Assessing the clinical utility of abdominal computed tomography in sepsis patients with unknown origin: A retrospective cohort study.

Early identification of the sources of infection in emergency department (ED) patients of sepsis remains challenging. Computed tomography (CT) has the potential to identify sources of infection. This retrospective study aimed to investigate the role of CT in identifying sources of infection in patients with sepsis without obvious infection foci in the ED. A retrospective chart review was conducted on patients with fever and sepsis visiting the ED of Linkou Chang Gung Memorial Hospital between July 1, 2020 and June 30, 2021. Data on patient demographics, vital signs, clinical symptoms, underlying medical conditions, laboratory results, administered interventions, length of hospital stay, and mortality outcomes were collected and analyzed. Of 218 patients included in the study, 139 (63.8%) had positive CT findings. The most common sources of infection detected by CT included liver abscesses, acute pyelonephritis, and cholangitis. Laboratory results showed that patients with positive CT findings had higher white blood cell and absolute neutrophil counts and lower hemoglobin levels. Positive blood culture results were more common in patients with positive CT findings. Additionally, the length of hospital stay was longer in the group with positive CT findings. Multivariate logistic regression analysis revealed that hemoglobin levels and positive blood culture results independently predicted positive CT findings in patients with fever or sepsis without an obvious source of infection. In patients with sepsis with an undetermined infection focus, those presenting with leukocytosis, anemia, and elevated absolute neutrophil counts tended to have positive findings on abdominal CT scans. These patients had high rates of bacteremia and longer lengths of stay. Abdominal CT remains a valuable diagnostic tool for identifying infection sources in carefully selected patients with sepsis of undetermined infection origins.

Diagnostic accuracy of procalcitonin in adult non-neutropenic cancer patients with suspected infection: a systematic review and meta-analysis.

BACKGROUND: Procalcitonin (PCT) has garnered attention as a potential diagnostic biomarker for infection in cancer patients. We performed a systematic review and meta-analysis to evaluate the diagnostic accuracy of procalcitonin (PCT) and to compare it with C-reactive protein (CRP) in adult non-neutropenic cancer patients with suspected infection. METHODS: A systematic literature search was performed in MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials to identify all relevant diagnostic accuracy studies. Original articles reporting the diagnostic accuracy of PCT for infection detection in adult patients with solid or hematological malignancies were included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the hierarchical summary receiver operator characteristic (HSROC) curve, and corresponding 95% confidence interval (CI) were calculated. RESULTS: Seven studies were included in the meta-analysis. The pooled sensitivity and specificity of PCT were 60% (95% CI [45-74%]) and 78% (95% CI [69-86%]). The diagnostic odds ratio was estimated at 5.47 (95% CI [2.86-10.46]). Three studies compared the diagnostic accuracies of PCT and CRP. The pooled sensitivity and specificity values for PCT were 57% (95% CI [26-83%]) and 75% (95% CI [68-82%]), and those for CRP were 67% (95% CI [35-88%]) and 73% (95% CI [69-77%]). The pooled sensitivity and specificity of PCT and CRP did not differ significantly (p = 0.61 and p = 0.63). The diagnostic accuracy of PCT was similar to that of CRP as measured by the area under the HSROC curve (0.73, CI = 0.61-0.91 vs. 0.74, CI = 0.61-0.95, p = 0.93). CONCLUSION: While elevated PCT levels can be indicative of potential infection, they should not be solely relied upon to exclude infection. We recommend not using the PCT test in isolation; Instead, it should be carefully interpreted in the context of clinical findings.

Novel Prediction Score for Arterial-Esophageal Fistula in Patients with Esophageal Cancer Bleeding: A Multicenter Study.

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.

Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review.

BACKGROUND: Unscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) models can incorporate a wide range of complex predictors to identify high-risk patients and reduce errors to save time and cost. However, the accuracy and practicality of such models are questionable. This review compares the predictive power of multiple ML models and examines the effects of multiple research factors on these models' performance in predicting URVs to EDs. METHODS: We conducted the present scoping review by searching eight databases for data from 2010 to 2023. The criteria focused on eligible articles that used ML to predict ED return visits. The primary outcome was the predictive performances of the ML models, and results were analyzed on the basis of intervals of return visits, patient population, and research scale. RESULTS: A total of 582 articles were identified through the database search, with 14 articles selected for detailed analysis. Logistic regression was the most widely used method; however, eXtreme Gradient Boosting generally exhibited superior performance. Variations in visit interval, target group, and research scale did not significantly affect the predictive power of the models. CONCLUSION: This is the first study to summarize the use of ML for predicting URVs in ED patients. The development of practical ML prediction models for ED URVs is feasible, but improving the accuracy of predicting ED URVs to beyond 0.75 remains a challenge. Including multiple data sources and dimensions is key for enabling ML models to achieve high accuracy; however, such inclusion could be challenging within a limited timeframe. The application of ML models for predicting ED URVs may improve patient safety and reduce medical costs by decreasing the frequency of URVs. Further research is necessary to explore the real-world efficacy of ML models.