To integrate the quantitative and qualitative data collected as part of the PEACH (Procalcitonin: Evaluation of Antibiotic use in COVID-19 Hospitalised patients) study, which evaluated whether procalcitonin (PCT) testing should be used to guide antibiotic prescribing and safely reduce antibiotic use among patients admitted to acute UK National Health Service (NHS) hospitals.

Triangulation to integrate quantitative and qualitative data.

Four data sources in 148 NHS hospitals in England and Wales including data from 6089 patients.

A triangulation protocol was used to integrate three quantitative data sources (survey, organisation-level data and patient-level data: data sources 1, 2 and 3) and one qualitative data source (clinician interviews: data source 4) collected as part of the PEACH study. Analysis of data sources initially took place independently, and then, key findings for each data source were added to a matrix. A series of interactive discussion meetings took place with quantitative, qualitative and clinical researchers, together with patient and public involvement (PPI) representatives, to group the key findings and produce seven statements relating to the study objectives. Each statement and the key findings related to that statement were considered alongside an assessment of whether there was agreement, partial agreement, dissonance or silence across all four data sources (convergence coding). The matrix was then interpreted to produce a narrative for each statement.

To explore whether PCT testing safely reduced antibiotic use during the first wave of the COVID-19 pandemic.

Seven statements were produced relating to the PEACH study objective. There was agreement across all four data sources for our first key statement, ‘During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing’. The second statement was related to this key statement, ‘During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing safely reduced antibiotic prescribing’. Partial agreement was found between data sources 3 (quantitative patient-level data) and 4 (qualitative clinician interviews). There were no data regarding safety from data sources 1 or 2 (quantitative survey and organisational-level data) to contribute to this statement. For statements three and four, ‘PCT was not used as a central factor influencing antibiotic prescribing’, and ‘PCT testing reduced antibiotic prescribing in the emergency department (ED)/acute medical unit (AMU),’ there was agreement between data source 2 (organisational-level data) and data source 4 (interviews with clinicians). The remaining two data sources (survey and patient-level data) contributed no data on this statement. For statement five, ‘PCT testing reduced antibiotic prescribing in the intensive care unit (ICU)’, there was disagreement between data sources 2 and 3 (organisational-level data and patient-level data) and data source 4 (clinician interviews). Data source 1 (survey) did not provide data on this statement. We therefore assigned dissonance to this statement. For statement six, ‘There were many barriers to implementing PCT testing during the first wave of COVID-19’, there was partial agreement between data source 1 (survey) and data source 4 (clinician interviews) and no data provided by the two remaining data sources (organisational-level data and patient-level data). For statement seven, ‘Local PCT guidelines/protocols were perceived to be valuable’, only data source 4 (clinician interviews) provided data. The clinicians expressed that guidelines were valuable, but as there was no data from the other three data sources, we assigned silence to this statement.

There was agreement between all four data sources on our key finding ‘during the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing’. Data, methodological and investigator triangulation, and a transparent triangulation protocol give validity to this finding.

ISRCTN66682918.

Skin temperature assessment is essential for the diagnosis of cellulitis and monitoring treatment response, but is currently subjective and can contribute to overdiagnosis. We aimed to characterise skin temperature changes over time in cellulitis and compare two objective measurement approaches, a thermal imaging camera (TIC) and a non-contact infrared thermometer (NCIT).

A device comparison study nested within a prospective cohort. We measured limb temperatures daily for 4 days using a TIC and two NCITs.

Two acute hospitals in the UK’s National Health Service.

202 adults (age ≥18 years) diagnosed with lower limb cellulitis who attended hospital for antibiotic treatment.

We used linear mixed-effects models to quantify changes in temperature over time and intraclass correlation coefficients (ICC) to assess reliability. We compared temperature measurements between devices using Lin’s concordance coefficients and Bland-Altman plots with estimated 95% limits of agreement.

202 patients were included: 95% white ethnicity. Baseline limb temperature differences varied between 2.4°C and 3.4°C, depending on the device. All devices showed significant reductions in affected limb temperature per day, with the largest decrease recorded by the TIC (–0.34°C per day, 95% CI –0.48°C to –0.19°C, p

Daily temperature changes may be too small for reliable monitoring at the individual patient level, but cumulative changes from day 0 to day 3 may be sufficient for clinical interpretation, despite limitations in the precision of device measurements. NCITs’ measurement capabilities differ widely, so these devices cannot be used interchangeably. Due to this and the potential benefits of advanced thermal image analysis, TICs should be prioritised for further study in cellulitis. Future research should confirm our findings in different skin tones and explore the clinical utility of thermal imaging in enabling earlier diagnosis or detecting signs of therapeutic failure.