Real-World Implementation of Artificial Intelligence/Machine Learning for Managing Surgical Spine Patients at 2 Academic Health Care Systems.

Decision-making in spine surgery is complex due to patients heterogeneity and complexity of spinal pathologies and the various surgical options applied to a given pathology. Artificial intelligence/machine learning algorithms provide an opportunity to improve patient selection, surgical planning, and outcomes. The purpose of this article is to present the experience and applications of in spine surgery at 2 large academic health care systems

Differential expression of APE1 and APE2 in germinal centers promotes error-prone repair and A:T mutations during somatic hypermutation

Somatic hypermutation (SHM) of antibody variable region genes is initiated in germinal center B cells during an immune response by activation-induced cytidine deaminase (AID), which converts cytosines to uracils. During accurate repair in nonmutating cells, uracil is excised by uracil DNA glycosylase (UNG), leaving abasic sites that are incised by AP endonuclease (APE) to create single-strand breaks, and the correct nucleotide is reinserted by DNA polymerase beta. During SHM, for unknown reasons, repair is error prone. There are two APE homologs in mammals and, surprisingly, APE1, in contrast to its high expression in both resting and in vitro-activated splenic B cells, is expressed at very low levels in mouse germinal center B cells where SHM occurs, and APE1 haploinsufficiency has very little effect on SHM. In contrast, the less efficient homolog, APE2, is highly expressed and contributes not only to the frequency of mutations, but also to the generation of mutations at A:T base pair (bp), insertions, and deletions. In the absence of both UNG and APE2, mutations at A:T bp are dramatically reduced. Single-strand breaks generated by APE2 could provide entry points for exonuclease recruited by the mismatch repair proteins Msh2-Msh6, and the known association of APE2 with proliferating cell nuclear antigen could recruit translesion polymerases to create mutations at AID-induced lesions and also at A:T bp. Our data provide new insight into error-prone repair of AID-induced lesions, which we propose is facilitated by down-regulation of APE1 and up-regulation of APE2 expression in germinal center B cells

Artificial Intelligence and Robotics in Spine Surgery

STUDY DESIGN: Narrative review. OBJECTIVES: Artificial intelligence (AI) and machine learning (ML) have emerged as disruptive technologies with the potential to drastically affect clinical decision making in spine surgery. AI can enhance the delivery of spine care in several arenas: (1) preoperative patient workup, patient selection, and outcome prediction; (2) quality and reproducibility of spine research; (3) perioperative surgical assistance and data tracking optimization; and (4) intraoperative surgical performance. The purpose of this narrative review is to concisely assemble, analyze, and discuss current trends and applications of AI and ML in conventional and robotic-assisted spine surgery. METHODS: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2019 examining AI, ML, and robotics in spine surgery. Key findings were then compiled and summarized in this review. RESULTS: The majority of the published AI literature in spine surgery has focused on predictive analytics and supervised image recognition for radiographic diagnosis. Several investigators have studied the use of AI/ML in the perioperative setting in small patient cohorts; pivotal trials are still pending. CONCLUSIONS: Artificial intelligence has tremendous potential in revolutionizing comprehensive spine care. Evidence-based, predictive analytics can help surgeons improve preoperative patient selection, surgical indications, and individualized postoperative care. Robotic-assisted surgery, while still in early stages of development, has the potential to reduce surgeon fatigue and improve technical precision

Late-week surgery and discharge to specialty care associated with higher costs and longer lengths of stay after elective lumbar laminectomy

OBJECTIVE In a healthcare landscape in which costs increasingly matter, the authors sought to distinguish among the clinical and nonclinical drivers of patient length of stay (LOS) in the hospital following elective lumbar laminectomy-a common spinal surgery that may be reimbursed using bundled payments-and to understand their relationships with patient outcomes and costs. METHODS Patients ≥ 18 years of age undergoing laminectomy surgery for degenerative lumbar spinal stenosis within the Cleveland Clinic health system between March 1, 2016, and February 1, 2019, were included in this analysis. Generalized linear modeling was used to assess the relationships between the day of surgery, patient discharge disposition, and hospital LOS, while adjusting for underlying patient health risks and other nonclinical factors, including the hospital surgery site and health insurance. RESULTS A total of 1359 eligible patients were included in the authors' analysis. The mean LOS ranged between 2.01 and 2.47 days for Monday and Friday cases, respectively. The LOS was also notably longer for patients who were ultimately discharged to a skilled nursing facility (SNF) or rehabilitation center. A prolonged LOS occurring later in the week was not associated with greater underlying health risks, yet it nevertheless resulted in greater costs of care: The average total surgical costs for lumbar laminectomy were 20% greater for Friday cases than for Monday cases, and 24% greater for late-week cases than for early-week cases ultimately transferred to SNFs or rehabilitation centers. A Poisson generalized linear model fit the data best and showed that the comorbidity burden, surgery at a tertiary care center versus a community hospital, and the incidence of any postoperative complication were associated with significantly longer hospital stays. Discharge to home healthcare, SNFs, or rehabilitation centers, and late-week surgery were significant nonclinical predictors of LOS prolongation, even after adjusting for underlying patient health risks and insurance, with LOSs that were, for instance, 1.55 and 1.61 times longer for patients undergoing their procedure on Thursday and Friday compared to Monday, respectively. CONCLUSIONS Late-week surgeries are associated with a prolonged LOS, particularly when discharge is to an SNF or rehabilitation center. These findings point to opportunities to lower costs and improve outcomes associated with elective surgical care. Interventions to optimize surgical scheduling and perioperative care coordination could help reduce prolonged LOSs, lower costs, and, ultimately, give service line management personnel greater flexibility over how to use existing resources as they remain ahead of healthcare reforms