• Eric Vibert, Emmanuel Boleslawski, Cyril Cosse, Rene Adam, Denis Castaing, Daniel Cherqui, Salima Naili, Jean-Marc Régimbeau, Antonio Sa Cunha, Stephanie Truant, Maher Fleyfel, François-René Pruvot, Catherine Paugam-Burtz, and Olivier Farges.
• *AP-HP Hôpital Paul-Brousse, Centre Hépato-Biliaire, Villejuif, France †CHRU de Lille, Hôpital Huriez, Service de Chirurgie Digestive et Transplantation, Lille, France ‡CHU d'Amiens Picardie, Hôpital Sud Amiens, Service de Chirurgie Digestive, Amiens, France §AP-HP Hôpital Paul-Brousse, Service d'Anesthésie-Réanimation, Villejuif, France ¶CHRU de Lille, Hôpital Huriez, Département d'Anesthésie-Réanimation, Lille, France ||AP-HP Hôpital Beaujon, Service d'Anesthésie-Réanimation, Paris, France **AP-HP Hôpital Beaujon, Service de Chirurgie Hépato-Biliaire et Transplantation, Clichy, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
• Ann. Surg. 2015 Nov 1;262(5):787-92; discussion 792-3.

ObjectivesTo test the prognostic impact of arterial lactate concentration at the end-of-surgery (LCT-EOS) on postoperative outcome after elective liver-resections and to identify the predictors of an increase in LCT-EOS.Background DataA recent systematic-review of risk-prediction-models for liver resections has evidenced their poor accuracy and a deficit in the evaluation of intraoperative events. LCT-EOS is a marker of impaired tissue oxygenation.MethodsThis prospectively-designed study was based on a training-cohort of 519 patients and a validation-cohort of 466 patients. For each of the endpoints (high comprehensive complication index (CCI) scores, 90-day-mortality and severe-morbidity), prognostic-models were built by logistic-regression using the training-cohort. These models were thereafter tested in the validation-cohort and their performance (discrimination, accuracy, calibration) assessed. Independent predictors of LCT-EOS were also identified.ResultsIn the training-cohort, LCT-EOS cutoff best discriminating high-CCI, 90-day-mortality and severe-morbidity were 3, 3 and 2.8 mmol/L (and the corresponding AUROC 0.86, 0.87 and 0.76). LCT-EOS was an independent predictor of endpoints and adding LCT-EOS to the other predictors increased by 16.4%, 34.5% and 17.7% the accuracy of the models for high-CCI, 90-day-mortality and severe-morbidity, respectively. The models had high calibration and accuracy. Diabetes, repeat-hepatectomy, major-hepatectomy, synchronous-major-procedure, inflow-occlusion and blood-transfusion were independent predictors of LCT-EOS >3 mmol/L.ConclusionsLCT-EOS >3 mmol/L is an early predictor of postoperative-outcome and should be used as a tool to determine patients requiring critical-care and as an endpoint in studies measuring the impact of perioperative interventions.

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