Smits F Jasmijn, Henry Anne Claire, Besselink Marc G, Busch Olivier R, van Eijck Casper H, Arntz Mark, Bollen Thomas L, van Delden Otto M, van den Heuvel Daniel, van der Leij Christiaan, van Lienden Krijn P, Moelker Adriaan, Bonsing Bert A, Borel Rinkes Inne H, Bosscha Koop, van Dam Ronald M, Derksen Wouter J M, den Dulk Marcel, Festen Sebastiaan, Groot Koerkamp Bas, de Haas Robbert J, Hagendoorn Jeroen, van der Harst Erwin, de Hingh Ignace H, Kazemier Geert, van der Kolk Marion, Liem Mike, Lips Daan J, Luyer Misha D, de Meijer Vincent E, Mieog J Sven, Nieuwenhuijs Vincent B, Patijn Gijs A, Te Riele Wouter W, Roos Daphne, Schreinemakers Jennifer M, Stommel Martijn W J, Wit Fennie, Zonderhuis Babs A, Daamen Lois A, van Werkhoven C Henri, Molenaar I Quintus, van Santvoort Hjalmar C
Department of Surgery, Regional Academic Cancer Centre Utrecht, St Antonius Hospital, Nieuwegein and University Medical Centre Utrecht, Utrecht, Netherlands.
Department of Surgery, Cancer Centre Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands.
Lancet. 2022 May 14;399(10338):1867-1875. doi: 10.1016/S0140-6736(22)00182-9. Epub 2022 Apr 28.
Early recognition and management of postoperative complications, before they become clinically relevant, can improve postoperative outcomes for patients, especially for high-risk procedures such as pancreatic resection.
We did an open-label, nationwide, stepped-wedge cluster-randomised trial that included all patients having pancreatic resection during a 22-month period in the Netherlands. In this trial design, all 17 centres that did pancreatic surgery were randomly allocated for the timing of the crossover from usual care (the control group) to treatment given in accordance with a multimodal, multidisciplinary algorithm for the early recognition and minimally invasive management of postoperative complications (the intervention group). Randomisation was done by an independent statistician using a computer-generated scheme, stratified to ensure that low-medium-volume centres alternated with high-volume centres. Patients and investigators were not masked to treatment. A smartphone app was designed that incorporated the algorithm and included the daily evaluation of clinical and biochemical markers. The algorithm determined when to do abdominal CT, radiological drainage, start antibiotic treatment, and remove abdominal drains. After crossover, clinicians were trained in how to use the algorithm during a 4-week wash-in period; analyses comparing outcomes between the control group and the intervention group included all patients other than those having pancreatic resection during this wash-in period. The primary outcome was a composite of bleeding that required invasive intervention, organ failure, and 90-day mortality, and was assessed by a masked adjudication committee. This trial was registered in the Netherlands Trial Register, NL6671.
From Jan 8, 2018, to Nov 9, 2019, all 1805 patients who had pancreatic resection in the Netherlands were eligible for and included in this study. 57 patients who underwent resection during the wash-in phase were excluded from the primary analysis. 1748 patients (885 receiving usual care and 863 receiving algorithm-centred care) were included. The primary outcome occurred in fewer patients in the algorithm-centred care group than in the usual care group (73 [8%] of 863 patients vs 124 [14%] of 885 patients; adjusted risk ratio [RR] 0·48, 95% CI 0·38-0·61; p<0·0001). Among patients treated according to the algorithm, compared with patients who received usual care there was a decrease in bleeding that required intervention (47 [5%] patients vs 51 [6%] patients; RR 0·65, 0·42-0·99; p=0·046), organ failure (39 [5%] patients vs 92 [10%] patients; 0·35, 0·20-0·60; p=0·0001), and 90-day mortality (23 [3%] patients vs 44 [5%] patients; 0·42, 0·19-0·92; p=0·029).
The algorithm for the early recognition and minimally invasive management of complications after pancreatic resection considerably improved clinical outcomes compared with usual care. This difference included an approximate 50% reduction in mortality at 90 days.
The Dutch Cancer Society and UMC Utrecht.
在术后并发症出现临床相关性之前进行早期识别和处理，可改善患者的术后结局，尤其是对于胰腺切除术等高风险手术。
我们开展了一项开放标签、全国性、阶梯楔形整群随机试验，纳入了荷兰22个月期间所有接受胰腺切除术的患者。在该试验设计中，所有17家进行胰腺手术的中心被随机分配从常规治疗（对照组）过渡到按照多模式、多学科算法进行术后并发症早期识别和微创处理的治疗（干预组）的时间。随机分组由一名独立统计学家使用计算机生成的方案进行，分层以确保低-中等手术量中心与高手术量中心交替。患者和研究人员未对治疗设盲。设计了一款智能手机应用程序，其中纳入了该算法，并包括对临床和生化指标的每日评估。该算法确定何时进行腹部CT检查、放射引流、开始抗生素治疗以及拔除腹部引流管。在过渡之后，临床医生在为期4周的导入期接受了如何使用该算法的培训；比较对照组和干预组结局的分析纳入了导入期内除接受胰腺切除术的患者之外的所有患者。主要结局是需要侵入性干预的出血、器官衰竭和90天死亡率的复合指标，由一个设盲的判定委员会进行评估。本试验已在荷兰试验注册库注册，注册号为NL6671。
从2018年1月8日至2019年11月9日，荷兰所有1805例接受胰腺切除术的患者均符合本研究的纳入标准并被纳入。57例在导入期接受手术的患者被排除在主要分析之外。纳入了1748例患者（885例接受常规治疗，863例接受以算法为中心的治疗）。以算法为中心的治疗组发生主要结局的患者少于常规治疗组（863例患者中的73例[8%] vs 885例患者中的124例[14%]；调整后风险比[RR]为0.48，95%置信区间[CI]为0.38 - 0.61；p<0.0001）。在按照算法治疗的患者中，与接受常规治疗的患者相比，需要干预的出血有所减少（47例[5%]患者 vs 51例[6%]患者；RR为0.65，0.42 - 0.99；p = 0.046），器官衰竭（39例[5%]患者 vs 92例[10%]患者；0.35，0.20 - 0.60；p = 0.0001），以及90天死亡率（23例[3%]患者 vs 44例[5%]患者；0.42，0.19 - 0.92；p = 0.029）。
与常规治疗相比，胰腺切除术后并发症的早期识别和微创处理算法显著改善了临床结局。这种差异包括90天死亡率降低约50%。
荷兰癌症协会和乌得勒支大学医学中心。
