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Browsing by Author "Kalbaugh, Corey"

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    Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study
    (JMIR, 2025-01-09) Holler, Emma; Ludema, Christina; Ben Miled, Zina; Rosenberg, Molly; Kalbaugh, Corey; Boustani, Malaz; Mohanty, Sanjay; Surgery, School of Medicine
    Background: Postoperative delirium (POD) is a common complication after major surgery and is associated with poor outcomes in older adults. Early identification of patients at high risk of POD can enable targeted prevention efforts. However, existing POD prediction models require inpatient data collected during the hospital stay, which delays predictions and limits scalability. Objective: This study aimed to develop and externally validate a machine learning-based prediction model for POD using routine electronic health record (EHR) data. Methods: We identified all surgical encounters from 2014 to 2021 for patients aged 50 years and older who underwent an operation requiring general anesthesia, with a length of stay of at least 1 day at 3 Indiana hospitals. Patients with preexisting dementia or mild cognitive impairment were excluded. POD was identified using Confusion Assessment Method records and delirium International Classification of Diseases (ICD) codes. Controls without delirium or nurse-documented confusion were matched to cases by age, sex, race, and year of admission. We trained logistic regression, random forest, extreme gradient boosting (XGB), and neural network models to predict POD using 143 features derived from routine EHR data available at the time of hospital admission. Separate models were developed for each hospital using surveillance periods of 3 months, 6 months, and 1 year before admission. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Each model was internally validated using holdout data and externally validated using data from the other 2 hospitals. Calibration was assessed using calibration curves. Results: The study cohort included 7167 delirium cases and 7167 matched controls. XGB outperformed all other classifiers. AUROCs were highest for XGB models trained on 12 months of preadmission data. The best-performing XGB model achieved a mean AUROC of 0.79 (SD 0.01) on the holdout set, which decreased to 0.69-0.74 (SD 0.02) when externally validated on data from other hospitals. Conclusions: Our routine EHR-based POD prediction models demonstrated good predictive ability using a limited set of preadmission and surgical variables, though their generalizability was limited. The proposed models could be used as a scalable, automated screening tool to identify patients at high risk of POD at the time of hospital admission.
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    Perioperative Anticholinergic Medication Use and Incident Dementia Among Older Surgical Patients: A Retrospective Cohort Study using Real-World Data
    (Springer, 2025) Holler, Emma; Mohanty, Sanjay; Rosenberg, Molly; Kalbaugh, Corey; Ben Miled, Zina; Boustani, Malaz; Ludema, Christina; Surgery, School of Medicine
    Background: Inpatient anticholinergic medications have been associated with a higher likelihood of postoperative delirium in older adults. However, it remains unclear whether administering anticholinergic medications after surgery adversely affects long-term cognitive function. Objective: We aimed to evaluate the relationship between in-hospital anticholinergic medications and time to incident dementia in a cohort of older surgical patients. We also sought to determine whether the association between in-hospital anticholinergic drugs and dementia differed by sex and prehospital anticholinergic exposure. Methods: This was a retrospective analysis of electronic health record data from a regional health information exchange. The study population included patients aged 50 years and older who underwent major surgery requiring an inpatient stay between 2014 and 2021. Orders for anticholinergic medications were identified using the anticholinergic cognitive burden (ACB) scale. A Cox proportional hazards model was used to estimate the association between inpatient orders for strong anticholinergics and incident dementia after hospital discharge. Cause-specific hazards were modeled. Stratification and relative excess risk due to interaction (RERI) were used to investigate multiplicative and additive interaction, respectively. Results: In total, 66,420 surgical encounters were analyzed. Approximately 90% of patients received one or more strong anticholinergics during hospitalization, and 3806 patients developed dementia during a median follow-up of 3.4 years. The median time to dementia was 2.2 years. Each one-order increase in inpatient anticholinergic medications was associated with a 0.60% increase in dementia risk (HR 1.006; 95% CI 1.003-1.008). This association was stronger among patients who were prescribed anticholinergics before hospitalization (RERI 0.10; 95% CI 0.08-1.12; p = 0.0122). Conclusions: Perioperative anticholinergics may increase the risk of dementia after major surgery. Avoiding these medications in hospitalized older adults may improve long-term cognitive outcomes.
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