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Using routine data
to measure recurrence
in Head and Neck Cancer
Zi Wei Liu
Matt Williams
Adam Gibson
Kate Ricketts
Heather Fitzke
[email protected]
Imperial 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
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
No. of
recurre recurren
nce
ce
events
events
falsely missed
identifi
ed
Initial
approac
h
7
25
1 week (62)
1 month (97)
1 week (1)
1 month (4)
21
5
19
Backdat
ing
alone
3
23
1 week (61)
1 month
(101)
1 week (5)
1 month (7)
21
5
19
Backdat
ing +
optimis
ed time
interval
s
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?