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Plain language summary An enlarged heart occurs during various medical conditions and can result in early death. However, it is unclear whether this is also the case in patients with chronic kidney disease (CKD). Although the size of the heart can be measured on chest X-rays, this process is time consuming. We used artificial intelligence to quantify the heart size of 3117 CKD patients based on their chest X-rays within hours. We found that CKD patients with an enlarged heart were more likely to develop end-stage kidney disease or die. This could improve monitoring of CKD patients with an enlarged heart and improve their care.

Abstract The fasting blood glucose (FBG) values extracted from electronic medical records (EMR) are assumed valid in existing research, which may cause diagnostic bias due to misclassification of fasting status. We proposed a machine learning (ML) algorithm to predict the fasting status of blood samples. This cross-sectional study was conducted using the EMR of a medical center from 2003 to 2018 and a total of 2,196,833 ontological FBGs from the outpatient service were enrolled. The theoretical true fasting status are identified by comparing the values of ontological FBG with average glucose levels derived from concomitant tested HbA1c based on multi-criteria. In addition to multiple logistic regression, we extracted 67 features to predict the fasting status by eXtreme Gradient Boosting (XGBoost). The discrimination and calibration of the prediction models were also assessed. Real-world performance was gauged by the prevalence of ineffective glucose measurement (IGM). Of the 784,340 ontologically labeled fasting samples, 77.1% were considered theoretical FBGs. The median (IQR) glucose and HbA1c level of ontological and theoretical fasting samples in patients without diabetes mellitus (DM) were 94.0 (87.0, 102.0) mg/dL and 5.6 (5.4, 5.9)%, and 92.0 (86.0, 99.0) mg/dL and 5.6 (5.4, 5.9)%, respectively. The XGBoost showed comparable calibration and AUROC of 0.887 than that of 0.868 in multiple logistic regression in the parsimonious approach and identified important predictors of glucose level, home-to-hospital distance, age, and concomitantly serum creatinine and lipid testing. The prevalence of IGM dropped from 27.8% based on ontological FBGs to 0.48% by using algorithm-verified FBGs. The proposed ML algorithm or multiple logistic regression model aids in verification of the fasting status.

Rationale & Objective: Poor sleep quality and insomnia are pervasive among patients with advanced chronic kidney disease (CKD); however, these health issues have not been systematically evaluated. Study Design: Systematic review and meta-analysis. Setting & Study Populations: Adult patients with CKD not receiving kidney replacement therapy (KRT), as well as adults receiving KRT, including hemodialysis, peritoneal dialysis, and kidney transplantation. Selection Criteria for Studies: A systematic literature search using PubMed, Embase, and PsycNET, was conducted for articles published between January 1, 1990, and September 28, 2018. Data Extraction: Data on the prevalences of poor sleep quality and insomnia in patients with CKD, including those receiving and not receiving KRT, were extracted. Analytical Approach: Pooled prevalences were estimated using a random-effects meta-analysis and were stratified according to age, CKD stage, World Health Organization region, risk of bias, Pittsburgh Sleep Quality Index score, and the different criteria for insomnia that were used at diagnosis. Results: Of 3,708 articles, 93 were selected, and significant methodological heterogeneity was present. The pooled prevalences of poor sleep quality for CKD without KRT, hemodialysis, peritoneal dialysis, and kidney transplantation were 59% (95% CI, 44%-73%), 68% (95% CI, 64%-73%), 67% (95% CI, 44%-86%), and 46% (95% CI, 34%-59%), respectively. The corresponding prevalences of insomnia were 48% (95% CI, 30%-67%), 46% (95% CI, 39%-54%), 61% (95% CI, 41%-79%), and 26% (95% CI, 9%-49%), respectively. Insomnia was significantly more prevalent among patients aged 51-60 years and those aged >60 years than among those aged

Glycosylated hemoglobin (HbA1c) targets for patients with chronic kidney disease (CKD) and type 2 diabetes remain controversial. To evaluate whether baseline HbA1c and HbA1c trajectories are associated with the risk of end-stage kidney disease (ESKD) and all-cause mortality, we recruited adult patients with CKD and type 2 diabetes from a “Pre-ESKD Program” at a medical center in Taiwan from 2003 to 2017. Group-based trajectory modeling was performed to identify distinct patient groups that contained patients with similar longitudinal HbA1c patterns. Cox proportional hazard models were used to estimate hazard ratios (HRs) of ESKD and mortality associated with baseline HbA1c levels and HbA1c trajectories. In the analysis related to baseline HbA1c (n = 4543), the adjusted HRs [95% confidence interval (CI)] of all-cause mortality were 1.06 (0.95–1.18) and 1.25 (95% CI, 1.07–1.46) in patients with an HbA1c level of 7%–9% (53–75 mmol/mol) and >9% (>75 mmol/mol), respectively, as compared with those with an HbA1c level < 7% (