- Browse by Author
Browsing by Author "Imler, Timothy D."
Now showing 1 - 6 of 6
Results Per Page
Sort Options
Item Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED)(BMC, 2021-04-03) Grout, Randall W.; Hui, Siu L.; Imler, Timothy D.; El‑Azab, Sarah; Sands, George H.; Ateya, Mohammad; Pike, Francis; Pediatrics, School of MedicineBackground: 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 CHA2DS2-VASc scores of patients identified by the model in the pilot are presented. 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 CHA2DS2-VASc score ≥ 2. 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.Item Lower provider volume is associated with higher failure rates for endoscopic retrograde cholangiopancreatography(Ovid Technologies (Wolters Kluwer) - Lippincott Williams & Wilkins, 2013-12) Coté, Gregory A.; Imler, Timothy D.; Xu, Huiping; Teal, Evgenia; French, Dustin D.; Imperiale, Thomas F.; Rosenman, Marc B.; Wilson, Jeffrey S.; Hui, Siu L.; Sherman, Stuart; Department of Medicine, IU School of MedicineBACKGROUND: Among physicians who perform endoscopic retrograde cholangiopancreatography (ERCP), the relationship between procedure volume and outcome is unknown. OBJECTIVE: Quantify the ERCP volume-outcome relationship by measuring provider-specific failure rates, hospitalization rates, and other quality measures. RESEARCH DESIGN: Retrospective cohort. SUBJECTS: A total of 16,968 ERCPs performed by 130 physicians between 2001 and 2011, identified in the Indiana Network for Patient Care. MEASURES: Physicians were classified by their average annual Indiana Network for Patient Care volume and stratified into low (<25/y) and high (≥25/y). Outcomes included failed procedures, defined as repeat ERCP, percutaneous transhepatic cholangiography or surgical exploration of the bile duct≤7 days after the index procedure, hospitalization rates, and 30-day mortality. RESULTS: Among 15,514 index ERCPs, there were 1163 (7.5%) failures; the failure rate was higher among low (9.5%) compared with high volume (5.7%) providers (P<0.001). A second ERCP within 7 days (a subgroup of failure rate) occurred more frequently when the original ERCP was performed by a low-volume (4.1%) versus a high-volume physician (2.3%, P=0.013). Patients were more frequently hospitalized within 24 hours when the ERCP was performed by a low-volume (28.3%) versus high-volume physician (14.8%, P=0.002). Mortality within 30 days was similar (low=1.9%, high=1.9%). Among low-volume physicians and after adjusting, the odds of having a failed procedure decreased 3.3% (95% confidence interval, 1.6%-5.0%, P<0.001) with each additional ERCP performed per year. CONCLUSIONS: Lower provider volume is associated with higher failure rate for ERCP, and greater need for postprocedure hospitalization.Item Measuring the quality of colonoscopy: Where are we now and where are we going?(Elsevier, 2015-09) Imler, Timothy D.; Department of Medicine, IU School of MedicineItem Provider Specific Quality Measurement for Endoscopic Retrograde Cholangiopancreatography Utilizing Natural Language Processing(Elsevier, 2018-01) Imler, Timothy D.; Sherman, Stuart; Imperiale, Thomas F.; Xu, Huiping; Ouyang, Fangqian; Beesley, Christopher; Hilton, Charity; Coté, Gregory A.; Medicine, School of MedicineBACKGROUND AND AIMS: Natural language processing (NLP) is an information retrieval technique that has been shown to accurately identify quality measures for colonoscopy. There are no systematic methods by which to track adherence to quality measures for ERCP, the highest risk endoscopic procedure widely used in practice. Our aim was to demonstrate the feasibility of using NLP to measure adherence to ERCP quality indicators across individual providers. METHODS: ERCPs performed by 6 providers at a single institution from 2006 to 2014 were identified. Quality measures were defined using society guidelines and from expert opinion, and then extracted using a combination of NLP and data mining (eg, ICD9-CM codes). Validation for each quality measure was performed by manual record review. Quality measures were grouped into preprocedure (5), intraprocedure (6), and postprocedure (2). NLP was evaluated using measures of precision and accuracy. RESULTS: A total of 23,674 ERCPs were analyzed (average patient age, 52.9 ± 17.8 years, 14,113 were women [59.6%]). Among 13 quality measures, precision of NLP ranged from 84% to 100% with intraprocedure measures having lower precision (84% for precut sphincterotomy). Accuracy of NLP ranged from 90% to 100% with intraprocedure measures having lower accuracy (90% for pancreatic stent placement). CONCLUSIONS: NLP in conjunction with data mining facilitates individualized tracking of ERCP providers for quality metrics without the need for manual medical record review. Incorporation of these tools across multiple centers may permit tracking of ERCP quality measures through national registries.Item Provider-specific quality measurement for ERCP using natural language processing(Elsevier, 2017) Imler, Timothy D.; Sherman, Stuart; Imperiale, Thomas F.; Xu, Huiping; Ouyang, Fangqian; Beesley, Christopher; Hilton, Charity; Coté, Gregory A.; Department of Medicine, IU School of MedicineBackground and Aims Natural language processing (NLP) is an information retrieval technique that has been shown to accurately identify quality measures for colonoscopy. There are no systematic methods by which to track adherence to quality measures for ERCP, the highest risk endoscopic procedure widely used in practice. Our aim was to demonstrate the feasibility of using NLP to measure adherence to ERCP quality indicators across individual providers. Methods ERCPs performed by 6 providers at a single institution from 2006 to 2014 were identified. Quality measures were defined using society guidelines and from expert opinion, and then extracted using a combination of NLP and data mining (eg, ICD9-CM codes). Validation for each quality measure was performed by manual record review. Quality measures were grouped into preprocedure (5), intraprocedure (6), and postprocedure (2). NLP was evaluated using measures of precision and accuracy. Results A total of 23,674 ERCPs were analyzed (average patient age, 52.9 ± 17.8 years, 14,113 were women [59.6%]). Among 13 quality measures, precision of NLP ranged from 84% to 100% with intraprocedure measures having lower precision (84% for precut sphincterotomy). Accuracy of NLP ranged from 90% to 100% with intraprocedure measures having lower accuracy (90% for pancreatic stent placement). Conclusions NLP in conjunction with data mining facilitates individualized tracking of ERCP providers for quality metrics without the need for manual medical record review. Incorporation of these tools across multiple centers may permit tracking of ERCP quality measures through national registries.Item Tailoring Surveillance Colonoscopy in Patients with Advanced Adenomas(Elsevier, 2021) Kahi, Charles J.; Myers, Laura J.; Stump, Timothy E.; Imler, Timothy D.; Sherer, Eric A.; Larson, Jason; Imperiale, Thomas F.; Medicine, School of MedicineBackground and Aims Patients with advanced colorectal adenomas (AA) are directed to undergo intensive surveillance. However, the benefit derived from surveillance may be outweighed by the risk of death from non-colorectal cancer (CRC) causes, leading to uncertainty on how best to individualize follow-up. The aim of this study was to derive a risk prediction model and risk index that estimates and stratifies the risk for non-colorectal cancer mortality (NCM) subsequent to diagnosis and removal of AA. Methods We conducted a retrospective cohort study of Veterans > 40 years who had colonoscopy for diagnostic or screening indications at 13 VAMCs between 2002 and 2009, and had one or more AAs. The primary outcome was non-CRC mortality (NCM) using a fixed follow-up time period of 5 years. Logistic regression using the lasso technique was used to identify factors independently associated with non-CRC mortality (NCM), and an index based on points from regression coefficients was constructed to estimate risk of 5-year NCM. Results We identified 2,943 Veterans with AA (mean age (SD) 63 (8.6) years, 98% male, 74% white), with an overall 5-year mortality of 16.7%, which was nearly all due to NCM (16.6%). Age, comorbidity burden, specific comorbid conditions, and hospitalization within the preceding year were independently associated with NCM. The risk prediction model had a goodness of fit (calibration) p-value of 0.41, and c-statistic (discrimination) of 0.74 (95% CI, 0.71-0.76). Based on comparable 5-year risks of NCM, the scores comprised 3 risk categories: low (score of 0-1), intermediate (score of 2-4) and high (score of ≥ 5), in which NCM occurred in 6.5%, 14.1%, and 33.2%, respectively. Conclusions We derived a risk prediction model that identifies Veterans at high risk of NCM within 5 years, and who are thus unlikely to benefit from further surveillance.