Pardo Àlex, Gómez Josep, Berrueta Julen, García Alejandro, Manrique Sara, Rodríguez Alejandro, Bodí María
Department of Medicine and Surgery, Rovira Virgili University, 43201 Tarragona, Spain.
Critical Care Department, Joan XXIII University Hospital, 43005 Tarragona, Spain.
J Clin Med. 2025 Jun 25;14(13):4515. doi: 10.3390/jcm14134515.
The Patient Outcome Assessment and Decision Support (PADS) model is a real-time framework designed to predict both mortality and the likelihood of discharge within 48 h in critically ill patients. By combining these predictions, PADS enables clinically meaningful stratification of patient trajectories, supporting bedside decision-making and the planning of critical care resources such as nursing allocation and surgical scheduling. PADS integrates routinely collected clinical data: SOFA variables, age, gender, admission type, and comorbidities. It consists of two Long Short-Term Memory (LSTM) neural networks-one predicting the probability of death and the other the probability of discharge within 48 h. The combination places each patient into one of four states: alive/discharged within 48 h, alive/not discharged, dead within 48 h, or dead later. The model was trained using MIMIC-IV data, emphasizing ease of implementation in units with electronic health records. Out of the 76,540 stays present in MIMIC-IV (53,150 patients), 32,875 (25,555 patients) were used after excluding those with short stays (<48 h) or life support treatment limitations. The code is open, well-documented, and designed for reproducibility and external validation. The model achieved strong performance: AUCROC of 0.94 (±0.03) for mortality and 0.89 (±0.07) for discharge on training data, and 0.87 (±0.02) and 0.88 (±0.03), respectively, on the test set. As a comparison, benchmark models obtain worse accuracy (-13.4% for APS III, -19% for OASIS, and -7.4% for SAPS II). Predictions are visualized in an intuitive format to support clinical interpretation. PADS offers a transparent, reproducible, and practical tool that supports both individual patient care and the strategic organization of intensive care resources by anticipating short-term outcomes.
患者预后评估与决策支持（PADS）模型是一个实时框架，旨在预测危重症患者的死亡率和48小时内出院的可能性。通过结合这些预测结果，PADS能够对患者的病程进行具有临床意义的分层，为床边决策以及诸如护理分配和手术安排等重症监护资源的规划提供支持。PADS整合了常规收集的临床数据：序贯器官衰竭评估（SOFA）变量、年龄、性别、入院类型和合并症。它由两个长短期记忆（LSTM）神经网络组成，一个预测死亡概率，另一个预测48小时内出院概率。综合这两个概率可将每个患者归入四种状态之一：48小时内存活/出院、存活/未出院、48小时内死亡或之后死亡。该模型使用多中心重症医学信息数据库（MIMIC-IV）数据进行训练，强调在配备电子健康记录的科室易于实施。在MIMIC-IV中的七万六千五百四十次住院病例（五万三千一百五十名患者）中，排除住院时间短（<48小时）或有生命支持治疗限制的病例后，使用了三万二千八百七十五次住院病例（二万五千五百五十五名患者）。该模型代码开放、文档完善，设计用于可重复性研究和外部验证。该模型表现出色：训练数据上死亡率的受试者工作特征曲线下面积（AUCROC）为0.94（±0.03），出院概率的AUCROC为0.89（±0.07），测试集上分别为0.87（±0.02）和0.88（±0.03）。作为对比，基准模型的准确率更低（急性生理和慢性健康状况评分系统III（APS III）低13.4%，器官功能障碍评估系统（OASIS）低19%，简化急性生理学评分系统II（SAPS II）低7.4%）。预测结果以直观的形式呈现，以支持临床解读。PADS提供了一个透明、可重复且实用的工具，通过预测短期预后，既支持个体患者护理，也支持重症监护资源的战略组织。
