1. Using routine data to measure recurrence in Head and Neck Cancer Zi Wei Liu Matt Williams Adam Gibson Kate Ricketts Heather Fitzke Zi-wei.liu@nhs.net Imperial E-oncology Conference 2015

2. Defining the problem Head and neck cancer – ~6000 new diagnoses of head and neck cancer a year – Strongly related to smoking – Increase in incidence recently due to HPV related H+N cancer – ~60% present at an advanced stage and require multi-modality treatment-surgery, radiotherapy, chemo.

3. Defining the problem Recurrence rates in H&N cancer are important  For staff (efficacy)  For patients (prognosis)  For service planning (costs) Not well measured in routine care population Relies on patchy manual data entry (9 th DAHNO 12% reported)

4. What is 'routine data'? Nationally collected patient data  Uniform coding scheme  Some linked to payments for activity  mandatory data collection Examples:  HES (hospital episodes statistic)  SACT (Systemic anti-cancer therapy)  RTDS (radiotherapy database)  DBS(Demographic batch service)  Cancer registry data

5. Hospital episodes statistic Patient demographics Inpatient (and now outpatient) attendances Diagnosis & Procedures Co-morbidities

6. SACT & RTDS SACT & RTDS cancer databases have a minimum dataset which usually contains the following:  Patient demographics: e.g NHS number, DOB, post code, consultant code  Primary diagnosis: ICD-10 code, staging, morphology  Regimen, intention of treatment, height and weight, PS  Start and end date of treatment, intended and actual treatment delivered  Date of death

7. Aims and importance of our study Can we determine recurrence rates and survival times from routine data ? How closely do they match manually-measured rates & times ? Pilot study assess feasibility and possible problems Follow-up study larger sample size, problems with scaling

8. Methods Pilot study: 20 patients with head and neck identified from local MDT lists  Received radical treatment  Weighted towards those diagnosed at UCH  Weighted towards advanced disease Paired datasets generated-'manual' and 'routine' Tests of correlation performed on key clinical outcome indicators such as overall survival, progression survival and recurrence events. Ref: Liu ZW, Fitzke H, Williams M. Using routine data to estimate survival and recurrence in head and neck cancer: our preliminary experience in twenty patients. (2013) Clinical Otolaryngology, 38(4):334-9.

9. Methods Second expanded study 122 patients Paired datasets generated-'manual' and 'routine' Optimization strategies including backdating, time interval optimization Survival curves Ref: Ricketts K, Williams M, Liu ZW, Gibson A. (2014). Automated estimation of disease recurrence in head and neck cancer using routine healthcare data. Computer Methods and Programs in Biomedicine. 7(3):412-24.

10. Methods

12. Date & Site of first head and neck cancer diagnosis code Radical treatment Collect HES, RTDS and SACT data (incl. Dates) If further major surgical resection or palliative chemotherapy, or palliative RT, assume recurrence  No intention on RT, so used a 3/12 cut-off for differentiating adjuvant vs. radical salvage RT

13. Results Pilot study: 20 patients  13 male  9 primary oropharynx  15 LAHNSCC Median OS 24.4 months Median PFS 9.6 months

14. Results Follow-up Study: 122 patients 82% locally advanced disease  51 oropharynx  26 larynx Median OS 88% (1 year), 77% (2 years) Median PFS 75% (1 year), 66% (2 years)

15. Results Optimization strategies – Backdating – Optimizing time intervals between primary and secondary treatment

16. Results Conditi ons No. patien ts out of bound s for routin e OS No. patie nts out of boun ds for routin e PFS Diagnosis dates in agreement {n = 122} ±1 week / ±1 month Recurrenc e dates in agreement {n = 40} ±1 week / ±1 month No. of recurrenc e events correctly identified No. of recurre nce events falsely identifi ed No. of recurren ce events missed Initial approac h 7251 week (62) 1 month (97) 1 week (1) 1 month (4) 21519 Backdat ing alone 3231 week (61) 1 month (101) 1 week (5) 1 month (7) 21519 Backdat ing + optimis ed time interval s 3211 week (61) 1 month (102) 1 week (7) 1 month (9) 21219

17. Results

19. Pilot study (n=20)Follow up study (n=122) OS95% good agreement98% good agreement PFS80% good agreement82% good agreement Recurrence events10/11 correctly identified21/40 correctly identified

20. Discussion Selected sample  LAHNSCC  Radical treatment only Reasonable agreement between routine and manual data Used national-level data, possible to automate, adds to existing knowledge Potentially inaccurate, esp. in palliative patients

21. Discussion Further optimisation work HES density data looking at ratio of inpatient to outpatient attendances to predict recurrence Measurement of non-OS outcomes – In addition to recurrence: – PEG dependency rates – Tracheostomy dependency rates

22. Future directions Phase III study using national cancer data under way Develop software to automate data handling and analysis  Experiments to optimise algorithm and utilise modelling to improve accuracy of predictions  Incorporate registry data  First comprehensive automated analysis of national cancer dataset in the UK  Different subsites- head and neck and breast will be pilot sites In collaboration with NCIN and Public Health England

23. Summary 2 studies using routine data validated against manually collected data demonstrating potential of analysing national databases for clinically relevant outcomes Can be automated and less resource-intensive than audit Algorithms can be tailored for other cancer subsites (GBM study under way) Third phase study

24. Questions?