Emergency and Critical Care Department, University of Guelph, Guelph, Ontario, Canada.
J Vet Intern Med. 2010 Sep-Oct;24(5):1034-47. doi: 10.1111/j.1939-1676.2010.0552.x. Epub 2010 Jul 9.
Objective risk stratification models are used routinely in human critical care medicine. Applications include quantitative and objective delineation of illness severity for patients enrolled in clinical research, performance benchmarking, and protocol development for triage and therapeutic management.
To develop an accurate, validated, and user-friendly model to stratify illness severity by mortality risk in hospitalized dogs.
Eight hundred and ten consecutive intensive care unit (ICU) admissions of dogs at a veterinary teaching hospital.
Prospective census cohort study. Data on 55 management, physiological, and biochemical variables were collected within 24 hours of admission. Data were randomly divided, with 598 patient records used for logistic regression model construction and 212 for model validation.
Patient mortality was 18.4%. Ten-variable and 5-variable models were developed to provide both a high-performance model and model maximizing accessibility, while maintaining good performance. The 10-variable model contained creatinine, WBC count, albumin, SpO(2) , total bilirubin, mentation score, respiratory rate, age, lactate, and presence of free fluid in a body cavity. Area under the receiver operator characteristic (AUROC) on the construction data set was 0.93, and on the validation data set was 0.91. The 5-variable model contained glucose, albumin, mentation score, platelet count, and lactate. AUROC on the construction data set was 0.87, and on the validation data set was 0.85.
Two models are presented that enable allocation of an accurate and user-friendly illness severity index for dogs admitted to an ICU. These models operate independent of primary diagnosis, and have been independently validated.
客观风险分层模型在人类重症医学中得到了常规应用。其应用包括对入组临床研究的患者进行疾病严重程度的定量和客观描述、绩效基准测试以及分诊和治疗管理方案的制定。
开发一种准确、验证良好且易于使用的模型，以对住院犬的死亡率风险进行疾病严重程度分层。
某兽医教学医院的 810 例连续重症监护病房（ICU）入院犬。
前瞻性普查队列研究。在入院后 24 小时内收集 55 项管理、生理和生化变量的数据。数据随机分为两组，其中 598 份患者记录用于逻辑回归模型构建，212 份用于模型验证。
患者死亡率为 18.4%。开发了 10 变量和 5 变量模型，以提供一种高性能模型和最大限度提高可访问性的模型，同时保持良好的性能。10 变量模型包含肌酐、白细胞计数、白蛋白、SpO2、总胆红素、神志评分、呼吸频率、年龄、乳酸和体腔中存在自由液体。构建数据集的受试者工作特征（ROC）曲线下面积（AUROC）为 0.93，验证数据集的 AUROC 为 0.91。5 变量模型包含血糖、白蛋白、神志评分、血小板计数和乳酸。构建数据集的 AUROC 为 0.87，验证数据集的 AUROC 为 0.85。
本文提出了两种模型，可用于为入住 ICU 的犬分配一种准确且易于使用的疾病严重程度指数。这些模型独立于主要诊断，且已经过独立验证。
