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C.C. Lin
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MINI 20 - Surgery (ID 137)
- Event: WCLC 2015
- Type: Mini Oral
- Track: Treatment of Locoregional Disease – NSCLC
- Presentations: 1
- Moderators:G. Veronesi, R. Flores
- Coordinates: 9/08/2015, 16:45 - 18:15, 201+203
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MINI20.02 - Risk-Adjusted Margin Positivity (RAMP) Rate as a Surgical Quality Metric for Non-Small-Cell Lung Cancer in the US National Cancer Data Base (NCDB) (ID 1247)
16:50 - 16:55 | Author(s): C.C. Lin
- Abstract
- Presentation
Background:
Surgical resection is the most important curative treatment modality for early-stage non-small-cell lung cancer (NSCLC). However, incomplete (margin-positive) resection is associated with inferior survival. We sought to develop a valid facility-based quality metric to measure surgical quality, adjusting related patient demographic and clinical characteristics.
Methods:
We identified facilities that performed cancer-directed surgery for patients diagnosed with AJCC stage I-IIIA NSCLC in the NCDB between 2004 and 2011. We used a multivariate logistic regression model, adjusting for patient risk-mix in each facility, to predict the expected number of risk-adjusted margin positivity (RAMP) cases for each facility. We divided the number of observed margin positivity (OMP) cases by the expected number of RAMP cases to obtain an observed: expected (O/E) ratio for each facility. We categorized facility performance as low outlier (O/E ratio<1 and p<.05), high outlier (O/E ratio>1 and p<.05), or non-outlier. Facility characteristics across performance categories were compared by chi-square test. Five-year unadjusted overall survival (OS) rates were estimated by Kaplan-Meier analyses and compared across categories with the log-rank test.
Results:
A total of 96,596 NSCLC stage I-IIIA patients underwent surgery in 941 facilities. The overall OMP rate was 4.6%. We identified 73 facilities as low outliers (mean O/E ratio=0.41), 755 as non-outliers (mean O/E ratio=1.28) and 113 as high outliers (mean O/E ratio=2.78). Compared to patients treated at high-outlier facilities, patients treated at low-outliers were more likely to be privately insured (34.7%[Low] vs. 32.9%[High]), reside in high-income neighborhoods, have no comorbidity (51.7% [Low] vs. 41.9 [High], p<.001), have adenocarcinoma (62.4%[Low] vs. 58.1%[High], p<.001), stage IA disease (41.6%[Low] vs. 39.6%[High], p<.001) and receive sub-lobectomy (11.7%[Low] vs. 9.9%[High], p<.001). Low-outlier facilities were more likely to be teaching/research or NCI-designated programs (54.8% [Low] vs. 18.5% [High], p<.001) and in the highest quartile of total cancer surgical volume (90.4% [Low] vs. 34.5% [High], p<.001) and lung cancer surgery volume (42.5% [Low] vs. 29.2% [High], p<.001) (Table 1). They also had smaller proportions of uninsured/Medicaid patients (45.2% [Low] vs. 36.2% [High], p=.006). The 5-year unadjusted OS estimates were: 0.62 (low-outliers), 0.58 (non-outliers), 0.57 (high-outliers); log-rank p<.001. Table 1. Facility characteristics across performance categoriesHigh-Outlier(N=113) Non-Outlier(N=755) Low-Outlier(N=73) p-value N(%) Census_region Northeast 18(15.9) 154(20.4) 19(26.0) 0.03 Midwest 39(34.5) 223(29.5) 15(20.6) South 37(32.7) 257(34.0) 35(48.0) West 19(16.8) 121(16.0) 4(5.5) Facility_type Community_Cancer_Program 23(20.4) 164(21.7) 0(0.0) <0.001 Comprehensive_Community_Cancer_Program 62(54.9) 419(55.5) 28(38.4) Teaching/Research 17(15.0) 128(17.0) 28(38.4) NCI_program 4(3.5) 17(2.3) 12(16.4) Other 7(6.2) 27(3.6) 5(6.9) Proportion_of_Medicaid/uninsure_patients Q1(low) 25(22.1) 206(27.3) 13(17.8) 0.006 Q2 16(14.2) 204(27.0) 20(27.4) Q3 41(36.3) 174(23.1) 21(28.8) Q4(high) 31(27.4) 171(22.7) 19(26.0) Lung_cancer_surgery_as_a_proportion of_all_surgery Q1(low) 8(7.1) 73(9.7) 0(0.0) <0.001 Q2 37(32.7) 224(29.7) 9(12.3) Q3 35(31.0) 226(29.9) 33(45.2) Q4(high) 33(29.2) 232(30.7) 31(42.5) Total_cancer_surgery_volume Q1(low) 12(10.6) 98(13.0) 0(0.0) <0.001 Q2 32(28.3) 193(25.6) 0(0.0) Q3 30(26.6) 253(33.5) 7(9.6) Q4(high) 39(34.5) 211(28.0) 66(90.4)
Conclusion:
Facility performance in lung cancer surgery can be captured by using the RAMP rate. Low-outlier facilities delivered superior OS than high-outliers. RAMP metrics could allow facilities to understand their performance and serve as a quality improvement benchmark.
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