Abstract
Background
Reported data regarding the relation between the incidence of spontaneous subarachnoid hemorrhage (SAH) and weather conditions are conflicting and do so far not allow prognostic models.
Methods
Admissions for spontaneous SAH (ICD I60.*) 2009–2018 were retrieved form our hospital data base. Historical meteorological data for the nearest meteorological station, Düsseldorf Airport, was retrieved from the archive of the Deutsche Wetterdienst (DWD). Airport is in the center of our catchment area with a diameter of approximately 100 km. Pearson correlation matrix between mean daily meteorological variables and the daily admissions of one or more patients with subarachnoid hemorrhage was calculated and further analysis was done using deep learning algorithms.
Results
For the 10-year period from January 1, 2009 until December 31, 2018, a total of 1569 patients with SAH were admitted. No SAH was admitted on 2400 days (65.7%), 1 SAH on 979 days (26.7%), 2 cases on 233 days (6.4%), 3 SAH on 37 days (1.0%), 4 in 2 days (0.05%), and 5 cases on 1 day (0.03%). Pearson correlation matrix suggested a weak positive correlation of admissions for SAH with precipitation on the previous day and weak inverse relations with the actual mean daily temperature and the temperature change from the previous days, and weak inverse correlations with barometric pressure on the index day and the day before. Clustering with admission of multiple SAH on a given day followed a Poisson distribution and was therefore coincidental. The deep learning algorithms achieved an area under curve (AUC) score of approximately 52%. The small difference from 50% appears to reflect the size of the meteorological impact.
Conclusion
Although in our data set a weak correlation of the probability to admit one or more cases of SAH with meteorological conditions was present during the analyzed time period, no helpful prognostic model could be deduced with current state machine learning methods. The meteorological influence on the admission of SAH appeared to be in the range of only a few percent compared with random or unknown factors.
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References
Abe T, Ohde S, Ishimatsu S, Ogata H, Hasegawa T, Nakamura T, Tokuda Y (2008) Effects of meteorological factors on the onset of subarachnoid hemorrhage: a time-series analysis. J Clin Neurosci 15(9):1005–1010. https://doi.org/10.1016/j.jocn.2007.07.081
Backes D, Rinkel GJ, Algra A, Vaartjes I, Donker GA, Vergouwen MD (2016) Increased incidence of subarachnoid hemorrhage during cold temperatures and influenza epidemics. J Neurosurg 125(3):737–745. https://doi.org/10.3171/2015.8.JNS151473
Baño-Ruiz E, Abarca-Olivas J, Duart-Clemente JM, Ballenilla-Marco F, García P, Botella-Asunción C (2010) Influence of the atmospheric pressure and other variable weather on the incidence of the subarachnoid hemorrhage. Neurocirugia (Astur) 21(1):14–21 Spanish
Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2:125–137
Beseoglu K, Hänggi D, Stummer W, Steiger HJ (2008) Dependence of subarachnoid hemorrhage on climate conditions: a systematic meteorological analysis from the Dusseldorf metropolitan area. Neurosurgery 62(5):1033–1038; discussion 1038-9. https://doi.org/10.1227/01.neu.0000325864.91584.c7
Brownlee J (2016) How to compare machine learning algorithms in Python with scikit-learn. https://machinelearningmastery.com/compare-machine-learning-algorithms-python-scikit-learn/. Accessed 30 May 2019
Cao Y, Wang X, Zheng D, Robinson T, Hong D, Richtering S, Leong TH, Salam A, Anderson C, Hackett ML (2016) Air pressure, humidity and stroke occurrence: a systematic review and meta-analysis. Int J Environ Res Public Health 5(7):13. https://doi.org/10.3390/ijerph13070675 Review
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: Synthetic Minority Over-sampling Technique. JAIR 16:321–357. https://doi.org/10.1613/jair.953
Chyatte D, Chen TL, Bronstein K, Brass LM (1994) Seasonal fluctuation in the incidence of intracranial aneurysm rupture and its relationship to changing climatic conditions. J Neurosurg 81(4):525–530
Cowperthwaite MC, Burnett MG (2011) The association between weather and spontaneous subarachnoid hemorrhage: an analysis of 155 US hospitals. Neurosurgery 68(1):132–138; discussion 138-9. https://doi.org/10.1227/NEU.0b013e3181fe23a1
Gill RS, Hambridge HL, Schneider EB, Hanff T, Tamargo RJ, Nyquist P (2013) Falling temperature and colder weather are associated with an increased risk of aneurysmal subarachnoid hemorrhage. World Neurosurg 79(1):136–142. https://doi.org/10.1016/j.wneu.2012.06.020
Grandi A (2018) Machine Learning: Pima Indians Diabetes. https://www.andreagrandi.it/2018/04/14/machine-learning-pima-indians-diabetes/. Accessed 30 May 2019
Han MH, Kim J, Choi KS, Kim CH, Kim JM, Cheong JH, Yi HJ, Lee SH (2017) Monthly variations in aneurysmal subarachnoid hemorrhage incidence and mortality: correlation with weather and pollution. PLoS One 12(10):e0186973. https://doi.org/10.1371/journal.pone.0186973 eCollection 2017
Hakan T, Kizilkilic O, Adaletli I, Karabagli H, Kocer N, Islak C (2003) Is there any seasonal influence in spontaneous bleeding of intracranial aneurysm and and/or AVM in Istanbul? Swiss Med Wkly 133(17-18):267–272
Herten A, Jabbarli R, Dammann P , Hütter BO, Sure U, Wrede K (2016) Epidemiology of aneurysmal subarachnoid hemorrhage with special focus on seasonal and circaseptan influences as well as the relation with short-term weather changes: a long-term study of 821 bleeding events. Abstracts of the 67th Annual Meeting of the German Society of Neurosurgery (DGNC). https://www.egms.de/static/en/meetings/dgnc2016/16dgnc003.shtml. Accessed 30 May 2019
Izumihara A (2012) Epidemiology of subarachnoid hemorrhage in the Yaeyama Islands, an isolated subtropical region of Japan most frequently affected by typhoons: a population-based study. Clin Neurol Neurosurg 114(9):1226–1231. https://doi.org/10.1016/j.clineuro.2012.03.001
Jehle D, Moscati R, Frye J, Reich N (1994) The incidence of spontaneous subarachnoid hemorrhage with change in barometric pressure. Am J Emerg Med 12(1):90–91
Kellogg M, Petrov D, Agarwal N, Patel NV, Hansberry DR, Agarwal P, Brimacombe M, Gandhi CD, Prestigiacomo C (2017) Effects of meteorological variables on the incidence of rupture of intracranial aneurysms in Central New Jersey. J Neurol Surg A Cent Eur Neurosurg 78(3):238–244. https://doi.org/10.1055/s-0036-1594308
Lai PM, Dasenbrock H, Du R (2014) The association between meteorological parameters and aneurysmal subarachnoid hemorrhage: a nationwide analysis. PLoS One 9(11):e112961. https://doi.org/10.1371/journal.pone.0112961 eCollection 2014
Landers AT, Narotam PK, Govender ST, van Dellen JR (1997) The effect of changes in barometric pressure on the risk of rupture of intracranial aneurysms. Br J Neurosurg 11(3):191–195
Law HY, Wong GK, Chan DT, Wong L, Poon WS (2009) Meteorological factors and aneurysmal subarachnoid haemorrhage in Hong Kong. Hong Kong Med J 15(2):85–89
Lee S, Guth M (2017) Associations between temperature and hospital admissions for subarachnoid hemorrhage in Korea. Int J Environ Res Public Health 14(4). https://doi.org/10.3390/ijerph14040449
Lejeune JP, Vinchon M, Amouyel P, Escartin T, Escartin D, Christiaens JL (1994) Association of occurrence of aneurysmal bleeding with meteorologic variations in the north of France. Stroke 25(2):338–341
McDonald RJ, McDonald JS, Bida JP, Kallmes DF, Cloft HJ (2012) Subarachnoid hemorrhage incidence in the United States does not vary with season or temperature. AJNR Am J Neuroradiol 33(9):1663–1668. https://doi.org/10.3174/ajnr.A3059
Muroi C, Yonekawa Y, Khan N, Rousson V, Keller E (2004) Seasonal variations in hospital admissions due to aneurysmal subarachnoid haemorrhage in the state of Zurich, Switzerland. Acta Neurochir 146(7):659–665
Neidert MC, Sprenger M, Wernli H, Burkhardt J-K, Krayenbühl N, Bozinov O, Regli L, Woernle CM (2013) Meteorological influences on the incidence of aneurysmal subarachnoid hemorrhage - a single center study of 511 patients. PLoS One 8:e81621. https://doi.org/10.1371/journal.pone.0081621
Patrice T, Rozec B, Desal H, Blanloeil Y (2017) Oceanic meteorological conditions influence incidence of aneurysmal subarachnoid hemorrhage. J Stroke Cerebrovasc Dis 26(7):1573–1581. https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.02.031
Rivera-Lara L, Kowalski RG, Schneider EB, Tamargo RJ, Nyquist P (2015) Elevated relative risk of aneurysmal subarachnoid hemorrhage with colder weather in the mid-Atlantic region. J Clin Neurosci 22(10):1582–1587. https://doi.org/10.1016/j.jocn.2015.03.033
Roberts SJ (1997) Parametric and non-parametric unsupervised cluster analysis. Pattern Recogn 30(2):261–272
Rosenørn J, Rønde F, Eskesen V, Schmidt K (1988) Seasonal variation of aneurysmal subarachnoid haemorrhage. Acta Neurochir 93(1-2):24–27
Rué M, Camiade E, Jecko V, Bauduer F, Vignes JR (2014) The relationship between aneurysmal subarachnoid hemorrhage and meteorological parameters based on a series of 236 French patients. Neurochirurgie 60(5):222–226. https://doi.org/10.1016/j.neuchi.2014.02.010 French
Setzer M, Beck J, Hermann E, Raabe A, Seifert V, Vatter H, Marquardt G (2007) The influence of barometric pressure changes and standard meteorological variables on the occurrence and clinical features of subarachnoid hemorrhage. Surg Neurol 67(3):264–272 discussion 272
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958
Staartjes VE, Schröder ML (2018) Class imbalance in machine learning for neurosurgical outcome prediction: are our models valid? J Neurosurg Spine 29:611–612. https://doi.org/10.3171/2018.5.SPINE18543
Tarnoki AD, Turker A, Tarnoki DL, Iyisoy MS, Szilagyi BK, Duong H, Miskolczi L (2017) Relationship between weather conditions and admissions for ischemic stroke and subarachnoid hemorrhage. Croat Med J 58(1):56–62
van Donkelaar CE, Potgieser ARE, Groen H, Foumani M, Abdulrahman H, Sluijter R, van Dijk JMC, Groen RJM (2018) Atmospheric pressure variation is a delayed trigger for aneurysmal subarachnoid hemorrhage. World Neurosurg 112:e783–e790. https://doi.org/10.1016/j.wneu.2018.01.155
Zheng Y, Wang X, Liu J, Zhao F, Zhang J, Feng H (2016) A community-based study of the correlation of hemorrhagic stroke occurrence with meteorologic factors. J Stroke Cerebrovasc Dis 25(10):2323–2330. https://doi.org/10.1016/j.jstrokecerebrovasdis.2014.12.028
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee (Medical Faculty of the Heinrich-Heine-University, Düsseldorf) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.
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@@Congratulations on this elegant approach to demystify the effect of meteorological variables on the frequency of aneurysm rupture. The approximation of 3-12% impact from weather per se on a SAH is fascinating, though not surprising. Their findings concur with reports on the relation of meteorological factors on daily admissions of type A aortic dissections, abdominal aortic aneurysm rupture and acute myocardial infarction. The findings in this article suggest a common background factor which very well may be blood pressure levels. Blood pressure elevation is a common risk factor for all these vascular emergencies and the known chronobiological circardian and seasonal variations in blood pressure seem to coincide with the respective admission rates.
Angelika Sorteberg
Oslo, Norway
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Steiger, HJ., Petridis, A.K., Tortora, A. et al. Meteorological factors for subarachnoid hemorrhage in the greater Düsseldorf area revisited: a machine learning approach to predict the probability of admission of patients with subarachnoid hemorrhage. Acta Neurochir 162, 187–195 (2020). https://doi.org/10.1007/s00701-019-04128-4
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DOI: https://doi.org/10.1007/s00701-019-04128-4