Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2

International audienceObjective To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic. Design, setting and participants This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic. Primary and secondary outcome measures The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation. Results Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was –4.72 mg/dL vs –4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March–April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March–April 2020 vs 14.3% in November 2020 to January 2021, p<0.001). Conclusions Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries

International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

10.1038/s41746-022-00623-8NPJ DIGITAL MEDICINE5

Meta-analysis of the risk of adverse clinical outcomes stratified by concurrent neurological status and outcome during acute COVID-19 hospitalizations in adults.

Adverse outcomes include lower risk of hospital discharge and higher risk of mortality. Neurological status during COVID-19 hospitalization included any central nervous system (CNS) diagnosis (A, C) or any peripheral nervous system (PNS) diagnosis (B, D). Black circles indicate the local healthcare system-level hazard ratio derived from the Cox proportional hazards model. The red diamond represents the pooled effect size derived from the random-effects meta-analysis. The effect size and associated p-value derived from meta-analysis are reported in Table 2 of the main text. We also report the following metrics: I2 (95% CI), the estimated proportion of variance due to differences among healthcare systems; (Tau) τ2, the between-healthcare system variance; Prediction Interval, the predicted effect size if we were to add a new healthcare system to the analysis. We excluded two adult healthcare systems (NUH and UKFR) from the meta-analysis due to low frequency of neurological diagnoses in their patient populations ( (PDF)</p

Study Population Characteristics.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients </div

Random-effects meta-analysis of the risk of adverse clinical outcomes in adults with concurrent CNS or PNS diagnosis during the acute COVID-19 hospitalization from the Cox-proportional hazard models locally run at each healthcare system.

Random-effects meta-analysis of the risk of adverse clinical outcomes in adults with concurrent CNS or PNS diagnosis during the acute COVID-19 hospitalization from the Cox-proportional hazard models locally run at each healthcare system.</p

Logistic Principal Component Analysis.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients </div

Demographic profile for each participating healthcare system arranged by country.

Cohort-wise breakdown of the number of patients, age range, sex, severity status, mortality outcome, readmission status, and race at each healthcare system for each of the following neurological status during acute COVID-19 hospitalization: no neurological condition (NNC), central nervous system (CNS) diagnosis, and peripheral nervous system (PNS) diagnosis. Healthcare systems are arranged by country in descending order by the number of included participating healthcare systems. The stacked bar charts indicate the normalized distribution of age and race. The nested pie-charts are stratified by the neurological status with the darker portion representing the proportion of patients having the value of the binary variable for the given column header.</p

Frequency of neurological diagnosis codes by age group.

For each age group, we report the total number and proportion of patients who had the associated ICD-10 code. Neurological diagnoses are listed in descending order of overall frequency. Please refer to S1 Fig for the incidence of severe COVID-19 status and mortality as stratified by concurrent neurological status in adults and children. S5 Table details the total counts and percentages of patients with ICD-10 (and ICD-9 codes) as stratified by adult and pediatric populations.</p

Pointwise mutual information (PMI) of a central nervous system (CNS) or peripheral nervous system (PNS) diagnosis co-occurring with severe COVID-19 disease during acute COVID-19 hospitalization.

Notes: 1. We report each healthcare system’s total number of severe and neurological patients used to calculate the PMI at each healthcare system. PMI >0 indicates more frequent co-occurrence (between a CNS or a PNS diagnosis and severe COVID-19 status) than independent assumptions. 2. Severe COVID-19 status was based on previously published computable phenotypes, including diagnosis of pneumonia and/or acute respiratory distress syndrome, need for mechanical ventilation, sedation, and/or medication administration for shock [1]. 3. 95% confidence intervals were estimated using 500 bootstrapped samples. 4. Bold findings indicate statistically significant results. Supplemental Citation 1. Klann, J. G. et al. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data. Journal of the American Medical Informatics Association 28: 1411–1420 (2021). (PDF)</p

Count and percentage of adult and pediatric patients with pre-admission health conditions as stratified by concurrent neurological status during acute COVID-19 hospitalization ¹.

Notes: 1. Neurological status during acute COVID-19 hospitalization: central nervous system diagnosis (CNS), peripheral nervous system diagnosis (PNS), no neurological condition (NNC). 2. Refer to S3–S4 Tables for detailed descriptions of ICD codes comprising each component of the Elixhauser Comorbidity Index. 3. N = the total number of adult or pediatric patients with the pre-admission health condition; the corresponding percentage is out of the total adult or pediatric population. 4. Percentages in the NNC, CNS, and PNS columns reflect the percent of patients with the respective neurological status who have the indicated pre-admission health condition. 5. Complicated and uncomplicated diabetes were combined as one condition. Likewise, complicated and uncomplicated hypertension were combined as one condition. (PDF)</p