ANALYSIS OF THE PREDICTIVE VALUE OF CLINICAL-DATA IN PATIENTS WITH SUSPECTED COLONIC DISEASE

Citation
S. Siles et al., ANALYSIS OF THE PREDICTIVE VALUE OF CLINICAL-DATA IN PATIENTS WITH SUSPECTED COLONIC DISEASE, Revista espanola de enfermedades digestivas, 89(6), 1997, pp. 451-456
Citations number
18
Categorie Soggetti
Gastroenterology & Hepatology
ISSN journal
11300108
Volume
89
Issue
6
Year of publication
1997
Pages
451 - 456
Database
ISI
SICI code
1130-0108(1997)89:6<451:AOTPVO>2.0.ZU;2-D
Abstract
Objective: To develop guidelines for predicting colonic disease on the basis of clinical parameters. Experimental design: A prospective stud y of the clinical data prior to colonoscopy. On the basis of the endos copic findings, the patients were divided into three diagnostic groups : absence of significant disease, significant benign disease and malig nant disease. The patient population was divided randomly into two sub groups. The clinical data from one of them teas used to build a databa se which, using Bayes' theorem, was compared with the variables from t he other subgroup to predict the diagnosis for each patient. Patients: A total of 336 patients (170 males and 166 females; mean age: 58 year s; range: 15 to 87 years) were evaluated. Results: When the endoscopic findings were grouped on the basis of their clinical importance, 211 patients (63%) belonged to the group without significant disease, 60 p atients (18%) had significant benign disease and 65 (19%) presented a neoplastic disease. Of the 21 variables selected for use in the databa se, 6 showed statistically significant differences in terms of the abs ence or presence of malignant disease: age, absence of previous simila r episodes, weight loss, rectal bleeding, lack of improvement and the presence of a mass on digital rectal examination. The predictive model differentiated patients with neoplasm from those without malignant di sease, but was not capable of identifying differences among the latter . The model was useful for assessing the risk of malignant disease for each patient. Conclusions: The predictive model obtained is a useful tool for establishing the diagnosis and the priority in the performanc e of colonoscopy.