Applicability of logistic regression (LR) risk modelling to decision making in lung cancer resection

Abstract

The objective of this study was to evaluate the performance of a locally derived risk-adjusted model to predict cardiorespiratory morbidity after major lung resection for bronchogenic carcinoma. A logistic regression risk model has been developed using a database of 515 patients undergoing major lung resection between 1994 and 2001. Independent studied variables were: age of the patient, body mass index, predicted postoperative forced expiratory volume in the first second (ppoFEV1%), cardiovascular co-morbidity, diabetes mellitus, induction chemotherapy, tumour staging, extent of resection, chest wall resection, and perioperative blood transfusion. The analyzed outcome was the occurrence of postoperative cardiorespiratory complications prospectively recorded and codified. Variables with an influence on the outcome on univariate analysis were entered in the risk model. The calculated probabilities of complication were compared to its actual occurrence in 53 consecutive cases operated on between January and June 2002 and a receiver operating characteristic (ROC) curve was constructed. On logistic regression analysis, age (P < 0.001) and ppoFEV1 (P = 0.003) independently correlated with the outcome. The accuracy for morbidity prediction (area under the ROC curve) was 0.55 (95% CI: 0.31-0.78). These data show that this locally derived lung resection risk-adjusted model fails to predict postoperative cardiorespiratory morbidity in individual patients.

Cross-Industry Standard Process for data mining is applicable to the lung cancer surgery domain, improving decision making as well as knowledge and quality management

“…Medical diagnosis usually prefers comprehensible patterns, even when they are less precise. This is because mistakes are too expensive in this domain (especially in surgical selection) and decisions must be made by specialists [8][9][10][11]. With logistic regression we can get a series of coeffi cients (odds ratio) [12] that measure every variable's capacity for classifying patients.…”

Analysis of survival for lung cancer resections cases with fuzzy and soft set theory in surgical decision making

“…We now comment on some approaches for the application of soft sets in DM practice in medicine. Various statistical and Artificial Intelligence techniques have been suggested as tools for the analysis of survival rate and surgical risk in lung cancer resections [39, 42, 62, 63] and in other medical fields [64, 65]. Clark et al .…”