Background: Researchers have suggested models to predict the risk of postoperative AKI (PO-AKI), but an externally validated risk index that can be practically implemented before patients undergo noncardiac surgery is needed. Methods: We performed a retrospective observational study of patients without preexisting renal failure who underwent a noncardiac operation (≥1 hour) at two tertiary hospitals in Korea. We fitted a proportional odds model for an ordinal outcome consisting of three categories: critical AKI (defined as Kidney Disease Improving Global Outcomes AKI stage ≥2, post-AKI death, or dialysis within 90 days after surgery), low-stage AKI (defined as PO-AKI events not fulfilling the definition of critical AKI), and no PO-AKI. Results: The study included 51,041 patients in a discovery cohort and 39,764 patients in a validation cohort. The Simple Postoperative AKI Risk (SPARK) index included a summation of the integer scores of the following variables: age, sex, expected surgery duration, emergency operation, diabetes mellitus, use of renin-angiotensin-aldosterone inhibitors, baseline eGFR, dipstick albuminuria hypoalbuminemia, anemia, and hyponatremia. The model calibration plot showed tolerable distribution of observed and predicted probabilities in both cohorts. The discrimination power of the SPARK index was acceptable in both the discovery (c-statistic 0.80) and validation (c-statistic 0.72) cohorts. When four SPARK classes were defined on the basis of the sum of the risk scores, the SPARK index and classes fairly reflected the risks of PO-AKI and critical AKI. Conclusions: Clinicians may consider implementing the SPARK index and classifications to stratify patients’ PO-AKI risks before performing noncardiac surgery.

Chronic kidney disease (CKD)-associated uremia aggravates—and is aggravated by—gut dysbiosis. However, the correlation between CKD severity and gut microbiota and/or their uremic metabolites is unclear. We enrolled 103 CKD patients with stage 1 to 5 and 46 healthy controls. We analyzed patients’ gut microbiota by MiSeq system and measured the serum concentrations of four uremic metabolites (p-cresyl sulfate, indoxyl sulfate, p-cresyl glucuronide, and trimethylamine N-oxide) by liquid chromatography–tandem mass spectrometry. Serum concentrations of the uremic metabolites increased with kidney function deterioration. Gut microbial diversity did not differ among the examined patient and control groups. In moderate or higher stage CKD groups, Oscillibacter showed positive interactions with other microbiota, and the proportions of Oscillibacter were positively correlated with those of the uremic metabolites. The gut microbiota, particularly Oscillibacter, was predicted to contribute to pyruvate metabolism which increased with CKD progression. Relative abundance of Oscillibacter was significantly associated with both serum uremic metabolite levels and kidney function. Predicted functional analysis suggested that kidney-function-associated changes in the contribution of Oscillibacter to pyruvate metabolism in CKD may greatly affect the gut environment according to kidney function, resulting in dysbiosis concomitant with uremic toxin production. The gut microbiota could be associated with uremia progression in CKD. These results may provide basis for further metagenomics analysis of kidney diseases.

Data regarding kidney transplantation (KT) and dialysis outcomes are rare in Asian populations. In the present study, we evaluated the clinical outcomes associated with KT using claims data from the Korean national public health insurance program. Among the 35,418 adult patients with incident dialysis treated between 2005 and 2008 in Korea, 1539 underwent KT. An optimal balanced risk set matching was attempted to compare the transplant group with the control group in terms of the overall survival and major adverse cardiac event-free survival. Before matching, the dialysis group was older and had more comorbidities. After matching, there were no differences in age, sex, dialysis modalities, or comorbidities. Patient survival was significantly better in the transplant group than in the matched control group (P < 0.001). In addition, the transplant group showed better major adverse cardiac event-free survival than the dialysis group (P < 0.001; hazard ratio, 0.49; 95% confidence interval, 0.32-0.75). Korean patients with incident dialysis who underwent long-term dialysis had significantly more cardiovascular events and higher all-cause mortality rates than those who underwent KT. Thus, KT should be more actively recommended in Korean populations.

We evaluated the impact of serum uric acid (SUA) on mortality in patients with chronic dialysis. A total of 4132 adult patients on dialysis were enrolled prospectively between August 2008 and September 2014. Among them, we included 1738 patients who maintained dialysis for at least 3 months and had available SUA in the database. We categorized the time averaged-SUA (TA-SUA) into 5 groups: <5.5, 5.5–6.4, 6.5–7.4, 7.5–8.4, and ≥8.5 mg/dL. Cox regression analysis was used to calculate the hazard ratio (HR) of all-cause mortality according to SUA group. The mean TA-SUA level was slightly higher in men than in women. Patients with lower TA-SUA level tended to have lower body mass index (BMI), phosphorus, serum albumin level, higher proportion of diabetes mellitus (DM), and higher proportion of malnourishment on the subjective global assessment (SGA). During a median follow-up of 43.9 months, 206 patients died. Patients with the highest SUA had a similar risk to the middle 3 TA-SUA groups, but the lowest TA-SUA group had a significantly elevated HR for mortality. The lowest TA-SUA group was significantly associated with increased all-cause mortality (adjusted HR, 1.720; 95% confidence interval, 1.007–2.937; P = 0.047) even after adjusting for demographic, comorbid, nutritional covariables, and medication use that could affect SUA levels. This association was prominent in patients with well nourishment on the SGA, a preserved serum albumin level, a higher BMI, and concomitant DM although these parameters had no significant interaction in the TA-SUA-mortality relationship except DM. In conclusion, a lower TA-SUA level <5.5 mg/dL predicted all-cause mortality in patients with chronic dialysis.

This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices.

Lee H, et al. J Korean Med Sci. 2014 Sep;29(Suppl 2):S91-S96. http://dx.doi.org/10.3346/jkms.2014.29.S2.S91