Comparing data generated by Abaxis and Imagyst platforms involves analyzing differences in hematology and blood chemistry parameters. For example, discrepancies might arise in white blood cell differentials, enzyme levels, or electrolyte concentrations due to variations in analytical methodologies or instrument-specific reference intervals. Understanding these differences is essential for accurate clinical interpretation.
Harmonizing results from different diagnostic platforms is crucial for consistent patient care, particularly in settings utilizing multiple analyzers or comparing in-house results with reference laboratories. This comparative analysis can contribute to improved diagnostic accuracy, facilitate inter-laboratory comparisons, and streamline clinical decision-making. Furthermore, such evaluations play a role in validating new diagnostic technologies and optimizing existing laboratory workflows.
The following sections will delve deeper into specific analytical variations between the two platforms, explore potential clinical implications, and offer guidance for interpreting discordant results.
1. Accuracy
Accuracy, in the context of comparing Abaxis and Imagyst results, refers to the closeness of the measured values to the true values for a given analyte. Evaluating accuracy is crucial for determining the reliability and clinical utility of each platform. Discrepancies in accuracy can lead to misdiagnosis or inappropriate treatment decisions.
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Calibration and Standardization
Regular calibration and standardization of both Abaxis and Imagyst instruments are essential for maintaining accuracy. Variations in calibration protocols or the use of different reference materials can contribute to inter-platform discrepancies. Traceability to internationally recognized standards is paramount.
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Methodological Differences
The analytical principles employed by each platform (e.g., impedance vs. optical analysis) can impact accuracy. For instance, differences in white blood cell differential methodologies may lead to variations in subpopulation counts. Understanding these methodological nuances is crucial for accurate result interpretation.
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Sample Matrix Effects
Sample matrix effects, such as hemolysis, lipemia, or icterus, can differentially affect the accuracy of Abaxis and Imagyst measurements. Each platform’s susceptibility to these interferences should be considered when evaluating discordant results, especially in challenging patient samples.
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Quality Control Procedures
Robust quality control procedures are essential for monitoring and maintaining accuracy. Regular participation in external quality assessment schemes provides an objective measure of performance and allows for inter-laboratory comparisons, enabling identification of systematic biases between platforms.
Ultimately, evaluating the accuracy of both Abaxis and Imagyst platforms requires a comprehensive approach encompassing calibration, methodology, sample matrix effects, and quality control. Understanding these factors allows for informed interpretation of results, facilitates inter-platform comparisons, and enhances the clinical utility of both diagnostic technologies.
2. Precision
Precision, within the context of comparing Abaxis and Imagyst results, denotes the reproducibility of measurements obtained from each platform. High precision implies minimal variation between replicate analyses of the same sample, contributing to reliable result interpretation and consistent patient monitoring. Conversely, low precision can lead to uncertainty in clinical decision-making, particularly when tracking trends or evaluating subtle changes in analyte concentrations.
Several factors can influence the precision of both Abaxis and Imagyst systems. Instrument maintenance, including regular cleaning and component replacement, plays a crucial role. Variations in temperature or humidity can also introduce variability. Furthermore, operator technique, particularly in sample handling and analysis procedures, can affect precision. For example, inconsistent pipetting or improper mixing of reagents can introduce measurement errors. Statistical measures like the coefficient of variation (CV) quantify precision, providing a standardized metric for inter-platform comparisons. A lower CV indicates higher precision. For instance, a CV of less than 5% for a specific analyte on both platforms suggests comparable and acceptable precision. However, a significantly higher CV on one platform raises concerns about its reliability and necessitates further investigation.
Understanding the precision characteristics of Abaxis and Imagyst systems is essential for interpreting results accurately. When discrepancies arise between platforms, considering their respective precision profiles can help determine the clinical significance of the difference. High precision on both platforms reinforces the validity of observed differences, whereas low precision on either platform necessitates cautious interpretation and potentially repeat analysis. Consistent and rigorous quality control procedures, including regular analysis of control materials with known values, are crucial for monitoring and maintaining precision. This proactive approach ensures reliable performance and minimizes the risk of erroneous clinical interpretations due to imprecision.
3. Methodologies
Understanding the different methodologies employed by Abaxis and Imagyst is crucial for interpreting discrepancies in results. These platforms utilize distinct analytical principles, impacting various hematological and biochemical parameters. A comparative analysis of these methodologies provides insights into the strengths and limitations of each platform, facilitating informed clinical decision-making.
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Hematology Analyzers
Abaxis hematology analyzers typically employ impedance-based cell counting, where cells suspended in an electrolyte solution pass through an aperture, creating measurable changes in electrical impedance. Imagyst, conversely, often utilizes fluorescence-based methods, employing fluorescent dyes to label specific cell types for identification and quantification. These differing approaches can lead to variations in white blood cell differentials, particularly in identifying and classifying abnormal cell populations. For example, impedance-based methods may struggle to differentiate certain immature or atypical cells, whereas fluorescence-based methods offer enhanced specificity.
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Clinical Chemistry Analyzers
In clinical chemistry analysis, Abaxis systems commonly utilize dry slide technology, where reagents are pre-deposited on slides for individual tests. Imagyst platforms often employ wet chemistry methods, involving liquid reagents and traditional spectrophotometry or other optical techniques. These methodological differences can influence the accuracy and precision of various biochemical measurements. For instance, dry slide technology might be more susceptible to interferences from sample matrix effects like lipemia or icterus compared to wet chemistry methods.
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Data Analysis Algorithms
Beyond the core analytical methods, Abaxis and Imagyst platforms employ distinct data analysis algorithms to process raw data and generate final results. These algorithms play a crucial role in interpreting complex signals, such as those generated in cell counting or spectral analysis. Differences in algorithms can impact the sensitivity and specificity of various tests, influencing the detection and quantification of target analytes. For instance, variations in algorithms for flagging abnormal results could lead to discrepancies in identifying clinically significant findings.
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Reference Intervals
While not strictly a methodological difference, the establishment of reference intervals plays a critical role in result interpretation. Abaxis and Imagyst may utilize distinct reference intervals based on their specific methodologies and target populations. Comparing results without considering these differences can lead to misinterpretations. It is essential to ensure that appropriate reference intervals are applied for each platform to avoid misclassifying results as normal or abnormal. For example, comparing potassium levels obtained from an Abaxis analyzer using its specific reference range to potassium levels from an Imagyst analyzer using a different reference range could lead to erroneous clinical conclusions.
Considering these methodological variations is essential when comparing Abaxis and Imagyst results. Recognizing the strengths and limitations of each platform’s analytical approach, data analysis algorithms, and reference intervals ensures accurate result interpretation and facilitates informed clinical decision-making. Failing to account for these differences can lead to diagnostic errors and potentially compromise patient care. Further investigation into specific methodological discrepancies, especially for critical analytes, may be warranted to reconcile conflicting results and optimize clinical utility.
4. Reference Intervals
Reference intervals play a crucial role in interpreting results from diagnostic platforms like Abaxis and Imagyst. These intervals represent the range of values expected in a healthy population and are essential for classifying results as normal or abnormal. Variations in reference intervals between Abaxis and Imagyst can arise due to differences in analytical methodologies, instrument-specific calibrations, and the demographics of the populations used to establish these intervals. Understanding these variations is paramount for accurate result interpretation and avoiding misdiagnosis.
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Methodology-Specific Intervals
Different analytical methodologies employed by Abaxis and Imagyst can influence the distribution of test results, necessitating platform-specific reference intervals. For example, if Abaxis uses an impedance-based method for measuring a specific analyte while Imagyst utilizes a photometric method, the resulting values might differ systematically, requiring distinct reference ranges for accurate interpretation.
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Calibration and Standardization
Instrument-specific calibration and standardization procedures can also contribute to variations in reference intervals. Even if both platforms utilize the same analytical principle, differences in calibration materials or protocols can shift the distribution of results, necessitating platform-specific reference ranges. Regular calibration against traceable standards is essential to minimize these variations and ensure comparability.
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Demographic Considerations
Reference intervals are often established based on specific populations, considering factors like age, sex, and ethnicity. If Abaxis and Imagyst derive their reference intervals from different demographic groups, variations in these intervals can arise. For instance, reference intervals for certain hematological parameters might differ significantly between pediatric and adult populations, necessitating age-specific interpretations.
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Clinical Implications of Discrepancies
Discrepancies in reference intervals between Abaxis and Imagyst can lead to misclassification of results and potentially impact clinical decision-making. A result considered normal based on Abaxis’s reference interval might fall outside the normal range for Imagyst, potentially leading to unnecessary further investigations or interventions. Careful consideration of platform-specific reference intervals is crucial to avoid such scenarios and ensure accurate clinical assessments.
In summary, variations in reference intervals between Abaxis and Imagyst underscore the importance of considering platform-specific ranges when interpreting results. Failing to account for these differences can lead to diagnostic errors and compromise patient care. Harmonizing reference intervals, where possible, or clearly documenting platform-specific ranges is crucial for ensuring accurate and consistent clinical interpretations. Further investigation into the underlying causes of these variations, such as methodological differences or demographic considerations, can contribute to improved standardization and enhance the clinical utility of both platforms.
5. Sample Types
Sample type significantly influences the comparability of Abaxis and Imagyst results. Each platform may exhibit varying performance characteristics depending on the nature of the sample being analyzed. Understanding these variations is crucial for accurate result interpretation and informed clinical decision-making. Factors such as sample volume, anticoagulant used, and sample handling procedures can differentially impact the performance of each platform.
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Whole Blood
Whole blood is commonly used for hematological analysis. Variations in red blood cell and platelet counts can arise between Abaxis and Imagyst platforms due to differences in cell counting methodologies or sensitivity to red blood cell aggregation. Furthermore, the presence of nucleated red blood cells or other interfering substances can differentially affect the accuracy of white blood cell differentials generated by each platform.
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Serum/Plasma
Serum and plasma are frequently employed for biochemical analyses. Differences in the separation techniques used to obtain these sample types can influence the concentration of certain analytes. For instance, residual platelets in plasma can release potassium, potentially leading to discrepancies in potassium levels measured by Abaxis and Imagyst. Furthermore, the type of anticoagulant used for plasma collection can interfere with specific assays, leading to platform-dependent variations in results.
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Urine
Urine analysis plays a crucial role in assessing renal function. Abaxis and Imagyst may employ different methodologies for detecting and quantifying urinary analytes. Variations in sensitivity and specificity can lead to discrepancies in results, particularly for low-concentration analytes or in the presence of interfering substances. Furthermore, differences in sample preparation protocols, such as centrifugation or filtration, can influence the final concentration of analytes measured by each platform.
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Other Body Fluids
Other body fluids, such as cerebrospinal fluid or pleural fluid, can present unique challenges for analysis. The limited volume and complex matrix of these samples can differentially affect the performance of Abaxis and Imagyst platforms. Variations in sensitivity and susceptibility to interferences can lead to discrepancies in results. Careful consideration of platform-specific performance characteristics is essential when analyzing these challenging sample types.
Considering the impact of sample type on Abaxis and Imagyst results is essential for accurate clinical interpretation. Understanding platform-specific performance characteristics across various sample types enables informed decision-making and minimizes the risk of misdiagnosis. Further investigation into the underlying causes of these variations, such as methodological differences or sample processing procedures, can contribute to improved standardization and enhance the comparability of results across platforms. For instance, comparing the performance of each platform using standardized samples can help identify systematic biases and improve the harmonization of results. This rigorous approach ensures reliable and consistent interpretations, regardless of the sample type analyzed.
6. Workflow Integration
Workflow integration significantly impacts the comparison of Abaxis and Imagyst results. The way each platform integrates into the overall laboratory workflow can influence turnaround time, data management, and result interpretation, ultimately affecting clinical decision-making. A well-integrated system streamlines processes, minimizes errors, and facilitates efficient data flow, whereas a poorly integrated system can introduce delays, increase the risk of discrepancies, and complicate result comparison.
Consider a scenario where a clinic utilizes Abaxis analyzers for point-of-care testing and sends samples to a central laboratory using Imagyst platforms for more specialized analyses. If the data management systems of these platforms are not seamlessly integrated, comparing and correlating results becomes cumbersome. Manual data entry increases the risk of transcription errors, while delays in data transfer can hinder timely clinical interventions. Conversely, a well-integrated system allows for automatic data transfer and facilitates direct comparison of results, enabling faster and more informed clinical decisions. This integration is particularly crucial in time-sensitive situations, such as emergency departments or critical care settings, where rapid and accurate result interpretation is paramount.
Furthermore, variations in sample handling protocols dictated by each platform’s workflow can introduce pre-analytical variability, impacting result comparability. For instance, differences in sample collection tubes, centrifugation speeds, or storage temperatures between Abaxis and Imagyst workflows can influence analyte stability and introduce discrepancies in measured values. Standardizing pre-analytical processes across platforms is essential for minimizing these variations and ensuring reliable result comparison. This standardization not only improves the accuracy of comparisons but also enhances the overall quality and efficiency of the laboratory workflow.
Effective workflow integration is essential for maximizing the comparability and clinical utility of Abaxis and Imagyst results. Streamlined data management, minimized pre-analytical variability, and efficient result reporting enhance clinical decision-making and contribute to improved patient care. Addressing challenges related to inter-platform connectivity and standardizing pre-analytical processes are crucial steps towards optimizing workflow integration and realizing the full potential of both diagnostic platforms. Ultimately, a well-integrated workflow facilitates a more comprehensive and efficient approach to diagnostics, enabling clinicians to leverage the strengths of each platform for improved patient outcomes.
7. Cost-effectiveness
Cost-effectiveness plays a critical role in evaluating the comparative utility of Abaxis and Imagyst platforms. Direct instrument costs, including acquisition, maintenance, and reagent expenses, contribute significantly to the overall financial burden. Indirect costs, such as personnel training, quality control procedures, and data management infrastructure, also factor into the economic equation. A comprehensive cost analysis requires considering both direct and indirect expenses to accurately assess the long-term financial implications of each platform.
For instance, Abaxis analyzers are often positioned as cost-effective solutions for point-of-care testing due to their relatively lower initial investment and smaller footprint. However, higher per-test reagent costs can accrue over time, especially in high-volume settings. Conversely, Imagyst platforms might entail a higher upfront investment but offer lower per-test costs, potentially proving more economical in the long run for laboratories processing a large number of samples. Furthermore, operational efficiency influences cost-effectiveness. Factors such as turnaround time, automation capabilities, and integration with existing laboratory information systems impact personnel time and resource utilization, ultimately affecting overall cost.
A thorough cost-effectiveness analysis requires considering the specific needs and resources of the clinical setting. Factors like test volume, required turnaround time, and available technical expertise influence the optimal choice. Evaluating the clinical value derived from each platform alongside its associated costs provides a comprehensive framework for decision-making. A more expensive platform might be justified if it offers superior diagnostic accuracy or enables faster clinical interventions, ultimately improving patient outcomes and potentially offsetting higher initial costs. Therefore, balancing financial considerations with clinical performance is crucial for maximizing value and ensuring sustainable diagnostic services. Failing to incorporate cost-effectiveness into the evaluation process can lead to suboptimal resource allocation and compromise the long-term viability of diagnostic services.
8. Data Interpretation
Data interpretation is crucial when comparing results from Abaxis and Imagyst platforms. Accurate interpretation requires understanding the nuances of each platform’s methodology, performance characteristics, and reference intervals. Failing to account for these factors can lead to misdiagnosis, inappropriate treatment decisions, and compromised patient care. The following facets highlight key considerations for interpreting data from these platforms.
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Reference Interval Harmonization
Discrepancies in reference intervals between platforms can complicate data interpretation. Harmonizing these intervals, where possible, or clearly documenting platform-specific ranges facilitates accurate comparisons. For example, a hemoglobin value within the normal range for Abaxis might fall below the normal range for Imagyst, necessitating careful consideration of platform-specific reference intervals to avoid misinterpretation.
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Methodological Considerations
Variations in analytical methodologies between platforms can lead to differences in measured values. Understanding these methodological differences is essential for interpreting discrepancies. For example, Abaxis hematology analyzers often employ impedance-based cell counting, while Imagyst may utilize optical or fluorescence-based methods, leading to potential variations in white blood cell differentials.
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Flags and Alerts
Each platform employs specific flags and alerts to highlight abnormal results or potential analytical issues. Interpreting these flags requires understanding their platform-specific definitions and clinical significance. For example, an “interference” flag on an Abaxis chemistry analyzer might indicate lipemia, whereas a similar flag on an Imagyst platform could represent hemolysis, requiring specific follow-up based on the respective platform’s criteria.
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Delta Checks and Trending
Monitoring changes in analyte values over time (trending) and comparing current results with previous results from the same patient (delta checks) are essential aspects of data interpretation. Implementing consistent delta check and trending parameters across platforms enhances the ability to detect clinically significant changes and minimizes the risk of overlooking subtle but important trends.
Effective data interpretation requires a comprehensive understanding of the interplay between these facets. Integrating knowledge of platform-specific methodologies, reference intervals, flags, and delta checks enables accurate result comparison and facilitates informed clinical decision-making. Harmonizing data interpretation practices across platforms enhances the overall quality and consistency of patient care, minimizing the risk of diagnostic errors and optimizing treatment strategies. Further, developing standardized interpretation guidelines for common scenarios encountered when comparing Abaxis and Imagyst results can enhance clarity and consistency across clinical teams.
9. Clinical Relevance
Clinical relevance, in the context of comparing Abaxis and Imagyst results, centers on the impact of platform discrepancies on patient management decisions. Variations in reported values between platforms can influence diagnoses, treatment choices, and patient outcomes. Understanding the clinical implications of these discrepancies is paramount for ensuring appropriate medical interventions and minimizing the risk of adverse events. A comprehensive assessment of clinical relevance requires considering the magnitude of discrepancies, the biological variability of the analyte in question, and the specific clinical context.
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Diagnostic Accuracy
Discrepancies between Abaxis and Imagyst results can impact diagnostic accuracy. For instance, variations in white blood cell differentials could lead to misdiagnosis of infections or hematological malignancies. Similarly, differences in enzyme levels might affect the diagnosis of myocardial infarction or liver disease. Clinicians must carefully evaluate discrepancies, considering the potential impact on diagnostic accuracy and the need for confirmatory testing using alternative methods.
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Therapeutic Monitoring
Monitoring drug therapy often relies on laboratory results. Discrepancies between Abaxis and Imagyst measurements, particularly for drugs with narrow therapeutic indices, can complicate dose adjustments and therapeutic monitoring. For example, variations in coagulation parameters might influence warfarin management, increasing the risk of bleeding or thromboembolic events. Consistent use of a single platform or careful calibration between platforms is crucial for reliable therapeutic monitoring.
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Patient Outcomes
Ultimately, the clinical relevance of platform discrepancies hinges on their potential impact on patient outcomes. Misdiagnosis or inappropriate treatment decisions stemming from inaccurate or inconsistent results can lead to adverse events, delayed recovery, and increased healthcare costs. For example, misinterpreting electrolyte imbalances due to platform differences could result in inappropriate fluid management, potentially leading to complications. Prioritizing clinical relevance ensures that laboratory results contribute meaningfully to improved patient care and outcomes.
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Resource Utilization
Discrepancies between platforms can lead to unnecessary repeat testing, specialist consultations, and extended hospital stays, impacting resource utilization. For example, if a discrepancy in a critical analyte arises between an Abaxis point-of-care result and an Imagyst laboratory result, it might necessitate repeat testing using a gold-standard method, delaying diagnosis and increasing costs. Minimizing platform discrepancies through careful calibration and standardization can optimize resource utilization and improve overall healthcare efficiency.
Understanding the clinical relevance of “Abaxis vs Imagyst results” requires a nuanced approach that considers the interplay between analytical performance and clinical context. While analytical concordance is desirable, it is the clinical impact of discrepancies that ultimately determines their significance. Evaluating the potential consequences of these discrepancies on diagnosis, treatment, patient outcomes, and resource utilization ensures that laboratory data contributes meaningfully to improved patient care. Prioritizing clinical relevance fosters a more judicious and effective utilization of diagnostic information, ultimately leading to better patient management decisions.
Frequently Asked Questions
This section addresses common inquiries regarding the comparison of results obtained from Abaxis and Imagyst platforms. Understanding these frequently asked questions clarifies potential discrepancies and facilitates informed interpretation of laboratory data.
Question 1: How do differences in calibration between Abaxis and Imagyst platforms affect result comparability?
Variations in calibration protocols or reference materials can lead to systematic differences in reported values. Regular calibration against traceable standards is essential to minimize inter-platform discrepancies.
Question 2: Can sample matrix effects, such as hemolysis or lipemia, differentially impact results from Abaxis and Imagyst analyzers?
Sample matrix effects can indeed influence results differently depending on the analytical methodology employed by each platform. Each platform’s susceptibility to specific interferences should be considered when evaluating discrepancies.
Question 3: Are reference intervals always consistent between Abaxis and Imagyst for the same analyte?
Reference intervals can vary between platforms due to differences in methodologies, calibration, and the demographics of the populations used to establish those intervals. Using platform-specific reference intervals is crucial for accurate interpretation.
Question 4: What are the key methodological differences between Abaxis and Imagyst hematology analyzers, and how might these differences affect complete blood count (CBC) results?
Abaxis hematology analyzers often employ impedance-based cell counting, while Imagyst may utilize optical or fluorescence-based methods. These differing approaches can lead to variations in white blood cell differentials and other CBC parameters.
Question 5: How does workflow integration impact the comparison of results?
Variations in sample handling protocols, data management systems, and turnaround times can influence result comparability. Standardized pre-analytical processes and seamless data integration facilitate more accurate comparisons.
Question 6: What factors should be considered when assessing the clinical relevance of discrepancies between Abaxis and Imagyst results?
The magnitude of the discrepancy, the biological variability of the analyte, and the specific clinical context determine the clinical relevance. Discrepancies impacting diagnoses, treatment decisions, or patient outcomes require careful consideration and potential confirmatory testing.
Considering these frequently asked questions provides a framework for understanding the complexities of comparing Abaxis and Imagyst results. Further investigation into platform-specific performance characteristics and consultation with laboratory professionals is recommended when significant discrepancies arise.
The subsequent sections will delve deeper into specific case studies illustrating the practical implications of comparing results from these platforms and offer practical guidance for resolving discrepancies and optimizing clinical decision-making.
Tips for Interpreting Comparative Results
Comparing results generated by different diagnostic platforms requires careful consideration of several factors. The following tips provide guidance for interpreting discrepancies and maximizing the clinical utility of comparative data.
Tip 1: Standardize Pre-analytical Processes
Variations in sample collection, handling, and processing can introduce significant variability. Standardizing these pre-analytical steps across platforms minimizes discrepancies and improves result comparability.
Tip 2: Verify Calibration and Quality Control
Regular calibration against traceable standards and robust quality control procedures are essential for ensuring the accuracy and reliability of results from both platforms. Verify that both instruments are properly calibrated and that quality control results fall within acceptable ranges.
Tip 3: Consult Platform-Specific Reference Intervals
Reference intervals can vary between platforms due to differences in methodologies and target populations. Always consult platform-specific reference intervals when interpreting results to avoid misclassification.
Tip 4: Understand Methodological Differences
Familiarize yourself with the analytical principles employed by each platform. Recognizing methodological differences aids in interpreting discrepancies and understanding the limitations of each technology.
Tip 5: Consider Sample Matrix Effects
Sample matrix effects, such as hemolysis or lipemia, can differentially impact results. Evaluate the potential influence of these effects when interpreting discrepancies, especially in challenging samples.
Tip 6: Correlate Results with Clinical Findings
Always interpret laboratory results in the context of the patient’s clinical presentation. Discrepancies between platforms should be evaluated in light of other clinical findings to determine their significance.
Tip 7: Consult Laboratory Professionals
When significant or unexplained discrepancies arise, consult with laboratory professionals to gain further insights into the potential causes and implications.
Adhering to these tips enhances the reliability and clinical utility of comparative data, facilitating informed decision-making and improved patient care. Consistent application of these principles strengthens the overall diagnostic process and contributes to a more comprehensive understanding of patient health.
The following conclusion summarizes the key takeaways of this comparative analysis and offers perspectives on future directions in optimizing diagnostic platforms and data interpretation strategies.
Conclusion
Comparing Abaxis and Imagyst results requires a nuanced understanding of each platform’s analytical methodologies, performance characteristics, and reference intervals. Variations in calibration, sample types, and workflow integration can influence result comparability and impact clinical interpretation. Accuracy, precision, and cost-effectiveness are key considerations when evaluating the relative merits of each platform within a specific clinical context. Harmonizing pre-analytical processes, consulting platform-specific documentation, and engaging with laboratory professionals are essential for mitigating discrepancies and maximizing the clinical utility of comparative data.
Continued evaluation and standardization of diagnostic platforms are crucial for advancing laboratory medicine. Further research focusing on inter-platform comparability, harmonization of reference intervals, and development of robust data interpretation guidelines will enhance clinical decision-making and contribute to improved patient care. Ultimately, leveraging the strengths of each platform while mitigating their limitations empowers clinicians to deliver more precise and personalized medical interventions.
