Medical comorbidities are a critical factor in the decision-making process for operative management and risk-stratification. The Hierarchical Condition Categories (HCC) risk adjustment model is a powerful measure of illness severity for patients treated by surgeons. The HCC is utilized by Medicare to predict medical expenditure risk and to reimburse physicians accordingly. HCC weighs comorbidities differently to calculate risk. This study determines the prevalence of medical comorbidities and the average HCC score in Medicare patients being evaluated by neurosurgeons and orthopaedic surgeon, as well as a subset of academic spine surgeons within both specialities, in the USA. The Medicare Provider Utilization and Payment Database, which is based on data from the Centers for Medicare and Medicaid Services’ National Claims History Standard Analytic Files, was analyzed for this study. Every surgeon who submitted a valid Medicare Part B non-institutional claim during the 2013 calendar year was included in this study. This database was queried for medical comorbidities and HCC scores of each patient who had, at minimum, a single office visit with a surgeon. This data included 21,204 orthopaedic surgeons and 4,372 neurosurgeons across 54 states/territories in the USA.Aims
Methods
Gram-negative infections are associated with comorbid patients, but outcomes are less well understood. This study reviewed diagnosis, management, and treatment for a cohort treated in a tertiary spinal centre. A retrospective review was performed of all gram-negative spinal infections (n = 32; median age 71 years; interquartile range 60 to 78), excluding surgical site infections, at a single centre between 2015 to 2020 with two- to six-year follow-up. Information regarding organism identification, antibiotic regime, and treatment outcomes (including clinical, radiological, and biochemical) were collected from clinical notes.Aims
Methods
As the world continues to fight successive waves of COVID-19 variants, we have seen worldwide infections surpass 100 million. London, UK, has been severely affected throughout the pandemic, and the resulting impact on the NHS has been profound. The aim of this study is to evaluate the impact of COVID-19 on theatre productivity across London’s four major trauma centres (MTCs), and to assess how the changes to normal protocols and working patterns impacted trauma theatre efficiency. This was a collaborative study across London’s MTCs. A two-month period was selected from 5 March to 5 May 2020. The same two-month period in 2019 was used to provide baseline data for comparison. Demographic information was collected, as well as surgical speciality, procedure, time to surgery, type of anaesthesia, and various time points throughout the patient journey to theatre.Aims
Methods
The first death in the UK caused by COVID-19 occurred on 5 March 2020. We aim to describe the clinical characteristics and outcomes of major trauma and orthopaedic patients admitted in the early COVID-19 era. A prospective trauma registry was reviewed at a Level 1 Major Trauma Centre. We divided patients into Group A, 40 days prior to 5 March 2020, and into Group B, 40 days after.Aims
Methods
Patient-reported outcome measures have become an important part of routine care. The aim of this study was to determine if Patient-Reported Outcomes Measurement Information System (PROMIS) measures can be used to create patient subgroups for individuals seeking orthopaedic care. This was a cross-sectional study of patients from Duke University Department of Orthopaedic Surgery clinics (14 ambulatory and four hospital-based). There were two separate cohorts recruited by convenience sampling (i.e. patients were included in the analysis only if they completed PROMIS measures during a new patient visit). Cohort #1 (n = 12,141; December 2017 to December 2018,) included PROMIS short forms for eight domains (Physical Function, Pain Interference, Pain Intensity, Depression, Anxiety, Sleep Quality, Participation in Social Roles, and Fatigue) and Cohort #2 (n = 4,638; January 2019 to August 2019) included PROMIS Computer Adaptive Testing instruments for four domains (Physical Function, Pain Interference, Depression, and Sleep Quality). Cluster analysis (K-means method) empirically derived subgroups and subgroup differences in clinical and sociodemographic factors were identified with one-way analysis of variance.Aims
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