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Objective: The evidence pertaining to the effects of asthma on Coronavirus disease 2019 outcomes has been unclear. To improve our understanding of the clinically important association of asthma and Coronavirus disease 2019., Methods: A matched cohort study was performed using data from the Mass General Brigham Health Care System (Boston, MA). Adult (age ≥18 years) patients with confirmed Coronavirus disease 2019 and without chronic obstructive pulmonary disease, cystic fibrosis, or interstitial lung disease between March 4, 2020 and July 2, 2020 were analyzed. Up to five non-asthma comparators were matched to each asthma patient based on age (within 5 years), sex, and date of positive test (within 7 days). The primary outcomes were hospitalization, mechanical ventilation, and death, using multivariable Cox-proportional hazards models accounting for competing risk of death, when appropriate. Patients were followed for these outcomes from diagnosis of Coronavirus disease 2019 until July 2, 2020., Results: Among 562 asthma patients, 199 (21%) were hospitalized, 15 (3%) received mechanical ventilation, and 7 (1%) died. Among the 2686 matched comparators, 487 (18%) were hospitalized, 107 (4%) received mechanical ventilation, and 69 (3%) died. The adjusted Hazard Ratios among asthma patients were 0.99 (95% Confidence Internal 0.80, 1.22) for hospitalization, 0.69 (95% Confidence Internal 0.36, 1.29) for mechanical ventilation, and 0.30 (95% Confidence Internal 0.11, 0.80) for death., Conclusions: In this matched cohort study from a large Boston-based healthcare system, asthma was associated with comparable risk of hospitalization and mechanical ventilation but a lower risk of mortality.

Importance: Although tumor-infiltrating lymphocytes (TILs) are an important histopathologic characteristic reflecting host immune response in patients with melanoma, their prognostic value remains controversial. Because manual review of medical records is labor intensive, a survival analysis using a large patient cohort with comprehensive clinical and histopathologic characteristics is lacking., Objective: To assess the prognostic significance of TILs among patients with cutaneous melanoma using a large cohort established through natural language processing (NLP) algorithms., Design, Setting, and Participants: This retrospective cohort study analyzed the medical records of 14 436 patients with cutaneous melanoma at Brigham and Women's Hospital between June 1, 2004, and December 31, 2019. Patients were followed up to death or censored at their last clinical visit., Main Outcome and Measures: The primary outcome was overall survival (OS). Survival analysis was conducted using Kaplan-Meier curves, the log-rank test, and Cox proportional hazards regression analysis., Results: A total of 14 436 patients with cutaneous melanoma were identified in the institution's pathology information system. Using NLP, we established a study cohort of 2624 patients (1462 men [55.7%]; median age, 61 years [interquartile range, 50-72 years]) who had vertical growth phase melanoma with TIL status scored. Absent TILs were identified in 434 patients (16.5%), nonbrisk TILs in 1916 patients (73.0%), and brisk TILs in 274 patients (10.4%). The 5-year survival rate was 71.0% (95% CI, 65.5%-76.9%) among patients with an absence of TILs, 73.8% (95% CI, 71.1%-76.5%) among patients with nonbrisk TILs, and 85.2% (95% CI, 80.0%-90.7%) among patients with brisk TILs. Brisk TILs were significantly associated with improved OS (adjusted hazard ratio, 0.63; 95% CI, 0.42-0.95; P = .03; 14.2% OS advantage at 5 years), and nonbrisk TILs were not associated with improved OS (adjusted hazard ratio, 0.87; 95% CI, 0.68-1.11; P = .25), compared with the absence of TILs., Conclusions and Relevance: This study provides evidence based on a large patient cohort from a single institution that suggests that brisk TILs represent an independent prognostic factor for OS among patients with primary cutaneous melanoma. The study also suggests that NLP is a highly efficient tool to facilitate large-scale analyses that involve free-text clinical data.

Importance: Although critical to patient safety, health care-related allergic reactions are challenging to identify and monitor., Objective: To develop a deep learning model to identify allergic reactions in the free-text narrative of hospital safety reports and evaluate its generalizability, efficiency, productivity, and interpretability., Design, Setting, and Participants: This cross-sectional study analyzed hospital safety reports filed between May 2004 and January 2019 at Brigham and Women's Hospital and between April 2006 and June 2018 at Massachusetts General Hospital in Boston. Training and validating a deep learning model involved extracting safety reports using 101 expert-curated keywords from Massachusetts General Hospital (data set I). The model was then evaluated on 3 data sets: reports without keywords (data set II), reports from a different time frame (data set III), and reports from a different hospital (Brigham and Women's Hospital; data set IV). Statistical analyses were performed between March 1, 2019, and July 18, 2020., Main Outcomes and Measures: The area under the receiver operating characteristic curve and area under the precision-recall curve were used on data set I. The precision at top-k was used on data sets II to IV., Results: A total of 299 028 safety reports with 172 854 patients were included. Of these patients, 86 544 were women (50.1%) and the median (interquartile range [IQR]) age was 59.7 (43.8-71.6) years. The deep learning model achieved an area under the receiver operating characteristic curve of 0.979 (95% CI, 0.973-0.985) and an area under the precision-recall curve of 0.809 (95% CI, 0.773-0.845). The model achieved precisions at the top 100 model-identified cases of 0.930 in data set II, 0.960 in data set III, and 0.990 in data set IV. Compared with the keyword-search approach, the deep learning model reduced the number of cases for manual review by 63.8% and identified 24.2% more cases of confirmed allergic reactions. The model highlighted important words (eg, rash, hives, and Benadryl) in prediction and extended the list of expert-curated keywords through an attention layer., Conclusions and Relevance: This study showed that a deep learning model can accurately and efficiently identify allergic reactions using free-text narratives written by a variety of health care professionals. This model could be used to improve allergy care, potentially enabling real-time event surveillance and guidance for medical errors and system improvement.