Development of an Electronic Health Record-Based Algorithm for Predicting Lung Cancer Screening Eligibility in the Population-Based Research to Optimize the Screening Process Lung Research Consortium.

PURPOSE: Lung cancer screening (LCS) guidelines in the United States recommend LCS for those age 50-80 years with at least 20 pack-years smoking history who currently smoke or quit within the last 15 years. We tested the performance of simple smoking-related criteria derived from electronic health record (EHR) data and developed and tested the performance of a multivariable model in predicting LCS eligibility. METHODS: Analyses were completed within the Population-based Research to Optimize the Screening Process Lung Consortium (PROSPR-Lung). In our primary validity analyses, the reference standard LCS eligibility was based on self-reported smoking data collected via survey. Within one PROSPR-Lung health system, we used a training data set and penalized multivariable logistic regression using the Least Absolute Shrinkage and Selection Operator to select EHR-based variables into the prediction model including demographics, smoking history, diagnoses, and prescription medications. A separate test data set assessed model performance. We also conducted external validation analysis in a separate health system and reported AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy metrics associated with the Youden Index. RESULTS: There were 14,214 individuals with survey data to assess LCS eligibility in primary analyses. The overall performance for assigning LCS eligibility status as measured by the AUC values at the two health systems was 0.940 and 0.938. At the Youden Index cutoff value, performance metrics were as follows: accuracy, 0.855 and 0.895; sensitivity, 0.886 and 0.920; specificity, 0.896 and 0.850; PPV, 0.357 and 0.444; and NPV, 0.988 and 0.992. CONCLUSION: Our results suggest that health systems can use an EHR-derived multivariable prediction model to aid in the identification of those who may be eligible for LCS.

Individuals Eligible for Lung Cancer Screening Less Likely to Receive Screening When Enrolled in Health Plans With Deductibles.

BACKGROUND: In 2015, the Centers for Medicare & Medicaid Services and commercial insurance plans began covering lung cancer screening (LCS) without patient cost-sharing for all plans. We explore the impact of enrolling into a deductible plan on the utilization of LCS services despite having no out-of-pocket cost requirement. METHODS: This retrospective study analyzed data from the Population-based Research to Optimize the Screening Process Lung Consortium. Our cohort included non-Medicare LCS-eligible individuals enrolled in managed care organizations between February 5, 2015, and February 28, 2019. We estimate a series of sequential logistic regression models examining utilization across the sequence of events required for baseline LCS. We report the marginal effects of enrollment into deductible plans compared with enrollment in no-deductible plans. RESULTS: The total effect of deductible plan enrollment was a 1.8 percentage-point (PP) decrease in baseline LCS. Sequential logistic regression results that explore each transition separately indicate deductible plan enrollment was associated with a 4.3 PP decrease in receipt of clinician visit, a 1.7 PP decrease in receipt of LCS order, and a 7.0 PP decrease in receipt of baseline LCS. Reductions persisted across all observable races and ethnicities. CONCLUSIONS: These findings suggest individuals enrolled in deductible plans are more likely to forgo preventive LCS services despite requiring no out-of-pocket costs. This result may indicate that increased cost-sharing is associated with suboptimal choices to forgo recommended LCS. Alternatively, this effect may indicate individuals enrolling into deductible plans prefer less health care utilization. Patient outreach interventions at the health plan level may improve LCS.

Application of team science best practices to the project management of a large, multi-site lung cancer screening research consortium.

Research is increasingly conducted through multi-institutional consortia, and best practices for establishing multi-site research collaborations must be employed to ensure efficient, effective, and productive translational research teams. In this manuscript, we describe how the Population-based Research to Optimize the Screening Process Lung Research Center (PROSPR-Lung) utilized evidence-based Science of Team Science (SciTS) best practices to establish the consortium's infrastructure and processes to promote translational research in lung cancer screening. We provide specific, actionable examples of how we: (1) developed and reinforced a shared mission, vision, and goals; (2) maintained a transparent and representative leadership structure; (3) employed strong research support systems; (4) provided efficient and effective data management; (5) promoted interdisciplinary conversations; and (6) built a culture of trust. We offer guidance for managing a multi-site research center and data repository that may be applied to a variety of settings. Finally, we detail specific project management tools and processes used to drive collaboration, efficiency, and scientific productivity.

Uptake of novel systemic therapy: Real world patterns among adults with advanced non-small cell lung cancer.

INTRODUCTION/BACKGROUND: Systemic treatment for advanced non-small cell lung cancer (NSCLC) is shifting from platinum-based chemotherapy to immunotherapy and targeted therapies associated with improved survival in clinical trials. As new therapies are approved for use, examining variations in use for treating patients in community practice can generate additional evidence as to the magnitude of their benefit. PATIENTS AND METHODS: We identified 1,442 patients diagnosed with de novo stage IV NSCLC between 3/1/2012 and 12/31/2020. Patient characteristics and treatment patterns are described overall and by type of first- and second-line systemic therapy received. Prevalence ratios estimate the association of patient and tumor characteristics with receipt of first-line therapy. RESULTS: Within 180 days of diagnosis, 949 (66%) patients received first-line systemic therapy, increasing from 53% in 2012 to 71% in 2020 (p = 0.0004). The proportion of patients receiving first-line immunotherapy+/-chemotherapy (IMO) increased from 14%-66% (p<0.0001). Overall, 380 (26%) patients received both first- and second-line treatment, varying by year between 16%-36% (p = 0.18). The proportion of patients receiving second-line IMO increased from 13%-37% (p<0.0001). Older age and current smoking status were inversely associated with receipt of first-line therapy. Higher BMI, receipt of radiation, and diagnosis year were positively associated with receipt of first-line therapy. No association was found for race, ethnicity, or socioeconomic status. CONCLUSION: The proportion of advanced NSCLC patients receiving first- and second-line treatment increased over time, particularly for IMO treatments. Additional research is needed to better understand the impact of these therapies on patient outcomes, including short-term, long-term, and financial toxicities. MICROABSTRACT: Systemic treatment for non-small cell lung cancer (NSCLC) is shifting from platinum-based therapies to immunotherapy and targeted therapies. Using de novo stage IV NSCLC patients identified from 4 healthcare systems, we examine trends in systemic therapy. We saw an increase in the portion of patients receiving any systemic therapy and a sharp increase in the proportion of patients receiving immunotherapy.

Smoking status and the association between patient-level factors and survival among lung cancer patients.

BACKGROUND: Declines in the prevalence of cigarette smoking, advances in targeted therapies, and implementation of lung cancer screening have changed the clinical landscape for lung cancer. The proportion of lung cancer deaths is increasing in those who have never smoked cigarettes. To better understand contemporary patterns in survival among patients with lung cancer, a comprehensive evaluation of factors associated with survival, including differential associations by smoking status, is needed. METHODS: Patients diagnosed with lung cancer between January 1, 2010, and September 30, 2019, were identified. We estimated all-cause and lung cancer-specific median, 5-year, and multivariable restricted mean survival time (RMST) to identify demographic, socioeconomic, and clinical factors associated with survival, overall and stratified by smoking status (never, former, and current). RESULTS: Analyses included 6813 patients with lung cancer: 13.9% never smoked, 54.2% formerly smoked, and 31.9% currently smoked. All-cause RMST through 5 years for those who never, formerly, and currently smoked was 32.1, 25.9, and 23.3 months, respectively. Lung cancer-specific RMST was 36.3 months, 30.3 months, and 26.0 months, respectively. Across most models, female sex, younger age, higher socioeconomic measures, first-course surgery, histology, and body mass index were positively associated, and higher stage was inversely associated with survival. Relative to White patients, Black patients had increased survival among those who formerly smoked. CONCLUSIONS: We identify actionable factors associated with survival between those who never, formerly, and currently smoked cigarettes. These findings illuminate opportunities to address underlying mechanisms driving lung cancer progression, including use of first-course treatment, and enhanced implementation of tailored smoking cessation interventions for individuals diagnosed with cancer.

Percentage Up to Date With Chest Computed Tomography Among Those Eligible for Lung Cancer Screening.

INTRODUCTION: Authors aimed to calculate the percentage up-to-date with testing in the context of lung cancer screening across 5 healthcare systems and evaluate differences according to patient and health system characteristics. METHODS: Lung cancer screening‒eligible individuals receiving care within the five systems in the Population-based Research to Optimize the Screening Process Lung consortium from October 1, 2018 to September 30, 2019 were included in analyses. Data collection was completed on June 15, 2021; final analyses were completed on April 1, 2022. Chest computed tomography scans and patient characteristics were obtained through electronic health records and used to calculate the percentage completing a chest computed tomography scan in the previous 12 months (considered up-to-date). The association of patient and healthcare system factors with being up-to-date was evaluated with adjusted prevalence ratios and 95% CIs using log-binomial regression models. RESULTS: There were 29,417 individuals eligible for lung cancer screening as of September 30, 2019; 8,333 (28.3%) were up-to-date with testing. Those aged 65-74 years (prevalence ratio=1.19; CI=1.15, 1.24, versus ages 55-64), those with chronic obstructive pulmonary disease (prevalence ratio=2.05; CI=1.98, 2.13), and those in higher SES census tracts (prevalence ratio=1.22; CI=1.16, 1.30, highest quintile versus lowest) were more likely to be up-to-date. Currently smoking (prevalence ratio=0.91; CI=0.88, 0.95), having a BMI ≥30 kg/m2 (prevalence ratio=0.83; CI=0.77, 0.88), identifying as Native Hawaiian or other Pacific Islander (prevalence ratio=0.79; CI=0.68, 0.92), and having a decentralized lung cancer screening program (prevalence ratio=0.77; CI=0.74, 0.80) were inversely associated with being up-to-date. CONCLUSIONS: The percentage up-to-date with testing among those eligible for lung cancer screening is well below up-to-date estimates for other types of cancer screening, and disparities in lung cancer screening participation remain.

Stage Migration and Lung Cancer Incidence After Initiation of Low-Dose Computed Tomography Screening.

INTRODUCTION: Despite evidence from clinical trials of favorable shifts in cancer stage and improvements in lung cancer-specific mortality, the effectiveness of lung cancer screening (LCS) in clinical practice has not been clearly revealed. METHODS: We performed a multicenter cohort study of patients diagnosed with a primary lung cancer between January 1, 2014, and September 30, 2019, at one of four U.S. health care systems. The primary outcome variables were cancer stage distribution and annual age-adjusted lung cancer incidence. The primary exposure variable was receipt of at least one low-dose computed tomography for LCS before cancer diagnosis. RESULTS: A total of 3678 individuals were diagnosed with an incident lung cancer during the study period; 404 (11%) of these patients were diagnosed after initiation of LCS. As screening volume increased, the proportion of patients diagnosed with lung cancer after LCS initiation also rose from 0% in the first quartile of 2014 to 20% in the third quartile of 2019. LCS did not result in a significant change in the overall incidence of lung cancer (average annual percentage change [AAPC]: -0.8 [95% confidence interval (CI): -4.7 to 3.2]) between 2014 and 2018. Stage-specific incidence rates increased for stage I cancer (AAPC = 8.0 [95% CI: 0.8-15.7]) and declined for stage IV disease (AAPC = -6.0 [95% CI: -11.2 to -0.5]). CONCLUSIONS: Implementation of LCS at four diverse health care systems has resulted in a favorable shift to a higher incidence of stage I cancer with an associated decline in stage IV disease. Overall lung cancer incidence did not increase, suggesting a limited impact of overdiagnosis.

Patterns of Medical Care Cost by Service Type for Patients With Recurrent and De Novo Advanced Cancer.

OBJECTIVES: There is limited knowledge about the cost patterns of patients who receive a diagnosis of de novo and recurrent advanced cancers in the United States. METHODS: Data on patients who received a diagnosis of de novo stage IV or recurrent breast, colorectal, or lung cancer between 2000 and 2012 from 3 integrated health systems were used to estimate average annual costs for total, ambulatory, inpatient, medication, and other services during (1) 12 months preceding de novo or recurrent diagnosis (preindex) and (2) diagnosis month through 11 months after (postindex), from the payer perspective. Generalized linear regression models estimated costs adjusting for patient and clinical factors. RESULTS: Patients who developed a recurrence <1 year after their initial cancer diagnosis had significantly higher total costs in the preindex period than those with recurrence ≥1 year after initial diagnosis and those with de novo stage IV disease across all cancers (all P < .05). Patients with de novo stage IV breast and colorectal cancer had significantly higher total costs in the postindex period than patients with cancer recurrent in <1 year and ≥1 year (all P < .05), respectively. Patients in de novo stage IV and those with recurrence in ≥1 year experienced significantly higher postindex costs than the preindex period (all P < .001). CONCLUSIONS: Our findings reveal distinct cost patterns between patients with de novo stage IV, recurrent <1-year, and recurrent ≥1-year cancer, suggesting unique care trajectories that may influence resource use and planning. Future cost studies among patients with advanced cancer should account for de novo versus recurrent diagnoses and timing of recurrence to obtain estimates that accurately reflect these care pattern complexities.

Community-based Lung Cancer Screening Results in Relation to Patient and Radiologist Characteristics: The PROSPR Consortium.

Rationale: Lung-RADS classification was developed to standardize reporting and management of lung cancer screening using low-dose computed tomographic (LDCT) imaging. Although variation in Lung-RADS distribution between healthcare systems has been reported, it is unclear if this is explained by patient characteristics, radiologist experience with lung cancer screening, or other factors. Objectives: Our objective was to determine if patient or radiologist factors are associated with Lung-RADS score. Methods: In the Population-based Research to Optimize the Screening Process (PROSPR) Lung consortium, we conducted a study of patients who received their first screening LDCT imaging at one of the five healthcare systems in the PROSPR Lung Research Center from May 1, 2014, through December 31, 2017. Data on LDCT scans, patient factors, and radiologist characteristics were obtained via electronic health records. LDCT scan findings were categorized using Lung-RADS (negative [1], benign [2], probably benign [3], or suspicious [4]). We used generalized estimating equations with a multinomial distribution to compare the odds of Lung-RADS 3, and separately Lung-RADS 4, versus Lung-RADS 1 or 2 and estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between Lung-RADS assignment and patient and radiologist characteristics. Results: Analyses included 8,556 patients; 24% were assigned Lung-RADS 1, 60% Lung-RADS 2, 10% Lung-RADS 3, and 5% Lung-RADS 4. Age was positively associated with Lung-RADS 3 (OR, 1.02; 95% CI, 1.01-1.03) and 4 (OR, 1.03; 95% CI, 1.01-1.05); chronic obstructive pulmonary disease (COPD) was positively associated with Lung-RADS 4 (OR, 1.78; 95% CI, 1.45-2.20); obesity was inversely associated with Lung-RADS 3 (OR, 0.70; 95% CI, 0.58-0.84) and 4 (OR, 0.58; 95% CI, 0.45-0.75). There was no association between sex, race, ethnicity, education, or smoking status and Lung-RADS assignment. Radiologist volume of interpreting screening LDCT scans, years in practice, and thoracic specialty were also not associated with Lung-RADS assignment. Conclusions: Healthcare systems that are comprised of patients with an older age distribution or higher levels of COPD will have a greater proportion of screening LDCT scans with Lung-RADS 3 or 4 findings and should plan for additional resources to support appropriate and timely management of noted positive findings.

Evaluation of Population-Level Changes Associated With the 2021 US Preventive Services Task Force Lung Cancer Screening Recommendations in Community-Based Health Care Systems.

Importance: The US Preventive Services Task Force (USPSTF) released updated lung cancer screening recommendations in 2021, lowering the screening age from 55 to 50 years and smoking history from 30 to 20 pack-years. These changes are expected to expand screening access to women and racial and ethnic minority groups. Objective: To estimate the population-level changes associated with the 2021 USPSTF expansion of lung cancer screening eligibility by sex, race and ethnicity, sociodemographic factors, and comorbidities in 5 community-based health care systems. Design, Setting, and Participants: This cohort study analyzed data of patients who received care from any of 5 community-based health care systems (which are members of the Population-based Research to Optimize the Screening Process Lung Consortium, a collaboration that conducts research to better understand how to improve the cancer screening processes in community health care settings) from January 1, 2010, through September 30, 2019. Individuals who had complete smoking history and were engaged with the health care system for 12 or more continuous months were included. Those who had never smoked or who had unknown smoking history were excluded. Exposures: Electronic health record-derived age, sex, race and ethnicity, socioeconomic status (SES), comorbidities, and smoking history. Main Outcomes and Measures: Differences in the proportion of the newly eligible population by age, sex, race and ethnicity, Charlson Comorbidity Index, chronic obstructive pulmonary disease diagnosis, and SES as well as lung cancer diagnoses under the 2013 recommendations vs the expected cases under the 2021 recommendations were evaluated using χ2 tests. Results: As of September 2019, there were 341 163 individuals aged 50 to 80 years who currently or previously smoked. Among these, 34 528 had electronic health record data that captured pack-year and quit-date information and were eligible for lung cancer screening according to the 2013 USPSTF recommendations. The 2021 USPSTF recommendations expanded screening eligibility to 18 533 individuals, representing a 53.7% increase. Compared with the 2013 cohort, the newly eligible 2021 population included 5833 individuals (31.5%) aged 50 to 54 years, a larger proportion of women (52.0% [n = 9631]), and more racial or ethnic minority groups. The relative increases in the proportion of newly eligible individuals were 60.6% for Asian, Native Hawaiian, or Pacific Islander; 67.4% for Hispanic; 69.7% for non-Hispanic Black; and 49.0% for non-Hispanic White groups. The relative increase for women was 13.8% higher than for men (61.2% vs 47.4%), and those with a lower comorbidity burden and lower SES had higher relative increases (eg, 68.7% for a Charlson Comorbidity Index score of 0; 61.1% for lowest SES). The 2021 recommendations were associated with an estimated 30% increase in incident lung cancer diagnoses compared with the 2013 recommendations. Conclusions and Relevance: This cohort study suggests that, in diverse health care systems, adopting the 2021 USPSTF recommendations will increase the number of women, racial and ethnic minority groups, and individuals with lower SES who are eligible for lung cancer screening, thus helping to minimize the barriers to screening access for individuals with high risk for lung cancer.

Real-world Clinical Implementation of Lung Cancer Screening-Evaluating Processes to Improve Screening Guidelines-Concordance.

BACKGROUND: Lung cancer screening (LCS) requires complex processes to identify eligible patients, provide appropriate follow-up, and manage findings. It is unclear whether LCS in real-world clinical settings will realize the same benefits as the National Lung Screening Trial (NLST). OBJECTIVE: To evaluate the impact of process modifications on compliance with LCS guidelines during LCS program implementation, and to compare patient characteristics and outcomes with those in NLST. DESIGN: Retrospective cohort study. SETTING: Kaiser Permanente Colorado (KPCO), a non-profit integrated healthcare system. PATIENTS: A total of 3375 patients who underwent a baseline lung cancer screening low-dose computed tomography (S-LDCT) scan between May 2014 and June 2017. MEASUREMENTS: Among those receiving an S-LDCT, proportion who met guidelines-based LCS eligibility criteria before and after LCS process modifications, differences in patient characteristics and outcomes between KPCO LCS patients and the NLST cohort, and factors associated with a positive screen. RESULTS: After modifying LCS eligibility confirmation processes, patients receiving S-LDCT who met guidelines-based LCS eligibility criteria increased from 45.6 to 92.7% (P < 0.001). Prior to changes, patients were older (68 vs. 67 years; P = 0.001), less likely to be current smokers (51.3% vs. 52.5%; P < 0.001), and less likely to have a ≥ 30-pack-year smoking history (50.0% vs. 95.3%; P < 0.001). Compared with NLST participants, KPCO LCS patients were older (67 vs. 60 years; P < 0.001), more likely to currently smoke (52.3% vs. 48.1%; P < 0.001), and more likely to have pulmonary disease. Among those with a positive baseline S-LDCT, the lung cancer detection rate was higher at KPCO (9.4% vs. 3.8%; P < 0.001) and was positively associated with prior pulmonary disease. CONCLUSION: Adherence to LCS guidelines requires eligibility confirmation procedures. Among those with a positive baseline S-LDCT, comorbidity burden and lung cancer detection rates were notably higher than in NLST, suggesting that the study of long-term outcomes in patients undergoing LCS in real-world clinical settings is warranted.

Long-Term Patterns of Oral Anticancer Agent Adoption, Duration, and Switching in Patients With CML.

BACKGROUND: Oral tyrosine kinase inhibitors (TKIs) have been the standard of care for chronic myeloid leukemia (CML) since 2001. However, few studies have evaluated changes in the treatment landscape of CML over time. This study assessed the long-term treatment patterns of oral anticancer therapies among patients with CML. METHODS: This retrospective cohort study included patients newly diagnosed with CML between January 1, 2000, and December 31, 2016, from 10 integrated healthcare systems. The proportion of patients treated with 5 FDA-approved oral TKI agents-bosutinib, dasatinib, imatinib, nilotinib, and ponatinib-in the 12 months after diagnosis were measured, overall and by year, between 2000 and 2017. We assessed the use of each oral agent through the fourth-line setting. Multivariable logistic regression estimated the odds of receiving any oral agent, adjusting for sociodemographic and clinical characteristics. RESULTS: Among 853 patients with CML, 81% received an oral agent between 2000 and 2017. Use of non-oral therapies decreased from 100% in 2000 to 5% in 2005, coinciding with imatinib uptake from 65% in 2001 to 98% in 2005. Approximately 28% of patients switched to a second-line agent, 9% switched to a third-line agent, and 2% switched to a fourth-line agent. Adjusted analysis showed that age at diagnosis, year of diagnosis, and comorbidity burden were statistically significantly associated with odds of receiving an oral agent. CONCLUSIONS: A dramatic shift was seen in CML treatments away from traditional, nonoral chemotherapy toward use of novel oral TKIs between 2000 and 2017. As the costs of oral anticancer agents reach new highs, studies assessing the long-term health and financial outcomes among patients with CML are warranted.

Melanoma incidence, recurrence, and mortality in an integrated healthcare system: A retrospective cohort study.

BACKGROUND: Numerous studies have examined melanoma incidence and survival, but studies on melanoma recurrence are limited. We examined melanoma incidence, recurrence, and mortality among members of Kaiser Permanente Colorado (KPCO) between January 1, 2000 and December 31, 2015. METHODS: Age-adjusted incidence rates were computed to examine trends among KPCO members aged 21 years and older. Cox proportional hazards models were used to examine factors associated with recurrence and mortality. RESULTS: Our cohort included 1931 cases of invasive melanoma. Incidence rates increased over time and were higher than SEER rates; however, the increase was limited to early stage disease. In multivariable models, stage at initial diagnosis, gender, and age were associated with melanoma recurrence. Men were more likely to have a recurrence than women (adjusted hazard ratio [HR]: 1.70, 95% confidence interval [CI]: 1.19-2.43), and for each decade of increasing age, the adjusted HR = 1.20 (95% CI: 1.06-1.37). Factors associated with all-cause mortality included stage (HR = 12.87, 95% CI: 6.63-24.99, for stage IV vs stage I), male gender (HR = 1.42, 95% CI: 1.12-1.79), older age at diagnosis, lower socioeconomic status, and comorbidity index. For melanoma-specific mortality, results were similar, with one exception: age was not associated with melanoma-specific death (HR = 1.09, 95% CI: 0.94-1.25, P = 0.253). CONCLUSIONS: Data derived from an insured patient population, such as KPCO, have the potential to enhance our understanding of emerging trends in melanoma. This is the first population-based study in the United States to examine patient characteristics associated with risk of recurrence. Men have an increased risk of both recurrence and death, and thus may benefit from more intensive follow-up than women.

Spending for Advanced Cancer Diagnoses: Comparing Recurrent Versus De Novo Stage IV Disease.

PURPOSE: Spending for patients with advanced cancer is substantial. Past efforts to characterize this spending usually have not included patients with recurrence (who may differ from those with de novo stage IV disease) or described which services drive spending. METHODS: Using SEER-Medicare data from 2008 to 2013, we identified patients with breast, colorectal, and lung cancer with either de novo stage IV or recurrent advanced cancer. Mean spending/patient/month (2012 US dollars) was estimated from 12 months before to 11 months after diagnosis for all services and by the type of service. We describe the absolute difference in mean monthly spending for de novo versus recurrent patients, and we estimate differences after controlling for type of advanced cancer, year of diagnosis, age, sex, comorbidity, and other factors. RESULTS: We identified 54,982 patients with advanced cancer. Before diagnosis, mean monthly spending was higher for recurrent patients (absolute difference: breast, $1,412; colorectal, $3,002; lung, $2,805; all P < .001), whereas after the diagnosis, it was higher for de novo patients (absolute difference: breast, $2,443; colorectal, $4,844; lung, $2,356; all P < .001). Spending differences were driven by inpatient, physician, and hospice services. Across the 2-year period around the advanced cancer diagnosis, adjusted mean monthly spending was higher for de novo versus recurrent patients (spending ratio: breast, 2.39 [95% CI, 2.05 to 2.77]; colorectal, 2.64 [95% CI, 2.31 to 3.01]; lung, 1.46 [95% CI, 1.30 to 1.65]). CONCLUSION: Spending for de novo cancer was greater than spending for recurrent advanced cancer. Understanding the patterns and drivers of spending is necessary to design alternative payment models and to improve value.

Performance of Cancer Recurrence Algorithms After Coding Scheme Switch From International Classification of Diseases 9th Revision to International Classification of Diseases 10th Revision.

PURPOSE: We previously developed and validated informatic algorithms that used International Classification of Diseases 9th revision (ICD9)-based diagnostic and procedure codes to detect the presence and timing of cancer recurrence (the RECUR Algorithms). In 2015, ICD10 replaced ICD9 as the worldwide coding standard. To understand the impact of this transition, we evaluated the performance of the RECUR Algorithms after incorporating ICD10 codes. METHODS: Using publicly available translation tables along with clinician and other expertise, we updated the algorithms to include ICD10 codes as additional input variables. We evaluated the performance of the algorithms using gold standard recurrence measures associated with a contemporary cohort of patients with stage I to III breast, colorectal, and lung (excluding IIIB) cancer and derived performance measures, including the area under the receiver operating curve, average absolute prediction error, and correct classification rate. These values were compared with the performance measures derived from the validation of the original algorithms. RESULTS: A total of 659 colorectal, 280 lung, and 2,053 breast cancer cases were identified. Area under the receiver operating curve derived from the updated algorithms was 89.0% (95% CI, 82.3% to 95.7%), 88.9% (95% CI, 79.3% to 98.2%), and 80.5% (95% CI, 72.8% to 88.2%) for the colorectal, lung, and breast cancer algorithms, respectively. Average absolute prediction errors for recurrence timing were 2.7 (SE, 11.3%), 2.4 (SE, 10.4%), and 5.6 months (SE, 21.8%), respectively, and timing estimates were within 6 months of actual recurrence for more than 80% of colorectal, more than 90% of lung, and more than 50% of breast cancer cases using the updated algorithm. CONCLUSION: Performance measures derived from the updated and original algorithms had overlapping confidence intervals, suggesting that the ICD9 to ICD10 transition did not affect the RECUR Algorithm performance.

Comparing Survival After Recurrent vs De Novo Stage IV Advanced Breast, Lung, and Colorectal Cancer.

The treatments provided to and survival of patients with recurrent vs de novo stage IV advanced breast, lung, and colorectal cancer may differ but have not been well studied. Using population-based data from the Cancer Research Network for 4510 patients with advanced breast, lung, or colorectal cancer, we matched recurrent/de novo patients on demographic factors. We found longer survival for recurrent vs de novo lung cancer (182 matched pairs); no significant difference for colorectal cancer (332 matched pairs); and shorter survival for recurrent vs de novo breast cancer (219 matched pairs). Compared with recurrent cases, chemotherapy use and radiation therapy use were more common among de novo cases. Differences in treatment and survival between recurrent and de novo advanced cancer patients could inform prognostic estimates and clinical trial design.

Medical Care Costs for Recurrent versus De Novo Stage IV Cancer by Age at Diagnosis.

OBJECTIVE: To address the knowledge gap regarding medical care costs for advanced cancer patients, we compared costs for recurrent versus de novo stage IV breast, colorectal, and lung cancer patients. DATA SOURCES/STUDY SETTING: Virtual Data Warehouse (VDW) information from three Kaiser Permanente regions: Colorado, Northwest, and Washington. STUDY DESIGN: We identified patients aged ≥21 with de novo or recurrent breast (nde novo  = 352; nrecurrent  = 765), colorectal (nde novo  = 1,072; nrecurrent  = 542), and lung (nde novo  = 4,041; nrecurrent  = 340) cancers diagnosed 2000-2012. We estimated average total monthly and annual costs in the 12 months preceding, month of, and 12 months following the index de novo/recurrence date, stratified by age at diagnosis (<65, ≥65). Generalized linear repeated-measures models controlled for demographics and comorbidity. PRINCIPAL FINDINGS: In the pre-index period, monthly costs were higher for recurrent than for de novo breast (<65: +$2,431; ≥65: +$1,360), colorectal (<65: +$3,219; ≥65: +$2,247), and lung cancer (<65: +$3,086; ≥65: +$2,260) patients. Conversely, during the index and post-index periods, costs were higher for de novo patients. Average total annual pre-index costs were five- to ninefold higher for recurrent versus de novo patients <65. CONCLUSIONS: Cost differences by type of advanced cancer and by age suggest heterogeneous patterns of care that merit further investigation.

Development, Validation, and Dissemination of a Breast Cancer Recurrence Detection and Timing Informatics Algorithm.

Background: This study developed, validated, and disseminated a generalizable informatics algorithm for detecting breast cancer recurrence and timing using a gold standard measure of recurrence coupled with data derived from a readily available common data model that pools health insurance claims and electronic health records data. Methods: The algorithm has two parts: to detect the presence of recurrence and to estimate the timing of recurrence. The primary data source was the Cancer Research Network Virtual Data Warehouse (VDW). Sixteen potential indicators of recurrence were considered for model development. The final recurrence detection and timing models were determined, respectively, by maximizing the area under the ROC curve (AUROC) and minimizing average absolute error. Detection and timing algorithms were validated using VDW data in comparison with a gold standard recurrence capture from a third site in which recurrences were validated through chart review. Performance of this algorithm, stratified by stage at diagnosis, was compared with other published algorithms. All statistical tests were two-sided. Results: Detection model AUROCs were 0.939 (95% confidence interval [CI] = 0.917 to 0.955) in the training data set (n = 3370) and 0.956 (95% CI = 0.944 to 0.971) and 0.900 (95% CI = 0.872 to 0.928), respectively, in the two validation data sets (n = 3370 and 3961, respectively). Timing models yielded average absolute prediction errors of 12.6% (95% CI = 10.5% to 14.5%) in the training data and 11.7% (95% CI = 9.9% to 13.5%) and 10.8% (95% CI = 9.6% to 12.2%) in the validation data sets, respectively, and were statistically significantly lower by 12.6% (95% CI = 8.8% to 16.5%, P < .001) than those estimated using previously reported timing algorithms. Similar covariates were included in both detection and timing algorithms but differed substantially from previous studies. Conclusions: Valid and reliable detection of recurrence using data derived from electronic medical records and insurance claims is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for breast cancer patients and those who develop recurrence.

Determining the Time of Cancer Recurrence Using Claims or Electronic Medical Record Data.

PURPOSE: Data from claims and electronic medical records (EMRs) are frequently used to identify clinical events (eg, cancer diagnosis, stroke). However, accurately determining the time of clinical events can be challenging, and the methods used to generate time estimates are underdeveloped. We sought to develop an approach to determine the time of a clinical event-cancer recurrence-using high-dimensional longitudinal structured data. METHODS: Manual chart abstraction provided information regarding the actual time of cancer recurrence. These data were linked to claims from Medicare or structured EMR data from the Cancer Research Network, which were used to determine time of recurrence for patients with lung or colorectal cancer. We analyzed the longitudinal profile of codes that could help determine the time of recurrence, adjusted for systematic differences between code dates and recurrence dates, and integrated time estimates from different codes to empirically derive an optimal algorithm. RESULTS: We identified twelve code groups that could help determine the time of recurrence. Using claims data for patients with lung cancer, the optimal algorithm consisted of three code groups and provided an average prediction error of 4.8 months. Using EMR data or applying this approach to patients with colorectal cancer yielded similar results. CONCLUSION: Time estimates were improved by selecting codes not necessarily the same as those used to identify recurrence, combining time estimates from multiple code groups, and adjusting for systematic bias between code dates and recurrence dates. Improving the accuracy of time estimates for clinical events can facilitate research, quality measurement, and process improvement.

Detecting Lung and Colorectal Cancer Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.

INTRODUCTION: Recurrent cancer is common, costly, and lethal, yet we know little about it in community-based populations. Electronic health records and tumor registries contain vast amounts of data regarding community-based patients, but usually lack recurrence status. Existing algorithms that use structured data to detect recurrence have limitations. METHODS: We developed algorithms to detect the presence and timing of recurrence after definitive therapy for stages I-III lung and colorectal cancer using 2 data sources that contain a widely available type of structured data (claims or electronic health record encounters) linked to gold-standard recurrence status: Medicare claims linked to the Cancer Care Outcomes Research and Surveillance study, and the Cancer Research Network Virtual Data Warehouse linked to registry data. Twelve potential indicators of recurrence were used to develop separate models for each cancer in each data source. Detection models maximized area under the ROC curve (AUC); timing models minimized average absolute error. Algorithms were compared by cancer type/data source, and contrasted with an existing binary detection rule. RESULTS: Detection model AUCs (>0.92) exceeded existing prediction rules. Timing models yielded absolute prediction errors that were small relative to follow-up time (<15%). Similar covariates were included in all detection and timing algorithms, though differences by cancer type and dataset challenged efforts to create 1 common algorithm for all scenarios. CONCLUSIONS: Valid and reliable detection of recurrence using big data is feasible. These tools will enable extensive, novel research on quality, effectiveness, and outcomes for lung and colorectal cancer patients and those who develop recurrence.