Predictive potential of platelet mass index for 30-day mortality in acute ischemic stroke

Acute ischemic stroke (AIS) is a leading cause of morbidity and mortality worldwide, with early mortality risk prediction essential for guiding treatment decisions. Platelet Mass Index (PMI), a composite measure derived from platelet count and mean platelet volume (MPV), has shown promise as a biomarker in cardiovascular conditions. This study aimed to assess the predictive value of PMI for 30-day mortality in AIS patients. This retrospective cohort study included patients diagnosed with AIS who presented to the emergency department of a tertiary hospital between January 1, 2019, and January 1, 2024. The primary outcome was 30-day mortality. To determine the optimal PMI cutoff for predicting mortality, we calculated sensitivity, specificity, and likelihood ratios and the area under the curve (AUC) was obtained for overall diagnostic accuracy. A total of 117 AIS patients were analyzed, with a mean age of 68,2±14,6 years, and 58,1% were female. The 30-day mortality rate was 27,4%, with deceased patients being significantly older than survivors. PMI values were notably lower in deceased patients, and the ROC analysis yielded an AUC of 0,775. The optimal PMI cutoff provided a sensitivity of 71,8% and a specificity of 75%, with higher values associated with decreased survival. PMI may serve as a valuable prognostic tool for predicting 30-day mortality in AIS patients. These findings support the potential utility of PMI in early risk stratification, though further prospective studies are needed to validate its use in diverse clinical settings.

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Publication Details

Journal
Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi
Published
2025-03-23
DOI
https://doi.org/10.37989/gumussagbil.1608849
Citations
1
Primary Topic
Acute Ischemic Stroke Management
Type
article
Field-Weighted Citation Impact
1.35
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article

Predictive potential of platelet mass index for 30-day mortality in acute ischemic stroke

Nurhayat Başkaya, İzzet Ustaalioğlu
1 citations
Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi
Acute Ischemic Stroke Management
1.35
article

Predictive potential of platelet mass index for 30-day mortality in acute ischemic stroke

Nurhayat Başkaya, İzzet Ustaalioğlu
article en
1 citations

Abstract

Acute ischemic stroke (AIS) is a leading cause of morbidity and mortality worldwide, with early mortality risk prediction essential for guiding treatment decisions. Platelet Mass Index (PMI), a composite measure derived from platelet count and mean platelet volume (MPV), has shown promise as a biomarker in cardiovascular conditions. This study aimed to assess the predictive value of PMI for 30-day mortality in AIS patients. This retrospective cohort study included patients diagnosed with AIS who presented to the emergency department of a tertiary hospital between January 1, 2019, and January 1, 2024. The primary outcome was 30-day mortality. To determine the optimal PMI cutoff for predicting mortality, we calculated sensitivity, specificity, and likelihood ratios and the area under the curve (AUC) was obtained for overall diagnostic accuracy. A total of 117 AIS patients were analyzed, with a mean age of 68,2±14,6 years, and 58,1% were female. The 30-day mortality rate was 27,4%, with deceased patients being significantly older than survivors. PMI values were notably lower in deceased patients, and the ROC analysis yielded an AUC of 0,775. The optimal PMI cutoff provided a sensitivity of 71,8% and a specificity of 75%, with higher values associated with decreased survival. PMI may serve as a valuable prognostic tool for predicting 30-day mortality in AIS patients. These findings support the potential utility of PMI in early risk stratification, though further prospective studies are needed to validate its use in diverse clinical settings.

Gümüşhane Üniversitesi Sağlık Bilimleri DergisiVol. 14(1)
State Hospital (GB), Dr Lütfi Kırdar Kartal Eğitim ve Araştırma Hastanesi (TR), University of Health Sciences (SO)
Openalex Percentile: Top 22%
Acute Ischemic Stroke Management
1.35
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