首页|Chengdu Second People’s Hospital Reports Findings in Heart Failure (Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index)
Chengdu Second People’s Hospital Reports Findings in Heart Failure (Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Heart Failure is the subject of a report. According to news originating from Chengdu, People’s Republic of China, by NewsRx correspondents, research st ated, “There is a lack of tools for accurately identifying the risk of readmissi on for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [len gth of stay (‘L’), acute (emergent) admission (‘A’), Charlson comorbidity index (‘C’) and visits to the emergency department during the previous 6 months (‘E’)] index and machine learning in predicting 1 year readmission for heart failure in elderly patients with arrhythmia.” Our news journalists obtained a quote from the research from Chengdu Second Peop le’s Hospital, “Elderly patients with arrhythmia who were hospitalized at Sichua n Provincial People’s Hospital between 1 June 2018 and 31 May 2020 were enrolled . The LACE index was calculated for each patient, and the area under the receive r operating characteristic curve (AUROC) was calculated. Six machine learning al gorithms, combined with three variable selection methods and clinically relevant features available at the time of hospital discharge, were used to develop mach ine learning models. AUROC and area under the precision-recall curve (AUPRC) wer e used to assess discrimination. Shapley additive explanations (SHAP) analysis w as used to explain the contributions of the features. A total of 523 patients we re enrolled, and 108 patients experienced 1 year hospital readmission for heart failure. The AUROC of the LACE index was 0.5886. The complete machine learning m odel had the best predictive performance, with an AUROC of 0.7571 and an AUPRC o f 0.4096. The most important predictors for 1 year readmission were educational level, total triiodothyronine (TT3), aspartate aminotransferase/alanine aminotra nsferase (AST/ALT), number of medications (NOM) and triglyceride (TG) level.”
ChengduPeople’s Republic of ChinaAsi aAminotransferaseArrhythmiaCardiologyCardiovascular Diseases and Conditi onsCyborgsEmerging TechnologiesEnzymes and CoenzymesHealth and MedicineHeart DiseaseHeart Disorders and DiseasesHeart FailureHospitalsMachine Learning
NETL
NSTL
