Faculty Bibliography

Importance/UNASSIGNED:To date, the association of psychiatric diagnoses with mortality in patients infected with coronavirus disease 2019 (COVID-19) has not been evaluated. Objective/UNASSIGNED:To assess whether a diagnosis of a schizophrenia spectrum disorder, mood disorder, or anxiety disorder is associated with mortality in patients with COVID-19. Design, Setting, and Participants/UNASSIGNED:This retrospective cohort study assessed 7348 consecutive adult patients for 45 days following laboratory-confirmed COVID-19 between March 3 and May 31, 2020, in a large academic medical system in New York. The final date of follow-up was July 15, 2020. Patients without available medical records before testing were excluded. Exposures/UNASSIGNED:Patients were categorized based on the following International Statistical Classification of Diseases, Tenth Revision, Clinical Modification diagnoses before their testing date: (1) schizophrenia spectrum disorders, (2) mood disorders, and (3) anxiety disorders. Patients with these diagnoses were compared with a reference group without psychiatric disorders. Main Outcomes and Measures/UNASSIGNED:Mortality, defined as death or discharge to hospice within 45 days following a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test result. Results/UNASSIGNED:Of the 26 540 patients tested, 7348 tested positive for SARS-CoV-2 (mean [SD] age, 54 [18.6] years; 3891 [53.0%] women). Of eligible patients with positive test results, 75 patients (1.0%) had a history of a schizophrenia spectrum illness, 564 (7.7%) had a history of a mood disorder, and 360 (4.9%) had a history of an anxiety disorder. After adjusting for demographic and medical risk factors, a premorbid diagnosis of a schizophrenia spectrum disorder was significantly associated with mortality (odds ratio [OR], 2.67; 95% CI, 1.48-4.80). Diagnoses of mood disorders (OR, 1.14; 95% CI, 0.87-1.49) and anxiety disorders (OR, 0.96; 95% CI, 0.65-1.41) were not associated with mortality after adjustment. In comparison with other risk factors, a diagnosis of schizophrenia ranked behind only age in strength of an association with mortality. Conclusions and Relevance/UNASSIGNED:In this cohort study of adults with SARS-CoV-2-positive test results in a large New York medical system, adults with a schizophrenia spectrum disorder diagnosis were associated with an increased risk for mortality, but those with mood and anxiety disorders were not associated with a risk of mortality. These results suggest that schizophrenia spectrum disorders may be a risk factor for mortality in patients with COVID-19.

During the COVID-19 pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images, and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an AUC of 0.786 (95% CI: 0.742-0.827) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions, and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at NYU Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

Clinical interactions are initially recorded and documented in free text medical notes. ICD coding is the task of classifying and coding all diagnoses, symptoms and procedures associated with a patient's visit. The process is often manual and extremely time-consuming and expensive for hospitals. In this paper, we propose a machine learning model, BERT-XML, for large scale automated ICD coding from EHR notes, utilizing recently developed unsupervised pretraining that have achieved state of the art performance on a variety of NLP tasks. We train a BERT model from scratch on EHR notes, learning with vocabulary better suited for EHR tasks and thus outperform off-the-shelf models. We adapt the BERT architecture for ICD coding with multi-label attention. While other works focus on small public medical datasets, we have produced the first large scale ICD-10 classification model using millions of EHR notes to predict thousands of unique ICD codes

Artificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.