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

Can routinely collected administrative data effectively be used to evaluate and validate endpoints used in breast cancer clinical trials? Protocol for a scoping review of the literature

BACKGROUND

Randomized controlled trials (RCTs) are a critical component of evidence-based medicine and the evolution of patient care. However, the costs of conducting a RCT can be prohibitive. A promising approach toward reduction of costs and lessening of the burden of intensive and lengthy patient follow-up is the use of routinely collected healthcare data (RCHD), commonly called real-world data. We propose a scoping review to identify existing RCHD case definitions of breast cancer progression and survival and their diagnostic performance.

METHODS

We will search MEDLINE, EMBASE, and CINAHL to identify primary studies of women with either early-stage or metastatic breast cancer, managed with established therapies, that evaluated the diagnostic accuracy of one or more RCHD-based case definitions or algorithms of disease progression (i.e., recurrence, progression-free survival, disease-free survival, or invasive disease-free survival) or survival (i.e., breast-cancer-free survival or overall survival) compared with a reference standard measure (e.g., chart review or a clinical trial dataset). Study characteristics and descriptions of algorithms will be extracted along with measures of the diagnostic accuracy of each algorithm (e.g., sensitivity, specificity, positive predictive value, negative predictive value), which will be summarized both descriptively and in structured figures/tables.

DISCUSSION

Findings from this scoping review will be clinically meaningful for breast cancer researchers globally. Identification of feasible and accurate strategies to measure patient-important outcomes will potentially reduce RCT budgets as well as lessen the burden of intensive trial follow-up on patients.

SYSTEMATIC REVIEW REGISTRATION

Open Science Framework ( https://doi.org/10.17605/OSF.IO/6D9RS ).

Long-term Mortality in Individuals Diagnosed With Cancer During Pregnancy or Postpartum

Importance

Outcomes among patients with pregnancy-associated cancers (diagnosed during pregnancy or 1-year postpartum) other than breast cancer have received relatively little research attention. High-quality data from additional cancer sites are needed to inform the care of this unique group of patients.

Objective

To assess mortality and survival in premenopausal women with pregnancy-associated cancers, with a particular focus on cancers other than those of the breast.

Design, Setting, and Participants

This population-based retrospective cohort study included premenopausal women (aged 18-50 years) living in 3 Canadian provinces (Alberta, British Columbia, and Ontario) diagnosed with cancer between January 1, 2003, and December 31, 2016, with follow-up until December 31, 2017, or date of death. Data analysis occurred in 2021 and 2022.

Exposures

Participants were categorized as being diagnosed with cancer during pregnancy (from conception to delivery), during the postpartum period (up to 1 year after delivery), or during a time that was remote from pregnancy.

Main Outcomes and Measures

Outcomes were overall survival at 1 and 5 years and time from diagnosis to death due to any cause. Cox proportional hazard models were used to estimate mortality adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs), adjusting for age at cancer diagnosis, cancer stage, cancer site, and days from diagnosis to first treatment. Meta-analysis was used to pool results across all 3 provinces.

Results

During the study period there were 1014, 3074, and 20 219 participants diagnosed with cancer during pregnancy, postpartum, and periods remote from pregnancy, respectively. One-year survival was similar across the 3 groups, but 5-year survival was lower among those diagnosed with cancer during pregnancy or postpartum. Overall, there was a greater risk of death due to pregnancy-associated cancer among those diagnosed during pregnancy (aHR, 1.79; 95% CI, 1.51-2.13) and postpartum (aHR, 1.49; 95% CI, 1.33-1.67); however, these results varied across cancer sites. Increased hazard of mortality was observed for breast (aHR, 2.01; 95% CI, 1.58-2.56), ovarian (aHR, 2.60; 95% CI, 1.12-6.03), and stomach (aHR, 10.37; 95% CI, 3.56-30.24) cancers diagnosed during pregnancy, and brain (aHR, 2.75; 95% CI, 1.28-5.90), breast (aHR, 1.61; 95% CI, 1.32-1.95), and melanoma (aHR, 1.84; 95% CI, 1.02-3.30) cancers diagnosed postpartum.

Conclusions and Relevance

This population-based cohort study found that pregnancy-associated cancers had increased overall 5-year mortality, though not all cancer sites presented the same risk.

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.

Fracture rate increases after immune checkpoint inhibitor treatment: a potential new immune related adverse event

INTRODUCTION

T cell activation can lead to osteoporosis and while there are several case reports of fractures occurring after immune checkpoint inhibitor (ICI) use, to date, there are no population level studies looking at fracture risk related to ICI use.

METHODS

Using Alberta Cancer Registry data, we identified all individuals treated with ICI for cancer between September 29, 2010, and March 31, 2019. Linked records from Alberta's healthcare administrative databases were assessed for the presence of fracture diagnostic codes in the year prior to and up to two years after ICI initiation. Fracture rate was stratified based on the time-period before and after ICI initiation. Fracture rates after ICI were compared to baseline.

RESULTS

The study cohort consisted of 1600 ICI users (mean age 65.7 years, 60% male). Most patients were treated with an anti-PD-1 agent (73.9%). ICIs were initiated on average 707.8 days after cancer diagnosis. 76 (4.8%) individuals had a remote history of a major fracture, and 141 (8.8%) had been treated with an osteoporosis medication prior to ICI treatment. The fracture rate in the year prior to ICI initiation was 11.3 per 1000 patient-years. The fracture rate in the year after ICI initiation was significantly higher at 27.3 per 1000 patient-years. The fracture rate dropped to 17.6 per 1000 patient-years in the second year after ICI initiation. The incidence rate ratio of sustaining a major fracture in the year after compared to the year prior to ICI initiation was 2.43 (95% CI 1.34-4.27).

CONCLUSIONS

Fracture risk may be increased in cancer patients early after initiation of ICI, and this may represent a novel immune-related adverse event.

Attitudes of Australian breast cancer patients toward the secondary use of administrative and clinical trial data

AIM

Little is known about the attitudes of Australian patients with a history of breast cancer toward the reuse of administrative health data and clinical trial data. Issues of consent, privacy, and information security are key to the discussion. Cancer care and research provides an opportune setting to develop an understanding of attitudes toward data sharing and reuse in individuals with a history of breast cancer.

METHODS

An anonymous, online questionnaire for individuals with a history or diagnosis of breast cancer was distributed by two peak bodies (Breast Cancer Trials [BCT] and Breast Cancer Network of Australia [BCNA]) to their memberships between July 14, 2020 and October 17, 2020. Results were captured in RedCap; data analysis was undertaken using Stata, and a thematic analysis of free text responses was undertaken using NVivo.

RESULTS

One hundred and thirty-two complete responses were received. Twenty-three percent of respondents had participated in a clinical trial, and 12% were currently receiving treatment (chemotherapy, radiotherapy, surgery, or endocrine). Respondents were supportive of the secondary use of de-identified administrative health data and clinical trial data, but showed concern about data security and privacy. Respondents emphasized that the reuse of data should be for improved societal health outcomes, not profit. Many assumed secondary analysis was already undertaken on de-identified administrative health data and clinical trial data.

CONCLUSIONS

Respondents were supportive of the secondary use of de-identified administrative health and clinal trial data within the established bounds of good clinical practice and ethical oversight.

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.

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.