Prognostic Impact of Paraneoplastic Cushing’s Syndrome in Small-Cell Lung Cancer

Introduction:Paraneoplastic Cushing’s syndrome (CushingPS) in small-cell lung cancer is rare but severe.Methods:We studied 383 patients with small-cell lung cancer diagnosed between 1998 and 2012. Among them, 23 patients had CushingPS, 56 had other paraneoplastic syndrome (OtherPS), and 304 had no paraneoplastic syndrome (NoPS).Results:After comparison of the three groups, we observed that CushingPS patients had more extensive disease: 82.6% versus 67.8% versus 53.3% (p = 0.005), respectively, with more than two metastatic sites: 63.2% versus 15.8% and 24.1% (p ⩽ 0.001), a higher World Health Organization performance status (2–4): 73.9% versus 57.1% versus 43.7% (p = 0.006), greater weight loss (≥10%): 47.8% versus 33.9% versus 16.4% (p ⩽ 0.001), reduced objective response to first-line treatment: 47.6% versus 74.1% versus 71.1% (p = 0.04), and poorer sensitivity to first-line treatment: 19% versus 38.9% versus 48.6% (p = 0.01). NoPS patients, with World Health Organization performance status of 3–4, had extensive disease at diagnosis, with response, sensitivity to first-line treatment, and survival similar to the CushingPS group. At relapse, the CushingPS group had no objective response to second-line treatment versus 25% versus 42.8% in OtherPS and NoPS groups, respectively (p = 0.005). The median survival of CushingPS patients was 6.6 months versus 9.2 months for OtherPS and 13.1 months for NoPS patients (p ⩽ 0.001). CushingPS is a prognostic factor of death (hazard ratio, 2.31; p ⩽ 0.001).Conclusion:CushingPS is the worst form of the paraneoplastic syndromes with particularly extensive tumors. Reduced objective response and sensitivity to first-line treatment and no response to second-line treatment suggest starting palliative care early at first line and exclusively at relapse

Smoking and asthma: disentangling their mutual influences using a longitudinal approach.

International audienceBACKGROUND: The association between smoking and asthma remains unclear and has mostly been assessed in cross-sectional studies, with potential selection bias ("healthy smoker effect"). AIMS: Using a longitudinal approach, the aims were to assess whether 1) childhood asthma modifies smoking initiation, 2) active smoking influences asthma incidence in adults and 3) active smoking among subjects with asthma influences the persistence of the disease or the 12-year evolution in lung function in children and adults. METHODS: Subjects (513 children and 1190 adults) were recruited and followed-up for 12 years in the context of the EGEA study (Epidemiological study on the Genetics and Environment of Asthma). RESULTS: Childhood asthma was not associated with a decreased probability of starting active smoking (Hazard Ratio, HR = 0.96; 95% confidence interval (CI): 0.72, 1.27). Smoking at baseline was associated with a higher risk for asthma incidence in adulthood (HR = 1.95, 95% CI: 1.00, 3.77). Among subjects with asthma, smoking was unrelated to lung function evolution; however, among children with moderate to severe asthma at inclusion, smoking tended to slow down the lung function growth (P = 0.04). CONCLUSION: These findings support the hypothesis that childhood asthma does not prevent smoking initiation and confirm that active smoking has a deleterious role on asthma. Altogether this study emphasizes the importance of active smoking as a serious public health problem particularly for children and women

Reporting and handling missing values in clinical studies in intensive care units

Missing values occur in nearly all clinical studies, despite the best efforts of the investigators, and cause frequently unrecognised biases. Our aims were (1) to assess the reporting and handling of missing values in the critical care literature; (2) to describe the impact of various techniques for handling missing values on the study results; (3) to provide guidance on the management of clinical study analysis in case of missing data. We reviewed 44 published manuscripts in three critical care research journals. We used the Conflicus study database to illustrate how to handle missing values. Among 44 published manuscripts, 16 (36.4 %) provided no information on whether missing data occurred, 6 (13.6 %) declared having no missing data, 20 (45.5 %) reported that missing values occurred but did not handle them and only 2 (4.5 %) used sophisticated missing data handling methods. In our example using the Conflicus study database, we evaluated correlations linking job strain intensity to the type and proportion of missing values. Overall, 8 % of data were missing; however, using only complete cases would have resulted in discarding 24 % of the questionnaires. A greater number and a higher percentage of missing values for a particular variable were significantly associated with a lower job strain score (indicating greater stress). Among respondents who fully completed the job strain questionnaire, the comparison of those whose questionnaires did and did not have missing values showed significant differences in terms of age, number of children and country of birth. We provided an algorithm to manage clinical studies analysis in case of missing data. Missing data are common and generate interpretation biases. They should be reported routinely and taken into account when modelling data from clinical studies

Asthma control assessed in the EGEA epidemiological survey and health-related quality of life

BACKGROUND: The aims were to assess 1) the relationship of asthma control assessed by combining epidemiological survey questions and lung function to Health-Related Quality of Life (HRQL) and 2) whether individuals with controlled asthma reach similar generic HRQL levels as individuals without asthma. METHODS: The analysis included 584 individuals without asthma and 498 with asthma who participated in the follow-up of the Epidemiological study on Genetics and Environment of Asthma (EGEA). Asthma control was assessed from survey questions and lung function, closely adapted from the 2006-2009 Global Initiative for Asthma guidelines. The Asthma Quality of Life Questionnaire (AQLQ, scores range:1-7) and the generic SF-36 (scores range: 0-100) were used. RESULTS: Adjusted mean total AQLQ score decreased by 0.5 points for each asthma control steps (6.4, 5.9 and 5.4 for controlled, partly-controlled and uncontrolled asthma respectively, p < 0.0001). The differences in SF-36 scores between individuals with controlled asthma and those without asthma were minor and not significant for the PCS (-1, p = 0.09), borderline significant for the MCS (-1.6, p = 0.05) and small for the 8 domains (<5.1) although statistically significant for 4 domains. CONCLUSION: These results support the discriminative properties of the proposed asthma control grading system and its use in epidemiology

Asthma control assessed in the EGEA epidemiological survey and health-related quality of life

BACKGROUND: The aims were to assess 1) the relationship of asthma control assessed by combining epidemiological survey questions and lung function to Health-Related Quality of Life (HRQL) and 2) whether individuals with controlled asthma reach similar generic HRQL levels as individuals without asthma. METHODS: The analysis included 584 individuals without asthma and 498 with asthma who participated in the follow-up of the Epidemiological study on Genetics and Environment of Asthma (EGEA). Asthma control was assessed from survey questions and lung function, closely adapted from the 2006-2009 Global Initiative for Asthma guidelines. The Asthma Quality of Life Questionnaire (AQLQ, scores range:1-7) and the generic SF-36 (scores range: 0-100) were used. RESULTS: Adjusted mean total AQLQ score decreased by 0.5 points for each asthma control steps (6.4, 5.9 and 5.4 for controlled, partly-controlled and uncontrolled asthma respectively, p < 0.0001). The differences in SF-36 scores between individuals with controlled asthma and those without asthma were minor and not significant for the PCS (-1, p = 0.09), borderline significant for the MCS (-1.6, p = 0.05) and small for the 8 domains (<5.1) although statistically significant for 4 domains. CONCLUSION: These results support the discriminative properties of the proposed asthma control grading system and its use in epidemiology

Determinants of procedural pain intensity in the intensive care unit: the Europain® study

Rationale:Intensive care unit (ICU) patients undergo several diagnostic and therapeutic procedures every day. The prevalence, intensity, and risk factors of pain related to these procedures are not well known. Objectives: To assess self-reported procedural pain intensity versus baseline pain, examine pain intensity differences across procedures, and identify risk factors for procedural pain intensity. Methods: Prospective, cross-sectional, multicenter, multinational study of pain intensity associated with 12 procedures. Data were obtained from 3,851 patients who underwent 4,812 procedures in 192 ICUs in 28 countries. Measurements andMain Results: Painintensity on a 0–10 numeric rating scale increased significantly from baseline pain during all procedures (P , 0.001). Chest tube removal, wound drain removal, and arterial line insertion were the three most painful procedures, with median pain scores of 5 (3–7), 4.5 (2–7), and 4 (2–6), respectively. By multivariate analysis, risk factors independently associated with greater procedural pain intensity were the specific procedure; opioid administration specifically for the procedure; preprocedural pain intensity; preprocedural pain distress; intensity of the worst pain on the same day, before the procedure; and procedure not performed by a nurse. A significant ICU effect was observed, with no visible effect of country because of its absorption by the ICU effect. Some of the risk factors became nonsignificant when each procedure was examined separately. Conclusions: Knowledge of risk factors for greater procedural pain intensity identified in this study may help clinicians select interventions that are needed to minimize procedural pain. Clinical trial registered with www.clinicaltrials.gov (NCT 01070082)