Purpose of Review Social determinants of health (SDH) are factors that affect patient health outcomes outside the hospital. SDH are “conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.” Current literature has shown SDH affecting patient reported outcomes in various specialties; however, there is a dearth in research relating spine surgery with SDH. The aim of this review article is to identify connections between SDH and post-operative outcomes in spine surgery. These are important, yet understudied predictors that can impact health outcomes and affect health equity. Recent Findings Few studies have shown associations between SDH pillars (environment, race, healthcare, economic, and education) and spine surgery outcomes. The most notable relationships demonstrate increased disability, return to work time, and pain with lower income, education, environmental locations, healthcare status and/or provider. Despite these findings, there remains a significant lack of understanding between SDH and spine surgery. Summary Our manuscript reviews the available literature comparing SDH with various spine conditions and surgeries. We organized our findings into the following narrative themes: 1) education, 2) geography, 3) race, 4) healthcare access, and 5) economics.
Objective: To construct, evaluate, and interpret a series of machine learning models to predict outcomes related to inpatient health care resource utilization for patients undergoing anterior cervical discectomy and fusion (ACDF).Summary of Background Data: Reducing postoperative health care utilization is an important goal for improving the delivery of surgical care and serves as a metric for quality assessment. Recent data has shown marked hospital resource utilization after ACDF surgery, including readmissions, and ED visits. The burden of postoperative health care use presents a potential application of machine learning techniques, which may be capable of accurately identifying at-risk patients using patient-specific predictors.Methods: Patients 18-88 years old who underwent ACDF from 2011 to 2021 at a multisite academic center and had preoperative lab values within 3 months of surgery were included. Outcomes analyzed included 90-day readmissions, postoperative length of stay, and nonhome discharge. Four machine learning models-Extreme Gradient Boosted Trees, Balanced Random Forest, Elastic-Net Penalized Logistic Regression, and a Neural Network-were trained and evaluated through the Area Under the Curve estimates. Feature importance scores were computed for the highest-performing model per outcome through modelspecific metrics.Results: A total of 1026 cases were included in the analysis cohort. All machine learning models were predictive for outcomes of interest, with the Random Forest algorithm consistently demonstrating the strongest average area under the curve performance, with a peak performance of 0.84 for nonhome discharge. Important features varied per outcome, though age, body mass index, American Society of Anesthesiologists classification > 2, and medical comorbidities were highly weighted in the studied outcomes.Conclusions: Machine learning models were successfully applied and predictive of postoperative health utilization after ACDF. Deployment of these tools can assist clinicians in determining high-risk patients.Level of Evidence: III.
BackgroundThe widespread societal effects of the COVID-19 pandemic connote public health and epidemiological changes for orthopedic injuries. The epidemiology of upper extremity injuries and the effects of the pandemic on these nationwide trends is poorly defined. MethodsThis cross-sectional, descriptive epidemiological study compares epidemiological trends among upper extremity (UE) orthopedic injuries presenting to emergency departments (EDs) prior to and during the COVID-19 pandemic. Upper extremity fracture and dislocation data was sourced from the National Electronic Injury Surveillance System (NEISS) database in years prior to (2015)(2016)(2017)(2018)(2019) and during the pandemic (2020-2021). Data on incidence, patient demographics, injury patterns, mechanisms of injury, incident locale, and patient disposition were collected and compared between years. ResultsThe pre-COVID-19 incidence rate (IR) of UE fractures at 2.03 per 1,000 persons (n=3038930 from 2015-2019) decreased to 1.84 per 1,000 in 2020 (n=474805) and 1.82 per 1,000 in 2021 (n=471793). Dislocation rates were largely unchanged at 0.34 per 1,000 people (n=476740) prior to the pandemic and with incidence rates of 0.33 per 1,000 (n=85582) and 0.34 per 1,000 (n=89386) in 2020 and 2021, respectively. Female patients over 65 had the highest injury IR at 4.85 per 1,000 (n=976948). Finger fractures (IR=0.38 per 1000, n=96009) overtook hand fractures (IR=0.51 per 1000, n=310710) as more common during COVID-19 in males, while wrist (IR=0.55 per 1000, n=350650) fractures remained most common in females. Injuries from individual sports, such as skateboarding and bicycling, increased during the pandemic, while injuries from team sports decreased. Hospital admission and observation increased in 2020, while discharge and transfer rates decreased. Admission, observation, and discharge rates moved closer to pre-pandemic levels in 2021. ConclusionsThe COVID-19 pandemic was associated with epidemiological and activity changes regarding UE fractures and dislocations presenting to EDs. The present study demonstrates notable decreases in rates of upper extremity fractures and dislocations, increases in rates of injuries related to outdoor and individual sports such as skateboarding with corresponding decreases in rates of injuries related to organized sports such as basketball, increases in the rates of injuries occurring in homes and in association with pet supplies, and decreases in rates of injuries occurring in schools and places of recreation observed during the pandemic. Additionally, trends observed among patient disposition specific to the pandemic, such as increasing rates of patient admission, observation, and against medical advice (AMA) departure with decreasing rates of discharge and transfer, offer insight into the burden of upper extremity injuries on the healthcare system during this critical time. While upper extremity orthopedic injuries remained common through the pandemic, the early pandemic was associated with higher rates of hospital admission ...
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