Evaluation of a downstaging, bidirectional version of the Montreal classification of Crohn's disease: Analysis of 5-year follow-up data from the prospective BioCrohn study

OBJECTIVE

Under the assumption of irreversibility, the Montreal classification provides a unidirectional assessment of the complications and behaviour of Crohn's disease (CD) that does not allow for downstaging. We examined the use of a bidirectional Montreal classification system that can capture disease regression.

DESIGN

From the BioCrohn Registry, an inception cohort of patients with CD for ≤12 months duration was defined and followed up for 5-years. Cumulative probabilities for developing complications were estimated using the Kaplan-Meier method. Potential associations of explanatory variables with disease progression were estimated with Cox regression.

RESULTS

Among 393 incident CD patients (of whom 255 completed the entire follow-up), the 5-year cumulative probability of developing complications was 41.5% (15.6% and 25.9% for stricturing and penetrating complications respectively). Perianal disease (hazard ratio [95% confidence interval]: 8.45 [4.74-15.07]) and surgical resection of the intestine (2.71 [1.50-4.92]) in the very early phase of the disease were associated with a higher risk of developing a penetrating complication within the 5-year follow-up. The use of a bidirectional Montreal classification system which can account for disease regression demonstrated that 90% of patients exhibited inflammatory disease behaviour at 5 years, in contrast to 58%, if the hierarchical, unidirectional Montreal classification system was used.

CONCLUSION

An additional bidirectional disease behaviour assessment capturing reversed or fully controlled complications may provide a more realistic appraisal of the complexity and unmet needs of patients treated with advanced therapies.

Dynamic impairment classification through arrayed comparisons

The multivariate normative comparison (MNC) method has been used for identifying cognitive impairment. When participants' cognitive brain domains are evaluated regularly, the longitudinal MNC (LMNC) has been introduced to correct for the intercorrelation among repeated assessments of multiple cognitive domains in the same participant. However, it may not be practical to wait until the end of study for diagnosis. For example, in participants of the Multicenter AIDS Cohort Study (MACS), cognitive functioning has been evaluated repeatedly for more than 35 years. Therefore, it is optimal to identify cognitive impairment at each assessment, while the family-wise error rate (FWER) is controlled with unknown number of assessments in future. In this work, we propose to use the difference of consecutive LMNC test statistics to construct independent tests. Frequency modeling can help predict how many assessments each participant will have, so Bonferroni-type correction can be easily adapted. A chi-squared test is used under the assumption of multivariate normality, and permutation test is proposed where this assumption is violated. We showed through simulation and the MACS data that our method controlled FWER below a predetermined level.

Diagnostic Strategies for Treatment Selection in Advanced Prostate Cancer

Prostate Cancer (PCa) is a leading cause of morbidity and mortality among men worldwide. For most men with PCa, their disease will follow an indolent course. However, advanced PCa is associated with poor outcomes. There has been an advent of new therapeutic options with proven efficacy for advanced PCa in the last decade which has improved survival outcomes for men with this disease. Despite this, advanced PCa continues to be associated with a high rate of death. There is a lack of strong evidence guiding the timing and sequence of these novel treatment strategies. This paper focuses on a review of the strategies for diagnostic and the current evidence available for treatment selection in advanced PCa.

Treatment of Advanced Prostate Cancer

The therapeutic landscape of prostate cancer has been transformed over the last decade by new therapeutics, advanced functional imaging, next-generation sequencing, and better use of existing therapies in early-stage disease. Until 2004, progression on androgen deprivation therapy for metastatic disease was treated with the addition of secondary hormonal manipulation; in the last decade, six systemic agents have been approved for the treatment of castration-resistant prostate cancer. We review clinical trials and survival benefit for these therapies and assess how the understanding of the disease shifted as these therapies were developed. We also discuss advances in noncastrate disease states, identification of biomarkers for prognosis and treatment selection, and opportunities in locoregional therapy to delay androgen deprivation therapy.

A dynamic and self-adaptive classification algorithm for motor imagery EEG signals

BACKGROUND

Brain-computer interface (BCI) is a communication pathway applied for pathological analysis or functional substitution. BCI based on functional substitution enables the recognition of a subject's intention to control devices such as prosthesis and wheelchairs. Discrimination of electroencephalography (EEG) trials related to left- and right-hand movements requires complex EEG signal processing to achieve good system performance.

NEW METHOD

In this study, a novel dynamic and self-adaptive algorithm (DSAA) based on the least-squares method is proposed to select the most appropriate feature extraction and classification algorithms couple for each subject. Specifically, the best couple identified during the training of the system is updated during online testing in order to check the stability of the selected couple and maintain high system accuracy.

RESULTS

Extensive and systematic experiments were conducted on public datasets of 17 subjects in the BCI-competition and the results show an improved performance for DSAA over other selected state-of-the-art methods.

COMPARISON WITH EXISTING METHODS

The results show that the proposed system enhanced the classification accuracy for the three chosen public datasets by 8% compared to other approaches. Moreover, the proposed system was successful in selecting the best path despite the unavailability of reference labels.

CONCLUSIONS

Performing dynamic and self-adaptive selection for the best feature extraction and classification algorithm couple increases the recognition rate of trials despite the unavailability of reference trial labels. This approach allows the development of a complete BCI system with excellent accuracy.

Identifying patients who will not reachieve remission after breakthrough seizures

Objective

We aim to identify people with epilepsy who are unlikely to reachieve a 12‐month remission within 2 years after experiencing a breakthrough seizure following an initial 12‐month remission.

Methods

We apply a novel longitudinal discriminant approach to data from the Standard and New Antiepileptic Drugs study to dynamically predict the risk of a patient not achieving a second remission after a breakthrough seizure by combining both baseline covariates (collected at the time of breakthrough seizure) and follow‐up data.

Results

The model classifies 83% of patients. Of these, 73% of patients (95% confidence interval [CI] = 58%‐88%) who did not achieve a second remission were correctly identified (sensitivity), and 84% of patients (95% CI = 69%‐96%) who achieved a second remission were correctly identified (specificity). The area under the curve from our model was 87% (95% CI = 80%‐94%). Patients who did not achieve a second remission were correctly identified on average after 10 months of observation postbreakthrough. Occurrence of seizures after breakthrough and the number of seizures experienced were the most informative longitudinal variables. These longitudinal profiles were influenced by the following baseline covariates: age at breakthrough seizure, presence of neurological insult, and number of antiepileptic drugs required to achieve first remission.

Significance

Using longitudinal data gathered during patient follow‐up allows more accurate predictions than using baseline covariates in a standard Cox model. The model developed in this paper is a useful first step in developing a tool for identifying patients who develop drug resistance after an initial remission.

Treatment of Advanced Prostate Cancer

The therapeutic landscape of prostate cancer has been transformed over the last decade by new therapeutics, advanced functional imaging, next-generation sequencing, and better use of existing therapies in early-stage disease. Until 2004, progression on androgen deprivation therapy for metastatic disease was treated with the addition of secondary hormonal manipulation; in the last decade, six systemic agents have been approved for the treatment of castration-resistant prostate cancer. We review clinical trials and survival benefit for these therapies and assess how the understanding of the disease shifted as these therapies were developed. We also discuss advances in noncastrate disease states, identification of biomarkers for prognosis and treatment selection, and opportunities in locoregional therapy to delay androgen deprivation therapy.