Li Xiao-Lei, Adi Dilare, Zhao Qian, Aizezi Aibibanmu, Keremu Munawaer, Li Yan-Peng, Liu Fen, Ma Xiang, Li Xiao-Mei, Azhati Adila, Ma Yi-Tong
State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Xinjiang Key Laboratory of Cardiovascular Disease, Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Front Cardiovasc Med. 2023 Mar 16;10:1043274. doi: 10.3389/fcvm.2023.1043274. eCollection 2023.
Unplanned admission to the intensive care unit (ICU) is the major in-hospital adverse event for patients with dilated cardiomyopathy (DCM). We aimed to establish a nomogram of individualized risk prediction for unplanned ICU admission in DCM patients.
A total of 2,214 patients diagnosed with DCM from the First Affiliated Hospital of Xinjiang Medical University from January 01, 2010, to December 31, 2020, were retrospectively analyzed. Patients were randomly divided into training and validation groups at a 7:3 ratio. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used for nomogram model development. The area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. The primary outcome was defined as unplanned ICU admission.
A total of 209 (9.44%) patients experienced unplanned ICU admission. The variables in our final nomogram included emergency admission, previous stroke, New York Heart Association Class, heart rate, neutrophil count, and levels of N-terminal pro b-type natriuretic peptide. In the training group, the nomogram showed good calibration (Hosmer-Lemeshow = 14.40, = 0.07) and good discrimination, with an optimal-corrected C-index of 0.76 (95% confidence interval: 0.72-0.80). DCA confirmed the clinical net benefit of the nomogram model, and the nomogram maintained excellent performances in the validation group.
This is the first risk prediction model for predicting unplanned ICU admission in patients with DCM by simply collecting clinical information. This model may assist physicians in identifying individuals at a high risk of unplanned ICU admission for DCM inpatients.
重症监护病房（ICU）的非计划收治是扩张型心肌病（DCM）患者住院期间的主要不良事件。我们旨在建立一个预测DCM患者非计划入住ICU的个体化风险预测列线图。
回顾性分析2010年1月1日至2020年12月31日在新疆医科大学第一附属医院诊断为DCM的2214例患者。患者按7:3的比例随机分为训练组和验证组。采用最小绝对收缩和选择算子及多变量逻辑回归分析建立列线图模型。采用受试者操作特征曲线下面积、校准曲线和决策曲线分析（DCA）对模型进行评估。主要结局定义为非计划入住ICU。
共有209例（9.44%）患者非计划入住ICU。我们最终列线图中的变量包括急诊入院、既往卒中史、纽约心脏协会心功能分级、心率、中性粒细胞计数以及N末端B型利钠肽原水平。在训练组中，列线图显示出良好的校准（Hosmer-Lemeshow检验χ² = 14.40，P = 0.07）和良好的区分度，最佳校正C指数为0.76（95%置信区间：0.72 - 0.80）。DCA证实了列线图模型的临床净效益，且该列线图在验证组中保持了优异的性能。
这是首个通过简单收集临床信息来预测DCM患者非计划入住ICU的风险预测模型。该模型可能有助于医生识别DCM住院患者中存在非计划入住ICU高风险的个体。
