Development and validation of a predictive model to predict and manage drug shortages. (4th April 2021)

• Record Type: Journal Article
• Authors: Liu, Ina; Colmenares, Evan; Tak, Casey; Vest, Mary-Haston; Clark, Henry; Oertel, Maryann; Pappas, Ashley
• Is Part Of: American journal of health-system pharmacy. Volume 78:Number 14(2021)
• Page Start: 1309
• Page End: 1316
• Publication Date: 2021-04-04
• Subjects: drug shortage -- predictive analytics -- predictors -- models -- statistical; Hospital pharmacies -- United States -- Periodicals; 615.1
• DOI: 10.1093/ajhp/zxab152
• Languages: English
• ISSNs: 1079-2082

Abstract

Abstract: Purpose: Pharmacy departments across the country are problem-solving the growing issue of drug shortages. We aim to change the drug shortage management strategy from a reactive process to a more proactive approach using predictive data analytics. By doing so, we can drive our decision-making to more efficiently manage drug shortages. Methods: Internal purchasing, formulary, and drug shortage data were reviewed to identify drugs subject to a high shortage risk ("shortage drugs") or not subject to a high shortage risk ("nonshortage drugs"). Potential candidate predictors of drug shortage risk were collected from previous literature. The dataset was trained and tested using 2 methods, including k-fold cross-validation and a 70/30 partition into a training dataset and a testing dataset, respectively. Results: A total of 1, 517 shortage and nonshortage drugs were included. The following candidate predictors were used to build the dataset: dosage form, therapeutic class, controlled substance schedule (Schedule II or Schedules III-V), orphan drug status, generic versus branded status, and number of manufacturers. Predictors that positively predicted shortages included classification of drugs as intravenous-only, both oral and intravenous, antimicrobials, analgesics, electrolytes, anesthetics, and cardiovascular agents. Predictors that negatively predicted a shortage included classification as an oral-only agent, branded-only agent, antipsychotic, Schedule II agent, or orphan drug, as well as the total number of manufacturers. The calculated sensitivity was 0.71; the specificity, 0.93; the accuracy, 0.87; and the C statistic, 0.93. Conclusion: The study demonstrated the use of predictive analytics to create a drug shortage model using drug characteristics and manufacturing variables.