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

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

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

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

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

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

Decisional Regret Among Parents of Children With Cancer.

Purpose Decision making is one of the ways in which parents serve as stewards of their children with cancer, but barriers to informed decision making among parents of children with cancer have been identified. We sought to evaluate the extent to which parents feel satisfied with, or regretful of, decisions made for their child's cancer treatment and to identify factors associated with heightened regret. Methods We surveyed 346 parents of children with cancer within 12 weeks of their initial cancer treatment decision and the children's physicians at Dana-Farber Cancer Institute/Boston Children's Hospital and the Children's Hospital of Philadelphia. Our main outcome measure was heightened regret as measured by the Decisional Regret Scale. Results Sixteen percent of parents (N = 54) met our definition of heightened decisional regret. In a multivariable logistic regression model, race/ethnicity was associated with regret, with black (odds ratio [OR], 6.55; 95% CI, 2.30 to 18.7), Hispanic (OR, 2.15; 95% CI, .69 to 6.65), and other race parents (OR, 4.68; 95% CI, 1.58 to 13.8) at increased risk for regret relative to whites ( P = .001 across all categories). In contrast, parents who reported receiving high-quality information (OR, .45; 95% CI, .23 to .91; P = .03) and detailed prognostic information (OR, .48; 95% CI, .24 to .96; P = .04), who trusted the oncologist completely (OR, .32; 95% CI, .17 to .63; P = .001), and who held their ideal role in decision making (OR, .49; 95% CI, .25 to .95; P = .04) were less likely to experience regret. Conclusion Although many parents are satisfied with decisions made for their children with cancer, racial and ethnic minority parents are at heightened risk for regret. Clinicians may be able to reduce this risk by providing high-quality information, including prognostic information, involving parents in decision making in the ways they wish, and serving as trusted providers.