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T. Zhang



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    MINI 29 - Meta Analyses and Trial Conduct (ID 156)

    • Event: WCLC 2015
    • Type: Mini Oral
    • Track: Treatment of Advanced Diseases - NSCLC
    • Presentations: 1
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      MINI29.06 - Are Clinical Trial Eligibility Criteria an Accurate Reflection of a Real World Population of Advanced Lung Cancer Patients? (ID 1398)

      18:30 - 20:00  |  Author(s): T. Zhang

      • Abstract
      • Presentation
      • Slides

      Background:
      Modern systemic treatment options for advanced NSCLC have largely been established from clinical trials (CTs). It is estimated that less than 10% of cancer patients enter a CT, but this subgroup drives oncology practice and impacts treatment decisions for other cancer patients. The advantage of CTs comes from solid internal validity and stringent methodology. Nonetheless, the generalizability of CTs could be questioned due to the high selectivity of eligibility criteria. We investigated clinical trial eligibility in an unselected NSCLC population

      Methods:
      With ethics approval, a retrospective chart review was performed of patients with de novo advanced NSCLC assessed by medical oncologists at a large academic cancer centre, serving a mixed urban and rural population, between September 2009 and September 2012. Data collected included patient demographics, stage, performance status, histology, treatment details and outcome. Two sets (A and B) of arbitrary eligibility criteria were created using common criteria from phase 3 CTs. These criteria were applied to this cohort to identify the proportions of patient who would hypothetically qualify for CT enrollment. Criteria A required: ECOG 0 or 1, absence of brain metastases, Creatinine < 120 and the absence of second malignancy. Criteria B, allowing broader inclusion, only required ECOG 0-2 and Creatinine < 120. We investigated survival among eligible/ineligible and treated/untreated patients.

      Results:
      528 patients were included: 55% male; 50% ECOG 0-1; 58% adenocarcinoma, 22% squamous cell; 7% stage IIIB and 93% stage IV. Using the strict CT criteria (A), only 144 (27%) patients were considered eligible. Of those, 79% actually received systemic therapy. From 384 patients who would have been ineligible for the CT, 178 patients (46%) still received systemic therapy. There was a trend to longer median overall survival (mOS) in the eligible treated compared to eligible non-treated patients (11.6 vs 8.1 months p=0.12). mOS was significantly longer in the non-treated eligible cohort compared to the non-treated ineligible cohort (8.1 vs 3.8 months p=0.003). The eligible treated and non-eligible treated had similar mOS ( 11.6 vs 10.2 months, p= 0.10). When less strict eligibility criteria (B) were applied, 343 patients (65%) would have been eligible, of whom 240 patients (70%) actually received systemic therapy. From the remaining ineligible 185 patients, only 51 (28%) received treatment. The mOS was similar in the treated patient whether eligible or ineligible (10.9 vs 10.1 months, p=0.57). As seen in criteria A, significantly longer mOS was observed in the eligible untreated compared to the ineligible untreated ( 4.9 vs 3.5 months p<0.001).

      Conclusion:
      While clinical trial criteria restrict study entry to the fittest patients, these results suggest that they do not reflect the broader patient population, as many ‘ineligible’ patients received therapy. Extrapolation of treatment paradigms to non-trial eligible populations is common, and may be reasonable based on these results. We observed similar survival among treated patients, whether trial eligible or not. This suggests that clinical judgement is more important than trial eligibility. In order to broaden trial participation, we could hypothesize that trial eligibility criteria could be relaxed.

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    P1.01 - Poster Session/ Treatment of Advanced Diseases – NSCLC (ID 206)

    • Event: WCLC 2015
    • Type: Poster
    • Track: Treatment of Advanced Diseases - NSCLC
    • Presentations: 1
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      P1.01-034 - Is There A "Physician Effect" in Medical Oncology? (ID 1408)

      09:30 - 17:00  |  Author(s): T. Zhang

      • Abstract
      • Slides

      Background:
      Non-small cell lung cancer (NSCLC) is the commonest cause of cancer death globally, with a 5-year survival of 16%. Known prognostic factors include stage, performance status (PS) and gender, but does the choice of physician affect patient outcome? We assessed practice variations of four medical oncologists treating advanced NSCLC, investigating this impact on overall survival (OS).

      Methods:
      Following ethics approval, a retrospective analysis was undertaken of all newly diagnosed stage 4 NSCLC patients seen in out-patient consultation at our institution between 2009 and 2012. All physicians accepted unselected lung cancer referrals and all patients are included. Baseline demographics, systemic therapy received, reasons for not receiving therapy, and OS data were collected. Cox regression analyses (univariate and multivariate) were employed to assess determinants of OS. The physicians were blinded to the results.

      Results:
      Overall 528 patients were included. Baseline characteristics are shown in table 1. A significant variation was noted in the proportion receiving any systemic chemotherapy (p≤0.01) [D(60%), L(65%), R(43%), M(52%)] (Figure 1A). However OS was not statistically significantly different among all patients (p=0.47), among treated patients (p=0.18) or among untreated patients (p=0.22)(Table and Figure 1B). In multivariate analysis, factors associated with survival were PS (p<0.01), weight loss (<5%, ≥5%)(p<0.01), WBC (<11, ≥11)(p=0.0588) and platelets (<400, ≥400)(p=0.0374).Figure 1

      Demographic Overall (n=528) Physician R (n=137) Physician M (n=118) Physician D (n=115) Physician L (n=158) p-value
      Median Age 68 70 68 67 67 0.23
      Gender (male) 55% 58% 58% 49% 56% 0.42
      PS (0-1) 50% 47% 48% 50% 55% 0.01
      Hg (<100) 6% 11% 3% 4% 4% 0.01
      LDH (<250) 28% 21% 30% 27% 33% 0.09
      Platelets (<400) 71% 64% 75% 78% 70% 0.12
      Weight loss (>5%) 48% 49% 48% 46% 49% 0.87
      WBC (<11) 62% 56% 68% 68% 60% 0.11
      Received ≥ 1 line systemic therapy 55% 43% 52% 60% 65% <0.01




      Conclusion:
      While practice size and proportion of patient treated did vary between oncologists, these did not translate into significantly different survival. There were statistically significant differences in the distribution of baseline characteristics between the 4 oncologists and this could cause the differences in proportion of patients treated. We hypothesize that as long as the oncologists are well trained and display good practice, survival is not dependant on the individual. This research does not measure other valuable characteristics or outcomes such as rapport, compassion, and quality of life, which may differ between physicians.

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    P2.01 - Poster Session/ Treatment of Advanced Diseases – NSCLC (ID 207)

    • Event: WCLC 2015
    • Type: Poster
    • Track: Treatment of Advanced Diseases - NSCLC
    • Presentations: 1
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      P2.01-033 - Patients with Advanced NSCLC Requiring Inpatient Oncology Consultation (ID 504)

      09:30 - 17:00  |  Author(s): T. Zhang

      • Abstract
      • Slides

      Background:
      Most newly diagnosed advanced lung cancer patients have an initial medical oncology consult as an outpatient. However, occasionally the initial referral occurs as an inpatient. We explored the characteristics of advanced NSCLC patients whose first medical oncology consultation occurred while hospitalized.

      Methods:
      With ethics approval, we performed a retrospective analysis of all advanced NSCLC patients at our institution whose initial consult occurred while hospitalized, from 2007 to 2012. Demographics, treatment and survival data were collected. This was an exploratory analysis. Multivariate survival analysis was performed using Cox regression models.

      Results:
      In total, 223 patients were included (baseline characteristics in Table 1). Overall, only 24% received chemotherapy while 72% received some palliative radiotherapy. Median time from diagnosis to chemotherapy was 43 days. Reasons for not receiving chemotherapy included poor performance status (PS) (72%), patient choice (9%), clinical deterioration (6%) or co-morbidities (4%). Factors associated with receiving chemotherapy were good PS (OR 11.11 [95% CI 5.56-25.00], p<0.001), no constitutional symptoms (OR 2.86 [95% CI 1.41-5.88], p=0.004), no leukocytosis (OR 2.38 [95% CI 1.23-4.55], p=0.01), fewer co-morbidities (OR 1.54 [95% CI 1.27-1.89], p<0.001) and younger age (OR 1.09 [95% CI 1.05-1.12], p<0.001). Median OS was shorter in those not receiving chemotherapy (1.7 v 7.1 months, HR 2.76 [95% CI 1.72-4.41], p-value<0.001). Figure 1 shows Kaplan-Meier survival curves. In multivariate analysis, in addition to not receiving chemotherapy, factors associated with shorter OS were PS 3-4, (HR 1.55 [CI 1.03-2.33, p=0.04]), leukocytosis (HR 2.23 [95% CI 1.51-3.28], p-value <0.001) and thrombocytosis (HR 1.52 [1.06-2.18], p=0.02).

      Conclusion:
      Patients whose first consultation with medical oncologists occurs while hospitalized are an inherently sick population and only a minority receive chemotherapy. The lung cancer community must advocate for earlier diagnosis and referral, so more patients have access to treatment options before a terminal functional decline.

      Table 1: Baseline Characteristics
      Demographic (N=223) %
      Age in years, median (range) 65 (23-89)
      Gender
      Male 48
      Female 52
      Charlson Comorbidity Index total score, median (range) 10 (6-18)
      Performance status
      0-2 24
      3-4 69
      Unknown 7
      Smoking status
      Current 49
      Ex 34
      Never 9
      Unknown 8
      Stage at diagnosis
      IIIB 10
      IV 89
      Unknown 1
      NSCLC subtype
      Adenocarcinoma 45
      Squamous cell 23
      Large cell 8
      Other 23
      Dominant presenting symptom
      Dyspnea 34
      Pain 23
      Constitutional symptoms 9
      Pneumonia 7
      Cough 5
      Hemoptysis 3
      Other 18
      Weight loss
      <5% 22
      >5% 52
      Unknown 25
      Figure 1



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