Positive pathogens in stool could predict the clinical outcomes of sepsis-associated acute kidney injury in critical ill patient.

In this study, we sought to evaluate the influence of positive pathogens in stool (PPS) on clinical outcomes in critical ill patients with Sepsis-associated acute kidney injury (S-AKI) from intensive care unit. Our sample consisted of 7338 patients, of whom 752 (10.25%) had PPS. We found that the presence of Clostridium difficile (C. difficile) and protists in stool samples was correlated with survival during hospitalization, as well as 30-day and 90-day survival. Interestingly, there was no significant difference in overall survival and 30-day in-hospital survival between the PPS group and the negative pathogens in stool (NPS) control group. However, the cumulative incidence of 90-day infection-related mortality was significantly higher in the PPS group (53 vs. 48%, P = 0.022), particularly in patients with C. difficile in their stool specimens. After adjusting for propensity scores, the results also have statistical significance. These findings suggest that PPS may affect the 90-days survival outcomes of S-AKI, particularly in patients with C. difficile and protists in their stool samples. Further research is warranted to further explore these associations.

Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study.

BACKGROUND: Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer the causality. METHODS: A total of 2898 patients with upper GI bleeding were included from the Medical Information Mart for Intensive Care-IV and eICU-Collaborative Research Database, respectively. To identify the most critical factors contributing to the prognostic model, we used SHAP (SHapley Additive exPlanations) for machine learning interpretability. We performed causal inference using inverse probability weighting for survival-associated prognostic factors. RESULTS: The optimal model using the light GBM (gradient boosting algorithm) algorithm achieved an AUC of .93 for in-hospital survival, .81 for 30-day survival in internal testing and .87 for in-hospital survival in external testing. Important factors for in-hospital survival, according to SHAP, were SOFA (Sequential organ failure assessment score), GCS (Glasgow coma scale) motor score and length of stay in ICU (Intensive critical care). In contrast, essential factors for 30-day survival were SOFA, length of stay in ICU, total bilirubin and GCS verbal score. Our model showed improved performance compared to SOFA alone. CONCLUSIONS: Our interpretable machine learning model for predicting in-hospital and 30-day mortality in critically ill patients with upper gastrointestinal bleeding showed excellent accuracy and high generalizability. This model can assist clinicians in managing these patients to improve the discrimination of high-risk patients.

DNA methylation analysis for smoking status prediction in the Chinese population based on the methylation-sensitive single-nucleotide primer extension method.

In some criminal cases, the identity of suspect is unknown and there is no matching DNA profile in the DNA database. Forensic DNA Phenotyping can provide useful investigative information for these cases. Most forensic studies focus on visible characteristics rather than behavioral characteristics. However, smoking is prevalent in the Chinese population, and DNA methylation is the most promising biomarker for smoking. We collected 204 whole blood samples from the Chinese population and measured methylation levels of 9 smoking-related CpG loci using the methylation-sensitive single-nucleotide primer extension method (Ms-SnuPE). But the single-base extension primers of loci cg12803068 and cg21566642 contained other CpG sites, which may introduce bias, and only the other 7 CpG loci were included in subsequent statistical analysis. The methylation level of locus cg05575921 near the aromatic hydrocarbon receptor repressor (AHRR) gene was much lower in the current smoker group than in the never smoker group. To evaluate the ability of each of 7 CpG loci to predict smoking status, the logistic regression (LR) models were established separately, and locus cg05575921 had the best ability to predict smoking status compared with the other 6 loci. Then, combined (including loci cg19572487, cg05575921, cg23480021, cg23576855, cg21161138, cg01940273, and cg09935388) and stepwise (including loci cg05575921 and cg01940273) multinomial logistic regression (MLR) models were also established. Both combined and stepwise MLR models had good efficiencies in predicting smoking status, and outperformed the above 7 LR models. However, the accuracy, specificity and area under the curve (AUC) of stepwise MLR model in the testing dataset were slightly higher than those of combined MLR model, and the stepwise MLR model required less loci information. Therefore, the stepwise MLR model based on 2 significant CpG loci was more recommended model for predicting smoking status in the Chinese population, and the formula was as follow: P = 1/(1 +e-(10.621-10.005*cg05575921-8.770*cg01940273)). Mainly 2 CpG loci (cg05575921 and cg01940273) played a major role in the prediction of smoking status, and the other 5 CpG loci contributed less. Moreover, for evaluating the ability of each of 7 CpG loci to predict cigarette consumption, the polynomial regression formulas were established separately. As the adjusted R2 was between 0.00 and 0.20, the methylation levels of these 7 loci were not closely associated with the cigarette consumption. Our methylation assay is simple, economical, and available in conventional forensic laboratories, and may be useful in assessing the smoking status of unknown suspects.