Les enjeux de l’équivalence écologique pour la conception et le dimensionnement de mesures compensatoires d’impacts sur la biodiversité et les milieux naturels,

L’évolution du contexte réglementaire a renforcé l’obligation de compenser " en nature " les impacts sur la biodiversité qui n’ont pas pu être évités ou réduits. Dans ce contexte, l’évaluation de l’équivalence entre les pertes causées par ces impacts et les gains de biodiversité attendus des actions de compensation suscite des questions scientifiques et techniques quant aux concepts et connaissances à mobiliser et aux méthodes d’évaluation à développer et mettre en ½uvre. On y trouve en particulier l'identification des éléments de biodiversité à considérer, le développement d’indicateurs appropriés permettant de comparer pertes et gains, la sélection d’un état de référence pour le calcul des pertes et gains, et la prise en compte des dynamiques écologiques et des incertitudes dans l’évaluation du devenir des sites de compensation. Par ces questions, l'équivalence écologique donne un cadre de raisonnement explicite à la conception et au dimensionnement de la compensation qui est appropriable par chacun des acteurs concernés. / Since 2007 France has seen a radical strengthening of its legislation concerning the mitigation of development impacts on biodiversity and ecosystems. Under pressure from the European Union and as an outcome of a national consultative process called the “Grenelle de l’Environnement”, the scope of the mitigation hierarchy of avoiding, reducing and offsetting impacts of plans, programs and projects has been expanded. It now includes stronger requirements in terms of monitoring and effective implementation. These changes – which have strong financial and legal implications for developers - have revealed the lack of technical guidelines for designing and sizing offsets. Assessing the ecological equivalence between losses caused by impacts and the gains expected from offset actions raises scientific and technical issues that remain unresolved. These include the identification of relevant components of biodiversity, the development of appropriate indicators, the identification of reference states and the incorporation of ecological dynamics and uncertainties into offset design and sizing

Bedside prediction of mortality from bacteremic sepsis. A dynamic analysis of ICU patients

The prognosis in patients with sepsis depends on severity of acute illness, underlying chronic diseases, and complications associated with infection. Adjusting for these factors is essential for evaluation of new therapies. The purpose of the present study was to determine variables readily identifiable at the bedside that predict mortality in intensive care unit (ICU) patients with sepsis and positive blood cultures. For a 5-yr period, all patients of a surgical ICU presenting with positive blood cultures and sepsis were systematically analyzed for clinical variables and organ dysfunctions at the day of onset of sepsis and bacteremia and during the subsequent clinical course. The prognostic value of these variables was determined using logistic regression procedures. Of the 5,457 admissions to the ICU, 176 patients developed sepsis with positive blood cultures (3.2 per 100 admissions). The fatality rate was 35% at 28 days after the onset of sepsis; in-hospital mortality was 43%. Independent predictors of mortality at onset of sepsis were previous antibiotic therapy (odds ratio [OR], 2.40; 95% confidence interval [CI95], 1.59 to 3.62; p = 0.034), hypothermia (OR, 1.43; CI95, 1.04 to 2.44; p = 0.030), requirement for mechanical ventilation (OR, 2.97; CI95, 1.96 to 4.51; p = 0.009), and onset-of-sepsis APACHE II score (OR, 1.21; CI95, 1.13 to 1.29; p < 0.001). Vital organ dysfunctions developing after the onset of sepsis influenced outcome markedly. The best two independent prognostic factors were the APACHE II score at the onset of sepsis (OR, 1.13 per unit; CI95, 1.08 to 1.17; p = 0.0016) and the number of organ dysfunctions developing thereafter (OR, 2.39; CI95, 2.02 to 2.82; p < 0.001). In ICU patients with sepsis and positive blood cultures, outcome can be predicted by the severity of illness at onset of sepsis and the number of vital organ dysfunctions developing subsequently. These variables are easily assessed at the bedside and should be included in the evaluation of new therapeutic strategies

Importance of pre-existing co-morbidities for prognosis of septicemia in critically ill patients

OBJECTIVE: To determine admission characteristics associated with the outcome of septicemia in critically ill patients and more specifically assess the prognostic value of pre-existing co-morbidities. DESIGN: 5 year-retrospective cohort study. SETTING: Surgical Intensive Care Unit (ICU-20 beds) in a 1600 bed-tertiary care center. PATIENTS: Among 5457 patients admitted to the ICU between 1984 and 1988, 176 (3.2%) met prospectively-defined criteria for blood culture-proven septicemia (8.77 per 1000 patient-days). Overall septicemic patients had a 5-fold increased risk of death compared to non-septicemic patients (relative risk 5.03, 95% confidence intervals 4.17-6.07, p < 0.0001), and this estimate persisted after stratification according to age, sex, primary diagnosis and conditions of admission to the ICU (emergency/elective). RESULTS: Prognostic factors recorded on admission to ICU that were associated with mortality from septicemia among 173 patients were older age, higher admission Apache II score, gastrointestinal surgery, ultimately and rapidly fatal diseases and the number of co-morbidities in addition to the principal diagnosis (active smoking, alcohol abuse, non-cured malignancy, diabetes mellitus, splenectomy, recent antibiotic therapy, major surgery, or major cardiac event). In the multivariate analysis with logistic regression procedures, Apache II and co-morbidities were identified as the two independent predictors of mortality. CONCLUSIONS: Pre-existing co-morbidities assessed at the admission to the ICU significantly improved the prediction of mortality from septicemia compared to Apache II score alone

Predicting survival of patients with sepsis by use of regression and neural network models

OBJECTIVES: (1) To predict at the time of diagnosis of sepsis the subsequent occurrence of multiple organ failure and patient death; and (2) to compare the prediction accuracies of standard multiple logistic regression (MLR) and neural network (NN) models. METHODS: The data were collected during a 5-year period for all patients (n=173) who met prospectively determined criteria for sepsis and had positive blood culture results while admitted in the surgical intensive care unit at the University Hospital of Geneva, Switzerland. These data formed the basis for a retrospective cohort study described elsewhere. The MLR model was adapted from existing data. An NN model of the feed-forward, back-propagation type was constructed for predicting the outcome of sepsis with bloodstream infection. Both models were constructed from randomly chosen subsets of patients and subsequently were evaluated on the remaining (independent) patients. RESULTS: Survival after sepsis was predicted with an accuracy of 80% by the NN model, which used only information collected at the time of the diagnosis of sepsis. The development of multiple organ failure after the diagnosis of sepsis was predicted accurately (81.5%) with either the MLR or the NN model. Both the MLR and the NN methods depended on the interpretation of a likelihood quantity, requiring the choice of a threshold to make a survival prediction. The accuracy of the MLR models was very sensitive to the threshold value. The accuracy of the NN models was not sensitive to the choice of threshold, because they generated likelihood predictions that were distributed far from the middle range where the threshold was placed. CONCLUSION: Compared with MLR models, the NN models were slightly more accurate and much less sensitive to the arbitrary threshold parameter