Modelo predictivo preliminar para la identificación de pacientes con oportunidades de mejora farmacoterapéutica

Objective: To develop a prediction model for identifying patients with the possibility of improving pharmacotherapy during the process of pharmaceutical validation of the prescription. Method: Cross-sectional study over two months, performed in the Internal Medicine and Infectious Disease divisions. Detecting opportunities for improving quality of pharmaco¬therapy is done by means of a pharmacist's validation of the prescription. Based on the information we obtained through this process, we performed a multivariate logistic regression analysis using as prognostic factors the demographic, pharmacotherapy and clinical variables related to identifying any drug-related problems (DRPs) in the patient. The model's prediction validity was assessed using the diagnostic performance curve and calculating the area under it. Results: The final prediction model included the variables age, cardiovascular drugs (digoxin) and drugs for which a dosage adjustment is recommended in the case of organ failures. Analysis of the ROC curve showed an estimated area under the curve AUCROC)of 84.0% (95% CI: 80.5-87.1), a sensitivity value of 28% (95% CI: 24.07-32.19), a specificity value of 99.10% (95% CI: 97.80-99.73), a positive predictive value of 77.78% and a negative predictive value of 92.41%. Conclusion: The resulting prediction model enables population-based detection of pharmacotherapy safety risks in adult patients admitted to the selected hospital units. The predictive variables used by the model are commonly used in daily practice.