■本研究旨在构建并内部验证中国心脏骤停(CA)患者人群中自发循环恢复(ROSC)速率列线图的概率。
■对接受标准心肺复苏(CPR)的CA患者进行回顾性研究。小绝对收缩和选择算子(LASSO)回归分析和多变量逻辑回归评估了各种人口统计学和临床病理特征。使用C指数构建预测列线图,并评估准确性和可靠性,接受者工作特性曲线下面积(AUC),校准图,和决策曲线分析(DCA)。
■将经历过CA并接受标准CPR的508名患者随机分为训练(70%,n=356)和验证组(30%,n=152)用于研究。LASSO回归分析和多变量逻辑回归显示,13个变量,比如年龄,CPR开始时间,电除颤,肾上腺素,碳酸氢钠(NaHCO3),CPR按压持续时间,术后凝血酶原(PT)时间,乳酸(Lac),心肌肌钙蛋白(cTn),钾(K+),D-二聚体,高血压(HBP),和糖尿病(DM),被发现是CPRROSC率的独立预测因子。列线图模型显示出异常的区分度,C指数为0.933(95%置信区间:0.882-0.984)。即使在内部验证中,仍获得显著的C指数值0.926(95%置信区间:0.875-0.977).训练组的AUC为0.923,验证组的AUC为0.926,验证了模型的准确性和可靠性。校正曲线显示模型与实际结果吻合。DCA提示预测性列线图具有临床实用性。
■成功建立了预测列线图模型,并证明了该模型可以识别CA患者ROSC率的影响因素。心肺复苏期间,建议根据影响ROSC率的因素调整急诊治疗,以提高CA患者的治疗率。
UNASSIGNED: This study aimed to construct and internally validate a probability of the return of spontaneous circulation (ROSC) rate nomogram in a Chinese population of patients with cardiac arrest (CA).
UNASSIGNED: Patients with CA receiving standard cardiopulmonary resuscitation (CPR) were studied retrospectively. The minor absolute shrinkage and selection operator (LASSO) regression analysis and multivariable logistic regression evaluated various demographic and clinicopathological characteristics. A predictive nomogram was constructed and evaluated for accuracy and reliability using C-index, the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis (DCA).
UNASSIGNED: A cohort of 508 patients who had experienced CA and received standard CPR was randomly divided into training (70 %, n = 356) and validation groups (30 %, n = 152) for the study. LASSO regression analysis and multivariable logistic regression revealed that thirteen variables, such as age, CPR start time, Electric defibrillation, Epinephrine, Sodium bicarbonate (NaHCO3), CPR Compression duration, The postoperative prothrombin (PT) time, Lactate (Lac), Cardiac troponin (cTn), Potassium (K+), D-dimer, Hypertension (HBP), and Diabetes mellitus (DM), were found to be independent predictors of the ROSC rate of CPR. The nomogram model showed exceptional discrimination, with a C-index of 0.933 (95 % confidence interval: 0.882-0.984). Even in the internal validation, a remarkable C-index value of 0.926 (95 % confidence interval: 0.875-0.977) was still obtained. The accuracy and reliability of the model were also verified by the AUC of 0.923 in the training group and 0.926 in the validation group. The calibration curve showed the model agreed with the actual results. DCA suggested that the predictive nomogram had clinical utility.
UNASSIGNED: A predictive nomogram model was successfully established and proved to identify the influencing factors of the ROSC rate in patients with CA. During cardiopulmonary resuscitation, adjusting the emergency treatment based on the influence factors on ROSC rate is suggested to improve the treatment rate of patients with CA.