Acta neurochirurgica · 2016
Multimodal analysis to predict shunt surgery outcome of 284 patients with suspected idiopathic normal pressure hydrocephalus.
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Luikku Antti J, Hall Anette, Nerg Ossi, Koivisto Anne M, Hiltunen Mikko, Helisalmi Seppo, Herukka Sanna-Kaisa, Sutela Anna, Kojoukhova Maria, Mattila Jussi, Lötjönen Jyrki, Rummukainen Jaana, Alafuzoff Irina, Jääskeläinen Juha E, Remes Anne M, Soininen Hilkka, Leinonen Ville
Аннотация
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.
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.