Development and Validation of a Novel Scoring System for Severity of Plantar Fasciitis

Predictive Prognostic Factors in Non-Calcific Supraspinatus Tendinopathy Treated with Focused Extracorporeal Shock Wave Therapy: An Artificial Neural Network Approach

The supraspinatus tendon is one of the most involved tendons in the development of shoulder pain. Extracorporeal shockwave therapy (ESWT) has been recognized as a valid and safe treatment. Sometimes the symptoms cannot be relieved, or a relapse develops, affecting the patient's quality of life. Therefore, a prediction protocol could be a powerful tool aiding our clinical decisions. An artificial neural network was run, in particular a multilayer perceptron model incorporating input information such as the VAS and Constant-Murley score, administered at T0 and at T1 after six months. It showed a model sensitivity of 80.7%, and the area under the ROC curve was 0.701, which demonstrates good discrimination. The aim of our study was to identify predictive factors for minimal clinically successful therapy (MCST), defined as a reduction of ≥40% in VAS score at T1 following ESWT for chronic non-calcific supraspinatus tendinopathy (SNCCT). From the male gender, we expect greater and more frequent clinical success. The more severe the patient's initial condition, the greater the possibility that clinical success will decrease. The Constant and Murley score, Roles and Maudsley score, and VAS are not just evaluation tools to verify an improvement; they are also prognostic factors to be taken into consideration in the assessment of achieving clinical success. Due to the lower clinical improvement observed in older patients and those with worse clinical and functional scales, it would be preferable to also provide these patients with the possibility of combined treatments. The ANN predictive model is reasonable and accurate in studying the influence of prognostic factors and achieving clinical success in patients with chronic non-calcific tendinopathy of the supraspinatus treated with ESWT.

Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty

Background

Frailty is a dynamic and complex geriatric condition characterized by multi-domain declines in physiological, gait and cognitive function. This study examined whether digital health technology can facilitate frailty identification and improve the efficiency of diagnosis by optimizing analytical and machine learning approaches using select factors from comprehensive geriatric assessment and gait characteristics.

Methods

As part of an ongoing study on observational study of Aging, we prospectively recruited 214 individuals living independently in the community of Southern China. Clinical information and fragility were assessed using comprehensive geriatric assessment (CGA). Digital tool box consisted of wearable sensor-enabled 6-min walk test (6MWT) and five machine learning algorithms allowing feature selections and frailty classifications.

Results

It was found that a model combining CGA and gait parameters was successful in predicting frailty. The combination of these features in a machine learning model performed better than using either CGA or gait parameters alone, with an area under the curve of 0.93. The performance of the machine learning models improved by 4.3-11.4% after further feature selection using a smaller subset of 16 variables. SHapley Additive exPlanation (SHAP) dependence plot analysis revealed that the most important features for predicting frailty were large-step walking speed, average step size, age, total step walking distance, and Mini Mental State Examination score.

Conclusion

This study provides evidence that digital health technology can be used for predicting frailty and identifying the key gait parameters in targeted health assessments.

Clinical, Biochemical, Radiological, and Genetic Analyses of a Patient with VCP Gene Variant-Induced Paget's Disease of Bone

Paget's disease of bone (PDB) is a rare metabolic bone disorder, which is extremely rare in Asian population. This study aimed to investigate the phenotypes and the pathogenic mutations of woman with early-onset PDB. The clinical features, bone mineral density, x-ray, radionuclide bone scan, and serum levels of alkaline phosphatase (ALP), procollagen type 1 N-terminal propeptide (P1NP), and β-carboxy-terminal cross-linked telopeptide of type 1 collagen (β-CTX) were measured in detail. The pathogenic mutations were identified by whole-exon sequencing and confirmed by Sanger sequencing. We also evaluated the effects of intravenous infusion of zoledronic acid on the bones of the patient and summarized the phenotypic characteristics of reported patients with mutation at position 155 of the valosin-containing protein (VCP). The patient only exhibited bone pain as the initial manifestation with vertebral compression fracture and extremely elevated ALP, P1NP, and β-CTX levels; she had no inclusion body myopathy and frontotemporal dementia. The missense mutation in exon 5 of the VCP gene (p.Arg155His) was identified by whole-exome sequencing and further confirmed by Sanger sequencing. No mutation in candidate genes of PDB, such as SQSTM1, CSF1, TM7SF4, OPTN, PFN1, and TNFRSF11A, were identified in the patient by Sanger sequencing. Rapid relief of bone pain and a marked decline in ALP, P1NP, and β-CTX levels were observed after zoledronic acid treatment. Previously reported patients with VCP missense mutation at position 155 (R155H) always had myopathy, frontotemporal dementia, and PDB, but the patient in this study exhibited only PDB. This was the first report of R155H mutation-induced early-onset in the VCP gene in Asian population. PDB was the only manifestation having a favorable response to zoledronic acid treatment. We broadened the genetic and clinical phenotype spectra of the VCP mutation.