Comparison of Maryland hospital discharge and trauma registry data

BACKGROUND: This study aimed to examine the validity of using Maryland hospital discharge data to characterize injuries sustained by trauma patients. METHODS: Maryland hospital discharge and Maryland trauma registry data for 1999 were merged, and the extent of agreement regarding the presence and severity of injuries sustained was evaluated. RESULTS: The mean Injury Severity Score was 8.4 according to the Maryland hospital discharge data and 10 according to the Maryland trauma registry data (p < 0.0001). The Maryland hospital discharge data identified 95% or more of all moderate to severe injuries (Abbreviated Injury Score, > or =2) for all body regions except the head. There was substantial agreement between the two data sets for mechanism of injury (weighted kappa, 0.62), the number of preexisting conditions present (weighted kappa, 0.45) and final disposition (weighted kappa, 0.78). CONCLUSIONS: The Maryland hospital discharge data are a valid source for documenting the nature and severity of injuries sustained by trauma patients, except for those with a relatively minor head injury.     

Classification of atrial fibrillation

In his editorial on the Euro Heart Survey on atrial fibrillation,Wyse¹ rightly criticizes the current     

Linked selection and recombination rate variation drive the evolution of the genomic landscape of differentiation across the speciation continuum of Ficedula flycatchers

Speciation is a continuous process during which genetic changes gradually accumulate in the genomes of diverging species. Recent studies have documented highly heterogeneous differentiation landscapes, with distinct regions of elevated differentiation (“differentiation islands”) widespread across genomes. However, it remains unclear which processes drive the evolution of differentiation islands; how the differentiation landscape evolves as speciation advances; and ultimately, how differentiation islands are related to speciation. Here, we addressed these questions based on population genetic analyses of 200 resequenced genomes from 10 populations of four Ficedula flycatcher sister species. We show that a heterogeneous differentiation landscape starts emerging among populations within species, and differentiation islands evolve recurrently in the very same genomic regions among independent lineages. Contrary to expectations from models that interpret differentiation islands as genomic regions involved in reproductive isolation that are shielded from gene flow, patterns of sequence divergence (d_xy and relative node depth) do not support a major role of gene flow in the evolution of the differentiation landscape in these species. Instead, as predicted by models of linked selection, genome-wide variation in diversity and differentiation can be explained by variation in recombination rate and the density of targets for selection. We thus conclude that the heterogeneous landscape of differentiation in Ficedula flycatchers evolves mainly as the result of background selection and selective sweeps in genomic regions of low recombination. Our results emphasize the necessity of incorporating linked selection as a null model to identify genome regions involved in adaptation and speciation.Uncovering the genetic architecture of reproductive isolation and its evolutionary history are central tasks in evolutionary biology. The identification of genome regions that are highly differentiated between closely related species, and thereby constitute candidate regions involved in reproductive isolation, has recently been a major focus of speciation genetic research. Studies from a broad taxonomic range, involving organisms as diverse as plants (Renaut et al. 2013), insects (Turner et al. 2005; Lawniczak et al. 2010; Nadeau et al. 2012; Soria-Carrasco et al. 2014), fishes (Jones et al. 2012), mammals (Harr 2006), and birds (Ellegren et al. 2012) contribute to the emerging picture of a genomic landscape of differentiation that is usually highly heterogeneous, with regions of locally elevated differentiation (“differentiation islands”) widely spread over the genome. However, the evolutionary processes driving the evolution of the differentiation landscape and the role of differentiation islands in speciation are subject to controversy (Turner and Hahn 2010; Cruickshank and Hahn 2014; Pennisi 2014).Differentiation islands were originally interpreted as “speciation islands,” regions that harbor genetic variants involved in reproductive isolation and are shielded from gene flow by selection (Turner et al. 2005; Soria-Carrasco et al. 2014). During speciation-with-gene-flow, speciation islands were suggested to evolve through selective sweeps of locally adapted variants and by hitchhiking of physically linked neutral variation (“divergence hitchhiking”) (Via and West 2008); gene flow would keep differentiation in the remainder of the genome at bay (Nosil 2008; Nosil et al. 2008). In a similar way, speciation islands can arise by allopatric speciation followed by secondary contact. In this case, genome-wide differentiation increases during periods of geographic isolation, but upon secondary contact, it is reduced by gene flow in genome regions not involved in reproductive isolation. In the absence of gene flow in allopatry, speciation islands need not (but can) evolve by local adaptation, but may consist of intrinsic incompatibilities sensu Bateson-Dobzhansky-Muller (Bateson 1909; Dobzhansky 1937; Muller 1940) that accumulated in spatially isolated populations.However, whether differentiation islands represent speciation islands has been questioned. Rather than being a cause of speciation, differentiation islands might evolve only after the onset of reproductive isolation as a consequence of locally accelerated lineage sorting (Noor and Bennett 2009; Turner and Hahn 2010; White et al. 2010; Cruickshank and Hahn 2014; Renaut et al. 2014), such as in regions of low recombination (Nachman 2002; Sella et al. 2009; Cutter and Payseur 2013). In these regions, the diversity-reducing effects of both positive selection and purifying selection (background selection [BGS]) at linked sites (“linked selection”) impact physically larger regions due to the stronger linkage among sites. The thereby locally reduced effective population size (N_e) will enhance genetic drift and hence inevitably lead to increased differentiation among populations and species.These alternative models for the evolution of a heterogeneous genomic landscape of differentiation are not mutually exclusive, and their population genetic footprints can be difficult to discern. In the cases of (primary) speciation-with-gene-flow and gene flow at secondary contact, shared variation outside differentiation islands partly stems from gene flow. In contrast, under linked selection, ancestral variation is reduced and differentiation elevated in regions of low recombination, while the remainder of the genome may still share considerable amounts of ancestral genetic variation and show limited differentiation. Many commonly used population genetic statistics do not capture these different origins of shared genetic variation and have the same qualitative expectations under both models, such as reduced diversity (π) and skews toward an excess of rare variants (e.g., lower Tajima''s D) in differentiation islands relative to the remainder of the genome. However, since speciation islands should evolve by the prevention or breakdown of differentiation by gene flow in regions not involved in reproductive isolation, substantial gene flow should be detectable in these regions (Cruickshank and Hahn 2014) and manifested in the form of reduced sequence divergence (d_xy) or as an excess of shared derived alleles in cases of asymmetrical gene flow (Patterson et al. 2012). Under linked selection, predictions are opposite for d_xy (Cruickshank and Hahn 2014), owing to reduced ancestral diversity in low-recombination regions. Further predictions for linked selection include positive and negative relationships of recombination rate with genetic diversity (π) and differentiation (F_ST), respectively, and inverse correlations of the latter two with the density of targets for selection. Finally, important insights into the nature of differentiation islands may be gained by studying the evolution of differentiation landscapes across the speciation continuum. Theoretical models and simulations of speciation-with-gene-flow predict that after an initial phase during which differentiation establishes in regions involved in adaptation, differentiation should start spreading from these regions across the entire genome (Feder et al. 2012, 2014; Flaxman et al. 2013).Unravelling the processes driving the evolution of the genomic landscape of differentiation, and hence understanding how genome differentiation unfolds as speciation advances, requires genome-wide data at multiple stages of the speciation continuum and in a range of geographical settings from allopatry to sympatry (Seehausen et al. 2014). Although studies of the speciation continuum are emerging (Hendry et al. 2009; Kronforst et al. 2013; Shaw and Mullen 2014, and references therein), empirical examples of genome differentiation at multiple levels of species divergence remain scarce (Andrew and Rieseberg 2013; Kronforst et al. 2013; Martin et al. 2013), and to our knowledge, have so far not jointly addressed the predictions of alternative models for the evolution of the genomic landscape of differentiation. In the present study, we implemented such a study design encompassing multiple populations of four black-and-white flycatcher sister species of the genus Ficedula (Fig. 1A,B; Supplemental Fig. S1; for a comprehensive reconstruction of the species tree, see Nater et al. 2015). Previous analyses in collared flycatcher (F. albicollis) and pied flycatcher (F. hypoleuca) revealed a highly heterogeneous differentiation landscape across the genome (Ellegren et al. 2012). An involvement of gene flow in its evolution would be plausible, as hybrids between these species occur at low frequencies in sympatric populations in eastern Central Europe and on the Baltic Islands of Gotland and Öland (Alatalo et al. 1990; Sætre et al. 1999), although a recent study based on genome-wide markers identified no hybrids beyond the F₁ generation (Kawakami et al. 2014a). Still, gene flow from pied into collared flycatcher appears to have occurred (Borge et al. 2005; Backström et al. 2013; Nadachowska-Brzyska et al. 2013) despite premating isolation (for review, see Sætre and Sæther 2010), hybrid female sterility (Alatalo et al. 1990; Tegelström and Gelter 1990), and strongly reduced long-term fitness of hybrid males (Wiley et al. 2009). Atlas flycatcher (F. speculigera) and semicollared flycatcher (F. semitorquata) are two closely related species, which have been less studied, but may provide interesting insights into how genome differentiation evolves over time. Here, we take advantage of this system to identify the processes underlying the evolution of differentiation islands based on the population genetic analysis of whole-genome resequencing data of 200 flycatchers.Open in a separate windowFigure 1.A recurrently evolving genomic landscape of differentiation across the speciation continuum in Ficedula flycatchers. (A) Species’ neighbor-joining tree based on mean genome-wide net sequence divergence (d_A). The same species tree topology was inferred with 100% bootstrap support from the distribution of gene trees under the multispecies coalescent (Supplemental Fig. S1). (B) Map showing the locations of population sampling and approximate species ranges. (C) Population genomic parameters along an example chromosome (Chromosome 4A) (see Supplemental Figs. S2, S4 for all chromosomes). Color codes for specific–specific parameters: (blue) collared; (green) pied; (orange) Atlas; (red) semicollared. Color codes for d_xy: (green) collared-pied; (light blue) collared-Atlas; (blue) collared-semicollared; (orange) pied-Atlas; (red) pied-semicollared; (black) Atlas-semicollared. For differentiation within species, comparisons with the Italian (collared) and Spanish (pied) populations are shown. Color codes for F_ST within collared flycatchers: (cyan) Italy–Hungary; (light blue) Italy–Czech Republic; (dark blue) Italy–Baltic. Color codes for F_ST within pied flycatchers: (light green) Spain–Sweden; (green) Spain–Czech Republic; (dark green) Spain–Baltic. (D) Distributions of differentiation (F_ST) from collared flycatcher along the speciation continuum. Distributions are given separately for three autosomal recombination percentiles (33%; 33%–66%; 66%–100%) corresponding to high (>3.4 cM/Mb, blue), intermediate (1.3–3.4 cM/Mb, orange), and low recombination rate (0–1.3 cM/Mb, red), and the Z Chromosome (green). Geographically close within-species comparison: Italy–Hungary. Comparisons within species include the geographically close Italian and Hungarian populations (within [close]), and the geographically distant Italian and Baltic populations (within [far]). Geographically far within-species comparison: Italy–Baltic. (E) Differentiation from collared flycatcher along an example chromosome (Chromosome 11) (see Supplemental Fig. S3 for all chromosomes). Color codes for between-species comparisons: (green) pied; (orange) Atlas; (red) semicollared; (dark red) red-breasted; (black) snowy-browed flycatcher. Color codes for within-species comparisons: (cyan) Italy–Hungary; (blue) Italy–Baltic. Flycatcher artwork in panel A courtesy of Dan Zetterström.     

Arthrodesis of proximal inter-phalangeal joint for hammertoe: intramedullary device options

Background

Proximal inter-phalangeal (PIP) joint arthrodesis today represents the standard treatment for structured hammertoes; however, recently, a lot of new intramedullary devices for the fixation of this arthrodesis have been introduced. The purpose of this work is to look at the currently available devices and to perform a review of the present literature.

Materials and methods

A literature search of PubMed/Medline and Google Scholar databases, considering works published up until September 2014 and using the keywords: hammertoe, arthrodesis, PIP joint, fusion, intramedullary devices, and K-wire, was performed. The published papers were included in the present study only if they met the following inclusion criteria: English articles, arthrodesis of PIP joints for hammertoes with new generation intramedullary devices, series with n > 10. Studies using absorbable pins or screws that are considered as another kind of fixation that involved more than one articulation, as well as comments, letters to the editor, or newsletters were excluded.

Results

Nine publications were included. Of the patients’ reports, 93–100 % were good or excellent concerning satisfaction. Radiological arthrodesis was achieved in 60.5–100 % of cases. Three of the publications compared the new devices with the K-wire. Of these three articles, two employed the traditional technique and one the buried technique. The AOFAS score, evaluated in three publications, showed a delta of 19, 45 and 58 points. Major complications, which required a secondary surgical revision, were between 0 and 8.6 %. The complications of the K-wire and the new devices were similar; also the reoperation rate was close to equal (maximal difference 2 %). On the other hand, these kinds of devices definitely have a higher price, compared to the K-wire.

Conclusion

The use of these new devices provides good results; however, their high price is currently a problem. For this reason, cost-benefit studies seem to be necessary to justify their use as standard treatment.

Level of evidence

Level III systematic review.     

Selecting Constant Work Rates for Endurance Testing in COPD: The Role of the Power-Duration Relationship

Constant work rate (CWR) exercise testing is highly responsive to therapeutic interventions and reveals physiological and functional benefits. No consensus exists, however, regarding optimal methods for selecting the pre-intervention work rate. We postulate that a CWR whose tolerated duration (t_lim) is 6 minutes (WR₆) may provide a useful interventional study baseline. WR₆ can be extracted from the power-duration relationship, but requires 4 CWR tests. We sought to develop prediction algorithms for easier WR₆ identification using backward stepwise linear regression, one in 69 COPD patients (FEV₁ 45 ± 15% pred) and another in 30 healthy subjects (HLTH), in whom cycle ergometer ramp incremental (RI) and CWR tests with t_lim of ～6 minutes had been performed. Demographics, pulmonary function, and RI responses were used as predictors. We validated these algorithms against power-duration measurements in 27 COPD and 30 HLTH (critical power 43 ± 18W and 231 ± 43W; curvature constant 5.1 ± 2.7 kJ and 18.5 ± 3.1 kJ, respectively). This analysis revealed that, on average, only corrected peak work rate ( = WR_peak–1 min × WR_slope) in RI was required to predict WR₆ (COPD SEE = 5.0W; HLTH SEE = 5.6W; R² > 0.96; p < 0.001). In the validation set, predicted and actual WR₆ were strongly correlated (COPD R² = 0.937; HLTH 0.978; p < 0.001). However, in COPD, unlike in HLTH, there was a wide range of t_lim values at predicted WR₆: COPD 8.3 ± 4.1 min (range 3.6 to 22.2 min), and HLTH 5.5 ± 0.7 min (range 3.9 to 7.0 min). This analysis indicates that corrected WR_peak in an incremental test can yield an acceptable basis for calculating endurance testing work rate in HLTH, but not in COPD patients.     

Skeletal Histomorphometry in Subjects on Teriparatide or Zoledronic Acid Therapy (SHOTZ) Study: A Randomized Controlled Trial

Context: Recent studies on the mechanism of action (MOA) of bone-active drugs have rekindled interest in how to present and interpret dynamic histomorphometric parameters of bone remodeling. Objective: We compared the effects of an established anabolic agent, teriparatide (TPTD), with those of a prototypical antiresorptive agent, zoledronic acid (ZOL). Design: This was a 12-month, randomized, double-blind, active-comparator controlled, cross-sectional biopsy study. Setting: The study was conducted at 12 U.S. and Canadian centers. Subjects: Healthy postmenopausal women with osteoporosis participated in the study. Interventions: Subjects received TPTD 20 μg once daily by sc injection (n = 34) or ZOL 5 mg by iv infusion at baseline (n = 35). Main Outcome Measures: The primary end point was mineralizing surface/bone surface (MS/BS), a dynamic measure of bone formation, at month 6. A standard panel of dynamic and static histomorphometric indices was also assessed. When specimens with missing labels were encountered, several methods were used to calculate mineral apposition rate (MAR). Serum markers of bone turnover were also measured. Results: Among 58 subjects with evaluable biopsies (TPTD = 28; ZOL = 30), MS/BS was significantly higher in the TPTD group (median: 5.60 vs. 0.16%, P < 0.001). Other bone formation indices, including MAR, were also higher in the TPTD group (P < 0.05). TPTD significantly increased procollagen type 1 N-terminal propeptide (PINP) at months 1, 3, 6, and 12 and carboxyterminal cross-linking telopeptide of collagen type 1 (CTX) from months 3 to 12. ZOL significantly decreased PINP and CTX below baseline at all time points. Conclusions: TPTD and ZOL possess fundamentally different mechanisms of action with opposite effects on bone formation based on this analysis of both histomorphometric data and serum markers of bone formation and resorption. An important mechanistic difference was a substantially higher MS/BS in the TPTD group. Overall, these results define the dynamic histomorphometric characteristics of anabolic activity relative to antiresorptive activity after treatment with these two drugs.