Objective Activity Parameters Track Patient-specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States.
Journal
Annals of surgery
ISSN: 1528-1140
Titre abrégé: Ann Surg
Pays: United States
ID NLM: 0372354
Informations de publication
Date de publication:
01 03 2023
01 03 2023
Historique:
pubmed:
8
2
2022
medline:
9
2
2023
entrez:
7
2
2022
Statut:
ppublish
Résumé
The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established. Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. Although phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity. Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state. The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, 6 distinct domains of physical function and sleep are identified to represent the objective temporal patterns: ''activity capacity'' and ''moderate and overall activity (declined immediately after surgery); ''sleep disruption and sedentary activity (increased after surgery); ''overall sleep'', ''sleep onset'', and ''light activity'' (no clear changes were observed after surgery). These patterns can be linked to individual patients preopera-tive immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in monocytic myeloid-derived suppressor cells predicted a slower recovery. Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.
Sections du résumé
OBJECTIVE
The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established.
BACKGROUND
Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. Although phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity.
METHODS
Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state.
RESULTS
The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, 6 distinct domains of physical function and sleep are identified to represent the objective temporal patterns: ''activity capacity'' and ''moderate and overall activity (declined immediately after surgery); ''sleep disruption and sedentary activity (increased after surgery); ''overall sleep'', ''sleep onset'', and ''light activity'' (no clear changes were observed after surgery). These patterns can be linked to individual patients preopera-tive immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in monocytic myeloid-derived suppressor cells predicted a slower recovery.
CONCLUSIONS
Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.
Identifiants
pubmed: 35129529
doi: 10.1097/SLA.0000000000005250
pii: 00000658-900000000-93245
pmc: PMC9040386
mid: NIHMS1797969
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e503-e512Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM137936
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM138353
Pays : United States
Organisme : NINDS NIH HHS
ID : R61 NS114926
Pays : United States
Informations de copyright
Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.
Déclaration de conflit d'intérêts
R.F., F.V., E.G., N.S., I.M., A.L.C., M.B., T.P., M.X., D.D.F., C.E., P.S., D.F.A., J.I.H., S.B.G., B.G., M.S.A., and N.A. contributed to conception and design. E.G., X.G., A.T., M.T., B.G., M.S.A, and N.A. acquired the data. R.F., F.V., A.C., B.G., M.S.A, and N.A. analyzed the data and interpreted the results. R.F., F.V., B.G., M.S.A., and N.A. wrote the manuscript with input from all authors. The authors report no conflicts of interest.
Références
Weiser TG, Haynes AB, Molina G, et al. Size and distribution of the global volume of surgery in 2012. Bull World Health Organ. 2016;94:201–209F.
Kehlet H, Dahl JB. Anaesthesia, surgery, and challenges in postoperative recovery. Lancet. 2003;362:1921–1928.
Jensen MB, Houborg KB, Nørager CB, et al. Postoperative changes in fatigue, physical function and body composition: an analysis of the amalgamated data from six randomized trials on patients undergoing colorectal surgery. Color Dis. 2011;13:588–593.
Bisgaard T, Klarskov B, Rosenberg J, et al. Characteristics and prediction of early pain after laparoscopic cholecystectomy. Pain. 2001;90:261–269.
Aarts MA, Okrainec A, Glicksman A, et al. Adoption of enhanced recovery after surgery (ERAS) strategies for colorectal surgery at academic teaching hospitals and impact on total length of hospital stay. Surg Endosc. 2012;26:442–450.
Kehlet H, Wilmore DW. Evidence-based surgical care and the evolution of fast-track surgery. Ann Surg. 2008;248:189–198.
Lee L, Tran T, Mayo NE, et al. What does it really mean to ‘recover’ from an operation? Surgery. 2014;155:211–216.
Bishwajit G, O’Leary DP, Ghosh S, et al. Physical inactivity and self-reported depression among middle- and older-aged population in South Asia: world health survey. BMC Geriatr. 2017;17:100.
Bauman A, Ainsworth BE, Sallis JF, et al. The descriptive epidemiology of sitting: a 20-country comparison using the international physical activity questionnaire (IPAQ). Am J Prev Med. 2011;41:228–235.
Tarasenko Y, Chen C, Schoenberg N. Self-reported physical activity levels of older cancer survivors: results from the 2014 National Health Interview survey. J Am Geriatr Soc. 2017;65:e39–e44.
Killen CJ, Murphy MP, Hopkinson WJ, et al. Minimum twelve-year follow-up of fixed- vs mobile-bearing total knee arthroplasty: double blinded randomized trial. J Clin Orthop Trauma. 2020;11:154–159.
Prince SA, Adamo KB, Hamel ME, et al. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act. 2008;5:56.
Cerin E, Cain KL, Oyeyemi AL, et al. Correlates of agreement between accelerometry and self-reported physical activity. Med Sci Sports Exerc. 2016;48:1075–1084.
Hallal PC, Andersen LB, Bull FC, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380:247–257.
Cornacchia M, Ozcan K, Zheng Y, et al. A survey on activity detection and classification using wearable sensors. IEEE Sens J. 2017;17:386–403.
Conci J, Sprint G, Cook D, et al. Utilizing consumer-grade wearable sensors for unobtrusive rehabilitation outcome prediction. 2019 IEEE EMBS Int Conf Biomed Health Inform. 2019;1–4.
Xiao W, Mindrinos MN, Seok J, et al. A genomic storm in critically injured humans. J Exp Med. 2011;208:2581–2590.
Gaudilliere B, Fragiadakis GK, Bruggner RV, et al. Coordinated surgical immune signatures contain correlates of clinical recovery. Sci Transl Med. 2014;6:255ra131.
Gaudilliere B, Fragiadakis GK, Bruggner RV, et al. Clinical recovery from surgery correlates with single-cell immune signatures. Sci Transl Med. 2014;6:255ra131–1255ra.
Bendall SC, Nolan GP, Roederer M, et al. A deep profiler’s guide to cytometry. Trends Immunol. 2012;33:323–332.
Newell EW, Sigal N, Nair N, et al. Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization. Nat Biotechnol. 2013;31:623–629.
Olin A, Henckel E, Chen Y, et al. Stereotypic immune system development in newborn children. Cell. 2018;174:1277–1292.e14.
Aghaeepour N, Ganio EA, Mcilwain D, et al. An immune clock of human pregnancy. Sci Immunol. 2017;2:eaan2946.
Spitzer MH, Carmi Y, Reticker-Flynn NE, et al. Systemic immunity is required for effective cancer immunotherapy. Cell. 2017;168:487–502.e15.
Fragiadakis GK, Gaudilliѐre B, Ganio EA, et al. Patient-specific immune states before surgery are strong correlates of surgical recovery. Anesthesiology. 2015;123:1241–1255.
Cole RJ, Kripke DF, Gruen W, et al. Automatic sleep/wake identification from wrist activity. Sleep. 1992;15:461–469.
Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30:777–781.
Assarsson E, Lundberg M, Holmquist G, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One. 2014;9:e95192.
Olink Proteomics Inc. PEA – a high-multiplex immunoassay technology with qPCR or NGS readout. White Pap. v1.0. 2020.
Olink Proteomics Inc. Data normalization and standardization. White Pap. v1.0. 2018.
Ganio EA, Stanley N, Lindberg-Larsen V, et al. Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nat Commun. 2020;11:1–12.
Krishnan NC, Cook DJ. Activity recognition on streaming sensor data. Pervasive Mob Comput. 2014;10:138–154.
Preece SJ, Goulermas JY, Kenney LPJ, et al. A comparison of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans Biomed Eng. 2009;56:871–879.
Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006;63:3–42.
Breiman L. Random forests. Mach Learn. 2001;45:5–32.
Paddison JS, Sammour T, Kahokehr A, et al. Development and validation of the surgical recovery scale (SRS). J Surg Res. 2011;167:e85–e91.
Bellamy N, Buchanan WW, Goldsmith CH, et al. Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol. 1988;15:1833–1840.
Master H, Pennings JS, Coronado RA, et al. Physical performance tests provide distinct information in both predicting and assessing patient-reported outcomes following lumbar spine surgery. Spine (Phila Pa 1976). 2020;45:E1556–E1563.
Paddison JS, Booth RJ, Hill AG, et al. Comprehensive assessment of peri-operative fatigue: development of the Identity-Consequence Fatigue Scale. J Psychosom Res. 2006;60:615–622.
Singh PP, Srinivasa S, Lemanu DP, et al. The Surgical Recovery Score correlates with the development of complications following elective colec-tomy. J Surg Res. 2013;184:138–144.
Cuenca AG, Delano MJ, Kelly-Scumpia KM, et al. A paradoxical role for myeloid-derived suppressor cells in sepsis and trauma. Mol Med. 2011;17:281–292.
Aghaeepour N, Kin C, Ganio EA, et al. Deep immune profiling of an arginineenriched nutritional intervention in patients undergoing surgery. J Immunol. 2017;199:2171–2180.
Nagaraj S, Collazo M, Corzo CA, et al. Regulatory myeloid suppressor cells in health and disease. Cancer Res. 2009;69:7503–7506.
Stiff A, Trikha P, Mundy-Bosse B, et al. Nitric oxide production by myeloid-derived suppressor cells plays a role in impairing Fc receptor-mediated natural killer cell function. Clin Cancer Res. 2018;24:1891–1904.
Braun D, Caramalho I, Demengeot J. IFN-α/β enhances BCR-dependent B cell responses. Int Immunol. 2002;14:411–419.
Takayanagi H. Osteoimmunology: shared mechanisms and crosstalk between the immune and bone systems. Nat Rev Immunol. 2007;7:292–304.
Könnecke I, Serra A, Khassawna TE, et al. T and B cells participate in bone repair by infiltrating the fracture callus in a two-wave fashion. Bone. 2014;64:155–165.
Iwata Y, Yoshizaki A, Komura K, et al. CD19, a response regulator of B lymphocytes, regulates wound healing through hyaluronan-induced TLR4 signaling. Am J Pathol. 2009;175:649–660.