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Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on
Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on
Lung Cancer
Lung Cancer
Lung Cancer Histology
Lung Cancer Histology
Evaluation in Suspected Lung Cancer
Evaluation in Suspected Lung Cancer
Research Agenda: Lung Cancer
Research Agenda: Lung Cancer
CanCORS
CanCORS
Specific Aims: Wait Times
Specific Aims: Wait Times
Why Measure Wait Times
Why Measure Wait Times
Guidelines for Wait Times
Guidelines for Wait Times
Prior Research
Prior Research
Prior Research: Length of Delay
Prior Research: Length of Delay
Waiting for Cancer Surgery
Waiting for Cancer Surgery
Waiting for Cancer Surgery
Waiting for Cancer Surgery
Median Wait Times for Radiation and Chemotherapy
Median Wait Times for Radiation and Chemotherapy
Predictors of Delay
Predictors of Delay
Length of Delay and Outcomes
Length of Delay and Outcomes
Length of Delay and Outcomes
Length of Delay and Outcomes
Length of Delay and Outcomes: Stage Distribution
Length of Delay and Outcomes: Stage Distribution
Research Methods
Research Methods
Statistical Methods
Statistical Methods
Patient Characteristics
Patient Characteristics
Pre-treatment Imaging Tests
Pre-treatment Imaging Tests
Pre-treatment Staging Procedures
Pre-treatment Staging Procedures
Treatment Received
Treatment Received
Type and Length of Delay
Type and Length of Delay
Predictors of Delay <90 days
Predictors of Delay <90 days
Treatment and Delay
Treatment and Delay
Longer Treatment Delays in SPN
Longer Treatment Delays in SPN
Longer Delays in Surgical Patients
Longer Delays in Surgical Patients
MV Predictors of Treatment Delay
MV Predictors of Treatment Delay
ROC Curve for Predictors of Rx Delay
ROC Curve for Predictors of Rx Delay
Predictors of Diagnostic Delay
Predictors of Diagnostic Delay
Outcomes: Stage Distribution
Outcomes: Stage Distribution
Outcomes: Survival
Outcomes: Survival
Effect of Delay on Survival
Effect of Delay on Survival
Multivariable Predictors of Survival
Multivariable Predictors of Survival
Longer Delay=Better Survival
Longer Delay=Better Survival
Sources of Bias and Variation
Sources of Bias and Variation
Strategies for Dealing with Selection Bias
Strategies for Dealing with Selection Bias
Stratification by SPN
Stratification by SPN
Stratification by Surgery
Stratification by Surgery
Propensity Scores
Propensity Scores
Effect of chemotherapy on survival Method Hazard Ratio Cox PH 0.81
Effect of chemotherapy on survival Method Hazard Ratio Cox PH 0.81
Stratification by Propensity
Stratification by Propensity
Improving Propensity Model in CanCORS
Improving Propensity Model in CanCORS
Instrumental Variables
Instrumental Variables
Strengths & Limitations
Strengths & Limitations
Conclusions
Conclusions
Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on
Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on
Acknowledgements
Acknowledgements
Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on
Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on
Specific Aims: Staging Practices
Specific Aims: Staging Practices
Correlations
Correlations
Effect of Delay on Survival
Effect of Delay on Survival

Презентация на тему: «Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on Survival in Patients with Non-Small-Cell Lung Cancer». Автор: vhapalgouldm. Файл: «Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on Survival in Patients with Non-Small-Cell Lung Cancer.ppt». Размер zip-архива: 1234 КБ.

Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on Survival in Patients with Non-Small-Cell Lung Cancer

содержание презентации «Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on Survival in Patients with Non-Small-Cell Lung Cancer.ppt»
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1 Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on

Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on

Survival in Patients with Non-Small-Cell Lung Cancer

Michael K. Gould, MD, MS VA Palo Alto Health Care System Stanford School of Medicine

2 Lung Cancer

Lung Cancer

175,000 new cases in U.S. in 2004 160,000 deaths in U.S. in 2004 More deaths than breast, prostate and colon cancer combined Jemal et al. CA Cancer J Clin 2004;54:8-29 Common in veterans 6,600 cases in 2003 (~20% of all cancers) VA Central Cancer Registry: http://www1.va.gov/cancer/index.cfm

3 Lung Cancer Histology

Lung Cancer Histology

SEER: http://seer.cancer.gov

4 Evaluation in Suspected Lung Cancer

Evaluation in Suspected Lung Cancer

Diagnosis Imaging tests (e.g. CXR, chest CT, PET) Biopsy (e.g. bronchoscopy, TTNA) Staging Imaging tests (e.g. brain CT or MR) Biopsy (e.g. mediastinoscopy, adrenal Bx) Pre-operative assessment (PFTs, cardiac eval) Consultations Tumor Board

5 Research Agenda: Lung Cancer

Research Agenda: Lung Cancer

Defining Best Practices: Cost-effectiveness of low-dose CT for lung cancer screening Accuracy of FDG-PET for SPN diagnosis Cost of FDG-PET Cost-effectiveness of tests for SPN management Predictors of mediastinal metastasis Accuracy of CT and FDG-PET for staging in NSCLC Accuracy of TBNA for staging in NSCLC Accuracy of mediastinoscopy for staging in NSCLC Cost-effectiveness of tests for staging in NSCLC

Examining Current Practices: Quality of practices for lung cancer diagnosis and staging (with CanCORS) Aligning Current and Best Practices: Development, validation and evaluation of a computer-based decision support system for managing SPN Eliciting preferences for shared decision making in patients with lung nodules

6 CanCORS

CanCORS

NCI-funded collaboration Population based, prospective cohort study of practices and outcomes in patients with lung and colorectal cancer in diverse geographic regions of U.S. 8,000 lung cancer patients, including 1,000 U.S. veterans with lung cancer enrolled at 13 sites

7 Specific Aims: Wait Times

Specific Aims: Wait Times

Describe variation in time to diagnosis and treatment in U.S. veterans with non-small cell lung cancer (NSCLC) Identify facilitators and barriers to timely diagnosis and treatment in VA Examine the effect of delayed diagnosis and treatment on stage distribution and survival

8 Why Measure Wait Times

Why Measure Wait Times

Longer wait times contribute to emotional distress of patients and family members Longer wait times may lead to missed opportunities for cure and/or effective palliation Longer wait times may (arguably) result in increased health care costs

9 Guidelines for Wait Times

Guidelines for Wait Times

RAND Quality Indicators Diagnosis within 2 months of presentation Treatment within 6 weeks of diagnosis http://www.rand.org/publications/MR/MR1281/ BTS Referral & evaluation by respiratory specialist within 2-7 days Results of diagnostic test communicated within 2 weeks Thoracotomy within 8 weeks, palliative XRT within 4 weeks, radical XRT within 2 weeks, chemotherapy within 2 weeks Thorax 1998;53(Suppl 1):S1-8. ATS, ACCP, CCO: No recommendations

10 Prior Research

Prior Research

Type and length of delay n=17 studies between 1989 to 2004 Heterogeneous patient populations Most studies from Europe, 3 from North America, 1 from Japan Effect of delay on lung cancer outcomes n=11 studies between 1993 and 2004 4 studies of surgical patients (1 from U.S.) 2 studies of delays following screen-detection of lung cancer in Japan 1 European study of patients referred for curative XRT

11 Prior Research: Length of Delay

Prior Research: Length of Delay

Interval

# of Studies

Median Time

Symptom to first contact

5

~3 weeks

First contact to diagnosis

6

2-6 weeks ( 1 study >12 weeks)

First contact to treatment

5

~3 months

Diagnosis to radiation

2

5 to 6 weeks

Diagnosis to surgery

1

7 weeks

12 Waiting for Cancer Surgery

Waiting for Cancer Surgery

Simunovic et al. CMAJ 2001;165:421-5.

13 Waiting for Cancer Surgery

Waiting for Cancer Surgery

One U.S. study from SFVA (retrospective) 83 veterans with stage I or II lung cancer Underwent surgical resection between 1989-99 Median time from initial contact to resection was 82 days

Quarterman et al. J Thorac Cardiovasc Surg 2003;125:108-14.

14 Median Wait Times for Radiation and Chemotherapy

Median Wait Times for Radiation and Chemotherapy

Ontario, Canada 1 to 4.1 weeks from referral to radiation 1.9 to 6.3 weeks from referral to chemotherapy http://www.cancercare.on.ca/access_waitTimes.htm No data from U.S.

15 Predictors of Delay

Predictors of Delay

Longer symptom delay in patients <45 years old Bourke et al. Chest 1992;102:1723-9. Age not related to diagnostic or treatment delay Deegan et al. J Royal Coll Phys London 1998;32:339-43. Simunovic et al. CMAJ 2001;165:421-5. Pita-Fernandez et al. J Clin Epidemiol 2003;56:820-5. Kanashiki et al. Onc Reports 2003;10:649-52. Gender not related to symptom or treatment delay Pita-Fernandez et al. J Clin Epidemiol 2003;56:820-5. Kanashiki et al. Onc Reports 2003;10:649-52. No data for race/ethnicity, SES, education, physician or institutional factors

16 Length of Delay and Outcomes

Length of Delay and Outcomes

Delays of 18 to 131 days between diagnostic CT and XRT planning CT associated with 19% increase in tumor X-sectional area (range 0% to 373%) 6/29 patients (21%) progressed to incurable disease while waiting O’Rourke & Edwards. Clin Oncol 2000;12:141-4. Delays in patients with screen-detected lung cancer associated with 2-fold reduction in survival time Kanashiki et al. Onc Reports 2003;10:649-52. Kashiwabara et al. Lung Cancer 2003;40:67-72.

17 Length of Delay and Outcomes

Length of Delay and Outcomes

No association between different types of delay and survival in 4 studies of surgical patients Quarterman et al. J Thorac Cardiovasc Surg 2003;125:108-14. Pita-Fernandez et al. J Clin Epidemiol 2003;56:820-5. Aragoneses et al. Lung Cancer 2002;36:59-63. Billing and Wells. Thorax 1996;51:903-6.

18 Length of Delay and Outcomes: Stage Distribution

Length of Delay and Outcomes: Stage Distribution

Christensen et al. Eur J Cardio-thorac Surg 1997;12:880-4.

N=103

N=103

N=69

N=69

19 Research Methods

Research Methods

Retrospective cohort study 129 U.S. veterans with NSCLC Consecutive patients diagnosed and treated at VAPAHCS between 1/1/02 and 12/31/03 Median follow-up: 270 days from 1st x-ray abnormality 194 days from histologic diagnosis 147 days from treatment

20 Statistical Methods

Statistical Methods

Associations between length of delay and potential predictors of delay Non-parametric correlations for continuous predictors Pearson chi-square for categorical predictors Multiple logistic regression Associations between length of delay and survival Kaplan-Meier, Cox proportional hazards

21 Patient Characteristics

Patient Characteristics

Characteristic

n=129

Age (years)

67.2 ± 9.5

Gender (Male), %

97.7

White, %

82.4

Tumor size, cm

3.9 ± 2.4

Adenocarcinoma, %

50.0

Squamous cell, %

28.8

Central location, %

55.6

Any symptom, %

58.3

Any CXR finding, %

25.0

SPN, %

18.2

22 Pre-treatment Imaging Tests

Pre-treatment Imaging Tests

N % >1 test X-ray chest 128 99 30% CT chest 126 98 11% PET 107 83 3% CT abdomen/pelvis 51 40 CT brain/spinal cord 29 22 MRI head 23 18 X-ray bone 19 15 MRI spinal cord 15 12 MRI chest 10 8

PET imaging more common in patients without symptoms (p=0.02), and those with centrally located tumors (p=0.02) or malignant solitary nodules (p=0.07)

23 Pre-treatment Staging Procedures

Pre-treatment Staging Procedures

N % >1 test Bronchoscopy/TBNA 15 12 4% Mediastinoscopy 7 5 Endoscopic ultrasound 1 1

Mediastinal biopsy more common in patients with primary tumors that were centrally located (p=0.02) or spiculated (p<0.05)

24 Treatment Received

Treatment Received

Characteristic

%, n=129

Surgery

27.3

Radiation

35.6

Chemotherapy

40.2

No treatment

19.7

Admit within 7 days

33.3

25 Type and Length of Delay

Type and Length of Delay

Length of Delay (Days)

42d 11-117

84d 38-153

22d 8-41

26 Predictors of Delay <90 days

Predictors of Delay <90 days

Tumor size, cm*

Any symptom, %*

SPN, %*

*p=0.001; † p=0.04

Characteristic

Delay<90 d (n=67)

Delay>90d (n=62)

Age (years)

66.5 ± 9.8

67.9 ± 9.2

Gender (Male), %

98.5

96.9

White, %

77.4

87.8

4.7 ± 2.8

3.1 ± 1.8

Adenocarcinoma, %

57.4

42.2

Squamous cell, %

25.0

32.8

Central location, %

54.7

56.5

72.1

43.8

Any CXR finding, % †

32.4

17.2

7.4

29.7

27 Treatment and Delay

Treatment and Delay

Surgery *

Admit within 7 days *

Characteristic

All, % (n=129)

Delay<90 d, % (n=67)

Delay>90d, % (n=62)

27.3

13.2

42.2

Radiation

35.6

41.2

29.7

Chemotherapy

40.2

45.6

34.4

No treatment †

19.7

26.5

12.5

33.3

48.5

17.2

*p<0.0001; † p=0.04

28 Longer Treatment Delays in SPN

Longer Treatment Delays in SPN

N=23 222 days P=0.002

N=106 116 days

29 Longer Delays in Surgical Patients

Longer Delays in Surgical Patients

N=36 208 days P<0.0001

N=93 106 days

30 MV Predictors of Treatment Delay

MV Predictors of Treatment Delay

Predictor

OR

95% CI

Admit within 7 days of 1st abnormal CXR

6.0

2.2 – 16.2

Tumor Size > 3.0 cm

5.4

2.1 – 14.1

Any additional abnormality on CXR

2.6

0.9 – 7.5

Any symptom

2.5

1.0 – 6.0

R2= 0.37; p= 0.82 for Hosmer-Lemeshow test; all correlations< 0.35

31 ROC Curve for Predictors of Rx Delay

ROC Curve for Predictors of Rx Delay

AUC= 0.80; (0.73 to 0.87); P<0.0001

Model included admission within 7 days, presence of any symptom, presence of any additional CXR abnormality, tumor size, age, sex and race/ethnicity

32 Predictors of Diagnostic Delay

Predictors of Diagnostic Delay

Independent predictors of diagnosis within 42 days included hospitalization within 7 days (OR 10.3, 95% CI 3.5 to 30), tumor size greater than 3 cm (OR 5.5, 95% CI 2.0 to 15), and white race (OR 3.0, 95% CI 1.1 to 8.0)

33 Outcomes: Stage Distribution

Outcomes: Stage Distribution

Stage

All, % (n=129)

Delay<90 d, % (n=67)

Delay>90d, % (n=62)

Stage I

15.9

9.7

23.5

Stage II

15.0

11.3

19.6

Stage III

32.7

29.0

37.3

Stage IV

36.3

50.0

19.6

P=0.006

34 Outcomes: Survival

Outcomes: Survival

Treatment within 90 days of presentation associated with an increased risk of death RR=1.45 (95% CI 79.4% vs. 54.7%) P=0.002

35 Effect of Delay on Survival

Effect of Delay on Survival

Med survival = 321 vs. 122 days, P=0.001

Med survival = 570 vs. 161 days, P<0.0001

36 Multivariable Predictors of Survival

Multivariable Predictors of Survival

In Cox proportional hazards models, TNM stage III (HR 11.4, P=0.01) and TNM stage IV (HR 24.0, P=0.001) were the only statistically significant predictors of survival Trend towards worse survival in patients with symptoms (HR 3.1, P=0.08) and patients with shorter treatment delays (HR 1.5, P=0.09) Age, ethnicity, tumor size, histology not associated with survival

37 Longer Delay=Better Survival

Longer Delay=Better Survival

After adjusting for age, sex, stage & surgery, longer symptom delay (HR 0.79) and hospital delay (HR 0.87) were associated with better survival. Myrdal et al. Thorax 2004;59:45-9.

Symptom Delay

Hospital Delay

38 Sources of Bias and Variation

Sources of Bias and Variation

Sources of Bias Selection bias Confounding by severity of disease Lead-time bias Sources of Variation Heterogeneous patient populations Heterogeneous health care systems

39 Strategies for Dealing with Selection Bias

Strategies for Dealing with Selection Bias

Stratification Should be performed according to baseline characteristics Propensity score methods Adjust, match or stratify by propensity or likelihood of receiving intervention/exposure Connors et al. JAMA 1996;276:889-97. Instrumental variable methods Newhouse & McClellan. Ann Rev Pub Health 1998;19:17-34. McClellan et al. JAMA 1994;272:859-866.

40 Stratification by SPN

Stratification by SPN

Med survival = 467 vs. 142 days, P=0.001

P=0.19

41 Stratification by Surgery

Stratification by Surgery

Med survival =478 vs. 142 days, P=0.001

P=0.08

42 Propensity Scores

Propensity Scores

Used to control for selection bias in observational studies of valve surgery for endocarditis, chemotherapy for advanced lung cancer, coronary angiography following acute myocardial infarction and right heart catheterization for critical illness Controls for observed differences between groups Typically use logistic regression to predict use of intervention Adjust, match or stratify by propensity to receive intervention/exposure 5 strata usually sufficient to remove over 90% of bias due to selection

43 Effect of chemotherapy on survival Method Hazard Ratio Cox PH 0.81

Effect of chemotherapy on survival Method Hazard Ratio Cox PH 0.81

Propensity score 1st 0.78 2nd 0.81 3rd 0.85 4th 0.80 5th 0.78

Earle et al. J Clin Oncol 2001; 19:1064-1070.

44 Stratification by Propensity

Stratification by Propensity

P=0.06

P=0.43

45 Improving Propensity Model in CanCORS

Improving Propensity Model in CanCORS

Patient characteristics Age, sex, race/ethnicity, education, marital status, SES Measures of disease severity, sypmtoms and co-morbidity Institutional characteristics Lung cancer volume; frequency of thoracic tumor board meetings Presence of dedicated thoracic surgeon, number of other specialists Availability of PET scanner, number of CT scanners Availability of OR time for thoracic surgeons Other non-clinical factors Distance of residence to VA Means test category Other insurance

46 Instrumental Variables

Instrumental Variables

Can control for unobserved characteristics Instrument” should be associated with use of intervention, but not with health status or outcome Example: Heart catheterization following acute MI—differential distance from home to hospital with/without cardiac catheterization lab.

47 Strengths & Limitations

Strengths & Limitations

Strengths Study sample captured full spectrum of NSCLC Objective measurement of time intervals avoided faulty recall Measurement of survival from time of 1st abnormal CXR minimized lead time bias Limitations Small sample size Stratification limited statistical power further Single center limited variability in practices Retrospective design—unable to assess symptom delay Not able to fully control for severity at presentation

48 Conclusions

Conclusions

Important biases complicate the interpretation of previous studies of delayed treatment in NSCLC Delays in diagnosis and treatment are longer than is currently recommended Patients with aggressive tumors tend to experience the shortest delays Reducing delays in patients with malignant SPNs and other potentially resectable tumors may yield greatest benefits Future studies should be large & prospective, avoid selection & lead time biases, and use sophisticated methods to account for confounding by severity of disease at presentation

49 Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on
50 Acknowledgements

Acknowledgements

Funding Advanced RCDA, VA HSR&D Service Collaborators David Au, MD, MS Dawn Provenzale, MD, MS Sharfun Ghaus CanCORS Ancillary Study Investigators Jay Bhattacharya, PhD Todd Wagner, PhD Doug Owens, MD, MS

51 Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on
52 Specific Aims: Staging Practices

Specific Aims: Staging Practices

Describe variation in use of FDG-PET imaging and invasive mediastinal biopsy procedures for staging in U.S. veterans with NSCLC Examine the effect of PET imaging and mediastinal biopsy on survival and rate of thoracotomy without cure in VA Measure pre-treatment resource utilization and evaluate the cost-effectiveness of selected imaging tests and biopsy procedures for lung cancer staging

53 Correlations

Correlations

Age not correlated with time to treatment Spearman’s rho= 0.10, P=0.26 Tumor size negatively correlated with time to treatment Spearman’s rho= -0.32, P<0.0001

54 Effect of Delay on Survival

Effect of Delay on Survival

Med survival = 321 vs. 122 days, P=0.001

Med survival = 570 vs. 161 days, P<0.0001

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