Acute Kidney Injury after Heart Valve Surgery in Elderly Patients: any Risk Factors to Modify?

Introduction

Postoperative acute kidney injury contributes to longer hospital stays and increased costs related to cardiac surgery in the elderly. We analyse the influence of the patient’s age on risk factors for acute kidney injury after cardiac valve surgery.

Methods

We evaluated the prevalence and risk factors for acute kidney injury in 939 consecutive patients undergoing valve surgery, between 2013 and 2018.

Results

The prevalence of acute kidney injury was 19.5%. Hypertension (P=0.017); RR (95% CI): 1.74 (1.10-3.48), age ≥70 years (P=0.006); RR (95% CI): 1.79 (1.17-2.72), preoperative haematocrit <33% (P=0.009); RR (95% CI): 2.04 (1.19-3.48), glomerular filtration rate <60 ml/min/1.73 m² (P<0.0001); RR (95%) CI: 2.36 (1.54-3.62) and cardiac catheterization <8 days before surgery (P=0.021); RR (95% CI): 2.15 (1.12-4.11) were identified as independent risk factors. In patients older than 70 years, with no kidney disease diagnosed preoperatively, glomerular filtration rate <70 ml/min/1.73 m², male gender, cardiopulmonary bypass time, preoperative haematocrit <36% and preoperative therapy with angiotensin-converting enzyme inhibitors were risk factors for acute kidney injury after valve surgery.

Conclusions

In elderly patients, postoperative acute kidney injury develops with higher values of preoperative glomerular filtration rate than those observed in a younger population. Preoperative correction of anaemia, discontinuation of angiotensin-converting enzyme inhibitors and surgical techniques reducing cardiopulmonary bypass time would be considered to reduce the prevalence of renal failure.

Predicting Acute Kidney Injury After Cardiac Surgery Using a Simpler Model

OBJECTIVE

To develop a simple model for the prediction of acute kidney injury (AKI) and renal replacement therapy (RRT) that could be used in clinical or research risk stratification.

DESIGN

Retrospective analysis.

SETTING

Multi-institutional.

PARTICIPANTS

All cardiac surgery patients from September 2016 to December 2018.

INTERVENTIONS

Observational.

MEASUREMENTS AND MAIN RESULTS

The study cohort was divided into a development set (75%) and validation set (25%). The following 2 data epochs were used: preoperative data and immediate postoperative data (within 4 h of intensive care unit admission). Univariate statistics were used to identify variables associated with AKI or RRT. Stepwise logistic regression was used to develop a parsimonious model. Model discrimination and calibration were evaluated in the test set. Models were compared with previously published models and with a more comprehensive model developed using the least absolute shrinkage and selection operator. The study included 22,731 patients at 33 hospitals. The incidences of AKI (any stage) and RRT for the present analysis were 5,829 patients (25.6%) and 488 patients (2.1%), respectively. Models were developed for AKI, with an area under the receiver operating curve (AU-ROC) of 0.67 and 0.69 preoperatively and postoperatively, respectively. Models for RRT had an AU-ROC of 0.77 and 0.80 preoperatively and postoperatively, respectively. These models contained between 3 and 5 variables. Comparatively, comprehensive least absolute shrinkage and selection operator models contained between 21 and 26 variables, with an AU-ROC of 0.71 and 0.72 for AKI and 0.84 and 0.87 for RRT respectively.

CONCLUSION

In the present study, simple, clinically applicable models for predicting AKI and RRT preoperatively and immediate postoperatively were developed. Even though AKI prediction remained poor, RRT prediction was good with a parsimonious model.

Cardiothoracic Critical Care

High-value CCC is rapidly evolving to meet the demands of increased patient acuity and to incorporate advances in technology. The high-performing CCC system and culture should aim to learn quickly and continuously improve. CCC demands a proactive, interactive, precise, an expert team, and continuity.