Lung cancer treatment patterns and factors relating to systemic therapy use in Australia

Socioeconomic differences in health-related quality of life among cancer survivors and comparison with a cancer-free population: a PROFILES study

PURPOSE

This study investigates the association between socioeconomic position (SEP) and health-related quality of life (HRQoL) in a cross-sectional cohort among cancer survivors and compares with cancer-free people.

METHODS

Survivors of colorectal, hematological, gynecological, prostate, thyroid cancer, and melanoma diagnosed 2000-2014 were identified in the PROFILES registry, and an age- and sex-matched cancer-free population were identified in the CentER panel. HRQoL, education, and comorbidity were self-reported. Street-level income and clinical factors were obtained from Statistics Netherlands and the Netherlands Cancer Registry. Multivariable logistic regression was used to examine associations of SEP (measured by education and income) and impaired HRQoL among cancer survivors and the cancer-free population, adjusting for age, sex, and time since diagnosis.

RESULTS

We included 6693 cancer survivors and 565 cancer-free people. Cancer survivors with low versus medium SEP more frequently reported impaired HRQoL (odds ratio (OR) range for all HRQoL outcomes, 1.06-1.78 for short education and 0.94-1.56 for low income). Survivors with high compared to medium SEP reported impaired HRQoL less frequently (OR range for all HRQoL outcomes, 0.46-0.81 for short education and 0.60-0.84 for low income). The association between SEP and HRQoL was similar in the matched cancer-free population.

CONCLUSION

Low SEP was associated with impaired HRQoL in both cancer survivors and cancer-free people.

IMPLICATIONS FOR CANCER SURVIVORS

Targeted care is warranted for cancer survivors with impaired HRQoL, especially among those with low SEP.

Development and Validation of txSim: A Model of Advanced Lung Cancer Treatment in Australia

BACKGROUND AND OBJECTIVE

Since 2016, new therapies have transformed the standard of care for lung cancer, creating a need for up-to-date evidence for health economic modelling. We developed a discrete event simulation of advanced lung cancer treatment to provide estimates of survival outcomes and healthcare costs in the Australian setting that can be updated as new therapies are introduced.

METHODS

Treatment for advanced lung cancer was modelled under a clinician-specified treatment algorithm for Australia in 2022. Prevalence of lung cancer subpopulations was extracted from cBioPortal and the Sax Institute's 45 and Up Study, a large prospective cohort linked to cancer registrations. All costs were from the health system perspective for the year 2020. Pharmaceutical and molecular diagnostic costs were obtained from public reimbursement fees, while other healthcare costs were obtained from health system costs in the 45 and Up Study. Treatment efficacy was obtained from clinical trials and observational study data. Costs and survival were modelled over a 10-year horizon. Uncertainty intervals were generated with probabilistic sensitivity analyses. Overall survival predictions were validated against real-world studies.

RESULTS

Under the 2022 treatment algorithm, estimated mean survival and costs for advanced lung cancer 10 years post-diagnosis were 16.4 months (95% uncertainty interval [UI]: 14.7-18.1) and AU$116,069 (95% UI: $107,378-$124,933). Survival and costs were higher assuming optimal treatment utilisation rates (20.5 months, 95% UI: 19.1-22.5; $154,299, 95% UI: $146,499-$161,591). The model performed well in validation, with good agreement between predicted and observed survival in real-world studies.

CONCLUSIONS

Survival improvements for advanced lung cancer have been accompanied by growing treatment costs. The estimates reported here can be used for budget planning and economic evaluations of interventions across the spectrum of cancer control.

Development of an Australia and New Zealand Lung Cancer Clinical Quality Registry: a protocol paper

INTRODUCTION

Lung cancer is the leading cause of cancer mortality, comprising the largest national cancer disease burden in Australia and New Zealand. Regional reports identify substantial evidence-practice gaps, unwarranted variation from best practice, and variation in processes and outcomes of care between treating centres. The Australia and New Zealand Lung Cancer Registry (ANZLCR) will be developed as a Clinical Quality Registry to monitor the safety, quality and effectiveness of lung cancer care in Australia and New Zealand.

METHODS AND ANALYSIS

Patient participants will include all adults >18 years of age with a new diagnosis of non-small-cell lung cancer (NSCLC), SCLC, thymoma or mesothelioma. The ANZLCR will register confirmed diagnoses using opt-out consent. Data will address key patient, disease, management processes and outcomes reported as clinical quality indicators. Electronic data collection facilitated by local data collectors and local, state and federal data linkage will enhance completeness and accuracy. Data will be stored and maintained in a secure web-based data platform overseen by registry management. Central governance with binational representation from consumers, patients and carers, governance, administration, health department, health policy bodies, university research and healthcare workers will provide project oversight.

ETHICS AND DISSEMINATION

The ANZLCR has received national ethics approval under the National Mutual Acceptance scheme. Data will be routinely reported to participating sites describing performance against measures of agreed best practice and nationally to stakeholders including federal, state and territory departments of health. Local, regional and (bi)national benchmarks, augmented with online dashboard indicator reporting will enable local targeting of quality improvement efforts.

Carboplatin dose calculations for patients with lung cancer: significant dose differences found depending on dosing equation choice

Background

Carboplatin is the backbone cytotoxic agent for many chemotherapy regimens for lung cancer. Dosing of carboplatin is complicated due to its relationship to renal function and narrow therapeutic index. Overestimation of renal function may lead to supratherapeutic dosing and toxicity, while underestimation may lead to underdosing and therapeutic failure. Although the Modification of Diet in Renal Disease (MDRD) and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations have higher accuracy in estimating glomerular filtration rate (eGFR), the Cockcroft Gault (CG) formula has been historically used for carboplatin dosing internationally.

Methods

We compared these formulae to identify patient profiles that were associated with significant carboplatin dose variation by retrospectively analysing the carboplatin dosing of 96 patients with lung cancer. Carboplatin doses were calculated using eGFR generated by MDRD, CKD-EPI ²⁰⁰⁹ and CKD-EPI ²⁰²¹ equations. These three hypothetical doses were compared to actual CG-based doses prescribed.

Results

MDRD and CKD-EPI equations resulted in comparable carboplatin doses; however, CG doses diverged markedly with up to 17% of the patients receiving a carboplatin dose that was at least 20% higher than a non-CG formula would have predicted, and 20% received a dose that was at least 20% lower than a non-CG formula would have predicted. Our data suggest CG use overestimates kidney function in patients with a higher bodyweight and body surface area (BSA) while underestimating it in patients with a lower bodyweight and BSA. Importantly, we demonstrate potential real-world benefit as CKD-EPI predicted lower doses for patients whose (CG-derived) carboplatin dose was later reduced following clinical assessment prior to infusion.

Conclusions

We have therefore confirmed significant differences in carboplatin dosing depending on the equation used in our modern patient population and suggest that use of CKD-EPI provides the most clinically appropriate carboplatin dosing and should be implemented as the new standard of care internationally.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09885-7.