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Related Concept Videos

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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A Novel Human Epithelial Enteroid Model of Necrotizing Enterocolitis
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Predictive Models for Necrotizing Soft Tissue Infections: Are the Available Scores Trustable?

Sophie Tran1, Kerry J Pullano1, Sharon Henry2

  • 1School of Medicine, University of Maryland, Baltimore, MD 21201, USA.

Journal of Clinical Medicine
|July 12, 2025
PubMed
Summary
This summary is machine-generated.

Necrotizing soft tissue infections (NSTIs) require better prognostic tools. Current scoring systems like LRINEC, PLR, NLR, NECROSIS, and POTTER have limitations, necessitating further development for reliable early assessment.

Keywords:
Laboratory Risk Indicator for Necrotizing Fasciitis Score (LRINEC Score)NECROSIS scorePOTTER scorein-hospital mortalitynecrotizing soft tissue infections (NSTI)neutrophil-to-lymphocyte ratio (NLR)platelet-to-lymphocyte ratio (PLR)

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Area of Science:

  • Infectious Diseases
  • Surgical Pathology
  • Medical Informatics

Background:

  • Necrotizing soft tissue infections (NSTIs) are severe conditions with high morbidity and mortality.
  • Early and reliable prognostic assessment tools for NSTIs are lacking in current medical literature.
  • Existing scoring systems demonstrate limitations in sensitivity, specificity, and validation for NSTI patients.

Purpose of the Study:

  • To review and analyze existing prognostic scoring systems for NSTIs.
  • To identify limitations and areas for improvement in current NSTI assessment tools.
  • To explore the potential of novel approaches, including AI, for NSTI prognosis.

Main Methods:

  • Systematic literature search of PubMed and Google Scholar.
  • Overview and critical analysis of five scoring systems: LRINEC, PLR, NLR, NECROSIS, and POTTER.
  • Evaluation of each system's strengths, weaknesses, and potential for improvement or novel application.

Main Results:

  • The LRINEC score lacks sensitivity and requires supplementary clinical data.
  • The NECROSIS score shows promise but needs external validation.
  • NLR and PLR offer insights into immune response but lack NSTI specificity.
  • The POTTER score, utilizing AI, is innovative but not yet validated for NSTI.

Conclusions:

  • A reliable, comprehensive scoring system for NSTI prognosis is still needed.
  • Further development is required to address the limitations of existing tools.
  • Integrative models, potentially incorporating AI like the POTTER score, may offer future solutions.