Department of Surgery, University of Massachusetts Medical School, Surgical Outcomes Analysis and Research, Worcester, MA, USA.
Surgery. 2012 Sep;152(3 Suppl 1):S120-7. doi: 10.1016/j.surg.2012.05.018. Epub 2012 Jul 3.
Undergoing a pancreatectomy obligates the patient to risks and benefits. For complex operations such as pancreatectomy, the objective assessment of baseline risks may be useful in decision-making. We developed an integer-based risk score estimating in-hospital mortality after pancreatectomy, incorporating institution-specific mortality rates to enhance its use.
Pancreatic resections were identified from the Nationwide Inpatient Sample (1998-2006), and categorized as proximal, distal, or nonspecified by the International Classification of Diseases, 9th edition. Logistic regression and bootstrap methods were used to estimate in-hospital mortality using demographics, diagnosis, comorbidities (Charlson index), procedure, and hospital volume; 80% of this cohort was selected randomly to create the score and 20% was used for validation. Score assignments were subsequently individually fitted to risk distributions around specific mortality rates.
Sixteen thousand one hundred sixteen patient discharges were identified. Nationwide in-hospital mortality was 5.3%. Integers were assigned to predictors (age group, Charlson index, sex, diagnosis, pancreatectomy type, and hospital volume) and applied to an additive score. Three score groups were defined to stratify in-hospital mortality (national mortality, 1.3%, 4.9%, and 14.3%; P < .0001), with sufficient discrimination of derivation and validation sets (C statistics, 0.72 and 0.74). Score groups were shifted algorithmically to calculate risk based on institutional data (eg, with institutional mortality of 2.0%, low-, medium-, and high-risk patient groups had 0.5%, 1.9%, and 5.4% mortality, respectively). A web-based tool was developed and is available online (http://www.umassmed.edu/surgery/panc_mortality_custom.aspx).
To maximize patient benefit, objective assessment of risk for major procedures is necessary. We developed a Surgical Outcomes Analysis and Research risk score predicting pancreatectomy mortality that combines national and institution-specific data to enhance decision-making. This type of risk stratification tool may identify opportunities to improve care for patients undergoing specific operative procedures.
接受胰腺切除术会给患者带来风险和获益。对于胰腺切除术等复杂手术,对基线风险进行客观评估可能有助于决策。我们开发了一种基于整数的风险评分系统,用于估计胰腺切除术后的院内死亡率,该系统结合了特定机构的死亡率,以提高其使用效果。
从全国住院患者样本(1998-2006 年)中确定胰腺切除术病例,并通过第 9 版国际疾病分类进行近端、远端或未特指分类。使用逻辑回归和自举方法,根据人口统计学、诊断、合并症(Charlson 指数)、手术和医院规模,对住院死亡率进行估计;该队列的 80%被随机选择用于创建评分,20%用于验证。随后,将评分分配给特定死亡率周围的风险分布。
确定了 16116 例患者出院。全国住院死亡率为 5.3%。整数被分配给预测因子(年龄组、Charlson 指数、性别、诊断、胰腺切除术类型和医院规模),并应用于一个加性评分中。定义了三个评分组来分层院内死亡率(全国死亡率为 1.3%、4.9%和 14.3%;P<0.0001),具有推导和验证集的充分区分度(C 统计量分别为 0.72 和 0.74)。通过算法调整评分组,根据机构数据计算风险(例如,机构死亡率为 2.0%,低、中、高危患者组的死亡率分别为 0.5%、1.9%和 5.4%)。开发了一个基于网络的工具,并可在网上获得(http://www.umassmed.edu/surgery/panc_mortality_custom.aspx)。
为了最大限度地提高患者的获益,对主要手术的风险进行客观评估是必要的。我们开发了一种外科手术结果分析和研究风险评分系统,用于预测胰腺切除术死亡率,该系统结合了全国和特定机构的数据,以提高决策水平。这种风险分层工具可能会发现改善特定手术患者护理的机会。