Validating billing/encounter codes as indicators of lung, colorectal, breast, and prostate cancer recurrence using 2 large contemporary cohorts

Differences in survival and recurrence of colorectal cancer by stage across population-based European registries

Recurrence after colorectal cancer resection is rarely documented in the general population while a key clinical determinant for patient survival. We identified 8785 patients with colorectal cancer diagnosed between 2010 and 2013 and clinically followed up to 2020 in 15 cancer registries from seven European countries (Bulgaria, Switzerland, Germany, Estonia, France, Italy, and Spain). We estimated world age-standardized net survival using a flexible cumulative excess hazard model. Recurrence rates were calculated for patients with initially resected stage I, II, or III cancer in six countries, using the actuarial survival method. The proportion of nonmetastatic resected colorectal cancers varied from 58.6% to 78.5% according to countries. The overall 5-year net survival by country ranged between 60.8% and 74.5%. The absolute difference between the 5-year survival extremes was 12.8 points for stage II (Bulgaria vs Switzerland), 19.7 points for stage III (Bulgaria vs. Switzerland) and 14.8 points for Stage IV and unresected cases (Bulgaria vs. Switzerland or France). Five-year cumulative rate of recurrence among resected patients with stage I-III was 17.7%. As compared to the mean of the whole cohort, the risk of developing a recurrence did not differ between countries except a lower risk in Italy for both stage I/II and stage III cancers and a higher risk in Spain for stage III. Survival after colorectal cancer differed across the concerned European countries while there were slight differences in recurrence rates. Population-based collection of cancer recurrence information is crucial to enhance efforts for evidence-based management of colorectal cancer follow up.

Racial and Ethnic Differences in Psychosocial Care Use Among Adults With Metastatic Breast Cancer: A Retrospective Analysis Across Six New York City Health Systems

PURPOSE

A metastatic breast cancer (mBC) diagnosis can affect physical and emotional well-being. However, racial and ethnic differences in receipt of outpatient psychosocial care and supportive care medications in adults with mBC are not well described.

METHODS

Adults with mBC were identified in the INSIGHT-Clinical Research Network, a database inclusive of >12 million patients receiving care across six New York City health systems. Outpatient psychosocial care was operationalized using Common Procedure Terminology codes for outpatient psychotherapy or counseling. Psychosocial/supportive care medications were defined using Rx Concept Unique Identifier codes. Associations between race/ethnicity and outpatient care and medication use were evaluated using logistic regression.

RESULTS

Among 5,429 adults in the analytic cohort, mean age was 61 years and <1% were male; 53.6% were non-Hispanic White (NHW), 21.4% non-Hispanic Black (NHB), 15.9% Hispanic, 6.1% Asian/Native Hawaiian/Pacific Islander (A/NH/PI), and 3% other or unknown. Overall, 4.1% had ≥one outpatient psychosocial care visit and 63.4% were prescribed ≥one medication. Adjusted for age, compared with NHW, Hispanic patients were more likely (odds ratio [OR], 2.14 [95% CI, 1.55 to 2.92]) and A/NH/PI patients less likely (OR, 0.35 [95% CI, 0.12 to 0.78]) to have an outpatient visit. NHB (OR, 0.59 [95% CI, 0.51 to 0.68]) and Asian (OR, 0.36 [95% CI, 0.29 to 0.46]) patients were less likely to be prescribed medications.

CONCLUSION

Despite the prevalence of depression, anxiety, and distress among patients with mBC, we observed low utilization of psychosocial outpatient care. Supportive medication use was more prevalent, although differences observed by race/ethnicity suggest that unmet needs exist.

Association between trajectories of adherence to endocrine therapy and risk of treated breast cancer recurrence among US nonmetastatic breast cancer survivors

BACKGROUND

Endocrine therapy is the mainstay treatment for breast cancer (BC) to reduce BC recurrence risk. During the first year of endocrine therapy use, nearly 30% of BC survivors are nonadherent, which may increase BC recurrence risk. This study is to examine the association between endocrine therapy adherence trajectories and BC recurrence risk in nonmetastatic BC survivors.

METHODS

This retrospective cohort study included Medicare beneficiaries in the United States (US) with incident nonmetastatic BC followed by endocrine therapy initiation in 2010-2019 US Surveillance, Epidemiology, and End Results linked Medicare data. We calculated monthly fill-based proportion of days covered in the first year of endocrine therapy. We applied group-based trajectory models to identify distinct endocrine therapy adherence patterns. After the end of the first-year endocrine therapy trajectory measurement period, we estimated the risk of time to first treated BC recurrence within 4 years using Cox proportional hazards models.

RESULTS

We identified 5 trajectories of adherence to endocrine therapy in BC Stages 0-I subgroup (n = 28,042) and in Stages II-III subgroup (n = 7781). A trajectory of discontinuation before 6 months accounted for 7.0% in Stages 0-I and 5.8% in Stages II-III subgroups, and this trajectory was associated with an increased treated BC recurrence risk compared to nearly perfect adherence (Stages 0-I: adjusted hazard [aHR] = 1.84, 95% CI = 1.46-2.33; Stages II-III: aHR = 1.38, 95% CI = 1.07-1.77).

CONCLUSIONS

Nearly 7% of BC survivors who discontinued before completing 6 months of treatment was associated with an increased treated BC recurrence risk compared to those with nearly perfect adherence among Medicare nonmetastatic BC survivors.

Accuracy of a Cancer Registry Versus Clinical Care Team Chart Abstraction in Identifying Cancer Recurrence

Objective

To evaluate the completeness and reliability of recurrence data from an institutional cancer registry for patients with head and neck cancer.

Patients and Methods

Recurrence information was collected by radiation oncology and otolaryngology researchers. This was compared with the institutional cancer registry for continuous patients treated with radiation therapy for head and neck cancer at a tertiary cancer center. The sensitivity and specificity of institutional cancer registry data was calculated using manual review as the gold standard. False negative recurrences were compared to true positive recurrences to assess for differences in patient characteristics.

Results

A total of 1338 patients who were treated from January 1, 2010, through December 31, 2017, were included in a cancer registry and underwent review. Of them, 375 (30%) had confirmed cancer recurrences, 45 (3%) had concern for recurrence without radiologic or pathologic confirmation, and 31 (2%) had persistent disease. Most confirmed recurrences were distant (37%) or distant plus locoregional (29%), whereas few were local (11%), regional (9%), or locoregional (14%) alone. The cancer registry accuracy was 89.4%, sensitivity 61%, and specificity 99%. Time to recurrence was associated with registry accuracy. True positives had recurrences at a median of 414 days vs 1007 days for false negatives.

Conclusion

Currently, institutional cancer registry recurrence data lacks the required accuracy for implementation into studies without manual confirmation. Longer follow-up of cancer status will likely improve sensitivity. No identified differences in patients accounted for differences in sensitivity. New, ideally automated, data abstraction tools are needed to improve detection of cancer recurrences and minimize manual chart review.

Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences

BACKGROUND

We updated algorithms to identify breast cancer recurrences from administrative data, extending previously developed methods.

METHODS

In this validation study, we evaluated pairs of breast cancer recurrence algorithms (vs. individual algorithms) to identify recurrences. We generated algorithm combinations that categorized discordant algorithm results as no recurrence [High Specificity and PPV (positive predictive value) Combination] or recurrence (High Sensitivity Combination). We compared individual and combined algorithm results to manually abstracted recurrence outcomes from a sample of 600 people with incident stage I-IIIA breast cancer diagnosed between 2004 and 2015. We used Cox regression to evaluate risk factors associated with age- and stage-adjusted recurrence rates using different recurrence definitions, weighted by inverse sampling probabilities.

RESULTS

Among 600 people, we identified 117 recurrences using the High Specificity and PPV Combination, 505 using the High Sensitivity Combination, and 118 using manual abstraction. The High Specificity and PPV Combination had good specificity [98%, 95% confidence interval (CI): 97-99] and PPV (72%, 95% CI: 63-80) but modest sensitivity (64%, 95% CI: 44-80). The High Sensitivity Combination had good sensitivity (80%, 95% CI: 49-94) and specificity (83%, 95% CI: 80-86) but low PPV (29%, 95% CI: 25-34). Recurrence rates using combined algorithms were similar in magnitude for most risk factors.

CONCLUSIONS

By combining algorithms, we identified breast cancer recurrences with greater PPV than individual algorithms, without additional review of discordant records.

IMPACT

Researchers should consider tradeoffs between accuracy and manual chart abstraction resources when using previously developed algorithms. We provided guidance for future studies that use breast cancer recurrence algorithms with or without supplemental manual chart abstraction.

Characteristics Associated With Survival in Surgically Nonresected Pancreatic Adenocarcinoma in the Military Health System

OBJECTIVES

Pancreatic cancer is often diagnosed at advanced stages with high-case fatality. Many tumors are not surgically resectable. We aimed to identify features associated with survival in patients with surgically nonresected pancreatic cancer in the Military Health System.

METHODS

We used the Military Cancer Epidemiology database to identify the Department of Defense beneficiaries aged 18 and older diagnosed with a primary pancreatic adenocarcinoma between January 1998 and December 2014 who did not receive oncologic surgery as treatment. We used Cox Proportional Hazard regression with stepwise procedures to select the sociodemographic and clinical characteristics related to 2-year overall survival, expressed as adjusted hazard ratios (aHR) and 95% CIs.

RESULTS

Among 1148 patients with surgically nonresected pancreatic cancer, sex, race-ethnicity, marital status, and socioeconomic indicators were not selected in association with survival. A higher comorbidity count (aHR 1.30, 95% CI: 1.06-1.59 for 5 vs. 0), jaundice at diagnosis (aHR 1.57, 95% CI: 1.33-1.85 vs. no), tumor grade G3 or G4 (aHR 1.32, 95% CI: 1.05-1.67 vs. G1/G2), tumor location in pancreas tail (aHR 1.49, 95% CI: 1.22-1.83 vs. head) or body (aHR 1.30, 95% CI: 1.04-1.62 vs. head), and metastases were associated with survival. Patients receiving chemotherapy (aHR 0.66, 95% CI: 0.57-0.76) had better survival compared with no treatment.

CONCLUSIONS

In a comprehensive health system, sociodemographic characteristics were not related to survival in surgically nonresected pancreatic cancer. This implicates access to care in reducing survival disparities in advanced pancreatic cancer and emphasizes the importance of treating patients based on clinical features.

Treatment of pancreatic adenocarcinoma in relation to survival in the U.S. Military Health System

BACKGROUND

Pancreatic cancer has a high case fatality and relatively short survival after diagnosis. Treatment is paramount to improving survival, but studies on the effects of standard treatment by surgery or chemotherapy on survival in U.S. healthcare settings is limited. Further, variability in access to care may impact treatment and outcomes for patients. We aimed to assess the relationship between standard treatment(s) and survival of pancreatic adenocarcinoma in a population with access to comprehensive healthcare.

METHODS

We used the Military Cancer Epidemiology (MilCanEpi) database, which includes data from the Department of Defense cancer registry and medical encounter data from the Military Health System (MHS), to study a cohort of 1408 men and women who were diagnosed with pancreatic adenocarcinoma between 1998 and 2014. Treatment with surgery or chemotherapy in relation to overall survival was examined in multivariable time-dependent Cox regression models.

RESULTS

Overall, 75 % of 441 patients with early-stage and 51 % of 967 patients with late-stage pancreatic adenocarcinoma received treatment. In early-stage disease, surgery alone or surgery with chemotherapy were both associated with statistically significant 52 % reduced risks of death, but chemotherapy alone was not. In late-stage disease, surgery alone, chemotherapy alone, or both surgery and chemotherapy significantly reduced the risk of death by 42 %, 25 %, and 52 %, respectively.

CONCLUSIONS

Our findings from the MHS demonstrate improved survival after treatment with surgery or surgery with chemotherapy for early- or late-stage pancreatic cancer and after chemotherapy for late-stage pancreatic cancer. In the era of immunotherapy and personalized medicine, further research on treatment and survival of pancreatic cancer in observational settings is needed.

Costs of breast cancer recurrence after initial treatment for HR+, HER2-, high-risk early breast cancer: estimates from SEER-Medicare linked data

OBJECTIVE

To assess the costs of treated recurrence and survival in elderly patients with early breast cancer (EBC) at high risk of recurrence using Surveillance Epidemiology and End Results (SEER) registry-Medicare linked claims data.

METHODS

This retrospective study included patients aged ≥65 years with hormone receptor-positive (HR+), human epidermal growth factor receptor 2 negative (HER2-), node-positive EBC at high risk of recurrence. Treated recurrences were defined based on treatment events/procedure codes from claims. Primary outcomes were monthly total extra costs and cumulative extra costs of treated recurrence relative to patients with non/untreated recurrence. Costs were calculated using a Kaplan-Meier sampling average estimator method and inflated to 2021 US$. Secondary outcomes included analysis by recurrence type and overall survival (OS) after recurrence. Subgroup analysis evaluated costs in patients with Medicare Part D coverage.

RESULTS

Among 3,081 eligible patients [mean (SD) age at diagnosis was 74.5 (7.1) years], the majority were females (97.4%) and white (87.8%). Treated recurrence was observed in 964 patients (31.3%). The monthly extra cost of treated recurrence was highest at the beginning of the first treated recurrence episode, with 6-year cumulative cost of $117,926. Six-year cumulative extra costs were higher for patients with distant recurrences ($168,656) than for patients with locoregional recurrences ($96,465). Median OS was 4.34 years for all treated recurrences, 1.92 years for distant recurrence, and 6.78 years for locoregional recurrence. Similar cumulative extra cost trends were observed in the subgroup with Part D coverage as in the overall population.

LIMITATIONS

This study utilizes claims data to identify treated recurrence. Due to age constraints of the dataset, results may not extrapolate to a younger population where EBC is commonly diagnosed.

CONCLUSION

EBC recurrence in this elderly population has substantial costs, particularly in patients with distant recurrences. Therapies that delay or prevent recurrence may reduce long-term costs significantly.

Chemotherapy Treatment Costs and Clinical Outcomes of Colon Cancer in the U.S. Military Health System's Direct and Private Sector Care Settings

INTRODUCTION

Identifying low-value cancer care may be an important step in containing costs associated with treatment. Low-value care occurs when the medical services, tests, or treatments rendered do not result in clinical benefit. These may be impacted by care setting and patients' access to care and health insurance. We aimed to study chemotherapy treatment and the cost paid by the Department of Defense (DoD) for treatment in relation to clinical outcomes among patients with colon cancer treated within the U.S. Military Health System's direct and private sector care settings to better understand the value of cancer care.

MATERIALS AND METHODS

A cohort of patients aged 18 to 64 years with primary colon cancer diagnosed between January 1, 1999, and December 31, 2014, were identified in the Military Cancer Epidemiology database. Multivariable time-dependent Cox proportional hazards regression models were used to assess the relationship between chemotherapy treatment and the cost paid by the DoD (in quartiles, Q) and the outcomes of cancer progression, cancer recurrence, and all-cause death modeled as adjusted hazard ratios (aHRs) and 95% confidence intervals (95% CIs). The Military Cancer Epidemiology data were approved for research by the Uniformed Services University of the Health Sciences' Institutional Review Board.

RESULTS

The study included 673 patients using direct care and 431 patients using private sector care. The median per patient chemotherapy costs in direct care ($111,202) were lower than in private sector care ($350,283). In direct care, higher chemotherapy costs were associated with an increased risk of any outcome but not with all-cause death. In private sector care, higher chemotherapy costs were associated with a higher risk of any outcome and with all-cause death (aHR, 2.67; 95% CI, 1.20-5.92 for Q4 vs. Q1).

CONCLUSIONS

The findings in the private sector may indicate low-value care in terms of the cost paid by the DoD for chemotherapy treatment and achieving desirable survival outcomes for patients with colon cancer in civilian health care. Comprehensive evaluations of value-based care among patients treated for other tumor types may be warranted.

Racial and Ethnic Bias in Risk Prediction Models for Colorectal Cancer Recurrence When Race and Ethnicity Are Omitted as Predictors

Importance

Including race and ethnicity as a predictor in clinical risk prediction algorithms has received increased scrutiny, but there continues to be a lack of empirical studies addressing whether simply omitting race and ethnicity from the algorithms will ultimately affect decision-making for patients of minoritized racial and ethnic groups.

Objective

To examine whether including race and ethnicity as a predictor in a colorectal cancer recurrence risk algorithm is associated with racial bias, defined as racial and ethnic differences in model accuracy that could potentially lead to unequal treatment.

Design, Setting, and Participants

This retrospective prognostic study was conducted using data from a large integrated health care system in Southern California for patients with colorectal cancer who received primary treatment between 2008 and 2013 and follow-up until December 31, 2018. Data were analyzed from January 2021 to June 2022.

Main Outcomes and Measures

Four Cox proportional hazards regression prediction models were fitted to predict time from surveillance start to cancer recurrence: (1) a race-neutral model that explicitly excluded race and ethnicity as a predictor, (2) a race-sensitive model that included race and ethnicity, (3) a model with 2-way interactions between clinical predictors and race and ethnicity, and (4) separate models by race and ethnicity. Algorithmic fairness was assessed using model calibration, discriminative ability, false-positive and false-negative rates, positive predictive value (PPV), and negative predictive value (NPV).

Results

The study cohort included 4230 patients (mean [SD] age, 65.3 [12.5] years; 2034 [48.1%] female; 490 [11.6%] Asian, Hawaiian, or Pacific Islander; 554 [13.1%] Black or African American; 937 [22.1%] Hispanic; and 2249 [53.1%] non-Hispanic White). The race-neutral model had worse calibration, NPV, and false-negative rates among racial and ethnic minority subgroups than non-Hispanic White individuals (eg, false-negative rate for Hispanic patients: 12.0% [95% CI, 6.0%-18.6%]; for non-Hispanic White patients: 3.1% [95% CI, 0.8%-6.2%]). Adding race and ethnicity as a predictor improved algorithmic fairness in calibration slope, discriminative ability, PPV, and false-negative rates (eg, false-negative rate for Hispanic patients: 9.2% [95% CI, 3.9%-14.9%]; for non-Hispanic White patients: 7.9% [95% CI, 4.3%-11.9%]). Inclusion of race interaction terms or using race-stratified models did not improve model fairness, likely due to small sample sizes in subgroups.

Conclusions and Relevance

In this prognostic study of the racial bias in a cancer recurrence risk algorithm, removing race and ethnicity as a predictor worsened algorithmic fairness in multiple measures, which could lead to inappropriate care recommendations for patients who belong to minoritized racial and ethnic groups. Clinical algorithm development should include evaluation of fairness criteria to understand the potential consequences of removing race and ethnicity for health inequities.

Racial Disparities in the Ascertainment of Cancer Recurrence in Electronic Health Records

PURPOSE

There is growing interest in using computable phenotypes or proxies to identify important clinical outcomes, such as cancer recurrence, in rich electronic health records data. However, the race/ethnicity-specific accuracies of these proxies remain unclear. We examined whether the accuracy of a proxy for colorectal cancer (CRC) recurrence differed by race/ethnicity and the possible mechanisms that drove the differences.

METHODS

Using data from a large integrated health care system, we identified a stratified random sample of 282 Black/African American (AA), Hispanic, and non-Hispanic White (NHW) patients with CRC who received primary treatment. Patient 5-year recurrence status was estimated using a utilization-based proxy and evaluated against the true recurrence status obtained using detailed chart review and by race/ethnicity. We used covariate-adjusted probit regression models to estimate the associations between race/ethnicity and misclassification.

RESULTS

The recurrence proxy had excellent overall accuracy (positive predictive value [PPV] 89.4%; negative predictive value 96.5%; mean difference in timing 1.96 months); however, accuracy varied by race/ethnicity. Compared with NHW patients, PPV was 14.9% lower (95% CI, 2.53 to 28.6) among Hispanic patients and 4.3% lower (95% CI, -4.8 to 14.8) among Black/AA patients. The proxy disproportionately inflated the 5-year recurrence incidence for Hispanic patients by 10.6% (95% CI, 4.2 to 18.2). Compared with NHW patients, proxy recurrences for Hispanic patients were almost three times as likely to have been misclassified as positive (adjusted risk ratio 2.91 [95% CI, 1.21 to 8.31]). Higher false positives among racial/ethnic minorities may be related to higher prevalence of noncancerous lung-related problems and substantial delays in primary treatment because of insufficient patient-provider communication and abnormal treatment patterns.

CONCLUSION

Using a proxy with worse accuracy among racial/ethnic minority patients to estimate population health may misdirect resources and support erroneous conclusions around treatment benefit for these patients.

Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data

Purpose

High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data.

Methods

Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009-2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not.

Results

A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8-87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8-87.8%), and accuracy of 96.7% (95% CI 95.4-97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4-91.3%), PPV of 84.1% (95% CI 74.4-91.3%), and an accuracy of 96.8% (95% CI 95.4-97.9%).

Conclusion

Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise.

Estimating the incidence of breast cancer recurrence using administrative data

BACKGROUND

Breast cancer is the most common cancer among women, but most cancer registries do not capture recurrences. We estimated the incidence of local, regional, and distant recurrences using administrative data.

METHODS

Patients diagnosed with stage I-III primary breast cancer in Ontario, Canada from 2013 to 2017 were included. Patients were followed until 31/Dec/2021, death, or a new primary cancer diagnosis. We used hospital administrative data (diagnostic and intervention codes) to identify local recurrence, regional recurrence, and distant metastasis after primary diagnosis. We used logistic regression to explore factors associated with developing a distant metastasis.

RESULTS

With a median follow-up 67 months, 5,431/45,857 (11.8%) of patients developed a distant metastasis a median 23 (9, 42) months after diagnosis of the primary tumor. 1086 (2.4%) and 1069 (2.3%) patients developed an isolated regional or a local recurrence, respectively. Patients with distant metastatic disease had a median overall survival of 15.4 months (95% CI 14.4-16.4 months) from the time recurrence/metastasis was identified. In contrast, the median survival for all other patients was not reached. Patients were more likely to develop a distant metastasis if they had more advanced stage, greater comorbidity, and presented with symptoms (p < 0.0001). Trastuzumab halved the risk of recurrence [OR 0.53 (0.45-0.63), p < 0.0001].

CONCLUSION

Distant metastasis is not a rare outcome for patients diagnosed with breast cancer, translating to an annual incidence of 2132 new cases (17.8% of all breast cancer diagnoses). Overall survival remains high for patients with locoregional recurrences, but was poor following a diagnosis of a distant metastasis.

Overall survival associated with CDK4/6 inhibitors in patients with HR+/HER2- metastatic breast cancer in the United States: A SEER-Medicare population-based study

BACKGROUND

Evidence on overall survival (OS) with cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors is generally limited to data from clinical trials or a few observational studies with limited generalizability to Medicare population. The aim of this study was to determine OS benefits associated with CDK4/6 inhibitors in older Medicare patients with hormone receptor (HR)-positive and human epidermal growth factor receptor-2 overexpressing (HER2-) metastatic breast cancer (MBC).

METHODS

In a retrospective cohort design, female patients aged ≥65 years with diagnosis of HR+/HER2- MBC from 2015 to 2017 who initiated first-line systemic therapy within 12 months of MBC diagnosis were selected from the Survey Epidemiology and End Results-Medicare database. The effect of treatment type (endocrine therapy [ET]+CDK4/6 inhibitor vs. ET alone) on OS was analyzed using Kaplan-Meier methods and multivariable Cox regression models. Adjusted hazard ratio (aHR) and 95% CIs were estimated.

RESULTS

A total of 630 eligible patients were identified (169 patients treated with ET+CDK4/6 inhibitor and 461 patients treated with ET alone). In the Kaplan-Meier analysis, OS rate at 3 years after first-line treatment initiation was 73.0% for ET+CDK4/6 inhibitor versus 49.1% for ET alone (log-rank p < .0001). In Cox regression analysis, first-line ET+CDK4/6 inhibitor therapy was associated with 41% lower rate of mortality versus ET alone (aHR, 0.590; 95% CI, 0.423-0.823).

CONCLUSIONS

The findings of this real-world study demonstrate significant OS benefit associated with ET+CDK4/6 inhibitor therapy over ET alone in an older Medicare population of patients with HR+/HER2- MBC, largely consistent with the evidence from clinical trials.

A Validated Register-Based Algorithm to Identify Patients Diagnosed with Recurrence of Surgically Treated Stage I Lung Cancer in Denmark

Introduction

Recurrence of cancer is not routinely registered in Danish national health registers. This study aimed to develop and validate a register-based algorithm to identify patients diagnosed with recurrent lung cancer and to estimate the accuracy of the identified diagnosis date.

Material and Methods

Patients with early-stage lung cancer treated with surgery were included in the study. Recurrence indicators were diagnosis and procedure codes recorded in the Danish National Patient Register and pathology results recorded in the Danish National Pathology Register. Information from CT scans and medical records served as the gold standard to assess the accuracy of the algorithm.

Results

The final population consisted of 217 patients; 72 (33%) had recurrence according to the gold standard. The median follow-up time since primary lung cancer diagnosis was 29 months (interquartile interval: 18-46). The algorithm for identifying a recurrence reached a sensitivity of 83.3% (95% CI: 72.7-91.1), a specificity of 93.8% (95% CI: 88.5-97.1), and a positive predictive value of 87.0% (95% CI: 76.7-93.9). The algorithm identified 70% of the recurrences within 60 days of the recurrence date registered by the gold standard method. The positive predictive value of the algorithm decreased to 70% when the algorithm was simulated in a population with a recurrence rate of 15%.

Conclusion

The proposed algorithm demonstrated good performance in a population with 33% recurrences over a median of 29 months. It can be used to identify patients diagnosed with recurrent lung cancer, and it may be a valuable tool for future research in this field. However, a lower positive predictive value is seen when applying the algorithm in populations with low recurrence rates.

Disparities in Care Access to Liver-Directed Therapy Among Medicare Beneficiaries with Colorectal Cancer Liver Metastasis

BACKGROUND

Liver-directed therapies (LDT) are important components of the multidisciplinary care of patients with colorectal cancer liver metastases (CRCLM) that contribute to improved long-term outcomes. Factors associated with receipt of LDT are poorly understood.

PATIENTS AND METHODS

Patients > 65 years old diagnosed with CRCLM were identified within the Medicare Standard Analytic File (2013-2017). Patients with extrahepatic metastatic disease were excluded. Mixed-effects analyses were used to assess patient factors associated with the primary outcome of LDT, defined as hepatectomy, ablation, and/or hepatic artery infusion chemotherapy (HAIC), as well as the secondary outcome of hepatectomy.

RESULTS

Among 23,484 patients with isolated CRCLM, only 2004 (8.5%) received LDT, although resectability status could not be determined for the entire cohort. Among patients who received LDT, 61.7% underwent hepatectomy alone, 28.1% received ablation alone, 8.5% underwent hepatectomy and ablation, and 1.8% received HAIC either alone (0.8%) or in combination with hepatectomy and/or ablation (0.9%). Patient factors independently associated with lower odds of LDT included older age, female sex, Black race, greater comorbidity burden, higher social vulnerability index, primary rectal cancer, synchronous liver metastasis, and further distance from a high-volume liver surgery center (p < 0.05). Results were similar for receipt of hepatectomy.

CONCLUSIONS

Despite the well-accepted role of LDT for CRCLM, only a small proportion of Medicare beneficiaries with CRCLM receive LDT. Increasing access to specialized centers with expertise in LDT, particularly for Black patients, female patients, and those with higher levels of social vulnerability or long travel distances, may improve outcomes for patients with CRCLM.

Disparities in access to care among patients with appendiceal or colorectal cancer and peritoneal metastases: A medicare insurance-based study in the United States

BACKGROUND

Prior studies attempting to identify disparities in the care of patients with appendiceal (AC) or colorectal cancer (CRC) with peritoneal metastasis (PM) are limited to single-institution, highly selected patient populations. This observational cohort study sought to identify factors associated with specialty care for Medicare beneficiaries with AC/CRC-PM.

MATERIALS AND METHODS

Patients >65 years old in the United States diagnosed with AC/CRC and isolated PM were identified within the Medicare Standard Analytic File (2013-2017). Mixed-effects analyses assessed patient factors associated with cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) and outpatient consultation with a peritoneal surface malignancy (PSM) surgeon, and Cox proportional-hazards analysis compared 3-year overall survival (OS) between patients receiving CRS/HIPEC versus systemic therapy alone.

RESULTS

Among 7,653 patients, only 250 (3.3%) underwent CRS/HIPEC. Among those individuals who did not undergo CRS/HIPEC (N=7,403), only 475 (6.4%) had outpatient consultation with a PSM surgeon. Patient factors independently associated with lower odds of CRS/HIPEC and PSM surgery consultation included older age, greater comorbidity burden, higher social vulnerability index, and further distance from a PSM center (p<0.05). CRS/HIPEC was independently associated with better 3-year OS compared with systemic therapy alone (HR=0.29, 95%CI=0.21-0.38).

CONCLUSION

An exceedingly small proportion of Medicare beneficiaries with AC/CRC-PM undergo CRS/HIPEC or even have an outpatient consultation with a PSM surgeon. Significant disparities in treatment and access to care exist for patients with higher levels of social vulnerability and those that live further away from a PSM center. Future research and interventions should focus on improving access to care for these at-risk patient populations.

Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation

Breast cancer recurrence is an important outcome for patients and healthcare systems, but it is not routinely reported in cancer registries. We developed an algorithm to identify patients who experienced recurrence or a second case of primary breast cancer (combined as a “second breast cancer event”) using administrative data from the population of Ontario, Canada. A retrospective cohort study design was used including patients diagnosed with stage 0-III breast cancer in the Ontario Cancer Registry between 1 January 2009 and 31 December 2012 and alive six months post-diagnosis. We applied the algorithm to healthcare utilization data from six months post-diagnosis until death or 31 December 2013, whichever came first. We validated the algorithm’s diagnostic accuracy against a manual patient record review (n = 2245 patients). The algorithm had a sensitivity of 85%, a specificity of 94%, a positive predictive value of 67%, a negative predictive value of 98%, an accuracy of 93%, a kappa value of 71%, and a prevalence-adjusted bias-adjusted kappa value of 85%. The second breast cancer event rate was 16.5% according to the algorithm and 13.0% according to manual review. Our algorithm’s performance was comparable to previously published algorithms and is sufficient for healthcare system monitoring. Administrative data from a population can, therefore, be interpreted using new methods to identify new outcome measures.

The Value of Federated Data Networks in Oncology: What Research Questions Do They Answer? Outcomes From a Systematic Literature Review

OBJECTIVES

Real-world evidence (RWE) plays an important role in addressing key research questions of interest to healthcare decision makers. Federated data networks (FDNs) apply novel technology to enable the conduct of RWE studies with multiple partners, without the need to share the individual partner's data set. A systematic review of the published literature was performed to determine which types of research questions can best be addressed through FDNs, specifically in the field of oncology.

METHODS

Systematic searches of MEDLINE and Embase were undertaken to identify the types of research questions that had been addressed in studies using FDNs. Additional information was retrieved about study characteristics, statistical methods, and the FDN itself.

RESULTS

In total, 40 publications were included where research questions on the following had been addressed (multiple categories possible): disease natural history (58%), safety surveillance (18%), treatment pathways (15%), comparative effectiveness (10%), and cost/resource use studies (3%)-13% of studies had to be left uncategorized. A total of 50% of the studies were run with data partners in networks of ≤5. The size of the networks ranged from 227 patients to >5 million patients. Statistical methods used included distributed learning and distributed regression methods.

CONCLUSIONS

Further work is needed to raise awareness of the important role that FDNs can play in leveraging readily available RWE to address key research questions of interest in cancer and the benefits to the research community in engaging in federated data initiatives with a long-term perspective.

Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial

CONTEXT

Large multisite clinical trials studying decision-making when facing serious illness require an efficient method for abstraction of advance care planning (ACP) documentation from clinical text documents. However, the current gold standard method of manual chart review is time-consuming and unreliable.

OBJECTIVES

To evaluate the ability to use natural language processing (NLP) to identify ACP documention in clinical notes from patients participating in a multisite trial.

METHODS

Patients with advanced cancer followed in three disease-focused oncology clinics at Duke Health, Mayo Clinic, and Northwell Health were identified using administrative data. All outpatient and inpatient notes from patients meeting inclusion criteria were extracted from electronic health records (EHRs) between March 2018 and March 2019. NLP text identification software with semi-automated chart review was applied to identify documentation of four ACP domains: (1) conversations about goals of care, (2) limitation of life-sustaining treatment, (3) involvement of palliative care, and (4) discussion of hospice. The performance of NLP was compared to gold standard manual chart review.

RESULTS

435 unique patients with 79,797 notes were included in the study. In our validation data set, NLP achieved F1 scores ranging from 0.84 to 0.97 across domains compared to gold standard manual chart review. NLP identified ACP documentation in a fraction of the time required by manual chart review of EHRs (1-5 minutes per patient for NLP, vs. 30-120 minutes for manual abstraction).

CONCLUSION

NLP is more efficient and as accurate as manual chart review for identifying ACP documentation in studies with large patient cohorts.

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.

Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records

PURPOSE

Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer.

METHODS

We used a total number of 4,412 patients with 483,782 clinical notes from the Stanford Cancer Institute Research Database containing patients with nonmetastatic prostate, oropharynx, and esophagus cancer. We trained treatment identification models for each cancer type separately and compared performance of using only structured, only unstructured (bag-of-words, doc2vec, fasttext), and combinations of both (structured + bow, structured + doc2vec, structured + fasttext). We optimized the identification model among five machine learning methods (logistic regression, multilayer perceptrons, random forest, support vector machines, and stochastic gradient boosting). The treatment information recorded in the cancer registry is the gold standard and compares our methods to an identification baseline with billing codes.

RESULTS

For prostate cancer, we achieved an f1-score of 0.99 (95% CI, 0.97 to 1.00) for radiation and 1.00 (95% CI, 0.99 to 1.00) for surgery using structured + doc2vec. For oropharynx cancer, we achieved an f1-score of 0.78 (95% CI, 0.58 to 0.93) for chemoradiation and 0.83 (95% CI, 0.69 to 0.95) for surgery using doc2vec. For esophagus cancer, we achieved an f1-score of 1.0 (95% CI, 1.0 to 1.0) for both chemoradiation and surgery using all combinations of structured and unstructured data. We found that employing the free-text clinical notes outperforms using the billing codes or only structured data for all three cancer types.

CONCLUSION

Our results show that treatment identification using free-text clinical notes greatly improves upon the performance using billing codes and simple structured data. The approach can be used for treatment cohort identification and adapted for longitudinal cancer treatment identification.

Evaluation of algorithms using administrative health and structured electronic medical record data to determine breast and colorectal cancer recurrence in a Canadian province : Using algorithms to determine breast and colorectal cancer recurrence

BACKGROUND

Algorithms that use administrative health and electronic medical record (EMR) data to determine cancer recurrence have the potential to replace chart reviews. This study evaluated algorithms to determine breast and colorectal cancer recurrence in a Canadian province with a universal health care system.

METHODS

Individuals diagnosed with stage I-III breast or colorectal cancer diagnosed from 2004 to 2012 in Manitoba, Canada were included. Pre-specified and conditional inference tree algorithms using administrative health and structured EMR data were developed. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) correct classification, and scaled Brier scores were measured.

RESULTS

The weighted pre-specified variable algorithm for the breast cancer validation cohort (N = 1181, 167 recurrences) demonstrated 81.1% sensitivity, 93.2% specificity, 61.4% PPV, 97.4% NPV, 91.8% correct classification, and scaled Brier score of 0.21. The weighted conditional inference tree algorithm demonstrated 68.5% sensitivity, 97.0% specificity, 75.4% PPV, 95.8% NPV, 93.6% correct classification, and scaled Brier score of 0.39. The weighted pre-specified variable algorithm for the colorectal validation cohort (N = 693, 136 recurrences) demonstrated 77.7% sensitivity, 92.8% specificity, 70.7% PPV, 94.9% NPV, 90.1% correct classification, and scaled Brier score of 0.33. The conditional inference tree algorithm demonstrated 62.6% sensitivity, 97.8% specificity, 86.4% PPV, 92.2% NPV, 91.4% correct classification, and scaled Brier score of 0.42.

CONCLUSIONS

Algorithms developed in this study using administrative health and structured EMR data to determine breast and colorectal cancer recurrence had moderate sensitivity and PPV, high specificity, NPV, and correct classification, but low accuracy. The accuracy is similar to other algorithms developed to classify recurrence only (i.e., distinguished from second primary) and inferior to algorithms that do not make this distinction. The accuracy of algorithms for determining cancer recurrence only must improve before replacing chart reviews.

Identification of endometrial cancer recurrence - a validated algorithm based on nationwide Danish registries

INTRODUCTION

Recurrence of endometrial cancer is not routinely registered in the Danish national health registers. The aim of this study was to develop and validate a register-based algorithm to identify women diagnosed with endometrial cancer recurrence in Denmark to facilitate register-based research in this field.

MATERIAL AND METHODS

We conducted a cohort study based on data from Danish health registers. The algorithm was designed to identify women with recurrence and estimate the accompanying diagnosis date, which was based on information from the Danish National Patient Registry and the Danish National Pathology Registry. Indicators of recurrence were pathology registrations and procedure or diagnosis codes suggesting recurrence and related treatment. The gold standard for endometrial cancer recurrence originated from a Danish nationwide study of 2612 women diagnosed with endometrial cancer, FIGO stage I-II during 2005-2009. Recurrence was suspected in 308 women based on pathology reports, and recurrence suspicion was confirmed or rejected in the 308 women based on reviews of the medical records. The algorithm was validated by comparing the recurrence status identified by the algorithm and the recurrence status in the gold standard.

RESULTS

After relevant exclusions, the final study population consisted of 268 women, hereof 160 (60%) with recurrence according to the gold standard. The algorithm displayed a sensitivity of 91.3% (95% confidence interval (CI): 85.8-95.1), a specificity of 91.7% (95% CI: 84.8-96.1) and a positive predictive value of 94.2% (95% CI: 89.3-97.3). The algorithm estimated the recurrence date within 30 days of the gold standard in 86% and within 60 days of the gold standard in 94% of the identified patients.

DISCUSSION

The algorithm demonstrated good performance; it could be a valuable tool for future research in endometrial cancer recurrence and may facilitate studies with potential impact on clinical practice.

Estimating Population-Based Recurrence Rates of Colorectal Cancer over Time in the United States

BACKGROUND

Population-based metastatic recurrence rates for patients diagnosed with nonmetastatic colorectal cancer cannot be estimated directly from population-based cancer registries because recurrence information is not reported. We derived population-based colorectal cancer recurrence rates using disease-specific survival data based on our understanding of the colorectal cancer recurrence-death process.

METHODS

We used a statistical continuous-time multistate survival model to derive population-based annual colorectal cancer recurrence rates from 6 months to 10 years after colorectal cancer diagnosis using relative survival data from the Surveillance, Epidemiology, and End Results Program. The model was based on the assumption that, after 6 months of diagnosis, all colorectal cancer-related deaths occur only in patients who experience a metastatic recurrence first, and that the annual colorectal cancer-specific death rate among patients with recurrence was the same as in those diagnosed with de novo metastatic disease. We allowed recurrence rates to vary by post-diagnosis time, age, stage, and location for two diagnostic time periods.

RESULTS

In patients diagnosed in 1975-1984, annual recurrence rates 6 months to 5 years after diagnosis ranged from 0.054 to 0.060 in stage II colon cancer, 0.094 to 0.105 in stage II rectal cancer, and 0.146 to 0.177 in stage III colorectal cancer, depending on age. We found a statistically significant decrease in colorectal cancer recurrence among patients diagnosed in 1994-2003 compared with those diagnosed in 1975-1984 for 6 months to 5 years after diagnosis (hazard ratios between 0.43 and 0.70).

CONCLUSIONS

We derived population-based annual recurrence rates for up to 10 years after diagnosis using relative survival data.

IMPACT

Our estimates can be used in decision-analytic models to facilitate analyses of colorectal cancer interventions that are more generalizable.

Transcatheter Versus Surgical Aortic Valve Replacement in Patients With Prior Mediastinal Radiation

OBJECTIVES

This study sought to evaluate the trends and outcomes of transcatheter aortic valve replacement (TAVR) versus surgical aortic valve replacement (SAVR) among patients with prior mediastinal radiation from a national database.

BACKGROUND

There is a paucity of data about the temporal trends and outcomes of TAVR versus SAVR in patients with prior mediastinal radiation.

METHODS

The National Inpatient Sample database years 2012 to 2017 was queried for hospitalizations of patients with prior mediastinal radiation who underwent isolated AVR. Using multivariable analysis, the study compared the outcomes of TAVR versus SAVR. The main study outcome was in-hospital mortality.

RESULTS

The final analysis included 3,675 hospitalizations for isolated AVR; of whom 2,170 (59.1%) underwent TAVR and 1,505 (40.9%) underwent isolated SAVR. TAVR was increasingly performed over time (p_trend = 0.01), but there was no significant increase in the rates of utilization of SAVR. The following factors were independently associated with TAVR utilization: older age, chronic lung disease, coronary artery disease, chronic kidney disease, prior cerebrovascular accidents, prior coronary artery bypass grafting, and larger-sized hospitals, while women were less likely to undergo TAVR. Compared with SAVR, TAVR was associated with lower in-hospital mortality (1.2% vs. 2.0%, adjusted odds ratio: 0.27; 95% confidence interval: 0.09 to 0.79; p = 0.02). TAVR was associated with lower rates of acute kidney injury, use of mechanical circulatory support, bleeding and respiratory complications, and shorter length of hospital stay. TAVR was associated with higher rates of pacemaker insertion.

CONCLUSIONS

This nationwide observational analysis showed that TAVR is increasingly performed among patients with prior mediastinal radiation. TAVR provides an important treatment option for this difficult patient population with desirable procedural safety when using SAVR as a benchmark.

Assessment of Studies Evaluating Incremental Costs, Effectiveness, or Cost-Effectiveness of Systemic Therapies in Breast Cancer Based on Claims Data: A Systematic Review

OBJECTIVES

Large secondary databases, such as those containing insurance claims data, are increasingly being used to compare the effects and costs of treatments in routine clinical practice. Despite their appeal, however, caution must be exercised when using these data. In this study, we aimed to identify and assess the methodological quality of studies that used claims data to compare the effectiveness, costs, or cost-effectiveness of systemic therapies for breast cancer.

METHODS

We searched Embase, the Cochrane Library, Medline, Web of Science, and Google Scholar for English-language publications and assessed the methodological quality using the Good Research for Comparative Effectiveness principles. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under number CRD42018103992.

RESULTS

We identified 1251 articles, of which 106 met the inclusion criteria. Most studies were conducted in the United States (74%) and Taiwan (9%) and were based on claims data sets (35%) or claims data linked to cancer registries (58%). Furthermore, most included large samples (mean 17 130 patients) and elderly patients, and they covered various outcomes (eg, survival, adverse events, resource use, and costs). Key methodological shortcomings were the lack of information on relevant confounders, the risk of immortal time bias, and the lack of information on the validity of outcomes. Only a few studies performed sensitivity analyses.

CONCLUSIONS

Many comparative studies of cost, effectiveness, and cost-effectiveness have been published in recent decades based on claims data, and the number of publications has increased over time. Despite the availability of guidelines to improve quality, methodological issues persist and are often inappropriately addressed or reported.

Validation of an Algorithm to Ascertain Late Breast Cancer Recurrence Using Danish Medical Registries

Purpose

About 70% of women with breast cancer survive at least 10 years after diagnosis. We constructed an algorithm to ascertain late breast cancer recurrence—which we define as breast cancer that recurs 10 years or more after primary diagnosis (excluding contralateral breast cancers)—using Danish nationwide medical registries. We used clinical information recorded in medical records as a reference standard.

Methods

Using the Danish Breast Cancer Group clinical database, we ascertained data on 21,134 women who survived recurrence-free 10 years or more after incident stage I–III breast cancer diagnosed in 1987–2004. We used a combination of Danish registries to construct the algorithm—the Danish National Patient Registry for information on diagnostic, therapeutic and procedural codes; and cancer diagnoses from the Danish Pathology Registry, the Danish Cancer Registry and the Contralateral Breast Cancer database. To estimate the positive predictive value (PPV), we selected 105 patients who, according to our algorithm, had late recurrence diagnosed at Aarhus University Hospital. To estimate the sensitivity, specificity and negative predictive value (NPV), we selected 114 patients diagnosed with primary breast cancer at Aalborg University Hospital. We abstracted clinical information on late recurrence for patients with medical record-confirmed late recurrence at Aarhus University Hospital.

Results

Our algorithm had a PPV of late recurrence of 85.7% (95% CI: 77.5–91.3%), a sensitivity of 100.0% (95% CI, 39.8–100.0%), a specificity of 97.3 (95% CI, 92.2–99.4) and a NPV of 100% (95% CI, 96.6–100.0%).

Conclusion

Our algorithm for late recurrence showed a moderate to high PPV and high sensitivity, specificity and negative predictive value. The algorithm could be an important tool for future studies of late breast cancer recurrence.

A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data

BACKGROUND

Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data.

METHODS

The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy.

RESULTS

Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%).

CONCLUSIONS

Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.

Patterns of Surveillance Advanced Imaging and Serum Tumor Biomarker Testing Following Launch of the Choosing Wisely Initiative

BACKGROUND

The purpose of this study was to assess advanced imaging (bone scan, CT, or PET/CT) and serum tumor biomarker use in asymptomatic breast cancer survivors during the surveillance period.

PATIENTS AND METHODS

Cancer registry records for 2,923 women diagnosed with primary breast cancer in Washington State between January 1, 2007, and December 31, 2014, were linked with claims data from 2 regional commercial insurance plans. Clinical data including demographic and tumor characteristics were collected. Evaluation and management codes from claims data were used to determine advanced imaging and serum tumor biomarker testing during the peridiagnostic and surveillance phases of care. Multivariable logistic regression models were used to identify clinical factors and patterns of peridiagnostic imaging and biomarker testing associated with surveillance advanced imaging.

RESULTS

Of 2,923 eligible women, 16.5% (n=480) underwent surveillance advanced imaging and 31.8% (n=930) received surveillance serum tumor biomarker testing. Compared with women diagnosed before the launch of the Choosing Wisely campaign in 2012, later diagnosis was associated with lower use of surveillance advanced imaging (odds ratio [OR], 0.68; 95% CI, 0.52-0.89). Factors significantly associated with use of surveillance advanced imaging included increasing disease stage (stage III: OR, 3.65; 95% CI, 2.48-5.38), peridiagnostic advanced imaging use (OR, 1.76; 95% CI, 1.33-2.31), and peridiagnostic serum tumor biomarker testing (OR, 1.35; 95% CI, 1.01-1.80).

CONCLUSIONS

Although use of surveillance advanced imaging in asymptomatic breast cancer survivors has declined since the launch of the Choosing Wisely campaign, frequent use of surveillance serum tumor biomarker testing remains prevalent, representing a potential target for further efforts to reduce low-value practices.

Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study

BACKGROUND

There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries.

OBJECTIVE

The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical claims data on the occurrence and timing of second breast cancer events (SBCE).

METHODS

We used supervised data from 3092 stage I and II breast cancer cases (with 394 recurrences), diagnosed between 1993 and 2006 inclusive, of patients at Kaiser Permanente Washington and cases in the Puget Sound Cancer Surveillance System. Our goal was to classify each month after primary treatment as pre- versus post-SBCE. The prediction feature set for a given month consisted of registry variables on disease and patient characteristics related to the primary breast cancer event, as well as features based on monthly counts of diagnosis and procedure codes for the current, prior, and future months. A month was classified as post-SBCE if the predicted probability exceeded a probability threshold (PT); the predicted time of the SBCE was taken to be the month of maximum increase in the predicted probability between adjacent months.

RESULTS

The Kaplan-Meier net probability of SBCE was 0.25 at 14 years. The month-level receiver operating characteristic curve on test data (20% of the data set) had an area under the curve of 0.986. The person-level predictions (at a monthly PT of 0.5) had a sensitivity of 0.89, a specificity of 0.98, a positive predictive value of 0.85, and a negative predictive value of 0.98. The corresponding median difference between the observed and predicted months of recurrence was 0 and the mean difference was 0.04 months.

CONCLUSIONS

Data mining of medical claims holds promise for the streamlining of cancer registry operations to feasibly collect information about second breast cancer events.

Cost-Efficiency of Breast Cancer Care in the US Military Health System: An Economic Evaluation in Direct and Purchased Care

INTRODUCTION

With the rising costs of cancer care, it is critical to evaluate the overall cost-efficiency of care in real-world settings. In the United States, breast cancer accounts for the largest portion of cancer care spending due to high incidence and prevalence. The purpose of this study is to assess the relationship between breast cancer costs in the first 6 months after diagnosis and clinical outcomes by care source (direct or purchased) in the universal-access US Military Health System (MHS).

MATERIALS AND METHODS

We conducted a retrospective analysis of data from the Department of Defense Central Cancer Registry and MHS Data Repository administrative records. The institutional review boards of the Walter Reed National Military Medical Center and the Defense Health Agency reviewed and approved the data linkage. We used the linked data to identify women aged 40-64 who were diagnosed with pathologically-confirmed breast cancer between 2003 and 2007 with at least 1 year of follow-up through December 31, 2008. We identified cancer treatment from administrative data using relevant medical procedure and billing codes and extracted costs paid by the MHS for each claim. Multivariable Cox proportional hazards models estimated hazards ratios (HR) and 95% confidence intervals (CI) for recurrence or all-cause death as a function of breast cancer cost in tertiles.

RESULTS

The median cost per patient (n = 2,490) for cancer care was $16,741 (interquartile range $9,268, $28,742) in the first 6 months after diagnosis. In direct care, women in the highest cost tertile had a lower risk for clinical outcomes compared to women in the lowest cost tertile (HR 0.58, 95% CI 0.35, 0.96). When outcomes were evaluated separately, there was a statistically significant inverse association between higher cost and risk of death (p-trend = 0.025) for women receiving direct care. These associations were not observed among women using purchased care or both care sources.

CONCLUSIONS

In the MHS, higher breast cancer costs in the first 6 months after diagnosis were associated with lower risk for clinical outcomes in direct care, but not in purchased care. Organizational, institutional, and provider-level factors may contribute to the observed differences by care source. Replication of our findings in breast and other tumor sites may have implications for informing cancer care financing and value-based reimbursement policy.

Advance Care Planning: Promoting Effective and Aligned Communication in the Elderly (ACP-PEACE): the study protocol for a pragmatic stepped-wedge trial of older patients with cancer

INTRODUCTION

Advance care planning (ACP) is associated with improved health outcomes for patients with cancer, and its absence is associated with unfavourable outcomes for patients and their caregivers. However, older adults do not complete ACP at expected rates due to patient and clinician barriers. We present the original design, methods and rationale for a trial aimed at improving ACP for older patients with advanced cancer and the modified protocol in response to changes brought by the COVID-19 pandemic.

METHODS AND ANALYSIS

The Advance Care Planning: Promoting Effective and Aligned Communication in the Elderly study is a pragmatic, stepped-wedge cluster randomised trial examining a Comprehensive ACP Program. The programme combines two complementary evidence-based interventions: clinician communication skills training (VitalTalk) and patient video decision aids (ACP Decisions). We will implement the programme at 36 oncology clinics across three unique US health systems. Our primary outcome is the proportion of eligible patients with ACP documentation completed in the electronic health record. Our secondary outcomes include resuscitation preferences, palliative care consultations, death, hospice use and final cancer-directed therapy. From a subset of our patient population, we will collect surveys and video-based declarations of goals and preferences. We estimate 11 000 patients from the three sites will be enrolled in the study.

ETHICS AND DISSEMINATION

Regulatory and ethical aspects of this trial include Institutional Review Board (IRB) approval via single IRB of record mechanism at Dana-Farber Cancer Institute, Data Use Agreements among partners and a Data Safety and Monitoring Board. We plan to present findings at national meetings and publish the results.

TRIAL REGISTRATION NUMBER

NCT03609177; Pre-results.

Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data

PURPOSE

Hospitalizations are a common occurrence during chemotherapy for advanced cancer. Validated risk stratification tools could facilitate proactive approaches for reducing hospitalizations by identifying at-risk patients.

PATIENTS AND METHODS

We assembled two retrospective cohorts of patients receiving chemotherapy for advanced nonhematologic cancer; cohorts were drawn from three integrated health plans of the Cancer Research Network. We used these cohorts to develop and validate logistic regression models estimating 30-day hospitalization risk after chemotherapy initiation. The development cohort included patients in two health plans from 2005 to 2013. The validation cohort included patients in a third health plan from 2007 to 2016. Candidate predictor variables were derived from clinical data in institutional data warehouses. Models were validated based on the C-statistic, positive predictive value, and negative predictive value. Positive predictive value and negative predictive value were calculated in reference to a prespecified risk threshold (hospitalization risk ≥ 18.0%).

RESULTS

There were 3,606 patients in the development cohort (median age, 63 years) and 634 evaluable patients in the validation cohort (median age, 64 years). Lung cancer was the most common diagnosis in both cohorts (26% and 31%, respectively). The selected risk stratification model included two variables: albumin and sodium. The model C-statistic in the validation cohort was 0.69 (95% CI, 0.62 to 0.75); 39% of patients were classified as high risk according to the prespecified threshold; 30-day hospitalization risk was 24.2% (95% CI, 19.9% to 32.0%) in the high-risk group and 8.7% (95% CI, 6.1% to 12.0%) in the low-risk group.

CONCLUSION

A model based on data elements routinely collected during cancer treatment can reliably identify patients at high risk for hospitalization after chemotherapy initiation. Additional research is necessary to determine whether this model can be deployed to prevent chemotherapy-related hospitalizations.

Validation in Alberta of an administrative data algorithm to identify cancer recurrence

Background

Readily available population-based data about cancer recurrence would improve surveillance and research for women of reproductive age.

Methods

We randomly selected 200 women from the Alberta Cancer Registry who had received a cancer diagnosis and who ever had a pregnancy between 2003 and 2012. Administrative data were obtained and linked. Several definitions of recurrence were assessed using various minimum lengths of time between the initial diagnosis date and subsequent diagnoses or treatments, or both. Chart review was used as a "gold standard" definition of recurrence.

Results

Chart review identified recurrences in 26 women. The definition that best captured "recurrence" was 2 or more cancer diagnosis codes 10 or more months from the diagnosis date [sensitivity: 80.8%; 95% confidence interval (ci): 60.7% to 93.5%; specificity: 81.0%; 95% ci: 74.4% to 86.6%; positive predictive value: 38.9%; 95% ci: 25.9% to 53.1%; negative predictive value: 96.6%; 95% ci: 92.2% to 98.9%; kappa = 0.42; 95% ci: 0.28 to 0.57].

Conclusions

Recurrence in reproductive-aged women can be captured with moderate validity using administrative data, but should be interpreted with caution.

Association between cancer-specific adverse event triggers and mortality: A validation study

Background

As there are few validated measures of patient safety in clinical oncology, creating an efficient measurement instrument would create significant value. Accordingly, we sought to assess the validity of a novel patient safety measure by examining the association of oncology‐specific triggers and mortality using administrative claims data.

Methods

We examined a retrospective cohort of 322 887 adult cancer patients enrolled in commercial or Medicare Advantage products for one year after an initial diagnosis of breast, colorectal, lung, or prostate cancer in 2008‐2014. We used diagnosis and procedure codes to calculate the prevalence of 16 cancer‐specific "triggers"–events that signify a potential adverse event. We compared one‐year mortality rates among patients with and without triggers by cancer type and metastatic status using logistic regression models.

Results

Trigger events affected 19% of patients and were most common among patients with metastatic colorectal (41%) and lung (50%) cancers. There was increased one‐year mortality among patients with triggers compared to patients without triggers across all cancer types in unadjusted and multivariate analyses. The increased mortality rate among patients with trigger events was particularly striking for nonmetastatic prostate cancer (1.3% vs 7.5%, adjusted odds ratio 1.96 [95% CI 1.49‐2.57]) and nonmetastatic colorectal cancer (4.1% vs 11.7%, 1.44 [1.19‐1.75]).

Conclusions

The association between adverse event triggers and poor survival among a cohort of cancer patients supports the validity of a cancer‐specific, administrative claims‐based trigger tool.

Application of the Adaptive Validation Substudy Design to Colorectal Cancer Recurrence

Background

Among men and women diagnosed with colorectal cancer (CRC), 20-50% will develop a cancer recurrence. Cancer recurrences are not routinely captured by most population-based registries; however, linkage across Danish registries allows for the development of predictive models to detect recurrence. Successful application of such models in population-based settings requires validation against a gold standard to ensure the accuracy of recurrence identification.

Objective

We apply a recently developed validation study design for prospectively collected validation data to validate predicted CRC recurrences against gold standard diagnoses from medical records in an actively followed cohort of CRC patients in Denmark.

Methods

We use a Bayesian monitoring framework, traditionally used in clinical trials, to iteratively update classification parameters (positive and negative predictive values, and sensitivity and specificity) in an adaptive validation substudy design. This design allows determination of the sample size necessary to estimate the corresponding parameters and to identify when validation efforts can cease based on predefined criteria for parameter values and levels of precision.

Results

Among 355 men and women diagnosed with CRC in Denmark and actively followed semi-annually, there were 63 recurrences diagnosed by active follow-up and 70 recurrences identified by a predictive algorithm. The adaptive validation design met stopping criteria for the classification parameters after 120 patients had their recurrence information validated. This stopping point yielded parameter estimates for the classification parameters similar to those obtained when the entire cohort was validated, with 66% less patients needed for the validation study.

Conclusion

In this proof of concept application of the adaptive validation study design for outcome misclassification, we demonstrated the ability of the method to accurately determine when sufficient validation data have been collected. This method serves as a novel validation substudy design for prospectively collected data with simultaneous implementation of a validation study.

Developing a cancer-specific trigger tool to identify treatment-related adverse events using administrative data

Background

As there are few validated tools to identify treatment‐related adverse events across cancer care settings, we sought to develop oncology‐specific “triggers” to flag potential adverse events among cancer patients using claims data.

Methods

322 887 adult patients undergoing an initial course of cancer‐directed therapy for breast, colorectal, lung, or prostate cancer from 2008 to 2014 were drawn from a large commercial claims database. We defined 16 oncology‐specific triggers using diagnosis and procedure codes. To distinguish treatment‐related complications from comorbidities, we required a logical and temporal relationship between a treatment and the associated trigger. We tabulated the prevalence of triggers by cancer type and metastatic status during 1‐year of follow‐up, and examined cancer trigger risk factors.

Results

Cancer‐specific trigger events affected 19% of patients over the initial treatment year. The trigger burden varied by disease and metastatic status, from 6% of patients with nonmetastatic prostate cancer to 41% and 50% of those with metastatic colorectal and lung cancers, respectively. The most prevalent triggers were abnormal serum bicarbonate, blood transfusion, non‐contrast chest CT scan following radiation therapy, and hypoxemia. Among patients with metastatic disease, 10% had one trigger event and 29% had two or more. Triggers were more common among older patients, women, non‐whites, patients with low family incomes, and those without a college education.

Conclusions

Oncology‐specific triggers offer a promising method for identifying potential patient safety events among patients across cancer care settings.

Identifying monoclonal gammopathy of undetermined significance in electronic health data

PURPOSE

Monoclonal gammopathy of undetermined significance (MGUS) is a prevalent yet largely asymptomatic precursor to multiple myeloma. Patients with MGUS must undergo regular surveillance and testing, with few known predictors of progression. We developed an algorithm to identify MGUS patients in electronic health data to facilitate large-scale, population-based studies of this premalignant condition.

METHODS

We developed a four-step algorithm using electronic health record and health claims data from men and women aged 50 years or older receiving care from a large, multispecialty medical group between 2007 and 2015. The case definition required patients to have at least two MGUS ICD-9 diagnosis codes within 12 months, at least one serum and/or urine protein electrophoresis and one immunofixation test, and at least one in-office hematology/oncology visit. Medical charts for selected cases were abstracted then adjudicated independently by two physicians. We assessed algorithm validity by positive predictive value (PPV).

RESULTS

We identified 833 people with at least two MGUS diagnosis codes; 429 (52%) met all four algorithm criteria. We randomly selected 252 charts for review, including 206 from patients meeting all four algorithm criteria. The PPV for the 206 algorithm-identified charts was 76% (95% CI, 70%-82%). Among the 49 cases deemed to be false positives (24%), 33 were judged to have multiple myeloma or another lymphoproliferative condition, such as lymphoma.

CONCLUSIONS

We developed a simple algorithm that identified MGUS cases in electronic health data with reasonable accuracy. Inclusion of additional steps to eliminate cases with malignant disease may improve algorithm performance.

Survival benefit of radiation in high-risk, early-stage endometrioid carcinoma

OBJECTIVE

To better delineate optimal management of high-risk, early-stage endometrial cancer, as national guidelines permit substantial practice variations.

METHODS

Patients with International Federation of Gynecology and Obstetrics (FIGO) stage IB grade 3 and stage II endometrioid carcinoma who underwent at least total hysterectomy were identified in SEER-Medicare. Adjuvant treatments were brachytherapy (BT), external beam radiation therapy (EBRT), and chemotherapy. Death from endometrial cancer (cancer-specific mortality [CSM]) and local recurrence were analyzed using Gray's test and Fine-Gray regression.

RESULTS

In total, 1,095 patients were identified: 52% received BT, 56% received EBRT, 16% received chemotherapy, and 29% received no adjuvant treatment. Survival outcomes were significantly worse for stage IB grade 3 and stage II grade 3 relative to stage II grades 1-2 (5-year CSM: 18% and 23% vs. 10%; p<0.001 and p=0.003, respectively), while there was no difference between stage IB grade 3 and stage II grade 3 (p=0.618). BT had a local control benefit across all patients (p<0.001) that translated into a survival benefit in stage IB grade 3 (adjusted hazard ratio [HR] for CSM=0.47, p=0.003). EBRT had a survival benefit in stage II grade 3 (adjusted HR for CSM=0.36; p=0.031), as did lymph node dissection (p=0.015). Chemotherapy was not significantly correlated with CSM.

CONCLUSIONS

High-risk, early-stage endometrioid carcinoma is a heterogeneous population. BT was associated with a survival benefit in stage IB grade 3, whereas regional treatment with EBRT and lymphadenectomy was associated with a survival benefit in stage II grade 3.

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.

An Approach to Identify Delivery of Palliative Radiation Therapy Using Health Care Claims Data: A Proof-of-Concept Application of a Visual Analytics Tool

PURPOSE

There is limited information on the use of data visualization tools for health services research applications. We provide a proof-of-concept application that focuses on claims-based measures of palliative radiation therapy. We investigate whether a guided, data-driven investigation contributes information for subsequent statistical analysis and algorithm development.

METHODS

This retrospective cohort study used linked registry and claims data on men who were diagnosed with stage IV M0 or stage IV M1b prostate cancer between 2005 and 2009, with associated claims from 2005 through 2010, and receiving radiation therapy. Preprocessing of data was accomplished by using EventFlow software to investigate longitudinal patterns in claims for radiation therapy in the 13 months after cancer diagnosis. Guided by results from EventFlow, we developed descriptive statistics to investigate the length of radiation therapy, use of bone metastasis coding, and mortality between M1b and M0 patients.

RESULTS

A total of 1,151 patients met the inclusion criteria. Taking advantage of the novel aggregation capability of EventFlow, we observed differences in the length of radiation therapy and the use of bone metastasis coding between men with (M1b) and without (M0) a diagnosis of bone metastasis. Seventy-nine percent of M1b patients received radiation for a duration ≤ 4 weeks, which suggested palliative radiation (to the bone). Seventy-six percent of M0 patients received radiation for ≥ 6 weeks, which suggested radiation to the prostate. Mortality was higher among those who received a shorter duration of radiation therapy compared with those who received a longer duration of therapy.

CONCLUSION

Use of EventFlow, followed by statistical analysis of the linked registry and claims data, identified useful components of a claims-based measure of radiation to the bone.

Using Medicare Claims to Examine Long-term Prostate Cancer Risk of Finasteride in the Prostate Cancer Prevention Trial

Background

Investigators have used administrative claims to better understand cancer outcomes when a research question cannot feasibly be examined within a study. The Prostate Cancer Prevention Trial (PCPT) showed that seven years of finasteride reduced prostate cancer (PC) risk by 25% in men age 55 years or older. However, it was unclear whether the observed reduction in PC for finasteride participants would be maintained after finasteride discontinuation.

Methods

We examined PC diagnoses identified by PCPT study records and Medicare claims (finasteride = 9423, placebo = 9457). A Medicare-defined PC diagnosis algorithm was defined using diagnosis and procedure codes. Multivariable Cox regression was used to examine time to PC within prespecified follow-up windows (<6.5, 6.5-7.5, and >7.5 years) using time-dependent covariates interacting with intervention assignment to account for the PCPT protocol-specified end-of-study biopsy at seven years. All statistical tests were two-sided.

Results

Median follow-up using the linked database was 16 years. Overall, finasteride arm participants had a 21.1% decrease in the hazard ratio of PC (hazard ratio [HR] = 0.79, 95% confidence interval [CI] = 0.74 to 0.84, P < .001). The beneficial effect of finasteride in reducing the hazard ratio of PC was most pronounced in the first 7.5 years (HR = 0.71, 95% CI = 0.66 to 0.77, P < .001), consistent with the original study findings; after 7.5 years, there was no increased risk of PC for finasteride arm participants (HR = 1.10, 95% CI = 0.96 to 1.26, P = .18).

Conclusions

Finasteride provides a substantial reduction in PC through 16 years of follow-up. There was no strong evidence that the benefit of finasteride diminished after the end-of-study follow-up. Utilizing Medicare claims to augment PCPT follow-up illustrates how the novel use of secondary data sources can enhance the ability to detect long-term outcomes from prospective studies.

Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data

Objectives

Most population-based cancer databases lack information on metastatic recurrence. Electronic medical records (EMR) and cancer registries contain complementary information on cancer diagnosis, treatment and outcome, yet are rarely used synergistically. To construct a cohort of metastatic breast cancer (MBC) patients, we applied natural language processing techniques within a semisupervised machine learning framework to linked EMR-California Cancer Registry (CCR) data.

Materials and Methods

We studied all female patients treated at Stanford Health Care with an incident breast cancer diagnosis from 2000 to 2014. Our database consisted of structured fields and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results Program (SEER). We identified de novo MBC patients from CCR and extracted information on distant recurrences from patient notes in EMR. Furthermore, we trained a regularized logistic regression model for recurrent MBC classification and evaluated its performance on a gold standard set of 146 patients.

Results

There were 11 459 breast cancer patients in total and the median follow-up time was 96.3 months. We identified 1886 MBC patients, 512 (27.1%) of whom were de novo MBC patients and 1374 (72.9%) were recurrent MBC patients. Our final MBC classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.917, with sensitivity 0.861, specificity 0.878, and accuracy 0.870.

Discussion and Conclusion

To enable population-based research on MBC, we developed a framework for retrospective case detection combining EMR and CCR data. Our classifier achieved good AUC, sensitivity, and specificity without expert-labeled examples.

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.

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.

A validated algorithm for register-based identification of patients with recurrence of breast cancer-Based on Danish Breast Cancer Group (DBCG) data

BACKGROUND

Cancer recurrence is not routinely and completely registered in Danish national health registers, which challenges register-based research. The aim of this study was to develop and validate a register-based algorithm to identify patients with recurrence of breast cancer (BC).

METHODS

We conducted a cohort study based on data from Danish national health registers and used the Danish National Patient Register and the Danish National Pathology Register as sources to identify BC recurrence. We used data from the Danish Breast Cancer Group (DBCG) validated against medical records on recurrence status and recurrence date for 471 women with early stage unilateral BC as the gold standard of BC recurrence to assess the accuracy of the algorithm to identify BC recurrence.

RESULTS

The algorithm displayed a sensitivity of 97.3% (95% confidence interval (CI): 93.2-99.3), a specificity of 97.2% (95% CI: 94.8-98.7) and a positive predictive value of 94.4% (95% CI: 89.2-97.3). The concordance correlation coefficient for the agreement between recurrence dates generated by the algorithm and the gold standard was 0.97 (95% CI: 0.96-0.98), and the date was estimated within +/-30 days of the gold standard in 66% of the patients and within +/-60 days in 76% of the patients.

CONCLUSION

The developed algorithm almost perfectly identified BC recurrence and with reasonable timing compared to the gold standard.