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.
CONCLUSION: The high prevalence of untreated underlying PMRFs was significantly associated with poorer outcomes among Malaysian patients with ischaemic stroke in Sarawak. Efforts are needed to promote early screening and treatment of cardiovascular risk factors to reduce the burdens and improve stroke outcomes in this region.
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.
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.
CONCLUSIONS: The COVID-19 pandemic is evolving gradually in Africa. Among patients with COVID-19 in Africa, older age, presence of chronic disease, travel history, and the locations of Central Africa and West Africa were associated with increased mortality. A regional response should prioritize strategies that will protect these populations. Also, conducting a further in-depth study could provide more insights into additional factors predictive of mortality in COVID-19 patients.
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.
TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04920942.
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.
RESULTS: Of the total number of procedures analyzed, 83% (n = 13,294) were from LMI centers. Among all centers, the mean age at operation was 2.2 years, with 36% (n = 5,743) less than six months; 85% (n = 11,307) of procedures were STAT I/II for LMI centers compared with 77% (n = 2127) for HI centers (P
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.
CONCLUSIONS: ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.
METHODS: This is a retrospective case-control study (ratio 1:1) where a patient with CRE infection or colonisation was matched with a control. The control was an individual who tested negative for CRE but was a close contact of a patient testing positive and was admitted at the same time and place. Univariate and multivariate statistical analyses were done.
RESULTS: The study included 154 patients. The majority of the CRE was Klebsiella species (83%). From univariate analysis, the significant risk factors were having a history of indwelling devices (OR: 2.791; 95% CI: 1.384-5.629), concomitant other MDRO (OR: 2.556; 95% CI: 1.144-5.707) and hospitalisation for more than three weeks (OR: 2.331; 95% CI: 1.163-4.673). Multivariate analysis showed that being unable to ambulate on admission (adjusted OR: 2.345; 95% CI: 1.170-4.699) and antibiotic exposure (adjusted OR: 3.515; 95% CI: 1.377-8.972) were independent predictors. The in-hospital mortality rate of CRE infection was high (64.5%). CRE acquisition resulted in prolonged hospitalisation (median=35 days; P<0.001).
CONCLUSION: CRE infection results in high morbidity and mortality. On top of the common risk factors, patients with mobility restriction, prior antibiotic exposures and hospitalisation for more than three weeks should be prioritised in the screening strategy to control the spread of CRE.
METHODS: A prospective cohort study of all patients with COVID-19 found in the Electronic Medical Records of Jaber Al-Ahmad Al-Sabah Hospital in Kuwait was conducted. The study included 3995 individuals (symptomatic and asymptomatic) of all ages who tested positive from February 24th to May 27th, 2020, out of which 315 were treated in the ICU and 3619 were discharged including those who were transferred to a different healthcare unit without having previously entered the ICU. A competing risk analysis considering two events, namely, ICU admission and hospital discharge using flexible hazard models was performed to describe the association between event-specific probabilities and patient characteristics.
RESULTS: Results showed that being male, increasing age and comorbidities such as chronic kidney disease (CKD), asthma or chronic obstructive pulmonary disease and weakened immune system increased the risk of ICU admission within 10 days of entering the hospital. CKD and weakened immune system decreased the probabilities of discharge in both females and males however, the age-related pattern differed by gender. Diabetes, which was the most prevalent comorbid condition, had only a moderate impact on both probabilities (18% overall) in contrast to CKD which had the largest effect, but presented only in 7% of those admitted to ICU and in 1% of those who got discharged. For instance, within 5 days a 50-year-old male had 19% (95% C.I.: [15,23]) probability of entering the ICU if he had none of these comorbidities, yet this risk jumped to 31% (95% C.I.: [20,46]) if he had also CKD, and to 27% in the presence of asthma/COPD (95% C.I.: [19,36]) or of weakened immune system (95% C.I.: [16,42]).
CONCLUSIONS: This study provides useful insight in describing the probabilities of ICU admission and hospital discharge according to age, gender, and comorbidities among confirmed COVID-19 cases in Kuwait. A web-tool is also provided to allow the user to estimate these probabilities for any combination of these covariates. These probabilities enable deeper understanding of the hospital demand according to patient characteristics which is essential to hospital management and useful for developing a vaccination strategy.