Inpatient opioid use varies by construct length among laminoplasty versus laminectomy and fusion patients.

Background: Laminoplasty (LP) and laminectomy and fusion (LF) are utilized to achieve decompression in patients with symptomatic degenerative cervical myelopathy (DCM). Comparative analyses aimed at determining outcomes and clarifying indications between these procedures represent an area of active research. Accordingly, we sought to compare inpatient opioid use between LP and LF patients and to determine if opioid use correlated with length of stay. Methods: Sociodemographic information, surgical and hospitalization data, and medication administration records were abstracted for patients >18 years of age who underwent LP or LF for DCM in the Mass General Brigham (MGB) health system between 2017 and 2019. Specifically, morphine milligram equivalents (MME) of oral and parenteral pain medication given after arrival in the recovery area until discharge from the hospital were collected. Categorical variables were analyzed using chi-squared analysis or Fisher exact test when appropriate. Continuous variables were compared using Independent samples t tests and Mann-Whitney U tests. Results: One hundred eight patients underwent LF, while 138 patients underwent LP. Total inpatient opioid use was significantly higher in the LF group (312 vs. 260 MME, p=.03); this difference was primarily driven by higher postoperative day 0 pain medication requirements. Furthermore, more LF patients required high dose (>80 MME/day) regimens. While length of stay was significantly different between groups, with LF patients staying approximately 1 additional day, postoperative day 0 MME was not a significant predictor of this difference. When operative levels including C2, T1, and T2 were excluded, the differences in total opioid use and average length of stay lost significance. Conclusions: Inpatient opioid use and length of stay were significantly greater in LF patients compared to LP patients; however, when constructs including C2, T1, T2 were excluded from analysis, these differences lost significance. Such findings highlight the impact of operative extent between these procedures. Future studies incorporating patient reported outcomes and evaluating long-term pain needs will provide a more complete understanding of postoperative outcomes between these 2 procedures.

What Factors Lead to Racial Disparities in Outcomes After Total Knee Arthroplasty?

Total knee arthroplasty (TKA) is one of the most commonly performed, major elective surgeries in the USA. African American TKA patients on average experience worse clinical outcomes than whites, including lower improvements in patient-reported outcomes and higher rates of complications, hospital readmissions, and reoperations. The mechanisms leading to these racial health disparities are unclear, but likely involve patient, provider, healthcare system, and societal factors. Lower physical and mental health at baseline, lower social support, provider bias, lower rates of health insurance coverage, higher utilization of lower quality hospitals, and systemic racism may contribute to the inferior outcomes that African Americans experience. Limited evidence suggests that improving the quality of surgical care can offset these factors and lead to a reduction in outcome disparities.

Social Determinants of Health and Patient-Reported Outcomes Following Total Hip and Knee Arthroplasty in Veterans.

BACKGROUND: Social determinants of health (SDOH) are the conditions in which people are born, grow, live, work, and age. They are associated with disparities in outcomes following total joint arthroplasty (TJA). These disparities occur even in equal-access healthcare systems such as the Veterans Health Administration (VHA). Our goal was to determine whether SDOH affect patient-reported outcome measures (PROMs) following TJA in VHA patients. METHODS: Patients scheduled to undergo total hip or knee arthroplasty at VHA Hospitals in Minneapolis, MN, Palo Alto, CA, and San Francisco, CA, prospectively completed PROMs before and 1 year after surgery. PROMs included the Hip disability and Osteoarthritis Outcome Score, the Knee injury and Osteoarthritis Outcome Score, and their Joint Replacement subscores. SDOH included race, ethnicity, marital status, education, and employment status. The level of poverty in each patient's neighborhood was determined. Medical comorbidities were recorded. Univariate and multivariate analyses were performed to determine whether SDOH were significantly associated with PROM improvement after surgery. RESULTS: On multivariate analysis, black race was significantly negatively correlated with knee PROM improvement and Hispanic ethnicity was significantly negatively correlated with hip PROM improvement compared to whites. Higher baseline PROM scores and lower age were significantly associated with lower PROM improvement. Significant associations were also found based on education, gender, comorbidities, and neighborhood poverty. CONCLUSION: Minority VHA patients have lower improvement in PROM scores after TJA than white patients. Further research is required to identify the reasons for these disparities and to design interventions to reduce them.

Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning.

PURPOSE: To investigate the feasibility of automatic identification and classification of hip fractures using deep learning, which may improve outcomes by reducing diagnostic errors and decreasing time to operation. MATERIALS AND METHODS: Hip and pelvic radiographs from 1118 studies were reviewed, and 3026 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous open reduction and internal fixation, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (or DenseNet) was trained on a subset of the bounding box images, and its performance was evaluated on a held-out test set and by comparison on a 100-image subset with two groups of human observers: fellowship-trained radiologists and orthopedists; senior residents in emergency medicine, radiology, and orthopedics. RESULTS: The binary accuracy for detecting a fracture of this model was 93.7% (95% confidence interval [CI]: 90.8%, 96.5%), with a sensitivity of 93.2% (95% CI: 88.9%, 97.1%) and a specificity of 94.2% (95% CI: 89.7%, 98.4%). Multiclass classification accuracy was 90.8% (95% CI: 87.5%, 94.2%). When compared with the accuracy of human observers, the accuracy of the model achieved an expert-level classification, at the very least, under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance in the multiclass classification. CONCLUSION: A deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians.Supplemental material is available for this article.© RSNA, 2020.

Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial.

BACKGROUND: Tracking patient-generated health data (PGHD) following total joint arthroplasty (TJA) may enable data-driven early intervention to improve clinical results. We aim to demonstrate the feasibility of combining machine learning (ML) with PGHD in TJA to predict patient-reported outcome measures (PROMs). METHODS: Twenty-two TJA patients were recruited for this pilot study. Three activity trackers collected 35 features from 4 weeks before to 6 weeks following surgery. PROMs were collected at both endpoints (Hip and Knee Disability and Osteoarthritis Outcome Score, Knee Osteoarthritis Outcome Score, and Veterans RAND 12-Item Health Survey Physical Component Score). We used ML to identify features with the highest correlation with PROMs. The algorithm trained on a subset of patients and used 3 feature sets (A, B, and C) to group the rest into one of the 3 PROM clusters. RESULTS: Fifteen patients completed the study and collected 3 million data points. Three sets of features with the highest R2 values relative to PROMs were selected (A, B and C). Data collected through the 11th day had the highest predictive value. The ML algorithm grouped patients into 3 clusters predictive of 6-week PROM results, yielding total sum of squares values ranging from 3.86 (A) to 1.86 (C). CONCLUSION: This small but critical proof-of-concept study demonstrates that ML can be used in combination with PGHD to predict 6-week PROM data as early as 11 days following TJA surgery. Further study is needed to confirm these findings and their clinical value.

Optimal Sampling Frequency for Wearable Sensor Data in Arthroplasty Outcomes Research. A Prospective Observational Cohort Trial.

BACKGROUND: Wearable sensors can track patient activity after surgery. The optimal data sampling frequency to identify an association between patient-reported outcome measures (PROMs) and sensor data is unknown. Most commercial grade sensors report 24-hour average data. We hypothesize that increasing the frequency of data collection may improve the correlation with PROM data. METHODS: Twenty-two total joint arthroplasty (TJA) patients were prospectively recruited and provided wearable sensors. Second-by-second (Raw) and 24-hour average data (24Hr) were collected on 7 gait metrics on the 1st, 7th, 14th, 21st, and 42nd days postoperatively. The average for each metric as well as the slope of a linear regression for 24Hr data (24HrLR) was calculated. The R2 associations were calculated using machine learning algorithms against individual PROM results at 6 weeks. The resulting R2 values were defined having a mild, moderate, or strong fit (R2 ≥ 0.2, ≥0.3, and ≥0.6, respectively) with PROM results. The difference in frequency of fit was analyzed with the McNemar's test. RESULTS: The frequency of at least a mild fit (R2 ≥ 0.2) for any data point at any time frame relative to either of the PROMs measured was higher for Raw data (42%) than 24Hr data (32%; P = .041). There was no difference in frequency of fit for 24hrLR data (32%) and 24Hr data values (32%; P > .05). Longer data collection improved frequency of fit. CONCLUSION: In this prospective trial, increasing sampling frequency above the standard 24Hr average provided by consumer grade activity sensors improves the ability of machine learning algorithms to predict 6-week PROMs in our total joint arthroplasty cohort.

Neurologic deterioration in a child undergoing treatment for tuberculosis meningitis.

Clinical deterioration while receiving antituberculosis (anti-TB) therapy can be due to a number of etiologies, including drug resistance, disease progression despite effective therapy, or alternative diagnoses. We present the case of a 22-month-old girl diagnosed with TB meningitis 4 months prior to presentation. At time of her initial diagnosis, computed tomography showed hydrocephalus and basilar meningitis with some evidence of ischemic damage. She required placement of a ventriculoperitoneal shunt and was discharged on multidrug anti-TB therapy and corticosteroids. At the time of her second emergency department presentation, she had developed new-onset seizures and hemiparesis. Her steroids had been tapered and discontinued. Differential diagnosis included shunt malfunction and/or shunt infection. Magnetic resonance imaging of the brain showed interval development of tuberculomas. Symptomatic and radiographic improvement was seen after initiation of corticosteroids for immune reconstitution inflammatory syndrome, which can be seen in immunocompetent children, with onset weeks to months after starting antituberculous therapy.

Adolescents with tuberculosis: a review of 145 cases.

BACKGROUND: Adolescents comprise one-third of pediatric tuberculosis (TB) cases in the United States, but there are few specific data on the epidemiology and clinical course in this population. METHODS: This was a retrospective review of adolescents (12-18 years old) seen at a Children's Tuberculosis Clinic in Houston, TX, from 1987 to 2012. RESULTS: One hundred forty-five adolescents were identified; median age was 15.4 years: 50% female, 55% were Hispanic, 26% black, 13% Asian and 1% white; 54 were born abroad. Diagnoses were made after symptomatic presentation in 79%, during contact investigations in 14% and after screening tuberculin skin testing in the remainder. The most common symptoms were fever (63%), cough (60%) and weight loss (30%), but 21% were asymptomatic at diagnosis. Only 8% of adolescents with intrathoracic TB had hemoptysis. One hundred fourteen (78.6%) had isolated intrathoracic TB, 4 (2.8%) had intra- and extrathoracic TB and 27 (18.6%) had extrathoracic TB. The most common sites of extrathoracic TB were peripheral lymphadenopathy (10) and meningitis (6). The most common radiographic findings were infiltrates (34%), lymphadenopathy (27%), cavitary lesions (26%), pleural effusions (19%) and miliary disease (10%). Acid-fast bacillus smears and mycobacterial cultures were attempted for 97 of 118 adolescents with intrathoracic and 22 of 27 with extrathoracic disease, respectively, resulting in smear/culture positivity in 25% and 54% and 18% and 45%, respectively. Two patients died, 2 had relapse, 7 had significant sequelae and 92% recovered without complication. Seventy three percent of cases potentially were preventable. CONCLUSIONS: The clinical, radiologic and microbiologic findings in adolescents with TB have features seen in both younger children and adults; most cases were preventable.