Keeping up with innovation: a predictive framework for modeling healthcare data with evolving clinical interventions

Medical outcomes are inexorably linked to patient illness and clinical interventions. Interventions change the course of disease, crucially determining outcome. Traditional outcome prediction models build a single classifier by augmenting interventions with disease information. Interventions, however, differentially affect prognosis, thus a single prediction rule may not suffice to capture variations. Interventions also evolve over time as more advanced interventions replace older ones. To this end, we propose a Bayesian nonparametric, supervised framework that models a set of intervention groups through a mixture distribution building a separate prediction rule for each group, and allows the mixture distribution to change with time. This is achieved by using a hierarchical Dirichlet process mixture model over the interventions. The outcome is then modeled as conditional on both the latent grouping and the disease information through a Bayesian logistic regression. Experiments on synthetic and medical cohorts for 30-day readmission prediction demonstrate the superiority of the proposed model over clinical and data mining baselines.

Interventions for treating obesity in children (Review)

Childhood obesity affects both the physical and psychosocial health of children and may put them at risk of ill health as adults. The number of obese children in developed countries is increasing dramatically. There are many different lifestyle change programmes to help parents and children control their weight. Although 18 research studies were found, most of these were very small studies and so evidence from them is limited. In conclusion, there is a limited amount of quality data on the effects of programs to treat childhood obesity, and as such no conclusions can be drawn with confidence.

Diabetes prevention research: a systematic review of external validity in lifestyle interventions

Context

Type 2 diabetes is a major contributor to disease burden globally. A number of systematic reviews support the efficacy of lifestyle interventions in preventing Type 2 diabetes in adults; however, relatively little attention has been paid to the generalizability of study findings. This study systematically reviews the reporting of external validity components and generalizability of diabetes prevention studies.

Evidence acquisition

Lifestyle intervention studies for the prevention of Type 2 diabetes in adults with at least 6 months' follow-up, published between 1990 and 2011, were identified through searches of major electronic databases. External validity reporting was rated using an assessment tool, and all analysis was undertaken in 2011.

Evidence synthesis

A total of 31 primary studies (n=95 papers) met the selection criteria. All studies lacked full reporting on external validity elements. Description of the study sample, intervention, delivery agents, and participant attrition rates were reported by most studies. However, few studies reported on the representativeness of individuals and settings, methods for recruiting settings and delivery agents, costs, and how interventions could be institutionalized into routine service delivery. It is uncertain to what extent the findings of diabetes prevention studies apply to men, socioeconomically disadvantaged individuals, those living in rural and remote communities, and to low- and middle-income countries.

Conclusions

Reporting of external validity components in diabetes prevention studies needs to be enhanced to improve the evidence base for the translation and dissemination of these programs into policy and practice.