Division of Thoracic and Foregut Surgery, Department of Cardiothoracic Surgery, University of Pittsburgh, Pittsburg, PA, USA.
J Thorac Cardiovasc Surg. 2013 Mar;145(3):721-9. doi: 10.1016/j.jtcvs.2012.12.026. Epub 2013 Jan 11.
In the current era, giant paraesophageal hernia repair by experienced minimally invasive surgeons has excellent perioperative outcomes when performed electively. However, nonelective repair is associated with significantly greater morbidity and mortality, even when performed laparoscopically. We hypothesized that clinical prediction tools using pretreatment variables could be developed that would predict patient-specific risk of postoperative morbidity and mortality.
We assessed 980 patients who underwent giant paraesophageal hernia repair (1997-2010; 80% elective and 97% laparoscopic). We assessed the association between clinical predictor covariates, including demographics, comorbidity, and urgency of operation, and risk for in-hospital or 30-day mortality and major morbidity. By using forward stepwise logistic regression, clinical prediction models for mortality and major morbidity were developed.
Urgency of operation was a significant predictor of mortality (elective 1.1% [9/778] vs nonelective 8% [16/199]; P < .001) and major morbidity (elective 18% [143/781] vs nonelective 41% [81/199]; P < .001). The most common adverse outcomes were pulmonary complications (n = 199; 20%). A 4-covariate prediction model consisting of age 80 years or more, urgency of operation, and 2 Charlson comorbidity index variables (congestive heart failure and pulmonary disease) provided discriminatory accuracy for postoperative mortality of 88%. A 5-covariate model (sex, age by decade, urgency of operation, congestive heart failure, and pulmonary disease) for major postoperative morbidity was 68% predictive.
Predictive models using pretreatment patient characteristics can accurately predict mortality and major morbidity after giant paraesophageal hernia repair. After prospective validation, these models could provide patient-specific risk prediction, tailored for individual patient characteristics, and contribute to decision-making regarding surgical intervention.
在当前时代，经验丰富的微创外科医生进行择期巨大食管裂孔疝修补术具有极好的围手术期结果。然而，非择期修补与更高的发病率和死亡率相关，即使是腹腔镜下进行也如此。我们假设，可以开发使用术前变量的临床预测工具，以预测患者术后发病率和死亡率的特定风险。
我们评估了 980 例接受巨大食管裂孔疝修补术（1997-2010 年；80%为择期手术，97%为腹腔镜手术）的患者。我们评估了临床预测变量（包括人口统计学、合并症和手术紧迫性）与院内或 30 天死亡率和主要并发症风险之间的关联。通过使用逐步向前逻辑回归，我们开发了死亡率和主要并发症的临床预测模型。
手术紧迫性是死亡率（择期 1.1%[778/728] vs 非择期 8%[199/247]；P<0.001）和主要并发症（择期 18%[728/394] vs 非择期 41%[199/487]；P<0.001）的显著预测因素。最常见的不良后果是肺部并发症（n=199；20%）。一个由年龄 80 岁或以上、手术紧迫性和 2 个 Charlson 合并症指数变量（充血性心力衰竭和肺部疾病）组成的 4 个协变量预测模型，对术后死亡率的预测准确率为 88%。一个由性别、年龄（以十年为单位）、手术紧迫性、充血性心力衰竭和肺部疾病组成的 5 个协变量模型对主要术后并发症的预测准确率为 68%。
使用术前患者特征的预测模型可以准确预测巨大食管裂孔疝修补术后的死亡率和主要并发症。经过前瞻性验证后，这些模型可以为个体患者特征提供特定风险预测，并有助于针对手术干预做出决策。
