➤ Sagittal alignment of the spine has gained attention in the field of spinal deformity surgery for decades. However, emerging data support the importance of restoring segmental lumbar lordosis and lumbar spinal shape according to the pelvic morphology when surgically addressing degenerative lumbar pathologies such as degenerative disc disease and spondylolisthesis. ➤ The distribution of caudal lordosis (L4-S1) and cranial lordosis (L1-L4) as a percentage of global lordosis varies by pelvic incidence (PI), with cephalad lordosis increasing its contribution to total lordosis as PI increases. ➤ Spinal fusion may lead to iatrogenic deformity if performed without attention to lordosis magnitude and location in the lumbar spine. ➤ A solid foundation of knowledge with regard to optimal spinal sagittal alignment is beneficial when performing lumbar spinal surgery, and thoughtful planning and execution of lumbar fusions with a focus on alignment may improve patient outcomes.

Background: Orthopaedic surgery has become increasingly specialized, and most trainees currently complete subspecialty fellowship training. The purposes of this investigation were to evaluate recent trends in U.S. orthopaedic fellowship matches and to provide relevant analyses for future orthopaedic fellowship applicants and fellowship program directors. Methods: This study analyzed data from orthopaedic fellowship match programs from 2010 to 2017. For each fellowship, the following variables were analyzed: numbers of positions offered, participating programs, applicant registrations, rank lists submitted by applicants (i.e., completed applications), applicants matched, and filled positions. Applicant-matching success rate and percentage of total fellowship positions filled for each subspecialty were calculated, and trends were evaluated for significance and difference between subspecialties utilizing ordinary least-square regressions, with p < 0.05 indicating significance. Results: From 2010 to 2017, the number of fellowship positions that were offered increased in all subspecialties (p < 0.05) except for spine (p = 0.44) and trauma (p = 0.92). Participating fellowship programs increased in all subspecialties (p < 0.05) except spine (p = 0.38) and sports medicine; the latter experienced the only significant decrease (p < 0.05). The largest significant increases (p < 0.05) in both applicant registrations (33.5%) and rank lists submitted by applicants (45.3%) were in adult reconstruction. The subspecialty with the highest applicant-matching success rate during the study period of 2010 to 2017 was sports (mean, 93.5%). Spine and trauma had the lowest applicant-matching success rates in 2016 to 2017. The percentage of positions filled across all subspecialties increased from 2011 to 2017 (p < 0.05); hand had the highest mean (96.6% filled), and adult reconstruction had the largest significant increase from 82.0% in 2010 to 95.5% in 2017 (p < 0.05). Conclusions: This investigation provides data with regard to current trends in the orthopaedic fellowship match. Specifically, adult reconstruction fellowship training has recently gained popularity at a more rapid rate than the other subspecialty fellowship pathways, although hand surgery consistently maintains a very high rate of positions filled. Our results for orthopaedic subspecialty fellowship match trends may assist fellowship directors with program planning and career advising and may also assist current residents with fellowship application expectations and career planning.

➤ Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedic research. ➤ There are several domains in which machine learning has application to orthopaedics, including radiographic diagnosis, gait analysis, implant identification, and patient outcome prediction. ➤ Several limitations prevent the widespread use of machine learning in the daily clinical environment. However, future work can overcome these issues and enable machine learning tools to be a useful adjunct for orthopaedic surgeons in their clinical decision-making.

(1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treatment for Adult Spine Deformity (ASD) will experience a prolonged LOS. (2) Methods: Patients undergoing treatment for ASD with posterior spinal fusion surgery were selected from the American College of Surgeon’s NSQIP dataset. Prolonged LOS was defined as a LOS greater than or equal to 9 days. Data was analyzed with the Logistic Regression, Decision Tree, Random Forest, XGBoost, and Gradient Boosting functions in Python with the Sci-Kit learn package. Prediction accuracy and area under the curve (AUC) were calculated. (3) Results: 1281 posterior patients were analyzed. The five algorithms had prediction accuracies between 68% and 83% for posterior cases (AUC: 0.566–0.821). Multivariable regression indicated that increased Work Relative Value Units (RVU), elevated American Society of Anesthesiologists (ASA) class, and longer operating times were linked to longer LOS. (4) Conclusions: Machine learning algorithms can predict if patients will experience an increased LOS following ASD surgery. Therefore, medical resources can be more appropriately allocated towards patients who are at risk of prolonged LOS.

Antibiotic bone cement (ABC) is an effective tool for the prophylaxis and treatment of osteomyelitis due to the controlled, sustained release of local antibiotics. ABC has been proven to be effective in the orthopedic fields of arthroplasty and extremity trauma, but the adoption of ABC in spine surgery is limited. The characteristics of ABC make it an optimal solution for treating vertebral osteomyelitis (VO), a serious complication following spine surgery, typically caused by bacterial and sometimes fungal and parasitic pathogens. VO can be devastating, as infection can result in pathogenic biofilms on instrumentation that is dangerous to remove. New techniques, such as kyphoplasty and novel vertebroplasty methods, could amplify the potential of ABC in spine surgery. However, caution should be exercised when using ABC as there is some evidence of toxicity to patients and surgeons, antibiotic allergies, bone cement structural impairment, and possible development of antibiotic resistance. The purpose of this article is to describe the basic science of antibiotic cement utilization and review its usage in spine surgery.

Surgical robots were invented in the 1980s, and since then, robotic-assisted surgery has become commonplace. In the field of spine surgery, robotic assistance is utilized mainly to place pedicle screws, and multiple studies have demonstrated that robots can increase the accuracy of screw placement and reduce radiation exposure to the patient and the surgeon. However, this may be at the cost of longer operative times, complications, and the risk of errors in mapping the patient’s anatomy.