Acute Kidney Injury After Heart Transplant: The Importance of Pulmonary Hypertension

Perioperative Risk Factors of Acute Kidney Injury After Heart Transplantation and One-Year Clinical Outcomes: A Retrospective Cohort Study

OBJECTIVES

This study aimed to identify perioperative risk factors of acute kidney injury after heart transplantation and to evaluate 1-year clinical outcomes.

DESIGN

A retrospective single-center cohort study.

SETTING

At a university hospital.

PARTICIPANTS

All patients who underwent heart transplantation from January 2015 to December 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The authors recorded acute kidney injury after heart transplantation. One-year mortality and renal function also were recorded. Risk factors of acute kidney injury were evaluated using a multivariate logistic regression model. Long-term survival was compared between patients developing acute kidney injury and those who did not, using a log-rank test. Among 209 patients included in this study, 134 patients (64% [95% CI (58; 71)]) developed posttransplantation acute kidney injury. Factors independently associated with acute kidney injury were high body mass index (odds ratio [OR]: 1.18 [1.02-1.38] per kg/m2; p = 0.030), prolonged duration of cold ischemic period (OR: 1.11 [1.01-1.24] per 10 minutes; p = 0.039), and high dose of intraoperative dobutamine support (OR: 1.24 [1.06-1.46] per µg/kg/min; p = 0.008). At 1 year, patients who developed postoperative acute kidney injury had higher mortality rates (20% v 8%, p = 0.015). Among 172 survivors at 1 year, 82 survivors (48%) had worsened their renal function compared with preheart transplantation.

CONCLUSIONS

This study highlighted the high incidence of acute kidney injury after heart transplantation and its impact on patient outcomes. Risk factors such as body mass index, prolonged cold ischemic period duration, and level of inotropic support with dobutamine were identified, providing insights for preventive strategies.

Severe Acute Kidney Injury Postheart Transplantation: Analysis of Risk Factors

Background

Acute kidney injury (AKI) is a common complication postheart transplantation and is associated with significant morbidity and increased mortality.

Methods

We conducted a single-center, retrospective, observational cohort study of 109 consecutive patients undergoing heart transplantation between September 2019 and September 2021 to determine major risk factors for, and the incidence of, severe postoperative AKI as defined by Kidney Disease Improving Global Outcomes criteria in the first 48-h posttransplantation and the impact that this has on mortality and dialysis dependence.

Results

One hundred nine patients were included in our study, 83 of 109 (78%) patients developed AKI, 42 (39%) developed severe AKI, and 37 (35%) required renal replacement therapy in the first-week posttransplantation. We found preoperative estimated glomerular filtration rate (eGFR), postoperative noradrenaline dose, and the need for postoperative mechanical circulatory support to be independent risk factors for the development of severe AKI. Patients who developed severe AKI had a 19% 12-mo mortality compared with 1% for those without. Of those who survived to hospital discharge, 20% of patients in the severe AKI group required dialysis at time of hospital discharge compared with 3% in those without severe AKI.

Conclusion

Severe AKI is common after heart transplantation. Preoperative kidney function, postoperative vasoplegia with high requirements for vasoactive drugs, and graft dysfunction with the need for mechanical circulatory supports were independently associated with the development of severe AKI in the first-week following heart transplantation. Severe AKI is associated with a significantly increased mortality and dialysis dependence at time of hospital discharge.

Heart Transplantation-Postoperative Considerations

Heart transplantation (HT) remains the best treatment of patients with severe heart failure who are deemed to be transplant candidates. The authors discuss postoperative management of the HT recipient by system, emphasizing areas where care might differ from other cardiac surgery patients. Working together, critical care physicians, heart transplant surgeons and cardiologists, advanced practice providers, pharmacists, transplant coordinators, nursing staff, physical therapists, occupational therapists, rehabilitation specialists, nutritionists, health psychologists, social workers, and the patient and their loved ones partner to increase the likelihood of a successful outcome.

Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study

BACKGROUND

Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) surgery is associated with poor outcomes. The objective of this study was to apply a new machine learning (ML) method to establish prediction models of AKI after CABG.

METHODS

A total of 2,780 patients from two medical centers in East China who underwent primary isolated CABG were enrolled. The dataset was randomly divided for model training (80%) and model testing (20%). Four ML models based on LightGBM, Support vector machine (SVM), Softmax and random forest (RF) algorithms respectively were established in Python. A total of 2,051 patients from two other medical centers were assigned to an external validation group to verify the performances of the ML prediction models. The models were evaluated using the area under the receiver operating characteristics curve (AUC), Hosmer-Lemeshow goodness-of-fit statistic, Bland-Altman plots, and decision curve analysis. The outcome of the LightGBM model was interpreted using SHapley Additive exPlanations (SHAP).

RESULTS

The incidence of postoperative AKI in the modeling group was 13.4%. Similarly, the incidence of postoperative AKI of the two medical centers in the external validation group was 8.2% and 13.6% respectively. LightGBM performed the best in predicting, with an AUC of 0.8027 in internal validation group and 0.8798 and 0.7801 in the external validation group. The SHAP revealed the top 20 predictors of postoperative AKI ranked according to the importance, and the top three features on prediction were the serum creatinine in the first 24 h after operation, the last preoperative Scr level, and body surface area.

CONCLUSION

This study provides a LightGBM predictive model that can make accurate predictions for AKI after CABG surgery. The LightGBM model shows good predictive ability in both internal and external validation. It can help cardiac surgeons identify high-risk patients who may experience AKI after CABG surgery.

Inhaled milrinone in cardiac surgical patients: pharmacokinetic and pharmacodynamic exploration

Mean arterial pressure to mean pulmonary arterial pressure ratio (mAP/mPAP) has been identified as a strong predictor of perioperative complications in cardiac surgery. We therefore investigated the pharmacokinetic/pharmacodynamic (PK/PD) relationship of inhaled milrinone in these patients using this ratio (R) as a PD marker. Following approval by the ethics and research committee and informed consent, we performed the following experiment. Before initiation of cardiopulmonary bypass in 28 pulmonary hypertensive patients scheduled for cardiac surgery, milrinone (5 mg) was nebulized, plasma concentrations measured (up to 10 h) and compartmental PK analysis carried out. Baseline (R₀) and peak (R_max) ratios as well as magnitude of peak response (∆_Rmax-R0) were measured. During inhalation, individual area under effect-time (AUEC) and plasma concentration-time (AUC) curves were correlated. Potential relationships between PD markers and difficult separation from bypass (DSB) were explored. In this study, we observed that milrinone peak concentrations (41-189 ng ml^-1) and Δ_Rmax-R0 (- 0.12-1.5) were obtained at the end of inhalation (10-30 min). Mean PK parameters agreed with intravenous milrinone published data after correction for the estimated inhaled dose. Paired comparisons yielded a statistically significant increase between R₀ and R_max (mean difference, 0.58: 95% CI 0.43-0.73; P < 0.001). Individual AUEC correlated with AUC (r = 0.3890, r² = 0.1513; P = 0.045); significance increased after exclusion of non-responders (r = 4787, r² = 0.2292; P = 0.024). Individual AUEC correlated with ∆R_max-R0 (r = 5973, r² = 0.3568; P = 0.001). Both ∆R_max-R0 (P = 0.009) and CPB duration (P < 0.001) were identified as predictors of DSB. In conclusion, both magnitude of peak response of the mAP/mPAP ratio and CPB duration were associated with DSB.

Risk factors of renal replacement therapy after heart transplantation: a retrospective single-center study

BACKGROUND

Acute kidney injury (AKI) and renal replacement therapy (RRT) are common after heart transplantation (HT). The need for RRT has been reported to be one of the most important predictors of a poor prognosis after HT. Therefore, it is important to early identify risk factors of RRT after HT. However, in the heart transplantation setting, the risk factors are less well studied, and some of the conclusions are controversial. This study aimed to identify the clinical predictors of RRT after HT.

METHODS

This single-center, retrospective study from January 2010 to June 2021 analyzed risk factors (pre-, intra-, and postoperative characteristics) of 163 patients who underwent HT. The endpoint of the study was RRT within 7 days of HT. Risk factors were analyzed by multivariable logistic regression models.

RESULTS

Fifty-five (33.74%) recipients required RRT within 7 days of HT. Factors independently associated with RRT after HT were as follows: a baseline estimated glomerular filtration rate (eGFR) <60 mL/min per 1.73 m² [odds ratio (OR) =3.123; 95% confidence interval (CI): 1.183-8.244; P=0.022], a dose of intraoperative methylprednisolone >10 mg/kg (OR =3.197; 95% CI: 1.290-7.923; P=0.012), the use of mechanical circulatory support (MCS) during surgery (OR =4.903; 95% CI: 1.628-14.766; P=0.005), a cardiopulmonary bypass (CPB) time ≥5 hours (OR =3.929; 95% CI: 1.222-12.634; P=0.022), and postoperative serum total bilirubin (TBIL) ≥60 umol/L (OR =5.105; 95% CI: 1.868-13.952; P=0.001). Protective factors were higher postoperative serum albumin (OR =0.907; 95% CI: 0.837-0.983; P=0.017) and higher postoperative left ventricular ejection fraction (LVEF) (OR =0.908; 95% CI: 0.838-0.985; P=0.020).

CONCLUSIONS

A low preoperative eGFR, a high intraoperative dose of methylprednisolone, a long CPB time, the use of mechanical circulatory support, and a high postoperative TBIL were risk factors for RRT after HT. While a high postoperative serum albumin level and a high left ventricular ejection fraction were protective factors. Understanding these risk factors may help us identify high-risk patients and intervene early.