Alonso-Morán Edurne, Nuño-Solinis Roberto, Onder Graziano, Tonnara Giuseppe
O+berri, Basque Institute for Healthcare Innovation, Torre del BEC (Bilbao Exhibition Centre), Ronda de Azkue 1, 48902 Barakaldo, Spain.
Department of Geriatrics, Centro Medicina dell'Invecchiamento, Università Cattolica del Sacro Cuore, Rome, Italy; Agenzia Italiana del Farmaco (AIFA), Rome, Italy.
Eur J Intern Med. 2015 Apr;26(3):182-9. doi: 10.1016/j.ejim.2015.02.010. Epub 2015 Mar 6.
Risk stratification tools were developed to assess risk of negative health outcomes. These tools assess a variety of variables and clinical factors and they can be used to identify targets of potential interventions and to develop care plans. The role of multimorbidity in these tools has never been assessed.
To summarize validated risk stratification tools for predicting negative outcomes, with a specific focus on multimorbidity.
MEDLINE, Cochrane Central Register of Controlled Trials and PubMed database were interrogated for studies concerning risk prediction models in medical populations. Review was conducted to identify prediction models tested with patients in both derivation and validation cohorts. A qualitative synthesis was performed focusing particularly on how multimorbidity is assessed by each algorithm and how much this weighs in the ability of discrimination.
Of 3674 citations reviewed, 36 articles met criteria. Of these, 29 had as outcome hospital admission/readmission. The most common multimorbidity measure employed in the models was the Charlson Comorbidity Index (12 articles). C-statistics ranged between 0.5 and 0.85 in predicting hospital admission/ readmission. The highest c-statistics was 0.83 in models with disability as outcome. For healthcare cost, models which used ACG-PM case mix explained better the variability of total costs.
This review suggests that predictive risk models which employ multimorbidity as predictor variable are more accurate; CHF, cerebro-vascular disease, COPD and diabetes were strong predictors in some of the reviewed models. However, the variability in the risk factors used in these models does not allow making assumptions.
风险分层工具旨在评估不良健康结局的风险。这些工具评估各种变量和临床因素，可用于确定潜在干预的目标并制定护理计划。多重疾病在这些工具中的作用从未得到评估。
总结用于预测不良结局的经过验证的风险分层工具，特别关注多重疾病。
检索MEDLINE、Cochrane对照试验中央注册库和PubMed数据库，查找有关医疗人群风险预测模型的研究。进行综述以识别在推导队列和验证队列中均对患者进行测试的预测模型。进行定性综合分析，特别关注每种算法如何评估多重疾病以及这在辨别能力中所占的权重。
在审查的3674条引用文献中，有36篇符合标准。其中，29篇以住院/再入院作为结局。模型中使用最普遍的多重疾病测量方法是Charlson合并症指数（12篇文章）。在预测住院/再入院方面，C统计量在0.5至0.85之间。以残疾为结局的模型中，最高C统计量为0.83。对于医疗保健成本，使用ACG-PM病例组合（ACG-PM case mix）的模型能更好地解释总成本的变异性。
本综述表明，将多重疾病作为预测变量的预测风险模型更准确；在一些综述模型中，心力衰竭、脑血管疾病、慢性阻塞性肺疾病和糖尿病是强有力的预测因素。然而，这些模型中使用的风险因素存在变异性，无法做出假设。
