利用外周免疫细胞的可解释机器学习模型预测急性心力衰竭患者 90 天再入院或死亡率

IF 2.3 4区 医学 Q2 HEMATOLOGY
Junming Chen, Liting Yang, Jiangchuan Han, Liang Wang, Tingting Wu, Dongsheng Zhao
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引用次数: 0

摘要

背景:急性心力衰竭(AHF)的预后很差,出院后 90 天内的再入院率和死亡率都很高。这凸显了在这一关键时期加强护理转换、早期监测和对高危人群进行精确干预的迫切需要:我们的研究旨在开发并验证一种可解释的机器学习(ML)模型,该模型将外周免疫细胞数据与传统临床标记物相结合。我们的目标是准确预测 AHF 患者的 90 天再入院率或死亡率:在研究中,我们对 1210 名 AHF 患者进行了回顾性分析,将他们分为训练组和外部验证组。根据患者出院后 90 天的结果将其分为 "再入院/死亡 "组和 "无再入院/死亡 "组。我们利用外周免疫细胞、传统临床指标或两者的数据开发了多种 ML 模型,然后对这些模型进行了内部验证。通过夏普利相加解释(SHAP)方法对最有希望的模型的特征重要性进行了检验,最终进行了外部验证:在我们的 1210 名患者中,28.4%(344 人)在出院后 90 天内面临再入院或死亡。我们的研究确定了 10 个重要指标,包括外周免疫细胞和传统的临床指标,这些指标都能预测这些结果,其中支持向量机 (SVM) 模型显示出卓越的性能。SHAP分析进一步将这些预测因素提炼为五个关键决定因素,包括三个临床指标和两种免疫细胞类型,这对评估90天再入院或死亡风险至关重要:我们的分析确定了 SVM 模型,该模型融合了传统的临床指标和外周免疫细胞,是预测 AHF 患者 90 天再入院或死亡率的最有效方法。这种创新方法有望完善风险评估,并通过持续改进为高危人群提供更有针对性的干预。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients.

Background: Acute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This underscores the urgent need for enhanced care transitions, early monitoring, and precise interventions for at-risk individuals during this critical period.

Objective: Our study aims to develop and validate an interpretable machine learning (ML) model that integrates peripheral immune cell data with conventional clinical markers. Our goal is to accurately predict 90-day readmission or mortality in patients AHF.

Methods: In our study, we conducted a retrospective analysis on 1210 AHF patients, segregating them into training and external validation cohorts. Patients were categorized based on their 90-day outcomes post-discharge into groups of 'with readmission/mortality' and 'without readmission/mortality'. We developed various ML models using data from peripheral immune cells, traditional clinical indicators, or both, which were then internally validated. The feature importance of the most promising model was examined through the Shapley Additive Explanations (SHAP) method, culminating in external validation.

Results: In our cohort of 1210 patients, 28.4% (344) faced readmission or mortality within 90 days post-discharge. Our study pinpointed 10 significant indicators-spanning peripheral immune cells and traditional clinical metrics-that predict these outcomes, with the support vector machine (SVM) model showing superior performance. SHAP analysis further distilled these predictors to five key determinants, including three clinical indicators and two immune cell types, essential for assessing 90-day readmission or mortality risks.

Conclusion: Our analysis identified the SVM model, which merges traditional clinical indicators and peripheral immune cells, as the most effective for predicting 90-day readmission or mortality in AHF patients. This innovative approach promises to refine risk assessment and enable more targeted interventions for at-risk individuals through continuous improvement.

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来源期刊
CiteScore
4.40
自引率
3.40%
发文量
150
审稿时长
2 months
期刊介绍: CATH is a peer-reviewed bi-monthly journal that addresses the practical clinical and laboratory issues involved in managing bleeding and clotting disorders, especially those related to thrombosis, hemostasis, and vascular disorders. CATH covers clinical trials, studies on etiology, pathophysiology, diagnosis and treatment of thrombohemorrhagic disorders.
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