Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

Generalized,severe epidermolysis bullosa simplex caused by a Keratin 5 p.E477K mutation

Epidermolysis bullosa simplex (EBS) is a skin fragility disorder resulting from mutations of structural proteins in the epidermis. We provide a brief report of long‐term survival and reproduction in a mother with EBS due to keratin 5 (KRT5) c.1429G > A (p.E477K) mutation, which causes a particularly severe form of the disease.     

Comparison of outcomes between minimally invasive and median sternotomy for double and triple valve surgery: A meta-analysis

Background

Limited data exists demonstrating the efficacy of minimally invasive surgery (MIS) compared to median sternotomy (MS) for multiple valvular disease (MVD). This systematic review and meta-analysis aims to compare operative and peri-operative outcomes of MIS vs MS in MVD.

Methods

PubMed, Ovid, and Embase were searched from inception until August 2019 for randomized and observational studies comparing MIS and MS in patients with MVD. Clinical outcomes of intra- and postoperative times, reoperation for bleeding and surgical site infection were evaluated.

Results

Five observational studies comparing 340 MIS vs 414 MS patients were eligible for qualitative and quantitative review. The quality of evidence assessed using the Newcastle-Ottawa scale was good for all included studies. Meta-analysis demonstrated increased cardiopulmonary bypass time for MIS patients (weighted mean difference [WMD], 0.487; 95% confidence interval [CI], 0.365-0.608; P < .0001). Similarly, aortic cross-clamp time was longer in patients undergoing MIS (WMD, 0.632; 95% CI, 0.509-0.755; P < .0001). No differences were found in operative mortality, reoperation for bleeding, surgical site infection, or hospital stay.

Conclusions

MIS for MVD have similar short-term outcomes compared to MS. This adds value to the use of minimally invasive methods for multivalvular surgery, despite conferring longer operative times. However, the paucity in literature and learning curve associated with MIS warrants further evidence, ideally randomized control trials, to support these findings.

     

Measured and predicted resting energy expenditure in wheelchair rugby athletes

Objective: Report measured resting energy expenditure (REE) in wheelchair rugby athletes and evaluate agreement between REE and the prediction models of Chun, Cunningham, Harris-Benedict, Mifflin, Nightingale and Gorgey, and Owen.

Design: Cohort-based validation study.

Setting. Paralympic team training camp.

Participants: Fourteen internationally competitive athletes who play wheelchair rugby, 13 of whom had cervical spinal cord injuries (SCI).

Outcome Measures: A portable metabolic analyzer was used to measure REE following an overnight fast and dual-energy X-ray absorptiometry (DXA) was used to assess lean body mass for the prediction equations.

Results: REE in the current sample was 1735?±?257?kcal?×?day^?1 ranging from 1324 to 2068?kcal?×?day^{?1 Bhambhani Y. Physiology of wheelchair racing in athletes with spinal cord injury. Sports Med 2002;32(1):23–51.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]}. Bland–Altman analyses revealed negative mean bias but similar limits of agreement between measured REE and scores predicted by Chun, Cunningham, Mifflin, Nightingale and Gorgey, and Owen models in elite athletes who play wheelchair rugby.

Conclusion: Prediction models regressed on persons with and without SCI under-predicted REE of competitive wheelchair rugby athletes. This outcome may be explained by the higher REE/fat-free mass (FFM) ratio of current athletes compared to less active samples. Findings from the current study will help practitioners to determine nutrient intake needs on training days of varied intensity.