Johnson Michael L, Bush Ruth L, Collins Tracie C, Lin Peter H, Liles Debra R, Henderson William G, Khuri Shukri F, Petersen Laura A
Houston Center for Quality of Care and Utilization Studies, and Michael E. DeBakey Veterans Affairs Medical Center, Baylor College of Medicine, Houston VAMC, 2002 Holcomb Blvd (112), Houston, TX 77030, USA.
Am J Surg. 2006 Sep;192(3):336-43. doi: 10.1016/j.amjsurg.2006.03.009.
Large databases composed of well-designed prospectively collected cohort data provide an opportunity to examine and compare healthcare treatments in actual clinical practice settings. Because the analysis of these data often leads to a retrospective cohort design, it is essential to adequately adjust for lack of balance in patient characteristics when making treatment comparisons. We used matched propensity scoring in a cohort of patients undergoing elective aneurysm repair as an illustrative example of this important statistical method that adjusts for baseline characteristics and selection bias by matching covariables.
By using prospectively collected clinical data from the National Surgical Quality Improvement Program of the Department of Veterans Affairs, we studied 30-day mortality, 1-year survival, and postoperative complications in 1904 patients who underwent elective AAA repair (endovascular aneurysm repair [EVAR], n=717 (37.7%); open aneurysm repair, n=1187 [62.3%]) at 123 Veterans Health Administration's hospitals between May 1, 2001, and September 30, 2003. In bivariate analysis, patient characteristics and operative and hospital variables were associated with both type of surgery and outcomes of surgery. Therefore, the predicted probability of receiving EVAR was tabulated for all patients by using multiple logistic regression to control for 32 independent demographic and clinical characteristics and then stratified into 5 groups. Patients were matched within strata based on similar levels of the independent measures (a propensity score technique), creating a pseudo-randomized control design. The proportion of patients with the morbidity and mortality outcomes was then compared between the EVAR and open procedures within strata to control for selection.
Patients undergoing EVAR had significantly lower unadjusted 30-day (3.1% versus 5.6%, P=.01) and 1-year mortality (8.7% versus 12.1%, P=.018) than patients undergoing open repair. By using propensity scoring, the proportions of EVAR patients experiencing 30-day mortality were equal or less than patients undergoing open procedures for all levels of probability and decreased as the probability of EVAR increased. Furthermore, propensity scoring also showed that patients having EVAR had lower 1-year mortality and experienced fewer perioperative complications.
We used a propensity score approach to examine outcomes after elective AAA repair to statistically control for many factors affecting both treatment selection and outcome. Patients who underwent elective EVAR had substantially lower perioperative mortality and morbidity rates compared with patients having open repair, which was not explained solely by patient selection in an observational dataset.
由精心设计的前瞻性收集队列数据组成的大型数据库为在实际临床实践环境中检查和比较医疗保健治疗方法提供了机会。由于对这些数据的分析通常会导致回顾性队列设计，因此在进行治疗比较时充分调整患者特征的不平衡至关重要。我们在一组接受择期动脉瘤修复的患者中使用匹配倾向评分法，作为这种重要统计方法的一个示例，该方法通过匹配协变量来调整基线特征和选择偏倚。
通过使用美国退伍军人事务部国家外科质量改进计划前瞻性收集的临床数据，我们研究了2001年5月1日至2003年9月30日期间在123家退伍军人健康管理局医院接受择期腹主动脉瘤修复（血管内动脉瘤修复[EVAR]，n = 717[37.7%]；开放性动脉瘤修复，n = 1187[62.3%]）的1904例患者的30天死亡率、1年生存率和术后并发症。在双变量分析中，患者特征以及手术和医院变量与手术类型和手术结果均相关。因此，通过使用多元逻辑回归控制32个独立的人口统计学和临床特征，为所有患者列出接受EVAR的预测概率，然后将其分为5组。根据独立测量的相似水平（倾向评分技术）在各层内对患者进行匹配，创建一个伪随机对照设计。然后比较各层内EVAR组和开放手术组患者出现发病和死亡结果的比例，以控制选择因素。
接受EVAR的患者未经调整的30天死亡率（3.1%对5.6%，P = 0.01）和1年死亡率（8.7%对12.1%，P = 0.018）显著低于接受开放性修复的患者。通过使用倾向评分法，在所有概率水平下，经历30天死亡率的EVAR患者比例等于或低于接受开放手术的患者，并且随着EVAR概率的增加而降低。此外，倾向评分法还显示接受EVAR的患者1年死亡率较低，围手术期并发症较少。
我们使用倾向评分法来检查择期腹主动脉瘤修复后的结果，以便在统计学上控制影响治疗选择和结果的许多因素。与接受开放性修复的患者相比，接受择期EVAR的患者围手术期死亡率和发病率显著较低，这在观察性数据集中不仅仅是由患者选择来解释的。
