Browsing by Author "Beiser, David G."

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    Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021
    (PloS, 2021-03) Kline, Jeffrey A.; Camargo, Carlos A.; Courtney, D. Mark; Kabrhel, Christopher; Nordenholz, Kristen E.; Aufderheide, Thomas; Baugh, Joshua J.; Beiser, David G.; Bennett, Christopher L.; Bledsoe, Joseph; Castillo, Edward; Chisolm-Straker, Makini; Goldberg, Elizabeth M.; House, Hans; House, Stacey; Jang, Timothy; Lim, Stephen C.; Madsen, Troy E.; McCarthy, Danielle M.; Meltzer, Andrew; Moore, Stephen; Newgard, Craig; Pagenhardt, Justine; Pettit, Katherine L.; Pulia, Michael S.; Puskarich, Michael A.; Southerland, Lauren T.; Sparks, Scott; Turner-Lawrence, Danielle; Vrablik, Marie; Wang, Alfred; Weekes, Anthony J.; Westafer, Lauren; Wilburn, John; Emergency Medicine, School of Medicine
    Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. Results Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79–0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8–96.3%), specificity of 20.0% (19.0–21.0%), negative likelihood ratio of 0.22 (0.19–0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). Conclusion Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.
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    Computer adaptive testing to assess impairing behavioral health problems in emergency department patients with somatic complaints
    (Wiley, 2022-09-22) O’Reilly, Lauren M.; Dalal, Azhar I.; Maag, Serena; Perry, Matthew T.; Card, Alex; Bohrer, Max B.; Hamersly, Jackson; Nader, Setarah Mohammad; Peterson, Kelli; Beiser, David G.; Gibbons, Robert D.; D’Onofrio, Brian M.; Musey, Paul I.; Emergency Medicine, School of Medicine
    Objectives: To assess: (1) the prevalence of mental health and substance use in patients presenting to the emergency department (ED) through use of a computer adaptive test (CAT-MH), (2) the correlation among CAT-MH scores and self- and clinician-reported assessments, and (3) the association between CAT-MH scores and ED utilization in the year prior and 30 days after enrollment. Methods: This was a single-center observational study of adult patients presenting to the ED for somatic complaints (97%) from May 2019 to March 2020. The main outcomes were computer-adaptive-assessed domains of suicidality, depression, anxiety, post-traumatic stress disorder (PTSD), and substance use. We conducted Pearson correlations and logistic regression for objectives 2 and 3, respectively. Results: From a sample of 794 patients, the proportion of those at moderate/severe risk was: 24.1% (suicidality), 8.3% (depression), 16.5% (anxiety), 12.3% (PTSD), and 20.4% (substance use). CAT-MH domains were highly correlated with self-report assessments (r = 0.49-0.79). Individuals who had 2 or more ED visits in the prior year had 62% increased odds of being in the intermediate-high suicide risk category (odds ratio [OR], 1.62; 95% confidence interval [CI], 1.07-2.44) compared to those with zero prior ED visits. Individuals who scored in the intermediate-high-suicide risk group had 63% greater odds of an ED visit within 30 days after enrollment compared to those who scored as low risk (OR, 1.63; 95% CI, 1.09, 2.44). Conclusion: The CAT-MH documented that a considerable proportion of ED patients presenting for somatic problems had mental health conditions, even if mild. Mental health problems were also associated with ED utilization.
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    Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study
    (Wiley, 2021) Beiser, David G.; Jarou, Zachary J.; Kassir, Alaa A.; Puskarich, Michael A.; Vrablik, Marie C.; Rosenman, Elizabeth D.; McDonald, Samuel A.; Meltzer, Andrew C.; Courtney, D. Mark; Kabrhel, Christopher; Kline, Jeffrey A.; RECOVER Investigators; Emergency Medicine, School of Medicine
    Objectives: Identification of patients with coronavirus disease 2019 (COVID-19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID-19 at risk for return and hospital admission within 30 days of ED discharge. Methods: We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID-19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches. Results: Among patients with COVID-19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine-learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71–0.78), with a sensitivity of 0.46 (95% CI, 0.39–0.54) and a specificity of 0.84 (95% CI, 0.82–0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models. Conclusions: A predictive model identifying adult ED patients with COVID-19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods (eg, GBM, RF, and LASSO) outperform the single-tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow-up resources.