PDF(Pubmed)
Abstract：
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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
背景：急性心力衰竭（AHF）预后严重，出院后90天内的高再入院率和死亡率。这凸显了加强护理过渡的迫切需要，早期监测,以及在这一关键时期对高危个体的精确干预。
目的：我们的研究旨在开发和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常规临床标志物整合在一起。我们的目标是准确预测AHF患者的90天再入院或死亡率。
方法：在我们的研究中，我们对1210例AHF患者进行了回顾性分析，将它们分为培训和外部验证队列。根据出院后90天的结果将患者分为“有再入院/死亡率”和“没有再入院/死亡率”组。我们使用来自外周免疫细胞的数据开发了各种ML模型，传统的临床指标，或者两者兼而有之,然后进行内部验证。通过Shapley加法解释(SHAP)方法检验了最有前途的模型的特征重要性，最终导致外部验证。
结果：在我们的1210名患者队列中，28.4%(344)在出院后90天内面临再次入院或死亡。我们的研究确定了10个重要指标-跨越外周免疫细胞和传统的临床指标-预测这些结果，支持向量机(SVM)模型表现出优越的性能。SHAP分析将这些预测因素进一步提炼为五个关键决定因素，包括三种临床指标和两种免疫细胞类型，对于评估90天的再入院或死亡风险至关重要。
结论：我们的分析确定了SVM模型，它融合了传统的临床指标和外周免疫细胞，作为预测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，并通过持续改进为有风险的个人提供更有针对性的干预措施。
目的：我们的研究旨在开发和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常规临床标志物整合在一起。我们的目标是准确预测AHF患者的90天再入院或死亡率。
方法：在我们的研究中，我们对1210例AHF患者进行了回顾性分析，将它们分为培训和外部验证队列。根据出院后90天的结果将患者分为“有再入院/死亡率”和“没有再入院/死亡率”组。我们使用来自外周免疫细胞的数据开发了各种ML模型，传统的临床指标，或者两者兼而有之,然后进行内部验证。通过Shapley加法解释(SHAP)方法检验了最有前途的模型的特征重要性，最终导致外部验证。
结果：在我们的1210名患者队列中，28.4%(344)在出院后90天内面临再次入院或死亡。我们的研究确定了10个重要指标-跨越外周免疫细胞和传统的临床指标-预测这些结果，支持向量机(SVM)模型表现出优越的性能。SHAP分析将这些预测因素进一步提炼为五个关键决定因素，包括三种临床指标和两种免疫细胞类型，对于评估90天的再入院或死亡风险至关重要。
结论：我们的分析确定了SVM模型，它融合了传统的临床指标和外周免疫细胞，作为预测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，并通过持续改进为有风险的个人提供更有针对性的干预措施。
