Using routine datato measure recurrence

in Head and Neck Cancer

Zi Wei LiuMatt WilliamsAdam GibsonKate Ricketts

Heather Fitzke

Zi-wei.liu@nhs.netImperial E-oncology Conference 2015

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.

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 (9th DAHNO 12% reported)

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

Hospital episodes statistic

Patient demographics Inpatient (and now outpatient) attendances Diagnosis & Procedures Co-morbidities

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

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

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.

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.

Methods

Methods

Methods

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

Results

Pilot study:

20 patients

13 male

9 primary oropharynx

15 LAHNSCC

Median OS 24.4 months

Median PFS 9.6 months

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)

Results

Optimization strategies

– Backdating

– Optimizing time intervals between primary and secondary treatment

Results

Conditions

No. patients out

of bounds for

routine OS

No. patients

out of bounds for routine PFS

Diagnosis dates in

agreement{n = 122}

±1 week / ±1 month

Recurrence dates in agreement

{n = 40} ±1 week / ±1 month

No. of recurrence events correctly identified

No. of recurre

nce events falsely identifi

ed

No. of recurren

ce events missed

Initial approach

7 25 1 week (62)1 month (97)

1 week (1)1 month (4)

21 5 19

Backdating alone

3 23 1 week (61)1 month

(101)

1 week (5)1 month (7)

21 5 19

Backdating + optimised time intervals

3 21 1 week (61)1 month

(102)

1 week (7)1 month (9)

21 2 19

Results

Results

Results

Pilot study (n=20) Follow up study (n=122)

OS 95% good agreement 98% good agreement

PFS 80% good agreement 82% good agreement

Recurrence events 10/11 correctly identified 21/40 correctly identified

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

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

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

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

Questions?