2 resultados para Upper bound estimate

em DigitalCommons@The Texas Medical Center


Relevância:

80.00% 80.00%

Publicador:

Resumo:

Adolescents 15 – 19 years of age have the highest prevalence of Chlamydia trachomatis out of any age group, reaching 28.3% among detained youth [1]. The 2010 Center for Disease Control guidelines recommend one dose of azithromycin for the treatment of uncomplicated chlamydia infections based on 97% cure rate with azithromycin. Recent studies found an 8% or higher failure rate of azithromycin treatment in adolescents [2-5]. We conducted a prospective study beginning May, 2012 in the Harris County Juvenile Justice Center (HCJJC) medical department. Study subjects were detainees with positive urine NAAT tests for chlamydia on intake. We provided treatment with Azithromycin, completed questionnaires assessing risk factors and performed a test of cure for chlamydia three weeks after successful treatment. Those with treatment failure (positive TOC) received doxycycline for seven days. The preliminary results summarized herein are based on data collected from May 2012 to January 2013. Of the 97 youth enrolled in the study to date, 4 (4.1%) experienced treatment failure after administration of Azithromycin. Of these four patients, all were male, African-American and asymptomatic at the time of initial diagnosis and treatment. Of note, 37 (38%) patients in the cohort complained of abdominal pain with administration of Azithromycin. Results to date suggest that the efficacy of Azithromycin in our study is higher than the recent reported studies indicating a possible upper bound of Azithromycin. These results are preliminary and recruitment will continue until a sample size of 127 youth is reached.^

Relevância:

30.00% 30.00%

Publicador:

Resumo:

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^