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MULTIDIMENSIONAL DATA VISUALIZATION TO EVALUATE THE POST-TREATMENT PROGNOSIS OF PATIENTS PRESENTING WITH METHANOL INTOXICATION: CHERNOFF FACES

Büşra Dilara Dayıoğlu, Ejder Saylav Bora, Mehmet Göktuğ Efgan, Mustafa Agah Tekindal, Elif Kaymaz

Journal of Academic Research in Medicine - 2026;16(1):29-35

University of Health Sciences Türkiye, Kartal Dr. Lütfi Kırdar City Hospital, Department of Emergency Medicine, İstanbul, Türkiye

 

Objective: Methanol intoxication is life-threatening and often needs urgent intervention due to severe metabolic disturbances. This study aims to evaluate Chernoff faces (CF) as a visual tool for representing patient prognosis. It also aims to support rapid clinical decision-making in cases of methanol intoxication. Methods: This retrospective study included 81 patients who presented to the emergency department with methanol intoxication. Key biochemical parameters-pH, bicarbonate (HCO3), base excess, and creatinine-were recorded before and after dialysis. CF, a graphical method for displaying multidimensional data, were used to visualize these values. The technique illustrated both individual-and group-level changes in metabolic status. Results: The cohort comprised 81 patients (6 females, 75 males; mean age: 52.9 years). After dialysis, significant improvements were observed: pH and HCO3 levels increased, while blood urea nitrogen and creatinine levels decreased. Chernoff's facial features clearly depict these changes. Pre-dialysis patients exhibited distressed facial expressions, reflecting severe acidosis. Post-dialysis faces appeared more harmonious and "smiling", suggesting metabolic recovery. This visual shift was consistent across the entire group and in selected individuals. The method effectively and intuitively demonstrated clinical improvement. Conclusion: CF provides a practical, visually intuitive method for monitoring metabolic recovery in methanol intoxication. By enabling rapid assessment of complex data, this tool enhances decision-making, particularly in time-sensitive settings like mass poisonings. Further studies will expand its use to other clinical contexts requiring the swift interpretation of multidimensional parameters.