Prediction of late seizures after ischaemic stroke with a novel prognostic model (the SeLECT score): a multivariable prediction model development and validation study

Galovic, Marian; Döhler, Nico; Erdélyi-Canavese, Barbara; Felbecker, Ansgar; Siebel, Philip; Conrad, Julian; Evers, Stefan; Winklehner, Michael; Oertzen, Tim J. von; Haring, Hans‐Peter; Serafini, Anna; Gregoraci, Giorgia; Valente, Mariarosaria; Janes, Francesco; Gigli, Gian Luigi; Keezer, Mark R; Duncan, John S.; Sander, Josemir W.; Koepp, Matthias J.; Tettenborn, Barbara

doi:10.1016/s1474-4422(17)30404-0

Cited by 209 publications

(277 citation statements)

References 40 publications

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“…Stroke‐related epilepsy prediction tools can help to risk‐stratify stroke survivors who are most likely to benefit from AED therapy. These could also become an important basic model in research that aims to prevent the transformation of infarcted tissue into an epileptic focus . Although many studies have identified variables associated with STRE, a well‐validated instrument that synthesizes clinical characteristics individually and objectively is lacking.…”

Section: Epidemiologymentioning

confidence: 99%

Stroke‐related epilepsy

Feyissa

Hasan

Meschia

2018

Euro J of Neurology

119

120

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Stroke is the cause of about 10% of all epilepsy and 55% of newly diagnosed seizures among the elderly. Although recent advances in acute stroke therapy have improved longevity, there has been a consequent rise in the prevalence of stroke-related epilepsy (STRE). Many clinical studies make a distinction between early (within 7 days of onset of stroke) and late (beyond 7 days of onset of stroke) seizures based on presumed pathophysiological differences. Although early seizures are thought to be the consequence of local metabolic disturbances without altered neuronal networks, late seizures are thought to occur when the brain has acquired a predisposition for seizures. Overall, STRE has a good prognosis, being well controlled by antiepileptic drugs. However, up to 25% of cases become drug resistant. STRE can also result in increased morbidity, longer hospitalization, greater disability at discharge and greater resource utilization. Additional controlled trials are needed to explore the primary and secondary prevention of STRE as well as to provide highquality evidence on efficacy and tolerability of antiepileptic drugs to guide treatment of STRE. Robust pre-clinical and clinical prediction models of STRE are also needed to develop treatments to prevent the transformation of infarcted tissue into an epileptic focus.

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Section: Epidemiologymentioning

confidence: 99%

Stroke‐related epilepsy

Feyissa

Hasan

Meschia

2018

Euro J of Neurology

119

120

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“…Monitoring tools have been presented in the clinical literature, but have limited appearances in the statistical literature. A recent clinical study, which was aimed at predicting late seizures after ischemic stroke using Cox proportional hazard modeling, presented a downloadable smart phone app for making predictions with new patients . A joint longitudinal‐survival model for prostate cancer recurrence was accompanied by an online risk calculator, providing predictions of up to 3 years in advance .…”

Section: Introductionmentioning

confidence: 99%

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Szczesniak

Brokamp

et al. 2019

Statistics in Medicine

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Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung‐function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for “nowcasting” rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between‐patient heterogeneity through random effects. Corresponding lung‐function decline at time t is defined as the rate of change, S′(t). We predict S′(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single‐center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real‐time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1‐Q3) were 0.817 (0.814‐0.822) and 0.745 (0.741‐0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical‐monitoring approach.

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“…However, our choice of flow cytometry markers reflects all major leukocyte lineages. Additionally,multiple papers have developed scores and multi-dimensional models based on pre-selected markers (Galovic et al, 2018;Liu et al, 2017;Schrag et al, 2017). Nonetheless, we cannot exclude that there are further useful markers to distinguish MS and NS, that we did not investigate and did not include in our model.…”

Section: Discussionmentioning

confidence: 97%

Leukocyte profiles in blood and CSF distinguish neurosarcoidosis from multiple sclerosis

Heming

Schulte‐Mecklenbeck

Brix

et al. 2020

Journal of Neuroimmunology

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A B S T R A C TDistinguishing neurosarcoidosis (NS) from multiple sclerosis (MS) remains challenging and available parameters lack discriminatory power. Comprehensive flow cytometry data of blood and CSF leukocytes of patients with NS (n = 24), MS (n = 49) and idiopathic intracranial hypertension (IIH, n = 52) were analyzed by machine learning algorithms. NS featured a specific immune cell pattern with increased activated CD4+ T cells in CSF and increased plasma cells in blood. Combining blood and CSF parameters improved the differentiation. We thereby identify and independently validate a multi-dimensional model of blood and CSF supporting the difficult differential diagnosis between NS and MS.

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Prediction of late seizures after ischaemic stroke with a novel prognostic model (the SeLECT score): a multivariable prediction model development and validation study

Abstract: Background: Stroke is one of the leading causes of acquired epilepsy in adults. An

Cited by 209 publications

References 40 publications

Stroke‐related epilepsy

Stroke‐related epilepsy

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Leukocyte profiles in blood and CSF distinguish neurosarcoidosis from multiple sclerosis

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