当前位置： SCI文献检索 > ARTIFICIAL INTELLIGENCE IN MEDICINE期刊下所有文献 > Case-based distance measurements for the selection of controls in case-matched studies: application in coronary interventions.

Case-based distance measurements for the selection of controls in case-matched studies: application in coronary interventions.

Abstract：

:In case-based studies, controls are retrospectively assigned to patients in order to permit a statistical evaluation of the study results through a comparison of the main outcome measures for the patient and retrieved control groups. Inappropriate selection of the controls by using false retrieval parameters or a false algorithm might lead to an incorrect data analysis and a false interpretation of the results. The main hypothesis of the presented study was that, if the matching baseline parameters were at least nearly perfectly selected, then the outcomes of the matched pairs should be similar, or no significant differences in study outcome should be observed between the patients and the matched controls. In the present study, the case-based domain was created from 1566 patients who had undergone intracoronary stent implantation. Uni- and multivariate logistic regression analysis determined nine significant predictors (matching variables) for the occurrence of major adverse cardiac events. An additional 425 consecutive patients undergoing intracoronary stent implantation were then matched with all the potential controls from the database by calculating the individual distance between the patient and the matched control on the basis of the elaborated retrieval algorithm. To test the matching hypothesis, different distance measurements, and an altered outcome and retrieval process were examined. With "flexible" distance measurements, the mean distance between the patients and the first matched controls was 1.31. The major adverse cardiac events were compared in the patient and matched control groups. The best sensitivity and specificity values of the matching system could be achieved in matched pairs with the distance < or =3.00 (95.1% of all patients). On the further stepwise exclusion of the matched pairs exhibiting a distance greater than 2 and 1, then the number of "matchable" controls and the specificity of our matching concept decreased considerably. When the short- or mid-term outcome was compared by using the long-term follow-up matching parameters, no correlation could be found between the treated subjects and controls, indicating that for the other study, main measures of other appropriate parameters must be selected, and demonstrating the importance of the outcome-oriented selection of the retrieval parameters. Furthermore, the outcome measures of the patients and randomly (non-systematically) selected controls did not correlate, revealing the impossibility of drawing correct study conclusions from a non-matched, randomly assigned pairs. In conclusion, the sensitivity and specificity of the matching program, and the study conclusions depend on the appropriately predefined matching parameters and retrieval algorithm.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Gyöngyösi M,Ploner M,Porenta G,Sperker W,Wexberg P,Strehblow C,Glogar D

doi

10.1016/s0933-3657(02)00084-2

subject

Has Abstract

pub_date

2002-11-01 00:00:00

pages

237-53

issue

eissn

0933-3657

issn

1873-2860

pii

S0933365702000842

journal_volume

pub_type

杂志文章

在线工具

Case-based distance measurements for the selection of controls in case-matched studies: application in coronary interventions.