Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95–0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012–0.030 in C3PO vs. 0.028–0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research.

Background Performance of existing atrial fibrillation (AF) risk prediction models in poststroke populations is unclear. We evaluated predictive utility of an AF risk model in patients with acute stroke and assessed performance of a fully refitted model. Methods and Results Within an academic hospital, we included patients aged 46 to 94 years discharged for acute ischemic stroke between 2003 and 2018. We estimated 5‐year predicted probabilities of AF using the Cohorts for Heart and Aging Research in Genomic Epidemiology for Atrial Fibrillation (CHARGE‐AF) model, by recalibrating CHARGE‐AF to the baseline risk of the sample, and by fully refitting a Cox proportional hazards model to the stroke sample (Re‐CHARGE‐AF) model. We compared discrimination and calibration between models and used 200 bootstrap samples for optimism‐adjusted measures. Among 551 patients with acute stroke, there were 70 incident AF events over 5 years (cumulative incidence, 15.2%; 95% CI, 10.6%–19.5%). Median predicted 5‐year risk from CHARGE‐AF was 4.8% (quartile 1–quartile 3, 2.0–12.6) and from Re‐CHARGE‐AF was 16.1% (quartile 1–quartile 3, 8.0–26.2). For CHARGE‐AF, discrimination was moderate (C statistic, 0.64; 95% CI, 0.57–0.70) and calibration was poor, underestimating AF risk (Greenwood‐Nam D’Agostino chi‐square, P <0.001). Calibration with recalibrated baseline risk was also poor (Greenwood‐Nam D’Agostino chi‐square, P <0.001). Re‐CHARGE‐AF improved discrimination ( P =0.001) compared with CHARGE‐AF (C statistic, 0.74 [95% CI, 0.68–0.79]; optimism‐adjusted, 0.70 [95% CI, 0.65–0.75]) and was well calibrated (Greenwood‐Nam D’Agostino chi‐square, P =0.97). Conclusions Covariates from an established AF risk model enable accurate estimation of AF risk in a poststroke population after recalibration. A fully refitted model was required to account for varying baseline AF hazard and strength of associations between covariates and incident AF.

Objective:To determine if providing teleneurology (TN) consultations aiding in determination of death by neurologic criteria (DNC) to a bedside intensivist is feasible; and whether timely access and expert input increases the quality of the DNC exam and identification of potential organ donors, we reviewed retrospective data related to outcomes of such consultations.Methods:Between March 2017 and April 2019, TN consults were requested for sequential ICU comatose patients. We recorded patients’ demographic information, causes leading to coma or suspected death by neurologic criteria and the results of TN consultations. We obtained data regarding the number of referrals to organ bank and number of organ donors.Results:Ninety-nine consults were performed with a median time from request to start of the consult of 20.2 minutes (IQR 5.4-65.3 min). Eighty consults were requested for determination of prognosis, whereas 19 consults were requested for supervision of the DNC examination. In 1 of 80 (1.2%) prognostication consults, the patient was determined by the neurologist to require assessment of DNC and was found to meet DNC criteria; determination of DNC occurred in 11 of the 19 (57.9%) consultations for supervised DNC exam. When comparing pre (94 months) and post teleneurology (17 months) periods, there was 2.56-fold increase in the proportion of patients meeting DNC criteria who were medically suitable for donation (pre-TN 8.9% vs. post-TN 21.1%, p=0.02) and a 2.12-fold increase in the proportion of donors (pre-TN 6.14%, vs. post-TN 13.1%, p=0.14).Conclusions:It is feasible to perform TN consultations for patients with severe neurologic damage and to allow expert supervision for DNC examination. Having a teleneurologist as part of the ICU assessment team helped differentiate severe neurologic deficits from DNC and was associated with increase in organ donation.