The impact of spontaneous intracranial hypotension on social life and health-related quality of life

Objective Spontaneous intracranial hypotension (SIH), which is often caused by a spinal cerebrospinal fluid leak, is an important cause of disabling headaches. Many patients report devastating changes in their quality of life because of their symptoms. This study aimed to evaluate the impact of SIH on patients' social/ working life and health-related quality of life (HRQoL). Methods We included consecutive patients with proven SIH treated at our institution from January 2013 to May 2020. Patients were contacted and asked to complete the 15D questionnaire for the collection of HRQoL data and to provide additional information on their social life status. Results Of 112 patients, 79 (70.5%) returned the questionnaire and were included in the analysis. Of those, 69 were treated surgically (87.3%), and 10 were managed non-operatively (12.7%). Twenty-five (31.6%) patients reported a severe impact on their partnership, 32 (41.5%) reported a moderate or severe impact on their social life. Forty (54.8%) patients reported sick leave for more than 3 months. The mean 15D score was 0.890 (+/- 0.114) and significantly impaired compared to an age- and sex-matched general population (p = 0.001), despite treatment. Patients with residual SIH-symptoms (36, 45.6%) had significantly impaired HRQoL compared to those without any residual symptoms (41, 51.9%) (p < 0.001). Conclusion SIH had a notable impact on the patients' social life and HRQoL. It caused long periods of incapacity for work, and is therefore, associated with high economic costs. Although all patients were appropriately treated, reduced HRQoL persisted after treatment, underlining the chronic character of this disease.Peer reviewe

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe

Peripheral metabolite pattern in heart failure patients is an independent predictor of survival

Background: Measurement of small molecules of intermediary metabolism is a method for characterizing biologic and disease states. Heart failure (HF) is an ideal candidate for this since evidence indicates that energetic derangements contribute to disease progression and metabolite correlations with survival have been described. We set out to assess whether a targeted metabolomics profile could predict survival in HF independent of established predictors. Methods: We analyzed plasma samples from 400 patients with chronic HF. All participants met Framingham criteria for HF. Data on demographics, ejection fraction, and comorbid conditions were collected, a blood sample was obtained and aliquoted plasma stored at -70C. Eight-six amino acids (AA), organic acids (OA), and acylcarnitines (AC) were quantified using targeted metabolomic profiling. Analytes with a coefficient of variation Results: The cohort was 50% African American, 50% female, 67% HFrEF, and had an average age of 70 years. Eleven metabolites had significant associations to survival time, with α-ketoglutarate being the strongest (p=1.24 x 10-9). Cross-validation of the lasso penalized model yielded a significant multi-metabolite predictor (p=3.73 x 10-5); the dichotomized risk score was associated with a 2.4 fold risk of death (Figure 1). When the multi-metabolite risk category was added to the conventional predictive model (which included NTproBNP, p=1.98 x 10-4), the multi-metabolite predictor remained independently associated with survival (HR 2.59, p=2.38 x 10-4). Conclusion: Plasma metabolite profile was a strong predictor of survival among HF patients, independent of clinical factors and NTproBNP. The key drivers included citric acid cycle intermediates, arginine, and short-chain acylcarnitines. Validation in a larger independent data set is warranted

Metabolomic patterns in heart failure patients vary across demographic and clinical factors

Background: Measurement of small molecules of intermediary metabolism (‘metabolites’) is an emerging method for characterizing a host of diverse disease states. Heart Failure (HF) is of particular interest since there is evidence that energetic derangements contribute to progression and certain metabolites have correlated with prognosis. Systematic description of metabolite patterns in large numbers of HF patients has not been described. Methods: We analyzed plasma samples from 400 patients with chronic HF. All participants met Framingham criteria for HF and had a previous echocardiogram. Data on demographics, comorbid conditions, functional status (6 minute walk distance [6MWD]), and quality of life (Kansas City Cardiomyopathy Questionnaire [KCCQ]) were collected. A blood sample was obtained and aliquoted plasma stored at -70 OC. Eighty-six amino acids (AA), organic acids (OA) and acylcarnitines (AC) were quantified using targeted metabolomic profiling. Analytes with coefficient of variation Results: The cohort was 50% African American, 50% female, 67% HFrEF, and had an average age of 70. There were significant differences in metabolite abundance by each characteristic examined, including race and gender. A strong pattern emerged for citric acid cycle intermediates and HF phenotype; one or more of these intermediates showed significant association with HF type, NYHA class, and 6MWD. Also of note was an increased abundance of short branched-chain AC among diabetics, which was not accompanied by the expected increase in corresponding branched-chain AA. Each of these associations persisted after adjustment for renal function. Conclusion: There are significant differences in plasma metabolomic profiles among HF patients. Metabolites vary by demographics and diabetes status, and citric acid cycle intermediates may be associated with disease severity/prognosis. Analysis in larger data sets is warranted