TY - JOUR
T1 - Multimodal analysis to predict shunt surgery outcome of 284 patients with suspected idiopathic normal pressure hydrocephalus
AU - Luikku, Antti J.
AU - Hall, Anette
AU - Nerg, Ossi
AU - Koivisto, Anne M.
AU - Hiltunen, Mikko
AU - Helisalmi, Seppo
AU - Herukka, Sanna-Kaisa
AU - Sutela, Anna
AU - Kojoukhova, Maria
AU - Mattila, Jussi
AU - Lötjönen, Jyrki
AU - Rummukainen, Jaana
AU - Alafuzoff, Irina
AU - Jääskeläinen, Juha E.
AU - Remes, Anne M.
AU - Soininen, Hilkka
AU - Leinonen, Ville
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Idiopathic normal pressure hydrocephalus
KW - Shunt response
KW - Computer-assisted diagnosis
KW - Prognosis
KW - Cognitive decline
U2 - 10.1007/s00701-016-2980-4
DO - 10.1007/s00701-016-2980-4
M3 - Article
SN - 0001-6268
VL - 158
SP - 2311
EP - 2319
JO - Acta Neurochirurgica
JF - Acta Neurochirurgica
IS - 12
ER -