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.
Highlights
METHODSAneurysmal subarachnoid hemorrhage is a common cause of stroke with high mortality and morbidity
Six different deep learning models were programmed using the Keras framework and were trained for aneurysmal subarachnoid hemorrhage (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
Summary
METHODSAneurysmal subarachnoid hemorrhage (aSAH) is a common cause of stroke with high mortality and morbidity. Further risk factors are alcohol and drug abuse ( cocaine), polycystic kidney disease, and fibromuscular dysplasia [3, 4] Several scores such as “UIATS”, “ELAPSS,” or “PHASES” have been created to predict aneurysm growth and the rates of aSAH using those risk [5,6,7]. The availability of large datasets allows event predictions, pattern recognition, and detailed image analysis. These networks can aid clinicians for diagnosis and treatment and can, improve the quality of patient care [24]. The big data and deep learning approach allowed to the simultaneous analysis of 13 different weather and climate parameters and 1,271 aSAH events over a course of 6,334 days (83,613 data points)
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