Back

The prognostic value of mean platelet volume in patients with coronary artery disease: a systematic review with meta-analyses

Galimzhanov, A.; Naung Tun, H.; Sabitov, Y.; Perone, F.; Tigen Kursat, M.; Tenekecioglu, E.; Mamas, M. A.

2024-03-22 cardiovascular medicine
10.1101/2024.03.20.24304646 medRxiv
Show abstract

BackgroundMean platelet volume (MPV) is a widely available laboratory index, however its prognostic significance in patients with coronary artery disease (CAD) is still unclear. We intended to investigate and pool the evidence on the prognostic utility of admission MPV in predicting clinical outcomes in patients with CAD. MethodsPubMed, Web of Science, and Scopus were the major databases used for literature search. The risk of bias was assessed using the quality in prognostic factor studies. We used random-effects pairwise analysis with the Knapp and Hartung approach supported further with permutation tests and prediction intervals (PIs). ResultsWe identified 52 studies with 47066 patients. A meta-analysis of 9 studies with 14,864 patients demonstrated that 1 femtoliter increase in MPV values was associated with a rise of 29% in the risk of long-term mortality (hazard ratio (HR) 1.29, 95% confidence interval (CI) 1.22-1.37) in CAD as a whole. The results were further supported with PIs, permutation tests and leave-one-out sensitivity analyses. MPV also demonstrated its stable and significant prognostic utility in predicting long-term mortality as a linear variable in patients treated with percutaneous coronary intervention (PCI) and presented with acute coronary syndrome (ACS) (HR 1.29, 95% CI 1.20-1.39, and 1.29, 95% CI 1.19-1.39, respectively). ConclusionThe meta-analysis found robust evidence on the link between admission MPV and the increased risk of long-term mortality in patients with CAD patients, as well as in patients who underwent PCI and patients presented with ACS. PROSPERO numberCRD42023495287

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.