Department of Critical Care Medicine, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
Department of Cardiology, Shantou Central Hospital, Shantou, China.
BMC Cardiovasc Disord. 2022 Dec 6;22(1):528. doi: 10.1186/s12872-022-02973-3.
Acute heart failure is a serious condition. Atrial fibrillation is the most frequent arrhythmia in patients with acute heart failure. The occurrence of atrial fibrillation in heart failure patients worsens their prognosis and leads to a substantial increase in treatment costs. There is no tool that can effectively predict the onset of atrial fibrillation in patients with acute heart failure in the ICU currently.
We retrospectively analyzed the MIMIC-IV database of patients admitted to the intensive care unit (ICU) for acute heart failure and who were initially sinus rhythm. Data on demographics, comorbidities, laboratory findings, vital signs, and treatment were extracted. The cohort was divided into a training set and a validation set. Variables selected by LASSO regression and multivariate logistic regression in the training set were used to develop a model for predicting the occurrence of atrial fibrillation in acute heart failure in the ICU. A nomogram was drawn and an online calculator was developed. The discrimination and calibration of the model was evaluated. The performance of the model was tested using the validation set.
This study included 2342 patients with acute heart failure, 646 of whom developed atrial fibrillation during their ICU stay. Using LASSO and multiple logistic regression, we selected six significant variables: age, prothrombin time, heart rate, use of vasoactive drugs within 24 h, Sequential Organ Failure Assessment (SOFA) score, and Acute Physiology Score (APS) III. The C-index of the model was 0.700 (95% CI 0.672-0.727) and 0.682 (95% CI 0.639-0.725) in the training and validation sets, respectively. The calibration curves also performed well in both sets.
We developed a simple and effective model for predicting atrial fibrillation in patients with acute heart failure in the ICU.
急性心力衰竭是一种严重的病症。心房颤动是急性心力衰竭患者中最常见的心律失常。心力衰竭患者发生心房颤动会使其预后恶化，并导致治疗费用大幅增加。目前，还没有一种工具可以有效地预测 ICU 中急性心力衰竭患者心房颤动的发作。
我们回顾性分析了因急性心力衰竭入住重症监护病房（ICU）且初始窦性心律的 MIMIC-IV 数据库。提取了人口统计学、合并症、实验室检查、生命体征和治疗数据。该队列分为训练集和验证集。在训练集中，通过 LASSO 回归和多变量逻辑回归选择变量，并用于开发预测 ICU 中急性心力衰竭患者心房颤动发生的模型。绘制了列线图并开发了在线计算器。评估了模型的判别和校准。使用验证集测试了模型的性能。
这项研究共纳入 2342 例急性心力衰竭患者，其中 646 例在 ICU 期间发生了心房颤动。使用 LASSO 和多变量逻辑回归，我们选择了六个重要变量：年龄、凝血酶原时间、心率、24 小时内使用血管活性药物、序贯器官衰竭评估（SOFA）评分和急性生理学评分（APS）III。模型在训练集和验证集中的 C 指数分别为 0.700（95%CI 0.672-0.727）和 0.682（95%CI 0.639-0.725）。校准曲线在两组中表现也良好。
我们开发了一种简单有效的预测 ICU 中急性心力衰竭患者心房颤动的模型。
