A Prediction Model for Acute Kidney Injury After Pericardiectomy: An Observational Study

Pericardiectomy for Constrictive Pericarditis with or without Cardiopulmonary Bypass

Aim

We aim to access the effect of pericardiectomy for constrictive pericarditis with or without cardiopulmonary bypass.

Methods

This was a review of pericardiectomy for constrictive pericarditis.

Results

Cardiopulmonary bypass is actually an important maneuver to attain complete relief of the constriction. The short additional time of cardiopulmonary bypass during the procedure has very little effect on the risk of morbidity of the main operation.

Conclusion

Incomplete pericardiectomy perhaps was the cause of postoperative remnant constriction and high diastolic filling pressure leading to multiorgan failure. Complete pericardiectomy (removal of phrenic-to-phrenic and the postero-lateral and inferior wall pericardial thickening) using cardiopulmonary bypass should be the routine for total relief of the constriction of the heart.

Postoperative acute kidney injury after on-pump cardiac surgery in patients with connective tissue disease

Objective

Patients with connective tissue disease have a poor prognosis after receiving cardiac surgery. This study described the clinical scenarios and investigated factors correlated with acute kidney injury (AKI) after on-pump cardiac surgery in patients with systemic lupus erythematosus (SLE) or vasculitis.

Methods

Patients with SLE or vasculitis who underwent on-pump cardiac surgery from March 2002 to March 2022 were enrolled, while patients with preoperative renal dysfunction were excluded. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Uni- and multivariable analyses were performed to identify potential factors associated with postoperative AKI.

Results

Among 123 patients enrolled, 39 (31.7%) developed AKI within seven days after receiving on-pump cardiac surgery. Four patients died in the hospital, resulting in an overall in-hospital mortality of 3.3%, and all deaths occurred in the AKI group. Patients in the AKI group also had longer ICU stays (median difference 3.0 day, 95% CI: 1.0-4.0, P < 0.001) and extubation time (median difference 1.0 days, 95% CI: 0-2.0, P < 0.001) than those in the non-AKI group. Multivariable logistic regression revealed that BMI over 24 kg/m2 (OR: 3.00, 95% CI: 1.24-7.28) and comorbid SLE (OR: 4.73, 95% CI: 1.73-12.93) were independently correlated with postoperative AKI.

Conclusion

Factors potentially correlated with AKI following on-pump cardiac surgery in patients with connective tissue disease were explored. Clinicians should pay more attention to preoperative evaluation and intraoperative management in patients with risk factors.

Construction and Validation of a Nomogram for Predicting Acute Kidney Injury After Hip Fracture Surgery

Background

Acute kidney injury (AKI), characterized by sudden impairment of kidney function, is an uncommon complication following hip fracture surgery that is associated with increased morbidity and mortality. We constructed a nomogram to stratify patients according to risk of AKI after hip fracture surgery to guide clinicians in the implementation of timely interventions.

Methods

Patients who received hip fracture surgery from January 2015 to December 2021 were retrospectively identified and divided into a training set (n=448, surgery from January 2015 to December 2019) and a validation set (n=200, surgery from January 2020 to December 2021). Univariate and multivariate logistic regression were used to identify risk factors for AKI after surgery in the training set. A nomogram was constructed based the risk factors for AKI, and was evaluated by receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).

Results

The mean age was 82.0±6.22 years-old and the prevalence of post-surgical AKI was 13.3%. Age, American Society of Anesthesiologists (ASA) score, the preexistence of chronic kidney disease (CKD), cemented surgery and the decrease of hemoglobin on the first day after surgery were identified as independent risk factors of AKI after hip fracture surgery, and a predictive nomogram was established based on the multivariable model. The predictive nomogram had good discrimination ability (training set: AUC: 0.784, 95% CI: 0.720-0.848; validation set: AUC: 0.804, 95% CI: 0.704-0.903), and showed good validation ability and clinical usefulness based on a calibration plot and decision curve analysis.

Conclusion

A nomogram that incorporated five risk factors including age, ASA score, preexisting CKD, cemented surgery and the decrease of hemoglobin on the first day after surgery had good predictive performance and discrimination. Use of our results for early stratification and intervention has the potential to improve the outcomes of patients receiving hip fracture surgery. Future large, multicenter cohorts are needed to verify the model's performance.

Machine learning in predicting cardiac surgery-associated acute kidney injury: A systemic review and meta-analysis

Background

Cardiac surgery-associated acute kidney injury (CSA-AKI) is a common complication following cardiac surgery. Early prediction of CSA-AKI is of great significance for improving patients' prognoses. The aim of this study is to systematically evaluate the predictive performance of machine learning models for CSA-AKI.

Methods

Cochrane Library, PubMed, EMBASE, and Web of Science were searched from inception to 18 March 2022. Risk of bias assessment was performed using PROBAST. Rsoftware (version 4.1.1) was used to calculate the accuracy and C-index of CSA-AKI prediction. The importance of CSA-AKI prediction was defined according to the frequency of related factors in the models.

Results

There were 38 eligible studies included, with a total of 255,943 patients and 60 machine learning models. The models mainly included Logistic Regression (n = 34), Neural Net (n = 6), Support Vector Machine (n = 4), Random Forest (n = 6), Extreme Gradient Boosting (n = 3), Decision Tree (n = 3), Gradient Boosted Machine (n = 1), COX regression (n = 1), κNeural Net (n = 1), and Naïve Bayes (n = 1), of which 51 models with intact recording in the training set and 17 in the validating set. Variables with the highest predicting frequency included Logistic Regression, Neural Net, Support Vector Machine, and Random Forest. The C-index and accuracy wer 0.76 (0.740, 0.780) and 0.72 (0.70, 0.73), respectively, in the training set, and 0.79 (0.75, 0.83) and 0.73 (0.71, 0.74), respectively, in the test set.

Conclusion

The machine learning-based model is effective for the early prediction of CSA-AKI. More machine learning methods based on noninvasive or minimally invasive predictive indicators are needed to improve the predictive performance and make accurate predictions of CSA-AKI. Logistic regression remains currently the most commonly applied model in CSA-AKI prediction, although it is not the one with the best performance. There are other models that would be more effective, such as NNET and XGBoost.

Systematic review registration

https://www.crd.york.ac.uk/; review registration ID: CRD42022345259.