Identifying necrotizing soft tissue infection using infectious fluid analysis and clinical parameters based on machine learning algorithms.

Background: Determining the presence of necrotizing soft tissue infection (NSTI) poses a significant hurdle. As of late, there has been a notable increase in the application of artificial intelligence (AI) machine learning techniques in identifying diseases, a shift that can be attributed to their exceptional efficiency, unbiased nature, and high precision. Methods: Information was gathered from a cohort of 13 patients suffering from NSTI, alongside 12 patients with cellulitis. The construction of NSTI diagnostic machine learning models utilized four different algorithms, specifically random forest, k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression. These models were constructed based on 28 distinctive attributes identified through statistical examination. Following this, the diagnostic efficiency of each algorithms was evaluated. A novel random forest model, streamlined for clinical use, was later developed by focusing on 6 attributes that had the most pronounced influence on the accuracy of our initial random forest model. Results: The following data was noted regarding the sensitivity and specificity of the four NSTI diagnostic models:logistic regression displayed 78.2 % and 83.7 %, KNN presented 79.1 % and 87.1 %, SVM showed 83.5 % and 86.3 %, and random forest exhibited 89.6 % and 92.9 %, respectively. In comparison, lactate levels in fluid demonstrated 100 % sensitivity and 76.9 % specificity at an optimal cut-off point of 69.6 mg/dL. Among all four machine learning models, random forest outperformed the others and also showed better results than fluid lactate. A newly constructed random forest model, created using 6 of the 13 identified features, displayed promising results in diagnosing NSTI, having a sensitivity and specificity of 90.2 % and 92.2 %, respectively. Conclusions: Developing a diagnostic model for NSTI employing the random forest algorithm has resulted in a diagnostic technique that is more efficient, cost-effective, and expedient. This approach could provide healthcare practitioners with the tools to identify and manage NSTI with greater efficacy.

Performance of Six Clinical Physiological Scoring Systems in Predicting In-Hospital Mortality in Elderly and Very Elderly Patients with Acute Upper Gastrointestinal Bleeding in Emergency Department.

Background and Objectives: The aim of this study is to compare the performance of six clinical physiological-based scores, including the pre-endoscopy Rockall score, shock index (SI), age shock index (age SI), Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), and Modified Early Warning Score (MEWS), in predicting in-hospital mortality in elderly and very elderly patients in the emergency department (ED) with acute upper gastrointestinal bleeding (AUGIB). Materials and Methods: Patients older than 65 years who visited the ED with a clinical diagnosis of AUGIB were enrolled prospectively from July 2016 to July 2021. The six scores were calculated and compared with in-hospital mortality. Results: A total of 336 patients were recruited, of whom 40 died. There is a significant difference between the patients in the mortality group and survival group in terms of the six scoring systems. MEWS had the highest area under the curve (AUC) value (0.82). A subgroup analysis was performed for a total of 180 very elderly patients (i.e., older than 75 years), of whom 27 died. MEWS also had the best predictive performance in this subgroup (AUC, 0.82). Conclusions: This simple, rapid, and obtainable-by-the-bed parameter could assist emergency physicians in risk stratification and decision making for this vulnerable group.

Effect of moxibustion on meridian in a warm needling model: A protocol for a prospective observational study.

BACKGROUND: Warm needling is a type of traditional Chinese medicine (TCM), which uses burned moxa to generate warmth and exerts a therapeutic effect on human skin. Additionally, based on TCM theory, warm needling could improve deficiency-cold syndrome. Research has found that warm needling has analgesic effects as well as effects on the immune system, endocrine system, nervous system, and lower urinary tract symptoms. However, the mechanism of warm needling on the meridians is not fully understood. In modern studies, the electric characteristics of the meridians have been discovered and the combination of semiconductor and TCM theory could explain the meridian phenomena through physics. However, there is no known research exploring the relationship between thermal effect and electric property on the meridians. METHODS: A total of 30 healthy participants older than 20 years of age will be recruited for this study. The participants would first be administered acupuncture, following which the electric characteristics will be measured using the semiconductor analyzer Agilent B1500A/Agilent 4156C. The visual analog score (VAS) will also be rated. Thereafter, a burned moxa will be added to the acupuncture needles as a method for warm needling. The electric characteristics and VAS will be measured again. We will use the paired t-test and repeated measure two-way ANOVA to compare the electric characteristics and VAS before and after warm needling in each participant. OBJECTIVES: This protocol aims to explore the thermal effect on the electric characteristics of meridians in a warm needling model and provide a scientific explanation of TCM through the aspect of physics.

Does medical disparity exist while treating severe mental illness patients with acute appendicitis in emergency departments? A real-world database study.

BACKGROUND: Patients with severe mental illness (SMI) have a shorter life expectancy and have been considered by the World Health Organization (WHO) as a vulnerable group. As the causes for this mortality gap are complex, clarification regarding the contributing factors is crucial to improving the health care of SMI patients. Acute appendicitis is one of the most common indications for emergency surgery worldwide. A higher perforation rate has been found among psychiatric patients. This study aims to evaluate the differences in appendiceal perforation rate, emergency department (ED) management, in-hospital outcomes, and in-hospital expenditure among acute appendicitis patients with or without SMI via the use of a multi-centre database. METHODS: Relying on Chang Gung Research Database (CGRD) for data, we selectively used its data from January 1st, 2007 to December 31st, 2017. The diagnoses of acute appendicitis and SMI were confirmed by combining ICD codes with relevant medical records. A non-SMI patient group was matched at the ratio of 1:3 by using the Greedy algorithm. The outcomes were appendiceal perforation rate, ED treatment, in-hospital outcome, and in-hospital expenditure. RESULTS: A total of 25,766 patients from seven hospitals over a span of 11 years were recruited; among them, 11,513 were excluded by criteria, with 14,253 patients left for analysis. SMI group was older (50.5 vs. 44.4 years, p < 0.01) and had a higher percentage of females (56.5 vs. 44.4%, p = 0.01) and Charlson Comorbidity Index. An analysis of the matched group has revealed that the SMI group has a higher unscheduled 72-hour revisit to ED (17.9 vs. 10.4%, p = 0.01). There was no significant difference in appendiceal perforation rate, ED treatment, in-hospital outcome, and in-hospital expenditure. CONCLUSIONS: Our study demonstrated no obvious differences in appendiceal perforation rate, ED management, in-hospital outcomes, and in-hospital expenditure among SMI and non-SMI patients with acute appendicitis. A higher unscheduled 72-hour ED revisit rate prior to the diagnosis of acute appendicitis in the SMI group was found. ED health providers need to be cautious when it comes to SMI patients with vague symptoms or unspecified abdominal complaints.

Performance Assessment of the Mortality in Emergency Department Sepsis Score, Modified Early Warning Score, Rapid Emergency Medicine Score, and Rapid Acute Physiology Score in Predicting Survival Outcomes of Adult Renal Abscess Patients in the Emergency Department.

BACKGROUND: Renal abscess is a relatively uncommon yet debilitating and potentially fatal disease. There is no clearly defined, objective risk stratification tool available for emergency physicians' and surgeons' use in the emergency department (ED) to quickly determine the appropriate management strategy for these patients, despite early intervention having a beneficial impact on survival outcomes. OBJECTIVE: This case control study evaluates the performance of Mortality in Emergency Department Sepsis Score (MEDS), Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS), and Rapid Acute Physiology Score (RAPS) in predicting risk of mortality in ED adult patients with renal abscess. This will help emergency physicians, surgeons, and intensivists expedite the time-sensitive decision-making process. METHODS: Data from 152 adult patients admitted to the EDs of two training and research hospitals who had undergone a contrast-enhanced computed tomography scan of the abdomen and was diagnosed with renal abscess from January 2011 to December 2015 were analyzed, with the corresponding MEDS, MEWS, REMS, RAPS, and mortality risks calculated. Ability to predict patient mortality was assessed via receiver operating curve analysis and calibration analysis. RESULTS: MEDS was found to be the best performing physiologic scoring system, with sensitivity, specificity, and accuracy of 87.50%, 88.89%, and 88.82%, respectively. Area under receiver operating characteristic curve (AUROC) value was 0.9440, and negative predictive value was 99.22% with a cutoff of 9 points. CONCLUSION: Our study is the largest of its kind in examining ED patients with renal abscess. MEDS has been demonstrated to be superior to MEWS, REMS, and RAPS in predicting mortality for this patient population. We recommend its use for evaluation of disease severity and risk stratification in these patients, to expedite identification of critically ill patients requiring urgent intervention.