Effects of Spinal Deformities on Hiatal Hernia Occurrence and Recurrence.

BACKGROUND: Spinal deformities such as kyphosis, lordosis, and scoliosis have demonstrated a possible association between these deformities. Our hypothesis is that the presence of spinal deformities will increase the risk of hiatal hernia recurrence after repair. METHODS: The following data was retrospectively gleaned for patients undergoing hiatal hernia repair (1997-2022): age, sex, date of hiatal hernia repair, presence and type of spinal deformity, Cobb angle, type of hiatal hernia and size, type of hiatal hernia repair, recurrence and size, time to recurrence, reoperation, type of reoperation, and time to reoperation. RESULTS: Spinal deformities were present in 15.8% of 546 patients undergoing hiatal hernia repair, with a distribution of 21.8% kyphosis, 2.3% lordosis, 58.6% scoliosis, and 17.2% multiple. There was no difference in sex or age between groups. Spinal deformity patients were more likely to have types III and IV hiatal hernias (52.3% vs. 38.9%, p = 0.02) and larger hernias (median 5 [3-8] vs. 4 [2-6], p = 0.01). There was no difference in access, fundoplication use, or mesh use between groups. However, these patients had a higher recurrence rate (47.7% vs 30.0%, p = 0.001) and a shorter time to recurrence (months) (10.3 [5.6-25.1] vs 19.2 [9.8-51.0], p = 0.02). Cobb angle did not affect recurrence. CONCLUSIONS: Spinal deformity patients were more likely to have more complex and larger hiatal hernias. They were at higher risk of hiatal hernia recurrence after repair with shorter times to recurrence. This is a group that requires special attention with additional preoperative counseling and possibly use of surgical adjuncts in repair.

AI and machine learning ethics, law, diversity, and global impact.

Artificial intelligence (AI) and its machine learning (ML) algorithms are offering new promise for personalized biomedicine and more cost-effective healthcare with impressive technical capability to mimic human cognitive capabilities. However, widespread application of this promising technology has been limited in the medical domain and expectations have been tampered by ethical challenges and concerns regarding patient privacy, legal responsibility, trustworthiness, and fairness. To balance technical innovation with ethical applications of AI/ML, developers must demonstrate the AI functions as intended and adopt strategies to minimize the risks for failure or bias. This review describes the new ethical challenges created by AI/ML for clinical care and identifies specific considerations for its practice in medicine. We provide an overview of regulatory and legal issues applicable in Europe and the United States, a description of technical aspects to consider, and present recommendations for trustworthy AI/ML that promote transparency, minimize risks of bias or error, and protect the patient well-being.