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Baseline characteristics of patients predicting suitability for rapid naltrexone induction.

ABSTRACT: Extended-release (XR) injection naltrexone has proved promising in the treatment of opioid dependence. Induction onto naltrexone is often accomplished with a procedure known as rapid naltrexone induction. The purpose of this study was to evaluate pre-treatment patient characteristics as predictors of successful completion of a rapid naltrexone induction procedure prior to XR naltrexone treatment.A chart review of 150 consecutive research participants (N = 84 completers and N = 66 non-completers) undergoing a rapid naltrexone induction with the buprenorphone-clonidine procedure were compared on a number of baseline demographic, clinical and psychosocial factors. Logistic regression was used to identify client characteristics that may predict successful initiation of naltrexone after a rapid induction-detoxification.Patients who failed to successfully initiate naltrexone were younger (AOR: 1.040, CI: 1.006, 1.075), and using 10 or more bags of heroin (or equivalent) per day (AOR: 0.881, CI: 0.820, 0.946). Drug use other than opioids was also predictive of failure to initiate naltrexone in simple bivariate analyses, but was no longer significant when controlling for age and opioid use level.Younger age, and indicators of greater substance dependence severity (more current opioid use, other substance use) predict difficulty completing a rapid naltrexone induction procedure. Such patients might require a longer period of stabilization and/or more gradual detoxification prior to initiating naltrexone.Our study findings identify specific characteristics of patients who responded positively to rapid naltrexone induction.

SUBMITTER: Mogali S

PROVIDER: S-EPMC4496800 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Baseline characteristics of patients predicting suitability for rapid naltrexone induction.

Mogali Shanthi S Khan Nabil A NA Drill Esther S ES Pavlicova Martina M Sullivan Maria A MA Nunes Edward E Bisaga Adam A

The American journal on addictions 20150401 3

<h4>Background and objectives</h4>Extended-release (XR) injection naltrexone has proved promising in the treatment of opioid dependence. Induction onto naltrexone is often accomplished with a procedure known as rapid naltrexone induction. The purpose of this study was to evaluate pre-treatment patient characteristics as predictors of successful completion of a rapid naltrexone induction procedure prior to XR naltrexone treatment.<h4>Methods</h4>A chart review of 150 consecutive research particip ...[more]

PMID: 25907815

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Project description:BACKGROUND:Different treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level. OBJECTIVE:This study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation. METHODS:Outcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment. RESULTS:Predicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%). CONCLUSIONS:Our results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs.

Project description:Microarray technology has enabled the measurement of comprehensive transcriptomic information. However, each data entry may reflect trivial individual differences among samples and also contain technical noise. Therefore, the certainty of each observed difference should be confirmed at earlier steps of the analyses, and statistical tests are frequently used for this purpose. Since a microarray measures a huge number of genes and the results are processed simultaneously, concerns regarding problems of multiplicity have been raised to the tests. To deal with these problems, several methodologies have been proposed, making the tests very conservative. Indeed, arbitrary tuning of the test threshold has also been introduced to relax the test conditions. However, the appropriateness of the multiplicity problems as well as the compensation methods has not been confirmed. The appropriateness of the compensation methods was checked by means of coincidence of the premises of the methodologies with the observed characteristics found in real data of two typical platforms of microarray analysis. Normality was observed in within-group data variations, supporting applications of parametric tests. However, genes displayed their own tendencies in the magnitude of variations, and the distributions of P-values were rather complex and varied; these characteristics are inconsistent with the premises of the compensation methodologies. Additionally, the appropriateness of the proposed multiplicities is reconsidered. When we observed differences in the transcriptome, the family-wise error rate should not be considered, since analyses at higher levels would not be influenced by a few false positives among the huge numbers of true information. Likely, concerns for a false discovery rate are not suitable for the point null hypotheses on expression levels, since the rate of true null hypotheses should be rare in contradiction to the premise of the methodology. Although compensation methods have been recommended to deal with the problem of multiplicity, the compensations are actually inappropriate for many of the applications of transcriptome analyses. Compensations are not only unnecessary, but will increase the occurrence of false negative errors, and arbitrary adjustment of the threshold damages the objectivity of the tests. Rather, the results of parametric tests should be evaluated directly. This SuperSeries is composed of the SubSeries listed below.

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Baseline characteristics of patients predicting suitability for rapid naltrexone induction.

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Baseline characteristics of patients predicting suitability for rapid naltrexone induction.

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