Dataset Information

The Subarachnoid Hemorrhage-Weather Myth: A Long-Term Big Data and Deep Learning Analysis.

ABSTRACT: Objective: The frequency of aneurysmal subarachnoid hemorrhage (aSAH) presents complex fluctuations that have been attributed to weather and climate changes in the past. In the present long-term big data and deep learning analysis, we have addressed this long-held myth. Methods: Bleeding dates and basic demographic data for all consecutive patients (n = 1,271) admitted to our vascular center for treatment of aSAH between January 2003 and May 2020 (6,334 days) were collected from our continuously maintained database. The meteorological data of the local weather station, including 13 different weather and climate parameters, were retrieved from Germany's National Meteorological Service for the same period. Six different deep learning models were programmed using the Keras framework and were trained for aSAH event prediction with meteorological data from January 2003 to June 2017, with 10% of this dataset applied for data validation and model improvement. The dataset from July 2017 to May 2020 was tested for aSAH event prediction accuracy for all six models using the area under the receiver operating characteristic curve (AUROC) as the metric. Results: The study group comprised of 422 (33.2%) male and 849 (66.8%) female patients with an average age of 55 ± 14 years. None of the models showed an AUROC larger than 60.2. From the presented data, the influence of weather and climate on the occurrence of aSAH events is extremely unlikely. Conclusion: The myth of special weather conditions influencing the frequency of aSAH is disenchanted by this long-term big data and deep learning analysis.

SUBMITTER: Helsper M

PROVIDER: S-EPMC8131675 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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The Subarachnoid Hemorrhage-Weather Myth: A Long-Term Big Data and Deep Learning Analysis.

Helsper Moritz M Agarwal Aashish A Aker Ahmet A Herten Annika A Darkwah-Oppong Marvin M Gembruch Oliver O Deuschl Cornelius C Forsting Michael M Dammann Philipp P Pierscianek Daniela D Jabbarli Ramazan R Sure Ulrich U Wrede Karsten Henning KH

Frontiers in neurology 20210505

Objective: The frequency of aneurysmal subarachnoid hemorrhage (aSAH) presents complex fluctuations that have been attributed to weather and climate changes in the past. In the present long-term big data and deep learning analysis, we have addressed this long-held myth. Methods: Bleeding dates and basic demographic data for all consecutive patients (n = 1,271) admitted to our vascular center for treatment of aSAH between January 2003 and May 2020 (6,334 days) were collected ...[more]

PMID: 34025556

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Project description:IntroductionRadiological scores used to assess the extent of subarachnoid hemorrhage are limited by intrarater and interrater variability and do not utilize all available information from the imaging. Image segmentation enables precise identification and delineation of objects or regions of interest and offers the potential for automatization of score assessments using precise volumetric information. Our study aims to develop a deep learning model that enables automated multiclass segmentation of structures and pathologies relevant for aneurysmal subarachnoid hemorrhage outcome prediction.MethodsA set of 73 non-contrast CT scans of patients with aneurysmal subarachnoid hemorrhage were included. Six target classes were manually segmented to create a multiclass segmentation ground truth: subarachnoid, intraventricular, intracerebral and subdural hemorrhage, aneurysms and ventricles. We used the 2d and 3d configurations of the nnU-Net deep learning biomedical image segmentation framework. Additionally, we performed an interrater reliability analysis in our internal test set (n = 20) and an external validation on a set of primary intracerebral hemorrhage patients (n = 104). Segmentation performance was evaluated using the Dice coefficient, volumetric similarity and sensitivity.ResultsThe nnU-Net-based segmentation model demonstrated performance closely matching the interrater reliability between two senior raters for the subarachnoid hemorrhage, ventricles, intracerebral hemorrhage classes and overall hemorrhage segmentation. For the hemorrhage segmentation a median Dice coefficient of 0.664 was achieved by the 3d model (0.673 = 2d model). In the external test set a median Dice coefficient of 0.831 for the hemorrhage segmentation was achieved.ConclusionDeep learning enables automated multiclass segmentation of aneurysmal subarachnoid hemorrhage-related pathologies and achieves performance approaching that of a human rater. This enables automatized volumetries of pathologies identified on admission CTs in patients with subarachnoid hemorrhage potentially leading to imaging biomarkers for improved outcome prediction.

Project description:In about 25% of patients with spontaneous subarachnoid hemorrhage (SAH), a bleeding source cannot be identified during radiological diagnostics. Generally, the outcome of perimesencephalic or prepontine (PM) SAH is known to be significantly better than after non-PM SAH. Data about long-term follow-up concerning physical and mental health are scarce, so this study is reports on long-term results. We measured the influence of PM SAH on a quality-of-life modified Rankin (mRs) scale after six months. For long-term follow-up, a SF-36 questionnaire was used. Questionnaires were sent out between 18 and 168 months after ictus. In 37 patients, a long-term follow-up was available (up to 14 years after SAH). Data detected with the SF-36 questionnaire are compared to reference applicability to the standard population. In total, 37 patients were included for further analysis and divided in 2 subgroups; 13 patients (35%) received subsequent rehabilitation after clinical stay and 24 (65%) did not. In the short-term outcome, a significant improvement from discharge until follow-up was identified in patients with subsequent rehabilitation, but not in the matched pair group without rehabilitation. When PM SAH was compared to the standard population, a reduction in quality of life was identified in physical items (role limitations because of physical health problems, physical functioning) as well as in psychological items (role limitations because of emotional problems). Subsequent rehabilitation on PM SAH patients probably leads to an increase in independence and better mRs. While better mRs was shown at discharge in patients without subsequent rehabilitation, the mRs of rehabilitants was nearly identical after rehabilitation. Patients with good mRs also reached high levels of health-related quality of life (HRQoL) without rehabilitation. Thus, subsequent rehabilitation needs to be encouraged on an individual basis. Indication criteria for subsequent rehabilitation should be defined in further studies to improve patient treatment and efficiency in health care.

Dataset Information

The Subarachnoid Hemorrhage-Weather Myth: A Long-Term Big Data and Deep Learning Analysis.

Publications

The Subarachnoid Hemorrhage-Weather Myth: A Long-Term Big Data and Deep Learning Analysis.

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