Risk Factor Analysis for Predicting the Onset of Rotator Cuff Calcific Tendinitis Based on Artificial Intelligence

Abstract

Background. Symptomatic rotator cuff calcific tendinitis (RCCT) is a common shoulder disorder, and approaches combined with artificial intelligence greatly facilitate the development of clinical practice. Current scarce knowledge of the onset suggests that clinicians may need to explore this disease thoroughly. Methods. Clinical data were retrospectively collected from subjects diagnosed with RCCT at our institution within the period 2008 to 2020. A standardized questionnaire related to shoulder symptoms was completed in all cases, and standardized radiographs of both shoulders were extracted using a human-computer interactive electronic medical system (EMS) to clarify the clinical diagnosis of symptomatic RCCT. Based on the exclusion of asymptomatic subjects, risk factors in the baseline characteristics significantly associated with the onset of symptomatic RCCT were assessed via stepwise logistic regression analysis. Results. Of the 1,967 consecutive subjects referred to our academic institution for shoulder discomfort, 237 were diagnosed with symptomatic RCCT (12.05%). The proportion of women and the prevalence of clinical comorbidities were significantly higher in the RCCT cohort than those in the non-RCCT cohort. Stepwise logistic regression analysis confirmed that female gender, hyperlipidemia, diabetes mellitus, and hypothyroidism were independent risk factors for the entire cohort. Stratified by gender, the study found a partial overlap of risk factors contributing to morbidity in men and women. Diagnosis of hyperlipidemia, diabetes mellitus, and hypothyroidism in male cases and diabetes mellitus in female cases were significantly associated with symptomatic RCCT. Conclusion. Independent predictors of symptomatic RCCT are female, hyperlipidemia, diabetes mellitus, and hypothyroidism. Men diagnosed with hyperlipidemia, diabetes mellitus, and hypothyroidism are at high risk for symptomatic RCCT, while more medical attention is required for women with diabetes mellitus. Artificial intelligence offers pioneering innovations in the diagnosis and treatment of musculoskeletal disorders, and careful assessment through individualized risk stratification can help predict onset and targeted early stage treatment.

Application of Machine Learning Algorithms for Prognostic Assessment in Rotator Cuff Pathologies: A Clinical Data-Based Approach

“…The most statistically significant values for prediction were the Jobe test, Bear hug test, and the age of the patients, with mean Shapley additive explanation (SHAP) values of 1.458, 0.950, and 0.790, respectively. Similarly, Dong et al [ 60 ] studied a cohort of 1967 patients through a human–computer interactive Electronic Medical System (EMS) and demonstrated the presence of predictors of RC calcific tendinitis stratified according to the patients’ sex: women diagnosed with diabetes mellitus and men diagnosed with hyperlipidemia, diabetes mellitus, and hypothyroidism showed a higher risk of developing RC calcific tendinitis. Clinical factors such as age and sex were investigated in both articles, similarly to this paper.…”

Calcific Shoulder Tendinopathy (CT): Influence of the Biochemical Process of Hydrolysis of HA (Hydroxyapatite) on the Choice of Ultrasound-Guided Percutaneous Treatment (with the Three-Needle Technique)

“…The literature reports a wide range of risk factors involved in the onset of calcific rotator cuff tendinitis, such as female sex, diabetes mellitus, dyslipidemia, hypothyroidism and other endocrine disorders [20], and in our experience a crucial role in the development of this condition is played by repetitive overhead movements and microtrauma, although individuals involved in strenuous manual labour or athletic activities are no more commonly affected than those leading sedentary lives. (Figure 2A,B) [21].…”

Application of Artificial Intelligence in Shoulder Pathology

“…AI technology offers promising advancements in predicting RCCT onset and facilitating targeted early-stage treatment. Dong et al [63] retrospectively analyzed clinical data from individuals diagnosed with RCCT at their institution. Logistic regression analysis revealed that female gender, hyperlipidemia, diabetes mellitus, and hypothyroidism were independent risk factors for symptomatic RCCT.…”