BACKGROUND Postoperative acute kidney injury (AKI) is a major complication associated with increased morbidity and mo-rtality after surgery for acute type A aortic dissection (AAAD). To the best of our knowledge, risk prediction models for AKI fol-lowing AAAD surgery have not been reported. The goal of the present study was to develop a prediction model to predict severe AKI after AAAD surgery.METHODS A total of 485 patients who underwent AAAD surgery were enrolled and randomly divided into the training coh-ort (70％) and the validation cohort (30％). Severe AKI was defined as AKI stage Ⅲ following the Kidney Disease: Improving Glo-bal Outcomes criteria. Preoperative variables, intraoperative variables and postoperative data were collected for analysis. Multiv-ariable logistic regression analysis was performed to select predictors and develop a nomogram in the study cohort. The final predic-tion model was validated using the bootstrapping techniques and in the validation cohort. RESULTS The incidence of severe AKI was 23.0％ (n = 78), and 14.7％ (n = 50) of patients needed renal replacement treatment. The hospital mortality rate was 8.3％ (n = 28), while for AKI patients, the mortality rate was 13.1％, which increased to 20.5％ for severe AKI patients. Univariate and multivariate analyses showed that age, cardiopulmonary bypass time, serum creatinine, and D-dimer were key predictors for severe AKI following AAAD surgery. The logistic regression model incorporated these pred-ictors to develop a nomogram for predicting severe AKI after AAAD surgery. The nomogram showed optimal discrimination abil-ity, with an area under the curve of 0.716 in the training cohort and 0.739 in the validation cohort. Calibration curve analysis dem-onstrated good correlations in both the training cohort and the validation cohort. CONCLUSIONS We developed a prognostic model including age, cardiopulmonary bypass time, serum creatinine, and D-di-mer to predict severe AKI after AAAD surgery. The prognostic model demonstrated an effective predictive capability for severe AKI, which may help improve risk stratification for poor in-hospital outcomes after AAAD surgery.

Cong-Cong LUO;Yong-Liang ZHONG;Zhi-Yu QIAO;Cheng-Nan LI;Yong-Min LIU;Jun ZHENG;Li-Zhong SUN;Yi-Peng GE;Jun-Ming ZHU

Department of Cardiovascular Surgery,Beijing Aortic Disease Center,Beijing Anzhen Hospital,Capital Medical Unive-rsity,Beijing,China;Department of Thoracic Surgery,Shanghai 9th People's Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai,China

老年心脏病学杂志（英文版）

[1]Cong-Cong LUO;Yong-Liang ZHONG;Zhi-Yu QIAO;Cheng-Nan LI;Yong-Min LIU;Jun ZHENG;Li-Zhong SUN;Yi-Peng GE;Jun-Ming ZHU-.Development and validation of a nomogram for postoperative severe acute kidney injury in acute type A aortic dissection)[J].老年心脏病学杂志（英文版）,2022(10):734-742

rtality,coh,ort,Multiv,ariable,ictors

Development,validation,nomogram,postoperative,severe,acute,kidney,injury,type,aortic,dissection,BACKGROUND,Postoperative,AKI,major,complication,associated,increased,morbidity,after,surgery,AAAD,To,best,our,knowledge,risk,prediction,models,have,not,been,reported,goal,present,study,was,METHODS,total,patients,who,underwent,were,enrolled,randomly,divided,into,training,cohort,Severe,defined,stage,following,Kidney,Disease,Improving,Glo,bal,Outcomes,criteria,Preoperative,variables,intraoperative,data,collected,analysis,logistic,regression,performed,select,predictors,final,validated,using,bootstrapping,techniques,RESULTS,incidence,needed,renal,replacement,treatment,hospital,mortality,while,which,Univariate,multivariate,analyses,showed,that,cardiopulmonary,bypass,serum,creatinine,dimer,key,incorporated,these,predicting,optimal,discrimination,area,curve,Calibration,good,correlations,both,CONCLUSIONS,We,developed,prognostic,including,demonstrated,effective,predictive,capability,may,help,improve,stratification,poor,outcomes

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