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.

Medication Reconciliation during Transitions of Care Across Institutions: A Quantitative Analysis of Challenges and Opportunities.

OBJECTIVE: Medication discrepancies between clinical systems may pose a patient safety hazard. In this paper, we identify challenges and quantify medication discrepancies across transitions of care. METHODS: We used structured clinical data and free-text hospital discharge summaries to compare active medications' lists at four time points: preadmission (outpatient), at-admission (inpatient), at-discharge (inpatient), and postdischarge (outpatient). Medication lists were normalized to RxNorm. RxNorm identifiers were further processed using the RxNav API to identify the ingredient. The specific drugs and ingredients from inpatient and outpatient medication lists were compared. RESULTS: Using RxNorm drugs, the median percentage intersection when comparing active medication lists within the same electronic health record system ranged between 94.1 and 100% indicating substantial overlap. Similarly, when using RxNorm ingredients the median percentage intersection was 94.1 to 100%. In contrast, the median percentage intersection when comparing active medication lists across EHR systems was significantly lower (RxNorm drugs: 6.1-7.1%; RxNorm ingredients: 29.4-35.0%) indicating that the active medication lists were significantly less similar (p < 0.05).Medication lists in the same EHR system are more similar to each other (fewer discrepancies) than medication lists in different EHR systems when comparing specific RxNorm drug and the more general RxNorm ingredients at transitions of care. Transitions of care that require interoperability between two EHR systems are associated with more discrepancies than transitions where medication changes are expected (e.g., at-admission vs. at-discharge). Challenges included lack of access to structured, standardized medication data across systems, and difficulty distinguishing medications from orderable supplies such as lancets and diabetic test strips. CONCLUSION: Despite the challenges to medication normalization, there are opportunities to identify and assist with medication reconciliation across transitions of care between institutions.

A cluster-randomized controlled trial of automated internet weight-loss programs in primary care: Role of automated provider feedback.

OBJECTIVE: Despite the high prevalence of obesity and associated health risks in the United States adult population, few primary care providers (PCPs) have time and training to provide weight-management counseling to their patients. This study aims to compare the effect of referral to a comprehensive automated digital weight-loss program, with or without provider email feedback, with usual care on weight loss in patients with overweight or obesity. METHODS: A total of 550 adults (mean [SD], 51.4 [11.2] years, BMI = 35.1 [5.5] kg/m2 , 72.0% female) were enrolled through their PCPs (n = 31). Providers were randomly assigned to refer their patients to a 12-month internet weight-loss intervention only (IWL), the intervention plus semiautomated feedback from the provider (IWL + PCP), or to usual care (EUC). Weight was measured at baseline and at 3, 6, and 12 months. RESULTS: Weight changes (mean [SE]) at 12 months were -0.92 (0.46), -3.68 (0.46), and -3.58 (0.48) kg in the EUC, IWL, and IWL + PCP groups, respectively. Outcomes were significantly different in EUC versus IWL and EUC versus IWL + PCP (p < 0.001), but not in IWL versus IWL + PCP. CONCLUSIONS: Referral by PCPs to an automated weight-loss program holds promise for patients with obesity. Future research should explore ways to further promote accountability and adherence.

Predicting health-related social needs in Medicaid and Medicare populations using machine learning.

Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66-0.70) was achieved by the "any HRSNs" outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.

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.

Prevalence and Factors Associated With Mental Health Symptoms in Adults Undergoing Covid-19 Testing.

BACKGROUND AND OBJECTIVE: Understanding the mental health impact of the COVID-19 pandemic on persons receiving COVID-19 testing will help guide mental health interventions. We aimed to determine the association between sociodemographic factors and mental health symptoms at 8 weeks (baseline) after a COVID-19 test, and compare prevalence of mental health symptoms at baseline to those at 16-week follow-up. MATERIALS AND METHODS: Prospective cohort study of adults who received outpatient COVID-19 testing at primary care clinics. Logistic regression analyses were used to assess the association between sociodemographic characteristics and COVID-19 test results with mental health symptoms. Mental health symptoms reported at baseline were compared to symptoms at 16 weeks follow-up using conditional logistic regression analyses. RESULTS: At baseline, a total of 124 (47.51%) participants reported at least mild depressive symptoms, 110 (42.15%) participants endorsed at least mild anxiety symptoms, and 94 participants (35.21%) endorsed hazardous use of alcohol. Females compared to males were at increased risk of at least mild depressive symptoms at baseline (Adjusted Odds Ratio (AOR): 2.08; 95% CI: 1.14-3.79). The odds of at least mild depressive symptoms was significantly lower among those residing in zip codes within the highest quartile compared to lowest quartile of household income (AOR: 0.37; 95% CI: 0.17-0.81). Also, non-Hispanic Whites had significantly higher odds of reporting hazardous alcohol use compared to non-Whites at baseline (AOR: 1.94; 95% CI: 1.05-3.57). The prevalence of mental health symptoms remained elevated after 16 weeks. CONCLUSION AND RELEVANCE: We found a high burden of symptoms of depression and anxiety as well as hazardous alcohol use in a diverse population who received testing for COVID-19 in the primary care setting. Primary care providers need to remain vigilant in screening for symptoms of mental health disorders in patients tested for COVID-19 well after initial testing.

Protein Folding Stability Changes Across the Proteome Reveal Targets of Cu Toxicity in E. coli.

The ability of metal ionophores to induce cellular metal hyperaccumulation endows them with potent antimicrobial activity; however, the targets and mechanisms behind these outcomes are not well understood. This work describes the first utilization of proteome-wide measurements of protein folding stability in combination with protein expression level analysis to identify protein targets of copper, thereby providing new insight into ionophore-induced copper toxicity in E. coli. The protein folding stability analysis employed a one-pot protocol for pulse proteolysis (PP) in combination with a semi-tryptic peptide enrichment strategy for proteolysis procedures (STEPP) to generate stability profiles for proteins in cell lysates derived from E. coli exposed to copper with and without two ionophores, the antimicrobial agent pyrithione and its ß-lactamase-activated prodrug, PcephPT. As part of this work, the above cell lysates were also subject to protein expression level analysis using conventional quantitative bottom-up proteomic methods. The protein folding stability and expression level profiles generated here enabled the effects of ionophore vs copper to be distinguished and revealed copper-driven stability changes in proteins involved in processes spanning metabolism, translation, and cell redox homeostasis. The 159 differentially stabilized proteins identified in this analysis were significantly more numerous (∼3×) than the 53 proteins identified with differential expression levels. These results illustrate the unique information that protein stability measurements can provide to decipher metal-dependent processes in drug mode of action studies.

Author Correction: Analysis of the Human Protein Atlas Image Classification competition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

Publisher Correction: Analysis of the Human Protein Atlas Image Classification competition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Independent control over size, valence, and elemental composition in the synthesis of DNA-nanoparticle conjugates.

DNA-nanoparticle conjugates have found widespread use in sensing, imaging, and as components of devices. However, their synthesis remains relatively complicated and empirically based, often requiring specialized protocols for conjugates of different size, valence, and elemental composition. Here we report a novel, bottom-up approach for the synthesis of DNA-nanoparticle conjugates, based on ring-opening metathesis polymerization (ROMP), intramolecular crosslinking, and template synthesis. Using size, valence, and elemental composition as three independent synthetic parameters, various conjugates can be obtained using a facile and universal procedure. Examples are given to show the usefulness of these conjugates as sensing probes, building blocks for self-assembly, and as model particles for structure-property relationship studies.

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.

Clinical Prediction of Coronavirus Disease-2019: How Accurate Can One Be?

Background Some models based on clinical information have been reported to predict which patients have Coronavirus Disease-2019 (COVID-19) pneumonia but have failed so far to yield reliable results. We aimed to determine if physicians were able to accurately predict which patients, as described in clinical vignettes, had, or did not have this infection using their clinical acumen and epidemiological data. Methods Of 1177 patients under investigation for COVID-19 admitted, we selected 20 and presented them in a vignette form. We surveyed physicians from different levels of training (<5, and five or more years after graduation from medical school) and included non-medical participants as a control group. We asked all participants to predict the result of the PCR test for COVID-19. We measured the accuracy of responses as a whole, and at three stages of the pandemic associated with a growing incidence of COVID-19 in the community. We calculated the inter-rater reliability, sensitivity, and specificity of the clinical prediction as a whole and by pandemic stage.  Results Between June 8 and August 28, 2020, 82 doctors and 20 non-medical participants completed the survey. The accuracy was 58% (59% for doctors and 52% for non-medical, p=0.002). The lowest accuracy was noted for cases in the pandemic middle stage; years of post-graduate training represented no difference. Of the 2040 total answers, 1176 were accurate and 864 inaccurate (349 false positives and 515 false negatives). Conclusion The influence of symptomatic positivity, confirmation bias, and rapid expertise acquisition on accuracy is discussed, as the disease is new, time after graduation made no difference in the response accuracy. The limited clinical diagnostic capacity emphasizes the need for a reliable diagnostic test.

Feasibility and potential efficacy of commercial mHealth/eHealth tools for weight loss in African American breast cancer survivors: pilot randomized controlled trial.

Weight management after breast cancer (BC) treatment in African American (AA) women is crucial to reduce comorbid conditions and health disparities. We examined feasibility and potential efficacy of commercial eHealth/mHealth tools for weight management in AA BC survivors in New Jersey. Participants (N = 35) were randomized to an intervention (SparkPeople) plus activity tracker, Fitbit Charge (n = 18), or wait-list active control group (Fitbit only, n = 17). Anthropometric, behavioral, and quality of life (QOL) outcomes were collected at baseline, 3, 6, and 12 months. Differences in outcomes were assessed using intent-to-treat analysis. Retention was 97.1%. Both groups lost weight, with no significant differences between groups. At month 6, mean weight change was: intervention: -1.71 kg (SD 2.33; p = .006), 33.3% lost ≥3% of baseline weight; control: -2.54 kg (SD 4.00, p = .002), 23.5% lost ≥3% weight. Intervention participants achieved significant improvements in waist circumference (-3.56 cm, SD 4.70, p = .005), QOL (p = .030), and use of strategies for healthy eating (p = .025) and decreasing calories (p < .001). Number of days logged food per week was associated with decreases in waist circumference at 6 months (ß -0.79, 95% CI, -1.49, -0.09, p = .030) and 12 months (ß -2.16, 95% CI, -4.17, -0.15, p = .038). Weight loss was maintained at 12 months. This is the first study to demonstrate potential efficacy of commercial eHealth/mHealth tools for weight loss in AA BC survivors, without additional counseling from the research team. If effective, they may be convenient weight loss tools that can be easily and widely disseminated. Clinical Trials registration: ClinicalTrials.gov NCT02699983.

Coaction Between Physical Activity and Fruit and Vegetable Intake in Racially Diverse, Obese Adults.

PURPOSE: There is minimal understanding of the potential for coaction, defined as action on one behavior increasing the likelihood of taking action on another behavior, between physical activity (PA) and fruit and vegetable (FV) intake. The purpose of this study was to assess the bidirectional coaction between FV intake and PA, as well as self-efficacy for these behaviors, in a racially diverse sample of obese adults. DESIGN: This is a secondary analysis using data collected from the Path to Health study, a randomized controlled trial. ClinicalTrials.gov Identifier: NCT03674229. SAMPLE: Obese adults who completed baseline and 6-month follow-up assessments. MEASURES: For this study, data on FV intake, leisure time PA, and 7-day accelerometer data were analyzed at baseline and 6-month follow-up. ANALYSIS: We interchanged modeling the FV intake and PA change variables as the independent and dependent variables. We conducted multiple imputation and both linear and multinomial regression. RESULTS: The sample (n = 168) was 59% female and mainly split between white (42%) and African American (42%). Change in self-efficacy for PA was predictive of change in self-efficacy for FV intake and vice versa. When compared with participants with no change in FV intake, someone with a positive change in FV intake was more likely to have a positive change in self-reported PA (adjusted risk ratio [RR] = 6.72, 95% confidence interval [CI] = 1.69-26.68). Likewise, when compared with no change, participants with a positive change in self-reported PA were more likely to report a positive change in FV intake (adjusted RR = 6.79, 95% CI = 1.70-27.17). CONCLUSION: Findings suggest coaction between self-efficacy for FV intake and PA as well as between FV intake and PA. Coaction could be capitalized on to more effectively promote both energy-balance behaviors.

Publisher Correction: Analysis of the Human Protein Atlas Image Classification competition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Analysis of the Human Protein Atlas Image Classification competition.

Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.

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.

DataGauge: A Practical Process for Systematically Designing and Implementing Quality Assessments of Repurposed Clinical Data.

The well-known hazards of repurposing data make Data Quality (DQ) assessment a vital step towards ensuring valid results regardless of analytical methods. However, there is no systematic process to implement DQ assessments for secondary uses of clinical data. This paper presents DataGauge, a systematic process for designing and implementing DQ assessments to evaluate repurposed data for a specific secondary use. DataGauge is composed of five steps: (1) Define information needs, (2) Develop a formal Data Needs Model (DNM), (3) Use the DNM and DQ theory to develop goal-specific DQ assessment requirements, (4) Extract DNM-specified data, and (5) Evaluate according to DQ requirements. DataGauge's main contribution is integrating general DQ theory and DQ assessment methods into a systematic process. This process supports the integration and practical implementation of existing Electronic Health Record-specific DQ assessment guidelines. DataGauge also provides an initial theory-based guidance framework that ties the DNM to DQ testing methods for each DQ dimension to aid the design of DQ assessments. This framework can be augmented with existing DQ guidelines to enable systematic assessment. DataGauge sets the stage for future systematic DQ assessment research by defining an assessment process, capable of adapting to a broad range of clinical datasets and secondary uses. Defining DataGauge sets the stage for new research directions such as DQ theory integration, DQ requirements portability research, DQ assessment tool development and DQ assessment tool usability.