Is Health Literacy Associated With Surgical Outcomes? A Systematic Review.

INTRODUCTION: Low levels of health literacy have been shown to increase healthcare utilization and negatively affect health outcomes within medical specialties. However, the relationship of health literacy with clinical, patient-centered, and process-oriented surgical outcomes is not as well understood. MATERIALS AND METHODS: We sought to systematically review the current evidence base regarding the relationship between health literacy and a range of outcomes in patients experiencing surgical care. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched six databases and then identified and extracted data from 25 cross-sectional or cohort studies deemed eligible for a systematic review. RESULTS: Among included studies, strong evidence exists to support an association between low health literacy and worse patient-centered outcomes, as well as an association between low health literacy and poorer process-oriented surgical outcomes. However, the relationship between health literacy and clinical outcomes remains unclear. CONCLUSIONS: Substantial opportunities remain to improve our understanding of the impact of health literacy on surgical outcomes. Future work should expand the range of institutional and specialized surgical settings studied, implement a standardized set of validated health literacy assessment tools, include more diverse patient populations, and investigate a comprehensive range of patient-reported outcomes.

Association of Unmet Social Needs With Level of Capability in People With Persistent Knee Pain.

BACKGROUND: Musculoskeletal providers are increasingly recognizing the importance of social factors and their association with health outcomes as they aim to develop more comprehensive models of care delivery. Such factors may account for some of the unexplained variation between pathophysiology and level of pain intensity and incapability experienced by people with common conditions, such as persistent nontraumatic knee pain secondary to osteoarthritis (OA). Although the association of one's social position (for example, income, employment, or education) with levels of pain and capability are often assessed in OA research, the relationship between aspects of social context (or unmet social needs) and such symptomatic and functional outcomes in persistent knee pain are less clear. QUESTIONS/PURPOSES: (1) Are unmet social needs associated with the level of capability in patients experiencing persistently painful nontraumatic knee conditions, accounting for sociodemographic factors? (2) Do unmet health-related social needs correlate with self-reported quality of life? METHODS: We performed a prospective, cross-sectional study between January 2021 and August 2021 at a university academic medical center providing comprehensive care for patients with persistent lower extremity joint pain secondary to nontraumatic conditions such as age-related knee OA. A final 125 patients were included (mean age 62 ± 10 years, 65% [81 of 125] women, 47% [59 of 125] identifying as White race, 36% [45 of 125] as Hispanic or Latino, and 48% [60 of 125] with safety-net insurance or Medicaid). We measured patient-reported outcomes of knee capability (Knee injury and Osteoarthritis Outcome Score for Joint Replacement), quality of life (Patient-Reported Outcome Measure Information System [PROMIS] Global Physical Health and PROMIS Global Mental Health), and unmet social needs (Accountable Health Communities Health-Related Social Needs Survey, accounting for insufficiencies related to housing, food, transportation, utilities, and interpersonal violence), as well as demographic factors. RESULTS: After controlling for demographic factors such as insurance status, education attained, and household income, we found that reduced knee-specific capability was moderately associated with experiencing unmet social needs (including food insecurity, housing instability, transportation needs, utility needs, or interpersonal safety) (standardized beta regression coefficient [ß] = -4.8 [95% confidence interval -7.9 to -1.7]; p = 0.002 and substantially associated with unemployment (ß = -13 [95% CI -23 to -3.8]; p = 0.006); better knee-specific capability was substantially associated with having Medicare insurance (ß = 12 [95% CI 0.78 to 23]; p = 0.04). After accounting for factors such as insurance status, education attained, and household income, we found that older age was associated with better general mental health (ß = 0.20 [95% CI 0.0031 to 0.39]; p = 0.047) and with better physical health (ß = 0.004 [95% CI 0.0001 to 0.008]; p = 0.04), but effect sizes were small to negligible, respectively. CONCLUSION: There is an association of unmet social needs with level of capability and unemployment in patients with persistent nontraumatic knee pain. This finding signals a need for comprehensive care delivery for patients with persistent knee pain that screens for and responds to potentially modifiable social risk factors, including those based on one's social circumstances and context, to achieve better outcomes. LEVEL OF EVIDENCE: Level II, prognostic study.

Practical Artificial Intelligence: Realistic Ways It Can Help Orthopaedic Surgeons and the Challenges It Will Face.

Artificial intelligence can improve various orthopaedic subspecialties in the next 5 to 10 years. There are several image recognition applications particularly in orthopedic trauma and orthopedic spine. Specifically, convolutional neural networks have been shown to work well for making diagnoses and recreating more advanced imaging form radiographs. There are many applications of artificial intelligence with predictive in total joint arthroplasty, particularly with shared decision making. And there are many day-to-day applications that can be improved with natural language processing, particularly administrative tasks. This includes several applications in billing and charting. When investigating the landscape of artificial intelligence in healthcare, there are many barriers to their adoption. This includes overcoming bias, incorporating new applications into clinical workflow, regulatory approval, and billing.

Utility of the LET-IN-OUT Clinical Decision Support Tool for Medical Risk Stratification Prior to Outpatient Total Hip or Knee Arthroplasty.

BACKGROUND: Musculoskeletal care teams can benefit from simple, standardized, and reliable preoperative tools for assessing discharge disposition after total joint arthroplasty. Our objective was to compare the predictive strength of the Ascension Seton Lower Extremity Inpatient-Outpatient (LET-IN-OUT) tool versus the American Society of Anesthesiologists Physical Status (ASA-PS) score for predicting early postoperative discharge. METHODS: We retrospectively extracted sociodemographic, surgical admission, postoperative day (POD) of discharge, 90-day readmissions, and predictions of the LET-IN-OUT and ASA-PS tools from the electronic records of 563 consecutive hip or knee arthroplasty patients (mean age 65 [SD 9.6], 54% women). Included patients who underwent a total hip arthroplasty (THA) or total knee arthroplasty (TKA) at a single health system between June 2020 and March 2021. We performed descriptive statistics and analyzed predictive values of each tool, defining "early discharge" primarily as discharge before the second postoperative day (POD 2), and secondarily as before 24 hours, and on the same calendar day (POD 0) as surgery. RESULTS: The LET-IN-OUT tool demonstrated superior predictive power among hip and knee arthroplasty patients compared to the ASA-PS tool for discharge prior to POD 2 (positive predictive value [PPV] 89 versus 83%, positive likelihood ratio [+LR] 2.0 versus 1.2), discharge before 24 hours (PPV 86 versus 70%, +LR 2.9 versus 1.2), and discharge on POD 0 (PPV 34% versus 30%, +LR 1.2 versus 1.1). CONCLUSIONS: The Ascension Seton Lower Extremity Inpatient-Outpatient tool predicted patients suitable for early discharge following THA or TKA and did so more effectively than the ASA-PS score.

AgRP Neurons Can Increase Food Intake during Conditions of Appetite Suppression and Inhibit Anorexigenic Parabrachial Neurons.

To maintain energy homeostasis, orexigenic (appetite-inducing) and anorexigenic (appetite suppressing) brain systems functionally interact to regulate food intake. Within the hypothalamus, neurons that express agouti-related protein (AgRP) sense orexigenic factors and orchestrate an increase in food-seeking behavior. In contrast, calcitonin gene-related peptide (CGRP)-expressing neurons in the parabrachial nucleus (PBN) suppress feeding. PBN CGRP neurons become active in response to anorexigenic hormones released following a meal, including amylin, secreted by the pancreas, and cholecystokinin (CCK), secreted by the small intestine. Additionally, exogenous compounds, such as lithium chloride (LiCl), a salt that creates gastric discomfort, and lipopolysaccharide (LPS), a bacterial cell wall component that induces inflammation, exert appetite-suppressing effects and activate PBN CGRP neurons. The effects of increasing the homeostatic drive to eat on feeding behavior during appetite suppressing conditions are unknown. Here, we show in mice that food deprivation or optogenetic activation of AgRP neurons induces feeding to overcome the appetite suppressing effects of amylin, CCK, and LiCl, but not LPS. AgRP neuron photostimulation can also increase feeding during chemogenetic-mediated stimulation of PBN CGRP neurons. AgRP neuron stimulation reduces Fos expression in PBN CGRP neurons across all conditions. Finally, stimulation of projections from AgRP neurons to the PBN increases feeding following administration of amylin, CCK, and LiCl, but not LPS. These results demonstrate that AgRP neurons are sufficient to increase feeding during noninflammatory-based appetite suppression and to decrease activity in anorexigenic PBN CGRP neurons, thereby increasing food intake during homeostatic need.SIGNIFICANCE STATEMENT The motivation to eat depends on the relative balance of activity in distinct brain regions that induce or suppress appetite. An abnormal amount of activity in neurons that induce appetite can cause obesity, whereas an abnormal amount of activity in neurons that suppress appetite can cause malnutrition and a severe reduction in body weight. The purpose of this study was to determine whether a population of neurons known to induce appetite ("AgRP neurons") could induce food intake to overcome appetite-suppression following administration of various appetite-suppressing compounds. We found that stimulating AgRP neurons could overcome various forms of appetite suppression and decrease neural activity in a separate population of appetite-suppressing neurons, providing new insights into how the brain regulates food intake.

The genetic architecture of the human skeletal form.

The human skeletal form underlies our ability to walk on two legs, but unlike standing height, the genetic basis of limb lengths and skeletal proportions is less well understood. Here we applied a deep learning model to 31,221 whole body dual-energy X-ray absorptiometry (DXA) images from the UK Biobank (UKB) to extract 23 different image-derived phenotypes (IDPs) that include all long bone lengths as well as hip and shoulder width, which we analyzed while controlling for height. All skeletal proportions are highly heritable (∻40-50%), and genome-wide association studies (GWAS) of these traits identified 179 independent loci, of which 102 loci were not associated with height. These loci are enriched in genes regulating skeletal development as well as associated with rare human skeletal diseases and abnormal mouse skeletal phenotypes. Genetic correlation and genomic structural equation modeling indicated that limb proportions exhibited strong genetic sharing but were genetically independent of width and torso proportions. Phenotypic and polygenic risk score analyses identified specific associations between osteoarthritis (OA) of the hip and knee, the leading causes of adult disability in the United States, and skeletal proportions of the corresponding regions. We also found genomic evidence of evolutionary change in arm-to-leg and hip-width proportions in humans consistent with striking anatomical changes in these skeletal proportions in the hominin fossil record. In contrast to cardiovascular, auto-immune, metabolic, and other categories of traits, loci associated with these skeletal proportions are significantly enriched in human accelerated regions (HARs), and regulatory elements of genes differentially expressed through development between humans and the great apes. Taken together, our work validates the use of deep learning models on DXA images to identify novel and specific genetic variants affecting the human skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.

The genetic architecture and evolution of the human skeletal form.

The human skeletal form underlies bipedalism, but the genetic basis of skeletal proportions (SPs) is not well characterized. We applied deep-learning models to 31,221 x-rays from the UK Biobank to extract a comprehensive set of SPs, which were associated with 145 independent loci genome-wide. Structural equation modeling suggested that limb proportions exhibited strong genetic sharing but were independent of width and torso proportions. Polygenic score analysis identified specific associations between osteoarthritis and hip and knee SPs. In contrast to other traits, SP loci were enriched in human accelerated regions and in regulatory elements of genes that are differentially expressed between humans and great apes. Combined, our work identifies specific genetic variants that affect the skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.

Deep learning based phenotyping of medical images improves power for gene discovery of complex disease.

Electronic health records are often incomplete, reducing the power of genetic association studies. For some diseases, such as knee osteoarthritis where the routine course of diagnosis involves an X-ray, image-based phenotyping offers an alternate and unbiased way to ascertain disease cases. We investigated this by training a deep-learning model to ascertain knee osteoarthritis cases from knee DXA scans that achieved clinician-level performance. Using our model, we identified 1931 (178%) more cases than currently diagnosed in the health record. Individuals diagnosed as cases by our model had higher rates of self-reported knee pain, for longer durations and with increased severity compared to control individuals. We trained another deep-learning model to measure the knee joint space width, a quantitative phenotype linked to knee osteoarthritis severity. In performing genetic association analysis, we found that use of a quantitative measure improved the number of genome-wide significant loci we discovered by an order of magnitude compared with our binary model of cases and controls despite the two phenotypes being highly genetically correlated. In addition we discovered associations between our quantitative measure of knee osteoarthritis and increased risk of adult fractures- a leading cause of injury-related death in older individuals-, illustrating the capability of image-based phenotyping to reveal epidemiological associations not captured in the electronic health record. For diseases with radiographic diagnosis, our results demonstrate the potential for using deep learning to phenotype at biobank scale, improving power for both genetic and epidemiological association analysis.

Pediatric Septic Arthritis of the Knee due to Fusobacterium necrophorum in a Patient with Down Syndrome: A Case Report.

CASE: A 16-year-old boy with a history of Down syndrome presented with right knee pain and swelling. He was diagnosed with isolated septic arthritis of the knee due to Fusobacterium necrophorum in the absence of current or recent oropharyngeal infection. He was successfully treated with arthroscopic irrigation and debridement and 12 weeks of oral antibiotics. CONCLUSION: Fusobacterium necrophorum is a part of the normal oral flora and a rare cause of septic arthritis, typically associated with recent oropharyngeal infection. However, patients with immune dysregulation such as those with Down syndrome may develop isolated septic arthritis due to transient hematogenous seeding.

A pragmatic parallel arm randomized-controlled trial of a multi-pronged electronic health record-based clinical decision support tool protocol to reduce low-value antipsychotic prescriptions among older adults with Alzheimer's and related dementias.

Among patients with Alzheimer's disease and its related dementias (ADRD) with behavioral disturbances, antipsychotic prescriptions have limited efficacy and increase the risk of death. Yet, physicians continue to routinely prescribe low-value antipsychotic medications for behavioral disturbances among patients with ADRD. We designed a pragmatic randomized-controlled trial to measure the impact of a behavioral economic electronic health record (EHR) clinical decision support (CDS) intervention to reduce physician prescriptions of new antipsychotic medications among patients with ADRD. Utilizing a pragmatic parallel arm randomized-controlled trial design, the study will randomize eligible physicians from a large academic health system to either receive a EHR CDS intervention or not (control) when they prescribe a new antipsychotic medication during visits with patients with ADRD. The intervention will include three components: 1) alerts prescribers that antipsychotic prescriptions increase mortality risk (motivating physicians' intrinsic desire for non-malfeasance); 2) offers non-pharmacological behavioral resources for caregivers; 3) auto-defaults the prescription to contain the lowest dose and number of pill-days (n = 30) without refills if the prescriber does not cancel the order (appealing to default bias). Over 1 year, we will compare the cumulative total of new antipsychotic pill-days prescribed (primary outcome) by physicians in the intervention group versus in the control group. The study protocol meets international SPIRIT guidelines. Behavioral economics, or the study of human behavior as a function of more than rational incentives, considering a whole host of cognitive and social psychological preferences, tendencies, and biases, is increasingly recognized as an important conceptual framework to improve physician behavior. This pragmatic trial is among the first to combine two distinct behavioral economic principles, a desire for non-malfeasance and default bias, to improve physician prescribing patterns for patients with ADRD. We anticipate this trial will substantially advance understanding of how behavioral-economic informed EHR CDS tools can potentially reduce harmful, low-value care among patients with ADRD.