Examining implementation of the first risk stratification-based programme for evaluating penicillin allergy labels in Western Norway hospitals: a study protocol

Introduction

Penicillin allergy is a significant barrier to optimal antibiotic stewardship, contributing to increased antimicrobial resistance, higher treatment costs, prolonged hospital stays and elevated mortality rates. However, approximately 90% of penicillin allergy labels are disproven on evaluation. Risk stratification-based penicillin allergy evaluation (PAE) has proven safe and effective outside allergology departments, yet its adoption in routine clinical practice remains limited. This study aims to examine the implementation of a risk stratification-based PAE programme across hospitals in Western Norway.

Methods and analysis

We aim to examine implementation of a risk stratification-based programme for evaluating and de-labelling declared penicillin allergy in Western Norway hospitals in a multicentre study. The study employs a combined stepped wedge- and interrupted time series design. It includes hospitals of varying service levels in both urban and rural settings. Each participating centre receives 12 months of active implementation support, followed by a 6 month postintervention evaluation. The study is guided by the Consolidated Framework for Implementation Research and the Expert Recommendations for Implementing Change. Implementation outcomes assessed include acceptability, fidelity, adoption, penetration, feasibility and sustainability.

Ethics and dissemination

The study was approved by the Regional Ethics Committee of Western Norway (approval number: 199210). All legal and formal research requirements are met. Results will be published open access in peer-reviewed journals and included in the first author’s publicly available PhD. Nationwide implementation of PAE is planned, informed by the study’s findings.

Trial registration number

Current Research Information System (Cristin) in Norway project identification number: 2500736.

An Evaluation of the Multifactorial Model of Cancer-Related Cognitive Impairment

Background Up to 45% of patients report cancer-related cognitive impairment (CRCI). A variety of characteristics are associated with the occurrence and/or severity of CRCI. However, an important gap in knowledge of risk factors for CRCI is the relative contribution of each factor. The multifactorial model of cancer-related cognitive impairment (MMCRCI) is a conceptual model of CRCI that can be used to evaluate the strength of relationships between various factors and CRCI. Objectives The purpose of this study was to use structural regression methods to evaluate the MMCRCI using data from a large sample of outpatients receiving chemotherapy (n = 1,343). Specifically, the relationships between self-reported CRCI and four MMCRCI concepts (i.e., social determinants of health, patient-specific factors, treatment factors, and co-occurring symptoms) were examined. The goals were to determine how well the four concepts predicted CRCI and determine the relative contribution of each concept to deficits in perceived cognitive function. Methods This study is part of a larger, longitudinal study that evaluated the symptom experience of oncology outpatients receiving chemotherapy. Adult patients were diagnosed with breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding 4 weeks; were scheduled to receive at least two additional cycles of chemotherapy; were able to read, write, and understand English; and gave written informed consent. Self-reported CRCI was assessed using the attentional function index. Available study data were used to define the latent variables. Results On average, patients were 57 years of age, college educated, and with a mean Karnofsky Performance Status score of 80. Of the four concepts evaluated, whereas co-occurring symptoms explained the largest amount of variance in CRCI, treatment factors explained the smallest amount of variance. A simultaneous structural regression model that estimated the joint effect of the four exogenous latent variables on the CRCI latent variable was not significant. Discussion These findings suggest that testing individual components of the MMCRCI may provide useful information on the relationships among various risk factors, as well as refinements of the model. In terms of risk factors for CRCI, co-occurring symptoms may be more significant than treatment factors, patient-specific factors, and/or social determinants of health in patients receiving chemotherapy.