Status and Future Perspectives of Utilizing Big Data in Neurosurgical and Stroke Research

Nishimura, Ataru; Nishimura, Kunihiro; Kada, Akiko; Iihara, Koji

doi:10.2176/nmc.ra.2016-0174

Cited by 17 publications

(19 citation statements)

References 23 publications

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“…Despite several limitations, using nationwide databases provides large sample sizes and produces more generalizable results than randomized trials because it eliminates potential selection and referral biases. 10 The J-ASPECT study is the first nationwide survey on the clinical practice of stroke care in Japan that uses data obtained from hospitals subject to the DPC-based payment system. 10 This study had a larger sample of clinical patients than did previous reports comparing DOAC- and warfarin-associated ICHs.…”

Section: Discussionmentioning

confidence: 99%

“… 7 The DPC database includes data about all patients admitted to participating hospitals, including each patient's profile (e.g., age, sex); principal diagnoses and comorbidities at admission (both coded by the International Classification of Diseases and Injuries, 10th revision); complications after admission (coded similarly); procedures including surgeries, medications, and devices used during hospitalization; length of stay; discharge status; and medical expense. 8 The J-ASPECT study group has analyzed the DPC database to gain new clinical insights, 7 , 9 – 11 an approach we applied again for this cross-sectional survey.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comparing intracerebral hemorrhages associated with direct oral anticoagulants or warfarin

Kurogi¹,

Nishimura

Nakai³

et al. 2018

Neurology

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ObjectivesThis cross-sectional survey explored the characteristics and outcomes of direct oral anticoagulant (DOAC)–associated nontraumatic intracerebral hemorrhages (ICHs) by analyzing a large nationwide Japanese discharge database.MethodsWe analyzed data from 2,245 patients who experienced ICHs while taking anticoagulants (DOAC: 227; warfarin: 2,018) and were urgently hospitalized at 621 institutions in Japan between April 2010 and March 2015. We compared the DOAC- and warfarin-treated patients based on their backgrounds, ICH severities, antiplatelet therapies at admission, hematoma removal surgeries, reversal agents, mortality rates, and modified Rankin Scale scores at discharge.ResultsDOAC-associated ICHs were less likely to cause moderately or severely impaired consciousness (DOAC-associated ICHs: 31.3%; warfarin-associated ICHs: 39.4%; p = 0.002) or require surgical removal (DOAC-associated ICHs: 5.3%; warfarin-associated ICHs: 9.9%; p = 0.024) in the univariate analysis. Propensity score analysis revealed that patients with DOAC-associated ICHs also exhibited lower mortality rates within 1 day (odds ratio [OR] 4.96, p = 0.005), within 7 days (OR 2.29, p = 0.037), and during hospitalization (OR 1.96, p = 0.039).ConclusionsThis nationwide study revealed that DOAC-treated patients had less severe ICHs and lower mortality rates than did warfarin-treated patients, probably due to milder hemorrhages at admission and lower hematoma expansion frequencies.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comparing intracerebral hemorrhages associated with direct oral anticoagulants or warfarin

Kurogi¹,

Nishimura

Nakai³

et al. 2018

Neurology

Self Cite

View full text Add to dashboard Cite

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“…At present, big data [ 132 ] are very broadly used, and we can use big data technology to improve this problem. There are a lot of big data applications on the field of stroke therapy [ 133 – 135 ], but there is little reference to big data in the cerebral blood flow autoregulation research. There are numerous imaging techniques such as SPECT, CT, MRI, and PET used in the cerebral blood flow research field [ 136 ], and as we know, the amount of image data is far more than the other data, and we can use big data technology and its 3V (velocity, volumes, and variety) [ 132 ] feature to improve the algorithm or model.…”

Section: Transcriptional Signals For the Autoregulationmentioning

confidence: 99%

Neural Vascular Mechanism for the Cerebral Blood Flow Autoregulation after Hemorrhagic Stroke

Xiao

Feng

et al. 2017

Neural Plasticity

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During the initial stages of hemorrhagic stroke, including intracerebral hemorrhage and subarachnoid hemorrhage, the reflex mechanisms are activated to protect cerebral perfusion, but secondary dysfunction of cerebral flow autoregulation will eventually reduce global cerebral blood flow and the delivery of metabolic substrates, leading to generalized cerebral ischemia, hypoxia, and ultimately, neuronal cell death. Cerebral blood flow is controlled by various regulatory mechanisms, including prevailing arterial pressure, intracranial pressure, arterial blood gases, neural activity, and metabolic demand. Evoked by the concept of vascular neural network, the unveiled neural vascular mechanism gains more and more attentions. Astrocyte, neuron, pericyte, endothelium, and so forth are formed as a communicate network to regulate with each other as well as the cerebral blood flow. However, the signaling molecules responsible for this communication between these new players and blood vessels are yet to be definitively confirmed. Recent evidence suggested the pivotal role of transcriptional mechanism, including but not limited to miRNA, lncRNA, exosome, and so forth, for the cerebral blood flow autoregulation. In the present review, we sought to summarize the hemodynamic changes and underline neural vascular mechanism for cerebral blood flow autoregulation in stroke-prone state and after hemorrhagic stroke and hopefully provide more systematic and innovative research interests for the pathophysiology and therapeutic strategies of hemorrhagic stroke.

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“…The use of big data in stroke care has garnered substantial notice as a vital resource for generating new evidence-based research. Several researchers attempted to uncover new verification and awareness from a real neurosurgical clinic, focusing on stroke care using Big Data, therefore developing more efficient methods for bridging the evidence-based practice gap in acute stroke knowledge (Nishimura et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Healthcare Driven by Big Data Analytics

Alexander

Wang²

2018

American Journal of Engineering and Applied Sciences

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Text messages and social network posts are often included in big data and are frequently invaluable sources of health data. When machine learning or data mining is used, it is important to perform an automatic process for integrating all available and related health data. Artificial Neural Network (ANN) is one of the machine learning methods flexible in using algorithms to detect complicated nonlinear relationships within huge datasets. Pharmacokinetics uses genetics to individualize drug therapy. Because genetic and pharmacological analysis often require large scale computation, Big Data analytics has shown potential in this area. Personal data from various sensors can often be used for making health and treatment recommendations, taking appropriate action for a patient's lifestyle choices and early diagnosis vital to advancing quality of care. Big data techniques such as hadoop and spark have been used in various areas of healthcare and big data analytics has been employed in great datasets to expose hidden patterns or correlations for effective decision-making. Challenges of big data in healthcare, concerned with gathering material from multifaceted heterogonous patient sources still exist, although hadoop has been employed as the processing unit for heterogeneous data gathered from various body sensors. The most critical necessity in a healthcare big data system is the data security; however, users are continuously applying big data-driven strategies to solve the various problems and challenges.

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Status and Future Perspectives of Utilizing Big Data in Neurosurgical and Stroke Research

Cited by 17 publications

References 23 publications

Comparing intracerebral hemorrhages associated with direct oral anticoagulants or warfarin

Comparing intracerebral hemorrhages associated with direct oral anticoagulants or warfarin

Neural Vascular Mechanism for the Cerebral Blood Flow Autoregulation after Hemorrhagic Stroke

Healthcare Driven by Big Data Analytics

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