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Multimodal analysis to predict shunt surgery outcome of 284 patients with suspected idiopathic normal pressure hydrocephalus

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Abstract

Objectives

Optimal selection of idiopathic normal pressure hydrocephalus (iNPH) patients for shunt surgery is challenging. Disease State Index (DSI) is a statistical method that merges multimodal data to assist clinical decision-making. It has previously been shown to be useful in predicting progression in mild cognitive impairment and differentiating Alzheimer’s disease (AD) and frontotemporal dementia. In this study, we use the DSI method to predict shunt surgery response for patients with iNPH.

Methods

In this retrospective cohort study, a total of 284 patients (230 shunt responders and 54 non-responders) from the Kuopio NPH registry were analyzed with the DSI. Analysis included data from patients’ memory disorder assessments, age, clinical symptoms, comorbidities, medications, frontal cortical biopsy, CT/MRI imaging (visual scoring of disproportion between Sylvian and suprasylvian subarachnoid spaces, atrophy of medial temporal lobe, superior medial subarachnoid spaces), APOE genotyping, CSF AD biomarkers, and intracranial pressure.

Results

Our analysis showed that shunt responders cannot be differentiated from non-responders reliably even with the large dataset available (AUC = 0.58).

Conclusions

Prediction of the treatment response in iNPH is challenging even with our extensive dataset and refined analysis. Further research of biomarkers and indicators predicting shunt responsiveness is still needed.

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Abbreviations

ACC:

Accuracy

AD:

Alzheimer’s disease

AUC:

Area under the receiver-operator curve

Aβ:

Amyloid-beta

Aβ42:

Amyloid-beta 42

BMI:

Body mass index

CDR:

Clinical dementia rating

CT:

Computed tomography

CSF:

Cerebrospinal fluid

DSI:

Disease state index

HPτ:

Hyperphosphorylated tau

ICP:

Intracranial pressure

iNPH:

Idiopathic normal pressure hydrocephalus

KUH:

Kuopio University Hospital

MMSE:

Mini-Mental State Examination

NPH:

Normal pressure hydrocephalus

VPS:

Ventriculoperitoneal shunt

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Acknowledgments

We would like to thank Marita Voutilainen, RN, for maintaining the NPH registry.

Author information

Authors and Affiliations

  1. Institute of Clinical Medicine–Neurology, University of Eastern Finland, Kuopio, Finland

    Antti J. Luikku, Anette Hall, Ossi Nerg, Anne M. Koivisto, Mikko Hiltunen, Seppo Helisalmi, Sanna-Kaisa Herukka, Irina Alafuzoff, Anne M. Remes & Hilkka Soininen

  2. Neurosurgery of Neuro Center, Kuopio University Hospital, PO Box 100 FIN-70029 KYS, Kuopio, Finland

    Antti J. Luikku, Maria Kojoukhova, Juha E. Jääskeläinen & Ville Leinonen

  3. Neurology of NeuroCenter, Kuopio University Hospital, Kuopio, Finland

    Ossi Nerg, Anne M. Koivisto, Mikko Hiltunen, Sanna-Kaisa Herukka, Anne M. Remes & Hilkka Soininen

  4. Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland

    Mikko Hiltunen

  5. Department of Radiology, Kuopio University Hospital, Kuopio, Finland

    Anna Sutela & Maria Kojoukhova

  6. VTT Technical Research Centre of Finland, Tampere, Finland

    Jussi Mattila & Jyrki Lötjönen

  7. Combinostics Ltd, Tampere, Finland

    Jussi Mattila & Jyrki Lötjönen

  8. Department of Pathology, Kuopio University Hospital, Kuopio, Finland

    Jaana Rummukainen

  9. Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University and Department of Pathology and Cytology, Uppsala University Hospital, Uppsala, Sweden

    Irina Alafuzoff

Authors
  1. Antti J. Luikku
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  2. Anette Hall
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  7. Sanna-Kaisa Herukka
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  8. Anna Sutela
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  9. Maria Kojoukhova
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  13. Irina Alafuzoff
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  14. Juha E. Jääskeläinen
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  15. Anne M. Remes
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  17. Ville Leinonen
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Corresponding author

Correspondence to Ville Leinonen.

Ethics declarations

Funding

This study was funded by Academy of Finland (decision no 263193), VTR grant V16001 of Kuopio University Hospital, The Finnish Medical Foundation, Sigrid Juselius Foundation, Maire Taponen Foundation, the Strategic Funding of the University of Eastern Finland (UEF-Brain), VPH-DARE@IT project funded by European Union’s Seventh Framework Programme (FP7/2007-2013) grant agreement no. 601055, From Patient Data to Clinical Diagnosis in Neurodegenerative Diseases PredictND project funded by the European Union’s Seventh Framework Programme (FP7/2007-2013) grant agreement no. 611005, and is part of the BIOMARKAPD project in the frame of JPND. The sponsors had no role in the design or conduct of this research.

Conflict of interest

J. Mattila and J. Lötjönen report that VTT Technical Research Centre of Finland owns the patents (U.S. Patent No. 7,840,510, Inventors: JL; PCT/FI2010/050545, pending, Inventors: JM, JL) that cover parts of the methods presented in the paper.

All other authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Brain biopsy and invasive ICP measurement were part of clinical routine. The Finnish National Supervisory Authority for Welfare and Health has approved to use that information for the research purposes in cases the informed consent was not available. Informed consents were obtained from all patients for APOE genotyping and AD biomarker assessments.

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Comments

This Finnish group has developed a statistical method, the Disease State Index (DSI), which merges multimodal data to assist in clinical decision-making. The authors have so far published nine articles on the method since 2011. From these publications, it seems as if this tool has the power to predict progression, etc., in patients with Alzheimer’s disease. In the present study, the DSI method was used in an attempt to predict the response to shunt surgery in iNPH patients—and the authors found that it does not. Predicting outcome after shunt surgery in these patients is still a challenge. Although negative, these authors’ attempt at introducing a new tool was based on a fair assumption and therefore deserves to be published.

Knut Wester

Bergen, Norway

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Luikku, A.J., Hall, A., Nerg, O. et al. Multimodal analysis to predict shunt surgery outcome of 284 patients with suspected idiopathic normal pressure hydrocephalus. Acta Neurochir 158, 2311–2319 (2016). https://doi.org/10.1007/s00701-016-2980-4

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