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Toward Identifying a Multivariate Correlation of Septic Arthritis With a Machine Learning Approach: Time to Reset the

Sourav Bhattacharjee1,2, Imal C Hemachandra3, Sudharsan Venkatesan3

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Logistic regression models are more reliable for diagnosing septic arthritis than arbitrary synovial fluid white blood cell count thresholds. This approach better identifies key factors contributing to septic arthritis diagnosis.

Keywords:
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Area of Science:

  • Rheumatology and Infectious Diseases
  • Biostatistics and Medical Informatics

Background:

  • Septic arthritis diagnosis often relies on arbitrary thresholds, such as synovial leucocyte counts of ≥ 100,000/μL.
  • The complexity of septic arthritis pathology necessitates a more nuanced diagnostic approach than single-factor analysis.

Purpose of the Study:

  • To evaluate the diagnostic reliability of logistic regression models compared to arbitrary synovial leucocyte count thresholds for septic arthritis.
  • To identify significant independent variables contributing to septic arthritis diagnosis.

Main Methods:

  • Statistical analysis of a 360-episode patient dataset, including patient attributes and synovial fluid analysis.
  • Logistic regression modeling to predict septic arthritis, with results compared against synovial leucocyte count thresholds (≥ 100,000/μL and ≥ 50,000/μL).

Main Results:

  • The logistic regression model demonstrated superior performance (sensitivity 50%, specificity 97.04%) compared to arbitrary thresholds (e.g., ≥ 100,000/μL: sensitivity 48.21%, specificity 88.16%).
  • Significant predictors of septic arthritis included age, comorbidities (gout, autoimmune arthritis), hip joint involvement, synovial leucocyte count, and synovial crystals (p < 0.05).

Conclusions:

  • Septic arthritis diagnosis is multivariate and requires a holistic approach, not solely relying on individual factors.
  • Logistic regression is a valuable tool for analyzing the complex interplay of variables in diagnosing septic arthritis and other multisystem diseases.