Good function and high patient satisfaction at mean 2.8 years after dual mobility THA following femoral neck fracture: a cross-sectional study of 124 patients

Steffan Tabori-Jensen,1,2 Torben B Hansen,1,2 Søren Bøvling,1 Peter Aalund,1 Morten Homilius,1 Maiken Stilling1,2 1University Clinic for Hand, Hip and Knee Surgery, Regional Hospital West, Holstebro, Denmark; 2Department of Clinical Medicine, Aarhus University, Aarhus, Denmark Aims: Our aim was to investigate function, health status and satisfaction in patients treated with primary dual mobility (DM) total hip arthroplasty (THA) after displaced femoral neck fracture (FNF). Patients and methods: From 2005–2011, 414 consecutive FNF patients received Saturne DM THA. At a minimum of 1-year follow-up, 124 (95 women) were evaluated with Oxford Hip Score (OHS), Harris Hip Score (HHS), health-related quality of life (HRQoL) measure (EQ-5D) and two functional tests: Timed Up and Go (TUG) and Sit to Stand 10 times (STS). The FNF patients were matched 1:2 by age, sex and surgery date with patients receiving THA due to osteoarthrosis (OA group) and 1-year OHS and EQ5D were compared. FNF patients were matched by age and sex with the general population index (GPI) for EQ-5D comparison. Results: Patient age at surgery after FNF was mean 74.8 (range 30–92) years. At mean follow-up of 2.8 (range 1.0–7.7) years, mean EQ-5D score was 0.79 (SD 0.15) in the FNF group, which was similar to the matched GPI (p = 0.4), but lower (p = 0.014) compared to the OA group. Mean OHS was 36.4 (SD 9.5) in the FNF group and 38.4 (SD 7.2) in the OA group (p = 0.18). HHS in the FNF group was 78.7 (SD 15.5). Mean TUG time was 13.5 (SD 4.9) secs, and mean STS was 37.9 (SD 15.3) secs. Eighty nine percent (n = 111) of FNF patients were satisfied with the operation result. Conclusion: DM THA following displaced FNF provides a good functional result and quality of life in addition to high patient satisfaction. Keywords: dual mobility cup, femoral neck fracture, hip arthroplasty, EQ-5D, Oxford Hip Score, patient reported outcome measure

FNTM: A server for predicting functional networks of tissues in mouse

Functional Networks of Tissues in Mouse (FNTM) provides biomedical researchers with tissue-specific predictions of functional relationships between proteins in the most widely used model organism for human disease, the laboratory mouse. Users can explore FNTM-predicted functional relationships for their tissues and genes of interest or examine gene function and interaction predictions across multiple tissues, all through an interactive, multi-tissue network browser. FNTM makes predictions based on integration of a variety of functional genomic data, including over 13 000 gene expression experiments, and prior knowledge of gene function. FNTM is an ideal starting point for clinical and translational researchers considering a mouse model for their disease of interest, researchers already working with mouse models who are interested in discovering new genes related to their pathways or phenotypes of interest, and biologists working with other organisms to explore the functional relationships of their genes of interest in specific mouse tissue contexts. FNTM predicts tissue-specific functional relationships in 200 tissues, does not require any registration or installation and is freely available for use at http://fntm.princeton.edu

Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors.

BackgroundResearchers routinely evaluate novel biomarkers for incorporation into clinical risk models, weighing tradeoffs between cost, availability, and ease of deployment. For risk assessment in population health initiatives, ideal inputs would be those already available for most patients. We hypothesized that common hematologic markers (eg, hematocrit), available in an outpatient complete blood count without differential, would be useful to develop risk models for cardiovascular events.MethodsWe developed Cox proportional hazards models for predicting heart attack, ischemic stroke, heart failure hospitalization, revascularization, and all-cause mortality. For predictors, we used 10 hematologic indices (eg, hematocrit) from routine laboratory measurements, collected March 2016 to May 2017 along with demographic data and diagnostic codes. As outcomes, we used neural network-based automated event adjudication of 1 028 294 discharge summaries. We trained models on 23 238 patients from one hospital in Boston and evaluated them on 29 671 patients from a second one. We assessed calibration using Brier score and discrimination using Harrell's concordance index. In addition, to determine the utility of high-dimensional interactions, we compared our proportional hazards models to random survival forest models.ResultsEvent rates in our cohort ranged from 0.0067 to 0.075 per person-year. Models using only hematology indices had concordance index ranging from 0.60 to 0.80 on an external validation set and showed the best discrimination when predicting heart failure (0.80 [95% CI, 0.79-0.82]) and all-cause mortality (0.78 [0.77-0.80]). Compared with models trained only on demographic data and diagnostic codes, models that also used hematology indices had better discrimination and calibration. The concordance index of the resulting models ranged from 0.75 to 0.85 and the improvement in concordance index ranged up to 0.072. Random survival forests had minimal improvement over proportional hazards models.ConclusionsWe conclude that low-cost, ubiquitous inputs, if biologically informative, can provide population-level readouts of risk