The association between menopausal status and clinical outcomes of female patients with carpal tunnel release: a retrospective observational study

BACKGROUND

The relationship between menopausal status and clinical outcomes of female patients with carpal tunnel release (CTR) has been not clearly investigated. This study aimed to evaluate the clinical outcomes of CTR among women with different menopausal statuses.

METHODS

Two hundred-eighteen consecutive female patients with carpal tunnel syndrome (CTS) who underwent surgery at our hospital between May 2016 and May 2020 were included in this study. Based on whether subjects had undergone menopause before surgery, they were divided into three groups: the premenopausal group, the early postmenopausal group (less than 10 years since menopause) and the late postmenopausal group (more than 10 years since menopause). The clinical outcome was assessed before surgery and at six-month postoperative visits using the Boston Carpal Tunnel Questionnaire (BCTQ).

RESULTS

There were 86 patients in the premenopausal group, 70 patients in the early postmenopausal group and 62 patients in the late postmenopausal group. All the groups showed significant improvements in their Symptom Severity Scale (SSS) and Functional Status Scale (FSS) compared with preoperative scores. Although, there was a significant difference between the groups based and post-surgical SSS and FSS (p < 0.05). Patients in the postmenopausal group had less improvement in SSS and FSS in comparison with those in the other groups (p < 0.05).

CONCLUSIONS

Our results showed that all the groups had significant improvement in their SSS and FSS. However, the clinical outcomes were poorer in late postmenopausal patients than in premenopausal or early postmenopausal patients. These findings can be used in pre-operative counseling and interpretation of outcomes in women with CTR.

The value of machine learning technology and artificial intelligence to enhance patient safety in spine surgery: a review

Machine learning algorithms have the potential to significantly improve patient safety in spine surgeries by providing healthcare professionals with valuable insights and predictive analytics. These algorithms can analyze preoperative data, such as patient demographics, medical history, and imaging studies, to identify potential risk factors and predict postoperative complications. By leveraging machine learning, surgeons can make more informed decisions, personalize treatment plans, and optimize surgical techniques to minimize risks and enhance patient outcomes. Moreover, by harnessing the power of machine learning, healthcare providers can make data-driven decisions, personalize treatment plans, and optimize surgical interventions, ultimately enhancing the quality of care in spine surgery. The findings highlight the potential of integrating artificial intelligence in healthcare settings to mitigate risks and enhance patient safety in surgical practices. The integration of machine learning holds immense potential for enhancing patient safety in spine surgeries. By leveraging advanced algorithms and predictive analytics, healthcare providers can optimize surgical decision-making, mitigate risks, and personalize treatment strategies to improve outcomes and ensure the highest standard of care for patients undergoing spine procedures. As technology continues to evolve, the future of spine surgery lies in harnessing the power of machine learning to transform patient safety and revolutionize surgical practices. The present review article was designed to discuss the available literature in the field of machine learning techniques to enhance patient safety in spine surgery.