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Updated: Apr 15, 2026
Dynamic Multiparameter Platelet Function Assessment Using a Capacitive Biosensor
Published on: May 2, 2025
Statistical and prognostic analysis of dynamic changes of platelet count in ICU patients
M Puertas1, J L Zayas-Castro, P J Fabri
1Industrial and Management Systems Engineering, University of South Florida, 4202 E. Fowler Ave. Tampa, FL 33620-5350, USA.
Insights
Analyzing dynamic platelet counts in intensive care unit (ICU) patients reveals trends that improve disease diagnosis and patient outcome prediction. Monitoring these dynamic changes offers a more accurate diagnostic approach.
Area of Science:
- Clinical Medicine
- Biomedical Engineering
- Data Science
Background:
- Laboratory tests are crucial for disease diagnosis, but static results limit physician insight.
- Physicians often rely on single test results, missing the diagnostic value of parameter trends.
- Critically ill patients, especially in the intensive care unit (ICU), require continuous monitoring.
Purpose of the Study:
- To investigate the diagnostic and prognostic value of dynamic changes in platelet counts in ICU patients.
- To assess if analyzing platelet count trends enhances diagnostic accuracy and predicts patient complications.
- To determine the utility of dynamic platelet analysis in both homogeneous (cardiac surgery) and heterogeneous ICU populations.
Main Methods:
- Applied analytic methods to datasets of critically ill patients.
- Analyzed platelet count dynamics in a homogeneous group of ICU cardiac surgery patients.
- Validated findings in a heterogeneous group of ICU patient cohort.
Main Results:
- Dynamic changes in platelet counts were found to be predictive of patient complications and mortality in ICU settings.
- Trend analysis of platelet counts offers enhanced diagnostic insights beyond static values.
- The predictive value of platelet count dynamics was confirmed across different ICU patient groups.
Conclusions:
- Monitoring dynamic changes in platelet counts provides a more accurate diagnostic picture for ICU patients.
- Incorporating trend analysis of laboratory parameters can improve clinical decision-making and patient management.
- Dynamic patient profiling, particularly using platelet counts, aids in anticipating recovery trajectories and optimizing interventions.
Abstract:
Laboratory tests are a primary resource to diagnose patient's diseases. However, physicians often make decisions based on the available information, which commonly includes the last test results as a static picture and have limited perspective of the role of trends in commonly measured parameters in enhancing the diagnostic process. By providing a dynamic patient profile the diagnosis could be more accurate and, as a consequence, physicians could anticipate changes in recovery trajectory and prescribe interventions more effectively. Intensive care unit (ICU) patients need continuous monitoring, which commonly includes the assessment of several blood components. One of these components is the platelet count which is used in assessing blood clotting. However, platelet counts represent a dynamic equilibrium of many simultaneous processes including altered capillary permeability, inflammatory cascades, as well as the coagulation process. To characterize the value of dynamic changes in platelet counts we applied analytic methods to datasets of critically ill patients in (i) a homogeneous population of ICU cardiac surgery patients, where an observation appears to be predictive of patient's complications and mortality and (ii) a heterogeneous group of ICU patients to confirm the previous observation.
