Hurry Up and Wait: The Effect of Delayed Diagnosis and Treatment on Survival in Patients with NonSma

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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

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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 2004548-29
• Common in veterans
• 6,600 cases in 2003 (20 of all cancers)
• VA Central Cancer Registry http//www1.va.gov/ca
  ncer/index.cfm

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Lung Cancer Histology
SEER http//seer.cancer.gov
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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

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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

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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

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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

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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

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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 199853(Suppl 1)S1-8.
• ATS, ACCP, CCO No recommendations

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Prior Research

• Type and length of delay
• n17 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
• n11 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

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Prior Research Length of Delay
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Waiting for Cancer Surgery
Simunovic et al. CMAJ 2001165421-5.
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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
2003125108-14.
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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.

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Predictors of Delay

• Longer symptom delay in patients
• Bourke et al. Chest 19921021723-9.
• Age not related to diagnostic or treatment delay
• Deegan et al. J Royal Coll Phys London
  199832339-43.
• Simunovic et al. CMAJ 2001165421-5.
• Pita-Fernandez et al. J Clin Epidemiol
  200356820-5.
• Kanashiki et al. Onc Reports 200310649-52.
• Gender not related to symptom or treatment delay
• Pita-Fernandez et al. J Clin Epidemiol
  200356820-5.
• Kanashiki et al. Onc Reports 200310649-52.
• No data for race/ethnicity, SES, education,
  physician or institutional factors

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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
• ORourke Edwards. Clin Oncol 200012141-4.
• Delays in patients with screen-detected lung
  cancer associated with 2-fold reduction in
  survival time
• Kanashiki et al. Onc Reports 200310649-52.
• Kashiwabara et al. Lung Cancer 20034067-72.

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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
  2003125108-14.
• Pita-Fernandez et al. J Clin Epidemiol
  200356820-5.
• Aragoneses et al. Lung Cancer 20023659-63.
• Billing and Wells. Thorax 199651903-6.

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Length of Delay and Outcomes Stage Distribution
N103
N103
N69
N69
Christensen et al. Eur J Cardio-thorac Surg
199712880-4.
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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

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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

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Patient Characteristics
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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 (p0.02), and those with centrally
located tumors (p0.02) or malignant solitary
nodules (p0.07)
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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
(p0.02) or spiculated (p
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Treatment Received
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Type and Length of Delay
Length of Delay (Days)
42d 11-117
84d 38-153
22d 8-41
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Predictors of Delay p0.001 p0.04
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Treatment and Delay
p 28
Longer Treatment Delays in SPN
N23 222 days P0.002
N106 116 days
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Longer Delays in Surgical Patients
N36 208 days PN93 106 days
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MV Predictors of Treatment Delay
R2 0.37 p 0.82 for Hosmer-Lemeshow test all
correlations 31
ROC Curve for Predictors of Rx Delay
AUC 0.80 (0.73 to 0.87) PModel included admission within 7 days, presence
of any symptom, presence of any additional CXR
abnormality, tumor size, age, sex and
race/ethnicity
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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)

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Outcomes Stage Distribution
P0.006
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Outcomes Survival

• Treatment within 90 days of presentation
  associated with an increased risk of death
• RR1.45 (95 CI 79.4 vs. 54.7)
• P0.002

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Effect of Delay on Survival
Med survival 321 vs. 122 days, P0.001
Med survival 570 vs. 161 days, P 36
Multivariable Predictors of Survival

• In Cox proportional hazards models, TNM stage III
  (HR 11.4, P0.01) and TNM stage IV (HR 24.0,
  P0.001) were the only statistically significant
  predictors of survival
• Trend towards worse survival in patients with
  symptoms (HR 3.1, P0.08) and patients with
  shorter treatment delays (HR 1.5, P0.09)
• Age, ethnicity, tumor size, histology not
  associated with survival

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Longer DelayBetter Survival
Symptom Delay
Hospital Delay
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 20045945-9.
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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

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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 1996276889-97.
• Instrumental variable methods
• Newhouse McClellan. Ann Rev Pub Health
  19981917-34.
• McClellan et al. JAMA 1994272859-866.

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Stratification by SPN
P0.19
Med survival 467 vs. 142 days, P0.001
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Stratification by Surgery
Med survival 478 vs. 142 days, P0.001
P0.08
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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

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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 0.7
8
Earle et al. J Clin Oncol 2001 191064-1070.
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Stratification by Propensity
P0.06
P0.43
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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

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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
  MIdifferential distance from home to hospital
  with/without cardiac catheterization lab.

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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 designunable to assess symptom
  delay
• Not able to fully control for severity at
  presentation

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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

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Acknowledgements

• Funding
• Advanced RCDA, VA HSRD 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

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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

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Correlations

• Age not correlated with time to treatment
• Spearmans rho 0.10, P0.26
• Tumor size negatively correlated with time to
  treatment
• Spearmans rho -0.32, P

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Effect of Delay on Survival
Med survival 321 vs. 122 days, P0.001
Med survival 570 vs. 161 days, P