预测冠状动脉旁路移植术后中国GBV患者喉部并发症的风险模型

Jiangyun Peng,Yinghong Zhang,Xuan Liu,Xue Feng,Zijun Yin,Yanhong Hu,Wen Zhang,Jing Zhang,Jingping Li
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摘要

方法纳入2020年1月至2022年6月期间武汉亚洲心脏病医院收治的1476名中国CABG患者,将其分为建模队列和验证队列。通过单变量分析确定喉部症状的风险因素,并应用多变量逻辑回归建立 CABG 术后喉部症状的预测模型。根据接收者操作特征曲线下面积(ROC)和Hosmer-Lemeshow(H-L)检验,分别验证了该模型的识别性和校准性。模型中包含了四个独立的风险因素,已建立的咽喉并发症风险计算公式为:Logit (P) = -4.525 + 0.824 × 女性 + 2.09 × 体重指数 < 18.5 Kg/m2 + 0.793 × 经食道超声心动图 + 1.218 × 重症监护室插管时间。在喉部症状方面,衍生队列的 ROC 曲线下面积为 0.769(95% 置信区间 [CI]:0.698-0.840),验证队列的 ROC 曲线下面积为 0.811(95% 置信区间 [CI]:0.742-0.879)。根据 H-L 检验,建模组和验证组的 P 值分别为 0.659 和 0.838。结论本研究建立的预测模型可用于识别接受 CABG 手术的喉部症状高危患者,并帮助临床医生实施后续治疗。
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Risk Model for Laryngeal Complications Prediction in Chinese PatientGBVs After Coronary Artery Bypass Grafting.
INTRODUCTION The aim of this study was to identify perioperative risk factors of laryngeal symptoms and to develop an implementable risk prediction model for Chinese hospitalized patients undergoing coronary artery bypass grafting (CABG). METHODS A total of 1476 Chinese CABG patients admitted to Wuhan Asian Heart Hospital from January 2020 to June 2022 were included and then divided into a modeling cohort and a verification cohort. Univariate analysis was used to identify laryngeal symptoms risk factors, and multivariate logistic regression was applied to construct a prediction model for laryngeal symptoms after CABG. Discrimination and calibration of this model were validated based on the area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow (H-L) test, respectively. RESULTS The incidence of laryngeal symptoms in patients who underwent CABG was 6.48%. Four independent risk factors were included in the model, and the established aryngeal complications risk calculation formula was Logit (P) = -4.525 + 0.824 × female + 2.09 × body mass index < 18.5 Kg/m2 + 0.793 × transesophageal echocardiogram + 1.218 × intensive care unit intubation time. For laryngeal symptoms, the area under the ROC curve was 0.769 in the derivation cohort (95% confidence interval [CI]: 0.698-0.840) and 0.811 in the validation cohort (95% CI: 0.742-0.879). According to the H-L test, the P-values in the modeling group and the verification group were 0.659 and 0.838, respectively. CONCLUSION The prediction model developed in this study can be used to identify high-risk patients for laryngealsymptoms undergoing CABG, and help clinicians implement the follow-up treatment.
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The Usefulness of Microcirculatory Assessment After Cardiac Surgery: Illustrative Case Report. Risk Model for Laryngeal Complications Prediction in Chinese PatientGBVs After Coronary Artery Bypass Grafting. Artificial Intelligence in the Hands of Perfusionists: Revolutionizing Cardiopulmonary Bypass. Assessment of EuroSCORE II and STS Score Performance and the Impact of Surgical Urgency in Isolated Coronary Artery Bypass Graft Surgery at a Referral Center in São Paulo, Brazil. Comparison of the Effect of Pump Flow Type (Pulsatile or Non-Pulsatile) on Postoperative Neurocognitive Functions in Coronary Artery Surgery.
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