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Background: In diagnostic evaluation, it is necessary to assess the clinical impact of a new diagnostic as well as its diagnostic accuracy. The comparative interrupted time series design has been proposed as a quasi-experimental approach to evaluating interventions. We show how it can be used in the design of a study to evaluate a point-of-care diagnostic test for C-reactive protein in out-of-hours primary care services, to guide antibiotic prescribing among patients presenting with possible respiratory tract infection. This study consisted of a retrospective phase that used routinely collected monthly antibiotic prescribing data from different study sites, and a prospective phase in which antibiotic prescribing rates were monitored after the C-reactive protein diagnostic was introduced at some of the sites., Methods: Of 8 study sites, 3 were assigned to receive the diagnostic and 5 were assigned as controls. We obtained retrospective monthly time series of respiratory tract targeted antibiotic prescriptions at each site. Separate ARIMA models at each site were used these to forecast monthly prescription counts that would be expected in the prospective phase, using simulation to obtain a set of 1-year predictions alongside their standard errors. We show how these forecasts can be combined to test for a change in prescription rates after introduction of the diagnostic and estimate power to detect this change., Results: Fitted time series models at each site were stationary and showed second-order annual seasonality, with a clear December peak in prescriptions, although the timing and extent of the peak varied between sites and between years. Mean one-year predictions of antibiotic prescribing rates based on the retrospective time series analysis differed between sites assigned to receive the diagnostic and those assigned to control. Adjusting for the trend in the retrospective time series at each site removed these differences., Conclusions: Quasi-experimental designs such as comparative interrupted time series can be used in diagnostic evaluation to estimate effect sizes before conducting a full randomised controlled trial or if a randomised trial is infeasible. In multi-site studies, existing retrospective data should be used to adjust for underlying differences between sites to make outcome data from different sites comparable, when possible., (© 2022. The Author(s).)

Background: Serious infections in older people are associated with unplanned hospital admissions and high mortality. Recognising the presence of a serious infection and making an accurate diagnosis are important challenges for General Practice. This study aimed to explore the issues UK GPs face when diagnosing serious infections in older patients., Methods: Qualitative study using semi-structured interviews. 28 GPs from 27 practices were purposively sampled from across the UK to achieve maximum variation in terms of GP role, experience and practice population. Interviews began by asking participants to describe recent or memorable cases where they had assessed older patients with suspected serious infections. Additional questions from the topic guide were used to explore the challenges further. Interview transcripts were coded and analysed using a modified framework approach., Results: Diagnosing serious infection in older adults was perceived to be challenging by participating GPs and the diagnosis was often uncertain. Contributing factors included patient complexity, atypical presentations, as well as a lack of knowledge of patients due to a loss in continuity. Diagnostic challenges were present at each stage of the patient assessment. Scoring systems were mainly used as communication tools. Investigations were sometimes used to resolve diagnostic uncertainty, but availability and speed of result limited their practical use. Clear safety-net plans shared with patients and their families helped GPs manage ongoing uncertainty., Conclusions: Diagnostic challenges are present throughout the assessment of an older adult with a serious infection in primary care. Supporting GPs to provide continuity of care may improve the recognition and developing point of care testing for use in community settings may reduce diagnostic uncertainty.