A Dynamic Prognostic Model for Identifying Vulnerable COVID-19 Patients at High Risk of Rapid Deterioration.

Autor: Anand P; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA., D'Andrea E; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA., Feldman W; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA., Wang SV; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA., Liu J; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA., Brill G; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA., DiCesare E; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA., Lin KJ; Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.; Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Jazyk: angličtina
Zdroj: Pharmacoepidemiology and drug safety [Pharmacoepidemiol Drug Saf] 2024 Aug; Vol. 33 (8), pp. e5872.
DOI: 10.1002/pds.5872
Abstrakt: Purpose: We aimed to validate and, if performance was unsatisfactory, update the previously published prognostic model to predict clinical deterioration in patients hospitalized for COVID-19, using data following vaccine availability.
Methods: Using electronic health records of patients ≥18 years, with laboratory-confirmed COVID-19, from a large care-delivery network in Massachusetts, USA, from March 2020 to November 2021, we tested the performance of the previously developed prediction model and updated the prediction model by incorporating data after availability of COVID-19 vaccines. We randomly divided data into development (70%) and validation (30%) cohorts. We built a model predicting worsening in a published severity scale in 24 h by LASSO regression and evaluated performance by c-statistic and Brier score.
Results: Our study cohort consisted of 8185 patients (Development: 5730 patients [mean age: 62; 44% female] and Validation: 2455 patients [mean age: 62; 45% female]). The previously published model had suboptimal performance using data after November 2020 (N = 4973, c-statistic = 0.60. Brier score = 0.11). After retraining with the new data, the updated model included 38 predictors including 18 changing biomarkers. Patients hospitalized after Jun 1st, 2021 (when COVID-19 vaccines became widely available in Massachusetts) were younger and had fewer comorbidities than those hospitalized before. The c-statistic and Brier score were 0.77 and 0.13 in the development cohort, and 0.73 and 0.14 in the validation cohort.
Conclusion: The characteristics of patients hospitalized for COVID-19 differed substantially over time. We developed a new dynamic model for rapid progression with satisfactory performance in the validation set.
(© 2024 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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