The impact of COVID-19 on antibiotic resistance and clinical outcomes among critically ill patients.

BACKGROUND: There is conflicting evidence regarding the impact of the Coronavirus 2019 (COVID-19) pandemic on antimicrobial resistance, with few studies conducted in low- and middle-income countries. We investigated the impact of the COVID-19 pandemic on multidrug resistant organisms (MDROs) among critically ill patients and their clinical outcomes. METHODS: This was a retrospective observational study of patients admitted to the medical Intensive Care Unit at Jordan University Hospital and had blood, urine, or bronchoalveolar bacterial cultures taken during 2 time periods: prepandemic (ie, 1/2016 to 1/2019) and pandemic (ie, 1/2020 to 1/2023). We compared the clinical outcomes (ie, in-hospital deaths, the need for O2 devices, intubation, the length of hospital stay, and the occurrence of complications) and prevalence of MDROs between the 2 periods and conducted multivariate analyses. RESULTS: There were 1,254 patients (479 prepandemic and 775 postpandemic. The percentage of patients who had MDROs was significantly higher among patients with a culture taken during the pandemic (82.4%) compared to before it (17.6%) (P-value=.000). Multivariate analysis demonstrated that patients cultured during the pandemic were more than 3 times as likely to have an MDRO (odds ratio = 3.210; 95% confidence interval: 2.236-4.609). CONCLUSIONS: The increase in MDROs during the COVID-19 pandemic is an alarming threat to public health; thus, investigating the antibiotic resistance situation as the pandemic subsides is crucial.

Comparative Analysis of Heart Failure with Preserved Vs Reduced Ejection Fraction: Patient Characteristics, Outcomes, Mortality Prediction, and Machine Learning Model Development in the JoHFR.

Background: Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction. Methodology: The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF ≥ 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction. Results: Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study. Conclusion: The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.

Geographic variation in the association between Hashimoto's thyroiditis and Papillary thyroid carcinoma, a meta-analysis.

PURPOSE: The association between papillary thyroid cancer (PTC) and Hashimoto's thyroiditis (HT) remains a matter of debate. Several genetic and environmental factors have been found to influence this association. Because of the variation in these factors among different populations, we conducted a country- and region-based meta-analysis to examine whether the geographic area influences this association. METHODS: We searched PubMed and Web of Science databases for original articles that investigated the association between HT and PTC from February 1955 to February 28, 2023. The included studies were stratified according to their country and region of origin. Various subgroup analyses were conducted. The primary outcome was the pooled relative risk (RR) and its 95% confidence interval (CI) for each region and country. RESULTS: Forty-six studies including a total of 93,970 participants met our inclusion criteria. They originated from 16 countries distributed in five regions. Significant variation was found among countries but not among regions. Upon analysis of all 46 included studies, countries were classified based on their RR and its 95% CI. Excluding countries with pooled sample sizes <500, Sri Lanka (RR 4.23, 95% CI 2.91-6.14), Poland (RR 3.16, 95% CI 2.79-3.57) and Japan (2.68, 2.14-3.36) showed the strongest association between HT and PTC while Greece (RR 1.06, 95% CI 1.00-1.13), Spain (RR 0.70, 95% CI 0.23-2.11), and Jordan (0.62, 0.32-1.32) showed no significant association. CONCLUSION: Our findings revealed a variation in the association between HT and PTC among countries but not among regions. The country-to-country variation could be due to certain genetic and/or environmental factors subject to geographic variation that influence this association. These findings may help guide health policies aiming to mitigate the risk of PTC in the HT population by helping identify high-risk and low-risk countries.