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Derivation and Validation of a Biomarker-Based Clinical Algorithm to Rule Out Sepsis From Noninfectious Systemic Inflammatory Response Syndrome at Emergency Department Admission

A Multicenter Prospective Study*

Mearelli, Filippo, MD1; Fiotti, Nicola, MD1; Giansante, Carlo, MD1; Casarsa, Chiara, MD1; Orso, Daniele, MD1; De Helmersen, Marco, MD1; Altamura, Nicola, MD1; Ruscio, Maurizio, PhD6; Castello, Luigi Mario, MD3; Colonetti, Efrem, MD4; Marino, Rossella, MD5; Barbati, Giulia, PhD1; Bregnocchi, Andrea, MD7; Ronco, Claudio, MD8; Lupia, Enrico, MD9; Montrucchio, Giuseppe, MD9; Muiesan, Maria Lorenza, MD4; Di Somma, Salvatore, MD5; Avanzi, Gian Carlo, MD3; Biolo, Gianni, MD1

doi: 10.1097/CCM.0000000000003206
Clinical Investigations

Objectives: To 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.

Design: Multicenter prospective study.

Setting: At emergency department admission in five University hospitals.

Patients: Nine-hundred forty-seven adults in inception cohort and 185 adults in validation cohort.

Interventions: None.

Measurements and Main Results: A 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 2 group IIA">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 2 group IIA">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.

Conclusions: We 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.

1Unit of Internal Medicine, Department of Medical Surgical and Health Sciences, University of Trieste, Trieste, Italy.

2Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy.

3Unit of Emergency Medicine, Department of Translational Medicine, Eastern Piedmont University, Novara, Italy.

4Unit of Internal Medicine, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.

5Unit of Emergency Medicine, Department of Medical Surgery Sciences and Translational medicine, University “Sapienza” of Rome, Rome, Italy.

6Biostatistics Unit, Department of Medical Sciences, University of Trieste, Trieste, Italy.

7Unit of Internal Medicine, General Hospital of Susa, Susa (TO), Italy.

8Unit of Nephrology, Department of Nephrology, Dialysis and Transplantation International Renal Research Institute St Bortolo Hospital, Vicenza, Italy.

9Unit of Emergency Medicine, Department of Medical Sciences, University of Turin, Turin, Italy.

*See also p. 1553.

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Supported, in part, by the Italian Ministry of Health.

Drs. Mearelli, Fiotti, Giansante, Casarsa, De Helmersen, Altamura, Barbati, and Bregnocchi disclosed government work. Dr. Orso received support for article research from the Italian Ministry of Education, Universities and Research. The remaining authors have disclosed that they do not have any potential conflicts of interest.

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