Background Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing continuous renal replacement therapy (CRRT) for severe acute kidney injury. Accordingly, the present study applies machine learning algorithms to improve prediction accuracy for this patient subset. Methods We randomly divided a total of 1571 adult patients who started CRRT for acute kidney injury into training (70%, n = 1094) and test (30%, n = 477) sets. The primary output consisted of the probability of mortality during admission to the intensive care unit (ICU) or hospital. We compared the area under the receiver operating characteristic curves (AUCs) of several machine learning algorithms with that of the APACHE II, SOFA, and the new abbreviated mortality scoring system for acute kidney injury with CRRT (MOSAIC model) results. Results For the ICU mortality, the random forest model showed the highest AUC (0.784 [0.744–0.825]), and the artificial neural network and extreme gradient boost models demonstrated the next best results (0.776 [0.735–0.818]). The AUC of the random forest model was higher than 0.611 (0.583–0.640), 0.677 (0.651–0.703), and 0.722 (0.677–0.767), as achieved by APACHE II, SOFA, and MOSAIC, respectively. The machine learning models also predicted in-hospital mortality better than APACHE II, SOFA, and MOSAIC. Conclusion Machine learning algorithms increase the accuracy of mortality prediction for patients undergoing CRRT for acute kidney injury compared with previous scoring models.

Smoking affected healing failure after arthroscopic rotator cuff repair. Attention should be paid to smokers, especially current heavy smokers, in cases of rotator cuff repair surgery.

A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant -expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.

Background/AimsThis nationwide, multicenter prospective randomized controlled trial aimed to compare the efficacy and safety of 10-day concomitant therapy (CT) and 10-day sequential therapy (ST) with 7-day clarithromycin-containing triple therapy (TT) as first-line treatment for Helicobacter pylori infection in the Korean population.MethodsPatients with H. pylori infection were assigned randomly to 7d-TT (lansoprazole 30 mg, amoxicillin 1 g, and clarithromycin 500 mg twice daily for 7 days), 10d-ST (lansoprazole 30 mg and amoxicillin 1 g twice daily for the first 5 days, followed by lansoprazole 30 mg, clarithromycin 500 mg, and metronidazole 500 mg twice daily for the remaining 5 days), or 10d-CT (lansoprazole 30 mg, amoxicillin 1 g, clarithromycin 500 mg, and metronidazole 500 mg twice daily for 10 days). The primary endpoint was eradication rate by intention-to-treat (ITT) and per-protocol (PP) analyses.ResultsA total of 1,141 patients were included. The 10d-CT protocol achieved a markedly higher eradication rate than the 7d-TT protocol in both the ITT (81.2% vs 63.9%) and PP analyses (90.6% vs 71.4%). The eradication rate of the 10d-ST protocol was superior to that of the 7d-TT protocol (76.3% vs 63.9%, ITT analysis; 85.0% vs 71.4%, PP analysis). No significant differences in adherence or serious side effects were found among the three treatment arms.ConclusionsThe 10d-CT and 10d-ST regimens were superior to the 7d-TT regimen as standard first-line treatment in Korea.

Aim On the basis of the worst outcomes of patients undergoing continuous renal replacement therapy (CRRT) in intensive care unit, previously developed mortality prediction model, Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) needs to be modified. Methods A total of 828 patients who underwent CRRT were recruited. Mortality prediction model was developed for the prediction of death within 7 days after starting the CRRT. Based on regression analysis, modified scores were assigned to each variable which were originally used in the APACHE II and SOFA scoring models. Additionally, a new abbreviated Mortality Scoring system for AKI with CRRT (MOSAIC) was developed after stepwise selection analysis. Results We used all the variables included in the APACHE II and SOFA scoring models. The prediction powers indicated by C‐statistics were 0.686 and 0.683 for 7‐day mortality by the APACHE II and SOFA systems, respectively. After modification of these models, the prediction powers increased up to 0.752 for the APACHE II and 0.724 for the SOFA systems. Using multivariate analysis, seven significant variables were selected in the MOSAIC model wherein its C‐statistic value was 0.772. These models also showed good performance with 0.720, 0.734 and 0.773 of C‐statistics in the modified APACHE II, modified SOFA and MOSAIC scoring models in the external validation cohort (n = 497). Conclusion The modified APACHE II/SOFA and newly developed MOSAIC models could be more useful tool for predicting mortality for patients receiving CRRT.

Abstract Background Diet is a modifiable factor of chronic kidney disease (CKD) progression. However, the effect of dietary salt intake on CKD progression remains unclear. Therefore, we analyzed the effect of dietary salt intake on renal outcome in Korean patients with CKD. Methods We measured 24-h urinary sodium (Na) excretion as a marker of dietary salt intake in the prospective, multi-center, longitudinal KoreaN cohort study for Outcome in patients With CKD (KNOW-CKD). Data were analyzed from CKD patients at Stages G3a to G5 (n = 1254). We investigated the association between dietary salt intake and CKD progression. Patients were divided into four quartiles of dietary salt intake, which was assessed using measured 24-h urinary Na excretion. The study endpoint was composite renal outcome, which was defined as either halving the estimated glomerular filtration rate or developing end-stage renal disease. Results During a median (interquartile range) follow-up of 4.3 (2.8–5.8) years, 480 (38.7%) patients developed the composite renal event. Compared with the reference group (Q2, urinary Na excretion: 104.2 ≤ Na excretion < 145.1 mEq/day), the highest quartile of measured 24-h urinary Na excretion was associated with risk of composite renal outcome [Q4, urinary Na excretion ≥192.9 mEq/day, hazard ratio 1.8 (95% confidence interval 1.12–2.88); P = 0.015] in a multivariable hazards model. Subgroup analyses showed that high-salt intake was particularly associated with a higher risk of composite renal outcome in women, in patients <60 years of age, in those with uncontrolled hypertension and in those with obesity. Conclusions High salt intake was associated with increased risk of progression in CKD.

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