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.

Posterolateral or direct lateral approach for cemented hemiarthroplasty after femoral neck fracture (APOLLO): protocol for a multicenter randomized controlled trial with economic evaluation and natural experiment alongside.

BACKGROUND AND PURPOSE: The posterolateral and direct lateral surgical approach are the 2 most common surgical approaches for performing a hemiarthroplasty in patients with a hip fracture. It is unknown which surgical approach is preferable in terms of (cost-)effectiveness and quality of life. METHODS AND ANALYSIS: We designed a multicenter randomized controlled trial (RCT) with an economic evaluation and a natural experiment (NE) alongside. We will include 555 patients ≥ 18 years with an acute femoral neck fracture. The primary outcome is patient-reported health-related quality of life assessed with the EQ-5D-5L. Secondary outcomes include healthcare costs, complications, mortality, and balance (including fear of falling, actual falls, and injuries due to falling). An economic evaluation will be performed for quality adjusted life years (QALYs). We will use variable block randomization stratified for hospital. For continuous outcomes, we will use linear mixed-model analysis. Dichotomous secondary outcome measures will be analyzed using chi-square statistics and logistic regression models. Primary analyses are based on the intention-to-treat principle. Additional as treated analyses will be performed to evaluate the effect of protocol deviations. Study summary: (i) Largest RCT addressing the health-related patient outcome of the main surgical approaches of hemiarthroplasty. (ii) Focus on outcomes that are important for the patient. (iii) Pragmatic and inclusive RCT with few exclusion criteria, e.g., patients with dementia can participate. (iv) Natural experiment alongside to amplify the generalizability. (v) The first study conducting a costutility analysis comparing both surgical approaches.

A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients.

BACKGROUND: The use of somatic mutations for predicting clinical outcome is difficult because a mutation can indirectly influence the function of many genes, and also because clinical follow-up is sparse in the relatively young next generation sequencing (NGS) databanks. Here we approach this problem by linking sequence databanks to well annotated gene-chip datasets, using a multigene transcriptomic fingerprint as a link between gene mutations and gene expression in breast cancer patients. METHODS: The database consists of 763 NGS samples containing mutational status for 22,938 genes and RNA-seq data for 10,987 genes. The gene chip database contains 5,934 patients with 10,987 genes plus clinical characteristics. For the prediction, mutations present in a sample are first translated into a 'transcriptomic fingerprint' by running ROC analysis on mutation and RNA-seq data. Then correlation to survival is assessed by computing Cox regression for both up- and downregulated signatures. RESULTS: According to this approach, the top driver oncogenes having a mutation prevalence over 5 % included AKT1, TRANK1, TRAPPC10, RPGR, COL6A2, RAPGEF4, ATG2B, CNTRL, NAA38, OSBPL10, POTEF, SCLT1, SUN1, VWDE, MTUS2, and PIK3CA, and the top tumor suppressor genes included PHEX, TP53, GGA3, RGS22, PXDNL, ARFGEF1, BRCA2, CHD8, GCC2, and ARMC4. The system was validated by computing correlation between RNA-seq and microarray data (r(2) = 0.73, P < 1E-16). Cross-validation using 20 genes with a prevalence of approximately 5 % confirmed analysis reproducibility. CONCLUSIONS: We established a pipeline enabling rapid clinical validation of a discovered mutation in a large breast cancer cohort. An online interface is available for evaluating any human gene mutation or combinations of maximum three such genes ( http://www.g-2-o.com ).

A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer.

To date, three molecular markers (ER, PR, and CYP2D6) have been used in clinical setting to predict the benefit of the anti-estrogen tamoxifen therapy. Our aim was to validate new biomarker candidates predicting response to tamoxifen treatment in breast cancer by evaluating these in a meta-analysis of available transcriptomic datasets with known treatment and follow-up. Biomarker candidates were identified in Pubmed and in the 2007-2012 ASCO and 2011-2012 SABCS abstracts. Breast cancer microarray datasets of endocrine therapy-treated patients were downloaded from GEO and EGA and RNAseq datasets from TCGA. Of the biomarker candidates, only those identified or already validated in a clinical cohort were included. Relapse-free survival (RFS) up to 5 years was used as endpoint in a ROC analysis in the GEO and RNAseq datasets. In the EGA dataset, Kaplan-Meier analysis was performed for overall survival. Statistical significance was set at p < 0.005. The transcriptomic datasets included 665 GEO-based and 1,208 EGA-based patient samples. All together 68 biomarker candidates were identified. Of these, the best performing genes were PGR (AUC = 0.64, p = 2.3E-07), MAPT (AUC = 0.62, p = 7.8E-05), and SLC7A5 (AUC = 0.62, p = 9.2E-05). Further genes significantly correlated to RFS include FOS, TP53, BTG2, HOXB7, DRG1, CXCL10, and TPM4. In the RNAseq dataset, only ERBB2, EDF1, and MAPK1 reached statistical significance. We evaluated tamoxifen-resistance genes in three independent platforms and identified PGR, MAPT, and SLC7A5 as the most promising prognostic biomarkers in tamoxifen treated patients.