Mayo Clinic, Rochester, MN.
Mayo Clinic, Rochester, MN.
Curr Probl Cardiol. 2023 Dec;48(12):102011. doi: 10.1016/j.cpcardiol.2023.102011. Epub 2023 Aug 4.
Accurate ECG interpretation is vital, but variations in skills exist among healthcare professionals. This study aims to identify factors contributing to ECG interpretation proficiency. Survey data and ECG interpretation test scores from participants in the EDUCATE Trial were analyzed to identify predictors of performance for 30 sequential 12-lead ECGs. Nonmodifiable factors (being a physician, clinical experience, patient care impact) and modifiable factors (weekly interpretation volume, training hours, expert supervision frequency) were analyzed. Bivariate and multivariate analyses were used to generate a Comprehensive Model (incorporating all factors) and Actionable Model (incorporating modifiable factors only). Among 1206 participants analyzed, there were 72 (6.0%) primary care physicians, 146 (12.1%) cardiology fellows-in-training, 353 (29.3%) resident physicians, 182 (15.1%) medical students, 84 (7.0%) advanced practice providers, 120 (9.9%) nurses, and 249 (20.7%) allied health professionals. Among them, 571 (47.3%) were physicians and 453 (37.6%) were nonphysicians. The average test score was 56.4% ± 17.2%. Bivariate analysis demonstrated significant associations between test scores and >10 weekly ECG interpretations, being a physician, >5 training hours, patient care impact, and expert supervision but not clinical experience. In the Comprehensive Model, independent associations were found with weekly interpretation volume (9.9 score increase; 95% CI, 7.9-11.8; P < 0.001), being a physician (9.0 score increase; 95% CI, 7.2-10.8; P < 0.001), and training hours (5.7 score increase; 95% CI, 3.7-7.6; P < 0.001). In the Actionable Model, scores were independently associated with weekly interpretation volume (12.0 score increase; 95% CI, 10.0-14.0; P < 0.001) and training hours (4.7 score increase; 95% CI, 2.6-6.7; P < 0.001). The Comprehensive and Actionable Models explained 18.7% and 12.3% of the variance in test scores, respectively. Predictors of ECG interpretation proficiency include nonmodifiable factors like physician status and modifiable factors such as training hours and weekly ECG interpretation volume.
准确的心电图解读至关重要,但医疗保健专业人员的技能水平存在差异。本研究旨在确定导致心电图解读能力的因素。分析了 EDUCATE 试验中参与者的调查数据和心电图解读测试成绩,以确定 30 份连续 12 导联心电图的表现预测因素。分析了不可改变的因素(是否为医生、临床经验、对患者护理的影响)和可改变的因素(每周解读量、培训时间、专家监督频率)。使用双变量和多变量分析生成综合模型(包含所有因素)和可操作模型(仅包含可改变的因素)。在分析的 1206 名参与者中,有 72 名(6.0%)初级保健医生、146 名(12.1%)正在接受培训的心脏病学研究员、353 名(29.3%)住院医生、182 名(15.1%)医学生、84 名(7.0%)高级执业医师、120 名(9.9%)护士和 249 名(20.7%)辅助医疗专业人员。其中,571 名(47.3%)为医生,453 名(37.6%)为非医生。平均测试得分为 56.4%±17.2%。双变量分析表明,测试成绩与每周>10 次心电图解读、是否为医生、>5 小时培训、对患者护理的影响和专家监督显著相关,但与临床经验无关。在综合模型中,与每周解读量(增加 9.9 分;95%CI,7.9-11.8;P<0.001)、是否为医生(增加 9.0 分;95%CI,7.2-10.8;P<0.001)和培训时间(增加 5.7 分;95%CI,3.7-7.6;P<0.001)独立相关。在可操作模型中,评分与每周解读量(增加 12.0 分;95%CI,10.0-14.0;P<0.001)和培训时间(增加 4.7 分;95%CI,2.6-6.7;P<0.001)独立相关。综合和可操作模型分别解释了测试成绩方差的 18.7%和 12.3%。心电图解读能力的预测因素包括不可改变的因素,如医生身份和可改变的因素,如培训时间和每周心电图解读量。