key: cord-0275824-evbnewfq
authors: McCreary, E. J.; Kip, K. E.; Bariola, J. R.; Schmidhofer, M.; Minnier, T.; Mayak, K.; Albin, D.; Daley, J.; Linstrum, K.; Hernandez, E.; Sackrowitz, R.; Hughes, K.; Horvat, C.; Snyder, G. M.; McVerry, B. J.; Yealy, D. M.; Huang, D. T.; Angus, D. C.; Marroquin, O. C.
title: A Learning Health System Approach to the COVID-19 Pandemic: System-Wide Changes in Clinical Practice and 30-Day Mortality Among Hospitalized Patients
date: 2021-08-28
journal: nan
DOI: 10.1101/2021.08.26.21262661
sha: 5e02a32b293961a148864c6e76e40ff45253e502
doc_id: 275824
cord_uid: evbnewfq

Introduction: Rapid, continuous implementation of credible scientific findings and regulatory approvals is often slow in large, diverse health systems. The COVID 19 pandemic created a new threat to this common 'slow to learn and adapt' model in healthcare. We describe how UPMC committed to a rapid learning health system (LHS) model to respond to the COVID 19 pandemic. Methods: An observational cohort study was conducted among 11,429 hospitalized patients from 22 hospitals (PA, NY) with a primary diagnosis of COVID 19 infection (March 19, 2020 to June 6, 2021). Sociodemographic and clinical data were captured from UPMC electronic medical record (EMR) systems. Patients were grouped into four time-defined patient 'waves' based on nadir of daily hospital admissions, with wave 3 (September 20, 2020 to March 10, 2021) split at its zenith due to high volume with steep acceleration and deceleration. Outcomes included changes in clinical practice (e.g., use of corticosteroids, antivirals, and other therapies) in relation to timing of internal system analyses, scientific publications, and regulatory approvals, along with 30-day rate of mortality over time. Results: Mean (SD) daily number of hospital admissions was 26 (28) with a maximum 7 day moving average of 107 patients. System wide implementation of the use of dexamethasone, remdesivir, and tocilizumab occurred within days of release of corresponding seminal publications and regulatory actions. After adjustment for differences in patient clinical profiles over time, each month of hospital admission was associated with an estimated 5% lower odds of 30 day mortality (adjusted OR = 0.95, 95% confidence interval: 0.92 to 0.97, p < .001). Conclusions: In our large LHS, near real-time changes in clinical management of COVID 19 patients happened promptly as scientific publications and regulatory approvals occurred throughout the pandemic. Alongside these changes, patients with COVID 19 experienced lower adjusted 30 day mortality following hospital admission over time.

Integration of evidence-based practices is notoriously slow, especially at larger, diverse, health care systems. The emergence of a rapidly-spreading, severe respiratory virus pandemic created a heightened need for change in this common approach. 1, 2 Current research infrastructure and information technology systems facilitate unprecedented volume and speed of pandemicrelated information, and data sharing in the biomedical literature, social media, and other resources allow insights to flow much more quickly. 3 Making efficient, optimal use of this massive, constantly changing information is paramount to minimize the deadly impact of the pandemic, and for the health and welfare of humanity at large.

In addition to the need for coordinated global approaches to pandemics, 1 individual health care delivery systems must seek to give equitable, evidence-based care across institutions regardless of geographical region or hospital type. 4 In this realm, a learning health system (LHS) is an ideal organizing principle to inform evidence-based responses to public health emergencies like COVID-19. 4 The LHS concept is characterized as an environment in which "science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience." 5 Seeking to embrace the LHS model, the UPMC health system leveraged its science, data, and analytics capabilities and established the multidisciplinary COVID-19 Therapeutics Committee in early 2020. The purpose of this Committee was to evaluate any possible COVID-19 treatment option and rapidly disseminate updated guidelines to all institutions within the system. The Committee also coordinated with information technology specialists to build forcing functions into several electronic medical records (EMRs) to enforce practice guideline . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 recommendations, and also collaborated with research teams to integrate clinical practice with clinical trial enrollment across the enterprise. This LHS process, coupled with regular internal COVID-19 analyses from the UPMC Clinical Analytics Team (described in Methods), formed the basis for establishing, disseminating, and documenting data-driven clinical recommendations to all UPMC outpatient and in-patient facilities caring for patients with COVID-19.

We describe the UPMC LHS approach to the COVID-19 pandemic since March 2020.

We share processes on the development and dissemination of clinical guidelines that occurred in a near real-time manner across the entire UPMC system. We also share quantitative results of how such changes mirrored credible findings and information from key scientific publications and regulatory approvals. This is followed by temporal assessment of the 30-day rate of mortality of hospitalized patients with COVID-19.

How have hospital patient volumes, patient clinical management, and 30-day mortality changed since the onset of the COVID-19 pandemic within a large, multi-hospital learning health system (LHS)?

We used data captured in the EMR and ancillary clinical systems, all of which are aggregated and harmonized in a Clinical Data Warehouse (CDW). UPMC is a 40hospital integrated academic healthcare system providing care principally within central and western Pennsylvania (USA). For the 22 hospitals with complete EMR data in the CDW, we accessed all key clinical data, including detailed sociodemographic and medical history data, . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 diagnostic and clinical tests conducted, surgical and other treatment procedures performed, prescriptions ordered, and billing charges on all outpatient and in-hospital encounters, with diagnoses and procedures coded based on the International Classification of Diseases, Ninth and Tenth revisions (ICD-9 and ICD-10, respectively).

Influence of UPMC COVID-19 Therapeutics Committee. We evaluated the influence of the UPMC COVID-19 Therapeutics Committee on change in COVID-19 clinical practice by time series plotting of the prevalence of in-hospital use of selected medications in relation to internal analyses and key scientific publications and regulatory approvals routinely reviewed by the committee. The UPMC COVID-19 Therapeutics Committee charge was to continuously evaluate evidence to create and disseminate treatment recommendations across the UPMC system. The process included: (1) weekly to bi-weekly review of internal analyses of COVID-19 patient testing, clinical practice, and outcomes generated from the CDW; (2) interim review of results from UPMC-led Randomized Embedded Multifactorial Adaptive Platform for COVID-19 (REMAP-COVID) trials, a global adaptive platform for trials of hospitalized and ambulatory patients with COVID-19; 6,7 (3) weekly and ad-hoc review of key external scientific publications, press releases, and regulatory approvals of COVID-19 treatment approaches; (4) consensus determination of patient criteria and clinical instructions for use (and non-use) of established and emerging treatment approaches; (5) creation of EMR-embedded forcing functions to enforce therapeutics recommendations and guide prescribing at the point of care; (6) empowerment of local pharmacists to review and approve all COVID-19-related medications within the context of the guidelines; and (7) system-wide dissemination of continuously updated treatment recommendations using multimodal media sources.

. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 The system-wide dissemination of treatment guidelines to all physicians and other clinicians affiliated with UPMC occurred through email notifications, computer screensavers, educational webinars, and formal directives from the chair of the Committee. A COVID-19 therapeutics webpage was built into the system intranet. The COVID-19 Therapeutics Committee also created continuous, updated recommendations on the use of monoclonal antibodies for ambulatory COVID-19 patients beginning in November 2020; however, the present analysis is restricted to treatment of hospitalized patients and omits that intervention. Figure S1 ).

We assessed changes in utilization of COVID-19 pharmacotherapy, level of oxygen support during hospitalization, and 30-day mortality from the index date of hospital admission. Pharmacotherapy and oxygen support were determined by the presence of charge codes within UPMC billing software. We assessed 30-day mortality by the hospital discharge disposition of "Ceased to Breathe" sourced from the inpatient Medical Record System, as well as deaths after discharge identified with the Death Master File (DMF) from the Social Security Administration (SSA)(NTIS 2021) as an external data source.

Explanatory Variables. For assessment of temporal changes, we categorized the study analysis period into 4 discrete "waves" based on empirical change in hospital admissions within the UPMC system. We chose the 4-wave classification scheme (Figure 1 ) based on the start and . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101/2021.08.26.21262661 doi: medRxiv preprint nadir of individual waves. However, because Wave 3 (September 29, 2020 -March 10, 2021 had dramatically higher hospital admissions and discharges, we split this wave at its zenith to assess its impact during rapid acceleration and deceleration. For assessment of variation between waves, we considered demographic variables, clinical history and medical comorbidities, laboratory values, vital signs, and medication use, with a focus on indicators of changing clinical practice such as use and timing of specific medications. We further assessed changes in COVID-19 clinical practice by the date of important scientific and regulatory events, as formally reviewed by the UPMC COVID-19 Therapeutics Committee. We also assessed potential variation in clinical practice across the 22 hospitals by classification as "large academic" (n=5), "large community" (n=8), or "small community" (n=9) (see Supplemental Table 1 ).

Statistical Methods. We describe changes over time in COVID-19 hospital admissions using 7-day moving mean and median values. We plotted temporal changes in pharmacotherapy used in-hospital on a weekly basis and anchored to important scientific and regulatory events.

Medication use and oxygen support (proportion of patients) plots by wave of hospital admission were evaluated by the Cochran-Mantel-Haenszel test of trend. We compared presenting characteristics of hospitalized patients across the 4 waves using analysis of variance (ANOVA) or Wilcoxon tests for continuous variables (based on distributional properties) and chi-square tests for categorical variables. Crude rates of 30-day mortality for test positive and hospitalized patients by wave were censored at May 7, 2021 (i.e. to allow 30-day follow-up for all patients). A logistic regression model was fit using 30-day mortality as the dependent variable with stepwise selection of pre-treatment explanatory variables. Date of hospital admission was added to the model at the last stage to assess whether the odds of 30-day mortality changed over time after adjustment for different patient characteristics. We did not impute missing values in any . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101/2021.08.26.21262661 doi: medRxiv preprint analyses. A two-sided type I error rate of 0.05 was used, and all analyses were conducted using the SAS System, Version 9.4 (SAS Institute, Cary, NC). We used The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) approach 8 (see Supplemental Table 2 ). Our study received formal ethics approval by the UPMC Ethics and Quality Improvement Review Committee (Project ID Project ID 2882).

Hospital Admissions. Over the 14+ month study period, the mean (SD) daily number of hospital admissions was 26 (28) with median of 17, IQR of 6 -31, maximum 7-day moving mean of 107, and steep acceleration and deceleration during wave 3 from late September 2020 to early March 2021 (Figure 1) . The mean hospital admission rate per day by wave was 4.0 (wave 1), 9.1 (wave 2), 46.1 (wave 3a), 48.0 (wave 3b), and 24.0 (wave 4).

Among patients who received any form of supplemental oxygen, there was rapid system-wide implementation in the use of dexamethasone immediately around the date in which initial positive results of the RECOVERY trial were published as a pre-print, 9 (Figure 2 ). Of note, subsequent peer-review publication 10 did not trigger an added uptake in use of dexamethasone. A steep increase in the use of remdesivir among patients on oxygen therapy occurred after Emergency Use Authorization (EUA) granted by the FDA 11 and subsequent public announcements and regulatory actions 12-14 (Figure 3 ).

There was no appreciable variation in the use of dexamethasone or remdesivir by volume or type across the 22 UPMC hospitals (Supplemental Figures S2 and S3 ).

In contrast, despite widespread publicity 15,16 our group recommended no role for hydroxychloroquine outside of the context of a clinical trial. Subsequently, in-hospital use was very low (<6%) and did not vary over time, including after FDA EUA revocation of . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ;  hydroxychloroquine, 17,18 and thus may be represented solely by patients taking this medication for a non-COVID-19 indication or enrolled in a clinical trial (Figure 4 ). More recently, an increase in the use of tocilizumab among eligible patients occurred immediately following preprint release of the REMAP-CAP trial results, 19 ( Figure 5 ) and a few weeks after publication of the RECOVERY 20 and REMAP-CAP trial results 21 .

Among patients who received oxygen therapy, in-hospital use of corticosteroids was 80% or higher starting in wave 2. Two-thirds or more of all patients received steroids within 1 day of admission. Beginning in wave 3, about three-quarters of all clinically appropriate patients received remdesivir (almost always within 1 day of admission) (Supplemental Figure S4 ). Use of non-invasive ventilation did not vary appreciably across waves, whereas use of mechanical ventilation was markedly lower after wave 1 (Supplemental Figure S4 ).

Hospitalized patients in waves 1 and 3a/3b were significantly older than patients in wave 2 (about 3-4 years), and the most recent wave 4 patients were the youngest with mean (median) age of 59. 5 (62) years and about a quarter (27%) being age 50 or younger (Supplemental Table 3 ). In aggregate, patients in waves 1 and 3 generally presented with more comorbidities, higher estimated 90-day probability of mortality, and higher neutrophil to lymphocyte ratio (NLR) and systemic inflammatory index than patients in waves 2 and 4 (Supplemental Table 3 ).

Among all test positive patients (hospitalized and not hospitalized), the 30-day mortality rate ranged from a high of 5.6% in wave 1 to a low of 2.2% in wave 4 ( Table 2) . Similar in direction, among hospitalized patients, the 30-day mortality rate ranged from a high of 19.8% in wave 1 to a low of 8.5% in wave 4. Consistent with different risk profiles across the 4 waves, 30day mortality was highest in wave 1, intermediate in wave 3, and lowest in waves 2 and 4. After . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101/2021.08.26.21262661 doi: medRxiv preprint statistical adjustment, factors independently associated with 30-day mortality rate after hospitalization included older age (15% increased odds per 5 years), male gender (27% increased odds), estimated risk of mortality within 90 days after being hospitalized (12% increased odds per 5 percentage points), and higher white blood cell, ALT, and NLRs ( Table 3) . Of note, adjusting for different risk profiles, each month of hospital admission to the UPMC system was associated with an estimated 5% lower odds of 30-day mortality (adjusted OR = 0.95, 95% confidence interval: 0.92 -0.97, p < .001).

In 2009, the National Academy of Medicine (NAM) called for development of a learning health system (LHS), setting a goal that by 2020, "…90 percent of clinical decisions will be supported by accurate, timely, and up-to-date clinical information, and will reflect the best available evidence." 22 The importance of this LHS goal is emblematic with the COVID-19 pandemic. 23 While lacking the ability to demonstrate cause and effect, the fact that the adjusted risk of in-hospital mortality among hospitalized COVID-19 patients at UPMC hospitals has decreased monthly by an average of 5% suggests a consistent learning effect to improved patient care. Additionally, there was no appreciable variation in type or volume of pharmacotherapy agents utilized for patients with COVID-19 across 22 hospitals, achieving the goal of equity and access regardless of patient zip code. Importantly, this model continues and can rapidly adapt as needed for SARS-CoV-2 variants, vaccination efforts, and other key variables.

In addition to continuous evaluation of UPMC internal analyses and controlled clinical trials, the COVID-19 Therapeutics Committee has evaluated the surge of COVID-19-related preprints and peer-reviewed publications that have emanated on an unprecedented scale . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101/2021.08.26.21262661 doi: medRxiv preprint throughout the pandemic. 24, 25, 26 This placed a premium on expertise in evaluating the merits of published information. While our committee recognized the benefits of and thus implemented steroids, remdesivir, and tocilizumab in selected COVID-19 patients, it refuted use of hydroxychloroquine despite its EUA, given the existing data. 27

The time between clinical evidence arising and uniform implementation of use was in days-to-weeks, rather than months-to-years, which has been the traditional gap for implementation of findings from RCTs into clinical practice. 28, 29, 30 We invested substantial efforts in the use of near-real time data and evidence (as per NAM LHS guidance), especially when the lack of available therapies fueled adoption of both warranted and unwarranted treatments. The average monthly risk-adjusted decrease in mortality of 5% observed in our healthcare system is noteworthy given the overall worse clinical profile of patients seen in wave 3. While utilization of pharmacotherapy is the focus of this analysis, it is likely that the observed improvements are multifactorial in nature. Alongside the Therapeutics Committee, an intensive care unit management group made real-time recommendations surrounding respiratory support strategies and other critical, supportive care and a systemwide infection prevention taskforce guided testing, tracing, isolation, and use of personal protective equipment. Accordingly, we posit there were changes of unmeasured practice patterns (i.e., ventilation strategies) learned over time that also contributed to the improved outcomes, and the rapid implementation of approved pharmacotherapies is a surrogate marker of systemwide learning. Lastly, while improvements in outcomes over time are natural to the progression of science and medical practice, the fact that the improvement seen in our healthcare system happened in a short time and mostly prior to mass vaccination, speaks, at least in part, to the importance of our system's embracing the organizational push to be an LHS.

. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10. 1101 /2021 While desirable, no formal criteria or certification process exist for an institution to be designated as an LHS. 4 One component we believe is essential is embedding of randomized controlled trial procedures into routine care processes using existing institutional infrastructure and electronic health records. 6 This approach defines broad eligibility criteria and aims to enroll as many "real-world" patients as possible to continuously evaluate therapies believed to be potentially efficacious. The key is avoiding "research" and "care" schisms, but rather use all care as an opportunity to learn about care improvement. Randomization is an added tool for some efforts, allowing adaptation as the trial evolves such that subjects are preferentially randomized to receive better performing arms based on interim analyses-termed "response adaptive randomization." 31 This was accomplished at our hospitals by embedding REMAP-CAP enrollment into the EMR, screening all patients with COVID-19 at all hospitals for trial eligibility, and integrating trial enrollment with Therapeutics Committee treatment guidelines. 32

There are some limitations to our study; because this is the experience in one, albeit large, integrated healthcare system in Western Pennsylvania, the generalizability of our findings may be questioned. However, the fact that we saw similar findings across our different sites suggests that our findings are applicable across academic, community, and rural hospitals. In addition, we cannot determine the extent to which the therapeutic interventions implemented uniformly by the UPMC COVID-19 Therapeutics Committee contributed to lower adjusted mortality over time, as opposed to other less well documented clinical practices that may have been implemented over time (i.e., mechanical ventilation).

The LHS description and results presented herein are not meant to be content-or institution-specific, but rather to illustrate some of the processes that can be used to support the NAM imperative for clinical decisions that are supported by accurate, timely, and up-to-date . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101/2021.08.26.21262661 doi: medRxiv preprint clinical information that reflects the best available evidence. 22 On a broader level, we support the stated advocacy for a learning health network that promotes collaboration among health systems, community-based organizations, and government agencies, especially during public health emergencies. 4

Other institutions have qualitatively described their respective LHS processes employed in response to the COVID-19 pandemic, 33,34 with limited quantitative temporal assessment of clinical outcomes. 35 We believe our analysis and description is the first to empirically document how COVID-specific processes employed within an LHS were actually implemented to achieve timely changes in clinical practice on a system level, which likely in turn favorably influenced clinical outcomes as evidenced by lower mortality over time. We recommend that institutions in describing their respective LHS do so by linking (and presenting) processes and sources of information that were used in the establishment and dissemination of clinical care guidelines with data-documented temporal changes in clinical practice and patient outcomes.

. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Plot of weekly prevalence (%) of in-hospital use of hydroxychloroquine. On the x-axis, negative numbers reflect weeks prior to seminal event "A", the date (March 28, 2020) in which the FDA granted EUA of hydroxychloroquine for COVID-19 patients.

. CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101/2021.08.26.21262661 doi: medRxiv preprint Plot of weekly prevalence (%) of in-hospital use of tocilizumab among patients who received high-flow nasal cannula (HFNC), BiPAP/CPAP (NIV), or mechanical ventilation (MV). On the x-axis, negative numbers reflect weeks prior to seminal event "A", the date (January 9, 2021) in which tocilizumab trial results were published among critically ill patients with COVID-19 who were receiving organ support. Figure S1 .

hospitalized patients aggregated from patients tested within and outside of the UPMC system. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-updatefda-issues-emergency-use-authorization-potential-covid-19-treatment. May 1, 2020.

Accessed June 10, 2021.

Patients with Moderate COVID-19. https://www.gilead.com/news-and-press/pressroom/press-releases/2020/6/gilead-announces-results-from-phase-3-trial-of-remdesivir- . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101/2021.08.26.21262661 doi: medRxiv preprint . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101/2021.08.26.21262661 doi: medRxiv preprint . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted August 28, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 Supplemental Methods, Pages 8-9 RECORD 6.1: The methods of study population selection (such as codes or algorithms used to identify subjects) should be listed in detail. If this is not possible, an explanation should be provided.

Case-control study -Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls Cross-sectional study -Give the eligibility criteria, and the sources and methods of selection of participants Figure S1 study, completing follow-up, and analysed) (b) Give reasons for nonparticipation at each stage. (c) Consider use of a flow diagram Methods, Pages 8-9 Supplemental Figure  S1 selection of included persons can be described in the text and/or by means of the study flow diagram.

Descriptive data 14 (a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders (b) Indicate the number of participants with missing data for each variable of interest (c) Cohort study -summarise follow-up time (e.g., average and total amount) Results, pages 10-11, Supplemental Table 3 Not listed due to extensive number of variables examined Results, Page 9

Outcome data 15 Cohort study -Report numbers of outcome events or summary measures over time Case-control study -Report numbers in each exposure category, or summary measures of exposure Cross-sectional study -Report numbers of outcome events or summary measures

Results, Pages 11-12, Table 1 Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence interval). Make clear which confounders were adjusted for and why they were included (b) Report category boundaries when continuous variables were categorized (c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period

Results, Pages 11-12, Tables 1 and 2 Results, Pages 9-11

Other analyses 17 Report other analyses done-e.g., analyses of subgroups and interactions, and sensitivity analyses 

Innovation for pandemics

Responding to Covid-19 -A once-in-a-century pandemic

Data explosion during COVID-19: A call for collaboration with the tech industry & data scrutiny. EClinicalMedicine

Learning health system in crisis: Lessons from the COVID-19 pandemic

The Learning Healthcare System: Workshop Summary. Olsen L, Aisner D, McGinnis JM

on behalf of the REMAP-CAP Investigators. History of COPD

Medications ACE

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