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Nevin Manimala Statistics

Symptomatic Intracranial Hemorrhage After Endovascular Stroke Treatment: External Validation of Prediction Models

Stroke. 2023 Feb;54(2):476-487. doi: 10.1161/STROKEAHA.122.040065. Epub 2023 Jan 23.

ABSTRACT

BACKGROUND: Symptomatic intracranial hemorrhage (sICH) is a severe complication of reperfusion therapy for ischemic stroke. Multiple models have been developed to predict sICH or intracranial hemorrhage (ICH) after reperfusion therapy. We provide an overview of published models and validate their ability to predict sICH in patients treated with endovascular treatment in daily clinical practice.

METHODS: We conducted a systematic search to identify models either developed or validated to predict sICH or ICH after reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) for ischemic stroke. Models were externally validated in the MR CLEAN Registry (n=3180; Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands). The primary outcome was sICH according to the Heidelberg Bleeding Classification. Model performance was evaluated with discrimination (c-statistic, ideally 1; a c-statistic below 0.7 is considered poor in discrimination) and calibration (slope, ideally 1, and intercept, ideally 0).

RESULTS: We included 39 studies describing 40 models. The most frequently used predictors were baseline National Institutes of Health Stroke Scale (NIHSS; n=35), age (n=22), and glucose level (n=22). In the MR CLEAN Registry, sICH occurred in 188/3180 (5.9%) patients. Discrimination ranged from 0.51 (SPAN-100 [Stroke Prognostication Using Age and National Institutes of Health Stroke Scale]) to 0.61 (SITS-SICH [Safe Implementation of Treatments in Stroke Symptomatic Intracerebral Hemorrhage] and STARTING-SICH [STARTING Symptomatic Intracerebral Hemorrhage]). Best calibrated models were IST-3 (intercept, -0.15 [95% CI, -0.01 to -0.31]; slope, 0.80 [95% CI, 0.50-1.09]), SITS-SICH (intercept, 0.15 [95% CI, -0.01 to 0.30]; slope, 0.62 [95% CI, 0.38-0.87]), and STARTING-SICH (intercept, -0.03 [95% CI, -0.19 to 0.12]; slope, 0.56 [95% CI, 0.35-0.76]).

CONCLUSIONS: The investigated models to predict sICH or ICH discriminate poorly between patients with a low and high risk of sICH after endovascular treatment in daily clinical practice and are, therefore, not clinically useful for this patient population.

PMID:36689584 | DOI:10.1161/STROKEAHA.122.040065

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Nevin Manimala Statistics

Distal Embolization in Relation to Radiological Thrombus Characteristics, Treatment Details, and Functional Outcome

Stroke. 2023 Feb;54(2):448-456. doi: 10.1161/STROKEAHA.122.040542. Epub 2023 Jan 23.

ABSTRACT

BACKGROUND: Distal embolization (DE) is a common complication of endovascular treatment (EVT). We investigated the association of radiological thrombus characteristics and treatment details with DE.

METHODS: Patients with thin-slice (≤2.5 mm) baseline noncontrast computed tomography and computed tomography angiography from the ESCAPE-NA1 trial (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischemic Stroke) were included. Thrombus annotation was performed manually on coregistered scans by experienced readers. We assessed thrombus location, distance from internal carotid artery terminus, length, perviousness, absolute attenuation, and hyperdense artery sign. In addition, we evaluated balloon guide catheter use during EVT, first-line EVT approach, the number of thrombectomy passes, and prior intravenous thrombolysis administration. DE was defined as the occurrence of emboli distal to the target artery or in new territories during EVT. The association between thrombus characteristics, treatment details, and DE was evaluated using descriptive statistics and multivariable mixed-effects logistic regression, resulting in adjusted odds ratios (aOR) with 95% CI. Interaction between IVT and radiological thrombus characteristics was assessed by adding interaction terms in separate models.

RESULTS: In total, 496 out of 1105 (44.9%) ESCAPE-NA1 patients were included. DE was detected in 251 out of 496 patients (50.6%). Patients with DE had longer thrombi (median, 28.5 [interquartile range, 20.8-42.3] mm versus 24.4 [interquartile range, 17.1-32.4] mm; P<0.01). There were no statistically significant differences in the other thrombus characteristics. Factors associated with DE were thrombus length (aOR, 1.02 [95% CI, 1.01-1.04]), balloon guide catheter use (aOR, 0.49 [95% CI, 0.29-0.85]), and number of passes (aOR, 1.24 [95% CI, 1.04-1.47]). In patients with hyperdense artery sign, IVT was associated with reduced odds of DE (aOR, 0.55 [95% CI, 0.31-0.97]), P for interaction=0.04.

CONCLUSIONS: DE was associated with longer thrombi, no balloon guide catheter use, and more EVT passes. IVT was associated with a reduced risk of DE in patients with hyperdense artery sign. These findings may support treatment decisions on IVT and EVT approaches.

PMID:36689583 | DOI:10.1161/STROKEAHA.122.040542

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Nevin Manimala Statistics

Decomposing heritability and genetic covariance by direct and indirect effect paths

PLoS Genet. 2023 Jan 23;19(1):e1010620. doi: 10.1371/journal.pgen.1010620. Online ahead of print.

ABSTRACT

Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.

PMID:36689559 | DOI:10.1371/journal.pgen.1010620

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Nevin Manimala Statistics

Exploring the role of financial empowerment in mitigating the gender differentials in subjective and objective health outcomes among the older population in India

PLoS One. 2023 Jan 23;18(1):e0280887. doi: 10.1371/journal.pone.0280887. eCollection 2023.

ABSTRACT

BACKGROUND: Despite the progress in achieving gender equality to a certain extent, women are found to be more susceptible to health disadvantages compared to men in the older ages. However, research in the Indian context has mainly remained restricted to subjective health that heavily depends on the individual’s perception, which may affect the validity of results. This study addresses this gap by complementing the investigation of the gender differentials in self-reported health outcomes (mobility and functional limitations) with that of objectively measured health status (hand-grip strength and static balance) among the older population of India. Besides, there is a dearth of literature that considers financial empowerment in explaining the gender differentials in health. Women’s ability to participate in household decision-making, especially for important matters like major purchases, including property, indicates their empowerment status. Furthermore, the ability to extend financial support can be considered an important ‘non-altruistic’ driver for kins to care for older adults, indirectly affecting their health and well-being. Thus, the present paper explores the influence of financial empowerment on gender differentials in poor health outcomes.

METHODS: Using the Longitudinal Aging Study in India, Wave-1 (2017-18), six logistic regression models have been specified to capture the adjusted association between gender and poor health outcomes. The first three models successively control for the demographic and social support factors; socioeconomic factors and pre-existing health conditions; and financial empowerment indicators. The last three models investigate the interactions between gender and marital status, living arrangement and involvement in financial decisions, respectively.

RESULTS: The findings reveal that women tend to be more perceptive about their physical discomfort than men and reported a higher prevalence of poor subjective health. In terms of objectively measured health status, older men had a higher prevalence of low hand-grip strength but a lower prevalence of poor balance. Gender demonstrated a strong, adjusted association with poor health outcomes among older adults. However, the magnitude of gender difference either shrunk considerably or became statistically insignificant for all the poor health outcomes after controlling the effect of indicators of financial empowerment. Further, the interaction between gender and involvement in financial matters demonstrated a stronger effect for men in reversing poor subjective health.

CONCLUSION: The study reinforced the positive effect of financial empowerment in mitigating gender disparity in health among older adults.

PMID:36689542 | DOI:10.1371/journal.pone.0280887

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Nevin Manimala Statistics

Experimental and machine learning approaches to investigate the effect of waste glass powder on the flexural strength of cement mortar

PLoS One. 2023 Jan 23;18(1):e0280761. doi: 10.1371/journal.pone.0280761. eCollection 2023.

ABSTRACT

Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches’ results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics.

PMID:36689541 | DOI:10.1371/journal.pone.0280761

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Nevin Manimala Statistics

Attitudes towards receiving COVID-19 vaccine and its associated factors among Southwest Ethiopian adults, 2021

PLoS One. 2023 Jan 23;18(1):e0280633. doi: 10.1371/journal.pone.0280633. eCollection 2023.

ABSTRACT

INTRODUCTION: Many countries around the world are still affected by the global pandemic of coronavirus disease. The vaccine is the most effective method of controlling Coronavirus Disease 2019 (COVID-19). However, attitudes toward vaccination are heavily affected by different factors besides vaccine availability.

OBJECTIVES: This study aimed to determine community attitudes toward the COVID-19 vaccine in Gurage Zone, Ethiopia.

METHODS: A community-based cross-sectional study was conducted from November 15th to December 15th, 2021. A simple random sampling technique was used to select 364 participants in the study area. An interview-administered structured questionnaire was used to collect the data; the data was entered into Epidata 3.1 version, and then exported to SPSS version 23 for further analysis. Descriptive statistics were used to determine the characteristics of study participants. Binary and multivariable logistic regression analyses with a p-value of less than 0.05 were used as a measure of significance.

RESULTS: In this study, 44.7% of study participants had a favorable attitude toward the COVID-19 vaccine. Perceived potential vaccine harm [AOR: 1.85; 95% CI (1.15-2.96)], Having ever had a chronic disease [AOR: 3.22; 95% CI (2.02-5.14)], community belief on the effectiveness of the vaccine [AOR: 2.02; 95% CI (1.27-3.22)], and average monthly income 3001-5000 ETB [AOR: 0.54; 95% CI (0.30-0.97)], average monthly income 5001-10000 ETB [AOR: 0.48; 95% CI(0.27-0.86)] were statistically significantly towards COVID-19 vaccination.

CONCLUSIONS: Overall, less than half of the participants had a favorable attitude toward the COVID-19 vaccine. Perceived potential vaccine harm, having ever had a chronic disease, community belief in the effectiveness of the vaccine, and average monthly income were determinant factors of the community’s attitude toward COVID-19 vaccination. As a result, information conversation with the community’s awareness of the COVID-19 vaccination in reducing vaccine-related suspicion.

PMID:36689539 | DOI:10.1371/journal.pone.0280633

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Nevin Manimala Statistics

Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks

PLoS Comput Biol. 2023 Jan 23;19(1):e1010855. doi: 10.1371/journal.pcbi.1010855. Online ahead of print.

ABSTRACT

How the connectivity of cortical networks determines the neural dynamics and the resulting computations is one of the key questions in neuroscience. Previous works have pursued two complementary approaches to quantify the structure in connectivity. One approach starts from the perspective of biological experiments where only the local statistics of connectivity motifs between small groups of neurons are accessible. Another approach is based instead on the perspective of artificial neural networks where the global connectivity matrix is known, and in particular its low-rank structure can be used to determine the resulting low-dimensional dynamics. A direct relationship between these two approaches is however currently missing, and in particular it remains to be clarified how local connectivity statistics and the global low-rank connectivity structure are inter-related and shape the low-dimensional activity. To bridge this gap, here we develop a method for mapping local connectivity statistics onto an approximate global low-rank structure. Our method rests on approximating the global connectivity matrix using dominant eigenvectors, which we compute using perturbation theory for random matrices. We demonstrate that multi-population networks defined from local connectivity statistics for which the central limit theorem holds can be approximated by low-rank connectivity with Gaussian-mixture statistics. We specifically apply this method to excitatory-inhibitory networks with reciprocal motifs, and show that it yields reliable predictions for both the low-dimensional dynamics, and statistics of population activity. Importantly, it analytically accounts for the activity heterogeneity of individual neurons in specific realizations of local connectivity. Altogether, our approach allows us to disentangle the effects of mean connectivity and reciprocal motifs on the global recurrent feedback, and provides an intuitive picture of how local connectivity shapes global network dynamics.

PMID:36689488 | DOI:10.1371/journal.pcbi.1010855

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Nevin Manimala Statistics

Effectiveness of mHealth on Adherence to Antiretroviral Therapy in Patients Living With HIV: Meta-analysis of Randomized Controlled Trials

JMIR Mhealth Uhealth. 2023 Jan 23;11:e42799. doi: 10.2196/42799.

ABSTRACT

BACKGROUND: The World Health Organization recommends that all adults with HIV adhere to antiretroviral therapy (ART). Good adherence to ART is beneficial to patients and the public. Furthermore, mHealth has shown promise in improving HIV medication adherence globally.

OBJECTIVE: The aim of this meta-analysis is to analyze the effectiveness of mHealth on adherence to antiretroviral therapy in patients living with HIV.

METHODS: Randomized controlled trials (RCTs) of the association between mHealth and adherence to ART published until December 2021 were searched in electronic databases. Odds ratios (ORs), weighted mean differences, and 95% CIs were calculated. This meta-analysis was performed using the Mantel-Haenszel method or the inverse variance test. We evaluated heterogeneity with the I2 statistic. If I2 was ≤50%, heterogeneity was absent, and a fixed effect model was used. If I2 was >50%, heterogeneity was present, and a random effects model was used.

RESULTS: A total of 2163 participants in 8 studies were included in this meta-analysis. All included studies were RCTs. The random effects model was used for a meta-analysis of the effects of various intervention measures compared to routine nursing; the outcome was not statistically significant (OR 1.54, 95% CI 0.99-2.38; P=.05). In the subgroups, only short messaging service (SMS)-based interventions significantly increased adherence to ART (OR 1.76, 95% CI 1.07-2.89; P=.03). Further analysis showed that only interactive or bidirectional SMS could significantly increase ART adherence (OR 1.69, 95% CI 1.22-2.34; P=.001). After combining the difference in CD4 cell count before and after the interventions, we concluded that there was no statistical heterogeneity among the studies (I2=0%; tau2=0.37; P=.95).

CONCLUSIONS: Interactive or bidirectional SMS can enhance intervention effects. However, whether mHealth can improve adherence to ART in patients with HIV needs further study. Owing to a lack of the required significant staff time, training, and ongoing supervision, there is still much more to do to apply mHealth to the clinical use of ART for patients living with HIV.

TRIAL REGISTRATION: PROSPERO CRD42022358774; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=358774.

PMID:36689267 | DOI:10.2196/42799

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Nevin Manimala Statistics

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

JMIR Ment Health. 2023 Jan 23;10:e37225. doi: 10.2196/37225.

ABSTRACT

BACKGROUND: Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful.

OBJECTIVE: We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common.

METHODS: We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma.

RESULTS: A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes.

CONCLUSIONS: Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.

PMID:36689265 | DOI:10.2196/37225

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Nevin Manimala Statistics

Impacts of HER2 Immunohistochemical Scores on Response and Outcomes of HER2-Positive Breast Cancers after Neoadjuvant Therapy

J Chin Med Assoc. 2023 Jan 23. doi: 10.1097/JCMA.0000000000000883. Online ahead of print.

ABSTRACT

BACKGROUND: Neoadjuvant systemic therapy (NST) is conducted in increased patients with breast cancer overexpressing human epidermal growth factor receptor 2 (HER2). Whether the intensity of HER2 protein expression determines response to treatment is challenged. This study aims to analyse the impact of HER2 immunohistochemical (IHC) scores on NST response and survival outcome.

METHODS: We analysed a total of 197 patients with HER2-positive breast cancer receiving NST and definite surgery from a prospectively collected database. The analysed end points included pathological complete response (pCR), disease-free survival (DFS) and overall survival (OS). More patients with IHC 2+/in situ hybridization (ISH)-positive tumours presented positive for hormonal receptors, compared to those with IHC 3+ tumours. No clinicopathological features except tumour necrosis were significantly associated with pCR.

RESULTS: Both positive hormone receptors and IHC scores stood on the borderline in statistical analysis. IHC 3+ group tends to present a higher pCR rate than IHC 2+/ISH+ groups (52.5% vs. 34.3%). Patients who achieved pCR had better survival outcome than that of non-pCR group. The impact of pCR on survival reached the statistical significance in the IHC 3+ group both in DFS (90.9% vs. 76.5%, p=0.004) and OS (97.4% vs. 83.2%, p=0.002). Multivariate analysis demonstrated IHC scores as an independent predictor of survival outcome with the adjustment of tumour staging and pCR.

CONCLUSION: HER2 IHC score is an independent predictor for outcome. IHC 3+ tumours presented a trend of higher pCR rate and better outcome in HER2-positive breast cancer patients who receive NST.

PMID:36689250 | DOI:10.1097/JCMA.0000000000000883