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Objectives: To validate and recalibrate the CURB-65 and pneumonia severity index (PSI) in predicting 30-day mortality and critical care intervention (CCI) in a multiethnic population with COVID-19, along with evaluating both models in predicting CCI. Methods: Retrospective data was collected for 1181 patients admitted to the largest hospital in Qatar with COVID-19 pneumonia. The area under the curve (AUC), calibration curves, and other metrics were bootstrapped to examine the performance of the models. Variables constituting the CURB-65 and PSI scores underwent further analysis using the Least Absolute Shrinkage and Selection Operator (LASSO) along with logistic regression to develop a model predicting CCI. Complex machine learning models were built for comparative analysis. Results: The PSI performed better than CURB-65 in predicting 30-day mortality (AUC 0.83, 0.78 respectively), while CURB-65 outperformed PSI in predicting CCI (AUC 0.78, 0.70 respectively). The modified PSI/CURB-65 model (respiratory rate, oxygen saturation, hematocrit, age, sodium, and glucose) predicting CCI had excellent accuracy (AUC 0.823) and good calibration. Conclusions: Our study recalibrated, externally validated the PSI and CURB-65 for predicting 30-day mortality and CCI, and developed a model for predicting CCI. Our tool can potentially guide clinicians in Qatar to stratify patients with COVID-19 pneumonia.

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability &lt, 5%), Moderate (5% &lt, probability &lt, 50%), and High (probability &gt, 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.

Importance Obesity is associated with increased prevalence of gastroesophageal reflux disease (GERD); with recent reports suggesting improvement in GERD symptoms and weight loss following bariatric surgical intervention. However, the exact impact of the type of bariatric surgery on the evolution of GERD symptoms have remained unexamined. Objective To characterize the exact evolution of GERD symptoms, post bariatric surgery.Data sources We systematically searched electronic databases (PubMed, EMBASE, Web of Science, and the Cochrane Library from inception to December 2018) for eligible studies that satisfy pre-specified inclusion criteria. Study selection We included clinical trials of all designs (prospective and retrospective) that reported on GERD outcomes following Laparoscopic Sleeve Gastrectomy (LSG) or laparoscopic Roux-en-Y gastric bypass (LRYGB).Data extraction and synthesis Two independent reviewers extracted relevant data based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Data were pooled using a random-effects model.Main outcomes Symptomatic improvement in GERD symptoms following bariatric surgery Results A total of 39 studies were analyzed and a robust-error meta-regression model was used to conduct a Dose-Response Meta-Analysis (DRMA) synthesizing data on 39 studies that reported GERD outcomes after bariatric surgery. Out of 43,994 patients who underwent either LSG (N = 9,547 patients) or LRYGB (N = 34,447 patients), 32.4% experienced improvement in symptoms (95% CI 20.62 to 45.45); The DRMA demonstrated a window period of two years for sustained improvement after which symptoms began to recur in those that were asymptomatic. Conclusion and relevance Bariatric surgery may improve GERD symptoms in obese patients who underwent LSG, however, the most favorable effect is likely to be found after Roux-en-Y gastric bypass surgery. The effects were not sustained and returned to baseline within 4 years.

Purpose This paper aims to explore Internet of Things (IoT)-enabled monitoring in a multi-national petrochemical organization in Qatar and finds that the technology does not negatively influence employee perceptions of fairness, challenging current propositions on monitoring and highlighting the emerging role of culture, competition and paradoxical leadership in moderating the relationship between IoT-enabled monitoring and perceptions of fairness. Design/methodology/approach The authors adopted qualitative research as the methodological premise to explore the relationship between IoT-enabled monitoring and perceptions of fairness. They collected data from an oil and gas organization in Qatar to test the validity of the proposed hypotheses. Findings While I0T-enabled monitoring was perceived as pervasive, tracking every move and recording conversations, the diffusion of the technology throughout Qatar desensitized employees who felt it was the new reality around workspaces. The following three important factors reshaped employees’ perceptions toward IoT-enabled monitoring: a culture that is driven by productivity and strongly adheres by policies and standards to reach set goals; a highly competitive job market; and a paradoxical leadership who balances between the competition and lucrative rewards. Research limitations/implications The limitation of this research is that the authors conducted a case study in similar organizations within the oil and gas industry in the State of Qatar to refute the theory that electronic monitoring of employees in the workspace elicits perceptions of unfairness. Future research can conduct quantitative surveys of employee perceptions in different industries within different cultures to be able to generalize and evolve a universal theory. Practical implications The research findings shed light on the escalating pressure global competition exerts on employees that nervousness about pervasive monitoring systems is replaced with fear of job loss and analytics on monitoring data is welcomed as a means of readjusting behavior to meet performance expectations. Originality/value The case study is the first to highlight the desensitization of employees to monitoring and the increasing pressure competition plays in motivating them to exceed expectations.