Dolor agudo en fosa ilíaca derecha. Un nuevo algoritmo de diagnóstico basado en árboles de clasificación y red neuronal artificial / Diagnosis of pain in the right iliac fossa. A new diagnostic score based on Decision-Tree and Artificial Neural Network Methods

Introducción: El dolor en fosa ilíaca derecha (FID) sigue planteando problemas diagnósticos. El objetivo de este estudio es la elaboración de un modelo diagnóstico de dolor en FID basado en árboles de clasificación (CHAID) y en una red neuronal artificial (RNA). Métodos: Estudio prospectivo de 252 pacientes que acudieron al hospital por presentar dolor en FID. Se recogieron datos demográficos, clínicos, exploración física y analíticos. Se clasificaron en 4 grupos: dolor simple en FID (dFID), apendicitis aguda (AA), dolor abdominal sin proceso inflamatorio (DASPI) y dolor abdominal con proceso inflamatorio (DACPI). Se construyó un modelo de árbol de clasificación tipo Chi-Square Automatic Interaction Detection (CHAID) y un modelo de RNA. Se evaluaron también los modelos clásicos (Alvarado [ALS], Appendicitis Inflammatory Response [AIR] y Fenyö-Lindberg FLS]). Se evaluó la discriminación mediante curvas ROC (ABC [IC 95%]) y porcentaje de correcta clasificación (PCC). Resultados: El 53% eran varones. Edad media 33,3±16 años. El grupo más numeroso fue el de dFID (45%), AA (37%), DASPI (12%) y DACPI (6%). Discriminación de ALS (0,82 [0,76-0,87]), AIR (0,83 [0,77-0,88]) y FLS (0,88 [0,84-0,92]). El CHAID determina 10 grupos de decisión: 3 con probabilidad altas para dFID, 3 altas para AA y 4 especiales sin diagnóstico predominante. PCC de RNA y CHAID con el 75 y 74,2%, respectivamente. Conclusiones: La metodología basada en árboles de clasificación tipo CHAID permite establecer un modelo diagnóstico basado en cuatro grupos de dolor en FID y genera reglas de decisión que pueden ayudarnos en el diagnóstico de procesos con dolor en FID

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

Diagnosis of pain in the right iliac fossa. A new diagnostic score based on Decision-Tree and Artificial Neural Network Methods. / Dolor agudo en fosa ilíaca derecha. Un nuevo algoritmo de diagnóstico basado en árboles de clasificación y red neuronal artificial.

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.

How is intensive care reimbursed? A review of eight European countries.

Reimbursement schemes in intensive care are more complex than in other areas of healthcare, due to special procedures and high care needs. Knowledge regarding the principles of functioning in other countries can lead to increased understanding and awareness of potential for improvement. This can be achieved through mutual exchange of solutions found in other countries. In this review, experts from eight European countries explain their respective intensive care unit reimbursement schemes. Important conclusions include the apparent differences in the countries' reimbursement schemes-despite all of them originating from a DRG system-, the high degree of complexity found, and the difficulties faced in several countries when collecting the data for this collaborative work. This review has been designed to assist the intensivist clinician and researcher in understanding neighbouring countries' approaches and in putting research into the context of a European perspective. In addition, steering committees and decision makers might find this a valuable source to compare different reimbursement schemes.