Objective: To develop a diagnostic algorithm (decision tree) to improve the ability to identify or predict medically important spider bites (funnel-web and redback spiders) from information about the circumstances and initial clinical effects of spider bites.
Methods: A dataset of definite spider bites with expert identification of all spiders was used from a previous Australia-wide prospective study. Spider bites were categorized as: big black spider (BBS), redback spider (RED) and other spider (OTH). Big black spider included funnel-web spiders (most medically significant), but also other spiders of similar appearance. Fifteen predictor variables were based on univariate analysis from previous studies and clinical experience. They included information about the circumstances and early clinical effects of bites. The data were analyzed using CART (Classification and Regression Trees), a 'decision tree' algorithm used to create a tree-like structure to describe a data set.
Results: Of 789 spider bites there were 49 (6.2%) bites by BBS, 68 (8.6%) bites by RED and 672 (85.2%) bites by OTH. A decision tree was developed that included six predictor variables (fang marks/bleeding; state/territory; local diaphoresis; month; time of day; and proximal or distal bite region). The decision tree accurately classified 47 out of the 49 (96%) BBS, and no funnel-web spiders were incorrectly classified (100% sensitivity). Two hundred and forty-four of 789 were classified as OTH and included no BBS.
Conclusions: A decision tree based on a small amount of information about the circumstances and early clinical effects of spider bites safely predicted all funnel-web spider bites. Application of this algorithm would allow the early institution of appropriate treatment for funnel-web spider bites and the immediate discharge of 31% as other spider bites (reassurance only).