Recent studies have reported the presence of acetylcholine (ACh) receptor subtypes in bone tissue, and have demonstrated that inhibition of the ACh receptors has negative effects on bone mass and fracture healing capacity. However, little is known about the potential clinical effects that increased ACh signaling might have on bone. Accordingly, this study was designed to determine whether the use of acetylcholinesterase inhibitors (AChEIs), a group of drugs that stimulate ACh receptors and are used to treat Alzheimer's disease (AD), is associated with a decreased risk of hip fracture in AD patients. To accomplish this objective, a case-control analysis was performed using the AD population, aged above 75 years, based in the local health area of the Carlos Haya Hospital, in Malaga, Spain. The cases were 80 AD patients that suffered a hip fracture between January 2004 and December 2008. The controls were 2178 AD patients without hip fracture followed at our health care area during the same period of time. Compared with patients who did not use AChEIs, the hip fracture adjusted odds ratio (OR) for users of AChEIs was 0.42 (95% confidence interval [CI], 0.24–0.72), for users of rivastigmine was 0.22 (95% CI, 0.10–0.45), and for users of donepezil was 0.39 (95% CI, 0.19–0.76). Data were adjusted for the following parameters: body mass index, fall risk, smoking habits, cognition, dependence, degree of AD, comorbidity score, treatment with selective serotonin reuptake inhibitors, age, and gender. Our data suggests that use of AChEIs donepezil and rivastigmine is associated with a reduced risk of fractures in AD patients. Many elderly patients with AD disease who are at risk of developing osteoporosis may potentially benefit from therapy with the AChEIs donepezil and rivastigmine. © 2012 American Society for Bone and Mineral Research.

Supervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited. It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee. Cross-sectional study; Level of evidence, 3. Included were 50 adults who had undergone primary ACL reconstruction between 2008 and 2015. All patients were between 18 and 40 years of age at the time of surgery. Patients with a previous ACL injury, multiligament knee injury, previous ACL reconstruction, history of ACL revision surgery, complete meniscectomy, infection, missing data, and associated fracture were excluded. We also identified 50 sex-matched controls who had not sustained an ACL injury. For all participants, we used the preoperative magnetic resonance images to measure the anteroposterior lengths of the medial and lateral tibial plateaus as well as the lateral and medial bone slope (LBS and MBS), lateral and medial meniscal height (LMH and MMH), and lateral and medial meniscal slope (LMS and MMS). The AI predictor was created using Matlab R2019b. A Gaussian naïve Bayes model was selected to create the predictor. Patients in the ACL injury group had a significantly increased posterior LBS (7.0° ± 4.7° vs 3.9° ± 5.4°; P = .008) and LMS (–1.7° ± 4.8° vs –4.0° ± 4.2°; P = .002) and a lower MMH (5.5 ± 0.1 vs 6.1 ± 0.1 mm; P = .006) and LMH (6.9 ± 0.1 vs 7.6 ± 0.1 mm; P = .001). The AI model selected LBS and MBS as the best possible predictive combination, achieving 70% validation accuracy and 92% testing accuracy. A prediction model for primary ACL injury, created using machine learning techniques, achieved a >90% testing accuracy. Compared with patients who did not sustain an ACL injury, patients with torn ACLs had an increased posterior LBS and LMS and a lower MMH and LMH.