Duan Hongwei, Li Xiaojing, Yang Xingju, Wang Fei, Yang Fengyong
Department of Critical Care Medicine, Jinan People's Hospital (People's Hospital Affiliated to Shandong First Medical University), Jinan 271100, Shandong, China.
Department of Nursing, Jinan People's Hospital (People's Hospital Affiliated to Shandong First Medical University), Jinan 271100, Shandong, China.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2024 Apr;36(4):358-363. doi: 10.3760/cma.j.cn121430-20230925-00821.
To explore the independent risk factors of acute respiratory distress syndrome (ARDS) in patients with sepsis, establish an early warning model, and verify the predictive value of the model based on synthetic minority oversampling technique (SMOTE) algorithm.
A retrospective case-control study was conducted. 566 patients with sepsis who were admitted to Jinan People's Hospital from October 2016 to October 2022 were enrolled. General information, underlying diseases, infection sites, initial cause, severity scores, blood and arterial blood gas analysis indicators at admission, treatment measures, complications, and prognosis indicators of patients were collected. The patients were grouped according to whether ARDS occurred during hospitalization, and the clinical data between the two groups were observed and compared. Univariate and binary multivariate Logistic regression analysis were used to select the independent risk factors of ARDS during hospitalization in septic patients, and regression equation was established to construct the early warning model. Simultaneously, the dataset was improved using the SMOTE algorithm to build an enhanced warning model. Receiver operator characteristic curve (ROC curve) was drawn to validate the prediction efficiency of the model.
566 patients with sepsis were included in the final analysis, of which 163 developed ARDS during hospitalization and 403 did not. Univariate analysis showed that there were statistically significant differences in age, body mass index (BMI), malignant tumor, blood transfusion history, pancreas and peripancreatic infection, gastrointestinal tract infection, pulmonary infection as the initial etiology, acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score, albumin (Alb), blood urea nitrogen (BUN), mechanical ventilation therapy, septic shock and length of intensive care unit (ICU) stay between the two groups. Binary multivariate Logistic regression analysis showed that age [odds ratio (OR) = 3.449, 95% confidence interval (95%CI) was 2.197-5.414, P = 0.000], pulmonary infection as the initial etiology (OR = 2.309, 95%CI was 1.427-3.737, P = 0.001), pancreas and peripancreatic infection (OR = 1.937, 95%CI was 1.236-3.035, P = 0.004), septic shock (OR = 3.381, 95%CI was 1.890-6.047, P = 0.000), SOFA score (OR = 9.311, 95%CI was 5.831-14.867, P = 0.000) were independent influencing factors of ARDS during hospitalization in septic patients. An early warning model was established based on the above risk factors: P = -4.558+1.238×age+0.837×pulmonary infection as the initial etiology+0.661×pancreas and peripancreatic infection+1.218×septic shock+2.231×SOFA score. ROC curve analysis showed that the area under the ROC curve (AUC) of the model for ARDS during hospitalization in septic patients was 0.882 (95%CI was 0.851-0.914) with sensitivity of 79.8% and specificity of 83.4%. The dataset was improved based on the SMOTE algorithm, and the early warning model was rebuilt: P = -3.279+1.288×age+0.763×pulmonary infection as the initial etiology+0.635×pancreas and peripancreatic infection+1.068×septic shock+2.201×SOFA score. ROC curve analysis showed that the AUC of the model for ARDS during hospitalization in septic patients was 0.890 (95%CI was 0.867-0.913) with sensitivity of 85.3% and specificity of 79.1%. This result further confirmed that the early warning model constructed by the independent risk factors mentioned above had high predictive performance.
Risk factors for the occurrence of ARDS during hospitalization in patients with sepsis include age, pulmonary infection as the initial etiology, pancreatic and peripancreatic infection, septic shock, and SOFA score. Clinically, the probability of ARDS in patients with sepsis can be assessed based on the warning model established using these risk factors, allowing for early intervention and improvement of prognosis.
探讨脓毒症患者发生急性呼吸窘迫综合征（ARDS）的独立危险因素，建立预警模型，并基于合成少数过采样技术（SMOTE）算法验证该模型的预测价值。
进行一项回顾性病例对照研究。纳入2016年10月至2022年10月期间在济南市人民医院住院的566例脓毒症患者。收集患者的一般资料、基础疾病、感染部位、初始病因、严重程度评分、入院时血液及动脉血气分析指标、治疗措施、并发症及预后指标。根据患者住院期间是否发生ARDS进行分组，观察并比较两组间的临床资料。采用单因素及二元多因素Logistic回归分析筛选脓毒症患者住院期间发生ARDS的独立危险因素，建立回归方程构建预警模型。同时，使用SMOTE算法对数据集进行优化，构建增强预警模型。绘制受试者工作特征曲线（ROC曲线）验证模型的预测效能。
最终纳入566例脓毒症患者，其中163例住院期间发生ARDS，403例未发生。单因素分析显示，两组患者在年龄、体重指数（BMI）、恶性肿瘤、输血史、胰腺及胰周感染、胃肠道感染、以肺部感染作为初始病因、急性生理与慢性健康状况评分II（APACHE II）、序贯器官衰竭评估（SOFA）评分、白蛋白（Alb）、血尿素氮（BUN）、机械通气治疗、脓毒性休克及重症监护病房（ICU）住院时间等方面差异有统计学意义。二元多因素Logistic回归分析显示，年龄[比值比（OR）=3.449，95%置信区间（95%CI）为2.197 - 5.414，P = 0.000]、以肺部感染作为初始病因（OR = 2.309，95%CI为1.427 - 3.737，P = 0.001）、胰腺及胰周感染（OR = 1.937，95%CI为1.236 - 3.035，P = 0.004）、脓毒性休克（OR = 3.381，95%CI为1.890 - 6.047，P = 0.000）、SOFA评分（OR = 9.311，95%CI为5.831 - 14.867，P = 0.000）是脓毒症患者住院期间发生ARDS的独立影响因素。基于上述危险因素建立预警模型：P = -4.558 + 1.238×年龄 + 0.837×以肺部感染作为初始病因 + 0.661×胰腺及胰周感染 + 1.218×脓毒性休克 + 2.231×SOFA评分。ROC曲线分析显示，该模型对脓毒症患者住院期间ARDS的ROC曲线下面积（AUC）为0.882（95%CI为0.851 - 0.914），灵敏度为79.8%。基于SMOTE算法对数据集进行优化后，重新构建预警模型：P = -3.279 + 1.288×年龄 + 0.763×以肺部感染作为初始病因 + 0.635×胰腺及胰周感染 + 1.068×脓毒性休克 + 2.201×SOFA评分。ROC曲线分析显示，该模型对脓毒症患者住院期间ARDS的AUC为0.890（95%CI为0.867 - 0.913），灵敏度为85.3%，特异度为79.1%。这一结果进一步证实了上述独立危险因素构建的预警模型具有较高的预测性能。
脓毒症患者住院期间发生ARDS的危险因素包括年龄、以肺部感染作为初始病因、胰腺及胰周感染、脓毒性休克及SOFA评分。临床上可根据这些危险因素建立的预警模型评估脓毒症患者发生ARDS的概率，以便早期干预，改善预后。
