Selenoprotein P predicting mortallity in acute ill patients with dyspnea.

Background And Aims: Good Selenium (Se) status predicts favorable prognoses for various diseases and a reduced overall mortality. The primary objective of the study was to determine whether Selenium status, i.e.

The association between TNF-receptors (TNFR1 and TNFR2) and mortality as well as kidney function decline in patients with chronic kidney disease.

Background: Higher circulating levels of tumor necrosis factor (TNF) alpha receptors 1 (TNFR1) and 2 (TNFR2) are associated with increased long-term mortality and impaired kidney function.

Aim: To study associations between levels of TNFR1 and TNFR2 and all-cause mortality as well as estimated glomerular filtration rate (eGFR) decline.

Population And Methods: Patients with chronic kidney disease (CKD) stages 3-5 in the Salford Kidney Study were included.

A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.

Aims: We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals with diabetes in primary health care (PHC) through the analysis of diagnostic data.

Methods: We used a sex- and age-matched case-control design. Cases of new CHF were identified across all outpatient care settings 2015-2022 (n = 9098).

Predicting new cases of hypertension in Swedish primary care with a machine learning tool.

Background: Many individuals with hypertension remain undiagnosed. We aimed to develop a predictive model for hypertension using diagnostic codes from prevailing electronic medical records in Swedish primary care.

Methods: This sex- and age-matched case-control (1:5) study included patients aged 30-65 years living in the Stockholm Region, Sweden, with a newly recorded diagnosis of hypertension during 2010-19 (cases) and individuals without a recorded hypertension diagnosis during 2010-19 (controls), in total 507,618 individuals.

A machine learning tool for identifying patients with newly diagnosed diabetes in primary care.

Background And Aim: It is crucial to identify a diabetes diagnosis early. Create a predictive model utilizing machine learning (ML) to identify new cases of diabetes in primary health care (PHC).

Methods: A case-control study utilizing data on PHC visits for sex-, age, and PHC-matched controls.