acute kidney injury ( aki ) is a common complication after cardiac surgery , with reported incidences between 8.9% and 39% as well as mortality of 3.8% to 54.4% , and even slight renal function changes were reported to affect short and longterm outcome after cardiac surgery.1 , 2 , 3 because early prediction in those patients at high risk of developing cardiac surgery  associated acute kidney injury ( csaaki ) may enable prevention of complications , in the last 2 decades various predictive models have been developed to forecast csaaki or renal replacement therapy ( rrt)aki after cardiac surgery,4 , 5 , 6 , 7 , 8 , 9 in which adequate predictive power was validated among whites.10 , 11 however , by now , the predictive value for estimating aki , rrtaki , or death incidence of 5 established models , including the method published by palomba et  al,7 the cleveland clinic score,8 the scoring system published by mehta et  al,6 the simplified renal index score,9 and the euro score12 in other racial cohorts after cardiac surgery is uncertain .
( 1 ) there was no consensus in the definition of aki when those models were developed . in the akics score
, aki was defined as  an increase of serum creatine ( cr ) levels above 2.0  mg / dl in patients with baseline serum cr lower than 1.5  mg / dl .  in patients with baseline serum cr between 1.5 and 3.0  mg / dl , aki was defined as  a serum cr increase of 50% over the baseline value .  in the new 2012 kidney disease : improving global outcomes ( kdigo ) aki definition,13 urine output , as another indicator besides serum creatine , was not utilized to define aki in palomba 's study , and aki may be missed .
( 2 ) with the recognition of intraoperative and postoperative risk factors , especially the modifiable factors , contributing to csaaki , risk scores6 , 8 , 9 including only preoperative risk factors can hardly meet the predictive requirement .
( 3 ) in some developing countries such as china , the proportions of valve surgery , comorbidities , and population race14 are quite different from those of the existing model derivation cohorts . with the development of medical technology , cardiac surgery in developing countries
meanwhile complications of surgery and economic burdens are also increasing due to restriction of medical technology and management .
we aimed to develop a dynamic predictive model for csaaki based on the recent kdigo aki definition , which was devised to predict the incidence of csaaki from preoperative to early postoperative periods and to validate its predictive capacity for improved clinical application .
in our prospective study we included 7233 patients who were divided into a derivation cohort ( 6081 patients between january 2010 and october 2012 ) and a validation cohort ( 1152 patients between november 2012 and april 2013 ) ; all were admitted to the zhongshan hospital , shanghai medical college , fudan university .
the study was approved by the ethical committee of the zhongshan hospital , and all participants provided written informed consent .
inclusion criteria were adult patients who underwent coronary artery bypass grafting ( cabg ) , offpump coronary artery bypass ( opcab ) , valve surgery , or valve combined with cabg surgery .
excluded were patients with previous heart transplantation , preoperative mechanical ventilation or tracheotomy , preoperative defibrillator or ventricular assist devices , preoperative rrt , preoperative liver dysfunction , or sepsis .
the clinical characteristics of both derivation and validation cohorts have been collected and were extracted from the database of zhongshan hospital cardiac surgery .
all data were checked twice by medical personnel ( postgraduate and resident doctor ) before being admitted to the database .
we collected data including sex , age , weight , length of stay , length of icu stay , and outcome ; preoperative data including diagnosis , comorbidities including hypertension , diabetes mellitus , atrial fibrillation , endocarditis , coronary artery disease , cerebral vascular disease , anemia , chronic obstructive pulmonary disease , kidney disease without renal replacement therapy ; and laboratory reports including estimated glomerular filtration rate ( egfr ) , which were calculated with ckdepi formulas,15 in addition to new york heart association ( nyha ) functional classification , left ventricular ejection fraction ( lvef ) , preoperative congestive heart failure , preoperative intraaortic balloon pump ( iabp ) , previous cardiac surgery history , recent contrast media ( cm ) exposure , and renal toxic drug exposure .
intraoperative data included emergency surgery , procedure type , cardiopulmonary bypass ( cpb ) application and time , transfusion , and fluid balance ; postoperative data comprised low cardiac output syndrome ( lcos ) , central venous pressure , medicine application , mechanical ventilation , rrt , and renal recovery . 
aki was defined according to the kdigo guideline13 as any of the following : increase in scr by 0.3  mg / dl ( 26.5 
mol / l ) within 48  hours ; or increase in scr to 1.5 times baseline that is known or presumed to have occurred within the prior 7  days or urine volume < 0.5  ml / kg per hour for 6  hours .
if there were more than 1 cardiac surgery procedures performed during the same hospitalization , only the data on the first surgery were considered .
if there were more than 1 hospitalization between january 2010 and april 2013 , only the data on the first hospitalization were considered .
continuous variables were expressed as meansd and analyzed by unpaired t tests , with welch adjustment when necessary .
continuous variables that violated the normality assumption were expressed as median and 25th to 75th percentiles and analyzed by a mannwhitney u test .
categorical variables were expressed as absolute ( n ) and relative ( % ) frequency and were analyzed by the pearson 2test or the fisher exact test whenever appropriate .
univariate analysis was carried out for patient data , and multivariate analysis by logistic regression was used to obtain data about the risk factors for aki at the 3 time points : preoperative , icu admittance , and 24  hours after icu admittance . in the multivariate analysis , results were expressed by the odds ratio and the 95% confidence interval .
the regression  of those variables obtained from logistic regression were used to develop the final 3 stage scores to predict csaaki .
the 1152 patients in the validation cohort were scored for validation , and the area under the receiver operating characteristic curves ( auroc ) as well as hosmerlemeshow goodnessoffit test were applied to estimate the reliability of the scoring for predicting csaaki .
in our prospective study we included 7233 patients who were divided into a derivation cohort ( 6081 patients between january 2010 and october 2012 ) and a validation cohort ( 1152 patients between november 2012 and april 2013 ) ; all were admitted to the zhongshan hospital , shanghai medical college , fudan university .
the study was approved by the ethical committee of the zhongshan hospital , and all participants provided written informed consent .
inclusion criteria were adult patients who underwent coronary artery bypass grafting ( cabg ) , offpump coronary artery bypass ( opcab ) , valve surgery , or valve combined with cabg surgery .
excluded were patients with previous heart transplantation , preoperative mechanical ventilation or tracheotomy , preoperative defibrillator or ventricular assist devices , preoperative rrt , preoperative liver dysfunction , or sepsis .
the clinical characteristics of both derivation and validation cohorts have been collected and were extracted from the database of zhongshan hospital cardiac surgery .
all data were checked twice by medical personnel ( postgraduate and resident doctor ) before being admitted to the database .
we collected data including sex , age , weight , length of stay , length of icu stay , and outcome ; preoperative data including diagnosis , comorbidities including hypertension , diabetes mellitus , atrial fibrillation , endocarditis , coronary artery disease , cerebral vascular disease , anemia , chronic obstructive pulmonary disease , kidney disease without renal replacement therapy ; and laboratory reports including estimated glomerular filtration rate ( egfr ) , which were calculated with ckdepi formulas,15 in addition to new york heart association ( nyha ) functional classification , left ventricular ejection fraction ( lvef ) , preoperative congestive heart failure , preoperative intraaortic balloon pump ( iabp ) , previous cardiac surgery history , recent contrast media ( cm ) exposure , and renal toxic drug exposure .
intraoperative data included emergency surgery , procedure type , cardiopulmonary bypass ( cpb ) application and time , transfusion , and fluid balance ; postoperative data comprised low cardiac output syndrome ( lcos ) , central venous pressure , medicine application , mechanical ventilation , rrt , and renal recovery . 
aki was defined according to the kdigo guideline13 as any of the following : increase in scr by 0.3  mg / dl ( 26.5 
mol / l ) within 48  hours ; or increase in scr to 1.5 times baseline that is known or presumed to have occurred within the prior 7  days or urine volume < 0.5  ml / kg per hour for 6  hours .
if there were more than 1 cardiac surgery procedures performed during the same hospitalization , only the data on the first surgery were considered .
if there were more than 1 hospitalization between january 2010 and april 2013 , only the data on the first hospitalization were considered .
continuous variables were expressed as meansd and analyzed by unpaired t tests , with welch adjustment when necessary .
continuous variables that violated the normality assumption were expressed as median and 25th to 75th percentiles and analyzed by a mannwhitney u test .
categorical variables were expressed as absolute ( n ) and relative ( % ) frequency and were analyzed by the pearson 2test or the fisher exact test whenever appropriate .
univariate analysis was carried out for patient data , and multivariate analysis by logistic regression was used to obtain data about the risk factors for aki at the 3 time points : preoperative , icu admittance , and 24  hours after icu admittance . in the multivariate analysis ,
the regression  of those variables obtained from logistic regression were used to develop the final 3 stage scores to predict csaaki .
the 1152 patients in the validation cohort were scored for validation , and the area under the receiver operating characteristic curves ( auroc ) as well as hosmerlemeshow goodnessoffit test were applied to estimate the reliability of the scoring for predicting csaaki .
the derivation cohort consisted of 6081 patients , and the validation cohort consisted of 1152 patients .
the incidence of csaaki and mortality in the derivation cohort were 35.5% ( 2159/6081 ) and 2.8% ( 170/6081 ) , whereas in the validation cohort they were 33.0% ( 380/1152 ) and 3.4% ( 39/1152 ) .
clinical characteristics of derivation and validation cohort aki indicates acute kidney injury ; cabg , coronary artery bypass grafting ; cm , contrast media ; copd , chronic obstructive pulmonary disease ; cpb , cardiopulmonary bypass ; cvd , cerebral vascular disease ; cvp , central venous pressure by icu admittance ; dm , diabetes mellitus ; egfr , estimated glomerular filtration rate , as was calculated based on the ckdepi formulas ; lcos , low cardiac output syndrome ; lvef , left ventricular ejection fraction ; nyha , new york heart association ; opcab , offpump coronary artery bypass .
we categorized the following variables for subgroup observation : age ( 40 , 4160 , 6180 , 81 ) , estimated gfr ( > 60 , > 30 to 60 , 30  ml / min ) , and lvef ( > 50 , 3650 , 35 ) .
there were statistically significant differences between the csaaki patients and the unaffected group regarding sex , age , preoperative kidney function , some comorbidities such as hypertension and copd , preoperative heart function , cm exposure , procedure type , perioperative erythrocyte transfusion , postoperative central venous pressure , and shortterm mortality ( table  2 ) .
the multivariate logistic regression analysis was conducted at 3 time points : preoperative , icu admittance , and 24  hours after icu admittance ; the statistically significant risk factors identified by the analysis are shown in table  3 , which shows that male sex , age , kidney disease without rrt , nyha score > 2 , previous cardiac surgery , valve and cabg procedure , application of cpb , erythrocyte transfusion , and postoperative lcos at the indicated time points were risk factors for csaaki development ( table  3 ) .
characteristics of the patients in the derivation cohort aki indicates acute kidney injury ; cabg , coronary artery bypass grafting ; cm , contrast media ; copd , chronic obstructive pulmonary disease ; cpb , cardiopulmonary bypass ; cvd , cerebral vascular disease ; cvp , central venous pressure by icu admittance ; dm , diabetes mellitus ; egfr , estimated glomerular filtration rate , as was calculated based on the ckdepi formulas ; lcos , low cardiac output syndrome ; lvef , left ventricular ejection fraction ; nyha , new york heart association ; opcab , offpump coronary artery bypass .
multivariate logistic regression analysis of factors for csaaki * cabg indicates coronary artery bypass grafting ; csaaki , cardiac surgery associated acute kidney injury ; lcos , low cardiac output syndrome ; nyha , new york heart association ; rrt , renal replacement therapy . only the statistically significant risk factors in the logistic regression
the variable age was categorized and ( age 40 ) was set as the reference level .
next we developed a point system for predicting csaaki occurrence with the regression coefficient data from the multivariable regression analyses of the derivative cohort patients ( table  4 ) .
depending on the scores , the risks of csaaki development were categorized into low , medium , and high in accordance with the practically observed incidence of csaaki ( table  5 ) .
the preoperative predictive risks of csaaki occurrence were low with 0 to 1  patient ( 9.5% ) , medium with 2 to 3  patients ( 22.1% ) , and high with 4  patients ( 37.3% ) .
the predictive risks of csaaki development at the day of icu admission were low with 0 to 4  patients ( 11.5% ) , medium with 5 to 9  patients ( 42.6% ) , and high with 10  patients ( 83.3% ) .
the postoperative predictive risks for developing csaaki were low with 0 to 4  patients ( 11.6% ) , medium with 5 to 9  patients ( 44.2% ) , and high with 10  patients ( 88.8% ) .
the efficiency of the scores for predicting medium and high risk for csaaki improved with the procedure progress and additional risk factors being added .
we further analyzed the score models at the 3 time points by auroc determinations and derived 0.75 ( preoperative ) , 0.81 ( icu admittance ) , and 0.82 ( 24  hours after icu admittance ) area under curve ( auc ) values .
the calibrations of them by hosmerlemeshow statistics resulted in 0.569 ( preoperative ) , 0.684 ( icu admittance ) , 0.829 ( 24  hours after icu admittance ) .
factor scoring for predicting of csaaki development cabg indicates coronary artery bypass grafting ; cpb , cardiopulmonary bypass ; csaaki , cardiac surgery associated acute kidney injury ; icu , intensive care unit ; lcos , low cardiac output syndrome ; nyha , new york heart association .
risk stratification of csaaki development at indicated time points the distinction of low / medium / high was based on the incidence of csaaki in each sector ; eg , the incidence of aki in the sector of 10  pts in the 24  hours after icu admittance column was 88.8% , which meant in the patients who scored 10 points at the time of 24  hours after admission , the incidence of csaaki was 88.8% .
the clinical characteristics of the 1152 patients in the validation cohort are shown in table  1 .
application of the scoring system led to auc values of 0.74 ( preoperative ) , 0.75 ( at icu admission ) , and 0.82 ( postoperative ) ( figure  1 ) .
there was no statistically significant difference between the predicted and observed csaaki incidences in the validation cohort ( preoperative =6.346 , p=0.175 ; at the day of icu admittance =4.65 , p=0.703 ; and postoperative =3.669 , p=0.886 ) . 

roc curves for predicting csaaki in the validation cohort with point scores from table  5 .
* application of the scoring system led to auc values of 0.74 ( preoperative ) , 0.75 ( at icu admission ) , and 0.82 ( postoperative ) .
score calibrations for predicting csaaki ( a ) preoperative , ( b ) at the day of icu admittance , ( c ) postoperative .
* there was no statistically significant difference between the csaaki incidences predicted by point scores and those observed in the validation cohort ( preoperative =6.346 , p=0.175 ; at the day of icu admittance =4.65 , p=0.703 ; and postoperative =3.669 , p=0.886 ) .
we categorized the following variables for subgroup observation : age ( 40 , 4160 , 6180 , 81 ) , estimated gfr ( > 60 , > 30 to 60 , 30  ml / min ) , and lvef ( > 50 , 3650 , 35 ) .
there were statistically significant differences between the csaaki patients and the unaffected group regarding sex , age , preoperative kidney function , some comorbidities such as hypertension and copd , preoperative heart function , cm exposure , procedure type , perioperative erythrocyte transfusion , postoperative central venous pressure , and shortterm mortality ( table  2 ) .
the multivariate logistic regression analysis was conducted at 3 time points : preoperative , icu admittance , and 24  hours after icu admittance ; the statistically significant risk factors identified by the analysis are shown in table  3 , which shows that male sex , age , kidney disease without rrt , nyha score > 2 , previous cardiac surgery , valve and cabg procedure , application of cpb , erythrocyte transfusion , and postoperative lcos at the indicated time points were risk factors for csaaki development ( table  3 ) .
characteristics of the patients in the derivation cohort aki indicates acute kidney injury ; cabg , coronary artery bypass grafting ; cm , contrast media ; copd , chronic obstructive pulmonary disease ; cpb , cardiopulmonary bypass ; cvd , cerebral vascular disease ; cvp , central venous pressure by icu admittance ; dm , diabetes mellitus ; egfr , estimated glomerular filtration rate , as was calculated based on the ckdepi formulas ; lcos , low cardiac output syndrome ; lvef , left ventricular ejection fraction ; nyha , new york heart association ; opcab , offpump coronary artery bypass .
multivariate logistic regression analysis of factors for csaaki * cabg indicates coronary artery bypass grafting ; csaaki , cardiac surgery associated acute kidney injury ; lcos , low cardiac output syndrome ; nyha , new york heart association ; rrt , renal replacement therapy . only the statistically significant risk factors in the logistic regression
the variable age was categorized and ( age 40 ) was set as the reference level .
next we developed a point system for predicting csaaki occurrence with the regression coefficient data from the multivariable regression analyses of the derivative cohort patients ( table  4 ) .
depending on the scores , the risks of csaaki development were categorized into low , medium , and high in accordance with the practically observed incidence of csaaki ( table  5 ) .
the preoperative predictive risks of csaaki occurrence were low with 0 to 1  patient ( 9.5% ) , medium with 2 to 3  patients ( 22.1% ) , and high with 4  patients ( 37.3% ) .
the predictive risks of csaaki development at the day of icu admission were low with 0 to 4  patients ( 11.5% ) , medium with 5 to 9  patients ( 42.6% ) , and high with 10  patients ( 83.3% ) .
the postoperative predictive risks for developing csaaki were low with 0 to 4  patients ( 11.6% ) , medium with 5 to 9  patients ( 44.2% ) , and high with 10  patients ( 88.8% ) .
the efficiency of the scores for predicting medium and high risk for csaaki improved with the procedure progress and additional risk factors being added .
we further analyzed the score models at the 3 time points by auroc determinations and derived 0.75 ( preoperative ) , 0.81 ( icu admittance ) , and 0.82 ( 24  hours after icu admittance ) area under curve ( auc ) values .
the calibrations of them by hosmerlemeshow statistics resulted in 0.569 ( preoperative ) , 0.684 ( icu admittance ) , 0.829 ( 24  hours after icu admittance ) .
factor scoring for predicting of csaaki development cabg indicates coronary artery bypass grafting ; cpb , cardiopulmonary bypass ; csaaki , cardiac surgery associated acute kidney injury ; icu , intensive care unit ; lcos , low cardiac output syndrome ; nyha , new york heart association .
risk stratification of csaaki development at indicated time points the distinction of low / medium / high was based on the incidence of csaaki in each sector ; eg , the incidence of aki in the sector of 10  pts in the 24  hours after icu admittance column was 88.8% , which meant in the patients who scored 10 points at the time of 24  hours after admission , the incidence of csaaki was 88.8% .
the clinical characteristics of the 1152 patients in the validation cohort are shown in table  1 .
application of the scoring system led to auc values of 0.74 ( preoperative ) , 0.75 ( at icu admission ) , and 0.82 ( postoperative ) ( figure  1 ) .
there was no statistically significant difference between the predicted and observed csaaki incidences in the validation cohort ( preoperative =6.346 , p=0.175 ; at the day of icu admittance =4.65 , p=0.703 ; and postoperative =3.669 , p=0.886 ) . 

roc curves for predicting csaaki in the validation cohort with point scores from table  5 .
* application of the scoring system led to auc values of 0.74 ( preoperative ) , 0.75 ( at icu admission ) , and 0.82 ( postoperative ) .
score calibrations for predicting csaaki ( a ) preoperative , ( b ) at the day of icu admittance , ( c ) postoperative .
* there was no statistically significant difference between the csaaki incidences predicted by point scores and those observed in the validation cohort ( preoperative =6.346 , p=0.175 ; at the day of icu admittance =4.65 , p=0.703 ; and postoperative =3.669 , p=0.886 ) .
in our study we utilized the kdigo definition of aki to establish the dynamic risk score to predict csaaki . with the kdigo guideline , which is accepted by the global nephrology community
, some authors carried out risk factor analysis for csaaki16 , 17 with the new aki definition . to the best of our knowledge
, there were no dynamically designed risk scores for predicting csaaki from preoperative to early postoperative periods .
not only preop risk factors but also the intraop and postop risk factors were enrolled in the analysis for better predictive efficiency .
the result of our study showed that the predictive value of the dynamic risk scores was close to that of the previous scores and improved as the intraop ( auc=0.75 ) and postop ( auc=0.82 ) risk factors were admitted into the multivariate regression analysis and the subsequent scores .
these risk scores can help clinicians to evaluate the risk of csaaki from preoperative to early postoperative periods . in this study
all the major risk factors of csaaki were collected to derive the predictive score system , including the intraop and postop risk factors .
male sex and age were risk factors for csaaki in all 3 time points , and with increasing age , the incidence of csaaki also increased .
ageassociated aki was reported to occur more frequently in the elderly due to structural changes such as vascular sclerosis , increased percentages of sclerosing glomeruli , and other degenerative changes that occur with increasing age,18 , 19 and our finding is in agreement with other publications from fang et  al,20 , 21 who noted that the incidence of csaaki in the older patients was higher than that in younger patients . in our cohort
we conducted a subgroup analysis according to the categorized age and found significant differences in csaaki incidence between groups ( figure  3 ) .
the risk factor of male sex is in disagreement with other publications in which female sex was a risk factor,22 , 23 which needs further analysis .
the csaaki incidence was statistically significantly different within the subgroups , and increased with age .
preoperative renal dysfunction was related to csaaki in several other studies.4 , 8 , 23 however , we excluded preoperative rrt patients and also drew the same conclusion . in table  2 , the egfr was statistically significantly different between aki and nonaki cohorts ( p<0.01 ) .
in the subsequent subgroup analysis , we found significant differences in the incidence of csaaki among subgroups .
however , unlike age , categorized egfr were eliminated through logistic regression . the possible reason may be that we brought many more parameters , such as  previous kidney disease without rrt ,  into our study in order to improve prediction , so the relationship between egfr and csaaki could be somehow diluted in the multivariate analysis .
another possible reason was that the kidney function ( preoperative serum creatine and egfr ) in our cohorts was better than that of existing model derivation cohorts .
the predictors above were also listed in the previous scores ; the innovation and superiority of our scores result from our inclusion of intraop and postop predictors . with the awareness of intraop and postop risk factors contributing to csaaki
, the risk stratification based on only preop risk factors does not take into account the patients physiological response to the surgeon and postop treatment as well as the occurrence of adverse events in the intraop and postop periods , which increase the risk of csaaki .
patients undergoing cardiac surgery typically exhibit complex syndromes with numerous pathways that affect renal function and involve hemodynamic changes , tissue breakdown , reactive oxygen species , infection , and drug toxicity .
all of these factors can result in endothelial dysfunction , blood dynamics disorders , inflammatory responses , and tubular cell damage .
previous studies also found a correlation of erythrocyte transfusion and csaaki,24 , 25 and it has been reported that the posttransfusion recovery of red blood cells is lower after longterm storage26 ; the morphological and biochemical changes during storage have been suggested to promote a proinflammatory state , impair tissue oxygen delivery , and exacerbate tissue oxidative stress due to free iron , which in turn is an aki risk factor for susceptible cpb patients.27 some studies suggest that each unit of perioperative blood transfusion is significantly associated with a 10% to 20% increase in the risk of aki after cardiac surgery with cpb25 , 28 in spite of the different aki definitions .
some surgical teams trying to reduce the potential renal harm of erythrocyte transfusion have found promising options,29 including reducing perioperative hemodilution by minimizing fluid administration and retrograde autologous priming of the cardiopulmonary circuit , salvage of shed blood , tolerance of moderate hemodilution , and early identification and treatment with erythropoietinstimulating agents and iron therapy . in previous studies some complex procedures , especially valve surgery combined with cabg ,
were found to increase the incidence of csaaki.6 , 7 , 8 , 9 this conclusion was also proven in our study , in which valve combined with cabg was one of the risk factors .
chetow et  al4 found local renal ischemia injury due to insufficient perfusion contributes to csaaki .
preoperative nyha scores > 2 , previous cardiac surgery , and postoperative lcos were also found to be risk factors for csaaki in our study . among the above factors , preoperative nyha scores > 2 and previous cardiac surgery are not modifiable .
the lcos may result from delayed recovery of postop cardiac function and cause pulmonary edema and pulmonary infection , which in turn aggravate lcos in a vicious circle.30 for those csaaki patients who were unable to eliminate excess fluid and adverse inflammatory factors , either lcos or pulmonary infection would result in a worse prognosis .
some studies31 were carried out to break this vicious circle by means of  perioperative goaldirected hemodynamic resuscitation therapy ,  which was considered to be useful in reducing adverse complications including lcos as well as 30day mortality in highrisk patients undergoing cardiac surgery .
the lack of a consensus aki definition has led to inaccuracy in data regarding the actual incidence of this complication , as can be confirmed by the large variation ( 1% to 30% ) in the reported incidence of aki following cardiac surgery.22 by the time of this study , no consensus aki definition was utilized in the risk scores above , and so their worldwide applicability is suspect .
the kdigo criterion , published in 2012 , was regarded as a complement to previous aki definitions.32 , 33 sampaio et  al compared the kdigo aki definition with akin and rifle criteria and validated its superiority , especially for prognoses in unstable hemodynamic cardiac surgery patients.34 therefore , risk scores with the kdigo aki definition were better for predicting csaaki .
cardiac surgery in developing countries such as china , india , and brazil has grown at a steady pace for decades , but most centers performing open heart surgery are located in the major cities , which is a common problem for most developing countries . although as the economy has developed , and cardiac surgery has become affordable for more patients , its complications are adding to the social economic burden in developing countries.35 , 36 considering the tremendous population in developing countries , this is worthy of concern .
the occurrence of valve surgery is becoming higher in developing countries such as china than it was in previous risk stratification development centers , in which cabg is the major procedure ( table  6 )
. this change may be due to the lack in early diagnosis and imbalanced distribution of medical resources ; it is also one of the reasons why csaaki incidence was high in our cohort .
patients who undergo valve surgery typically demonstrate low stroke volume related to regurgitation , which increases the vulnerability of the kidney to injury during cardiac surgery .
the comorbidities in developing countries such as china are also different from those in the western population . in our derivation cohort ,
the ratios of hypertension and diabetes mellitus are also remarkably lower than those in the previous developmental populations ( table  6 ) .
comparison of derivation cohort and other risk scores in comorbidities and procedures cabg indicates coronary artery bypass grafting ; copd , chronic obstructive pulmonary disease ; cvd , cerebral vascular disease ; dm , diabetes mellitus ; rrt , renal replacement therapy ; sri , simplified renal index .
therefore , the difference in ethnicity , comorbidities , and  underlying disease may affect the efficacy of the existing models predicting csaaki in patients of developing countries .
as in thakar 's and palomba 's studies,7 , 8 the present risk scores are validated in a sample extracted from the original population with good discrimination and calibration . for simpler clinical application ,
the maximum predictive risk of 88.8% , which is higher than those in other studies , may be attributed to the kdigo aki definition , which was verified with better efficacy , and the inclusion of intraop and postop predictors , with which the course of disease can be well simulated .
the limitation of this study was that the data were derived from a singlecenter chinese database , implying differences in procedures and comorbidities with other existing risk scores , so their predictive efficiency should be prospectively validated in more centers and on a larger scale .
we carried out this study prospectively and developed dynamic predictive scores for csaaki in cardiac surgical patients with the kdigo definition for aki .
this may improve the early recognition and intervention against csaaki , fix those modifiable factors , reduce the incidence of csaaki , and improve the renal and overall prognosis . embedding these scores into the electronic medical record ( emr ) system
the study was funded by natural science foundation of china ( no . 81430015 ) .
 backgroundcardiac surgery  associated acute kidney injury ( csaaki ) is a common complication with a poor prognosis . in order to identify modifiable perioperative risk factors for aki , 
 which existing risk scores are insufficient to predict , a dynamic clinical risk score to allow clinicians to estimate the risk of csaaki from preoperative to early postoperative periods is needed.methods and resultsa total of 7233 cardiac surgery patients in our institution from january 2010 to april 2013 were enrolled prospectively and distributed into 2 cohorts . among the derivation cohort , 
 logistic regression was used to analyze csaaki risk factors preoperatively , on the day of icu admittance and 24  hours after icu admittance . 
 sex , age , valve surgery combined with coronary artery bypass grafting , preoperative nyha score > 2 , previous cardiac surgery , preoperative kidney ( without renal replacement therapy ) disease , intraoperative cardiopulmonary bypass application , intraoperative erythrocyte transfusions , and postoperative low cardiac output syndrome were identified to be associated with csaaki . among the other 1152 patients who served as a validation cohort , the point scoring of risk factor combinations led to area under receiver operator characteristics curves ( auroc ) values for csaaki prediction of 0.74 ( preoperative ) , 0.75 ( on the day of icu admission ) , and 0.82 ( postoperative ) , and hosmer  
 lemeshow goodnessoffit tests revealed a good agreement of expected and observed csaaki rates.conclusionsthe first dynamic predictive score system , with kidney disease : improving global outcomes ( kdigo ) aki definition , was developed and predictive efficiency for csaaki was validated in cardiac surgery patients . 
Introduction
Patients and Methods
Patients
Data Collection
Statistical Analyses
Results
Risk Factor Analysis
Score Development
Score Validation
Discussion
Conclusion
Sources of Funding
Disclosures