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

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

Proposal of early CT morphological criteria for response of liver metastases to systemic treatments in gastroenteropancreatic neuroendocrine tumors:Alternatives to RECIST

RECIST 1.1 criteria are commonly used with computed tomography (CT) to evaluate the efficacy of systemic treatments in patients with neuroendocrine tumors (NETs) and liver metastases (LMs), but their relevance is questioned in this setting. We aimed to explore alternative criteria using different numbers of measured LMs and thresholds of size and density variation. We retrospectively studied patients with advanced pancreatic or small intestine NETs with LMs, treated with systemic treatment in the first-and/or second-line, without early progression, in 14 European expert centers. We compared time to treatment failure (TTF) between responders and non-responders according to various criteria defined by 0%, 10%, 20% or 30% decrease in the sum of LM size, and/or by 10%, 15% or 20% decrease in LM density, measured on two, three or five LMs, on baseline (≤1 month before treatment initiation) and first revaluation (≤6 months) contrast-enhanced CT scans. Multivariable Cox proportional hazard models were performed to adjust the association between response criteria and TTF on prognostic factors. We included 129 systemic treatments (long-acting somatostatin analogs 41.9%, chemotherapy 26.4%, targeted therapies 31.8%), administered as first-line (53.5%) or second-line therapies (46.5%) in 91 patients. A decrease ≥10% in the size of three LMs was the response criterion that best predicted prolonged TTF, with significance at multivariable analysis (HR 1.90; 95% CI: 1.06–3.40; p =.03). Conversely, response defined by RECIST 1.1 did not predict prolonged TTF (p =.91), and neither did criteria based on changes in LM density. A ≥10% decrease in size of three LMs could be a more clinically relevant criterion than the current 30% threshold utilized by RECIST 1.1 for the evaluation of treatment efficacy in patients with advanced NETs. Its implementation in clinical trials is mandatory for prospective validation. Criteria based on changes in LM density were not predictive of treatment efficacy. Clinical Trial Registration: Registered at CNIL-CERB, Assistance publique hopitaux de Paris as “E-NETNET-L-E-CT” July 2018. No number was assigned. Approved by the Medical Ethics Review Board of University Medical Center Groningen.</p

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