TY - JOUR
T1 - Predicting hospital admission from emergency department triage data for patients presenting with fall-related fractures
AU - Pai, Dinesh R.
AU - Rajan, Balaraman
AU - Jairath, Puneet
AU - Rosito, Stephen M.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Società Italiana di Medicina Interna (SIMI).
PY - 2023/1
Y1 - 2023/1
N2 - Purpose: Predict in advance the need for hospitalization of adult patients for fall-related fractures based on information available at the time of triage to help decision-making at the emergency department (ED). Methods: We developed machine learning models using routinely collected triage data at a regional hospital chain in Pennsylvania to predict admission to an inpatient unit. We considered all patients presenting to the ED for fall-related fractures. Patients who were 18 years or younger, who left the ED against medical advice, left the ED waiting room without being seen by a provider, and left the ED after initial diagnostics were excluded from the analysis. We compared models obtained using triage data (pre-model) with models developed using additional data obtained after physicians’ diagnoses (post-model). Results: Our results show good discriminatory power on predicting hospital admissions. Neural network models performed the best (AUC: pre-model = 0.938 [CI 0.920–0.956], post-model = 0.983 [0.974–0.992]). The logistic regression analysis provides additional insights into the data and the relationships between the variables. Conclusions: Using limited data available at the time of triage, we developed four machine learning models aimed at predicting hospitalization for patients presenting to the ED for fall-related fractures. All the four models were robust and performed well. Neural network method, however, performed the best for both pre- and post-models. Simple, parsimonious machine learning models can provide high accuracy for predicting hospital admission.
AB - Purpose: Predict in advance the need for hospitalization of adult patients for fall-related fractures based on information available at the time of triage to help decision-making at the emergency department (ED). Methods: We developed machine learning models using routinely collected triage data at a regional hospital chain in Pennsylvania to predict admission to an inpatient unit. We considered all patients presenting to the ED for fall-related fractures. Patients who were 18 years or younger, who left the ED against medical advice, left the ED waiting room without being seen by a provider, and left the ED after initial diagnostics were excluded from the analysis. We compared models obtained using triage data (pre-model) with models developed using additional data obtained after physicians’ diagnoses (post-model). Results: Our results show good discriminatory power on predicting hospital admissions. Neural network models performed the best (AUC: pre-model = 0.938 [CI 0.920–0.956], post-model = 0.983 [0.974–0.992]). The logistic regression analysis provides additional insights into the data and the relationships between the variables. Conclusions: Using limited data available at the time of triage, we developed four machine learning models aimed at predicting hospitalization for patients presenting to the ED for fall-related fractures. All the four models were robust and performed well. Neural network method, however, performed the best for both pre- and post-models. Simple, parsimonious machine learning models can provide high accuracy for predicting hospital admission.
UR - http://www.scopus.com/inward/record.url?scp=85138558123&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138558123&partnerID=8YFLogxK
U2 - 10.1007/s11739-022-03100-y
DO - 10.1007/s11739-022-03100-y
M3 - Article
C2 - 36136289
AN - SCOPUS:85138558123
SN - 1828-0447
VL - 18
SP - 219
EP - 227
JO - Internal and Emergency Medicine
JF - Internal and Emergency Medicine
IS - 1
ER -