Effect of a multidisciplinary approach on hospital visit continuation in the treatment of patients with alcohol dependence

AIMS

Given the high dropout rates from initial treatment for alcoholism among patients with alcohol dependence, it is highly essential to prevent alcohol-dependent patients from early dropout. This study aims to investigate whether a multidisciplinary approach can help achieve continuous hospital visits for this patient population for initial treatment.

METHODS

This is a retrospective cohort study based on the medical records of all sequential alcohol-dependent outpatients who visited Sodegaura Satsukidai Hospital for alcoholism at least once between October 2017 and March 2019. The primary outcome was the difference in the rates of patients who achieved 6 and 12 months of continuous hospital visits following the first visit with and without the multidisciplinary approach.

RESULTS

Of all the participants (n = 67), the female-to-male ratios for patients supported with and without the multidisciplinary approach were 6:30 and 5:26, respectively. It was found that the rate of alcoholic patients treated with the multidisciplinary approach (n = 33, 91.7%), who had continuous hospital visits, was significantly higher than that of those without (n = 12, 38.7%) (χ2  = 21.2, p < 0.0001) during the first 6 months of treatment. Similarly, the rate of alcoholic patients treated with the multidisciplinary approach (n = 29, 90.6%) having continuous visits was significantly higher than that of those who did not receive such support (n = 8, 25.8%) (χ2  = 27.3, p < 0.0001) during the first 12 months.

CONCLUSION

A multidisciplinary approach can be used to reduce dropout from initial treatment among outpatients with alcohol dependence.

Comparative Effectiveness of Dynamic Treatment Strategies for Medication Use and Dosage: Emulating a Target Trial Using Observational Data

BACKGROUND

Availability of detailed data from electronic health records (EHRs) has increased the potential to examine the comparative effectiveness of dynamic treatment strategies using observational data. Inverse probability (IP) weighting of dynamic marginal structural models can control for time-varying confounders. However, IP weights for continuous treatments may be sensitive to model choice.

METHODS

We describe a target trial comparing strategies for treating anemia with darbepoetin in hemodialysis patients using EHR data from the UK Renal Registry 2004 to 2016. Patients received a specified dose (microgram/week) or did not receive darbepoetin. We compared 4 methods for modeling time-varying treatment: (A) logistic regression for zero dose, standard linear regression for log dose; (B) logistic regression for zero dose, heteroscedastic linear regression for log dose; (C) logistic regression for zero dose, heteroscedastic linear regression for log dose, multinomial regression for patients who recently received very low or high doses; and (D) ordinal logistic regression.

RESULTS

For this dataset, method (C) was the only approach that provided a robust estimate of the mortality hazard ratio (HR), with less-extreme weights in a fully weighted analysis and no substantial change of the HR point estimate after weight truncation. After truncating IP weights at the 95th percentile, estimates were similar across the methods.

CONCLUSIONS

EHR data can be used to emulate target trials estimating the comparative effectiveness of dynamic strategies adjusting treatment to evolving patient characteristics. However, model checking, monitoring of large weights, and adaptation of model strategies to account for these is essential if an aspect of treatment is continuous.

Detoxification and decolorization of complex textile effluent in an enzyme membrane reactor: batch and continuous studies

There is an urgent need to look for bio-based technologies to address the pollution related to textile dyes in waterbodies. The aim of this study was to evaluate an engineered laccase variant, LCC1-62 of Cyathus bulleri, expressed in recombinant Pichia pastoris, for the decolorization and detoxification of real textile effluent. The partially purified laccase effectively (~60-100%) decolorized combined effluent from different dyeing units at a laccase concentration of 500 U/L at a 50-mL level. Decolorization and detoxification of the combined effluents, from a local textile mill, were evaluated at 0.3 L volumetric level in a ray-flow membrane reactor in batch and continuous modes of operation. In batch studies, maximum decolorization of 97% and detoxification of 96% occurred at a hydraulic retention time (HRT) of 6 h without any additional laccase requirement. In continuous studies, the reactor was operated at an HRT of 6 h with a lower enzyme dosage (~120 U/L of the effluent). Decolorization was accompanied by a loss in laccase activity which was restored to ~120 U/L by the addition of laccase in two regimes. The addition of laccase, when the residual laccase activity decreased to 40% (~50 U/L), resulted in high decolorization (~5 ppm residual dye concentration) and low variance (σ²) of 2.77, while laccase addition, when the residual dye concentration decreased to ~8% (~10 U/L), resulted in an average dye concentration of 13 ppm with a high variance of 62.08. The first regime was implemented, and the continuous reactor was operated for over 80 h at an HRT of 3 and 6 h, with the latter resulting in ~95% decolorization and 96% reduction in the mutagenicity of the effluent. Less than 10% membrane fouling was observed over long operations of the reactor. The findings strongly suggest the feasibility of using LCC1-62 in an enzyme membrane reactor for large-scale treatment of textile effluents.

Prognostic factors of traumatic optic neuropathy based on multimodal analysis-Especially the influence of postoperative dressing change and optic nerve blood supply on prognosis

Objective

To investigate the critical prognostic factors of patients with traumatic optic neuropathy (TON) treated with endoscopic transnasal optic canal decompression (ETOCD) and to perform multimodal analysis based on imaging examinations of optical coherence tomography angiography (OCTA) and CT scan. Subsequently, a new prediction model was established.

Methods

The clinical data of 76 patients with TON who underwent decompression surgery with the endoscope-navigation system in the Department of Ophthalmology, Shanghai Ninth People's Hospital from January 2018 to December 2021 were retrospectively analyzed. The clinical data included demographic characteristics, reasons for injury, interval between injury and surgery, multimode imaging information of CT scan and OCTA, including orbital fracture, optical canal fractures, vessel density of optic disc and macula, and the times of postoperative dressing change. Binary logistic regression was used to establish a model for best corrected visual acuity (BCVA) after treatment as a predictor of TON outcome.

Results

Postoperative BCVA improved in 60.5% (46/76) patients and did not improve in 39.5% (30/76) patients. The times of postoperative dressing change had a significant impact on the prognosis. Other factors affecting the prognosis were microvessel density of the central optic disc, the cause of injury, and the microvessel density above the macula. The area under the raw current curves of the predictive model was 0.7596.

Conclusions

The times of dressing changes after the operation, i.e., continuous treatment, is the key factor affecting prognosis. The microvessel density in the center of the optic disc and superior macula, quantitatively analyzed by OCTA, is the prognostic factor of TON and may be used as a prognostic marker of TON.

Treatment strategies for a rapidly evolving landscape in chronic lymphocytic leukemia management

With the advent of targeted therapies for chronic lymphocytic leukemia (CLL), treatment choice has expanded and patients are living longer. Careful consideration is needed regarding treatment duration and sequence, how best to meet patients' needs, balancing toxicities while improving long-term survival and maximizing depth of response. This review addresses these considerations and discusses current targeted treatment dilemmas. Targeted therapies have dramatically transformed the CLL treatment landscape. Two treatment paradigms have emerged using B-cell lymphoma 2 inhibitors (BCL2i) and Bruton's tyrosine kinase (BTK): (i) fixed duration and (ii) continuous treatment. The BCL2i venetoclax can attain deep remissions with a fixed-duration approach, resulting in high rates of undetectable minimal residual disease (uMRD) in treatment-naïve and relapsed/refractory (R/R) patients with CLL. BTKis such as ibrutinib and acalabrutinib achieve high objective response rates and long-term disease control, although they rarely attain complete response or uMRD status as monotherapy. Numerous studies are evaluating the clinical utility of BTKi and BCL2i as combination therapies, where deep remissions have been found to occur. MRD status may also be a useful marker for deciding when to stop continuous therapy, and randomized trials on MRD-guided treatment strategies are currently ongoing. The current treatment choice between continuous or fixed-duration therapy should be based on comorbidities, risks, preferences, and treatment goals, whilst areas of emerging clinical interest include the potential utility of BTKi-BCL2i combination therapies, as well as an MRD-guided treatment strategies in the future.

Propensity score stratification methods for continuous treatments

Continuous treatments propensity scoring remains understudied as the majority of methods are focused on the binary treatment setting. Current propensity score methods for continuous treatments typically rely on weighting in order to produce causal estimates. It has been shown that in some continuous treatment settings, weighting methods can result in worse covariate balance than had no adjustments been made to the data. Furthermore, weighting is not always stable, and resultant estimates may be unreliable due to extreme weights. These issues motivate the current development of novel propensity score stratification techniques to be used with continuous treatments. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) and the nonparametric GPS-CDF approaches are introduced. Empirical CDFs are used to stratify subjects based on pretreatment confounders in order to produce causal estimates. A detailed simulation study shows superiority of these new stratification methods based on the empirical CDF, when compared with standard weighting techniques. The proposed methods are applied to the "Mexican-American Tobacco use in Children" study to determine the causal relationship between continuous exposure to smoking imagery in movies, and smoking behavior among Mexican-American adolescents. These promising results provide investigators with new options for implementing continuous treatment propensity scoring.

Bayesian Meta-analysis of Multiple Continuous Treatments with Individual Participant-Level Data: An Application to Antipsychotic Drugs

Modeling dose-response relationships of drugs is essential to understanding their safety effects on patients under realistic circumstances. While intention-to-treat analyses of clinical trials provide the effect of assignment to a particular drug and dose, they do not capture observed exposure after factoring in nonadherence and dropout. We develop a Bayesian method to flexibly model the dose-response relationships of binary outcomes with continuous treatment, permitting multiple evidence sources, treatment effect heterogeneity, and nonlinear dose-response curves. In an application, we examine the risk of excessive weight gain for patients with schizophrenia treated with the second-generation antipsychotics paliperidone, risperidone, or olanzapine in 14 clinical trials. We define exposure as total cumulative dose (daily dose × duration) and convert to units equivalent to 100 mg of olanzapine (OLZ doses). Averaging over the sample population of 5891 subjects, the median dose ranged from 0 (placebo randomized participants) to 6.4 OLZ doses (paliperidone randomized participants). We found paliperidone to be least likely to cause excessive weight gain across a range of doses. Compared with 0 OLZ doses, at 5.0 OLZ doses, olanzapine subjects had a 15.6% (95% credible interval: 6.7, 27.1) excess risk of weight gain; corresponding estimates for paliperidone and risperidone were 3.2% (1.5, 5.2) and 14.9% (0.0, 38.7), respectively. Moreover, compared with nonblack participants, black participants had a 6.8% (1.0, 12.4) greater risk of excessive weight gain at 10.0 OLZ doses of paliperidone. Nevertheless, our findings suggest that paliperidone is safer in terms of weight gain risk than risperidone or olanzapine for all participants at low to moderate cumulative OLZ doses.

Rapid degradation of malachite green by CoFe2O4-SiC foam under microwave radiation

This study demonstrated rapid degradation of malachite green (MG) by a microwave (MW)-induced enhanced catalytic process with CoFe₂O₄-SiC foam. The catalyst was synthesized from CoFe₂O₄ particles and SiC foam by the hydrothermal method. X-ray diffraction and scanning electron microscopy techniques were used to confirm that CoFe₂O₄ particles were settled on the surface of SiC foam. In this experiment, a novel fixed-bed reactor was set up with this catalyst for a continuous flow process in a MW oven. The different parameters that affect the MW-induced degradation rate of MG were explored. The MW irradiation leads to the effective catalytic degradation of MG, achieving 95.01% degradation within 5 min at pH 8.5. At the same time, the good stability and applicability of CoFe₂O₄-SiC foam for the degradation process were also discussed, as well as the underlying mechanism. In brief, these findings make the CoFe₂O₄-SiC foam an excellent catalyst that could be used in practical rapid degradation of MG.