Development and validation of a risk prediction model for 30-day readmission in elderly type 2 diabetes patients complicated with heart failure: a multicenter, retrospective study.

IF 4.6 2区医学 Q2 ENDOCRINOLOGY & METABOLISM Frontiers in Endocrinology Pub Date : 2025-02-27 eCollection Date: 2025-01-01 DOI:10.3389/fendo.2025.1534516

Yuxin He, Yuan Yuan, Qingzhu Tan, Xiao Zhang, Yunyu Liu, Minglun Xiao

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Abstract

Background: Elderly type 2 diabetes mellitus (T2DM) patients complicated with heart failure (HF) exhibit a high rate of 30-day readmission. Predictive models have been suggested as tools for identifying high-risk patients. Thus, we aimed to develop and validate a predictive model using multicenter electronic medical records (EMRs) data to estimate the risk of 30-day readmission in elderly T2DM patients complicated with HF.

Methods: EMRs data of elderly T2DM patients complicated with HF from five tertiary hospitals, spanning 2012 to 2023, were utilized to develop and validate the 30-day readmission model. The model were evaluated using holdout data with the area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

Results: A total of 1899 patients were included, with 955, 409, and 535 in the derivation, internal validation, and external validation cohorts, respectively. Pulmonary infections (odds ratio [OR]: 3.816, 95% confidence interval [CI]: 2.377-6.128, P < 0.001), anti-hypertensive drug use (OR: 5.536, 95% CI: 1.658-18.486, P = 0.005), and neutrophil percentage-to-albumin ratio (NPAR) (OR: 1.144, 95% CI: 1.093-1.197, P < 0.001) were independent predictors of 30-day readmission risk. AUROC in the derivation, internal validation, and external validation cohorts were 0.782 (95% CI: 0.737-0.826), 0.746 (95% CI: 0.654-0.838), and 0.753 (95% CI: 0.684-0.813), respectively. The calibration curve, DCA results, and CIC results indicated that the model also possessed good predictive power. Additionally, an operation interface on a web page (https://cqykdxtjt.shinyapps.io/readmission/) was created for clinical practitioners to apply.

Conclusion: A 30-day readmission risk prediction model was developed and externally validated. This model facilitates the targeting of interventions for elderly T2DM patients complicated with HF who are at high risk of an early readmission.

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老年2型糖尿病合并心力衰竭患者30天再入院风险预测模型的建立与验证：一项多中心、回顾性研究

背景：老年2型糖尿病（T2DM）合并心力衰竭（HF）患者30天再入院率高。预测模型被认为是识别高危患者的工具。因此，我们的目的是利用多中心电子病历（EMRs）数据建立并验证一个预测模型，以估计老年T2DM合并心衰患者30天再入院的风险。方法：利用5家三级医院2012 - 2023年老年T2DM合并心衰患者的emr数据，建立并验证30天再入院模型。采用持位数据与受试者工作特征曲线（AUROC）、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）下的面积对模型进行评估。结果：共纳入1899例患者，推导组955例，内部验证组409例，外部验证组535例。肺部感染（优势比[OR]: 3.816, 95%可信区间[CI]: 2.377 ~ 6.128, P < 0.001）、抗高血压药物使用（OR: 5.536, 95% CI: 1.658 ~ 18.486, P = 0.005）和中性粒细胞百分比-白蛋白比（NPAR）（OR: 1.144, 95% CI: 1.093 ~ 1.197, P < 0.001）是30天再入院风险的独立预测因素。推导、内部验证和外部验证队列的AUROC分别为0.782 （95% CI: 0.737-0.826）、0.746 （95% CI: 0.654-0.838）和0.753 （95% CI: 0.684-0.813）。校正曲线、DCA结果和CIC结果表明，该模型也具有良好的预测能力。此外，还在网页（https://cqykdxtjt.shinyapps.io/readmission/）上创建了一个操作界面，供临床从业者使用。结论：建立了30天再入院风险预测模型并进行了外部验证。该模型有助于对早期再入院高风险的老年T2DM合并心衰患者进行针对性干预。

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来源期刊

Frontiers in Endocrinology Medicine-Endocrinology, Diabetes and Metabolism

CiteScore

5.70

自引率

9.60%

发文量

3023

审稿时长

14 weeks

期刊介绍： Frontiers in Endocrinology is a field journal of the "Frontiers in" journal series. In today’s world, endocrinology is becoming increasingly important as it underlies many of the challenges societies face - from obesity and diabetes to reproduction, population control and aging. Endocrinology covers a broad field from basic molecular and cellular communication through to clinical care and some of the most crucial public health issues. The journal, thus, welcomes outstanding contributions in any domain of endocrinology. Frontiers in Endocrinology publishes articles on the most outstanding discoveries across a wide research spectrum of Endocrinology. The mission of Frontiers in Endocrinology is to bring all relevant Endocrinology areas together on a single platform.