The use of NSWCI data to review clinical variations –

Early Breast Cancer Hypofractionation: A Case Study

Delaney G., Gandhidasan, S., Walton R., Terlich F., Baker D., Currow D.NSW Cancer Institute and SWSLHD

Aims of this presentation• Introduce the concept of clinical variation assessment using

NSWCCR data• Explore the variations of practice in hypofractionation for

early breast cancer XRT to assess what factors impact adoption of new treatments

• Hypofractionation in breast tangents– Controversial– Likely to be variation– Evidence evolution over time

NSW Cancer Registry• Combined data from the mandated Central registry and

clinical data from most LHDs (since 2005)• 388 000 cases within the registry with complete data– Lacking private hospital data (coming)– Lacking data from some rural LHDs (coming)– Long-term plans for an entire data set

Breast Cancer Hypofractionation

• Hypofractionation is giving XRT in a shorter number of fractions e.g. 16 versus conventional 25

• 5 RCTs with long-term follow-up have shown equivalence to standard fractionation for early breast cancer (local control and survival)

• Possible long-term toxicity concerns relate to high fraction size (2.6-2.7 Gy versus standard 1.8-2)

• Longer term case control studies have shown no additional toxicity

Tangent XRT HypofractionationAuthor Tmt years #

regimenn Median (range)

F/U (yrs)Year of publication

Baillet et al. 1982-1984 23/4 230 5.5 1990

Whelan et al. 1993-1996 42.5/16 1234 12 (>10) 2002/2010

Yarnold, Owen et al.

1986-1998 39/1342.9/13

1410 9.7 (7-18.4) 2005/2006

Yarnold et al. (START B)

1999-2001 40/15 2215 6.0 (<8) 2008

Yarnold et al. (START A)

1998-2002 41.6/1339Gy/13

2236 5.1 (<8) 2008

Lalani et al. 1994-2003 Various (non RCT) for DCIS

1609 9.2 2014

http://guidelines.canceraustralia.gov.au/guidelines/guideline_12.pdf = 2011

ASTRO and Australian guidelines

Recommend breast hypofractionation but perhaps insufficient evidence in some sub-groups

• Large breast• Chemotherapy• Young women• Node XRT

http://guidelines.canceraustralia.gov.au/guidelines/guideline_12.pdf

So what should the benchmark be? ASTRO/Australian guidelines

So what should the benchmark be? • All patients (believers) = 100%

– Long term follow up available– Adoption by some other groups as standard e.g. some parts of Canada– Leads to greater resource efficiency

• Selected patients (believe where evidence strong) = 40%• No patients (non-believers) = 0%

– Old way is tried and tested– Long-term toxicity still not long enough especially for rarer toxicities

such as cardiac risk

Methodology• All T1-2N0M0 treated with tangent XRT during the XRT study period 2008-

2012 in public XRT dept.• Analysis of dose/fraction (hypo# defined as >2.4Gy/f) against :

– age, – laterality, – residence distance from department, – year of tmt,– department treated– clinicianNO surrogate marker for breast size available

RESULTS• 10572 patients with T1-2N0M0• 6066 (63%) treated with XRT in a public facility

– 3947 (65%) received standard– 2119 (35%) received hypo

Estimate z value P value

Age at diagnosis (10 years) OR = 2.10 20.756 P<0.001

Laterality (right vs left) OR = 1.27 3.285 P=0.001Distance (100km) OR = 1.12 3.105 P=0.002Year OR = 1.15 4.858 P<0.001

AMO σ σ = 1.09Facility σ σ = 1.39

The greatest predictor for variability was clinician

50 55 60 65 70 75

Median age (yrs)

Facility 14 (n=118)

Facility 13 (n=260)

Facility 12 (n=368)

Facility 11 (n=520)

Facility 10 (n=670)

Facility 9 (n=571)

Facility 8 (n=438)

Facility 7 (n=826)

Facility 6 (n=526)

Facility 5 (n=244)

Facility 4 (n=98)

Facility 3 (n=280)

Facility 2 (n=888)

Facility 1 (n=259)

Total (n=6066)

0% 100%

6

8

9

10

16

22

26

28

32

43

69

71

72

92

35

94

92

91

90

84

78

74

72

68

57

31

29

28

8

65

Hypofractionation Standard

Treatment regimen received (%)

< 30 (n=18)

30-34 (n=68)

35-39 (n=133)

40-44 (n=322)

45-49 (n=581)

50-54 (n=842)

55-59 (n=854)

60-64 (n=1079)

65-69 (n=1030)

70-74 (n=521)

75-79 (n=353)

80-84 (n=195)

85+ (n=68)

0% 100%

6

10

5

20

18

27

31

38

39

48

53

65

82

94

90

95

80

82

73

69

62

61

52

47

35

18

Hypofractionation Standard

Treatment regimen received (%)

Age

grou

p at

trea

tmen

t

2008 2009 2010 2011 20120

100

25

34 3638 40

Year of treatment

Hyp

ofra

ction

ation

rece

ived

(%)

Facility 14 (n=260)

Facility 13 (n=118)

Facility 12 (n=518)

Facility 11 (n=368)

Facility 10 (n=670)

Facility 9 (n=571)

Facility 8 (n=437)

Facility 7 (n=826)

Facility 6 (n=521)

Facility 5 (n=244)

Facility 4 (n=280)

Facility 3 (n=98)

Facility 2 (n=884)

Facility 1 (n=259)

Total (n=6054)

0 100

14

7

10

7

17

30

27

29

34

45

75

71

74

88

36

4

4

9

10

15

16

25

28

31

42

67

68

71

97

34

Left Right

Hypofractionation received (%)

Summary findings• Difference in use of hypofractionation• Variation causes are multi-factorial (facility, AMO, age, laterality, distance)• Clearly the area remains controversial in NSW• Identifying some factors might help understand motivation and set future

research direction• Raises issues about whether variation is appropriate or not

Is follow-up long enough?

Other regionsAUTHOR REGION STUDY

PERIODHF RATE PREDICTORS

Dayes et al. Ontario, Canada

1999-2001 100% Substantially higher than a similar US cohort

Jagsi et al. Michigan, US

2011-2013 31% older age, smaller body habitus, no chemo

Jagsi et al., SEER, US 2004-2010 23% (2009-2010)

Older age, smaller T, increased comorbidity, higher education, region, year

Wang et al., NCDB, US 2004-2011 23% (2011) Year, travel distance, academic facility, high median income, smaller T, lower grade

Bekelman et al. 14 US centers

2008-2012 21% (2012) Year, older age, IMRT use, type of facility

THIS STUDY NSW 2008-2012 35% (2008-2012)

Facility, AMO, year, laterality, older age, travel distance

Where to from here?• Registry data

– Consider other variations in treatment e.g. SNB, WLE, post-mastectomy XRT, chemotherapy

• Hypofractionation- Feed data back to departments- Update data and include private departments- Focus groups, debate the evidence- Review literature- Consensus recommendations- Ideal to link these data to outcome data (e.g. long-term IHD data)- Feedback quality loop- Increased access to techniques that minimise heart dose might help

Thank you