• e-cigarettes
• perceptions of nicotine addiction
• scale development

• National Institute on Drug Abuse
• Tobacco-Related Disease Research Program
• Dr. Vikram and Priya Lakireddy Fellowship

• Anxiety
• Depression
• Patients with thoracolumbar fractures
• Social pressure
• Social support

• Humans
• Mental Health
• Prevalence
• Health Personnel/psychology
• Health Resources
• Wounds and Injuries/psychology
• Wounds and Injuries/therapy
• Musculoskeletal System/injuries

• depression
• orthopedic trauma
• outpatient clinic
• referral hospital

• Complications
• Open tibia fracture
• Outcomes
• Smokeless
• Tobacco

• Analgesics, Opioid
• CHRONIC PAIN
• Outcome Assessment, Health Care
• Pain Management

• Humans
• Analgesics, Opioid/therapeutic use
• Incidence
• Opioid-Related Disorders/epidemiology
• Pain/drug therapy
• Risk Factors
• Retrospective Studies

• K12 HD001441 NICHD NIH HHS
• K23 AT011389 NCCIH NIH HHS

• Adult
• Humans
• Male
• Fractures, Bone/epidemiology
• Fractures, Bone/etiology
• Fractures, Bone/surgery
• Scaphoid Bone/surgery
• Scaphoid Bone/injuries
• Fracture Healing
• Tobacco, Smokeless/adverse effects
• Fractures, Ununited/surgery
• Retrospective Studies
• Wrist Injuries
• Hand Injuries

• Trauma centers
• alcohol misuse
• blood alcohol content

Common mental disorders such as anxiety and depression in geriatric orthopedic trauma patients have received little attention in research.

To investigate the prevalence of emotional disorders among geriatric orthopedic trauma patients and identify demographic, social and clinical risk factors.

This cross-sectional study was performed in geriatric patients (aged ≥ 60 years, both sexes) with orthopedic trauma admitted to a level I trauma center between May 2015 and December 2017. Demographic, social, and clinical characteristics were described. Huaxi Emotional-Distress Index (HEI) was used to evaluate the severity of anxiety and depression status. Differences in continuous variables were tested using the t-test, and differences in categorical variables were assessed using the Pearson χ² test. Binary logistic regression analyses were used to identify the factors associated with a HEI score > 8.

Among the 966 patients, 487 were male and 479 were female, with a mean age of 70.2 ± 7.1 years. The age ranged from 60 to 90 years. Seventy-five patients had an HEI score > 8, accounting for about 7.8% of all patients. A higher Injury Severity Score (4.17 ± 3.10 vs 7.96 ± 6.68, P < 0.001), higher Visual Analog Score (5.05 ± 1.09 vs 6.89 ±1.23, P < 0.001), number of chronic diseases (P < 0.001), injury type (P = 0.038), and education level (P = 0.001) were significantly associated with HEI score > 8. On logistic regression, a higher education level was a protective factor for emotional disorders (P = 0.047), whereas Injury Severity Score (P = 0.024), Visual Analog Score (P < 0.001), two or more chronic diseases (P < 0.001) were the related independent risk factors.

Emotional disorders are common in geriatric patients with orthopedic trauma. Clinicians should remain vigilant of emotional disorders in geriatric patients and screen for anxiety and depression in higher risk groups.

• Anxiety
• Depression
• Geriatric
• Trauma
• Orthopedic

• Life Care Specialist
• Opioid epidemic
• Opioid use disorder
• Opioid utilization
• Orthopedic trauma
• Pain management
• Patient-reported outcomes
• Substance misuse

• foot & ankle
• knee
• musculoskeletal disorders
• spine

• Depression/diagnosis
• Depression/epidemiology
• Female
• Health Status
• Humans
• Information Systems
• Middle Aged
• Orthopedics
• Patient Reported Outcome Measures
• Retrospective Studies

• Australia/epidemiology
• Cost-Benefit Analysis/economics
• Health Care Costs
• Hospitals, Public
• Humans
• Smoking/epidemiology
• Surgical Wound Infection/economics
• Surgical Wound Infection/epidemiology

• vichealth

• Medicare
• complications
• depressive disorders
• readmissions
• total shoulder arthroplasty

• brain-derived neurotrophic factor
• orthopedic trauma
• symptom clusters
• symptoms

Patients with traumatic injuries are often accompanied by emotional disorders, which seriously impede functional gains. The objective of this study was to identify the prevalence and risk factors associated with underlying anxiety and depression in orthopaedic trauma patients.

From July 2015 to December 2017, all orthopaedic trauma patients were included in the retrospective study. Patients with conditions that might affect cognitive impairment were excluded from the study. Basic demographic data were collected. All patients were screened for emotional disorders on admission using a simple questionnaire called “Huaxi Emotional-Distress Index” (HEI). Bivariate analyses and logistic regression were used to identify the factors associated with a HEI score of > 8.

One hundred and sixty-two patients (8.1%) had a HEI score of > 8. About 1.0% of enrolled patients had severe emotional disorders (HEI score ≥ 17). The reasons caused by emotional disorders in patients with orthopaedic trauma were a higher Injury Severity Score (ISS), a higher visual analogue score (VAS) and type of surgery. On logistic regression, marital status was a protective factor for emotional disorders, while VAS and ISS were the risk factors for emotional disorders.

Although a significantly low percentage of orthopaedic trauma patients in our setting have emotional disorders, traumatic orthopaedic surgeons still need to pay attention to the risk of emotional disorders and integrate effective screening tools into clinical practice to screen for these factors and stratify emotional disorders. Appropriate targeted psychological intervention and treatment should be adopted according to the stratification of emotional disorders.

• Anxiety
• Depression
• Orthopaedic trauma
• Prevalence
• Risk factors

• Addiction
• Adolescence
• Anxiety
• Dopamine
• Mood disorders
• Neurodevelopment
• Nicotine

To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.

Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.

Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.

Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.

• depression
• electronic health records
• machine learning
• phenotyping

• cannabis use
• costs
• readmission rates
• thromboemboli
• total knee arthroplasty