Psychotropic Medication Use Is Associated With Greater 1-Year Incidence of Dementia After COVID-19 Hospitalization.

Background: COVID-19 has been associated with an increased risk of incident dementia (post-COVID dementia). Establishing additional risk markers may help identify at-risk individuals and guide clinical decision-making. Methods: We investigated pre-COVID psychotropic medication use (exposure) and 1-year incidence of dementia (outcome) in 1,755 patients (≥65 years) hospitalized with COVID-19. Logistic regression models were used to examine the association, adjusting for demographic and clinical variables. For further confirmation, we applied the Least Absolute Shrinkage and Selection Operator (LASSO) regression and a machine learning (Random Forest) algorithm. Results: One-year incidence rate of post-COVID dementia was 12.7% (N = 223). Pre-COVID psychotropic medications (OR = 2.7, 95% CI: 1.8-4.0, P < 0.001) and delirium (OR = 3.0, 95% CI: 1.9-4.6, P < 0.001) were significantly associated with greater 1-year incidence of post-COVID dementia. The association between psychotropic medications and incident dementia remained robust when the analysis was restricted to the 423 patients with at least one documented neurological or psychiatric diagnosis at the time of COVID-19 admission (OR = 3.09, 95% CI: 1.5-6.6, P = 0.002). Across different drug classes, antipsychotics (OR = 2.8, 95% CI: 1.7-4.4, P < 0.001) and mood stabilizers/anticonvulsants (OR = 2.4, 95% CI: 1.39-4.02, P = 0.001) displayed the greatest association with post-COVID dementia. The association of psychotropic medication with dementia was further confirmed with Random Forest and LASSO analysis. Conclusion: Confirming prior studies we observed a high dementia incidence in older patients after COVID-19 hospitalization. Pre-COVID psychotropic medications were associated with higher risk of incident dementia. Psychotropic medications may be risk markers that signify neuropsychiatric symptoms during prodromal dementia, and not mutually exclusive, contribute to post-COVID dementia.

A Novel Method to Improve the Identification of Time of Intubation for Retrospective EHR Data Analysis During a Time of Resource Strain, the COVID-19 Pandemic.

Accurate determinations of the time of intubation (TOI) are critical for retrospective electronic health record (EHR) data analyses. In a retrospective study, the authors developed and validated an improved query (Ti) to identify TOI across numerous settings in a large health system, using EHR data, during the COVID-19 pandemic. Further, they evaluated the affect of Ti on peri-intubation patient parameters compared to a previous method-ventilator parameters (Tv). Ti identified an earlier TOI for 84.8% (n = 1666) of cases with a mean (SD) of 3.5 hours (15.5), resulting in alternate values for: partial pressure of arterial oxygen (PaO 2 ) in 18.4% of patients (mean 43.95 mmHg [54.24]); PaO 2 /fractional inspired oxygen (FiO 2 ) in 17.8% of patients (mean 48.29 [69.81]), and oxygen saturation/FiO 2 in 62.7% (mean 16.75 [34.14]), using the absolute difference in mean values within the first 4 hours of intubation. Differences in PaO 2 /FiO 2 using Ti versus Tv resulted in the reclassification of 7.3% of patients into different acute respiratory distress syndrome (ARDS) severity categories.

Machine learning to assist clinical decision-making during the COVID-19 pandemic.

BACKGROUND: The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. MAIN BODY: While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. CONCLUSION: This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.