Validation of a Cyclic Algorithm to Proxy Number of Lines of Systemic Cancer Therapy Using Administrative Data

Old and New Drugs for Chronic Lymphocytic Leukemia: Lights and Shadows of Real-World Evidence

Several novel treatments for chronic lymphocytic leukemia (CLL) have been recently approved based on the results of randomized clinical trials. However, real-world evidence (RWE) is also requested before and after drug authorization in order to confirm safety and to provide data for health technology assessments. We conducted a scoping review of the available RWE for targeted treatments of CLL, namely ibrutinib, acalabrutinib, idelalisib, and venetoclax, as well as for chemoimmunotherapy (CIT). In particular, we searched studies published since 1 January 2010 and reported outcomes of the above treatments based on health databases, registries, or phase IV studies, including named-patient programs. We included both full papers and abstracts of studies presented at major meetings. Overall, 110 studies were selected and analyzed: 28,880 patients were treated with ibrutinib, 1424 with idelalisib, 751 with venetoclax, 496 with acalabrutinib, and 14,896 with CIT. Reported discontinuation rates were higher than in clinical trials, while effectiveness could not be indirectly compared with clinical trials since a detailed case mix, including cytogenetic risk factors, was partially available and propensity scores rarely applied. RWE on CLL can help to set realistic outcomes with novel treatments, however, real-world studies should be fostered, and available data shared.

Identifying Valid Algorithms for Number of Lines of Anti-Neoplastic Therapy in the Danish National Patient Registry Among Patients with Advanced Ovarian, Gastric, Renal Cell, Urothelial, and Non-Small Cell Lung Cancer Attending a Danish University Hospital

Purpose

To develop algorithms to identify number of lines of anti-neoplastic therapy per patient based on the Danish National Patient Registry (DNPR) and identify which algorithm has the highest percentage agreement with a reference standard of documentation in medical records.

Patients and Methods

We included 179 patients diagnosed between January 1, 2012, and December 31, 2016, with stage II, III, or IV urothelial cell carcinoma or stage III or IV epithelial ovarian cancer, gastric adenocarcinoma, renal cell carcinoma, or non-small cell lung cancer (NSCLC). We developed two algorithms for number of lines of anti-neoplastic therapy based on dates and treatment codes (eg, “treatment with cisplatin” or “cytostatic treatment”) in the DNPR. First, to denote a change in line of therapy the “Time-based algorithm” used the number of days between consecutive administrations. Second, the “Drug-based algorithm” used information on drug names if available or the number of days between consecutive administrations if no drug names were specified. We calculated the percentage agreement between the algorithms setting the number of allowed days between consecutive administrations from 28 to 50 and the reference standard – information on anti-neoplastic therapy drugs abstracted from medical records and subsequently coded according to lines of anti-neoplastic therapy.

Results

For the “Time-based algorithm”, the highest percentage agreement with the reference standard was found when using <45 days between consecutive administrations (67.6%; 95% CI: 60.1–73.8%). However, the percentage agreement was higher for the “Drug-based algorithm” using <45 days between consecutive administrations for registrations where the drug name was unspecified (90.5%; 95% CI: 85.0–93.7%).

Conclusion

The algorithm for number of lines of anti-neoplastic therapy that had the highest percentage agreement with the reference standard (medical records) incorporated both registration of specific drug names and <45 days between consecutive administrations if the drug name was unspecified in routinely recorded data from DNPR.

Early-stage economic analysis of research-based comprehensive genomic sequencing for advanced cancer care

Genomic research is driving discovery for future population benefit. Limited evidence exists on immediate patient and health system impacts of research participation. This study uses real-world data and quasi-experimental matching to examine early-stage cost and health impacts of research-based genomic sequencing. British Columbia’s Personalized OncoGenomics (POG) single-arm program applies whole genome and transcriptome analysis (WGTA) to characterize genomic landscapes in advanced cancers. Our cohort includes POG patients enrolled between 2014 and 2015 and 1:1 genetic algorithm–matched usual care controls. We undertake a cost consequence analysis and estimate 1-year effects of WGTA on patient management, patient survival, and health system costs reported in 2015 Canadian dollars. WGTA costs are imputed and forecast using system of equations modeling. We use Kaplan-Meier survival analysis to explore survival differences and inverse probability of censoring weighted linear regression to estimate mean 1-year survival times and costs. Non-parametric bootstrapping simulates sampling distributions and enables scenario analysis, revealing drivers of incremental costs, survival, and net monetary benefit for assumed willingness to pay thresholds. We identified 230 POG patients and 230 matched controls for cohort inclusion. The mean period cost of research-funded WGTA was $26,211 (SD: $14,191). Sequencing costs declined rapidly, with WGTA forecasts hitting $13,741 in 2021. The incremental healthcare system effect (non-research expenditures) was $5203 (95% CI: 75, 10,424) compared to usual care. No overall survival differences were observed, but outcome heterogeneity was present. POG patients receiving WGTA-informed treatment experienced incremental survival gains of 2.49 months (95% CI: 1.32, 3.64). Future cost consequences became favorable as WGTA cost drivers declined and WGTA-informed treatment rates improved to 60%. Our study demonstrates the ability of real-world data to support evaluations of only-in-research health technologies. We identify situations where precision oncology research initiatives may produce survival benefit at a cost that is within healthcare systems’ willingness to pay. This economic evidence informs the early-stage healthcare impacts of precision oncology research.

Determining lines of therapy in patients with solid cancers: a proposed new systematic and comprehensive framework

The complexity of neoplasia and its treatment are a challenge to the formulation of general criteria that are applicable across solid cancers. Determining the number of prior lines of therapy (LoT) is critically important for optimising future treatment, conducting medication audits, and assessing eligibility for clinical trial enrolment. Currently, however, no accepted set of criteria or definitions exists to enumerate LoT. In this article, we seek to open a dialogue to address this challenge by proposing a systematic and comprehensive framework to determine LoT uniformly across solid malignancies. First, key terms, including LoT and 'clinical progression of disease' are defined. Next, we clarify which therapies should be assigned a LoT, and why. Finally, we propose reporting LoT in a novel and standardised format as LoT N (CLoT + PLoT), where CLoT is the number of systemic anti-cancer therapies (SACT) administered with curative intent and/or in the early setting, PLoT is the number of SACT given with palliative intent and/or in the advanced setting, and N is the sum of CLoT and PLoT. As a next step, the cancer research community should develop and adopt standardised guidelines for enumerating LoT in a uniform manner.

Characterizing the Anticancer Treatment Trajectory and Pattern in Patients Receiving Chemotherapy for Cancer Using Harmonized Observational Databases: Retrospective Study

BACKGROUND

Accurate and rapid clinical decisions based on real-world evidence are essential for patients with cancer. However, the complexity of chemotherapy regimens for cancer impedes retrospective research that uses observational health databases.

OBJECTIVE

The aim of this study is to compare the anticancer treatment trajectories and patterns of clinical events according to regimen type using the chemotherapy episodes determined by an algorithm.

METHODS

We developed an algorithm to extract the regimen-level abstracted chemotherapy episodes from medication records in a conventional Observational Medical Outcomes Partnership (OMOP) common data model (CDM) database. The algorithm was validated on the Ajou University School Of Medicine (AUSOM) database by manual review of clinical notes. Using the algorithm, we extracted episodes of chemotherapy from patients in the EHR database and the claims database. We also developed an application software for visualizing the chemotherapy treatment patterns based on the treatment episodes in the OMOP-CDM database. Using this software, we generated the trends in the types of regimen used in the institutions, the patterns of the iterative chemotherapy use, and the trajectories of cancer treatment in two EHR-based OMOP-CDM databases. As a pilot study, the time of onset of chemotherapy-induced neutropenia according to regimen was measured using the AUSOM database. The anticancer treatment trajectories for patients with COVID-19 were also visualized based on the nationwide claims database.

RESULTS

We generated 178,360 treatment episodes for patients with colorectal, breast, and lung cancer for 85 different regimens. The algorithm precisely identified the type of chemotherapy regimen in 400 patients (average positive predictive value >98%). The trends in the use of routine clinical chemotherapy regimens from 2008-2018 were identified for 8236 patients. For a total of 12 regimens (those administered to the largest proportion of patients), the number of repeated treatments was concordant with the protocols for standard chemotherapy regimens for certain cases. In addition, the anticancer treatment trajectories for 8315 patients were shown, including 62 patients with COVID-19. A comparative analysis of neutropenia showed that its onset in colorectal cancer regimens tended to cluster between days 9-15, whereas it tended to cluster between days 2-8 for certain regimens for breast cancer or lung cancer.

CONCLUSIONS

We propose a method for generating chemotherapy episodes for introduction into the oncology extension module of the OMOP-CDM databases. These proof-of-concept studies demonstrated the usability, scalability, and interoperability of the proposed framework through a distributed research network.

Matching methods in precision oncology: An introduction and illustrative example

BACKGROUND

Randomized controlled trials (RCTs) are uncommon in precision oncology. We provide an introduction and illustrative example of matching methods for evaluating precision oncology in the absence of RCTs. We focus on British Columbia's Personalized OncoGenomics (POG) program, which applies whole-genome and transcriptome analysis (WGTA) to inform advanced cancer care.

METHODS

Our cohort comprises 230 POG patients enrolled between 2014 and 2015 and matched POG-naive controls. We generated our matched cohort using 1:1 propensity score matching (PSM) and genetic matching prior to exploring survival differences.

RESULTS

We find that genetic matching outperformed PSM when balancing covariates. In all cohorts, overall survival did not significantly differ across POG and POG-naive patients (p > 0.05). Stratification by WGTA-informed treatment indicated unmatched survival differences. Patients whose WGTA information led to treatment change were at a reduced hazard of death compared to POG-naive controls in all cohorts, with estimated hazard ratios ranging from 0.33 (95% CI: 0.13, 0.81) to 0.41 (95% CI: 0.17, 0.98).

CONCLUSION

These results signal that clinical effectiveness of precision oncology approaches will depend on rates of genomics-informed treatment change. Our study will guide future evaluations of precision oncology and support reliable effect estimation when RCT data are unavailable.