Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Article - Vascular Neurosurgery - Aneurysm
  • Published:
Acta Neurochirurgica Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. 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

    Article  PubMed  Google Scholar 

  2. 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

    Article  PubMed  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2:125–137

    Google Scholar 

  5. 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

    Article  PubMed  Google Scholar 

  6. 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

  7. 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

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. 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

    Article  CAS  Google Scholar 

  10. 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

    Article  PubMed  Google Scholar 

  11. 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

    Article  PubMed  Google Scholar 

  12. Grandi A (2018) Machine Learning: Pima Indians Diabetes. https://www.andreagrandi.it/2018/04/14/machine-learning-pima-indians-diabetes/. Accessed 30 May 2019

  13. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 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

    PubMed  Google Scholar 

  15. 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

  16. 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

    Article  PubMed  Google Scholar 

  17. 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

    Article  CAS  Google Scholar 

  18. 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

    Article  PubMed  Google Scholar 

  19. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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

    Article  CAS  Google Scholar 

  21. 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

    CAS  PubMed  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. 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

    Article  CAS  Google Scholar 

  24. 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

    Article  CAS  PubMed  Google Scholar 

  25. 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

    Article  CAS  Google Scholar 

  26. 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 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

    Article  PubMed  Google Scholar 

  28. 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

    Article  PubMed  Google Scholar 

  29. Roberts SJ (1997) Parametric and non-parametric unsupervised cluster analysis. Pattern Recogn 30(2):261–272

    Article  Google Scholar 

  30. Rosenørn J, Rønde F, Eskesen V, Schmidt K (1988) Seasonal variation of aneurysmal subarachnoid haemorrhage. Acta Neurochir 93(1-2):24–27

    Article  Google Scholar 

  31. 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

    Article  PubMed  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. 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

    Google Scholar 

  34. 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

    Article  PubMed  Google Scholar 

  35. 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

    Article  Google Scholar 

  36. 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

    Article  PubMed  Google Scholar 

  37. 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

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Neurosurgery, Medical Faculty, Heinrich-Heine-University, Moorenstraße 5, 40225, Düsseldorf, Germany

    Hans-Jakob Steiger, Athanasios K. Petridis, Angelo Tortora, Hendrik-Jan Mijderwijk, Kerim Beseoglu, Jasper H. van Lieshout & Marcel A. Kamp

  2. Division of Informatics and Statistics, Department of Neurosurgery, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany

    Igor Fischer

Authors
  1. Hans-Jakob Steiger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Athanasios K. Petridis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Angelo Tortora
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hendrik-Jan Mijderwijk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kerim Beseoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jasper H. van Lieshout
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marcel A. Kamp
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Igor Fischer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hans-Jakob Steiger.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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.

Additional information

Comments

@@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

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Vascular Neurosurgery - Aneurysm

Electronic supplementary material

ESM 1

(XLSX 5762 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00701-019-04128-4

Keywords

Search

Navigation