A cross-sectional database study.The aim of this study was to train and validate machine learning models to identify risk factors for complications following posterior lumbar spine fusion.Machine learning models such as artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex datasets. ANNs have yet to be used for risk factor analysis in orthopedic surgery.The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent posterior lumbar spine fusion. This query returned 22,629 patients, 70% of whom were used to train our models, and 30% were used to evaluate the models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society for Anesthesiology (ASA) class ≥3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating curves (AUC) was used to determine the accuracy of our machine learning models.On the basis of AUC values, ANN and LR both outperformed ASA class for predicting all four types of complications. ANN was the most accurate for predicting cardiac complications, and LR was most accurate for predicting wound complications, VTE, and mortality, though ANN and LR had comparable AUC values for predicting all types of complications. ANN had greater sensitivity than LR for detecting wound complications and mortality.Machine learning in the form of logistic regression and ANNs were more accurate than benchmark ASA scores for identifying risk factors of developing complications following posterior lumbar spine fusion, suggesting they are potentially great tools for risk factor analysis in spine surgery.3.
The purpose of this study was to analyze posts shared on social media sites, Twitter and Instagram, referencing scoliosis surgery for tone, content, and perspective of the posts.Public Twitter and Instagram posts from November 2020 to April 2021 were isolated using the hashtag #ScoliosisSurgery or the words "scoliosis surgery." A total of 5,022 Instagram and 1,414 Twitter posts were collected, of which 500 of each were randomly selected to be analyzed by the authors for the variables previously listed.Of the Instagram posts, 91.8% were associated with an image, and 47.8% were postoperative. 96.9% of the posts had either a positive or neutral tone. 38% delivered a progress update, and 29.9% disseminated education or sought to provide awareness. 48.6% of the posts were from the perspective of the patient. Of the Twitter posts, 60.1% contained only words, and 37.8% were postoperative. 75% of the posts had either a negative or neutral tone. 38.4% described a personal story, and 19.3% provided a progress update. 42.3% of the posts were from the perspective of the patient.Patients reported a positive tone on Instagram, displaying their progress updates and demonstrating contentment with scoliosis surgery, and a negative tone on Twitter, showing discontentment toward inadequate access to surgery. Although both platforms were used to distribute information and provide awareness, only a small percentage of posts were from physicians and hospitals, indicating opportunities for surgeons to use social media to connect with patients.
Background: An accurate knowledge of a patient’s risk of cord-level intraoperative neuromonitoring (IONM) data loss is important for an informed decision-making process prior to deformity correction, but no prediction tool currently exists. Methods: A total of 1,106 patients with spinal deformity and 205 perioperative variables were included. A stepwise machine-learning (ML) approach using random forest (RF) analysis and multivariable logistic regression was performed. Patients were randomly allocated to training (75% of patients) and testing (25% of patients) groups. Feature score weights were derived by rounding up the regression coefficients from the multivariable logistic regression model. Variables in the final scoring calculator were automatically selected through the ML process to optimize predictive performance. Results: Eight features were included in the scoring system: sagittal deformity angular ratio (sDAR) of ≥15 (score = 2), type-3 spinal cord shape (score = 2), conus level below L2 (score = 2), cervical upper instrumented vertebra (score = 2), preoperative upright largest thoracic Cobb angle of ≥75° (score = 2), preoperative lower-extremity motor deficit (score = 2), preoperative upright largest thoracic kyphosis of ≥80° (score = 1), and total deformity angular ratio (tDAR) of ≥25 (score = 1). Higher cumulative scores were associated with increased rates of cord-level IONM data loss: patients with a cumulative score of ≤2 had a cord-level IONM data loss rate of 0.9%, whereas those with a score of ≥7 had a loss rate of 86%. When evaluated in the testing group, the scoring system achieved an accuracy of 93%, a sensitivity of 75%, a specificity of 94%, and an AUC (area under the receiver operating characteristic curve) of 0.898. Conclusions: This is the first study to provide an ML-derived preoperative scoring system that predicts cord-level IONM data loss during pediatric and adult spinal deformity surgery with >90% accuracy. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.
This paper describes trends in HIV-related morbidity among people living with HIV/AIDS (PLWHA) admitted to a tertiary hospital in Chennai, South India, between 1997 and 2003. Patients comprised HIV-infected men, women and children who had been admitted at least once to YR Gaitonde Centre for AIDS Research and Education (YRGCARE). A non-parametric trends analysis was conducted to observe trends in clinical and demographic parameters and diagnoses at admission over the seven-year period. Among clinical and demographic parameters, we identified a significantly increasing time trend in the use of antiretroviral therapy (p<0.001) and a significant decrease in the mean hemoglobin level (p=0.01). Among diagnoses at admission, we identified a decreasing time trend for admissions due to pulmonary tuberculosis (p<0.001) and increasing trends for admissions due to extra pulmonary tuberculosis (p<0.01), toxoplasmosis (p<0.01), Pneumocystis carinii pneumonia (p=0.02) and anemia (p<0.001). The results indicate a changing pattern among the clinical conditions requiring admission. With increasing proportions of patients initiating highly active antiretroviral therapy (HAART), it is probable that adverse events due to HAART will account for larger proportions of admissions in the years to come, as is being seen in the industrialized countries.
The use of convalescent plasma in coronavirus disease 2019 (COVID‑19) in the general population has not been shown to have a clear benefit. However, there are limited data available on its use in specific populations, such as in persons with human immunodeficiency virus (HIV; PWH). The present case series study describes 12 hospitalized PWH who received convalescent plasma for severe COVID‑19 between March 2020 and July 2020. Demographics, pre‑existing comorbidities, HIV status, and COVID‑19 management were reported and examined in a multivariate analysis. A high mortality rate of 58%, (7 out of 12 PWH) was observed in those receiving the convalescent plasma. By contrast, a brief review of 13 previously published cohorts of PWH hospitalized with COVID‑19 revealed a cumulative mortality of 19% (85 of 439 PWH). In the present case series study, PWH had a significantly higher relative risk for in‑hospital COVID‑19‑associated mortality compared with individuals without HIV (unadjusted range, 2.10‑2.52; and adjusted range, 1.79‑2.08; P<0.02 in all analyses). Covariate‑adjustments were made for patient demographics, pre‑existing co‑morbidities, and mechanical ventilation needs. The high mortality rate of the present case series may be related to random sampling or an adverse effect of convalescent plasma in PWH and severe COVID‑19. Additional research is thus required to investigate the risks and benefits of the use of COVID‑19 convalescent plasma in PWH.
Tendon-to-bone repair remains a surgical challenge. Although bone tunnel fixation is a common surgical technique whereby soft tissue is expected to heal against a bone tunnel interface, contemporary methods have yet to recapitulate biomechanical similarity to the native enthesis. In this study, we aimed to understand how inside-out longitudinal tendon inversion affects bone tunnel healing with the hypothesis that inversion removes the gliding epitenon surface to facilitate interface healing. Forty male Sprague-Dawley rats underwent either native tendon tenodesis (control group) or tendon inversion tenodesis (experimental group). Interface tissue was harvested 8 weeks after surgery. Biomechanical testing was performed to assess tensile strength and modes of failure. Histology was performed to assess tissue architecture, and immunohistochemistry confirmed the disruption of epitendinous lubricin from interface tissues. Maximum tensile strength increased after tendon inversion compared with control surgery. The extracellular matrix protein lubricin was reduced with tendon inversion, and specimens with tendon inversion had greater healing scores and collagen fibril alignment at the healing interface. Tendon inversion has the potential to improve bone tunnel healing in rats. Our findings suggest that longitudinal tendon inversion, or inverse tubularization, in a rat biceps model improves tendon-to-bone healing in part because of disruption of the epitendinous surface at the bone healing interface. This work provides molecular insight into future improvements for tendon-to-bone repair surgical techniques.
Retrospective cohort study of 2016 Healthcare Cost and Utilization Project Nationwide Readmissions Database (NRD).The aim was to evaluate cost and outcomes associated with navigation use on posterior cervical fusion (PCF) surgery patients.Computer-assisted navigation systems demonstrate comparable outcomes with hardware placement and procedural speed compared with traditional techniques. Innovations in technology continue to improve surgeons' performance in complicated procedures, causing need to analyze the impact on patient care.The 2016 NRD was queried for patients with PCF surgery ICD-10 codes. Cost and readmission rates were compared with and without navigation. Nonelective cases and patients below 18 years of age were excluded. Univariate analysis on demographics, surgical data, and total charges was performed. Lastly, multivariate analysis was performed to assess navigation's impact on cost and postoperative outcomes.A total of 11,834 patients were identified, with 137 (1.2%) patients undergoing surgery with navigation and 11,697 (98.8%) patients without. Average total charge was $131,939.47 and $141,270.1 for the non-navigation and navigation cohorts, respectively ( P =0.349). Thirty-day and 90-day readmission rates were not significantly lower in patients who received navigation versus those that did not ( P =0.087). This remained insignificant after adjusting for several variables, age above 65, sex, medicare status, mental health history, and comorbidities. The model adjusting for demographic and comorbidities maintained insignificant results of navigation being associated with decreased 30-day and 90-day readmissions ( P =0.079).Navigation use in PCF surgery was not associated with increased cost, and patients operated on with navigation did not significantly have increased routine discharge or decreased 90-day readmission. As a result, future studies must continue to evaluate the cost-benefit of navigation use for cervical fusion surgery.Level III.
