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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. Lung Cancer. 175,000 new cases in U.S. in 2004 160,000 deaths in U.S. in 2004
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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
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
Lung Cancer Histology SEER: http://seer.cancer.gov
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
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 Research Agenda: Lung Cancer
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
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
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
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
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
Waiting for Cancer Surgery Simunovic et al. CMAJ 2001;165:421-5.
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.
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.
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
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.
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.
P=0.04 P=0.02 Length of Delay and Outcomes: Stage Distribution N=103 N=103 N=69 N=69 Christensen et al. Eur J Cardio-thorac Surg 1997;12:880-4.
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
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
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)
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)
Type and Length of Delay Length of Delay (Days) 42d 11-117 84d 38-153 22d 8-41
Predictors of Delay <90 days *p=0.001; † p=0.04
Treatment and Delay *p<0.0001; † p=0.04
Longer Treatment Delays in SPN N=23 222 days P=0.002 N=106 116 days
Longer Delays in Surgical Patients N=36 208 days P<0.0001 N=93 106 days
MV Predictors of Treatment Delay R2= 0.37; p= 0.82 for Hosmer-Lemeshow test; all correlations< 0.35
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
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)
Outcomes: Stage Distribution P=0.006
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
Effect of Delay on Survival Med survival = 321 vs. 122 days, P=0.001 Med survival = 570 vs. 161 days, P<0.0001
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
Longer Delay=Better 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 2004;59:45-9.
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
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.
Stratification by SPN Med survival = 467 vs. 142 days, P=0.001 P=0.19
Stratification by Surgery Med survival =478 vs. 142 days, P=0.001 P=0.08
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
Effect of chemotherapy on survival MethodHazard 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.
Stratification by Propensity P=0.06 P=0.43
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
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.
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
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
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