Disease phenotype prediction in multiple sclerosis

Herman, Stephanie; Arvidsson, Staffan; Zhukovsky, Christina; Khoonsari, Payam Emami; Svenningsson, Anders; Burman, Joachim; Spjuth, Ola; Kultima, Kim

doi:10.1016/j.isci.2023.106906

Cited by 2 publications

(1 citation statement)

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“…Conversely, if a CP generates empty predictions, it signifies that a valid prediction cannot be made. We have recently demonstrated that CP can substantially reduce the number of errors made by an AI classifier in grading prostate biopsies 20 , and that ML in combination with CP can aid in predicting the transition of SPMS based on biomarkers measured in cerebrospinal fluid (CSF) analysis 21 . However, this approach has not been assessed with EHR data alone, which could circumvent the need for invasive or costly biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis

Sreenivasan,

Vaivade,

Noui

et al. 2024

Preprint

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Accurate assessment of progression and disease course in multiple sclerosis (MS) is vital for timely and appropriate clinical intervention. The transition from relapsing-remitting MS (RRMS) to secondary progressive MS (SPMS) is gradual and diagnosed retrospectively with a typical delay of three years. To address this diagnostic delay, we developed a predictive model that is able to distinguish between RRMS and SPMS with high accuracy, trained on data from electronic health records collected at routine hospital visits obtained from the Swedish MS Registry containing 22,748 patients with 197,227 hospital visits. To be useful within a clinical setting, we applied conformal prediction to deliver valid measures of uncertainty in predictions at the level of the individual patient. We showed that the model was theoretically and empirically valid, having the highest efficiency at a 92% confidence level, and demonstrated on an external test set that it enables effective prediction of the clinical course of a patient with individual confidence measures. We applied the model to a set of patients who transitioned from RRMS to SPMS during the cohort timeframe and showed that we can accurately predict when patients transition from RRMS to SPMS. We also identified new patients who, with high probability, are in the transition phase from RRMS to SPMS but have not yet received a clinical diagnosis. We conclude that our methodology can assist in monitoring MS disease progression and proactively identify patients undergoing transition to SPMS. An anonymized, publically accessible version of the model is available athttps://msp-tracker.serve.scilifelab.se/.

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Section: Introductionmentioning

confidence: 99%