目的:为了外部验证SCORE2,AHA/ACC集合队列方程(PCE),弗雷明汉风险评分(FRS),非实验室INTERHEART风险评分(NL-IHRS),Globorisk-LAC,和世卫组织的预测模型,并比较它们的辨别和校准能力。
方法:根据哥伦比亚的前瞻性城市农村流行病学前瞻性队列研究(PURE),对40-69岁的个体进行了至少10年的随访,并且没有基线使用他汀类药物或心血管疾病。对于歧视,C统计量,使用并比较了具有曲线下积分面积(AUCi)的接收器工作特性曲线。对于校准,使用了平滑的事件时间方法,根据积分校准指数(ICI)选择重新校准因子。在NL-IHRS中,使用线性回归。
结果:在3,802名参与者(59.1%的女性)中,基线风险范围为4.8%(SCORE2女性)至55.7%(NL-IHRS).经过13.2年的平均随访,报告了234例事件(每1000人年4.8例)。C统计量在NL-IHRS的0.637(0.601-0.672)和AHA/ACCPCE的0.767(0.657-0.877)之间。AUCi之间的区分是相似的。在女性中,在Globorisk-LAC(61%)和世卫组织(59%)中观察到更高的过度预测。在男人中,在FRS(72%)和AHA/ACCPCE(71%)中观察到更高的过度预测。在乘以ICI得出的因子后,对高估进行了校正。
结论:六个预测模型具有相似的辨别能力,在乘以校正因子后支持它们的使用。如果无法进行血液检查,NL-IHRS是一个合理的选择。我们的结果表明,在用乘数校正高估后,这些模型可以在拉丁美洲的其他国家使用。
检测心血管疾病高危人群并在该人群中实施预防性干预措施是一级预防的关键策略。最近,开发了新的风险计算工具,但是在它们在与开发的人群不同的人群中应用和常规使用之前,有必要验证它们。哥伦比亚指南中预测心血管风险的建议是基于具有值得注意的局限性的研究。这项涉及哥伦比亚3,802名健康个体的研究支持使用这些预测模型的建议。估计结果应乘以校正因子,因为大多数预测模型高估了心血管风险。例如,女性AHA/ACCPCE和SCORE2的校正因子分别为0.54和0.75.在男人中,AHA/ACCPCE和SCORE2中建议的校正系数分别为0.28和0.61。因此,本研究以当代人群为研究对象,为哥伦比亚甚至拉丁美洲更新这些建议提供了更多证据.
OBJECTIVE: To external validate the SCORE2, AHA/ACC Pooled Cohort Equation (PCE), Framingham Risk Score (FRS), Non-Laboratory INTERHEART Risk Score (NL-IHRS), Globorisk-LAC, and WHO prediction models and compare their discrimination and calibration capacity.
METHODS: Validation in individuals aged 40-69 years with at least 10 years follow-up and without baseline use of statins or cardiovascular diseases from the Prospective Urban Rural Epidemiology prospective cohort
study (PURE)-Colombia. For discrimination, the C-statistic, and Receiver Operating Characteristic curves with the integrated area under the curve (AUCi) were used and compared. For calibration, the smoothed time-to-event method was used, choosing a recalibration factor based on the integrated calibration index (ICI). In the NL-IHRS, linear regressions were used.
RESULTS: In 3,802 participants (59.1% women), baseline risk ranged from 4.8% (SCORE2 women) to 55.7% (NL-IHRS). After a mean follow-up of 13.2 years, 234 events were reported (4.8 cases per 1000 person-years). The C-statistic ranged between 0.637 (0.601-0.672) in NL-IHRS and 0.767 (0.657-0.877) in AHA/ACC PCE. Discrimination was similar between AUCi. In women, higher overprediction was observed in the Globorisk-LAC (61%) and WHO (59%). In men, higher overprediction was observed in FRS (72%) and AHA/ACC PCE (71%). Overestimations were corrected after multiplying by a factor derived from the ICI.
CONCLUSIONS: Six prediction models had a similar discrimination capacity, supporting their use after multiplying by a correction factor. If blood tests are unavailable, NL-IHRS is a reasonable option. Our results suggest that these models could be used in other countries of Latin America after correcting the overestimations with a multiplying factor.
Detecting people at high risk of cardiovascular disease and implementing preventive interventions in this population is a key strategy in primary prevention. Recently, new risk calculation tools have been developed, but before their application and routine use in populations different from those where it was developed, it\'s necessary to validate them. The recommendations for predicting cardiovascular risk in Colombia\'s guidelines are based on studies with noteworthy limitations. This
study involving 3,802 healthy individuals in Colombia supports the recommendation of using these prediction models. The estimation result should be multiplied by a correction factor, because most of the prediction models overestimate cardiovascular risk. For example, the correction factors suggested in women for AHA/ACC PCE and SCORE2 are 0.54 and 0.75, respectively. In men, the correction factors suggested in AHA/ACC PCE and SCORE2 are 0.28 and 0.61, respectively. Therefore, the present
study with a contemporary population provides additional evidence to update these recommendations in Colombia and perhaps in Latin America.