Power calculation for detecting interaction effect in cross-sectional stepped-wedge cluster randomized trials: an important tool for disparity research.

BACKGROUND: The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. METHODS: We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. RESULTS: Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. CONCLUSIONS: We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.

Malnutrition risk assessment using a machine learning-based screening tool: A multicentre retrospective cohort.

BACKGROUND: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. METHODS: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days. RESULTS: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. CONCLUSION: MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.

A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.

BACKGROUND: Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs IMV, machine learning algorithms may help bring more prognostic value in a timely and systematic manner. Chest radiographs (CXRs) and electronic medical records (EMRs), typically obtained early in patients admitted with COVID-19, are the keys to deciding whether they need IMV. OBJECTIVE: We aimed to evaluate the use of a machine learning model to predict the need for intubation within 24 hours by using a combination of CXR and EMR data in an end-to-end automated pipeline. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City. METHODS: CXRs were first resized, rescaled, and normalized. Then lungs were segmented from the CXRs by using a U-Net algorithm. After splitting them into a training and a test set, the training set images were augmented. The augmented images were used to train an image classifier to predict the probability of intubation with a prediction window of 24 hours by retraining a pretrained DenseNet model by using transfer learning, 10-fold cross-validation, and grid search. Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR. Variables in the EMR included clinical and laboratory data routinely collected in the inpatient setting. The final fusion model gave a prediction likelihood for the need of intubation within 24 hours as well. RESULTS: At a prediction probability threshold of 0.5, the fusion model provided 78.9% (95% CI 59%-96%) sensitivity, 83% (95% CI 76%-89%) specificity, 0.509 (95% CI 0.34-0.67) F1-score, 0.874 (95% CI 0.80-0.94) area under the receiver operating characteristic curve (AUROC), and 0.497 (95% CI 0.32-0.65) area under the precision recall curve (AUPRC) on the holdout set. Compared to the image classifier alone, which had an AUROC of 0.577 (95% CI 0.44-0.73) and an AUPRC of 0.206 (95% CI 0.08-0.38), the fusion model showed significant improvement (P<.001). The most important predictor variables were respiratory rate, C-reactive protein, oxygen saturation, and lactate dehydrogenase. The imaging probability score ranked 15th in overall feature importance. CONCLUSIONS: We show that, when linked with EMR data, an automated deep learning image classifier improved performance in identifying hospitalized patients with severe COVID-19 at risk for intubation. With additional prospective and external validation, such a model may assist risk assessment and optimize clinical decision-making in choosing the best care plan during the critical stages of COVID-19.

Association between adjuvant radiation treatment and breast cancer-specific mortality among older women with comorbidity burden: A comparative effectiveness analysis of SEER-MHOS.

BACKGROUND: The National Comprehensive Cancer Network suggested that older women with low-risk breast cancer (LRBC; i.e., early-stage, node-negative, and estrogen receptor-positive) could omit adjuvant radiation treatment (RT) after breast-conserving surgery (BCS) if they were treated with hormone therapy. However, the association between RT omission and breast cancer-specific mortality among older women with comorbidity is not fully known. METHODS: 1105 older women (≥65 years) with LRBC in 1998-2012 were queried from the Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey data resource and were followed up through July 2018. Latent class analysis was performed to identify comorbidity burden classes. A propensity score-based inverse probability of treatment weighting (IPTW) was applied to Cox regression models to obtain subdistribution hazard ratios (HRs) and 95% CI for cancer-specific mortality considering other causes of death as competing risks, overall and separately by comorbidity burden class. RESULTS: Three comorbidity burden (low, moderate, and high) groups were identified. A total of 318 deaths (47 cancer-related) occurred. The IPTW-adjusted Cox regression analysis showed that RT omission was not associated with short-term, 5- and 10-year cancer-specific death (p = 0.202 and p = 0.536, respectively), regardless of comorbidity burden. However, RT omission could increase the risk of long-term cancer-specific death in women with low comorbidity burden (HR = 1.98, 95% CI = 1.17, 3.33), which warrants further study. CONCLUSIONS: Omission of RT after BCS is not associated with an increased risk of cancer-specific death and is deemed a reasonable treatment option for older women with moderate to high comorbidity burden.

Implementing a Machine Learning Screening Tool for Malnutrition: Insights From Qualitative Research Applicable to Other Machine Learning-Based Clinical Decision Support Systems.

BACKGROUND: Machine learning (ML)-based clinical decision support systems (CDSS) are popular in clinical practice settings but are often criticized for being limited in usability, interpretability, and effectiveness. Evaluating the implementation of ML-based CDSS is critical to ensure CDSS is acceptable and useful to clinicians and helps them deliver high-quality health care. Malnutrition is a common and underdiagnosed condition among hospital patients, which can have serious adverse impacts. Early identification and treatment of malnutrition are important. OBJECTIVE: This study aims to evaluate the implementation of an ML tool, Malnutrition Universal Screening Tool (MUST)-Plus, that predicts hospital patients at high risk for malnutrition and identify best implementation practices applicable to this and other ML-based CDSS. METHODS: We conducted a qualitative postimplementation evaluation using in-depth interviews with registered dietitians (RDs) who use MUST-Plus output in their everyday work. After coding the data, we mapped emergent themes onto select domains of the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. RESULTS: We interviewed 17 of the 24 RDs approached (71%), representing 37% of those who use MUST-Plus output. Several themes emerged: (1) enhancements to the tool were made to improve accuracy and usability; (2) MUST-Plus helped identify patients that would not otherwise be seen; perceived usefulness was highest in the original site; (3) perceived accuracy varied by respondent and site; (4) RDs valued autonomy in prioritizing patients; (5) depth of tool understanding varied by hospital and level; (6) MUST-Plus was integrated into workflows and electronic health records; and (7) RDs expressed a desire to eventually have 1 automated screener. CONCLUSIONS: Our findings suggest that continuous involvement of stakeholders at new sites given staff turnover is vital to ensure buy-in. Qualitative research can help identify the potential bias of ML tools and should be widely used to ensure health equity. Ongoing collaboration among CDSS developers, data scientists, and clinical providers may help refine CDSS for optimal use and improve the acceptability of CDSS in the clinical context.

Longitudinal Assessment of Association Between Tobacco Use and Tobacco Dependence Among Adults: Latent Class Analysis of the Population Assessment of Tobacco and Health Study Waves 1-4.

INTRODUCTION: With increasing tobacco product varieties, understanding tobacco use (TU) profiles and their associations with tobacco dependence (TD) has also become increasingly challenging. AIMS AND METHODS: We aimed to identify TU profiles and their associations with TD over time, and to identify subgroups with high risk of TD. We included 3463 adult recent tobacco users who had complete TU and TD data across waves 1-4 of the Population Assessment of Tobacco and Health (PATH) study. We used a composite index of TD and a summed TD score from an established 16-item TD measure. We applied a latent class analysis to identify TU profiles based on participants' usage of eight common tobacco product groups at each survey wave and to check the stability of the TU profiles over time. We then used generalized estimating equations regressions to evaluate the longitudinal TU-TD association, adjusting for potential confounders. RESULTS: We identified three distinct TU profiles that remained consistent across four survey waves: Dominant cigarette users (62%-68%), poly users with high propensity of using traditional cigarettes, e-cigarettes, and cigars (24%-31%), and dominant smokeless product users (7%-9%). Covariate-adjusted models showed that TD was significantly lower among the poly users and the dominant smokeless users, compared to that among the dominant cigarette users. CONCLUSIONS: Both TU profiles and their associations with TD were stable over time at the population level. Poly users and smokeless product users were consistently associated with lower TD than cigarette-dominant users, suggesting the need for tailored tobacco cessation interventions for users with different TU profiles. IMPLICATIONS: The finding of consistent TU profiles across four survey waves extends the current literature in capturing TU patterns in an evolving tobacco product landscape. The finding of the overall higher level of TD among the cigarette-dominant users compared to the other TU latent profiles (the Cig+eCig+Cigar dominant poly users and the dominant smokeless product users) can help identify high-risk groups for potential interventions. Our application of innovative statistical methods to high-quality longitudinal data from the PATH study helps improve the understanding of the dynamic TU-TD relationship over time.

Benefits of an Early Mobility Program for Hospitalized Patients With Cancer.

PURPOSE: Patients with cancer are often hospitalized with complications from cancer and cancer treatment. Many experience a decline in physical functioning, including loss of mobility, which likely contributes to increased length of stay (LOS) and increased readmissions. We aimed to determine whether a mobility program would improve quality of care and decrease health care utilization. METHODS: We implemented a mobility aide program on an oncology unit in a large academic medical center for all patients without bedrest orders between October 1, 2018, and February 28, 2021. The program consisted of nursing evaluation using the Activity Measure for Post-Acute Care (AMPAC), an ordinal scale ranging from bed rest to ambulating ≥ 250 feet, to quantify mobility. Plan of care was determined in a multidisciplinary manner with physical therapy (PT), nursing, and a mobility aide, who is a medical assistant with enhanced rehabilitation training. Patients were then mobilized two times per day 7 days a week. Using descriptive statistics and mixed effects logistic regression, we evaluated the programs impact on LOS, readmissions, and changes in mobility during this time period compared with the 6-month interval before implementation. RESULTS: A total of 1,496 hospitalized patients were identified. The odds of hospital readmission within 30 days of discharge was significantly less for those who received the intervention (OR, 0.53; 95% CI, 0.37 to 0.78; P = .001). The odds ratio (OR) of having a final AMPAC score at or above the median was significantly higher for those who received the intervention (OR, 1.60; 95% CI, 1.04 to 2.45; P < .05). There was no significant difference in LOS. CONCLUSION: Use of this mobility program resulted in a significant decrease in readmissions and maintained or improved patients' mobility. This demonstrates that non-PT professionals can effectively mobilize hospitalized patients with cancer, thereby decreasing the burden on PT and nursing resources. Future work will evaluate the sustainability of the program and evaluate association with health care costs.

Examining annual transitions in healthcare spending among U.S. medicare beneficiaries using multistate Markov models: Analysis of medicare current beneficiary survey data, 2003-2019.

Many studies have examined factors associated with individuals of high or low healthcare spending in a given year. However, few have studied how healthcare spending changes over multiple years and which factors are associated with the changes. In this study, we examined the dynamic patterns of healthcare spending over a three-year period, among a nationally representative cohort of Medicare beneficiaries in the U.S. and identified factors associated with these patterns. We extracted data for 30,729 participants from the national Medicare Current Beneficiary Survey (MCBS), for the period 2003-2019. Using multistate Markov (MSM) models, we estimated the probabilities of year-to-year transitions in healthcare spending categorized as three states (low (L), medium (M) and high (H)), or to the terminal state, death. The participants, 13,554 (44.1%), 13,715 (44.6%) and 3,460 (11.3%) were in the low, medium and high spending states at baseline, respectively. The majority of participants remained in the same spending category from one year to the next (L-to-L: 76.8%; M-to-M: 71.7%; H-to-H: 56.6 %). Transitions from the low to high spending state were significantly associated with older age (75-84, ≥85 years), residing in a long-term care facility, greater assistance with activities of daily living, enrollment in fee-for-service Medicare, not receiving a flu shot, and presence of specific medical conditions, including cancer, dementia, and heart disease. Using data from a large population-based longitudinal survey, we have demonstrated that MSM modelling is a flexible framework and useful tool for examining changes in healthcare spending over time.

The Burden of Postoperative Delirium After Shoulder Arthroplasty and Modifiable Pharmacological Perioperative Risk Factors: A Retrospective Nationwide Cohort Study.

Background: Increasing demand for shoulder arthroplasty and an aging population may increase the rate of complications associated with advanced age such as postoperative delirium, but little is known on its burden in this cohort. Purpose: We sought to answer the following questions: (1) What is the epidemiology of postoperative delirium after shoulder arthroplasty? (2) What modifiable risk factors can be identified for postoperative delirium after shoulder arthroplasty? (3) Do risk factors differ in those younger than and in those older than 70 years of age? Methods: In a retrospective nationwide cohort study, we extracted data from the Premier Healthcare database on inpatient total and reverse shoulder arthroplasties from 2006 to 2016. The primary outcome was postoperative delirium; modifiable risk factors of interest were perioperative opioid use (high, medium, or low), peripheral nerve block use, and perioperative prescription medications. Mixed-effects models assessed associations between risk factors and postoperative delirium. Odds ratios and confidence intervals are reported. We applied a cutoff of 70 years of age because it was the median age of the cohort, as well as the age at which we observed that delirium prevalence increased. Results: A total of 92,429 total and reverse shoulder arthroplasties were identified (age range: 14-89 years). Overall delirium prevalence was 3.1% (n = 2909). Age-specific prevalence of postoperative delirium was lower in patients aged 50 to 70 years and higher in those aged 70 years and older, up to 8% among those older than 88 years. After adjusting for relevant covariates, only long-acting and combined short-acting and long-acting benzodiazepines (compared with no benzodiazepines) were associated with increased odds of postoperative delirium. Corticosteroids were associated with decreased odds of postoperative delirium. Conclusion: Our retrospective cohort study demonstrated that benzodiazepine use and older patient age were significantly associated with postoperative delirium in shoulder arthroplasty patients. The relationship between benzodiazepine use and delirium was particularly notable among those 70 years of age and older. Further investigation is indicated, given the known adverse effects of benzodiazepines in older adults and our findings of higher than expected use of these medications in this surgical cohort.

Musculoskeletal Telemedicine Trends Preceding the COVID-19 Pandemic and Potential Implications of Rapid Telemedicine Expansion.

Introduction: Telemedicine was rapidly deployed at the onset of the COVID-19 pandemic. Little has been published on telemedicine in musculoskeletal care prior to the COVID-19 pandemic. This study is aimed at characterizing trends in telemedicine for musculoskeletal care preceding the COVID-19 pandemic. Methods: This retrospective study used insurance claims from the Truven MarketScan database. Musculoskeletal-specific outpatient visits from 2014 to 2018 were identified using the musculoskeletal major diagnostic category ICD-10 codes. Telemedicine visits were categorized using CPT codes and Healthcare Common Procedure Coding Systems. We described annual trends in telemedicine in the overall dataset and by diagnosis grouping. Multivariable logistic regression modeling estimated the association between patient-specific and telemedicine visit variables and telemedicine utilization. Results: There were 36,672 musculoskeletal-specific telemedicine visits identified (0.020% of all musculoskeletal visits). Overall, telemedicine utilization increased over the study period (0% in 2014 to 0.05% in 2018). Orthopedic surgeons had fewer telemedicine visits than primary care providers (OR 0.57, 95% CI 0.55-0.59). The proportion of unique patients utilizing telemedicine in 2018 was higher in the south (OR 2.28, 95% CI 2.19-2.38) and west (OR 5.58, 95% CI 5.36-5.81) compared to the northeast. Those with increased comorbidities and lower incomes and living in rural areas had lower rates of telemedicine utilization. Conclusions: From 2014 to 2018, there was an increase in telemedicine utilization for musculoskeletal visits, in part due to insurance reimbursement and telemedicine regulation. Despite this increase, the rates of telemedicine utilization are still lowest in some of the groups that could derive the most benefit from these services. Establishing this baseline is important for assessing how the roll-out of telemedicine during the pandemic impacted how/which patients and providers are utilizing telemedicine today.

A changing supply chain for a changing health care system: Barriers and facilitators of implementing enterprise resource planning.

BACKGROUND: No studies have examined how health care mergers and acquisitions affected the hospital supply chain and its employees since the passing of the Patient Protection and Affordable Care Act. OBJECTIVE: To describe the barriers and facilitators of digital transformation in a hospital supply chain from the employee perspective. METHODS: We conducted two rounds of interviews, one year apart, with supply chain employees at an urban academic health system preparing to adopt an enterprise resource planning (ERP) software (N = 11 in Round I and N = 8 in Round II). Two researchers coded transcripts for themes using NVivo 11. RESULTS: We identified the following barriers to technology integration: silos between supply chain groups (e.g. Purchasing, Information Management, Strategic Sourcing), between employees and management, and resulting from prior mergers; focus on short-term problems and fear of change; and lack of transparent communication about upcoming changes. Facilitators of technology integration included motivation to work in supply chain; long-term vision that allowed tolerance of change and positive outlook; and transparent communication. CONCLUSION: Desire for shared leadership among employees emerged as a major theme, indicating the need for active involvement of employees during transition to new integrative technology.

Latent class analysis of eHealth behaviors among adults with epilepsy.

OBJECTIVE: The objective of this study was to determine the proportions of uptake and factors associated with electronic health (eHealth) behaviors among adults with epilepsy. METHODS: The 2013, 2015, and 2017 National Health Interview Surveys were analyzed. We assessed the proportions of use of five domains of eHealth in those with epilepsy: looked up health information on the internet, filled a prescription on the internet, scheduled a medical appointment on the internet, communicated with a health care provider via email, and used chat groups to learn about health topics. Multivariate logistic regressions were conducted to identify factors associated with any eHealth behaviors among those with active epilepsy. Latent class analysis was performed to identify underlying patterns of eHealth activity. Survey participants were classified into three discrete classes: (1) frequent, (2) infrequent, and (3) nonusers of eHealth. Multinomial logistic regression was performed to identify factors associated with frequency of eHealth use. RESULTS: There were 1770 adults with epilepsy, of whom 65.87% had at least one eHealth behavior in the prior year. By domain, 62.61% looked up health information on the internet, 15.81% filled a prescription on the internet, 14.95% scheduled a medical appointment on the internet, 17.20% communicated with a health care provider via email, and 8.27% used chat groups to learn about health topics. Among those with active epilepsy, female sex, more frequent computer usage, and internet usage were associated with any eHealth behavior. Female sex and frequent computer use were associated with frequent eHealth use as compared to nonusers. SIGNIFICANCE: A majority of persons with epilepsy were found to use at least one form of eHealth. Various technological and demographic factors were associated with eHealth behaviors. Individuals with lower eHealth behaviors should be provided with targeted interventions that address barriers to the adoption of these technologies.

Diversity within the neurosurgical oncology workforce in the United States: A cross-sectional study with proposed strategies to pave the path forward.

BACKGROUND: Improving and fostering diversity within the neurosurgical workforce has become a high priority. This cross-sectional study aims to provide data on the diversity of neurosurgical oncology faculty (NSOF) in the US. METHODS: All 115 neurosurgery (NS) Accreditation Council for Graduate Medical Education (ACGME) accredited programs were included in this study. The academic rank, academic and clinical title(s), gender, race, and hiring date of neurosurgical faculty with a primary focus on neurosurgical oncology (NSOF) were recorded. Geographical distribution and "top 10" programs were tabulated according to published data. Underrepresented minorities in medicine (URiM) faculty were identified according to the AAMC definition. RESULTS: The NSOF workforce constitutes 21% of the total NS faculty. Of these, 10.1% are women and 9.9% are URiM (P < .001). Currently, 58% of neurosurgery programs (NSP) do not have URiM and/or women NSOF. The top 10 ranked NSP, according to Blue Ridge Institute for Medical Research, had a significantly less URiM NSOF (P = .019) than nontop 10 ranked programs. There was a decreasing trend in the proportion of URiM at higher academic ranks (P = .019). All of the URiM department chairs (3/113)-all men-and 1/3 women department chairs nationwide subspecialized in neurosurgical oncology. CONCLUSIONS: Neurosurgical oncology is a sought-after subspecialty attracting a fifth of neurosurgeons practicing in ACGME-accredited training programs. Changing demographics and the benefits of workforce diversity represent a great opportunity for our field to continue leading inclusion efforts and attracting the best and brightest.

Association of Drain Use in Ankle Arthrodesis With Increased Blood Transfusion Risk: A National Observational Study.

Background: Closed wound drainage has been extensively studied in the hip and knee arthroplasty literature with equivocal results on its clinical benefits. Although also used in orthopaedic surgeries like ankle arthrodesis and ankle arthroplasty, large-scale data are currently lacking on utilization patterns and real-world effectiveness. We, therefore, aimed to address this research gap in this distinct surgical cohort using national claims data. Methods: Using the Premier Healthcare claims database from 2006 to 2016, ankle arthrodesis (n=10,085) and ankle arthroplasty (n=4,977) procedures were included. The main effect was drain use, defined by detailed billing descriptions. Outcomes included blood transfusion, 90-day readmission, and length and cost of hospitalization. Mixed-effects models measured associations between drain use and outcomes. Odds ratios (OR, or % change), 95% CIs, and P values are reported. Results: Overall, drains were used in 11% (n=1,074) and 15% (n=755) of ankle arthrodesis and ankle arthroplasty procedures, respectively. Drain use dramatically decreased over the years in both surgery types: from 14% to 6% and 24% to 7% between 2006 and 2016, for arthrodesis and ankle arthroplasty procedures, respectively. After adjustment for relevant covariates, drain use was associated with increased odds of blood transfusion in ankle arthrodesis surgery (OR 1.4, CI 1.1-1.8, P = .0168), whereas differences that were statistically but not clinically significant were seen in cost and length of stay. In total ankle arthroplasty, no statistically significant associations were observed between drain use and the selected outcomes. Conclusion: This is the first national study on drain use in ankle surgery. We found a decrease in use over time. Drain use was associated with higher odds of blood transfusion in ankle arthrodesis patients. Although this negative effect may be mitigated by the rapidly decreasing use of drains, future studies are needed to discern drivers of drain use in this distinct surgical population. Level of Evidence: Level III, retrospective cohort study.

Racial Differences in Care and Outcomes After Total Hip and Knee Arthroplasties: Did the Comprehensive Care for Joint Replacement Program Make a Difference?

BACKGROUND: There is a paucity of literature on racial differences across a full total joint arthroplasty (TJA) "episode of care" and beyond. Given various incentives, the Comprehensive Care for Joint Replacement (CJR) program in the U.S. may have impacted preexisting racial differences across this care continuum. The purposes of the present study were (1) to assess trends in racial differences in care/outcome characteristics before, during, and after TJA surgery and (2) to assess if the CJR program coincided with reductions in these racial differences. METHODS: This retrospective cohort study includes data on 1,483,221 TJAs (based on Medicare claims data, 2013 to 2018). Racial differences between Black and White patients were assessed for (1) preoperative characteristics (Deyo-Charlson comorbidity index, patient sex, and age), (2) characteristics during hospitalization (length of stay, blood transfusions, and combined complications), and (3) postoperative characteristics (90 and 180-day readmission rates and institutional post-acute care). Additionally, Medicare payments for each period were assessed. Racial differences (Black versus White patients) were expressed in terms of odds ratios (ORs) and 95% confidence intervals (CIs) per year. A "difference-in-differences" analysis (comparing before and after CJR implementation, with non-CJR hospitals being used as controls) estimated the association of the CJR program with changes in racial differences. RESULTS: In both 2013 and 2018, Black patients (n = 74,390; 5.0%) were more likely than White patients to have a higher Deyo-Charlson comorbidity index (score of >0) (OR = 1.32 [95% CI = 1.28 to 1.36] and OR = 1.32 [95% CI = 1.28 to 1.37]), to require more transfusions (OR = 1.55 [95% CI = 1.49 to 1.62] and OR = 1.77 [95% CI = 1.56 to 2.01]), to be discharged to institutional post-acute care (OR = 1.40 [95% CI = 1.36 to 1.44] and OR = 1.49 [95% CI = 1.43 to 1.56]), and to be readmitted within 90 days (OR = 1.38 [95% CI = 1.32 to 1.44] and OR = 1.21 [95% CI = 1.13 to 1.29]) (p < 0.05 for all). Adjusted difference-in-differences analyses demonstrated that the CJR program coincided with reductions in racial differences in 90-day readmission (-1.24%; 95% CI, -2.46% to -0.03%) and 180-day readmission (-1.28%; 95% CI, -2.52% to -0.03%) (p = 0.044 for both). CONCLUSIONS: Racial differences persist among patients managed with TJA. The CJR program coincided with reductions in some racial differences, thus identifying bundle design as a potential novel strategy to target racial disparities. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Innovating in a crisis: a qualitative evaluation of a hospital and Google partnership to implement a COVID-19 inpatient video monitoring program.

OBJECTIVE: To describe adaptations necessary for effective use of direct-to-consumer (DTC) cameras in an inpatient setting, from the perspective of health care workers. METHODS: Our qualitative study included semi-structured interviews and focus groups with clinicians, information technology (IT) personnel, and health system leaders affiliated with the Mount Sinai Health System. All participants either worked in a coronavirus disease 2019 (COVID-19) unit with DTC cameras or participated in the camera implementation. Three researchers coded the transcripts independently and met weekly to discuss and resolve discrepancies. Abiding by inductive thematic analysis, coders revised the codebook until they reached saturation. All transcripts were coded in Dedoose using the final codebook. RESULTS: Frontline clinical staff, IT personnel, and health system leaders (N = 39) participated in individual interviews and focus groups in November 2020-April 2021. Our analysis identified 5 areas for effective DTC camera use: technology, patient monitoring, workflows, interpersonal relationships, and infrastructure. Participants described adaptations created to optimize camera use and opportunities for improvement necessary for sustained use. Non-COVID-19 patients tended to decline participation. DISCUSSION: Deploying DTC cameras on inpatient units required adaptations in many routine processes. Addressing consent, 2-way communication issues, patient privacy, and messaging about video monitoring could help facilitate a nimble rollout. Implementation and dissemination of inpatient video monitoring using DTC cameras requires input from patients and frontline staff. CONCLUSIONS: Given the resources and time it takes to implement a usable camera solution, other health systems might benefit from creating task forces to investigate their use before the next crisis.

Racial and Ethnical Diversity Within the Neurosurgery Resident and Faculty Workforce in the United States.

BACKGROUND: Promoting workplace diversity leads to a variety of benefits related to a broader range of perspectives and insights. Underrepresented in medicine (URiM), including African Americans, Latinx, and Natives (Americans/Alaskan/Hawaiians/Pacific Islanders), are currently accounting for approximately 40% of the US population. OBJECTIVE: To establish a snapshot of current URiM representation within academic neurosurgery (NS) programs and trends within NS residency. METHODS: All 115 NS residencies and academic programs accredited by the Accreditation Council for Graduate Medical Education in 2020 were included in this study. The National Residency Matching Program database was reviewed from 2011 to 2020 to analyze URiM representation trends over time within the NS resident workforce. The academic rank, academic and clinical title(s), subspecialty, sex, and race of URiM NS faculty (NSF) were obtained from publicly available data. RESULTS: The Black and Latinx NS resident workforce currently accounts for 4.8% and 5.8% of the total workforce, respectively. URiM NSF are present in 71% of the Accreditation Council for Graduate Medical Education-accredited NS programs and account for 8% (148 of 1776) of the workforce. Black and Latinx women comprise 10% of URiM NSF. Latinx NSFs are the majority within the URiM cohort for both men and women. URiM comprise 5% of all department chairs. All are men. Spine (26%), tumor (26%), and trauma (17%) were the top 3 subspecialties among URiM NSF. CONCLUSION: NS has evolved, expanded, and diversified in numerous directions, including race and gender representation. Our data show that ample opportunities remain to improve URiM representation within NS.