Background

Artificial intelligence tools and techniques such as machine learning (ML) are increasingly seen as a suitable manner in which to increase the prediction capacity of currently available clinical tools, including prognostic scores. However, studies evaluating the efficacy of ML methods in enhancing the predictive capacity of existing scores for community-acquired pneumonia (CAP) are limited. We aimed to apply and validate a causal probabilistic network (CPN) model to predict mortality in patients with CAP.

Research Question

Is a CPN model able to predict mortality in patients with CAP better than the commonly used severity scores?

Study Design and Methods

This was a derivation-validation retrospective study conducted in two Spanish university hospitals. The ability of a CPN designed to predict mortality in sepsis (SepsisFinder [SeF]), and adapted for CAP (SeF-ML), to predict 30-day mortality was assessed and compared with other scoring systems (Pneumonia Severity Index [PSI], Sequential Organ Failure Assessment [SOFA], quick Sequential Organ Failure Assessment [qSOFA], and CURB-65 criteria [confusion, urea, respiratory rate, BP, age ≥ 65 years]). The SeF models are proprietary software. Differences between receiver operating characteristic curves were assessed by the DeLong method for correlated receiver operating characteristic curves.

Results

The derivation cohort comprised 4,531 patients, and the validation cohort consisted of 1,034 patients. In the derivation cohort, the areas under the curve (AUCs) of SeF-ML, CURB-65, SOFA, PSI, and qSOFA were 0.801, 0.759, 0.671, 0.799, and 0.642, respectively, for 30-day mortality prediction. In the validation study, the AUC of SeF-ML was 0.826, concordant with the AUC (0.801) in the derivation data (P = .51). The AUC of SeF-ML was significantly higher than those of CURB-65 (0.764; P = .03) and qSOFA (0.729, P = .005). However, it did not differ significantly from those of PSI (0.830; P = .92) and SOFA (0.771; P = .14).

Interpretation

SeF-ML shows potential for improving mortality prediction among patients with CAP, using structured health data. Additional external validation studies should be conducted to support generalizability.

中文翻译：

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社区获得性肺炎患者死亡率预测的机器学习模型

背景

机器学习 (ML) 等人工智能工具和技术越来越被视为提高当前可用临床工具预测能力（包括预后评分）的合适方式。然而，评估 ML 方法在提高社区获得性肺炎 (CAP) 现有分数的预测能力方面的功效的研究是有限的。我们旨在应用和验证因果概率网络 (CPN) 模型来预测 CAP 患者的死亡率。

研究问题

CPN 模型是否能够比常用的严重程度评分更好地预测 CAP 患者的死亡率？

研究设计和方法

这是一项在西班牙两家大学医院进行的推导验证回顾性研究。设计用于预测脓毒症死亡率的 CPN (SepsisFinder [SeF]) 和适用于 CAP (SeF-ML) 的 CPN 预测 30 天死亡率的能力得到评估，并与其他评分系统（肺炎严重程度指数 [PSI]、序贯器官衰竭评估 [SOFA]、快速序贯器官衰竭评估 [qSOFA] 和 CURB-65 标准 [意识模糊、尿素、呼吸频率、血压、年龄 ≥ 65 岁]）。SeF 模型是专有软件。接受者操作特征曲线之间的差异通过相关接受者操作特征曲线的 DeLong 方法进行评估。

结果

推导队列包括 4,531 名患者，验证队列包括 1,034 名患者。在推导队列中，SeF-ML、CURB-65、SOFA、PSI 和 qSOFA 的曲线下面积 (AUC) 分别为 0.801、0.759、0.671、0.799 和 0.642，用于 30 天死亡率预测。在验证研究中，SeF-ML 的 AUC 为 0.826，与推导数据中的 AUC (0.801) 一致 ( P  = .51)。SeF-ML 的 AUC 显着高于 CURB-65 (0.764; P  = .03) 和 qSOFA (0.729, P  = .005)。然而，它与 PSI (0.830; P  = .92) 和 SOFA (0.771; P  = .14) 没有显着差异。

解释

SeF-ML 显示出使用结构化健康数据改善 CAP 患者死亡率预测的潜力。应进行额外的外部验证研究以支持普遍性。