Improved creatinine-based early detection of acute kidney injury after cardiac surgery

Ten years' experience with the sutureless aortic valve replacement: incidence and predictors for survival and valve durability at follow-up

OBJECTIVES

Aortic valve replacement with a sutureless prosthesis [sutureless aortic valve replacement (Su-AVR)] is an option for patients with severe aortic valve stenosis. However, data regarding long-term outcomes and prosthesis durability are still lacking.

METHODS

All consecutive patients who successfully underwent Su-AVR with the Perceval valve in our centre between 2010 and 2020 were included in the analysis and followed prospectively with echocardiography. Risk factor analysis was performed to assess variables associated with worse survival and bioprosthetic valve failure.

RESULTS

Study population consisted of 547 patients: the mean age was 76.4 (5.2) years, 51% were female and the mean logistic EuroSCORE was 13% (11). The median survival was 7.76 years [95% confidence interval (CI) = 6.9-8.6]. Risk factor analysis identified age [hazard ratio (HR) 1.06, 95% CI 1.03-1.11; P < 0.001), EuroSCORE II (HR 1.08, 1.02-1.13; P < 0.001), baseline dialysis (HR 2.14, 95% CI 1.4-4.4; P = 0.038) and postoperative acute kidney injury ≥2 (HR 8.97, 95% CI 4.58-17.6; P < 0.001) as factors significantly correlated with worse survival. The reported HRs for age are per 1 year and for EuroSCORE II is 1 percentage point. Structural valve deterioration (SVD) was observed in 23 patients, of whom 19 underwent reintervention (median freedom from SVD 10.3 years). In multivariable Cox analysis, age (HR 0.89, 95% CI 0.82-0.95; P < 0.001) was found to be a significant predictor of SVD. Overall, 1.8% was referred for prosthetic valve endocarditis (confirmed or suspected) during follow-up. One patient showed moderate non-SVD and none developed prosthetic valve thrombosis.

CONCLUSIONS

The sutureless valve represents a reliable bioprosthesis for aortic valve replacement in patients with a 10-year life expectancy. Younger age at the time of implant is the only factor associated with the risk of long-term SVD.

Enhanced Detection of Cardiac Surgery-Associated Acute Kidney Injury by a Composite Biomarker Panel in Patients with Normal Preoperative Kidney Function

We have recently shown that minor subclinical creatinine dynamic changes enable the excellent detection of acute kidney injury (AKI) within 6–12 h after cardiac surgery. The aim of the present study was to examine a combination of neutrophil gelatinase-associated lipocalin (NGAL), cystatin C (CysC) and creatinine for enhanced AKI detection early after cardiac surgery. Elective patients with normal renal function undergoing cardiac surgery using cardiopulmonary bypass were enrolled. Concentrations of plasma NGAL, serum CysC and serum creatinine were determined after the induction of general anesthesia, at the termination of the cardiopulmonary bypass and 2 h thereafter. Out of 119 enrolled patients, 51 (43%) developed AKI. A model utilizing an NGAL, CysC and creatinine triple biomarker panel including sequential relative changes provides a better prediction of cardiac surgery-associated acute kidney injury than any biomarker alone already 2 h after the termination of the cardiopulmonary bypass. The area under the receiver-operator curve was 0.77, sensitivity 77% and specificity 68%.

Artificial intelligence-based early detection of acute kidney injury after cardiac surgery

OBJECTIVES

This study aims to improve early detection of cardiac surgery-associated acute kidney injury using artificial intelligence-based algorithms.

METHODS

Data from consecutive patients undergoing cardiac surgery between 2008 and 2018 in our institution served as the source for artificial intelligence-based modeling. Cardiac surgery-associated acute kidney injury was defined according to the Kidney Disease Improving Global Outcomes criteria. Different machine learning algorithms were trained and validated to detect cardiac surgery-associated acute kidney injury within 12 hours after surgery. Demographic characteristics, comorbidities, preoperative cardiac status, intra- and postoperative variables including creatinine and hemoglobin values were retrieved for analysis.

RESULTS

From 7507 patients analyzed, 1699 patients (22.6%) developed cardiac surgery-associated acute kidney injury. The ultimate detection model, 'Detect-A(K)I', recognizes cardiac surgery-associated acute kidney injury within 12 hours with an area under the curve of 88.0%, sensitivity of 78.0%, specificity of 78.9%, and accuracy of 82.1%. The optimal parameter set includes serial changes of creatinine and hemoglobin, operative emergency, bleeding-associated variables, cardiac ischaemic time and cardiac function-associated variables, age, diuretics and active infection, chronic obstructive lung and peripheral vascular disease.

CONCLUSIONS

The 'Detect-A(K)I' model successfully detects cardiac surgery-associated acute kidney injury within 12 hours after surgery with the best discriminatory characteristics reported so far.