Predictive Accuracy of a Perioperative Laboratory Test–Based Prediction Model for Moderate to Severe Acute Kidney Injury After Cardiac Surgery

Abstract

IMPORTANCEEffective treatment of acute kidney injury (AKI) is predicated on timely diagnosis; however, the lag in the increase in serum creatinine levels after kidney injury may delay therapy initiation.OBJECTIVE To determine the derivation and validation of predictive models for AKI after cardiac surgery.DESIGN, SETTING, AND PARTICIPANTS Multivariable prediction models were derived based on a retrospective observational cohort of adult patients undergoing cardiac surgery between January 2000 and December 2019 from a US academic medical center (n = 58 526) and subsequently validated on an external cohort from 3 US community hospitals (n = 4734). The date of final follow-up was January 15, 2020.EXPOSURES Perioperative change in serum creatinine and postoperative blood urea nitrogen, serum sodium, potassium, bicarbonate, and albumin from the first metabolic panel after cardiac surgery. MAIN OUTCOMES AND MEASURESArea under the receiver-operating characteristic curve (AUC) and calibration measures for moderate to severe AKI, per Kidney Disease: Improving Global Outcomes (KDIGO), and AKI requiring dialysis prediction models within 72 hours and 14 days following surgery. RESULTSIn a derivation cohort of 58 526 patients (median [IQR] age, 66 [56-74] years; 39 173 [67%] men; 51 503 [91%] White participants), the rates of moderate to severe AKI and AKI requiring dialysis were 2674 (4.6%) and 868 (1.48%) within 72 hours and 3156 (5.4%) and 1018 (1.74%) within 14 days after surgery. The median (IQR) interval to first metabolic panel from conclusion of the surgical procedure was 10 (7-12) hours. In the derivation cohort, the metabolic panel-based models had excellent predictive discrimination for moderate to severe AKI within 72 hours (AUC, 0.876 [95% CI, 0.869-0.883]) and 14 days (AUC, 0.854 [95% CI, 0.850-0.861]) after the surgical procedure and for AKI requiring dialysis within 72 hours (AUC, 0.916 [95% CI, 0.907-0.926]) and 14 days (AUC, 0.900 [95% CI, 0.889-0.909]) after the surgical procedure. In the validation cohort of 4734 patients (median [IQR] age, 67 (60-74) years; 3361 [71%] men; 3977 [87%] White participants), the models for moderate to severe AKI after the surgical procedure showed AUCs of 0.860 (95% CI, 0.838-0.882) within 72 hours and 0.842 (95% CI, 0.820-0.865) within 14 days and the models for AKI requiring dialysis and 14 days had an AUC of 0.879 (95% CI, 0.840-0.918) within 72 hours and 0.873 (95% CI, 0.836-0.910) within 14 days after the surgical procedure. Calibration assessed by Spiegelhalter z test showed P >.05 indicating adequate calibration for both validation and derivation models.CONCLUSIONS AND RELEVANCE Among patients undergoing cardiac surgery, a prediction model based on perioperative basic metabolic panel laboratory values demonstrated good predictive accuracy for moderate to severe acute kidney injury within 72 hours and 14 days after the surgical procedure. Further research is needed to determine whether use of the risk prediction tool improves clinical outcomes.

Nomogram Prediction Model of Serum Chloride and Sodium Ions on the Risk of Acute Kidney Injury in Critically Ill Patients

“…In a study of patients undergoing cardiac surgery, a prediction model for AKI based on perioperative basic metabolic laboratory values showed that the indicators ultimately included in the model were perioperative blood creatinine and postoperative changes in blood urea nitrogen, serum sodium, potassium, bicarbonate, and albumin. 36 This study confirmed the predictive power of electrolyte imbalance for AKI, but this study did not specify the effect of hypernatremia on AKI and did not address chloride-related parameters. In the predictive model for the development of AKI after orthotopic liver transplantation, the analysis showed that the MELD Na score had potential predictive value for patients who developed AKI postoperatively.…”

“… 35 Acute kidney injury is associated with metabolic acidosis, volume overload, electrolyte imbalance, and other complications. But we found fewer electrolyte factors to be incorporated in most prediction models, with the highest focus on sodium ions; 36–45 Some studies have reported potassium ions, 40–42 , 45 but few have investigated the role of chloride ions in the model, only individual studies have explored the case of chloride ions, 44 but the results are still controversial. Therefore, the efficacy of sodium and chloride ions in the serum for predicting the risk of AKI occurrence in the ICU is uncertain.…”

Performance of resistive index and semi-quantitative power doppler ultrasound score in predicting acute kidney injury: A meta-analysis of prospective studies

“…Nowadays, numerous studies have constructed prediction model for AKI in patients undergoing various conditions, and the model were constructed based on perioperative laboratory test and history of disease status [22][23][24][25][26][27]. However, the prediction model for AKI were not contained the parameters from ultrasound.…”

Construction and validation of a nomogram for predicting survival in elderly patients with cardiac surgery

“…Creatinine is a typical indicator of kidney injury in the clinic that has been linked to various other diseases ( 28 ). Acute kidney injury is one of the most common and serious complications of cardiac surgery, and it can substantially impact the morbidity and mortality of patients ( 29 , 30 ). Cardiac surgery-associated acute kidney injury has a complicated pathogenesis, mainly involving inflammatory response, ischemia-reperfusion injury, metabolic disorder, and oxidative stress.…”