正常人左心室功能指标的参考值及其预计公式的初步研究报告*

目的: 当前评估左心室容量和功能仍常用正常值范围,个体化分析也仅使用体表面积进行校正。尚缺少个体化因素相关的大样本参考值和预计公式。方法: 本研究纳入美国加州洛杉矶县南湾地区1200名健康志愿者,其中男807女393,年龄20岁-94岁,心脏CT造影（CTA）,经过高精度三维成像技术处理,计算左心室容积在收缩和舒张过程中的连续动态变化,测定左心室（LV）容量和功能指标：舒张末期容积（EDV)、收缩末期容积(ESV)、每搏输出量(SV)、射血分数(EF)和心输出量(CO)。将以上指标与一般特征指标进行多因素相关分析,以探索正常人预计值计算公式。结果: 男性除LVEF小于女性外（P<0.001）,其余各指标均大于女性（P<0.001）。多元线性回归分析提示, 性别、年龄、身高和体质量均为EDV、ESV、SV的独立影响因子(P<0.001); 而CO仅受年龄、性别、体质量显著影响(P<0.001),但与身高无关(P>0.05)。CO的预测公式CO (L·min^-1)= 6.963+0.446(Male) -0.037×年龄(yr)+0.013×体质量(kg)。结论: 性别、年龄、身高、体质量均影响左心室容量和功能,建立预测值计算公式,对心血管疾病的无创评估和个体化精准医疗具有重要参考价值。

Normal reference values and predict equations of heart function

Objective: For heart functional parameters, we commonly used normal range. The reference values and predict formulas of heart functional parameters and their relationships with individual characteristics are still lack. Methods: Left ventricular (LV) volumes (end-diastolic volume and end-systolic volume), stroke volume(SV), ejection fraction(EF) and cardiac output (CO) were measured by cardiac CT angiography (CAT) in 1 200 healthy Caucasian volunteers, men 807 and women 393, and age 20~90yr. The results are analyzed by high-accuracy three-dimensional imaging technology, and then measured the dynamic changes of the volumes of each atriam and ventricule during their contractions and relaxations. The gender, age, height and weight were analyzed by multiple linear regression to predict LV functional parameters. Results: Except the LVEF was lower in man than in women (P<0.001), all other LV functional parameters of EDV, ESV, SV, FE and CO were higher in man (P<0.001). Multiple linear regression indicated that age, gender, height and weight are all independent factors of EDV, ESV and SV (P<0.001). CO could be significantly predicted by age, gender and weight (P<0.001), but not height (P>0.05). The predict equation for CO (L·min^-1)= 6.963+0.446(Male)-0.037×age (yr) +0.013×weight (kg). Conclusion: Age, gender, height and weight are predictors of heart functions. The reference values and predict equations are important for noninvasive and accurate evaluation of cardiovascular disease and individualized treatment.