BACKGROUND Risk prediction tools are routinely utilised in cardiothoracic surgery but have not been developed for pulmonary endarterectomy (PEA). There is no data on whether patients undergoing PEA may benefit from a tailored risk modelling approach. We develop and validate a clinically-usable tool to predict PEA 90-day mortality (90 DM) with the secondary aim of informing factors that may influence five-year mortality (5 YM) and improvement in patient-reported outcomes (PROchange) using common clinical assessment parameters. Derived model predictions were compared to those of the currently most widely implemented cardiothoracic surgery risk tool, EuroSCORE II. METHODS Consecutive patients undergoing PEA for chronic thromboembolic pulmonary hypertension (CTEPH) between 2007 and 2018 (n=1334) were included in a discovery dataset. Outcome predictors included an intentionally broad array of variables, incorporating demographic, functional and physiological measures. Three statistical models (linear regression, penalised linear regression and random forest) were considered per outcome, each calibrated, fitted and assessed using cross-validation, ensuring internal consistency. The best predictive models were incorporated into an open-source PEA risk tool and validated using a separate prospective PEA cohort from 2019 to 2021 (n=443) at the same institution. RESULTS Random forest models had the greatest predictive accuracy for all three outcomes. Novel risk models had acceptable discriminatory ability for outcome 90 DM (AUROC 0.82) outperforming that of EuroSCORE II (AUROC 0.65). CTEPH related factors were important for outcome 90 DM but 5 YM was driven by non-CTEPH factors, dominated by generic cardiovascular risk. We were unable to accurately predict a positive improvement in PRO status (AUROC 0.47). CONCLUSIONS Operative mortality from PEA can be predicted pre-operatively to a potentially clinically useful degree. Our validated models enable individualised risk stratification at clinician point-of-care to better inform shared decision making.

中文翻译：

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开发用于肺动脉内膜切除术风险评估的开源工具。


背景 风险预测工具通常用于心胸外科手术，但尚未开发用于肺动脉内膜切除术 （PEA）。没有关于接受 PEA 的患者是否可以从量身定制的风险建模方法中受益的数据。我们开发并验证了一种临床上可用的工具来预测 PEA 90 天死亡率 （90 DM），次要目的是告知可能影响五年死亡率 （5 YM） 和患者报告结果改善 （PROchange） 的因素使用常见的临床评估参数。将得出的模型预测与目前最广泛实施的心胸外科手术风险工具 EuroSCORE II 的预测进行了比较。方法 2007 和 2018 年连续接受 PEA 治疗慢性血栓栓塞性肺动脉高压 （CTEPH） 的患者 （n=1334） 被纳入发现数据集。结果预测因子包括有意广泛的变量，包括人口统计学、功能和生理学指标。每个结果考虑了三种统计模型 （线性回归、惩罚线性回归和随机森林），每种模型都使用交叉验证进行校准、拟合和评估，以确保内部一致性。最佳预测模型被纳入开源 PEA 风险工具，并在同一机构使用 2019 年至 2021 年的单独前瞻性 PEA 队列 （n=443） 进行验证。结果 随机森林模型对所有 3 个结局的预测准确性最高。新风险模型对结局 90 DM （AUROC 0.82） 具有可接受的区分能力，优于 EuroSCORE II （AUROC 0.65）。CTEPH 相关因素对结局 90 DM 很重要，但 5 YM 由非 CTEPH 因素驱动，以一般心血管风险为主。 我们无法准确预测 PRO 状态的积极改善 （AUROC 0.47）。结论 PEA 的手术死亡率可以在术前预测，具有潜在的临床有用程度。我们经过验证的模型能够在临床医生的床旁进行个体化风险分层，以更好地为共同决策提供信息。