TY - JOUR
T1 - To Scan or Not to Scan
T2 - Development of a Clinical Decision Support Tool to Determine if Imaging Would Aid in the Diagnosis of Appendicitis
AU - Gunasingha, Rathnayaka M.K.D.
AU - Grey, Scott F.
AU - Munoz, Beau
AU - Schobel, Seth
AU - Lee, Joseph
AU - Erwin, Casey
AU - Irons, Thomas
AU - McMillan, Elizabeth
AU - Unselt, Desiree
AU - Elster, Eric
AU - Bradley, Matthew
N1 - Publisher Copyright:
© 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
PY - 2021/10
Y1 - 2021/10
N2 - Background: Appendicitis is one of the most common surgically treated diseases in the world. CT scans are often over-utilized and ordered before a surgeon has evaluated the patient. Our aim was to develop a tool using machine learning (ML) algorithms that would help determine if there would be benefit in obtaining a CT scan prior to surgeon consultation. Methods: Retrospective chart review of 100 randomly selected cases who underwent appendectomy and 100 randomly selected controls was completed. Variables included components of the patient’s history, laboratory values, CT readings, and pathology. Pathology was used as the gold standard for appendicitis diagnosis. All variables were then used to build the ML algorithms. Random Forest (RF), Support Vector Machine (SVM), and Bayesian Network Classifiers (BNC) models with and without CT scan results were trained and compared to CT scan results alone and the Alvarado score using area under the Receiver Operator Curve (ROC), sensitivity, and specificity measures as well as calibration indices from 500 bootstrapped samples. Results: Among the cases that underwent appendectomy, 88% had pathology-confirmed appendicitis. All the ML algorithms had better sensitivity, specificity, and ROC than the Alvarado score. SVM with and without CT had the best indices and could predict if imaging would aid in appendicitis diagnosis. Conclusion: This study demonstrated that SVM with and without CT results can be used for selective imaging in the diagnosis of appendicitis. This study serves as the initial step and proof-of-concept to externally validate these results with larger and more diverse patient population.
AB - Background: Appendicitis is one of the most common surgically treated diseases in the world. CT scans are often over-utilized and ordered before a surgeon has evaluated the patient. Our aim was to develop a tool using machine learning (ML) algorithms that would help determine if there would be benefit in obtaining a CT scan prior to surgeon consultation. Methods: Retrospective chart review of 100 randomly selected cases who underwent appendectomy and 100 randomly selected controls was completed. Variables included components of the patient’s history, laboratory values, CT readings, and pathology. Pathology was used as the gold standard for appendicitis diagnosis. All variables were then used to build the ML algorithms. Random Forest (RF), Support Vector Machine (SVM), and Bayesian Network Classifiers (BNC) models with and without CT scan results were trained and compared to CT scan results alone and the Alvarado score using area under the Receiver Operator Curve (ROC), sensitivity, and specificity measures as well as calibration indices from 500 bootstrapped samples. Results: Among the cases that underwent appendectomy, 88% had pathology-confirmed appendicitis. All the ML algorithms had better sensitivity, specificity, and ROC than the Alvarado score. SVM with and without CT had the best indices and could predict if imaging would aid in appendicitis diagnosis. Conclusion: This study demonstrated that SVM with and without CT results can be used for selective imaging in the diagnosis of appendicitis. This study serves as the initial step and proof-of-concept to externally validate these results with larger and more diverse patient population.
UR - http://www.scopus.com/inward/record.url?scp=85112080765&partnerID=8YFLogxK
U2 - 10.1007/s00268-021-06246-6
DO - 10.1007/s00268-021-06246-6
M3 - Article
C2 - 34370058
AN - SCOPUS:85112080765
SN - 0364-2313
VL - 45
SP - 3056
EP - 3064
JO - World Journal of Surgery
JF - World Journal of Surgery
IS - 10
ER -