PDF(Pubmed)
Abstract：
BACKGROUND: Patients with heart failure (HF) are the most commonly readmitted group of adult patients in Germany. Most patients with HF are readmitted for noncardiovascular reasons. Understanding the relevance of HF management outside the hospital setting is critical to understanding HF and factors that lead to readmission. Application of machine learning (ML) on data from statutory health insurance (SHI) allows the evaluation of large longitudinal data sets representative of the general population to support clinical decision-making.
OBJECTIVE: This study aims to evaluate the ability of ML methods to predict 1-year all-cause and HF-specific readmission after initial HF-related admission of patients with HF in outpatient SHI data and identify important predictors.
METHODS: We identified individuals with HF using outpatient data from 2012 to 2018 from the AOK Baden-Württemberg SHI in Germany. We then trained and applied regression and ML algorithms to predict the first all-cause and HF-specific readmission in the year after the first admission for HF. We fitted a random forest, an elastic net, a stepwise regression, and a logistic regression to predict readmission by using diagnosis codes, drug exposures, demographics (age, sex, nationality, and type of coverage within SHI), degree of rurality for residence, and participation in disease management programs for common chronic conditions (diabetes mellitus type 1 and 2, breast cancer, chronic obstructive pulmonary disease, and coronary heart disease). We then evaluated the predictors of HF readmission according to their importance and direction to predict readmission.
RESULTS: Our final data set consisted of 97,529 individuals with HF, and 78,044 (80%) were readmitted within the observation period. Of the tested modeling approaches, the random forest approach best predicted 1-year all-cause and HF-specific readmission with a C-statistic of 0.68 and 0.69, respectively. Important predictors for 1-year all-cause readmission included prescription of pantoprazole, chronic obstructive pulmonary disease, atherosclerosis, sex, rurality, and participation in disease management programs for type 2 diabetes mellitus and coronary heart disease. Relevant features for HF-specific readmission included a large number of canonical HF comorbidities.
CONCLUSIONS: While many of the predictors we identified were known to be relevant comorbidities for HF, we also uncovered several novel associations. Disease management programs have widely been shown to be effective at managing chronic disease; however, our results indicate that in the short term they may be useful for targeting patients with HF with comorbidity at increased risk of readmission. Our results also show that living in a more rural location increases the risk of readmission. Overall, factors beyond comorbid disease were relevant for risk of HF readmission. This finding may impact how outpatient physicians identify and monitor patients at risk of HF readmission.
OBJECTIVE: This study aims to evaluate the ability of ML methods to predict 1-year all-cause and HF-specific readmission after initial HF-related admission of patients with HF in outpatient SHI data and identify important predictors.
METHODS: We identified individuals with HF using outpatient data from 2012 to 2018 from the AOK Baden-Württemberg SHI in Germany. We then trained and applied regression and ML algorithms to predict the first all-cause and HF-specific readmission in the year after the first admission for HF. We fitted a random forest, an elastic net, a stepwise regression, and a logistic regression to predict readmission by using diagnosis codes, drug exposures, demographics (age, sex, nationality, and type of coverage within SHI), degree of rurality for residence, and participation in disease management programs for common chronic conditions (diabetes mellitus type 1 and 2, breast cancer, chronic obstructive pulmonary disease, and coronary heart disease). We then evaluated the predictors of HF readmission according to their importance and direction to predict readmission.
RESULTS: Our final data set consisted of 97,529 individuals with HF, and 78,044 (80%) were readmitted within the observation period. Of the tested modeling approaches, the random forest approach best predicted 1-year all-cause and HF-specific readmission with a C-statistic of 0.68 and 0.69, respectively. Important predictors for 1-year all-cause readmission included prescription of pantoprazole, chronic obstructive pulmonary disease, atherosclerosis, sex, rurality, and participation in disease management programs for type 2 diabetes mellitus and coronary heart disease. Relevant features for HF-specific readmission included a large number of canonical HF comorbidities.
CONCLUSIONS: While many of the predictors we identified were known to be relevant comorbidities for HF, we also uncovered several novel associations. Disease management programs have widely been shown to be effective at managing chronic disease; however, our results indicate that in the short term they may be useful for targeting patients with HF with comorbidity at increased risk of readmission. Our results also show that living in a more rural location increases the risk of readmission. Overall, factors beyond comorbid disease were relevant for risk of HF readmission. This finding may impact how outpatient physicians identify and monitor patients at risk of HF readmission.
摘要：
背景：心力衰竭（HF）患者是德国最常再入院的成年患者。大多数HF患者因非心血管原因再次入院。了解医院以外的HF管理的相关性对于了解HF和导致再入院的因素至关重要。机器学习（ML）对来自法定健康保险（SHI）的数据的应用允许评估代表一般人群的大型纵向数据集，以支持临床决策。
目的：本研究旨在评估ML方法在门诊SHI数据中预测HF患者初次入院后1年全因和特定HF再入院的能力，并确定重要的预测因素。
方法：我们使用2012年至2018年德国AOKBaden-WürttembergSHI的门诊数据确定了HF患者。然后，我们对回归和ML算法进行了训练和应用，以预测HF首次入院后一年内的首次全因和特定于HF的再入院。我们拟合了一个随机森林，一个弹性网，逐步回归，以及使用诊断代码预测再入院的逻辑回归，药物暴露,人口统计（年龄，性别,国籍,和SHI内的覆盖类型)，居住的乡村程度，并参与常见慢性病（1型和2型糖尿病，乳腺癌，慢性阻塞性肺疾病,和冠心病）。然后，我们根据其重要性和预测再入院的方向评估了HF再入院的预测因子。
结果：我们的最终数据集包括97,529名HF患者，和78,044（80％）在观察期内再次入院。在经过测试的建模方法中，随机森林方法最好地预测了1年全因和HF特异性再入院，C统计量分别为0.68和0.69。1年全因再入院的重要预测因素包括泮托拉唑的处方，慢性阻塞性肺疾病,动脉粥样硬化,性别,rurality,并参与2型糖尿病和冠心病的疾病管理计划。HF特异性再入院的相关特征包括大量典型的HF合并症。
结论：虽然我们确定的许多预测因子已知与HF的合并症有关，我们还发现了几个新颖的联想。疾病管理计划已被广泛证明是有效的管理慢性疾病;然而，我们的结果表明,在短期内,它们可能有助于针对再次入院风险增加的合并有并发症的HF患者.我们的结果还表明，生活在更农村的地方会增加再次入院的风险。总的来说,共病以外的因素与HF再入院风险相关.这一发现可能会影响门诊医生如何识别和监测有HF再入院风险的患者。
目的：本研究旨在评估ML方法在门诊SHI数据中预测HF患者初次入院后1年全因和特定HF再入院的能力，并确定重要的预测因素。
方法：我们使用2012年至2018年德国AOKBaden-WürttembergSHI的门诊数据确定了HF患者。然后，我们对回归和ML算法进行了训练和应用，以预测HF首次入院后一年内的首次全因和特定于HF的再入院。我们拟合了一个随机森林，一个弹性网，逐步回归，以及使用诊断代码预测再入院的逻辑回归，药物暴露,人口统计（年龄，性别,国籍,和SHI内的覆盖类型)，居住的乡村程度，并参与常见慢性病（1型和2型糖尿病，乳腺癌，慢性阻塞性肺疾病,和冠心病）。然后，我们根据其重要性和预测再入院的方向评估了HF再入院的预测因子。
结果：我们的最终数据集包括97,529名HF患者，和78,044（80％）在观察期内再次入院。在经过测试的建模方法中，随机森林方法最好地预测了1年全因和HF特异性再入院，C统计量分别为0.68和0.69。1年全因再入院的重要预测因素包括泮托拉唑的处方，慢性阻塞性肺疾病,动脉粥样硬化,性别,rurality,并参与2型糖尿病和冠心病的疾病管理计划。HF特异性再入院的相关特征包括大量典型的HF合并症。
结论：虽然我们确定的许多预测因子已知与HF的合并症有关，我们还发现了几个新颖的联想。疾病管理计划已被广泛证明是有效的管理慢性疾病;然而，我们的结果表明,在短期内,它们可能有助于针对再次入院风险增加的合并有并发症的HF患者.我们的结果还表明，生活在更农村的地方会增加再次入院的风险。总的来说,共病以外的因素与HF再入院风险相关.这一发现可能会影响门诊医生如何识别和监测有HF再入院风险的患者。
