Prediction Of Treatment Effect Research Articles

Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI.

Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method – support vector machine (SVM) classification – to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8–15 yrs) and 42 unaffected controls (age, IQ, in‐scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

Risk stratification and treatment effect of statins in secondary cardiovascular prevention in old age: Additive value of N-terminal pro-B-type natriuretic peptide.

To date, no validated risk scores exist for prediction of recurrence risk or potential treatment effect for older people with a history of a cardiovascular event. Therefore, we assessed predictive values for recurrent cardiovascular disease of models with age and sex, traditional cardiovascular risk markers, and 'SMART risk score', all with and without addition of N-terminal pro-B-type natriuretic peptide (NT-proBNP). Treatment effect of pravastatin was assessed across low and high risk groups identified by the best performing models. Post-hoc analysis in 2348 participants (age 70-82 years) with a history of cardiovascular disease within the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) study. Composite endpoint was a recurrent cardiovascular event/cardiovascular mortality. The models with age and sex, traditional risk markers and SMART risk score had comparable predictive values (area under the curve (AUC) 0.58, 0.61 and 0.59, respectively). Addition of NT-proBNP to these models improved AUCs with 0.07 (p for difference ((pdiff)) = 0.003), 0.05 (pdiff = 0.009) and 0.06 (pdiff < 0.001), respectively. For the model with age, sex and NT-proBNP, the hazard ratio for the composite endpoint in pravastatin users compared with placebo was 0.67 (95% confidence interval 0.49-0.90) for those in the highest third of predicted risk and 0.91 (0.57-1.46) in the lowest third, number needed to treat 12 and 115 (pdiff = 0.038) respectively. In secondary cardiovascular prevention in old age addition of NT-proBNP improves prediction of recurrent cardiovascular disease, cardiovascular mortality and treatment effect of pravastatin. A minimal model including age, sex and NT-proBNP predicts as accurately as complex risk models including NT-proBNP.

Prediction of treatment benefit in high-dimensional cox models via gene signatures in randomized clinical trials

Methods We investigated four approaches: penalize biomarker main effects and biomarker-by-treatment interactions using a lasso penalty (full-lasso); control of main effects by principal components or ridge penalty, and lasso on interactions (sPCA+lasso or ridge+lasso); and ‘modified covariates’ in a penalized regression model (Tian et al. 2014). We performed simulations under null and alternative scenarios by varying the sample size n, number of biomarkers H, number of true main effects or treatment-modifiers, effect sizes and correlations. We proposed two novel measures of treatment effect prediction for gene signatures: a difference in C-indices and a Wald-based interaction statistic. We used gene expression data from a RCT of adjuvant chemotherapy in non-small cell lung cancer (n=133) for illustration.

MODELING AND OPTIMIZATION OF ACID TREATMENT OF FHF FRACTURE IN CARBONATE RESERVOIRS

The subject of the study presented is a process of acid treatment of a fracture generated by FHF using proppant in carbonate reservoirs. For prediction of acidizing treatment effectiveness the averaged mathematical model of this process was developed based on the laws of acid mass conservation and acid transport in fractures taking into account the acid solution leakage due to diffusion towards the treated surface. The modeling aids in the pressure field predic-tion, as well as the acid concentration profile along the fracture and the fracture width variation resulted from the face etching, and the skin factor as a function of injected acid amount and injection rate.

Computational Approaches to the Examination of the Functional Architecture of the Human Brain

In this talk I will discuss how we can establish functional correspondence in neuroimaging data across individuals. We will briefly review the challenges in inferring multivariate fine-grained functional structure shared across individuals from neuroimaging data despite anatomical variability. Specifically, we will discuss the relevance of discrepancies between anatomical and functional correspondence in light of evolution, brain development and disease. I will outline how to use manifold learning techniques to capture the functional connectivity architecture in individuals. These methods capture a multitude of relationships, such as functional connectivity and map them into a space that represents the entire relationship structure. This enables learning of a joint functional atlas in an embedding space representing the structure shared in a population. The resulting functional correspondence has several uses. First, it allows to remove possible anatomical confounds when comparing cohorts, secondly it allows to study the relationship between function and the location of its anatomical anchors. The talk will conclude with a discussion of results in pre-surgery language mapping, resting state networks, and treatment effect prediction.

Individualised prediction of alternate-day aspirin treatment effects on the combined risk of cancer, cardiovascular disease and gastrointestinal bleeding in healthy women

BackgroundThe value of aspirin in primary prevention of cancer and cardiovascular disease (CVD) remains unclear. The aim of this study was to identify women who benefit from alternate-day aspirin with...

Prediction of Treatment Effects for Individual Cancer Patients Using Data from Randomized Clinical Trials

ABSTRACT Aim: Results from randomized clinical trials (RCTs) evaluating the efficacy of chemotherapy are typically reported as relative risk or difference in median survival on a group level. Patients are most interested in their personal expected absolute treatment effect. Translating group level estimates to individual patients in clinical practice is challenging, as absolute treatment effects can vary substantially among individuals. We propose a method for individualized treatment effect prediction using data from RCTs, to identify cancer patients who benefit from specific treatments. Methods: Data from the NVALT7 study, a randomized phase II trial comparing pemetrexed (P) with P plus carboplatin (C) in 240 patients with non-small cell lung cancer relapsing after platinum based chemotherapy (Smit et al. J. Clin. Oncol. 2009), were analyzed. Cox proportional hazards models, including routinely available patient and tumor characteristics, were developed for individualized prediction of the absolute effects of adding C to P in terms of absolute increase in overall survival (OS), progression free survival (PFS) and risk of grade 3/4 toxicity. Models were internally validated and an external validation in the GOIRC 02 2006 randomized phase II study is planned. Results: The predicted individual benefit in OS of adding C to P ranged from -1.2 to 10.8 months. Patients with squamous tumors, particularly those with better performance status, lower leucocyte count and longer treatment free interval, had the highest predicted median OS benefit from addition of C (3.2 months ), though with a wide range (1.4 to 10.8 months). The difference in predicted PFS for individuals ranged from 0.1 to 3.2 months. Internal validation showed a satisfactory model calibration and discrimination (c-index: 0.69/0.65 for OS/PFS). The predicted absolute risk increase (ARI) of toxicity with addition of C was similar in patients with ≤3 months predicted OS (ARI 5.0%) and those with longer predicted OS (ARI 5.2%). Conclusions: Data from RCTs may be used to predict absolute treatment effects for individual patients before start of therapy. Individualized treatment effect predictions from validated models may guide treatment decisions in cancer patients by identifying those who benefit most in terms of OS and PFS and have low risk of toxicity. Disclosure: E.F. Smit: EFS has received honoraria from Eli Lilly. All other authors have declared no conflicts of interest.

Neural Predictors of Treatment Response in Depression

Depression is a leading cause of disability and treatment response is highly variable between patients. The current review focuses on recent findings from studies exploring neural predictors of treatment response to conventional antidepressant drugs, psychological treatment, and novel candidate interventions. Structural markers of response have revealed that decreased volume of key substrates such as the hippocampus and orbitofrontal cortex are predictive of poor response to antidepressant drug treatment, and diffusion tensor imaging offers potentially relevant information for future studies. Pretreatment measures of rostral anterior cingulate activity (from functional magnetic resonance imaging- and electroencephalogram-based approaches) have also been highlighted as a biomarker for antidepressant drug response. The relative utility of measurements taken at baseline versus early change for refining treatment in individual patients is considered. The response to novel treatments such as scopolamine, ketamine, and deep brain stimulation also offer promise for understanding targets of treatment and predictors for tailoring treatment. There is considerable overlap between neural biomarkers for different treatments although positron emission tomography metabolism in the anterior insula may offer a differential predictor of response to pharmacotherapy versus psychotherapy in depression. These insights provide important information about the potential targets and mechanisms underlying the prediction of treatment effects in depression. Future work needs to explore these different predictors between treatment arms in prospectively defined studies to evaluate whether these markers may have utility in the clinic.

Clinical relevance of the K-ras oncogene in colorectal cancer: Experience in a Mexican population

BackgroundColorectal cancer is frequent in the developed countries, with a cancer-specific mortality rate of 33%. Different biomarkers are associated with overall survival and the prediction of monoclonal treatment effectiveness. The presence of mutations in the K-ras oncogene alters the response to target therapy with cetuximab and could be an independent prognostic factor. AimsTo analyze the difference in survival between patients with mutated K-ras and those with K-ras wild-type status. MethodsThirty-one clinical records were retrospectively analyzed of patients presenting with colorectal cancer that underwent K-ras sequencing through real-time polymerase chain reaction within the time frame of 2009 to 2012 at the Hospital de Alta Especialidad de Veracruz of the Instituto para la Salud y Seguridad Social de los Trabajadores del Estado (HAEV-ISSSTE). Survival analysis for patients with and without K-ras mutation was performed using the Kaplan Meier method. Contrast of covariates was performed using logarithmic transformations. ResultsNo statistically significant difference was found in relation to survival in the patients with mutated K-ras vs. those with K-ras wild-type (P=.416), nor were significant differences found when analyzing the covariants and survival in the patients with mutated K-ras: ECOG scale (P=.221); age (less than, equal to or greater than 65years, P=.441); clinical stage according to the AJCC (P=.057), and primary lesion site (P=.614). ConclusionsNo relation was found between the K-ras oncogene mutation and reduced survival, in contrast to what has been established in the international medical literature. Further studies that include both a larger number of patients and those receiving monoclonal treatment, need to be conducted. There were only 5 patients in the present study that received cetuximab, resulting in a misleading analysis.

Personalized cardiovascular disease prevention by applying individualized prediction of treatment effects

Large-scale randomized clinical trials have established the efficacy of cholesterol-lowering, blood pressure-lowering, and anti-platelet therapy to prevent cardiovascular diseases. A challenge for clinicians is to apply group-level evidence from these trials to individual patients. Trials typically report a single treatment effect estimate which is the average effect of all participants, comprising patients who respond poorly, intermediately, and well. Clinicians would preferably make patient-tailored treatment decisions. Therefore, one would require an estimate of an individual patient's response to therapy. Although not yet widely recognized, trials contain this type of information. In this paper, we show how available information from landmark trials can be translated to an individual 'treatment score' through the use of multivariable therapeutic prediction models. These models provide an individual estimate of the absolute risk reduction in cardiovascular events given the specific combination of multiple clinical characteristics of a patient under care. Based on this individualized treatment estimate and metrics such as the individual number-needed-to-treat, clinicians together with their patients can decide whether drug treatment or what treatment intensity is worthwhile. Selective treatment of those who can anticipate the greatest benefit and the least harm on an individualized basis could reduce the number of unnecessary treatments and healthcare costs beyond that currently achievable by subgroup analyses based on single patient characteristics.

Development of Korean spine database and ontology for realizing e-Spine

By 2026, Korea is expected to surpass the UNs definition of an aged society and reach the level of a super-aged society. As a result, degenerative spinal disease and the related surgical procedures will increase exponentially. To prevent unnecessary spinal surgery and support scientific diagnosis of spinal disease and systematic prediction of treatment effects, we have been developing e-Spine which is a computerized simulation model of human spines. In this paper, we present the Korean spine database and ontology that are used as a background data for realizing e-Spine. Generally, Korean physical function is different from foreign physical function. For example, ossification of posterior longitudinal ligament is only occurred in Asians. However, developed countries are currently constructing digital human data to improve the medical and biomedical researches, while the digital human data for Korean are inadequate. Therefore, we constructed Korean spine database on Korean with normal spine or degenerative spinal diseases. To date, we have collected spine data from 72 cadavers and 298 patients. The spine data consists of 2D images such as CT, MRI, or X-ray, 3D shapes, geometry data and property data. The volume and quality of Korean spine database are now the worlds highest. Also, we constructed spinal ontology to provide a wealth of information related to spine. The spinal ontology contains anatomy of spine, method of treatment, cause, classification information related to spine. Finally, we implemented a management service for efficiently searching and managing the data. As a result, our database and ontology will offer great value and utility in the diagnosis, treatment, and rehabilitation of patients suffering from spinal diseases.

Patient characteristics in low back pain subgroups based on an existing classification system. A descriptive cohort study in chiropractic practice

Sub-grouping of low back pain (LBP) is believed to improve prediction of prognosis and treatment effects. The objectives of this study were: (1) to examine whether chiropractic patients could be sub-grouped according to an existing pathoanatomically-based classification system, (2) to describe patient characteristics within each subgroup, and (3) to determine the proportion of patients in whom clinicians considered the classification to be unchanged after approximately 10 days. A cohort of 923 LBP patients was included during their first consultation. Patients completed an extensive questionnaire and were examined according to a standardised protocol. Based on the clinical examination, patients were classified into diagnostic subgroups. After approximately 10 days, chiropractors reported whether they considered the subgroup had changed. The most frequent subgroups were reducible and partly reducible disc syndromes followed by facet joint pain, dysfunction and sacroiliac (SI)-joint pain. Classification was inconclusive in 5% of the patients. Differences in pain, activity limitation, and psychological factors were small across subgroups. Within 10 days, 82% were reported to belong to the same subgroup as at the first visit. In conclusion, LBP patients could be classified according to a standardised protocol, and chiropractors considered most patient classifications to be unchanged within 10 days. Differences in patient characteristics between subgroups were very small, and the clinical relevance of the classification system should be investigated by testing its value as a prognostic factor or a treatment effect modifier. It is recommended that this classification system be combined with psychological and social factors if it is to be useful.

High-Dose Statin Therapy in Patients With Stable Coronary Artery Disease

Background— Clinicians need to identify coronary artery disease patients for whom the benefits of high-dose versus usual-dose statin therapy outweigh potential harm. We therefore aimed to develop and validate a model for prediction of the incremental treatment effect of high-dose statins for individual patients in terms of reduction of 5-year absolute risk for myocardial infarction, stroke, coronary death, or cardiac resuscitation. Methods and Results— Based on data from the Treating to New Targets trial (TNT; n=10 001), a Cox proportional hazards model was developed comprising 13 easy-to-measure clinical predictors: age, sex, smoking, diabetes mellitus, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, history of myocardial infarction, coronary artery bypass grafting, congestive heart failure or abdominal aortic aneurysm, glomerular filtration rate, and treatment status (ie, atorvastatin 80 mg or 10 mg). External validation in the Incremental Decrease in End Points Through Aggressive Lipid Lowering trial (IDEAL; n=8888) confirmed adequate goodness-of-fit and calibration, but moderate discrimination (C-statistic, 0.63; 95% confidence interval, 0.62–0.65). Still, among participants of both trials combined, the model identified a group of 11.7% whose predicted 5-year number needed to treat was ≤25 and a group of 41.9% whose predicted needed to treat was ≥50. A decision curve shows that making treatment decisions on the basis of predictions using our model may improve net benefit. Conclusions— Estimation of the incremental treatment effect of high-dose versus usual-dose statin therapy in individual coronary artery disease patients enables selection of high-risk patients that benefit most from more aggressive therapy. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifiers: NCT00327691 and NCT00159835.

IDENTIFYING SOCIAL NORMS USING COORDINATION GAMES: WHY DOES DICTATOR GAME SHARING VARY?

We introduce an incentivized elicitation method for identifying social norms that uses simple coordination games. We demonstrate that concern for the norms we elicit and for money predict changes in behavior across several variants of the dictator game, including data from a novel experiment and from prior published laboratory studies, that are unaccounted for by most current theories of social preferences. Moreover, we find that the importance of social norm compliance and of monetary considerations is fairly constant across different experiments. This consistency allows prediction of treatment effects across experiments, and implies that subjects have a generally stable willingness to sacrifice money to take behaviors that are socially appropriate.

Early serum hepatitis B virus large surface protein level: A stronger predictor of virological response to peginterferon alfa-2a than that to entecavir in HBeAg-positive patients with chronic hepatitis B

BackgroundThe response rate to antiviral therapy varies greatly among individuals, and its prediction is still very challenging. ObjectivesTo evaluate the usefulness of serum hepatitis B virus large surface protein (LHBs) levels compared with HBsAg in prediction of the antiviral treatment effect. Study designQuantification of LHBs, HBsAg and HBV DNA was carried out at baseline and during antiviral therapy (weeks 4, 12, 24, 36 and 48) in HBeAg-positive patients treated with peginterferon alfa-2a (n=21) or entecavir (n=41). ResultsThe serum LHBs concentration was correlated positively with HBV DNA and HBsAg (r=0.635 and 0.588, respectively). LHBs and HBV DNA levels decreased significantly in a biphasic manner and HBsAg level tended to decrease slowly in both treatment groups. In peginterferon alfa-2a group, the cutoff of 88.46ng/ml in serum LHBs at week 4 gave the best AUC (=0.96) with positive and negative predictive values of 88.9% and 100%, in association with virological response (VR). Serum LHBs level at week 4 also showed an association with VR in entecavir group (AUC 0.78). The predictive model incorporating LHBs, HBsAg and HBV DNA could discriminate VR at baseline (AUC 0.79) and showed an association with serological response (SR) at week 12 (AUC 0.80) in peginterferon alfa-2a group. ConclusionsOn-treatment quantification of serum LHBs may be a more useful parameter for predicting VR in patients on peginterferon alfa-2a than those on entecavir. Combining LHBs, HBsAg and HBV DNA can predict VR and SR more effectively and earlier.

Treatment effect prediction in panic disorder patients - fmri study

Introduction Some patients have a chronic and/or recurrent form of the disorder. Reasons of treatment resistance have been described, but the neurobiological basis of disorder gravity is unclear. Objectives Examine if the hyperactivity of right and left amygdala, left posterior cingulate cortex or left dorsolateral prefrontal cortex predict the treatment effect of Cognitive Behavioral Therapy (CBT). Aims The aim of our study is to test the hypothesis of treatment predictability in panic disorder patients in fMRI. Method 24 patients meeting the ICD-10 criteria for panic disorder with or without agoraphobia. Patients were studied in fMRI during the exposition of threat-related words compared with neutral words and emotional faces compared with fixation points. Patients were studied in fMRI scanner before and after CBT treatment. Psychopathology was assessed using Clinical Global Impression rating scale (CGI) and Beck Anxiety Inventory (BAI). Patients were divided into responders and non-responders groups by the 25% reduction in BAI scale. Results Significant reduction of activity was observed in responders on the right amygdala, while the left amygdala decreased activity is lower, and could be the effect of habituation. Incresed activation of left Broca’s area 46, and 45 was negative treatment effect predictor. Conclusion Negative treatment effect predictor is hyperactivity in left dorsolateral prefrontal cortex BA 46 and 45. Positive predictors are higher reactivity of right amygdala for anxious faces, activation of right angular gyrus, right insula, right temporal pole. Supported by the project IGA MZ CR NT 11047-4/2010

P.4.a.012 Treatment effect prediction of cognitive behavioural therapy in panic disorder patients – fMRI study

P.4.a.012 Treatment effect prediction of cognitive behavioural therapy in panic disorder patients – fMRI study

Prediction and monitoring of treatment effect using T1-weighted dynamic contrast-enhanced magnetic resonance imaging in colorectal liver metastases: Potential of whole tumour ROI and selective ROI analysis

PurposeTo evaluate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for prediction and early monitoring of treatment in colorectal liver metastases. Materials and methodsTen patients were included. Baseline and follow-up DCE-MRI examinations were evaluated by whole tumour and selected ROI placements calculating Kep-values. Selective ROIs, concentric-like and hot spot, were drawn on early arterial phase images. Monitoring of treatment was performed comparing RECIST1.1 criteria with whole tumour and selected ROI placement. To evaluate treatment effect between responders and non-responders, independent samples t-test was used on Kep-values. ResultsIn each patient largest lesion was evaluated totalling 10 target lesions. At baseline, for whole tumour ROI placements mean Kep-values in responders were significantly higher than mean Kep-values in non-responders (t=7.481, p<0.001). Selective ROI placement comparison of mean Kep-values at baseline and after 6 weeks of treatment (first follow-up measurement) showed significant decrease in responding patients (t=4.706, p=0.003) whereas increase in Kep-values in non-responding patients was not statistically significant. ConclusionThis preliminary study shows that baseline Kep for whole tumour ROI is a predictor for treatment outcome. Decrease of Kep using selective ROIs allows early identification of response after 6 weeks of treatment.

Transcranial Magnetic Stimulation for the Prediction and Enhancement of Rehabilitation Treatment Effects

In this update on rehabilitation technology, transcranial magnetic stimulation (TMS), a technique that allows noninvasive stimulation of the brain, is examined. The background and basic principles of TMS are reviewed, and its usefulness as a tool to inform and possibly augment the rehabilitation process is discussed. The three main paradigms by which TMS is applied-physiological measurement, disruption/virtual lesion studies, and modulation of cortical excitability-are discussed relative to the types of scientific information each paradigm can provide and their potential clinical usefulness in the future. One of the more exciting prospects is that, when combined with rehabilitation training, TMS modulation of cortical excitability could potentially enhance the effects of rehabilitation and lead to greater levels of recovery than are currently attainable with rehabilitation alone. It is concluded that current studies must focus on the mechanisms of recovery based on the specific structures and processes affected by the disorder and the neural effects of specific rehabilitation interventions in order for the potential of TMS-augmented rehabilitation to be realized.

Aspirin for primary prevention of vascular events in women: individualized prediction of treatment effects.

AIMS To identify women who benefit from aspirin 100 mg on alternate days for primary prevention of vascular events by using treatment effect prediction based on individual patient characteristics. METHODS AND RESULTS Randomized controlled trial data from the Women's Health Study were used to predict treatment effects for individual women in terms of absolute risk reduction for major cardiovascular events (i.e. myocardial infarction, stroke, or cardiovascular death). Predictions were based on existing risk scores, i.e. Framingham (FRS), and Reynolds (RRS), and on a newly developed prediction model. The net benefit of different aspirin treatment-strategies was compared: (i) treat no one, (ii) treat everyone, (iii) treatment according to the current guidelines (i.e. selective treatment of women >65 years of age or having >10% FRS), and (iv) prediction-based treatment (i.e. selective treatment of patients whose predicted treatment effect exceeds a given decision threshold). The predicted reduction in 10-year absolute risk for major cardiovascular events was <1% in 97.8% of 27 939 study subjects when based on the refitted FRS, in 97.0% when based on the refitted RRS, and in 90.0% when based on the newly developed model. Of the treatment strategies considered, only prediction-based treatment using the newly developed model and selective treatment of women >65 years of age yielded more net benefit than treating no one, provided that the 10-year number-willing-to-treat (NWT) to prevent one cardiovascular event was above 50. CONCLUSION Aspirin was ineffective or even harmful in the majority of patients. Age was positively related to treatment effect, whereas current smoking and baseline risk for cardiovascular events were not. When the NWT is 50 or lower, the aspirin treatment strategy that is associated with optimal net benefit in primary prevention of vascular events in women is to treat none.