Predictive models for identifying risk of readmission after index hospitalization for heart failure: a systematic review
Mahajan, S.M.; Heidenreich, P.; Abbott, B.; Newton, A.; Ward, D.
European Journal of Cardiovascular Nursing Journal of the Working Group on Cardiovascular Nursing of the European Society of Cardiology 17(8): 675-689
2018
ISSN/ISBN: 0008-7335 PMID: 30189743 Document Number: 696971
Larva migrans cutanea is a typical skin parasitosis of tropical and subtropical regions. In Central European countries, such as Slovakia and Czech Republic, larva migrans cutanea is just an imported disease. Its clinical symptoms are characterized by formation of erythematous focus with serpiginous morphology, which is caused by migration of helminth in epidermis. The disease does not threaten the patient's life, but causes significant discomfort, especially in form of pruritus in the affected area. Thanks to growing trend of today's tourism more tourists are exposed to the harmful effects of the environment in final destinations. This leads to an increase in frequency of imported diseases, with which physicians in our latitudes may not have enough experience. Readmission rates for patients with heart failure have consistently remained high over the past two decades. As more electronic data, computing power, and newer statistical techniques become available, data-driven care could be achieved by creating predictive models for adverse outcomes such as readmissions. We therefore aimed to review models for predicting risk of readmission for patients admitted for heart failure. We also aimed to analyze and possibly group the predictors used across the models. Major electronic databases were searched to identify studies that examined correlation between readmission for heart failure and risk factors using multivariate models. We rigorously followed the review process using PRISMA methodology and other established criteria for quality assessment of the studies. We did a detailed review of 334 papers and found 25 multivariate predictive models built using data from either health system or trials. A majority of models was built using multiple logistic regression followed by Cox proportional hazards regression. Some newer studies ventured into non-parametric and machine learning methods. Overall predictive accuracy with C-statistics ranged from 0.59 to 0.84. We examined significant predictors across the studies using clinical, administrative, and psychosocial groups. Complex disease management and correspondingly increasing costs for heart failure are driving innovations in building risk prediction models for readmission. Large volumes of diverse electronic data and new statistical methods have improved the predictive power of the models over the past two decades. More work is needed for calibration, external validation, and deployment of such models for clinical use.