Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,611

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The LRINEC (Laboratory Risk Indicator for Necrotizing Fasciitis) score: A tool for distinguishing necrotizing fasciitis from other soft tissue infections*

Wong, Chin-Ho, Khin, Lay-Wai, Heng, Kien-Seng, Tan, Kok-Chai, Low, Cheng-Ooi·2004·Critical Care Medicine

Abstract

OBJECTIVE: Early operative debridement is a major determinant of outcome in necrotizing fasciitis. However, early recognition is difficult clinically. We aimed to develop a novel diagnostic scoring system for distinguishing necrotizing fasciitis from other soft tissue infections based on laboratory tests routinely performed for the evaluation of severe soft tissue infections: the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score. DESIGN: Retrospective observational study of patients divided into a developmental cohort (n = 314) and validation cohort (n = 140) SETTING: Two teaching tertiary care hospitals. PATIENTS: One hundred forty-five patients with necrotizing fasciitis and 309 patients with severe cellulitis or abscesses admitted to the participating hospitals. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The developmental cohort consisted of 89 consecutive patients admitted for necrotizing fasciitis. Control patients (n = 225) were randomly selected from patients admitted with severe cellulitis or abscesses during the same period. Hematologic and biochemical results done on admission were converted into categorical variables for analysis. Univariate and multivariate logistic regression was used to select significant predictors. Total white cell count, hemoglobin, sodium, glucose, serum creatinine, and C-reactive protein were selected. The LRINEC score was constructed by converting into integer the regression coefficients of independently predictive factors in the multiple logistic regression model for diagnosing necrotizing fasciitis. The cutoff value for the LRINEC score was 6 points with a positive predictive value of 92.0% and negative predictive value of 96.0%. Model performance was very good (Hosmer-Lemeshow statistic, p =.910); area under the receiver operating characteristic curve was 0.980 and 0.976 in the developmental and validation cohorts, respectively. CONCLUSIONS: The LRINEC score is a robust score capable of detecting even clinically early cases of necrotizing fasciitis. The variables used are routinely measured to assess severe soft tissue infections. Patients with a LRINEC score of > or = 6 should be carefully evaluated for the presence of necrotizing fasciitis.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1097/01.ccm.0000129486.35458.7d

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

Metadata

Authors
Wong, C.-H., Khin, L.-W., Heng, K.-S., Tan, K.-C., & Low, C.-O.
Venue
Critical Care Medicine
Published
2004-07
Volume
32
Issue
7
Pages
1535-1541
ISSN
0090-3493 (ISSN-L)
OpenAlex
W2116080470
Cited by
1,611
References
2
FWCI
12.13
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.