Long-term Mental Health Morbidity Among Survivors of COVID-19 Critical Illness – A Population-Based Cohort Study
Creators: name: Slutsky, Arthur
name: Munshi, Laveena
name: Talarico, Robert
name: Fiest, Kirsten
name: Ferguson, Niall
name: Fan, Eddy
name: Vigod, Simone
name: Myran, Daniel
name: Brodie, Daniel
name: Hodgson, Carol L
name: Fernando, Shannon
name: Tanuseputro, Peter
name: Needham, Dale M
name: Fowler, Robert
name: McIsaac, Daniel I
name: Scales, Damon
name: Qureshi, Danial
name: Rochwerg, Bram
name: Cook, Deborah
name: Herridge, Margaret
name: Kwado Kyeremanteng
name: Sood, Manish
name: Bienvenu, Joseph
Publisher: name: Open Science Framework
Year: 2023
Subjects: subject: Medicine and Health Sciences

description: AUTHORS: Shannon M. Fernando, Danial Qureshi, Robert Talarico, Eddy Fan, Daniel I. McIsaac, Simone N. Vigod, Manish M. Sood, Daniel T. Myran, Carol L. Hodgson, Bram Rochwerg, Laveena Munshi, Kirsten M. Fiest, O. Joseph Bienvenu, Dale M. Needham, Daniel Brodie, Niall D. Ferguson, Robert A. Fowler, Deborah J. Cook, Arthur S. Slutsky, Damon C. Scales, Margaret S. Herridge, Peter Tanuseputro, Kwadwo Kyeremanteng BACKGROUND Severe Coronavirus 2019 (COVID-19) is a common cause of critical illness and intensive care unit (ICU) admission, most often due to hypoxemic respiratory failure. Incidence of ICU admission and hospital mortality among COVID-19 patients has varied geographically and across the duration of the pandemic (Tzotzos et al., Crit Care, 2020). The initial focus of research in critical care focused upon treatments for critically ill COVID-19 patients, and adequate use of resources (Alhazzani et al., Intensive Care Med, 2020; Weissman et al., Ann Intern Med, 2020). Since then, there has been great emphasis upon understanding survivorship after COVID-19 critical illness, and the long-term outcomes among COVID-19 ICU survivors (Hosey and Needham, Nat Rev Dis Primers, 2020). Survivors of critical illness are known to have substantial physical morbidity (Herridge et al., N Engl J Med, 2003; Herridge et al., N Engl J Med, 2011), and existing data also shows that these patients are at increased risk of downstream psychiatric morbidity (Wunsch et al., JAMA, 2014; Sivanathan et al., Intensive Care Med, 2019; Olafson et al., Intensive Care Med, 2021), including suicide and self-harm (Fernando et al., BMJ, 2021). Understanding long-term outcomes among survivors of COVID-19 remains an important avenue for future research (Marshall et al., Lancet Infect Dis, 2020). Survivors of COVID-19 critical illness may have experienced invasive critical care interventions, such as invasive mechanical ventilation and extracorporeal life support (Wunsch, Am J Respir Crit Care Med, 2020; Barbaro et al., Lancet, 2020), treatments that have been associated with higher physical and mental health morbidity (Wunsch et al., JAMA, 2014; Hodgson et al., Lancet Respir Med, 2022; Fernando et al., JAMA, 2022). While existing data suggest that survivors of COVID-19 critical illness may experience substantial physical morbidity (Heesakkers et al., JAMA, 2022), data on mental health morbidity is less clear (Sankar et al., Chest, 2022). Furthermore, how mental health morbidity among survivors of COVID-19 critical illness compares to other survivors of critical illness is unknown, and is an important question as the physical morbidity experienced by these patient populations does not appear to differ (Hodgson et al., Am J Respir Crit Care Med, 2022). We seek to investigate the incidence of long-term mental health morbidity in survivors of COVID-19 critical illness, using population-based data from the province of Ontario, and compare this incidence to other survivors of critical illness, as well as non-ICU hospitalized patients with COVID-19. STUDY OBJECTIVES 1. To describe the sociodemographic characteristics of survivors of COVID-19 critical illness, and to examine the incidence of new mental health diagnoses, and self-harm. 2. To examine if COVID-19 critical illness is associated with a higher incidence of mental health diagnoses among survivors, as compared to ICU survivors without COVID-19; 3. To examine the proportion of COVID-19 critical illness survivors using inpatient (i.e., requiring hospitalization for mental health diagnoses) and outpatient (e.g., visiting a psychiatrist as an outpatient, number of visits within 1-year, time to first visit, and costs related to outpatient resource use) mental health services; 4. To investigate the prognostic factors associated with incident downstream mental health diagnoses among survivors of COVID-19 critical illness. METHODS Data Sources and Setting This will be a population-level cohort study using health administrative databases from the province of Ontario in Canada (population 14.6 million). Within Ontario’s single payer healthcare system, all publicly funded healthcare services, physician, hospital, and demographic information for residents are recorded in administrative databases. These datasets are linked using unique encoded identifiers, and analyzed at ICES, an independent, non-profit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyze healthcare and demographic data, without consent, for health system evaluation and improvement. ICES is funded by an annual grant from the Ontario Ministry of Health and the Ministry of Long-term Care. Patients are linked across provincial databases using their Ontario Health Insurance Plan (OHIP) number, which is unique to each citizen in Ontario. We will link ten databases at ICES, as performed previously (Fernando et al., BMJ, 2021; Fernando et al., JAMA, 2022) at the individual patient level, from January 1, 2020, through March 31, 2022. Data on illness severity (Multiple Organ Dysfunction Score [MODS]) and co-interventions will be obtained from the Critical Care Information System (CCIS). CCIS provides near-real time information on every patient admitted to a level 2 (designation for those requiring increased observation, those “stepping down” from higher levels of care, or those requiring monitoring and support for an organ system) or level 3 (designation for those requiring advanced respiratory support alone, or monitoring and support for two or more organ systems) critical care unit in Ontario’s acute care hospitals. The system captures data on bed availability, critical care service utilization and patient outcomes. This provides consistent and reliable information on the utilization of critical care resources across the province. The system provides an important medium for monitoring and managing the province’s critical care resources more effectively, and for highlighting opportunities to implement quality improvement initiatives at individual hospitals and across Local Health Integration Networks (LHINs). Data contained in ICES are full and complete, with the exception of emigration from Ontario, which represents approximately 0.5% of patients per year. Patients The entire study period (including outcome ascertainment) will be from January 1, 2020 to September 30, 2022. We will include consecutive adult patients (≥ 18 years of age), with an index intensive care unit (ICU) discharge in Ontario from January 1, 2020, through March 30, 2022, with a diagnosis of COVID-19, and who survived to hospital discharge. For patients with multiple ICU admissions, we will randomly select one admission per patient during the accrual period. We will identify ICU admission through the use of previously validated algorithms from the Canadian Institute for Health Information Discharge Abstract Database (Scales et al., J Clin Epidemiol, 2006). Since routine SARS-CoV-2 testing has been done in Ontario hospitals during the study period, patients with COVID-19 will be identified by a positive polymerase chain reaction (PCR) test for SARS-CoV-2, linked within 14 days to an index admission where the most responsible diagnosis is COVID-19 (using International Classification of Diseases, Version 10 [ICD-10] codes U071 and U072), as performed previously (McNaughton et al., CMAJ, 2022). We will not exclusively rely on PCR testing, due to concerns surrounding incidental positive cases that may be found during ICU admission, particularly in 2022 with the Omicron variant. The primary control group will be adult patients admitted to an ICU with pulmonary infection, but without a positive PCR test for SARS-CoV-2 linked to the index admission, and surviving to hospital discharge. Pulmonary infection will be identified using validated ICD-10 coding for either pneumonia or influenza (J09-J18; Skull et al., Epidemiol Infect, 2010). We will also include additional control groups: 1) Adult patients admitted to the ICU during the study period for any cause, without a positive PCR test for SARS-CoV-2 linked to the index admission, and surviving to hospital discharge; 2) Adult patients admitted to hospital with a positive PCR test for SARS-CoV-2 linked to the index admission, but without ICU admission, and surviving to hospital discharge; and 3) Adult patients with an outpatient positive PCR test for SARS-CoV-2, and not requiring hospital admission within 30 days prior to or after the date of the positive test. We will identify important patient characteristics at the time of the index admission, including age, sex, Charlson comorbidity index (CCI), date of admission, and the number of hospital admissions in the previous year. We will calculate duration of ICU and hospital length of stay from admission and discharge dates. We will obtain neighbourhood income (categorized into quintiles), rurality, and area-level measures of essential worker and visible minority volume through postal code conversion files based on Statistics Canada census data. Recent immigrant status will be captured from the Immigration, Refugees and Citizenship Canada (IRCC) database, which includes all immigration records for people landing in Ontario from 1985 onwards. We will also capture history of pre-existing mental health diagnoses that occurred in the 5 years prior to the index admission, through the use of ICD-10 codes and whether patients had any outpatient mental health visits with a primary care provider or psychiatrist in the previous year (Fernando et al., JAMA, 2022). We will also capture the Charlson Comorbidity Index in the 5 years prior to index admission. Finally, we will record life support interventions received during hospital admission, including invasive mechanical ventilation (delivered through an endotracheal or tracheostomy tube), non-invasive mechanical ventilation (by facemask), renal replacement therapy, tracheostomy, and extracorporeal life support. Outcomes The primary outcome will be incidence of the composite of new mental health diagnoses post-discharge occurring prior to the end of the study period (September 30, 2022), the patient’s date of death, or emigration from Ontario. New mental health diagnoses are categorized in ICES using the Mental Health and Addictions Scorecard (as utilized previously: Gatov et al., Can J Psychiatry, 2017; Fernando et al., JAMA, 2022), and can be noted from either inpatient hospital admissions, or outpatient encounters (including family physician or emergency department visits). This composite outcome includes any of the following: mood or anxiety disorders (depression, anxiety, posttraumatic stress disorder), schizophrenia or psychotic disorders, and other mental health diagnoses (including adjustment reaction, reactive depression, anxiety neurosis, hysteria, neurasthenia, obsessive-compulsive neurosis, personality disorders, sexual deviations, and psychosomatic illness). In addition to a composite variable, we will also separately evaluate the incidence of each of these individual diagnoses. Secondary outcomes include social problems (including economic problems, marital difficulties, family disruption or divorce, parent-child problems, problems with aged parents, educational problems, social maladjustment, occupational problems, and legal problems), substance misuse (secondary to alcohol or drug dependence), and hospital visit for deliberate self-harm. Self-harm behaviour (e.g., deliberate drug overdose or self-inflicted traumatic injury) will be identified using the ICES Mental Health and Addictions Scorecard (Fernando et al., BMJ, 2021; Bayoumi et al., CMAJ Open, 2020). Statistical Analyses We will conduct all statistical analyses using SAS Enterprise Guide 7.1 (SAS Institute, Cary, NC). We will present data as mean values with standard deviations, or medians with interquartile ranges. Based on previous work we anticipate &lt;1% of neighborhood income quintile and rurality and &lt;3% of MODS to be missing completely at random. We will impute the mode for the former and the 5-yr age group mean MODS for the latter. To compare crude mental health outcomes rates we will report the incidence proportion and rate per 100 person years with accompanying 95% Confidence Intervals between COVID-19 patients versus matched controls To identify the association between COVID-19 critical illness and post-discharge mental health diagnoses and self-harm, we will follow existing recommendations for causal inference in critical care (Lederer et al., Ann Am Thorac Soc, 2020), and compare survivors of COVID-19 critical illness to each of the selected control population using overlap propensity score weighting. Overlap weights is a technique that assigns less weight to those with outlier propensity scores, and more weight to those with propensity scores close to 0.5. This technique has shown improved covariate balance and overlap of probabilities in creating propensity scores, as compared with other approaches (Thomas et al., JAMA, 2020). Furthermore, this method does not exclude any potential study participants and is not affected by extreme outliers over dominating the analysis, which can occur with traditional propensity score matching, or inverse probability treatment weighting. We will calculate weights using relevant variables that we have determined a priori (based on existing literature and hypotheses about potential for confounding, as suggested by existing recommendations). Confounding variables are listed below: Pre-Admission Confounders: Age, sex, immigrant status (yes or no), neighborhood income quintile, rural residency, area-level essential worker volume quintile, area-level visible minority volume quintile, number of hospital admissions in the past year, number of outpatient psychiatry visits in the past year, number of total outpatient physician visits in the past year, outpatient mental health and substance use visits to primary care providers, comorbidity burden (Charlson Comorbidity Index), specific comorbidities that might influence ICU candidacy (malignancy, renal failure, chronic liver disease, and chronic obstructive pulmonary disease), previous controlled narcotic treatment, pre-existing mental health disorders, and previous history of self-harm. Admission Confounders: Year and month of admission, severity of illness (MODS; for sensitivity analysis only – see below), invasive mechanical ventilation, renal replacement therapy, tracheostomy, extracorporeal life support, hospital length of stay, ICU length of stay, and discharge disposition. We will compare cohorts before and after overlap weights for balance by using weighted standardized differences. We will fit an overlap weighted cause specific Cox proportional hazards model to examine COVID-19 status versus controls with the study outcomes, while accounting for mortality as a competing risk. Furthermore, we will use overlap weighted Cumulative Incidence Function (CIF) curves to plot the weighted CIF of new mental health diagnoses in the follow-up. The CIF equality test will be used to test if the curves are statistically significant. To account for unmeasured confounders, we will compute an E value (VanderWeele et al., Ann Intern Med, 2017). A large E value implies that considerable unmeasured confounding would be needed to explain away the effect estimate. To identify prognostic factors associated with our primary outcome of interest, we will generate separate cause-specific Cox proportional hazards regression models using the COVID-19 critical illness survivor cohort. In model creation, we will follow the PROGRESS (prognosis research strategy) guidelines (Riley et al., PLoS Med, 2013; Steyerberg et al., PLoS Med, 2013), and recommendations for development of prognostic models in critical care (Leisman et al., Crit Care Med, 2020). These guidelines recommend a clinically hypothesis driven approach for a priori selection of all model variables, as opposed to bivariate association testing methods. We plan to include the following variables in our model: Pre-Admission Confounders: Age, sex, immigrant status (yes or no), income quintile, rural residency, essential worker quintile, visible minority quintile, number of outpatient psychiatry visits in the past year, outpatient mental health and substance use visits to primary care providers, comorbidity burden (Charlson), pre-existing mental health disorders, and previous history of self-harm. Admission Confounders: Year and month of admission, severity of illness (MODS; for sensitivity analysis only – see below), invasive mechanical ventilation, renal replacement therapy, tracheostomy, extracorporeal life support, hospital length of stay, ICU length of stay, and discharge disposition. SENSITIVITY ANALYSES Here we included pre-planned sensitivity analyses that are designed to further evaluate the impact of potential confounders in influencing our causal inference analyses. 1. New vs. Worsening Mental Health Diagnoses Among COVID-19 Critical Illness Survivors While the ICES databases have strong validity in identifying new mental health diagnoses, they may not be able to as easily identify worsening or decompensated mental illness among patients with pre-existing mental health diagnoses. For example, it is possible that a patient with known, well-controlled depression for years may have a deterioration in the severity of their depression following ICU admission for COVID-19, and thus may have more frequent follow-up with their providers for care of their depression, or may present to the emergency department with thoughts of self-harm. Since this is not a “new” diagnosis at follow-up, the patient would not meet our pre-determined outcome, even though it is of interest to identify patients with worsening of pre-existing mental illness that may be associated with COVID-19 infection. Therefore, we will plan to perform a sensitivity analysis following exclusion of patients with any mental health diagnoses in the previous five years. This would provide greater reliability in results that find patients with new mental health diagnoses do or do not experience psychiatric morbidity following their ICU admission. 2. Exclusion of patients not receiving invasive mechanical ventilation While many COVID-19 patients were able to be maintained on non-invasive mechanical ventilation, a substantial proportion of patients did require invasive ventilation. While data showing an association between severity of illness (and receipt of invasive ICU interventions) and downstream mental health morbidity is mixed, we propose a sensitivity analysis excluding patients who did not receive invasive ventilation. This would only apply to the comparison between critically ill COVID-19 patients and control populations from the ICU. 3. Exclusion of patients with Omicron variant of SARS-CoV-2 Unlike the initial wild-type strain and the first several variants of SARS-CoV-2, the Omicron variant was associated with lower severity of illness, and lower case fatality in immunocompetent individuals (Menni et al., Lancet, 2022). Furthermore, risk of post-COVID-19 sequelae appear to be reduced among patients infected with the Omicron variant (Antonelli et al., Lancet, 2022). Finally, given the higher rate of infection with the Omicron variant, cases of hospital admission due to COVID-19 infection, and incidental cases of COVID-19 were difficult to differentiate (Bouzid et al., Ann Intern Med, 2022). Unfortunately, the ICES databases do not contain data that would allow for distinction between SARS-CoV-2 variants. The first cases of the Omicron variant in Ontario were recorded on November 24, 2021. As such, in this sensitivity analysis, we would exclude any patients admitted to ICU on or after this date. 4. Inclusion of Illness Severity (Multiple Organ Dysfunction Score) The Multiple Organ Dysfunction Score (MODS) is a measure of illness severity that is commonly used in the critical care literature (Marshall et al. Crit Care Med, 1995). The MODS is available in the ICES databases for ICU patients only up to February 28, 2021, and as such cannot be applied to all patients in the study period. For this reason, it will not be included as a covariable in the primary analysis. However, we will perform a sensitivity analysis including ICU patients admitted from April 1, 2020 to February 28, 2021, and incorporating the MODS as a covariate in the overlap weights analysis. This would only apply to the comparison of critically ill COVID-19 patients and critically ill patients with pulmonary infection but without a positive SARS-CoV-2 test. Ethics Studies conducted at ICES using administrative data fall under section 45 of the Personal Health Information Act of Ontario, and do not require research ethics board approval.