A risk score of cardiovascular autonomic dysfunction for targeted screening in the Chinese population

Abstract

Cardiovascular autonomic (CA) dysfunction has become a major health concern in China following rapid lifestyle changes [1,2]. Individuals with previously undiagnosed CA dysfunction have an unfavorable cardiovascular risk profile [3,4]. Delay and lack of detection of the disease mostly results from patients being asymptomatic during its early stages [5,6]; therefore, the development of a simple and accurate screening tool to identify those at high risk of developing CA dysfunction will be of great value. The aim of this study was to develop and evaluate a simple, noninvasive and informative scoring system to characterize individuals according to their future risk of CA dysfunction. This study is a CA dysfunction factor survey carried out in a random sample of Chinese population [7]. Survey participants with undiagnosed CA dysfunction, aged 30–80 years, were included in this study. Subjects were excluded from the study to eliminate potential confounding factors thatmayhave influenced theirCA function [7].Of these subjects, complete baseline datawere obtained for 2092 (69.46%) of the participantswithout prior CA dysfunction history. Written consent was obtained from all patients before the study. The present study was approved by the Ethics Committee of the Huashan Hospital, Shanghai, China. The subjects were interviewed for the documentation of medical histories and medication, laboratory assessment of cardiovascular disease risk factors, and standardized examination for HRV. All study subjects underwent a complete CA function evaluation after an eighthour fast. CAdysfunctionwas diagnosed based on at least two abnormal CA reflex test results [5]. The potential risk factors for CA dysfunction were age (categorized into three groups: ≤50, 51–60, and N60 years; code 0, 1 and 2), gender, BMI, abdominal obesity (WC ≥ °90 inmen and≥80 cm inwomen; code 0 and 1), current smoking, resting HR (categorized into four groups: ≤70, 71–80, 81–90, and N90 bpm; code 0, 1, 2and 3), diabetes, hypertension (HT, code 0 and 1), blood glucose and lipid profile. Univariate analyses were performed to estimate significant predictors of CA dysfunction.Multiple logistic regression (MLR)was used to compute β-coefficients for known risk factors. Only parameters that are easy to assess without any laboratory tests were entered into the model. Variables significant at 5% were included in the MLR using stepwise elimination, with CA dysfunction as the dependent variable. For each significant variable in theMLR analysis, a risk scorewas calculated by the regression coefficients (β) dividing by a common factor (0.10) and rounding to the nearest integer. A sum score was calculated for each participant by adding the score for each variable in the risk model. A receiver-operating characteristic (ROC) curve and area under the curve (AUC)wereproduced. Sensitivityandspecificitywere calculated foreach cutoff score. The cutoff score that gave the maximum sum of sensitivity and specificity was taken as the optimum. The performance of the risk scorewas evaluatedbyusing theAUC inROC curve, sensitivity, specificity, the positive predictive value (PPV), and the negative predictive value (NPV) in the three different sets. Furthermore, the proportion of individuals who needed subsequent testing (NST) was compared. The baseline characteristics of the 2092 subjects were listed in Table 1. The CA dysfunction prevalence was 18.51% in entire sample. A total of 1066 individuals and 1026 individuals were randomly selected to be the exploratory set and validation set, respectively. The baseline characteristics were similar between the exploratory and validation set (p b 0.05, Table 1). Univariate association analysis to include potential risk factors showed that resting HR, DM, SBP, DBP, HT, BMI, WC, age, TG, and IRwere significantly associatedwith CAdysfunction. After stepwise elimination of the non-significant variables, the final MLR model included risk factors of age, WC, HT, and HR. The risk score was calculatedusing the formula of 4*age + 3*WC+ 5*HT+7*HR. The total score ranged from 0 to 37. In exploratory set, the cutoff score of 16 was optimum (sensitivity =69.78%, specificity = 69.30%, Youden index = 39.08%,