English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background  The clinical prediction of survival is among the most challenging tasks because it refers to the process whereby the medical team assimilates clinical data using subjective methods. The purpose of this prospective observational study was to develop a model for evaluating survival time using objective laboratory parameters.    Methods  Albumin (ALB), creatinine (CRE), C-reactive protein (CRP) and the neutrophilic leukocyte count (NEU) were measured using automated analysers. A total of 177 subjects with any one positive item of 4 items were included in the study. Age on the observation date and date of death were recorded.    Results  ALB, CRE, CRP and the NEU were all significant predictors of survival time (p < 0.05). The median survival time of patients with anyone of the 4 items positive would be over 1 year; if any 2 items were positive, the median survival time was approximately 1 year; if any 3 items were positive, the median survival time was approximately 4 months and if 4 items were positive, the median survival time was approximately 20 days.    Conclusions  This study suggests that a model using ALB, CRE, CRP and the NEU is potentially useful in the objective evaluation of survival time in terminally ill patients.

journal of translational medicine

Nonspecific changes in clinical laboratory indicators in unselected terminally ill patients and a model to predict survival time based on a prospective observational study