Comparison of prognostic models for severe burn patients in an Indonesian tertiary hospital: retrospective study

• Published in: Bali medical journal Vol. 11; no. 1; pp. 211 - 215
• Main Authors: Oenarta, Eunice Geraldine, Hamid, Agus Roy Rusly Hariantana, Sanjaya, I Gusti Putu Hendra, Adnyana, I Made Suka, Mahadewa, Tjokorda Gede Bagus, Agustinus, I Wayan Harimawan
• Format: Journal Article
• Language: English
• Published: 11.04.2022
• ISSN: 2089-1180; 2302-2914
• DOI: 10.15562/bmj.v11i1.3378

Background: Despite the high burn-related mortality rate, few studies investigate the discriminating power of various mortality prognostic models for burn patients in the Indonesian setting. Thus, the objective of our study is to describe and compare the discriminating power of several prognostic models for burn patients, including the Prognostic Burn Index (PBI), Belgian Outcome of Burn In-jury (BOBI), Revised Baux Score (rBaux), and Abbreviated Burn Severity Index (ABSI). Patients and methods: We conducted a retrospective study using medical records data with analysis including patients with severe burn (>20% Total Body Surface Area (TBSA), >10% for children and elderly), excluding those with known chronic conditions. Statistical analyses were performed on IBM SPSS 23.0 and included ROC curve and diagnostic values calculation, including sensitivity, specificity, positive, and negative predictive values with 95%CI. Results: Final analysis included 117 subjects with a 38.9% mortality rate. All prognostic models were statistically significant discriminating power with Area Under the Curve (AUC) values of 0.817, 0.849, 0.929, and 0.859, respectively. Comparing diagnostic values identified rBaux to have the strongest discriminating power with the highest specificity, PPV, and NPV. PBI score was identified with the highest sensitivity score. Conclusion: Discrepancy of results between our study and another Indonesian study with a similar design pointed to the need for a multi-centered study with a larger sample size to improve generalizability and statistical power of analysis.