Prediction models for childhood asthma: a systematic review

Background The inability to objectively diagnose childhood asthma before age five often results in both under‐treatment and over‐treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school‐age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school‐age asthma. Methods Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school‐age children (6‐13 years). Validation studies were evaluated as a secondary objective. Results Twenty‐four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression‐based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression‐based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62‐0.83). Conclusion Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school‐age asthma predictio

an introduction to personalized ehealth

Personalized medicine can be defined as the adaptation of medical treatments to the specific characteristics of patients. This approach allows health providers to develop therapies and interventions by taking into account the heterogeneity of illnesses and external factors such as the environment, patients' needs, and lifestyle. Technology could play an important role to achieve this new approach to medicine. An example of technology's utility regards real-time monitoring of individual well-being (subjective and objective), in order to improve disease management through data-driven personalized treatment recommendations. Another important example is an interface designed based on patient's capabilities and preferences. These could improve patient-doctor communication: on one hand, patients have the possibility to improve health decision-making; on the other hand, health providers could coordinate care services more easily, because of continual access to patient's data. This contribution deepens these technologies and related opportunities for health, as well as recommendation for successful development and implementation