Effect of a Computer-Based Decision Support Intervention on Autism Spectrum Disorder Screening in Pediatric Primary Care Clinics: A Cluster Randomized Clinical Trial

Importance: Universal early screening for autism spectrum disorder (ASD) is recommended but not routinely performed. Objective: To determine whether computer-automated screening and clinical decision support can improve ASD screening rates in pediatric primary care practices. Design, Setting, and Participants: This cluster randomized clinical trial, conducted between November 16, 2010, and November 21, 2012, compared ASD screening rates among a random sample of 274 children aged 18 to 24 months in urban pediatric clinics of an inner-city county hospital system with or without an ASD screening module built into an existing decision support software system. Statistical analyses were conducted from February 6, 2017, to June 1, 2018. Interventions: Four clinics were matched in pairs based on patient volume and race/ethnicity, then randomized within pairs. Decision support with the Child Health Improvement Through Computer Automation system (CHICA) was integrated with workflow and with the electronic health record in intervention clinics. Main Outcomes and Measures: The main outcome was screening rates among children aged 18 to 24 months. Because the intervention was discontinued among children aged 18 months at the request of the participating clinics, only results for those aged 24 months were collected and analyzed. Rates of positive screening results, clinicians' response rates to screening results in the computer system, and new cases of ASD identified were also measured. Main results were controlled for race/ethnicity and intracluster correlation. Results: Two clinics were randomized to receive the intervention, and 2 served as controls. Records from 274 children (101 girls, 162 boys, and 11 missing information on sex; age range, 23-30 months) were reviewed (138 in the intervention clinics and 136 in the control clinics). Of 263 children, 242 (92.0%) were enrolled in Medicaid, 138 (52.5%) were African American, and 96 (36.5%) were Hispanic. Screening rates in the intervention clinics increased from 0% (95% CI, 0%-5.5%) at baseline to 68.4% (13 of 19) (95% CI, 43.4%-87.4%) in 6 months and to 100% (18 of 18) (95% CI, 81.5%-100%) in 24 months. Control clinics had no significant increase in screening rates (baseline, 7 of 64 children [10.9%]; 6-24 months after the intervention, 11 of 72 children [15.3%]; P = .46). Screening results were positive for 265 of 980 children (27.0%) screened by CHICA during the study period. Among the 265 patients with positive screening results, physicians indicated any response in CHICA in 151 (57.0%). Two children in the intervention group received a new diagnosis of ASD within the time frame of the study. Conclusions and Relevance: The findings suggest that computer automation, when integrated with clinical workflow and the electronic health record, increases screening of children for ASD, but follow-up by physicians is still flawed. Automation of the subsequent workup is still needed

Dysmenorrhea Symptom-Based Phenotypes: A Replication and Extension Study

BACKGROUND: Dysmenorrhea is a prevalent pain condition among women and a risk factor for other chronic pain conditions. Individuals vary in dysmenorrhea pain severity, the number of painful sites, and co-occurring gastrointestinal symptoms. Three dysmenorrhea symptom-based phenotypes were previously identified using latent class analysis; however, there is a need to validate these in an independent sample, so they can be used in mechanistic and interventional research. There also is a need to further characterize dysmenorrhea symptom-based phenotypes in terms of demographic, clinical, and psychobehavioral characteristics so they can be used to inform precision dysmenorrhea treatment. OBJECTIVES: The study objectives were to: (a) determine whether the same dysmenorrhea symptom-based phenotypes would be found in a new sample; (b) determine whether including demographic, clinical, and psychobehavioral covariates in latent class analyses would change individuals’ phenotype memberships; and (c) investigate relationships between dysmenorrhea symptom-based phenotypes and demographic, clinical, and psychobehavioral characteristics. METHODS: This cross-sectional survey study included 678 women (aged 14 to 42 years) with dysmenorrhea. Participants reported dysmenorrhea symptom severity, demographic, clinical (comorbid chronic pain and gynecological conditions), and psychobehavioral characteristics (perceived stress, anxiety, depression, sleep disturbance, and pain catastrophizing). We used latent class analysis to identify symptom-based phenotypes. We compared analyses with and without covariates (i.e., demographic, clinical, and psychobehavioral characteristics) to determine if individuals’ phenotype memberships changed. We then examined associations between phenotypes and demographic, clinical, and psychobehavioral characteristics. RESULTS: We reproduced three dysmenorrhea symptom-based phenotypes: the “mild localized pain” phenotype (characterized by mild abdominal cramps), the “severe localized pain” phenotype (characterized by severe abdominal cramps), and the “multiple severe symptoms” phenotype (characterized by severe pain at multiple locations and gastrointestinal symptoms). Analyses with and without covariates had little effect on individuals’ phenotype membership. Race, comorbid chronic pain condition, endometriosis, and pain catastrophizing were significantly associated with the dysmenorrhea phenotypes. DISCUSSION: Findings provide a foundation to further study mechanisms of dysmenorrhea symptom heterogeneity and develop dysmenorrhea precision treatments. The three dysmenorrhea symptom-based phenotypes were validated in a second sample. Demographic, clinical, and psychobehavioral factors were associated with dysmenorrhea symptom-based phenotypes

Symptoms-Based Phenotypes Among Women With Dysmenorrhea: A Latent Class Analysis

Dysmenorrhea is highly prevalent and may increase women’s risk for developing other chronic pain conditions. Although it is highly variable, symptom-based dysmenorrhea phenotypes have not been identified. The study aims were to identify symptom-based dysmenorrhea phenotypes and examine their relationships with demographic and clinical characteristics. In a cross-sectional study, 762 women with dysmenorrhea rated severity of 14 dysmenorrhea-related symptoms. Using latent class analysis, we identified three distinctive phenotypes. Women in the “mild localized pain” phenotype (n=202, 26.51%) had mild abdominal cramps and dull abdominal pain/discomfort. Women in the “severe localized pain” phenotype (n=412 54.07%) had severe abdominal cramps. Women in the “multiple severe symptoms” phenotype (n=148, 19.42%) had severe pain at multiple locations and multiple gastrointestinal symptoms. Race, ethnicity, age, and comorbid chronic pain conditions were significantly associated with phenotypes. Identification of these symptom-based phenotypes provides a foundation for research examining genotype-phenotype associations, etiologic mechanisms, and/or variability in treatment responses

Bleeding Risk, Physical Functioning, and Non-use of Anticoagulation Among Patients with Stroke and Atrial Fibrillation

Background: Atrial fibrillation (AF) is common among people with stroke. Anticoagulation medications can be used to manage the deleterious impact of AF after stroke, however may not be prescribed due to concerns about post-stroke falls and decreased functioning. Thus, the purpose of this study was to identify, among people with stroke and AF, predictors of anticoagulation prescription at hospital discharge. Methods: This is a secondary analysis of a retrospective cohort study of data retrieved via medical records, including: National Institutes of Health Stroke Scale score; Functional Independence Measure (FIM) motor score (motor or physical function); ambulation on 2nd day of hospitalization; Morse Falls Scale (fall risk); and HAS-BLED score (Hypertension; Abnormal renal and liver function; Stroke; Bleeding; Labile INRs; Elderly > 65; and Drugs or alcohol). Data analyses included bivariate comparisons between people with and without anticoagulation at discharge. Logistic-regression modeling was used to assess predictors of discharge anti-coagulation. Results: There were 334 subjects included in the analyses, average age was 75 years old. Anticoagulation was prescribed at discharge for 235 (70%) of patients. In the adjusted regression analyses, only the FIM motor score (adjusted OR = 1.015, 95%CI 1.001-1.028) and the HAS-BLED score (adjusted OR = 0.36, 95%CI 0.22-0.58) were significantly associated with anticoagulation prescription at discharge. Conclusion: It appears that in this sample, post-stroke anti-coagulation decisions appear to be made based on clinical factors associated with bleed risk and motor deficits or physical functioning. However, opportunities may exist for improving clinician documentation of specific reasoning for non-anticoagulation prescription

Correlation of inpatient and outpatient measures of stroke care quality within veterans health administration hospitals

Background and Purpose—Quality of care delivered in the inpatient and ambulatory settings may be correlated within an integrated health system such as the Veterans Health Administration. We examined the correlation between stroke care quality at hospital discharge and within 6 months postdischarge. Methods—We conducted a cross-sectional hospital-level correlation analyses of chart-abstracted data for 3467 veterans discharged alive after an acute ischemic stroke from 108 Veterans Health Administration medical centers and 2380 veterans with postdischarge follow-up within 6 months in fiscal year 2007. Four risk-standardized processes of care represented discharge care quality: prescription of antithrombotic and antilipidmic therapy, anticoagulation for atrial fibrillation, and tobacco cessation counseling along with a composite measure of defect-free care. Five risk-standardized intermediate outcomes represented postdischarge care quality: achievement of blood pressure, low-density lipoprotein, international normalized ratio, and glycosylated hemoglobin target levels, and delivery of appropriate treatment for poststroke depression along with a composite measure of achieved outcomes. Results—Median risk-standardized composite rate of defect-free care at discharge was 79%. Median risk-standardized postdischarge rates of achieving goal were 56% for blood pressure, 36% for low-density lipoprotein, 41% for international normalized ratio, 40% for glycosylated hemoglobin, and 39% for depression management and the median risk-standardized composite 6-month outcome rate was 44%. The hospital composite rate of defect-free care at discharge was correlated with meeting the low-density lipoprotein goal (r=0.31; P=0.007) and depression management (r=0.27; P=0.03) goal but was not correlated with blood pressure, international normalized ratio, glycosylated hemoglobin goals, nor with the composite measure of achieved postdischarge outcomes (probability values >0.13). Conclusions—Hospital discharge care quality was not consistently correlated with ambulatory care quality

Processes of Care Associated With Risk of Mortality and Recurrent Stroke Among Patients With Transient Ischemic Attack and Nonsevere Ischemic Stroke

Importance: Early evaluation and management of patients with transient ischemic attack (TIA) and nonsevere ischemic stroke improves outcomes. Objective: To identify processes of care associated with reduced risk of death or recurrent stroke among patients with TIA or nonsevere ischemic stroke. Design, Setting, and Participants: This cohort study included all patients with TIA or nonsevere ischemic stroke at Department of Veterans Affairs emergency department or inpatient settings from October 2010 to September 2011. Multivariable logistic regression was used to model associations of processes of care and without-fail care, defined as receiving all guideline-concordant processes of care for which patients are eligible, with risk of death and recurrent stroke. Data were analyzed from March 2018 to April 2019. Main Outcomes and Measures: Risk of all-cause mortality and recurrent ischemic stroke at 90 days and 1 year was calculated. Overall, 28 processes of care were examined. Without-fail care was assessed for 6 processes: brain imaging, carotid artery imaging, hypertension medication intensification, high- or moderate-potency statin therapy, antithrombotics, and anticoagulation for atrial fibrillation. Results: Among 8076 patients, the mean (SD) age was 67.8 (11.6) years, 7752 patients (96.0%) were men, 5929 (73.4%) were white, 474 (6.1%) had a recurrent ischemic stroke within 90 days, 793 (10.7%) had a recurrent ischemic stroke within 1 year, 320 (4.0%) died within 90 days, and 814 (10.1%) died within 1 year. Overall, 9 processes were independently associated with lower odds of both 90-day and 1-year mortality after adjustment for multiple comparisons: carotid artery imaging (90-day adjusted odds ratio [aOR], 0.49; 95% CI, 0.38-0.63; 1-year aOR, 0.61; 95% CI, 0.52-0.72), antihypertensive medication class (90-day aOR, 0.58; 95% CI, 0.45-0.74; 1-year aOR, 0.70; 95% CI, 0.60-0.83), lipid measurement (90-day aOR, 0.68; 95% CI, 0.51-0.90; 1-year aOR, 0.64; 95% CI, 0.53-0.78), lipid management (90-day aOR, 0.46; 95% CI, 0.33-0.65; 1-year aOR, 0.67; 95% CI, 0.53-0.85), discharged receiving statin medication (90-day aOR, 0.51; 95% CI, 0.36-0.73; 1-year aOR, 0.70; 95% CI, 0.55-0.88), cholesterol-lowering medication intensification (90-day aOR, 0.47; 95% CI, 0.26-0.83; 1-year aOR, 0.56; 95% CI, 0.41-0.77), antithrombotics by day 2 (90-day aOR, 0.56; 95% CI, 0.40-0.79; 1-year aOR, 0.69; 95% CI, 0.55-0.87) or at discharge (90-day aOR, 0.59; 95% CI, 0.41-0.86; 1-year aOR, 0.69; 95% CI, 0.54-0.88), and neurology consultation (90-day aOR, 0.67; 95% CI, 0.52-0.87; 1-year aOR, 0.74; 95% CI, 0.63-0.87). Anticoagulation for atrial fibrillation was associated with lower odds of 1-year mortality only (aOR, 0.59; 95% CI, 0.40-0.85). No processes were associated with reduced risk of recurrent stroke after adjustment for multiple comparisons. The rate of without-fail care was 15.3%; 1216 patients received all guideline-concordant processes of care for which they were eligible. Without-fail care was associated with a 31.2% lower odds of 1-year mortality (aOR, 0.69; 95% CI, 0.55-0.87) but was not independently associated with stroke risk. Conclusions and Relevance: Patients who received 6 readily available processes of care had lower adjusted mortality 1 year after TIA or nonsevere ischemic stroke. Clinicians caring for patients with TIA and nonsevere ischemic stroke should seek to ensure that patients receive all guideline-concordant processes of care for which they are eligible

Exploring the Relationships Among Social Support, Patient Activation, and Pain-Related Outcomes

Objective Social support has been linked to more effective pain adaptation. The relationship between social support and other relevant constructs is less well understood. Chief among these is patient activation, which has robust links to effective self-management, yet has not been well studied in chronic pain. We sought to better understand these relationships in an effort to inform future intervention strategies for patients with chronic pain. Methods Using baseline data from a clinical trial with patients with chronic pain (N = 213), we analyzed the relationships among perceived social support and patient activation, depression, anxiety, general health perceptions, pain centrality, pain catastrophizing, and pain intensity and interference. Multiple linear regression was used to examine the effect of social support on outcomes. Patient activation was explored as a mediator of the effect of social support on outcomes. Results Social support was significantly associated with all outcomes except pain. Social support explained the greatest variance in patient activation (squared semi-partial correlation = 0.081), followed by depression (0.073) and general health perceptions (0.072). Patient activation was not found to be a significant mediator of the effect of social support on pain-related outcomes. Conclusions These findings provide insight into the roles of patient activation and social support in chronic pain management. Although patient activation did not mediate the relationship between social support and outcomes, this study is an important step toward gaining a more complete understanding of constructs thought to be related to pain self-management and points to the need to advance theory in this area to guide future research. Such work is needed to optimize interventions for patients with chronic pain

Does Inclusion of Stroke Severity in a 30-day Mortality Model Change Standardized Mortality Rates at VA Hospitals?

Background—The Centers for Medicare and Medicaid Services is considering developing a 30-day ischemic stroke hospital-level mortality model using administrative data. We examined whether inclusion of the National Institutes of Health Stroke Scale (NIHSS), a measure of stroke severity not included in administrative data, would alter 30-day mortality rates in the Veterans Health Administration. Methods and Results—A total of 2562 veterans admitted with ischemic stroke to 64 Veterans Health Administration Hospitals in the fiscal year 2007 were included. First, we examined the distribution of unadjusted mortality rates across the Veterans Health Administration. Second, we estimated 30-day all-cause, risk standardized mortality rates (RSMRs) for each hospital by adjusting for age, sex, and comorbid conditions using hierarchical models with and without the inclusion of the NIHSS. Finally, we examined whether adjustment for the NIHSS significantly changed RSMRs for each hospital compared with other hospitals. The median unadjusted mortality rate was 3.6%. The RSMR interquartile range without the NIHSS ranged from 5.1% to 5.6%. Adjustment with the NIHSS did not change the RSMR interquartile range (5.1%–5.6%). Among veterans ≥65 years, the RSMR interquartile range without the NIHSS ranged from 9.2% to 10.3%. With adjustment for the NIHSS, the RSMR interquartile range changed from 9.4% to 10.0%. The plot of 30-day RSMRs estimated with and without the inclusion of the NIHSS in the model demonstrated overlapping 95% confidence intervals across all hospitals, with no hospital significantly below or above the mean-unadjusted 30-day mortality rate. The 30-day mortality measure did not discriminate well among hospitals. Conclusions—The impact of the NIHSS on RSMRs was limited. The small number of stroke admissions and the narrow range of 30-day stroke mortality rates at the facility level in the Veterans Health Administration cast doubt on the value of using 30-day RSMRs as a means of identifying outlier hospitals based on their stroke care quality