Prediction of Readmission Following Sepsis Using Social Determinants of Health.

Q4 Medicine Critical care explorations Pub Date : 2024-05-24 eCollection Date: 2024-06-01 DOI:10.1097/CCE.0000000000001099
Fatemeh Amrollahi, Brent D Kennis, Supreeth Prajwal Shashikumar, Atul Malhotra, Stephanie Parks Taylor, James Ford, Arianna Rodriguez, Julia Weston, Romir Maheshwary, Shamim Nemati, Gabriel Wardi, Angela Meier
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Abstract

Objectives: To determine the predictive value of social determinants of health (SDoH) variables on 30-day readmission following a sepsis hospitalization as compared with traditional clinical variables.

Design: Multicenter retrospective cohort study using patient-level data, including demographic, clinical, and survey data.

Settings: Thirty-five hospitals across the United States from 2017 to 2021.

Patients: Two hundred seventy-one thousand four hundred twenty-eight individuals in the AllofUs initiative, of which 8909 had an index sepsis hospitalization.

Interventions: None.

Measurements and main results: Unplanned 30-day readmission to the hospital. Multinomial logistic regression models were constructed to account for survival in determination of variables associate with 30-day readmission and are presented as adjusted odds rations (aORs). Of the 8909 sepsis patients in our cohort, 21% had an unplanned hospital readmission within 30 days. Median age (interquartile range) was 54 years (41-65 yr), 4762 (53.4%) were female, and there were self-reported 1612 (18.09%) Black, 2271 (25.49%) Hispanic, and 4642 (52.1%) White individuals. In multinomial logistic regression models accounting for survival, we identified that change to nonphysician provider type due to economic reasons (aOR, 2.55 [2.35-2.74]), delay of receiving medical care due to lack of transportation (aOR, 1.68 [1.62-1.74]), and inability to afford flow-up care (aOR, 1.59 [1.52-1.66]) were strongly and independently associated with a 30-day readmission when adjusting for survival. Patients who lived in a ZIP code with a high percentage of patients in poverty and without health insurance were also more likely to be readmitted within 30 days (aOR, 1.26 [1.22-1.29] and aOR, 1.28 [1.26-1.29], respectively). Finally, we found that having a primary care provider and health insurance were associated with low odds of an unplanned 30-day readmission.

Conclusions: In this multicenter retrospective cohort, several SDoH variables were strongly associated with unplanned 30-day readmission. Models predicting readmission following sepsis hospitalization may benefit from the addition of SDoH factors to traditional clinical variables.

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利用健康的社会决定因素预测败血症后的再入院情况。
目的与传统的临床变量相比,确定健康的社会决定因素(SDoH)变量对脓毒症住院后 30 天再入院的预测价值:多中心回顾性队列研究,使用患者层面的数据,包括人口统计学、临床和调查数据:2017年至2021年,全美35家医院:干预措施:无:干预措施:无:非计划 30 天再入院。在确定与 30 天再入院相关的变量时,建立了多项式逻辑回归模型以考虑生存率,并以调整后的几率(aORs)表示。在我们队列中的 8909 名脓毒症患者中,21% 的患者在 30 天内发生了计划外再入院。中位年龄(四分位数间距)为 54 岁(41-65 岁),女性 4762 人(53.4%),自述黑人 1612 人(18.09%),西班牙裔 2271 人(25.49%),白人 4642 人(52.1%)。在考虑生存率的多项式逻辑回归模型中,我们发现,由于经济原因(aOR,2.55 [2.35-2.74])而改用非医生医疗服务提供者类型、由于交通不便而延迟接受医疗护理(aOR,1.68 [1.62-1.74])以及无法负担流动医疗护理(aOR,1.59 [1.52-1.66]),在考虑生存率的情况下,与 30 天再入院密切且独立相关。居住在贫困和没有医疗保险的患者比例较高的邮政编码内的患者也更有可能在 30 天内再次入院(aOR,分别为 1.26 [1.22-1.29] 和 aOR,1.28 [1.26-1.29])。最后,我们发现拥有初级保健提供者和医疗保险与30天内非计划再入院的低几率相关:在这个多中心回顾性队列中,几个 SDoH 变量与 30 天非计划再入院密切相关。在传统临床变量的基础上增加 SDoH 因素,可能会对预测脓毒症住院后再入院的模型有所帮助。
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CiteScore
5.70
自引率
0.00%
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审稿时长
8 weeks
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