Heart rate variability helps classify phenotype in systemic sclerosis.
Cardiovascular function
Heart rate variability
Machine learning
Nonlinear dynamics
Systemic sclerosis
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
15 05 2024
15 05 2024
Historique:
received:
21
09
2023
accepted:
24
04
2024
medline:
16
5
2024
pubmed:
16
5
2024
entrez:
15
5
2024
Statut:
epublish
Résumé
We aimed to develop a systemic sclerosis (SSc) subtypes classifier tool to be used at the patient's bedside. We compared the heart rate variability (HRV) at rest (5-min) and in response to orthostatism (5-min) of patients (n = 58) having diffuse (n = 16, dcSSc) and limited (n = 38, lcSSc) cutaneous forms. The HRV was evaluated from the beat-to-beat RR intervals in time-, frequency-, and nonlinear-domains. The dcSSc group differed from the lcSSc group mainly by a higher heart rate (HR) and a lower HRV, in decubitus and orthostatism conditions. Stand-up maneuver lowered HR standard deviation (sd_HR), the major axis length of the fitted ellipse of Poincaré plot of RR intervals (SD2), and the correlation dimension (CorDim) in the dcSSc group while increased these HRV indexes in the lcSSc group (p = 0.004, p = 0.002, and p = 0.004, respectively). We identified the 5 most informative and discriminant HRV variables. We then compared 341 classifying models (1 to 5 variables combinations × 11 classifier algorithms) according to mean squared error, logloss, sensitivity, specificity, precision, accuracy, area under curve of the ROC-curves and F1-score. F1-score ranged from 0.823 for the best 1-variable model to a maximum of 0.947 for the 4-variables best model. Most specific and precise models included sd_HR, SD2, and CorDim. In conclusion, we provided high performance classifying models able to distinguish diffuse from limited cutaneous SSc subtypes easy to perform at the bedside from ECG recording. Models were based on 1 to 5 HRV indexes used as nonlinear markers of autonomic integrated influences on cardiac activity.
Identifiants
pubmed: 38750078
doi: 10.1038/s41598-024-60553-1
pii: 10.1038/s41598-024-60553-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
11151Subventions
Organisme : Laënnec Institute Research Program - Excellence Initiative of Aix-Marseille Université
ID : AMX-21-IET-017
Informations de copyright
© 2024. The Author(s).
Références
Volkmann, E. R., Andréasson, K. & Smith, V. Systemic sclerosis. Lancet 401(10373), 304–318 (2023).
doi: 10.1016/S0140-6736(22)01692-0
pubmed: 36442487
Hermosillo, A. G., Ortiz, R., Dábague, J., Casanova, J. M. & Martínez-Lavín, M. Autonomic dysfunction in diffuse scleroderma vs CREST: An assessment by computerized heart rate variability. J. Rheumatol. 21(10), 1849–1854 (1994).
pubmed: 7837149
Morelli, S. et al. Twenty-four-hour heart period variability in systemic sclerosis. J. Rheumatol. 23(4), 643–645 (1996).
pubmed: 8730119
Ferri, C. et al. Autonomic dysfunction in systemic sclerosis: Time and frequency domain 24 hour heart rate variability analysis. Br. J. Rheumatol. 36(6), 669–676 (1997).
doi: 10.1093/rheumatology/36.6.669
pubmed: 9236677
Pancera, P. et al. Autonomic nervous system dysfunction in sclerodermic and primary Raynaud’s phenomenon. Clin. Sci. 96(1), 49–57 (1999).
doi: 10.1042/cs0960049
Colaci, M. et al. Reduction of carotid baroreceptor sensitivity in systemic sclerosis. Clin. Exp. Rheumatol. 40(10), 1964–1969 (2022).
pubmed: 35916301
Grossman, P. Respiratory sinus arrhythmia (RSA), vagal tone and biobehavioral integration: Beyond parasympathetic function. Biol. Psychol. 25(186), 108739 (2023).
Wozniak, J. et al. Evaluation of heart rhythm variability and arrhythmia in children with systemic and localized scleroderma. J. Rheumatol. 36(1), 191–196 (2009).
doi: 10.3899/jrheum.080021
pubmed: 19040309
Rodrigues, G. D. et al. Cardiac autonomic modulation at rest and during orthostatic stress among different systemic sclerosis subsets. Eur. J. Intern. Med. 66, 75–80 (2019).
doi: 10.1016/j.ejim.2019.06.003
pubmed: 31202484
Delliaux, S., Delaforge, A., Deharo, J. C. & Chaumet, G. Mental workload alters heart rate variability, lowering non-linear dynamics. Front. Physiol. 14(10), 565 (2019).
doi: 10.3389/fphys.2019.00565
van den Hoogen, F. et al. 2013 classification criteria for systemic sclerosis: An American college of rheumatology/European league against rheumatism collaborative initiative. Ann. Rheum. Dis. 72(11), 1747–1755 (2013).
doi: 10.1136/annrheumdis-2013-204424
pubmed: 24092682
LeRoy, E. C. et al. Scleroderma (systemic sclerosis): Classification, subsets and pathogenesis. J. Rheumatol. 15(2), 202–205 (1988).
pubmed: 3361530
Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Circulation. 93(5), 1043–1065 (1996).
doi: 10.1161/01.CIR.93.5.1043
Billman, G. E. Heart rate variability—a historical perspective. Front. Physiol. 29(2), 86 (2011).
doi: 10.3389/fphys.2011.00086
Akselrod, S. et al. Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control. Science. 213(4504), 220–222 (1981).
doi: 10.1126/science.6166045
pubmed: 6166045
Hoshi, R. A., Pastre, C. M., Vanderlei, L. C. & Godoy, M. F. Poincaré plot indexes of heart rate variability: Relationships with other nonlinear variables. Auton. Neurosci. 177(2), 271–274 (2013).
doi: 10.1016/j.autneu.2013.05.004
pubmed: 23755947
Mourot, L. et al. Decrease in heart rate variability with overtraining: Assessment by the Poincaré plot analysis. Clin. Physiol. Funct. Imaging. 24(1), 10–18 (2004).
doi: 10.1046/j.1475-0961.2003.00523.x
pubmed: 14717743
De Vito, G., Galloway, S. D., Nimmo, M. A., Maas, P. & McMurray, J. J. Effects of central sympathetic inhibition on heart rate variability during steady-state exercise in healthy humans. Clin. Physiol. Funct. Imaging. 22(1), 32–38 (2002).
doi: 10.1046/j.1475-097X.2002.00395.x
pubmed: 12003097
Acharya, R. U., Lim, C. M. & Joseph, P. Heart rate variability analysis using correlation dimension and detrended fluctuation analysis. ITBM-RBM 23, 333–339 (2002).
doi: 10.1016/S1297-9562(02)90002-1
Skinner, J. E., Zebrowski, J. J. & Kowalik, Z. J. New nonlinear algorithms for analysis of heart rate variability: Low dimensional chaos predicts lethal arrhythmias. In Nonlinear Analysis of Physiological Data (eds Kantz, H. et al.) 129–166 (Springer, 1998).
doi: 10.1007/978-3-642-71949-3_9
Sammer, G. Heart period variability and respiratory changes associated with physical and mental load: Non-linear analysis. Ergonomics. 41(5), 746–755 (1998).
doi: 10.1080/001401398186892
pubmed: 9613233
Grassberger, P. & Procaccia, I. Characterization of strange attractors. Phys. Rev. Lett. 50, 346–349 (1983).
doi: 10.1103/PhysRevLett.50.346
Pons, J. F. et al. Heart rhythm characterization through induced physiological variables. Sci. Rep. 7(1), 5059 (2017).
doi: 10.1038/s41598-017-04998-7
pubmed: 28698645
pmcid: 5505978
Michel, P., Ngo, N., Pons, J. F., Delliaux, S. & Giorgi, R. A filter approach for feature selection in classification: Application to automatic atrial fibrillation detection in electrocardiogram recordings. BMC Med. Inform. Decis. Mak. 21(Suppl 4), 130 (2021).
doi: 10.1186/s12911-021-01427-8
pubmed: 33947379
pmcid: 8094578
Ormea, F. Studio comparativo del sistema neurovegetativo periferico nella sclerodermia diffusa e nel tessuto connettivale alterato di alcune dermatosi. Dermatologia 105, 8–17 (1952).
doi: 10.1159/000256881
deBoer, R. W., Karemaker, J. M. & Strackee, J. Hemodynamic fluctuations and baroreflex sensitivity in humans: A beat-to-beat model. Am. J. Physiol. 253(3 Pt 2), H680–H689 (1987).
doi: 10.1152/ajpheart.1987.253.3.H680
pubmed: 3631301
Zlatanovic, M. et al. Cardiac mechanics and heart rate variability in patients with systemic sclerosis: The association that we should not miss. Rheumatol. Int. 37(1), 49–57 (2017).
doi: 10.1007/s00296-016-3618-9
pubmed: 27888320
Tadic, M. et al. Systemic sclerosis impacts right heart and cardiac autonomic nervous system. J. Clin. Ultrasound. 46(3), 188–194 (2018).
doi: 10.1002/jcu.22552
pubmed: 29064088
Rodrigues, G. D. et al. Sympatho-vagal dysfunction in systemic sclerosis: A follow-up study. Life 13(1), 34 (2022).
doi: 10.3390/life13010034
pubmed: 36675983
pmcid: 9863978
Reyes del Paso, G. A., Langewitz, W., Mulder, L. J., van Roon, A. & Duschek, S. The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: A review with emphasis on a reanalysis of previous studies. Psychophysiology. 50(5), 477–487 (2013).
doi: 10.1111/psyp.12027
pubmed: 23445494
Tulppo, M. P., Mäkikallio, T. H., Takala, T. E., Seppänen, T. & Huikuri, H. V. Quantitative beat-to-beat analysis of heart rate dynamics during exercise. Am. J. Physiol. 271(1 Pt 2), H244–H252 (1996).
pubmed: 8760181
Huikuri, H. V. et al. Abnormalities in beat-to-beat dynamics of heart rate before the spontaneous onset of life-threatening ventricular tachyarrhythmias in patients with prior myocardial infarction. Circulation. 93(10), 1836–1844 (1996).
doi: 10.1161/01.CIR.93.10.1836
pubmed: 8635263
Guzik, P. et al. Correlations between the Poincaré plot and conventional heart rate variability parameters assessed during paced breathing. J. Physiol. Sci. 57(1), 63–71 (2007).
doi: 10.2170/physiolsci.RP005506
pubmed: 17266795
Baranger, M. Complexity, Chaos, and Entropy (New England Complex Systems Institute, 2000).
Pincus, S. M. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA. 88(6), 2297–2301 (1991).
doi: 10.1073/pnas.88.6.2297
pubmed: 11607165
pmcid: 51218
Pincus, S. M., Gladstone, I. M. & Ehrenkranz, R. A. A regularity statistic for medical data analysis. J. Clin. Monit. 7(4), 335–345 (1991).
doi: 10.1007/BF01619355
pubmed: 1744678
Pincus, S. M. & Goldberger, A. L. Physiological time-series analysis: What does regularity quantify?. Am. J. Physiol. 266(4 Pt 2), H1643–H1656 (1994).
pubmed: 8184944
Pincus, S. M. Greater signal regularity may indicate increased system isolation. Math Biosci. 122(2), 161–181 (1994).
doi: 10.1016/0025-5564(94)90056-6
pubmed: 7919665
Butler, G. C., Yamamoto, Y., Xing, H. C., Northey, D. R. & Hughson, R. L. Heart rate variability and fractal dimension during orthostatic challenges. J. Appl. Physiol. 75(6), 2602–2612 (1993).
doi: 10.1152/jappl.1993.75.6.2602
pubmed: 8125880
Henriksen, O., Kristensen, J. K. & Wadskov, S. Local regulation of blood flow in subcutaneous tissue in generalized scleroderma. J. Investig. Dermatol. 68(5), 318–321 (1977).
doi: 10.1111/1523-1747.ep12494588
pubmed: 870565
Masini, F. et al. Autonomic nervous system dysfunction correlates with microvascular damage in systemic sclerosis patients. J. Scleroderma Relat. Disord. 6(3), 256–263 (2021).
doi: 10.1177/23971983211020617
pubmed: 35387218
pmcid: 8922659
Saul, J. P., Berger, R. D., Chen, M. H. & Cohen, R. J. Transfer function analysis of autonomic regulation. II. Respiratory sinus arrhythmia. Am. J. Physiol. Heart Circ. Physiol. 256(1), H153–H161 (1989).
doi: 10.1152/ajpheart.1989.256.1.H153
Grossman, P., Wilhelm, F. H. & Spoerle, M. Respiratory sinus arrhythmia, cardiac vagal control, and daily activity. Am. J. Physiol.-Heart Circ. Physiol. 287(2), H728–H734 (2004).
doi: 10.1152/ajpheart.00825.2003
pubmed: 14751862
Ritz, T. Studying noninvasive indices of vagal control: The need for respiratory control and the problem of target specificity. Biol. Psychol. 80, 158–168 (2009).
doi: 10.1016/j.biopsycho.2008.08.003
pubmed: 18775468
Sobanski, V. et al. Phenotypes determined by cluster analysis and their survival in the prospective European scleroderma trials and research cohort of patients with systemic sclerosis. Arthritis Rheumatol. 71(9), 1553–1570 (2019).
doi: 10.1002/art.40906
pubmed: 30969034
pmcid: 6771590
Castiglioni, P. & Parati, G. Present trends and future directions in the analysis of cardiovascular variability. J. Hypertens. 29(7), 1285–1288 (2011).
doi: 10.1097/HJH.0b013e3283491d97
pubmed: 21659819
Silva, L. E. V. et al. Comparison between spectral analysis and symbolic dynamics for heart rate variability analysis in the rat. Sci. Rep. 7(1), 8428 (2017).
doi: 10.1038/s41598-017-08888-w
pubmed: 28814785
pmcid: 5559602
Säkki, M., Kalda, J., Vainu, M. & Laan, M. What does measure the scaling exponent of the correlation sum in the case of human heart rate?. Chaos 14(1), 138–144 (2004).
doi: 10.1063/1.1636151
pubmed: 15003054