The feasibility, acceptability, safety, and effects of early weight bearing in humeral fractures - a scoping review.

PURPOSE: Non-weight bearing is often recommended after humeral fractures. This review aims to summarise the extent and nature of the evidence for the feasibility, acceptability, safety, and effects of early weight bearing (EWB) in people with humeral fractures, treated operatively or non-operatively. . METHODS: Data sources identified published (PUBMED, EMBASE, CINAHL) and unpublished (ClinicalTrials.gov, CENTRAL, NIHR Open Research, OpenGrey) literature. Independent data extraction was conducted by two reviewers. RESULTS: 13 901 records were retrieved. Ten studies, involving 515 post-operative patients and 351 healthcare professionals, were included. EWB was found to be feasible in nine studies. There was limited evidence regarding adherence to EWB. Trauma and orthopaedic surgeons reported that EWB was acceptable. This depended on surgery type and whether it was a post-operative polytrauma case. No acceptability data was reported from patients' perspectives. Only one study reported two patients who developed unsatisfactory outcomes from excessive post-operative EWB. Positive effects of EWB were reported on disability level, pain, shoulder and elbow motion, and union. CONCLUSION: There is some evidence for the feasibility, safety, and effectiveness of post-operative EWB after humeral fractures. There was limited data on the acceptability of EWB. Heterogeneous study designs, and variations in EWB protocols limit conclusions.

There is some evidence to support the feasibility, safety, and effectiveness of early weight bearing following operative management of humeral fractures.Early weight bearing after some humeral fractures is acceptable to some subspecialities of orthopaedic surgeons but is not universally accepted.Rehabilitation professionals should discuss the option of early weight bearing after operative management of humeral fracture with patients and their multidisciplinary team.

Disparities in Demographics in Hip Arthroplasty Between U.S. Active Duty Military and the ACS-NSQIP Clinical Registry.

INTRODUCTION: Patient demographics, such as sex and age, are known risk factors for undergoing revision following primary total hip arthroplasty (THA). The military population is unique because of the increased rates of primary and secondary osteoarthritis of the hip. Treatment options are limited for returning patients to their line of duty; however, THA has been shown to be an effective option. The primary purpose of this study was to evaluate and contrast the demographic differences of patients undergoing primary THA between the U.S. active duty military population and the general population. The secondary goal was to identify the proportion of primary THA performed at the MTF within the military health system (MHS). METHODS: This was an exempt study determined by the local institutional review board. A retrospective analysis of the MHS Data Repository (MDR) and the National Surgical Quality Improvement Program (NSQIP) was performed. The databases were used to identify the patients who underwent THA from January 1, 2015 to December 31, 2020. The MDR was used to identify demographics such as sex, age, setting of surgery, geographic location, previous military deployments, history of deployment-related injuries, branch of service, and rank. The NSQIP database was queried for sex and age. The median age of the population was compared using the Mann-Whitney U test and gender was compared using the Chi-square test. RESULTS: The MDR was used to evaluate 2,734 patients, whereas the NSQIP database was used to evaluate 223,832 patients. In the military population, patients who underwent THA were 87.7% male with an average age of 45 years, whereas in the general population as measured via the NSQIP database, 45.2% patients were male with an average age of 66.0 years. Comparing the two groups, we demonstrated that the military patients were significantly more likely to be younger (P < .001) and males (P < .001). Only 29.6% of primary THAs were performed within the MTF. CONCLUSIONS: Patients in the MHS are undergoing THA at a younger age and are more likely to be male compared to the general population. A significant portion of primary THAs in the MHS are also being performed at civilian institutions. These demographics may result in increased risk of revision; however, long-term studies are warranted to evaluate survivorship in this unique population.

A Lack of Generalizability-Total Knee Demographics in the Active Duty Population.

INTRODUCTION: Age and sex are known demographic risk factors for requiring revision surgery following primary total knee arthroplasty (TKA). Military service members are a unique population with barriers to long-term follow up after surgery. This study aims to compare demographic data between active duty military personnel and a nationwide sample to identify differences that may impact clinical and economic outcomes. METHODS: A retrospective observational analysis was performed using the Military Health System Data Repository (MDR) and the National Surgical Quality Improvement Program (NSQIP). Databases were queried for patients undergoing primary TKA between January 1, 2015 and December 31, 2020. The MDR was queried for demographic data including age, sex, duty status, facility type, geographic region, history of prior military deployment, history of deployment-related health condition, branch of military service, and military rank. National Surgical Quality Improvement Program was queried for age and sex. Median age between populations was compared with the Mann-Whitney U test, and gender was compared with a chi-squared test. RESULTS: During the study period, 2,094 primary TKA patients were identified from the MDR, and 357,865 TKA patients were identified from the NSQIP database. Military TKA patients were 79.4% male with a median age of 49.0, and NSQIP TKA patients were 38.9% were male, with a median age of 67. Military TKA patients were significantly more likely to be male (P < .001) and younger (P < .001). CONCLUSION: Patients undergoing TKA in the military are younger and more likely to be male compared to national trends. Current evidence suggests these factors may place them at a significant revision risk in the future. The application of quality metrics based on nationwide demographics may not be applicable to military members within the Military Health System.

Machine-learning Models Predict 30-Day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty.

BACKGROUND: Aseptic revision THA and TKA are associated with an increased risk of adverse outcomes compared with primary THA and TKA. Understanding the risk profiles for patients undergoing aseptic revision THA or TKA may provide an opportunity to decrease the risk of postsurgical complications. There are risk stratification tools for postoperative complications after aseptic revision TKA or THA; however, current tools only include nonmodifiable risk factors, such as medical comorbidities, and do not include modifiable risk factors. QUESTIONS/PURPOSES: (1) Can machine learning predict 30-day mortality and complications for patients undergoing aseptic revision THA or TKA using a cohort from the American College of Surgeons National Surgical Quality Improvement Program database? (2) Which patient variables are the most relevant in predicting complications? METHODS: This was a temporally validated, retrospective study analyzing the 2014 to 2019 National Surgical Quality Improvement Program database, as this database captures a large cohort of aseptic revision THA and TKA patients across a broad range of clinical settings and includes preoperative laboratory values. The training data set was 2014 to 2018, and 2019 was the validation data set. Given that predictive models learn expected prevalence of outcomes, this split allows assessment of model performance in contemporary patients. Between 2014 and 2019, a total of 24,682 patients underwent aseptic revision TKA and 17,871 patients underwent aseptic revision THA. Of those, patients with CPT codes corresponding to aseptic revision TKA or THA were considered as potentially eligible. Based on excluding procedures involving unclean wounds, 78% (19,345 of 24,682) of aseptic revision TKA procedures and 82% (14,711 of 17,871) of aseptic revision THA procedures were eligible. Ten percent of patients in each of the training and validation cohorts had missing predictor variables. Most of these missing data were preoperative sodium or hematocrit (8% in both the training and validation cohorts). No patients had missing outcome data. No patients were excluded due to missing data. The mean patient was age 66 ± 12 years, the mean BMI was 32 ± 7 kg/m 2 , and the mean American Society of Anesthesiologists (ASA) Physical Score was 3 (56%). XGBoost was then used to create a scoring tool for 30-day adverse outcomes. XGBoost was chosen because it can handle missing data, it is nonlinear, it can assess nuanced relationships between variables, it incorporates techniques to reduce model complexity, and it has a demonstrated record of producing highly accurate machine-learning models. Performance metrics included discrimination and calibration. Discrimination was assessed by c-statistics, which describe the area under the receiver operating characteristic curve. This quantifies how well a predictive model discriminates between patients who have the outcome of interest versus those who do not. Relevant ranges for c-statistics include good (0.70 to 0.79), excellent (0.80 to 0.89), and outstanding (> 0.90). We estimated 95% confidence intervals (CIs) for c-statistics by 500-sample bootstrapping. Calibration curves quantify reliability of model predictions. Reliable models produce prediction probabilities for outcomes that are similar to observed probabilities of those outcomes, so a well-calibrated model should demonstrate a calibration curve that does not deviate substantially from a line of slope 1 and intercept 0. Calibration curves were generated on the 2019 validation data. Shapley Additive Explanations (SHAP) visualizations were used to investigate feature importance to gain insight into how models made predictions. The models were built into an online calculator for ongoing testing and validation. The risk calculator, which is freely available ( http://nb-group.org/rev2/ ), allows a user to input patient data to calculate postoperative risk of 30-day mortality, cardiac, and respiratory complications after aseptic revision TKA or THA. A post hoc analysis was performed to assess whether using data from 2020 would improve calibration on 2019 data. RESULTS: The model accurately predicted mortality, cardiac complications, and respiratory complications after aseptic revision THA or TKA, with c-statistics of 0.88 (95% CI 0.83 to 0.93), 0.80 (95% CI 0.75 to 0.84), and 0.78 (95% CI 0.74 to 0.82), respectively, on internal validation and 0.87 (95% CI 0.77 to 0.96), 0.70 (95% CI 0.61 to 0.78), and 0.82 (95% CI 0.75 to 0.88), respectively, on temporal validation. Calibration curves demonstrated slight over-confidence in predictions (most predicted probabilities were higher than observed probabilities). Post hoc analysis of 2020 data did not yield improved calibration on the 2019 validation set. Important risk factors for all models included increased age and higher ASA, BMI, hematocrit level, and sodium level. Hematocrit and ASA were in the top three most important features for all models. The factor with the strongest association for mortality and cardiac complication models was age, and for the respiratory model, chronic obstructive pulmonary disease. Risk related to sodium followed a U-shaped curve. Preoperative hyponatremia and hypernatremia predicted an increased risk of mortality and respiratory complications, with a nadir of 138 mmol/L; hyponatremia was more strongly associated with mortality than hypernatremia. A hematocrit level less than 36% predicted an increased risk of all three adverse outcomes. A BMI less than 24 kg/m 2 -and especially less than 20 kg/m 2 -predicted an increased risk of all three adverse outcomes, with little to no effect for higher BMI. CONCLUSION: This temporally validated model predicted 30-day mortality, cardiac complications, and respiratory complications after aseptic revision THA or TKA with c-statistics ranging from 0.78 to 0.88. This freely available risk calculator can be used preoperatively by surgeons to educate patients on their individual postoperative risk of these specific adverse outcomes. Unanswered questions that remain include whether altering the studied preoperative patient variables, such as sodium or hematocrit, would affect postoperative risk of adverse outcomes; however, a prospective cohort study is needed to answer this question. LEVEL OF EVIDENCE: Level III, therapeutic study.

Nuclear oligo hashing improves differential analysis of single-cell RNA-seq.

Single-cell RNA sequencing (scRNA-seq) offers a high-resolution molecular view into complex tissues, but suffers from high levels of technical noise which frustrates efforts to compare the gene expression programs of different cell types. "Spike-in" RNA standards help control for technical variation in scRNA-seq, but using them with recently developed, ultra-scalable scRNA-seq methods based on combinatorial indexing is not feasible. Here, we describe a simple and cost-effective method for normalizing transcript counts and subtracting technical variability that improves differential expression analysis in scRNA-seq. The method affixes a ladder of synthetic single-stranded DNA oligos to each cell that appears in its RNA-seq library. With improved normalization we explore chemical perturbations with broad or highly specific effects on gene regulation, including RNA pol II elongation, histone deacetylation, and activation of the glucocorticoid receptor. Our methods reveal that inhibiting histone deacetylation prevents cells from executing their canonical program of changes following glucocorticoid stimulation.