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Aims: An investigation of the impact of a multidisciplinary bone infection unit (BIU) undertaking osteomyelitis surgery with a single-stage protocol on clinical outcomes and healthcare utilisation compared to national outcomes in England. Patients and Methods: A tertiary referral multidisciplinary BIU was compared to the rest of England (ROE) and a subset of the 10 next busiest centres based on osteomyelitis treatment episode volume (Top Ten), using the Hospital Episodes Statistics database (HES). A total of 25 006 patients undergoing osteomyelitis surgery between April 2013 and March 2017 were included. Data on secondary healthcare resource utilisation and clinical indicators were extracted for 24 months before and after surgery. Results: Patients treated at the BIU had higher orthopaedic healthcare utilisation in the 2 years prior to their index procedure, with more admissions (p< 0.001) and a mean length of stay (LOS) over 4 times longer than other groups (10.99 d, compared to 2.79 d for Top Ten and 2.46 d for the ROE, p< 0.001). During the index inpatient period, the BIU had fewer mean theatre visits (1.25) compared to the TT (1.98, p< 0.001) and the ROE (1.64, p= 0.001). The index inpatient period was shorter in the BIU (11.84 d), 33.6 % less than the Top Ten (17.83 d, p< 0.001) and 29.9 % shorter than the ROE (16.88 d, p< 0.001). During follow-up, BIU patients underwent fewer osteomyelitis-related reoperations than Top Ten centres (p= 0.0139) and the ROE (p= 0.0137). Mortality was lower (4.71 %) compared to the Top Ten (20.06 %, p< 0.001) and the ROE (22.63 %, p< 0.001). The cumulative BIU total amputation rate was lower (6.47 %) compared to the Top Ten (15.96 %, p< 0.001) and the ROE (12.71 %, p< 0.001). Overall healthcare utilisation was lower in the BIU for all inpatient admissions, LOS, and Accident and Emergency (A&E) attendances. Conclusion: The benefits of managing osteomyelitis in a multi-disciplinary team (MDT) specialist setting included reduced hospital stays, lower reoperation rates for infection recurrence, improved survival, lower amputation rates, and lower overall healthcare utilisation. These results support the establishment of centrally funded multidisciplinary bone infection units that will improve patient outcomes and reduce healthcare utilisation.

Objective The “Bow-tie” optimal pathway discovery analysis uses large clinical event datasets to map clinical pathways and to visualize risks (improvement opportunities) before, and outcomes after, a specific clinical event. This proof-of-concept study assesses the use of NHS Hospital Episode Statistics (HES) in England as a potential clinical event dataset for this pathway discovery analysis approach. Materials and Methods A metaheuristic optimization algorithm was used to perform the “bow-tie” analysis on HES event log data for sepsis (ICD-10 A40/A41) in 2016. Analysis of hospital episodes across inpatient and outpatient departments was performed for the period 730 days before and 365 days after the index sepsis hospitalization event. Results HES data captured a sepsis event for 76 523 individuals (>13 years), relating to 580 000 coded events (across 220 sepsis and non-sepsis event classes). The “bow-tie” analysis identified several diagnoses that most frequently preceded hospitalization for sepsis, in line with the expectation that sepsis most frequently occurs in vulnerable populations. A diagnosis of pneumonia (5 290 patients) and urinary tract infections (UTIs; 2 057 patients) most often preceded the sepsis event, with recurrent UTIs acting as a potential indicative risk factor for sepsis. Discussion This proof-of-concept study demonstrates that a “bow-tie” pathway discovery analysis of the HES database can be undertaken and provides clinical insights that, with further study, could help improve the identification and management of sepsis. The algorithm can now be more widely applied to HES data to undertake targeted clinical pathway analysis across multiple healthcare conditions.

Rupert Jones,1– 3 Andrew Davis,1,2 Brooklyn Stanley,1,2 Steven Julious,4 Dermot Ryan,5 David J Jackson,6 David MG Halpin,7 Katherine Hickman,8 Hilary Pinnock,9 Jennifer K Quint,10 Kamlesh Khunti,11 Liam G Heaney,12 Phillip Oliver,4 Salman Siddiqui,13 Ian Pavord,14 David HM Jones,15 Michael Hyland,3,16 Lewis Ritchie,17 Pam Young,18 Tony Megaw,18 Steve Davis,19 Samantha Walker,20 Stephen Holgate,21 Sue Beecroft,22 Anu Kemppinen,1,2 Francis Appiagyei,1,2 Emma-Jane Roberts,1,2 Megan Preston,1,2 Antony Hardjojo,1,2 Victoria Carter,1,2 Marije van Melle,1,2 David Price1,2,17 1Optimum Patient Care, Cambridge, UK; 2Observational and Pragmatic Research Institute, Singapore, Singapore; 3Faculty of Health, University of Plymouth, Plymouth, UK; 4University of Sheffield, South Yorkshire, UK; 5Usher Institute, University of Edinburgh, Edinburgh, UK; 6Guy’s & St Thomas’ NHS Trust, School of Immunology & Microbial Sciences, King’s College London, London, UK; 7University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK; 8Bradford and Leeds Clinical Commissioning Group, Leeds, UK; 9Asthma UK Centre for Applied Research, Usher Institute, The University of Edinburgh, Edinburgh, UK; 10National Heart & Lung Institute, Imperial College London, London, UK; 11Diabetes Research Centre, University of Leicester, Leicester, UK; 12Wellcome-Wolfson Centre for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland; 13Institute for Lung Health, Leicester National Institute for Health Research Biomedical Research Centre, University of Leicester, Leicester, UK; 14Respiratory Medicine Unit and Oxford Respiratory NIHR BRC, Nuffield Department of Medicine, University of Oxford, Oxford, UK; 15Box Surgery, Wiltshire, UK; 16Plymouth Marjon University, Plymouth, UK; 17Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK; 18Wellbeing Software, Mansfield, UK; 19Interface Clinical Services, Leeds, UK; 20Asthma UK & British Lung Foundation, London, UK; 21Clinical and Experimental Sciences, University of Southampton, Southampton, UK; 22OPEN Health, Buckinghamshire, UKCorrespondence: David PriceAcademic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Polwarth Building, Foresterhill, Aberdeen, AB25 2ZD, UKTel +65 3105 1489Email dprice@opri.sgIntroduction: Symptoms may persist after the initial phases of COVID-19 infection, a phenomenon termed long COVID. Current knowledge on long COVID has been mostly derived from test-confirmed and hospitalized COVID-19 patients. Data are required on the burden and predictors of long COVID in a broader patient group, which includes both tested and untested COVID-19 patients in primary care.Methods: This is an observational study using data from Platform C19, a quality improvement program-derived research database linking primary care electronic health record data (EHR) with patient-reported questionnaire information. Participating general practices invited consenting patients aged 18– 85 to complete an online questionnaire since 7th August 2020. COVID-19 self-diagnosis, clinician-diagnosis, testing, and the presence and duration of symptoms were assessed via the questionnaire. Patients were considered present with long COVID if they reported symptoms lasting ≥ 4 weeks. EHR and questionnaire data up till 22nd January 2021 were extracted for analysis. Multivariable regression analyses were conducted comparing demographics, clinical characteristics, and presence of symptoms between patients with long COVID and patients with shorter symptom duration.Results: Long COVID was present in 310/3151 (9.8%) patients with self-diagnosed, clinician-diagnosed, or test-confirmed COVID-19. Only 106/310 (34.2%) long COVID patients had test-confirmed COVID-19. Risk predictors of long COVID were age ≥ 40 years (adjusted Odds Ratio [AdjOR]=1.49 [1.05– 2.17]), female sex (adjOR=1.37 [1.02– 1.85]), frailty (adjOR=2.39 [1.29– 4.27]), visit to A&E (adjOR=4.28 [2.31– 7.78]), and hospital admission for COVID-19 symptoms (adjOR=3.22 [1.77– 5.79]). Aches and pain (adjOR=1.70 [1.21– 2.39]), appetite loss (adjOR=3.15 [1.78– 5.92]), confusion and disorientation (adjOR=2.17 [1.57– 2.99]), diarrhea (adjOR=1.4 [1.03– 1.89]), and persistent dry cough (adjOR=2.77 [1.94– 3.98]) were symptom features statistically more common in long COVID.Conclusion: This study reports the factors and symptom features predicting long COVID in a broad primary care population, including both test-confirmed and the previously missed group of COVID-19 patients.Keywords: SARS-CoV-2, questionnaire, observational study, frailty, chronic diseases