基于临床数据库建立急性肾损伤风险预测列线图模型

Establishment of nomogram model of acute kidney injury risk prediction based on clinical database

目的基于大样本临床数据库平台,应用R语言及传统统计学方法构建急性肾损伤(acute kidney injury,AKI)的风险预测列线图模型,同时验证模型的准确性。方法该研究为回顾性病例对照研究。筛选临床数据库2021年1月1日至12月31日于同济大学附属同济医院就诊患者中的AKI患者,同时纳入48 h内监测血肌酐但未发生AKI的患者作为对照组。收集患者的人口学、疾病史、手术史、用药史以及实验室检查等资料,以筛选临床上发生AKI的危险因素。首先,采用多因素Logistic回归分析及正向逐步Logistic回归分析筛选危险因素,利用这些危险因素构建列线图模型,同时使用交叉验证、Bootstrap验证和随机拆分样本验证方法进行内部验证,并收集同院后1年(即2022年1月至12月)患者的临床资料进行外部验证,通过受试者工作特征曲线来确定模型的区分度,同时采用校准曲线和决策曲线分别评估准确性和临床净效益。结果共有5671例患者纳入该研究,AKI患者1884例(33.2%),非AKI患者3787例(66.7%)。与非AKI组相比,AKI组年龄、手术史比例、肾脏替代治疗比例、高血压比例、糖尿病比例、脑血管意外比例、慢性肾脏病比例、药物使用史比例及死亡比例均较高(均P<0.05)。多因素Logistic回归分析结果显示,AKI发生的独立影响因素为手术史、高血压、脑血管意外、糖尿病、慢性肾脏病、使用利尿剂、使用硝酸甘油、使用抗利尿激素、体温、血肌酐、C反应蛋白、红细胞、白细胞、D-二聚体、肌红蛋白、血红蛋白、血尿素氮、脑钠肽、谷草转氨酶、谷丙转氨酶、三酰甘油、乳酸脱氢酶、总胆红素、活化部分凝血活酶时间、血尿酸和钾离子(均P<0.05)。正向逐步Logistic回归分析最终确定纳入列线图的预测因素,包括慢性肾脏病、高血压、肌红蛋白、血肌酐和血尿素氮(均P<0.05),列线图预测模型的受试者工作特征曲线下面积为0.926(95%CI 0.918~0.933,P<0.001),校准曲线显示列线图校准效果良好(P>0.05),决策曲线显示当列线图模型风险阈值>0.04时,该模型构建在临床上有用。此外,列线图模型在外部验证集预测的受试者工作特征曲线下面积为0.876(95%CI 0.865~0.886),提示列线图模型在外部验证集上有较高的区分度。结论预测AKI发生风险的列线图模型成功建立,该模型的建立有助于临床医师及早发现高危AKI患者,及时干预,改善预后。

Objective To construct the risk prediction nomogram model of acute kidney injury(AKI)with R language and traditional statistical methods based on the large sample clinical database,and verify the accuracy of the model.Methods It was a a retrospective case control study.The patients who met the diagnostic criteria of AKI in Tongji Hospital of Tongji University from January 1 to December 31,2021 were screened in the clinical database,and the patients with monitored serum creatinine within 48 hours but without AKI were included as the control group.The demographic data,disease history,surgical history,medication history and laboratory test data were collected to screen the risk factors of AKI in clinic.Firstly,based on multivariate logistic regression analysis and forward stepwise logistic regression analysis,the selected risk factors were included to construct the nomogram model.At the same time,cross validation,bootstrap validation and randomly split sample validation were used for internal verification,and clinical data of patients in the sane hospital after one year(January to December,2022)were collected for external verification.The receiver-operating characteristic curve was used to determine the discrimination of the model,and calibration curve and decision curve analysis were carried out to evaluate the accuracy and clinical net benefit,respectively.Results A total of 5671 patients were enrolled in the study,with 1884 AKI patients(33.2%)and 3787 non-AKI patients(66.7%).Compared with non-AKI group,age,and proportions of surgical history,renal replacement therapy,hypertension,diabetes,cerebrovascular accident,chronic kidney disease,drug use histories and mortality in AKI group were all higher(all P<0.05).Multivariate logistic regression analysis showed that the independent influencing factors of AKI were surgical history,hypertension,cerebrovascular accident,diabetes,chronic kidney disease,diuretics,nitroglycerin,antidiuretic hormones,body temperature,serum creatinine,C-reactive protein,red blood cells,white blood cells,D-dimer,myoglobin,hemoglobin,blood urea nitrogen,brain natriuretic peptide,aspartate aminotransferase,alanine aminotransferase,triacylglycerol,lactate dehydrogenase,total bilirubin,activated partial thromboplastin time,blood uric acid and potassium ion(all P<0.05).Finally,the predictive factors in the nomogram were determined by forward stepwise logistic regression analysis,including chronic kidney disease,hypertension,myoglobin,serum creatinine and blood urea nitrogen,and the area under the curve of the prediction nomogram model was 0.926[95%CI 0.918-0.933,P<0.001].The calibration curve showed that the calibration effect of nomogram was good(P>0.05).The decision curve showed that when the risk threshold of nomogram model was more than 0.04,the model construction was useful in clinic.In addition,the area under the curve of receiver-operating characteristic curve predicted by nomograph model in external validation set was 0.876(95%CI 0.865-0.886),which indicated that nomograph model had a high discrimination degree.ConclusionA nomogram model for predicting the occurrence of AKI is established successfully,which is helpful for clinicians to find high-risk AKI patients early,intervene in time and improve the prognosis.