A Nomogram for Predicting the Effectiveness of Consultations on Multi-Drug Resistant Infections: An Exploration for Clinical Pharmacy Services

用于预测多重耐药感染咨询效果的列线图:临床药学服务的探索

阅读:1

Abstract

PURPOSE: 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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。