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

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.

Validated algorithms for identifying timing of second event of oropharyngeal squamous cell carcinoma using real-world data

BACKGROUND

Understanding occurrence and timing of second events (recurrence and second primary cancer) is essential for cancer specific survival analysis. However, this information is not readily available in administrative data.

METHODS

Alberta Cancer Registry, physician claims, and other administrative data were used. Timing of second event was estimated based on our developed algorithm. For validation, the difference, in days between the algorithm estimated and the chart-reviewed timing of second event. Further, the result of Cox-regression modeling cancer-free survival was compared to chart review data.

RESULTS

Majority (74.3%) of the patients had a difference between the chart-reviewed and algorithm-estimated timing of second event falling within the 0-60 days window. Kaplan-Meier curves generated from the estimated data and chart review data were comparable with a 5-year second-event-free survival rate of 75.4% versus 72.5%.

CONCLUSION

The algorithm provided an estimated timing of second event similar to that of the chart review.

Identifying breast cancer recurrence histories via patient-reported outcomes

PURPOSE

To test accuracy of patient self-report of breast cancer recurrence for enhancing standard population-based cancer registries that do not routinely collect cancer recurrence data despite the importance of this outcome.

METHODS

Potential research subjects were identified in the Breast Cancer Research Database (BCRD) of the Swedish Cancer Institute (SCI). The BCRD has collected data within 45 days of each medical encounter on new primary breast cancer patients receiving all or part of their initial care at SCI. Females diagnosed with a new primary breast cancer 2004-2016, Stages I-III, and alive at the time of study initiation (2018) were identified. Recurrent breast cancer patients were matched 1:1 to surviving non-recurrent patients by patient age, date of diagnosis, and single or multiple primary tumors. Consented research subjects were surveyed about their initial and subsequent diagnostic, therapeutic, and recurrent events. PRO survey responses were compared with BCRD information for each individual participant. Discrepancies were reviewed in medical records.

RESULTS

A matched sample of 88 recurrent and 88 non-recurrent patients were used in analyses. Respondents correctly identified the date of diagnosis of first primary breast cancer within 1 year 94% (165/176). Recurrence was reported by 97% (85/88) of recurrent patients. No recurrence was reported by 100% (88/88) of non-recurrent patients. Recurrence date within 1 year was correctly identified in 79% (67/85). Recurrence site was correctly identified in 82% (70/85). Medical record review of survey-registry discrepancies led to BCRD corrections in 4.5% (8/176) of cases.

IMPLICATIONS FOR CANCER SURVIVORS

Breast cancer patients can accurately report their disease characteristics, treatments, and recurrence history. Patient-reported information would enhance cancer registry data.

New method for determining breast cancer recurrence-free survival using routinely collected real-world health data

Background

In cancer survival analyses using population-based data, researchers face the challenge of ascertaining the timing of recurrence. We previously developed algorithms to identify recurrence of breast cancer. This is a follow-up study to detect the timing of recurrence.

Methods

Health events that signified recurrence and timing were obtained from routinely collected administrative data. The timing of recurrence was estimated by finding the timing of key indicator events using three different algorithms, respectively. For validation, we compared algorithm-estimated timing of recurrence with that obtained from chart-reviewed data. We further compared the results of cox regressions models (modeling recurrence-free survival) based on the algorithms versus chart review.

Results

In total, 598 breast cancer patients were included. 121 (20.2%) had recurrence after a median follow-up of 4 years. Based on the high accuracy algorithm for identifying the presence of recurrence (with 94.2% sensitivity and 79.2% positive predictive value), the majority (64.5%) of the algorithm-estimated recurrence dates fell within 3 months of the corresponding chart review determined recurrence dates. The algorithm estimated and chart-reviewed data generated Kaplan–Meier (K-M) curves and Cox regression results for recurrence-free survival (hazard ratios and P-values) were very similar.

Conclusion

The proposed algorithms for identifying the timing of breast cancer recurrence achieved similar results to the chart review data and were potentially useful in survival analysis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09333-6.