The Long-Term Risk of Knee Arthroplasty in Patients with Arthroscopically Verified Focal Cartilage Lesions: A Linkage Study with the Norwegian Arthroplasty Register, 1999 to 2020

Background: Focal cartilage lesions are common in the knee. The risk of later ipsilateral knee arthroplasty remains unknown. The purposes of the present study were to evaluate the long-term cumulative risk of knee arthroplasty after arthroscopic identification of focal cartilage lesions in the knee, to investigate the risk factors for subsequent knee arthroplasty, and to estimate the subsequent cumulative risk of knee arthroplasty compared with that in the general population. Methods: Patients who had undergone surgical treatment of focal cartilage lesions at 6 major Norwegian hospitals between 1999 and 2012 were identified. The inclusion criteria were an arthroscopically classified focal cartilage lesion in the knee, an age of ≥18 years at the time of surgery, and available preoperative patient-reported outcomes (PROMs). The exclusion criteria were osteoarthritis or “kissing lesions” at the time of surgery. Demographic data, later knee surgery, and PROMs were collected with use of a questionnaire. A Cox regression model was used to adjust for and investigate the impact of risk factors, and Kaplan-Meier analysis was performed to estimate cumulative risk. The risk of knee arthroplasty in the present cohort was compared with that in the age-matched general Norwegian population. Results: Of the 516 patients who were eligible, 322 patients (328 knees) consented to participate. The mean age at the time of the index procedure was 36.8 years, and the mean duration of follow-up was 19.8 years. The 20-year cumulative risk of knee arthroplasty in the cartilage cohort was 19.1% (95% CI, 14.6% to 23.6%). Variables that had an impact on the risk of knee arthroplasty included an ICRS grade of 3 to 4 (hazard ratio [HR], 3.1; 95% CI, 1.1 to 8.7), an age of ≥40 years at time of cartilage surgery (HR, 3.7; 95% CI, 1.8 to 7.7), a BMI of 25 to 29 kg/m2 (HR, 3.9; 95% CI, 1.7 to 9.0), a BMI of ≥30 kg/m2 (HR, 5.9; 95% CI, 2.4 to 14.3) at the time of follow-up, autologous chondrocyte implantation (ACI) at the time of the index procedure (HR, 3.4; 95% CI, 1.0 to 11.4), >1 focal cartilage lesion (HR, 2.1; 95% CI, 1.1 to 3.7), and a high preoperative visual analog scale (VAS) score for pain at the time of the index procedure (HR, 1.1; 95% CI, 1.0 to 1.1). The risk ratio of later knee arthroplasty in the cartilage cohort as compared with the age-matched general Norwegian population was 415.7 (95% CI, 168.8 to 1,023.5) in the 30 to 39-year age group. Conclusions: In the present study, we found that the 20-year cumulative risk of knee arthroplasty after a focal cartilage lesion in the knee was 19%. Deep lesions, higher age at the time of cartilage surgery, high BMI at the time of follow-up, ACI, and >1 cartilage lesion were associated with a higher risk of knee arthroplasty.publishedVersio

Chest compression rate measurement from smartphone video

© 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Background: Out-of-hospital cardiac arrest is a life threatening situation where the first person performing cardiopulmonary resuscitation (CPR) most often is a bystander without medical training. Some existing smartphone apps can call the emergency number and provide for example global positioning system (GPS) location like Hjelp 113-GPS App by the Norwegian air ambulance. We propose to extend functionality of such apps by using the built in camera in a smartphone to capture video of the CPR performed, primarily to estimate the duration and rate of the chest compression executed, if any. Methods: All calculations are done in real time, and both the caller and the dispatcher will receive the compression rate feedback when detected. The proposed algorithm is based on finding a dynamic region of interest in the video frames, and thereafter evaluating the power spectral density by computing the fast fourier transform over sliding windows. The power of the dominating frequencies is compared to the power of the frequency area of interest. The system is tested on different persons, male and female, in different scenarios addressing target compression rates, background disturbances, compression with mouth-to-mouth ventilation, various background illuminations and phone placements. All tests were done on a recording Laerdal manikin, providing true compression rates for comparison. Results: Overall, the algorithm is seen to be promising, and it manages a number of disturbances and light situations. For target rates at 110 cpm, as recommended during CPR, the mean error in compression rate (Standard dev. over tests in parentheses) is 3.6 (0.8) for short hair bystanders, and 8.7 (6.0) including medium and long haired bystanders. Conclusions: The presented method shows that it is feasible to detect the compression rate of chest compressions performed by a bystander by placing the smartphone close to the patient, and using the built-in camera combined with a video processing algorithm performed real-time on the device