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Purpose Despite progress in the treatment of coronavirus disease 2019 (COVID-19), including the development of monoclonal antibodies (mAbs), more clinical data to support the use of mAbs in outpatients with COVID-19 is needed. This study is designed to determine the impact of bamlanivimab, bamlanivimab/etesevimab, or casirivimab/imdevimab on clinical outcomes within 30 days of COVID-19 diagnosis. Methods A retrospective cohort study was conducted at a single academic medical center with 3 campuses in Manhattan, Brooklyn, and Long Island, NY. Patients 12 years of age or older who tested positive for COVID-19 or were treated with a COVID-19–specific therapy, including COVID-19 mAb therapies, at the study site between November 24, 2020, and May 15, 2021, were included. The primary outcomes included rates of emergency department (ED) visit, inpatient admission, intensive care unit (ICU) admission, or death within 30 days from the date of COVID-19 diagnosis. Results A total of 1,344 mAb-treated patients were propensity matched to 1,344 patients with COVID-19 patients who were not treated with mAb therapy. Within 30 days of diagnosis, among the patients who received mAb therapy, 101 (7.5%) presented to the ED and 79 (5.9%) were admitted. Among the patients who did not receive mAb therapy, 165 (12.3%) presented to the ED and 156 (11.6%) were admitted (relative risk [RR], 0.61 [95% CI, 0.50-0.75] and 0.51 [95% CI, 0.40-0.64], respectively). Four mAb patients (0.3%) and 2.64 control patients (0.2%) were admitted to the ICU (RR, 01.51; 95% CI, 0.45-5.09). Six mAb-treated patients (0.4%) and 3.37 controls (0.3%) died and/or were admitted to hospice (RR, 1.61; 95% CI, 0.54-4.83). mAb therapy in ambulatory patients with COVID-19 decreases the risk of ED presentation and hospital admission within 30 days of diagnosis.

Background: Corticosteroids and tocilizumab have been shown to improve survival in patients who require supplemental oxygen from coronavirus disease 2019 (COVID-19) pneumonia. The optimal dose of immunosuppression for the treatment of COVID-19 acute respiratory distress syndrome (ARDS) is still unknown. Objective: The objective of this study was to evaluate the effectiveness and safety of high- versus low-dose corticosteroids with or without tocilizumab for the treatment of COVID-19 ARDS. Methods: This was a retrospective study of patients admitted to the intensive care unit (ICU) requiring mechanical ventilation who received high- versus low-dose corticosteroids with or without tocilizumab. The primary outcome was survival to discharge. Safety outcomes included infections and incidence of hyperglycemia. Results: In this cohort, 110 (54%) and 95 (46%) patients received high-dose (≥10 mg dexamethasone equivalent) and low-dose (Conclusions: In critically ill patients with COVID-19 ARDS requiring mechanical ventilation, we found no difference in high- versus low-dose corticosteroids with regard to survival to hospital discharge. However, patients receiving only low-dose corticosteroids without tocilizumab did worse than the other groups. Larger prospective studies are needed to determine the optimal immunosuppression dosing strategy in this patient population.

ObjectiveTo explore the regulation of the inflammatory response in acute SARS-CoV-2 infection, we examined effects of single nucleotide variants (SNVs) ofIL1RN, the gene encoding the anti-inflammatory IL-1 receptor antagonist (IL-1Ra), on the cytokine release syndrome and mortality.MethodsWe studied 2589 patients hospitalized with SARS-CoV-2 between March 2020 and March 2021 at NYU Langone’s Tisch Hospital. CTA and TTG haplotypes formed from three SNVs (rs419598, rs315952, rs9005) and the individual SNVs of theIL1RNgene were assessed for association with laboratory markers of the cytokine release syndrome (CRS) and mortality.ResultsMortality in the population was 15.3%, and was lower in women than men (13.1% vs.17.3%, pIL1RNhaplotypes exhibiteddecreasedinflammatory markers andincreasedplasma IL-1Ra relative to TTG carriers. Decreased mortality among CTA-1/2 carriers was observed in male patients between the ages of 55-74 [9.2% vs. 17.9%, p=0.001]. Evaluation of individual SNVs of theIL1RNgene (rs419598, rs315952, rs9005) indicated that carriers of theIL1RNrs419598 CC SNV exhibited lower inflammatory biomarker levels, and was associated with reduced mortality compared to the CT/TT genotype in men (OR 0.49 (0.23 – 1.00); 0.052), with the most pronounced effect observed between the ages of 55-74 [5.5% vs. 18.4%, pConclusionTheIL1RNhaplotype CTA, and sequence variant of rs419598 are associated with attenuation of the cytokine release syndrome and decreased mortality in males with acute SARS-CoV2 infection. The data suggest thatIL1RNmodulates the COVID-19 cytokine release syndrome via endogenous “ anti-inflammatory” mechanisms.Significance statementWe provide evidence that variants ofIL1RNmodulate the severity of SARS-CoV-2 infection. TheIL1RN CTA haplotype andrs419598 CC single nucleotide variant are associated with decreased plasma levels of inflammatory markers, interleukin-1 beta (IL-1β), interleukin-6 (IL-6), interleukin-2 (IL-2), C-reactive protein (CRP), D-dimer, ferritin, and procalcitonin, in association with higher levels of IL-1Ra and IL-10, anti-inflammatory proteins. Both haplotype CTA and rs419598 CC genotype are associated with a significant reduction in the mortality of men. These data provide genetic evidence that inflammasome activation and the IL-1 pathway plays an important role in the mortality and morbidity associated with severe SARS-CoV-2 infection, and that genetic regulation of inflammatory pathways by variants ofIL1RNmerits further evaluation in severe SARS-CoV-2 infection.

BACKGROUND The introduction of electronic workflows has allowed for the flow of raw uncontextualized clinical data into medical documentation. As a result, many electronic notes have become replete of “noise” and deplete clinically significant “signals.” There is an urgent need to develop and implement innovative approaches in electronic clinical documentation that improve note quality and reduce unnecessary bloating. OBJECTIVE This study aims to describe the development and impact of a novel set of templates designed to change the flow of information in medical documentation. METHODS This is a multihospital nonrandomized prospective improvement study conducted on the inpatient general internal medicine service across 3 hospital campuses at the New York University Langone Health System. A group of physician leaders representing each campus met biweekly for 6 months. The output of these meetings included (1) a conceptualization of the note bloat problem as a dysfunction in information flow, (2) a set of guiding principles for organizational documentation improvement, (3) the design and build of novel electronic templates that reduced the flow of extraneous information into provider notes by providing link outs to best practice data visualizations, and (4) a documentation improvement curriculum for inpatient medicine providers. Prior to go-live, pragmatic usability testing was performed with the new progress note template, and the overall user experience was measured using the System Usability Scale (SUS). Primary outcome measures after go-live include template utilization rate and note length in characters. RESULTS In usability testing among 22 medicine providers, the new progress note template averaged a usability score of 90.6 out of 100 on the SUS. A total of 77% (17/22) of providers strongly agreed that the new template was easy to use, and 64% (14/22) strongly agreed that they would like to use the template frequently. In the 3 months after template implementation, general internal medicine providers wrote 67% (51,431/76,647) of all inpatient notes with the new templates. During this period, the organization saw a 46% (2768/6191), 47% (3505/7819), and 32% (3427/11,226) reduction in note length for general medicine progress notes, consults, and history and physical notes, respectively, when compared to a baseline measurement period prior to interventions. CONCLUSIONS A bundled intervention that included the deployment of novel templates for inpatient general medicine providers significantly reduced average note length on the clinical service. Templates designed to reduce the flow of extraneous information into provider notes performed well during usability testing, and these templates were rapidly adopted across all hospital campuses. Further research is needed to assess the impact of novel templates on note quality, provider efficiency, and patient outcomes.

Background: There is mixed evidence about the relations of current versus past cancer with severe COVID-19 outcomes and how they vary by patient and cancer characteristics. Methods: Electronic health record data of 104,590 adult hospitalized patients with COVID-19 were obtained from 21 United States health systems from February 2020 through September 2021. In-hospital mortality and ICU admission were predicted from current and past cancer diagnoses. Moderation by patient characteristics, vaccination status, cancer type, and year of the pandemic was examined. Results: 6.8% of the patients had current (n = 7,141) and 6.5% had past (n = 6,749) cancer diagnoses. Current cancer predicted both severe outcomes but past cancer did not; adjusted odds ratios (aOR) for mortality were 1.58 [95% confidence interval (CI), 1.46–1.70] and 1.04 (95% CI, 0.96–1.13), respectively. Mortality rates decreased over the pandemic but the incremental risk of current cancer persisted, with the increment being larger among younger vs. older patients. Prior COVID-19 vaccination reduced mortality generally and among those with current cancer (aOR, 0.69; 95% CI, 0.53–0.90). Conclusions: Current cancer, especially among younger patients, posed a substantially increased risk for death and ICU admission among patients with COVID-19; prior COVID-19 vaccination mitigated the risk associated with current cancer. Past history of cancer was not associated with higher risks for severe COVID-19 outcomes for most cancer types. Impact: This study clarifies the characteristics that modify the risk associated with cancer on severe COVID-19 outcomes across the first 20 months of the COVID-19 pandemic. See related commentary by Egan et al., p. 3

Introduction Available evidence is mixed concerning associations between smoking status and COVID-19 clinical outcomes. Effects of nicotine replacement therapy (NRT) and vaccination status on COVID-19 outcomes in smokers are unknown. Methods Electronic health record data from 104 590 COVID-19 patients hospitalized February 1, 2020 to September 30, 2021 in 21 U.S. health systems were analyzed to assess associations of smoking status, in-hospital NRT prescription, and vaccination status with in-hospital death and ICU admission. Results Current (n = 7764) and never smokers (n = 57 454) did not differ on outcomes after adjustment for age, sex, race, ethnicity, insurance, body mass index, and comorbidities. Former (vs never) smokers (n = 33 101) had higher adjusted odds of death (aOR, 1.11; 95% CI, 1.06–1.17) and ICU admission (aOR, 1.07; 95% CI, 1.04–1.11). Among current smokers, NRT prescription was associated with reduced mortality (aOR, 0.64; 95% CI, 0.50–0.82). Vaccination effects were significantly moderated by smoking status; vaccination was more strongly associated with reduced mortality among current (aOR, 0.29; 95% CI, 0.16–0.66) and former smokers (aOR, 0.47; 95% CI, 0.39–0.57) than for never smokers (aOR, 0.67; 95% CI, 0.57, 0.79). Vaccination was associated with reduced ICU admission more strongly among former (aOR, 0.74; 95% CI, 0.66–0.83) than never smokers (aOR, 0.87; 95% CI, 0.79–0.97). Conclusions Former but not current smokers hospitalized with COVID-19 are at higher risk for severe outcomes. SARS-CoV-2 vaccination is associated with better hospital outcomes in COVID-19 patients, especially current and former smokers. NRT during COVID-19 hospitalization may reduce mortality for current smokers. Implications Prior findings regarding associations between smoking and severe COVID-19 disease outcomes have been inconsistent. This large cohort study suggests potential beneficial effects of nicotine replacement therapy on COVID-19 outcomes in current smokers and outsized benefits of SARS-CoV-2 vaccination in current and former smokers. Such findings may influence clinical practice and prevention efforts and motivate additional research that explores mechanisms for these effects.

Background There is concern about the potential of an increased risk related to medications that act on the renin–angiotensin–aldosterone system in patients exposed to coronavirus disease ...

Male sex, elevated troponin levels, and elevated D-dimer levels are associated with more complicated COVID-19 illness and greater mortality; however, while there are known sex differences in the prognostic value of troponin and D-dimer in other disease states, it is unknown whether they exist in the setting of COVID-19.We assessed whether sex modified the relationship between troponin, D-dimer, and severe COVID-19 illness (defined as mechanical ventilation, ICU admission or transfer, discharge to hospice, or death).We conducted a retrospective cohort study of patients hospitalized with COVID-19 at a large, academic health system. We used multivariable regression to assess associations between sex, troponin, D-dimer, and severe COVID-19 illness, adjusting for demographic, clinical, and laboratory covariates. To test whether sex modified the relationship between severe COVID-19 illness and troponin or D-dimer, models with interaction terms were utilized.Among 4,574 patients hospitalized with COVID-19, male sex was associated with higher levels of troponin and greater odds of severe COVID-19 illness, but lower levels of initial D-dimer when compared with female sex. While sex did not modify the relationship between troponin level and severe COVID-19 illness, peak D-dimer level was more strongly associated with severe COVID-19 illness in male patients compared to female patients (males: OR=2.91, 95%CI=2.63-2.34, p0.001; females: OR=2.31, 95%CI=2.04-2.63, p0.001; p-interaction=0.005).Sex did not modify the association between troponin level and severe COVID-19 illness, but did modify the association between peak D-dimer and severe COVID-19 illness, suggesting greater prognostic value for D-dimer in males with COVID-19.

In 2021, Delta became the predominant SARS-CoV-2 variant worldwide. While vaccines have effectively prevented COVID-19 hospitalization and death, vaccine breakthrough infections increasingly occurred. The precise role of clinical and genomic determinants in Delta infections is not known, and whether they contributed to increased rates of breakthrough infections compared to unvaccinated controls.We studied SARS-CoV-2 variant distribution, dynamics, and adaptive selection over time in relation to vaccine status, phylogenetic relatedness of viruses, full genome mutation profiles, and associated clinical and demographic parameters.We show a steep and near-complete replacement of circulating variants with Delta between May and August 2021 in metropolitan New York. We observed an increase of the Delta sublineage AY.25 (14% in vaccinated, 7% in unvaccinated), its spike mutation S112L, and AY.44 (8% in vaccinated, 2% in unvaccinated) with its nsp12 mutation F192V in breakthroughs. Delta infections were associated with younger age and lower hospitalization rates than Alpha. Delta breakthrough infections increased significantly with time since vaccination, and, after adjusting for confounders, they rose at similar rates as in unvaccinated individuals.We observed a modest adaptation of Delta genomes in breakthrough infections in New York, suggesting an improved genomic framework to support Delta's epidemic growth in times of waning vaccine protection despite limited impact on vaccine escape.The study was supported by NYU institutional funds. The NYULH Genome Technology Center is partially supported by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center.

Background The epidemiology of nosocomial bloodstream infections (NBSIs) in patients with coronavirus disease 2019 (COVID-19) is poorly understood, due in part to substantial disease heterogeneity resulting from multiple potential pathogens. Methods We identified risk factors for NBSIs and examined the association between NBSIs and mortality in a retrospective cohort of patients hospitalized with COVID-19 in 2 New York City hospitals during the height of the pandemic. We adjusted for the potential effects of factors likely to confound that association, including age, race, illness severity upon admission, and underlying health status. Results Between January 1 and October 1, 2020, 1403 patients had a positive blood culture, and 79 and 101 met the stringent criteria for NBSI among non-COVID-19 and COVID-19 patients, respectively. NBSIs occurred almost exclusively among patients who were severely ill with COVID-19 at hospital admission. NBSIs were associated with elevated mortality, even after adjusting for baseline differences in COVID-19 illness (55% cases vs 45% controls; P = .13). Mortality was concentrated in patients with early-onset pneumonia caused by S. aureus and gram-negative bacteria. Less virulent Candida (49%) and Enterococcus (12%) species were the predominant cause of NBSI in the latter stages of hospitalization, after antibiotic treatment and COVID-19 treatments that attenuate immune response. Most Enterococcus and Candida infections did not have an identifiable source and were not associated with common risk factors for infection by these organisms. Conclusions Pathogen species and mortality exhibited temporal differences. Early recognition of risk factors among COVID-19 patients could potentially decrease NBSI-associated mortality through early COVID-19 and antimicrobial treatment.

In 2021, Delta has become the predominant SARS-CoV-2 variant worldwide. While vaccines effectively prevent COVID-19 hospitalization and death, vaccine breakthrough infections increasingly occur. The precise role of clinical and genomic determinants in Delta infections is not known, and whether they contribute to increased rates of breakthrough infections compared to unvaccinated controls. Here, we show a steep and near complete replacement of circulating variants with Delta between May and August 2021 in metropolitan New York. We observed an increase of the Delta sublineage AY.25, its spike mutation S112L, and nsp12 mutation F192V in breakthroughs. Delta infections were associated with younger age and lower hospitalization rates than Alpha. Delta breakthroughs increased significantly with time since vaccination, and, after adjusting for confounders, they rose at similar rates as in unvaccinated individuals. Our data indicate a limited impact of vaccine escape in favor of Delta’s increased epidemic growth in times of waning vaccine protection.

BACKGROUND Telemedicine as a mode of healthcare work has grown dramatically during the COVID-19 pandemic; the impact of this transition on clinicians’ after-hours EHR-based clinical and administrative work is unclear. OBJECTIVE This study assesses the impact of the transition to telemedicine work during the COVID-19 pandemic on physicians’ EHR-based after-hours workload (“work outside work”) at a large academic medical center in New York City. METHODS We conducted an EHR-based retrospective cohort study of ambulatory care physicians providing telemedicine services during the pre-pandemic, acute pandemic, and post-acute pandemic periods, relating EHR-based work after work to telemedicine intensity (percentage of care provided via telemedicine), and clinical load (patient load per provider). RESULTS 2,129 physicians were included in this study. During the acute pandemic, the volume of care provided via telemedicine significantly increased across all physicians, while patient volume decreased. When normalizing for clinical load (average appointments per day by average clinical days per week), telemedicine intensity was positively associated with work outside work across time periods. This association was strongest in the post-acute period. CONCLUSIONS Taking physicians’ clinical load into account, physicians who devoted a higher proportion of their clinical time to telemedicine throughout the various stages of the pandemic engaged in higher levels of EHR-based after-hours than those who used telemedicine less intensively. This suggests that telemedicine, as currently delivered, may be less efficient than in-person-based care, and may increase the after-hours work burden of physicians. CLINICALTRIAL N/a

Background Telemedicine as a mode of health care work has grown dramatically during the COVID-19 pandemic; the impact of this transition on clinicians’ after-hours electronic health record (EHR)–based clinical and administrative work is unclear. Objective This study assesses the impact of the transition to telemedicine during the COVID-19 pandemic on physicians’ EHR-based after-hours workload (ie, “work outside work”) at a large academic medical center in New York City. Methods We conducted an EHR-based retrospective cohort study of ambulatory care physicians providing telemedicine services before the pandemic, during the acute pandemic, and after the acute pandemic, relating EHR-based after-hours work to telemedicine intensity (ie, percentage of care provided via telemedicine) and clinical load (ie, patient load per provider). Results A total of 2129 physicians were included in this study. During the acute pandemic, the volume of care provided via telemedicine significantly increased for all physicians, whereas patient volume decreased. When normalized by clinical load (ie, average appointments per day by average clinical days per week), telemedicine intensity was positively associated with work outside work across time periods. This association was strongest after the acute pandemic. Conclusions Taking physicians’ clinical load into account, physicians who devoted a higher proportion of their clinical time to telemedicine throughout various stages of the pandemic engaged in higher levels of EHR-based after-hours work compared to those who used telemedicine less intensively. This suggests that telemedicine, as currently delivered, may be less efficient than in-person–based care and may increase the after-hours work burden of physicians.

Background: Although definitions from the CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety, the algorithm is still laborious for manual use. We implemented an automated surveillance system that combines 2 CDC pneumonia surveillance definitions to identify pneumonia infection in inpatients. Methods: The program was implemented at an academic health center with >40,000 inpatient admission per year. We used Window Task Scheduler with a batch file daily to run a validated pneumonia surveillance algorithm program written with SAS version 9.4 software (SAS Institute, Cary, NC) and a natural language processing tool that queries variables (Table 1) and text found in the electronic medical records (EMR) to identify pneumonia cases (Fig. 1). We uploaded all computer-identified positive cases into a Microsoft Access database daily to be reviewed by a hospital epidemiologist. Every week, we also validated 5 computer-identified negative cases from the prior 2 weeks to ensure accuracy of the computer algorithm. We defined negative cases as pneumonia present on admission or chest x-ray indicative of pneumonia but without CDC-defined surveillance symptoms. We also wrote a program to automatically send e-mails to key stakeholders and to prepare summary reports. Results: Since November 2019, we have successfully implemented the automated computer algorithm or program to notify, via e-mail, infection prevention staff and respiratory therapy providers of CDC-defined pneumonia cases on a daily basis. This automated program has reduced the number of manual hours spent reviewing each admission case for pneumonia. A summary report is created each week and month for distribution to hospital staff and the Department of Health, respectively. Conclusions: The implementation of an automated pneumonia surveillance system proves to be a timelier, more cost-effective approach compared to manual pneumonia surveillance. By allowing an automated algorithm to review pneumonia, timely reports can be sent to infection prevention control staff, respiratory therapy providers, and unit staff about individual cases. Hospitals should leverage current technology to automate surveillance definitions because automated programs allow near real-time identification and critical review for infection and prevention activities.Funding: NoneDisclosures: None

Objective The widespread deployment of electronic health records (EHRs) has introduced new sources of error and inefficiencies to the process of ordering medications in the hospital setting. Existing work identifies orders that require pharmacy intervention by comparing them to a patient’s medical records. In this work, we develop a machine learning model for identifying medication orders requiring intervention using only provider behavior and other contextual features that may reflect these new sources of inefficiencies. Materials and Methods Data on providers’ actions in the EHR system and pharmacy orders were collected over a 2-week period in a major metropolitan hospital system. A classification model was then built to identify orders requiring pharmacist intervention. We tune the model to the context in which it would be deployed and evaluate global and local feature importance. Results The resultant model had an area under the receiver-operator characteristic curve of 0.91 and an area under the precision-recall curve of 0.44. Conclusions Providers’ actions can serve as useful predictors in identifying medication orders that require pharmacy intervention. Careful model tuning for the clinical context in which the model is deployed can help to create an effective tool for improving health outcomes without using sensitive patient data.

Background: Myocardial injury is associated with higher mortality of COVID-19 and more complicated disease course. Little is known about sex differences in COVID-19-associated myocardial injury. We hypothesized that sex modifies the relationship between myocardial injury and severe COVID-19 infection. Methods: This is a cross-sectional study of all hospitalized patients with COVID-19 at a large health system in New York City between 3/2/20 - 5/12/20. We defined severe COVID-19 infection as ICU admission, mechanical ventilation, discharge to hospice, or death. Using multivariable logistic regression, we assessed whether sex modified the effect of peak troponin on the odds of severe COVID-19 infection, adjusting for demographics, co-morbidity, and laboratory values, and did the same for D-dimer. Results: Among 2,746 patients hospitalized with COVID-19, 650 (23.7%) required ICU admission, 648 (23.7%) required mechanical ventilation, and 680 (24.8%) died or were discharged to hospice. Peak troponin was higher in men than women (median 0.06, interquartile range [IQR] 0.01- 0.10 vs. 0.05 [IQR 0.01-0.10], p=0.02). Men were more likely to be admitted to the ICU and to receive mechanical ventilation (Table). After adjustment for demographics, medical history and other laboratory values, peak troponin level was associated with higher odds of severe COVID-19 illness: OR 1.51 per ng/mL, 95% CI 1.27-1.88, p Conclusions: Sex modifies the relationship between myocardial injury and severe COVID-19, with men having higher odds of severe COVID-19 infection associated with increasing peak troponin. Sex differences in myocardial injury may play a role in poorer outcome among men.

Background: Guidelines recommend moderate to high-intensity statins and antithrombotic agents in patients with atherosclerotic cardiovascular disease (ASCVD). However, guideline-directed medical therapy (GDMT) remains suboptimal. Methods: In this quality initiative, best practice alerts (BPA) in the electronic health record (EHR) were utilized to alert providers to prescribe to GDMT upon hospital discharge in ASCVD patients. Rates of GDMT were compared for 5 months pre- and post-BPA implementation. Multivariable regression was used to identify predictors of GDMT. Results: In 5985 pre- and 5568 post-BPA patients, the average age was 69.1 ± 12.8 years and 58.5% were male. There was a 4.0% increase in statin use from 67.3% to 71.3% and a 3.1% increase in antithrombotic use from 75.3% to 78.4% in the post-BPA cohort. Conclusions: This simple EHR-based initiative was associated with a modest increase in ASCVD patients being discharged on GDMT. Leveraging clinical decision support tools provides an opportunity to influence provider behavior and improve care for ASCVD patients, and warrants further investigation.

Objective To determine the clinical significance of tachycardia in the postoperative period. Patients and Methods Individuals 18 years or older undergoing hip and knee arthroplasty were included in the study. Two data sets were collected from different time periods: development data set from January 1, 2011, through December 31, 2011, and validation data set from December 1, 2012, through September 1, 2014. We used the development data set to identify the optimal definition of tachycardia with the strongest association with the vascular composite outcome (pulmonary embolism and myocardial necrosis and infarction). The predictive value of this definition was assessed in the validation data set for each outcome of interest, pulmonary embolism, myocardial necrosis and infarction, and infection using multiple logistic regression to control for known risk factors. Results In 1755 patients in the development data set, a maximum heart rate (HR) greater than 110 beats/min was found to be the best cutoff as a correlate of the composite vascular outcome. Of the 4621 patients who underwent arthroplasty in the validation data set, 40 (0.9%) had pulmonary embolism. The maximum HR greater than 110 beats/min had an odds ratio (OR) of 9.39 (95% CI, 4.67-18.87; sensitivity, 72.5%; specificity, 78.0%; positive predictive value, 2.8%; negative predictive value, 99.7%) for pulmonary embolism. Ninety-seven patients (2.1%) had myocardial necrosis (elevated troponin). The maximum HR greater than 110 beats/min had an OR of 4.71 (95% CI, 3.06-7.24; sensitivity, 47.4%; specificity, 78.1%; positive predictive value, 4.4%; negative predictive value, 98.6%) for this outcome. Thirteen (.3%) patients had myocardial infarction according to our predetermined definition, and the maximum HR greater than 110 beats/min had an OR of 1.72 (95% CI, 0.47-6.27). Conclusion Postoperative tachycardia within the first 4 days of surgery should not be dismissed as a postoperative variation in HR, but may precede clinically significant adverse outcomes.

Background Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)–integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician’s interaction with these alerts in general. Objective This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the signal of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts. Methods We built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician’s interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients. Results During the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; P=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; P=.20). Conclusions All key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.

Author(s): Koziatek, Christian; Swartz, Jordan; Iturrate, Eduardo; Levy-Lambert, Dina; Testa, Paul | Abstract: Introduction: Emergency department (ED) patient care often hinges on the result of a diagnostic test. Frequently there is a lag time between a test result becoming available for review and physician decision-making or disposition based on that result. We implemented a system that electronically alerts ED providers when test results are available for review via a smartphone- and smartwatch- push notification. We hypothesized this would reduce the time from result to clinical decision-making.Methods: We retrospectively assessed the impact of the implementation of a push notification system at three EDs on time-to-disposition or time-to-follow-up order in six clinical scenarios of interest: chest radiograph (CXR) to disposition, basic metabolic panel (BMP) to disposition, urinalysis (UA) to disposition, respiratory pathogen panel (RPP) to disposition, hemoglobin (Hb) to blood transfusion order, and abnormal D-dimer to computed tomography pulmonary angiography (CTPA) order. All ED patients during a one-year period of push-notification availability were included in the study. The primary outcome was median time in each scenario from result availability to either disposition order or defined follow-up order. The secondary outcome was the overall usage rate of the opt-in push notification system by providers.Results: During the study period there were 6115 push notifications from 4183 ED encounters (2.7% of all encounters). Of the six clinical scenarios examined in this study, five were associated with a decrease in median time from test result availability to patient disposition or follow-up order when push notifications were employed: CXR to disposition, 80 minutes (interquartile range [IQR] 32-162 minutes) vs 56 minutes (IQR 18-141 minutes), difference 24 minutes (pl0.01); BMP to disposition, 128 minutes (IQR 62-225 minutes) vs 116 minutes (IQR 33-226 minutes), difference 12 minutes (pl0.01); UA to disposition, 105 minutes (IQR 43-200 minutes) vs 55 minutes (IQR 16-144 minutes), difference 50 minutes (pl0.01); RPP to disposition, 80 minutes (IQR 28-181 minutes) vs 37 minutes (IQR 10-116 minutes), difference 43 minutes (pl0.01); and D-dimer to CTPA, 14 minutes (IQR 6-30 minutes) vs 6 minutes (IQR 2.5-17.5 minutes), difference 8 minutes (pl0.01). The sixth scenario, Hb to blood transfusion (difference 19 minutes, p=0.73), did not meet statistical significance.Conclusion: Implementation of a push notification system for test result availability in the ED was associated with a decrease in lag time between test result and physician decision-making in the examined clinical scenarios. Push notifications were used in only a minority of ED patient encounters.

Purpose To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations. Methods and Materials We searched the electronic health records for patients who underwent chest CT during 2014 and 2017, before and after implementation of a department-wide dictation macro of the Fleischner Society recommendations. We randomly selected 950 unstructured chest CT reports and reviewed manually for ILNs. An NLP tool was trained and validated against the manually reviewed set, for the task of automated detection of ILNs with exclusion of previously known or definitively benign nodules. For ILNs found in the training and validation sets, we assessed whether reported management recommendations agreed with Fleischner Society guidelines. The guideline concordance of management recommendations was compared between 2014 and 2017. Results The NLP tool identified ILNs with sensitivity and specificity of 91.1% and 82.2%, respectively, in the validation set. Positive and negative predictive values were 59.7% and 97.0%. In reports of ILNs in the training and validation sets before versus after introduction of a Fleischner reporting macro, there was no difference in the proportion of reports with ILNs (108 of 500 [21.6%] versus 101 of 450 [22.4%]; P = .8), or in the proportion of reports with ILNs containing follow-up recommendations (75 of 108 [69.4%] versus 80 of 101 [79.2%]; P = .2]. Rates of recommendation guideline concordance were not significantly different before and after implementation of the standardized macro (52 of 75 [69.3%] versus 60 of 80 [75.0%]; P = .43). Conclusion NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management.

The purpose of this study is to decrease overutilization of laboratory testing by eliminating a feature of the electronic ordering system that allowed providers to order laboratory tests to occur daily without review.We collected rates of utilization of a group of commonly ordered laboratory tests (number of tests per patient per day) throughout the entire hospital from June 10, 2013 through June 10, 2015. Our intervention, which eliminated the ability to order daily recurring tests, was implemented on June 11, 2014. We compared pre- and postintervention rates in order to assess the impact and surveyed providers about their experience with the intervention.We examined 1,296,742 laboratory tests performed on 92,799 unique patients over 434,059 patient days. Before the intervention, the target tests were ordered using this daily recurring mechanism 33% of the time. After the intervention we observed an 8.5% (P.001) to 20.9% (P.001) reduction in tests per patient per day. The reduction in rate for some of the target tests persisted during the study period, but not for the 2 most commonly ordered tests. We estimated an approximate reduction in hospital costs of $300,000 due to the intervention.A simple modification to the order entry system significantly and immediately altered provider practices throughout a large tertiary care academic center. This strategy is replicable by the many hospitals that use the same electronic health record system, and possibly, by users of other systems. Future areas of study include evaluating the additive effects of education and real-time decision support.

Key Points Question Do outcomes among patients with coronavirus disease 2019 (COVID-19) differ by race/ethnicity, and are observed disparities associated with comorbidity and neighborhood characteristics? Findings This cohort study including 9722 patients found that Black and Hispanic patients were more likely than White patients to test positive for COVID-19. Among patients hospitalized with COVID-19 infection, Black patients were less likely than White patients to have severe illness and to die or be discharged to hospice. Meaning Although Black patients were more likely than White patients to test positive for COVID-19, after hospitalization they had lower mortality, suggesting that neighborhood characteristics may explain the disproportionately higher out-of-hospital COVID-19 mortality among Black individuals., This cohort study of patients testing positive for COVID-19 in a large New York City health system compares rates of hospitalization, critical illness, mortality across racial/ethnic categories., Importance Black and Hispanic populations have higher rates of coronavirus disease 2019 (COVID-19) hospitalization and mortality than White populations but lower in-hospital case-fatality rates. The extent to which neighborhood characteristics and comorbidity explain these disparities is unclear. Outcomes in Asian American populations have not been explored. Objective To compare COVID-19 outcomes based on race and ethnicity and assess the association of any disparities with comorbidity and neighborhood characteristics. Design, Setting, and Participants This retrospective cohort study was conducted within the New York University Langone Health system, which includes over 260 outpatient practices and 4 acute care hospitals. All patients within the system’s integrated health record who were tested for severe acute respiratory syndrome coronavirus 2 between March 1, 2020, and April 8, 2020, were identified and followed up through May 13, 2020. Data were analyzed in June 2020. Among 11 547 patients tested, outcomes were compared by race and ethnicity and examined against differences by age, sex, body mass index, comorbidity, insurance type, and neighborhood socioeconomic status. Exposures Race and ethnicity categorized using self-reported electronic health record data (ie, non-Hispanic White, non-Hispanic Black, Hispanic, Asian, and multiracial/other patients). Main Outcomes and Measures The likelihood of receiving a positive test, hospitalization, and critical illness (defined as a composite of care in the intensive care unit, use of mechanical ventilation, discharge to hospice, or death). Results Among 9722 patients (mean [SD] age, 50.7 [17.5] years; 58.8% women), 4843 (49.8%) were positive for COVID-19; 2623 (54.2%) of those were admitted for hospitalization (1047 [39.9%] White, 375 [14.3%] Black, 715 [27.3%] Hispanic, 180 [6.9%] Asian, 207 [7.9%] multiracial/other). In fully adjusted models, Black patients (odds ratio [OR], 1.3; 95% CI, 1.2-1.6) and Hispanic patients (OR, 1.5; 95% CI, 1.3-1.7) were more likely than White patients to test positive. Among those who tested positive, odds of hospitalization were similar among White, Hispanic, and Black patients, but higher among Asian (OR, 1.6, 95% CI, 1.1-2.3) and multiracial patients (OR, 1.4; 95% CI, 1.0-1.9) compared with White patients. Among those hospitalized, Black patients were less likely than White patients to have severe illness (OR, 0.6; 95% CI, 0.4-0.8) and to die or be discharged to hospice (hazard ratio, 0.7; 95% CI, 0.6-0.9). Conclusions and Relevance In this cohort study of patients in a large health system in New York City, Black and Hispanic patients were more likely, and Asian patients less likely, than White patients to test positive; once hospitalized, Black patients were less likely than White patients to have critical illness or die after adjustment for comorbidity and neighborhood characteristics. This supports the assertion that existing structural determinants pervasive in Black and Hispanic communities may explain the disproportionately higher out-of-hospital deaths due to COVID-19 infections in these populations.

This study examines the incidence of and risk factors for venous and arterial thrombosis in patients hospitalized with COVID-19 in 4 New York City hospitals.

Background Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance. Methods Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network’s (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians’ interpretations. Results The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1–97.8), specificity 97.1% (93.4–99.1), PPV 90.4% (79.0–96.8), NPV 97.7% (94.1–99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours. Conclusion The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities. Disclosures All authors: No reported disclosures.

BACKGROUND Late afternoon hospital discharges are thought to contribute to admission bottlenecks. We previously described an intervention that resulted in a statistically significant increase in the discharge before noon (DBN) rate on 2 inpatient medicine units. OBJECTIVE To evaluate (1) the effect of an increased DBN rate on the admission arrival time and the number of admissions per hour and (2) the sustainability of our DBN initiative. DESIGN Pre-/postintervention retrospective analysis. SETTING Two acute-care inpatient medicine units in a tertiary care, urban, academic medical center. PATIENTS: For the admission arrival time and admissions per hour analysis, all inpatients admitted to the medical units from June 1, 2011 to June 31, 2013. For the sustainability analysis, all patients discharged from July 1, 2013 to December 31, 2014. INTERVENTION A multidisciplinary intervention to increase the DBN rate. MEASUREMENTS: Date and time of arrival to all inpatient sites, and discharge date and time of all patients from 2 inpatient medicine units. RESULTS Concurrent with our increase in DBN rate, we found a statistically significant change in the median arrival time of emergency department (ED) admissions and transfers from 5 pm to 4 pm. High-frequency admission peaks were statistically significantly reduced for ED admissions. The statistically significant increase in DBN rate is sustained at 35%. CONCLUSIONS Increasing the DBN rate correlates with admissions arriving earlier in the day and reductions in high-frequency peaks of ED admissions. Statistically significant improvements in DBN rates are sustainable. Journal of Hospital Medicine 2015;10:664–669. © 2015 Society of Hospital Medicine

A 49-year-old woman with cholangiocarcinoma metastatic to the lungs presented with new-onset unrelenting headaches. A lumbar puncture revealed malignant cells consistent with leptomeningeal metastasis from her cholangiocarcinoma. Magnetic resonance imaging (MRI) of the brain revealed leptomeningeal enhancement. An intrathecal (IT) catheter was placed and IT chemotherapy was initiated with methotrexate. Her case is notable for the rarity of cholangiocarcinoma spread to the leptomeninges, the use of IT chemotherapy with cytologic and potentially symptomatic response, and a possible survival benefit in comparison to previously reported cases of leptomeningeal carcinomatosis secondary to cholangiocarcinoma.

Background Testing for venous thromboembolism (VTE) is associated with cost and risk to patients (e.g. radiation). To assess the appropriateness of imaging utilization at the provider level, it is important to know that provider’s diagnostic yield (percentage of tests positive for the diagnostic entity of interest). However, determining diagnostic yield typically requires either time-consuming, manual review of radiology reports or the use of complex and/or proprietary natural language processing software. Objectives The objectives of this study were twofold: 1) to develop and implement a simple, user-configurable, and open-source natural language processing tool to classify radiology reports with high accuracy and 2) to use the results of the tool to design a provider-specific VTE imaging dashboard, consisting of both utilization rate and diagnostic yield. Methods Two physicians reviewed a training set of 400 lower extremity ultrasound (UTZ) and computed tomography pulmonary angiogram (CTPA) reports to understand the language used in VTE-positive and VTE-negative reports. The insights from this review informed the arguments to the five modifiable parameters of the NLP tool. A validation set of 2,000 studies was then independently classified by the reviewers and by the tool; the classifications were compared and the performance of the tool was calculated. Results The tool was highly accurate in classifying the presence and absence of VTE for both the UTZ (sensitivity 95.7%; 95% CI 91.5–99.8, specificity 100%; 95% CI 100–100) and CTPA reports (sensitivity 97.1%; 95% CI 94.3–99.9, specificity 98.6%; 95% CI 97.8–99.4). The diagnostic yield was then calculated at the individual provider level and the imaging dashboard was created. Conclusions We have created a novel NLP tool designed for users without a background in computer programming, which has been used to classify venous thromboembolism reports with a high degree of accuracy. The tool is open-source and available for download at http://iturrate.com/simpleNLP . Results obtained using this tool can be applied to enhance quality by presenting information about utilization and yield to providers via an imaging dashboard.

To determine the clinical significance of tachycardia in the postoperative period.Individuals 18 years or older undergoing hip and knee arthroplasty were included in the study. Two data sets were collected from different time periods: development data set from January 1, 2011, through December 31, 2011, and validation data set from December 1, 2012, through September 1, 2014. We used the development data set to identify the optimal definition of tachycardia with the strongest association with the vascular composite outcome (pulmonary embolism and myocardial necrosis and infarction). The predictive value of this definition was assessed in the validation data set for each outcome of interest, pulmonary embolism, myocardial necrosis and infarction, and infection using multiple logistic regression to control for known risk factors.In 1755 patients in the development data set, a maximum heart rate (HR) greater than 110 beats/min was found to be the best cutoff as a correlate of the composite vascular outcome. Of the 4621 patients who underwent arthroplasty in the validation data set, 40 (0.9%) had pulmonary embolism. The maximum HR greater than 110 beats/min had an odds ratio (OR) of 9.39 (95% CI, 4.67-18.87; sensitivity, 72.5%; specificity, 78.0%; positive predictive value, 2.8%; negative predictive value, 99.7%) for pulmonary embolism. Ninety-seven patients (2.1%) had myocardial necrosis (elevated troponin). The maximum HR greater than 110 beats/min had an OR of 4.71 (95% CI, 3.06-7.24; sensitivity, 47.4%; specificity, 78.1%; positive predictive value, 4.4%; negative predictive value, 98.6%) for this outcome. Thirteen (.3%) patients had myocardial infarction according to our predetermined definition, and the maximum HR greater than 110 beats/min had an OR of 1.72 (95% CI, 0.47-6.27).Postoperative tachycardia within the first 4 days of surgery should not be dismissed as a postoperative variation in HR, but may precede clinically significant adverse outcomes.

Background Computed tomography pulmonary angiography (CTA) is the gold standard for diagnosing pulmonary embolism (PE) but involves radiation and iodinated contrast exposure. Of orthopedic patients evaluated for PE, a minority have a positive CTA study. Herein, we evaluate end tidal carbon dioxide (ETCO 2 ) as a method to identify patients at low risk for PE and may not require a CTA. We hypothesize that ETCO 2 will be useful for predicting the absence of PE in postoperative orthopedic patients. Methods In this prospective study, all patients older than 18 years who were admitted for orthopedic surgery and who had a CTA performed for PE were eligible. These patients underwent an ETCO 2 measurement. Patients were determined to have PE if they had a positive PE-protocol CT. Results Between May 2014 and April 2015, 121 patients met the inclusion criteria for the study. Of these patients, 84 had a negative CTA examination, 25 had a positive examination, and 12 had a nondiagnostic examination. We found a statistically significant difference ( P = .03) when comparing the average ETCO 2 values for the positive and negative CTA groups. An ETCO 2 cutoff value of 43 mm Hg was 100% sensitive with a negative predictive value of 100% for absence of PE on CTA. Conclusion This study demonstrates a significant difference in ETCO 2 measurements between postoperative orthopedic patients with and without CTA-detected PE. A cutoff value of >43 mm Hg may be useful in excluding patients from undergoing CTA.

Late afternoon hospital discharges are thought to contribute to admission bottlenecks. We previously described an intervention that resulted in a statistically significant increase in the discharge before noon (DBN) rate on 2 inpatient medicine units.To evaluate (1) the effect of an increased DBN rate on the admission arrival time and the number of admissions per hour and (2) the sustainability of our DBN initiative.Pre-/postintervention retrospective analysis.Two acute-care inpatient medicine units in a tertiary care, urban, academic medical center.For the admission arrival time and admissions per hour analysis, all inpatients admitted to the medical units from June 1, 2011 to June 31, 2013. For the sustainability analysis, all patients discharged from July 1, 2013 to December 31, 2014.A multidisciplinary intervention to increase the DBN rate.Date and time of arrival to all inpatient sites, and discharge date and time of all patients from 2 inpatient medicine units.Concurrent with our increase in DBN rate, we found a statistically significant change in the median arrival time of emergency department (ED) admissions and transfers from 5 pm to 4 pm. High-frequency admission peaks were statistically significantly reduced for ED admissions. The statistically significant increase in DBN rate is sustained at 35%.Increasing the DBN rate correlates with admissions arriving earlier in the day and reductions in high-frequency peaks of ED admissions. Statistically significant improvements in DBN rates are sustainable.