Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications.
Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications.
复制标题
DOI：
10.1111/j.1537-2995.2012.03886.x
复制
发表时间：
2013-06
期刊：
影响因子：
2.9
通讯作者：
Kor DJ
中科院分区：
文献类型：
--
作者：
Clifford L;Singh A;Wilson GA;Toy P;Gajic O;Malinchoc M;Herasevich V;Pathak J;Kor DJ
输血相关急性肺损伤（TRALI）和输血相关循环负荷（TACO）是输血相关死亡的主要原因。值得注意的是，对综合征认识不足和少报可能导致低估其真正可归因负担。我们的目标是开发准确的基于电子健康记录的筛查算法，以改进TRALI/输血急性肺损伤（ALI）和TACO的检测。这是一项回顾性观察性研究。该研究队列来自先前美国国立卫生研究院（National Institutes of health）赞助的一项前瞻性调查，包括223名接受TRALI、输血ALI、TACO或无并发症对照的输血患者。使用分类和回归树（CART）分析确定最佳病例检测算法。用敏感性、特异性、似然比和总体误分类率来评估算法性能。对于TRALI/输血ALI检测，CART分析的敏感性和特异性分别为83.9%（95%可信区间[CI]， 74.4% ~ 90.4%）和89.7% （95% CI, 80.3% ~ 95.2%）。TACO的敏感性和特异性分别为86.5% （95% CI, 73.6% ~ 94.0%）和92.3% （95% CI, 83.4% ~ 96.8%）。降低的PaO2/FiO2比率和输血后胸片的获取是两种综合征病例与对照状态的主要决定因素。在使用筛选算法（TRALI/输血ALI， n = 78； TACO, n = 45）确定的真阳性病例中，医生分别仅向血库报告了11例（14.1%）和5例（11.1%）。在本院，电子筛选算法在识别TRALI/输血ALI和TACO患者方面显示出良好的敏感性和特异性。这支持了主动电子监测可以改善病例识别的观点，从而提供对TRALI/输血ALI和TACO流行病学更准确的理解。
Transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO) are leading causes of transfusion-related mortality. Notably, poor syndrome recognition and underreporting likely result in an underestimate of their true attributable burden. We aimed to develop accurate electronic health record–based screening algorithms for improved detection of TRALI/transfused acute lung injury (ALI) and TACO. This was a retrospective observational study. The study cohort, identified from a previous National Institutes of Health–sponsored prospective investigation, included 223 transfused patients with TRALI, transfused ALI, TACO, or complication-free controls. Optimal case detection algorithms were identified using classification and regression tree (CART) analyses. Algorithm performance was evaluated with sensitivities, specificities, likelihood ratios, and overall misclassification rates. For TRALI/transfused ALI detection, CART analysis achieved a sensitivity and specificity of 83.9% (95% confidence interval [CI], 74.4%–90.4%) and 89.7% (95% CI, 80.3%–95.2%), respectively. For TACO, the sensitivity and specificity were 86.5% (95% CI, 73.6%–94.0%) and 92.3% (95% CI, 83.4%–96.8%), respectively. Reduced PaO2/FiO2 ratios and the acquisition of posttransfusion chest radiographs were the primary determinants of case versus control status for both syndromes. Of true-positive cases identified using the screening algorithms (TRALI/transfused ALI, n = 78; TACO, n = 45), only 11 (14.1%) and five (11.1%) were reported to the blood bank by physicians, respectively. Electronic screening algorithms have shown good sensitivity and specificity for identifying patients with TRALI/transfused ALI and TACO at our institution. This supports the notion that active electronic surveillance may improve case identification, thereby providing a more accurate understanding of TRALI/transfused ALI and TACO epidemiology.