Diagnosis of pain in the right iliac fossa. A new diagnostic score based on Decision-Tree and Artificial Neural Network Methods.

Autor: Gudelis M; Departamento de Cirugía, Hospital Universitario Arnau de Vilanova, Universidad de Lérida, Lérida, España., Lacasta Garcia JD; Departamento de Cirugía, Hospital Universitario Arnau de Vilanova, Universidad de Lérida, Lérida, España. Electronic address: jdlacasta@gmail.com., Trujillano Cabello JJ; Departamento de Cirugía, Hospital Universitario Arnau de Vilanova, Universidad de Lérida, Lérida, España.
Jazyk: English; Spanish; Castilian
Zdroj: Cirugia espanola [Cir Esp (Engl Ed)] 2019 Jun - Jul; Vol. 97 (6), pp. 329-335. Date of Electronic Publication: 2019 Apr 18.
DOI: 10.1016/j.ciresp.2019.02.006
Abstrakt: Introduction: Pain in the right iliac fossa (RIF) continues to pose diagnostic challenges. The objective of this study is the development of a RIF pain diagnosis model based on classification trees of type CHAID (Chi-Square Automatic Interaction Detection) and on an artificial neural network (ANN).
Methods: Prospective study of 252 patients who visited the hospital due to RIF pain. Demographic, clinical, physical examination and analytical data were registered. Patients were classified into 4 groups: NsP (nonspecific RIFP group), AA (acute appendicitis), NIRIF (RIF pain with no inflammation) and IRIF (RIF pain with inflammation). A CHAID-type classification tree model and an ANN were constructed. The classic models (Alvarado [ALS], Appendicitis Inflammatory Response [AIR] and Fenyö-Linberg [FLS]) were also evaluated. Discrimination was assessed using ROC curves (AUC [95% CI]) and the correct classification rate (CCR).
Results: 53% were men. Mean age 33.3±16 years. The largest group was the NsP (45%), AA (37%), NRIF (12%) and IRIF (6%). The analytical model results were: ALS (0.82 [0.76-0.87]), AIR (0.83 [0.77-0.88]) and FLS (0.88 [0.84-0.92]). CHAID determined 10 decision groups: 3 with high probability for NsP, 3 high for AA and 4 special groups with no predominant diagnosis. CCR of ANN and CHAID were 75% and 74.2%, respectively.
Conclusions: The methodology based on CHAID-type classification trees establishes a diagnostic model based on four pain groups in RIF and generates decision rules that can help us in the diagnosis of processes with RIF pain.
(Copyright © 2019 AEC. Publicado por Elsevier España, S.L.U. All rights reserved.)
Databáze: MEDLINE
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