METHODS: We analysed the data from ischaemic stroke cases admitted to Sarawak General Hospital between June 2013 and June 2021. We matched the underlying PMRFs with prior medications and categorised them as treated, untreated, or no PMRF. We calculated the prevalence and assessed the association between untreated PMRFs and in-hospital mortality or favourable functional outcome (FFO) at discharge, which was adjusted for age, sex, and other covariates in multivariable models.

RESULTS: We included 1963 patients [65.4% male, 59.8 (SD 13.4) years]; 43.8% who had at least one untreated PMRF had triple the odds of in-hospital mortality [adjusted OR (aOR) 2.86, (95%CI 1.44, 5.70)], whereas 30.2% who had all PMRFs treated showed no significant association. Untreated hypertension [aOR 2.19 (95%CI 1.21, 3.98)], treated [aOR 3.02 (95%CI 1.32, 6.92)], and untreated atrial fibrillation [aOR 1.89 (95%CI 1.18, 3.03)] were significantly associated with more in-hospital death, whereas treated prior stroke was associated with fewer in-hospital death [aOR 0.31 (95%CI 0.11, 0.84)]. Treated diabetes [aOR 0.66 (95%CI 0.49, 0.88)] and untreated prior stroke [aOR 0.53 (95%CI 0.33, 0.83)] were associated with fewer FFO.

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.

METHODS: A total of 1028 confirmed cases of COVID-19 from Africa with definite survival outcomes were identified retrospectively from an open-access individual-level worldwide COVID-19 database. The live version of the dataset is available at https://github.com/beoutbreakprepared/nCoV2019 . Multivariable logistic regression was conducted to determine the risk factors that independently predict mortality among patients with COVID-19 in Africa.

RESULTS: Of the 1028 cases included in study, 432 (42.0%) were females with a median (interquartile range, IQR) age of 50 (24) years. Older age (adjusted odds ratio {aOR} 1.06; [95% confidence intervals {95% CI}, 1.04-1.08]), presence of chronic disease (aOR 9.63; [95% CI, 3.84-24.15]), travel history (aOR 2.44; [95% CI, 1.26-4.72]), as well as locations of Central Africa (aOR 0.14; [95% CI, 0.03-0.72]) and West Africa (aOR 0.12; [95% CI, 0.04-0.32]) were identified as the independent risk factors significantly associated with increased mortality among the patients with COVID-19.

OBJECTIVE: To determine the efficacy of ivermectin in preventing progression to severe disease among high-risk patients with COVID-19.

DESIGN, SETTING, AND PARTICIPANTS: The Ivermectin Treatment Efficacy in COVID-19 High-Risk Patients (I-TECH) study was an open-label randomized clinical trial conducted at 20 public hospitals and a COVID-19 quarantine center in Malaysia between May 31 and October 25, 2021. Within the first week of patients' symptom onset, the study enrolled patients 50 years and older with laboratory-confirmed COVID-19, comorbidities, and mild to moderate disease.

INTERVENTIONS: Patients were randomized in a 1:1 ratio to receive either oral ivermectin, 0.4 mg/kg body weight daily for 5 days, plus standard of care (n = 241) or standard of care alone (n = 249). The standard of care consisted of symptomatic therapy and monitoring for signs of early deterioration based on clinical findings, laboratory test results, and chest imaging.

MAIN OUTCOMES AND MEASURES: The primary outcome was the proportion of patients who progressed to severe disease, defined as the hypoxic stage requiring supplemental oxygen to maintain pulse oximetry oxygen saturation of 95% or higher. Secondary outcomes of the trial included the rates of mechanical ventilation, intensive care unit admission, 28-day in-hospital mortality, and adverse events.

RESULTS: Among 490 patients included in the primary analysis (mean [SD] age, 62.5 [8.7] years; 267 women [54.5%]), 52 of 241 patients (21.6%) in the ivermectin group and 43 of 249 patients (17.3%) in the control group progressed to severe disease (relative risk [RR], 1.25; 95% CI, 0.87-1.80; P = .25). For all prespecified secondary outcomes, there were no significant differences between groups. Mechanical ventilation occurred in 4 (1.7%) vs 10 (4.0%) (RR, 0.41; 95% CI, 0.13-1.30; P = .17), intensive care unit admission in 6 (2.4%) vs 8 (3.2%) (RR, 0.78; 95% CI, 0.27-2.20; P = .79), and 28-day in-hospital death in 3 (1.2%) vs 10 (4.0%) (RR, 0.31; 95% CI, 0.09-1.11; P = .09). The most common adverse event reported was diarrhea (14 [5.8%] in the ivermectin group and 4 [1.6%] in the control group).

CONCLUSIONS AND RELEVANCE: In this randomized clinical trial of high-risk patients with mild to moderate COVID-19, ivermectin treatment during early illness did not prevent progression to severe disease. The study findings do not support the use of ivermectin for patients with COVID-19.

METHODS: A total of 16,040 primary procedures were identified over a two-year period. Centers that submitted procedures were dichotomized to low/middle income (LMI) and high income (HI) by the Gross National Income per capita categorization. Mortality was defined as any death following the primary procedure to discharge or 90 days inpatient. Multiple logistic regression models were utilized to identify independent predictors of mortality.

OBJECTIVE: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score.

METHODS: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score.

RESULTS: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation.

OBJECTIVE: This study aimed to assess the incidence rate of AKI in hospitalized COVID-19 patients and identify risk factors and prognostic predictors.

METHOD: In this retrospective study, we recruited hospitalized COVID-19 patients from January 2021 until June 2021 at the University Malaya Medical Center. The inclusion criteria were hospitalized for ≥ 48 h with confirmed COVID-19 infection and at least 18 years old. Patient demographic and clinical data were collected from electronic medical records. The staging of AKI was based on criteria as per KDIGO guidelines.

METHODS: We recruited and analyzed SARS-CoV-2-infected adult patients (age ≥18 years) who were admitted to the ICU at Jaber Al-Ahmad Al Sabah Hospital, Kuwait, between March 1, 2020, and April 30, 2020. The risk factors associated with in-hospital mortality were assessed using multiple regression analysis.

RESULTS: We recruited a total of 103 ICU patients in this retrospective cohort. The median age of the patients was 53 years and the fatality rate was 45.6%; majority (85.5%) were males and 37% patients had more than 2 comorbidities. Preexisting hypertension, moderate/severe acute respiratory distress syndrome, lymphocyte count <0.5 × 109, serum albumin <22 g/L, procalcitonin >0.2 ng/mL, D-dimer >1,200 ng/mL, and the need for continuous renal replacement therapy were significantly associated with mortality.