title: Multimorbidity in risk stratification tools to predict negative outcomes in adult population.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  To summarize validated risk stratification tools for predicting negative outcomes, with a specific focus on multimorbidity.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: O+berri, Basque Institute for Healthcare Innovation, Torre del BEC (Bilbao Exhibition Centre), Ronda de Azkue 1, 48902 Barakaldo, Spain.
authors.forename: Edurne
authors.initials: E
authors.lastname: Alonso-Morán
authors.affiliation: O+berri, Basque Institute for Healthcare Innovation, Torre del BEC (Bilbao Exhibition Centre), Ronda de Azkue 1, 48902 Barakaldo, Spain.
authors.forename: Roberto
authors.initials: R
authors.lastname: Nuño-Solinis
authors.affiliation: Department of Geriatrics, Centro Medicina dell'Invecchiamento, Università Cattolica del Sacro Cuore, Rome, Italy; Agenzia Italiana del Farmaco (AIFA), Rome, Italy.
authors.forename: Graziano
authors.initials: G
authors.lastname: Onder
authors.affiliation: Department of Geriatrics, Centro Medicina dell'Invecchiamento, Università Cattolica del Sacro Cuore, Rome, Italy. Electronic address: giuseppe.tonnara@gmail.com.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Tonnara
mesh_terms: D000328:Adult; D015897:Comorbidity; D016208:Databases, Factual; D017048:Health Care Costs; D006760:Hospitalization; D006801:Humans; D017063:Outcome Assessment, Health Care; D010359:Patient Readmission; D011379:Prognosis; D018570:Risk Assessment
keywords: Adverse outcomes; Multimorbidity; Predictive models; Stratification tools