Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No 37 Guoxue Alley, Chengdu, 610041, Sichuan, China.
Department of Infectional Inpatient Ward Two, Chengdu Public Health Clinical Medical Center, Chengdu, Sichuan, China.
BMC Pulm Med. 2022 Sep 12;22(1):343. doi: 10.1186/s12890-022-02143-3.
Emerging evidence shows that cardiovascular injuries and events in coronavirus disease 2019 (COVID-19) should be considered. The current study was conducted to develop an early prediction model for major adverse cardiovascular events (MACE) during hospitalizations of COVID-19 patients.
This was a retrospective, multicenter, observational study. Hospitalized COVID-19 patients from Wuhan city, Hubei Province and Sichuan Province, China, between January 14 and March 9, 2020, were randomly divided into a training set (70% of patients) and a testing set (30%). All baseline data were recorded at admission or within 24 h after admission to hospitals. The primary outcome was MACE during hospitalization, including nonfatal myocardial infarction, nonfatal stroke and cardiovascular death. The risk factors were selected by LASSO regression and multivariate logistic regression analysis. The nomogram was assessed by calibration curve and decision curve analysis (DCA).
Ultimately, 1206 adult COVID-19 patients were included. In the training set, 48 (5.7%) patients eventually developed MACE. Six factors associated with MACE were included in the nomogram: age, PaO/FiO under 300, unconsciousness, lymphocyte counts, neutrophil counts and blood urea nitrogen. The C indices were 0.93 (95% CI 0.90, 0.97) in the training set and 0.81 (95% CI 0.70, 0.93) in the testing set. The calibration curve and DCA demonstrated the good performance of the nomogram.
We developed and validated a nomogram to predict the development of MACE in hospitalized COVID-19 patients. More prospective multicenter studies are needed to confirm our results.
有新证据表明，2019 年冠状病毒病（COVID-19）患者应考虑心血管损伤和事件。本研究旨在为 COVID-19 住院患者发生主要不良心血管事件（MACE）建立早期预测模型。
这是一项回顾性、多中心、观察性研究。2020 年 1 月 14 日至 3 月 9 日期间，来自中国湖北省武汉市和四川省的住院 COVID-19 患者被随机分为训练集（70%的患者）和测试集（30%）。所有基线数据均在入院时或入院后 24 小时内记录。主要结局是住院期间的 MACE，包括非致命性心肌梗死、非致命性卒中和心血管死亡。采用 LASSO 回归和多变量逻辑回归分析选择危险因素。通过校准曲线和决策曲线分析（DCA）评估列线图。
最终纳入 1206 例成年 COVID-19 患者。在训练集中，48（5.7%）例患者最终发生 MACE。列线图纳入 6 个与 MACE 相关的因素：年龄、PaO/FiO 小于 300、意识不清、淋巴细胞计数、中性粒细胞计数和血尿素氮。训练集的 C 指数为 0.93（95%CI 0.90，0.97），测试集的 C 指数为 0.81（95%CI 0.70，0.93）。校准曲线和 DCA 表明列线图具有良好的性能。
我们开发并验证了一个列线图，用于预测住院 COVID-19 患者发生 MACE 的风险。需要更多前瞻性多中心研究来证实我们的结果。
