NOVA Medical School|Faculdade de Ciências Médicas. Lisboa; Serviço de Medicina Interna. Hospital da Luz. Lisboa. Portugal.
NOVA Medical School|Faculdade de Ciências Médicas. Lisboa. Centro de Estatística e Aplicações. Universidade de Lisboa. Lisboa. Portugal.
Acta Med Port. 2021 Feb 1;34(2):118-127. doi: 10.20344/amp.12996. Epub 2020 Nov 9.
Increased life expectancy leads to older and frailer surgical patients. Co-management between medical and surgical specialities has proven favourable in complex situations. Selection of patients for co-management is full of difficulties. The aim of this study was to develop a clinical decision support tool to select surgical patients for co-management.
Clinical data was collected from patient electronic health records with an ICD-9 code for colorectal surgery from January 2012 to December 2015 at a hospital in Lisbon. The outcome variable consists in co-management signalling. A dataset from 344 patients was used to develop the prediction model and a second data set from 168 patients was used for external validation.
Using logistic regression modelling the authors built a five variable (age, burden of comorbidities, ASA-PS status, surgical risk and recovery time) predictive referral model for co-management. This model has an area under the curve (AUC) of 0.86 (95% CI: 0.81 - 0.90), a predictive Brier score of 0.11, a sensitivity of 0.80, a specificity of 0.82 and an accuracy of 81.3%.
Early referral of high-risk patients may be valuable to guide the decision on the best level of post-operative clinical care. We developed a simple bedside decision tool with a good discriminatory and predictive performance in order to select patients for comanagement.
A simple bed-side clinical decision support tool of patients for co-management is viable, leading to potential improvement in early recognition and management of postoperative complications and reducing the 'failure to rescue'. Generalizability to other clinical settings requires adequate customization and validation.
预期寿命的延长导致接受手术的患者年龄更大、身体更脆弱。医学和外科专业之间的共同管理已被证明在复杂情况下是有利的。选择适合共同管理的患者存在诸多困难。本研究旨在开发一种临床决策支持工具，以选择接受共同管理的外科患者。
从 2012 年 1 月至 2015 年 12 月，在里斯本的一家医院，使用国际疾病分类第 9 版（ICD-9）代码收集接受结直肠手术的患者的电子健康记录中的临床数据。结果变量包括共同管理信号。使用 344 名患者的数据集来开发预测模型，使用另外 168 名患者的数据集进行外部验证。
使用逻辑回归建模，作者构建了一个包含 5 个变量（年龄、合并症负担、ASA-PS 状态、手术风险和恢复时间）的预测性转诊模型，用于共同管理。该模型的曲线下面积（AUC）为 0.86（95%置信区间：0.81-0.90），预测 Brier 评分 0.11，敏感性 0.80，特异性 0.82，准确性 81.3%。
早期转诊高风险患者可能有助于指导术后最佳临床护理水平的决策。我们开发了一种简单的床边决策工具，具有良好的区分和预测性能，可用于选择共同管理的患者。
一种简单的床边临床决策支持工具用于共同管理患者是可行的，可能有助于早期识别和管理术后并发症，并减少“抢救失败”。在其他临床环境中的推广应用需要进行适当的定制和验证。
