Department of Vascular and Thoracic Surgery, Regional Hospital, Bolzano, Italy.
Department of Vascular and Thoracic Surgery, Regional Hospital, Bolzano, Italy.
Semin Thorac Cardiovasc Surg. 2021;33(2):581-592. doi: 10.1053/j.semtcvs.2020.08.012. Epub 2020 Aug 25.
Validation of predictive risk models for prolonged air leak (PAL) is essential to understand if they can help to reduce its incidence and complications. This study aimed to evaluate both the clinical and statistical performances of 4 existing models. We selected 4 predictive PAL risk models based on their scientific relevance. We referred to these models as Chicago, Bordeaux, Leeds and Pittsburgh model, respectively, according to the affiliation place of the first author. These predicting risk models were retrospectively applied to patients recorded on the second edition of the Italian Video-Assisted Thoracoscopic Surgery Group registry. Predictions for each patient were calculated based on the logistic regression coefficient values provided in the original manuscripts. All models were tested for their overall performance, discrimination, and calibration. We recalibrated the original models with the re-estimation of the model intercept and slope. We used curve decision analysis to describe and compare the clinical effects of the studied risk models. Better statistical metrics characterize the models developed on larger populations (Chicago and Bordeaux models). However, no model has a valid benefit for threshold probability greater than 0.30. The Net benefit of the most performing model (Bordeaux model) at the threshold probability of 0.11 is 23 of 1000 patients, burdened by 333 false positive cases. One of 1000 is the Net benefit at the threshold probability of 0.3. The use of PAL scores based on preoperative predictive factors cannot be currently used in a clinical setting because of a high false positive rate and low positive predictive value.
验证预测性气胸持续时间（PAL）风险模型对于了解它们是否有助于降低其发生率和并发症至关重要。本研究旨在评估 4 种现有模型的临床和统计学性能。我们根据科学相关性选择了 4 种预测性 PAL 风险模型。我们根据第一作者的所属单位分别将这些模型称为芝加哥模型、波尔多模型、利兹模型和匹兹堡模型。这些预测风险模型被回顾性地应用于记录在意大利胸腔镜手术协会注册中心第二版中的患者。根据原始手稿中提供的逻辑回归系数值，为每位患者计算了预测值。我们对原始模型进行了重新校准，重新估算了模型截距和斜率。我们使用曲线决策分析来描述和比较所研究风险模型的临床效果。在较大的人群中，更好的统计指标可以更好地描述模型（芝加哥和波尔多模型）。然而，没有任何一个模型在阈值概率大于 0.30 时具有有效的获益。在阈值概率为 0.11 时，表现最佳的模型（波尔多模型）的净获益为 1000 例患者中有 23 例，假阳性病例为 333 例。在阈值概率为 0.3 时，1000 例中有 1 例。由于假阳性率高和阳性预测值低，基于术前预测因素的 PAL 评分目前不能在临床环境中使用。
