Nomogram-based predictive models are able to effectively predict patients at risk of verbal memory decline based on common measures of verbal memory following temporal lobe resection (TLR) for pharmacoresistant epilepsy.
Why this matters
Verbal memory decline is a well-known and common side effect of TLR, but difficult to predict in an individual pre-operative patient. Known risk factors include dominant-sided surgery, strong pre-operative memory, older age at seizure onset and surgery, and absence of hippocampal sclerosis. Although some predictive models have been developed, they carry a range of shortcomings that limit their use.
Nomograms are simple statistical tools that can integrate multivariate data effectively and are used in several areas of clinical medicine to inform decision-making. It is possible that nomogram-based predictive models could be useful in predicting verbal memory decline following TLR.