Related Experiment Video
Updated: Jul 10, 2026
A Contemporary Warming/Restraining Device for Efficient Tail Vein Injections in a Murine Fungal Sepsis Model
Published on: November 6, 2020
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.
Background:
Melioidosis, a disease induced by Burkholderia pseudomallei, poses a significant health threat in tropical areas where it is endemic. Despite the availability of effective treatments, mortality rates remain notably elevated. Many risk factors are associated with mortality. This study aims to develop a scoring system for predicting the in-hospital mortality from melioidosis using readily available clinical data.
Methods:
The data were collected from Surin Hospital, Surin, Thailand, during the period from April 2014 to March 2017. We included patients aged 15 years and above who had cultures that tested positive for Burkholderia pseudomallei. The clinical prediction rules were developed using significant risk factors from the multivariable analysis.
Results:
A total of 282 patients with melioidosis were included in this study. In the final analysis model, 251 patients were used for identifying the significant risk factors of in-hospital fatal melioidosis. Five factors were identified and used for developing the clinical prediction rules, and the factors were as follows: qSOFA ≥ 2 (odds ratio [OR] = 2.39, p= 0.025), abnormal chest X-ray findings (OR = 5.86, p < 0.001), creatinine ≥ 1.5 mg/dL (OR = 2.80, p = 0.004), aspartate aminotransferase ≥50 U/L (OR = 4.032, p < 0.001), and bicarbonate ≤ 20 mEq/L (OR = 2.96, p = 0.002). The prediction scores ranged from 0 to 7. Patients with high scores (4-7) exhibited a significantly elevated mortality rate exceeding 65.0% (likelihood ratio [LR+] 2.18, p < 0.001) compared to the low-risk group (scores 0-3) with a lower mortality rate (LR + 0.18, p < 0.001). The area under the receiver operating characteristic curve (AUC) was 0.84, indicating good model performance.
Conclusions:
This study presents a simple scoring system based on easily obtainable clinical parameters to predict in-hospital mortality in melioidosis patients. This tool may facilitate the early identification of high-risk patients who could benefit from more aggressive treatment strategies, potentially improving clinical decision-making and patient outcomes.
Insights
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.
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.
More Related Videos
07:27Simplified Whole Body Plethysmography to Characterize Lung Function During Respiratory Melioidosis
Published on: February 24, 2023
06:19Author Spotlight: Characterizing Airway Environments to Advance Model Systems and Antimicrobial Discovery in Cystic Fibrosis and Chronic Respiratory Infections
Published on: October 11, 2024
