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Shah NP, Willsey MS, Hahn N, Kamdar F, Avansino DT, Fan C, Hochberg LR, Willett FR, Henderson JM. A flexible intracortical brain-computer interface for typing using finger movements. bioRxiv 2024:2024.04.22.590630. [PMID: 38712189 PMCID: PMC11071346 DOI: 10.1101/2024.04.22.590630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Keyboard typing with finger movements is a versatile digital interface for users with diverse skills, needs, and preferences. Currently, such an interface does not exist for people with paralysis. We developed an intracortical brain-computer interface (BCI) for typing with attempted flexion/extension movements of three finger groups on the right hand, or both hands, and demonstrated its flexibility in two dominant typing paradigms. The first paradigm is "point-and-click" typing, where a BCI user selects one key at a time using continuous real-time control, allowing selection of arbitrary sequences of symbols. During cued character selection with this paradigm, a human research participant with paralysis achieved 30-40 selections per minute with nearly 90% accuracy. The second paradigm is "keystroke" typing, where the BCI user selects each character by a discrete movement without real-time feedback, often giving a faster speed for natural language sentences. With 90 cued characters per minute, decoding attempted finger movements and correcting errors using a language model resulted in more than 90% accuracy. Notably, both paradigms matched the state-of-the-art for BCI performance and enabled further flexibility by the simultaneous selection of multiple characters as well as efficient decoder estimation across paradigms. Overall, the high-performance interface is a step towards the wider accessibility of BCI technology by addressing unmet user needs for flexibility.
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Pun TK, Khoshnevis M, Hosman T, Wilson GH, Kapitonava A, Kamdar F, Henderson JM, Simeral JD, Vargas-Irwin CE, Harrison MT, Hochberg LR. Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces. bioRxiv 2024:2024.02.29.582733. [PMID: 38496552 PMCID: PMC10942277 DOI: 10.1101/2024.02.29.582733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.
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Fan C, Hahn N, Kamdar F, Avansino D, Wilson GH, Hochberg L, Shenoy KV, Henderson JM, Willett FR. Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication. Adv Neural Inf Process Syst 2023; 36:42258-42270. [PMID: 38738213 PMCID: PMC11086983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Abstract
Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.
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Affiliation(s)
- Chaofei Fan
- Department of Computer Science, Stanford University
| | - Nick Hahn
- Department of Neurosurgery, Stanford University
| | | | | | | | - Leigh Hochberg
- School of Engineering and Carney Institute for Brain Science, Brown University
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence Healthcare System
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School
| | - Krishna V. Shenoy
- Bio-X Program, Stanford University
- Department of Neurobiology, Stanford University
- Department of Bioengineering, Stanford University
- Wu Tsai Neurosciences Institute, Stanford University
- Howard Hughes Medical Institute at Stanford University
- Department of Electrical Engineering, Stanford University
| | - Jaimie M. Henderson
- Department of Neurosurgery, Stanford University
- Wu Tsai Neurosciences Institute, Stanford University
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Fan C, Hahn N, Kamdar F, Avansino D, Wilson GH, Hochberg L, Shenoy KV, Henderson JM, Willett FR. Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication. ArXiv 2023:arXiv:2311.03611v1. [PMID: 37986728 PMCID: PMC10659441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.
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Shah NP, Avansino D, Kamdar F, Nicolas C, Kapitonava A, Vargas-Irwin C, Hochberg L, Pandarinath C, Shenoy K, Willett FR, Henderson J. Pseudo-linear Summation explains Neural Geometry of Multi-finger Movements in Human Premotor Cortex. bioRxiv 2023:2023.10.11.561982. [PMID: 37873182 PMCID: PMC10592742 DOI: 10.1101/2023.10.11.561982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such hand gestures or playing piano)? Motor cortical activity was recorded using intracortical multi-electrode arrays in two people with tetraplegia as they attempted single, pairwise and higher order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity was normalized, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers (e.g. middle) changed significantly as a result of the movement of other fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Overall, simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation.
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Affiliation(s)
| | - Donald Avansino
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | | | - Claire Nicolas
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anastasia Kapitonava
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carlos Vargas-Irwin
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Department of Neuroscience, Brown University, Providence, RI, USA
| | - Leigh Hochberg
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering, Brown University, Providence, RI, USA
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Department of Neurosurgery, Emory University, Atlanta, GA, USA
| | - Krishna Shenoy
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Francis R Willett
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | - Jaimie Henderson
- Department of Neurosurgery, Stanford University
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Bio-X Institute, Stanford University, Stanford, CA, USA
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Willett FR, Kunz EM, Fan C, Avansino DT, Wilson GH, Choi EY, Kamdar F, Glasser MF, Hochberg LR, Druckmann S, Shenoy KV, Henderson JM. A high-performance speech neuroprosthesis. Nature 2023; 620:1031-1036. [PMID: 37612500 PMCID: PMC10468393 DOI: 10.1038/s41586-023-06377-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 06/27/2023] [Indexed: 08/25/2023]
Abstract
Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text1,2 or sound3,4. Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary1-7. Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant-who can no longer speak intelligibly owing to amyotrophic lateral sclerosis-achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI2) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant's attempted speech was decoded at 62 words per minute, which is 3.4 times as fast as the previous record8 and begins to approach the speed of natural conversation (160 words per minute9). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak.
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Affiliation(s)
- Francis R Willett
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA.
| | - Erin M Kunz
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Chaofei Fan
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Donald T Avansino
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | - Guy H Wilson
- Department of Neuroscience, Stanford University, Stanford, CA, USA
| | - Eun Young Choi
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Foram Kamdar
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Matthew F Glasser
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Leigh R Hochberg
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Shaul Druckmann
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Krishna V Shenoy
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Bio-X Program, Stanford University, Stanford, CA, USA
| | - Jaimie M Henderson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
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Willett FR, Kunz E, Fan C, Avansino D, Wilson G, Choi EY, Kamdar F, Hochberg LRH, Druckmann S, Shenoy K, Henderson J. A high-performance speech neuroprosthesis. bioRxiv 2023:2023.01.21.524489. [PMID: 36711591 PMCID: PMC9882398 DOI: 10.1101/2023.01.21.524489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text or sound. Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary. Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI2) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI and begins to approach the speed of natural conversation (160 words per minute). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.
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Shah NP, Willsey MS, Hahn N, Kamdar F, Avansino DT, Hochberg LR, Shenoy KV, Henderson JM. A brain-computer typing interface using finger movements. Int IEEE EMBS Conf Neural Eng 2023; 2023:10.1109/ner52421.2023.10123912. [PMID: 37465143 PMCID: PMC10353344 DOI: 10.1109/ner52421.2023.10123912] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Intracortical brain computer interfaces (iBCIs) decode neural activity from the cortex and enable motor and communication prostheses, such as cursor control, handwriting and speech, for people with paralysis. This paper introduces a new iBCI communication prosthesis using a 3D keyboard interface for typing using continuous, closed loop movement of multiple fingers. A participant-specific BCI keyboard prototype was developed for a BrainGate2 clinical trial participant (T5) using neural recordings from the hand-knob area of the left premotor cortex. We assessed the relative decoding accuracy of flexion/extension movements of individual single fingers (5 degrees of freedom (DOF)) vs. three groups of fingers (thumb, index-middle, and ring-small fingers, 3 DOF). Neural decoding using 3 independent DOF was more accurate (95%) than that using 5 DOF (76%). A virtual keyboard was then developed where each finger group moved along a flexion-extension arc to acquire targets that corresponded to English letters and symbols. The locations of these letter/symbols were optimized using natural language statistics, resulting in an approximately a 2× reduction in distance traveled by fingers on average compared to a random keyboard layout. This keyboard was tested using a simple real-time closed loop decoder enabling T5 to type with 31 symbols at 90% accuracy and approximately 2.3 sec/symbol (excluding a 2 second hold time) on average.
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Affiliation(s)
| | | | | | | | | | - Leigh R Hochberg
- Neurol., Mass. Gen. Hosp; Boston, MA; Brown Univ./VA Medical Center, Providence, RI
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Wilson GH, Willett FR, Stein EA, Kamdar F, Avansino DT, Hochberg LR, Shenoy KV, Druckmann S, Henderson JM. Long-term unsupervised recalibration of cursor BCIs. bioRxiv 2023:2023.02.03.527022. [PMID: 36778458 PMCID: PMC9915729 DOI: 10.1101/2023.02.03.527022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time. Compensating for this nonstationarity would enable consistently high performance without the need for supervised recalibration periods, where users cannot engage in free use of their device. Here we introduce a hidden Markov model (HMM) to infer what targets users are moving toward during iBCI use. We then retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms the state of the art in large-scale, closed-loop simulations over two months and in closed-loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we further show how recently proposed data distribution-matching approaches to recalibration fail over long time scales; only target-inference methods appear capable of enabling long-term unsupervised recalibration. Our results demonstrate how task structure can be used to bootstrap a noisy decoder into a highly-performant one, thereby overcoming one of the major barriers to clinically translating BCIs.
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El Rafei A, Schultz J, Masotti M, Maharaj V, Fraser M, Mutschler M, Martin C, Alexy T, Kamdar F, Knoper R, Shaffer A, John R, Cogswell R. Risk Factors and Clinical Significance of Vasoplegia after LVAD Implantation. J Heart Lung Transplant 2021. [DOI: 10.1016/j.healun.2021.01.1123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Shaaban A, Schultz J, Leonard J, John R, Martin C, Alexy T, Pritzker M, Thenappan T, Kamdar F, Shaffer A, Cogswell R. Outcomes of Patients Referred for Cardiac Rehabilitation after Left Ventricular Assist Device Implantation. J Heart Lung Transplant 2021. [DOI: 10.1016/j.healun.2021.01.532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Schultz J, Trachtenberg B, Estep J, Goodwin K, Araujo R, Rafei A, Pritzker M, Kamdar F, John R, Cogswell R. Association of Use of Angiotensin-Converting Enzyme Inhibitors or Angiotensin II Receptor Blockers on LVAD Support and Risk of Gastrointestinal Bleeding: A Multi-Center Analysis. J Heart Lung Transplant 2020. [DOI: 10.1016/j.healun.2020.01.1169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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Schultz J, Goodwin K, John R, Alexy T, Kamdar F, Martin C, Cogswell R. Association Between Angiotensin II Receptor Blockade and Recurrent Gastrointestinal Bleeding on Left Ventricular Assist Device Support. J Heart Lung Transplant 2018. [DOI: 10.1016/j.healun.2018.01.395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
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Cogswell R, John R, Shultz J, Martin C, Thenappan T, Kamdar F, Earthman C, Teigen L. Pre-Operative Pectoralis Muscle Quantity and Attenuation by Computed Tomography are Predictive of Recurrent Gastrointestinal Bleeding on Left Ventricular Assist Device Support. J Heart Lung Transplant 2018. [DOI: 10.1016/j.healun.2018.01.164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
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Kamdar F, Urban M, Szczech D, John R, Starling R, Moazami N. The Impact of LVAD Pump Thrombosis (PT) on Renal Function in Patients Undergoing Continuous Flow (CF) Left Ventricular Device (LVAD) Exchange. J Heart Lung Transplant 2017. [DOI: 10.1016/j.healun.2017.01.672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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Kamdar F, Sathnur N, Klaassen Kamdar A, Eckman P, John R. Low Cardiac Power Index (CPI) Is Associated With Higher Mortality in Cardiogenic Shock: Stratifying INTERMACS 1 and 2 Patients Undergoing Continuous-Flow LVAD (CF-LVAD) Implantation. J Heart Lung Transplant 2015. [DOI: 10.1016/j.healun.2015.01.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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Kamdar P, Kamdar F, Roettger M, Eckman P. Dental Care in Left Ventricular Assist Devices (LVAD) Patients: A Survey of Dentists. J Heart Lung Transplant 2015. [DOI: 10.1016/j.healun.2015.01.605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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18
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Kamdar F, Doyle M, Chapman C, Lohr J, Koyano Nakagawa N, Garry D. In Vitro Modeling of Duchenne Muscular Dystrophy (DMD) Cardiomyopathy Using Human Induced Pluripotent Stem Cells (hiPSC). J Heart Lung Transplant 2013. [DOI: 10.1016/j.healun.2013.01.632] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Kamdar F, Liao K, Eckman P, Colvin-Adams M, Shumway S, John R. 583 Post-Cardiac Transplant Survival in the Current Era in Patients Receiving Continuous-Flow LVADs. J Heart Lung Transplant 2012. [DOI: 10.1016/j.healun.2012.01.596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
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Kamdar F, Eckman P, Liao K, Colvin-Adams M, John R. 554 Safety of Discontinuation of Anticoagulation in Patients with Continuous-Flow LVADs. J Heart Lung Transplant 2012. [DOI: 10.1016/j.healun.2012.01.567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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21
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Kamdar F, Eckman P, Goldstein D, Sai-Sudhaker C, Aggarwal S, Pagani F, John R. 31 Pump-Related Infections (PRI) after Implantation of Continuous-Flow Left Ventricular Devices (CF LVADs): Analysis of 2900 Patients from the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS). J Heart Lung Transplant 2012. [DOI: 10.1016/j.healun.2012.01.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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22
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Kamdar F, Caccamo M, Martin C, Masri C, Eckman P, Colvin-Adams M. 675 CD19 Monitoring after Rituxamab in Patients with Cardiac Transplant Antibody Mediated Rejection (AMR). J Heart Lung Transplant 2012. [DOI: 10.1016/j.healun.2012.01.690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Kamdar F, Nieto D, Eckman P, Colvin-Adams M, Liao K, John R. 640 Poor Pre-Operative Pulmonary Function Tests (PFT) Do Not Predict Worse Outcomes in Patients Undergoing LVAD Placement. J Heart Lung Transplant 2011. [DOI: 10.1016/j.healun.2011.01.653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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Lee S, Kamdar F, John R. 65: Changing Patterns and Regional Differences in the Use of Ventricular Assist Devices as a Bridge-to-Transplant: An Analysis of UNOS Data. J Heart Lung Transplant 2010. [DOI: 10.1016/j.healun.2009.11.073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Kamdar F, Boyle A, Colvin-Adams M, Liao K, Joyce L, John R. 690: Left Ventricular Unloading Is Comparable between Axial and Centrifugal Continuous-Flow LVADs. J Heart Lung Transplant 2009. [DOI: 10.1016/j.healun.2008.11.697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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26
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Lee S, Kamdar F, Madlon-Kay R, John R. 608: Effects of the HeartMate II Continuous-Flow Left Ventricular Assist Device on RV Function. J Heart Lung Transplant 2009. [DOI: 10.1016/j.healun.2008.11.615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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27
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Kamdar F, Liao K, Martinez F, Colvin-Adams M, Boyle A, Joyce L, John R. 49: Obesity – No Longer an Exclusion Criteria for LVAD Implantation. J Heart Lung Transplant 2009. [DOI: 10.1016/j.healun.2008.11.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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28
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Kamdar F, Boyle A, Colvin-Adams M, Pritzker M, Missov E, Liao K, Joyce L, John R. 286: The Effect of Centrifugal, Axial, and Pulsatile Left Ventricular Assist Device Support on End-Organ Function in Heart Failure Patients. J Heart Lung Transplant 2008. [DOI: 10.1016/j.healun.2007.11.295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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29
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John R, Kamdar F, Toninato C, Boyle A, Colvin-Adams M, Liao K, Miller L, Joyce L. 539: Low thromboembolic risk with the Heartmate II left ventricular assist device. J Heart Lung Transplant 2007. [DOI: 10.1016/j.healun.2006.11.563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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