Publications by authors named "Toral Bhakta"

Objective: To investigate the potential impact of implementing alternatives to opioids (ALTOs) protocol in a community emergency department (ED) in North Texas. We hypothesize that the ALTO protocol is associated with decreased opioid utilization without affecting patient satisfaction to pain control and ED flow.

Design: A retrospective, single-center, cohort study.

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Article Synopsis
  • - The study focuses on using machine learning algorithms to better predict COVID-19 infections based on patient data from emergency departments (EDs), emphasizing the importance of timely diagnoses to control the disease spread
  • - Researchers developed and validated a predictive model using data from suspected COVID-19 patients at two different EDs: the first cohort from the US during the early pandemic and the second from a different country later on
  • - Three machine learning methods (random forest, gradient boosting, and extra trees classifiers) were tested for their effectiveness, with random forest showing the best performance in accurately identifying COVID-19 cases among patients in the testing cohort
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Background: The coronavirus disease 2019 (COVID-19) outbreak is an international public health emergency. Early identification of COVID-19 patients with false-negative RT-PCR tests is paramount in the ED to prevent both nosocomial and community transmission. This study aimed to compare clinical characteristics of repeat emergency department (ED) visits among coronavirus disease 2019 (COVID-19) patients with initial false-negative reverse transcriptase-polymerase chain reaction (RT-PCR)-based COVID-19 test.

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There are only a few models developed for risk-stratifying COVID-19 patients with suspected pneumonia in the emergency department (ED). We aimed to develop and validate a model, the COVID-19 ED pneumonia mortality index (CoV-ED-PMI), for predicting mortality in this population. We retrospectively included adult COVID-19 patients who visited EDs of five study hospitals in Texas and who were diagnosed with suspected pneumonia between March and November 2020.

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Introduction: Diverse coronavirus disease 2019 (COVID-19) mortalities have been reported but focused on identifying susceptible patients at risk of more severe disease or death. This study aims to investigate the mortality variations of COVID-19 from different hospital settings during different pandemic phases.

Methods: We retrospectively included adult (≥18 years) patients who visited emergency departments (ED) of five hospitals in the state of Texas and who were diagnosed with COVID-19 between March-November 2020.

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Introduction: Within a few months coronavirus disease 2019 (COVID-19) evolved into a pandemic causing millions of cases worldwide, but it remains challenging to diagnose the disease in a timely fashion in the emergency department (ED). In this study we aimed to construct machine-learning (ML) models to predict severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection based on the clinical features of patients visiting an ED during the early COVID-19 pandemic.

Methods: We retrospectively collected the data of all patients who received reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 at the ED of Baylor Scott & White All Saints Medical Center, Fort Worth, from February 23-May 12, 2020.

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Coronavirus (severe acute respiratory syndrome coronavirus 2) outbreak is a public health emergency and a global pandemic. During the present coronavirus disease (COVID-19) crisis, telemedicine has been recommended to screen suspected patients to limit risk of exposure and maximise medical staff protection. We constructed the protective physical barrier with telemedicine technology to limit COVID-19 exposure in ED.

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Purpose: To determine the trends of infection sites and outcome of sepsis using a national population-based database.

Materials And Methods: Using the Nationwide Inpatient Sample database of the US, adult sepsis hospitalizations and infection sites were identified using a validated approach that selects admissions with explicit ICD-9-CM codes for sepsis and diagnosis/procedure codes for acute organ dysfunctions. The primary outcome was the trend of incidence and in-hospital mortality of specific infection sites in sepsis patients.

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