An economic evaluation of the NightWatch for children with refractory epilepsy:Insight into the cost-effectiveness and cost-utility

IntroductionWe performed an economic evaluation, from a societal perspective, to examine the cost-utility and cost-effectiveness of a wearable multimodal seizure detection device: NightWatch.MethodsWe collected data from the PROMISE study (NCT03909984), including children aged 4-16 years with refractory epilepsy living at home. Caregivers completed questionnaires on stress (Caregiver Strain Index), quality of life (EQ-5D-5L), health care consumption and productivity costs after the two-month baseline and the two-month intervention period with NightWatch. We used costs, stress levels and quality-adjusted life years (QALYs) to calculate incremental cost-effectiveness ratios (ICERs) and cost-effectiveness acceptability curves using bootstrapping. Missing items was handled with mean imputation. Three univariate sensitivity analyses examined the robustness of the results.ResultsWe included 41 children (18% female; mean age 9.8 years). We observed a decrease in mean costs of €775 during the intervention, compared to baseline. The QALYs were similar between both periods, yet at a ceiling ratio of €50,000, NightWatch showed a 72% cost-effective probability. Of the bootstrapped ICERs of the CSI, 82% lay in the dominant southeast quadrant (i.e., cost-effective). Univariate sensitivity analysis demonstrated result robustness.ConclusionsOur study shows that NightWatch may be a cost-effective addition to current standard care for children with refractory epilepsy living at home.Paroxysmal Cerebral Disorder

Parental preferences for seizure detection devices:A discrete choice experiment

AbstractIntorductionPrevious studies identified essential user preferences for seizure detection devices (SDDs), without addressing their relative strength. We performed a discrete choice experiment (DCE) to quantify attributes' strength, and to identify the determinants of user SDD preferences.MethodsWe designed an online questionnaire targeting parents of children with epilepsy to define the optimal balance between SDD sensitivity and positive predictive value (PPV) while accounting for individual seizure frequency. We selected five DCE attributes from a recent study. Using a Bayesian design, we constructed eleven unique choice tasks and analyzed these using a mixed multinomial logit model.ResultsOne hundred parents responded to the online questionnaire link; 49 completed all tasks, whereas 28 completed the questions, but not the DCE. Most parents preferred a relatively high sensitivity (80%-90%) over a high PPV (>50%). The preferred sensitivity-to-PPV ratio correlated with seizure frequency (r = −.32), with a preference for relative high sensitivity and low PPV among those with relative low seizure frequency (p = .04). All DCE attributes significantly impacted parental choices. Parents expressed preferences for consulting a neurologist before device use, personally training the device's algorithm, interaction with their child via audio and video, alarms for all seizure types, and an interface detailing measurements during an alarm. Preferences varied between subgroups (learning disability or not, SDD experience, relative low vs. high seizure frequency based on the population median).ConclusionsVarious attributes impact parental SDD preferences and may explain why preferences vary among users. Tailored approaches may help to meet the contrasting needs among SDD users.Paroxysmal Cerebral Disorder

Ictal autonomic changes as a tool for seizure detection:a systematic review

\u3cp\u3ePurpose: Adequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters. Methods: The PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms. Results: Twenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability (n = 10), heart rate (n = 4), or QRS morphology (n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71–100% vs. 64–96%, and mean FAR per participant 0.0–2.4/h vs. 0.7–5.4/h). Conclusions: The overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection.\u3c/p\u3

Ictal autonomic changes as a tool for seizure detection: a systematic review

Purpose: Adequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters. Methods: The PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms. Results: Twenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability (n = 10), heart rate (n = 4), or QRS morphology (n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71–100% vs. 64–96%, and mean FAR per participant 0.0–2.4/h vs. 0.7–5.4/h). Conclusions: The overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection

Ictal autonomic changes as a tool for seizure detection: a systematic review

PURPOSE: Adequate epileptic seizure detection may have the potential to minimize seizure-related complications and improve treatment evaluation. Autonomic changes often precede ictal electroencephalographic discharges and therefore provide a promising tool for timely seizure detection. We reviewed the literature for seizure detection algorithms using autonomic nervous system parameters. METHODS: The PubMed and Embase databases were systematically searched for original human studies that validate an algorithm for automatic seizure detection based on autonomic function alterations. Studies on neonates only and pilot studies without performance data were excluded. Algorithm performance was compared for studies with a similar design (retrospective vs. prospective) reporting both sensitivity and false alarm rate (FAR). Quality assessment was performed using QUADAS-2 and recently reported quality standards on reporting seizure detection algorithms. RESULTS: Twenty-one out of 638 studies were included in the analysis. Fifteen studies presented a single-modality algorithm based on heart rate variability (n = 10), heart rate (n = 4), or QRS morphology (n = 1), while six studies assessed multimodal algorithms using various combinations of HR, corrected QT interval, oxygen saturation, electrodermal activity, and accelerometry. Most studies had small sample sizes and a short follow-up period. Only two studies performed a prospective validation. A tendency for a lower FAR was found for retrospectively validated algorithms using multimodal autonomic parameters compared to those using single modalities (mean sensitivity per participant 71-100% vs. 64-96%, and mean FAR per participant 0.0-2.4/h vs. 0.7-5.4/h). CONCLUSIONS: The overall quality of studies on seizure detection using autonomic parameters is low. Unimodal autonomic algorithms cannot reach acceptable performance as false alarm rates are still too high. Larger prospective studies are needed to validate multimodal automatic seizure detection.status: publishe

Diagnostic markers for CNS lymphoma in blood and cerebrospinal fluid : a systematic review

Diagnosing central nervous system (CNS) lymphoma remains a challenge. Most patients have to undergo brain biopsy to obtain tissue for diagnosis, with associated risks of serious complications. Diagnostic markers in blood or cerebrospinal fluid (CSF) could facilitate early diagnosis with low complication rates. We performed a systematic literature search for studies on markers in blood or cerebrospinal fluid for the diagnosis CNS lymphoma and assessed the methodological quality of studies with the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). We evaluated diagnostic value of the markers at a given threshold, as well as differences between mean or median levels in patients versus control groups. Twenty-five studies were included, reporting diagnostic value for 18 markers in CSF (microRNAs -21, -19b, and -92a, RNU2-1f, CXCL13, interleukins -6, -8, and -10, soluble interleukin-2-receptor, soluble CD19, soluble CD27, tumour necrosis factor-alfa, beta-2-microglobulin, antithrombin III, soluble transmembrane activator and calcium modulator and cyclophilin ligand interactor, soluble B cell maturation antigen, neopterin and osteopontin) and three markers in blood (microRNA-21 soluble CD27, and beta-2-microglobulin). All studies were at considerable risk of bias and there were concerns regarding the applicability of 15 studies. CXCL-13, beta-2-microglobulin and neopterin have the highest potential in diagnosing CNS lymphoma, but further study is still needed before they can be used in clinical practice

Specific EEG markers in POLG1 Alpers’ syndrome

Objective: To examine whether rhythmic high-amplitude delta with superimposed (poly)spikes (RHADS) in EEG allow a reliable early diagnosis of Alpers-Huttenlocher syndrome (AHS) and contribute to recognition of this disease. Methods: EEGs of nine patients with DNA-proven AHS and fifty age-matched patients with status epilepticus were retrospectively examined by experts for the presence of RHADS and for accompanying clinical signs and high-frequency ripples. Reproducibility of RHADS identification was tested in a blinded panel. Results: Expert defined RHADS were found in at least one EEG of all AHS patients and none of the control group. RHADS were present at first status epilepticus in six AHS patients (67%). Sometimes they appeared 5–10 weeks later and disappeared over time. RHADS were symptomatic in three AHS patients and five AHS patients showed distinct ripples on the (poly)spikes of RHADS. Independent RHADS identification by the blinded panel resulted in a sensitivity of 87.5% (95% CI 47–100) and a specificity of 87.5% (95% CI 77–94) as compared to the experts’ reporting. Conclusion: RHADS are a highly specific EEG phenomenon for diagnosis of AHS and can be reliably recognized. Clinical expression and EEG ripples suggest that they signify an epileptic phenomenon. Significance: RHADS provide a specific tool for AHS diagnosis

Specific EEG markers in POLG1 Alpers’ syndrome

Objective: To examine whether rhythmic high-amplitude delta with superimposed (poly)spikes (RHADS) in EEG allow a reliable early diagnosis of Alpers-Huttenlocher syndrome (AHS) and contribute to recognition of this disease. Methods: EEGs of nine patients with DNA-proven AHS and fifty age-matched patients with status epilepticus were retrospectively examined by experts for the presence of RHADS and for accompanying clinical signs and high-frequency ripples. Reproducibility of RHADS identification was tested in a blinded panel. Results: Expert defined RHADS were found in at least one EEG of all AHS patients and none of the control group. RHADS were present at first status epilepticus in six AHS patients (67%). Sometimes they appeared 5–10 weeks later and disappeared over time. RHADS were symptomatic in three AHS patients and five AHS patients showed distinct ripples on the (poly)spikes of RHADS. Independent RHADS identification by the blinded panel resulted in a sensitivity of 87.5% (95% CI 47–100) and a specificity of 87.5% (95% CI 77–94) as compared to the experts’ reporting. Conclusion: RHADS are a highly specific EEG phenomenon for diagnosis of AHS and can be reliably recognized. Clinical expression and EEG ripples suggest that they signify an epileptic phenomenon. Significance: RHADS provide a specific tool for AHS diagnosis