Propensity score analysis with partially observed covariates: How should multiple imputation be used?

Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement multiple imputation for propensity score analysis: (a) should we apply Rubin's rules to the inverse probability of treatment weighting treatment effect estimates or to the propensity score estimates themselves? (b) does the outcome have to be included in the imputation model? (c) how should we estimate the variance of the inverse probability of treatment weighting estimator after multiple imputation? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome. We also compared the performance of these methods to complete case analysis and the missingness pattern approach, which uses a different propensity score model for each pattern of missingness, and a third multiple imputation approach in which the propensity score parameters are combined rather than the propensity scores themselves (MIpar). Under a missing at random mechanism, complete case and missingness pattern analyses were biased in most cases for estimating the marginal treatment effect, whereas multiple imputation approaches were approximately unbiased as long as the outcome was included in the imputation model. Only MIte was unbiased in all the studied scenarios and Rubin's rules provided good variance estimates for MIte. The propensity score estimated in the MIte approach showed good balancing properties. In conclusion, when using multiple imputation in the inverse probability of treatment weighting context, MIte with the outcome included in the imputation model is the preferred approach

High Burden of Non-Influenza Viruses in Influenza-Like Illness in the Early Weeks of H1N1v Epidemic in France

BACKGROUND: Influenza-like illness (ILI) may be caused by a variety of pathogens. Clinical observations are of little help to recognise myxovirus infection and implement appropriate prevention measures. The limited use of molecular tools underestimates the role of other common pathogens. OBJECTIVES: During the early weeks of the 2009-2010 flu pandemic, a clinical and virological survey was conducted in adult and paediatric patients with ILI referred to two French University hospitals in Paris and Tours. Aims were to investigate the different pathogens involved in ILI and describe the associated symptoms. METHODS: H1N1v pandemic influenza diagnosis was performed with real time RT-PCR assay. Other viral aetiologies were investigated by the molecular multiplex assay RespiFinder19®. Clinical data were collected prospectively by physicians using a standard questionnaire. RESULTS: From week 35 to 44, endonasal swabs were collected in 413 patients. Overall, 68 samples (16.5%) were positive for H1N1v. In 13 of them, other respiratory pathogens were also detected. Among H1N1v negative samples, 213 (61.9%) were positive for various respiratory agents, 190 in single infections and 23 in mixed infections. The most prevalent viruses in H1N1v negative single infections were rhinovirus (62.6%), followed by parainfluenza viruses (24.2%) and adenovirus (5.3%). 70.6% of H1N1v cases were identified in patients under 40 years and none after 65 years. There was no difference between clinical symptoms observed in patients infected with H1N1v or with other pathogens. CONCLUSION: Our results highlight the high frequency of non-influenza viruses involved in ILI during the pre-epidemic period of a flu alert and the lack of specific clinical signs associated with influenza infections. Rapid diagnostic screening of a large panel of respiratory pathogens may be critical to define and survey the epidemic situation and to provide critical information for patient management

Dyspnoea in patients receiving noninvasive ventilation for acute respiratory failure: prevalence, risk factors and prognostic impact: A prospective observational study

Dyspnoea is a frequent and intense symptom in intubated patients, but little attention has been paid to dyspnoea during noninvasive mechanical ventilation in the intensive care unit (ICU).The objectives of this study were to quantify the prevalence, intensity and prognostic impact of dyspnoea in patients receiving noninvasive ventilation (NIV) for acute respiratory failure (ARF) based on secondary analysis of a prospective observational cohort study in patients who received ventilatory support for ARF in 54 ICUs in France and Belgium. Dyspnoea was measured by a modified Borg scale.Among the 426 patients included, the median (interquartile range) dyspnoea score was 4 (3-5) on admission and 3 (2-4) after the first NIV session (p=0.001). Dyspnoea intensity ≥4 after the first NIV session was associated with the Sequential Organ Failure Assessment Score (odds ratio (OR) 1.12, p=0.001), respiratory rate (OR 1.03, p=0.032), anxiety (OR 1.92, p=0.006), leaks (OR 2.5, p=0.002) and arterial carbon dioxide tension (OR 0.98, p=0.025). Dyspnoea intensity ≥4 was independently associated with NIV failure (OR 2.41, p=0.001) and mortality (OR 2.11, p=0.009), but not with higher post-ICU burden and altered quality of life.Dyspnoea is frequent and intense in patients receiving NIV for ARF and is associated with a higher risk of NIV failure and poorer outcome

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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Lymphoscintigraphy in the sentinel lymph node technique for breast tumor: value of early and late images for the learning curve1

As the performance of early (H+1 to H+4) and late (D1) lymphoscintigraphic images raises organizational problems in outpatient surgery for breast cancer, only early images are generally obtained. The present study evaluated whether two series of images are better than one and defined the advantages of both methodologies. One hundred and eighteen patients with infiltrating breast carcinoma (T(0), T(1) and T(2)) were included in the study: 87 in group A (early and late images) and 31 in group B (only early images). All patients received two peritumoral injections of (99m)Tc-sulfur colloid, 15-18 MBq (group A) and <15 MBq (group B). During the operation, the patent blue bye technique was associated with radioactivity detection. The two groups were comparable for histological type and tumor size and localization. Successful localization of sentinel nodes on early lymphoscintigraphic images was significantly greater for group B. The identification of a sentinel node focus on early lymphoscintigraphy increased by 10% during the study. Sentinel node detection by the isotopic method alone, or the two methods combined, was comparable for both groups. In radioactivity detection, the count rate for sentinel nodes versus background (contralateral breast) was similar for the two groups. During the learning phase, two series of images gave a definite advantage. Subsequently, lymphoscintigraphy performed at +2 h was sufficient (the results for the two groups became indistinguishable