Interpretable deep learning survival predictions in sporadic Creutzfeldt–Jakob disease

Abstract

Background: Sporadic Creutzfeldt–Jakob disease (sCJD) is a rapidly progressive and fatal prion disease with significant public health implications. Survival is heterogenous, posing challenges for prognostication and care planning. We developed a survival model using diagnostic data from comprehensive UK sCJD surveillance. Methods: Using national CJD surveillance data from the United Kingdom (UK), we included 655 cases of probable or definite sCJD according to 2017 international consensus diagnostic criteria between 01/2017 and 01/2022. Data included symptoms at diagnosis, CSF RT-QuIC and 14-3-3, MRI and EEG findings, as well as sex, age, PRNP codon 129 polymorphism, CSF total protein and S100b. An artificial neural network based multitask logistic regression was used for survival analysis. Model-agnostic interpretation methods was used to assess the contribution of individual features on model outcome. Results: Our algorithm had a c-index of 0.732, IBS of 0.079, and AUC at 5 and 10 months of 0.866 and 0.872, respectively. This modestly improved on Cox proportional hazard model (c-index 0.730, IBS 0.083, AUC 0.852 and 0863) but was not statistically significant. Both models identified codon 129 polymorphism and CSF 14-3-3 to be significant predictive features. Conclusions: sCJD survival can be predicted using routinely collected clinical data at diagnosis. Our analysis pipeline has similar levels of performance to classical methods and provide clinically meaningful interpretation which help deepen clinical understanding of the condition. Further development and clinical validation will facilitate improvements in prognostication, care planning, and stratification to clinical trials.