Radhachandran Ashwath, Garikipati Anurag, Zelin Nicole S, Pellegrini Emily, Ghandian Sina, Calvert Jacob, Hoffman Jana, Mao Qingqing, Das Ritankar
Dascena, Inc, 12333 Sowden Rd Ste B PMB 65148, Houston, TX, 77080-2059, USA.
BioData Min. 2021 Mar 31;14(1):23. doi: 10.1186/s13040-021-00255-w.
Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.
When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG.
A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient's risk profiles.
急性心力衰竭（AHF）与较高的发病率和死亡率相关。有效的患者风险分层对于指导住院决策和AHF的临床管理至关重要。临床决策支持系统可用于改善急诊环境中对死亡率的预测，以进行AHF风险分层。在本研究中，通过将机器学习技术应用于来自236,275次急诊科（ED）就诊的回顾性患者数据，开发了几种预测AHF患者七天死亡率的模型，其中1881次就诊被认为AHF呈阳性，并用于模型训练和测试。这些模型使用了年龄、性别、生命体征和实验室值的不同子集。将模型性能与急诊心力衰竭死亡率风险分级（EHMRG）模型进行比较，EHMRG模型是一种常用的用于预测ED中七天死亡率的系统，其输入相似（或在某些情况下更广泛）。通过受试者操作特征曲线下面积（AUROC）、敏感性和特异性来评估模型性能。
在一个大型学术数据集上进行训练和测试时，表现最佳的模型和EHMRG在预测七天死亡率方面的测试集AUROC分别为0.84和0.78。仅给出呼吸频率、体温、平均动脉压和FiO的测量值时，一个模型的测试集AUROC为0.83。逻辑回归比较器和简单决策树的表现均未超过EHMRG。
在预测AHF患者七天死亡率方面，仅使用四个临床变量测量值的模型优于EHMRG。有了这些输入，该模型不能被逻辑回归替代，也不能简化为简单决策树而不造成显著的性能损失。在急诊环境中，这种最小输入风险分层工具可以通过对个体患者风险概况提供早期和准确的见解，协助临床医生做出关于患者处置的关键决策。
