A simple Chinese risk score model for screening cardiovascular autonomic neuropathy

Xiaoli Ge, Shu-Ming Pan, Fangfang Zeng, Zi-Hui Tang, Ying-Wei Wang, Xiaoli Ge, Shu-Ming Pan, Fangfang Zeng, Zi-Hui Tang, Ying-Wei Wang

Abstract

Background: The purpose of the present study was to develop and evaluate a risk score to predict people at high risk of cardiovascular autonomic dysfunction neuropathy (CAN) in Chinese population.

Methods and materials: A population-based sample of 2,092 individuals aged 30-80 years, without previously diagnosed CAN, was surveyed between 2011 and 2012. All participants underwent short-term HRV test. The risk score was derived from an exploratory set. The risk score was developed by stepwise backward multiple logistic regression. The coefficients from this model were transformed into components of a CAN score. This score was tested in a validation and entire sample.

Results: The final risk score included age, body mass index, hypertension, resting hear rate, items independently and significantly (P<0.05) associated with the presence of previously undiagnosed CAN. The area under the receiver operating curve was 0.726 (95% CI 0.686-0.766) for exploratory set, 0.784 (95% CI 0.749-0.818) for validation set, and 0.756 (95% CI 0.729-0.782) for entire sample. In validation set, at optimal cutoff score of 5 of 10, the risk score system has the sensitivity, specificity, and percentage that needed subsequent testing were 69, 78, and 30%, respectively.

Conclusion: We developed a CAN risk score system based on a set of variables not requiring laboratory tests. The score system is simple fast, inexpensive, noninvasive, and reliable tool that can be applied to early intervention to delay or prevent the disease in China.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Receiver operating characteristic curves showed…
Figure 1. Receiver operating characteristic curves showed the performance of each cardiovascular autonomic neuropathy (CAN) risk score (CRS) in predicting prevalence of diabetes in the exploratory, validation and entire cohorts.
The 95% confidence interval (CI) is given in parentheses. AUC - area under the curve.

References

    1. Spallone V, Ziegler D, Freeman R, Bernardi L, Frontoni S, et al. (2011) Cardiovascular autonomic neuropathy in diabetes: clinical impact, assessment, diagnosis, and management. Diabetes Metab Res Rev
    1. Garruti G, Giampetruzzi F, Vita MG, Pellegrini F, Lagioia P, et al. (2012) Links between metabolic syndrome and cardiovascular autonomic dysfunction. Exp Diabetes Res 2012: 615835.
    1. Hazari MA, Khan RT, Reddy BR, Hassan MA (2012) Cardiovascular autonomic dysfunction in type 2 diabetes mellitus and essential hypertension in a South Indian population. Neurosciences (Riyadh) 17: 173–175.
    1. Laitinen T, Lindstrom J, Eriksson J, Ilanne-Parikka P, Aunola S, et al. (2011) Cardiovascular autonomic dysfunction is associated with central obesity in persons with impaired glucose tolerance. Diabet Med 28: 699–704.
    1. Iodice V, Low DA, Vichayanrat E, Mathias CJ (2011) Cardiovascular autonomic dysfunction in MSA and Parkinson's disease: similarities and differences. J Neurol Sci 310: 133–138.
    1. Vinik AI, Ziegler D (2007) Diabetic cardiovascular autonomic neuropathy. Circulation 115: 387–397.
    1. Ziegler D, Zentai C, Perz S, Rathmann W, Haastert B, et al. (2006) Selective contribution of diabetes and other cardiovascular risk factors to cardiac autonomic dysfunction in the general population. Exp Clin Endocrinol Diabetes 114: 153–159.
    1. Kamphuis MH, Geerlings MI, Dekker JM, Giampaoli S, Nissinen A, et al. (2007) Autonomic dysfunction: a link between depression and cardiovascular mortality? The FINE Study. Eur J Cardiovasc Prev Rehabil 14: 796–802.
    1. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 17: 354–381.
    1. Min KB, Min JY, Paek D, Cho SI, Son M (2008) Is 5-minute heart rate variability a useful measure for monitoring the autonomic nervous system of workers? Int Heart J 49: 175–181.
    1. Tang ZH, Liu J, Zeng F, Li Z, Yu X, et al. (2013) Comparison of prediction model for cardiovascular autonomic dysfunction using artificial neural network and logistic regression analysis. PLoS One 8: e70571.
    1. Liu J, Tang ZH, Zeng F, Li Z, Zhou L (2013) Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population. BMC medical informatics and decision making 13: 80.
    1. Bei-Fan Z (2002) Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults: study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Asia Pacific journal of clinical nutrition 11 Suppl 8: S685–693.
    1. Grundy SM, Hansen B, Smith SC Jr, Cleeman JI, Kahn RA (2004) Clinical management of metabolic syndrome: report of the American Heart Association/National Heart, Lung, and Blood Institute/American Diabetes Association conference on scientific issues related to management. Circulation 109: 551–556.
    1. Swartz MD, Yu RK, Shete S (2008) Finding factors influencing risk: comparing Bayesian stochastic search and standard variable selection methods applied to logistic regression models of cases and controls. Stat Med 27: 6158–6174.
    1. Li Y, Wang YG (2008) Smooth bootstrap methods for analysis of longitudinal data. Stat Med 27: 937–953.
    1. Bian RW, Lou QL, Gu LB, Kong AP, So WY, et al. (2011) Delayed gastric emptying is related to cardiovascular autonomic neuropathy in Chinese patients with type 2 diabetes. Acta Gastroenterol Belg 74: 28–33.
    1. Ding W, Zhou L, Bao Y, Yang Y, Lu B, et al. (2011) Autonomic nervous function and baroreflex sensitivity in hypertensive diabetic patients. Acta Cardiol 66: 465–470.
    1. Ayad F, Belhadj M, Paries J, Attali JR, Valensi P (2010) Association between cardiac autonomic neuropathy and hypertension and its potential influence on diabetic complications. Diabet Med 27: 804–811.
    1. Nanaiah A, Chowdhury SD, Jeyaraman K, Thomas N (2012) Prevalence of cardiac autonomic neuropathy in Asian Indian patients with fibrocalculous pancreatic diabetes. Indian J Endocrinol Metab 16: 749–753.
    1. Lambrecht R, McNeeley K, Tusing L, Chelimsky T (2007) Evaluation of a brief cardiovascular autonomic screen. Auton Neurosci 131: 102–106.
    1. Vespasiani G, Bruni M, Meloncelli I, Clementi L, Amoretti R, et al. (1996) Validation of a computerised measurement system for guided routine evaluation of cardiovascular autonomic neuropathy. Comput Methods Programs Biomed 51: 211–216.
    1. Jarczok MN, Li J, Mauss D, Fischer JE, Thayer JF (2012) Heart Rate Variability is Associated with Glycemic Status After Controlling for Components of the Metabolic Syndrome. Int J Cardiol
    1. Meinhold JA, Maslowska-Wessel E, Bender R, Sawicki PT (2001) Low prevalence of cardiac autonomic neuropathy in Type 1 diabetic patients without nephropathy. Diabet Med 18: 607–613.
    1. Erdem A, Uenishi M, Matsumoto K, Kucukdurmaz Z, Kato R, et al. (2012) Cardiac autonomic function in metabolic syndrome: a comparison of ethnic Turkish and Japanese patients. J Interv Card Electrophysiol 35: 253–258.

Source: PubMed

Sponsors et collaborateurs

Les conditions médicales

Interventions en matière de drogue

3
S'abonner