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Diagnosis of pain in the right iliac fossa. A new diagnostic score based on Decision-Tree and Artificial Neural
Mindaugas Gudelis1, José Daniel Lacasta Garcia1, José Javier Trujillano Cabello1
1Departamento de Cirugía, Hospital Universitario Arnau de Vilanova, Universidad de Lérida, Lérida, España.
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.
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