Usefulness of High Estimated Pulmonary Artery Systolic Pressure to Predict Acute Kidney Injury After Cardiac Valve Operations

Long-term follow-up in outpatients with mildly elevated pulmonary artery systolic pressure on echocardiography: a single-centre retrospective cohort study in Shanghai, China

OBJECTIVE

To investigate the correlation between mildly elevated pulmonary artery systolic pressure (PASP) on echocardiography and mortality, as well as long-term changes in PASP.

DESIGN

Retrospective cohort study.

SETTING

Shanghai, China, a single centre.

PARTICIPANTS

A total of 910 patients were enrolled in this study. From January to June 2016, 1869 patients underwent echocardiography at the Zhongshan Hospital affiliated with Fudan University. Patients with malignant tumours, previous heart or other solid organ transplantation, previous or scheduled ventricular assist device implantation, severe kidney dysfunction (uraemia and patients on dialysis) and a life expectancy of less than 1 year for any medical condition were excluded.

INTERVENTIONS

No interventions were done.

PRIMARY AND SECONDARY OUTCOME MEASURES

The predictors of death in patients with mild echocardiographic pulmonary hypertension were analysed using univariate and multivariate Cox regression analyses. Paired t-tests were used to calculate changes in the PASP values at baseline and follow-up for different patient groups.

RESULTS

The 5-year survival of patients was 93.2%. Patients were grouped according to whether they had combined organic heart disease (OHD). The PASP value was an independent predictor of all-cause mortality in patients with OHD, with each 1 mm Hg increase associated with an HR of 1.02 (95% CI: 1.01-1.03, p=0.038) but not in patients without OHD. Of the total, 46% (419/910) of the patients with 5-6 years of echocardiography were investigated for changes in the PASP value. We found significant PASP reduction in patients without OHD (42.8±2.4 mm Hg vs 39.3±8.2 mm Hg, p<0.001), but no significant change was observed for patients with OHD (42.8±2.5 mm Hg vs 42.4±8.8 mm Hg, p=0.339).

CONCLUSIONS

The PASP was associated with all-cause mortality in patients with OHD and mildly elevated PASP compared with patients without OHD. After 5-6 years of follow-up, the PASP on echocardiography was not further elevated in patients without OHD.

Predictive Value of the Transthoracic Echocardiography Index for Acute Kidney Injury after Cardiac Valve Surgery

Background: We aimed to demonstrate whether the preoperative transthoracic echocardiography index (TTEI) could improve the predictive value of clinical parameters for cardiac valve surgery-associated acute kidney injury (CVS−AKI). Methods: A total of 213 patients who underwent surgical CVS at Renmin Hospital of Wuhan University were consecutively recruited in this retrospective study. TTE assessments were performed within 7 days before surgery and logistic regression was used to determine TTEI. A nomogram was constructed by integrating TTEI and clinical features, and the net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement in TTEI for CVS−AKI. Results: Among them, 66 patients (30.9%) developed CVS−AKI. The TTEI was calculated as follows: −6.579 + 0.068 × pulmonary artery systolic pressure (mmHg) −0.742 × LVEF (>55%, yes or no) + 0.346 × left ventricle posterior wall thickness (mm). The nomogram based on the TEEI and other clinical factors possessed excellent performance (C-index = 0.880), had great calibration and discrimination, and was clinically useful. Furthermore, NRI (0.07, 95% confidence interval, 95%CI, 0.01−0.12, p = 0.02) and IDI (0.08, 95%CI, 0.01−0.20, p = 0.02) indicated that TTEI could significantly improve the predictive value of clinical features for CVS−AKI. Conclusions: As a simple access and cost-effective parameter, the preoperative TTEI may be a reliable and useful factor for CVS−AKI.

Machine learning to predict post-operative acute kidney injury stage 3 after heart transplantation

BACKGROUND

Acute kidney injury (AKI) stage 3, one of the most severe complications in patients with heart transplantation (HT), is associated with substantial morbidity and mortality. We aimed to develop a machine learning (ML) model to predict post-transplant AKI stage 3 based on preoperative and perioperative features.

METHODS

Data from 107 consecutive HT recipients in the provincial center between 2018 and 2020 were included for analysis. Logistic regression with L2 regularization was used for the ML model building. The predictive performance of the ML model was assessed using the area under the curve (AUC) in tenfold stratified cross-validation and was compared with that of the Cleveland-clinical model.

RESULTS

Post-transplant AKI occurred in 76 (71.0%) patients including 15 (14.0%) stage 1, 18 (16.8%) stage 2, and 43 (40.2%) stage 3 cases. The top six features selected for the ML model to predicate AKI stage 3 were serum cystatin C, estimated glomerular filtration rate (eGFR), right atrial long-axis dimension, left atrial anteroposterior dimension, serum creatinine (SCr) and FVII. The predictive performance of the ML model (AUC: 0.821; 95% confidence interval [CI]: 0.740-0.901) was significantly higher compared with that of the Cleveland-clinical model (AUC: 0.654; 95% [CI]: 0.545-0.763, p < 0.05).

CONCLUSIONS

The ML model, which achieved an effective predictive performance for post-transplant AKI stage 3, may be helpful for timely intervention to improve the patient's prognosis.

Clinical and Operative Determinants of Acute Kidney Injury after Cardiac Surgery

INTRODUCTION

Cardiac surgery-associated acute kidney injury (CSA-AKI) is associated with increased morbidity and mortality.

OBJECTIVES

We aimed to identify potentially modifiable risk factors for CSA-AKI.

METHODS

This was asingle-center retrospective cohort study of 495 adult patients undergoing cardiac surgery. AKI was diagnosed and staged using full KDIGO criteria incorporating baseline serum creatinine (SC) levels and correction of postoperative SC levels for fluid balance. We examined the association of routinely available clinical and laboratory data with AKI using multivariate logistic regression modeling.

RESULTS

A total of 103 (20.8%) patients developed AKI: 16 (15.5%) patients were diagnosed with AKI upon hospital admission, and 87 (84.5%) patients were diagnosed with CSA-AKI. Correction of SC levels for fluid balance increased the number of AKI cases to 104 (21.0%), with 6 patients categorized to different AKI stages. Univariate logistic regression analysis identified five preoperative (age, sex, diabetes mellitus, preoperative systolic pulmonary arterial pressure [PSPAP], acute decompensated heart failure) and five intraoperative predictors of AKI (age, sex, red blood cell [RBC] volume transfused, use of minimally invasive surgery, duration of cardiopulmonary bypass). When all preoperative and intraoperative variables were incorporated into one model, six predictors remained significant (age, sex, use of minimally invasive surgery, RBC volume transfused, PSPAP, duration of cardiopulmonary bypass). Model discrimination performance showed an area under the curve of 0.69 for the model including only preoperative variables, 0.76 for the model including only intraoperative variables, and 0.77 for the model including all preoperative and intraoperative variables.

CONCLUSIONS

Use of minimally invasive surgery and therapies mitigating PSPAP and intraoperative blood loss may offer protection against CSA-AKI.