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 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. 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

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

Fiotti, Nicola;Giansante, Carlo;Casarsa, Chiara;Orso, Daniele;MARINO, ROSSELLA;Barbati, Giulia;Biolo, Gianni
2018-01-01

Abstract

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 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. 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
Pubblicato
https://journals.lww.com/ccmjournal/Fulltext/2018/09000/Derivation_and_Validation_of_a_Biomarker_Based.5.aspx
File in questo prodotto:
File Dimensione Formato  
Mearelli_2018 (2).pdf

Accesso chiuso

Descrizione: articolo
Tipologia: Documento in Versione Editoriale
Licenza: Digital Rights Management non definito
Dimensione 589.73 kB
Formato Adobe PDF
589.73 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
2932070_Mearelli_2018 (2)-Post_print.pdf

accesso aperto

Descrizione: Post Print VQR3
Tipologia: Bozza finale post-referaggio (post-print)
Licenza: Digital Rights Management non definito
Dimensione 1.15 MB
Formato Adobe PDF
1.15 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2932070
Citazioni
  • ???jsp.display-item.citation.pmc??? 16
  • Scopus 28
  • ???jsp.display-item.citation.isi??? 26
social impact