Posterolateral or Direct Lateral Surgical Approach for Hemiarthroplasty After a Hip Fracture: A Randomized Clinical Trial Alongside a Natural Experiment.

Importance: Hip fractures in older adults are serious injuries that result in disability, higher rates of illness and death, and a substantial strain on health care resources. High-quality evidence to improve hip fracture care regarding the surgical approach of hemiarthroplasty is lacking. Objective: To compare 6-month outcomes of the posterolateral approach (PLA) and direct lateral approach (DLA) for hemiarthroplasty in patients with acute femoral neck fracture. Design, Setting, and Participants: This multicenter, randomized clinical trial (RCT) comparing DLA and PLA was performed alongside a natural experiment (NE) at 14 centers in the Netherlands. Patients aged 18 years or older with an acute femoral neck fracture were included, with or without dementia. Secondary surgery of the hip, pathological fractures, or patients with multitrauma were excluded. Recruitment took place between February 2018 and January 2022. Treatment allocation was random or pseudorandom based on geographical location and surgeon preference. Statistical analysis was performed from July 2022 to September 2022. Exposure: Hemiarthroplasty using PLA or DLA. Main Outcome and Measures: The primary outcome was health-related quality of life 6 months after surgery, quantified with the EuroQol Group 5-Dimension questionnaire (EQ-5D-5L). Secondary outcomes included dislocations, fear of falling and falls, activities of daily living, pain, and reoperations. To improve generalizability, a novel technique was used for data fusion of the RCT and NE. Results: A total of 843 patients (542 [64.3%] female; mean [SD] age, 82.2 [7.5] years) participated, with 555 patients in the RCT (283 patients in the DLA group; 272 patients in the PLA group) and 288 patients in the NE (172 patients in the DLA group; 116 patients in the PLA group). In the RCT, mean EQ-5D-5L utility scores at 6 months were 0.50 (95% CI, 0.45-0.55) after DLA and 0.49 (95% CI, 0.44-0.54) after PLA, with 77% completeness. The between-group difference (-0.04 [95% CI, -0.11 to 0.04]) was not statistically significant nor clinically meaningful. Most secondary outcomes were comparable between groups, but PLA was associated with more dislocations than DLA (RCT: 15 of 272 patients [5.5%] in PLA vs 1 of 283 patients [0.4%] in DLA; NE: 6 of 113 patients [5.3%]) in PLA vs 2 of 175 patients [1.1%] in DLA). Data fusion resulted in an effect size of 0.00 (95% CI, -0.04 to 0.05) for the EQ-5D-5L and an odds ratio of 12.31 (95% CI, 2.77 to 54.70) for experiencing a dislocation after PLA. Conclusions and Relevance: This combined RCT and NE found that among patients treated with a cemented hemiarthroplasty after an acute femoral neck fracture, PLA was not associated with a better quality of life than DLA. Rates of dislocation and reoperation were higher after PLA. Randomized and pseudorandomized data yielded similar outcomes, which suggests a strengthening of these findings. Trial Registration: ClinicalTrials.gov Identifier: NCT04438226.

Semi-supervised empirical Bayes group-regularized factor regression.

The features in a high-dimensional biomedical prediction problem are often well described by low-dimensional latent variables (or factors). We use this to include unlabeled features and additional information on the features when building a prediction model. Such additional feature information is often available in biomedical applications. Examples are annotation of genes, metabolites, or p-values from a previous study. We employ a Bayesian factor regression model that jointly models the features and the outcome using Gaussian latent variables. We fit the model using a computationally efficient variational Bayes method, which scales to high dimensions. We use the extra information to set up a prior model for the features in terms of hyperparameters, which are then estimated through empirical Bayes. The method is demonstrated in simulations and two applications. One application considers influenza vaccine efficacy prediction based on microarray data. The second application predicts oral cancer metastasis from RNAseq data.

Adaptive group-regularized logistic elastic net regression.

In high-dimensional data settings, additional information on the features is often available. Examples of such external information in omics research are: (i) $p$-values from a previous study and (ii) omics annotation. The inclusion of this information in the analysis may enhance classification performance and feature selection but is not straightforward. We propose a group-regularized (logistic) elastic net regression method, where each penalty parameter corresponds to a group of features based on the external information. The method, termed gren, makes use of the Bayesian formulation of logistic elastic net regression to estimate both the model and penalty parameters in an approximate empirical-variational Bayes framework. Simulations and applications to three cancer genomics studies and one Alzheimer metabolomics study show that, if the partitioning of the features is informative, classification performance, and feature selection are indeed enhanced.

Applying Pattern Recognition as a Robust Approach for Silicone Oil Droplet Identification in Flow-Microscopy Images of Protein Formulations.

Discrimination between potentially immunogenic protein aggregates and harmless pharmaceutical components, like silicone oil, is critical for drug development. Flow imaging techniques allow to measure and, in principle, classify subvisible particles in protein therapeutics. However, automated approaches for silicone oil discrimination are still lacking robustness in terms of accuracy and transferability. In this work, we present an image-based filter that can reliably identify silicone oil particles in protein therapeutics across a wide range of parenteral products. A two-step classification approach is designed for automated silicone oil droplet discrimination, based on particle images generated with a flow imaging instrument. Distinct from previously published methods, our novel image-based filter is trained using silicone oil droplet images only and is, thus, independent of the type of protein samples imaged. Benchmarked against alternative approaches, the proposed filter showed best overall performance in categorizing silicone oil and non-oil particles taken from a variety of protein solutions. Excellent accuracy was observed particularly for higher resolution images. The image-based filter can successfully distinguish silicone oil particles with high accuracy in protein solutions not used for creating the filter, showcasing its high transferability and potential for wide applicability in biopharmaceutical studies.

Gene Network Reconstruction using Global-Local Shrinkage Priors.

Reconstructing a gene network from high-throughput molecular data is an important but challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done locally in the neighbourhood of each node or gene. However, estimation of the many regularization parameters is often difficult and can result in large statistical uncertainties. In this paper we propose to combine local regularization with global shrinkage of the regularization parameters to borrow strength between genes and improve inference. We employ a simple Bayesian model with non-sparse, conjugate priors to facilitate the use of fast variational approximations to posteriors. We discuss empirical Bayes estimation of hyper-parameters of the priors, and propose a novel approach to rank-based posterior thresholding. Using extensive model- and data-based simulations, we demonstrate that the proposed inference strategy outperforms popular (sparse) methods, yields more stable edges, and is more reproducible. The proposed method, termed ShrinkNet, is then applied to Glioblastoma to investigate the interactions between genes associated with patient survival.

Transcriptomic Heterogeneity in Cancer as a Consequence of Dysregulation of the Gene-Gene Interaction Network.

Many pathways are dysregulated in cancer. Dysregulation of the regulatory network results in less control of transcript levels in the cell. Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity. We identify four scenarios for a transcriptomic heterogeneity increase (i.e., pathway dysregulation) in cancer: (1) activation of a molecular switch, (2) a structural change in a regulator, (3) a temporal change in a regulator, and (4) weakening of gene-gene interactions. These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

CHEK2*1100delC homozygosity is associated with a high breast cancer risk in women.

BACKGROUND: Mutations in the CHEK2 gene confer a moderately increased breast cancer risk. The risk for female carriers of the CHEK2*1100delC mutation is twofold increased. Breast cancer risk for carrier women is higher in a familial breast cancer setting which is due to coinheritance of additional genetic risk factors. This study investigated the occurrence of homozygosity for the CHEK2*1100delC allele among familial breast cancer cases and the associated breast cancer risk. METHODS AND RESULTS: Homozygosity for the CHEK2*1100delC allele was identified in 8/2554 Dutch independent familial non-BRCA1/2 breast cancer cases. The genotype relative risk for breast cancer of homozygous and heterozygous familial breast cancer cases was 101.34 (95% CI 4.47 to 121 000) and 4.04 (95% CI 0.88 to 21.0), respectively. Female homozygotes appeared to have a greater than twofold increased breast cancer risk compared to familial CHEK2*1100delC heterozygotes (p=0.044). These results and the occurrence of multiple primary tumours in 7/10 homozygotes indicate a high cancer risk in homozygous women from non-BRCA1/2 families. CONCLUSIONS: Intensive breast surveillance is therefore justified in these homozygous women. It is concluded that diagnostic testing for biallelic mutations in CHEK2 is indicated in non-BRCA1/2 breast cancer families, especially in populations with a relatively high prevalence of deleterious mutations in CHEK2.

Statistical analysis of the cancer cell's molecular entropy using high-throughput data.

MOTIVATION: As cancer progresses, DNA copy number aberrations accumulate and the genomic entropy (chromosomal disorganization) increases. For this surge to have any oncogenetic effect, it should (to some extent) be reflected at other molecular levels of the cancer cell, in particular that of the transcriptome. Such a coincidence of cancer progression and the propagation of an entropy increase through the molecular levels of the cancer cell would enhance the understanding of cancer evolution. RESULTS: A statistical argument reveals that (under some assumptions) an entropy increase in one random variable (DNA copy number) leads to an entropy increase in another (gene expression). Statistical methodology is provided to investigate the relation between the genomic and transcriptomic entropy using high-throughput data. Analyses of multiple high-throughput datasets using this methodology show a close, concordant relation among the genomic and transcriptomic entropy. Hence, as cancer evolves, and the genomic entropy increases, the transcriptomic entropy is also expected to surge.