Citations

Objectives: Early identification of patients living with cancer at higher risk of death from an infection is critical in infection mortality prevention. We characterize patients living with cancer at the highest risk of dying from an infection. Methods: 7,529,481 US cancer survivors (1992 to 2015) were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Standardized mortality ratios (SMRs) and 95% CIs were calculated. Fine-gray survival analysis was performed to calculate hazard ratios by adjusting for the effects of competing risks (eg, deaths due to causes other than infection). Results: Among 7,529,481 patients living with cancer (1992 to 2015), 101,167 (1.3%) died of infection. The rate of infection-specific mortality was 27.19/10,000 person-years, with an SMR of 3.29 (95% CI: 3.26-3.32, P <0.001). Patients who were older, male, black, and unmarried were at a greater risk of fatal infection. Overall, the risk of infection-specific mortality for patients living with cancer is greatest 1 year after diagnosis compared with the general population (SMR: 8.68, 95% CI: 8.53-8.84; P< 0.0001), and this risk decreases with follow-up time (SMR at >10 y after diagnosis: 2.93, 95% CI: 2.87-3.00; P< 0.0001). Among patients with Hodgkin Lymphoma, Non-Hodgkin Lymphoma, and Kaposi Sarcoma, 9.2%, 11.5%, and 82.2% of all deaths within the first year after cancer diagnosis occurred due to acute infectious disease. In contrast, for patients with liver cancer, the relative percentage of infection-specific mortality increases with follow-up time from 3.5% at <1 year after cancer diagnosis and 10.4% at 10+ years of follow-up. Conclusion: The results of this study characterize infection mortality in patients living with cancer, which can guide more targeted research and interventions in this population.

Background: Benefits of hospice care, such as improvement in quality of life and reduced costs, depend on duration of enrollment in hospice services, making timely hospice referral essential. Patient-, provider-, and system-level factors associated with hospice referral timing and length of stay (LOS) are unclear. 1 – 6 Objective: To review existing hospice referral patterns to identify patient- and system-level factors associated with hospice LOS. Methods: We performed a retrospective review of all adult patients referred from our institution (located in Rochester, Minnesota, USA) to a nonprofit hospice agency between 2013 and 2017. The primary dependent variable was hospice LOS. Patient-level variables included demographic characteristics, place of residence, and hospice diagnosis. System-level variables included referral setting and provider-specific variables, such as title and gender. Statistical analyses were performed using multivariate logistic regression. Results: A total of 2072 patients were referred to hospice during the study period. Patient-level factors associated with LOS included hospice diagnosis and place of residence. Patients referred for cancer had a higher odds of a long LOS, while patients from long-term care facilities had a higher odds of a short LOS. System-level factors associated with LOS included provider gender and title. Referral by a female provider or by a physician, rather than an advanced practice provider, was associated with a lower odds of a short LOS. Conclusion: Based on a review of hospice referral patterns, the integration of hospice care into subspecialty practices, long-term care facilities, and advanced practice education could be an effective strategy to improve hospice LOS.

Abstract Objectives The aim of this study was to compare the measured physiological factors that limit running performance with real marathon results from world-class distance runners, evaluating the compatibility between measured data and predicted results based on the previously suggested model. Methods Four world-class East African marathon runners (three male, one female) underwent physiological running assessments to predict marathon performance times using a model based on V ˙ $\dot {\mathrm{V}}$ O 2 peak, percentage of V ˙ $\dot {\mathrm{V}}$ O 2 peak at the second ventilatory threshold, and running economy. Predictions were then compared to participants’ best marathon times. Results The measured V ˙ $\dot {\mathrm{V}}$ O 2 peak of the world-class runners was 75.1 ± 2.7 mL/kg/min. The second ventilatory threshold occurred at 85 ± 3 % of the peak, with a running economy of 63.7 ± 2.4 mL/kg/min at 19.6 ± 0.9 km/h. The predicted marathon performance time was 2:06:51 ± 0:03:17 h:min:s for the males and 2:17:36 h:min:s for the female. Comparing these predictions to their personal best times, the average difference was 00:55 ± 00:51 min:s (range: 00:20-02:08). Conclusions This research provides laboratory data on world-class road running athletes, reinforcing the link between marathon performance and V ˙ $\dot {\mathrm{V}}$ O 2 peak, the percentage of V ˙ $\dot {\mathrm{V}}$ O 2 peak at the second ventilatory threshold, and running economy. The examined athletes had lower V ˙ $\dot {\mathrm{V}}$ O 2 peak compared to predicted values, highlighting the importance of running economy and fractional utilization of V ˙ $\dot {\mathrm{V}}$ O 2 peak in achieving such performances. Future studies should continue to advance the field by including additional bioenergetic parameters measured during race conditions and expanding the participant cohort of elite marathoners, encompassing both sexes.

AimsContemporary outcomes of total femoral arthroplasties (TFAs) in the non-oncological setting are limited. This study aimed to assess implant survival free from revision and reoperation, clinical outcomes, and radiological results associated with TFAs for non-oncological conditions.MethodsWe identified 24 patients who received TFAs between 1 January 2007 and 31 December 2021 through our institutional total joint registry. The patients’ mean age was 66 years (48 to 83), mean BMI was 33 kg/m2 (21 to 54), and median follow-up was four years (IQR 2 to 5). Bearings used included eight flat-faced polyethylene bearings, seven dual-mobility constructs, six constrained liners, and three face-changing liners. Of the 24 patients, 17 had a previously documented periprosthetic joint infection (PJI) and 12 were treated with chronic antibiotic suppression following their TFAs.ResultsThe two-, five-, and ten-year survivals free of any revision were 66%, 51%, and 34%, respectively. There were 12 revisions: five for hip dislocation, four for PJI, one for aseptic tibial loosening, one for prosthetic knee dislocation, and one for acetabular loosening with pelvic discontinuity. The two-, five-, and ten-year survivals free of any reoperation were 53%, 47%, and 34%, respectively. In addition to the stated TFA revisions, one patient required open reduction of a hip dislocation, and two patients required superficial irrigation and debridement procedures for wound complications. Mean Harris Hip Scores improved from a mean score of 48 to 69 (p = 0.002), and Knee Society Scores improved from a mean score of 56 to 71 (p = 0.110). At final follow-up, 20 patients had TFAs in situ, but four patients had been treated with hip disarticulation due to recalcitrant infection. All but one tibial and all non-revised acetabular components were radiologically well-fixed at final follow-up.ConclusionThe use of TFAs in the non-oncological setting, mostly for salvage of multiply revised hips with previous PJI, had a low five-year survival free from any revision of 51%, mostly for hip dislocation and infection. Nevertheless, clinical outcomes improved in this challenging cohort of patients.Cite this article: Bone Joint J 2025;107-B(4):449–454.

Abstract Background and Aims Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET for attenuation correction and potentially contain valuable data about body tissue composition. Deep learning and image processing were combined to automatically quantify skeletal muscle (SM), bone and adipose tissue from these scans and then evaluate their associations with death or myocardial infarction (MI). Methods In PET MPI from three sites, deep learning quantified SM, bone, epicardial adipose tissue (EAT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT). Sex-specific thresholds for abnormal values were established. Associations with death or MI were evaluated using unadjusted and multivariable models adjusted for clinical and imaging factors. Results This study included 10 085 patients, with median age 68 (interquartile range 59–76) and 5767 (57%) male. Body tissue segmentations were completed in 102 ± 4 s. Higher VAT density was associated with an increased risk of death or MI in both unadjusted [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.37–1.43] and adjusted (HR 1.24, 95% CI 1.19–1.28) analyses, with similar findings for IMAT, SAT, and EAT. Patients with elevated VAT density and reduced myocardial flow reserve had a significantly increased risk of death or MI (adjusted HR 2.49, 95% CI 2.23–2.77). Conclusions Volumetric body tissue composition can be obtained rapidly and automatically from standard cardiac PET/CT. This new information provides a detailed, quantitative assessment of sarcopenia and cardiometabolic health for physicians.

Abstract Background There remains a lack of consensus regarding the postoperative treatment regimen in patients with native valve Staphylococcus aureus infective endocarditis who undergo surgical valve replacement or repair. Methods We conducted a multicenter retrospective review of patients with S aureus native valve endocarditis who underwent surgical valve replacement or repair at Mayo Clinic Enterprise (Minnesota, Florida, Arizona, and Mayo Clinic Health Systems) between 1 January 2012 and 31 December 2022. Postoperative treatment regimens were classified as either monotherapy with a methicillin-sensitive or methicillin-resistant S aureus active agent or combination therapy that included rifampin and/or gentamicin with either active agent. Results Of the 339 patients diagnosed with S aureus native valve endocarditis, 61 underwent surgical valve replacement or repair within the initial 6 weeks of antimicrobial therapy. A total of 12 (20.2%) patients died during the 1-year follow-up period. Between patients postoperatively treated with monotherapy (n = 33) and combination therapy (n = 28), a propensity score–weighted analysis revealed that combination therapy was associated with increased risk of 1-year mortality (P = .039), 6-month relapse (P = .016), and treatment-related adverse events (P &lt; .001). Conclusions Among patients treated for native valve infective endocarditis caused by S aureus after valvular surgical intervention, all study outcomes—adverse events and drug-drug interactions, 6-month infective endocarditis relapse, and 1-year mortality—were higher in the combination therapy group than the monotherapy group.

The approach to eosinophilia and mast cell disorders in the bone marrow is diverse and depends on multiple factors including access to ancillary testing, resources to support testing, type of practice setting (eg, community, remote, tertiary care center or specialized referral center for these disorders) and whether there are options for clinical trial enrollment. That said, while there are some basic principles to the workup that we can all likely agree upon, individual practice habits will need to be tailored to suit an individual setting. As such, the approach presented in this manuscript is meant to serve as a practical guide and not as dogma per se. Importantly, an in-depth discussion of individual diseases and International Consensus Classification diagnostic criteria will not be covered, as the main focus of this article is the approach to these disorders. The reader is referred to a comprehensive discussion of these diseases and diagnostic criteria in several excellent articles. While there are clear areas of overlap between eosinophilia and mast cell conditions (eg, systemic mastocytosis associated with eosinophilia, myeloid neoplasm with eosinophilia, and tyrosine kinase rearrangements), it is the authors’ opinion that it is perhaps easier to navigate these entities separately (eg, eosinophilia as one broad topic and mast cell conditions as another) and to recognize the settings in which overlap may exist and what testing might be considered. Eosinophilia and mast cell conditions will be discussed separately supplemented by generous use of figures and tables to highlight key points.

Abstract Background and Aims Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular regurgitation and stratify MR-progression risk. Methods Using transthoracic echocardiograms (TTEs) at two sites (internal development/test, external test), the DELINEATE-Regurgitation system was developed to classify AR, MR, and TR severity using colour Doppler videos. Methods of summating video-level classifications into study-level predictions were tested, comparing single-view with multiview approaches integrating predictions across multiple videos. Model agreement with cardiologists was assessed by weighted kappa. A separate AI system (DELINEATE-MR-Progression) analysing colour Doppler videos was developed to predict which patients with mild, mild–moderate, and moderate MR were most likely to progress to moderate–severe or severe MR with analysis by Kaplan–Meier and Cox proportional hazards models. Results A total of 71 660 TTEs with 1 203 980 colour Doppler videos were included. The weighted kappa in internal/external test sets for regurgitation classification was 0.81/0.76 for AR, 0.76/0.72 for MR, and 0.73/0.64 for TR using a multiview approach taking all colour Doppler videos in a study, demonstrating substantial agreement with cardiologist interpretation with superiority of multiview over single view approaches. In the progression analysis, the AI score stratified MR-progression risk even when controlled for clinical factors known to be associated with MR progression [internal test set hazard ratio 4.1 (95% confidence interval 2.5–6.6)]. Conclusions An AI system can accurately classify AR, MR, and TR and predict MR progression beyond currently known risk factors.