Evaluating mortality in intensive care units: contribution of competing risks analyses

INTRODUCTION: Kaplan–Meier curves and logistic models are widely used to describe and explain the variability of survival in intensive care unit (ICU) patients. The Kaplan–Meier approach considers that patients discharged alive from hospital are 'non-informatively' censored (for instance, representative of all other individuals who have survived to that time but are still in hospital); this is probably wrong. Logistic models are adapted to this so-called 'competing risks' setting but fail to take into account censoring and differences in exposure time. To address these issues, we exemplified the usefulness of standard competing risks methods; namely, cumulative incidence function (CIF) curves and the Fine and Gray model. METHODS: We studied 203 mechanically ventilated cancer patients with acute respiratory failure consecutively admitted over a five-year period to a teaching hospital medical ICU. Among these patients, 97 died before hospital discharge. After estimating the CIF of hospital death, we used Fine and Gray models and logistic models to explain variability hospital mortality. RESULTS: The CIF of hospital death was 35.5% on day 14 and was 47.8% on day 60 (97/203); there were no further deaths. Univariate models, either the Fine and Gray model or the logistic model, selected the same eight variables as carrying independent information on hospital mortality at the 5% level. Results of multivariate were close, with four variables selected by both models: autologous stem cell transplantation, absence of congestive heart failure, neurological impairment, and acute respiratory distress syndrome. Two additional variables, clinically documented pneumonia and the logistic organ dysfunction, were selected by the Fine and Gray model. CONCLUSION: The Fine and Gray model appears of interest when predicting mortality in ICU patients. It is closely related to the logistic model, through direct modeling of times to death, and can be easily extended to model non-fatal outcomes

Multiple Imputation for Multilevel Data with Continuous and Binary Variables

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset includes a large number of clusters. In addition, it highlights that heteroscedastic multiple imputation methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, guidelines are given to choose the most suitable multiple imputation method according to the structure of the data

Acute respiratory failure in patients with hematological malignancies : outcomes according to initial ventilation strategy : a Groupe de recherche respiratoire en réanimation onco-hématologique (Grrr-OH) study

Background: In patients with hematological malignancies and acute respiratory failure (ARF), noninvasive ventilation was associated with a decreased mortality in older studies. However, mortality of intubated patients decreased in the last years. In this study, we assess outcomes in those patients according to the initial ventilation strategy. Methods: We performed a post hoc analysis of a prospective multicentre study of critically ill hematology patients, in 17 intensive care units in France and Belgium. Patients with hematological malignancies admitted for ARF in 2010 and 2011 and who were not intubated at admission were included in the study. A propensity score-based approach was used to assess the impact of NIV compared to oxygen only on hospital mortality. Results: Among 1011 patients admitted to ICU during the study period, 380 met inclusion criteria. Underlying diseases included lymphoid (n = 162, 42.6 %) or myeloid (n = 141, 37.1 %) diseases. ARF etiologies were pulmonary infections (n = 161, 43 %), malignant infiltration (n = 65, 17 %) or cardiac pulmonary edema (n = 40, 10 %). Mechanical ventilation was ultimately needed in 94 (24.7 %) patients, within 3 [2-5] days of ICU admission. Hospital mortality was 32 % (123 deaths). At ICU admission, 142 patients received first-line noninvasive ventilation (NIV), whereas 238 received oxygen only. Fifty-five patients in each group (NIV or oxygen only) were matched according the propensity score. NIV was not associated with decreased hospital mortality [OR 1.5 (0.62-3.65)]. Conclusions: In hematology patients with acute respiratory failure, initial treatment with NIV did not improve survival compared to oxygen only

Cardiac troponin and skeletal muscle oxygenation in severe post-partum haemorrhage

International audienceIntroductionCardiac troponin has been shown to be elevated in one-half of the parturients admitted for post-partum haemorrhage. The purpose of the study was to assess whether increased cardiac troponin was associated with a simultaneous alteration in haemoglobin tissue oxygen saturation in peripheral muscles in post-partum haemorrhage.MethodsTissue haemoglobin oxygen saturation of thenar eminence muscle (StO2) was measured via near-infrared spectroscopy technology. Two sets of StO2 parameters (both isolated baseline and during forearm ischaemia-reperfusion tests) were collected at two time points: upon intensive care unit admission and prior to intensive care unit discharge. Comparisons were performed using Wilcoxon paired tests, and univariate associations were assessed using logistic regression model and Wald tests.ResultsThe 42 studied parturients, admitted for post-partum haemorrhage, had clinical and biological signs of severe blood loss. Initial cardiac troponin I was increased in 24/42 parturients (0.43 ± 0.60 μrg/l). All measured parameters of muscular haemoglobin oxygen saturation, including Srecovery, were also altered at admission and improved together with improved haemodynamics, when bleeding was controlled. Multivariate analysis showed that muscular Srecovery ConclusionsOur study confirmed the high incidence of increased cardiac troponin, and demonstrated the simultaneous impairment in the reserve of oxygen delivery to peripheral muscles in parturients admitted for severe post-partum haemorrhage

Increased mortality in hematological malignancy patients with acute respiratory failure from undetermined etiology : a Groupe de Recherche en Réanimation Respiratoire en Onco-Hématologique (Grrr-OH) study

Background: Acute respiratory failure (ARF) is the most frequent complication in patients with hematological malignancies and is associated with high morbidity and mortality. ARF etiologies are numerous, and despite extensive diagnostic workflow, some patients remain with undetermined ARF etiology. Methods: This is a post-hoc study of a prospective multicenter cohort performed on 1011 critically ill hematological patients. Relationship between ARF etiology and hospital mortality was assessed using a multivariable regression model adjusting for confounders. Results: This study included 604 patients with ARF. All patients underwent noninvasive diagnostic tests, and a bronchoscopy and bronchoalveolar lavage (BAL) was performed in 155 (25.6%). Definite diagnoses were classified into four exclusive etiological categories: pneumonia (44.4%), non-infectious diagnoses (32.6%), opportunistic infection (10.1%) and undetermined (12.9%), with corresponding hospital mortality rates of 40, 35, 55 and 59%, respectively. Overall hospital mortality was 42%. By multivariable analysis, factors associated with hospital mortality were invasive pulmonary aspergillosis (OR 7.57 (95% CI 3.06-21.62); p 7 (OR 3.32 (95% CI 2.15-5.15); p < 0.005) and an undetermined ARF etiology (OR 2.92 (95% CI 1.71-5.07); p < 0.005). Conclusions: In patients with hematological malignancies and ARF, up to 13% remain with undetermined ARF etiology despite comprehensive diagnostic workup. Undetermined ARF etiology is independently associated with hospital mortality. Studies to guide second-line diagnostic strategies are warranted

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.Medical Research Council (project grant MR/M013278/1 and Unit Programme number U105260558

The FOXO1 Transcription Factor Instructs the Germinal Center Dark Zone Program

SummaryThe pathways regulating formation of the germinal center (GC) dark zone (DZ) and light zone (LZ) are unknown. In this study we show that FOXO1 transcription factor expression was restricted to the GC DZ and was required for DZ formation, since its absence in mice led to the loss of DZ gene programs and the formation of LZ-only GCs. FOXO1-negative GC B cells displayed normal somatic hypermutation but defective affinity maturation and class switch recombination. The function of FOXO1 in sustaining the DZ program involved the trans-activation of the chemokine receptor CXCR4, and cooperation with the BCL6 transcription factor in the trans-repression of genes involved in immune activation, DNA repair, and plasma cell differentiation. These results also have implications for the role of FOXO1 in lymphomagenesis because they suggest that constitutive FOXO1 activity might be required for the oncogenic activity of deregulated BCL6 expression