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S.D. Ramsey
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OA01 - Risk Assessment and Follow up in Surgical Patients (ID 371)
- Event: WCLC 2016
- Type: Oral Session
- Track: Surgery
- Presentations: 1
- Moderators:W. Zhong, E. Lim
- Coordinates: 12/05/2016, 11:00 - 12:30, Schubert 2
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OA01.02 - A Lung Cancer Surgical Mortality Risk-Prediction Algorithm to Inform Lung Cancer Screening Shared Decision-Making (ID 4601)
11:00 - 12:30 | Author(s): S.D. Ramsey
- Abstract
- Presentation
Background:
Low-dose computed tomography lung cancer screening has been demonstrated to increase detection of cases at an early-stage and reduce lung cancer mortality (vs. x-ray or no screening). However, screening benefits are greatly reduced in persons who are poor candidates for curative intent surgery in the event of screen-detected early-stage disease. To date, no practical tools have been developed to assess potential suitability for surgical treatment at the time of screening shared decision-making. The objective of this study was to use readily available socio-demographic and medical history variables to develop a prediction model that estimates the risk of 30-day mortality following surgical treatment for early-stage non-small cell lung cancer (NSCLC).
Methods:
We used logistic regression to develop a risk-prediction model for 30-day mortality following surgical treatment for Stage I/II NSCLC in patients age 65 to 79 using SEER-Medicare linked databases (2007-2012). Additionally, all patients had at least 1 year of Medicare enrollment prior to NSCLC diagnosis and received initial surgical treatment within 6 months of diagnosis. We developed the model with a training sample of 1,571 surgical cases and conducted internal validation exercises with a sample 4,632 independent surgical cases. Models included age, sex, race, country of birth, urban-rural status, and comorbidities in the year prior to NSCLC diagnosis. The Hosmer-Lemeshow test (by decile) and area under the receiver-operating characteristic curve (AUC) were assessed as measures of model calibration and discrimination, respectively.
Results:
Within the full sample of 6,203 cases, 201 deaths were identified within 30 days of surgical treatment (3.2% of sample). In the training and internal validation sets, the AUC was 0.831 and 0.734, respectively. The observed risk of 30-day mortality was 9.3-fold greater in the highest decile of predicted risk (8.3%) vs. the lowest decile (0.7%), and the Hosmer-Lemeshow test indicated satisfactory model fit (p=0.92). The model had similar performance in women, men, whites, and non-whites; and also had similar calibration and discrimination for 60- and 90-day mortality.
Conclusion:
Our risk-prediction model has good ability to identify patients at increased risk of mortality following surgical treatment for early-stage NSCLC, and pending additional development and validation, can potentially be applied in clinic to inform lung cancer screening shared decision-making with minimal time or resource impacts.
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P1.03 - Poster Session with Presenters Present (ID 455)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:
- Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)
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P1.03-036 - Adherence to Eligibility Criteria for Low-Dose CT Screening in an Academic Center (ID 4957)
14:30 - 15:45 | Author(s): S.D. Ramsey
- Abstract
Background:
The United States Preventive Services Task Force (USPSTF), Centers for Medicare and Medicaid Services (CMS), and the National Comprehensive Cancer Network (NCCN) recommend low dose computed tomography (LDCT) lung cancer screening for high risk patients, defined as those between age 55-77 (CMS) or 55-80 (USPSTF), with ≥30 pack-year smoking history who currently smoke or quit within the past 15 years. The NCCN guidelines also recommend screening for patients over 50 years with ≥20 pack-year smoking history and at least one additional lung cancer risk factor. To better understand community practices, we describe adherence to screening eligibility criteria for the population screened at the Seattle Cancer Care Alliance (SCCA).
Methods:
The SCCA developed a multidisciplinary LDCT screening program that executes LDCT screening orders when patients’ regional primary care providers deem them eligible for screening, and provides follow-up evaluations based on screening results. From a prospective registry study of patients screened at the SCCA, we collected baseline sociodemographic, smoking history, and clinical data to retroactively assess patients’ screening eligibility based on USPSTF, CMS, and NCCN criteria, respectively. We define adherence as the proportion of patients meeting at least 1 set of guidelines criteria for screening and used univariate logistic regression to identify potential sociodemographic predictors of adherence, excluding age and smoking history.
Results:
Of 252 patients screened between 5/8/2012 and 8/19/2015, 111 (44%) consented to participate in the study. Median age was 63, 67% were male, 89% were white, 99% were insured, median household income was $75,000, 56% were current smokers, and median cigarette use was 36 pack-years. Of 106 patients with complete eligibility data, 61 (58%), 60 (57%), and 60 (57%) met the USPSTF, CMS, and NCCN screening criteria, respectively. Seventy-nine (75%) patients met eligibility criteria for at least one guideline. Of the 27 patients ineligible by any guidelines, 17 (63%) had <20 pack-years smoking history and 5 (19%) were under age 50. White patients were more likely to meet eligibility for at least one guideline (Odds Ratio= 7.3; 95% CI = 1.9-27.8).
Conclusion:
In this single-center registry study, 25% of patients did not meet screening eligibility criteria when primary care providers were responsible for identifying screening candidates. In response to these results, the program employed a coordinator to pro-actively review screening orders to confirm guideline compliance. An opportunity exists to prioritize LDCT screening to high risk patients through patient counseling, provider education and pro-active review of screening CT orders.
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P3.07 - Poster Session with Presenters Present (ID 493)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Regional Aspects/Health Policy/Public Health
- Presentations: 1
- Moderators:
- Coordinates: 12/07/2016, 14:30 - 15:45, Hall B (Poster Area)
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P3.07-013 - Determining EGFR and ALK Status in a Population-Based Cancer Registry: A Natural Language Processing Validation Study (ID 5061)
14:30 - 15:45 | Author(s): S.D. Ramsey
- Abstract
Background:
Population-based data on Epidermal Growth Factor Receptor (EGFR) and Anaplastic Lymphoma Kinase (ALK) gene test status can inform about real-world molecular testing practices and their impact on treatment decisions and outcomes. Yet no efficient methods are available for population-based cancer registries to ascertain molecular testing data of non-squamous non-small cell lung cancer (NS-NSCLC) from electronic pathology (e-path) records. We sought to validate natural language processing (NLP) systems to accurately ascertain EGFR and ALK test use and results in patients with stage IV NS-NSCLC included in the Fred Hutchinson Cancer Research Center’s Cancer Surveillance System (CSS), a part of the U.S. Surveillance, Epidemiology, and End Results (SEER) program.
Methods:
We identified 4,279 e-path reports available in the CSS corresponding to 1,634 patients diagnosed with stage IV NS-NSCLC between 09/1/2011 and 12/31/2013. Using a random sample of 426 (10%) reports, we developed and trained an NLP system to detect EGFR mutation and ALK gene rearrangement test use (test result reported vs. not reported), and test results (positive vs. negative among reported tests). Two oncologists reviewed all e-path reports and resolved discrepancies by consensus to determine the gold-standard classification of test use and results. We report preliminary estimates of the NLP sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for EGFR and ALK test use based on a second random sample of 426 reports (testing subsample).
Results:
Of 1,634 patients, mean age was 68 years, 815 (50%) were male, 1424 (87%) were white, and 1,347 (82%) had adenocarcinoma histology. Based on the gold-standard classification, in the training subsample, 126 (30%) and 103 (24%) reports contained information about EGFR and ALK test results, respectively. In the testing subsample, 139 (32%) and 115 (27%) had information about EGFR and ALK test results, respectively. In the testing subsample, the NLP system correctly detected 135 reports that contained EGFR test results and 285 that did not (sensitivity=97%; specificity=99%; PPV=99%; NPV=99%), respectively. The NLP system correctly detected 113 reports that contained ALK test results and 307 that did not (sensitivity=98%; specificity=99%; PPV=97%; NPV=99%), respectively.
Conclusion:
NLP is likely a valid method for capture of EGFR and ALK test use from e-path reports. Ongoing analyses include the NLP validity for ascertainment of test results among reported EGFR and ALK tests in this initial dataset and in a separate validation dataset of 3,427 pathology reports, all of which will be reported subsequently.
