Tag: nevin manimala

Clin Infect Dis. 2021 Dec 24:ciab1047. doi: 10.1093/cid/ciab1047. Online ahead of print.

ABSTRACT

BACKGROUND: Household contact tracing for tuberculosis (TB) may facilitate TB diagnosis and identify individuals who may benefit from TB preventive therapy (TPT). In this cluster-randomised trial, we investigated whether household contact tracing and intensive TB/HIV screening would improve TB-free survival.

METHODS: Household contacts of index TB patients in two Provinces of South Africa were randomised to home tracing and intensive HIV/TB screening (sputum Xpert and culture; HIV testing with treatment linkage; and TPT, if eligible), or standard of care (SOC, clinic referral letters). The primary outcome was incident TB or death at 15-months. Secondary outcomes included tuberculin skin test (TST) positivity in children ≤14 years and undiagnosed HIV. (ISRCTN16006202).

RESULTS: From December 2016-March 2019, 1,032 index patients (4,459 contacts) and 1,030 (4,129 contacts) were randomised to the intervention and SOC arms. 3.2% (69/2166) of intervention arm contacts had prevalent microbiologically-confirmed TB. At 15-months, the cumulative incidence of TB or death did not differ between the intensive screening (93/3230, 2.9%) and SOC (80/2600, 3.1%) arms (hazard ratio: 0.90, 95% confidence interval (CI): 0.66-1.24). TST positivity was higher in the intensive screening arm (38/845, 4.5%) compared to the SOC arm (15/800, 1.9%, odds ratio: 2.25, 95% CI: 1.07-4.72). Undiagnosed HIV was similar between arms (41/3185, 1.3% vs. 32/2543, 1.3%; odds ratio: 1.02, 95% CI: 0.64-1.64).

CONCLUSIONS: Household contact tracing with intensive screening and referral did not reduce incident TB or death. Providing referral letters to household contacts of index patients is an alternative strategy to home visits in high TB/HIV-prevalence settings.

PMID:34950944 | DOI:10.1093/cid/ciab1047

J Gerontol A Biol Sci Med Sci. 2021 Dec 24:glab382. doi: 10.1093/gerona/glab382. Online ahead of print.

ABSTRACT

This review identifies frequent design and analysis errors in aging and senescence research and discusses best practices in study design, statistical methods, analyses, and interpretation. Recommendations are offered for how to avoid these problems. The following issues are addressed: 1) errors in randomization, 2) errors related to testing within-group instead of between-group differences, 3) failing to account for clustering, 4) failing to consider interference effects, 5) standardizing metrics of effect size, 6) maximum lifespan testing, 7) testing for effects beyond the mean, 8) tests for power and sample size, 9) compression of morbidity versus survival curve-squaring, and 10) other hot topics, including modeling high-dimensional data and complex relationships and assessing model assumptions and biases. We hope that bringing increased awareness of these topics to the scientific community will emphasize the importance of employing sound statistical practices in all aspects of aging and senescence research.

PMID:34950945 | DOI:10.1093/gerona/glab382

Patterns (N Y). 2021 Dec 10;2(12):100393. doi: 10.1016/j.patter.2021.100393. eCollection 2021 Dec 10.

ABSTRACT

The paper highlights how each step of a data science pipeline can be performed in a “responsible” way, taking into account privacy, ethics, and quality issues. Several examples from the Italian public sector contribute to clarifying how data collections and data analyses can be carried out under a responsible view.

PMID:34950908 | PMC:PMC8672189 | DOI:10.1016/j.patter.2021.100393

Patterns (N Y). 2021 Dec 10;2(12):100368. doi: 10.1016/j.patter.2021.100368. eCollection 2021 Dec 10.

ABSTRACT

Numerous arguments strongly support the practice of open science, which offers several societal and individual benefits. For individual researchers, sharing research artifacts such as data can increase trust and transparency, improve the reproducibility of one’s own work, and catalyze new collaborations. Despite a general appreciation of the benefits of data sharing, research data are often only available to the original investigators. For data that are shared, lack of useful metadata and documentation make them challenging to reuse. In this paper, we argue that a lack of incentives and infrastructure for making data useful is the biggest barrier to creating a culture of widespread data sharing. We compare data with code, examine computational environments in the context of their ability to facilitate the reproducibility of research, provide some practical guidance on how one can improve the chances of their data being reusable, and partially bridge the incentive gap. While previous papers have focused on describing ideal best practices for data and code, we focus on common-sense ideas for sharing tabular data for a target audience of academics working in data science adjacent fields who are about to submit for publication.

PMID:34950899 | PMC:PMC8672137 | DOI:10.1016/j.patter.2021.100368

Patterns (N Y). 2021 Oct 27;2(12):100372. doi: 10.1016/j.patter.2021.100372. eCollection 2021 Dec 10.

ABSTRACT

We introduce a new method for single-cell cytometry studies, FAUST, which performs unbiased cell population discovery and annotation. FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited for the analysis of complex datasets. We provide simulation studies that compare FAUST with existing methodology, exemplifying its strength. We apply FAUST to data from a Merkel cell carcinoma anti-PD-1 trial and discover pre-treatment effector memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. Using FAUST, we then validate these correlates in cryopreserved peripheral blood mononuclear cell samples from the same study, as well as an independent CyTOF dataset from a published metastatic melanoma trial. Finally, we show how FAUST’s phenotypes can be used to perform cross-study data integration in the presence of diverse staining panels. Together, these results establish FAUST as a powerful new approach for unbiased discovery in single-cell cytometry.

PMID:34950900 | PMC:PMC8672150 | DOI:10.1016/j.patter.2021.100372

Palliat Care Soc Pract. 2021 Dec 16;15:26323524211063217. doi: 10.1177/26323524211063217. eCollection 2021.

ABSTRACT

AIMS: The study aimed to explore the quality and impact of care provided through an innovative palliative care project to improve the quality of life of older people in an urban informal settlement in Bangladesh.

METHODS: Center for Palliative Care (CPC) at Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, in collaboration with the Worldwide Hospice Palliative Care Alliance (WHPCA) has been operating this community project since 2015. A cross-sectional observational design was used in this program evaluation study. A total of 594 people received services including 227 patients (Group-1) receiving regular and intensive palliative care and 367 patients with less intense needs (Group-2) receiving relatively less support based on need. In addition, current group-1 patients (total 114) and a matched cohort of 58 group-2 patients were interviewed with an experience of care survey questionnaire. Baseline and demographic data were presented in tables. The Z-test was used to measure mean statistical differences between two groups.

RESULTS: Multiple comorbidities were common. Pain was the most frequently noted physical symptom along with anxiety, sadness, and depression as common psychological concerns. Compassionate palliative care for the older people had significant (p < 0.05) impact on psycho-social and spiritual care, caregiver training, responding to emergencies, and reduction of out of pocket healthcare expenditure among the intensive intervention group.

CONCLUSION: Using a community-based approach following this model may play a significant part in expansion of palliative care throughout Bangladesh to meet the huge need and scarcity of such services.

PMID:34950874 | PMC:PMC8689427 | DOI:10.1177/26323524211063217

Patterns (N Y). 2021 Oct 4;2(12):100364. doi: 10.1016/j.patter.2021.100364. eCollection 2021 Dec 10.

ABSTRACT

Current cardiovascular risk assessment tools use a small number of predictors. Here, we study how machine learning might: (1) enable principled selection from a large multimodal set of candidate variables and (2) improve prediction of incident coronary artery disease (CAD) events. An elastic net-based Cox model (ML4H_EN-COX) trained and evaluated in 173,274 UK Biobank participants selected 51 predictors from 13,782 candidates. Beyond most traditional risk factors, ML4H_EN-COX selected a polygenic score, waist circumference, socioeconomic deprivation, and several hematologic indices. A more than 30-fold gradient in 10-year risk estimates was noted across ML4H_EN-COX quintiles, ranging from 0.25% to 7.8%. ML4H_EN-COX improved discrimination of incident CAD (C-statistic = 0.796) compared with the Framingham risk score, pooled cohort equations, and QRISK3 (range 0.754-0.761). This approach to variable selection and model assessment is readily generalizable to a broad range of complex datasets and disease endpoints.

PMID:34950898 | PMC:PMC8672148 | DOI:10.1016/j.patter.2021.100364

Gen Psychiatr. 2021 Dec 1;34(6):e100635. doi: 10.1136/gpsych-2021-100635. eCollection 2021.

ABSTRACT

BACKGROUND: The corpus callosum (CC) is the most targeted region in the cerebrum that integrates cognitive data between homologous areas in the right and left hemispheres.

AIMS: Our study used statistical analysis to determine whether there was a correlation between shape changes in the CC in patients with schizophrenia (SZ) (deficit syndrome (DS) and non-deficit syndrome (NDS)) and healthy control (HC) subjects.

METHODS: This study consisted of 27 HC subjects and 50 schizophrenic patients (20 with DS and 30 with NDS). 3 patients with DS and 4 patients with NDS were excluded. Three-dimensional, sagittal, T1-spoiled, gradient-echo imaging was used. Standard anatomical landmarks were selected and marked on each image using specific software.

RESULTS: As to comparing the Procrustes mean shapes of the CC, statistically significant differences were observed between HC and SZ (DS+NDS) (p=0.017, James’s F_j=73.732), HC and DS (p<0.001, James’s F_j=140.843), HC and NDS (p=0.006, James’s F_j=89.178) and also DS and NDS (p<0.001, James’s F_j=152.967). Shape variability in the form of CC was 0.131, 0.085, 0.082 and 0.086 in the HC, SZ (DS+NDS), DS and NDS groups, respectively.

CONCLUSIONS: This study reveals callosal shape variations in patients with SZ and their DS and NDS subgroups that take into account the CC’s topographic distribution.

PMID:34950854 | PMC:PMC8638449 | DOI:10.1136/gpsych-2021-100635

Brain Commun. 2021 Dec 4;3(4):fcab288. doi: 10.1093/braincomms/fcab288. eCollection 2021.

ABSTRACT

Our inability to reliably predict disease outcomes in multiple sclerosis remains an issue for clinicians and clinical trialists. This study aims to create, from available clinical, genetic and environmental factors; a clinical-environmental-genotypic prognostic index to predict the probability of new relapses and disability worsening. The analyses cohort included prospectively assessed multiple sclerosis cases (N = 253) with 2858 repeated observations measured over 10 years. N = 219 had been diagnosed as relapsing-onset, while N = 34 remained as clinically isolated syndrome by the 10th-year review. Genotype data were available for 199 genetic variants associated with multiple sclerosis risk. Penalized Cox regression models were used to select potential genetic variants and predict risk for relapses and/or worsening of disability. Multivariable Cox regression models with backward elimination were then used to construct clinical-environmental, genetic and clinical-environmental-genotypic prognostic index, respectively. Robust time-course predictions were obtained by Landmarking. To validate our models, Weibull calibration models were used, and the Chi-square statistics, Harrell’s C-index and pseudo–R ² were used to compare models. The predictive performance at diagnosis was evaluated using the Kullback-Leibler and Brier (dynamic) prediction error (reduction) curves. The combined index (clinical-environmental-genotypic) predicted a quadratic time-dynamic disease course in terms of worsening (HR = 2.74, CI: 2.00-3.76; pseudo–R ²=0.64; C-index = 0.76), relapses (HR = 2.16, CI: 1.74-2.68; pseudo–R ² = 0.91; C-index = 0.85), or both (HR = 3.32, CI: 1.88-5.86; pseudo–R ² = 0.72; C-index = 0.77). The Kullback-Leibler and Brier curves suggested that for short-term prognosis (≤5 years from diagnosis), the clinical-environmental components of disease were more relevant, whereas the genetic components reduced the prediction errors only in the long-term (≥5 years from diagnosis). The combined components performed slightly better than the individual ones, although their prognostic sensitivities were largely modulated by the clinical-environmental components. We have created a clinical-environmental-genotypic prognostic index using relevant clinical, environmental, and genetic predictors, and obtained robust dynamic predictions for the probability of developing new relapses and worsening of symptoms in multiple sclerosis. Our prognostic index provides reliable information that is relevant for long-term prognostication and may be used as a selection criterion and risk stratification tool for clinical trials. Further work to investigate component interactions is required and to validate the index in independent data sets.

PMID:34950873 | PMC:PMC8691056 | DOI:10.1093/braincomms/fcab288

JNCI Cancer Spectr. 2021 Dec 8;5(6):pkab090. doi: 10.1093/jncics/pkab090. eCollection 2021 Dec.

ABSTRACT

BACKGROUND: Recreational physical activity (RPA) is associated with improved survival after breast cancer (BC) in average-risk women, but evidence is limited for women who are at increased familial risk because of a BC family history or BRCA1 and BRCA2 pathogenic variants (BRCA1/2 PVs).

METHODS: We estimated associations of RPA (self-reported average hours per week within 3 years of BC diagnosis) with all-cause mortality and second BC events (recurrence or new primary) after first invasive BC in women in the Prospective Family Study Cohort (n = 4610, diagnosed 1993-2011, aged 22-79 years at diagnosis). We fitted Cox proportional hazards regression models adjusted for age at diagnosis, demographics, and lifestyle factors. We tested for multiplicative interactions (Wald test statistic for cross-product terms) and additive interactions (relative excess risk due to interaction) by age at diagnosis, body mass index, estrogen receptor status, stage at diagnosis, BRCA1/2 PVs, and familial risk score estimated from multigenerational pedigree data. Statistical tests were 2-sided.

RESULTS: We observed 1212 deaths and 473 second BC events over a median follow-up from study enrollment of 11.0 and 10.5 years, respectively. After adjusting for covariates, RPA (any vs none) was associated with lower all-cause mortality of 16.1% (95% confidence interval [CI] = 2.4% to 27.9%) overall, 11.8% (95% CI = -3.6% to 24.9%) in women without BRCA1/2 PVs, and 47.5% (95% CI = 17.4% to 66.6%) in women with BRCA1/2 PVs (RPA*BRCA1/2 multiplicative interaction P = .005; relative excess risk due to interaction = 0.87, 95% CI = 0.01 to 1.74). RPA was not associated with risk of second BC events.

CONCLUSION: Findings support that RPA is associated with lower all-cause mortality in women with BC, particularly in women with BRCA1/2 PVs.

PMID:34950851 | PMC:PMC8692829 | DOI:10.1093/jncics/pkab090