To investigate health-related quality of life (HRQoL) of older adults (aged ≥ 60 years) after tibial plateau fracture (TPF) compared to preinjury and population matched values, and what aspects of treatment were most important to patients. We undertook a retrospective, case-control study of 67 patients at mean 3.5 years (SD 1.3; 1.3 to 6.1) after TPF (47 patients underwent fixation, and 20 nonoperative management). Patients completed EuroQol five-dimension three-level (EQ-5D-3L) questionnaire, Lower Limb Function Scale (LEFS), and Oxford Knee Scores (OKS) for current and recalled prefracture status. Propensity score matching for age, sex, and deprivation in a 1:5 ratio was performed using patient level data from the Health Survey for England to obtain a control group for HRQoL comparison. The primary outcome was the difference in actual (TPF cohort) and expected (matched control) EQ-5D-3L score after TPF.Aims
Methods
The development of spinal deformity in children with underlying neurodisability can affect their ability to function and impact on their quality of life, as well as compromise provision of nursing care. Patients with neuromuscular spinal deformity are among the most challenging due to the number and complexity of medical comorbidities that increase the risk for severe intraoperative or postoperative complications. A multidisciplinary approach is mandatory at every stage to ensure that all nonoperative measures have been applied, and that the treatment goals have been clearly defined and agreed with the family. This will involve input from multiple specialities, including allied healthcare professionals, such as physiotherapists and wheelchair services. Surgery should be considered when there is significant impact on the patients’ quality of life, which is usually due to poor sitting balance, back or costo-pelvic pain, respiratory complications, or problems with self-care and feeding. Meticulous preoperative assessment is required, along with careful consideration of the nature of the deformity and the problems that it is causing. Surgery can achieve good curve correction and results in high levels of satisfaction from the patients and their caregivers. Modern modular posterior instrumentation systems allow an effective deformity correction. However, the risks of surgery remain high, and involvement of the family at all stages of decision-making is required in order to balance the risks and anticipated gains of the procedure, and to select those patients who can mostly benefit from spinal correction.
The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
Methods
