Development and validation of a predictive model to predict and manage drug shortages

Abstract

Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. 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.

Development and validation of the nomogram to predict the risk of hospital drug shortages: A prediction model

“…Seven predictors were included in the nomogram: volume-based procurement, therapeutic class, drug dosage form, distribution firm, take orders, order date, and unit price. Among them, drug dosage forms and therapeutic classes are consistent with previous reports [24].…”

“…The study also found no statistical significance in the probability of shortage for both Chinese and foreign-funded drugs. This is inconsistent with some literature reports [24]. The reason may be that the national conditions of each country are different or the…”

“…After the model is visualized, it can be more intuitively displayed using the nomogram. Although some studies [24] have reported that logistic regression-based models are used to predict the drug shortage risk in the pharmacy sector, some of these variables are difficult to obtain. In addition, these studies were not comprehensive enough to evaluate the model visualize it, or show its clinical utility.…”

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“…A few publications studied the costs of specific shortages but excluded personnel costs [ 45 – 47 ]. For our framework, costs can be based on historical costs, actual costs as well as forecasting costs using financial or economic modelling [ 48 , 49 ].…”

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