A Clinical Prediction Model of Medication Adherence in Hypertensive Patients in a Chinese Community Hospital in Beijing.

BACKGROUND Hypertension remains a global health problem. Since there is a significant positive correlation between antihypertensive medication adherence and blood pressure control, it is therefore of great importance to elucidate the determinants of adherence to antihypertensive medications among hypertensive patients. METHODS Hereby, we retrospectively analyzed the medical records of a hypertensive cohort recruited from a community hospital in Beijing, China, to investigate the factors affecting adherence to antihypertensive medications using decision trees. In addition, all data were assigned into a training set (75%) and testing set (25%) by the random number seed method to build and validate a compliance predictive model. We identified that how many times patients became nonadherent to antihypertensive medications in the year before the first prescription, types of antihypertensive drugs used in the year before the first prescription, body weight, smoking history, total number of hospital visits in the past year, total number of days of medication use in the year before enrollment, age, total number of outpatient follow-ups in the year after the first prescription, and concurrent diabetes greatly affected the compliance to antihypertensive medications. RESULTS The compliance predictive model we built showed a 0.78 sensitivity and 0.69 specificity for the prediction of the compliance to antihypertensive medications, with an area under the representative operating characteristics (ROC) curve of 0.810. CONCLUSIONS Our data provide new insights into the improvements of the compliance to antihypertensive medications, which is beneficial for the management of hypertension, and the compliance predictive model may be used in community-based hypertension management.