Measuring quality of recovery (QoR-15) after degenerative spinal surgery: A prospective observational study

Introduction: The Quality of Recovery (QoR-15) score evaluates patient's recovery after surgery and anesthesia. There is a lack of studies focusing on the patients' quality of recovery in the early post-discharge phase after elective lumbar spine surgery. Research question: We aimed to identify the QoR-15 score in patients who underwent surgery for degenerative low back conditions. Furthermore, we aimed to identify the individual items of the QoR-15 that are crucial for the patients’ quality of recovery. Material and methods: The study was conducted at a spine center in Denmark from December 2021 to September 2022. Data were collected, using a mobile health application, preoperatively and at 3 time points after hospital discharge. Descriptive analysis followed by within-subjects longitudinal repeated measures was conducted. The individual items of the QoR-15 score were explored using a heatmap. Results: Data from 46 patients were analysed. The mean QoR-15 sum score at baseline was 105.4 ± 18.3. The mean QoR-15 sum scores were 108.1 ± 19.2 on post-discharge day 1, 118.5 ± 17.4 on day 7, and 120.7 ± 20.9 on day 14. The mean QoR-15 score from day 1 to day 7 improved significantly. Eight of the 15 items influenced the overall QoR-15 score. Discussion and conclusion: This study applied the QoR-15 score in lumbar spine surgery patients. We identified specific items from the QoR-15 scale that are crucial to improving patients’ recovery after hospital discharge. Further research is needed to identify specific needs in the post-discharge period in this group of patients

Quantification of Lipoprotein Subclasses by Proton Nuclear Magnetic Resonance–Based Partial Least-Squares Regression Models

Background: Cardiovascular disease risk can be estimated in part on the basis of the plasma lipoprotein profile. Analysis of lipoprotein subclasses improves the risk evaluation, but the traditional methods are very time-consuming. Novel, rapid, and productive methods are therefore needed. Methods: We obtained plasma samples from 103 fasting people and determined the plasma lipoprotein subclass profiles by an established ultracentrifugation-based method. Proton nuclear magnetic resonance (NMR) spectra were obtained from replicate samples on a 600 MHz NMR spectrometer. From the ultracentrifugationbased reference data and the NMR spectra, we developed partial least-squares (PLS) regression models to predict cholesterol and triglyceride (TG) concentration