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Clinical Prediction Rules for In-Hospital Mortality Outcome in Melioidosis Patients.

Sunee Chayangsu1, Chusana Suankratay2, Apichat Tantraworasin3,4

  • 1Department of Internal Medicine, Surin Hospital, Surin 32000, Thailand.

Tropical Medicine and Infectious Disease
|July 26, 2024
PubMed
Summary

A new scoring system predicts in-hospital melioidosis mortality using clinical data. This tool aids early identification of high-risk patients for improved outcomes in endemic tropical regions.

Keywords:
Burkholderia pseudomalleiclinical prediction rulesmelioidosismortality rateoutcomesepsis

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Area of Science:

  • Infectious Diseases
  • Clinical Medicine
  • Epidemiology

Background:

  • Melioidosis, caused by *Burkholderia pseudomallei*, is a significant threat in tropical areas.
  • High mortality rates persist despite available treatments, with numerous associated risk factors.
  • Predicting in-hospital mortality is crucial for timely intervention.

Purpose of the Study:

  • To develop a clinical scoring system for predicting in-hospital mortality in melioidosis patients.
  • To utilize readily available clinical data for risk stratification.
  • To improve clinical decision-making and patient outcomes.

Main Methods:

  • Retrospective data collection from Surin Hospital, Thailand (April 2014 - March 2017).
  • Inclusion of patients aged 15+ with positive *Burkholderia pseudomallei* cultures.
  • Development of clinical prediction rules using multivariable analysis of significant risk factors.

Main Results:

  • A scoring system was developed using five key predictors: qSOFA ≥ 2, abnormal chest X-ray, creatinine ≥ 1.5 mg/dL, AST ≥ 50 U/L, and bicarbonate ≤ 20 mEq/L.
  • High-risk scores (4-7) were associated with >65% mortality, while low-risk scores (0-3) had lower mortality.
  • The model demonstrated good performance with an AUC of 0.84.

Conclusions:

  • A simple, clinically applicable scoring system for predicting melioidosis in-hospital mortality has been developed.
  • This tool enables early identification of high-risk individuals.
  • Facilitates aggressive treatment strategies and potentially improves patient outcomes.