Dominici Carmelo, Salsano Antonio, Nenna Antonio, Spadaccio Cristiano, Barbato Raffaele, Mariscalco Giovanni, Santini Francesco, Biancari Fausto, Chello Massimo
Department of Cardiovascular Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
Department of Cardiac Surgery, Università di Genova, Genova, Italy.
J Cardiothorac Vasc Anesth. 2020 Nov;34(11):2951-2961. doi: 10.1053/j.jvca.2020.06.015. Epub 2020 Jun 12.
Many papers evaluated predictive factors for prolonged intensive care unit (ICU) stay after cardiac surgery, but efforts in translating those models in practical clinical tools is lacking. The aim of this study was to build a new nomogram score and test its calibration and discrimination power for predicting a long length of stay in the ICU among patients undergoing coronary artery bypass graft surgery (CABG).
Retrospective analysis of an international registry.
Multicentric.
Based on the european multicenter study on coronary artery bypass grafting (E-CABG) registry (NCT02319083), a total of 7,352 consecutive patients who underwent isolated CABG were analyzed.
A "long length of stay" in the ICU was considered when equal to or more than 3 days. Predictive factors were analyzed through a multivariate logistic regression model that was used for the nomogram.
Long length of ICU stay was observed in 2,665 patients (36.2%). Ten independent variables were included in the final regression model: the SYNTAX score class critical preoperative state, left ventricular ejection fraction class, angina at rest, poor mobility, recent potent antiplatelet use, estimated glomerular filtration rate class, body mass index, sex, and age. Based on this 10-risk factors logistic regression model, a nomogram has been designed.
The authors defined a nomogram model that can provide an individual prediction of long length of ICU stay in cardiovascular surgical patients undergoing CABG. This type of model would allow an early recognition of high-risk patients who might receive different preoperative and postoperative treatments to improve outcomes.
许多论文评估了心脏手术后重症监护病房(ICU)延长住院时间的预测因素,但在将这些模型转化为实用的临床工具方面仍有所欠缺。本研究的目的是构建一个新的列线图评分,并测试其校准和区分能力,以预测冠状动脉旁路移植术(CABG)患者在ICU的长时间住院情况。
对一个国际注册库进行回顾性分析。
多中心。
基于欧洲冠状动脉旁路移植术多中心研究(E-CABG)注册库(NCT02319083),共分析了7352例连续接受单纯CABG的患者。
当ICU住院时间等于或超过3天时,被视为“长时间住院”。通过用于列线图的多变量逻辑回归模型分析预测因素。
2665例患者(36.2%)出现ICU长时间住院情况。最终回归模型纳入了10个独立变量:SYNTAX评分分级、术前临界状态、左心室射血分数分级、静息性心绞痛、活动能力差、近期强效抗血小板药物使用、估计肾小球滤过率分级、体重指数、性别和年龄。基于这个包含10个危险因素的逻辑回归模型,设计了一个列线图。
作者定义了一个列线图模型,该模型可以对接受CABG的心血管外科患者在ICU的长时间住院情况进行个体预测。这种类型的模型将有助于早期识别可能接受不同术前和术后治疗以改善预后的高危患者。