Alexandre Cereuil1, Romain Ronflé2, Aurélien Culver3, Mohamed Boucekine4, Laurent Papazian5, Laurent Lefebvre3, Marc Leone1,6. 1. Réanimation et Surveillance Continue Médico-Chirurgicales Polyvalentes, Hôpital Nord, Service d'Anesthésie et de Réanimation, Aix Marseille Université, APHM, Avenue des tamaris, 13100, Marseille, Aix-en-Provence, France. 2. Réanimation et Surveillance Continue Médico-Chirurgicales Polyvalentes, Centre Hospitalier du Pays d'Aix, Marseille, Aix-en-Provence, France. rronfle@ch-aix.fr. 3. Réanimation et Surveillance Continue Médico-Chirurgicales Polyvalentes, Centre Hospitalier du Pays d'Aix, Marseille, Aix-en-Provence, France. 4. EA 3279 CEReSS, School of Medicine - La Timone Medical Campus, Health Service Research and Quality of Life Center, Aix Marseille Université, APHM, Marseille, France. 5. Hôpital Nord, Médecine Intensive - Réanimation, Aix Marseille Université, APHM, Marseille, France. 6. Centre d'Investigation Clinique, Hôpital Nord, Aix Marseille Université, APHM, Marseille, France.
Abstract
INTRODUCTION: Sepsis is a heterogeneous syndrome that results in life-threatening organ dysfunction. Our goal was to determine the relevant variables and patient phenotypes to use in predicting sepsis outcomes. METHODS: We performed an ancillary study concerning 119 patients with septic shock at intensive care unit (ICU) admittance (T0). We defined clinical worsening as having an increased sequential organ failure assessment (SOFA) score of ≥ 1, 48 h after admission (ΔSOFA ≥ 1). We performed univariate and multivariate analyses based on the 28-day mortality rate and ΔSOFA ≥ 1 and determined three patient phenotypes: safe, intermediate and unsafe. The persistence of the intermediate and unsafe phenotypes after T0 was defined as a poor outcome. RESULTS: At T0, the multivariate analysis showed two variables associated with 28-day mortality rate: norepinephrine dose and serum lactate concentration. Regarding ΔSOFA ≥ 1, we identified three variables at T0: norepinephrine dose, lactate concentration and venous-to-arterial carbon dioxide difference (P(v-a)CO2). At T0, the three phenotypes (safe, intermediate and unsafe) were found in 28 (24%), 70 (59%) and 21 (18%) patients, respectively. We thus suggested using an algorithm featuring norepinephrine dose, lactate concentration and P(v-a)CO2 to predict patient outcomes and obtained an area under the curve (AUC) of 74% (63-85%). CONCLUSION: Our findings highlight the fact that identifying relevant variables and phenotypes may help physicians predict patient outcomes.
INTRODUCTION: Sepsis is a heterogeneous syndrome that results in life-threatening organ dysfunction. Our goal was to determine the relevant variables and patient phenotypes to use in predicting sepsis outcomes. METHODS: We performed an ancillary study concerning 119 patients with septic shock at intensive care unit (ICU) admittance (T0). We defined clinical worsening as having an increased sequential organ failure assessment (SOFA) score of ≥ 1, 48 h after admission (ΔSOFA ≥ 1). We performed univariate and multivariate analyses based on the 28-day mortality rate and ΔSOFA ≥ 1 and determined three patient phenotypes: safe, intermediate and unsafe. The persistence of the intermediate and unsafe phenotypes after T0 was defined as a poor outcome. RESULTS: At T0, the multivariate analysis showed two variables associated with 28-day mortality rate: norepinephrine dose and serum lactate concentration. Regarding ΔSOFA ≥ 1, we identified three variables at T0: norepinephrine dose, lactate concentration and venous-to-arterial carbon dioxide difference (P(v-a)CO2). At T0, the three phenotypes (safe, intermediate and unsafe) were found in 28 (24%), 70 (59%) and 21 (18%) patients, respectively. We thus suggested using an algorithm featuring norepinephrine dose, lactate concentration and P(v-a)CO2 to predict patient outcomes and obtained an area under the curve (AUC) of 74% (63-85%). CONCLUSION: Our findings highlight the fact that identifying relevant variables and phenotypes may help physicians predict patient outcomes.
Authors: Mervyn Singer; Clifford S Deutschman; Christopher Warren Seymour; Manu Shankar-Hari; Djillali Annane; Michael Bauer; Rinaldo Bellomo; Gordon R Bernard; Jean-Daniel Chiche; Craig M Coopersmith; Richard S Hotchkiss; Mitchell M Levy; John C Marshall; Greg S Martin; Steven M Opal; Gordon D Rubenfeld; Tom van der Poll; Jean-Louis Vincent; Derek C Angus Journal: JAMA Date: 2016-02-23 Impact factor: 56.272
Authors: Christopher W Seymour; Vincent X Liu; Theodore J Iwashyna; Frank M Brunkhorst; Thomas D Rea; André Scherag; Gordon Rubenfeld; Jeremy M Kahn; Manu Shankar-Hari; Mervyn Singer; Clifford S Deutschman; Gabriel J Escobar; Derek C Angus Journal: JAMA Date: 2016-02-23 Impact factor: 56.272
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