Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED).
| Autor: | Grout RW; Center for Biomedical Informatics, Regenstrief Institute, 1101 W Tenth St, Indianapolis, IN, 46202, USA. rgrout@iu.edu.; Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA. rgrout@iu.edu., Hui SL; Center for Biomedical Informatics, Regenstrief Institute, 1101 W Tenth St, Indianapolis, IN, 46202, USA.; Research Services, Regenstrief Institute, Indianapolis, IN, USA.; Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA., Imler TD; Center for Biomedical Informatics, Regenstrief Institute, 1101 W Tenth St, Indianapolis, IN, 46202, USA.; Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN, USA., El-Azab S; Research Services, Regenstrief Institute, Indianapolis, IN, USA., Baker J; Center for Biomedical Informatics, Regenstrief Institute, 1101 W Tenth St, Indianapolis, IN, 46202, USA., Sands GH; Pfizer Inc, US Medical Affairs, New York, NY, USA., Ateya M; Pfizer Inc, US Medical Affairs, New York, NY, USA., Pike F; Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA. |
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| Jazyk: | angličtina |
| Zdroj: | BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2021 Apr 03; Vol. 21 (1), pp. 112. Date of Electronic Publication: 2021 Apr 03. |
| DOI: | 10.1186/s12911-021-01482-1 |
| Abstrakt: | Background: Many patients with atrial fibrillation (AF) remain undiagnosed despite availability of interventions to reduce stroke risk. Predictive models to date are limited by data requirements and theoretical usage. We aimed to develop a model for predicting the 2-year probability of AF diagnosis and implement it as proof-of-concept (POC) in a production electronic health record (EHR). Methods: We used a nested case-control design using data from the Indiana Network for Patient Care. The development cohort came from 2016 to 2017 (outcome period) and 2014 to 2015 (baseline). A separate validation cohort used outcome and baseline periods shifted 2 years before respective development cohort times. Machine learning approaches were used to build predictive model. Patients ≥ 18 years, later restricted to age ≥ 40 years, with at least two encounters and no AF during baseline, were included. In the 6-week EHR prospective pilot, the model was silently implemented in the production system at a large safety-net urban hospital. Three new and two previous logistic regression models were evaluated using receiver-operating characteristics. Number, characteristics, and CHA Results: After restricting age to ≥ 40 years, 31,474 AF cases (mean age, 71.5 years; female 49%) and 22,078 controls (mean age, 59.5 years; female 61%) comprised the development cohort. A 10-variable model using age, acute heart disease, albumin, body mass index, chronic obstructive pulmonary disease, gender, heart failure, insurance, kidney disease, and shock yielded the best performance (C-statistic, 0.80 [95% CI 0.79-0.80]). The model performed well in the validation cohort (C-statistic, 0.81 [95% CI 0.8-0.81]). In the EHR pilot, 7916/22,272 (35.5%; mean age, 66 years; female 50%) were identified as higher risk for AF; 5582 (70%) had CHA Conclusions: Using variables commonly available in the EHR, we created a predictive model to identify 2-year risk of developing AF in those previously without diagnosed AF. Successful POC implementation of the model in an EHR provided a practical strategy to identify patients who may benefit from interventions to reduce their stroke risk. |
| Databáze: | MEDLINE |
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