• Filippo Mearelli, Nicola Fiotti, Carlo Giansante, Chiara Casarsa, Daniele Orso, Marco De Helmersen, Nicola Altamura, Maurizio Ruscio, Luigi Mario Castello, Efrem Colonetti, Rossella Marino, Giulia Barbati, Andrea Bregnocchi, Claudio Ronco, Enrico Lupia, Giuseppe Montrucchio, Maria Lorenza Muiesan, Salvatore Di Somma, Gian Carlo Avanzi, and Gianni Biolo.
• Unit of Internal Medicine, Department of Medical Surgical and Health Sciences, University of Trieste, Trieste, Italy.
• Crit. Care Med. 2018 Sep 1; 46 (9): 1421-1429.

ObjectivesTo derive and validate a predictive algorithm integrating a nomogram-based prediction of the pretest probability of infection with a panel of serum biomarkers, which could robustly differentiate sepsis/septic shock from noninfectious systemic inflammatory response syndrome.DesignMulticenter prospective study.SettingAt emergency department admission in five University hospitals.PatientsNine-hundred forty-seven adults in inception cohort and 185 adults in validation cohort.InterventionsNone.Measurements And Main ResultsA nomogram, including age, Sequential Organ Failure Assessment score, recent antimicrobial therapy, hyperthermia, leukocytosis, and high C-reactive protein values, was built in order to take data from 716 infected patients and 120 patients with noninfectious systemic inflammatory response syndrome to predict pretest probability of infection. Then, the best combination of procalcitonin, soluble phospholipase A2 group IIA, presepsin, soluble interleukin-2 receptor α, and soluble triggering receptor expressed on myeloid cell-1 was applied in order to categorize patients as "likely" or "unlikely" to be infected. The predictive algorithm required only procalcitonin backed up with soluble phospholipase A2 group IIA determined in 29% of the patients to rule out sepsis/septic shock with a negative predictive value of 93%. In a validation cohort of 158 patients, predictive algorithm reached 100% of negative predictive value requiring biomarker measurements in 18% of the population.ConclusionsWe have developed and validated a high-performing, reproducible, and parsimonious algorithm to assist emergency department physicians in distinguishing sepsis/septic shock from noninfectious systemic inflammatory response syndrome.

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