Search

Hypertension is a widely prevalent disease and uncontrolled hypertension predisposes affected individuals to severe adverse effects. Though the importance of controlling hypertension is clear, the multitude of therapeutic regimens and patient factors that affect the success of blood pressure control makes it difficult to predict the likelihood to predict whether a patient's blood pressure will be controlled. This project endeavors to investigate whether machine learning can accurately predict the control of a patient's hypertension within 12 months of a clinical encounter. To build the machine learning model, a retrospective review of the electronic medical records of 350,008 patients 18 years of age and older between January 1, 2015 and June 1, 2022 was performed to form model training and testing cohorts. The data included in the model included medication combinations, patient laboratory values, vital sign measurements, comorbidities, healthcare encounters, and demographic information. The mean age of the patient population was 65.6 years with 161,283 (46.1%) men and 275,001 (78.6%) white. A sliding time window of data was used to both prohibit data leakage from training sets to test sets and to maximize model performance. This sliding window resulted in using the study data to create 287 predictive models each using 2 years of training data and one week of testing data for a total study duration of five and a half years. Model performance was combined across all models. The primary outcome, prediction of blood pressure control within 12 months demonstrated an area under the curve of 0.76 (95% confidence interval; 0.75–0.76), sensitivity of 61.52% (61.0–62.03%), specificity of 75.69% (75.25–76.13%), positive predictive value of 67.75% (67.51–67.99%), and negative predictive value of 70.49% (70.32–70.66%). An AUC of 0.756 is considered to be moderately good for machine learning models. While the accuracy of this model is promising, it is impossible to state with certainty the clinical relevancy of any clinical support ML model without deploying it in a clinical setting and studying its impact on health outcomes. By also incorporating uncertainty analysis for every prediction, the authors believe that this approach offers the best-known solution to predicting hypertension control and that machine learning may be able to improve the accuracy of hypertension control predictions using patient information already available in the electronic health record. This method can serve as a foundation with further research to strengthen the model accuracy and to help determine clinical relevance. [ABSTRACT FROM AUTHOR]

Polypharmacy, or use of multiple medications, is associated with patient factors. Less is known regarding variation in polypharmacy by individual physicians. The objective of this study was to assess patient and physician factors associated with polypharmacy among older patients. This is a cross-sectional study of patients aged ≥65 years with a primary care visit at Cleveland Clinic Health System in 2015 and their physicians. We collected patient demographics, comorbidities and current medications from the electronic health record, including potentially inappropriate medications (PIMs). We used mixed effects linear regression to estimate adjusted differences in the number of medications by patient factors. We generated adjusted prescribing rates for individual physicians and assessed differences in physician performance on quality measures by their prescribing rate. Our study included 44,570 patients who were prescribed an average of 6.8 medications (standard deviation: 4.0) by 701 physicians. Female sex, higher BMI, having Medicaid insurance, current or former smoking status, comorbidities and seeing a specialist were associated with number of medications. Age was not. Among 267 physicians who saw ≥20 study-eligible patients, the adjusted mean number of medications per patient ranged from 5.2 to 9.6. Compared to physicians who prescribed above the mean, lower prescribing physicians performed significantly better on medication reconciliation (p =.007) and hypertension control (p <.001) and prescribed fewer PIMs (p <.001). Individual physicians varied in their prescribing practices, even after adjusting for patient demographic and clinical characteristics. Interventions to reduce polypharmacy in older adults should target high prescribing physicians, as physician behavior is more actionable than patient factors. [ABSTRACT FROM AUTHOR]