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

IF 2.4 4区医学 Q3 PHARMACOLOGY & PHARMACY

Pharmacoepidemiology and Drug Safety Pub Date : 2024-08-01 DOI:10.1002/pds.5872

Priyanka Anand, Elvira D'Andrea, William Feldman, Shirley V Wang, Jun Liu, Gregory Brill, Elyse DiCesare, Kueiyu Joshua Lin

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引用次数: 0

Abstract

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.

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用于识别 COVID-19 快速恶化高危患者的动态预后模型

目的：我们旨在利用疫苗上市后的数据，验证之前发表的预测 COVID-19 住院患者临床病情恶化的预后模型，如果效果不理想，则对其进行更新：2020年3月至2021年11月期间，我们使用美国马萨诸塞州一个大型医疗服务网络中经实验室确诊为COVID-19的≥18岁患者的电子健康记录，测试了之前开发的预测模型的性能，并通过纳入COVID-19疫苗上市后的数据更新了预测模型。我们将数据随机分为开发组群（70%）和验证组群（30%）。我们建立了一个模型，通过 LASSO 回归预测 24 小时内已公布的严重程度量表中的恶化情况，并通过 c 统计量和 Brier 评分评估模型的性能：我们的研究队列由 8185 名患者组成（发展期：5730 名患者[平均年龄：62 岁]；成熟期：530 名患者[平均年龄：65 岁]）：5730名患者[平均年龄：62岁；44%为女性]；验证：2455名患者[平均年龄：62岁；45%为女性]）。之前发布的模型在使用 2020 年 11 月之后的数据时表现不佳（N = 4973，c 统计量 = 0.60。）使用新数据重新训练后，更新后的模型包含 38 个预测因子，其中包括 18 个不断变化的生物标志物。2021 年 6 月 1 日（COVID-19 疫苗在马萨诸塞州广泛上市）之后住院的患者比之前住院的患者更年轻，合并症更少。开发队列的 c 统计量和 Brier 评分分别为 0.77 和 0.13，验证队列的 c 统计量和 Brier 评分分别为 0.73 和 0.14：结论：COVID-19住院患者的特征随着时间的推移有很大差异。我们开发了一种新的快速进展动态模型，在验证组中表现令人满意。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Pharmacoepidemiology and Drug Safety 医学-药学

CiteScore

4.80

自引率

7.70%

发文量

173

审稿时长

3 months

期刊介绍： The aim of Pharmacoepidemiology and Drug Safety is to provide an international forum for the communication and evaluation of data, methods and opinion in the discipline of pharmacoepidemiology. The Journal publishes peer-reviewed reports of original research, invited reviews and a variety of guest editorials and commentaries embracing scientific, medical, statistical, legal and economic aspects of pharmacoepidemiology and post-marketing surveillance of drug safety. Appropriate material in these categories may also be considered for publication as a Brief Report. Particular areas of interest include: design, analysis, results, and interpretation of studies looking at the benefit or safety of specific pharmaceuticals, biologics, or medical devices, including studies in pharmacovigilance, postmarketing surveillance, pharmacoeconomics, patient safety, molecular pharmacoepidemiology, or any other study within the broad field of pharmacoepidemiology; comparative effectiveness research relating to pharmaceuticals, biologics, and medical devices. Comparative effectiveness research is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition, as these methods are truly used in the real world; methodologic contributions of relevance to pharmacoepidemiology, whether original contributions, reviews of existing methods, or tutorials for how to apply the methods of pharmacoepidemiology; assessments of harm versus benefit in drug therapy; patterns of drug utilization; relationships between pharmacoepidemiology and the formulation and interpretation of regulatory guidelines; evaluations of risk management plans and programmes relating to pharmaceuticals, biologics and medical devices.