Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis.

IF 4.7 2区医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of the American Medical Informatics Association Pub Date : 2025-03-24 DOI:10.1093/jamia/ocaf036

Fahad Kamran, Donna Tjandra, Thomas S Valley, Hallie C Prescott, Nigam H Shah, Vincent X Liu, Eric Horvitz, Jenna Wiens

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引用次数: 0

Abstract

Objectives: To quantify differences between (1) stratifying patients by predicted disease onset risk alone and (2) stratifying by predicted disease onset risk and severity of downstream outcomes. We perform a case study of predicting sepsis.

Materials and methods: We performed a retrospective analysis using observational data from Michigan Medicine at the University of Michigan (U-M) between 2016 and 2020 and the Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2012. We measured the correlation between the estimated sepsis risk and the estimated effect of sepsis on mortality using Spearman's correlation. We compared patients stratified by sepsis risk with patients stratified by sepsis risk and effect of sepsis on mortality.

Results: The U-M and BIDMC cohorts included 7282 and 5942 ICU visits; 7.9% and 8.1% developed sepsis, respectively. Among visits with sepsis, 21.9% and 26.3% experienced mortality at U-M and BIDMC. The effect of sepsis on mortality was weakly correlated with sepsis risk (U-M: 0.35 [95% CI: 0.33-0.37], BIDMC: 0.31 [95% CI: 0.28-0.34]). High-risk patients identified by both stratification approaches overlapped by 66.8% and 52.8% at U-M and BIDMC, respectively. Accounting for risk of mortality identified an older population (U-M: age = 66.0 [interquartile range-IQR: 55.0-74.0] vs age = 63.0 [IQR: 51.0-72.0], BIDMC: age = 74.0 [IQR: 61.0-83.0] vs age = 68.0 [IQR: 59.0-78.0]).

Discussion: Predictive models that guide selective interventions ignore the effect of disease on downstream outcomes. Reformulating patient stratification to account for the estimated effect of disease on downstream outcomes identifies a different population compared to stratification on disease risk alone.

Conclusion: Models that predict the risk of disease and ignore the effects of disease on downstream outcomes could be suboptimal for stratification.

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通过考虑由疾病发作引起的下游结果的严重程度，重新制定针对干预措施的患者分层：败血症的案例研究。

目标：量化(1)仅根据预测的发病风险对患者进行分层与(2)根据预测的发病风险和下游结果的严重程度对患者进行分层之间的差异。我们对脓毒症的预测进行了个案研究：我们利用密歇根大学密歇根医学中心（U-M）在 2016 年至 2020 年间和贝斯以色列女执事医疗中心（BIDMC）在 2008 年至 2012 年间的观察数据进行了回顾性分析。我们使用斯皮尔曼相关性测量了估计的败血症风险与估计的败血症对死亡率影响之间的相关性。我们比较了按败血症风险分层的患者与按败血症风险和败血症对死亡率的影响分层的患者：U-M和BIDMC队列包括7282人次和5942人次的ICU就诊者；分别有7.9%和8.1%的就诊者出现败血症。在出现败血症的就诊者中，21.9%和26.3%在马大和BIDMC出现死亡。败血症对死亡率的影响与败血症风险呈弱相关（U-M：0.35 [95% CI：0.33-0.37]；BIDMC：0.31 [95% CI：0.28-0.34]）。在马大和 BIDMC，两种分层方法确定的高危患者重叠率分别为 66.8% 和 52.8%。考虑死亡风险后，发现高危人群的年龄更大（U-M：年龄 = 66.0 [四分位数间距-IQR：55.0-74.0] vs 年龄 = 63.0 [IQR：51.0-72.0]；BIDMC：年龄 = 74.0 [IQR：61.0-83.0] vs 年龄 = 68.0 [IQR：59.0-78.0]）：讨论：指导选择性干预的预测模型忽略了疾病对下游结果的影响。讨论：指导选择性干预的预测模型忽略了疾病对下游结果的影响。重新对患者进行分层，以考虑疾病对下游结果的估计影响，与仅根据疾病风险进行分层相比，可识别出不同的人群：结论：预测疾病风险而忽视疾病对下游结果影响的模型可能不是分层的最佳选择。

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

Journal of the American Medical Informatics Association 医学-计算机：跨学科应用

CiteScore

14.50

自引率

7.80%

发文量

230

审稿时长

3-8 weeks

期刊介绍： JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.