Online calculator to predict early mortality in patient with surgically treated recurrent lower-grade glioma

Abstract

Abstract Purpose The aim of this study was to investigate the epidemiological characteristics and associated risk factors of recurrent lower-grade glioma [LGG] (WHO grades II and III) according to the 2016 updated WHO classification paradigm and finally develop a model for predicting early mortality (succumb within a year after reoperation) in recurrent LGG patients. Methods Data were obtained from consecutive patients who underwent surgery for primary LGG and reoperation for tumor recurrence. The end point “early mortality” was defined as death within 1 year after the reoperation. Predictive factors, including basic clinical characteristics and laboratory data, were retrospectively collected. Results A final nomogram was generated for surgically treated recurrent LGG. Factors that increased the probability of early mortality included older age (P = 0.042), D-dimer> 0.187 (P = 0.007), RDW > 13.4 (P = 0.048), PLR > 100.749 (P = 0.014), NLR > 1.815 (P = 0.047), 1p19q intact (P = 0.019), IDH1-R132H Mutant (P = 0.048), Fib≤2.80 (P = 0.018), lack of Stupp concurrent chemoradiotherapy (P = 0.041), and an initial symptom of epilepsy (P = 0.047). The calibration curve between the prediction from this model and the actual observations showed good agreement. Conclusion: A nomogram that predicts individualized probabilities of early mortality for surgically treated recurrent LGG patients could be a practical clinical tool for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free online software implementing this nomogram is provided at https://warrenwrl.shinyapps.io/RecurrenceGliomaEarlyM/

Keywords

• Lower-grade glioma
• Early mortality
• Recurrent glioma
• Nomogram