A prediction model for colon cancer surveillance data

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112258/1/sim6500-sup-0001-Supplementary1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/112258/2/sim6500.pd

Parental-reported allergic disorders and emergency department presentations for allergy in the first five years of life; a longitudinal birth cohort

Abstract Background To measure rates of parental-report of allergic disorders and ED presentations for allergic disorders in children, and to describe factors associated with either. Methods An existing cohort of 3404 children born between 2006 and 2011 (Environments for Healthy Living) with prospectively collected pre-natal, perinatal and follow-up data were linked to i) nationwide Medicare and pharmaceutical data and ii) Emergency Department (ED) data from four hospitals in Australia. Parental-reported allergy was assessed in those who returned follow-up questionnaires. ED presentation was defined as any presentation for a suite of allergic disorders, excluding asthma. Univariate analysis and multivariate logistic regression were used to descibe risk factors for both parental-reported allergy and ED presentation for an allergic disorder. Results The incidence of parental-reported child allergy at 1, 3 and 5 years of age was 7.8, 7.8 and 12.6%, respectively. Independent predictors of parental-report of allergy in multivariate analysis were parental-report of asthma (OR 2.2, 95% CI 1.4–3.4) or eczema (OR 4.3, 95% CI 3.1–6.1) and age > 6 months at introduction of solids (OR 1.3, 95% CI 1.0–1.7). Factors associated with ED presentations for allergy, which occurred in 3.6% of the cohort, were presence of maternal asthma (OR 2.3 95% CI:1.1, 4.9) and child born in spring (OR 1.7, 95% CI 1.1, 2.7). Conclusions More than 10% of children up to 5 years have a parental-reported allergic disorder, and 3.6% presented to ED. Parental-report of eczema and/or asthma and late introduction of solids were predictors of parental-report of allergy. Spring birth and maternal asthma were predictors for ED presentation for allergy

Predicting patient deterioration: A review of tools in the digital hospital setting

Background: Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. Objective: This review describes published studies on the development, validation, and implementation of tools for predicting patient deterioration in general wards in hospitals. Methods: An electronic database search of peer reviewed journal papers from 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration, defined by unplanned transfer to the intensive care unit, cardiac arrest, or death. Studies conducted solely in intensive care units, emergency departments, or single diagnosis patient groups were excluded. Results: A total of 46 publications were eligible for inclusion. These publications were heterogeneous in design, setting, and outcome measures. Most studies were retrospective studies using cohort data to develop, validate, or statistically evaluate prediction tools. The tools consisted of early warning, screening, or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time data, deal with complexities of longitudinal data, and warn of deterioration risk earlier. Only a few studies detailed the results of the implementation of deterioration warning tools. Conclusions: Despite relative progress in the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvements in patient outcomes. Further work is needed to realize the potential of automated predictions and update dynamic risk estimates as part of an operational early warning system for inpatient deterioration.</p

A web-based normative data tool for assessing cognitive performance in healthy older Australians

• A decline in cognition greater than expected with ageing and accompanied by subjective cognitive concerns or functional changes may be indicative of a dementing disorder. • The capacity to correctly identify cognitive decline relies on comparisons with normative data from a suitably matched healthy reference group with relatively homogeneous demographic features. • Formal assessment of cognition is usually performed by specialist neuropsychologists trained in administration and interpretation of psychometric tests. With a scarcity of normative data from large cohorts of older adults, Australian neuropsychologists commonly use representative data from small international studies. • Data from 727 healthy older Australians participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing have been used to create a normative dataset. • A web-based calculator was developed to simplify the time-consuming process of comparing cognitive performance scores with these representative data