Determining clinically important differences in health-related quality of life in older patients with cancer undergoing chemotherapy or surgery.

PURPOSE: Using the EORTC Global Health Status (GHS) scale, we aimed to determine minimal clinically important differences (MCID) in health-related quality of life (HRQOL) changes for older cancer patients with a geriatric risk profile, as defined by the geriatric 8 (G8) health screening tool, undergoing treatment. Simultaneously, we assessed baseline patient characteristics prognostic for HRQOL changes. METHODS: Our analysis included 1424 (G8 ≤ 14) older patients with cancer scheduled to receive chemotherapy (n = 683) or surgery (n = 741). Anchor-based methods, linking the GHS score to clinical indicators, were used to determine MCID between baseline and follow-up at 3 months. A threshold of 0.2 standard deviation (SD) was used to exclude MCID estimates too small for interpretation. Logistic regressions analysed baseline patient characteristics prognostic for HRQOL changes. RESULTS: The 15-item Geriatric Depression Scale (GDS15), Visual Analogue Scale (VAS) for Fatigue and ECOG Performance Status (PS) were selected as clinical anchors. In the surgery group, MCID estimates for improvement and deterioration were ECOG PS (5*, 11*), GDS15 (5*, 2) and VAS Fatigue (3, 9*). In the chemotherapy group, MCID estimates for improvement and deterioration were ECOG PS (8*, 7*), GDS15 (5, 4) and VAS Fatigue (5, 5*). Estimates with * were > 0.2 SD threshold. Patients experiencing pain or malnutrition (surgery group) or fatigue (chemotherapy group) at baseline showed a significantly stable or improved HRQOL (p < 0.05) after their treatment. CONCLUSION: The reported MCID for improvement and deterioration depended on the anchor used and treatment received. The estimates can be used to evaluate significant changes in HRQOL and to determine sample sizes in clinical trials.

Does change in health-related quality of life score predict survival? Analysis of EORTC 08975 lung cancer trial.

BACKGROUND: Little is known about whether changes in health-related quality of life (HRQoL) scores from baseline during treatment also predict survival, which we aim to investigate in this study. METHODS: We analysed data from 391 advanced non-small-cell lung cancer (NSCLC) patients enrolled in the EORTC 08975 study, which compared palliative chemotherapy regimens. HRQoL was assessed at baseline and after each chemotherapy cycle using the EORTC QLQ-C30 and QLQ-LC13. The prognostic significance of HRQoL scores at baseline and their changes over time was assessed with Cox regression, after adjusting for clinical and socio-demographic variables. RESULTS: After controlling for covariates, every 10-point increase in baseline pain and dysphagia was associated with 11% and 12% increased risk of death with hazard ratios (HRs) of 1.11 and 1.12, respectively. Every 10-point improvement of physical function at baseline (HR=0.93) was associated with 7% lower risk of death. Every 10-point increase in pain (HR=1.08) was associated with 8% increased risk of death at cycle 1. Every 10-point increase in social function (HR=0.91) at cycle 2 was associated with 9% lower risk of death. CONCLUSIONS: Our findings suggest that changes in HRQoL scores from baseline during treatment, as measured on subscales of the EORTC QLQ-C30 and QLQ-LC13, are significant prognostic factors for survival.

Effect of completion-time windows in the analysis of health-related quality of life outcomes in cancer patients.

BACKGROUND: We examined if cancer patients' health-related quality of life (HRQoL) scores on the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 are affected by the specific time point, before or during treatment, at which the questionnaire is completed, and whether this could bias the overall treatment comparison analyses. PATIENTS AND METHODS: A 'completion-time window' variable was created on three closed EORTC randomised control trials in lung (non-small cell lung cancer, NSCLC) and colorectal cancer (CRC) to indicate when the QLQ-30 was completed relative to chemotherapy cycle dates, defined as 'before', 'on' and 'after'. HRQoL mean scores were calculated using a linear mixed model. RESULTS: Statistically significant differences (P<0.05) were observed on 6 and 5 scales for 'on' and 'after' comparisons in the NSCLC and two-group CRC trial, respectively. As for the three-group CRC trial, several statistical differences were observed in the 'before' to 'on' and the 'on' to 'after' comparisons. For all three trials, including the 'completion-time window' variable in the model resulted in a better fit, but no substantial changes in the treatment effects were noted. CONCLUSIONS: We showed that considering the exact timing of completion within specified windows resulted in statistical and potentially clinically significant differences, but it did not alter the conclusions of treatment comparison in these studies.

Minimal clinically meaningful differences for the EORTC QLQ-C30 and EORTC QLQ-BN20 scales in brain cancer patients.

BACKGROUND: We aimed to determine the smallest changes in health-related quality of life (HRQoL) scores in the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire core 30 and the Brain Cancer Module (QLQ-BN20), which could be considered as clinically meaningful in brain cancer patients. MATERIALS AND METHODS: World Health Organisation performance status (PS) and mini-mental state examination (MMSE) were used as clinical anchors appropriate to related subscales to determine the minimal clinically important differences (MCIDs) in HRQoL change scores (range 0-100) in the QLQ-C30 and QLQ-BN20. A threshold of 0.2 standard deviation (SD) (small effect) was used to exclude anchor-based MCID estimates considered too small to inform interpretation. RESULTS: Based on PS, our findings support the following integer estimates of the MCID for improvement and deterioration, respectively: physical (6, 9), role (14, 12), and cognitive functioning (8, 8); global health status (7, 4*), fatigue (12, 9), and motor dysfunction (4*, 5). Anchoring with MMSE, cognitive functioning MCID estimates for improvement and deterioration were (11, 2*) and for communication deficit were (9, 7). Estimates with asterisks were <0.2 SD and were excluded from our MCID range of 5-14. CONCLUSION: These estimates can help clinicians evaluate changes in HRQoL over time, assess the value of a health care intervention and can be useful in determining sample sizes in designing future clinical trials.

Childhood tuberculosis in the European Union/European Economic Area, 2000 to 2009.

Childhood tuberculosis (TB) has been neglected for decades as a key component of TB control. However, ensuring proper monitoring of childhood TB has recently been given renewed emphasis. A descriptive analysis of surveillance data was performed to assess burden and trends of paediatric TB in the European Union/European Economic Area (EU/EEA) between 2000 and 2009. From 2000 to 2009, 39,695 notified paediatric (defined as 0­14 years of age) TB cases were reported by the 27 EU countries plus Norway, Iceland and Liechtenstein. These paediatric cases accounted for 4.3% of all notified cases. However, across the EU/EEA Member States, paediatric case notification rates ranged from 29.6 per 100,000 to 0.3 per 100,000 for the latest reporting year, 2009. Overall,though, these rates dropped from 5.5 per 100,000 in 2000 to 4.2 per 100,000 in 2009. The EU/EEA average annual percent changes (AAPC) in paediatric notification rates decreased between 2000 and 2004 by 1.3%and between 2005 and 2009 by 2.4%, with an overall decrease between 2000 and 2009 of 2.8%. Of all paediatric cases reported from 2000 to 2009, only 16.9%were culture-confirmed, amongst which the overall treatment success was 80.5% for all culture-confirmed pulmonary paediatric TB cases. Childhood TB in the EU/EEA remains a public health issue. Due attention should be paid to assessing paediatric trends as they could provide an insight in recent transmission. Whilst the primary aim of further reducing TB rates among children is paramount, better rates of appropriate diagnosis should also be achieved, along with a further improvement of therapeutic success rates.

Clinical implications of defining the gross tumor volume with combination of CT and 18FDG-positron emission tomography in non-small-cell lung cancer.

PURPOSE: To compare the planning target volume (PTV) definitions for computed tomography (CT) vs. positron emission tomography (PET) in non-small-cell lung cancer (NSCLC). METHODS AND MATERIALS: A total of 21 patients with NSCLC underwent three-dimensional conformal radiotherapy planning. All underwent a staging F-18 fluorodeoxyglucose-position emission tomography (18FDG-PET) scan and underwent treatment simulation using CT plus a separate planning 18FDG-PET scan. Three sets of target volumes were defined: Set 1, CT volumes (CT tumor + staging PET nodal disease); Set 2, PET volumes (planning PET tumor {gross tumor volume (GTV) = [(0.3069 x mean standardized uptake value) + 0.5853])}; Set 3, composite CT-PET volumes (fused CT-PET tumor). Sets 1 and 2 were compared using a matching index. Three-dimensional conformal radiotherapy plans were created using the Set 1 (CT) volumes; and coverage of the Set 3 (composite) volumes was evaluated. Separate three-dimensional conformal radiotherapy plans were designed for the Set 3 volumes. RESULTS: For the primary tumor GTV, the Set 1 (CT) volume was larger than the Set 2 (PET) volume in 48%, smaller in 33%, and equal in 19%. The mean matching index was 0.65 (35% CT-PET mismatch). Although quantitatively similar, the volumes differed qualitatively. The Set 3 (composite) volume was larger than either CT or PET alone in 62%, smaller in 24%, and equal in 14%. The dose-volume histogram parameters did not differ among the plans for Set 1 (CT) vs. Set 3 (composite) volumes. Small portions of the Set 3 PTV were significantly underdosed in 40% of cases using the CT-only plan. CONCLUSION: Computed tomography and PET are complementary and should be obtained in the treatment position and fused to define the GTV for NSCLC. Although the quantitative absolute target volume is sometimes similar, the qualitative target locations can be substantially different, leading to underdosage of the target when planning is done using CT alone without PET fusion.

Defining a radiotherapy target with positron emission tomography.

PURPOSE: F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) imaging is now considered the most accurate clinical staging study for non-small-cell lung cancer (NSCLC) and is also important in the staging of multiple other malignancies. Gross tumor volume (GTV) definition for radiotherapy, however, is typically based entirely on computed tomographic data. We performed a series of phantom studies to determine an accurate and uniformly applicable method for defining a GTV with FDG-PET. METHODS AND MATERIALS: A model-based method was tested by a phantom study to determine a threshold, or unique cutoff of standardized uptake value based on body weight (standardized uptake value [SUV]) for FDG-PET based GTV definition. The degree to which mean target SUV, background FDG concentration, and target volume influenced that GTV definition were evaluated. A phantom was constructed consisting of a 9.0-L cylindrical tank. Glass spheres with volumes ranging from 12.2 to 291.0 cc were suspended within the tank, with a minimum separation of 4 cm between the edges of the spheres. The sphere volumes were selected based on the range of NSCLC patient tumor volumes seen in our clinic. The tank and spheres were filled with a variety of known concentrations of FDG in several experiments and then scanned using a General Electric Advance PET scanner. In the initial experiment, six spheres with identical volumes were filled with varying concentrations of FDG (mean SUV = 1.85 approximately 9.68) and suspended within a background bath of FDG at a similar concentration to that used in clinical practice (0.144 muCi/mL). The second experiment was identical to the first, but was performed at 0.144 and 0.036 muCi/mL background concentrations to determine the effect of background FDG concentration on sphere definition. In the third experiment, six spheres with volumes of 12.2 to 291.0 cc were filled with equal concentrations of FDG and suspended in a standard background FDG concentration of 0.144 muCi/mL. Sphere images in each experiment were auto-contoured (simulating a GTV) using the threshold SUV that yielded a volume matching that of the known sphere volume. A regressive function was constructed to represent the relationship between the threshold SUV and the mean target SUV. This function was then applied to define the GTV of 15 NSCLC patients. The GTV volumes were compared to those determined by a fixed image intensity threshold proposed by other investigators. RESULTS: There was a strong linear relationship between the threshold SUV and the mean target SUV. The linear regressive function derived was: threshold SUV = 0.307 x (mean target SUV) + 0.588. The background concentration and target volume indirectly affect the threshold SUV by way of their influence on the mean target SUV. We applied the linear regressive function, as well as a fixed image intensity threshold (42% of maximum intensity) to the sphere phantoms and 15 patients with NSCLC. The results indicated that a much smaller deviation occurred when the threshold SUV regressive function was utilized to estimate the phantom volume as compared to the fixed image intensity threshold. The average absolute difference between the two methods was 21% with respect to the true phantom volume. The deviation became even more pronounced when applied to true patient GTV volumes, with a mean difference between the two methods of 67%. This was largely due to a greater degree of heterogeneity in the SUV of tumors over phantoms. CONCLUSIONS: An FDG-PET-based GTV can be systematically defined using a threshold SUV according to the regressive function described above. The threshold SUV for defining the target is strongly dependent on the mean target SUV of the target, and can be uniquely determined through the proposed iteration process.