Antti J Luikku1,2, Anette Hall1, Ossi Nerg1,3, Anne M Koivisto1,3, Mikko Hiltunen1,3,4, Seppo Helisalmi1, Sanna-Kaisa Herukka1,3, Anna Sutela5, Maria Kojoukhova2,5, Jussi Mattila6,7, Jyrki Lötjönen6,7, Jaana Rummukainen8, Irina Alafuzoff1,9, Juha E Jääskeläinen2, Anne M Remes1,3, Hilkka Soininen1,3, Ville Leinonen10. 1. Institute of Clinical Medicine-Neurology, University of Eastern Finland, Kuopio, Finland. 2. Neurosurgery of Neuro Center, Kuopio University Hospital, PO Box 100 FIN-70029 KYS, Kuopio, Finland. 3. Neurology of NeuroCenter, Kuopio University Hospital, Kuopio, Finland. 4. Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland. 5. Department of Radiology, Kuopio University Hospital, Kuopio, Finland. 6. VTT Technical Research Centre of Finland, Tampere, Finland. 7. Combinostics Ltd, Tampere, Finland. 8. Department of Pathology, Kuopio University Hospital, Kuopio, Finland. 9. Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University and Department of Pathology and Cytology, Uppsala University Hospital, Uppsala, Sweden. 10. Neurosurgery of Neuro Center, Kuopio University Hospital, PO Box 100 FIN-70029 KYS, Kuopio, Finland. ville.leinonen@kuh.fi.
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.
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.
Authors: Majid Rastegar-Mojarad; Sunghwan Sohn; Liwei Wang; Feichen Shen; Troy C Bleeker; William A Cliby; Hongfang Liu Journal: Int J Med Inform Date: 2017-10-10 Impact factor: 4.046
Authors: Ville E Korhonen; Seppo Helisalmi; Aleksi Jokinen; Ilari Jokinen; Juha-Matti Lehtola; Minna Oinas; Kimmo Lönnrot; Cecilia Avellan; Anna Kotkansalo; Janek Frantzen; Jaakko Rinne; Antti Ronkainen; Mikko Kauppinen; Antti Junkkari; Mikko Hiltunen; Hilkka Soininen; Mitja Kurki; Juha E Jääskeläinen; Anne M Koivisto; Hidenori Sato; Takeo Kato; Anne M Remes; Per Kristian Eide; Ville Leinonen Journal: Neurol Genet Date: 2018-12-03
Authors: A Junkkari; A J Luikku; N Danner; H K Jyrkkänen; T Rauramaa; V E Korhonen; A M Koivisto; O Nerg; M Kojoukhova; T J Huttunen; J E Jääskeläinen; V Leinonen Journal: Fluids Barriers CNS Date: 2019-07-25
Authors: Jani Sirkka; Marita Parviainen; Henna-Kaisa Jyrkkänen; Anne M Koivisto; Laura Säisänen; Tuomas Rauramaa; Ville Leinonen; Nils Danner Journal: Acta Neurochir (Wien) Date: 2021-07-08 Impact factor: 2.216