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Abstract Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome in critical care, yet it remains an understudied area. This retrospective cohort study, conducted in a medical center in Taiwan from 2015 to 2020, enrolled patients with AKI-D during intensive care unit stays. We aimed to develop and temporally test models for predicting dialysis liberation before hospital discharge using machine learning algorithms and explore early predictors. The dataset comprised 90 routinely collected variables within the first three days of dialysis initiation. Out of 1,381 patients who received acute dialysis, 27.3% experienced renal recovery. The cohort was divided into the training group (N = 1135) and temporal testing group (N = 251). The models demonstrated good performance, with an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.81–0.88) and an area under the precision-recall curve of 0.69 (95% CI, 0.62–0.76) for the XGBoost model. Key predictors included urine volume, Charlson comorbidity index, vital sign derivatives (trend of respiratory rate and SpO2), and lactate levels. We successfully developed early prediction models for renal recovery by integrating early changes in vital signs and inputs/outputs, which have the potential to aid clinical decision-making in the ICU.

Conclusions: This multicenter study demonstrated that high BAR, which is a ready-for-use biomarker in critical care, correlated with increased mortality in critically ill septic patients, and more studies are warranted to explore the underlying mechanism.

Abstract 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 registration CRD42020207883 , October 06, 2020.

BackgroundHyperuricemia and gout are risk factors of nephrolithiasis. However, it is unclear whether the ABCG2 gene contributes to the development of nephrolithiasis. We aimed to investigate the interaction between the ABCG2 rs2231142 variant and incident nephrolithiasis in the Taiwanese population.MethodsA total of 120,267 adults aged 30–70 years were enrolled from the Taiwan Biobank data-base in this retrospective case–control study and genotyped for rs2231142. The primary outcome was the prevalence of self-reported nephrolithiasis. The odds ratio (OR) of incident nephrolithiasis was analyzed by multivariable logistic regression models with adjustment for multifactorial confounding factors. Associations of the ABCG2 rs2231142 variant with serum uric acid levels, and the incident nephrolithiasis were explored.ResultsThe frequency of rs2231142 T allele was 53%, and 8,410 participants had nephrolithiasis. The multivariable-adjusted OR (95% confidence interval) of nephrolithiasis was 1.18 (1.09–1.28) and 1.12 (1.06–1.18) for TT and GT genotypes, respectively, compared with the GG genotype (p

IntroductionEarly fluid balance has been found to affect short-term mortality in critically ill patients; however, there is little knowledge regarding the association between early cumulative fluid balance (CFB) and long-term mortality. This study aims to determine the distinct association between CFB day 1–3 (CFB 1–3) and day 4–7 (CFB 4–7) and long-term mortality in critically ill patients.Patients and MethodsThis study was conducted at Taichung Veterans General Hospital, a tertiary care referral center in central Taiwan, by linking the hospital critical care data warehouse 2015–2019 and death registry data of the Taiwanese National Health Research Database. The patients followed up until deceased or the end of the study on 31 December 2019. We use the log-rank test to examine the association between CFB 1–3 and CFB 4–7 with long-term mortality and multivariable Cox regression to identify independent predictors during index admission for long-term mortality in critically ill patients.ResultsA total of 4,610 patients were evaluated. The mean age was 66.4 ± 16.4 years, where 63.8% were men. In patients without shock, a positive CFB 4–7, but not CFB 1–3, was associated with 1-year mortality, while a positive CFB 1–3 and CFB 4–7 had a consistent and excess hazard of 1-year mortality among critically ill patients with shock. The multivariate Cox proportional hazard regression model identified that CFB 1–3 and CFB 4–7 (with per 1-liter increment, HR: 1.047 and 1.094; 95% CI 1.037–1.058 and 1.080–1.108, respectively) were independently associated with high long-term mortality in critically ill patients after adjustment of relevant covariates, including disease severity and the presence of shock.ConclusionsWe found that the fluid balance in the first week, especially on days 4–7, appears to be an early predictor for long-term mortality in critically ill patients. More studies are needed to validate our findings and elucidate underlying mechanisms.

Chlorfenapyr is a new contact and stomach insecticide derived from natural pyrroles secreted by Streptomyces spp. It is a pro-insecticide and acts after metabolic transformation to its active metabolite tralopyril. Tralopyril is an uncoupler of oxidative phosphorylation in the mitochondria of the target insects and of experiment animals, leading to the disruption of adenosine triphosphate synthesis and death. Several fatal human poisonings had been reported and no blood chlorfenapyr or tralopyril measurements were available. The treatment remains supportive. A 32-year-old healthy man ingested 200 mL of 10% chlorfenapyr as a suicide attempt. Unfortunately, he succumbed at 157 h post-ingestion, shortly after having fever and seizures. His serum level of chlorfenapyr at 4 h post-exposure was 77.4 ng/mL, and was undetectable at 113 and 156 h, respectively. The serum levels of tralopyril were 723.6, 14,179, and 9654.2 ng/mL at 4, 113, and 156 h post-ingestion, respectively. The delay in the rise of serum tralopyril levels was noticeable, which seems to correlate with the patient’s signs and symptoms. The information may have therapeutic implications in the management of this deadly poisoning.

Acute kidney injury (AKI) refers to rapid decline of kidney function and is manifested by decreasing urine output or abnormal blood test (elevated serum creatinine). Electronic health records (EHRs) is fundamental for clinicians and machine learning algorithms to predict the clinical outcome of patients in the Intensive Care Unit (ICU). Early prediction of AKI could automatically warn the clinicians to review the possible risk factors and act in advance to prevent it. However, the enormous amount of patient data usually consists of a relatively incomplete data set and is very challenging for supervised machine learning process. In this paper, we propose an entropy-based feature engineering framework for vital signs based on their frequency of records. In particular, we address the missing at random (MAR) and missing not at random (MNAR) types of missing data according to different clinical scenarios. Regarding its applicability, we applied it to establish a prediction model for future AKI in ICU patients using 4278 ICU admissions from a tertiary hospital. Our result shows that the proposed entropy-based features are feasible to be used in the AKI prediction model and its performance improves as the data availability increases. In addition, we study the performance of AKI prediction model by comparing different time gaps and feature windows with the proposed vital sign entropy features. This work could be used as a guidance for feature windows selection and missing data processing during the development of a prediction model in ICU.

Background: Candidemia is associated with markedly high intensive care unit (ICU) mortality rates. Although the Impact of Early Enteral vs. Parenteral Nutrition on Mortality in Patients Requiring Mechanical Ventilation and Catecholamines (NUTRIREA-2) trial indicated that early enteral nutrition (EN) did not reduce 28-day mortality rates among critically ill patients with shock, the European Society for Clinical Nutrition and Metabolism (ESPEN) guidelines recommend avoiding EN in cases of uncontrolled shock. Whether increased caloric intake from EN positively impacts clinical outcomes in patients with candidemia and shock remains unclear. Methods: We retrospectively collected data from a tertiary medical center between January 2015 and December 2018. We enrolled patients who developed shock within the first 7 days following ICU admission and received a diagnosis of candidemia during their ICU stay. Patients with an ICU stay shorter than 48 hours were excluded. Results: The study included 106 patients, among whom the hospital mortality rate was 77.4% (82 patients). The median age of the patients was 71 years, and the median Acute Physiology and Chronic Health Evaluation II score was 29. The Cox regression model revealed that a higher 7-day average caloric intake through EN (hazard ratio, 0.61; 95% CI, 0.44-0.83) was significantly associated with lower hospital mortality rates. Our findings suggest EN as the preferred feeding route for critically ill patients with shock. Conclusions: Increased caloric intake through EN may be associated with lower hospital mortality rates in patients with candidemia and shock. [ABSTRACT FROM AUTHOR]