The Role of Hematological Parameters in Critically Ill Patients: Mortality Prediction in Elderly Intensive Care Unit Patients

Background Elderly patients constitute a high-risk population in intensive care units (ICUs) due to multiple comorbidities, frailty, and limited physiological reserves. Despite advances in intensive care, mortality rates remain significantly high in this demographic. Identifying reliable predictors of mortality is crucial for early intervention and improved clinical outcomes. Hematological parameters, such as red cell distribution width (RDW), platelet-related measurements (including platelet count, mean platelet volume [MPV], and platelet distribution width [PDW]), and inflammatory markers like procalcitonin (PCT), have emerged as potential prognostic indicators. However, their predictive value in critically ill elderly patients remains controversial. Aims This study aims to determine whether hematological parameters, including RDW, platelet-related indices (platelet count, MPV, and PDW), and inflammatory markers such as PCT, can predict mortality in elderly patients admitted to ICUs. By establishing their prognostic significance, we seek to enhance early risk stratification and improve clinical decision-making. Methods A retrospective observational study was conducted on 238 patients aged 65 years and older who were admitted to the ICU. Laboratory values, demographic data, and clinical severity scores were analyzed. A receiver operating characteristic (ROC) curve analysis and logistic regression analysis were performed to assess the predictive value of hematological markers in relation to 39-day mortality. Results Among the study population, mortality occurred in 87 patients (36.6%), highlighting the high-risk nature of the geriatric intensive care cohort. RDW, PDW, MPV, and PCT levels were significantly higher in patients who did not survive (p < 0.0001). The ROC curve analysis identified PCT (area under the curve [AUC]: 0.925, p < 0.0001) and urea (AUC: 0.767, p < 0.0001) as the strongest classifiers of mortality. Logistic regression analysis confirmed PCT (odds ratio [OR]: 1.410, p < 0.0001) and PDW (OR: 1.427, p = 0.013) as independent mortality predictors. Conclusions Hematological parameters, particularly RDW, PDW, MPV, and PCT, are strong predictors of mortality in elderly patients in ICUs. These findings emphasize the role of systemic inflammation and coagulation in clinical outcomes within the intensive care setting. Integrating these parameters into clinical risk assessment models may improve early mortality prediction and allow for more targeted management strategies, ultimately improving survival in critically ill elderly patients.

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Journal
Cureus
Published
2025-04-13
DOI
https://doi.org/10.7759/cureus.82183
Citations
3
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
4.06
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article

The Role of Hematological Parameters in Critically Ill Patients: Mortality Prediction in Elderly Intensive Care Unit Patients

Reeta Bora, İsmail Demir, Mehmet C. Oz, Burak Toprak
3 citations
Cureus
Sepsis Diagnosis and Treatment
4.06
article

The Role of Hematological Parameters in Critically Ill Patients: Mortality Prediction in Elderly Intensive Care Unit Patients

Reeta Bora, İsmail Demir, Mehmet C. Oz, Burak Toprak
article en
3 citations

Abstract

Background Elderly patients constitute a high-risk population in intensive care units (ICUs) due to multiple comorbidities, frailty, and limited physiological reserves. Despite advances in intensive care, mortality rates remain significantly high in this demographic. Identifying reliable predictors of mortality is crucial for early intervention and improved clinical outcomes. Hematological parameters, such as red cell distribution width (RDW), platelet-related measurements (including platelet count, mean platelet volume [MPV], and platelet distribution width [PDW]), and inflammatory markers like procalcitonin (PCT), have emerged as potential prognostic indicators. However, their predictive value in critically ill elderly patients remains controversial. Aims This study aims to determine whether hematological parameters, including RDW, platelet-related indices (platelet count, MPV, and PDW), and inflammatory markers such as PCT, can predict mortality in elderly patients admitted to ICUs. By establishing their prognostic significance, we seek to enhance early risk stratification and improve clinical decision-making. Methods A retrospective observational study was conducted on 238 patients aged 65 years and older who were admitted to the ICU. Laboratory values, demographic data, and clinical severity scores were analyzed. A receiver operating characteristic (ROC) curve analysis and logistic regression analysis were performed to assess the predictive value of hematological markers in relation to 39-day mortality. Results Among the study population, mortality occurred in 87 patients (36.6%), highlighting the high-risk nature of the geriatric intensive care cohort. RDW, PDW, MPV, and PCT levels were significantly higher in patients who did not survive (p < 0.0001). The ROC curve analysis identified PCT (area under the curve [AUC]: 0.925, p < 0.0001) and urea (AUC: 0.767, p < 0.0001) as the strongest classifiers of mortality. Logistic regression analysis confirmed PCT (odds ratio [OR]: 1.410, p < 0.0001) and PDW (OR: 1.427, p = 0.013) as independent mortality predictors. Conclusions Hematological parameters, particularly RDW, PDW, MPV, and PCT, are strong predictors of mortality in elderly patients in ICUs. These findings emphasize the role of systemic inflammation and coagulation in clinical outcomes within the intensive care setting. Integrating these parameters into clinical risk assessment models may improve early mortality prediction and allow for more targeted management strategies, ultimately improving survival in critically ill elderly patients.

CureusVol. 17(4)
Tarsus University (TR), Izmir Bozyaka Eğitim ve Araştırma Hastanesi (TR), Mersin Üniversitesi (TR), Kastamonu University (TR)
Good health and well-being
Openalex Percentile: Top 7%
Sepsis Diagnosis and Treatment
4.06
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