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Franz Schramel
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EP1.01 - Advanced NSCLC (ID 150)
- Event: WCLC 2019
- Type: E-Poster Viewing in the Exhibit Hall
- Track: Advanced NSCLC
- Presentations: 1
- Now Available
- Moderators:
- Coordinates: 9/08/2019, 08:00 - 18:00, Exhibit Hall
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EP1.01-36 - Quality of Life in Advanced Lung Cancer Patients in a Dutch Population (Now Available) (ID 2623)
08:00 - 18:00 | Author(s): Franz Schramel
- Abstract
Background
In advanced lung cancer chemotherapy is associated with a statistically significant improvement in overall survival (OS) compared to best-supportive care (BSC) alone. The palliative benefit of systemic therapy in incurable cancers cannot be deduced from response rates and survival benefits, but needs to be assessed directly, through validated patient reported tools. Studies have suggested that systemic therapy also improves quality of life (OoL) due to better overall physical functioning and alleviation of cancer-related symptoms. We aimed to investigate the QoL of patients with advanced lung cancer treated with systemic therapy or best-supportive care (BSC) alone in real-world practice.
Method
The European Organisation for Research and Treatment of Cancer (EORTC) QOL questionnaire (QLQ-C30) and the EORTC QLQ-LC13 were used to assess patients reported outcome measures (PROMs) at baseline, 3, 6 and 12 months in 235 patients with stage IIIb and IV NSCLC or SCLC diagnosed and treated between Janury 2013 and September 2017 in four large teaching hospitals in the Netherlands. Demographic and baseline data were compared with unpaired T- or c2-tests. Cox proportional hazard model was used to compare survival among these two groups and investigate the influence of age at diagnosis (continuous variable), gender and performance status. The change over time in raw questionnaire scores were analysed by using linear mixed modelling.
Result
Systemically treated patients (n=177) had a significantly prolonged OS compared to BSC (median OS of 691 vs 219 days (p<0.0001)) and a higher number of patients in the BSC arm (n= 58) died during the observation period (62.1% vs 44.6%, p=0.02). ‘Physical functioning’, ‘role functioning’, ‘shortness of breath’ and ‘bothered by shortness of breath’ were significantly different between the PST/BSC groups (p<0.001, p=0.025, p=0.017 and p=0.031 resp.) at baseline. Over time there was no significant time-treatment interaction in global health, functional or symptom scores between systemic therapy and BSC (p >0.09 in all cases). Patients deceased during observation had significantly worse global health and functional status and experienced more disease and treatment related symptoms.
Conclusion
Treatment with systemic therapy in advanced lung cancer led as expected to longer OS than BSC alone. However, unlike the evidence obtained from clinical trials, the QoL of patients in real-world practice, decreased in both groups equally over time.
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P1.17 - Treatment of Early Stage/Localized Disease (ID 188)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Treatment of Early Stage/Localized Disease
- Presentations: 1
- Now Available
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.17-39 - Preoperative Prediction of Incomplete Resection in Non-Small Cell Lung Cancer: An Externally Validated Clinical Nomogram (Now Available) (ID 1722)
09:45 - 18:00 | Author(s): Franz Schramel
- Abstract
Background
Patients who are surgically treated for stage I-III non-small cell lung cancer have a worse prognosis after incomplete (R1-R2) resection. Predictive factors for completeness of resection have not satisfactorily been established. Our study aimed to develop, and internally and externally validate a prediction model to estimate the risk of incomplete resection, based on preoperative patient-, tumor-, and treatment-related factors.
Method
From a Dutch national database (NKR) all consecutive NSCLC patients diagnosed from 2011 to 2014 who had surgery without neoadjuvant therapy were selected. Fifteen possible predictors were analyzed. Multivariable logistic regression analysis with stepwise backward elimination was used to create a prediction model. Discriminatory ability and calibration of the model was determined after internal validation. External validation was applied in an American dataset from the NCDB, whereupon the model was adjusted. The prediction model was presented as a nomogram.
Result
In the development set of 7,124 patients an incomplete resection was reached in 496 patients (7.0%). Remaining predictors were gender, histology, cT-stage, cN-stage, extent of surgical resection, time interval from diagnosis to surgery, open versus thoracoscopic procedure, and the interaction between procedure and cN-stage. After internal validation, the corrected c-statistic of the resulting nomogram was 0.73. Application of the nomogram to the external dataset of 85,235 patients with R1-R2 resections in 2,485 patients (2.9%) resulted in a c-statistic of 0.70. Calibration revealed good overall fit of the nomogram in both cohorts.
Conclusion
An internationally validated nomogram is presented providing the ability to predict the individual risk of an incomplete resection in patients with stage I-III NSCLC planned for surgery. In case of a relevant probability of incomplete resection, alternative treatment strategies could be considered, such as a larger extent of surgery, neoadjuvant or definitive chemoradiotherapy. In contrast, with a small predicted probability of incomplete resection, the use of surgery is further supported.
