Aims. The aims of this study were to assess quality of life after hip fractures, to characterize respondents to patient-reported outcome measures (PROMs), and to describe the recovery trajectory of hip fracture patients. Methods. Data on 35,206 hip fractures (2014 to 2018; 67.2% female) in the Norwegian Hip Fracture Register were linked to data from the Norwegian Patient Registry and Statistics Norway. PROMs data were collected using the EuroQol five-dimension three-level questionnaire (EQ-5D-3L) scoring instrument and living patients were invited to respond at four, 12, and 36 months post fracture. Multiple imputation procedures were performed as a model to substitute missing PROM data. Differences in response rates between categories of covariates were analyzed using chi-squared test statistics. The association between patient and socioeconomic characteristics and the reported EQ-5D-3L scores was analyzed using linear regression. Results. The median age was 83 years (interquartile range 76 to 90), and 3,561 (10%) lived in a healthcare facility. Observed mean pre-fracture EQ-5D-3L index score was 0.81 (95% confidence interval 0.803 to 0.810), which decreased to 0.66 at four months, to 0.70 at 12 months, and to 0.73 at 36 months. In the imputed datasets, the reduction from pre-fracture was similar (0.15 points) but an improvement up to 36 months was modest (0.01 to 0.03 points). Patients with higher age, male sex, severe comorbidity, cognitive impairment, lower income, lower education, and those in residential care facilities had a lower proportion of respondents, and systematically reported a lower health-related quality of life (HRQoL). The response pattern of patients influenced scores significantly, and the highest scores are found in patients reporting scores at all observation times. Conclusion. Hip fracture leads to a persistent reduction in measured HRQoL, up to 36 months. The patients’ health and socioeconomic status were associated with the proportion of patients returning PROM data for analysis, and affected the results reported. Observed EQ-5D-3L scores are affected by attrition and selection bias mechanisms and motivate the use of statistical modelling for adjustment. Cite this article: Bone Joint J 2024;106-B(4):394–400

Aims. This study aimed to identify risk factors (patient, healthcare system, and socioeconomic) for mortality after hip fractures and estimate their relative importance. Further, we aimed to elucidate mortality and survival patterns following fractures and the duration of excess mortality. Methods. Data on 37,394 hip fractures in the Norwegian Hip Fracture Register from January 2014 to December 2018 were linked to data from the Norwegian Patient Registry, Statistics Norway, and characteristics of acute care hospitals. Cox regression analysis was performed to estimate risk factors associated with mortality. The Wald statistic was used to estimate and illustrate relative importance of risk factors, which were categorized in modifiable (healthcare-related) and non-modifiable (patient-related and socioeconomic). We calculated standardized mortality ratios (SMRs) comparing deaths among hip fracture patients to expected deaths in a standardized reference population. Results. Mean age was 80.2 years (SD 11.4) and 67.5% (n = 25,251) were female. Patient factors (male sex, increasing comorbidity (American Society of Anesthesiologists grade and Charlson Comorbidity Index)), socioeconomic factors (low income, low education level, living in a healthcare facility), and healthcare factors (hip fracture volume, availability of orthogeriatric services) were associated with increased mortality. Non-modifiable risk factors were more strongly associated with mortality than modifiable risk factors. The SMR analysis suggested that cumulative excess mortality among hip fracture patients was 16% in the first year and 41% at six years. SMR was 2.48 for the six-year observation period, most pronounced in the first year, and fell from 10.92 in the first month to 3.53 after 12 months and 2.48 after six years. Substantial differences in median survival time were found, particularly for patient-related factors. Conclusion. Socioeconomic, patient-, and healthcare-related factors all contributed to excess mortality, and non-modifiable factors had stronger association than modifiable ones. Hip fractures contributed to substantial excess mortality. Apparently small survival differences translate into substantial disparity in median survival time in this elderly population. Cite this article: Bone Joint J 2022;104-B(7):884–893

Aims

Hip fractures are some of the most common fractures encountered in orthopaedic practice. We aimed to identify whether perioperative hypotension is a predictor of 30-day mortality, and to stratify patient groups that would benefit from closer monitoring and early intervention. While there is literature on intraoperative blood pressure, there are limited studies examining pre- and postoperative blood pressure.