Sir Peter MacCallum Department of Oncology - Research Publications
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ItemNo Preview AvailableRisk-Directed Ambulatory Thromboprophylaxis in Lung and Gastrointestinal Cancers The TARGET-TP Randomized Clinical Trial(American Medical Association, 2023-11)IMPORTANCE: Thromboprophylaxis for individuals receiving systemic anticancer therapies has proven to be effective. Potential to maximize benefits relies on improved risk-directed strategies, but existing risk models underperform in cohorts with lung and gastrointestinal cancers. OBJECTIVE: To assess clinical benefits and safety of biomarker-driven thromboprophylaxis and to externally validate a biomarker thrombosis risk assessment model for individuals with lung and gastrointestinal cancers. DESIGN, SETTING, AND PARTICIPANTS: This open-label, phase 3 randomized clinical trial (Targeted Thromboprophylaxis in Ambulatory Patients Receiving Anticancer Therapies [TARGET-TP]) conducted from June 2018 to July 2021 (with 6-month primary follow-up) included adults aged 18 years or older commencing systemic anticancer therapies for lung or gastrointestinal cancers at 1 metropolitan and 4 regional hospitals in Australia. Thromboembolism risk assessment based on fibrinogen and d-dimer levels stratified individuals into low-risk (observation) and high-risk (randomized) cohorts. INTERVENTIONS: High-risk patients were randomized 1:1 to receive enoxaparin, 40 mg, subcutaneously daily for 90 days (extending up to 180 days according to ongoing risk) or no thromboprophylaxis (control). MAIN OUTCOMES AND MEASURES: The primary outcome was objectively confirmed thromboembolism at 180 days. Key secondary outcomes included bleeding, survival, and risk model validation. RESULTS: Of 782 eligible adults, 328 (42%) were enrolled in the trial (median age, 65 years [range, 30-88 years]; 176 male [54%]). Of these participants, 201 (61%) had gastrointestinal cancer, 127 (39%) had lung cancer, and 132 (40%) had metastatic disease; 200 (61%) were high risk (100 in each group), and 128 (39%) were low risk. In the high-risk cohort, thromboembolism occurred in 8 individuals randomized to enoxaparin (8%) and 23 control individuals (23%) (hazard ratio [HR], 0.31; 95% CI, 0.15-0.70; P = .005; number needed to treat, 6.7). Thromboembolism occurred in 10 low-risk individuals (8%) (high-risk control vs low risk: HR, 3.33; 95% CI, 1.58-6.99; P = .002). Risk model sensitivity was 70%, and specificity was 61%. The rate of major bleeding was low, occurring in 1 participant randomized to enoxaparin (1%), 2 in the high-risk control group (2%), and 3 in the low-risk group (2%) (P = .88). Six-month mortality was 13% in the enoxaparin group vs 26% in the high-risk control group (HR, 0.48; 95% CI, 0.24-0.93; P = .03) and 7% in the low-risk group (vs high-risk control: HR, 4.71; 95% CI, 2.13-10.42; P < .001). CONCLUSIONS AND RELEVANCE: In this randomized clinical trial of individuals with lung and gastrointestinal cancers who were stratified by risk score according to thrombosis risk, risk-directed thromboprophylaxis reduced thromboembolism with a desirable number needed to treat, without safety concerns, and with reduced mortality. Individuals at low risk avoided unnecessary intervention. The findings suggest that biomarker-driven, risk-directed primary thromboprophylaxis is an appropriate approach in this population. TRIAL REGISTRATION: ANZCTR Identifier: ACTRN12618000811202.
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ItemImpact of COVID-19 on cancer service delivery: a follow-up international survey of oncology clinicians(ELSEVIER, 2020-10)BACKGROUND: The COVID-19 pandemic has had a vast impact on cancer service delivery around the world. Previously reported results from our international survey of oncology clinicians, conducted through March-April 2020, found that clinicians reported altering management in both the curative and palliative settings and not in proportion to the COVID-19 case burden in their region of practice. This follow-up survey, conducted from 27th September to 7th November 2020, aimed to explore how attitudes and practices evolved over the 2020 pandemic period. PARTICIPANTS AND METHODS: Participants were medical, radiation and surgical oncologist and trainees. Surveys were distributed electronically via ESMO and other collaborating professional societies. Participants were asked to compare their practice prior to the pandemic to both the period of March-April 2020, referred to as the 'early' period, and the current survey period, referred to as the 'later' period. RESULTS: One hundred and seventy-two oncology clinicians completed the survey. The majority of respondents were medical oncologists (n = 136, 79%) and many were from Europe (n = 82, 48%). In the 'early' period, 88% (n = 133) of clinicians reported altering their practice compared to 63% (n = 96) in the 'later' period. Compared to prior to the pandemic, clinicians reported fewer new patient presentations in the 'early' period and a trend towards more patients presenting with advanced disease in the 'later' period. CONCLUSIONS: Results indicate a swing back towards pre-COVID-19 practices despite an increase in the rate of cumulative COVID-19 cases across 2020. The impact of these changes on cancer associated morbidity and mortality remains to be measured over the months and years to come.
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ItemImpact of COVID-19 on cancer service delivery; results from an international survey of oncology clinicians(WILEY, 2020-11)OBJECTIVES: To report clinician-perceived changes to cancer service delivery in response to COVID-19. DESIGN: Multidisciplinary Australasian cancer clinician survey in collaboration with the European Society of Medical Oncology. SETTING: Between May and June 2020 clinicians from 70 countries were surveyed; majority from Europe (n=196; 39%) with 1846 COVID-19 cases per million people, Australia (AUS)/New Zealand (NZ) (n=188; 38%) with 267/236 per million and Asia (n=75; 15%) with 121 per million at time of survey distribution. PARTICIPANTS: Medical oncologists (n=372; 74%), radiation oncologists (n=91; 18%) and surgical oncologists (n=38; 8%). RESULTS: Eighty-nine per cent of clinicians reported altering clinical practices; more commonly among those with versus without patients diagnosed with COVID-19 (n=142; 93% vs n=225; 86%, p=0.03) but regardless of community transmission levels (p=0.26). More European clinicians (n=111; 66.1%) had treated patients diagnosed with COVID-19 compared with Asia (n=20; 27.8%) and AUS/NZ (n=8; 4.8%), p<0.001. Many clinicians (n=307; 71.4%) reported concerns that reduced access to standard treatments during the pandemic would negatively impact patient survival. The reported proportion of consultations using telehealth increased by 7.7-fold, with 25.1% (n=108) of clinicians concerned that patient survival would be worse due to this increase. Clinicians reviewed a median of 10 fewer outpatients/week (including non-face to face) compared with prior to the pandemic, translating to 5010 fewer specialist oncology visits per week among the surveyed group. Mental health was negatively impacted for 52.6% (n=190) of clinicians. CONCLUSION: Clinicians reported widespread changes to oncology services, in regions of both high and low COVID-19 case numbers. Clinician concerns of potential negative impacts on patient outcomes warrant objective assessment, with system and policy implications for healthcare delivery at large.
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ItemLung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer(NATURE PUBLISHING GROUP, 2017-08-22)INTRODUCTION: Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. METHODS: Prospective registry and study data were analysed using Cox proportional hazards regression to derive a prognostic model (hospital 1, n=695), which was subsequently tested (Harrell's c-statistic for discrimination and Cox-Snell residuals for calibration) in two independent validation cohorts (hospital 2, n=479 and hospital 3, n=284). RESULTS: The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score⩽9); 61, 61, and 42% (LCPI 2: score 10-13); 33, 32, and 14% (LCPI 3: score 14-16); 7, 16, and 5% (LCPI 4: score ⩾15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts. CONCLUSIONS: The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses.
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ItemDynamic Thromboembolic Risk Modelling to Target Appropriate Preventative Strategies for Patients with Non-Small Cell Lung Cancer(MDPI, 2019-01)Prevention of cancer-associated thromboembolism (TE) remains a significant clinical challenge and priority world-wide safety initiative. In this prospective non-small cell lung cancer (NSCLC) cohort, longitudinal TE risk profiling (clinical and biomarker) was undertaken to develop risk stratification models for targeted TE prevention. These were compared with published models from Khorana, CATS, PROTECHT, CONKO, and CATS/MICA. The NSCLC cohort of 129 patients, median follow-up 22.0 months (range 5.6-31.3), demonstrated a hypercoagulable profile in >75% patients and TE incidence of 19%. High TE risk patients were those receiving chemotherapy with baseline fibrinogen ≥ 4 g/L and d-dimer ≥ 0.5 mg/L; or baseline d-dimer ≥ 1.5 mg/L; or month 1 d-dimer ≥ 1.5 mg/L. The model predicted TE with 100% sensitivity and 34% specificity (c-index 0.67), with TE incidence 27% vs. 0% for high vs. low-risk. A comparison using the Khorana, PROTECHT, and CONKO methods were not discriminatory; TE incidence 17⁻25% vs. 14⁻19% for high vs. low-risk (c-index 0.51⁻0.59). Continuous d-dimer (CATS/MICA model) was also not predictive of TE. Independent of tumour stage, high TE risk was associated with cancer progression (HR 1.9, p = 0.01) and mortality (HR 2.2, p = 0.02). The model was tested for scalability in a prospective gastrointestinal cancer cohort with equipotency demonstrated; 80% sensitivity and 39% specificity. This proposed TE risk prediction model is simple, practical, potent and can be used in the clinic for real-time, decision-making for targeted thromboprophylaxis. Validation in a multicentre randomised interventional study is underway (ACTRN12618000811202).
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ItemEvaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients(OXFORD UNIV PRESS, 2020-07)OBJECTIVE: The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital. METHODS: A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I-III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility. RESULTS: The study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53-100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243-4132). Median time for the system to run a query and return results was 15.5 s (range 7.2-37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen's kappa 0.70-1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%. DISCUSSION AND CONCLUSION: The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment.