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Despite evidence that psychotherapy has a positive impact on psychological disorders, 30% of patients fail to respond during clinical trials, and as many as 65% of patients in routine care leave treatment without a measured benefit. In addition, therapists appear to overestimate positive outcomes in their patients relative to measured outcomes and are particularly poor at identifying patients at risk for a negative outcome. These problems suggest the need for measuring and monitoring patient treatment response over the course of treatment while applying standardized methods of identifying at-risk cases. Computer-assisted methods for measuring, monitoring, identifying potential deteriorators, and providing feedback to clinicians are described along with a model that explains why feedback is likely to be beneficial to patients. The results of 12 clinical trials are summarized and suggest that deterioration rates can be substantially reduced in at-risk cases (from baseline rates of 21% down to 13%) and that recovery rates are substantially increased in this subgroup of cases (from a baseline of 20% up to 35%) when therapists are provided this information. When problem-solving methods are added to feedback, deterioration in at-risk cases is further reduced to 6% while recovery/improvement rates rise to about 50%. It is suggested that the feedback methods become a standard of practice. Such a change in patterns of care can be achieved through minimal modification to routine practice but may require discussions with patients about their clinical progress.

Background: The preoperative selection of patients with intermediate-stage hepatocellular carcinoma (HCC) who are likely to have an objective response to first transarterial chemoembolization (TACE) remains challenging. Objective: To develop and validate a clinical-radiomic model (CR model) for preoperatively predicting treatment response to first TACE in patients with intermediate-stage HCC. Methods: A total of 595 patients with intermediate-stage HCC were included in this retrospective study. A tumoral and peritumoral (10 mm) radiomic signature (TPR-signature) was constructed based on 3,404 radiomic features from 4 regions of interest. A predictive CR model based on TPR-signature and clinical factors was developed using multivariate logistic regression. Calibration curves and area under the receiver operating characteristic curves (AUCs) were used to evaluate the model’s performance. Results: The final CR model consisted of 5 independent predictors, including TPR-signature (p < 0.001), AFP (p = 0.004), Barcelona Clinic Liver Cancer System Stage B (BCLC B) subclassification (p = 0.01), tumor location (p = 0.039), and arterial hyperenhancement (p = 0.050). The internal and external validation results demonstrated the high-performance level of this model, with internal and external AUCs of 0.94 and 0.90, respectively. In addition, the predicted objective response via the CR model was associated with improved survival in the external validation cohort (hazard ratio: 2.43; 95% confidence interval: 1.60–3.69; p < 0.001). The predicted treatment response also allowed for significant discrimination between the Kaplan-Meier curves of each BCLC B subclassification. Conclusions: The CR model had an excellent performance in predicting the first TACE response in patients with intermediate-stage HCC and could provide a robust predictive tool to assist with the selection of patients for TACE.

A propensity weighted (PSW) methodology was recently proposed for assessing the treatment effect conditional to the probability of non‐specific response to a treatment (prob‐NSRT). Prob‐NSRT was estimated using an artificial neural network (ANN) model applied to pre‐randomization and study endpoint observations in a placebo arm of a placebo‐controlled clinical trial. Placebo data were initially used to estimate prob‐NSRT, then the ANN model was applied to the data of each individual in each treatment arm (placebo + active) for estimating the individual prob‐NSRT, and finally all data in the trial enriched by the prob‐NSRT values were used to assess the treatment effect. One of the major limitations of this methodology was that the ANN model was developed and applied to analyze data in the same dataset. To overcome this limitation, a new artificial intelligence driven nonlinear mixed effect modeling approach (AI‐NLME) is proposed. This approach involves the development of the ANN model using a dataset that is independent from the dataset used to estimate the treatment effect. A case study is presented using data from a randomized, placebo‐controlled trial in major depressive disorders. The AI‐NLME approach provided an effective tool for controlling the confounding effect of treatment non‐specific response, for increasing signal detection, for decreasing heterogeneity in the response, for increasing the effect size, for better assessing the responder rate, and for providing a reliable estimate of the “true” treatment effect. These findings provide convergent evidence on the potential role of AI‐NLME to become the reference approach for analyzing placebo‐controlled clinical trials.

Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to develop antidepressant medications appropriate for outperforming placebo. Treatment effect in randomized, placebo‐controlled trials, is usually estimated by the mean baseline adjusted difference of treatment response in active and placebo arms and is function of treatment‐specific and non‐specific effects. The non‐specific treatment effect varies subject by subject conditional to the individual propensity to respond to placebo. This effect is not estimable at an individual level using the conventional parallel‐group study design, since each subject enrolled in the trial is assigned to receive either active treatment or placebo, but not both. The objective of this study was to conduct a comparative analysis of the machine learning methodologies to estimate the individual probability of a non‐specific treatment effect. The estimated probability is expected to support novel methodological approaches for better controlling effect of excessively high placebo response. At this purpose, six machine learning methodologies (gradient boosting machine, lasso regression, logistic regression, support vector machines, k‐nearest neighbors, and random forests) were compared to the multilayer perceptrons artificial neural network (ANN) methodology for predicting the probability of individual non‐specific treatment response. ANN achieved the highest overall accuracy among all methods tested. A fivefold cross‐validation was used to assess performances and risks of overfitting of the ANN model. The analysis conducted without subjects with non‐specific effect indicated a significant increase of signal detection with significant increase in effect size.

During the past several years, we have achieved a deeper understanding of the etiology/pathophysiology of major depressive disorder (MDD). However, this improved understanding has not translated to improved treatment outcome. Treatment often results in symptomatic improvement, but not full recovery. Clinical approaches are largely trial-and-error, and when the first treatment does not result in recovery for the patient, there is little proven scientific basis for choosing the next. One approach to enhancing treatment outcomes in MDD has been the use of standardized sequential treatment algorithms and measurement-based care. Such treatment algorithms stand in contrast to the personalized medicine approach, in which biomarkers would guide decision making. Incorporation of biomarker measurements into treatment algorithms could speed recovery from MDD by shortening or eliminating lengthy and ineffective trials. Recent research results suggest several classes of physiologic biomarkers may be useful for predicting response. These include brain structural or functional findings, as well as genomic, proteomic, and metabolomic measures. Recent data indicate that such measures, at baseline or early in the course of treatment, may constitute useful predictors of treatment outcome. Once such biomarkers are validated, they could form the basis of new paradigms for antidepressant treatment selection.

Objective There is no consensus on the type or duration of the posttreatment EEG needed for assessing treatment response for infantile spasms (IS). We assessed whether outpatient electroencephalograms (EEGs) are sufficient to confirm infantile spasms (IS) treatment response. Methods Three‐year retrospective review identified new‐onset IS patients. Only presumed responder to IS treatment at 2 weeks with a prolonged (>90 minutes) outpatient EEG to assess treatment response and at least 3‐month follow‐up were included. Hypsarrhythmia, electroclinical spasms, and sleep were evaluated for the first hour and for the duration of the EEG. Results We included 37 consecutive patients with new‐onset IS and presumed clinical response at 2 weeks posttreatment. Follow‐up outpatient prolonged EEGs (median: 150 minutes, range: 90‐240 minutes) were obtained 14 days (IQR: 13‐17) after treatment initiation. EEGs detected ongoing IS in 11 of 37 (30%) presumed early responders. Prolonged outpatient EEG had a sensitivity of 85% (confidence interval [CI] 55%‐98%) for detecting treatment failure. When hypsarrhythmia and/or electroclinical spasms were not seen, EEG had a negative predictive value 92% (CI: 75%‐99%) for confirming continued IS resolution. Outpatient EEG combined with clinical assessment, however, identified all treatment failures at 2 weeks. Compared with the entire prolonged EEG, the first‐hour recording missed IS in 45% (5/11). While sleep was captured in 95% (35/37) of the full EEG recording, the first hour of recording captured sleep in only 54% (20/37). Significance Infantile spasms treatment response can be confirmed with a clinical history of spasm freedom and an outpatient prolonged EEG without evidence for ongoing spasms (hypsarrhythmia/electroclinical spams on EEG). Outpatient prolonged EEG, but not routine EEGs, represents an alternative to inpatient long‐term monitoring for IS posttreatment EEG follow‐up.

Demodicosis is a vital skin problem in dogs. The present study has determined the prevalence and associated risk factors of demodicosis in dogs and the response to treatment. A total of 100 skin scrapings were collected from dogs having dermatological lesions brought to the Teaching and Training Pet Hospital and Research Center of Chattogram Veterinary and Animal Sciences University for treatment purpose. The collected scrapings were dissolved in 10% potassium hydroxide to detect mites through microscopic examination. Various risk factors like breed, age, sex, hair type, health status, and management system (indoor and outdoor) were analyzed using the logistic regression model. Positive cases were treated with oral ivermectin (Scabo ; at 0.6 mg/kg/day) along with amitraz 12.5% (Ridd ) diluted to 0.05% for rubbing on the body after bathing with chlorhexidine shampoo (PetHex ). Clavulanate amoxicillin (Moxaclav ) and omega-3 fatty acids (OMG-3 ) were also suggested to prevent secondary bacterial infection and to maintain skin and hair coat integrity. The recovery rate was observed every 2-4 weeks of treatment upto 11-13 weeks. The overall prevalence of demodicosis was recorded as 27%. Hair type, health status, and management system were significantly ( < 0.05) associated with the occurrence of demodicosis in dogs. Following treatment, the first negative skin scraping for mite was noticed at 8-10 weeks of treatment, and in all cases, clinical signs completely disappeared at 80-90 days of treatment. A good percentage of the dogs having dermatological lesions was determined as demodicosis. Long-term oral ivermectin and topical amitraz, an oral antibiotic, and nutritional therapy are effective against canine demodicosis. Keeping long-haired dogs with good body condition and indoor management is highly suggestive of preventing and controlling the disease.

Background: Prolonged grief disorder (PGD) causes significant impairment in approximately 7% of bereaved people. Although cognitive behaviour therapy (CBT) has been shown to effectively treat PGD, there is a need to identify predictors of treatment non-response. Methods: PGD patients (N = 80) were randomly allocated to receive 10 weekly two-hour group CBT sessions and (a) four individual sessions of exposure therapy or (b) CBT without exposure. PGD was assessed by self-report measures at baseline, post-treatment (N = 61), and six-months (N = 56) after treatment. Results: Post-treatment assessments indicated that greater reduction in grief severity relative to pretreatment levels was associated with being in the CBT/Exposure condition, and lower baseline levels of self-blame and avoidance. At follow-up, greater grief symptom reduction was associated with being in the CBT/Exposure condition and lower levels of avoidance. Conclusions: These patterns suggest that strategies that target excessive self-blame and avoidance during treatment may enhance response to grief-focused cognitive behaviour therapy.

We examined longitudinally the course and predictors of treatment resistance in a large cohort of first-episode psychosis (FEP) patients from initiation of antipsychotic treatment. We hypothesized that antipsychotic treatment resistance is: (a) present at illness onset; and (b) differentially associated with clinical and demographic factors. The study sample comprised 323 FEP patients who were studied at first contact and at 10-year follow-up. We collated clinical information on severity of symptoms, antipsychotic medication and treatment adherence during the follow-up period to determine the presence, course and predictors of treatment resistance. From the 23% of the patients, who were treatment resistant, 84% were treatment resistant from illness onset. Multivariable regression analysis revealed that diagnosis of schizophrenia, negative symptoms, younger age at onset, and longer duration of untreated psychosis predicted treatment resistance from illness onset. The striking majority of treatment-resistant patients do not respond to first-line antipsychotic treatment even at time of FEP. Clinicians must be alert to this subgroup of patients and consider clozapine treatment as early as possible during the first presentation of psychosis.