Search

Highlights • Outcomes tools can inform the management of unruptured intracranial aneurysms. • Relevant aspects of patient health are not measured by the modified Rankin Scale. • Half of surgical patients experience adverse quality of life or functional outcomes. • Multi-dimensional assessment provides a more complete representation of outcomes. Abstract Outcomes on the modified Rankin Scale (mRS) are commonly used to guide and evaluate the management of unruptured intracranial aneurysms (uIA). However, the mRS is unlikely to measure all the relevant aspects of the clinical health of a patient. The current study therefore investigated the relationship between the mRS and additional measures of outcome. Between January 2011 and January 2016 patients with a new diagnosis of uIA were prospectively examined at referral and 12-month follow-up. Assessment included the Physical and Mental Component Scores of the Short Form 36 (SF-36), the computerized driver screening instrument DriveSafe (DS), and the mRS. Minimally Important Change (MIC) for each outcome measure was used to identify adverse outcomes for individual patients. A total of 128 patients (98 surgery; 30 untreated) completed the minimal dataset for analysis. In the surgical group, 6% (95% CI 3–14%) experienced morbidity at 12-months, as defined by the MIC for mRS. This risk rate increased to 51% (95% CI 41–61%) when defined as an MIC on any outcome. A combined MIC also identified a downgrade in outcomes, not detectable on the mRS, in 42% (95% CI 26–61%) of untreated patients. Correlation and regression analyses were unable to identify any significant relationships between the different outcomes instruments. In sum, there were considerably more adverse outcomes reported by quality of life (SF-36) and functional (DS) instruments than by the mRS for either treated or untreated uIA. To obtain a more complete representation of patient outcomes requires administration of a multi-dimensional assessment. [ABSTRACT FROM AUTHOR]

Background and aims The Visual Analogue Scale (VAS) is a popular tool for the measurement of pain. A variety of statistical methods are employed for its analysis as an outcome measure, not all of them optimal or appropriate. An issue which has attracted much discussion in the literature is whether VAS is at a ratio or ordinal level of measurement. This decision has an influence on the appropriate method of analysis. The aim of this article is to provide an overview of current practice in the analysis of VAS scores, to propose a method of analysis which avoids the shortcomings of more traditional approaches, and to provide best practice recommendations for the analysis of VAS scores. Methods We report on the current usage of statistical methods, which fall broadly into two categories: those that assume a probability distribution for VAS, and those that do not. We give an overview of these methods, and propose continuous ordinal regression, an extension of current ordinal regression methodology, which is appropriate for VAS at an ordinal level of measurement. We demonstrate the analysis of a published data set using a variety of methods, and use simulation to compare the power of the various methods to detect treatment differences, in differing pain situations. Results We demonstrate that continuous ordinal regression provides the most powerful statistical analysis under a variety of conditions. Conclusions and Implications We recommend that in the situation in which no covariates besides treatment group are included in the analysis, distribution-free methods (Wilcoxon, Mann–Whitney) be used, as their power is indistinguishable from that of the proposed method. In the situation in which there are covariates which affect VAS, the proposed method is optimal. However, in this case, if the VAS scores are not concentrated around either extreme of the scale, normal-distribution methods ( t -test, linear regression) are almost as powerful, and are recommended as a pragmatic choice. In the case of small sample size and VAS skewed to either extreme of the scale, the proposed method has vastly superior power to other methods. [ABSTRACT FROM AUTHOR]

Objective: To perform risk factor analysis for the prediction of hyperbilirubinemia in an African American male neonatal cohort. Study design: A database of 500 previously published term and near-term African American male neonates was further analyzed to determine the role of risk factors for hyperbilirubinemia. Factors studied included birth weight ≥4.0 kg, gestational age ≤37 weeks, breast-feeding, glucose-6-phosphate dehydrogenase (G-6-PD) deficiency, and predischarge bilirubin ≥75th percentile. Hyperbilirubinemia was defined as any bilirubin value ≥95th percentile on the hour-of-life–specific bilirubin nomogram. Results: Forty-three (8.6%) neonates developed hyperbilirubinemia. At 48 ± 12 hours, median transcutaneous bilirubin was 8.3 mg/dL, 75th percentile 10.0 mg/dL, and 95th percentile 12.6 mg/dL. Of the risk factors, only exclusive breast-feeding, G-6-PD deficiency and predischarge bilirubin ≥75th percentile were significant (Adjusted Odds Ratios [95% Confidence Intervals; CI] 3.15 [1.39-7.14], P = .006; 4.96 [2.28-10.80], P = .001; and 7.47 [3.50-15.94], P < .0001, respectively). G-6-PD–deficient neonates who were also premature and breast-feeding had the highest incidence of hyperbilirubinemia (60%). Conclusions: African American male neonates may be at higher risk for hyperbilirubinemia than previously thought. Screening for G-6-PD deficiency and predischarge bilirubin determination may be useful adjuncts in hyperbilirubinemia prediction in these newborns. [Copyright &y& Elsevier]

Background: Missing cases following treatment are common in Web-based psychotherapy trials. Without the ability to directly measure and evaluate the outcomes for missing cases, the ability to measure and evaluate the effects of treatment is challenging. Although common, little is known about the characteristics of Web-based psychotherapy participants who present as missing cases, their likely clinical outcomes, or the suitability of different statistical assumptions that can characterize missing cases. Objective: Using a large sample of individuals who underwent Web-based psychotherapy for depressive symptoms (n=820), the aim of this study was to explore the characteristics of cases who present as missing cases at posttreatment (n=138), their likely treatment outcomes, and compare between statistical methods for replacing their missing data. Methods: First, common participant and treatment features were tested through binary logistic regression models, evaluating the ability to predict missing cases. Second, the same variables were screened for their ability to increase or impede the rate symptom change that was observed following treatment. Third, using recontacted cases at 3-month follow-up to proximally represent missing cases outcomes following treatment, various simulated replacement scores were compared and evaluated against observed clinical follow-up scores. Results: Missing cases were dominantly predicted by lower treatment adherence and increased symptoms at pretreatment. Statistical methods that ignored these characteristics can overlook an important clinical phenomenon and consequently produce inaccurate replacement outcomes, with symptoms estimates that can swing from -32% to 70% from the observed outcomes of recontacted cases. In contrast, longitudinal statistical methods that adjusted their estimates for missing cases outcomes by treatment adherence rates and baseline symptoms scores resulted in minimal measurement bias (<8%). Conclusions: Certain variables can characterize and predict missing cases likelihood and jointly predict lesser clinical improvement. Under such circumstances, individuals with potentially worst off treatment outcomes can become concealed, and failure to adjust for this can lead to substantial clinical measurement bias. Together, this preliminary research suggests that missing cases in Web-based psychotherapeutic interventions may not occur as random events and can be systematically predicted. Critically, at the same time, missing cases may experience outcomes that are distinct and important for a complete understanding of the treatment effect. [ABSTRACT FROM AUTHOR]