Seeing the forest beyond the trees: Predicting survival in burn patients with machine learning - 24/04/18
, Witawat Daungjaiboon c , Sarah A. Brownlee b , Anthony J. Baldea a , Arthur P. Sanford a , Michael M. Mosier a , Paul C. Kuo a, b, ⁎ Abstract |
Background |
This study aims to identify predictors of survival for burn patients at the patient and hospital level using machine learning techniques.
Methods |
The HCUP SID for California, Florida and New York were used to identify patients admitted with a burn diagnosis and merged with hospital data from the AHA Annual Survey. Random forest and stochastic gradient boosting (SGB) were used to identify predictors of survival at the patient and hospital level from the top performing model.
Results |
We analyzed 31,350 patients from 670 hospitals. SGB (AUC 0.93) and random forest (AUC 0.82) best identified patient factors such as age and absence of renal failure (p < 0.001) and hospital factors such as full time residents (p < 0.001) and nurses (p = 0.004) to be associated with increased survival.
Conclusions |
Patient and hospital factors are predictive of survival in burn patients. It is difficult to control patient factors, but hospital factors can inform decisions about where burn patients should be treated.
Il testo completo di questo articolo è disponibile in PDF.Highlights |
• | Patient factors predicting survival were age, absence of renal failure, electrolyte abnormalities, coagulopathy and weekend admission. |
• | Hospital factors included having full-time residents and nursing staff, access to advanced imaging, number of ICU beds and overall surgical volume. |
• | Hospital and patient factors can be used to inform decisions on where burn patients should be treated. |
Keywords : Burns, Survival, Machine learning, Random forest, Outcomes
Mappa
Vol 215 - N° 3
P. 411-416 - marzo 2018 Ritorno al numero
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