Development and validation of a multivariable prediction model for infection-related complications in patients with common infections in UK primary care and the extent of risk-based prescribing of antibiotics

Chirag Mistry, Victoria Palin, Yan Li, Glen P. Martin, David Jenkins, William Welfare, Darren M. Ashcroft, Tjeerd Van Staa*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Background: Antimicrobial resistance is driven by the overuse of antibiotics. This study aimed to develop and validate clinical prediction models for the risk of infection-related hospital admission with upper respiratory infection (URTI), lower respiratory infection (LRTI) and urinary tract infection (UTI). These models were used to investigate whether there is an association between the risk of an infection-related complication and the probability of receiving an antibiotic prescription. Methods: The study used electronic health record data from general practices contributing to the Clinical Practice Research Datalink (CPRD GOLD) and Welsh Secure Anonymised Information Linkage (SAIL), both linked to hospital records. Patients who visited their general practitioner with an incidental URTI, LRTI or UTI were included and followed for 30 days for hospitalisation due to infection-related complications. Predictors included age, gender, clinical and medication risk factors, ethnicity and socioeconomic status. Cox proportional hazards regression models were used with predicted risks independently validated in SAIL. Results: The derivation and validation cohorts included 8.1 and 2.7 million patients in CPRD and SAIL, respectively. A total of 7125 (0.09%) hospital admissions occurred in CPRD and 7685 (0.28%) in SAIL. Important predictors included age and measures of comorbidity. Initial attempts at validating in SAIL (i.e. transporting the models with no adjustment) indicated the need to recalibrate the models for age and underlying incidence of infections; internal bootstrap validation of these updated models yielded C-statistics of 0.63 (LRTI), 0.69 (URTI) and 0.73 (UTI) indicating good calibration. For all three infection types, the rate of antibiotic prescribing was not associated with patients' risk of infection-related hospital admissions. Conclusion: The risk for infection-related hospital admissions varied substantially between patients, but prescribing of antibiotics in primary care was not associated with risk of hospitalisation due to infection-related complications. Our findings highlight the potential role of clinical prediction models to help inform decisions of prescribing of antibiotics in primary care.

    Original languageEnglish
    Article number118
    JournalBMC Medicine
    Volume18
    Issue number1
    DOIs
    Publication statusPublished - 21 May 2020

    Bibliographical note

    Funding Information:
    This study was funded by Connected Health Cities. Connected Health Cities is a Northern Health Science Alliance-led programme funded by the Department of Health and delivered by a consortium of academic and NHS organisations across the north of England.

    Publisher Copyright:
    © 2020 The Author(s).

    Keywords

    • Antimicrobial resistance
    • Clinical risk prediction
    • Common infections
    • Cox regression
    • Risk-based prescribing

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