ArticlePloS one2023
Development and validation of the nomogram to predict the risk of hospital drug shortages: A prediction model.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Navigating Drug Shortages in Obstetric Care.O&G open · 2026Article
- Addressing Drug Shortages at Mediclinic Parkview Hospital: A Five-Year Study of Challenges, Impact, and Strategies.Cureus · 2024Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionReasons for drug shortages are multi-factorial, and patients are greatly injured. So we needed to reduce the frequency and risk of drug shortages in hospitals. At present, the risk of drug shortages in medical institutions rarely used prediction models. To this end, we attempted to proactively predict the risk of drug shortages in hospital drug procurement to make further decisions or implement interventions.
objectivesThe aim of this study is to establish a nomogram to show the risk of drug shortages.
methodsWe collated data obtained using the centralized procurement platform of Hebei Province and defined independent and dependent variables to be included in the model. The data were divided into a training set and a validation set according to 7:3. Univariate and multivariate logistic regression were used to determine independent risk factors, and discrimination (using the receiver operating characteristic curve), calibration (Hosmer-Lemeshow test), and decision curve analysis were validated.
resultsAs a result, volume-based procurement, therapeutic class, dosage form, distribution firm, take orders, order date, and unit price were regarded as independent risk factors for drug shortages. In the training (AUC = 0.707) and validation (AUC = 0.688) sets, the nomogram exhibited a sufficient level of discrimination.
conclusionsThe model can predict the risk of drug shortages in the hospital drug purchase process. The application of this model will help optimize the management of drug shortages in hospitals.
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