Risk estimation model for acute kidney injury defined by KDIGO classification after heart valve replacement surgery

Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis

Importance

Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.

Objective

To systematically review published AKI prediction models across all clinical subsettings.

Data Sources

MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.

Study Selection

All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.

Data Extraction and Synthesis

Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.

Main Outcomes and Measures

C statistic was used to measure the discrimination of prediction models.

Results

Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.

Conclusions and Relevance

In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

High preoperative serum strontium levels increase the risk of acute kidney injury after cardiopulmonary bypass

BACKGROUND

Acute kidney injury (AKI) is a common complication of cardiac surgeries. The incidence of AKI after cardiac surgeries using cardiopulmonary bypass (CPB-AKI) is high, emphasizing the need to determine strategies to prevent CPB-AKI. This study investigates the correlation between CPB-AKI and trace metal levels in clinical and animal studies.

METHODS

Samples and clinical data were obtained from 74 patients from the Nagoya City University Hospital and Okazaki City Hospital. Blood samples were collected before, immediately after, and 2 h after CPB withdrawal. Trace metal levels were measured using inductively coupled plasma mass spectrometry. Sr or vehicle treatment was orally administered to the rats to determine if Sr was associated with CPB-AKI. After the treatment, ischemia-reperfusion (IR) injury was induced, and serum creatinine (SCr) and blood urea nitrogen (BUN) levels were measured.

RESULTS

In this clinical study, the incidence of CPB-AKI was found to be 28% (21/74). The body mass index and estimated glomerular filtration rate were significantly different in patients with AKI. The intensive care unit and hospital stay were longer in AKI patients than in non-AKI patients. The Na, Fe, and Sr levels were significantly higher in AKI patients before CPB. Also, Fe and Sr were higher immediately after CPB withdrawal, and Sr was higher 2 h after CPB withdrawal in AKI patients. Animal studies showed that Sr-treated rats had significantly increased SCr and BUN levels than vehicle-treated rats at 24 h post-IR injury.

CONCLUSIONS

High preoperative serum Sr levels may be associated with CPB-AKI.

Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery

Background

Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population.

Methods

Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score.

Results

A total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66-0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65-0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63-0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score.

Conclusion

Among the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery.

Clinical trial registration

Trial registration: Clinicaltrials.gov, NCT04237636.

Activating Transcription Factor 3 Based Early Alarm Model of Acute Kidney Injury after Cardiopulmonary Bypass in Adults

Acute kidney injury (AKI) is a common complication after cardiopulmonary bypass (CPB) for cardiac surgery, and there is no effective treatment. This study was aimed at constructing an early warning model of AKI after CPB in adults and investigating the performance of this model. Patients who underwent CPB in the Department of Cardiac Surgery, Shanghai Tenth People's Hospital, from January 2018 to December 2019 were recruited into the present study. Blood and urine samples were collected preoperatively (0 h) and 2 h, 6 h, 12 h, 24 h, and 48 h after surgery, and the creatinine and activating transcription factor 3 (ATF3) were detected. According to the diagnostic criteria of AKI, patients were divided into the AKI group and the non-AKI group, and the risk factors for AKI after CPB were screened. The receiver operating characteristic (ROC) curve analysis was used to identify the optimal biomarkers for the establishment of early warning model of AKI after CPB. Finally, the performance of this model was further verified. A total of 83 patients were included in this study, 42 of whom developed AKI after surgery. After CPB, the serum and urine levels of creatinine and ATF3 increased to different degrees, and the increase in urine ATF3 was the most obvious in the AKI group. The area under ROC (AUC) of urine ATF3 at 12 h after surgery was 0.691 (95% CI: 0.576-0.807). When ATF3 was higher than 1216 pg/mL, the sensitivity and specificity of ATF3 in the diagnosis of AKI were 0.43 and 0.85, respectively. The height, conjugated bilirubin on the surgery day, urine ATF3 12 h after surgery, and serum creatinine 24 h after surgery were independent risk factors for postoperative AKI. Urine ATF3 and other factors were used to establish AKI warning model after CPB, which showed good fitting and accuracy. In conclusion, ATF3 is an early biomarker of post-CPB AKI. Addition of urine ATF3 to AKI risk factors can improve the accuracy of early AKI prediction.

Higher Mean Arterial Pressure during Cardiopulmonary Bypass May Not Prevent Acute Kidney Injury in Elderly Patients Undergoing Cardiac Surgery

We aimed to evaluate the role of higher mean arterial pressure (MAP) during cardiopulmonary bypass (CPB) in preventing development of acute kidney injury (AKI). Methods. We evaluated a population of elderly individuals >60 years of age undergoing CPB to find correlation of MAP during CPB with development of AKI after the surgery. Patients who experienced sustained low MAP during the CPB defined as that of <65 mmHg were compared with those that had sustained high MAP of >65 mmHg for their outcome with regard to AKI. The KDIGO criteria were used to define presence of acute kidney injury. Results. Of the total 92 patients, 50 were in the low-pressure group and 42 were in the high-pressure group. The MAP was 61.14 ± 5.54 mmHg in the low-pressure group and 68.97 ± 3.65 mmHg in the high-pressure group (p < 0.001). 13 (26%) in the low-pressure group and 17 (40.48%) in the high-pressure group developed AKI (p = 0.140). Male sex was associated with an increased incidence of cardiac surgery-associated AKI (p = 0.034). Conclusions. A higher MAP in the range of 65-75 mmHg during the cardiopulmonary bypass does not significantly prevent acute kidney injury in elderly patients undergoing cardiac valve surgery.

A New Method of Isolation of Mouse Renal Primary Tubular Epithelial Cells

Kidney diseases are becoming an emerging public health problem. In order to further explore the etiology of various kidney diseases, we improved the methods of isolation of primary cultures of mouse renal tubular epithelial cells. At the first stage, the kidneys were perfused with collagenase solution. To this end, the superior mesenteric artery, celiac artery and thoracic aorta were ligated and perfusion was performed through the abdominal aorta. Then, the cells were isolated ex vivo and their integrity, purity, viability, and concentration were evaluated. The proposed cost-effective and simple method provides high purity and high concentration of primary renal epithelial cells for molecular biology studies of the kidneys.

Incidence and Predictive Factors of Acute Kidney Injury After Off-pump Lung Transplantation

OBJECTIVES

To determine the incidence and predictive factors of acute kidney injury (AKI) after off-pump lung transplantation.

DESIGN

A retrospective cohort study.

SETTING

The operating room and intensive care unit.

PARTICIPANTS

Adult patients who underwent lung transplant without cardiopulmonary bypass or extracorporeal membrane oxygenator between 2006 and 2016 at the Vanderbilt University Medical Center.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The presence of postoperative AKI was assessed by the Kidney Disease: Improving Global Outcomes criteria in the first seven postoperative days. Multivariate logistic regression analysis was used to determine the independent predictive factors of AKI. One hundred forty-eight patients were included in the final analysis, of whom 63 (42.6%) subsequently developed AKI: 43 (29.0%) stage 1, ten (6.8%) stage 2, and ten (6.8%) stage 3. Patients who had AKI had a longer hospital length of stay (12 days [interquartile range (IQR): 10-17] vs ten days [IQR: 8-12], p < 0.001). For every one-year increase in age, the odds of AKI decreased by 8% (odds ratio [OR] 0.92, 95% confidence interval [CI]: 0.87-0.98, p = 0.008). The odds of having AKI in patients with bilateral lung transplant was lower than patients with unilateral transplant (OR 0.09, 95% CI: 0.01-0.63, p = 0.015). Additionally, a diagnosis of chronic obstructive pulmonary disease increased the odds of AKI by four-fold compared with a diagnosis of idiopathic pulmonary fibrosis (OR 4.73, 95% CI: 1.44-15.56, p = 0.011).

CONCLUSIONS

AKI is a common complication after off-pump lung transplantation and is associated with increased hospital length of stay. Younger age, unilateral lung transplant, and diagnosis of chronic obstructive pulmonary disease are independently associated with AKI.