Peer Review in a General Medical Research Journal Before and During the COVID-19 Pandemic.

Importance: Although peer review is an important component of publication for new research, the viability of this process has been questioned, particularly with the added stressors of the COVID-19 pandemic. Objective: To characterize rates of peer reviewer acceptance of invitations to review manuscripts, reviewer turnaround times, and editor-assessed quality of reviews before and after the start of the COVID-19 pandemic at a large, open-access general medical journal. Design, Setting, and Participants: This retrospective, pre-post cohort study examined all research manuscripts submitted to JAMA Network Open between January 1, 2019, and June 29, 2021, either directly or via transfer from other JAMA Network journals, for which at least 1 peer review of manuscript content was solicited. Measures were compared between the period before the World Health Organization declaration of a COVID-19 pandemic on March 11, 2020 (14.3 months), and the period during the pandemic (15.6 months) among all reviewed manuscripts and between pandemic-period manuscripts that did or did not address COVID-19. Main Outcomes and Measures: For each reviewed manuscript, the number of invitations sent to reviewers, proportions of reviewers accepting invitations, time in days to return reviews, and editor-assessed quality ratings of reviews were determined. Results: In total, the journal sought review for 5013 manuscripts, including 4295 Original Investigations (85.7%) and 718 Research Letters (14.3%); 1860 manuscripts were submitted during the prepandemic period and 3153 during the pandemic period. Comparing the prepandemic with the pandemic period, the mean (SD) number of reviews rated as high quality (very good or excellent) per manuscript increased slightly from 1.3 (0.7) to 1.5 (0.7) (P < .001), and the mean (SD) time for reviewers to return reviews was modestly shorter (from 15.8 [7.6] days to 14.4 [7.0] days; P < .001), a difference that persisted in linear regression models accounting for manuscript type, study design, and whether the manuscript addressed COVID-19. Conclusions and Relevance: In this cohort study, the speed and editor-reported quality of peer reviews in an open-access general medical journal improved modestly during the initial year of the pandemic. Additional study will be necessary to understand how the pandemic has affected reviewer burden and fatigue.

Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients.

OBJECTIVE: To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19). METHOD: Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission. RESULTS: Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave. CONCLUSION: This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.