Neuroautonomic risk factors for the labor abnormalities on the data of maternal cardiorhythm variability

Responsive changes of the heart rate variability (HRV) indices were analyzed in women with normal pregnancy outcome (NP, n = 97) and with the labor abnormalities (LA, n = 137) under mental (calculation in mind) and respiratory (deep breathing with a voluntary periodicity) loading tests. Signifi cant differences were found between NP and LA groups concerning vago-sympathetic index (as a ratio between high and very low HRV spectral components, VSI) and a parameter of detrended fl uctuation analysis (DFA) of maternal cardiac rhythm against respiratory probe in subgroups of women with initially high barorefl ex (HB) and sympathetic (HS) activity: LA (HB): increase of VSI +11 %; NP (HB): decrease of VSI–10 %, p < 0.05; LA (HS): increase of VSI +33 %; NP (HS): decrease of VSI–3 %, p < 0.10). LA (HB): decrease of DFA–7 %; NP (HB): increase of DFA +8 %, p < 0.10; LA (HS): decrease of DFA -10 %; NP (HS): increase of DFA +13 %, p < 0.001). It has been concluded that maternal heart rate variability under condition of respiratory loading test is the valuable criteria for prediction of the labor abnormalities at early gestational terms (in mid-pregnancy).

1.   Yannaeva N.E. Contemporary concepts of the cervical dilatation mechanisms at delivery and of the cervical dystocia formation. Review // Vest. RNCRR MZ RF. 2010. (10). 18. (in Russian)

2.   Podtetenev A.D. Prognostication, prophylaxis and treatment of the uterine inertia and dystocia: dis. … dr. med. sci. M., 2004. (in Russian)

3.  Gazazyan M.G. On some pathogenetic mechanisms of the uterine contractility abnormalities development // Akusherstvo i ginecologiya. 1989. (6). 67–68. (in Russian)

4.  Chernuha E.A. Obstetric department. M.: Medicina, 1991. (in Russian)

5.  Kleshchenogov S.A., Fleishman A.N. Computer spectral analysis of the heart rate variability in pregnant women: assessment of gestation course and prediction complications: Method. manual for practitioners. Novokuznetsk, 2003. (in Russian)

6.  Camm A., Malic M., Bigger J. et al. Heart rate variability: Standards of measurement, physiological interpretation and clinical use / Task Force of European Society of Cardiology and the North American Society of Pacing and Electrophysiology // Circulation. 1996. 93. 1043–1065.

7.  Fleishman A.N. Slow hemodynamic oscillation. The theory, practical use and prevention. Novosibirsk: Nauka, 1999. (in Russian)

8.   Peng C., Havlin S., Stanley A. et al. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series // Chaos. 1995. 5. 82–87.

9.   Pincus S. Approximate entropy as a measure of system complexity // Proc. Natl. Acad. Sci. USA. 1991. 88. 2297–2301.

10. Gubler E.V. Calculating methods for analyzing and recognition of the pathological conditions. Leningrad: Meditsina, 1978. (in Russian)

11. Goldberger A.L. Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside // Lancet. 1996. 347. 312–1314.

12. Glass L. Chaos and heart rate variability // J. Cardiovasc. Electrophysiol. 1999. 10. 1358–1360.

Kleshchenogov S.A. – candidate of medical sciences, senior researcher of the laboratory of physiology, e-mail: serg_kle29@mail.ru