Vancomycin: Predictive risk factors for nephrotoxicity and implication for monitoring

The increased use of vancomycin in hospitals has resulted in a standard practice to monitor serum vancomycin levels because of possible nephrotoxicity. However, the routine monitoring of vancomycin serum concentration is under criticism and the cost effectiveness of such routine monitoring is in question because frequent monitoring neither results in increase efficacy nor decrease nephrotoxicity. The purpose of the present study is to determine factors that may place patients at increased risk of developing vancomycin induced nephrotoxicity and for whom monitoring may be most beneficial.^ From September to December 1992, 752 consecutive in patients at The University of Texas M. D. Anderson Cancer Center, Houston, were prospectively evaluated for nephrotoxicity in order to describe predictive risk factors for developing vancomycin related nephrotoxicity. Ninety-five patients (13 percent) developed nephrotoxicity. A total of 299 patients (40 percent) were considered monitored (vancomycin serum levels determined during the course of therapy), and 346 patients (46 percent) were receiving concurrent moderate to highly nephrotoxic drugs.^ Factors that were found to be significantly associated with nephrotoxicity in univariate analysis were: gender, base serum creatinine greater than 1.5mg/dl, monitor, leukemia, concurrent moderate to highly nephrotoxic drugs, and APACHE III scores of 40 or more. Significant factors in the univariate analysis were then entered into a stepwise logistic regression analysis to determine independent predictive risk factors for vancomycin induced nephrotoxicity.^ Factors, with their corresponding odds ratios and 95% confidence limits, selected by stepwise logistic regression analysis to be predictive of vancomycin induced nephrotoxicity were: Concurrent therapy with moderate to highly nephrotoxic drugs (2.89; 1.76-4.74), APACHE III scores of 40 or more (1.98; 1.16-3.38), and male gender (1.98; 1.04-2.71).^ Subgroup (monitor and non-monitor) analysis showed that male (OR = 1.87; 95% CI = 1.01, 3.45) and moderate to highly nephrotoxic drugs (OR = 4.58; 95% CI = 2.11, 9.94) were significant for nephrotoxicity in monitored patients. However, only APACHE III score (OR = 2.67; 95% CI = 1.13,6.29) was significant for nephrotoxicity in non-monitored patients.^ The conclusion drawn from this study is that not every patient receiving vancomycin therapy needs frequent monitoring of vancomycin serum levels. Such routine monitoring may be appropriate in patients with one or more of the identified risk factors and low risk patients do not need to be subjected to the discomfort and added cost of multiple blood sampling. Such prudent selection of patients to monitor may decrease cost to patients and hospital. ^

PREDICTIVE VARIABLES FOR WEIGHT CHANGE 2 YEARS FOLLOWING PARTICIPATION IN A WEIGHT LOSS INTERVENTION PROGRAM

A historical prospective study was designed to assess the man weight status of subjects who participated in a behavioral weight reduction program in 1983 and to determine whether there was an association between the dependent variable weight change and any of 31 independent variables after a 2 year follow-up period. Data was obtained by abstracting the subjects records and from a follow-up questionnaire administered 2 years following program participation. Five hundred nine subjects (386 females and 123 males) of 1460 subjects who participated in the program, completed and returned the questionnaire. Results showed that mean weight was significantly different (p < 0.001) between the measurement at baseline and after a 2 year follow-up period. The mean weight loss of the group was 5.8 pounds, 10.7 pounds for males and 4.2 pounds for females after a 2 year follow-up period. A total of 63.9% of the group, 69.9% of males and 61.9% of females were still below their initial weight after the 2 year follow-up period. Sixteen of the 31 variables assessed utilizing bivariate analyses were found to be significantly (p (LESSTHEQ) 0.05) associated with weight change after a 2 year follow-up period. These variables were then entered into a multivariate linear regression model. A total of 37.9% of the variance of the dependent variable, weight change, was accounted for by all 16 variables. Eight of these variables were found to be significantly (p (LESSTHEQ) 0.05) predictive of weight change in the stepwise multivariate process accounting for 37.1% of the variance. These variables included: Two baseline variables (percent over ideal body weight at enrollment and occupation) and six follow-up variables (feeling in control of eating habits, percent of body weight lost during treatment, frequency of weight measurement, physical activity, eating in response to emotions, and number of pounds of weight gain needed to resume a diet). It was concluded that a greater amount of emphasis should be placed on the six follow-up variables by clinicians involved in the treatment of obesity, and by the subjects themselves to enhance their chances of success at long-term weight loss. ^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

Predictive factors for vaginal douching and tampon use among US women: Are these behaviors risk factors for bacterial vaginosis and trichomonas?

The use of feminine products such as vaginal douches, tampons, and sanitary napkins are common among women. Despite the results of some studies that suggest an association between douching and bacterial vaginosis, douching remains a topic that is understudied. The possibility of an association between tampon use and infection has not been significantly investigated since the toxic shock outbreak in the 1980s. The first objective of our study was to evaluate demographic, reproductive health, and sexual behavior variables to establish an epidemiologic profile of menstruating women who reported douching and women who reported using sanitary napkins only. The second objective of our study was to evaluate whether the behaviors of douching and using tampons were associated with an increased risk of bacterial vaginosis or trichomonas. We analyzed these factors, using logistic regression, among the 3,174 women from the NHANES cross sectional data from 2001-2004, who met the inclusion criteria determined for our study. We established an epidemiologic profile for women who had the highest frequency of douching reported as women who were age 36-49, had a high school education or GED, black race, not taking oral contraceptives, reported vaginal symptoms in the last month, two or more sexual partners in the last year, or tested positive for bacterial vaginosis or trichomonas. The profile for those who had the highest frequency of exclusive sanitary napkin use included women with less than a high school education, married women, women classified as black or "other" in race, and women who were not on oral contraceptives. While we were able to establish a significant increase in the odds of douching among women who tested positive for bacterial vaginosis or trichomonas, we did not find any significant difference in the odds of exclusive napkin use and testing negative for bacterial vaginosis or trichomonas.^

Selection bias and the cross-validation of regression models for prediction

Strategies are compared for the development of a linear regression model with stochastic (multivariate normal) regressor variables and the subsequent assessment of its predictive ability. Bias and mean squared error of four estimators of predictive performance are evaluated in simulated samples of 32 population correlation matrices. Models including all of the available predictors are compared with those obtained using selected subsets. The subset selection procedures investigated include two stopping rules, C$\sb{\rm p}$ and S$\sb{\rm p}$, each combined with an 'all possible subsets' or 'forward selection' of variables. The estimators of performance utilized include parametric (MSEP$\sb{\rm m}$) and non-parametric (PRESS) assessments in the entire sample, and two data splitting estimates restricted to a random or balanced (Snee's DUPLEX) 'validation' half sample. The simulations were performed as a designed experiment, with population correlation matrices representing a broad range of data structures.^ The techniques examined for subset selection do not generally result in improved predictions relative to the full model. Approaches using 'forward selection' result in slightly smaller prediction errors and less biased estimators of predictive accuracy than 'all possible subsets' approaches but no differences are detected between the performances of C$\sb{\rm p}$ and S$\sb{\rm p}$. In every case, prediction errors of models obtained by subset selection in either of the half splits exceed those obtained using all predictors and the entire sample.^ Only the random split estimator is conditionally (on $\\beta$) unbiased, however MSEP$\sb{\rm m}$ is unbiased on average and PRESS is nearly so in unselected (fixed form) models. When subset selection techniques are used, MSEP$\sb{\rm m}$ and PRESS always underestimate prediction errors, by as much as 27 percent (on average) in small samples. Despite their bias, the mean squared errors (MSE) of these estimators are at least 30 percent less than that of the unbiased random split estimator. The DUPLEX split estimator suffers from large MSE as well as bias, and seems of little value within the context of stochastic regressor variables.^ To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample be used for model development, and a leave-one-out statistic (e.g. PRESS) be used for assessment. ^

Logistic regression with incompletely observed binary

Logistic regression is one of the most important tools in the analysis of epidemiological and clinical data. Such data often contain missing values for one or more variables. Common practice is to eliminate all individuals for whom any information is missing. This deletion approach does not make efficient use of available information and often introduces bias.^ Two methods were developed to estimate logistic regression coefficients for mixed dichotomous and continuous covariates including partially observed binary covariates. The data were assumed missing at random (MAR). One method (PD) used predictive distribution as weight to calculate the average of the logistic regressions performing on all possible values of missing observations, and the second method (RS) used a variant of resampling technique. Additional seven methods were compared with these two approaches in a simulation study. They are: (1) Analysis based on only the complete cases, (2) Substituting the mean of the observed values for the missing value, (3) An imputation technique based on the proportions of observed data, (4) Regressing the partially observed covariates on the remaining continuous covariates, (5) Regressing the partially observed covariates on the remaining continuous covariates conditional on response variable, (6) Regressing the partially observed covariates on the remaining continuous covariates and response variable, and (7) EM algorithm. Both proposed methods showed smaller standard errors (s.e.) for the coefficient involving the partially observed covariate and for the other coefficients as well. However, both methods, especially PD, are computationally demanding; thus for analysis of large data sets with partially observed covariates, further refinement of these approaches is needed. ^

Predictive validity of the personal background and preparation survey and effectiveness of targeted interventions among health science community college students

Existing data, collected from 1st-year students enrolled in a major Health Science Community College in the south central United States, for Fall 2010, Spring 2011, Fall 2011 and Spring 2012 semesters as part of the "Online Navigational Assessment Vehicle, Intervention Guidance, and Targeting of Risks (NAVIGATOR) for Undergraduate Minority Student Success" with CPHS approval number HSC-GEN-07-0158, was used for this thesis. The Personal Background and Preparation Survey (PBPS) and a two-question risk self-assessment subscale were administered to students during their 1st-year orientation. The PBPS total risk score, risk self-assessment total and overall scores, and Under Representative Minority Student (URMS) status were recorded. The purpose of this study is to evaluate and report the predictive validity of the indicators identified above for Adverse Academic Status Events (AASE) and Nonadvancement Adverse Academic Status Events (NAASE) as well as the effectiveness of interventions targeted using the PBPS among a diverse population of health science community college students. The predictive validity of the PBPS for AASE has previously been demonstrated among health science professions and graduate students (Johnson, Johnson, Kim, & McKee, 2009a; Johnson, Johnson, McKee, & Kim, 2009b). Data will be analyzed using binary logistic regression and correlation using SPSS 19 statistical package. Independent variables will include baseline- versus intervention-year treatments, PBPS, risk self-assessment, and URMS status. The dependent variables will be binary AASE and NAASE status. ^ The PBPS was the first reliable diagnostic and prescriptive instrument to establish documented predictive validity for student Adverse Academic Status Events (AASE) among students attending health science professional schools. These results extend the documented validity for the PBPS in predicting AASE to a health science community college student population. Results further demonstrated that interventions introduced using the PBPS were followed by approximately one-third reduction in the odds of Nonadvancement Adverse Academic Status Events (NAASE), controlling for URMS status and risk self-assessment scores. These results indicate interventions introduced using the PBPS may have potential to reduce AASE or attrition among URMS and nonURMS attending health science community colleges on a broader scale; positively impacting costs, shortages, and diversity of health science professionals.^

Inappropriate imaging utilization in Texas: Geographic variation and predictive factors

Objective::Describe and understand regional differences and associated multilevel factors (patient, provider and regional) to inappropriate utilization of advance imaging tests in the privately insured population of Texas. Methods: We analyzed Blue Cross Blue Shield of Texas claims dataset to study the advance imaging utilization during 2008-2010 in the PPO/PPO+ plans. We used three of CMS "Hospital Outpatient Quality Reporting" imaging efficiency measures. These included ordering MRI for low back pain without prior conservative management (OP-8) and utilization of combined with and without contrast abdominal CT (OP-10) and thorax CT (OP-11). Means and variation by hospital referral regions (HRR) in Texas were measured and a multilevel logistic regression for being a provider with high values for any the three OP measures was used in the analysis. We also analyzed OP-8 at the individual level. A multilevel logistic regression was used to identify predictive factors for having an inappropriate MRI for low back pain. Results: Mean OP-8 for Texas providers was 37.89%, OP-10 was 29.94% and OP-11 was 9.24%. Variation was higher for CT measure. And certain HRRs were consistently above the mean. Hospital providers had higher odds of high OP-8 values (OP-8: OR, 1.34; CI, 1.12-1.60) but had smaller odds of having high OP-10 and OP-11 values (OP-10: OR, 0.15; CI, 0.12-0.18; OP-11: OR, 0.43; CI, 0.34-0.53). Providers with the highest volume of imaging studies performed, were less likely to have high OP-8 measures (OP-8: OR, 0.58; CI, 0.48-0.70) but more likely to perform combined thoracic CT scans (OP-11: OR, 1.62; CI, 1.34-1.95). Males had higher odds of inappropriate MRI (OR, 1.21; CI, 1.16-1.26). Pattern of care in the six months prior to the MRI event was significantly associated with having an inappropriate MRI. Conclusion::We identified a significant variation in advance imaging utilization across Texas. Type of facility was associated with measure performance, but the associations differ according to the type of study. Last, certain individual characteristics such as gender, age and pattern of care were found to be predictors of inappropriate MRIs.^