Nevin Manimala

PLoS One. 2022 Jul 21;17(7):e0271504. doi: 10.1371/journal.pone.0271504. eCollection 2022.

ABSTRACT

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.

PMID:35862480 | DOI:10.1371/journal.pone.0271504

PLoS One. 2022 Jul 21;17(7):e0271094. doi: 10.1371/journal.pone.0271094. eCollection 2022.

ABSTRACT

The one-sample log-rank test is the method of choice for single-arm Phase II trials with time-to-event endpoint. It allows to compare the survival of patients to a reference survival curve that typically represents the expected survival under standard of care. The one-sample log-rank test, however, assumes that the reference survival curve is known. This ignores that the reference curve is commonly estimated from historic data and thus prone to sampling error. Ignoring sampling variability of the reference curve results in type I error rate inflation. We study this inflation in type I error rate analytically and by simulation. Moreover we derive the actual distribution of the one-sample log-rank test statistic, when the sampling variability of the reference curve is taken into account. In particular, we provide a consistent estimate of the factor by which the true variance of the one-sample log-rank statistic is underestimated when reference curve sampling variability is ignored. Our results are further substantiated by a case study using a real world data example in which we demonstrate how to estimate the error rate inflation in the planning stage of a trial.

PMID:35862473 | DOI:10.1371/journal.pone.0271094

Brain Behav. 2022 Jul 21:e2712. doi: 10.1002/brb3.2712. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Fatigue is a common but poorly understood complaint in patients with immune-mediated polyneuropathies. We sought to evaluate changes in fatigue over 1 year in a cohort of chronic inflammatory demyelinating polyneuropathy (CIDP) patients and to correlate changes in fatigue with changes in disability and quality of life. Investigation into other factors that may contribute to fatigue with a particular interest in the role other chronic disease states may play was also performed.

METHODS: Fifty patients with CIDP who satisfied the 2010 EFNS/PNS diagnostic criteria were followed over the period of 1 year at three tertiary care centers in Serbia. Assessments of disability, quality of life, and patient perception of change and fatigue were collected at two time points 12 months apart. Comorbidities, treatment regimens, and sedating medication use was collected.

RESULTS: Disability, quality of life, and patient perception of change showed statistically significant correlations with change in fatigue (p < .01). Increased levels of fatigue were noted in patients who used sedating medications (p = .05) and who had a comorbid chronic medical condition (p = .01).

INTERPRETATION: Worsening fatigue correlates over time with increased disability and worse quality of life. Fatigue is not specific to CIDP, but is common in many chronic medical conditions and with the use of sedating medications. Our findings support the importance of identifying and supportively managing fatigue in patients with CIDP, but cautions against considering fatigue as a CIDP diagnostic symptom or using fatigue to justify immunotherapy utilization.

PMID:35862228 | DOI:10.1002/brb3.2712

Stroke. 2022 Jul 13:101161STROKEAHA122039732. doi: 10.1161/STROKEAHA.122.039732. Online ahead of print.

ABSTRACT

BACKGROUND: Imaging features derived from T1-weighted (T1w) images texture analysis were shown to be potential markers of poststroke cognitive impairment, with better sensitivity than atrophy measurement. However, in magnetic resonance images, the signal distribution is subject to variations and can limit transferability of the method between centers. This study examined the reliability of texture features against imaging settings using data from different centers.

METHODS: Data were collected from 327 patients within the Stroke and Cognition Consortium from centers in France, Germany, Australia, and the United Kingdom. T1w images were preprocessed to normalize the signal intensities and then texture features, including first- and second-order statistics, were measured in the hippocampus and the entorhinal cortex. Differences between the data led to the use of 2 methods of analysis. First, a machine learning modeling, using random forest, was used to build a poststroke cognitive impairment prediction model using one dataset and this was validated on another dataset as external unseen data. Second, the predictive ability of the texture features was examined in the 2 remaining datasets by ANCOVA with false discovery rate correction for multiple comparisons.

RESULTS: The prediction model had a mean accuracy of 90% for individual classification of patients in the learning base while for the validation base it was ≈ 77%. ANCOVA showed significant differences, in all datasets, for the kurtosis and inverse difference moment texture features when measured in patients with cognitive impairment and those without.

CONCLUSIONS: These results suggest that texture features obtained from routine clinical MR images are robust early predictors of poststroke cognitive impairment and can be combined with other demographic and clinical predictors to build an accurate prediction model.

PMID:35862196 | DOI:10.1161/STROKEAHA.122.039732

J Am Heart Assoc. 2022 Jul 20:e026379. doi: 10.1161/JAHA.121.026379. Online ahead of print.

ABSTRACT

Background Intracerebral hemorrhage is the most disabling and lethal form of stroke. We aimed to develop a novel clinical score for neurological deterioration during hospitalization after intracerebral hemorrhage. Methods and Results We analyzed data from the CHERRY (Chinese Cerebral Hemorrhage: Mechanism and Intervention) study. Two-thirds of eligible patients were randomly allocated into the training cohort (n=1027) and one-third into the validation cohort (n=515). Multivariable logistic regression was used to identify factors associated with neurological deterioration (an increase in National Institutes of Health Stroke Scale of ≥4 or death) within 15 days after symptom onset. A prediction score was developed based on regression coefficients derived from the logistic model. The site, size, gender, National Institutes of Health Stroke Scale, age, leukocyte, sugar (SIGNALS) score was developed as a sum of individual points (0-8) based on site (1 point for infratentorial location), size (3 points for >20 mL of supratentorial hematoma volume or 2 points for >10 mL of infratentorial hematoma volume), sex (1 point for male sex), National Institutes of Health Stroke Scale score (1 point for >10), age (1 point for ≥70 years), white blood cell (1 point for>9.0×10⁹/L), and fasting blood glucose (1 point>7.0 mmol/L). The proportion of patients who suffered from neurological deterioration increased with higher SIGNALS score, showing good discrimination and good calibration in the training cohort (C statistic, 0.821; Hosmer-Lemeshow test, P=0.687) and in the validation cohort (C statistic, 0.848; Hosmer-Lemeshow test, P=0.592), respectively. Conclusions The SIGNALS score reliably predicts the risk of in-hospital neurological deterioration of patients with intracerebral hemorrhage.

PMID:35862193 | DOI:10.1161/JAHA.121.026379

JMIR Res Protoc. 2022 Jul 21;11(7):e37729. doi: 10.2196/37729.

ABSTRACT

BACKGROUND: Digital technologies could contribute to health promotion and disease prevention. It is unclear if and how such digital technologies address the health needs of older people in nonclinical settings (ie, daily life).

OBJECTIVE: This study aims to identify digital technologies for health promotion and disease prevention that target the needs of older people in nonclinical settings by performing a scoping review of the published literature. The scoping review is guided by the framework of Arksey and O’Malley.

METHODS: Our scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The information sources are bibliographic databases (MEDLINE, PsycINFO, CINAHL, and SCOPUS) and bibliographies of any included systematic reviews. Manual searches for additional studies will be performed in Google Scholar and most relevant journals. The electronic search strategy was developed in collaboration with a librarian who performed the search for studies on digital technologies for health promotion and disease prevention targeting the needs of older people. Study selection and data coding will be performed independently by 2 authors. Consensus will be reached by discussion. Eligibility is based on the PCC (Population, Concept, and Context) criteria as follows: (1) older people (population); (2) any digital (health) technology, such as websites, smartphone apps, or wearables (concept); and (3) health promotion and disease prevention in nonclinical (daily life, home, or community) settings (context). Primary studies with any design or reviews with a systematic methodology published in peer-reviewed academic journals will be included. Data items will address study designs, PCC criteria, benefits or barriers related to digital technology use by older people, and evidence gaps. Data will be synthesized using descriptive statistics or narratively described by identifying common themes. Quality appraisal will be performed for any included systematic reviews, using a validated instrument for this study type (A Measurement Tool to Assess Systematic Reviews, version 2 [AMSTAR2]).

RESULTS: Following preliminary literature searches to test and calibrate the search syntax, the electronic literature search was performed in March 2022 and manual searches were completed in June 2022. Study selection based on titles and abstracts was completed in July 2022, and the full-text screen was initiated in July 2022.

CONCLUSIONS: Our scoping review will identify the types of digital technologies, health targets in the context of health promotion and disease prevention, and health benefits or barriers associated with the use of such technologies for older people in nonclinical settings. This knowledge could guide further research on how digital technologies can support healthy aging.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37729.

PMID:35862187 | DOI:10.2196/37729

JMIR Diabetes. 2022 Jul 21;7(3):e34699. doi: 10.2196/34699.

ABSTRACT

BACKGROUND: Diabetes is a chronic condition that necessitates regular monitoring and self-management of the patient’s blood glucose levels. People with type 1 diabetes (T1D) can live a productive life if they receive proper diabetes care. Nonetheless, a loose glycemic control might increase the risk of developing hypoglycemia. This incident can occur because of a variety of causes, such as taking additional doses of insulin, skipping meals, or overexercising. Mainly, the symptoms of hypoglycemia range from mild dysphoria to more severe conditions, if not detected in a timely manner.

OBJECTIVE: In this review, we aimed to report on innovative detection techniques and tactics for identifying and preventing hypoglycemic episodes, focusing on T1D.

METHODS: A systematic literature search following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines was performed focusing on the PubMed, GoogleScholar, IEEEXplore, and ACM Digital Library to find articles on technologies related to hypoglycemia detection in patients with T1D.

RESULTS: The presented approaches have been used or devised to enhance blood glucose monitoring and boost its efficacy in forecasting future glucose levels, which could aid the prediction of future episodes of hypoglycemia. We detected 19 predictive models for hypoglycemia, specifically on T1D, using a wide range of algorithmic methodologies, spanning from statistics (1.9/19, 10%) to machine learning (9.88/19, 52%) and deep learning (7.22/19, 38%). The algorithms used most were the Kalman filtering and classification models (support vector machine, k-nearest neighbors, and random forests). The performance of the predictive models was found to be satisfactory overall, reaching accuracies between 70% and 99%, which proves that such technologies are capable of facilitating the prediction of T1D hypoglycemia.

CONCLUSIONS: It is evident that continuous glucose monitoring can improve glucose control in diabetes; however, predictive models for hypo- and hyperglycemia using only mainstream noninvasive sensors such as wristbands and smartwatches are foreseen to be the next step for mobile health in T1D. Prospective studies are required to demonstrate the value of such models in real-life mobile health interventions.

PMID:35862181 | DOI:10.2196/34699

J Am Heart Assoc. 2022 Jul 20:e025519. doi: 10.1161/JAHA.122.025519. Online ahead of print.

ABSTRACT

Background A high prevalence of preclinical heart failure (HF) (Stages A and B) has previously been shown. The aim of this study was to explore factors associated with the incidence of preclinical HF in a community population. Methods and Results Retrospective review of 393 healthy community individuals aged ≥45 years from the Olmsted County Heart Function Study that returned for 2 visits, 4 years apart. At visit 2, individuals that remained normal were compared with those that developed preclinical HF. By the second visit, 191 (49%) developed preclinical HF (12.1 cases per 100 person-years of follow-up); 65 (34%) Stage A and 126 (66%) Stage B. Those that developed preclinical HF (n=191) were older (P=0.004), had a higher body mass index (P<0.001), and increased left ventricular mass index (P=0.006). When evaluated separately, increased body mass index was seen with development of Stage A (P<0.001) or Stage B (P=0.009). Echocardiographic markers of diastolic function were statistically different in those that developed Stage A [higher E/e’ (P<0.001), lower e’ (P<0.001)] and Stage B [higher left atrial volume index (P<0.001), higher E/e’ (P<0.001), lower e’ (P<0.001)]. NT-proBNP (N-terminal pro-B-type natriuretic peptide) was higher at visit 2 in those that developed Stage A or B (P<0.001 for both). Hypertension (57%), obesity (34%), and hyperlipidemia (25%) were common in the development of Stage A. Of patients who developed Stage B, 71% (n=84) had moderate or severe diastolic dysfunction. Conclusions There is a high incidence of preclinical HF in a community population. Development of Stage A was driven by hypertension and obesity, while preclinical diastolic dysfunction was seen commonly in those that developed Stage B.

PMID:35862175 | DOI:10.1161/JAHA.122.025519

Sci Med Footb. 2022 Aug;6(3):372-380. doi: 10.1080/24733938.2021.1944660. Epub 2021 Jun 27.

ABSTRACT

INTRODUCTION: Performance assessment in professional soccer often focusses on notational assessment like assists or pass accuracy. However, rather than statistics, performance is more about making the best possible tactical decision, in the context of aplayer’s positional role and the available options at the time. With the current paper, we aim to construct an improved model for the assessment of pass risk and reward across different positional roles, and validate that model by studying differences in decision-making between players with different positional roles.

METHODS: To achieve our aim, we collected position tracking data from an entire season of Dutch Eredivisie matches, containing 286.151 passes of 336 players. From that data, we derived several features on risk and reward, both for the pass that has been played, as well as for the pass options that were available at the time of passing.

RESULTS: Our findings indicate that we could adequately model risk and reward, outperforming previously published models, and that there were large differences in decision-making between players with different positional roles.

DISCUSSION: Our model can be used to assess player performance based on what could have happened, rather than solely based on what did happen in amatch.

PMID:35862167 | DOI:10.1080/24733938.2021.1944660

Sci Med Footb. 2022 Aug;6(3):389-397. doi: 10.1080/24733938.2021.1978106. Epub 2021 Sep 14.

ABSTRACT

In football research, ‘small’ trials with low statistical power are common. On the elite level, the inherently low number of participants obviously conflicts with the relevance of even tiny effects. However, general characteristics of football also contribute (e.g. multifactorially influenced and/or complex outcomes). Importantly, small sample sizes are problematic regardless of the study outcome with issues ranging from inconclusive results and low precision to unrepeatable ‘discoveries’ and overestimation of effect sizes. Therefore, meeting the calculated, target sample size is the first priority. If a suboptimal sample size must be accepted, a range of tools can improve insights. To begin with, some general aspects of data collection and analysis become more important and should be optimally implemented (e.g. reliability of measures). Building on this foundation, specific amendments are available on the levels of data collection (e.g. aggregated single-subject designs) and data analysis (e.g. Bayesian methods). The present commentary aims to give an overview of selected, practical tools for dealing with small sample sizes in football research and provide recommendations for their application in scenarios typical for the field. Importantly, versatility and adaptability are mirrored by the need for utmost transparency including a predetermined (ideally preregistered) study plan. Collaboration or counselling with an expert statistician is strongly encouraged.

PMID:35862155 | DOI:10.1080/24733938.2021.1978106