Early mortality in lung cancer: French prospective multicentre observational study.

BACKGROUND: Despite the progress seen in the last decade in diagnosis and treatment, lung cancer has still a bad prognosis and a substantial number of patients died within the weeks following diagnosis. The objective of this study was to quantify early mortality in lung cancer, to identify patients who are at high risk of early decease, and to describe their management in a real world. METHODS: Prospective observational study including consecutively all adult patients managed for primary lung cancer histologically or cytologically diagnosed in 2010 in the respiratory medicine department of one of the participating French general hospitals. Patients and cancer characteristics and first therapeutic strategy were collected at diagnosis. Dates of death were obtained from investigators or town council of the patient's birth place. All fatal cases were considered regardless of the cause of the death. Multivariate logistic regression model was used to determine the factors significantly and independently associated with death at 1 and 3 months. RESULTS: Seven thousand fifty-one patients from 104 centres were included in the study. Vital status was obtained for 6,981 patients. Respectively, 678 (9.7%) and 1,621 (23.2%) of the 6,981 patients with available data died within 1 and 3 months following diagnosis. As compared with the other patients, they were significantly older and frailer (based on performance status [PS] and recent weight loss) and more frequently reported stage IV tumour. Overall, 64.5% (1 month) and 42.8% (3 months) of patients had no cancer therapy and less than 1% were included in a therapeutic trial. CONCLUSION: About one in four patients died within 3 months following lung cancer diagnosis. Early mortality mainly involves frail patients with advanced cancer and is associated with lack of cancer therapy. This supports the need for early diagnosis and clinical trials in this population. Reducing early mortality to give supplementary time to patients to organise the future is a major challenge for 21(st) century physicians.

Multidimensional analyses to assess the relations between treatment choices by physicians and patients' characteristics: the example of COPD.

BACKGROUND: In some situations, practice guidelines do not provide firm evidence-based guidance regarding COPD treatment choices, especially when large trials have failed to identify subgroups of particularly good or poor responders to available medications. METHODS: This observational cross-sectional study explored the yield of four types of multidimensional analyses to assess the associations between the clinical characteristics of COPD patients and pharmacological and non-pharmacological treatments prescribed by lung specialists in a real-life context. RESULTS: Altogether, 2494 patients were recruited by 515 respiratory physicians. Multiple correspondence analysis and hierarchical clustering identified 6 clinical subtypes and 6 treatment subgroups. Strong bi-directional associations were found between clinical subtypes and treatment subgroups in multivariate logistic regression. However, although the overall frequency of prescriptions varied from one clinical subtype to the other for all types of pharmacological treatments, clinical subtypes were not associated with specific prescription profiles. When canonical analysis of redundancy was used, the proportion of variation in pharmacological treatments that was explained by clinical characteristics remained modest: 6.23%. This proportion was greater (14.29%) for non-pharmacological components of care. CONCLUSION: This study shows that, although pharmacological treatments of COPD are quantitatively very well related to patients' clinical characteristics, there is no particular patient profile that could be qualitatively associated to prescriptions. This underlines uncertainties perceived by physicians for differentiating the respective effects of available pharmacological treatments. The methodology applied here is useful to identify areas of uncertainty requiring further research and/or guideline clarification.