Chen-Bin Chen

Background and Objectives: Urinary tract infections (UTIs) are a major cause of emergency department (ED) visits and hospital admissions among older adults. Although most seniors present hemodynamically stable, a sizeable fraction deteriorate during hospitalization, and no ED-specific tool exists to identify those at greatest risk. We sought to determine risk factors for in-hospital mortality in this population and to develop a predictive model. Materials and Methods: We analyzed the MIMIC-IV-ED database (2011–2019) and enrolled culture-confirmed UTI patients aged ≥ 65 years who were hemodynamically stable—defined as a systolic blood pressure ≥ 100 mm Hg without vasopressor support. Demographics, comorbidities, triage vital signs, and initial laboratory tests were extracted. Least Absolute Shrinkage and Selection Operator (LASSO) regression with 10-fold cross-validation was performed for variable selection. Discrimination was quantified with the C-statistic, calibration with the Hosmer–Lemeshow test, and clinical utility with decision curve analysis. Internal validation was assessed via 1000-sample bootstrap resampling. Results: Among 1571 eligible encounters (median age 79 years, 33% male), in-hospital mortality was 4.5%. LASSO selected eight variables; six remained significant in multivariable analysis: age, systolic blood pressure, oxygen saturation, white blood cell count, red cell distribution width, and blood urea nitrogen. The predictive nomogram demonstrated a C-statistic of 0.73 (95% CI 0.66–0.79) and outperformed traditional early warning scores. Conclusions: A six-variable nomogram may stratify mortality risk in hemodynamically stable older adults with UTI. Because the model was developed in a single U.S. tertiary-care ED, it remains hypothesis-generating until validated in external, multicenter cohorts to confirm generalizability.

Background and Objectives: Spontaneous bacterial peritonitis (SBP) is both a prevalent and severe complication among individuals with cirrhosis. This systematic review and meta-analysis was designed to evaluate the diagnostic accuracy of procalcitonin (PCT) and compare it to C-reactive protein (CRP) in cirrhotic patients with suspected SBP. Materials and Methods: We performed an extensive literature review utilizing databases including MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Original investigations reporting PCT diagnostic accuracy for SBP in cirrhotic populations were included. We computed pooled measures of sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and SROC curve area under the curve, with corresponding 95% confidence intervals (CIs). Results: Meta-analytical synthesis encompassed twenty eligible studies. Diagnostic accuracy analysis revealed PCT sensitivity of 0.73 (95% CI, 0.61–0.83) and specificity of 0.88 (95% CI, 0.83–0.91). Likelihood ratio yielded positive values of 6.0 (95% CI, 4.1–8.8) and negative values of 0.30 (95% CI, 0.20–0.47). Overall discriminative ability, quantified through SROC curve analysis, demonstrated an AUC of 0.90 (95% CI, 0.87–0.92). Head-to-head comparisons between PCT and CRP were available from ten studies, demonstrating PCT’s superior diagnostic accuracy over CRP, with significantly higher AUC values (PCT: 0.89, 95% CI 0.86–0.91; CRP: 0.74, 95% CI 0.70–0.78, p < 0.01). Conclusions: Although PCT demonstrates higher diagnostic accuracy than CRP, it does not appear to provide sufficient accuracy to support treatment decisions for SBP. We recommend not relying solely on the PCT test and advise that it be interpreted in conjunction with clinical findings.

Background and Objectives: In patients with urolithiasis-related obstructive pyelonephritis (UROP), sepsis represents a critical and concerning complication that can substantially increase the mortality rate. This study aimed to identify the risk factors for sepsis in UROP patients and to develop a predictive nomogram model. Materials and Methods: We analyzed data from 148 patients who met the UROP criteria and were admitted to Chang Gung Memorial Hospital between 1 January 2016 and 31 December 2021. The primary outcome evaluated was the incidence of sepsis, as defined by the most recent Sepsis-3 guidelines. To identify potential risk factors for sepsis, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression technique. Subsequently, we utilized multivariable logistic regression to construct the predictive model. Results: There was a total of 102 non-sepsis cases and 46 sepsis cases. Risk factors for sepsis in multivariable analysis were a history of diabetes mellitus (DM) (OR = 4.24, p = 0.007), shock index (SI) (×10⁻¹) (OR = 1.55, p < 0.001), C-reactive protein (CRP) (mg/dL) (OR = 1.08, p = 0.005), and neutrophil to lymphocyte ratio (NLR) (×10) (OR = 1.58, p = 0.007). The nomogram exhibited an area under the receiver operating characteristic curve of 0.890 (95% CI 0.830–0.949). Conclusions: Our study demonstrated that patients with UROP who have DM, higher SI, higher NLR, and elevated CRP levels are significantly more likely to develop sepsis. These insights may aid in risk stratification, and it is imperative that clinicians promptly initiate treatment for those identified as high risk.

Purpose: To develop and internally validate a novel prediction score to predict the occurrence of arterial–esophageal fistula (AEF) in esophageal cancer bleeding. Methods: This retrospective cohort study enrolled patients with esophageal cancer bleeding in the emergency department. The primary outcome was the diagnosis of AEF. The patients were randomly divided into a derivation group and a validation group. In the derivation stage, a predictive model was developed using logistic regression analysis. Subsequently, internal validation of the model was conducted in the validation cohort during the validation stage to assess its discrimination ability. Results: A total of 257 patients were enrolled in this study. All participants were randomized to a derivation cohort (n = 155) and a validation cohort (n = 102). AEF occurred in 22 patients (14.2%) in the derivation group and 14 patients (13.7%) in the validation group. A predictive model (HEARTS-Score) comprising five variables (hematemesis, active bleeding, serum creatinine level >1.2 mg/dL, prothrombin time >13 s, and previous stent implantation) was established. The HEARTS-Score demonstrated a high discriminative ability in both the derivation and validation cohorts, with c-statistics of 0.90 (95% CI 0.82–0.98) and 0.82 (95% CI 0.72–0.92), respectively. Conclusions: By employing this novel prediction score, clinicians can make more objective risk assessments, optimizing diagnostic strategies and tailoring treatment approaches.