Category: Diabetes Introduction/Purpose: Diabetic foot ulcers (DFU) is a prevalent problem that can lead to devastating results such as limb loss if left untreated. Nevertheless, the prolonged treatment course can limit the patient’s overall function and quality of life. Utilization of Patient-Reported Outcomes Measurement Information System (PROMIS) in Orthopaedic practice has previously shown that preoperative PROMIS scores can predict postoperative outcomes in foot and ankle surgeries. However, PROMIS assessment has not been used to determine the impact of surgical treatment for DFU on patients’ physical function. We sought to investigate the impact of preoperative PROMIS scores (Physical Function (PF), Pain Interference (PI), Depression (D)), demographic and laboratory values on postoperative PF in this unique patient population. Methods: From an academic orthopaedic surgeon’s practice, we identified infected DFU patients who underwent surgical interventions between February 2015 and November 2018 using ICD-10 code E11.621 (n=240). Patients with at least 3 consecutive visits, 3 month minimum post-surgical follow up and completed PROMIS Computer Adaptive Testing (CAT) assessments for each visit were included (n=92). Demographic data, BMI, medical comorbidities, Hemoglobin A1C, procedure performed, and wound healing status were collected. Amputation level was categorized as the following: 0 = irrigation & debridement (I&D) (n=39), 1 = forefoot amputations (n=46), 2 = mid/hindfoot amputations (n=14), 3 = Syme or above amputations (n=12). Uni- and multivariate analysis were performed to identify factors affecting the post-operative PF within the cohort. Spearman’s rank correlation coefficient, Chi-Squared tests and multidimensional modelling were applied to all variables’ pre-operative and post-operative time points. Based on the results, we formulated a numeric equation to predict post-surgical PROMIS PF. Results: The mean age was 60.5 (33-96) and 4.7 (3-12) months follow up. Mean preoperative PF, PI, and D changed from 34.4, 58.7, 51.4 to postoperative 36.1, 58.8, 51.1, respectively (ΔPF = 1.7, ΔPI=0.1, ΔD = -0.3). Preoperative PF (p < 0.01), PI (p < 0.01), depression (p < 0.01), chronic renal failure (p < 0.02) and amputation level (p < 0.04) showed significant univariate correlation with post-operative PF. Multivariate model (r = 0.6) revealed postoperative PF is predicted by initial PF (p = 0.094), depression (p= 0.008), amputation level (p = 0.002), and wound healing status (p = 0.001). The model had greater prediction power than the best univariate association (Δr = +0.17). Follow up length was not significant (p = 0.08). Conclusion: This study demonstrates that preoperative PROMIS scores combined with clinical factors can predict postoperative PF in DFU patients. Postoperative PF is predicted by: PFlongest_FU = 45.4 +0.20 PFinitial -0.21 Dinitial -6.1 (Heal =1) -2.9 (Amputation Category, 1-3). Additional diseased states not captured in this study and psychosocial variables may improve prediction power of the multivariate model. 70% of the patients’ initial PF were 1 to 2 standard deviations below the US population (n = 49; 28). Therefore, the reported model may serve as a valuable tool for patient education, setting expectations and post-surgical PF prediction in infected DFU patients.
Category: Basic Sciences/Biologics; Diabetes Introduction/Purpose: Staphylococcus aureus is the major pathogen foot and ankle infections and osteomyelitis (>50%). There are currently no sensitive and specific diagnostic tools for monitoring a pathogen’s ongoing infection or providing prognostic measures. We have developed a novel immunoassay for S. aureus, and have applied this to diagnose and monitor its infectivity. We hypothesize that: 1) the species-specific immunoassay can serve as a reliable diagnostic tool for S. aureus foot and ankle infections and 2) the immunoassay provides a measure of treatment response and prognosis of clinical outcome to antibiotics therapy for S. aureus foot and ankle infections. Methods: From July 2015 to July 2019, 83 infected diabetic foot ulcer (DFU) patients undertaking surgical treatment were recruited. Blood were drawn from subjects at initial, 4-week, 8-week, and 12-week after surgery. Clinical wound healing status was determined by a fellowship-trained orthopaedic foot and ankle surgeon. Serum antibodies and plasmablast cultured antibodies (newly synthesized antibodies: NSA) were harvested. Eight unique S. aureusantigens from distinct functional classes were used for the immunoassay. All serum and NSA samples were run on a flow cytometer (Bio-Plex 200; Bio-Rad, Life Sciences Research) in duplicates and assessed for predictive ability in discriminating infection status and healing status using receiver operating characteristic (ROC) curve analysis, with accuracy summarized by the area under the ROC curve (AUC). Nonparametric estimates and 95% confidence intervals for the AUC were computed for each predictor along with p-values for testing the significance of each AUC. Results: Analysis of serum immunoassay showed significant difference in three anti-S. aureus antigens titers (IsdH (p = 0.037; AUC = 0.638), ClfB (p = 0.025; AUC = 0.644), and SCIN (p = 0.005; AUC = 0.677)) between S. aureus infected versus non- S. aureus infected DFU patients at initial presentation. NSA immunoassay showed elevation of two different S. aureus specific antigens, IsdH and LukS-PV, for S. aureus infected versus non- S. aureus infected DFU patients in ratios of approximately five and four, respectively. Changes of NSA based anti-S. aureus antibody titers over 12 weeks period, as single antigen or in combination, significantly correlated with clinical resolution of infection and wound healing status. Four anti-S. aureus antigen combinations achieved the highest AUC (Figure 1). Conclusion: Our results demonstrate that both the serum and NSA immunoassay can diagnose S. aureus infected DFUs. Furthermore, changes in NSA titers over a period of time against various S. aureus specific antigens significantly correlated with clinical representation of infection and wound healing status. The novel species-specific immunoassay can serve as a promising diagnostic system, tracking tool and prognostic potential in management of S. aureus associated foot and ankle infection.
Abstract Osteomyelitis is a devastating complication of orthopaedic surgery and commonly caused by Staphylococcus aureus ( S. aureus ) and Group B Streptococcus (GBS, S. agalactiae ). Clinically, S. aureus osteomyelitis is associated with local inflammation, abscesses, aggressive osteolysis, and septic implant loosening. In contrast, S. agalactiae orthopaedic infections generally involve soft tissue, with acute life‐threatening vascular spread. While preclinical models that recapitulate the clinical features of S. aureus bone infection have proven useful for research, no animal models of S. agalactiae osteomyelitis exist. Here, we compared the pathology caused by these bacteria in an established murine model of implant‐associated osteomyelitis. In vitro scanning electron microscopy and CFU quantification confirmed similar implant inocula for both pathogens (~10 5 CFU/pin). Assessment of mice at 14 days post‐infection demonstrated increased S. aureus virulence, as S. agalactiae infected mice had significantly greater body weight, and fewer CFU on the implant and in bone and adjacent soft tissue ( p < 0.05). X‐ray, µCT, and histologic analyses showed that S. agalactiae induced significantly less osteolysis and implant loosening, and fewer large TRAP + osteoclasts than S. aureus without inducing intraosseous abscess formation. Most notably, transmission electron microscopy revealed that although both bacteria are capable of digesting cortical bone, S. agalactiae have a predilection for colonizing blood vessels embedded within cortical bone while S. aureus primarily colonizes the osteocyte lacuno‐canalicular network. This study establishes the first quantitative animal model of S. agalactiae osteomyelitis, and demonstrates a vasculotropic mode of S. agalactiae infection, in contrast to the osteotropic behavior of S. aureus osteomyelitis.
Penalized methods for variable selection such as the Smoothly Clipped Absolute Deviation penalty have been increasingly applied to aid variable section in regression analysis. Much of the literature has focused on parametric models, while a few recent studies have shifted the focus and developed their applications for the popular semi-parametric, or distribution-free, generalized estimating equations (GEEs) and weighted GEE (WGEE). However, although the WGEE is composed of one main and one missing-data module, available methods only focus on the main module, with no variable selection for the missing-data module. In this paper, we develop a new approach to further extend the existing methods to enable variable selection for both modules. The approach is illustrated by both real and simulated study data.
Underrepresented populations are often excluded from genomic studies owing in part to a lack of resources supporting their analyses. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies. Here, we harmonized a high-quality set of 4094 whole genomes from 80 populations in the HGDP and 1kGP with data from the Genome Aggregation Database (gnomAD) and identified over 153 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels. We also show substantial added value from this data set compared with the prior versions of the component resources, typically combined via liftOver and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared with previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality-control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines. This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.
Staphylococcus aureus infection of bone is challenging to treat because it colonizes the osteocyte lacuno-canalicular network (OLCN) of cortical bone. To elucidate factors involved in OLCN invasion and identify novel drug targets, we completed a hypothesis-driven screen of 24 S. aureus transposon insertion mutant strains for their ability to propagate through 0.5 μm-sized pores in the Microfluidic Silicon Membrane Canalicular Arrays (μSiM-CA), developed to model S. aureus invasion of the OLCN. This screen identified the uncanonical S. aureus transpeptidase, penicillin binding protein 4 (PBP4), as a necessary gene for S. aureus deformation and propagation through nanopores. In vivo studies revealed that Δpbp4 infected tibiae treated with vancomycin showed a significant 12-fold reduction in bacterial load compared to WT infected tibiae treated with vancomycin (p<0.05). Additionally, Δpbp4 infected tibiae displayed a remarkable decrease in pathogenic bone-loss at the implant site with and without vancomycin therapy. Most importantly, Δpbp4 S. aureus failed to invade and colonize the OLCN despite high bacterial loads on the implant and in adjacent tissues. Together, these results demonstrate that PBP4 is required for S. aureus colonization of the OLCN and suggest that inhibitors may be synergistic with standard of care antibiotics ineffective against bacteria within the OLCN.
