Fruscione Mike, Kirks Russell, Cochran Allyson, Murphy Keith, Baker Erin H, Martinie John B, Iannitti David A, Vrochides Dionisios
Division of HPB Surgery, Department of General Surgery, Carolinas Medical Center, Charlotte, NC, USA.
Division of HPB Surgery, Department of General Surgery, Carolinas Medical Center, Charlotte, NC, USA.
HPB (Oxford). 2018 Aug;20(8):721-728. doi: 10.1016/j.hpb.2018.02.634. Epub 2018 Mar 15.
The American College of Surgeons NSQIP Surgical Risk Calculator (SRC) was developed to estimate postoperative outcomes. Our goal was to develop and validate an institution-specific risk calculator for patients undergoing major hepatectomy at Carolinas Medical Center (CMC).
Outcomes generated by the SRC were recorded for 139 major hepatectomies performed at CMC (2008-2016). Novel predictive models for seven postoperative outcomes were constructed and probabilities calculated. Brier score and area under the curve (AUC) were employed to assess accuracy. Internal validation was performed using bootstrap logistic regression. Logistic regression models were constructed using bivariate and multivariate analyses.
Brier scores showed no significant difference in the predictive ability of the SRC and CMC model. Significant differences in the discriminative ability of the models were identified at the individual level. Both models closely predicted 30-day mortality (SRC AUC: 0.867; CMC AUC: 0.815). The CMC model was a stronger predictor of individual postoperative risk for six of seven outcomes (SRC AUC: 0.531-0.867; CMC AUC: 0.753-0.970).
Institution-specific models provide superior outcome predictions of perioperative risk for patients undergoing major hepatectomy. If properly developed and validated, institution-specific models can be used to deliver more accurate, patient-specific care.
美国外科医师学会国家外科质量改进计划手术风险计算器(SRC)旨在评估术后结果。我们的目标是为卡罗莱纳医疗中心(CMC)接受大肝切除术的患者开发并验证一种特定于机构的风险计算器。
记录了CMC在2008年至2016年期间进行的139例大肝切除术的SRC生成的结果。构建了七个术后结果的新型预测模型并计算概率。采用Brier评分和曲线下面积(AUC)评估准确性。使用自助法逻辑回归进行内部验证。使用二元和多变量分析构建逻辑回归模型。
Brier评分显示SRC和CMC模型的预测能力无显著差异。在个体水平上发现了模型鉴别能力的显著差异。两种模型均能较好地预测30天死亡率(SRC AUC:0.867;CMC AUC:0.815)。对于七个结果中的六个,CMC模型是个体术后风险的更强预测指标(SRC AUC:0.531 - 0.867;CMC AUC:0.753 - 0.970)。
特定于机构的模型为接受大肝切除术的患者围手术期风险提供了更好的结果预测。如果开发和验证得当,特定于机构的模型可用于提供更准确的、针对患者的护理。