Category: Statistics

PLoS One. 2021 Jul 15;16(7):e0254818. doi: 10.1371/journal.pone.0254818. eCollection 2021.

ABSTRACT

INTRODUCTION: Unsafe abortion is a leading cause of maternal mortality, and access to safe abortion services remains a public health priority in sub-Saharan Africa (SSA). A considerable amount of abortion research exists in the region; however, the spread of existing evidence is uneven such that some countries have an acute shortage of data with others over-researched. The imbalance reflects the complexities in prioritization among researchers, academics, and funders, and undeniably impedes effective policy and advocacy efforts. This scoping review aims to identify and map the landscape of abortion research in SSA, summarize existing knowledge, and pinpoint significant gaps, both substantive and geographic, requiring further investigation. This review will provide direction for future research, investments, and offer guidance for policy and programming on safe abortion.

MATERIALS AND METHODS: We utilize the Joanna Briggs Institute’s methodology for conducting scoping reviews. We will perform the search for articles in 8 electronic databases (i.e., PubMed, AJOL, Science Direct, SCOPUS, HINARI, Web of Knowledge, CINAHL, and WHO Regional Databases). We will include studies written in English or French language, produced or published between January 1, 2011, and July 31, 2021, and pertain directly to the subject of abortion in SSA. Using a tailored extraction frame, we will extract relevant information from publications that meet the inclusion criteria. Data will be analyzed using descriptive statistics and thematic analysis in response to key review questions.

ETHICS AND DISSEMINATION: Formal ethical approval is not required, as no primary data will be collected. The findings of this study will be disseminated through peer-reviewed publications and conference presentations.

PMID:34264992 | DOI:10.1371/journal.pone.0254818

PLoS One. 2021 Jul 15;16(7):e0254366. doi: 10.1371/journal.pone.0254366. eCollection 2021.

ABSTRACT

BACKGROUND: To develop a clinical prediction model to identify children at risk for revisits with serious illness to the emergency department.

METHODS AND FINDINGS: A secondary analysis of a prospective multicentre observational study in five European EDs (the TRIAGE study), including consecutive children aged <16 years who were discharged following their initial ED visit (‘index’ visit), in 2012-2015. Standardised data on patient characteristics, Manchester Triage System urgency classification, vital signs, clinical interventions and procedures were collected. The outcome measure was serious illness defined as hospital admission or PICU admission or death in ED after an unplanned revisit within 7 days of the index visit. Prediction models were developed using multivariable logistic regression using characteristics of the index visit to predict the likelihood of a revisit with a serious illness. The clinical model included day and time of presentation, season, age, gender, presenting problem, triage urgency, and vital signs. An extended model added laboratory investigations, imaging, and intravenous medications. Cross validation between the five sites was performed, and discrimination and calibration were assessed using random effects models. A digital calculator was constructed for clinical implementation. 7,891 children out of 98,561 children had a revisit to the ED (8.0%), of whom 1,026 children (1.0%) returned to the ED with a serious illness. Rates of revisits with serious illness varied between the hospitals (range 0.7-2.2%). The clinical model had a summary Area under the operating curve (AUC) of 0.70 (95% CI 0.65-0.74) and summary calibration slope of 0.83 (95% CI 0.67-0.99). 4,433 children (5%) had a risk of > = 3%, which was useful for ruling in a revisit with serious illness, with positive likelihood ratio 4.41 (95% CI 3.87-5.01) and specificity 0.96 (95% CI 0.95-0.96). 37,546 (39%) had a risk <0.5%, which was useful for ruling out a revisit with serious illness (negative likelihood ratio 0.30 (95% CI 0.25-0.35), sensitivity 0.88 (95% CI 0.86-0.90)). The extended model had an improved summary AUC of 0.71 (95% CI 0.68-0.75) and summary calibration slope of 0.84 (95% CI 0.71-0.97). As study limitations, variables on ethnicity and social deprivation could not be included, and only return visits to the original hospital and not to those of surrounding hospitals were recorded.

CONCLUSION: We developed a prediction model and a digital calculator which can aid physicians identifying those children at highest and lowest risks for developing a serious illness after initial discharge from the ED, allowing for more targeted safety netting advice and follow-up.

PMID:34264983 | DOI:10.1371/journal.pone.0254366

PLoS One. 2021 Jul 15;16(7):e0254413. doi: 10.1371/journal.pone.0254413. eCollection 2021.

ABSTRACT

The best agro-industrial performance presented by a crop genotype in one environment may not be reproduced in another owing to complex edaphoclimatic variations. Therefore, breeding programs are constantly attempting to obtain, through artificial hybridization, novel genotypes with high adaptability and stability potential. The objective of this study was to analyze genetic divergence in sugarcane based on the genotypic values of adaptability and stability. A total of 11 sugarcane genotypes were analyzed for eight agro-industrial traits. The genotypic values of the traits were determined using mixed model methodology, and the genetic divergence based on phenotypic and genotypic values was measured using the Mahalanobis distance. The distance matrices were correlated using the Mantel test, and the genotypes were grouped using the Tocher method. Genetic divergence is more accurate when based on genotypic values free of genotype-environment interactions and will differ from genetic divergence based on phenotypic data, changing the genotype allocations in the groups. The above methodology can be applied to assess genetic divergence to obtain novel sugarcane genotypes with higher productivity that are adapted to intensive agricultural systems using diverse technologies. This methodology can also be tested in other crops to increase accuracy in selecting the parents to be crossed.

PMID:34264990 | DOI:10.1371/journal.pone.0254413

PLoS One. 2021 Jul 15;16(7):e0254343. doi: 10.1371/journal.pone.0254343. eCollection 2021.

ABSTRACT

This study aimed to assess the value of quick sequential organ failure assessment (qSOFA) combined with other risk factors in predicting in-hospital mortality in patients presenting to the emergency department with suspected infection. This post-hoc analysis of a prospective multicenter study dataset included 34 emergency departments across Japan (December 2017 to February 2018). We included adult patients (age ≥16 years) who presented to the emergency department with suspected infection. qSOFA was calculated and recorded by senior emergency physicians when they suspected an infection. Different types of sepsis-related risk factors (demographic, functional, and laboratory values) were chosen from prior studies. A logistic regression model was used to assess the predictive value of qSOFA for in-hospital mortality in models based on the following combination of predictors: 1) qSOFA-Only; 2) qSOFA+Age; 3) qSOFA+Clinical Frailty Scale (CFS); 4) qSOFA+Charlson Comorbidity Index (CCI); 5) qSOFA+lactate levels; 6) qSOFA+Age+CCI+CFS+lactate levels. We calculated the area under the receiver operating characteristic curve (AUC) and other key clinical statistics at Youden’s index, where the sum of sensitivity and specificity is maximized. Following prior literature, an AUC >0.9 was deemed to indicate high accuracy; 0.7-0.9, moderate accuracy; 0.5-0.7, low accuracy; and 0.5, a chance result. Of the 951 patients included in the analysis, 151 (15.9%) died during hospitalization. The AUC for predicting in-hospital mortality was 0.627 (95% confidence interval [CI]: 0.580-0.673) for the qSOFA-Only model. Addition of other variables only marginally improved the model’s AUC; the model that included all potentially relevant variables yielded an AUC of only 0.730 (95% CI: 0.687-0.774). Other key statistic values were similar among all models, with sensitivity and specificity of 0.55-0.65 and 0.60-0.75, respectively. In this post-hoc data analysis from a prospective multicenter study based in Japan, combining qSOFA with other sepsis-related risk factors only marginally improved the model’s predictive value.

PMID:34264977 | DOI:10.1371/journal.pone.0254343

J Magn Reson Imaging. 2021 Jul 15. doi: 10.1002/jmri.27823. Online ahead of print.

ABSTRACT

BACKGROUND: Although perfusion imaging plays a key role in the management of steno-occlusive diseases, the clinical usefulness of arterial spin labeling (ASL) is limited by technical issues.

PURPOSE: To examine the effect of arterial transit time (ATT) prolongation on cerebral blood flow (CBF) measurement accuracy and identify the best CBF measurement protocol for steno-occlusive diseases.

STUDY TYPE: Prospective.

POPULATION: Moyamoya (n = 10) and atherosclerotic diseases (n = 8).

FIELD STRENGTH/SEQUENCE: A 3.0T/3DT₁ -weighted and ASL.

ASSESSMENT: Hadamard-encoded multidelay ASL scans with/without vessel suppression (VS) and single-delay ASL scans with long-label duration (LD) and long postlabeling delay (PLD), referred to as long-label long-delay (LLLD), were acquired. CBF measurement accuracy and its ATT dependency, measured as the correlation between the relative CBF measurement difference (ASL-single-photon emission computed tomography [SPECT]) and ATT, were compared among 1) Combo (incorporating multidelay and LLLD data based on ATT), 2) standard (LD/PLD = 1333/2333 msec), and 3) LLLD (LD/PLD = 4000/4000 msec) protocols, using whole-brain voxel-wise correlation with reference standard SPECT CBF. The effect of VS on CBF measurement accuracy was also assessed.

STATISTICAL TESTS: Pearson’s correlation coefficient, repeated-measures analysis of variance, t-test. P< 0.05 was considered significant.

RESULTS: Pearson’s correlation coefficients between ASL and SPECT CBF measurements were as follows: Combo = 0.55 ± 0.09; standard = 0.52 ± 0.12; LLLD = 0.41 ± 0.10. CBF measurement was least accurate in LLLD and most accurate in Combo. VS significantly improved overall CBF measurement accuracy in the standard protocol and in moyamoya patients for the Combo. ATT dependency analysis revealed that, compared with Combo, the standard and LLLD protocols showed significantly lower and negative and significantly higher and positive correlations, respectively (standard = -0.12 ± 0.04, Combo = -0.04 ± 0.03, LLLD = 0.17 ± 0.03).

DATA CONCLUSION: By using ATT-corrected CBF derived from LD/PLD = 1333/2333 msec as a base and by compensating underestimation in delayed regions using multidelay scans, the ATT-based Combo strategy improves CBF measurement accuracy compared with single-delay protocols in severe steno-occlusive diseases.

EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

PMID:34263988 | DOI:10.1002/jmri.27823

Minerva Dent Oral Sci. 2021 Jul 15. doi: 10.23736/S2724-6329.21.04523-X. Online ahead of print.

ABSTRACT

AIM: The purpose of this research was to evaluate the dentinal microcracks formation after root canal preparation with Hyflex EDM (Electrical Discharge Machining), Neolix Neoniti A1 EDM, Wave One Gold and Edge File X1 under illumination and magnification.

MATERIALS AND METHODS: 150 mandibular molars with 2 different mesial canals extracted for periodontal reasons were included in the study. The samples were decoronated at 15mm from the apex using a carborundum disc under copious water cooling. Access opening was done using a round bur. Sectioning of the distal root was done. Patency of mesial canals were checked using a 10K file. The samples were randomly divided into 5 groups using simple randomization technique. Group 1: Uninstrumented group. Group 2: Hyflex EDM(HEDM). Group 3: Neolix Neoniti A1(NA1). Group 4: Wave One Gold (WOG). Group 5: Edge File X1(EFX). Biomechanical preparation in all groups was done following the manufacturer’s instructions. Sectioning was done at 3mm, 6mm, and 9mm from the apex using a 0.13mm circular saw under copious water cooling. Sections were observed at 16x magnification under the dental operating microscope and illumination using a Light Emitting Diode (LED) curing light. The chi-square test was used to determine the statistically significant differences at P < .05. Intergroup comparison was done by the Post hoc Tukey test.

RESULTS: Total of 1800 images were analyzed. The highest number of cracks was seen in Neoniti A1 group (43.33%) while the least number of cracks were seen with Wave One Gold group (13.33%). There was statistically difference between reciprocating groups and the rotary group.

CONCLUSIONS: Within the limitations of this study, it can be concluded that all the heat-treated file systems produced dentinal microcracks. Rotary group(NA1 and HEDM) produced significantly more cracks than Reciprocation group (WOG and EFX).

PMID:34264044 | DOI:10.23736/S2724-6329.21.04523-X

Transfusion. 2021 Jul 15. doi: 10.1111/trf.16587. Online ahead of print.

ABSTRACT

BACKGROUND: Prophylactic platelet transfusions prevent bleeding in hemato-oncology patients, but it is unclear how any benefit varies between patients. Our aim was to assess if patients with different baseline risks for bleeding benefit differently from a prophylactic platelet transfusion strategy.

STUDY DESIGN AND METHODS: Using the data from the randomized controlled TOPPS trial (Trial of Platelet Prophylaxis), we developed a prediction model for World Health Organization grades 2, 3, and 4 bleeding risk (defined as at least one bleeding episode in a 30 days period) and grouped patients in four risk-quartiles based on this predicted baseline risk. Predictors in the model were baseline platelet count, age, diagnosis, disease modifying treatment, disease status, previous stem cell transplantation, and the randomization arm.

RESULTS: The model had a c-statistic of 0.58 (95% confidence interval [CI] 0.54-0.64). There was little variation in predicted risks (quartiles 46%, 47%, and 51%), but prophylactic platelet transfusions gave a risk reduction in all risk quartiles. The absolute risk difference (ARD) was 3.4% (CI -12.2 to 18.9) in the lowest risk quartile (quartile 1), 7.4% (95% CI -8.4 to 23.3) in quartile 2, 6.8% (95% CI -9.1 to 22.9) in quartile 3, and 12.8% (CI -3.1 to 28.7) in the highest risk quartile (quartile 4).

CONCLUSION: In our study, generally accepted bleeding risk predictors had limited predictive power (expressed by the low c-statistic), and, given the wide confidence intervals of predicted ARD, could not aid in identifying subgroups of patients who might benefit more (or less) from prophylactic platelet transfusion.

PMID:34263930 | DOI:10.1111/trf.16587

Biometrics. 2021 Jul 15. doi: 10.1111/biom.13530. Online ahead of print.

ABSTRACT

When to initiate treatment on patients is an important problem in many medical studies such as AIDS and cancer. In this article, we formulate the treatment initiation time problem for time-to-event data and propose an optimal individualized regime that determines the best treatment initiation time for individual patients based on their characteristics. Different from existing optimal treatment regimes where treatments are undertaken at a pre-specified time, here new challenges arise from the complicated missing mechanisms in treatment initiation time data and the continuous treatment rule in terms of initiation time. To tackle these challenges, we propose to use restricted mean residual lifetime as a value function to evaluate the performance of different treatment initiation regimes, and develop a nonparametric estimator for the value function, which is consistent even when treatment initiation times are not completely observable and their distribution is unknown. We also establish the asymptotic properties of the resulting estimator in the decision rule and its associated value function estimator. In particular, the asymptotic distribution of the estimated value function is nonstandard, which follows a weighted chi-squared distribution. The finite-sample performance of the proposed method is evaluated by simulation studies and is further illustrated with an application to a breast cancer data. This article is protected by copyright. All rights reserved.

PMID:34263933 | DOI:10.1111/biom.13530

Stat Med. 2021 Jul 15. doi: 10.1002/sim.9144. Online ahead of print.

ABSTRACT

In biomedical studies, the causal mediation effect might be heterogeneous across individuals in the study population due to each study subject’s unique characteristics. While individuals’ mediation effects may differ from each other, it is often reasonable and more interpretable to assume that individuals belong to several distinct latent subgroups with similar attributes. In this article, we first show that the subgroup-specific mediation effect can be identified under the group-specific sequential ignorability assumptions. Then, we propose a simple mixture modeling approach to account for the latent subgroup structure where each mixture component corresponds to one latent subgroup in the linear structural equation model framework. Model parameters can be estimated using the standard expectation-maximization (EM) algorithm. Each individual’s subgroup membership can be inferred based on the posterior probability. We propose to use the singular Bayesian information criterion to consistently select the number of latent subgroups by recognizing that the Fisher information matrix for mixture models might be singular. We then propose to use nonparametric bootstrap method to compute standard errors and confidence intervals. We conducted simulation studies to evaluate the empirical performance of our proposed method named iMed. Finally, we reanalyzed a DNA methylation data set from the Normative Aging Study and found that the mediation effects of two well-documented DNA methylation CpG sites are heterogeneous across two latent subgroups in the causal pathway from smoking behavior to lung function. We also developed an R package iMed for public use.

PMID:34263963 | DOI:10.1002/sim.9144

Ginekol Pol. 2021 Jul 15. doi: 10.5603/GP.a2021.0064. Online ahead of print.

ABSTRACT

OBJECTIVES: Concentrations of soluble ICAM-2, -3, -4 and syndecan-1 and -4 have not yet been marked in the peritoneal fluid of women with endometriosis. The aim of the study was to determine whether these molecules can participate in formation and development of endometriosis.

MATERIAL AND METHODS: The study comprised of 80 women at the proliferative phase of the menstrual cycle, aged 21 to 49 years (mean age 31. 3 ± 6. 7 years) undergoing laparoscopy, to determine the causes of primary infertility and to confirm or exclude endometriosis. The study group consisted of 60 women with endometriosis in the pelvis as confirmed by laparoscopy and histopathology. The reference group consisted of 20 women in whom no endometriosis. Concentrations of selected sICAM and syndecans in the peritoneal fluid were determined with the use of ELISA method.

RESULTS: Decreased concentrations of sICAM-2 and increased concentrations of sICAM-3, sICAM-4 and syndnecan-1 and -4 were observed in the peritoneal fluid of women with endometriosis and compared with concentrations of this parameter in the reference group (p < 0.0001). Additionally, negative correlation was found between the concentrations of sICAM-3 and sICAM-2 among women with endometriosis. There was no statistically significant correlation between the concentration of sICAM-2 and sICAM-4, sICAM-3 and sICAM-4 and syndecan-1 and syndecan-4 in the examined women.

CONCLUSIONS: Changes in concentrations of all the evaluated molecules were observed in the peritoneal fluid in women suffering from endometriosis. Since they have a role in regulation of the immune response, in angiogenesis and apoptosis of the endometrial cells.

PMID:34263915 | DOI:10.5603/GP.a2021.0064