Laboratory data prediction models

OBJECTIVES: Risk stratification is a cornerstone for preventing atherosclerotic cardiovascular disease (ASCVD). Ghana has yet to develop a locally derived and validated ASCVD risk model. A critical first step towards this goal is assessing how the commonly available risk models perform in the Ghanaian population. This study compares the agreement and correlation between four ASCVD risk assessment models commonly used in Ghana. METHODS: The Ghana Heart Study collected data from four regions in Ghana (Ashanti, Greater Accra, Northern, and Central regions) and excluded people with a self-declared history of ASCVD...

This study aims to predict specific soil parameters based on quantitative and qualitative correlations between electrical and geotechnical data. A total of 21 geotechnical boreholes followed by sampling, 21 light dynamic penetrometer tests, and 76 vertical electrical sounding surveys were carried out in Maroua 1st. The electrical resistivity data of the soil layers were obtained through 1D and 2D inversions of the ERT surveys. The geotechnical data were obtained from field tests and laboratory experiments. The statistical analysis of the entire dataset indicates that most of the samples are clay formations, as demonstrated by the means of variables and low variance coefficients, enabling qualitative identification...

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with a high mortality rate. Differentiating between SFTS and hemorrhagic fever with renal syndrome (HFRS) is difficult and inefficient. Retrospective analysis of the medical records of individuals with SFTS and HFRS was performed. Clinical and laboratory data were compared, and a diagnostic model was developed based on multivariate logistic regression analyzes. Receiver operating characteristic curve analysis was used to evaluate the diagnostic model...

BACKGROUND: Lower gastrointestinal bleeding (LGIB) is a common reason for emergency department visits and subsequent hospitalizations. Recent data suggests that low-risk patients may be safely evaluated as an outpatient. Recommendations for healthcare systems to identify low-risk patients who can be safely discharged with timely outpatient follow-up have yet to be established. The primary objective of this study was to determine the role of patient predictors for the patients with LGIB to receive urgent endoscopic intervention...

This study aimed to develop and validate a nomogram model that includes clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in young Chinese individuals. This study retrospectively analyzed a cohort of young population who underwent health examination from November 2018 to December 2021 at The Affiliated Hospital of Southwest Medical University in Luzhou City, Sichuan Province, China. We extracted the clinical and laboratory data of 43,040 subjects and randomized participants into the training and validation groups (7:3)...

UNLABELLED: In many frankia, the ability to nodulate host plants (Nod+) and fix nitrogen (Fix+) is a common strategy. However, some frankia within the Pseudofrankia genus lack one or two of these traits. This phenomenon has been consistently observed across various actinorhizal nodule isolates, displaying Nod- and/or Fix- phenotypes. Yet, the mechanisms supporting the colonization and persistence of these inefficient frankia within nodules, both with and without symbiotic strains (Nod+/Fix+), remain unclear...

Stratospheric aerosol injection (SAI) is proposed as a means of reducing global warming and climate change impacts. Similar to aerosol enhancements produced by volcanic eruptions, introducing particles into the stratosphere would reflect sunlight and reduce the level of warming. However, uncertainties remain about the roles of nucleation mechanisms, ionized molecules, impurities (unevaporated residuals of injected precursors), and ambient conditions in the generation of SAI particles optimally sized to reflect sunlight...

BACKGROUND: Chronic kidney disease (CKD) affects >800 million individuals worldwide and is often underrecognized. Early detection, identification and treatment can delay disease progression. Klinrisk is a proprietary CKD progression risk prediction model based on common laboratory data to predict CKD progression. We aimed to externally validate the Klinrisk model for prediction of CKD progression in FIDELITY (a prespecified pooled analysis of two finerenone phase III trials in patients with CKD and type 2 diabetes)...

OBJECTIVES: Iron-deficiency anemia (IDA) is a common health problem worldwide, and up to 10% of adult patients with incidental IDA may have gastrointestinal cancer. A diagnosis of IDA can be established through a combination of laboratory tests, but it is often underrecognized until a patient becomes symptomatic. Based on advances in machine learning, we hypothesized that we could reduce the time to diagnosis by developing an IDA prediction model. Our goal was to develop 3 neural networks by using retrospective longitudinal outpatient laboratory data to predict the risk of IDA 3 to 6 months before traditional diagnosis...

No-till and mulching are typical management operations in conservation agriculture (CA). To model pesticide degradation and leaching under a CA scenario, as compared to a conventional-tillage scenario (CT), the mulch module of the agro-hydrological model Daisy was extended. A Daisy soil column was parameterized with measurements of topsoil, mulch, and a realistic subsoil, and tested against published experimental data of pesticide fate in laboratory soil columns covered by mulch. Uncertainty and sensitivity analyses of the new Daisy version were conducted for a series of weather, soil, pesticide, and mulch parameters, using 4939 Monte Carlo simulations under each scenario...

BACKGROUND: COVID-19 has had wide-reaching effects on healthcare services beyond the direct treatment of the pandemic. Most current studies have reported changes in realised service usage, but the dynamics of how patients engage with healthcare services are less well understood. We analysed the effects of COVID-19 on healthcare bookings and cancellations for various service channels between January 2020 and July 2021. METHODS: Our data includes 7.3 million bookings, 11...

BACKGROUND AND PURPOSE: The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses. METHODS: A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran...

[177 Lu]Lu-DOTATATE has been approved for progressive and inoperable gastroenteropancreatic neuroendocrine tumors (GEP-NETs) that overexpress somatostatin receptors. The absorbed doses by limiting organs and tumors can be quantified by serial postinfusion scintigraphy measurements of the γ-emissions from 177 Lu. The objective of this work was to explore how postinfusion [177 Lu]Lu-DOTATATE dosimetry could influence clinical management by predicting treatment efficacy (tumor shrinkage and survival) and toxicity...

Background Accurate identification of individuals at risk of developing chronic kidney disease (CKD) may improve clinical care. Nelson et al developed prediction equations to estimate the risk of incident eGFR of less than 60 ml/min/1.73m2 in diabetic and non-diabetes patients using data from 34 multinational cohorts. We aim to validate the non-diabetes equation in our local multi-ethnic cohort and develop further prediction models. Methods Demographics, clinical and laboratory data of hypertensive non-diabetes patients with baseline eGFR ≥60ml/min/1...

This work discusses the effectiveness of the previously developed comprehensive calculation model to optimize linear MALDI-TOF mass spectrometers. The model couples space- and velocity-focusing to precisely analyze the flight-time distribution of ions and predict optimal experimental parameters for the highest mass resolving power. Experimental validation was conducted using a laboratory-made instrument to analyze CsI3 and angiotensin I ions in low to medium m / z range. The results indicate that the predicted optimal extraction voltage and delay were reasonably accurate and effective...

OBJECTIVES: This study aims to analyse the risk factors associated with diffuse alveolar haemorrhage (DAH) in patients with ANCA-associated vasculitis (AAV) and construct a risk prediction model using line graph. METHODS: A retrospective study was conducted from January 2012 to May 2023 at the First Clinical College of Three Gorges University, focusing on patients diagnosed with AAV. Clinical and laboratory data were collected from these patients. The potential predictors subsets of high-risk AAV combined with DAH were screened by LASSO regression and 10-fold cross-validation method, and determined by using multivariate Logistic regression analysis, then were used for developing a prediction nomogram for high-risk AAV combined with DAH using the R software...

It is well established that individuals differ in their response to sleep loss. However, existing methods to predict an individual's sleep-loss phenotype are not scalable or involve effort-dependent neurobehavioural tests. To overcome these limitations, we sought to predict an individual's level of resilience or vulnerability to sleep loss using electroencephalographic (EEG) features obtained from routine night sleep. To this end, we retrospectively analysed five studies in which 96 healthy young adults (41 women) completed a laboratory baseline-sleep phase followed by a sleep-loss challenge...

In recent years, there has been an increase in the interest in adopting Explainable Artificial Intelligence (XAI) for healthcare. The proposed system includes•An XAI model for cancer drug value prediction. The model provides data that is easy to understand and explain, which is critical for medical decision-making. It also produces accurate projections.•A model outperformed existing models due to extensive training and evaluation on a large cancer medication chemical compounds dataset.•Insights into the causation and correlation between the dependent and independent actors in the chemical composition of the cancer cell...

BACKGROUND: The association between obesity and infectious diseases is increasingly reported in the literature. There are scarce studies on the association between obesity and urinary tract infection after kidney transplantation (KTx). These studies defined obesity based on body mass index, and their results were conflicting. The present study aimed to evaluate this association using bioelectrical impedance analysis for body composition evaluation, and obesity definition. METHODS: A single-center cohort study was conducted...

PURPOSE: Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes. PATIENTS AND METHODS: We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020...