Nevin Manimala

PLoS One. 2023 Apr 14;18(4):e0280892. doi: 10.1371/journal.pone.0280892. eCollection 2023.

ABSTRACT

Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients’ cognitive status one year after the attack. Using diffusion-weighted imaging, we apply the Tract-Based Spatial Statistics analysis and construct individual structural connectivity matrices by employing deterministic tractography. We further quantify the graph-theoretical properties of individual networks. The Tract-Based Spatial Statistic did identify lower fractional anisotropy as a predictor of cognitive status, although this effect was mostly attributable to the age-related white matter integrity decline. We further observed the effect of age propagating into other levels of analysis. Specifically, in the structural connectivity approach we identified pairs of regions significantly correlated with clinical scales, namely memory, attention, and visuospatial functions. However, none of them persisted after the age correction. Finally, the graph-theoretical measures appeared to be more robust towards the effect of age, but still were not sensitive enough to capture a relationship with clinical scales. In conclusion, the effect of age is a dominant confounder especially in older cohorts, and unless appropriately addressed, may falsely drive the results of the predictive modelling.

PMID:37058495 | DOI:10.1371/journal.pone.0280892

PLoS One. 2023 Apr 14;18(4):e0282590. doi: 10.1371/journal.pone.0282590. eCollection 2023.

ABSTRACT

BACKGROUND: There is no systematic insight into the effect of case management on common complications of chronic diseases, including depressive symptoms and symptoms of anxiety. This is a significant knowledge gap, given that people with a chronic disease such as Parkinson Disease or Alzheimer’s Disease have identified care coordination as one of their highest priorities. Furthermore, it remains unclear whether the putative beneficial effects of case management would vary by crucial patient characteristics, such as their age, gender, or disease characteristics. Such insights would shift from “one size fits all” healthcare resource allocation to personalized medicine.

OBJECTIVE: We systematically examined the effectiveness of case management interventions on two common complications associated PD and other chronic health conditions: Depressive symptoms and symptoms of anxiety.

METHODS: We identified studies published until November 2022 from PubMed and Embase databases using predefined inclusion criteria. For each study, data were extracted independently by two researchers. First, descriptive and qualitative analyses of all included studies were performed, followed by random-effects meta-analyses to assess the impact of case management interventions on anxiety and depressive symptoms. Second, meta-regression was performed to analyze potential modifying effects of demographic characteristics, disease characteristics and case management components.

RESULTS: 23 randomized controlled trials and four non-randomized studies reported data on the effect of case management on symptoms of anxiety (8 studies) or depressive symptoms (26 studies). Across meta-analyses, we observed a statistically significant effect of case management on reducing symptoms of anxiety (Standardized Mean Difference [SMD] = – 0.47; 95% confidence interval [CI]: -0.69, -0.32) and depressive symptoms (SMD = – 0.48; CI: -0.71, -0.25). We found large heterogeneity in effect estimates across studies, but this was not explained by patient population or intervention characteristics.

CONCLUSIONS: Among people with chronic health conditions, case management has beneficial effects on symptoms of depressive symptoms and symptoms of anxiety. Currently, research on case management interventions are rare. Future studies should assess the utility of case management for potentially preventative and common complications, focusing on the optimal content, frequency, and intensity of case management.

PMID:37058492 | DOI:10.1371/journal.pone.0282590

PLoS One. 2023 Apr 14;18(4):e0284103. doi: 10.1371/journal.pone.0284103. eCollection 2023.

ABSTRACT

Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the Cover statistic to identify risk factors for CAD. Shapely Additive Explanations (SHAP) explanations were utilized to visualize the relationship between these potential risk factors and CAD. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51%) were female, 2,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of other race. A total of 338 (4.5%) of patients had coronary artery disease. These were fitted into the XGBoost model and an AUROC = 0.89, Sensitivity = 0.85, Specificity = 0.87 were observed (Fig 1). The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were age (Cover = 21.1%), Platelet count (Cover = 5.1%), family history of heart disease (Cover = 4.8%), and Total Cholesterol (Cover = 4.1%). Machine learning models can effectively predict coronary artery disease using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

PMID:37058460 | DOI:10.1371/journal.pone.0284103

PLoS One. 2023 Apr 14;18(4):e0284181. doi: 10.1371/journal.pone.0284181. eCollection 2023.

ABSTRACT

This meta-analysis aims to compare corneal injuries and function after femtosecond laser-assisted cataract surgery (FLACS) and conventional phacoemulsification surgery (CPS). A comprehensive literature search of PubMed, EMBASE, and the Cochrane Controlled Trials Register was conducted to identify randomized controlled trials (RCT) and high-quality prospective comparative cohort studies comparing FLACS with CPS. Endothelial cell loss percentage (ECL%), central corneal thickness (CCT), endothelial cell density (ECD), endothelial cell loss (ECL), percentage of the hexagonal cell (6A), and coefficient of variance (CoV) were used as an indicator of corneal injury and function. Totally 42 trials (23 RCTs and 19 prospective cohort studies), including 3916 eyes, underwent FLACS, and a total of 3736 eyes underwent CPS. ECL% is significantly lower in the FLACS group at 1-3 days (P = 0.005), 1 week (P = 0.004), 1 month (P<0.0001), 3 months (P = 0.001), and 6 months (P = 0.004) after surgery compared to CPS. ECD and ECL appeared no statistically significant difference between the two groups, except for the significant reduction of ECD at 3 months in the CPS group (P = 0.002). CCT was significantly lower in the FLACS group at 1 week (P = 0.05) and 1 month (P = 0.002) early postoperatively. While at 1-3 days (P = 0.50), 3 months (P = 0.18), and 6 months (P = 0.11), there was no difference between the FLACS group and the CPS group. No significant difference was found in the percentage of hexagonal cells and the coefficient of variance. FLACS, compared with CPS, reduces corneal injury in the early postoperative period. Corneal edema recovered faster in the FLACS group in the early postoperative period. In addition, FLACS may be a better option for patients with corneal dysfunction.

PMID:37058458 | DOI:10.1371/journal.pone.0284181

JMIR Form Res. 2023 Apr 14;7:e44503. doi: 10.2196/44503.

ABSTRACT

BACKGROUND: Despite the declining prevalence of cigarette smoking in the United States, socioeconomically disadvantaged veterans receiving care from the Veterans Health Administration have a high prevalence of smoking. Currently, available treatment options for these veterans focus on tobacco users who are ready to quit and have limited reach. Consequently, there is a great need for accessible, effective smoking cessation interventions for veterans at all levels of readiness to quit smoking.

OBJECTIVE: To address these needs, we developed Vet Flexiquit, a web-based Acceptance and Commitment Therapy program for veterans, and evaluated its acceptability (primary aim), efficacy, and impact on theory-based change processes relative to the National Cancer Institute’s SmokefreeVET program in a pilot randomized controlled trial.

METHODS: Participants (N=49) were randomized 1:1 to receive either the Vet Flexiquit (n=25) or SmokefreeVET (n=24) web program. Both groups received SMS text messages as part of the intervention for 6 weeks. Both interventions are fully automated and self-guided. Primary outcome data were collected at 3 months after the randomization. Self-reported smoking abstinence was biochemically verified using saliva cotinine. Multivariable logistic regression, negative binomial regression, and linear regression models were used to evaluate the association between the treatment arm and outcomes of interest.

RESULTS: Acceptability, as measured by overall treatment satisfaction, was high and similar across treatment arms: 100% (17/17) for Vet Flexiquit and 95% (18/19) for SmokefreeVET. Acceptability, as measured by utilization, was more modest (log-ins: M=3.7 for Vet Flexiquit and M=3.2 for SmokefreeVET). There were no statistically significant differences between treatment arms for any acceptability measures. Similarly, there were no statistically significant differences between treatment arms in the secondary outcomes of smoking cessation or change in Acceptance and Commitment Therapy’s theory-based processes. In open-ended survey responses, some veterans in both treatment arms expressed interest in having support from a professional or peer to enhance their experience, as well as an expanded SMS text messaging program.

CONCLUSIONS: Both programs had high ratings of acceptability, limited utilization, and a similar impact on cessation and cessation processes. Taken together with the qualitative data suggesting that additional support may enhance participants’ experience of both programs, these preliminary findings suggest that the programs may have similar outcomes among veterans who are looking for a digital cessation treatment option and that integrating provider or peer support and enhancing the SMS text messaging program holds promise as a means of boosting engagement and outcomes for both programs.

TRIAL REGISTRATION: ClinicalTrials.gov NCT04502524; https://clinicaltrials.gov/ct2/show/NCT04502524.

PMID:37058346 | DOI:10.2196/44503

JMIR Res Protoc. 2023 Apr 14;12:e45771. doi: 10.2196/45771.

ABSTRACT

BACKGROUND: As researchers and implementors begin to acknowledge the repercussions of institutionalized colonialism on community and individual health, the need to decolonize research has become clear. Despite this, there is neither a singular definition of decolonizing methodologies nor an overview of the shared principles and characteristics of decolonized research needed to codify this work as common practice in global health.

OBJECTIVE: The review will identify papers that reference principles of decolonization and identify shared characteristics between them. The aim of this scoping review is to review decolonized research methodologies through the lens of sexual health as a step in creating a shared understanding of best practices. We will further examine the tools and methods used to collect and analyze data within the included studies.

METHODS: The protocol for this scoping review was developed using the framework from the Joanna Briggs Institute and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). The search strategy will comprise a search of electronic databases (JSTOR, Embase, EMCare, MEDLINE [Ovid], Global Health Database, Web of Science), gray literature sources, and key studies. Titles and abstracts will be reviewed by 2 or more independent reviewers against inclusion criteria. Bibliometric details, study design, methodology, community involvement, and other indicators will be collected using a data extraction tool developed for this review. Extracted data will be analyzed using descriptive statistics and qualitative analysis of content and themes to identify common practices in decolonized methodologies within sexual health. Narrative summaries will be used to describe results in relation to the research question, and identified gaps will be discussed.

RESULTS: The initial title or abstract review of 4967 studies identified by the search strategy was completed in November 2022. In total, 1777 studies met initial inclusion criteria and were sent to a second round of title or abstract review, which was completed in January 2023. In total, 706 studies were downloaded for full-text inclusion, which is expected to be completed by April 2023. We aim to complete data extraction and analysis by May 2023 and expect to publish the findings by the end of July 2023.

CONCLUSIONS: There remains a gap in the research on the meaning and application of decolonized research strategies, particularly within sexual and reproductive health. The findings of this study will contribute to a shared definition of decolonized methodologies and how they can be applied as a common practice in global health research. Applications include the development of decolonized frameworks, theoretical discourses, and methodologies. The study will inform the design and implementation of future decolonized research and evaluation strategies, particularly around sexual and reproductive health.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45771.

PMID:37058333 | DOI:10.2196/45771

JAMA Netw Open. 2023 Apr 3;6(4):e237043. doi: 10.1001/jamanetworkopen.2023.7043.

ABSTRACT

IMPORTANCE: The 2017 Clinical Practice Guideline (CPG) for the diagnosis and management of pediatric hypertension (PHTN) categorizes a greater proportion of children with elevated blood pressure and PHTN, yet several barriers to CPG adherence have been noted.

OBJECTIVE: To assess adherence to the 2017 CPG for the diagnosis and management of PHTN and use of a clinical decision support (CDS) tool to calculate blood pressure percentiles.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used electronic health record-extracted data from January 1, 2018, to December 31, 2019, among patients visiting 1 of 74 federally qualified health centers in AllianceChicago, a national Health Center Controlled Network. Children and adolescents (aged 3-17 years; hereinafter referred to as children) who attended at least 1 visit and had at least 1 blood pressure reading at or above the 90th percentile or diagnosis of elevated blood pressure or PHTN were eligible for data to be included in the analysis. Data were analyzed from September 1, 2020, to February 21, 2023.

EXPOSURES: Blood pressure at or above the 90th or 95th percentile.

MAIN OUTCOMES AND MEASURES: Diagnosis of PHTN (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10], code I10) or elevated blood pressure (ICD-10 code R03.0) and CDS tool use; blood pressure management (antihypertensive medication, lifestyle counseling, referral); and follow-up visit attendance. Descriptive statistics described the sample and rates of guideline adherence. Logistic regression analyses identified patient- and clinic-level associations with guideline adherence.

RESULTS: The sample consisted of 23 334 children (54.9% boys; 58.6% White race; median age, 8 [IQR, 4-12] years). Guideline-adherent diagnosis was observed in 8810 children (37.8%) with blood pressure at or above the 90th percentile and 146 of 2542 (5.7%) with blood pressure at or above the 95th percentile at 3 or more visits. The CDS tool was used to calculate blood pressure percentiles in 10 524 cases (45.1%) and was associated with significantly greater odds of PHTN diagnosis (odds ratio, 2.14 [95% CI, 1.10-4.15]). Among 15 422 children with blood pressure at or above the 95th percentile, antihypertensive medication was prescribed to 831 (5.4%), lifestyle counseling was provided to 14 841 (96.2%), and blood pressure-related referrals were given to 848 (5.5%). Guideline-adherent follow-up was observed in 8651 of 19 049 children (45.4%) with blood pressure at or above the 90th percentile and 2598 of 15 164 (17.1%) with blood pressure at or above the 95th percentile. Differences in guideline adherence by patient- and clinic-level factors were observed.

CONCLUSIONS AND RELEVANCE: In this study, fewer than 50% of children with elevated blood pressure had a guideline-adherent diagnosis code or attended guideline-adherent follow-up. Using a CDS tool was associated with guideline-adherent diagnosis, but the tool was underused. Further work is needed to understand how to best support implementation of tools promoting PHTN diagnosis, management, and follow-up.

PMID:37058305 | DOI:10.1001/jamanetworkopen.2023.7043

JAMA Netw Open. 2023 Apr 3;6(4):e238343. doi: 10.1001/jamanetworkopen.2023.8343.

ABSTRACT

IMPORTANCE: Digital health applications have been shown to be effective in the management of chronic diseases with simple treatment targets. The potential clinical value of digital health applications in rheumatoid arthritis (RA) has not been well studied.

OBJECTIVE: To investigate whether assessing patient-reported outcomes using digital health applications could result in disease control for patients with RA.

DESIGN, SETTING, AND PARTICIPANTS: This is a multicenter, open-label randomized clinical trial in 22 tertiary hospitals across China. Eligible participants were adult patients with RA. Participants were enrolled from November 1, 2018, to May 28, 2019, with a 12-month follow-up. The statisticians and rheumatologists who assessed disease activity were blinded. Investigators and participants were not blind to group assignment. Analysis was conducted from October 2020 to May 2022.

INTERVENTIONS: Participants were randomly assigned at a 1:1 ratio (block size of 4) to a smart system of disease management group (SSDM) or a conventional care control group. Upon the completion of the 6-month parallel comparison, patients in the conventional care control group were instructed to use the SSDM application for an extension of 6 months.

MAIN OUTCOMES AND MEASURES: The primary outcome was the rate of patients with disease activity score in 28 joints using the C-reactive protein (DAS28-CRP) of 3.2 or less at month 6.

RESULTS: Of 3374 participants screened, 2204 were randomized, and 2197 patients with RA (mean [SD] age, 50.5 [12.4] years; 1812 [82.5%] female) were enrolled. The study included 1099 participants in the SSDM group and 1098 participants in the control group. At month 6, the rate of patients with DAS28-CRP of 3.2 or less was 71.0% (780 of 1099 patients) in the SSDM group vs 64.5% (708 of 1098 patients) in the control group (difference between groups, 6.6%; 95% CI, 2.7% to 10.4%; P = .001). At month 12, the rate of patients with DAS28-CRP of 3.2 or less in the control group increased to a level (77.7%) that was comparable with that (78,2%) in the SSDM group (difference between groups, -0.2%; 95% CI, -3.9% to 3.4%; P = .90).

CONCLUSIONS AND RELEVANCE: In this randomized clinical trial of RA, the use of a digital health application with patient-reported outcomes was associated with an increase in disease control rate.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03715595.

PMID:37058302 | DOI:10.1001/jamanetworkopen.2023.8343

JAMA Netw Open. 2023 Apr 3;6(4):e238415. doi: 10.1001/jamanetworkopen.2023.8415.

ABSTRACT

IMPORTANCE: Food insecurity is associated with an increased likelihood of poor mental health and suicidality. The Supplemental Nutrition Assistance Program (SNAP) is the largest program addressing food insecurity in the US; under broad-based categorical eligibility (BBCE), states have the option to expand SNAP eligibility to a greater number of households by eliminating the asset test or increasing the income limit for eligibility.

OBJECTIVES: To examine the association of state elimination of the asset test and increases in the income limit for SNAP eligibility with rates of mental health and suicidality outcomes among adults.

DESIGN, SETTING, AND PARTICIPANTS: This ecological cross-sectional study used 2014 to 2017 data on US adults from the National Vital Statistics System and 2015 to 2019 data on US adults from the National Survey on Drug Use and Health (NSDUH) State-Level Small Area Estimates. Analyses were conducted between September and November 2022.

EXPOSURES: State elimination of the asset test only and state adoption of both SNAP eligibility policies (ie, state elimination of the asset test and increases in the income limit) for 2014 to 2017 from the SNAP Policy Database.

MAIN OUTCOMES AND MEASURES: Number of adults with a past-year major depressive disorder, mental illness, serious mental illness, or suicidal ideation and number of adults who died by suicide.

RESULTS: Analyses included 407 391 adult NSDUH participants and 173 085 adults who died by suicide. State elimination of the asset test only was associated with decreased rates of past-year major depressive episodes (rate ratio [RR], 0.92; 95% CI, 0.87-0.98) and mental illness (RR, 0.91; 95% CI, 0.87-0.97) among adults. State adoption of both SNAP eligibility policies (ie, state elimination of the asset test and increases in the income limit) was associated with decreased rates of past-year major depressive episodes (RR, 0.92; 95% CI, 0.86-0.99), mental illness (RR, 0.92; 95% CI, 0.87-0.98), serious mental illness (RR, 0.91; 95% CI, 0.84-0.99), and suicidal ideation (RR, 0.89; 95% CI, 0.82-0.96). Results suggested a decreased rate of suicide death (RR, 0.93; 95% CI, 0.84-1.02) in states with both policies compared with states with neither policy, although this result was not statistically significant.

CONCLUSIONS AND RELEVANCE: State adoption of policies that expand SNAP eligibility may contribute to decreased rates of multiple mental health and suicidality outcomes at the population level.

PMID:37058301 | DOI:10.1001/jamanetworkopen.2023.8415

Cell Biol Toxicol. 2023 Apr 14. doi: 10.1007/s10565-023-09806-9. Online ahead of print.

ABSTRACT

BACKGROUND: Prolonged exposure to toxic heavy metals leads to deleterious health outcomes including kidney injury. Metal exposure occurs through both environmental pathways including contamination of drinking water sources and from occupational hazards, including the military-unique risks from battlefield injuries resulting in retained metal fragments from bullets and blast debris. One of the key challenges to mitigate health effects in these scenarios is to detect early insult to target organs, such as the kidney, before irreversible damage occurs.

METHODS: High-throughput transcriptomics (HTT) has been recently demonstrated to have high sensitivity and specificity as a rapid and cost-effective assay for detecting tissue toxicity. To better understand the molecular signature of early kidney damage, we performed RNA sequencing (RNA-seq) on renal tissue using a rat model of soft tissue-embedded metal exposure. We then performed small RNA-seq analysis on serum samples from the same animals to identify potential miRNA biomarkers of kidney damage.

RESULTS: We found that metals, especially lead and depleted uranium, induce oxidative damage that mainly cause dysregulated mitochondrial gene expression. Utilizing publicly available single-cell RNA-seq datasets, we demonstrate that deep learning-based cell type decomposition effectively identified cells within the kidney that were affected by metal exposure. By combining random forest feature selection and statistical methods, we further identify miRNA-423 as a promising early systemic marker of kidney injury.

CONCLUSION: Our data suggest that combining HTT and deep learning is a promising approach for identifying cell injury in kidney tissue. We propose miRNA-423 as a potential serum biomarker for early detection of kidney injury.

PMID:37058270 | DOI:10.1007/s10565-023-09806-9