The feasibility of using of electronic health records to inform clinical decision making for community-onset urinary tract infection in England

Urinary tract infections (UTIs) are a major source of morbidity, yet differentiating UTI from other conditions and choosing the right treatment remains challenging. Using case studies from English primary and secondary care, this thesis investigates the potential use of electronic health records (EHR) - i.e., data recorded as part of routine care - to aid the diagnosis and management of community-onset UTI. I start by introducing sources of uncertainty in diagnosing UTI (Chapter 1) and review how EHRs have previously been used to study UTIs (Chapter 2). In Chapter 3, I discuss EHR sources available to study UTIs in England. In Chapter 4, I explore how EHRs from primary care can be used to guide antibiotic prescribing for UTI, by evaluating harms of delaying treatment in key patient groups. In Chapters 5 and 6, I explore the use of EHR data as a diagnostic tool to guide antibiotic de-escalation in patients with suspected UTI in the emergency department (ED). Cases of community-onset UTI could be identified in both primary and secondary care data but case definitions relied heavily on coarse diagnostic codes. A lack of information on patients' acute health status, clinical observations (e.g., urine dipstick tests), and reasons for antibiotic prescribing resulted in heterogeneous study cohorts, which likely confounded estimated effects of antibiotic treatment in primary care. In secondary care, early prediction of bacteriuria to guide antibiotic prescribing decisions in the ED proved promising, but model performance varied greatly by patient mix and variable definitions. Better recording of clinical information and a combination of retrospective EHR analysis with prospective cohorts and qualitative approaches will be required to derive actionable insights on UTI. Results based solely on currently available EHR data need to be interpreted carefully

Yet Another ICU Benchmark: A Flexible Multi-Center Framework for Clinical ML

Medical applications of machine learning (ML) have experienced a surge in popularity in recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of available data from electronic health records. Models have been proposed to address numerous ICU prediction tasks like the early detection of complications. While authors frequently report state-of-the-art performance, it is challenging to verify claims of superiority. Datasets and code are not always published, and cohort definitions, preprocessing pipelines, and training setups are difficult to reproduce. This work introduces Yet Another ICU Benchmark (YAIB), a modular framework that allows researchers to define reproducible and comparable clinical ML experiments; we offer an end-to-end solution from cohort definition to model evaluation. The framework natively supports most open-access ICU datasets (MIMIC III/IV, eICU, HiRID, AUMCdb) and is easily adaptable to future ICU datasets. Combined with a transparent preprocessing pipeline and extensible training code for multiple ML and deep learning models, YAIB enables unified model development. Our benchmark comes with five predefined established prediction tasks (mortality, acute kidney injury, sepsis, kidney function, and length of stay) developed in collaboration with clinicians. Adding further tasks is straightforward by design. Using YAIB, we demonstrate that the choice of dataset, cohort definition, and preprocessing have a major impact on the prediction performance - often more so than model class - indicating an urgent need for YAIB as a holistic benchmarking tool. We provide our work to the clinical ML community to accelerate method development and enable real-world clinical implementations. Software Repository: https://github.com/rvandewater/YAIB.Comment: Main benchmark: https://github.com/rvandewater/YAIB, Cohort generation: https://github.com/rvandewater/YAIB-cohorts, Models: https://github.com/rvandewater/YAIB-model

Generalisability of deep learning-based early warning in the intensive care unit: a retrospective empirical evaluation

Deep learning (DL) can aid doctors in detecting worsening patient states early, affording them time to react and prevent bad outcomes. While DL-based early warning models usually work well in the hospitals they were trained for, they tend to be less reliable when applied at new hospitals. This makes it difficult to deploy them at scale. Using carefully harmonised intensive care data from four data sources across Europe and the US (totalling 334,812 stays), we systematically assessed the reliability of DL models for three common adverse events: death, acute kidney injury (AKI), and sepsis. We tested whether using more than one data source and/or explicitly optimising for generalisability during training improves model performance at new hospitals. We found that models achieved high AUROC for mortality (0.838-0.869), AKI (0.823-0.866), and sepsis (0.749-0.824) at the training hospital. As expected, performance dropped at new hospitals, sometimes by as much as -0.200. Using more than one data source for training mitigated the performance drop, with multi-source models performing roughly on par with the best single-source model. This suggests that as data from more hospitals become available for training, model robustness is likely to increase, lower-bounding robustness with the performance of the most applicable data source in the training data. Dedicated methods promoting generalisability did not noticeably improve performance in our experiments

Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015)

[EN] Objectives To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015. Design Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording. Setting 400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES). Main outcomes The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45). Results Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%-62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (-34.4%) and unspecified stroke (-42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines. Conclusions Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process.VN is funded by a Public Health England PhD Studentship. RWA is supported by a Wellcome Trust Clinical Research Career Development Fellowship (206602/Z/17/Z). JMGG and CS contributions to this work were partially supported by the MTS4up Spanish project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R), the CrowdHealth H2020-SC1-2016-CNECT project (No. 727560) (JMGG) and the Inadvance H2020-SC1-BHC-2018-2020 project (No. 825750). PR and DA did not receive any direct funding for this project. Access to the Clinical Practice Research Datalink was supported by the UK Economic and Social Research Council (ES/P008321/1). Access to aggregated Hospital Episode Statistics was provided by Public Health England. This work was further supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust.Rockenschaub, P.; Nguyen, V.; Aldridge, RW.; Acosta, D.; Garcia-Gomez, JM.; Sáez Silvestre, C. (2020). Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015). BMJ Open. 10(2):1-9. https://doi.org/10.1136/bmjopen-2019-034396S19102Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. doi:10.1136/amiajnl-2012-001145Burton, P. R., Murtagh, M. J., Boyd, A., Williams, J. B., Dove, E. S., Wallace, S. E., … Knoppers, B. M. (2015). Data Safe Havens in health research and healthcare. Bioinformatics, 31(20), 3241-3248. doi:10.1093/bioinformatics/btv279Cruz-Correia R , Rodrigues P , Freitas A . Chapter: 4, Data quality and integration issues in electronic health records. In: Information discovery on electronic health records. CRC Press, 2009: 55–95.Massoudi, B. L., Goodman, K. W., Gotham, I. J., Holmes, J. H., Lang, L., Miner, K., … Fu, P. C. (2012). An informatics agenda for public health: summarized recommendations from the 2011 AMIA PHI Conference. Journal of the American Medical Informatics Association, 19(5), 688-695. doi:10.1136/amiajnl-2011-000507Schlegel, D. R., & Ficheur, G. (2017). Secondary Use of Patient Data: Review of the Literature Published in 2016. Yearbook of Medical Informatics, 26(01), 68-71. doi:10.15265/iy-2017-032Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. doi:10.1136/amiajnl-2011-000681Herrett, E., Thomas, S. L., Schoonen, W. M., Smeeth, L., & Hall, A. J. (2010). Validation and validity of diagnoses in the General Practice Research Database: a systematic review. British Journal of Clinical Pharmacology, 69(1), 4-14. doi:10.1111/j.1365-2125.2009.03537.xSáez, C., Zurriaga, O., Pérez-Panadés, J., Melchor, I., Robles, M., & García-Gómez, J. M. (2016). Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. Journal of the American Medical Informatics Association, 23(6), 1085-1095. doi:10.1093/jamia/ocw010Tate AR , Dungey S , Glew S , et al . Quality of recording of diabetes in the UK: how does the GP's method of coding clinical data affect incidence estimates? cross-sectional study using the CPRD database. BMJ Open 2017;7:e012905.doi:10.1136/bmjopen-2016-012905Calvert M , Shankar A , McManus RJ , et al . Effect of the quality and outcomes framework on diabetes care in the United Kingdom: retrospective cohort study. BMJ 2009;338:b1870.doi:10.1136/bmj.b1870Sáez, C., Robles, M., & García-Gómez, J. M. (2016). Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research, 26(1), 312-336. doi:10.1177/0962280214545122Herrett, E., Gallagher, A. M., Bhaskaran, K., Forbes, H., Mathur, R., van Staa, T., & Smeeth, L. (2015). Data Resource Profile: Clinical Practice Research Datalink (CPRD). International Journal of Epidemiology, 44(3), 827-836. doi:10.1093/ije/dyv098Herbert, A., Wijlaars, L., Zylbersztejn, A., Cromwell, D., & Hardelid, P. (2017). Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC). International Journal of Epidemiology, 46(4), 1093-1093i. doi:10.1093/ije/dyx015Chisholm J . The read clinical classification. BMJ 1990;300:1092.doi:10.1136/bmj.300.6732.1092Denaxas, S., Gonzalez-Izquierdo, A., Direk, K., Fitzpatrick, N. K., Fatemifar, G., Banerjee, A., … Hemingway, H. (2019). UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. Journal of the American Medical Informatics Association, 26(12), 1545-1559. doi:10.1093/jamia/ocz105Department for Communities and Local Government . The English Index of Multiple Deprivation (IMD) 2015 - Guidance. Available: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/464430/English_Index_of_Multiple_Deprivation_2015_-_Guidance.pdf [Accessed 8 Dec 2019].Sáez, C., Rodrigues, P. P., Gama, J., Robles, M., & García-Gómez, J. M. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery, 29(4), 950-975. doi:10.1007/s10618-014-0378-6Borg, I., & Groenen, P. (2003). Modern Multidimensional Scaling: Theory and Applications. Journal of Educational Measurement, 40(3), 277-280. doi:10.1111/j.1745-3984.2003.tb01108.xSáez, C., & García-Gómez, J. M. (2018). Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds. International Journal of Medical Informatics, 119, 109-124. doi:10.1016/j.ijmedinf.2018.09.015Conrad, N., Judge, A., Tran, J., Mohseni, H., Hedgecott, D., Crespillo, A. P., … Rahimi, K. (2018). Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. The Lancet, 391(10120), 572-580. doi:10.1016/s0140-6736(17)32520-5Herrett E , Shah AD , Boggon R , et al . Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study. BMJ 2013;346:f2350.doi:10.1136/bmj.f2350Pujades-Rodriguez M , Timmis A , Stogiannis D , et al . Socioeconomic deprivation and the incidence of 12 cardiovascular diseases in 1.9 million women and men: implications for risk prediction and prevention. PLoS One 2014;9:e104671.doi:10.1371/journal.pone.0104671Lee S , Shafe ACE , Cowie MR . Uk stroke incidence, mortality and cardiovascular risk management 1999-2008: time-trend analysis from the general practice research database. BMJ Open 2011;1:e000269.doi:10.1136/bmjopen-2011-000269Bhatnagar, P., Wickramasinghe, K., Williams, J., Rayner, M., & Townsend, N. (2015). The epidemiology of cardiovascular disease in the UK 2014. Heart, 101(15), 1182-1189. doi:10.1136/heartjnl-2015-307516Taylor, C. J., Ordóñez-Mena, J. M., Roalfe, A. K., Lay-Flurrie, S., Jones, N. R., Marshall, T., & Hobbs, F. D. R. (2019). Trends in survival after a diagnosis of heart failure in the United Kingdom 2000-2017: population based cohort study. BMJ, l223. doi:10.1136/bmj.l223Gho JMIH , Schmidt AF , Pasea L , et al . An electronic health records cohort study on heart failure following myocardial infarction in England: incidence and predictors. BMJ Open 2018;8:e018331.doi:10.1136/bmjopen-2017-018331Quint JK , Müllerova H , DiSantostefano RL , et al . Validation of chronic obstructive pulmonary disease recording in the clinical practice research Datalink (CPRD-GOLD). BMJ Open 2014;4:e005540.doi:10.1136/bmjopen-2014-005540Bhaskaran K , Forbes HJ , Douglas I , et al . Representativeness and optimal use of body mass index (BMI) in the UK clinical practice research Datalink (CPRD). BMJ Open 2013;3:e003389.doi:10.1136/bmjopen-2013-003389Booth, H. P., Prevost, A. T., & Gulliford, M. C. (2013). Validity of smoking prevalence estimates from primary care electronic health records compared with national population survey data for England, 2007 to 2011. Pharmacoepidemiology and Drug Safety, 22(12), 1357-1361. doi:10.1002/pds.3537Booth H , Dedman D , Wolf A . CPRD aurum frequently asked questions (FAQs). CPRD 2019.Burns, E. M., Rigby, E., Mamidanna, R., Bottle, A., Aylin, P., Ziprin, P., & Faiz, O. D. (2011). Systematic review of discharge coding accuracy. Journal of Public Health, 34(1), 138-148. doi:10.1093/pubmed/fdr054Marmot, M. G., Stansfeld, S., Patel, C., North, F., Head, J., White, I., … Smith, G. D. (1991). Health inequalities among British civil servants: the Whitehall II study. The Lancet, 337(8754), 1387-1393. doi:10.1016/0140-6736(91)93068-kKivimäki, M., Batty, G. D., Singh-Manoux, A., Britton, A., Brunner, E. J., & Shipley, M. J. (2017). Validity of Cardiovascular Disease Event Ascertainment Using Linkage to UK Hospital Records. Epidemiology, 28(5), 735-739. doi:10.1097/ede.0000000000000688Crosignani, P. G. (2003). Breast cancer and hormone-replacement therapy in the Million Women Study. Maturitas, 46(2), 91-92. doi:10.1016/j.maturitas.2003.09.002Wright FL , Green J , Canoy D , et al . Vascular disease in women: comparison of diagnoses in hospital episode statistics and general practice records in England. BMC Med Res Methodol 2012;12:161.doi:10.1186/1471-2288-12-161Herrett, E., Smeeth, L., Walker, L., & Weston, C. (2010). The Myocardial Ischaemia National Audit Project (MINAP). Heart, 96(16), 1264-1267. doi:10.1136/hrt.2009.192328Silver LE , Heneghan C , Mehta Z , et al . Substantial underestimation of incidence of acute myocardial infarction by hospital discharge diagnostic coding data: a prospective population-based study. Heart 2009;95.Health and Social Care Information Centre . Coding clinic guidance. 5th edn, 2010.Health & Social Care Information Centre . National Clinical Coding Standards - ICD-10. 4th edn, 2013. https://hscic.kahootz.com/connect.ti/t_c_home/view?objectId=31445829Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-33. doi:10.1080/07421222.1996.1151809

Hospital admission after primary care consultation for community-onset lower urinary tract infection: a cohort study of risks and predictors using linked data

BACKGROUND: Urinary tract infections (UTIs) are a common indication for antibiotic prescriptions, reductions in which would reduce antimicrobial resistance (AMR). Risk stratification of patients allows reductions to be made safely. AIM: To identify risk factors for hospital admission following UTI, to inform targeted antibiotic stewardship. DESIGN AND SETTING: Retrospective cohort study of East London primary care patients. METHOD: Hospital admission outcomes following primary care consultation for UTI were analysed using linked data from primary care, secondary care, and microbiology, from 1 April 2012 to 31 March 2017. The outcomes analysed were urinary infection-related hospital admission (UHA) and all-cause hospital admission (AHA) within 30 days of UTI in primary care. Odds ratios between specific variables (demographic characteristics, prior antibiotic exposure, and comorbidities) and the outcomes were predicted using generalised estimating equations, and fitted to a final multivariable model including all variables with a P-value <0.1 on univariable analysis. RESULTS: Of the 169 524 episodes of UTI, UHA occurred in 1336 cases (0.8%, 95% confidence interval [CI] = 0.7 to 0.8) and AHA in 6516 cases (3.8%, 95% CI = 3.8 to 3.9). On multivariable analysis, increased odds of UHA were seen in patients aged 55-74 years (adjusted odds ratio [AOR] 1.49) and ≥75 years (AOR 3.24), relative to adults aged 16-34 years. Increased odds of UHA were also associated with chronic kidney disease (CKD; AOR 1.55), urinary catheters (AOR 2.01), prior antibiotics (AOR 1.38 for ≥3 courses), recurrent UTI (AOR 1.33), faecal incontinence (FI; AOR 1.47), and diabetes mellitus (DM; AOR 1.37). CONCLUSION: Urinary infection-related hospital admission after primary care consultation for community-onset lower UTI was rare; however, increased odds for UHA were observed for some patient groups. Efforts to reduce antibiotic prescribing for suspected UTI should focus on patients aged <55 years without risk factors for complicated UTI, recurrent UTI, DM, or FI

An interdisciplinary mixed-methods approach to developing antimicrobial stewardship interventions:Protocol for the preserving antibiotics through safe stewardship (PASS) research programme [version 1; peer review: 2 approved]

Behaviour change is key to combating antimicrobial resistance. Antimicrobial stewardship (AMS) programmes promote and monitor judicious antibiotic use, but there is little consideration of behavioural and social influences when designing interventions. We outline a programme of research which aims to co-design AMS interventions across healthcare settings, by integrating data-science, evidence- synthesis, behavioural-science and user-centred design. The project includes three work-packages (WP): WP1 (Identifying patterns of prescribing): analysis of electronic health-records to identify prescribing patterns in care-homes, primary-care, and secondary-care. An online survey will investigate consulting/antibiotic-seeking behaviours in members of the public. WP2 (Barriers and enablers to prescribing in practice): Semi-structured interviews and observations of practice to identify barriers/enablers to prescribing, influences on antibiotic-seeking behaviour and the social/contextual factors underpinning prescribing. Systematic reviews of AMS interventions to identify the components of existing interventions associated with effectiveness. Design workshops to identify constraints influencing the form of the intervention. Interviews conducted with healthcare-professionals in community pharmacies, care-homes, primary-, and secondary-care and with members of the public. Topic guides and analysis based on the Theoretical Domains Framework. Observations conducted in care-homes, primary and secondary-care with analysis drawing on grounded theory. Systematic reviews of interventions in each setting will be conducted, and interventions described using the Behaviour Change Technique taxonomy v1. Design workshops in care-homes, primary-, and secondary care. WP3 (Co-production of interventions and dissemination). Findings will be integrated to identify opportunities for interventions, and assess whether existing interventions target influences on antibiotic use. Stakeholder panels will be assembled to co-design and refine interventions in each setting, applying the Affordability, Practicability, Effectiveness, Acceptability, Side-effects and Equity (APEASE) criteria to prioritise candidate interventions. Outputs will inform development of new AMS interventions and/or optimisation of existing interventions. We will also develop web-resources for stakeholders providing analyses of antibiotic prescribing patterns, prescribing behaviours, and evidence reviews

Incidence, healthcare-seeking behaviours, antibiotic use and natural history of common infection syndromes in England:results from the Bug Watch community cohort study

Background: Better information on the typical course and management of acute common infections in the community could inform antibiotic stewardship campaigns. We aimed to investigate the incidence, management, and natural history of a range of infection syndromes (respiratory, gastrointestinal, mouth/dental, skin/soft tissue, urinary tract, and eye). Methods: Bug Watch was an online prospective community cohort study of the general population in England (2018–2019) with weekly symptom reporting for 6 months. We combined symptom reports into infection syndromes, calculated incidence rates, described the proportion leading to healthcare-seeking behaviours and antibiotic use, and estimated duration and severity. Results: The cohort comprised 873 individuals with 23,111 person-weeks follow-up. The mean age was 54 years and 528 (60%) were female. We identified 1422 infection syndromes, comprising 40,590 symptom reports. The incidence of respiratory tract infection syndromes was two per person year; for all other categories it was less than one. 194/1422 (14%) syndromes led to GP (or dentist) consultation and 136/1422 (10%) to antibiotic use. Symptoms usually resolved within a week and the third day was the most severe. Conclusions: Most people reported managing their symptoms without medical consultation. Interventions encouraging safe self-management across a range of acute infection syndromes could decrease pressure on primary healthcare services and support targets for reducing antibiotic prescribing

Can the application of machine learning to electronic health records guide antibiotic prescribing decisions for suspected urinary tract infection in the Emergency Department?

Urinary tract infections (UTIs) are a major cause of emergency hospital admissions, but it remains challenging to diagnose them reliably. Application of machine learning (ML) to routine patient data could support clinical decision-making. We developed a ML model predicting bacteriuria in the ED and evaluated its performance in key patient groups to determine scope for its future use to improve UTI diagnosis and thus guide antibiotic prescribing decisions in clinical practice. We used retrospective electronic health records from a large UK hospital (2011-2019). Non-pregnant adults who attended the ED and had a urine sample cultured were eligible for inclusion. The primary outcome was predominant bacterial growth ≥104 cfu/mL in urine. Predictors included demography, medical history, ED diagnoses, blood tests, and urine flow cytometry. Linear and tree-based models were trained via repeated cross-validation, re-calibrated, and validated on data from 2018/19. Changes in performance were investigated by age, sex, ethnicity, and suspected ED diagnosis, and compared to clinical judgement. Among 12,680 included samples, 4,677 (36.9%) showed bacterial growth. Relying primarily on flow cytometry parameters, our best model achieved an area under the ROC curve (AUC) of 0.813 (95% CI 0.792-0.834) in the test data, and achieved both higher sensitivity and specificity compared to proxies of clinician's judgement. Performance remained stable for white and non-white patients but was lower during a period of laboratory procedure change in 2015, in patients ≥65 years (AUC 0.783, 95% CI 0.752-0.815), and in men (AUC 0.758, 95% CI 0.717-0.798). Performance was also slightly reduced in patients with recorded suspicion of UTI (AUC 0.797, 95% CI 0.765-0.828). Our results suggest scope for use of ML to inform antibiotic prescribing decisions by improving diagnosis of suspected UTI in the ED, but performance varied with patient characteristics. Clinical utility of predictive models for UTI is therefore likely to differ for important patient subgroups including women <65 years, women ≥65 years, and men. Tailored models and decision thresholds may be required that account for differences in achievable performance, background incidence, and risks of infectious complications in these groups

Antibiotic prescribing for lower UTI in elderly patients in primary care and risk of bloodstream infection: A cohort study using electronic health records in England.

BackgroundResearch has questioned the safety of delaying or withholding antibiotics for suspected urinary tract infection (UTI) in older patients. We evaluated the association between antibiotic treatment for lower UTI and risk of bloodstream infection (BSI) in adults aged ≥65 years in primary care.Methods and findingsWe analyzed primary care records from patients aged ≥65 years in England with community-onset UTI using the Clinical Practice Research Datalink (2007-2015) linked to Hospital Episode Statistics and census data. The primary outcome was BSI within 60 days, comparing patients treated immediately with antibiotics and those not treated immediately. Crude and adjusted associations between exposure and outcome were estimated using generalized estimating equations. A total of 147,334 patients were included representing 280,462 episodes of lower UTI. BSI occurred in 0.4% (1,025/244,963) of UTI episodes with immediate antibiotics versus 0.6% (228/35,499) of episodes without immediate antibiotics. After adjusting for patient demographics, year of consultation, comorbidities, smoking status, recent hospitalizations, recent accident and emergency (A&E) attendances, recent antibiotic prescribing, and home visits, the odds of BSI were equivalent in patients who were not treated with antibiotics immediately and those who were treated on the date of their UTI consultation (adjusted odds ratio [aOR] 1.13, 95% CI 0.97-1.32, p-value = 0.105). Delaying or withholding antibiotics was associated with increased odds of death in the subsequent 60 days (aOR 1.17, 95% CI 1.09-1.26, p-value ConclusionsIn this study, we observed that delaying or withholding antibiotics in older adults with suspected UTI did not increase patients' risk of BSI, in contrast with a previous study that analyzed the same dataset, but mortality was increased. Our findings highlight uncertainty around the risks of delaying or withholding antibiotic treatment, which is exacerbated by systematic differences between patients who were and were not treated immediately with antibiotics. Overall, our findings emphasize the need for improved diagnostic/risk prediction strategies to guide antibiotic prescribing for suspected UTI in older adults

Can the application of machine learning to electronic health records guide antibiotic prescribing decisions for suspected urinary tract infection in the Emergency Department?

Urinary tract infections (UTIs) are a major cause of emergency hospital admissions, but it remains challenging to diagnose them reliably. Application of machine learning (ML) to routine patient data could support clinical decision-making. We developed a ML model predicting bacteriuria in the ED and evaluated its performance in key patient groups to determine scope for its future use to improve UTI diagnosis and thus guide antibiotic prescribing decisions in clinical practice. We used retrospective electronic health records from a large UK hospital (2011-2019). Non-pregnant adults who attended the ED and had a urine sample cultured were eligible for inclusion. The primary outcome was predominant bacterial growth ≥104 cfu/mL in urine. Predictors included demography, medical history, ED diagnoses, blood tests, and urine flow cytometry. Linear and tree-based models were trained via repeated cross-validation, re-calibrated, and validated on data from 2018/19. Changes in performance were investigated by age, sex, ethnicity, and suspected ED diagnosis, and compared to clinical judgement. Among 12,680 included samples, 4,677 (36.9%) showed bacterial growth. Relying primarily on flow cytometry parameters, our best model achieved an area under the ROC curve (AUC) of 0.813 (95% CI 0.792-0.834) in the test data, and achieved both higher sensitivity and specificity compared to proxies of clinician's judgement. Performance remained stable for white and non-white patients but was lower during a period of laboratory procedure change in 2015, in patients ≥65 years (AUC 0.783, 95% CI 0.752-0.815), and in men (AUC 0.758, 95% CI 0.717-0.798). Performance was also slightly reduced in patients with recorded suspicion of UTI (AUC 0.797, 95% CI 0.765-0.828). Our results suggest scope for use of ML to inform antibiotic prescribing decisions by improving diagnosis of suspected UTI in the ED, but performance varied with patient characteristics. Clinical utility of predictive models for UTI is therefore likely to differ for important patient subgroups including women <65 years, women ≥65 years, and men. Tailored models and decision thresholds may be required that account for differences in achievable performance, background incidence, and risks of infectious complications in these groups