Prediction of cardiac surgery associated - acute kidney injury (CSA-AKI) by healthcare professionals and urine cell cycle arrest AKI biomarkers [TIMP-2]*[IGFBP7]: A single center prospective study (the PREDICTAKI trial)

Machine learning for dynamic and early prediction of acute kidney injury after cardiac surgery

OBJECTIVE

Acute kidney injury after cardiac surgery increases morbidity and mortality. Diagnosis relies on oliguria or increased serum creatinine, which develop 48 to 72 hours after injury. We hypothesized machine learning incorporating preoperative, operative, and intensive care unit data could dynamically predict acute kidney injury before conventional identification.

METHODS

Cardiac surgery patients at a tertiary hospital (2008-2019) were identified using electronic medical records in the Medical Information Mart for Intensive Care IV database. Preoperative and intraoperative parameters included demographics, Charlson Comorbidity subcategories, and operative details. Intensive care unit data included hemodynamics, medications, fluid intake/output, and laboratory results. Kidney Disease: Improving Global Outcomes creatinine criteria were used for acute kidney injury diagnosis. An ensemble machine learning model was trained for hourly predictions of future acute kidney injury within 48 hours. Performance was evaluated by area under the receiver operating characteristic curve and balanced accuracy.

RESULTS

Within the cohort (n = 4267), there were approximately 7 million data points. Median baseline creatinine was 1.0 g/dL (interquartile range, 0.8-1.2), with 17% (735/4267) of patients having chronic kidney disease. Postoperative stage 1 acute kidney injury occurred in 50% (2129/4267), stage 2 occurred in 8% (324/4267), and stage 3 occurred in 4% (183/4267). For hourly prediction of any acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.82, and balanced accuracy was 75%. For hourly prediction of stage 2 or greater acute kidney injury over the next 48 hours, area under the receiver operating characteristic curve was 0.95 and balanced accuracy was 86%. The model predicted acute kidney injury before clinical detection in 89% of cases.

CONCLUSIONS

Ensemble machine learning models using electronic medical records data can dynamically predict acute kidney injury risk after cardiac surgery. Continuous postoperative risk assessment could facilitate interventions to limit or prevent renal injury.

The NephroCheck bedside system for detecting stage 3 acute kidney injury after open thoracoabdominal aortic repair

Acute kidney injury (AKI) is a common complication after complex aortic procedures and it is associated with relevant mortality and morbidity. Biomarkers for early and specific AKI detection are lacking. The aim of this work is to investigate the reliability of the NephroCheck bedside system for diagnosing stage 3 AKI following open aortic surgery. In this prospective, multicenter, observational study,- https://clinicaltrials.gov/ct2/show/NCT04087161 -we included 45 patients undergoing open thoracoabdominal aortic repair. AKI risk (AKIRisk-Index) was calculated from urine samples at 5 timepoints: baseline, immediately postoperatively and at 12, 24, 48, and 72 h post-surgery. AKIs were classified according to the KDIGO criteria. Contributing factors were identified in univariable and multivariable logistic regression. Predictive ability was assessed with the area under the receiver operator curve (ROCAUC). Among 31 patients (68.8%) that developed AKIs, 21 (44.9%) developed stage-3 AKIs, which required dialysis. AKIs were correlated with increased in-hospital mortality (p = .006), respiratory complications (p < .001), sepsis (p < .001), and multi-organ dysfunction syndrome (p < .001). The AKIRisk-Index showed reliable diagnostic accuracy starting at 24 h post-surgery (ROCAUC: .8056, p = .001). In conclusion, starting at 24 h after open aortic repair, the NephroCheck system showed adequate diagnostic accuracy for detecting the patients at risk for stage 3 AKIs.

Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury

Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a surgery.

Postoperative Acute Kidney Injury

Postoperative AKI is a common complication of major surgery and is associated with significant morbidity and mortality. The Kidney Disease Improving Global Outcomes AKI definition allows consensus classification and identification of postoperative AKI through changes in serum creatinine and/or urine output. However, such conventional diagnostic criteria may be inaccurate in the postoperative period, suggesting a potential to refine diagnosis by application of novel diagnostic biomarkers. Risk factors for the development of postoperative AKI can be thought of in terms of preoperative, intraoperative, and postoperative factors and, as such, represent areas that may be targeted perioperatively to minimize the risk of AKI. The treatment of postoperative AKI remains predominantly supportive, although application of management bundles may translate into improved outcomes.