Background: Malnutrition and inflammation are highly prevalent and tightly regulated with each other in chronic kidney disease (CKD) patients. Inflammation can lead to malnutrition in patients with sufficient nourishment, while malnutrition may also induce an inflammatory response. This study investigated whether the albumin-globulin ratio (AGR) can predict the mortality risk in CKD patients. Methods: We enrolled 956 stage 3–5 CKD patients retrospectively at a medical center. Patients’ baseline characteristics including demographics, laboratory data, pharmacotherapy, and comorbidities were collected for statistical adjustments. The study patients were stratified into three AGR groups according to similar magnitudes of hazards for mortality as follows: low AGR group, AGR ≤ 1.0; moderate AGR group, 1.1 ≤ AGR < 1.3; high AGR group, AGR ≥1.3. Multivariate Cox proportional hazard analysis was performed to evaluate the association of the AGR with the study outcomes, including overall and cardiovascular disease (CVD) mortality. Results: During a median follow-up duration of 2.44 years, 108 (11.3%) deaths were recorded and 50 patients died from CVD. In adjusted model 1, the moderate AGR group was associated with hazard ratios (HR) of 0.57 (95% CI = 0.36–0.90, p = 0.016) and 0.52 (95% CI = 0.28–0.98, p = 0.043) for all-cause and CVD mortality compared with the low AGR group, respectively. The high AGR group was associated with HRs of 0.49 (95% CI = 0.27–0.90, p = 0.021) and 0.27 (95% CI = 0.1–0.74, p = 0.01) for all-cause and CVD mortality compared with the low AGR group, respectively. Similar results were obtained in the adjusted model 2 (inverse probability of the group weighted Cox model). In addition, the association between the AGR and mortality risk remained significant when the AGR was treated as a continuous variable. Conclusion: AGR is a significant biomarker predicting overall and cardiovascular mortality risk independent of various important factors amongst stage 3–5 CKD patients. We suggest that the AGR may be a simple and inexpensive measurement for detecting CKD patients at risk of mortality.

Background: The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. Methods: We conducted a retrospective case–control study. A total of 606 patients with COPD were screened for eligibility using registry data from the COPD Pay-for-Performance Program (COPD P4P program) database at Changhua Christian Hospital between January 2017 and December 2019. Recursive feature elimination technology was used to select the optimal subset of features for predicting the occurrence of AECOPD. We developed four ML models to predict first-time AECOPD, and the highest-performing model was applied. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) and a local explanation method were used to evaluate the risk of AECOPD and to generate individual explanations of the model’s decisions. Results: The gradient boosting machine (GBM) and support vector machine (SVM) models exhibited superior discrimination ability (area under curve [AUC] = 0.833 [95% confidence interval (CI) 0.745–0.921] and AUC = 0.836 [95% CI 0.757–0.915], respectively). The decision curve analysis indicated that the GBM model exhibited a higher net benefit in distinguishing patients at high risk for AECOPD when the threshold probability was <0.55. The COPD Assessment Test (CAT) and the symptom of wheezing were the two most important features and exhibited the highest SHAP values, followed by monocyte count and white blood cell (WBC) count, coughing, red blood cell (RBC) count, breathing rate, oral long-acting bronchodilator use, chronic pulmonary disease (CPD), systolic blood pressure (SBP), and others. Higher CAT score; monocyte, WBC, and RBC counts; BMI; diastolic blood pressure (DBP); neutrophil-to-lymphocyte ratio; and eosinophil and lymphocyte counts were associated with AECOPD. The presence of symptoms (wheezing, dyspnea, coughing), chronic disease (CPD, congestive heart failure [CHF], sleep disorders, and pneumonia), and use of COPD medications (triple-therapy long-acting bronchodilators, short-acting bronchodilators, oral long-acting bronchodilators, and antibiotics) were also positively associated with AECOPD. A high breathing rate, heart rate, or systolic blood pressure and methylxanthine use were negatively correlated with AECOPD. Conclusions: The ML model was able to accurately assess the risk of AECOPD. The ML model combined with SHAP and the local explanation method were able to provide interpretable and visual explanations of individualized risk predictions, which may assist clinical physicians in understanding the effects of key features in the model and the model’s decision-making process.

Age, obesity, and metabolic syndrome are known risk factors for gallstones; however, the combined impact of these different risk factors on gallstone formation has not yet been examined. This retrospective, cross-sectional study involved 3190 participants, including 207 participants (6.5%) with gallstones and 986 (30.9%) with metabolic syndrome. Participants were divided into four phenotypes according to metabolic syndrome and obesity status: 1378 participants were metabolically healthy and non-obese (MHNO); 826 were metabolically healthy but obese (MHO); 185 were metabolically abnormal but not obese (MANO); and 801 participants were metabolically abnormal and obese (MAO). The MAO and MANO phenotypes had more gallstones than the MHO and MHNO phenotypes, regardless of age (< 50 or ≥ 50 years old). Multivariate analyses showed that phenotype was an independent risk factor for gallstones in participants < 50 years old (odds ratio (OR) = 1.73, 95% confidence interval (CI) = 1.32–2.28). Younger participants also had a higher risk of gallstones in the MAO (OR = 5.41, 95% CI = 2.31–12.66), MANO (OR = 3.18, 95% CI = 0.86–11.75), and MHO (OR = 2.17, 95% CI = 0.90–5.22) phenotypes than the MHNO phenotype. Our retrospective results demonstrate an increased association of gallstones in younger people (< 50 years old) with metabolic syndrome and obesity.

Background: Spironolactone, a non-selective mineralocorticoid receptor antagonist, can protect against cardiac fibrosis and left ventricular dysfunction, and improve endothelial dysfunction and proteinuria. However, the safety and effects of spironolactone on patient-centered cardiovascular and renal endpoints remain unclear. Methods: We identified predialysis stage 3–4 chronic kidney disease (CKD) patients between 2000 and 2013 from the Longitudinal Health Insurance Database 2005 (LHID 2005). The outcomes of interest were end-stage renal disease (ESRD), major adverse cardiovascular events (MACE), hospitalization for heart failure (HHF), hyperkalemia-associated hospitalization (HKAH), all-cause mortality and cardiovascular mortality. The Fine and Gray sub-distribution hazards approach was adopted to adjust for the competing risk of death. Results: After the propensity score matching, 693 patients with stage 3–4 CKD were spironolactone users and 1386 were nonusers. During the follow-up period, spironolactone users had a lower incidence rate for ESRD than spironolactone non-users (39.2 vs. 53.69 per 1000 person-years) and a higher incidence rate for HKAH (54.79 vs. 18.57 per 1000 person-years). The adjusted hazard ratios for ESRD of spironolactone users versus non-users were 0.66 (95% CI, 0.51–0.84; p value < 0.001) and 3.17 (95% CI, 2.41–4.17; p value < 0.001) for HKAH. A dose-response relationship was found between spironolactone use and risk of ESRD and HKAH. There were no statistical differences in MACE, HHF, all-cause mortality and cardiovascular mortality between spironolactone users and non-users. Conclusion: Spironolactone represented a promising treatment option to retard CKD progression to ESRD amongst stage 3–4 CKD patients, but strategic treatments to prevent hyperkalemia should be enforced.

The morbidity and mortality of acute respiratory distress syndrome (ARDS) remains high, and the strategic focus of ARDS research has shifted toward identifying patients at high risk of mortality early in the course of illness. This study intended to identify the heart rate variability (HRV) measure that can predict the outcome of patients with ARDS on admission to the surgical intensive care unit (SICU). Patients who had lung or esophageal cancer surgery were included either in the ARDS group (n = 21) if they developed ARDS after surgery or in the control group (n = 11) if they did not. The ARDS patients were further stratified into survivors and non-survivors subgroups according to their outcomes. HRV measures of the patients were used for statistical analysis. The mean RR interval (mRRI), high-frequency power (HFP) and product of low-/high-frequency power ratio tidal volume and tidal volume (LHR*VT) were significantly lower (p < 0.05), while the normalized HFP to VT ratio (nHFP/VT) was significantly higher in the ARDS patients (p = 0.011). The total power (TP), low-frequency power (LFP), HFP and HFP/VT were all significantly higher in the non-survived ARDS patients, whereas Richmond Agitation-Sedation Scale (RASS) was significantly lower in the non-survived ARDS patients. After adjustment for RASS, age and gender, firth logistic regression analysis identified the HFP, TP as the significant independent predictors of mortality for ARDS patients. The vagal modulation of thoracic surgical patients with ARDS was enhanced as compared to that of non-ARDS patients, and the non-survived ARDS patients had higher vagal activity than those of survived ARDS patients. The vagal modulation-related parameters such as TP and HFP were independent predictors of mortality in patients with ARDS on admission to the SICU, and the HFP was found to be the best predictor of mortality for those ARDS patients. Increased vagal modulation might be an indicator for poor prognosis in critically ill patients following thoracic surgery.

Background: The results have been inconsistent with regards to the impact of uric acid (UA) on clinical outcomes both in the general population and in patients with chronic kidney disease. The aim of this study was to study the influence of serum UA levels on mortality in patients undergoing continuous ambulatory peritoneal dialysis. Methods: Data on 492 patients from a single peritoneal dialysis unit were retrospectively analyzed. The mean age of the patients was 53.5 ± 15.3 years, with 52% being female (n = 255). The concomitant comorbidities at the start of continuous ambulatory peritoneal dialysis (CAPD) encompassed diabetes mellitus (n = 179, 34.6%), hypertension (n = 419, 85.2%), and cardiovascular disease (n = 186, 37.9%). The study cohort was divided into sex-specific tertiles according to baseline UA level. A Cox proportional hazard model was used to calculate hazard ratios (HRs) of all-cause, cardiovascular, and infection-associated mortality with adjustments for demographic and laboratory data, medications, and comorbidities. Results: Multivariate Cox regression analysis showed that, using UA tertile 1 as the reference, the adjusted HR of all-cause, cardiovascular, and infection-associated mortality for tertile 3 was 0.4 (95% confidence interval (CI) 0.24–0.68, p = 0.001), 0.4 (95% CI 0.2–0.81, p = 0.01), and 0.47 (95% CI 0.19–1.08, p = 0.1). In the fully adjusted model, the adjusted HRs of all-cause, cardiovascular, and infection-associated mortality for each 1-mg/dL increase in UA level were 0.84 (95% CI, 0.69–0.9, p = 0.07), 0.79 (95% CI, 0.61–1.01, p = 0.06), and 0.79 (95% CI, 0.48–1.21, p = 0.32) for men and 0.57 (95% CI, 0.44–0.73, p < 0.001), 0.6 (95% CI, 0.41–0.87, p = 0.006), and 0.41 (95% CI, 0.26–0.6, p < 0.001) for women, respectively. Conclusions: Higher UA levels are associated with lower risks of all-cause, cardiovascular and infection-associated mortality in women treated with continuous ambulatory peritoneal dialysis.