Rao Ahsan, Bottle Alex, Bicknell Collin, Darzi Ara, Aylin Paul
Dr Foster Unit, Department of Public Health, Imperial College London, 3 Dorset Rise, London EC4Y 8EN, UK.
Department of Surgery, Imperial College London, St. Mary's Hospital, Praed Street, London W2 1NY, UK.
Surg Res Pract. 2018 Oct 21;2018:4321986. doi: 10.1155/2018/4321986. eCollection 2018.
The aim of the study was to use trajectory analysis to categorise high-impact users based on their long-term readmission rate and identify their predictors following AAA (abdominal aortic aneurysm) repair. Methods. In this retrospective cohort study, group-based trajectory modelling (GBTM) was performed on the patient cohort (2006-2009) identified through national administrative data from all NHS English hospitals. Proc Traj software was used in SAS program to conduct GBTM, which classified patient population into groups based on their annual readmission rates during a 5-year period following primary AAA repair. Based on the trends of readmission rates, patients were classified into low- and high-impact users. The high-impact group had a higher annual readmission rate throughout 5-year follow-up. Short-term high-impact users had initial high readmission rate followed by rapid decline, whereas chronic high-impact users continued to have high readmission rate.
Based on the trends in readmission rates, GBTM classified elective AAA repair (=16,973) patients into 2 groups: low impact (82.0%) and high impact (18.0%). High-impact users were significantly associated with female sex (=0.001) undergoing other vascular procedures (=0.003), poor socioeconomic status index ( < 0.001), older age ( < 0.001), and higher comorbidity score ( < 0.001). The AUC for c-statistics was 0.84. Patients with ruptured AAA repair (=4144) had 3 groups: low impact (82.7%), short-term high impact (7.2%), and chronic high impact (10.1%). Chronic high impact users were significantly associated with renal failure ( < 0.001), heart failure ( = 0.01), peripheral vascular disease ( < 0.001), female sex ( = 0.02), open repair ( < 0.001), and undergoing other related procedures (=0.05). The AUC for c-statistics was 0.71.
Patients with persistent high readmission rates exist among AAA population; however, their readmissions and mortality are not related to AAA repair. They may benefit from optimization of their medical management of comorbidities perioperatively and during their follow-up.
本研究的目的是使用轨迹分析方法,根据长期再入院率对高影响用户进行分类,并确定腹主动脉瘤(AAA)修复术后的预测因素。方法。在这项回顾性队列研究中,对通过英国国家医疗服务体系(NHS)所有英格兰医院的国家行政数据确定的患者队列(2006 - 2009年)进行基于组的轨迹建模(GBTM)。在SAS程序中使用Proc Traj软件进行GBTM,该软件根据初次AAA修复术后5年期间的年度再入院率将患者群体分类。根据再入院率趋势,将患者分为低影响用户和高影响用户。高影响组在5年随访期间的年度再入院率较高。短期高影响用户最初再入院率高,随后迅速下降,而慢性高影响用户持续保持高再入院率。
根据再入院率趋势,GBTM将择期AAA修复患者(=16,973例)分为两组:低影响组(82.0%)和高影响组(18.0%)。高影响用户与女性(=0.001)、接受其他血管手术(=0.003)、社会经济地位指数低(<0.001)、年龄较大(<0.001)以及合并症评分较高(<0.001)显著相关。c统计量的AUC为0.84。AAA破裂修复患者(=4144例)分为三组:低影响组(82.7%)、短期高影响组(7.2%)和慢性高影响组(10.1%)。慢性高影响用户与肾衰竭(<0.001)、心力衰竭(=0.01)、外周血管疾病(<0.001)、女性(=0.02)、开放修复(<0.001)以及接受其他相关手术(=0.05)显著相关。c统计量的AUC为0.71。
AAA患者群体中存在持续高再入院率的患者;然而,他们的再入院和死亡率与AAA修复无关。他们可能从围手术期和随访期间合并症的医疗管理优化中受益。