Tag: nevin manimala

J Int Med Res. 2024 Apr;52(4):3000605241232519. doi: 10.1177/03000605241232519.

ABSTRACT

OBJECTIVE: To develop and evaluate a novel feature selection technique, using photoplethysmography (PPG) sensors, for enhancing the performance of deep learning models in classifying vascular access quality in hemodialysis patients.

METHODS: This cross-sectional study involved creating a novel feature selection method based on SelectKBest principles, specifically designed to optimize deep learning models for PPG sensor data, in hemodialysis patients. The method effectiveness was assessed by comparing the performance of multiple deep learning models using the feature selection approach versus complete feature set. The model with the highest accuracy was then trained and tested using a 70:30 approach, respectively, with the full dataset and the SelectKBest dataset. Performance results were compared using Student’s paired t-test.

RESULTS: Data from 398 hemodialysis patients were included. The 1-dimensional convolutional neural network (CNN1D) displayed the highest accuracy among different models. Implementation of the SelectKBest-based feature selection technique resulted in a statistically significant improvement in the CNN1D model’s performance, achieving an accuracy of 92.05% (with feature selection) versus 90.79% (with full feature set).

CONCLUSION: These findings suggest that the newly developed feature selection approach might aid in accurately predicting vascular access quality in hemodialysis patients. This advancement may contribute to the development of reliable diagnostic tools for identifying vascular complications, such as stenosis, potentially improving patient outcomes and their quality of life.

PMID:38573764 | DOI:10.1177/03000605241232519

JMIR Perioper Med. 2024 Apr 4;7:e52125. doi: 10.2196/52125.

ABSTRACT

BACKGROUND: Pip is a novel digital health platform (DHP) that combines human health coaches (HCs) and technology with patient-facing content. This combination has not been studied in perioperative surgical optimization.

OBJECTIVE: This study’s aim was to test the feasibility of the Pip platform for deploying perioperative, digital, patient-facing optimization guidelines to elective surgical patients, assisted by an HC, at predefined intervals in the perioperative journey.

METHODS: We conducted an institutional review board-approved, descriptive, prospective feasibility study of patients scheduled for elective surgery and invited to enroll in Pip from 2.5 to 4 weeks preoperatively through 4 weeks postoperatively at an academic medical center between November 22, 2022, and March 27, 2023. Descriptive primary end points were patient-reported outcomes, including patient satisfaction and engagement, and Pip HC evaluations. Secondary end points included mean or median length of stay (LOS), readmission at 7 and 30 days, and emergency department use within 30 days. Secondary end points were compared between patients who received Pip versus patients who did not receive Pip using stabilized inverse probability of treatment weighting.

RESULTS: A total of 283 patients were invited, of whom 172 (60.8%) enrolled in Pip. Of these, 80.2% (138/172) patients had ≥1 HC session and proceeded to surgery, and 70.3% (97/138) of the enrolled patients engaged with Pip postoperatively. The mean engagement began 27 days before surgery. Pip demonstrated an 82% weekly engagement rate with HCs. Patients attended an average of 6.7 HC sessions. Of those patients that completed surveys (95/138, 68.8%), high satisfaction scores were recorded (mean 4.8/5; n=95). Patients strongly agreed that HCs helped them throughout the perioperative process (mean 4.97/5; n=33). The average net promoter score was 9.7 out of 10. A total of 268 patients in the non-Pip group and 128 patients in the Pip group had appropriate overlapping distributions of stabilized inverse probability of treatment weighting for the analytic sample. The Pip cohort was associated with LOS reduction when compared to the non-Pip cohort (mean 2.4 vs 3.1 days; median 1.9, IQR 1.0-3.1 vs median 3.0, IQR 1.1-3.9 days; mean ratio 0.76; 95% CI 0.62-0.93; P=.009). The Pip cohort experienced a 49% lower risk of 7-day readmission (relative risk [RR] 0.51, 95% CI 0.11-2.31; P=.38) and a 17% lower risk of 30-day readmission (RR 0.83, 95% CI 0.30-2.31; P=.73), though these did not reach statistical significance. Both cohorts had similar 30-day emergency department returns (RR 1.06, 95% CI 0.56-2.01, P=.85).

CONCLUSIONS: Pip is a novel mobile DHP combining human HCs and perioperative optimization content that is feasible to engage patients in their perioperative journey and is associated with reduced hospital LOS. Further studies assessing the impact on clinical and patient-reported outcomes from the use of Pip or similar DHPs HC combinations during the perioperative journey are required.

PMID:38573737 | DOI:10.2196/52125

J Emerg Manag. 2024 Special Issue on Climate Change and Sustainability in Emergency Management;22(7):101-112. doi: 10.5055/jem.0834.

ABSTRACT

The monthly search volumes for drought were extracted from Google® for South Africa using the Keywordsevery-where.com plugin from January 2004 until June 2022. To identify the potential qualitative drivers for such public interest the following data extracted by the plugin were investigated and analysed: the drought-related keywords, the long-tail keywords similar to drought, and the “people also searched for category” from the South African users. The Google Trends monthly score was extracted for South Africa and the Eastern Cape Province, and specific local municipalities/towns/cities in the province. The aim was to assess the relative significance of the drought interest in comparison to public interest in other search terms. The results of the Kruskal-Wallis analyses of variance by ranks showed that there was a statistically significant difference between individual values of the monthly search volumes for drought in South Africa, as a function of time of data extraction (5 percent level of significance; p-value ≤ 4.7 × 10^-14). The monthly search volumes increased with time, which is based on the results of the Mann-Kendall test at a 5 percent level of significance (p-value ≤ 0.0092). Analyses of the Google Trends scores indicate that the relative interest in drought in South Africa and the Eastern Cape Province increased with time between January 2004 and June 2022 (the Mann-Kendall test at a 5 percent level of significance; p-value = 0.0011). The population’s searches for drought were relatively low when compared to other search terms on Google. Drought adaptation of the South African community could be considered a driver of the Google searches for drought, but it is a marginal topic compared to other topics in Google searches. It might be necessary to increase this significance by investigating the “Google-search patterns for droughts” in the areas of Tshikaro, Mafusini, Cofimvaba, and Nxotsheni in the Eastern Cape Province of South Africa.

PMID:38573733 | DOI:10.5055/jem.0834

J Consult Clin Psychol. 2024 Apr;92(4):236-248. doi: 10.1037/ccp0000884.

ABSTRACT

BACKGROUND: Studies suggest that cognitive behavioral therapies (CBTs) may be efficacious in reducing symptoms of prolonged grief disorder (PGD), but no comprehensive overview and pooled estimate of CBTs’ effect on PGD in adulthood exist. We conducted a systematic review and meta-analysis of randomized controlled trials.

METHOD: Studies were selected independently by two researchers based on a systematic literature search in Pubmed, APA PsycInfo, Web of Science, and Embase. Meta-analyses provided pooled effect sizes for the effects of CBTs on PGD symptoms and secondary outcomes. We explored potential moderators of effect, risk of bias of included studies, and evaluated the quality of the meta-analytical evidence through the Grading of Recommendations, Assessment, Development, and Evaluation system.

RESULTS: The meta-analysis included 22 studies of 2,602 bereaved adults (averaged study M_age = 49 years). CBTs had a statistically significant medium effect on PGD symptoms at postintervention (K = 22, g = 0.65, 95% CI [0.49, 0.81]), and a large effect at follow-up (K = 7, g = 0.90, 95% CI [0.37, 1.43]). Statistically significant small-to-medium effects were found at postintervention on posttraumatic stress symptoms (K = 10, g = 0.74, 95% CI [0.49, 0.98]), depression (K = 19, g = 0.53, 95% CI [0.36, 0.71]), and anxiety (K = 9, g = 0.35, 95% CI [0.22, 0.49]). The effects on PGD remained unchanged when adjusted for possible outliers. None of the moderator analyses reached statistical significance.

CONCLUSION: This review suggests that CBTs are efficacious in reducing PGD symptoms in adulthood. Generalization of findings should be done with caution due to considerable inconsistency and indirectness of meta-analytic evidence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:38573714 | DOI:10.1037/ccp0000884

Clin Cancer Res. 2024 Apr 4. doi: 10.1158/1078-0432.CCR-23-3952. Online ahead of print.

ABSTRACT

PURPOSE: Preventing Anthracycline Cardiovascular Toxicity with Statins (PREVENT) (NCT01988571) randomized breast cancer or lymphoma patients receiving anthracyclines to atorvastatin 40 mg daily or placebo. We evaluated the effects of atorvastatin on oxidative and nitrosative stress biomarkers, and explored whether these biomarkers could explain the lack of effect of atorvastatin on LVEF in PREVENT.

PATIENTS AND METHODS: Blood samples were collected and cardiac magnetic resonance imaging was performed prior to doxorubicin initiation and at 6 and 24 months. Thirteen biomarkers (arginine-nitric oxide metabolites, paraoxonase-1 [PON-1] activity, and myeloperoxidase) were measured. Dimensionality reduction using principal component analysis was used to define biomarker clusters. Linear mixed-effects models determined the changes in biomarkers over time according to treatment group. Mediation analysis determined if biomarker clusters explained the lack of effect of atorvastatin on LVEF.

RESULTS: Among 202 participants with available biomarkers, median age was 53 years; 86.6% had breast cancer; median LVEF was 62%. Cluster 1 levels, reflecting arginine methylation metabolites, were lower over time with atorvastatin, although this was not statistically significant (p=0.081); cluster 2 levels, reflecting PON-1 activity, were significantly lower with atorvastatin (p=0.024). There were no significant changes in other biomarker clusters (p>0.05). Biomarker clusters did not mediate an effect of atorvastatin on LVEF (p>0.05) Conclusions: Atorvastatin demonstrated very modest effects on oxidative/nitrosative stress biomarkers in this low cardiovascular risk population. Our findings provide potential mechanistic insight into the lack of effect of atorvastatin on LVEF in the PREVENT trial.

PMID:38573708 | DOI:10.1158/1078-0432.CCR-23-3952

Rehabil Psychol. 2024 Apr 4. doi: 10.1037/rep0000561. Online ahead of print.

ABSTRACT

PURPOSE: This study examines (a) whether disability registration has anticipatory, immediate, and delayed effects on depressive symptoms and (b) how these effects differ by gender.

RESEARCH METHOD/DESIGN: Using data from the Korea Welfare Panel Study spanning over 16 waves between 2005 and 2020, this study employed the individual-level fixed effects models to estimate the trajectories of depressive symptoms before and after the registration of physical disability, for a cohort of 20,054 individuals. Furthermore, gender-stratified fixed effects models were used to examine gender differences.

RESULTS: Compared to the preregistration reference period (i.e., 4 or more years before disability registration), there was a sustained rise in depressive symptoms leading up to the year of registration, indicating the presence of anticipatory effects. After disability registration, depressive symptoms consistently remained at a statistically higher level than during the initial reference period, with a gradual return to the baseline level of depressive symptoms over time. These anticipatory, immediate, and delayed effects of disability registration were notably more pronounced among men than women.

CONCLUSION/IMPLICATIONS: To develop more effective mental health interventions for people with disability, policymakers should consider gendered trajectories of depressive symptoms before and after disability registration. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:38573669 | DOI:10.1037/rep0000561

Psychol Methods. 2024 Apr 4. doi: 10.1037/met0000657. Online ahead of print.

ABSTRACT

Latent moderated structural equation (LMS) is one of the most common techniques for estimating interaction effects involving latent variables (i.e., XWITH command in Mplus). However, empirical applications of LMS often overlook that this estimation technique assumes normally distributed variables and that violations of this assumption may lead to seriously biased parameter estimates. Against this backdrop, we study the robustness of LMS to different shapes and sources of nonnormality and examine whether various statistical tests can help researchers detect such distributional misspecifications. In four simulations, we show that LMS can be severely biased when the latent predictors or the structural disturbances are nonnormal. On the contrary, LMS is unaffected by nonnormality originating from measurement errors. As a result, testing for the multivariate normality of observed indicators of the latent predictors can lead to erroneous conclusions, flagging distributional misspecifications in perfectly unbiased LMS results and failing to reject seriously biased results. To solve this issue, we introduce a novel Hausman-type specification test to assess the distributional assumptions of LMS and demonstrate its performance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:38573667 | DOI:10.1037/met0000657

Psychol Methods. 2024 Apr 4. doi: 10.1037/met0000659. Online ahead of print.

ABSTRACT

Colliders, variables that serve as a common outcome of an independent and dependent variable, pose a major challenge in psychological research. Collider variables can induce bias in the estimation of a population relationship of interest when (a) the composition of a research sample is restricted by scores on a collider variable or (b) researchers adjust for a collider variable in their statistical analyses, as they might do for confounder variables. Both cases interfere with the accuracy and generalizability of statistical results. Despite their importance, however, collider effects remain relatively unknown in psychology. This tutorial article summarizes both the conceptual and the mathematical foundation for collider effects and their relevance to psychological research, and then proposes a method to correct for collider bias in cases of restrictive sample selection based on Thorndike’s Case III adjustment (1982). Two simulation studies demonstrated Thorndike’s correction as a viable solution for correcting collider bias in research studies, even when restriction on the collider variable was extreme and the selected sample size was as low as N = 100. Bias and relative bias results are reported to evaluate how well the correction equation approximates targeted population correlations under a variety of parameter conditions. We illustrate the application of the correction method to a hypothetical study of intelligence and conscientiousness, discuss the applicability of the method to more complex statistical models as a means of detection for collider bias, and provide code for researchers to apply to their own research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:38573665 | DOI:10.1037/met0000659

Psychol Methods. 2024 Apr 4. doi: 10.1037/met0000655. Online ahead of print.

ABSTRACT

Measurement invariance is an assumption underlying the regression of a latent variable on a background variable. It requires the measurement model parameters of the latent variable to be equal across the levels of the background variable. Item-specific violations of this assumption are referred to as differential item functioning and are ideally substantively explainable to warrant theoretically valid and meaningful results. Past research has focused on developing statistical approaches to explain differential item functioning effects in terms of item- or person-specific covariates. In this study, we propose a modeling approach that can be used to test if differences in item response times can be used to statistically explain differential item functioning. To this end, we operationalize a latent response process factor and test if item-specific group differences on this factor can account for the observed differences in item scores. We investigate the properties of the model in a simulation study, and we apply the model to a real data set. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

PMID:38573663 | DOI:10.1037/met0000655

JAMA Ophthalmol. 2024 Apr 4. doi: 10.1001/jamaophthalmol.2024.0540. Online ahead of print.

ABSTRACT

IMPORTANCE: Greater understanding of the association between strabismus and mental health conditions across sociodemographic backgrounds may inform strategies to improve mental well-being in this population.

OBJECTIVE: To describe the association of strabismus with mental health conditions in a diverse cohort of US adults.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used data from the National Institutes of Health’s All of Us Research Program, an ongoing program launched in 2015. The study included 3646 adults (aged ≥18 years) with strabismus and 3646 propensity score-matched controls. Statistical analysis was conducted from September 12, 2023, to January 29, 2024.

MAIN OUTCOMES AND MEASURES: Adults with strabismus were propensity score matched on age, gender, race and ethnicity, income, educational level, and insurance status in a 1:1 ratio with adults without strabismus. The prevalences of anxiety, depression, substance use and addiction, bipolar disorder, and schizophrenia spectrum disorder among adults with strabismus were compared with controls. Logistic regression was used to evaluate the association of mental health conditions with sociodemographic factors in each group.

RESULTS: This study included 3646 adults with strabismus (median age, 67 years [IQR, 53-76 years]; 2017 women [55%]) and 3646 propensity score-matched controls (median age, 67 years [IQR, 53-76 years]; 2017 women [55%]). Individuals with strabismus had higher prevalences of anxiety (1153 [32%] vs 519 [14%]; difference, 17%; 95% CI, 15%-19%; P < .001), depression (1189 [33%] vs 514 [14%]; difference, 19%; 95% CI, 17%-20%; P < .001), substance use and addiction (116 [3%] vs 51 [1%]; difference, 2%; 95% CI, 1%-3%; P < .001), bipolar disorder (253 [7%] vs 101 [3%]; difference, 4%; 95% CI, 3%-5%; P < .001), and schizophrenia spectrum disorder (103 [3%] vs 36 [1%]; difference, 2%; 95% CI, 1%-3%; P < .001) compared with individuals without strabismus. Among adults with strabismus, higher odds of mental health conditions were associated with younger age (odds ratio [OR], 1.11 per 10-year decrease; 95% CI, 1.06-1.16 per 10-year decrease), female gender (OR, 1.62; 95% CI, 1.41-1.85), Black or African American race and ethnicity (OR, 1.22; 95% CI, 1.01-1.48), low income (OR, 3.06; 95% CI, 2.56-3.67), and high school education or less (OR, 1.58; 95% CI, 1.34-1.85).

CONCLUSIONS AND RELEVANCE: In a diverse and nationwide cohort, adults with strabismus were more likely to have mental health conditions compared with adults without strabismus. Further investigation into the risk factors for poor mental health among adults with strabismus across sociodemographic backgrounds may offer novel opportunities for interventions to improve mental well-being in this population.

PMID:38573646 | DOI:10.1001/jamaophthalmol.2024.0540