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Guzek Z, Dziubek W, Stefańska M, Kowalska J. Evaluation of the functional outcome and mobility of patients after stroke depending on their cognitive state. Sci Rep 2024; 14:1515. [PMID: 38233519 PMCID: PMC10794689 DOI: 10.1038/s41598-024-52236-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 01/16/2024] [Indexed: 01/19/2024] Open
Abstract
The study aimed to analyze the functional outcome and mobility in stroke patients depending on their cognitive state. 180 patients after first stroke were divided into four groups: 48 patients without symptoms of cognitive impairment (G1); 38 with mild cognitive impairment without dementia (G2); 47 with mild dementia (G3); 47 with moderate dementia (G4). The Mini Mental State Examination (MMSE), Barthel Index (BI), Sitting Assessment Scale (SAS), Berg Balance Scale, Trunk Control Test and Test Up & Go were used. The tests were carried out at the time of admission to the ward (T1) and at the time of discharge (T2). A statistically significant improvement was demonstrated in all parameters in almost all groups. No significant difference was observed only in groups G1 and G4 in SAS head. Statistically significant differences in BI results in T2 between groups G1 and G4 were noted. The lowest change in BI was observed in the G4. Regression analysis showed that MMSE and BI at T1 and MMSE score at T2 explained the functional status at T2. Cognitive dysfunction at the time of admission to the ward and discharge may determining the patient's functional status at the time of discharge from the ward.
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Affiliation(s)
- Zbigniew Guzek
- Department of Neurological Rehabilitation, University Hospital in Zielona Góra, 65-046, Zielona Gora, Poland
- Faculty of Physiotherapy, Wroclaw University of Health and Sport Sciences, Paderewskiego 35 Street, 51-612, Wrocław, Poland
| | - Wioletta Dziubek
- Faculty of Physiotherapy, Wroclaw University of Health and Sport Sciences, Paderewskiego 35 Street, 51-612, Wrocław, Poland
| | - Małgorzata Stefańska
- Faculty of Physiotherapy, Wroclaw University of Health and Sport Sciences, Paderewskiego 35 Street, 51-612, Wrocław, Poland
| | - Joanna Kowalska
- Faculty of Physiotherapy, Wroclaw University of Health and Sport Sciences, Paderewskiego 35 Street, 51-612, Wrocław, Poland.
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Yang CC, Bamodu OA, Chan L, Chen JH, Hong CT, Huang YT, Chung CC. Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks. Front Neurol 2023; 14:1085178. [PMID: 36846116 PMCID: PMC9947790 DOI: 10.3389/fneur.2023.1085178] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/18/2023] [Indexed: 02/11/2023] Open
Abstract
Background Accurate estimation of prolonged length of hospital stay after acute ischemic stroke provides crucial information on medical expenditure and subsequent disposition. This study used artificial neural networks to identify risk factors and build prediction models for a prolonged length of stay based on parameters at the time of hospitalization. Methods We retrieved the medical records of patients who received acute ischemic stroke diagnoses and were treated at a stroke center between January 2016 and June 2020, and a retrospective analysis of these data was performed. Prolonged length of stay was defined as a hospital stay longer than the median number of days. We applied artificial neural networks to derive prediction models using parameters associated with the length of stay that was collected at admission, and a sensitivity analysis was performed to assess the effect of each predictor. We applied 5-fold cross-validation and used the validation set to evaluate the classification performance of the artificial neural network models. Results Overall, 2,240 patients were enrolled in this study. The median length of hospital stay was 9 days. A total of 1,101 patients (49.2%) had a prolonged hospital stay. A prolonged length of stay is associated with worse neurological outcomes at discharge. Univariate analysis identified 14 baseline parameters associated with prolonged length of stay, and with these parameters as input, the artificial neural network model achieved training and validation areas under the curve of 0.808 and 0.788, respectively. The mean accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of prediction models were 74.5, 74.9, 74.2, 75.2, and 73.9%, respectively. The key factors associated with prolonged length of stay were National Institutes of Health Stroke Scale scores at admission, atrial fibrillation, receiving thrombolytic therapy, history of hypertension, diabetes, and previous stroke. Conclusion The artificial neural network model achieved adequate discriminative power for predicting prolonged length of stay after acute ischemic stroke and identified crucial factors associated with a prolonged hospital stay. The proposed model can assist in clinically assessing the risk of prolonged hospitalization, informing decision-making, and developing individualized medical care plans for patients with acute ischemic stroke.
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Affiliation(s)
- Cheng-Chang Yang
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Research Center for Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
| | - Oluwaseun Adebayo Bamodu
- Department of Medical Research and Education, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Urology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Hematology and Oncology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Lung Chan
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Jia-Hung Chen
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chien-Tai Hong
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yi-Ting Huang
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Nursing, School of Nursing, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chen-Chih Chung
- Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan,*Correspondence: Chen-Chih Chung ✉
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Morais A, Locascio JJ, Sansing LH, Lamb J, Nagarkatti K, Imai T, van Leyen K, Aronowski J, Koenig JI, Bosetti F, Lyden P, Ayata C. Embracing Heterogeneity in The Multicenter Stroke Preclinical Assessment Network (SPAN) Trial. Stroke 2023; 54:620-631. [PMID: 36601951 PMCID: PMC9870939 DOI: 10.1161/strokeaha.122.040638] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The Stroke Preclinical Assessment Network (SPAN) is a multicenter preclinical trial platform using rodent models of transient focal cerebral ischemia to address translational failure in experimental stroke. In addition to centralized randomization and blinding and large samples, SPAN aimed to introduce heterogeneity to simulate the heterogeneity embodied in clinical trials for robust conclusions. Here, we report the heterogeneity introduced by allowing the 6 SPAN laboratories to vary most of the biological and experimental model variables and the impact of this heterogeneity on middle cerebral artery occlusion (MCAo) performance. We included the modified intention-to-treat population of the control mouse cohort of the first SPAN trial (n=421) and examined the biological and procedural independent variables and their covariance. We then determined their impact on the dependent variables cerebral blood flow drop during MCAo, time to achieve MCAo, and total anesthesia duration using multivariable analyses. We found heterogeneity in biological and procedural independent variables introduced mainly by the site. Consequently, all dependent variables also showed heterogeneity among the sites. Multivariable analyses with the site as a random effect variable revealed filament choice as an independent predictor of cerebral blood flow drop after MCAo. Comorbidity, sex, use of laser Doppler flow to monitor cerebral blood flow, days after trial onset, and maintaining anesthesia throughout the MCAo emerged as independent predictors of time to MCAo. Total anesthesia duration was predicted by most independent variables. We present with high granularity the heterogeneity introduced by the biological and model selections by the testing sites in the first trial of cerebroprotection in rodent transient filament MCAo by SPAN. Rather than trying to homogenize all variables across all sites, we embraced the heterogeneity to better approximate clinical trials. Awareness of the heterogeneity, its sources, and how it impacts the study performance may further improve the study design and statistical modeling for future multicenter preclinical trials.
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Affiliation(s)
- Andreia Morais
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Joseph J. Locascio
- Department of Biostatistics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Lauren H. Sansing
- Department of Neurology, Yale University School of Medicine, New Haven, CT USA
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT USA
| | - Jessica Lamb
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Los Angeles, CA USA
| | - Karisma Nagarkatti
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Los Angeles, CA USA
| | - Takahiko Imai
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Klaus van Leyen
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jaroslaw Aronowski
- Department of Neurology, McGovern Medical School, University of Texas HSC, Houston, TX, USA
| | - James I. Koenig
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD USA
| | - Francesca Bosetti
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD USA
| | - Patrick Lyden
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Los Angeles, CA USA
- Department of Neurology, Keck School of Medicine at USC, Los Angeles, CA USA
| | - Cenk Ayata
- Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
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Tam AKH. Sarcopenia in the Elderly Stroke Population-What does it Mean for Rehabilitation and Recovery? J Stroke Cerebrovasc Dis 2022; 31:106629. [PMID: 35810658 DOI: 10.1016/j.jstrokecerebrovasdis.2022.106629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Alan Ka Ho Tam
- Division of Physical Medicine & Rehabilitation, University of Toronto, Toronto, Canada; Toronto Rehabilitation Institute, University Health Network, Toronto, Canada.
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