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Life stressors significantly impact long-term outcomes and post-acute symptoms 12-months after COVID-19 hospitalization

Published:November 05, 2022DOI:https://doi.org/10.1016/j.jns.2022.120487

      Highlights

      • Over 50% of subjects reported significant life stressors at 12-months post COVID-19.
      • Life stressors predict worse outcomes 12-months after COVID-19 hospitalization.
      • Older age, female sex, baseline disability and COVID severity predict worse outcomes.

      Abstract

      Background

      Limited data exists evaluating predictors of long-term outcomes after hospitalization for COVID-19.

      Methods

      We conducted a prospective, longitudinal cohort study of patients hospitalized for COVID-19. The following outcomes were collected at 6 and 12-months post-diagnosis: disability using the modified Rankin Scale (mRS), activities of daily living assessed with the Barthel Index, cognition assessed with the telephone Montreal Cognitive Assessment (t-MoCA), Neuro-QoL batteries for anxiety, depression, fatigue and sleep, and post-acute symptoms of COVID-19. Predictors of these outcomes, including demographics, pre-COVID-19 comorbidities, index COVID-19 hospitalization metrics, and life stressors, were evaluated using multivariable logistic regression.

      Results

      Of 790 COVID-19 patients who survived hospitalization, 451(57%) completed 6-month (N = 383) and/or 12-month (N = 242) follow-up, and 77/451 (17%) died between discharge and 12-month follow-up. Significant life stressors were reported in 121/239 (51%) at 12-months. In multivariable analyses, life stressors including financial insecurity, food insecurity, death of a close contact and new disability were the strongest independent predictors of worse mRS, Barthel Index, depression, fatigue, and sleep scores, and prolonged symptoms, with adjusted odds ratios ranging from 2.5 to 20.8. Other predictors of poor outcome included older age (associated with worse mRS, Barthel, t-MoCA, depression scores), baseline disability (associated with worse mRS, fatigue, Barthel scores), female sex (associated with worse Barthel, anxiety scores) and index COVID-19 severity (associated with worse Barthel index, prolonged symptoms).

      Conclusions

      Life stressors contribute substantially to worse functional, cognitive and neuropsychiatric outcomes 12-months after COVID-19 hospitalization. Other predictors of poor outcome include older age, female sex, baseline disability and severity of index COVID-19.

      Keywords

      1. Introduction

      Long term sequelae of COVID-19 are increasingly recognized as major public health issues. We have previously reported that 87% of patients who survived COVID-19 hospitalization had abnormal scores on functional, cognitive, quality of life and/or activities of daily living batteries at 12-months [
      • Frontera J.A.
      • Yang D.
      • Medicherla C.
      • et al.
      Trajectories of neurologic recovery 12 months after hospitalization for COVID-19: a prospective longitudinal study.
      ]. Others have reported post-acute symptoms of COVID-19 in 33–90% of hospitalized patients [
      • Huang C.
      • Huang L.
      • Wang Y.
      • et al.
      6-month consequences of COVID-19 in patients discharged from hospital: a cohort study.
      ,
      • Bell M.L.
      • Catalfamo C.J.
      • Farland L.V.
      • et al.
      Post-acute sequelae of COVID-19 in a non-hospitalized cohort: results from the Arizona CoVHORT.
      ,
      • Misra S.
      • Kolappa K.
      • Prasad M.
      • et al.
      Frequency of neurologic manifestations in COVID-19: a systematic review and meta-analysis.
      ,
      • Groff D.
      • Sun A.
      • Ssentongo A.E.
      • et al.
      Short-term and long-term rates of Postacute sequelae of SARS-CoV-2 infection: a systematic review.
      ,
      • Maestre-Muniz M.M.
      • Arias A.
      • Mata-Vazquez E.
      • et al.
      Long-term outcomes of patients with coronavirus disease 2019 at one year after hospital discharge.
      ]. Among non-hospitalized cohorts, post-acute sequelae have been estimated to occur in 25–69% [
      • Bell M.L.
      • Catalfamo C.J.
      • Farland L.V.
      • et al.
      Post-acute sequelae of COVID-19 in a non-hospitalized cohort: results from the Arizona CoVHORT.
      ,
      • Frontera J.A.
      • Lewis A.
      • Melmed K.
      • et al.
      Prevalence and predictors of prolonged cognitive and psychological symptoms following COVID-19 in the United States.
      ,
      • Perez-Gonzalez A.
      • Araujo-Ameijeiras A.
      • Fernandez-Villar A.
      • Crespo M.
      • Poveda E.
      • Cohort C-otGSHRI
      Long COVID in hospitalized and non-hospitalized patients in a large cohort in Northwest Spain, a prospective cohort study.
      ,
      • Desgranges F.
      • Tadini E.
      • Munting A.
      • et al.
      PostCOVID19 syndrome in outpatients: a cohort study.
      ]. This variable prevalence can be explained by differences in study design, ascertainment, symptoms assessed, timing of assessment and whether objective metrics (in addition to subjective symptom reporting) were evaluated [
      • Frontera J.A.
      • Simon N.M.
      Bridging knowledge gaps in the diagnosis and management of neuropsychiatric sequelae of COVID-19.
      ]. Despite a plethora of literature reporting prevalence rates of post-COVID-19 symptoms, there is a paucity of data reporting predictors of long-term functional, cognitive and quality of life quantitative outcomes.
      In this prospective study, we evaluated the impact of four categories of predictors on 6- and 12-month outcome metrics including: demographics, pre-COVID-19 comorbidities, index COVID-19 hospitalization metrics, and life stressors within the month prior to interview. Outcome measures included not only long-term COVID-19 symptoms, but functional outcomes (modified Ranking Score), activities of daily living (Barthel Index), cognitive outcomes (telephone Montreal Cognitive Assessment [t-MoCA]) and NIH/PROMIS Neurological Quality Of Life (NeuroQoL) self-reported measures of anxiety, depression, fatigue and sleep.

      2. Methods

      2.1 Study design and patient cohort

      We conducted a prospective, observational study of consecutive COVID-19 patients hospitalized at four New York City area hospitals within the same hospital system between March 10, 2020 and May 20, 2020. Follow-up interviews were performed at 6 and 12 months after initial SARS-CoV-2 diagnosis. Detailed enrollment, methodology and outcome measures have been previously reported [
      • Frontera J.A.
      • Yang D.
      • Medicherla C.
      • et al.
      Trajectories of neurologic recovery 12 months after hospitalization for COVID-19: a prospective longitudinal study.
      ,
      • Frontera J.A.
      • Sabadia S.
      • Lalchan R.
      • et al.
      A prospective study of neurologic disorders in hospitalized patients with COVID-19 in new York City.
      ,
      • Frontera J.A.
      • Yang D.
      • Lewis A.
      • et al.
      A prospective study of long-term outcomes among hospitalized COVID-19 patients with and without neurological complications.
      ,
      • Valdes E.
      • Fuchs B.
      • Morrison C.
      • et al.
      Demographic and social determinants of cognitive dysfunction following hospitalization for COVID-19.
      ]. Inclusion criteria were: RT-PCR positive SARS-CoV-2 infection, age ≥ 18 years, hospital admission, and consent to participate in a follow-up interview. Exclusion criteria were: evaluation in an outpatient or emergency department setting only.

      2.2 Standard protocol approvals and patient consents

      This study was approved by the NYU Grossman School of Medicine Institutional Review Board. All patients or their surrogates provided informed consent for participation.

      2.3 Predictor variables

      Demographic data, past medical/neurological history, new neurological events or other complications during hospitalization, and COVID-19 specific medications administered during the acute phase of illness were recorded. Severity of illness during hospitalization was graded based on the Sequential Organ Failure Assessment (SOFA) score [
      • Vincent J.L.
      • Moreno R.
      • Takala J.
      • et al.
      The SOFA (Sepsis-related organ failure assessment) score to describe organ dysfunction/failure. On behalf of the working group on Sepsis-related problems of the European Society of Intensive Care Medicine.
      ] and requirement for intubation. Pre-COVID baseline functional status was assessed with modified Rankin Scale (mRS) [
      • van Swieten J.C.
      • Koudstaal P.J.
      • Visser M.C.
      • Schouten H.J.
      • van Gijn J.
      Interobserver agreement for the assessment of handicap in stroke patients.
      ] scores as reported by patients and/or their surrogate. Subjects were also asked to indicate if they had experienced any of 15 life stressors [
      • Frontera J.A.
      • Lewis A.
      • Melmed K.
      • et al.
      Prevalence and predictors of prolonged cognitive and psychological symptoms following COVID-19 in the United States.
      ] within the month prior to the 12-month interview (Supplemental Table 1).

      2.4 Study outcomes

      Longitudinal follow-up assessments were conducted by telephone interview among patients or their surrogates who consented to participate. Contact was attempted at 6-months (±1 month) and 12-months (±2 months) from the onset of COVID-19 symptoms. Three attempts at contact were required before patients/surrogates were coded as “unreachable”. Patients who were “unreachable” at 6-months, were also contacted at 12-months for participation. Functional status and disability were assessed using the modified Rankin Scale (mRS; 0 = no symptoms, 6 = dead, dichotomized as 0–3 versus 4–6) [
      • van Swieten J.C.
      • Koudstaal P.J.
      • Visser M.C.
      • Schouten H.J.
      • van Gijn J.
      Interobserver agreement for the assessment of handicap in stroke patients.
      ], activities of daily living were evaluated with the Barthel Index of activities of daily living (0 = completely dependent, 100 = independent for all activities, dichotomized as completely independent with a score of 100 versus 〈100) [
      • Mahoney F.I.
      • Barthel D.W.
      Functional evaluation: the Barthel index.
      ], cognition was assessed with the telephone-MoCA (t-MoCA; 22 = perfect score; ≤18 = abnormal cognition) [
      • Pendlebury S.T.
      • Welch S.J.
      • Cuthbertson F.C.
      • Mariz J.
      • Mehta Z.
      • Rothwell P.M.
      Telephone assessment of cognition after transient ischemic attack and stroke: modified telephone interview of cognitive status and telephone Montreal cognitive assessment versus face-to-face Montreal cognitive assessment and neuropsychological battery.
      ], and Quality of Life in Neurological Disorders [
      • Cella D.
      • Lai J.S.
      • Nowinski C.J.
      • et al.
      Neuro-QOL: brief measures of health-related quality of life for clinical research in neurology.
      ] (NeuroQoL) short form self-reported health measures of anxiety, depression, fatigue and sleep were collected. NeuroQoL raw scores were converted into T-scores with a mean of 50 and standard deviation of 10 in a reference population (U.S. general population or clinical sample) []. Higher T-scores indicate worse self-reported health for the anxiety, depression, fatigue and sleep metrics. NeuroQoL scores were dichotomized at the mean + 1 standard deviation (T-scores ≥60 versus <60). Patients with fewer than 13 years of education received an additional point when scoring the t-MoCA [
      • Rossetti H.C.
      • Lacritz L.H.
      • Cullum C.M.
      • Weiner M.F.
      Normative data for the Montreal cognitive assessment (MoCA) in a population-based sample.
      ]. With the exception of the t- MoCA, all of the above batteries have been validated for surrogate completion, and surrogates were asked to complete these metrics for patients who were unable to do so.
      The outcome of post-acute symptoms of COVID-19 was defined according to Centers for Disease Control and Prevention (CDC) criteria as new or persistent symptoms occurring ≥4 weeks after SARS-CoV-2 infection []. Symptoms were categorized following the World Health Organization (WHO) clinical case report form for post-acute COVID-19 symptoms [] (Supplemental Table 2). Post-acute symptom data was only collected at the 12-month follow-up interview.

      2.5 Statistical analyses

      Demographic variables, past medical/neurological history, life stressors, and hospital clinical variables were evaluated as predictors of dichotomized 6- and 12-month outcomes using univariate logistic regression analyses. Data on life stressors and post-COVID-19 symptoms were only available from the 12-month interview. Multivariable backward, stepwise logistic regression models were constructed utilizing univariate variables with P values <0.05. Discharge metrics including length of stay, and discharge disposition (home, skilled nursing facility, acute rehabilitation facility) were not entered into multivariable models due to collinearity. Receiver operating characteristic curves were used to determine the area under the curve (AUC) for each multivariable model.
      For patients who died, a mRS score of 6 was assigned, but no other outcome variables were scored or imputed. Incomplete or partial responses to a given metric were excluded from analysis. All analyses were conducted using IBM SPSS Statistics for Mac version 25 (IBM Corp., Armonk, NY).

      3. Results

      Follow-up interviews were attempted in 790 and 590 patients at 6 and 12 months post COVID-19 hospitalization, respectively [
      • Frontera J.A.
      • Yang D.
      • Medicherla C.
      • et al.
      Trajectories of neurologic recovery 12 months after hospitalization for COVID-19: a prospective longitudinal study.
      ]. A total of 451 patients completed follow-up either time point and were included in analyses. Fewer patients were eligible for the 12-month call due to language barrier, missing or defunct contact information or indication at the 6-month call that the respondent did not wish to be re-contacted. Interviews were completed in 382/790 (48%) patients at 6 months, 242/590 (41%) patients at 12-months and 174 patients completed follow-up at both time points. There were no differences in sex, race, education level, pre-COVID-19 history of psychiatric disease or dementia, pre-COVID-19 mRS scores, index COVID-19 severity, or rates of neurological events during index hospitalization between those who completed the 6-month versus 12-month follow-up interview. However, patients who completed only 6-month follow-up (and were then lost to follow-up) were significantly older than those who completed 12-month follow-up (median age 69 years versus 65 years, P < 0.001) and had slightly higher body mass index (Table 1). Among those who completed 12-month follow-up, the most common neurological post-COVID-19 symptoms reported were headache (22%), cognitive abnormalities (20%), anxiety (12%), depression (11%,), sleep disturbance (11%) and fatigue (10%, Supplemental Table 2) [
      • Frontera J.A.
      • Thorpe L.E.
      • Simon N.M.
      • et al.
      Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: a prospective, observational study.
      ].
      Table 1Demographics of the study population at 6 and 12 months post index SARS-CoV-2 hospitalization.
      6 months

      N = 382
      12 months

      N = 242
      Demographics
      Age (years), median (IQR)69 (57–78)*65 (53–73)*
      Sex (male), N (%)248 (65%)155 (64%)
      Race (white), N (%)213/309 (69%)128/189 (68%)
      Education level > 12 years, N (%)227/303 (75%)164/214 (77%)
      Comorbidities
      Pre-COVID disability (mRS score), median (IQR)0 (0–1)0 (0–1)
      BMI, median (IQR)27 (24–32)*28 (25–33)*
      Hypertension, N (%)164 (43%)98 (41%)
      Diabetes, N (%)109 (29%)60 (25%)
      COPD/Asthma, N (%)38 (10%)24 (10%)
      Headache Disorder, N (%)9 (2%)9 (4%)
      Dementia, N (%)40 (10%)20 (8%)
      Psychiatric history, N (%)47 (12%)28 (12%)
      Index COVID-19 Hospitalization
      Neuro complication, N (%)193 (50%)113 (47%)
      Mechanically ventilated, N (%)130 (34%)81 (34%)
      Worst Sequential Organ Failure Assessment (SOFA) score, median (IQR)4 (3–7)4 (3–7)
      mRS = modified Rankin Score; IQR = interquartile range, * indicates P < 0.05.
      The mean values for outcome metrics and percent of patients who had poor or abnormal test results at 6- and 12-months post COVID-19 are shown in Table 2. As previously reported [
      • Frontera J.A.
      • Yang D.
      • Medicherla C.
      • et al.
      Trajectories of neurologic recovery 12 months after hospitalization for COVID-19: a prospective longitudinal study.
      ,
      • Frontera J.A.
      • Yang D.
      • Lewis A.
      • et al.
      A prospective study of long-term outcomes among hospitalized COVID-19 patients with and without neurological complications.
      ], 90% of patients at 6 months and 87% of patients at 12 months had abnormalities on at least one of the metrics assessed (e.g. mRS > 0, Barthel Index<100, t-MoCa ≤18, or a NeuroQoL T score ≥ 60), with abnormalities in t-MoCA and mRS being most prevalent. There was a small but significant correlation between abnormal 12-month NeuroQoL anxiety scores≥60 and post-acute symptoms of COVID-19 (Pearson correlation coefficient 0.191, P = 0.005). There were no other significant correlations of post-acute symptoms with other 6- or 12-months outcomes (mRS 4–6, Barthel Index <100, t-MoCA scores<18, or depression, fatigue or sleep T-scores≥60).
      Table 2Number, mean (standard deviation), and percent of patients with abnormal or poor outcome metrics at 6- and 12-months post COVID-19.
      MetricMean, SDN (%) abnormal or poor
      6 months12 months6 months12 months
      Modified Rankin Scale,

      (poor = 4–6)
      N = 381

      3 (2)
      N = 236

      2 (2)
      189/381 (50%)79/236 (34%)
      Barthel Index,

      (abnormal <100)
      N = 304

      85.7 (25)
      N = 236

      87.2 (24)
      134/304 (44%)86/236 (36%)
      T-MoCA,

      (abnormal ≤18)
      N = 215

      17.0 (3.5)
      N = 170

      17.5 (3.8)
      106/215 (49%)69/170 (41%)
      NeuroQoL anxiety,

      (abnormal T-score ≥ 60)
      N = 280

      48.4 (9)
      N = 225

      46.8 (9)
      21/280 (8%)16/225 (7%)
      NeuroQoL depression,

      (abnormal T-score ≥ 60)
      N = 279

      44.6 (8)
      N = 225

      44.3 (8)
      8/279 (3%)9/225 (4%)
      NeuroQoL fatigue,

      (abnormal T-score ≥ 60)
      N = 272

      45.7 (10)
      N = 223

      45.6 (11)
      14/272 (5%)20/223 (9%)
      NeuroQoL sleep,

      (abnormal T-score ≥ 60)
      N = 278

      46.3 (10)
      N = 221

      46.1 (11)
      27/278 (10%)22/221 (10%)
      t-MOCA = telephone Montreal Cognitive Assessment; NeuroQoL = neurological quality of life.
      Table 3a, Table 3b demonstrate univariate demographic and pre-COVID comorbidity predictors of 6- and 12-month outcomes. Older age was consistently associated with worse mRS, Barthel Index and t-MoCA scores at both time points, as well as NeuroQoL depression scores at 12-months. Female sex was associated with worse Barthel scores at 6- and 12-months and higher anxiety scores at 12-months. Lower education levels were associated with worse cognitive scores at 6- and 12-months [
      • Valdes E.
      • Fuchs B.
      • Morrison C.
      • et al.
      Demographic and social determinants of cognitive dysfunction following hospitalization for COVID-19.
      ], as well as worse depression and fatigue NeuroQoL scores at 12-months. Pre-COVID disability (as measured by baseline mRS) was a strong predictor of worse mRS and Barthel scores at both time points, and was associated with worse NeuroQoL fatigue scores at 12-months. A pre-COVID history of dementia/cognitive disorder or psychiatric disease were associated with worse mRS and Barthel scores. A history of dementia/cognitive disorder was also associated with worse t-MoCA scores and higher anxiety NeuroQoL scores. The presence of post-acute COVID-19 symptoms was not associated with any demographic or comorbidity predictors.
      Table 3aAssociation of demographic and comorbidity variables and 6- and 12-month mRS, Barthel Index, T-MoCA and post-acute COVID-19 symptoms. Univariate logistic regression odds ratios, 95% confidence intervals (CI) and P values are shown.
      mRS 4–6

      6 months

      N = 381
      mRS 4–6

      12 months

      N = 236
      Barthel <100

      6 months

      N = 304
      Barthel <100

      12 months

      N = 236
      T-MoCA (≤18) 6-months

      N = 215
      T-MoCA (≤18) 12-months

      N = 170
      12-month Post-acute COVID-19 symptoms N = 242
      Demographics
      Age1.04 (1.02–1.05) P < 0.0011.04 (1.02–1.06) P < 0.0011.04 (1.02–1.05) P < 0.0011.05 (1.03–1.08) P < 0.0011.03 (1.01–1.05) P = 0.0091.03 (1.01–1.06) P = 0.0071.00 (0.98–1.02) P = 0.913
      Sex (male)0.74 (0.49–1.13) P = 0.1620.59 (0.34–1.03) P = 0.0630.53 (0.33–0.85) P = 0.0090.43 (0.25–0.74) P = 0.0030.88 (0.51–1.55) P = 0.6620.95 (0.50–1.81) P = 0.8800.73 (0.43–1.27) P = 0.265
      Race (white)0.94 (0.58–1.53) P = 0.8120.92 (0.48–1.75) P = 0.7900.91 (0.53–1.55) P = 0.7180.87 (0.47–1.63) P = 0.6710.44 (0.23–0.84) P = 0.0130.63 (0.30–1.31) P = 0.2170.54 (0.28–1.04) P = 0.066
      Education level > 12 years0.57 (0.33–0.96) P = 0.0350.68 (0.35–1.32) P = 0.2530.76 (0.45–1.29) P = 0.3150.72 (0.38–1.39) P = 0.3310.39 (0.19–0.79) P = 0.0090.45 (0.21–1.00) P = 0.0501.80 (0.94–3.46) P = 0.078
      Comorbidities
      Pre-COVID disability (mRS)2.06 (1.66–2.55) P < 0.0012.07 (1.63–2.64) P < 0.0011.95 (1.56–2.45) P < 0.0012.36 (1.78–3.13) P < 0.0011.27 (0.99–1.64) P = 0.0631.13 (0.84–1.53) P = 0.4270.84 (0.69–1.03) P = 0.091
      BMI0.97 (0.94–1.00) P = 0.0581.01 (0.97–1.05) P = 0.7700.99 (0.96–1.02) P = 0.5211.02 (0.98–1.07) P = 0.2991.02 (0.98–1.06) P = 0.4431.00 (0.95–1.04) P = 0.8551.04 (0.99–1.08) P = 0.106
      Hypertension1.50 (1.00–2.25) P = 0.0531.28 (0.74–2.22) P = 0.3771.51 (0.95–2.41) P = 0.0791.62 (0.95–2.78) P = 0.0790.74 (0.43–1.28) P = 0.2760.89 (0.48–1.65) P = 0.6990.73 (0.43–1.24) P = 0.244
      Diabetes1.48 (0.95–2.32) P = 0.0861.15 (0.62–2.14) P = 0.6511.45 (0.88–2.41) P = 0.1491.69 (0.93–3.08) P = 0.0871.20 (0.65–2.22) P = 0.5561.24 (0.62–2.47) P = 0.5391.12 (0.62–2.04) P = 0.707
      COPD/Asthma1.23 (0.62–2.42) P = 0.5581.01 (0.41–2.47) P = 0.9881.54 (0.70–3.35) P = 0.2821.55 (0.66–3.62) P = 0.3161.03 (0.43–2.49) P = 0.9450.79 (0.41–3.26) P = 0.7871.27 (0.54–2.99) P = 0.584
      Headache Disorder1.28 (0.34–4.86) P = 0.7131.29 (0.32–5.24) P = 0.7250.21 (0.03–1.70) P = 0.1430.61 (0.14–2.60) P = 0.5000.87 (0.20–3.78) P = 0.8561.81 (0.44–7.42) P = 0.410
      Dementia4.02 (1.86–8.69) P < 0.0013.39 (1.32–8.67) P = 0.0112.97 (1.24–7.11) P = 0.0152.69 (0.98–7.34) P = 0.0544.09 (1.11–15.11) P = 0.0351.90 (0.49–7.32) P = 0.3540.78 (0.31–2.00) P = 0.606
      Psychiatric history1.94 (1.03–3.66) P = 0.0402.22 (1.00–4.93) P = 0.0502.23 (1.07–4.64) P = 0.0321.72 (0.77–3.85) P = 0.1890.91 (0.35–2.34) P = 0.8430.96 (0.33–2.84) P = 0.9450.58 (0.25–1.30) P = 0.184
      Bold indicates P < 0.05; mRS = modified Rankin Scale, t-MOCA = telephone Montreal Cognitive Assessment; BMI = body mass index; COPD = chronic obstructive pulmonary disease.
      Table 3bAssociation of Demographic and Comorbidity variables and 6- and 12-month patient-reported NeuroQoL outcomes. Univariate logistic regression odds ratios, 95% confidence intervals (CI) and P values shown.
      Anxiety T-score ≥ 60 at 6-months

      N = 280
      Anxiety T-score ≥ 60 at 12-months

      N = 225
      Depression T-score ≥ 60 at 6-months

      N = 279
      Depression T-score ≥ 60 at 12-months

      N = 225
      Fatigue T-score ≥ 60 at 6-months

      N = 272
      Fatigue T-score ≥ 60 at 12-months

      N = 223
      Sleep T-score ≥ 60 at 6-months

      N = 278
      Sleep T-score ≥ 60 at 12-months

      N = 221
      Demographics
      Age1.03

      (1.00–1.06) P = 0.093
      1.00

      (0.96–1.03) P = 0.818
      1.05

      (0.99–1.11) P = 0.112
      1.10

      (1.03–1.17) P = 0.006
      0.99

      (0.96–1.03) P = 0.651
      1.02

      (0.99–1.06) P = 0.176
      0.99

      (0.97–1.02) P = 0.468
      0.99

      (0.96–1.02) P = 0.367
      Sex (male)0.72

      (0.29–1.78) P = 0.479
      0.17

      (0.05–0.55) P = 0.003
      0.31

      (0.07–1.34) P = 0.118
      0.28

      (0.07–1.14) P = 0.075
      0.40

      (0.14–1.19) P = 0.101
      0.54

      (0.21–1.35) P = 0.188
      0.92

      (0.40–2.09) P = 0.838
      0.56

      (0.23–1.34) P = 0.192
      Race (white)1.09

      (0.37–3.22) P = 0.875
      0.67

      (0.22–2.02) P = 0.473
      2.78

      (0.33–23.53) P = 0.348
      4.37

      (0.53–35.79) P = 0.169
      1.23

      (0.32–4.79) P = 0.765
      1.38

      (0.47–4.07) P = 0.563
      0.51

      (0.21–1.25) P = 0.140
      0.93

      (0.32–2.64) P = 0.885
      Education level > 12 years0.50

      (0.20–1.25) P = 0.137
      0.59

      (0.17–2.05) P = 0.406
      0.96

      (0.19–4.85) P = 0.957
      0.22

      (0.06–0.87) P = 0.031
      0.78

      (0.24–2.58) P = 0.687
      0.36

      (0.13–0.95) P = 0.039
      0.59

      (0.25–1.39) P = 0.231
      0.51

      (0.19–1.36) P = 0.178
      Comorbidities
      Pre-COVID disability (mRS)1.31

      (0.97–1.76) P = 0.078
      0.99

      (0.63–1.55) P = 0.959
      1.33

      (0.84–2.12) P = 0.221
      1.01

      (0.61–1.67) P = 0.983
      1.40

      (0.98–1.99) P = 0.064
      1.44

      (1.07–1.93) P = 0.017
      1.23

      (0.93–1.63) P = 0.150
      1.11

      (0.80–1.53) P = 0.547
      BMI0.97

      (0.90–1.04) P = 0.324
      1.03

      (0.96–1.11) P = 0.450
      0.95

      (0.85–1.07) P = 0.419
      0.94

      (0.81–1.09) P = 0.396
      0.97

      (0.89–1.06) P = 0.516
      0.96

      (0.88–1.05) P = 0.370
      0.97

      (0.91–1.04) P = 0.361
      1.04

      (0.98–1.11) P = 0.185
      Hypertension1.39

      (0.57–3.39) P = 0.469
      1.18

      (0.42–3.29) P = 0.751
      1.50

      (0.37–6.13) P = 0.572
      0.74

      (0.18–3.04) P = 0.678
      2.09

      (0.71–6.21) P = 0.183
      1.23

      (0.49–3.11) P = 0.658
      0.50 (0.20 = 1.23) P = 0.1291.52

      (0.63–3.67) P = 0.353
      Diabetes0.86

      (0.30–2.43) P = 0.770
      0.68

      (0.19–2.48) P = 0.558
      0.92

      (0.18–4.65) P = 0.916
      0.71

      (0.19–2.64) P = 0.614
      0.75

      (0.24–2.33) P = 0.613
      0.80

      (0.31–2.06) P = 0.639
      0.90

      (0.32–2.57) P = 0.844
      COPD/Asthma1.63

      (0.45–5.92) P = 0.462
      0.57

      (0.07–4.50) P = 0.591
      1.34

      (0.16–11.33) P = 0.788
      2.65

      (0.52–13.60) P = 0.242
      0.68

      (0.09–5.44) P = 0.719
      1.62

      (0.44–5.99) P = 0.474
      0.72

      (0.16–3.22) P = 0.667
      2.24

      (0.68–7.32) P = 0.184
      Headache Disorder1.79

      (0.21–15.30) P = 0.594
      1.34

      (0.16–11.28) P = 0.791
      1.14

      (0.14–9.54) P = 0.906
      Dementia1.32

      (0.29–6.12) P = 0.719
      7.46

      (2.23–24.95) P = 0.001
      1.79

      (0.21–15.24) P = 0.596
      1.56

      (0.18–13.29) P = 0.683
      0.91

      (0.11–7.33) P = 0.931
      2.38

      (0.62–9.12) P = 0.205
      0.97

      (0.21–4.42) P = 0.971
      2.09

      (0.55–7.92) P = 0.280
      Psychiatric history2.01

      (0.63–6.42) P = 0.237
      2.68

      (0.80–9.01) P = 0.111
      1.14

      (0.14–9.61) P = 0.902
      2.17

      (0.43–11.04) P = 0.350
      0.63

      (0.08–4.99) P = 0.661
      1.31

      (0.36–4.80) P = 0.685
      1.44

      (0.46–4.47) P = 0.532
      1.15

      (0.32–4.16) P = 0.837
      Bold indicates P < 0.05; mRS = modified Rankin Scale; NeuroQoL = neurological quality of life; BMI = body mass index; COPD = chronic obstructive pulmonary disease.
      Table 4a, Table 4b delineate index COVID-19 hospitalization metrics and their association with 6- and 12-month outcomes. Neurological complications including toxic metabolic encephalopathy and hypoxic ischemic brain injury were strong predictors of worse mRS and Barthel Index at 6 and 12 months and worse depression and fatigue scores at 12 months, while mechanical ventilation and worse SOFA scores (markers of severe COVID) were only predictive of worse Barthel Index at 6-months, and with much lower odds ratios. There was no consistent effect of COVID-19 related pharmaceuticals on outcome metrics, however, nitazoxanide (used in N = 14 patients) was associated with worse fatigue, depression and anxiety scores at 6 months. There were significantly worse Barthel Index scores in univariate analysis with corticosteroid use, but this medication was preferentially utilized in the most severely ill patients, suggesting a bias by indication. Post-acute COVID-19 symptoms at 12-months were more common in those with severe COVID-19 illness, as measured by the requirement for mechanical ventilation and worse SOFA scores. Worse mRS and Barthel scores were associated with discharge to a nursing home, but cognitive and NeuroQoL scores did not vary significantly with discharge disposition. We evaluated inflammatory markers collected during hospitalization including blood IL-6 (N = 300), D-dimer (N = 398), C-reactive peptide (N = 420) and ferritin levels (N = 414), but did not find any correlations with 6- or 12-month outcome metrics.
      Table 4aAssociation of index COVID-19 hospitalization variables and 6 and 12 month mRS, Barthel, T-MoCA and PASC outcomes. Univariate logistic regression odds ratios, 95% confidence intervals (CI) and P values shown.
      mRS 4–6

      6 months

      N = 381
      mRS 4–6

      12 months

      N = 236
      Barthel <100

      6 months

      N = 304
      Barthel <100

      12 months

      N = 236
      T-MoCA (≤18) 6-months

      N = 215
      T-MoCA (≤18) 12-months

      N = 170
      12-month Post-acute COVID-19 symptoms N = 242
      Index COVID-19 Hospitalization
      Neuro complication1.61 (1.07–2.41) P = 0.0211.31 (0.76–2.25) P = 0.3331.71 (1.08–2.70) P = 0.0211.21 (0.71–2.05) P = 0.4901.32 (0.77–2.26) P = 0.3120.92 (0.50–1.70) P = 0.7900.74 (0.44–1.24) P = 0.251
      Hypoxic/ischemic brain injury2.70 (1.37–5.34) P = 0.0043.52 (1.31–9.47) P = 0.0133.51 (1.56–7.92)

      P = 0.002
      3.00 (1.12–8.05) P = 0.0291.16 (0.45–2.97) P = 0.7611.51 (0.47–4.89) P = 0.4930.88 (0.34–2.29) P = 0.788
      Toxic-Metabolic Encephalopathy2.02 (1.28–3.19) P = 0.0022.73 (1.41–5.27) P = 0.0032.03 (1.20–3.43) P = 0.0082.39 (1.25–4.56) P = 0.0091.70 (0.88–3.28) P = 0.1131.72 (0.73–4.04) P = 0.2121.01 (0.53–1.92) P = 0.982
      Mechanically ventilated1.24 (0.81–1.89) P = 0.3301.10 (0.62–1.94) P = 0.7451.62 (1.01–2.61) P = 0.0481.10 (0.63–1.93) P = 0.7280.75 (0.43–1.32) P = 0.3180.90 (0.47–1.72) P = 0.7563.63 (2.01–6.58) P < 0.001
      Worst Sequential Organ Failure Assessment (SOFA) score1.03 (0.98–1.08) P = 0.1881.03 (0.96–1.10) P = 0.4101.07 (1.01–1.13) P = 0.0301.02 (0.96–1.09) P = 0.5180.98 (0.92–1.05) P = 0.5701.03 (0.96–1.11) P = 0.3731.10 (1.03–1.18) P = 0.006
      Lowest % oxygen saturation1.00 (0.99–1.01) P = 0.9401.00 (0.98–1.02) P = 0.7781.00 (0.99–1.01) P = 0.9831.01 (0.99–1.02) P = 0.4951.00 (0.98–1.01) P = 0.5850.99 (0.97–1.01) P = 0.4080.98 (0.96–1.00) P = 0.035
      Lowest mean arterial blood pressure (mmHg)1.00 (0.99–1.01) P = 0.6941.01 (0.99–1.02) P = 0.5800.99 (0.97–1.00) P = 0.1021.00 (0.98–1.02) P = 0.9761.00 (0.98–1.02) P = 0.9530.99 (0.97–1.01) P = 0.2250.99 (0.97–1.01) P = 0.150
      Acute renal failure1.60 (0.93–2.74) P = 0.0871.50 (0.71–3.16) P = 0.2871.48 (0.79–2.78) P = 0.2211.55 (0.74–3.26) P = 0.2481.11 (0.52–2.39) P = 0.7811.28 (0.54–3.06) P = 0.5731.83 (0.84–4.00) P = 0.128
      Medications during Index Hospitalization
      Tocilizumab/ clazakizumab0.70 (0.42–1.17) P = 0.1760.78 (0.41–1.48) P = 0.4400.84 (0.48–1.46) P = 0.5310.68 (0.36–1.29) P = 0.2350.47 (0.25–0.90) P = 0.0231.42 (0.72–2.81) P = 0.3082.81 (1.46–5.38) P = 0.002
      Corticosteroids1.51 (0.95–2.39) P = 0.0801.44 (0.74–2.84) P = 0.2862.08 (1.25–3.48) P = 0.0051.78 (0.91–3.47) P = 0.0910.99 (0.55–1.80) P = 0.9821.15 (0.54–2.44) P = 0.7212.35 (1.17–4.72) P = 0.016
      Remdesivir
      Hydroxychloroquine0.75 (0.48–1.17) P = 0.2070.82 (0.41–1.67) P = 0.5911.01 (0.61–1.68) P = 0.9700.93 (0.46–1.89) P = 0.8470.62 (0.33–1.15) P = 0.1280.69 (0.30–1.56) P = 0.3672.59 (1.27–5.28) P = 0.009
      Nitazoxanide1.81 (0.52–6.28) P = 0.3510.80 (0.15–4.22) P = 0.7931.28 (0.31–5.20) P = 0.7330.69 (0.13–3.64) P = 0.6620.20 (0.02–1.73) P = 0.1430.98 (0.16–5.99) P = 0.9780.43 (0.08–2.41) P = 0.340
      Zinc0.69 (0.46–1.05) P = 0.0830.79 (0.42–1.49) P = 0.4610.82 (0.52–1.31) P = 0.4071.18 (0.64–2.20) P = 0.5940.84 (0.49–1.44) P = 0.5231.12 (0.55–2.29) P = 0.7481.93 (1.04–3.58) P = 0.037
      Ascorbic acid0.94 (0.60–1.49) P = 0.7981.10 (0.62–1.97) P = 0.7421.13 (0.69–1.85) P = 0.6341.36 (0.77–2.39) P = 0.2871.30 (0.72–2.35) P = 0.3791.13 (0.59–2.17) P = 0.7171.03 (0.59–1.82) P = 0.908
      Lopinavir/ritonavir0.77 (0.37–1.60) P = 0.4900.70 (0.24–2.02) P = 0.5090.78 (0.34–1.77) P = 0.5470.93 (0.36–2.44) P = 0.8891.42 (0.57–3.52) P = 0.4512.21 (0.84–5.80) P = 0.1091.09 (0.43–2.74) P = 0.854
      Azithromycin0.61 (0.40–0.93) P = 0.0220.56 (0.29–1.08) P = 0.0850.65 (0.41–1.05) P = 0.0780.73 (0.38–1.41) P = 0.3440.68 (0.38–1.21) P = 0.1880.64 (0.29–1.39) P = 0.2572.27 (1.17–4.38) P = 0.015
      Therapeutic anticoagulation1.29 (0.85–1.97) P = 0.2361.26 (0.72–2.22) P = 0.4191.53 (0.95–2.46) P = 0.0781.02 (0.58–1.78) P = 0.9521.00 (0.57–1.76) P = 0.9950.80 (0.42–1.51) P = 0.4832.78 (1.56–4.95) P = 0.001
      Discharge metrics
      Length of stay1.01 (1.00–1.02) P = 0.1021.01 (1.00–1.03)

      P = 0.189
      1.02 (1.01–1.03)

      P = 0.004
      1.01 (1.00–1.03)

      P = 0.203
      1.00 (0.98–1.01)

      P = 0.549
      1.00 (0.98–1.02)

      P = 0.974
      1.03 (1.01–1.04)

      P = 0.002
      Discharge home0.18 (0.12–0.29) P < 0.0010.23 (0.13–0.42) P < 0.0010.17 (0.10–0.29)

      P < 0.001
      0.28 (0.16–0.50)

      P < 0.001
      0.86 (0.48–1.57)

      P = 0.629
      0.77 (0.39–1.50)

      P = 0.438
      0.89 (0.51–1.55)

      P = 0.683
      Discharge SNF4.92 (2.88–8.42)

      P < 0.001
      3.69 (1.91–7.11)

      P < 0.001
      5.18 (2.76–9.73)

      P < 0.001
      4.67 (2.38–9.18)

      P < 0.001
      2.06 (0.93–4.54)

      P = 0.074
      1.80 (0.80–4.04)

      P = 0.154
      0.94 (0.50–1.77)

      P = 0.842
      Discharge rehab1.71 (0.83–3.56)

      P = 0.148
      1.86 (0.82–4.26)

      P = 0.140
      2.50 (1.14–5.45)

      P = 0.022
      1.08 (0.45–2.60)

      P = 0.858
      0.47 (0.19–1.15)

      P = 0.097
      0.36 (0.12–1.15) P = 0.0841.21 (0.51–2.89)

      P = 0.667
      Bold indicates P < 0.05; mRS = modified Rankin Scale, t-MOCA = telephone Montreal Cognitive Assessment; SNF = skilled nursing facility.
      Table 4bAssociation of index COVID-19 hospitalization variables and 6- and 12-month patient-reported NeuroQoL outcomes. Univariate logistic regression odds ratios, 95% confidence intervals (CI) and P values shown.
      Anxiety T-score ≥ 60 at 6-months

      N = 280
      Anxiety T-score ≥ 60 at 12-months

      N = 225
      Depression T-score ≥ 60 at 6-months

      N = 279
      Depression T-score ≥ 60 at 12-months

      N = 225
      Fatigue T-score ≥ 60 at 6-months

      N = 272
      Fatigue T-score ≥ 60 at 12-months

      N = 223
      Sleep T-score ≥ 60 at 6-months

      N = 278
      Sleep T-score ≥ 60 at 12-months

      N = 221
      Index COVID-19 Hospitalization
      Neuro complication0.51

      (0.20–1.31) P = 0.162
      1.51

      (0.54–4.22) P = 0.428
      1.10

      (0.27–4.49) P = 0.894
      4.21

      (0.86–20.75) P = 0.077
      1.06

      (0.36–3.12) P = 0.910
      3.77

      (1.32–10.76) P = 0.013
      1.46

      (0.66–3.23) P = 0.358
      0.77

      (0.32–1.89) P = 0.573
      Hypoxic/ischemic brain injury1.03

      (0.23–4.70) P = 0.969
      3.43

      (0.66–17.94) P = 0.144
      1.46

      (0.17–12.40) P = 0.727
      0.75

      (0.09–5.99) P = 0.786
      1.30

      (0.28–6.10) P = 0.741
      0.35

      (0.05–2.67) P = 0.310
      1.14

      (0.25–5.34) P = 0.864
      Toxic-Metabolic Encephalopathy0.97

      (0.34–2.76) P = 0.958
      1.81

      (0.60–5.48) P = 0.296
      1.94

      (0.45–8.34) P = 0.373
      5.18

      (1.33–20.12) P = 0.018
      0.23

      (0.03–1.78) P = 0.159
      2.19

      (0.82–5.86) P = 0.117
      0.89

      (0.34–2.31) P = 0.810
      1.13

      (0.40–3.25) P = 0.816
      Mechanically ventilated0.60

      (0.21–1.68) P = 0.329
      0.92

      (0.31–2.76) P = 0.885
      0.65

      (0.13–3.28) P = 0.600
      1.11

      (0.36–3.42) P = 0.854
      0.83

      (0.31–2.26) P = 0.719
      0.66

      (0.27–1.62) P = 0.365
      1.43

      (0.58–3.51) P = 0.439
      Worst Sequential Organ Failure Assessment (SOFA) score0.90

      (0.78–1.03) P = 0.135
      0.98

      (0.86–1.12) P = 0.757
      0.82

      (0.62–1.08) P = 0.160
      0.79

      (0.59–1.05) P = 0.107
      0.90

      (0.76–1.07) P = 0.229
      0.94

      (0.83–1.07) P = 0.354
      0.89

      (0.78–1.01) P = 0.064
      0.97

      (0.87–1.09) P = 0.624
      Lowest % oxygen saturation1.01

      (0.98–1.05) P = 0.428
      1.01

      (0.97–1.05) P = 0.606
      1.07

      (0.97–1.19) P = 0.184
      1.05

      (0.97–1.14) P = 0.220
      1.07

      (0.99–1.16) P = 0.084
      1.02

      (0.98–1.06) P = 0.359
      1.03

      (0.99–1.07) P = 0.105
      1.01

      (0.98–1.05) P = 0.511
      Lowest mean arterial blood pressure (mmHg)1.03

      (1.00–1.07) P = 0.061
      1.00

      (0.96–1.03) P = 0.915
      1.05

      (1.00–1.11) P = 0.068
      1.02

      (0.97–1.07) P = 0.499
      1.02

      (0.98–1.07) P = 0.244
      1.01

      (0.98–1.05) P = 0.454
      1.04

      (1.01–1.08) P = 0.007
      1.00

      (0.97–1.03) P = 0.905
      Acute renal failure2.64

      (0.96–7.26) P = 0.061
      2.16

      (0.65–7.15) P = 0.210
      2.04

      (0.40–10.46) P = 0.395
      0.75

      (0.09–6.17) P = 0.786
      2.46

      (0.73–8.25) P = 0.146
      1.56

      (0.49–5.01) P = 0.453
      1.82

      (0.68–4.82) P = 0.232
      1.36

      (0.43–4.31) P = 0.604
      Medications during Index Hospitalization
      Tocilizumab/ clazakizumab0.89

      (0.29–2.76) P = 0.845
      1.62

      (0.43–6.09) P = 0.472
      0.53

      (0.06–4.35) P = 0.550
      0.28

      (0.04–2.22) P = 0.230
      0.33

      (0.04–2.56) P = 0.288
      0.62

      (0.21–1.87) P = 0.395
      1.05

      (0.29–3.80) P = 0.940
      Corticosteroids0.64

      (0.21–1.95) P = 0.429
      0.27

      (0.03–2.18) P = 0.217
      0.90

      (0.18–4.58) P = 0.903
      0.74

      (0.14–3.78) P = 0.714
      0.72

      (0.20–2.65) P = 0.619
      0.31

      (0.07–1.43) P = 0.134
      0.44

      (0.15–1.32) P = 0.143
      1.53

      (0.51–4.56) P = 0.445
      Remdesivir
      Hydroxychloroquine0.73

      (0.28–1.88) P = 0.509
      0.40

      (0.10–1.56) P = 0.187
      0.59

      (0.14–2.54) P = 0.480
      0.55

      (0.13–2.39) P = 0.422
      2.28

      (0.50–10.43) P = 0.289
      0.94

      (0.28–3.14) P = 0.923
      0.71

      (0.31–1.67) P = 0.435
      1.42

      (0.38–5.31) P = 0.602
      Nitazoxanide5.35

      (0.97–29.41) P = 0.054
      14.72

      (2.45–88.46) P = 0.003
      7.00

      (1.28–38.39) P = 0.025
      3.27

      (0.63–17.05) P = 0.160
      Zinc0.68

      (0.27–1.73) P = 0.417
      1.37

      (0.35–5.30) P = 0.648
      0.45

      (0.09–2.28) P = 0.336
      0.63

      (0.15–2.74) P = 0.540
      0.55

      (0.17–1.79) P = 0.318
      0.38

      (0.12–1.25) P = 0.110
      0.37

      (0.15–0.95) P = 0.040
      0.71

      (0.24–2.11) P = 0.543
      Ascorbic acid1.23

      (0.48–3.16) P = 0.672
      0.66

      (0.21–2.13) P = 0.488
      0.78

      (0.16–3.96) P = 0.766
      0.57

      (0.12–2.82) P = 0.492
      0.96

      (0.29–3.15) P = 0.944
      0.21

      (0.05–0.94) P = 0.041
      0.50

      (0.18–1.38) P = 0.183
      0.94

      (0.37–2.42) P = 0.899
      Lopinavir/ritonavir1.71

      (0.47–6.24) P = 0.417
      1.70

      (0.36–8.08) P = 0.503
      2.01

      (0.63–6.38) P = 0.237
      1.07

      (0.23–4.98) P = 0.931
      Azithromycin0.53

      (0.22–1.29) P = 0.162
      0.33

      (0.09–1.30) P = 0.113
      0.28

      (0.07–1.21) P = 0.089
      0.25

      (0.06–1.08) P = 0.063
      0.48

      (0.16–1.42) P = 0.186
      0.26

      (0.09–0.79) P = 0.017
      0.60

      (0.27–1.33) P = 0.206
      0.48

      (0.16–1.41) P = 0.183
      Therapeutic anticoagulation1.22

      (0.49–3.05) P = 0.675
      1.22

      (0.43–3.49) P = 0.714
      1.19

      (0.28–5.08) P = 0.817
      0.56

      0.11–2.76) P = 0.476
      0.54

      (0.15–1.97) P = 0.348
      0.47

      (0.15–1.44) P = 0.185
      0.53

      (0.21–1.36) P = 0.186
      1.13

      (0.45–2.82) P = 0.800
      Discharge metrics
      Length of stay0.99

      (0.96–1.02)

      P = 0.546
      1.01

      (0.98–1.04)

      P = 0.531
      0.98

      (0.94–1.03)

      P = 0.486
      0.95

      (0.88–1.02)

      P = 0.137
      0.99

      (0.95–1.02)

      P = 0.469
      0.98

      (0.95–1.01)

      P = 0.233
      0.98

      (0.95–1.01)

      P = 0.119
      1.01

      (0.99–1.03)

      P = 0.449
      Discharge home0.77

      (0.31–1.94)

      P = 0.585
      1.05

      (0.35–3.20)

      P = 0.927
      0.80

      (0.19–3.43)

      P = 0.764
      1.05

      (0.26–4.33)

      P = 0.944
      0.47

      (0.16–1.39)

      P = 0.171
      0.62

      (0.25–1.57)

      P = 0.315
      0.99

      (0.42–2.30)

      P = 0.977
      0.41

      (0.17–0.99)

      P = 0.048
      Discharge SNF1.39

      (0.49–4.00)

      P = 0.538
      0.28

      (0.04–2.22)

      P = 0.230
      1.53

      (0.30–7.79)

      P = 0.612
      0.47

      (0.06–3.89)

      P = 0.487
      2.59

      (0.83–8.10)

      P = 0.102
      2.35

      (0.88–6.31)

      P = 0.090
      1.29

      (0.49–3.37)

      P = 0.609
      1.13

      (0.39–3.26)

      P = 0.819
      Discharge rehab0.87

      (0.19–3.96)

      P = 0.860
      2.47

      (0.64–9.59)

      P = 0.192
      1.19

      (0.14–10.06)

      P = 0.871
      1.05

      (0.13–8.76)

      P = 0.967
      1.43

      (0.30–6.74)

      P = 0.652
      0.41

      (0.05–3.21)

      P = 0.395
      1.04

      (0.29–3.69)

      P = 0.953
      3.79

      (1.31–10.98)

      P = 0.014
      Bold indicates P < 0.05; NeuroQoL = neurological quality of life; SNF = skilled nursing facility.
      Next, we evaluated the impact of life stressors (Table 5 and Supplemental Table 1) on 12- month outcomes. Over 50% (121/239) of subjects reported experiencing at least one life stressor within the month prior to the 12-month follow-up interview (median 1 stressor, range 0–7 stressors). The most common stressors were: new personal illness within the month prior to the 12-month interview (23%), financial insecurity (17%), social isolation (13%) and death or illness of a close contact. The presence and number of stressors were strongly related to worse anxiety, depression, fatigue and sleep NeuroQoL scores and PASC. Social isolation, financial insecurity, unemployment, food insecurity, personal illness (within the month prior to 12-month interview), new disability and death of a close contact were all significantly associated with worse NeuroQoL measures, while personal illness, new disability and increased caregiver responsibilities were the only life stressors associated with mRS and Barthel scores. We did not identify associations with any of the measured stressors and cognitive scores.
      Table 5Impact of Life Stressors on 12-month outcomes. Univariate logistic regression odds ratios, 95% confidence intervals (CI) and P values shown.
      mRS 4–6

      N = 236
      Barthel < 100

      N = 236
      t-MoCA

      ≤ 18

      N = 170
      Anxiety T-score ≥ 60

      N = 225
      Depression T-score ≥ 60

      N = 225
      Fatigue T-score ≥ 60

      N = 223
      Sleep T-score ≥ 60

      N = 221
      12-month Post-acute COVID-19 symptoms N = 242
      Stressors
      At least one stressor1.32

      (0.76–2.28) P = 0.327
      1.55

      (0.91–2.64) P = 0.111
      1.03

      (0.56–1.90) P = 0.930
      7.07

      (1.57–31.87) P = 0.011
      5.61

      (1.60–19.74) P = 0.007
      3.43

      (1.22–9.67) P = 0.019
      2.08

      (1.23–3.52) P = 0.006
      Number of stressors1.10

      (0.92–1.31) P = 0.295
      1.18

      (0.99–1.39) P = 0.062
      1.02

      (0.84–1.23) P = 0.861
      1.37

      (1.06–1.77) P = 0.016
      1.42

      (1.03–1.96) P = 0.032
      1.47

      (1.16–1.87) P = 0.001
      1.43

      (1.14–1.80) P = 0.002
      1.29

      (1.07–1.56) P = 0.007
      Social Isolation1.28

      (0.59–2.79) P = 0.527
      1.91

      (0.90–4.06) P = 0.090
      1.09

      (0.47–2.54) P = 0.846
      4.22

      (1.42–12.59) P = 0.010
      3.22

      (0.76–13.61) P = 0.111
      2.17

      (0.73–6.46) P = 0.163
      2.50

      (0.90–6.95) P = 0.080
      1.83

      (0.84–4.00) P = 0.128
      Financial Insecurity0.90

      (0.43–1.90) P = 0.787
      1.01

      (0.50–2.03) P = 0.983
      1.06

      (0.50–2.23) P = 0.882
      4.15

      (1.44–11.92) P = 0.008
      1.34

      (0.27–6.70) P = 0.723
      2.13

      (0.76–5.94) P = 0.148
      2.98

      (1.16–7.69) P = 0.024
      1.31

      (0.66–2.60) P = 0.438
      Unemployment0.96

      (0.37–2.46) P = 0.933
      1.00

      (0.40–2.48) P = 0.994
      0.65

      (0.23–1.79) P = 0.399
      0.60

      (0.08–4.75) P = 0.626
      1.02

      (0.22–4.71) P = 0.983
      2.24

      (0.68–7.32) P = 0.184
      3.34

      (1.19–9.38) P = 0.022
      Food Insecurity0.68

      (0.07–6.69) P = 0.744
      0.43

      (0.05–3.91) P = 0.453
      1.48

      (0.20–10.75) P = 0.700
      9.81

      (1.51–63.60) P = 0.017
      6.63

      (0.66–66.23) P = 0.107
      7.41

      (1.16–47.25) P = 0.034
      1.34

      (0.22–8.15) P = 0.753
      Homelessness
      Domestic violence
      Relationship problems in household0.91

      (0.27–3.07) P = 0.884
      0.86

      (0.29–2.62) P = 0.796
      1.24

      (0.36–4.23) P = 0.734
      1.01

      (0.12–8.21) P = 0.996
      4.86

      (0.91–25.95) P = 0.065
      3.08

      (0.78–12.12) P = 0.107
      1.56

      (0.33–7.46) P = 0.579
      1.35

      (0.47–3.94) P = 0.578
      Education disruption0.48

      (0.05–4.72) P = 0.529
      4.58

      (0.45–46.73) P = 0.199
      3.51

      (0.35–35.04) P = 0.287
      Increased caregiver responsibilities5.15

      (1.72–15.40) P = 0.003
      3.52

      (1.25–9.89) P = 0.017
      1.90

      (0.49–7.32) P = 0.354
      1.85

      (0.38–8.90) P = 0.444
      3.83

      (0.73–20.07) P = 0.112
      1.39

      (0.30–6.58) P = 0.676
      1.23

      (0.26–5.76) P = 0.796
      0.77

      (0.29–2.08) P = 0.608
      New Disability4.42

      (1.69–11.60) P = 0.003
      8.34

      (2.69–25.88) P < 0.001
      0.72

      (0.21–2.48) P = 0.597
      1.72

      (0.36–8.26) P = 0.496
      3.57

      (0.69–18.63) P = 0.131
      8.57

      (2.89–24.45) P < 0.001
      4.21

      (1.34–13.22) P = 0.014
      1.57

      (0.60–4.15) P = 0.360
      Death of close contact0.71

      (0.27–1.87) P = 0.483
      1.28

      (0.54–3.02) P = 0.575
      1.74

      (0.64–4.77)

      P = 0.279
      1.21

      (0.26–5.70) P = 0.806
      12.96

      (3.21–52.38) P < 0.001
      2.29

      (0.70–7.51) P = 0.173
      1.99

      (0.61–6.46) P = 0.253
      1.89

      (0.77–4.60) P = 0.163
      Illness of close contact0.58

      (0.21–1.64) P = 0.303
      0.74

      (0.29–1.88) P = 0.530
      1.07

      (0.41–2.82) P = 0.886
      1.10

      (0.13–9.23) P = 0.928
      0.43

      (0.06–3.39) P = 0.433
      1.41

      (0.38–5.20) P = 0.603
      1.43

      (0.59–3.44) P = 0.430
      Political conflict with close contacts0.18

      (0.02–1.48) P = 0.112
      0.47

      (0.09–2.41) P = 0.368
      3.59

      (0.70–18.53) P = 0.127
      1.14

      (0.14–9.44) P = 0.907
      4.33

      (1.03–18.14) P = 0.045
      3.72

      (0.77–17.91) P = 0.101
      Bold indicates P < 0.05; mRS = modified Rankin Scale, t-MOCA = telephone Montreal Cognitive Assessment.
      Results of multivariable analyses for each outcome at 6 and 12 months, including variables entered into each model, are shown in Table 6 and Fig. 1. Older age and baseline disability (pre-COVID-19 mRS scores) were significantly predictive of worse mRS and Barthel Index scores at both 6 and 12 months. Older age was also associated with worse cognitive scores and worse NeuroQoL depression scores at 12-months. Neurological events during index hospitalization, specifically hypoxic ischemic encephalopathy, were independently associated with worse mRS scores at both timepoints. Severity of index COVID-19 illness was only associated with 6-month Barthel Index (SOFA scores) and post-acute COVID-19 symptoms at 12 months (mechanical ventilation). COVID-19 severity was not associated with mRS, t-MoCA or NeuroQoL anxiety, depression, fatigue or sleep scores at any time point. The presence of a variety of life stressors were independently associated with a number of 12-month outcomes including worse mRS, Barthel, depression, fatigue, and sleep scores, as well as post-acute COVID-19 symptoms. The AUCs for each model ranged from 0.664 to 0.903. Generally, 12-month models that included life stressors yielded more robust AUCs, however, models predicting cognitive outcomes and post-acute symptoms performed less well than models for other outcomes.
      Table 6Multivariable predictors of 6- and 12-month outcomes calculated using multivariable, backwards, stepwise logistic regression analyses. Adjusted odds ratios (OR), 95% confidence intervals (CI), p values, and area under the curve (AUC) for the entire model are shown.
      VariableAdjusted OR (95% CI)PModel AUC (95% CI)
      6-month mRS 4–60.755 (0.697–0.813)
      Age1.02 (1.00–1.04)0.021
      Baseline mRS1.99 (1.60–2.48)<0.001
      Neurological event during index hospitalization1.74 (1.02–2.98)0.043
      12-month mRS 4–60.830 (0.769–0.892)
      Age1.04 (1.01–1.06)0.002
      Baseline mRS2.05 (1.59–2.65)<0.001
      Hypoxic ischemic encephalopathy during index hospitalization3.58 (1.08–11.92)0.037
      Stressor: new disability4.88 (1.53–15.56)0.007
      6-month Barthel < 1000.789 (0.737–0.841)
      Age1.04 (1.02–1.06)<0.001
      Baseline mRS2.02 (1.58–2.59)<0.001
      Maximum SOFA score during index hospitalization1.10 (1.01–1.19)0.024
      12-month Barthel < 1000.882 (0.837–0.929)
      Age1.06 (1.03–1.09)<0.001
      Baseline mRS2.62 (1.90–3.62)<0.001
      Male sex0.33 (0.15–0.72)0.005
      Stressor: new disability11.74 (2.76–50.05)0.001
      6-month Telephone MoCA ≤ 180.688 (0.608–0.769)
      White race0.41 (0.21–0.83)0.012
      History of dementia6.82 (1.38–33.67)0.019
      Education>12 years0.30 (0.12–0.77)0.012
      12-month Telephone MoCA ≤ 180.664 (0.573–0.755)
      Age1.04 (1.01–1.07)0.003
      Education>12 years0.34 (0.15–0.80)0.014
      12-month NeuroQoL Anxiety T-score ≥ 600.731 (0.592–0.870)
      Male sex0.21 (0.06–0.74)0.015
      History of dementia6.42 (1.54–26.69)0.011
      12-month NeuroQoL Depression T-score ≥ 600.903 (0.812–0.993)
      Age1.11 (1.02–1.20)0.011
      Education>12 years0.14 (0.03–0.77)0.024
      Stressor: death of a close contact20.79 (3.57–121.14)0.001
      12-month NeuroQoL Fatigue T-score ≥ 600.840 (0.732–0.948)
      Stressor: food insecurity21.32 (1.92–236.80)0.013
      Stressor: new disability6.5 1(1.45–29.33)0.015
      Baseline mRS1.53 (1.05–2.23)0.027
      Azithromycin use during index hospitalization0.25 (0.08–0.82)0.022
      12-month NeuroQoL Sleep T-score ≥ 600.694 (0.574–0.814)
      Number of stressors1.43 (1.12–1.82)0.004
      12-month Post-Acute COVID-19 Symptoms0.685 (0.616–0.753)
      At least 1 stressor2.47 (1.39–4.40)0.002
      Mechanical ventilation during index hospitalization6.37 (2.16–18.78)0.001
      mRS = modified Rankin Scale, t-MOCA = telephone Montreal Cognitive Assessment; NeuroQoL = neurological quality of life; SOFA = Worst Sequential Organ Failure Assessment.
      Multivariable logistic regression analyses of 6-month NeuroQoL metrics not performed due to <2 univariate predictors with P < 0.05 Variables (univariate predictors with P < 0.05) assessed in each backwards, stepwise logistic regression model by outcome of interest:
      6-month mRS: age, education>12 years, pre-COVID disability, history of dementia, history of psychiatric disorder, any neurological complication during hospitalization, hypoxic ischemic brain injury, toxic-metabolic encephalopathy, azithromycin use during hospitalization.
      12-month mRS: age, pre-COVID disability, history of dementia, hypoxic ischemic brain injury, toxic-metabolic encephalopathy, increased caregiver responsibility, personal illness, new disability stressor.
      6-month Barthel Index: age, sex, pre-COVID disability, history of dementia, history of psychiatric disorder, any neurological complication during hospitalization, hypoxic ischemic brain injury, toxic-metabolic encephalopathy, mechanical ventilation, worst SOFA score, use of corticosteroids during hospitalization.
      12-month Barthel Index: age, sex, pre-COVID disability, hypoxic ischemic brain injury, toxic-metabolic encephalopathy, increased caregiver responsibility, personal illness, new disability stressor.
      6-month t-MoCA: age, race, education>12 years, history of dementia, tocilizumab use during hospitalization.
      12-month t-MoCA: age.
      12-month Anxiety: sex, history of dementia, at least one life stressor, number of stressors, social isolation, financial insecurity, food insecurity.
      12-month Depression: age, education>12 years, toxic-metabolic encephalopathy, number of stressors, death of a close contact,
      12-month Fatigue: education>12 years, pre-COVID disability, any neurological complication during hospitalization, ascorbic acid use during hospitalization, azithromycin use during hospitalization, at least one life stressor, number of stressors, food insecurity, personal illness, new disability.
      12-month Sleep: at least one life stressor, number of stressors, financial insecurity, personal illness, new disability, political conflict with close contacts.
      Post-acute COVID-19 symptoms: mechanical ventilation during index hospitalization, worst Sequential Organ Failure Assessment (SOFA) score during hospitalization, oxygen saturation during hospitalization, tocilizumab, corticosteroid, hydroxychloroquine, zinc, azithromycin or therapeutic anticoagulation during hospitalization, at least one life stressor, number of stressors, unemployment, personal illness.
      Fig. 1
      Fig. 1Independent predictors of outcome 6- and 12-months after COVID-19 hospitalization. Life stressors, age, female sex, baseline disability, and index COVID-19 severity were the most common predictors of functional status (measured by the modified Rankin Scale [mRS]), activities of daily living (ADLs, measured by the Barthel Index), cognition (measured by the telephone MoCA) and patient-reported anxiety, depression, fatigue and sleep (assessed using NeuroQoL metrics).

      4. Discussion

      In this prospective, longitudinal cohort study we identified independent predictors of 6- and 12-month functional (mRS, Barthel Index), cognitive (t-MoCA), quality of life (NeuroQoL depression, anxiety, fatigue and sleep) outcomes and post-acute COVID-19 symptoms following COVID-19 hospitalization. While predictors of disability (mRS, Barthel) were similar and largely consisted of age, baseline functional status, neurological complications during hospitalization and markers of higher severity of COVID-19 illness, life stressors, which were present in >50% of subjects, played a larger role in predicting NeuroQoL measures of depression, fatigue, sleep and post-acute symptoms of COVID-19. Indeed, the adjusted odds ratios for life stressors (including financial insecurity, food insecurity, death of a loved one and new disability) for predicting a variety of 12-month outcomes ranged from 2.5 to 20.8. While there are several reports of predictors of short term outcomes during hospitalization or within the first months following COVID-19 (e.g. mortality or discharge disposition) [
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      ,
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      • Elliott J.
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      Persistent COVID-19 symptoms in a community study of 606,434 people in England.
      ], this study is distinct in that it prospectively explores the impact of life stressors along with demographic, comorbid, and neurological events as predictors of quantitative long-term cognitive, functional, quality of life and post-acute symptoms outcomes in a large population.
      Life stressors were significantly associated with several 12-month outcomes, including worse mRS scores, activities of daily living, NeuroQoL depression, fatigue and sleep measures, and post-acute COVID-19 symptoms. The incorporation of pandemic-related stressors and related social determinants of health into predictive models is critical because these may represent areas of potential intervention. In a prior study of risk factors for post-acute COVID-19 symptoms among U.S. community dwellers with and without mild COVID-19 (not requiring hospitalization) conducted in February 2021, we identified multiple stressors (present within the month prior to interview) that were associated with the development of post-acute symptoms, most notably financial insecurity and unemployment [
      • Frontera J.A.
      • Lewis A.
      • Melmed K.
      • et al.
      Prevalence and predictors of prolonged cognitive and psychological symptoms following COVID-19 in the United States.
      ]. In that study, multivariable models predicting NeuroQoL measures of cognition, anxiety, depression, fatigue and sleep, demonstrated that several stressors were stronger predictors of abnormalities on quality of life testing than was SARS-CoV-2 infection itself. These data suggest an interplay of environmental and pandemic-related factors that may impact functional and neuropsychiatric outcomes.
      We found that older age was a consistent and prominent predictor of worse functional status (mRS and Barthel scores), cognitive abnormalities and depression. While these findings may appear intuitive, some have identified a paradoxical relationship, wherein older patients hospitalized with COVID-19 were more likely to make greater improvements in functional status and return to pre-hospitalization status at 18 weeks compared to patients <45 years old [
      • Capin J.J.
      • Wilson M.P.
      • Hare K.
      • et al.
      Prospective telehealth analysis of functional performance, frailty, quality of life, and mental health after COVID-19 hospitalization.
      ]. Female subjects and those who considered themselves “very fit” pre-COVID-19 were also less likely to recover to pre-hospitalization functional status33. These data may reflect a ceiling effect in frailty assessments that have limited ability to detect nuanced differences in functional status. Others have found that post-acute COVID-19 symptoms were more prevalent in older individuals [
      • Sudre C.H.
      • Murray B.
      • Varsavsky T.
      • et al.
      Attributes and predictors of long COVID.
      ,
      • Bai F.
      • Tomasoni D.
      • Falcinella C.
      • et al.
      Female gender is associated with long COVID syndrome: a prospective cohort study.
      ]. However, the types of post-acute COVID-19 symptoms may vary by age. For example, one study found that older individuals were more likely to have “any” post-acute COVID-19 feature (notably cognitive and respiratory symptoms), while younger patients more often reported headaches, anxiety/depression and abdominal symptoms [
      • Taquet M.
      • Dercon Q.
      • Luciano S.
      • Geddes J.R.
      • Husain M.
      • Harrison P.J.
      Incidence, co-occurrence, and evolution of long-COVID features: a 6-month retrospective cohort study of 273,618 survivors of COVID-19.
      ]. We also found that poor baseline functional status (pre-COVID mRS score) was a strong, independent predictor of 6- and 12-month mRS and Barthel Index scores and fatigue. Indeed, some studies have found that baseline frailty or disability scores are more closely associated with poor outcomes than age [
      • Simon N.R.
      • Jauslin A.S.
      • Rueegg M.
      • et al.
      Association of Frailty with adverse outcomes in patients with suspected COVID-19 infection.
      ].
      We identified female sex as an independent predictor of both anxiety and limitations in activities of daily living. Others have identified that female sex may be a predictor of post-acute COVID-19 symptoms [
      • Perez-Gonzalez A.
      • Araujo-Ameijeiras A.
      • Fernandez-Villar A.
      • Crespo M.
      • Poveda E.
      • Cohort C-otGSHRI
      Long COVID in hospitalized and non-hospitalized patients in a large cohort in Northwest Spain, a prospective cohort study.
      ,
      • Desgranges F.
      • Tadini E.
      • Munting A.
      • et al.
      PostCOVID19 syndrome in outpatients: a cohort study.
      ,
      • Sudre C.H.
      • Murray B.
      • Varsavsky T.
      • et al.
      Attributes and predictors of long COVID.
      ,
      • Taquet M.
      • Dercon Q.
      • Luciano S.
      • Geddes J.R.
      • Husain M.
      • Harrison P.J.
      Incidence, co-occurrence, and evolution of long-COVID features: a 6-month retrospective cohort study of 273,618 survivors of COVID-19.
      ,
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      Perception, prevalence, and prediction of severe infection and post-acute sequelae of COVID-19.
      ,
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      ,
      • Capin J.J.
      • Wilson M.P.
      • Hare K.
      • et al.
      Prospective telehealth analysis of functional performance, frailty, quality of life, and mental health after COVID-19 hospitalization.
      ,
      • Bai F.
      • Tomasoni D.
      • Falcinella C.
      • et al.
      Female gender is associated with long COVID syndrome: a prospective cohort study.
      ,
      • Kambhampati N.T.
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      • Ts D.
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      Assessment of post-Covid symptoms in Covid-19 recovered patients: a prospective cohort study in a tertiary Care Centre of South India.
      ,
      • Lorent N.
      • Vande Weygaerde Y.
      • Claeys E.
      • et al.
      Prospective longitudinal evaluation of hospitalised COVID-19 survivors 3 and 12 months after discharge.
      ,
      • Peghin M.
      • Palese A.
      • Venturini M.
      • et al.
      Post-COVID-19 symptoms 6 months after acute infection among hospitalized and non-hospitalized patients.
      ,
      • Townsend L.
      • Dyer A.H.
      • Jones K.
      • et al.
      Persistent fatigue following SARS-CoV-2 infection is common and independent of severity of initial infection.
      ,
      • Whitaker M.
      • Elliott J.
      • Chadeau-Hyam M.
      • et al.
      Persistent COVID-19 symptoms in a community study of 606,434 people in England.
      ,
      • Sneller M.C.
      • Liang C.J.
      • Marques A.R.
      • et al.
      A longitudinal study of COVID-19 sequelae and immunity: baseline findings.
      ]. In a survey of 999 community dwellers across the U.S, female sex, along with younger age, racial/ethnic minority status, baseline disability, fewer years of formal education, and/or a history of psychiatric disease were significant predictors of post-acute COVID-19 symptoms [
      • Frontera J.A.
      • Yang D.
      • Lewis A.
      • et al.
      A prospective study of long-term outcomes among hospitalized COVID-19 patients with and without neurological complications.
      ,
      • Xiong Q.
      • Xu M.
      • Li J.
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      Clinical sequelae of COVID-19 survivors in Wuhan, China: a single-Centre longitudinal study.
      ]. Mechanisms of injury more common in women, such as autoimmune disease, might explain some of these differences. Indeed, women have higher basal levels of immunoglobulins and respond more robustly to both infections and vaccines, with increased cytokine production and T-cell response, compared to men [
      • Rainville J.R.
      • Hodes G.E.
      Inflaming sex differences in mood disorders.
      ,
      • Nalbandian G.
      • Paharkova-Vatchkova V.
      • Mao A.
      • Nale S.
      • Kovats S.
      The selective estrogen receptor modulators, tamoxifen and raloxifene, impair dendritic cell differentiation and activation.
      ,
      • Bouman A.
      • Heineman M.J.
      • Faas M.M.
      Sex hormones and the immune response in humans.
      ]. Additionally, baseline pre-COVID-19 prevalence rates of anxiety and depression are nearly two-fold higher in women than men [
      • Albert P.R.
      Why is depression more prevalent in women?.
      ,
      • McLean C.P.
      • Asnaani A.
      • Litz B.T.
      • Hofmann S.G.
      Gender differences in anxiety disorders: prevalence, course of illness, comorbidity and burden of illness.
      ]. It is possible that subclinical or undiagnosed mood disorders may have been unmasked in the context of pandemic- or illness-related stressors.
      We found that severity of index COVID-19 illness (SOFA scores or intubation) was a predictor of limited activities of daily living and post-acute COVID-19 symptoms. Indeed, many others have identified index COVID-19 severity as a predictor of protracted COVID symptoms [
      • Taquet M.
      • Dercon Q.
      • Luciano S.
      • Geddes J.R.
      • Husain M.
      • Harrison P.J.
      Incidence, co-occurrence, and evolution of long-COVID features: a 6-month retrospective cohort study of 273,618 survivors of COVID-19.
      ,
      • Lorent N.
      • Vande Weygaerde Y.
      • Claeys E.
      • et al.
      Prospective longitudinal evaluation of hospitalised COVID-19 survivors 3 and 12 months after discharge.
      ,
      • Peghin M.
      • Palese A.
      • Venturini M.
      • et al.
      Post-COVID-19 symptoms 6 months after acute infection among hospitalized and non-hospitalized patients.
      ,
      • Al-Aly Z.
      • Xie Y.
      • Bowe B.
      High-dimensional characterization of post-acute sequelae of COVID-19.
      ,
      • Taquet M.
      • Geddes J.R.
      • Husain M.
      • Luciano S.
      • Harrison P.J.
      6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records.
      ,
      • Yoo S.M.
      • Liu T.C.
      • Motwani Y.
      • et al.
      Factors associated with post-acute sequelae of SARS-CoV-2 (PASC) after diagnosis of symptomatic COVID-19 in the inpatient and outpatient setting in a diverse cohort.
      ,
      • Abdelhafiz A.S.
      • Ali A.
      • Maaly A.M.
      • Mahgoub M.A.
      • Ziady H.H.
      • Sultan E.A.
      Predictors of post-COVID symptoms in Egyptian patients: drugs used in COVID-19 treatment are incriminated.
      ]. Some studies have suggested that the severity of acute respiratory failure, rather than the pathogen involved, predicts neuropsychiatric sequelae. One study compared electronic medical records of patients hospitalized for COVID-19 or a severe acute respiratory infection, to a reference population of hospitalized and non-hospitalized patients without these conditions [
      • Clift A.K.
      • Ranger T.A.
      • Patone M.
      • et al.
      Neuropsychiatric ramifications of severe COVID-19 and other severe acute respiratory infections.
      ], and found significantly higher rates of new onset anxiety, depression, bipolar disorder or psychotic disorder compared to the reference population. However, rates of neuropsychiatric sequelae were similar in COVID-19 and non-COVID-19 acute respiratory infection patients, suggesting that the prime driver of long-term events is disease severity, and not the specific pathogen.
      Conversely, other cohorts have found higher rates of post-acute symptoms among patients with COVID-19 compared to seasonal influenza, even after adjusting for severity of illness, suggesting that these long-term sequelae may be unique to SARS-CoV-2 [
      • Al-Aly Z.
      • Xie Y.
      • Bowe B.
      High-dimensional characterization of post-acute sequelae of COVID-19.
      ,
      • Taquet M.
      • Geddes J.R.
      • Husain M.
      • Luciano S.
      • Harrison P.J.
      6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records.
      ]. We did not identify index COVID-19 severity as a predictor of NeuroQoL metrics such as anxiety, depression, fatigue or sleep outcomes. Others have also failed to find an association of severity of index illness (defined by oxygen requirement or intubation status) when evaluating certain outcomes of hospitalized patients [
      • Bai F.
      • Tomasoni D.
      • Falcinella C.
      • et al.
      Female gender is associated with long COVID syndrome: a prospective cohort study.
      ]. Differing relationships of COVID-19 severity with sequelae may be explained by differences in comparator groups, e.g. some studies evaluated hospitalized versus non-hospitalized COVID-19 patients, whereas we compared mechanically ventilated hospital patients to non-intubated hospitalized patients. Additionally, these incongruities may simply reflect the fact that the sickest patients died or were too impaired to participate in long-term outcome batteries. Because we did not have a non-COVID-19 comparator group, we cannot make any assertions regarding whether outcomes were driven by severity of illness or are specific to the SARS-CoV-2 pathogen.
      Finally, most COVID-19 specific medications used during index hospitalization did not independently predict 12-month outcomes, with the exception of azithromycin, which was protective against severe fatigue scores. Since this cohort represents the first SARS-CoV-2 wave in the U.S., many subsequent studies that identified effective acute therapies were not yet published [
      • Cavalcanti A.B.
      • Zampieri F.G.
      • Rosa R.G.
      • et al.
      Hydroxychloroquine with or without azithromycin in mild-to-moderate Covid-19.
      ,
      • Group RC
      • Horby P.
      • Lim W.S.
      • et al.
      Dexamethasone in hospitalized patients with Covid-19.
      ,
      • Beigel J.H.
      • Tomashek K.M.
      • Dodd L.E.
      • et al.
      Remdesivir for the treatment of Covid-19 - final report.
      ,
      • Investigators R.-C.
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      • et al.
      Therapeutic anticoagulation with heparin in critically ill patients with Covid-19.
      ,
      • Investigators A.
      • Investigators AC-a, Investigators R-C
      • et al.
      Therapeutic anticoagulation with heparin in noncritically ill patients with Covid-19.
      ]. Indeed, many critically ill patients were treated with anticoagulation based on ferritin and D-Dimer levels per hospital protocol, while decadron was sporadically, and inconsistently used. There were too few patients who received remdesivir to even perform statistical analyses. Because certain COVID-19 specific medications may have variable beneficial or harmful impact depending on the population treated, it is likely we were unable to detect any effect due to both underpowering and poor patient selection. The relationship of in-hospital azithromycin use and 12-month fatigue scores is intriguing, since azithromycin has been reported to provide symptomatic relief to patients with chronic fatigue syndrome [
      • Vermeulen R.C.
      • Scholte H.R.
      Azithromycin in chronic fatigue syndrome (CFS), an analysis of clinical data.
      ]. Azithromycin has been shown to have immune modulating capacity and its utility in chronic fatigue syndrome patients is thought to be linked to its effect on chronically primed immune cells in the brain [
      • Vermeulen R.C.
      • Scholte H.R.
      Azithromycin in chronic fatigue syndrome (CFS), an analysis of clinical data.
      ]. Additionally, azithromycin has anti-inflammatory and anti-viral properties [
      • Venditto V.J.
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      Immunomodulatory effects of azithromycin revisited: potential applications to COVID-19.
      ], which may play a role in the pathophysiology of post-COVID-19 chronic fatigue. While acute COVID-19 studies did not demonstrate a beneficial effect of azithromycin on symptoms at 14–28 days [
      • Oldenburg C.E.
      • Pinsky B.A.
      • Brogdon J.
      • et al.
      Effect of Oral azithromycin vs placebo on COVID-19 symptoms in outpatients with SARS-CoV-2 infection: a randomized clinical trial.
      ,
      • Group PTC
      Azithromycin for community treatment of suspected COVID-19 in people at increased risk of an adverse clinical course in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial.
      ] post-infection, hospitalization rates [
      • Group PTC
      Azithromycin for community treatment of suspected COVID-19 in people at increased risk of an adverse clinical course in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial.
      ], requirement for invasive mechanical ventilation [
      • Group RC
      Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial.
      ], discharge disposition [
      • Group RC
      Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial.
      ], clinical recovery [
      • Furtado R.H.M.
      • Berwanger O.
      • Fonseca H.A.
      • et al.
      Azithromycin in addition to standard of care versus standard of care alone in the treatment of patients admitted to the hospital with severe COVID-19 in Brazil (COALITION II): a randomised clinical trial.
      ], or mortality [
      • Group RC
      Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial.
      ,
      • Hinks T.S.C.
      • Cureton L.
      • Knight R.
      • et al.
      Azithromycin versus standard care in patients with mild-to-moderate COVID-19 (ATOMIC2): an open-label, randomised trial.
      ], further study of azithromycin for the treatment of PASC fatigue may be warranted.
      Strengths of this study include the prospective ascertainment of data from hospitalization through 12-month follow-up, the robust characterization of neurological events during index hospitalization, accounting of pre-COVID functional status, assessment of life stressors, and the use of both quantitative and patient-reported long-term outcome metrics. There are also several limitations to this study that should be noted. First, we did not a priori investigate certain factors that may be important outcome predictors. For example, a multi-omic study of 309 patients (51% were hospitalized for index COVID-19) examined predictors of post-acute symptoms (most commonly fatigue, cough and anosmia/dysgeusia) at 2–3 months post SARS-CoV-2 infection. In this study, type 2 diabetes, Epstein-Barr virus (EBV) viremia, SARS-CoV-2 RNAemia and several autoantibodies were identified as risk factors for post-acute symptoms [
      • Su Y.
      • Yuan D.
      • Chen D.G.
      • et al.
      Multiple early factors anticipate post-acute COVID-19 sequelae.
      ]. Because index hospitalization occurred early in the pandemic in our study, we did not have measures of SARS-CoV-2 viral load, nor did we assess autoantibodies or EBV levels. Though we evaluated the impact of diabetes on our outcome measures, we did not find any significant associations. Differences in study populations (hospitalized versus not) and time frame of outcome assessments, may in part explain this discrepancy. While most of our models demonstrated robust AUCs, models for cognition and post-acute COVID-19 symptoms yielded middling AUC values, suggesting there are important factors that we did not account for in these models. We did not have baseline pre-COVID cognitive testing to evaluate change over time. It is also likely that sicker patients may not have been able to participate in cognitive testing or review of post-acute symptoms. Second, there were some differences in the number participants between the two time points, and the same individuals are not represented at each time point. However, aside from older age in those lost to follow-up at 12-months, there were no other significant differences between those who completed only 6-month follow-up and those who completed 12-month follow-up. Third, we did not collect life stressor data at the 6-month visit, so we were unable to account for these variables in 6-month outcome models. Fourth, some medications utilized to treat COVID-19 appeared to be associated with worse outcomes in univariate analyses. This is likely related to bias by indication, since many of these medications were reserved for the sickest patients, and little data existed to guide standardized management during the first wave of the pandemic. Last, we dichotomized NeuroQoL scores at ≥1 standard deviation above the mean. There is some data to suggest that a clinically meaningful threshold for dichotomization may be 0.5 standard deviations (SD) above the mean [,
      • Terwee C.B.
      • Peipert J.D.
      • Chapman R.
      • et al.
      Minimal important change (MIC): a conceptual clarification and systematic review of MIC estimates of PROMIS measures.
      ]. Utilization of a more liberal 0.5 SD threshold would increase the prevalence of worse NeuroQoL measures and could lead to differences in multivariable models.

      5. Conclusions

      In adults hospitalized with COVID-19, we found that traditional predictors of poor outcome, including older age, poor pre-COVID functional status and index severity of illness, were independently associated with worse mRS, Barthel Index, t-MoCA scores and persistent COVID-19 symptoms. We additionally found that life stressors significantly impacted disability, depression, fatigue, sleep and post-acute COVID-19 symptom metrics. Interventions targeted at ameliorating modifiable life stressors merit further investigation.

      Author contributions

      JAF contributed to conception, study design, data analysis and drafting of the manuscript.
      SS, DY, AL, ASL, KM, ST contributed to data curation, investigation, writing- review and editing of the manuscript.
      AdH, SY, AL, ASL, KM, LB, TW and SLG contributed to study conception and design, data interpretation and critical revision of the manuscript.

      Data availability

      De-identified data will be made available to qualified investigators upon written request to the corresponding author

      Declaration of Competing Interest

      Potential conflict of interest: JAF receives funding for the following COVID-19-related grants: NIH/NIA R01AG077422 , NIH/NINDS 3U24NS11384401S1 , NIH/NHLBI 1OT2HL161847-01 ; LJB and ST receive funding for the following COVID-19-related grant: NIH/NHLBI 1OT2HL161847-01 . TW receives funding for the following COVID-19 related grant: NIH/NIA R01AG077422 . JAF and TW have received funding for the COVID-19 related grant: NIH/NIA 3P30AG066512-01S1 . AdH, SY, SS, DY, AL, ASL, KM, and SLG do not report any relevant conflicts of interest.

      Acknowledgements

      We would like to thank the patients and clinicians who contributed data to this project.

      Appendix A. Supplementary data

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