Early recognition of cardiac surgery-associated acute kidney injury: lack of added value of TIMP2 IGFBP7 over short-term changes in creatinine (an observational pilot study)

Creatinine Reduction Ratio Is a Prognostic Factor for Acute Kidney Injury following Cardiac Surgery with Cardiopulmonary Bypass: A Single-Center Retrospective Cohort Study

Acute kidney injury (AKI) after cardiac surgery is a common complication that can lead to death. We previously reported that the creatinine reduction ratio (CRR) serves as a useful prognostic factor for AKI. The primary objective of this study was to determine the predictors of AKI after surgery. The secondary objective was to determine the reliability of the CRR for short- and long-term outcomes. We retrospectively collected information about cardiac surgery patients who underwent cardiopulmonary bypass. Patients were divided into AKI and non-AKI groups based on the AKIN and RIFLE criteria. We analyzed the two groups regarding the preoperative patient data and operative information. The CRR was calculated as follows: (preoperative creatinine-postoperative creatinine)/preoperative creatinine. The prognostic factors of AKI-CS were surgery time, CPB time, aorta clamp time, platelet transfusion, and CRR < 20%. In the multivariate logistical analysis, CRR was an independent predictor of AKI (adjusted odds ratio: 0.90 [0.87-0.93], p < 0.001). However, there were no significant differences in CRR in terms of the rate of new onset chronic kidney disease (CKD). After cardiac surgery with cardiopulmonary bypass, CRR has good diagnostic power for predicting perioperative AKI. However, we cannot use it as a prognostic factor over a long-term period.

Comparative accuracy of biomarkers for the prediction of hospital-acquired acute kidney injury: a systematic review and meta-analysis

Background

Several biomarkers have been proposed to predict the occurrence of acute kidney injury (AKI); however, their efficacy varies between different trials. The aim of this study was to compare the predictive performance of different candidate biomarkers for AKI.

Methods

In this systematic review, we searched PubMed, Medline, Embase, and the Cochrane Library for papers published up to August 15, 2022. We selected all studies of adults (> 18 years) that reported the predictive performance of damage biomarkers (neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP)), inflammatory biomarker (interleukin-18 (IL-18)), and stress biomarker (tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein-7 (TIMP-2 × IGFBP-7)) for the occurrence of AKI. We performed pairwise meta-analyses to calculate odds ratios (ORs) and 95% confidence intervals (CIs) individually. Hierarchical summary receiver operating characteristic curves (HSROCs) were used to summarize the pooled test performance, and the Grading of Recommendations, Assessment, Development and Evaluations criteria were used to appraise the quality of evidence.

Results

We identified 242 published relevant studies from 1,803 screened abstracts, of which 110 studies with 38,725 patients were included in this meta-analysis. Urinary NGAL/creatinine (diagnostic odds ratio [DOR] 16.2, 95% CI 10.1–25.9), urinary NGAL (DOR 13.8, 95% CI 10.2–18.8), and serum NGAL (DOR 12.6, 95% CI 9.3–17.3) had the best diagnostic accuracy for the risk of AKI. In subgroup analyses, urinary NGAL, urinary NGAL/creatinine, and serum NGAL had better diagnostic accuracy for AKI than urinary IL-18 in non-critically ill patients. However, all of the biomarkers had similar diagnostic accuracy in critically ill patients. In the setting of medical and non-sepsis patients, urinary NGAL had better predictive performance than urinary IL-18, urinary L-FABP, and urinary TIMP-2 × IGFBP-7: 0.3. In the surgical patients, urinary NGAL/creatinine and urinary KIM-1 had the best diagnostic accuracy. The HSROC values of urinary NGAL/creatinine, urinary NGAL, and serum NGAL were 91.4%, 85.2%, and 84.7%, respectively.

Conclusions

Biomarkers containing NGAL had the best predictive accuracy for the occurrence of AKI, regardless of whether or not the values were adjusted by urinary creatinine, and especially in medically treated patients. However, the predictive performance of urinary NGAL was limited in surgical patients, and urinary NGAL/creatinine seemed to be the most accurate biomarkers in these patients. All of the biomarkers had similar predictive performance in critically ill patients.

Trial registrationCRD42020207883, October 06, 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04223-6.

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.

Early Acute Kidney Injury in Patients with Non-Traumatic Subarachnoid Hemorrhage who Undergo Catheter Angiography: Incidence, Associated Risk Factors and Impact on Outcome

OBJECTIVES

After subarachnoid hemorrhage (SAH), potential renal insults are numerous but the burden of early acute kidney injury (AKI) is unclear. We determined its incidence, rate of persistence, risk factors, and impact on patients' outcomes.

MATERIALS AND METHODS

Patients with non-traumatic SAH were retrospectively included if they underwent catheter angiography within the 48 h after their admission to the intensive care unit. Early AKI was defined according to Kidney Disease Improving Global Outcome (KDIGO) criteria, analyzed from the time of catheter angiography. Early AKI was considered as persistent if the KDIGO stage did not decrease between the 48th and the 60th hour.

RESULTS

Among 499 consecutive patients, early AKI (mostly oliguria) occurred in 132 (26%): stage 1, 2 and 3 in 72 (14%), 44 (9%), and 16 (3%) patients, respectively. It persisted in 36% of cases. Early AKI occurred more likely when SAH was severe or renal function was impaired at hospital admission: adjusted odds ratio of 2.76 [95% 1.77-4.30] and 3.32 [1.17-9.46], respectively. ICU and hospital lengths of stay were longer in patients who developed early AKI than in patients who did not: 16 [9-29] versus 12 [4-24] days (p = 0.0003) and 21 [14-43] versus 16 [11-32] days (p = 0.007), respectively. There was an independent link between early AKI and renal outcome (n = 274 in the model) but not with hospital mortality (n = 453).

CONCLUSIONS

One quarter of our population developed early AKI, mostly oliguria. It persisted beyond the 48th hour in one third of cases. The associated risk factors we identified were non-modifiable.