bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU
Authors: Sikora Andrea , Zhang Tianyi , Murphy David J. , Smith Susan E. , Murray Brian , Kamaleswaran Rishikesan , Chen Xianyan , Buckley Mitchell S. , Rowe Sandra , Devlin John W. 
Published: June 2023

ABSTRACTBackgroundFluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors.MethodsThis was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 hours between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥10% of the admission body weight) in the 48-72 hours after ICU admission were created. Potential patient and medication fluid overload predictor variables (n=28) were collected at either baseline or 24 hours after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models.ResultsA total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload.ConclusionIn the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.
Prospective population-based observational study to estimate the incidence of T2DM in a metropolitan population in the north of Madrid (Spain) and to determine the effect of baseline glycaemic status through an explanatory Cox model. SPRINDIAP-1 study (Secondary PRevention of INcident DIAbetes in patients with Prediabetes)
Authors: Iriarte-Campo V , de Burgos-Lunar C , Mostaza J , Lahoz C , Cárdenas-Valladolid J , Gómez-Campelo P , Taulero-Escalera B , San-Andrés-Rebollo FJ , Rodriguez-Artalejo F , Salinero-Fort MA ,   SPREDIA-2 Group
Published: June 2023

ABSTRACTBackgroundT2DM (Type 2 Diabetes mellitus) is preceded by basal glycaemic states (BGS) such as normoglycaemia (NG) or pre-T2DM, including impaired fasting glucose (IFG); impaired glucose tolerance (IGT) or both (IFG-IGT). A better understanding of the role of pre-T2DM in the progression to T2DM may help in the prevention of T2DM in patients with pre-T2DM.MethodsPopulation-based cohort study to estimate the incidence of T2DM according to BGS and to study the adjusted effect of BGS on progression to T2DM using a Cox model (main model (MM) with reference category NG and sensitivity analysis (SA) in patients with pre-T2DM and reference IFG).Results1,209 patients aged 45-74 years (median follow-up=7.26 years). The crude T2DM incidence rate for the whole population was 11.21 per 1000 person-years (95%CI=9.09-13.68), 5.60 (95%CI=3.55-8.41) in patients with NG and 16.28 (95%CI=12.78-20.43) in patients with preT2DM. In both models, the significant variables showing risk of progression to T2DM were IGT BGS (MM: Hazard ratio HR=4.30; 95%CI=1.96-9.44; AS: HR=2.66, 95%CI=1.28-5.56) and IFG-IGT (MM: HR=3.71, 95%CI=1.97-6.99; AS: HR=2.45, 95%CI=1.41-4.23), and obesity (MM: HR=2.36, 95%CI=1.15-4.83; AS: HR=2.97, 95%CI=1.18-7.45). Being on diuretic treatment was a protective factor (MM: HR=0.47 CI95%=0.23-0.96; SA: HR=0.41, CI95%=0.19-0.92), as was, in SA only, self-perceived health status in the categories of: Very good (HR: 0.19, CI95%=0.06-0.67); Good (HR: 0.35, CI95%=0.13-0.96) and Fair (HR: 0.31, CI95%=0.11-0.93).ConclusionsOur T2DM incidence rates are in line with other Spanish studies. In people with NG or preT2DM, EBG (IGT and IFG-IGT) and obesity increase the risk of progression to T2DM and being on diuretics is a protective factor as is fair to very good self-perceived health in patients with preT2DM.
Environmental adversity moderates polygenic score effects on childhood behavioral problems in the United States
Authors: Thomas Taylor R. , Casten Lucas G. , Michaelson Jacob J. 
Published: June 2023

AbstractIMPORTANCEBehavioral problems in children are influenced by environmental and genetic factors, but it is still unclear how much each contributes and if there are gene-by-environment interactions (GxE).OBJECTIVEOur object was to investigate how environmental adversity moderates the effects of polygenic scores (PGS) on childhood behavioral problems through additive and interaction effects.DESIGN, SETTING, AND PARTICIPANTSParticipants wereN= 7, 191 children aged 7-15 years (50% autistic) from two United States cohorts, ABCD and SPARK.MAIN OUTCOMES AND MEASURESThe main outcomes were five dimensional subscales from the Child Behavior Checklist (CBCL). The genetic variables were 20 behavior-related PGS, including psychiatric diagnoses, substance use disorders, cognition, and personality PGS. Environmental adversity was estimated by the Area Deprivation Index (ADI). The ADI is a composite variable of neighborhood adversity based on education, income, and housing.RESULTSThirteen out of the 20 PGS were significantly associated with the ADI. PGS for psychiatric and substance use disorders were positively associated with the ADI, and PGS for educational attainment and cognitive performance were negatively associated. The ADI had significant SNP heritability:h2= 0.33 [0.24, 0.42], with the estimate similar between ABCD and SPARK. The ADI was positively associated with more behavioral problems and explained more variance than any PGS, but this effect was reduced after accounting for these potential genetic confounders. Several GxE effects were identified, including: 1.) the positive associations of the cannabis and alcohol dependency PGS with externalizing problems increased as the ADI increased, 2.) the positive associations of the anorexia PGS with thought and internalizing problems increased as the ADI increased, 3.) the positive associations of the autism PGS with internalizing problems decreased as the ADI increased, 4.) the negative associations of the educational attainment and cognitive performance PGS with several behavioral problems increased as the ADI increased, and 5.) the extraversion PGS association with social problems was negative in an advantaged environment but positive in a disadvantaged environment.CONCLUSIONS AND RELEVANCEEnvironmental adversity estimated by the ADI moderates the effects of some PGS on childhood behavioral problems through additive and interaction effects. This highlights the importance of considering both genetic and environmental factors in understanding childhood behavioral problems. Our findings emphasize the need to include PGS of personality and cognitive traits, in addition to psychiatric PGS.
A coupled electro-mechanical approach for early diagnostic of carpal tunnel syndrome
Authors: Peshin Saveliy , Karakulova Julia , Kuchumov Alex G. 
Published: June 2023

AbstractCarpal tunnel syndrome (CTS) is a pathology affecting hand function caused by median nerve overload. Numbness in the fingers, a loss of sensory and motor function in the hand, and pain are all symptoms of carpal tunnel syndrome. The lack of numerical data about the median nerve mechanical strain inside the carpal tunnel is the main disadvantage of current clinical approaches employed in carpal syndrome diagnostics. Moreover, application of each diagnostic method alone often leads to misdiagnosis. We proposed a combined approach including hand motion capture, finite element modelling (FEM), and electromechanical simulations to evaluate median nerve compression and find a correlation with hand mobility. The hand motion capture provided the boundary conditions for FEM. After that, FEM simulations of finger flexion and hand flexion / extension were performed. Further, FEM results were put in the electrical model of nerve conduction based on the Hodgkin-Huxley model and extended cable equation. It was exhibited median nerve conduction reduced significantly throughout the flexion and extension of the hand that compared to finger flexion. During finger flexion and hand flexion and extension, the load distribution over each of nine finger flexor tendons was evaluated. The tendons of the index finger were found to have the highest Mises stress values. It was found how tendon and connective tissue contact types affected carpal tunnel pressure. The difference between the contact types was 31.7% for hand extension and 59.9% for hand flexion. The developed approach has the potential to become an alternative diagnostic method for CTS at early stages. Additionally, it can be employed as non-invasive procedure for evaluation of carpal nerve stress.
An ecological examination of early adolescent e-cigarette use: A machine learning approach to understanding a health epidemic
Authors: Vázquez Alejandro L. , Navarro Flores Cynthia M. , Garcia Byron H. , Barrett Tyson S. , Domenech Rodríguez Melanie M. 
Published: June 2023

AbstractE-cigarette use among adolescents is a national health epidemic spreading faster than researchers can amass evidence for risk and protective factors and long-term consequences associated with use. New technologies, such as machine learning, may asset prevention programs in identifying at-risk youth and potential targets for intervention before adolescents enter developmental periods where e-cigarette use escalates. The current study utilized machine learning algorithms to explore a wide array of individual and socioecological variables in relation to patterns of lifetime e-cigarette use during early adolescence (i.e., exclusive, or with tobacco). Extant data was used from 14,346 students middle school students (Mage= 12.5, SD = 1.1; 6thand 8th grades) who participated in the Utah Prevention Needs Assessment survey. Students self-reported their substance use behaviors and related risk and protective factors. Machine learning algorithms examined 112 individual and socioecological factors as potential classifiers of lifetime e-cigarette use outcomes. The elastic net algorithm achieved outstanding classification for lifetime exclusive (AUC = .926) and dual use (AUC = .944) on a validation test set. Six high value classifiers were identified that varied in importance by outcome: Lifetime alcohol or marijuana use, perception of e-cigarette availability and risk, school suspension(s), and perceived risk of smoking marijuana regularly. Specific classifiers were important for lifetime exclusive (parent’s attitudes regarding student vaping, best friend[s] tried alcohol or marijuana) and dual use (best friend[s] smoked cigarettes, lifetime inhalant use). Our findings provide specific targets for the adaptation of existing substance use prevention programs to address early adolescent e-cigarette use.
Improving early infant diagnosis for HIV-exposed infants using unmanned aerial vehicles versus motorcycles for blood sample in Conakry, Guinea: a comparative cost-effectiveness analysis
Authors: Inghels Maxime , Mee Paul , Diallo Oumou Hawa , Cissé Mohamed , Nelson David , Tanser Frank , Asghar Zahid , Koita Youssouf , Laborde-Balen Gabrièle , Breton Guillaume 
Published: June 2023

BackgroundEarly infant diagnosis (EID) for HIV-exposed infants is essential due to high mortality during the first months of their lives. In Conakry (Guinea) timely EID is difficult as traffic congestion prevents the rapid transport of blood samples to the central laboratory. We investigated the cost-effectiveness of transporting EID blood samples by unmanned aerial vehicles (UAV), also known as drones.Methods and FindingsWe conducted a cost-effectiveness comparative analysis between EID blood samples transportation by UAV compared to motorcycle using Monte Carlo simulations. Incremental cost-effectiveness ratio (ICER) per life-year gained was computed with local annual GDP per capita (US$1,194) set as the threshold. Simulation models included parameters such as consultation timing (e.g. time of arrival), motorcycle and UAV characteristics, weather and traffic conditions. Sensitivity analyses were conducted. Over a 5-year program and 778 HIV-exposed infants seen in consultation on average every year, the UAV transport strategy was able to save 49.6 life-years [90% PI: -1.3 –100.5]. The UAV strategy costs an additional of $12.4 [10.2 –14.6] per infant tested compared to the motorcycle one. With an ICER of $979 per life-year gained, the UAV transportation strategy was below the cost-effectiveness threshold. The ICER is primarily sensitive to weather-related downtime, UAV loss risk, number of HIV-infected infants and travel time saved by UAV.ConclusionThe transportation of EID blood samples by UAVs in Conakry was found to be cost effective in that context. EID blood samples transportation by UAV could be a cost-effective strategy in other countries with traffic congestion and low rate of EID.What is already known on this topicThe cost-effectiveness of Unmanned aerial vehicles—UAV (drones) transportation for blood products remains limited in the Global South.We aimed to investigate the cost-effectiveness of urgent blood sample transportation for early HIV infant diagnosis (EID) by UAV compared to motorcycle in the region of Conakry, Guinea.What this study addsWe found that UAV transportation for EID was faster, saved more lives than motorcycles and was cost-effective (Incremental cost-effectiveness ratio: US$979 per life-year gained)The effectiveness of the drone strategy was primarily sensitive to weather-related downtime, UAV loss risk, number of HIV-infected infants and travel time saved by UAV.How this study might affect research, practice or policyOur study suggests that the transportation of EID blood could be cost-effective in the Conakry region. Other low-or middle-income cities experiencing important traffic congestion with low EID could benefit from this strategy.
Viraemia and HIV drug resistance among people receiving dolutegravir versus efavirenz-based first-line antiretroviral therapy
Authors: Dorward Jienchi , Sookrajh Yukteshwar , Lessells Richard , Bulo Elliot , Bodley Nicola , Singh Lavanya , Moodley Pravikrishnen , Samsunder Natasha , Drain Paul K. , Hayward Gail , Butler Christopher C , Garrett Nigel 
Published: June 2023

SUMMARYLimited data exists to inform management of viraemia among people receiving dolutegravir-based first-line ART in low- and middle-income countries. Among South-Africans with viraemia ≥1000copies/mL receiving dolutegravir (n=43) and efavirenz (n=37), we found no dolutegravir resistance, but high efavirenz resistance (66.7%). 12-week resuppression was higher with dolutegravir (85%) versus efavirenz (38%).
Development and validation of a neural network-based survival model for mortality in ischemic heart disease
Authors: Holm Peter C. , Haue Amalie D. , Westergaard David , Röder Timo , Banasik Karina , Tragante Vinicius , Christensen Alex H. , Thomas Laurent , Nøst Therese H. , Skogholt Anne-Heidi , Iversen Kasper K. , Pedersen Frants , Høfsten Dan E. , Pedersen Ole B. , Ostrowski Sisse Rye , Ullum Henrik , Svendsen Mette N. , Gjødsbøl Iben M. , Gudnason Thorarinn , Guðbjartsson Daníel F. , Helgadottir Anna , Hveem Kristian , Køber Lars V. , Holm Hilma , Stefansson Kari , Brunak Søren , Bundgaard Henning 
Published: June 2023

AbstractBackgroundCurrent risk prediction models for ischemic heart disease (IHD) use a limited set of established risk factors and are based on classical statistical techniques. Using machine-learning techniques and including a broader panel of features from electronic health records (EHRs) may improve prognostication.ObjectivesDeveloping and externally validating a neural network-based time-to-event model (PMHnet) for prediction of all-cause mortality in IHD.MethodsWe included 39,746 patients (training: 34,746, test: 5,000) with IHD from the Eastern Danish Heart Registry, who underwent coronary angiography (CAG) between 2006-2016. Clinical and genetic features were extracted from national registries, EHRs, and biobanks. The feature-selection process identified 584 features, including prior diagnosis and procedure codes, laboratory test results, and clinical measurements. Model performance was evaluated using time-dependent AUC (tdAUC) and the Brier score. PMHnet was benchmarked against GRACE Risk Score 2.0 (GRACE2.0), and externally validated using data from Iceland (n=8,287). Feature importance and model explainability were assessed using SHAP analysis.FindingsOn the test set, the tdAUC was 0.88 (95% CI 0.86-0.90, case count, cc=196) at six months, 0.88(0.86-0.90, cc=261) at one year, 0.84(0.82-0.86, cc=395) at three years, and 0.82(0.80-0.84, cc=763) at five years. On the same data, GRACE2.0 had a lower performance: 0.77 (0.73-0.80) at six months, 0.77(0.74-0.80) at one year, and 0.73(0.70-0.75) at three years. PMHnet showed similar performance in the Icelandic data.ConclusionPMHnet significantly improved survival prediction in patients with IHD compared to GRACE2.0. Our findings support the use of deep phenotypic data as precision medicine tools in modern healthcare systems.
Real-World Evaluation of an Automated Algorithm to Detect Patients with Potentially Undiagnosed Hypertension in an Ethnically Diverse, Large Health System in Hawaiʻi
Authors: Thompson Mika D. , Wu Yan Yan , Nett Blythe , Ching Lance K. , Taylor Hermina , Lemmen Tiffany , Sentell Tetine L. , McGurk Meghan D. , Pirkle Catherine M. 
Published: June 2023

ABSTRACTObjectiveThis real-world evaluation considers an algorithm designed to detect patients with potentially undiagnosed hypertension, receiving routine care, in a large health system in Hawaiʻi. It quantifies patients identified as potentially undiagnosed with hypertension, summarizes the individual, clinical, and health system factors associated with undiagnosed hypertension, and examines if the COVID-19 pandemic impacted detection.MethodsWe analyzed the electronic health records (EHR) of patients treated across 6 clinics from 2018-2021. We calculated total patients with potentially undiagnosed hypertension and compared patients flagged for undiagnosed hypertension to those with diagnosed hypertension and to the full patient panel across individual characteristics, clinical and health system factors (e.g., clinic of care), and timing. Modified Poisson regression was used to calculate crude and adjusted risk ratios.ResultsAmong the eligible patients (N=13,364), 52.6% had been diagnosed with hypertension, 2.7% were flagged as potentially undiagnosed, and 44.6% had no evidence of hypertension. Factors associated with a higher risk of potentially undiagnosed hypertension included: individual characteristics (ages 40-84 compared to 18-39 years), clinical (lack of diabetes diagnosis) and health system factors (clinic site and being a Medicaid versus a Medicare beneficiary), and timing (readings obtained after the COVID-19 Stay-At-Home Order in Hawaiʻi).ConclusionsThis evaluation provided evidence that a clinical algorithm implemented within a large health systems’s EHR could detect patients in need of follow-up to determine hypertension status, and it identified key individual characteristics, clinical and health system factors, and timing considerations that may contribute to undiagnosed hypertension among patients receiving routine care.
Collective synchrony of mating signals modulated by ecological cues and social signals in bioluminescent sea fireflies
Authors: Hensley Nicholai M. , Rivers Trevor J. , Gerrish Gretchen A. , Saha Raj , Oakley Todd H. 
Published: June 2023

AbstractIndividuals often employ simple rules that can emergently synchronise behaviour. Some collective behaviours are intuitively beneficial, but others like mate signalling in leks occur across taxa despite theoretical individual costs. Whether disparate instances of synchronous signalling are similarly organised is unknown, largely due to challenges observing many individuals simultaneously. Recording field collectives andex situplayback experiments, we describe principles of synchronous bioluminescent signals produced by marine ostracods (Crustacea; Luxorina) that seem behaviorally convergent with terrestrial fireflies, and with whom they last shared a common ancestor over 500 mya. Like synchronous fireflies, groups of signalling males use visual cues (intensity and duration of light) to decide when to signal. Individual ostracods also modulate their signal based on the distance to nearest neighbours. During peak darkness, luminescent “waves” of synchronous displays emerge and ripple across the sea floor every ∼60 seconds, but such periodicity decays within and between nights after the full moon. Our data reveal these bioluminescent aggregations are sensitive to both ecological and social light sources. Because the function of collective signals is difficult to dissect, evolutionary convergence, like in the synchronous visual displays of diverse arthropods, provides natural replicates to understand the generalities that produce emergent group behaviour.
The effect of Vitamin D (1,25-(OH)2-D3) on human theca and granulosa cell function
Authors: Seaward Brain Henrietta Philippa , Georgiou Christiana , Mason Helen D , Rice Suman 
Published: June 2023

AbstractNumerous observational and interventional studies have investigated the link between Vitamin D (VD) deficiency and reproductive outcomes, with contradictory results. VD is known to regulate steroidogenic enzymes crucial for human granulosa and cumulus cell function and genes that play a critical role in folliculogenesis have a vitamin D response element (VDRE) on their promoters. This study investigated whether deficient levels of 1,25-(OH)2-D3 altered ovarian cell function; and if the ovary could obtain bioactive 1,25-(OH)2-D3 via local enzymatic expression ofCYP27B1, to counteract systemic deficiency. A variety of cells and tissues were used for thein vitroexperiments as practicable.We have shown for the first time an increase in VDR expression inthecaof larger compared to smaller follicles, which along with the ability of 1,25-(OH)2-D3 to decrease Anti-Mullerian hormone (AMH) expression, supports a role for 1,25-(OH)2-D3 in theca and granulosa cell function. Conversely, we have shown that very levels of 1,25-(OH)2-D3 equivalent to hypovitaminosis, inhibited thecal production of androstenedione and cAMP-driven E2 production. Human thecal and unluteinised GC are incredibly hard to obtain for research purposes, highlighting the uniqueness of our data set. For the first time we have demonstrated that deficient levels of 1,25-(OH)2-D3 also down-regulated insulin receptor expression, potentially reducing insulin sensitivity. We have shown that the ovary expressesCYP27B1allowing it to make local bioactive 1,25-(OH)2-D3 which along with the upregulation in VDR expression in all ovarian cellular compartments, could be protective locally in counteracting systemic VD deficiency. To conclude a severely deficient VD environment (<2nM or <1ng/ml) could contribute to impaired ovarian cell function and hence potentially affect folliculogenesis/ovulation, but levels associated with mild deficiency may have less impact, apart from in the presence of hyperinsulinemia and insulin resistance.