Intensive care infection score outperforms inflammatory biomarkers in detecting infection in critically ill patients

Identification of infection in critically ill patients remains challenging, as conventional biomarkers such as C-reactive protein (CRP), procalcitonin (PCT) and white blood cell count (WBC) frequently lack diagnostic accuracy. The Intensive Care Infection Score (ICIS), a host-response biomarker derived from hematological parameters, reflects both innate and adaptive immune response. To compare diagnostic performance of ICIS, CRP, PCT, and WBC, expert adjudication was used to determine presence or absence of infection. Diagnostic accuracy parameters including receiver operating characteristic curve analysis were calculated. 770 samples from 382 critically ill patients were included. ICIS demonstrated the highest diagnostic accuracy with an area under the curve (AUC) of 0.914 (95% CI 0.892–0.933), compared with CRP (AUC 0.889; 95% CI 0.865–0.910), PCT (AUC 0.847; 95% CI 0.819–0.871), and WBC (AUC 0.697; 95% CI 0.663–0.729). At the optimal cut-off value of > 3, ICIS achieved a sensitivity of 80.7% and specificity of 89.3%. ICIS showed the lowest false-positive rate (10.7%) and a negative predictive value of 88.7%. ICIS demonstrated good diagnostic performance in critically ill patients and can contribute to antimicrobial stewardship as a novel host-response diagnostic.

Authors

Institutions

Publication Details

Journal
Infection
Published
2026-10-07
DOI
https://doi.org/10.1007/s15010-026-02984-8
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Intensive care infection score outperforms inflammatory biomarkers in detecting infection in critically ill patients

Alexander Zarbock, Marie-Luise Ruebsam, Stefanie Klatte, Noelle Nacke et al.
Infection
Sepsis Diagnosis and Treatment
article

Intensive care infection score outperforms inflammatory biomarkers in detecting infection in critically ill patients

Alexander Zarbock, Marie-Luise Ruebsam, Stefanie Klatte, Noelle Nacke, Bastian Blömer, Jens-Christian Schewe, Christian Hönemann
article en

Abstract

Identification of infection in critically ill patients remains challenging, as conventional biomarkers such as C-reactive protein (CRP), procalcitonin (PCT) and white blood cell count (WBC) frequently lack diagnostic accuracy. The Intensive Care Infection Score (ICIS), a host-response biomarker derived from hematological parameters, reflects both innate and adaptive immune response. To compare diagnostic performance of ICIS, CRP, PCT, and WBC, expert adjudication was used to determine presence or absence of infection. Diagnostic accuracy parameters including receiver operating characteristic curve analysis were calculated. 770 samples from 382 critically ill patients were included. ICIS demonstrated the highest diagnostic accuracy with an area under the curve (AUC) of 0.914 (95% CI 0.892–0.933), compared with CRP (AUC 0.889; 95% CI 0.865–0.910), PCT (AUC 0.847; 95% CI 0.819–0.871), and WBC (AUC 0.697; 95% CI 0.663–0.729). At the optimal cut-off value of > 3, ICIS achieved a sensitivity of 80.7% and specificity of 89.3%. ICIS showed the lowest false-positive rate (10.7%) and a negative predictive value of 88.7%. ICIS demonstrated good diagnostic performance in critically ill patients and can contribute to antimicrobial stewardship as a novel host-response diagnostic.

Infection
Universitätsmedizin Greifswald (DE), Marienhospital Stuttgart (DE), University Hospital Münster (DE), University of Rostock (DE)
Openalex Percentile: Top 12%
Sepsis Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.