Infection and Drug Resistance (Aug 2024)
A Nomogram for Predicting the Effectiveness of Consultations on Multi-Drug Resistant Infections: An Exploration for Clinical Pharmacy Services
Abstract
Hui Ao, Huizhu Song, Jing Li Department of Pharmacy, the Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, People’s Republic of ChinaCorrespondence: Jing Li, Department of Pharmacy, the Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299, Qingyang Road, Wuxi, Liangxi District, People’s Republic of China, Email [email protected]: The increasing multi-drug resistance (MDR) is a serious threat to human health. The appropriate use of antibiotics can control the progression of MDR and clinical pharmacists play an important role in the rational use of antibiotics. There are many factors that influence the effectiveness of multi-drug resistant organisms (MDRO) infection consultations. The study aimed to establish a model to predict the outcome of consultation and explore ways to improve clinical pharmacy services.Patients and methods: Patients diagnosed with MDRO infection and consulted by clinical pharmacists were included. Univariate analysis and multivariate logistic regression analysis were used to identify independent risk factors for MDRO infection consultation effectiveness, and then a nomogram was constructed and validated.Results: 198 patients were finally included. The number of underlying diseases (OR=1.720, 95% CI: 1.260– 2.348), whether surgery was performed prior to infection (OR=8.853, 95% CI: 2.668– 29.373), ALB level (OR=0.885, 95% CI: 0.805~0.974), pharmacist title (OR=3.463, 95% CI: 1.277~9.396) and whether the recommendation was taken up (OR=0.117, 95% CI: 0.030~0.462) were identified as independent influences on the effectiveness of the consultation. The nomogram prediction model was successfully constructed and the AUC of the training set and the verification set were 0.849 (95% CI: 0.780– 0.917) and 0.761 (95% CI: 0.616– 0.907) respectively. The calibration curves exhibited good overlap between the data predicted by the model and the actual data.Conclusion: A nomogram model was developed to predict the risk of consultation failure and was shown to be good accuracy and good prediction efficiency, which can provide proactive interventions to improve outcomes for potentially treatment ineffective patients.Keywords: multi-drug resistance, nomogram, clinical pharmacist, consultation, clinical pharmacy services
