Faculty Bibliography

There is a need to discriminate which COVID-19 inpatients are at higher risk for venous thromboembolism (VTE) to inform prophylaxis strategies. The IMPROVE-DD VTE risk assessment model (RAM) has previously demonstrated good discrimination in non-COVID populations. We aimed to externally validate the IMPROVE-DD VTE RAM in medical patients hospitalized with COVID-19. This retrospective cohort study evaluated the IMPROVE-DD VTE RAM in adult patients with COVID-19 admitted to one of thirteen Northwell Health hospitals in the New York metropolitan area between March 1, 2020 and April 27, 2020. VTE was defined as new-onset symptomatic deep venous thrombosis or pulmonary embolism. To assess the predictive value of the RAM, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Of 9407 patients who met study criteria, 274 patients developed VTE with a prevalence of 2.91%. The VTE rate was 0.41% for IMPROVE-DD score 0-1 (low risk), 1.21% for score 2-3 (moderate risk), and 5.30% for score ≥ 4 (high risk). Approximately 45.7% of patients were classified as high VTE risk, 33.3% moderate risk, and 21.0% low risk. Discrimination of low versus moderate-high VTE risk demonstrated sensitivity 0.971, specificity 0.215, PPV 0.036, and NPV 0.996. ROC AUC was 0.703. In this external validation study, the IMPROVE-DD VTE RAM demonstrated very good discrimination to identify hospitalized COVID-19 patients at low, moderate, and high VTE risk.

BACKGROUND:Clinicians often disregard potentially beneficial clinical decision support (CDS). OBJECTIVE:In this study, we sought to explore the psychological and behavioral barriers to the use of a CDS tool. METHODS:We conducted a qualitative study involving emergency medicine physicians and physician assistants. A semistructured interview guide was created based on the Capability, Opportunity, and Motivation-Behavior model. Interviews focused on the barriers to the use of a CDS tool built based on Wells' criteria for pulmonary embolism to assist clinicians in establishing pretest probability of pulmonary embolism before imaging. RESULTS:Interviews were conducted with 12 clinicians. Six barriers were identified, including (1) Bayesian reasoning, (2) fear of missing a pulmonary embolism, (3) time pressure or cognitive load, (4) gestalt includes Wells' criteria, (5) missed risk factors, and (6) social pressure. CONCLUSIONS:Clinicians highlighted several important psychological and behavioral barriers to CDS use. Addressing these barriers will be paramount in developing CDS that can meet its potential to transform clinical care.

Background/UNASSIGNED:Our objective was to characterize young adult patients hospitalized with coronavirus disease 2019 (COVID-19) and identify predictors of survival at 30 days. Methods/UNASSIGNED:This retrospective cohort study took place at 12 acute care hospitals in the New York City area. Patients aged 18-39 hospitalized with confirmed COVID-19 between March 1 and April 27, 2020 were included in the study. Demographic, clinical, and outcome data were extracted from electronic health record reports. Results/UNASSIGNED:= .002) were independent predictors of in-hospital 30-day mortality. Conclusions/UNASSIGNED:Obesity was identified as the strongest negative predictor of 30-day in-hospital survival in young adults with COVID-19.

Background - Social influencers of health (SIOH) namely race, ethnicity and structural inequities are known to affect the incidence of out of hospital sudden death (OHSD). We sought to examine the association between SIOH and the incidence of OHSD in the diverse neighborhoods of New York City (NYC) during the first wave of COVID-19 epidemic. Methods - NYC ZIP stratified data on OHSD were obtained from the Fire Department of New York during the first wave of COVID-19 epidemic (March 1 - April 10, 2019) and the same period in 2020. To assess associates of OHSD, ZIP code-specific sociodemographic characteristics for 8,491,238 NYC residents were obtained via the US Census Bureau's 2018 American Community Survey and the New York Police Department's crime statistics. Results - Between March 1 and April 10, 2020, the number of OHSD rose to 4,334 from 1,112 compared to the year prior. Of the univariate ZIP code level variables evaluated, proportions of: Black race, Hispanic/Latino ethnicity, single parent household, unemployed inhabitants, people completing less than high school education, inhabitants with no health insurance, people financially struggling or living in poverty, percent of non-citizens and population density were associated with increased rates of OHSD within ZIP codes. In multivariable analysis, ZIP codes with higher proportions of inhabitants with less than high school education (p < 0.001) and higher proportions of Black race (p = 0.04) were independent predictors for increases in ZIP code rates of OHSD. Conclusions - Educational attainment and the proportion of Black race in NYC ZIP codes remained independent predictors of increased rates of ZIP code level OHSD during the COVID-19 outbreak even after controlling for 2019 rates. To facilitate health equity, future research should focus on characterizing the impacts of structural inequities while exploring strategies to mitigate their effects.

BACKGROUND:Child maltreatment is a leading cause of pediatric morbidity and mortality. We previously reported on development and implementation of a child abuse clinical decision support system (CA-CDSS) in the Cerner electronic health record (EHR). Our objective was to develop a CA-CDSS in two different EHRs. METHODS:Using the CA-CDSS in Cerner as a template, CA-CDSSs were developed for use in four hospitals in the Northwell Health system who use Allscripts and two hospitals in the University of Wisconsin health system who use Epic. Each system had a combination of triggers, alerts and child abuse-specific order sets. Usability evaluation was done prior to launch of the CA-CDSS. RESULTS:Over an 18-month period, a CA-CDSS was embedded into Epic and Allscripts at two hospital systems. The CA-CDSSs vary significantly from each other in terms of the type of triggers which were able to be used, the type of alert, the ability of the alert to link directly to child abuse-specific order sets and the order sets themselves. CONCLUSIONS:Dissemination of CA-CDSS from one EHR into the EHR in other health care systems is possible but time-consuming and needs to be adapted to the strengths and limitations of the specific EHR. Site-specific usability evaluation, buy-in of multiple stakeholder groups and significant information technology support are needed. These barriers limit scalability and widespread dissemination of CA-CDSS.

BACKGROUND:Clinical decision support (CDS) is a promising tool for reducing antibiotic prescribing for acute respiratory infections (ARIs). OBJECTIVE:To assess the impact of previously effective CDS on antibiotic-prescribing rates for ARIs when adapted and implemented in diverse primary care settings. DESIGN/METHODS:Cluster randomized clinical trial (RCT) implementing a CDS tool designed to guide evidence-based evaluation and treatment of streptococcal pharyngitis and pneumonia. SETTING/METHODS:Two large academic health system primary care networks with a mix of providers. PARTICIPANTS/METHODS:All primary care practices within each health system were invited. All providers within participating clinic were considered a participant. Practices were randomized selection to a control or intervention group. INTERVENTIONS/METHODS:Intervention practice providers had access to an integrated clinical prediction rule (iCPR) system designed to determine the risk of bacterial infection from reason for visit of sore throat, cough, or upper respiratory infection and guide evidence-based evaluation and treatment. MAIN OUTCOME(S)/UNASSIGNED:Change in overall antibiotic prescription rates. MEASURE(S)/UNASSIGNED:Frequency, rates, and type of antibiotics prescribed in intervention and controls groups. RESULTS:33 primary care practices participated with 541 providers and 100,573 patient visits. Intervention providers completed the tool in 6.9% of eligible visits. Antibiotics were prescribed in 35% and 36% of intervention and control visits, respectively, showing no statistically significant difference. There were also no differences in rates of orders for rapid streptococcal tests (RR, 0.94; P = 0.11) or chest X-rays (RR, 1.01; P = 0.999) between groups. CONCLUSIONS:The iCPR tool was not effective in reducing antibiotic prescription rates for upper respiratory infections in diverse primary care settings. This has implications for the generalizability of CDS tools as they are adapted to heterogeneous clinical contexts. TRIAL REGISTRATION/BACKGROUND:Clinicaltrials.gov (NCT02534987). Registered August 26, 2015 at https://clinicaltrials.gov.

There is minimal information on COVID-19 in immunocompromised individuals. We have studied 10 patients treated at 12 adult care hospitals. Ten kidney transplant recipients tested positive for SARS-CoV-2 by PCR, and 9 were admitted. The median age was 57 (IQR 47-67), 60% were male, 40% Caucasian, and 30% Black/African American. Median time from transplant to COVID-19 testing was 2822 days (IQR 1272-4592). The most common symptom was fever, followed by cough, myalgia, chills, and fatigue. The most common CXR and CT abnormality was multifocal patchy opacities. 3 patients had no abnormal findings. Leukopenia was seen in 20% of patients, and allograft function was stable in 50% of patients. 9 patients were on tacrolimus and a mycophenolic antimetabolite, and 70% were on prednisone. Hospitalized patients had their antimetabolite agent stopped. All hospitalized patients received hydroxychloroquine (HCQ) and azithromycin. 3 patients died (30%), five (50%) developed acute kidney injury. Kidney transplant recipients infected with COVID-19 should be monitored closely in the setting of lowered immunosuppression. Most individuals required hospitalization and presenting symptoms were similar to those of non-transplant individuals.