Validation of the Ottawa Ankle Rules in Iran: A prospective survey

BACKGROUND: Acute ankle injuries are one of the most common reasons for presenting to emergency departments, but only a small percentage of patients – approximately 15% – have clinically significant fractures. However, these patients are almost always referred for radiography. The Ottawa Ankle Rules (OARs) have been designed to reduce the number of unnecessary radiographs ordered for these patients. The objective of this study was to validate the OARs in the Iranian population. METHODS: This prospective survey was done among 200 patients with acute ankle injury from January 2004 to April 2004 in the Akhtar Orthopedics Hospital Emergency Department. Main outcome measures of this survey were: sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratios (positive and negative) of the OARs. RESULTS: Sensitivity of the OARs for detecting 37 ankle fractures (23 in the malleolar zone and 14 in the midfoot zone) was 100% for each of the two zones, and 100% for both zones. Specificity of the OARs for detecting fractures was 40.50% for both zones, 40.50% for the malleolar zone, and 56.00% for the midfoot zone. Implementation of the OARs had the potential for reducing radiographs by 33%. CONCLUSION: OARs are very accurate and highly sensitive tools for detecting ankle fractures. Implementation of these rules would lead to significant reduction in the number of radiographs, costs, radiation exposure and waiting times in emergency departments

Hospital Readmission in General Medicine Patients: A Prediction Model

Background: Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models. Objective: To identify predictors of early hospital readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk. Design: Prospective observational cohort study. Patients: Participants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts. Measurements: We identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk. Results: Approximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, ≥1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of ≥25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively. Conclusions: Select patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission

Dual use of Medicare and the Veterans Health Administration: are there adverse health outcomes?

BACKGROUND: Millions of veterans are eligible to use the Veterans Health Administration (VHA) and Medicare because of their military service and age. This article examines whether an indirect measure of dual use based on inpatient services is associated with increased mortality risk. METHODS: Data on 1,566 self-responding men (weighted N = 1,522) from the Survey of Assets and Health Dynamics among the Oldest Old (AHEAD) were linked to Medicare claims and the National Death Index. Dual use was indirectly indicated when the self-reported number of hospital episodes in the 12 months prior to baseline was greater than that observed in the Medicare claims. The independent association of dual use with mortality was estimated using proportional hazards regression. RESULTS: 96 (11%) of the veterans were classified as dual users. 766 men (50.3%) had died by December 31, 2002, including 64.9% of the dual users and 49.3% of all others, for an attributable mortality risk of 15.6% (p < .003). Adjusting for demographics, socioeconomics, comorbidity, hospitalization status, and selection bias at baseline, as well as subsequent hospitalization for ambulatory care sensitive conditions, the independent effect of dual use was a 56.1% increased relative risk of mortality (AHR = 1.561; p = .009). CONCLUSION: An indirect measure of veterans' dual use of the VHA and Medicare systems, based on inpatient services, was associated with an increased risk of death. Further examination of dual use, especially in the outpatient setting, is needed, because dual inpatient and dual outpatient use may be different phenomena

Reporting and Methods in Clinical Prediction Research: A Systematic Review

Walter Bouwmeester and colleagues investigated the reporting and methods of prediction studies in 2008, in six high-impact general medical journals, and found that the majority of prediction studies do not follow current methodological recommendations

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams