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Acker L, Xu K, Ginsberg JP. The brain-heart-immune axis: a vago-centric framework for predicting and enhancing resilient recovery in older surgery patients. Bioelectron Med 2024; 10:21. [PMID: 39218887 PMCID: PMC11367755 DOI: 10.1186/s42234-024-00155-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 07/19/2024] [Indexed: 09/04/2024] Open
Abstract
Nearly all geriatric surgical complications are studied in the context of a single organ system, e.g., cardiac complications and the heart; delirium and the brain; infections and the immune system. Yet, we know that advanced age, physiological stress, and infection all increase sympathetic and decrease parasympathetic nervous system function. Parasympathetic function is mediated through the vagus nerve, which connects the heart, brain, and immune system to form, what we have termed, the brain-heart-immune axis. We hypothesize that this brain-heart-immune axis plays a critical role in surgical recovery among older adults. In particular, we hypothesize that the brain-heart-immune axis plays a critical role in the most common surgical complication among older adults: postoperative delirium. Further, we present heart rate variability as a measure that may eventually become a multi-system vital sign evaluating brain-heart-immune axis function. Finally, we suggest the brain-heart-immune axis as a potential interventional target for bio-electronic neuro-immune modulation to enhance resilient surgical recovery among older adults.
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Affiliation(s)
- Leah Acker
- Department of Anesthesiology, Duke University School of Medicine, 136 Sands Building, 303 Research Drive, Durham, NC, 27710, USA.
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA.
- Pratt School of Engineering, Duke University, Durham, NC, USA.
- Duke Center for the Study of Aging and Human Development, Durham, NC, USA.
- Claude D Pepper Older Americans Independence Center at Duke, Durham, NC, USA.
- Duke Center for Cognitive Neuroscience, Durham, NC, USA.
| | - Kevin Xu
- Department of Anesthesiology, Duke University School of Medicine, 136 Sands Building, 303 Research Drive, Durham, NC, 27710, USA
- Pratt School of Engineering, Duke University, Durham, NC, USA
| | - J P Ginsberg
- William Jennings Bryan Dorn VA Healthcare System, Columbia, SC, USA
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Oh MY, Jung YM, Kim WP, Lee HC, Kim TK, Ko SB, Lim J, Lee SM. Prediction for Perioperative Stroke Using Intraoperative Parameters. J Am Heart Assoc 2024; 13:e032216. [PMID: 39119968 DOI: 10.1161/jaha.123.032216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 06/20/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk. However, most existing models primarily base their predictions on the patient's baseline characteristics before surgery. We aimed to develop a machine-learning model incorporating both pre- and intraoperative variables to predict perioperative stroke. METHODS AND RESULTS This study included patients who underwent noncardiac surgery at 2 hospitals with the data of 15 752 patients from Seoul National University Hospital used for development and temporal internal validation, and the data of 449 patients from Boramae Medical Center used for external validation. Perioperative stroke was defined as a newly developed ischemic lesion on diffusion-weighted imaging within 30 days of surgery. We developed a prediction model composed of pre- and intraoperative factors (integrated model) and compared it with a model consisting of preoperative features alone (preoperative model). Perioperative stroke developed in 109 (0.69%) patients in the Seoul National University Hospital group and 11 patients (2.45%) in the Boramae Medical Center group. The integrated model demonstrated superior predictive performance with area under the curve values of 0.824 (95% CI, 0.762-0.880) versus 0.584 (95% CI, 0.499-0.667; P<0.001) in the internal validation; and 0.716 (95% CI, 0.560-0.859) versus 0.505 (95% CI, 0.343-0.654; P=0.018) in the external validation, compared to the preoperative model. CONCLUSIONS We suggest that incorporating intraoperative factors into perioperative stroke prediction models can improve their accuracy.
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Affiliation(s)
- Mi-Young Oh
- Department of Neurology Bucheon Sejong Hospital Bucheon-si Gyeonggi-do South Korea
| | - Young Mi Jung
- Department of Obstetrics and Gynecology Seoul National University College of Medicine Seoul South Korea
- Department of Obstetrics and Gynecology, Guro Hospital Korea University College of Medicine Seoul South Korea
| | | | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine Seoul National University College of Medicine Seoul South Korea
- Department of Anesthesiology and Pain Medicine Seoul National University Hospital Seoul South Korea
| | - Tae Kyong Kim
- Department of Anesthesiology and Pain Medicine Seoul National University College of Medicine Seoul South Korea
- Department of Anesthesiology and Pain Medicine Metropolitan Government Seoul National University Boramae Medical Center Seoul South Korea
| | - Sang-Bae Ko
- Department of Neurology Seoul National University Hospital Seoul South Korea
| | | | - Seung Mi Lee
- Department of Obstetrics and Gynecology Seoul National University College of Medicine Seoul South Korea
- Department of Obstetrics and Gynecology Seoul National University Hospital Seoul South Korea
- Innovative Medical Technology Research Institute Seoul National University Hospital Seoul South Korea
- Institute of Reproductive Medicine and Population & Medical Big Data Research Center, Medical Research Center Seoul National University Seoul South Korea
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Satomoto M. Predicting Postoperative Emergence Delirium From the Heart Rate Variability of Patients Undergoing Elective Cardiac Surgery. Cureus 2023; 15:e34613. [PMID: 36891021 PMCID: PMC9986650 DOI: 10.7759/cureus.34613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2023] [Indexed: 02/09/2023] Open
Abstract
Background and objective The complication of postoperative delirium is directly linked to prognosis, leading to prolonged hospital stays and an increase in mortality. Since there is no magic medicine that cures delirium, the prevention of its onset is important, and the development of simple tools that enable the early assessment of the risk is valuable. In the previous study, we hypothesized that postoperative delirium could be predicted from heart rate variability (HRV) measured by using an electrocardiogram (ECG) on the day before elective esophageal cancer surgery. HRV is calculated based on the fluctuation of RR intervals on ECG. The preoperative high-frequency (HF) power in delirium patients was significantly lower than that in non-delirium patients. The HF component is considered a reflection of parasympathetic function. In the current study, we evaluated the hypothesis that parasympathetic nerve activity is low in the resting HRV on the night before surgery in patients who go on to develop postoperative delirium. To that end, we recorded resting HRV in patients scheduled for cardiac surgery on the night before surgery. We then compared the HRV between patients with and without delirium in the postoperative intensive care unit (ICU). The Confusion Assessment Method for the ICU (CAM-ICU) was used to diagnose delirium. Methods This was a prospective observational study involving patients undergoing elective cardiac surgery. After obtaining approval from the institutional review board, patients aged 65 years and older were enrolled in the study. The day before surgery, a Mini-Mental State Examination (MMSE) was performed. The ECG was used in patients for five minutes. All patients were transferred to the ICU after surgery, and CAM-ICU was measured every eight hours until discharge from the ICU, and positive patients were diagnosed with delirium. Results In this study, 14 patients who developed delirium and 22 patients who did not were included in the analysis. The average MMSE score was 27.4, with no patients diagnosed with preoperative dementia. In the analysis of HRV, the HF component was significantly lower in the group with delirium compared to the group without delirium (Mann-Whitney U test, p<0.05). Conclusion Based on our findings, in patients with postoperative delirium, the activity of parasympathetic nerves was lower than before surgery, and we concluded that it is possible to predict the onset of postoperative delirium based on preoperative ECG measurement.
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Barthelemy JC, Pichot V, Hupin D, Berger M, Celle S, Mouhli L, Bäck M, Lacour JR, Roche F. Targeting autonomic nervous system as a biomarker of well-ageing in the prevention of stroke. Front Aging Neurosci 2022; 14:969352. [PMID: 36185479 PMCID: PMC9521604 DOI: 10.3389/fnagi.2022.969352] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/25/2022] [Indexed: 11/13/2022] Open
Abstract
Stroke prediction is a key health issue for preventive medicine. Atrial fibrillation (AF) detection is well established and the importance of obstructive sleep apneas (OSA) has emerged in recent years. Although autonomic nervous system (ANS) appears strongly implicated in stroke occurrence, this factor is more rarely considered. However, the consequences of decreased parasympathetic activity explored in large cohort studies through measurement of ANS activity indicate that an ability to improve its activity level and equilibrium may prevent stroke. In support of these observations, a compensatory neurostimulation has already proved beneficial on endothelium function. The available data on stroke predictions from ANS is based on many long-term stroke cohorts. These data underline the need of repeated ANS evaluation for the general population, in a medical environment, and remotely by emerging telemedicine digital tools. This would help uncovering the reasons behind the ANS imbalance that would need to be medically adjusted to decrease the risk of stroke. This ANS unbalance help to draw attention on clinical or non-clinical evidence, disclosing the vascular risk, as ANS activity integrates the cumulated risk from many factors of which most are modifiable, such as metabolic inadaptation in diabetes and obesity, sleep ventilatory disorders, hypertension, inflammation, and lack of physical activity. Treating these factors may determine ANS recovery through the appropriate management of these conditions. Natural aging also decreases ANS activity. ANS recovery will decrease global circulating inflammation, which will reinforce endothelial function and thus protect the vessels and the associated organs. ANS is the whistle-blower of vascular risk and the actor of vascular health. Such as, ANS should be regularly checked to help draw attention on vascular risk and help follow the improvements in response to our interventions. While today prediction of stroke relies on classical cardiovascular risk factors, adding autonomic biomarkers as HRV parameters may significantly increase the prediction of stroke.
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Affiliation(s)
- Jean-Claude Barthelemy
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
- *Correspondence: Jean-Claude Barthelemy,
| | - Vincent Pichot
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
| | - David Hupin
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
- Section of Translational Cardiology, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
| | - Mathieu Berger
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
- Centre d’Investigation et de Recherche sur le Sommeil, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Sébastien Celle
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
| | - Lytissia Mouhli
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- Département de Neurologie, Hôpital Universitaire Nord, Saint-Étienne, France
| | - Magnus Bäck
- Section of Translational Cardiology, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Cardiology, Karolinska University Hospital, Stockholm, Sweden
| | - Jean-René Lacour
- Laboratoire de Physiologie, Faculté de Médecine Lyon-Sud, Oullins, France
| | - Frederic Roche
- Physical Exercise and Clinical Physiology Department, CHU Nord, Saint-Étienne, France
- INSERM U1059 Santé Ingénierie Biologie, Université Jean Monnet, Saint-Étienne, France
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Preoperative heart rate variability analysis is as a potential simple and easy measure for predicting perioperative delirium in esophageal surgery. Ann Med Surg (Lond) 2021; 70:102856. [PMID: 34584685 PMCID: PMC8452778 DOI: 10.1016/j.amsu.2021.102856] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/09/2021] [Accepted: 09/10/2021] [Indexed: 11/23/2022] Open
Abstract
Background Delirium is one of the most common but severe perioperative complications. Autonomic activity evaluated by heart rate variability (HRV) has been recently reported as a useful tool for prediction and for early detection of delirium in acute care medicine, especially in postoperative intensive care unit (ICU) patients. We hypothesized that HRV, by 3-lead electrocardiogram (ECG), one day prior to surgery might correlate with the presence of postoperative delirium. Materials and methods This study was cohort prospective pilot study. We measured preoperative HRV and postoperative delirium in patients who underwent surgery for elective esophageal cancer. ECG of the participants was performed for 10 min 6–12 h preceding surgery. Postoperatively, patients were admitted to the ICU or critical care unit and stayed for at least 3 days. Delirium was diagnosed by psychiatrist rounds twice a day. Results Delirium was assessed for 3 days after surgery and 30 patients performed the study. Seven patients developed delirium during their ICU stay, while the remaining twenty-three did not. After HRV analysis, the preoperative high frequency power in delirium patients was significantly lower than that in non-delirium patient. Other parameters of HRV, including lower frequency power, total power and the ratio showed no statistically significant difference between the groups. Conclusion The results of current study demonstrated that preoperative measurement of HRV may be a useful predictor of delirium. Further investigation could pave the way to a non-invasive, minimally stressful method of predicting postoperative delirium. Delirium is one of the most common and severe postoperative complications. Delirium prediction can provide better treatment for patients. Heart rate variability analysis might predict delirium in esophageal cancer surgery.
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Kawai H, Obuchi S, Hirayama R, Watanabe Y, Hirano H, Fujiwara Y, Ihara K, Kim H, Kobayashi Y, Mochimaru M, Tsushima E, Nakamura K. Intra-day variation in daily outdoor walking speed among community-dwelling older adults. BMC Geriatr 2021; 21:417. [PMID: 34238238 PMCID: PMC8268528 DOI: 10.1186/s12877-021-02349-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/15/2021] [Indexed: 12/30/2022] Open
Abstract
Background Walking speed is an important measure associated with health outcomes in older individuals, such as dependency and death. This study aimed to examine whether the walking speed of community-dwelling older adults varies between time periods within a day, as measured outdoors in daily life. We aimed to determine the types of walking speed variations and examine the factors associated with them. Methods Daily life outdoor walking speed was measured in 92 participants (average age 71.9 years±5.64) using a GPS smartphone app for 1 month. Average walking speeds for five time periods were analyzed with a linear mixed model. Intra-day walking speed variation patterns were classified by latent class analysis. Factors associated with the class were identified by logistic regression analysis. Results A statistically significant difference in average walking speed was found between early morning (1.33 m/s), and afternoon (1.27 m/s) and evening (1.26 m/s) (p < 0.01). The intra-day variation in walking speed was attributed to variation in cadence. Two classes were identified: (1) fast walking speed with large variation and (2) slow walking speed with little variation; hypertension and frailty level were associated with the class. Conclusion The results suggest that there is intra-day variation in walking speed in daily life, wherein the speed is the fastest early in the morning and slower in the afternoon and evening. A larger variation in the walking speed was related to the health status without hypertension or frailty. These results suggest that if a person shows less intra-day variation in walking speed, this could be a sign that they are susceptible to hypertension and an increased frailty level. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-021-02349-w.
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Affiliation(s)
- Hisashi Kawai
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.
| | - Shuichi Obuchi
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Ryo Hirayama
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.,Osaka City University, Osaka, Japan
| | - Yutaka Watanabe
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.,Gerodontology, Department of Oral Health Science, Faculty of Dental Medicine, Hokkaido University, Sapporo, Japan
| | - Hirohiko Hirano
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Yoshinori Fujiwara
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | | | - Hunkyung Kim
- Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan
| | - Yoshiyuki Kobayashi
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Masaaki Mochimaru
- Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Eiki Tsushima
- Faculty of Medicine, Hirosaki University, Aomori, Japan
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