Comparison and validation of algorithms for asthma diagnosis in an electronic medical record system.

BACKGROUND: Asthma is one of the most common chronic health conditions, and to leverage the wealth of data in the electronic medical record (EMR), it is important to be able to accurately identify asthma diagnosis. OBJECTIVE: We aimed to determine the rule-based algorithm with the most balanced performance for sensitivity and positive predictive value of asthma diagnosis. METHODS: We performed a diagnostic accuracy study of multiple rule-based algorithms intended to identify asthma diagnosis in the EMR. Algorithm performance was validated by manual chart review of 795 charts of patients seen in a multisite, tertiary-level, pulmonary specialty clinic using explicit diagnostic criteria to distinguish asthma cases from controls. RESULTS: An asthma diagnosis anywhere in the medical record had a 97% sensitivity and a 77% specificity for asthma (F-score 80) when tested on a validation set of asthma cases and nonasthma respiratory disease from a pulmonary specialty clinic. The most balanced performance was seen with asthma diagnosis restricted to an encounter, hospital problem, or problem list diagnosis with a sensitivity of 94% and specificity of 85% (F-score 84). High sensitivity was achieved with the modified Health Plan Employer Data and Information Set criteria and high specificity was achieved with the NUgene algorithm, an algorithm developed for identifying asthma cases by EMR for genome-wide association studies. CONCLUSION: Asthma diagnosis can be accurately identified for research purposes by restricting to encounter, hospital problem, or problem list diagnosis in a pulmonary specialty clinic. Additional rules lead to steep drop-offs in algorithm sensitivity in our population.

Autoimmune and Chronic Inflammatory Disease Patients with COVID-19.

OBJECTIVE: There are limited data on the impact of coronavirus disease 2019 (COVID-19) on hospitalized patients with autoimmune and chronic inflammatory disease (AICID) compared with patients who do not have AICID. We sought to evaluate whether patients with AICID who have confirmed COVID-19 presenting to the hospital are at higher risk of adverse outcomes compared with those patients without AICID who are infected with COVID-19 and whether immunosuppressive medications impact this risk. METHODS: We performed a multicenter retrospective cohort study with patients presenting to five hospitals in a large academic health system with polymerase chain reaction-confirmed COVID-19 infection. We evaluated the impact of having an AICID and class of immunosuppressive medication being used to treat patients with AICID (biologics, nonbiologic immunosuppressives, or systemic corticosteroids) on the risk of developing severe COVID-19 defined as requiring mechanical ventilation (MV) and/or death. RESULTS: A total of 6792 patients with confirmed COVID-19 were included in the study, with 159 (2.3%) having at least one AICID. On multivariable analysis, AICIDs were not significantly associated with severe COVID-19 (adjusted odds ratio [aOR] 1.3, 95% confidence interval [CI]: 0.9-1.8). Among patients with AICID, use of biologics or nonbiologic immunosuppressives did not increase the risk of severe COVID-19. In contrast, systemic corticosteroid use was significantly associated with an increased risk of severe COVID-19 (aOR 6.8, 95% CI: 2.5-18.4). CONCLUSION: Patients with AICID are not at increased risk of severe COVID-19 with the exception of those on corticosteroids. These data suggest that patients with AICID should continue on biologic and nonbiologic immunosuppression but limit steroids during the COVID-19 pandemic.

Repeated cross-sectional sero-monitoring of SARS-CoV-2 in New York City.

In late 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first detected in China and has since caused a pandemic of coronavirus disease 2019 (COVID-19). The first case of COVID-19 in New York City was officially confirmed on 1 March 2020 followed by a severe local epidemic1. Here, to understand seroprevalence dynamics, we conduct a retrospective, repeated cross-sectional analysis of anti-SARS-CoV-2 spike antibodies in weekly intervals from the beginning of February to July 2020 using more than 10,000 plasma samples from patients at Mount Sinai Hospital in New York City. We describe the dynamics of seroprevalence in an 'urgent care' group, which is enriched in cases of COVID-19 during the epidemic, and a 'routine care' group, which more closely represents the general population. Seroprevalence increased at different rates in both groups; seropositive samples were found as early as mid-February, and levelled out at slightly above 20% in both groups after the epidemic wave subsided by the end of May. From May to July, seroprevalence remained stable, suggesting lasting antibody levels in the population. Our data suggest that SARS-CoV-2 was introduced in New York City earlier than previously documented and describe the dynamics of seroconversion over the full course of the first wave of the pandemic in a major metropolitan area.

Introductions and early spread of SARS-CoV-2 in the New York City area.

New York City (NYC) has emerged as one of the epicenters of the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. To identify the early transmission events underlying the rapid spread of the virus in the NYC metropolitan area, we sequenced the virus that causes coronavirus disease 2019 (COVID-19) in patients seeking care at the Mount Sinai Health System. Phylogenetic analysis of 84 distinct SARS-CoV-2 genomes indicates multiple, independent, but isolated introductions mainly from Europe and other parts of the United States. Moreover, we found evidence for community transmission of SARS-CoV-2 as suggested by clusters of related viruses found in patients living in different neighborhoods of the city.

Assessing stroke severity using electronic health record data: a machine learning approach.

BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structured real-world evidence databases seldom include the severity. The aim of this study was to use machine learning models to impute NIHSS scores for all patients with newly diagnosed stroke from multi-institution electronic health record (EHR) data. METHODS: NIHSS scores available in the Optum© de-identified Integrated Claims-Clinical dataset were extracted from physician notes by applying natural language processing (NLP) methods. The cohort analyzed in the study consists of the 7149 patients with an inpatient or emergency room diagnosis of ischemic stroke, hemorrhagic stroke, or transient ischemic attack and a corresponding NLP-extracted NIHSS score. A subset of these patients (n = 1033, 14%) were held out for independent validation of model performance and the remaining patients (n = 6116, 86%) were used for training the model. Several machine learning models were evaluated, and parameters optimized using cross-validation on the training set. The model with optimal performance, a random forest model, was ultimately evaluated on the holdout set. RESULTS: Leveraging machine learning we identified the main factors in electronic health record data for assessing stroke severity, including death within the same month as stroke occurrence, length of hospital stay following stroke occurrence, aphagia/dysphagia diagnosis, hemiplegia diagnosis, and whether a patient was discharged to home or self-care. Comparing the imputed NIHSS scores to the NLP-extracted NIHSS scores on the holdout data set yielded an R2 (coefficient of determination) of 0.57, an R (Pearson correlation coefficient) of 0.76, and a root-mean-squared error of 4.5. CONCLUSIONS: Machine learning models built on EHR data can be used to determine proxies for stroke severity. This enables severity to be incorporated in studies of stroke patient outcomes using administrative and EHR databases.

Risks of Stroke and Mortality in Atrial Fibrillation Patients Treated With Rivaroxaban and Warfarin.

Background and Purpose- Oral anticoagulation therapy is standard of care for patients with nonvalvular atrial fibrillation to prevent stroke. This study compared rivaroxaban and warfarin for stroke and all-cause mortality risk reduction in a real-world setting. Methods- This retrospective cohort study (2011-2017) included de-identified patients from the Optum Clinformatics Database who started treatment with rivaroxaban or warfarin within 30 days following initial diagnosis of nonvalvular atrial fibrillation. Before nonvalvular atrial fibrillation diagnosis, patients had 6 months of continuous health plan enrollment and CHA2DS2-VASc score ≥2. Stroke severity was determined by the National Institutes of Health Stroke Scale, imputed based on machine learning algorithms. Stroke and all-cause mortality risks were compared by treatment using Cox proportional hazard regression, with inverse probability of treatment weighting to balance cohorts for baseline risk factors. Stratified analysis by treatment duration was also performed. Results- During a mean follow-up of 27 months, 175 (1.33/100 patient-years [PY]) rivaroxaban-treated and 536 (1.66/100 PY) warfarin-treated patients developed stroke. The inverse probability of treatment weighting model showed that rivaroxaban reduced stroke risk by 19% (hazard ratio [HR], 0.81 [95% CI, 0.73-0.91]). Analysis by stroke severity revealed risk reductions by rivaroxaban of 48% for severe stroke (National Institutes of Health Stroke Scale score, 16-42; HR, 0.52 [95% CI, 0.33-0.82]) and 19% for minor stroke (National Institutes of Health Stroke Scale score, 1 to <5; HR, 0.81 [95% CI, 0.68-0.96]), but no difference for moderate stroke (National Institutes of Health Stroke Scale score, 5 to <16; HR, 0.93 [95% CI, 0.78-1.10]). A total of 41 (0.31/100 PY) rivaroxaban-treated and 147 (0.44/100 PY) warfarin-treated patients died poststroke, 12 (0.09/100 PY) and 67 (0.20/100 PY) of whom died within 30 days, representing mortality risk reductions by rivaroxaban of 24% (HR, 0.76 [95% CI, 0.61-0.95]) poststroke and 59% (HR, 0.41 [95% CI, 0.28-0.60]) within 30 days. Conclusions- After the initial diagnosis of atrial fibrillation, patients treated with rivaroxaban versus warfarin had significant risk reduction for stroke, especially severe stroke, and all-cause mortality after a stroke. Findings from this observational study may help inform anticoagulant choice for stroke prevention in patients with nonvalvular atrial fibrillation.

Production of cold beams of ND3 with variable rotational state distributions by electrostatic extraction of He and Ne buffer-gas-cooled beams.

The measurement of the rotational state distribution of a velocity-selected, buffer-gas-cooled beam of ND3 is described. In an apparatus recently constructed to study cold ion-molecule collisions, the ND3 beam is extracted from a cryogenically cooled buffer-gas cell using a 2.15 m long electrostatic quadrupole guide with three 90° bends. (2+1) resonance enhanced multiphoton ionization spectra of molecules exiting the guide show that beams of ND3 can be produced with rotational state populations corresponding to approximately T(rot) = 9-18 K, achieved through manipulation of the temperature of the buffer-gas cell (operated at 6 K or 17 K), the identity of the buffer gas (He or Ne), or the relative densities of the buffer gas and ND3. The translational temperature of the guided ND3 is found to be similar in a 6 K helium and 17 K neon buffer-gas cell (peak kinetic energies of 6.92(0.13) K and 5.90(0.01) K, respectively). The characterization of this cold-molecule source provides an opportunity for the first experimental investigations into the rotational dependence of reaction cross sections in low temperature collisions.