ObjectivesThis study aimed to examine the prevalence of frailty coding within the Dr Foster Global Comparators (GC) international database. We then aimed to develop and validate a risk prediction model, based on frailty syndromes, for key outcomes using the GC data set.DesignA retrospective cohort analysis of data from patients over 75 years of age from the GC international administrative data. A risk prediction model was developed from the initial analysis based on seven frailty syndrome groups and their relationship to outcome metrics. A weighting was then created for each syndrome group and summated to create the Dr Foster Global Frailty Score. Performance of the score for predictive capacity was compared with an established prognostic comorbidity model (Elixhauser) and tested on another administrative database Hospital Episode Statistics (2011-2015), for external validation.Setting34 hospitals from nine countries across Europe, Australia, the UK and USA.ResultsOf 6.7 million patient records in the GC database, 1.4 million (20%) were from patients aged 75 years or more. There was marked variation in coding of frailty syndromes between countries and hospitals. Frailty syndromes were coded in 2% to 24% of patient spells. Falls and fractures was the most common syndrome coded (24%). The Dr Foster Global Frailty Score was significantly associated with in-hospital mortality, 30-day non-elective readmission and long length of hospital stay. The score had significant predictive capacity beyond that of other known predictors of poor outcome in older persons, such as comorbidity and chronological age. The score’s predictive capacity was higher in the elective group compared with non-elective, and may reflect improved performance in lower acuity states.ConclusionsFrailty syndromes can be coded in international secondary care administrative data sets. The Dr Foster Global Frailty Score significantly predicts key outcomes. This methodology may be feasibly utilised for case-mix adjustment for older persons internationally.

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In this article, Roberts et al discuss the importance of SNOMED CT in improving electronic health records, and the benefits this can bring healthcare organisations, professionals and patients.

Objectives Patient non-attendance at outpatient appointments (DNA) is a major concern for healthcare providers. Non-attendances increase waiting lists, reduce access to care and may be detrimental not for the patient who did not attend. However, non-targeted interventions to reduce the DNA rate may not be effective and thus we aim to produce a model which can accurately predict which appointments will be attended. Methods In this work, a random forest classification algorithm was trained to predict whether an appointment will be missed using 7 million past outpatient appointments. The model was applied to patients of all ages and appointments with all specialties at a major London teaching hospital including a validation set covering the COVID-19 pandemic. Results The model achieves an AUROC score of 0.76 and accuracy of 73% on test data. We find that the waiting period between booking an the appointment, the patient's past DNA behaviour, and the levels of deprivation in their local area are important factors in predicting future DNAs. Discussion Our model is strongly predictive of whether a hospital outpatient appointment will be attended. Its performance on both patients who did not appear in the training data and appointments from a different time period which covers the Covid-19 pandemic indicate it generalized well across both face to face and virtual appointments and could be used to target resources and intervention towards those patients who are likely to miss an appointment. Moreover, it highlights the impact of deprivation on patient access to healthcare Conclusion Our model successfully predicts patient attendance at outpatient appointments.

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