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Rob Stirling



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    MA 18 - Global Tobacco Control and Epidemiology II (ID 676)

    • Event: WCLC 2017
    • Type: Mini Oral
    • Track: Epidemiology/Primary Prevention/Tobacco Control and Cessation
    • Presentations: 1
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      MA 18.12 - Quality of Data Informing Epidemiological Studies in Patients with Lung Cancer  (ID 7550)

      15:45 - 17:30  |  Author(s): Rob Stirling

      • Abstract
      • Presentation
      • Slides

      Background:
      Epidemiological studies commonly use data from clinical (i.e. medical records) and administrative (i.e. claims data) datasets for the purposes of exploratory analyses, as well as clinical and quality reporting, benchmarking, risk adjustment, and machine learning. Validity is contingent on accurate and detailed reporting of data, demanding robust methodological validation.

      Method:
      Single centre retrospective comparative study assessing completeness and agreement (kappa-statistic (κ)) of data reporting for key prognostic variables across three independent data sources, among patients with lung cancer. The study population was formed by random selection of patients from an Australian single centre prospective study. Prospectively collected research study-data (SD) was extracted, and then compared to data extracted from individual patient medical records (MR) as well as International Classification of Diseases (ICD) coding from administrative data (AD).

      Result:
      The study population included 10% of patients from an Australian lung cancer cohort (n=111/1090), and represented the overall cohort in terms of patient demographics and disease characteristics. Prognostic data for stage, comorbidities, smoking history, performance status, and weight loss at diagnosis, was reported for >96% of patients in SD. Comparatively, AD did not report any prognostic data for 42% (47/111) of patients treated in ambulatory settings, and indeed when reported was grossly inaccurate. By way of examples, according to AD, 23% of patients had ≥1 comorbidity versus 68% by MR and 64% by SD; 38% had positive smoking history versus 78% by MR and 81% by SD; 2% had respiratory comorbidity versus 28% by MR and 37% by SD. Similar patterns were observed for other comorbid conditions. Complete TNM staging was captured in only 45% of MR at the time of first treatment, although with good concordance with SD (κ=0.9, 95%CI 0.7, 1.0). Equally when factors were documented in MR they were reasonably concordant with SD: smoking status (completeness 96.4%, κ=0.9, 95%CI 0.8, 1.0), performance status (completeness 82.0%, κ=0.5, 95%CI 0.4, 0.7) and weight loss (completeness 71.1%, κ=0.3, 95%CI 0.1, 0.5).

      Conclusion:
      Poor capture of factors (either omission or inaccuracy) limit the potential contribution of both MR and AD for use in clinical, epidemiological, and machine learning research – particularly when being utilised to derive diagnostic, prognostic and classification systems. Use of this data for purposes other than intended may misinform estimates of comorbidity disease burden and fail to appropriately adjust for competing mortality risks in models that inform outcomes reporting and ensuing policy decisions.

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    P1.05 - Early Stage NSCLC (ID 691)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Early Stage NSCLC
    • Presentations: 1
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      P1.05-022c - Screening for Psychosocial Distress in Lung Cancer: Defining the Unmet Gaps (ID 8412)

      09:30 - 16:00  |  Presenting Author(s): Rob Stirling

      • Abstract

      Background:
      Objective: The evaluation of supportive care needs in lung cancer patients may be enhanced by engaging systematic screening using a validated distress screening tool, the distress thermometer (DT). We aimed to identify the extent of use of the screening tool, levels of distress and psychosocial problems identified by the tool and to determine associations with distress and the impacts of distress screening on patient outcomes in an Australian university teaching hospital.

      Method:
      We recruited all new lung cancer diagnoses recruited via the Victorian Lung Cancer Registry at the Alfred Hospital, Melbourne, Australia, during the period 14 July 2011 to 24 September 2016. We evaluated the presence of documented supportive care screening using the distress thermometer and demographic, clinical, treatment and outcome measures.

      Result:
      Levels of screening were very low (15.2%) amongst this cohort and yet 49.2% respondents described high levels of distress (median DT 3.5; IQR 1-6). High levels of distress (DT≥4) were associated with higher levels of practical, family, emotional and physical problems. Patients reporting higher levels of distress experienced an accelerated rate of decline in physical component of quality of life and had increased risk of death.

      Conclusion:
      The identification of the supportive care needs for lung cancer patients may be augmented by the use of a systematic screening tool. This study identifies significant gap in supportive care screening, high levels of distress amongst screened subjects and poorer patient related outcomes for distressed patients. This study provides an important platform for institutional supportive care screening strategy planning.

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    P2.13 - Radiology/Staging/Screening (ID 714)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      P2.13-026c - Impact of Lung Cancer Multidisciplinary Meeting Presentation on Quality of Life and Survival: A Victorian Retrospective Cohort Study (ID 8633)

      09:30 - 16:00  |  Presenting Author(s): Rob Stirling

      • Abstract

      Background:
      The creation of an effective management plan for non-small cell lung cancer (NSCLC) requires clinical and functional evaluation, a series of diagnostic and staging investigations and an evaluation of suitability for treatment. This process requires diverse multidisciplinary input and modern clinical guidelines therefore recommend presentation of all new lung cancer diagnoses to a multidisciplinary meeting (MDM) to facilitate evaluation and the development of an informed multidisciplinary management plan

      Method:
      We sought to evaluate the characteristics of patients presented to the lung cancer MDM and to evaluate the impact of MDM presentation on (i) management related outcomes including timeliness, supportive care screening, receipt of treatment and clinical trial participation, and (ii) patient related outcomes including survival and quality of life (QoL) in a metropolitan university teaching hospital.

      Result:
      In this cohort of cancer patients we found that just 59.6% of all new cancer diagnoses were presented to the lung cancer multidisciplinary meeting for clinical assessment and treatment planning despite the recommendation that all patients with lung cancer receive treatment in the context of a multidisciplinary setting. The likelihood of presentation was doubled for those with early clinical stage IA and halved for those with stage IV. Measures of quality of life (vitality and role emotion domain scores from the SF12v2) improved for those presented to the MDM between 3 and 12 months following presentation compared to those not presented. Advanced clinical stage was a strong predictor of mortality for all patients. MDM presentation conferred a significant crude protective effect on mortality for all patients (HR 0.63, 0.49-0.81; p<0.001) which was diminished when adjusted for confounding factors (0.79, 0.56-1.10;p=0.16), although this benefit was sustained for those with clinical stage IIIA (adjusted HR 0.31 0.12—0.79; p=0.01). The referral source for MDM presented patients were approximately one third from respiratory medicine, one third from lung cancer specialities and one third from medical and surgical specialty units with mortality risk increased for those referred by general medicine and surgical specialties.

      Conclusion:
      We found significant disparities in the utilisation of lung multidisciplinary meeting presentation which was associated with significant differences in uptake of active cancer therapy and ultimately survival. This study identifies significant benefit to those being presented to a lung cancer MDM and provides evidence to support multidisciplinary evaluation.

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    P3.13 - Radiology/Staging/Screening (ID 729)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      P3.13-003 - The Lung Cancer Prognostic Index – a Risk Score to Predict Overall Survival after the Diagnosis of Non-Small Cell Lung Cancer (ID 7551)

      09:30 - 16:00  |  Author(s): Rob Stirling

      • Abstract
      • Slides

      Background:
      Outcomes in Non-Small Cell Lung Cancer (NSCLC) are poor but heterogeneous, even within TNM stage groups. To improve prognostic precision we aimed to develop and validate a simple model for the prediction of overall survival (OS) using patient and disease variables.

      Method:
      The study population included 1458 patients from three independent cohorts. Associations between baseline variables and OS were estimated in a derivation cohort from a prospective single-centre study (n=695) using Cox proportional hazards regression. Points were allocated to variables based on the strength of association to create the Lung Cancer Prognostic Index (LCPI). Model performance was assessed (by a c-statistic for discrimination and Cox-Snell residuals for calibration) in two independent validation cohorts (n=479 and n=284).

      Result:
      Three disease-related and six patient-related variables were found to predict OS: stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex and age. Patients were classified according to predicted LCPI score. Two-year OS 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). Predictive performance (Harrell’s c-statistics) were 0·74 for the derivation cohort, 0·72 and 0·71 for the two validation cohorts.

      Conclusion:
      The LCPI contributes additional prognostic information which, in conjunction with other validated tools and evidence based management guidelines, may be applied to counsel patients, guide clinical trial eligibility, or standardise mortality risk for epidemiological analyses.

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