An overview of real-world data sources for oncology and considerations for research

Comparison of incident breast cancer cases in the largest national US tumor registries

BACKGROUND

This study compared incident breast cancer cases in the National Cancer Database (NCDB) and Surveillance, Epidemiology, End Results Program (SEER) to a national population cancer registry.

METHODS

Patients with malignant or in situ breast cancer (BC) 2010-2019 in the NCDB and SEER were compared to the US Cancer Statistics (USCS). Case coverage was estimated as the number of patients in the NCDB/SEER as a proportion of USCS cases.

RESULTS

The USCS reported 3,047,509 patients; 77.5% patients were included in the NCDB and 46.0% in SEER. Case ascertainment varied significantly by patient sex (both registries, p < .001). For males, 84.1% were captured in the NCDB, whereas only 77.5% of females were included. Case coverage in SEER was better for females than males (46.1% vs. 43.5%). Registries varied significantly by race/ethnicity (both p < .001). Case coverage in the NCDB was highest for non-Hispanic White (78.2%), non-Hispanic Black (77.7%), and non-Hispanic Asian or Pacific Islander (72.5%) BC patients, and lowest for Hispanic (56.4%) and non-Hispanic American Indian/Alaska Native (41.1%) patients. In SEER, case coverage was highest for non-Hispanic Asian or Pacific Islander (78.1%) and Hispanic (69.6%) patients and it was significantly lower for all other subgroups (non-Hispanic Black, 44.8%; non-Hispanic White, 42.4%; and non-Hispanic American Indian/Alaska Native, 36.6%).

CONCLUSIONS

National US tumor registries provide data for a large sampling of breast cancer patients, yet significant differences in case coverage were observed based on age, sex, and race/ethnicity. These findings suggest that analyses using these data sets and interpretation of findings should account for these meaningful variances.

Nanomaterials: leading immunogenic cell death-based cancer therapies

The field of oncology has transformed in recent years, with treatments shifting from traditional surgical resection and radiation therapy to more diverse and customized approaches, one of which is immunotherapy. ICD (immunogenic cell death) belongs to a class of regulatory cell death modalities that reactivate the immune response by facilitating the interaction between apoptotic cells and immune cells and releasing specific signaling molecules, and DAMPs (damage-associated molecular patterns). The inducers of ICD can elevate the expression of specific proteins to optimize the TME (tumor microenvironment). The use of nanotechnology has shown its unique potential. Nanomaterials, due to their tunability, targeting, and biocompatibility, have become powerful tools for drug delivery, immunomodulators, etc., and have shown significant efficacy in clinical trials. In particular, these nanomaterials can effectively activate the ICD, trigger a potent anti-tumor immune response, and maintain long-term tumor suppression. Different types of nanomaterials, such as biological cell membrane-modified nanoparticles, self-assembled nanostructures, metallic nanoparticles, mesoporous materials, and hydrogels, play their respective roles in ICD induction due to their unique structures and mechanisms of action. Therefore, this review will explore the latest advances in the application of these common nanomaterials in tumor ICD induction and discuss how they can provide new strategies and tools for cancer therapy. By gaining a deeper understanding of the mechanism of action of these nanomaterials, researchers can develop more precise and effective therapeutic approaches to improve the prognosis and quality of life of cancer patients. Moreover, these strategies hold the promise to overcome resistance to conventional therapies, minimize side effects, and lead to more personalized treatment regimens, ultimately benefiting cancer treatment.

Landscape analysis of environmental data sources for linkage with SEER cancer patients database

One of the challenges associated with understanding environmental impacts on cancer risk and outcomes is estimating potential exposures of individuals diagnosed with cancer to adverse environmental conditions over the life course. Historically, this has been partly due to the lack of reliable measures of cancer patients' potential environmental exposures before a cancer diagnosis. The emerging sources of cancer-related spatiotemporal environmental data and residential history information, coupled with novel technologies for data extraction and linkage, present an opportunity to integrate these data into the existing cancer surveillance data infrastructure, thereby facilitating more comprehensive assessment of cancer risk and outcomes. In this paper, we performed a landscape analysis of the available environmental data sources that could be linked to historical residential address information of cancer patients' records collected by the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. The objective is to enable researchers to use these data to assess potential exposures at the time of cancer initiation through the time of diagnosis and even after diagnosis. The paper addresses the challenges associated with data collection and completeness at various spatial and temporal scales, as well as opportunities and directions for future research.

Real-World Research on Retinal Diseases Using Health Claims Database: A Narrative Review

Real-world data (RWD) has emerged as a crucial component in understanding and improving patient outcomes across various medical conditions, including retinal diseases. Health claims databases, generated from healthcare reimbursement claims, offer a comprehensive source of RWD, providing insights into patient outcomes, healthcare utilization, and treatment effectiveness. However, the use of these databases for research also presents unique challenges. This narrative review explores the role of real-world research on retinal diseases using health claims databases, highlighting their advantages, limitations, and potential contributions to advancing our understanding and management of the diseases. The review examines the applications of health claims databases in retinal disease research, including epidemiological studies, comparative effectiveness and safety analyses, economic burden assessments, and evaluations of patient outcomes and quality of care. Previous findings demonstrate the value of these databases in generating prevalence and incidence estimates, identifying risk factors and predictors, evaluating treatment effectiveness and safety, and understanding healthcare utilization patterns and costs associated with retinal diseases. Despite their strengths, health claims databases face challenges related to data limitations, biases, privacy concerns, and methodological issues. Accordingly, the review also explores future directions and opportunities, including advancements in data collection and analysis, integration with electronic health records, collaborative research networks and consortia, and the evolving regulatory landscape. These developments are expected to enhance the utility of health claims databases for retinal disease research, resulting in more comprehensive and impactful findings across diverse retinal disorders and robust real-world insights from a large population.

Real-World-Time Data and RCT Synergy: Advancing Personalized Medicine and Sarcoma Care through Digital Innovation

This manuscript examines the synergistic potential of prospective real-world/time data/evidence (RWTD/E) and randomized controlled trials (RCTs) to enrich healthcare research and operational insights, with a particular focus on its impact within the sarcoma field. Through exploring RWTD/E's capability to provide real-world/time, granular patient data, it offers an enriched perspective on healthcare outcomes and delivery, notably in the complex arena of sarcoma care. Highlighting the complementarity between RWTD/E's expansive real-world/time scope and the structured environment of RCTs, this paper showcases their combined strength, which can help to foster advancements in personalized medicine and population health management, exemplified through the lens of sarcoma treatment. The manuscript further outlines methodological innovations such as target trial emulation and their significance in enhancing the precision and applicability of RWTD/E, underscoring the transformative potential of these advancements in sarcoma care and beyond. By advocating for the strategic incorporation of prospective RWTD/E into healthcare frameworks, it aims to create an evidence-driven ecosystem that significantly improves patient outcomes and healthcare efficiency, with sarcoma care serving as a pivotal domain for these developments.

Validation and clinical discovery demonstration of breast cancer data from a real-world data extraction platform

Objective

To validate and demonstrate the clinical discovery utility of a novel patient-mediated, medical record collection and data extraction platform developed to improve access and utilization of real-world clinical data.

Materials and Methods

Clinical variables were extracted from the medical records of 1011 consented patients with breast cancer. To validate the extracted data, case report forms completed using the structured data output of the platform were compared to manual chart review for 50 randomly-selected patients with metastatic breast cancer. To demonstrate the platform's clinical discovery utility, we identified 194 patients with early-stage clinical data who went on to develop distant metastases and utilized the platform-extracted data to assess associations between time to distant metastasis (TDM) and early-stage tumor histology, molecular type, and germline BRCA status.

Results

The platform-extracted data for the validation cohort had 97.6% precision (91.98%-100% by variable type) and 81.48% recall (58.15%-95.00% by variable type) compared to manual chart review. In our discovery cohort, the shortest TDM was significantly associated with metaplastic (739.0 days) and inflammatory histologies (1005.8 days), HR-/HER2- molecular types (1187.4 days), and positive BRCA status (1042.5 days) as compared to other histologies, molecular types, and negative BRCA status, respectively. Multivariable analyses did not produce statistically significant results.

Discussion

The precision and recall of platform-extracted clinical data are reported, although specificity could not be assessed. The data can generate clinically-relevant insights.

Conclusion

The structured real-world data produced by a novel patient-mediated, medical record-extraction platform are reliable and can power clinical discovery.

Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns

PURPOSE

Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM).

METHODS

Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method.

RESULTS

Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days.

CONCLUSION

We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.

Oncologists Must Consider Participant Data When Using Large-Scale Cancer Data Sets

Primer that helps clarify large-scale clinical data sets and participant demographics for oncologists.

Impact of Real-World Outpatient Cancer Rehabilitation Services on Health-Related Quality of Life of Cancer Survivors across 12 Diagnosis Types in the United States

Compared to adults without cancer, cancer survivors report poorer health-related quality of life (HRQOL), which is associated with negative treatment outcomes and increased healthcare use. Cancer-specialized physical and occupational therapy (PT/OT) could optimize HRQOL; however, the impact among survivors with non-breast malignancies is unknown. This retrospective (2020-2022), observational, study of medical record data of 12 cancer types, examined pre/post-HRQOL among cancer survivors who completed PT/OT. PROMIS® HRQOL measures: Global Health (physical [GPH] and mental [GMH]), Physical Function (PF), and Ability to Participate in Social Roles and Activities (SRA) were evaluated using linear mixed effect models by cancer type, then compared to the minimal important change (MIC, 2 points). Survivors were 65.44 ± 12.84 years old (range: 19-91), male (54%), with a median of 12 visits. Improvements in GPH were significant (p < 0.05) for all cancer types and all achieved MIC. Improvements in GMH were significant for 11/12 cancer types and 8/12 achieved MIC. Improvements in PF were significant for all cancer types and all achieved the MIC. Improvements in SRA were significant for all cancer types and all groups achieved the MIC. We observed statistically and clinically significant improvements in HRQOL domains for each of the 12 cancer types evaluated.

The individualized delineation of clinical target volume for primary nasopharyngeal carcinoma based on invasion risk of substructures: A prospective, real-world study with a large population

BACKGROUND AND PURPOSE

The delineation of clinical target volume (CTV) for primary nasopharyngeal carcinoma (NPC) is currently controversial and the international guideline still recommend a uniform border for CTV regardless of the tumor extent. We conducted this prospective, real-world study to evaluate the clinical outcomes of our individualized CTV delineation method based on distance plus substructures.

MATERIALS AND METHODS

We preliminarily investigated the local extension patterns of NPC on 354 newly diagnosed patients and defined the structures surrounding the nasopharynx as Level-1 to Level-4 substructures stratified by the risk of invasion. We then enrolled patients with newly diagnosed NPC without distant metastasis to investigate our individualized CTV delineation protocol. All patients received intensity modulated radiotherapy. CTV1 and CTV2 were prescribed doses of 60 Gy and 54 Gy in 30 ∼ 33 fractions. The primary endpoint was local recurrence-free survival (LRFS); secondary endpoints included regional control and survival, estimated using the Kaplan-Meier method. The local failure patterns were also analyzed.

RESULTS

From January 2008 to December 2012 and from January 2013 to September 2019, 356 and 648 patients were enrolled, named as training set and validation set, respectively. With a median follow-up of 104.6 (interquartile, 73.1-126.9) and 51.4 (39.5-78.5) months, 31 (8.7 %) and 38 (5.9 %) patients in training and validation sets experienced local recurrence, and the 5-year LRFS was 93.0 % and 93.2 %, respectively; 63 (17.7 %) and 39 (6 %) patients died in training and validation sets, and the 5-year overall survival (OS) was 88.5 % and 93.4 %, respectively. For the whole study cohort (N = 1004) with a median follow-up of 66.6 (41.5-98.0) months, the 5-year LRFS and OS was 93.2 % and 91.5 %. The grade 3 late toxicities included xerostomia, subcutaneous fibrosis, hearing impairment, trismus, visuality impairment and skin atrophy, with a total incidence of 1.5 %. Sixty-seven of 69 (97.1 %) local recurrence was in high-dose area.

CONCLUSION

Our individualized CTV delineation method can achieve favorable local tumor control and long-term survival outcomes with acceptable late toxicities.

The Landsteiner lung cancer research platform (LALUCA) : Objectives, methodology and implementation of a real-world clinical lung cancer registry

BACKGROUND

Lung cancer is a major health burden in Austria; however, limited real-world data exist on the diagnostic and treatment reality of lung cancer patients in Austria. The collection of high-quality data in a clinical setting is needed to gain insights into the real-world diagnostic and therapeutic management of lung cancer patients.

METHODS

The Karl Landsteiner Institute for Lung Research and Pulmonary Oncology implemented the Landsteiner lung cancer research platform (LALUCA), recruiting unselected lung cancer patients from two high volume centers in Vienna. This article describes the objectives, design, methodology of the registry and the process of implementation.

RESULTS

A multidisciplinary team of lung cancer specialists created a custom designed variable catalogue for the LALUCA platform consisting of 17 categories with 180 variables. Detailed information on clinical characteristics, diagnostic interventions, molecular pathology as well as curative and palliative treatment modalities are collected. During an implementation phase in 2020, the platform was optimized using the data of 50 patients. Since then a total of 1200 patients have been enrolled. Recruitment for the registry is ongoing with a recruitment rate of approximately 400 patients per year.

CONCLUSION

The LALUCA registry is a web-based platform for the collection of real-world clinical data of lung cancer patients. Combining a large number of patients with a focus on gathering comprehensive data on diagnosis and treatment, the LALUCA registry provides a tool for investigation, evaluation, and improvement of the clinical management, survival and quality of care of Austrian lung cancer patients.

The prevalence of anxiety and depression in cancer around the world: An overview of systematic reviews evaluating 128 meta-analyses

BACKGROUND

This overview of systematic reviews with meta-analysis summarized the prevalence of anxiety and depression in different types of tumors around the world.

METHODS

The quality of reviews was assessed. The degree of overlap between reviews was calculated. The regional prevalence of anxiety and depression was shown in the main text based on the World Health Organization regions.

RESULTS

Twenty-five reviews including 128 meta-analyses of interest were selected. The pooled prevalence of anxiety in breast tumors was in Africa (19 %), the Americas (38 %), Eastern Mediterranean (56 %), Europe (38 %), South-East Asia (42 %), and Western Pacific (26 %). The pooled prevalence of depression in breast tumors was in Africa (40 %), the Americas (23-25 %), Eastern Mediterranean (49-51 %), Europe (27-29 %), South-East Asia (23-33 %), Western Pacific (29 %). The pooled prevalence of depression in digestive tumors was in the Americas (4-44 %), Eastern Mediterranean (42 %), Europe (20-27 %), South-East Asia (66 %), and Western Pacific (24-40 %). The pooled national prevalence of anxiety and depression was mainly evaluated in China and Iran.

LIMITATIONS

Important methodological issues were identified. For example, no reviews judged the certainty of evidence.

CONCLUSIONS

The Eastern Mediterranean region showed the highest prevalence of anxiety and depression for breast tumors. The South-East Asian region showed the highest prevalence of depression for digestive tumors. In these regions, many countries are considered low-income and middle-income countries. Further research funding would help increase the investigation on this topic, which may allow the development of preventive strategies that reduce the prevalence of anxiety and depression.

Real-World Data Quality Framework for Oncology Time to Treatment Discontinuation Use Case: Implementation and Evaluation Study

BACKGROUND

The importance of real-world evidence is widely recognized in observational oncology studies. However, the lack of interoperable data quality standards in the fragmented health information technology landscape represents an important challenge. Therefore, adopting validated systematic methods for evaluating data quality is important for oncology outcomes research leveraging real-world data (RWD).

OBJECTIVE

This study aims to implement real-world time to treatment discontinuation (rwTTD) for a systemic anticancer therapy (SACT) as a new use case for the Use Case Specific Relevance and Quality Assessment, a framework linking data quality and relevance in fit-for-purpose RWD assessment.

METHODS

To define the rwTTD use case, we mapped the operational definition of rwTTD to RWD elements commonly available from oncology electronic health record-derived data sets. We identified 20 tasks to check the completeness and plausibility of data elements concerning SACT use, line of therapy (LOT), death date, and length of follow-up. Using descriptive statistics, we illustrated how to implement the Use Case Specific Relevance and Quality Assessment on 2 oncology databases (Data sets A and B) to estimate the rwTTD of an SACT drug (target SACT) for patients with advanced head and neck cancer diagnosed on or after January 1, 2015.

RESULTS

A total of 1200 (24.96%) of 4808 patients in Data set A and 237 (5.92%) of 4003 patients in Data set B received the target SACT, suggesting better relevance of the former in estimating the rwTTD of the target SACT. The 2 data sets differed with regard to the terminology used for SACT drugs, LOT format, and target SACT LOT distribution over time. Data set B appeared to have less complete SACT records, longer lags in incorporating the latest data, and incomplete mortality data, suggesting a lack of fitness for estimating rwTTD.

CONCLUSIONS

The fit-for-purpose data quality assessment demonstrated substantial variability in the quality of the 2 real-world data sets. The data quality specifications applied for rwTTD estimation can be expanded to support a broad spectrum of oncology use cases.

Use of Novel National Data Sets to Monitor Chemotherapy Use and Outcomes: A Retrospective Cohort Study of Non-Small-Cell Lung Cancer in Aotearoa New Zealand

PURPOSE

Te Aho o Te Kahu, the New Zealand Cancer Control Agency, is establishing a systemic anticancer therapy (SACT) database (Anti-Cancer Therapy-Nationally Organized Workstream [ACT-NOW]) which can be linked to other national health data collections. In this article, we explore the application of ACT-NOW data in the monitoring of uptake and outcomes after the public funding of pemetrexed in Aotearoa New Zealand.

METHODS

We used the ACT-NOW collection to identify patients with advanced nonsquamous non-small-cell lung cancer, who were treated with first-line platinum-based doublet chemotherapy over an 8-year period. Data were extracted for a period of 4 years before and 4 years after the national funding of pemetrexed (November 1, 2017). Treatments were classified as historical platinum doublet (cisplatin or carboplatin with gemcitabine, vinorelbine, paclitaxel, or docetaxel) or platinum pemetrexed doublet (cisplatin or carboplatin with pemetrexed). The primary outcome was the proportion of patients receiving each treatment type, before and after November 1, 2017. To prototype linkage to outcomes data, we evaluated hospitalization and 1-year overall survival (OS) rates by treatment.

RESULTS

A total of 331 patients were included from four cancer centers. All patients (116 of 116) who were treated with first-line platinum-based doublet chemotherapy between November 2013 and November 2017 received historical platinum doublet chemotherapy. After the introduction of pemetrexed, between November 2017 and November 2021, 94% (203 of 215) were treated with platinum pemetrexed doublet chemotherapy and 6% (12 of 215) with historical platinum doublet chemotherapy. Linkage to outcomes data for 1-year OS, hospitalization rates, and lengths of stay outcome data were achievable.

CONCLUSION

The ACT-NOW data set has the potential to facilitate evaluation of the impact of national-level SACT funding decisions on prescribing practice and specific patient outcomes. Our results support the use of these data to inform resource planning and quality improvement.

Evaluation of the effectiveness of a nationwide precision medicine program for patients with advanced non-small cell lung cancer in Germany: a historical cohort analysis

Background

The national Network Genomic Medicine (nNGM) Lung Cancer provides comprehensive and high-quality multiplex molecular diagnostics and standardized personalized treatment recommendation for patients with advanced non-small cell lung cancer (aNSCLC) in Germany. The primary aim of this study was to investigate the effectiveness of the nNGM precision medicine program in terms of overall survival (OS) using real-world data (RWD).

Methods

A historical nationwide cohort analysis of patients with aNSCLC and initial diagnosis between 04/2019 and 06/2020 was conducted to compare treatment and OS of patients with and without nNGM-participation. Patients participating within the nNGM (nNGM group) were selected based on a prospective nNGM database. The electronic health records (EHR) of the prospective nNGM database were case-specifically linked to claims data (AOK, German health insurance). The control group was selected from claims data of patients receiving usual care without nNGM-participation (non-nNGM group). The minimum follow-up period was six months.

Findings

Overall, n = 509 patients in the nNGM group and n = 7213 patients in the non-nNGM group met the inclusion criteria. Patients participating in the nNGM had a significantly improved OS compared to the non-nNGM group (median OS: 10.5 months vs. 8.7 months, p = 0.008, HR = 0.84, 95% CI: 0.74-0.95). The 1-year survival rates were 46.8% (nNGM) and 41.3% (non-nNGM). The use of approved tyrosine kinase inhibitors (TKI) in the first-line setting was significantly higher in the nNGM group than in the non-nNGM group (nNGM: 8.4% (43/509) vs. non-nNGM: 5.1% (366/7213), p = 0.001). Overall, patients receiving first-line TKI treatment had significantly higher 1-year OS rates than patients treated with PD-1/PD-L1 inhibitors and/or chemotherapy (67.2% vs. 40.2%, p < 0.001).

Interpretation

This is the first study to demonstrate a significant survival benefit and higher utilization of targeted therapies for aNSCLC patients participating within nNGM. Our data indicate that precision medicine programs can enhance collaborative personalized lung cancer care and promote the implementation of treatment innovations and the latest scientific knowledge into clinical routine care.

Funding

The study was funded by the AOK Federal Association Germany.

Diagnosis, treatment, and survival from kidney cancer: real-world National Health Service England data between 2013 and 2019

OBJECTIVES

To report the NHS Digital (NHSD) data for patients diagnosed with kidney cancer (KC) in England. We explore the incidence, route to diagnosis (RTD), treatment, and survival patterns from 2013 to 2019.

MATERIALS AND METHODS

Data was extracted from the Cancer Data NHSD portal for International Classification of Diseases, 10th edition coded KC; this included Cancer Registry data, Hospital Episode Statistics, and cancer waiting times data.

RESULTS

Registrations included 66 696 individuals with KC. Incidence of new KC diagnoses increased (8998 in 2013, to 10 232 in 2019), but the age-standardised rates were stable (18.7-19.4/100 000 population). Almost half of patients (30 340 [45.5%]) were aged 0-70 years and the cohort were most frequently diagnosed with Stage 1-2 KC (n = 26 297 [39.4%]). Most patients were diagnosed through non-urgent general practitioner referrals (n = 16 814 [30.4%]), followed by 2-week-wait (n = 15 472 [28.0%]) and emergency routes (n = 11 796 [21.3%]), with older patients (aged ≥70 years), Stage 4 KCs, and patients with non-specified renal cell carcinoma being significantly more likely to present through the emergency route (all P < 0.001). Invasive treatment (surgery or ablation), radiotherapy, or systemic anti-cancer therapy use varied with disease stage, patient factors, and treatment network (Cancer Alliance). Survival outcomes differed by Stage, histological subtype, and social deprivation class (P < 0.001). Age-standardised mortality rates did not change over the study duration, although immunotherapy usage is likely not captured in this study timeline.

CONCLUSION

The NHSD resource provides useful insight about the incidence, diagnostic pathways, treatment, and survival of patients with KC in England and a useful benchmark for the upcoming commissioned National Kidney Cancer Audit. The RTD data may be limited by incidental diagnoses, which could confound the high proportion of 'emergency' diagnoses. Importantly, survival outcomes remained relatively unchanged.

Treatment Patterns and Outcomes Among Patients With Advanced or Recurrent Endometrial Cancer Initiating First-Line Therapy in the United States

Background: Patients with advanced or recurrent endometrial cancer (EC) typically have limited treatment options and poor long-term survival outcomes following first-line therapy. Real-world treatment patterns and survival outcomes data are limited for patients in this setting. Objectives: The objective of this retrospective study was to describe real-world demographics, clinical characteristics, treatment patterns, and overall survival among patients in the United States with primary advanced or recurrent EC who initiated at least 1 line of therapy (LOT). Methods: Patients with a diagnosis of primary advanced or recurrent EC in a real-world database from January 1, 2013, to July 31, 2021, were included. The date for inclusion was the date of EC diagnosis documentation; patients were indexed for treatment patterns and outcomes at the start of the first LOT and at the start of each subsequent LOT they initiated. Data were stratified by subgroups of patients who had mismatch repair deficient (dMMR) or microsatellite instability-high (MSI-H) tumors. Results: A total of 1961 patients who received at least 1 LOT were included. Most patients in this cohort, and the dMMR/MSI-H subgroup, received a platinum combination as first-line treatment, with carboplatin-paclitaxel being the most common regimen. Only 53% of patients who received first-line treatment subsequently received second-line therapy. Of the patients who received at least 1 LOT, use of immunotherapy in the second-line setting was more common in the dMMR/MSI-H subgroup. Median overall survival ranged from 14.1 to 31.8 months across the 5 most frequently used first-line treatment regimens in the ≥1 LOT cohort and became shorter with each subsequent LOT. Discussion: The use of platinum-based chemotherapy for first-line treatment of advanced or recurrent EC predominates in the real-world setting, despite the poor long-term survival outcomes associated with most of these regimens. Conclusions: Patients with recurrent/advanced EC have a poor prognosis, highlighting the need for therapies with more durable benefits.

Real-World Treatment Patterns and Outcomes of First-Line Immunotherapy Among Patients With Advanced Nonsquamous NSCLC Harboring BRAF, MET, or HER2 Alterations

Introduction

Data on utilization and clinical outcomes of programmed cell death protein or programmed death-ligand 1 (PD-[L]1) inhibitors in NSCLC with uncommon oncogenic alterations is limited.

Methods

This retrospective study used a deidentified U.S. nationwide clinicogenomic database to select patients with advanced nonsquamous NSCLC without EGFR, ALK, or ROS1 alterations, diagnosed from January 1, 2016 to September 30, 2020, who initiated first-line therapy. Our objectives were to summarize characteristics and treatment patterns for patients with four little-studied genomic alterations or driver-negative NSCLC. We estimated Kaplan-Meier real-world time on treatment (rwTOT) and time to next treatment for patients receiving PD-(L)1 inhibitors. The data cutoff was September 30, 2021.

Results

Of the 3971 eligible patients, 84 (2%) had NSCLC with BRAF V600E mutation, 117 (3%) had MET exon 14 skipping mutation, 130 (3%) had MET amplification, 91 (2%) had ERBB2 activation mutation, and 691 patients (17%) had driver-negative NSCLC. Patient characteristics differed among cohorts as expected. The most common first-line regimen in each cohort was a PD-(L)1 inhibitor as monotherapy or in combination with chemotherapy. The median rwTOT with anti-PD-(L)1 monotherapy was 4.6 months in the driver-negative cohort and ranged from 2.9 months (ERBB2 mutation) to 7.6 months (BRAF V600E mutation). The median rwTOT with anti-PD-(L)1-chemotherapy combination was 5.2 months in the driver-negative cohort and 6 months in all but the BRAF V600E cohort (17.5 mo). The patterns of real-world time to next treatment results were similar.

Conclusions

Substantial use of anti-PD-(L)1 therapy and associated clinical outcomes are consistent with previous real-world findings and suggest no detriment from PD-(L)1 inhibitors for advanced nonsquamous NSCLC harboring one of these four genomic alterations relative to driver-negative NSCLC.

Unlocking the Power of Benchmarking: Real-World-Time Data Analysis for Enhanced Sarcoma Patient Outcomes

Benchmarking is crucial for healthcare providers to enhance quality and efficiency, notably for complex conditions like sarcomas. Multidisciplinary teams/sarcoma boards (MDT/SBs) are vital in sarcoma management, but differences in their processes can affect patient outcomes and treatment costs, despite adherence to international guidelines. To address this issue, this study aimed to compare two MDT/SBs and establish an interoperable digital platform, Sarconnector®, for real-time-world data assessment and automated analysis. The study included 983 patients, 46.0% of whom female, with a median age of 58 years, and 4.5% of patients presented with metastasis at diagnosis. Differences were observed in the number of first-time presentations, follow-up presentations, primary sarcomas, biopsies and chemotherapy indications between the two MDT/SB. The results highlight the importance of benchmarking and utilizing a harmonized data approach, such as the RWT approach provided by the Sarconnector®, to standardize and evaluate quality and cost metrics. By identifying areas of improvement and making data-driven decisions on the meta-level, healthcare providers can optimize resources and improve patient outcomes. In conclusion, benchmarking with the RWT harmonized data approach provided by the Sarconnector® can help healthcare providers improve the overall effectiveness of the healthcare system and achieve better outcomes for their patients in terms of both outcomes and costs.

A novel AIE fluorescence probe featuring with high quantum yield for high-fidelity lysosomal targeting and tracking

High-fidelity imaging and long-term visualization of lysosomes are pivotal factors in the functional assessment of lysosomes, which perform an instrumental role in the physiological activity of cells. However, commercial probes have great limitations in lysosome exploration resulting from the aggregation-caused quenching effect as well as photobleaching instability and small Stokes shift. Therefore, we constructed a novel probe named TTAM with triphenylamine as the matrix and morpholine ring as the targeting group. In contrast with commonly accessible Lyso-tracker Red, TTAM has the merits of aggregation-induced emission effect, extremely high quantum yields (51.57 % solid-state) as well as fluorescence intensity, significant photostability, and high resolution. These properties make it ideal for imaging and activity monitoring lysosomes, which provides a powerful condition for bio-imaging.

MilCanEpi: Increased Capability for Cancer Care Research in the Department of Defense

The Military Health System (MHS) of the US Department of Defense (DoD) provides comprehensive medical care to over nine million beneficiaries, including active-duty members, reservists, activated National Guard, military retirees, and their family members. The MHS generates an extensive database containing administrative claims and medical encounter data, while the DoD also maintains a cancer registry that collects information about the occurrence of cancer among its beneficiaries who receive care at military treatment facilities. Collating data from the two sources diminishes the limitations of using registry or medical claims data alone for cancer research and extends their usage. To facilitate cancer research using the unique military health resources, a computer interface linking the two databases has been developed, called Military Cancer Epidemiology, or MilCanEpi. The intent of this article is to provide an overview of the MilCanEpi data system, describing its components, structure, potential uses, and limitations.

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 ).

Evaluation of reporting quality of cohort studies using real-world data based on RECORD: systematic review

OBJECTIVE

Real-world data (RWD) and real-world evidence (RWE) have been paid more and more attention in recent years. We aimed to evaluate the reporting quality of cohort studies using real-world data (RWD) published between 2013 and 2021 and analyze the possible factors.

METHODS

We conducted a comprehensive search in Medline and Embase through the OVID interface for cohort studies published from 2013 to 2021 on April 29, 2022. Studies aimed at comparing the effectiveness or safety of exposure factors in the real-world setting were included. The evaluation was based on the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. Agreement for inclusion and evaluation was calculated using Cohen's kappa. Pearson chi-square test or Fisher's exact test and Mann-Whitney U test were used to analyze the possible factors, including the release of RECORD, journal IFs, and article citations. Bonferroni's correction was conducted for multiple comparisons. Interrupted time series analysis was performed to display the changes in report quality over time.

RESULTS

187 articles were finally included. The mean ± SD of the percentage of adequately reported items in the 187 articles was 44.7 ± 14.3 with a range of 11.1-87%. Of 23 items, the adequate reporting rate of 10 items reached 50%, and the reporting rate of some vital items was inadequate. After Bonferroni's correction, the reporting of only one item significantly improved after the release of RECORD and there was no significant improvement in the overall report quality. For interrupted time series analysis, there were no significant changes in the slope (p = 0.42) and level (p = 0.12) of adequate reporting rate. The journal IFs and citations were respectively related to 2 areas and the former significantly higher in high-reporting quality articles.

CONCLUSION

The endorsement of the RECORD cheklist was generally inadequate in cohort studies using RWD and has not improved in recent years. We encourage researchers to endorse relevant guidelines when utilizing RWD for research.

The potential of FCRL genes as targets for cancer treatment: insights from bioinformatics and immunology

Cancer is a prevalent and dangerous disease that requires a multifaceted approach to treatment. The FCRL family gene has been linked to immune function and tumor progression. Bioinformatics may help unravel their role in cancer treatment. We conducted a comprehensive analysis of the FCRL family genes in pan-cancer using publicly available databases and online tools. Specifically, we examined gene expression, prognostic significance, mutation profiles, drug resistance, as well as biological and immunomodulatory roles. Our data were sourced from The Cancer Genome Atlas, Genotype-Tissue Expression, cBioPortal, STRING, GSCALite, Cytoscape, and R software. The expression of FCRL genes varies significantly across different tumor types and normal tissues. While high expression of most FCRL genes is associated with a protective effect in many cancers, FCRLB appears to be a risk factor in several types of cancer. Alterations in FCRL family genes, particularly through amplification and mutation, are common in cancers. These genes are closely linked to classical cancer pathways such as apoptosis, epithelial-mesenchymal transition (EMT), estrogen receptor (ER) signaling, and DNA damage response. Enrichment analysis indicates that FCRL family genes are predominantly associated with immune cell activation and differentiation. Immunological assays demonstrate a strong positive correlation between FCRL family genes and tumor-infiltrating lymphocytes (TILs), immunostimulators, and immunoinhibitors. Furthermore, FCRL family genes can enhance the sensitivity of various anticancer drugs. The FCRL family genes are vital in cancer pathogenesis and progression. Targeting these genes in conjunction with immunotherapy could enhance cancer treatment efficacy. Further research is required to determine their potential as therapeutic targets.

Outcomes and a prognostic classifier in patients with microsatellite instability-high metastatic gastric cancer receiving PD-1 blockade

BACKGROUND

Subgroup analyses of randomized trials suggest the superiority of immune checkpoint inhibitor-based therapy over chemotherapy in patients with mismatch-repair deficient (dMMR) and/or microsatellite instability-high (MSI-high) advanced gastric or gastroesophageal junction adenocarcinoma. However, these subgroups are small and studies examining prognostic features within dMMR/MSI-high patients are lacking.

METHODS

We conducted an international cohort study at tertiary cancer centers and collected baseline clinicopathologic features of patients with dMMR/MSI-high metastatic or unresectable gastric cancer treated with anti-programmed cell death protein-1 (PD-1)-based therapies. The adjusted HRs of variables significantly associated with overall survival (OS) were used to develop a prognostic score.

RESULTS

One hundred and thirty patients were included. At a median follow-up of 25.1 months, the median progression-free survival (PFS) was 30.3 months (95% CI: 20.4 to NA) and 2-year PFS rate was 56% (95% CI: 48% to 66%). Median OS was of 62.5 months (95% CI: 28.4 to NA) and 2-year OS rate was 63% (95% CI: 55% to 73%). Among the 103 Response Evaluation Criteria in Solid Tumors-evaluable patients, objective response rate was 66% and disease control rate 87% across lines of therapy. In the multivariable models, Eastern Cooperative Oncology Group Performance Status of 1 or 2, non-resected primary tumor, presence of bone metastases and malignant ascites were independently associated with poorer PFS and OS. These four clinical variables were used to build a three-category (ie, good, intermediate, and poor risk) prognostic score. Compared with patients with good risk, patients with intermediate risk score had numerically inferior PFS and OS (2-year PFS rate: 54.3% versus 74.5%, HR 1.90, 95% CI: 0.99 to 3.66; 2-year OS rate: 66.8% versus 81.2%, HR 1.86, 95% CI: 0.87 to 3.98), whereas patients with poor risk score had significantly inferior PFS and OS (2-year PFS rate: 10.6%, HR 9.65, 95% CI: 4.67 to 19.92; 2-year OS rate: 13.3%, HR 11.93, 95% CI: 5.42 to 26.23).

CONCLUSIONS

Overall outcomes with anti-PD-1-based therapies are favorable in MSI-high gastroesophageal adenocarcinomas. However, within this overall favorable subgroup a more accurate prognostication using baseline clinical characteristics might identify patients at higher risk of rapid disease progression who may deserve intensified immunotherapy combination strategies.

Postprediction Inference for Clinical Characteristics Extracted With Machine Learning on Electronic Health Records

PURPOSE

Real-world data (RWD) derived from electronic health records (EHRs) are often used to understand population-level relationships between patient characteristics and cancer outcomes. Machine learning (ML) methods enable researchers to extract characteristics from unstructured clinical notes, and represent a more cost-effective and scalable approach than manual expert abstraction. These extracted data are then used in epidemiologic or statistical models as if they were abstracted observations. Analytical results derived from extracted data in this way may differ from those given by abstracted data, and the magnitude of this difference is not directly informed by standard ML performance metrics.

METHODS

In this paper, we define the task of postprediction inference, which is to recover similar estimation and inference from an ML-extracted variable that would be obtained from abstracting the variable. We consider fitting a Cox proportional hazards model that uses a binary ML-extracted variable as a covariate and evaluate four approaches for postprediction inference in this setting. The first two approaches only require the ML-predicted probability, while the latter two additionally require a labeled (human abstracted) validation data set.

RESULTS

Our results for both simulated data and EHR-derived RWD from a national cohort demonstrate that we can improve inference from ML-extracted variables by leveraging a limited amount of labeled data.

CONCLUSION

We describe and evaluate methods for fitting statistical models using ML-extracted variables subject to model error. We show that estimation and inference is generally valid when using extracted data from high-performing ML models. More complex methods that incorporate auxiliary labeled data provide further improvements.

Preliminary Attainability Assessment of Real-World Data for Answering Major Clinical Research Questions in Breast Cancer Brain Metastasis: Framework Development and Validation Study

BACKGROUND

In recent decades, real-world evidence (RWE) in oncology has rapidly gained traction for its potential to answer clinical questions that cannot be directly addressed by randomized clinical trials. Integrating real-world data (RWD) into clinical research promises to contribute to more sustainable research designs, including extension, augmentation, enrichment, and pragmatic designs. Nevertheless, clinical research using RWD is still limited because of concerns regarding the shortage of best practices for extracting, harmonizing, and analyzing RWD. In particular, pragmatic screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research with RWD have not yet been established.

OBJECTIVE

We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastasis (BCBM), which has an unmet medical need for data attainability screening at the preliminary step of observational studies that use RWD.

METHODS

The PAR framework was proposed to assess data attainability from a particular data source during the early research process. The PAR framework has four sequential stages, starting with clinical question clarification: (1) operational definition of variables, (2) data matching (structural/semantic), (3) data screening and extraction, and (4) data attainability diagramming. We identified 5 clinical questions to be used for PAR framework evaluation through interviews and validated them with a survey of breast cancer experts. We used the Samsung Medical Center Breast Cancer Registry, a hospital-based real-time registry implemented in March 2021, leveraging the institution's anonymized and deidentified clinical data warehouse platform. The number of breast cancer patients in the registry was 45,129; it covered the period from June 1995 to December 2021. The registry consists of 24 base data marts that represent disease-specific breast cancer characteristics and care pathways. The outcomes included screening results of the clinical questions via the PAR framework and a procedural diagram of data attainability for each research question.

RESULTS

Data attainability was tested for study feasibility according to the PAR framework with 5 clinical questions for BCBM. We obtained data sets that were sufficient to conduct studies with 4 of 5 clinical questions. The research questions stratified into 3 types when we developed data fields for clearly defined research variables. In the first, only 1 question could be answered using direct data variables. In the second, the other 3 questions required surrogate definitions that combined data variables. In the third, the question turned out to be not feasible for conducting further analysis.

CONCLUSIONS

The adoption of the PAR framework was associated with more efficient preliminary clinical research using RWD from BCBM. Furthermore, this framework helped accelerate RWE generation through clinical research by enhancing transparency and reproducibility and lowering the entry barrier for clinical researchers.

The Sarcoma-Specific Instrument to Longitudinally Assess Health-Related Outcomes of the Routine Care Cycle

Patient-based health related quality of life (HRQoL) measurements are associated with an improvement in quality of care and outcomes. For a complex disease such as sarcoma, there is no disease-specific questionnaire available which covers all clinically relevant dimensions. Herein, we report on the development of an electronically implemented, sarcoma-specific instrument to assess health-related outcomes, which encompasses a combination of generic questionnaires tailored to the respective disease and treatment status covering the entire longitudinal care cycle. An interoperable digital platform was designed to provide a node between patients and physicians and to integrate the sarcoma-specific HRQoL instrument with patient and physician-based quality indicators to allow longitudinal structured real-world-time data evidence analytics. This approach enables the prediction modeling of disease, and by attributing cost tags to quality indicators, treatment effectiveness for a given disease will be directly correlated with financial expenses, which may ultimately lead to a more sustainable healthcare system.

Worldwide Trends in Registering Real-World Studies at ClinicalTrials.gov: A Cross-Sectional Analysis

Objective

The purpose of this study was to characterize real-world studies (RWSs) registered at ClinicalTrials.gov to help investigators better conduct relevant research in clinical practice.

Methods

A retrospective analysis of 944 studies was performed on February 28, 2023.

Results

A total of 944 studies were included. The included studies involved a total of 48 countries. China was the leading country in terms of the total number of registered studies (37.9%, 358), followed by the United States (19.7%, 186). Regarding intervention type, 42.4% (400) of the studies involved drugs, and only 9.1% (86) of the studies involved devices. Only 8.5% (80) of the studies mentioned both the detailed study design type and data source in the "Brief Summary". A total of 49.4% (466) of studies had a sample size of 500 participants and above. Overall, 63% (595) of the studies were single-center studies. A total of 213 conditions were covered in the included studies. One-third of the studies (32.7%, 309) involved neoplasms (or tumors). China and the United States were very different regarding the study of different conditions.

Conclusion

Although the pandemic has provided new opportunities for RWSs, the rigor of scientific research still needs to be emphasized. Special attention needs to be given to the correct and comprehensive description of the study design in the Brief Summary of registered studies, thereby promoting communication and understanding. In addition, deficiencies in ClinicalTrials.gov registration data remain prominent.

The Utility of Oncology Information Systems for Prognostic Modelling in Head and Neck Cancer

Cancer centres rely on electronic information in oncology information systems (OIS) to guide patient care. We investigated the completeness and accuracy of routinely collected head and neck cancer (HNC) data sourced from an OIS for suitability in prognostic modelling and other research. Three hundred and fifty-three adults diagnosed from 2000 to 2017 with head and neck squamous cell carcinoma, treated with radiotherapy, were eligible. Thirteen clinically relevant variables in HNC prognosis were extracted from a single-centre OIS and compared to that compiled separately in a research dataset. These two datasets were compared for agreement using Cohen's kappa coefficient for categorical variables, and intraclass correlation coefficients for continuous variables. Research data was 96% complete compared to 84% for OIS data. Agreement was perfect for gender (κ = 1.000), high for age (κ = 0.993), site (κ = 0.992), T (κ = 0.851) and N (κ = 0.812) stage, radiotherapy dose (κ = 0.889), fractions (κ = 0.856), and duration (κ = 0.818), and chemotherapy treatment (κ = 0.871), substantial for overall stage (κ = 0.791) and vital status (κ = 0.689), moderate for grade (κ = 0.547), and poor for performance status (κ = 0.110). Thirty-one other variables were poorly captured and could not be statistically compared. Documentation of clinical information within the OIS for HNC patients is routine practice; however, OIS data was less correct and complete than data collected for research purposes. Substandard collection of routine data may hinder advancements in patient care. Improved data entry, integration with clinical activities and workflows, system usability, data dictionaries, and training are necessary for OIS data to generate robust research. Data mining from clinical documents may supplement structured data collection.

An overview of methodological flaws of real-world studies investigating drug safety in the post-marketing setting

INTRODUCTION

The evaluation of the post-marketing safety profile of drugs is a continuous monitoring process for approved and marketed medicines and it is crucial for detecting new adverse drug reactions. As such, real-world studies are essential to complement pre-marketing evidence with information concerning drug risk-benefit profile and use in wider patient populations and they have a great potential to support post-marketing drug safety evaluations.

AREAS COVERED

A detailed description of the main limitations of real-world data sources (i.e. claims databases, electronic healthcare records, drug/disease registers and spontaneous reporting system databases) and of the main methodological challenges of real-world studies in generating real-world evidence is provided.

EXPERT OPINION

Real-world evidence biases can be ascribed to both the methodological approach and the specific limitations of the different real-world data sources used to carry out the study. As such, it is crucial to characterize the quality of real-world data, by establishing guidelines and best practices for the assessment of data fitness for purpose. On the other hand, it is important that real-world studies are conducted using a rigorous methodology, aimed at minimizing the risk of bias.

Survival Outcomes of Metastatic Non-small Cell Lung Cancer Patients With Limited Access to Immunotherapy and Targeted Therapy in a Cancer Center of a Low- and Middle-Income Country

OBJECTIVE

To describe the survival outcomes of metastatic non-small cell lung cancer patients with limited access to immunotherapy and targeted therapy in a cancer reference center in Colombia.

METHODS

A retrospective analysis of metastatic non-small cell lung cancer patients treated between 2013 and 2018 was performed, majority diagnosed with adenocarcinoma. It was carried out in a public cancer reference center that provides care to patients of low and middle socioeconomic status. Overall survival and progression-free survival were evaluated by Kaplan-Meier analysis and log-rank test. A Cox regression model was performed for univariate and multivariate analysis.

RESULTS

209 patients were included with majority of adenocarcinoma (79.5%). First-line treatment was cytotoxic chemotherapy (50.2%), EGFR-targeted therapy (14.8%), chemoimmunotherapy (1.9%), and ALK-targeted therapy (1.4%). 31.6% received best supportive care. Median time of follow-up was 13 months, median overall survival was 11.2 months (95% CI, 7.9-14.4), 13 months for adenocarcinoma (95% CI, 8.1-17.9), and 2.5 months for squamous cell carcinoma (95% CI, 0.6-4.4) (P < .001). Median progression-free survival was 9.3 months (95% CI, 7.9-10.7) without differences according to the type of first-line therapy. Median time-to-treatment was 55 days and only 54% of patients with a tested actionable mutation in EGFR received an EGFR-targeted therapy as the first-line treatment. Multivariate analysis showed that squamous cell carcinoma histology and receiving best supportive care were independent factors for worse overall survival ((HR:1.8, 95% CI, 1.076-3.082, P=.026) and (HR:14.6, 95% CI, 8.921-24.049, P < .001), respectively). Meanwhile, squamous cell carcinoma histology was an independent factor for worse progression-free survival (HR:3.4, 95% CI, 1.540-7.464, P=.002).

CONCLUSIONS

Despite advances in precision medicine, during the study period, cytotoxic chemotherapy was the most used treatment in our patients. Furthermore, about a third of them received best supportive care. The use of targeted therapies has been restricted by access to molecular diagnosis and remained low until 2018. Access to immunotherapy should be prioritized.

Enhancing Administrative Claims Data: Feasibility, Validation and Application of Linking Medicare Claims Data and National Marrow Donor Program Search Data

PURPOSE

Administrative claims data provide real-world service utilization of acute myeloid leukemia (AML) treatment, but lacks insight into treatment delays or barriers. The National Marrow Donor Program (NMDP)/Be The Match Search (Search) data contains information on donor search, but lacks information on treatment received if allogeneic hematopoietic cell transplant (HCT) is not performed. We hypothesized that linking these two data sets would create a rich resource to define factors associated with receiving HCT that could not be evaluated with either data set alone.

METHODS

A subset of 2010-2016 Medicare administrative claims data was linked with Search data. A total of 5,351 patients with AML age 65-74 years (HCT = 607, no HCT = 4,744) were identified using Medicare. These patients were then linked to 93,800 records with a donor search between 2009 and 2016. Patient date of birth, sex, disease, ZIP code, transplant center/hospital, and diagnosis date were used for matching. Exploratory analysis was conducted to identify predictors associated with receiving HCT for patients with AML who received a search.

RESULTS

The data sets were successfully linked, showing high sensitivity and specificity. The final cohort included 5,085 patients with AML (HCT = 533, no HCT = 4,552). Of 97 patients who received HCT without a matched search, more than 85% received a related donor HCT. Of those not receiving HCT, 609 had a matched NMDP search and 3,943 did not have a matched NMDP search. Multivariate analysis showed time to search, age, diagnosis year, race/ethnicity, and neighborhood education status associated with receiving HCT.

CONCLUSION

Methods herein demonstrate the feasibility of linking Search and Medicare data. Similar methods may be applied to answer critical questions regarding barriers to HCT, thereby identifying areas to improve access to care.