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中华危重症医学杂志(电子版) ›› 2026, Vol. 19 ›› Issue (03) : 208 -214. doi: 10.3877/cma.j.issn.1674-6880.2026.03.003

论著

急诊手术患者术后28 d死亡风险预测列线图模型的开发与验证
陈进敏1, 陆远强2,()   
  1. 1310016 杭州,浙江大学医学院附属邵逸夫医院急诊科
    2310003 杭州,浙江大学医学院附属第一医院急诊科、全省理化与增龄损伤性疾病诊治研究重点实验室
  • 收稿日期:2025-11-16 出版日期:2026-06-30
  • 通信作者: 陆远强

Development and validation of a nomogram model for predicting 28-day mortality in emergency surgery patients

Jinmin Chen1, Yuanqiang Lu2,()   

  1. 1Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, China
    2Department of Emergency Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang Key Laboratory for Diagnosis and Treatment of Physic-chemical and Aging-related Injuries, Hangzhou 310003, China
  • Received:2025-11-16 Published:2026-06-30
  • Corresponding author: Yuanqiang Lu
引用本文:

陈进敏, 陆远强. 急诊手术患者术后28 d死亡风险预测列线图模型的开发与验证[J/OL]. 中华危重症医学杂志(电子版), 2026, 19(03): 208-214.

Jinmin Chen, Yuanqiang Lu. Development and validation of a nomogram model for predicting 28-day mortality in emergency surgery patients[J/OL]. Chinese Journal of Critical Care Medicine(Electronic Edition), 2026, 19(03): 208-214.

目的

开发并验证用于预测急诊手术患者28 d生存率的列线图模型。

方法

收集2020年1月至2024年6月期间在浙江大学医学院附属邵逸夫医院接受急诊手术的1 956例成年患者临床资料,其中训练集1 369例、验证集587例。根据患者术后28 d是否死亡将训练集患者分为生存组(1 188例)和非生存组(181例)。通过最小绝对收缩与选择算子(LASSO)回归筛选变量,再经多因素logistic回归建立预测模型,并绘制列线图模型。模型的区分度以受试者工作特征(ROC)曲线的曲线下面积(AUC)评估,校准性通过Bootstrap自助法1 000次重采样验证,决策曲线分析(DCA)用于评价其临床实用性。

结果

非生存组患者Charlson合并症指数(CCI)、白细胞计数(WBC)、中性粒细胞计数、淋巴细胞计数、肌酐、血尿素氮(BUN)、尿酸以及术中失血量和并发症发生情况均显著高于生存组(P均< 0.05),男性患者占比、舒张压、纤维蛋白原(FIB)、C反应蛋白(CRP)和钾离子水平均显著低于生存组(P均< 0.05)。多因素logistic回归筛选出7个独立预测因子,分别为男性、CCI、中性粒细胞计数、淋巴细胞计数、FIB、BUN以及尿酸(P均< 0.05)。整合7个独立预测因子构建急诊手术患者术后28 d死亡的列线图模型。ROC曲线分析结果显示,在训练集和测试集中评估急诊手术患者术后28 d死亡的AUC值分别为0.759[95%置信区间(CI)(0.721,0.798),P < 0.001]及0.725[95%CI(0.701,0.820),P < 0.001],说明列线图模型具有良好的区分度。校准曲线提示预测概率与实际观察值吻合良好。DCA结果表明模型具有显著的净临床获益。

结论

本研究建立的列线图模型可有效预测急诊手术患者的28 d生存率。

Objective

To develop and validate a nomogram for predicting the 28-day survival rate of patients undergoing emergency surgery.

Methods

Clinical data were collected from 1 956 adult patients who underwent emergency surgery at the Sir Run Run Shaw Hospital, Zhejiang University School of Medicine between January 2020 and June 2024. The cohort was randomly divided into a training set (n = 1 369) and a validation set (n = 587). According to the 28-day postoperative survival status, patients in the training set were classified into a survival group (n = 1 188) and a non-survival group (n = 181). The least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection, followed by multivariable logistic regression to establish a prediction model, which was subsequently visualized as a nomogram. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed by bootstrap resampling with 1 000 iterations, and decision curve analysis (DCA) was performed to evaluate the clinical utility of the model.

Results

Compared with the survival group, patients in the non-survival group had significantly higher Charlson comorbidity index (CCI), white blood cell count, neutrophil count, lymphocyte count, serum creatinine, blood urea nitrogen (BUN), uric acid, intraoperative blood loss, and incidence of postoperative complications (all P < 0.05). In contrast, the proportion of male patients, diastolic blood pressure, fibrinogen (FIB), C-reactive protein, and serum potassium were significantly lower in the non-survival group than in the survival group (all P < 0.05). Multivariable logistic regression identified seven independent predictors of 28-day postoperative mortality: male, CCI, neutrophil count, lymphocyte count, FIB, BUN, and uric acid (all P < 0.05). A nomogram integrating these seven predictors was constructed to estimate the risk of 28-day postoperative mortality in patients undergoing emergency surgery. Receiver operating characteristic curve analysis demonstrated that the nomogram achieved AUCs of 0.759 [95% confidence interval (CI) (0.721, 0.798), P < 0.001] in the training set and 0.725 [95%CI (0.701, 0.820), P < 0.001] in the validation set, indicating its good discriminatory performance. Calibration curves showed good agreement between the predicted probabilities and the observed outcomes. DCA further demonstrated that the nomogram provided substantial net clinical benefit.

Conclusion

The nomogram developed in this study can effectively predict the 28-day survival rate of patients undergoing emergency surgery.

表1 训练集急诊手术患者病历资料比较
指标 生存组(n = 1 188) 非生存组(n = 181) t/χ2/Z P
年龄(岁, ± s 62(51,71) 62(52,73) 1.135 0.314
男性[例(%)] 828(69.7) 111(61.3) 4.728 0.030
BMI(kg/m2 ± s 22.9 ± 4.0 22.6 ± 3.8 0.998 0.319
创伤手术[例(%)] 210(17.7) 43(23.8) 3.461 0.063
CCI[MP25P75)] 5(3,7) 7(5,9) 7.727 < 0.001
收缩压[mmHg,MP25P75)] 126(110,144) 123(106,146) 0.704 0.481
舒张压[mmHg,MP25P75)] 74(64,84) 70(60,82) 2.703 0.007
心率[次/min,MP25P75)] 88(75,104) 92(75,113) 1.947 0.052
呼吸频率[次/min,MP25P75)] 19(16,20) 18(14,21) 0.706 0.480
WBC[× 109/L,MP25P75)] 10.40(6.76,14.64) 12.49(8.64,17.80) 4.120 < 0.001
中性粒细胞计数[× 109/L,MP25P75)] 8.20(4.77,12.42) 9.50(5.90,14.52) 2.591 0.010
淋巴细胞计数[× 109/L,MP25P75)] 0.94(0.62,1.64) 1.32(0.67,2.59) 3.847 < 0.001
单核细胞计数[× 109/L,MP25P75)] 0.57(0.37,0.83) 0.57(0.38,0.95) 0.937 0.349
RBC[× 109/L,MP25P75)] 4.03(3.30,4.60) 4.20(3.31,4.79) 1.387 0.166
血红蛋白[g/L,MP25P75)] 123(98,140) 123(101,143) 0.524 0.600
HCT[%,MP25P75)] 36.7(30.0,41.7) 37.9(31.1,42.7) 1.105 0.269
PLT[× 109/L,MP25P75)] 192.0(133.0,255.0) 189.0(140.0,251.0) 0.439 0.661
INR[MP25P75)] 1.06(0.97,1.20) 1.06(0.96,1.25) 0.139 0.890
FIB[g/L,MP25P75)] 3.11(2.19,4.56) 2.65(1.87,4.36) 2.639 0.008
PT[s,MP25P75)] 12.2(11.3,13.8) 12.3(11.3,14.4) 0.802 0.423
APTT[s,MP25P75)] 27.10(24.50,32.63) 27.40(23.90,36.70) 0.409 0.682
白蛋白[g/L,MP25P75)] 38.7(31.7,44.3) 40.4(30.2,45.2) 0.638 0.523
肌酐[μmol/L,MP25P75)] 73.5(60.0,95.0) 89.0(66.0,126.0) 4.483 < 0.001
BUN[mmol/L,MP25P75)] 6.05(4.64,8.38) 6.24(4.99,10.70) 2.406 0.016
尿酸[μmol/L,MP25P75)] 292.00(210.75,379.00) 341.00(255.00,423.00) 4.313 < 0.001
CRP[mg/L,MP25P75)] 9.90(1.58,48.60) 4.56(1.00,38.80) 2.066 0.039
LVEF[%,MP25P75)] 65(60,69) 63(60,68) 1.228 0.220
钾离子[mmol/L,MP25P75)] 3.78(3.43,4.19) 3.68(3.24,4.20) 2.045 0.041
钠离子[mmol/L,MP25P75)] 139(137,142) 140(137,143) 1.768 0.077
手术时长[min,MP25P75)] 175.50(104.75,297.25) 181.00(93.00,290.00) 0.171 0.864
术中失血量[mL,MP25P75)] 100(50,500) 200(50,600) 2.479 0.013
术中输血[例(%)] 589(49.6) 95(52.5) 0.421 0.516
术中血管活性药使用[例(%)] 921(77.5) 150(82.9) 2.333 0.127
术中并发症[例(%)] 407(34.3) 83(45.9) 8.694 0.003
图1 急诊手术患者术后28 d死亡影响因素的LASSO回归分析注:LASSO.最小绝对收缩和选择算子;a图为LASSO回归10折交叉验证筛选变量模型误差随λ的对数变化图;b图为LASSO回归筛选变量数量与回归系数随λ的对数变化图
图2 急诊手术患者术后28 d死亡预测因子的列线图模型注:CCI. Charlson合并症指数;FIB.纤维蛋白原;BUN.血尿素氮
图3 急诊手术患者术后28 d死亡预测模型的ROC曲线分析注:ROC.受试者工作特征
图4 列线图模型预测急诊手术患者术后28 d死亡的校正曲线(a)及决策曲线(b)
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