Development and validation of the nomogram to predict the risk of hospital drug shortages: A prediction model.
PLoS One
; 18(4): e0284528, 2023.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2294383
ABSTRACT
INTRODUCTION:
Reasons 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.OBJECTIVES:
The aim of this study is to establish a nomogram to show the risk of drug shortages.METHODS:
We 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 73. 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.RESULTS:
As 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.CONCLUSIONS:
The 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.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Nomogramas
/
Hospitales
Tipo de estudio:
Estudio observacional
/
Estudio pronóstico
/
Ensayo controlado aleatorizado
Límite:
Humanos
Idioma:
Inglés
Revista:
PLoS One
Asunto de la revista:
Ciencia
/
Medicina
Año:
2023
Tipo del documento:
Artículo
Similares
MEDLINE
...
LILACS
LIS