Name: A Data-Driven Approach to Quantifying Immune States in Sepsis (Video) | JoVE Journal
Uploaded: 2025-02-07T15:00:00.000+00:00
Duration: 7 min 42 s
Description: Discover a data-driven approach to quantifying immune states in sepsis using data visualization and three-dimensional numerical fitting. This computational method identifies distinct immune states to improve disease monitoring and treatment strategies.

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This study received support from two sources: the seventh batch of the Master-Apprentice Inheritance Project organized by the National Administration of Traditional Chinese Medicine of China (Project number: [2021] No. 272) and the 2024 Chinese Medicine Research Capacity Enhancement Project of Municipal-level Chinese Medicine Hospital (SZY-NLTL-2024-003) from the Shaanxi Provincial Administration of Traditional Chinese Medicine.

This study explores the immune condition in sepsis patients by investigating the relationships among white blood cells, lymphocytes, and neutrophils. The patients were enrolled in the intensive care unit (ICU) of Dongzhimen Hospital in Beijing, China, and underwent standard blood tests after providing informed consent. The study was conducted as per the guidelines of the institutional human research ethics committee. Data grouping and detailed data content can be found in Supplementary Table 1. The software tools utilized in this study are enumerated in the Table of Materials. 
1. Data collection and preparation
NOTE: White blood cells, neutrophils, and lymphocytes were selected as key indicators of the immune condition in sepsis patients. This choice is based on the well-established clinical observations that lymphocyte counts tend to be suppressed and gradually decrease, while neutrophil counts often oscillate in sepsis patients. These two cell types have been empirically recognized as important markers of the immune status in sepsis patient populations. However, the precise quantitative relationship among these parameters has not been clearly reported in the literature. Therefore, white blood cells, neutrophils, and lymphocytes were chosen as a starting point for quantifying the immune condition in sepsis patients.
<ol>
	<li>Installing MATLAB Spreadsheet Link for Excel Add-In.
	<ol>
		<li>Open Microsoft Excel and navigate to the Insert tab on the ribbon.</li>
		<li>Click on Get Add-ins in the Add-ins section.</li>
		<li>In the Office Add-ins dialog box, search for MATLAB Spreadsheet Link in the search bar.</li>
		<li>Locate the MATLAB Spreadsheet Link for Excel add-in in the search results and click on the Add button.</li>
		<li>Read and accept the terms and conditions, then click Continue to proceed with the installation.</li>
		<li>If prompted, log in with a MathWorks account or create a new account to access the add-in.</li>
		<li>Once the installation is complete, the MATLAB Spreadsheet Link tab will appear on the Excel ribbon.</li>
		<li>Click on the MATLAB Spreadsheet Link tab to verify that the add-in is installed and ready to use.</li>
	</ol>
	</li>
	<li>Send data into MATLAB workspace.
	<ol>
		<li>Open the spreadsheet containing the sepsis patient data, including white blood cell counts, lymphocyte counts, and neutrophil counts.</li>
		<li>Ensure that the data is organized in a structured format, with each variable (white blood cell count, lymphocyte count, and neutrophil count) in a separate column and each patient in a separate row.</li>
		<li>Select the range of cells containing the white blood cell counts, lymphocyte counts, and neutrophil counts.</li>
		<li>Click on the MATLAB Spreadsheet Link tab in the Excel ribbon.</li>
		<li>In the MATLAB Spreadsheet Link tab, click the Send Data to MATLAB button.</li>
		<li>In the Send Data to MATLAB dialog box, choose the appropriate MATLAB instance from the dropdown menu if multiple instances are running.</li>
		<li>Specify the variable name for the data in the Variable name field. For example, sepsis_immune_data.</li>
		<li>Select the desired data type for the imported data in MATLAB (e.g., numeric matrix).</li>
		<li>Click OK to send the data to the MATLAB workspace.</li>
		<li>Switch to the MATLAB application and verify that the data has been successfully imported by checking the workspace for the specified variable name (e.g., sepsis_immune_data).</li>
	</ol>
	</li>
	<li>Checking the data in MATLAB
	<ol>
		<li>After sending the sepsis patient data (white blood cell counts, lymphocyte counts, and neutrophil counts) to MATLAB using the MATLAB Spreadsheet Link add-in, switch to the MATLAB application.</li>
		<li>To check the contents of the imported data, type the variable name in the MATLAB command window and press Enter.</li>
	</ol>
	</li>
</ol>
2. Three-dimensional visualization of white blood cells, lymphocytes, and neutrophils
<ol>
	<li>Generating the plot using the Immune_scatter3 function
	<ol>
		<li>The variable A stores the immune data of sepsis patients. Call the function Immune_scatter3(A) to obtain a graphical user interface (GUI) displaying the three-dimensional scatter plot of the samples, as shown in Figure 1. 
		NOTE: The fitting error between the three-dimensional plane and the sample distribution in the plot is very small. Section 3 will provide the exact formula.</li>
	</ol>
	</li>
	<li>The GUI usage 
	<ol>
		<li>Explore and analyze the three-dimensional scatter plot using the interactive features provided by the generated GUI.
		<ol>
			<li>Rotate: Click and drag the plot to rotate it in 3D space, allowing viewing the sample distribution from different angles.</li>
			<li>Pan: Right-click and drag the plot to move it horizontally or vertically, adjusting the visible area of the plot.</li>
			<li>Zoom: Use the mouse wheel or the zoom controls in the toolbar to zoom in or out of the plot, focusing on specific regions or samples.</li>
			<li>Data cursor: Click on individual samples to display their corresponding values for white blood cells, lymphocytes, and neutrophils. 
			NOTE: By utilizing these interactive features, clinicians can gain insights into the relationships and patterns among white blood cells, lymphocytes, and neutrophils in sepsis patients, facilitating the exploration and analysis of the immune data.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. The formula 
<ol>
	<li>In the MATLAB workspace, assign the neutrophil count, lymphocyte count, and white blood cell count to the variables X, Y, and Z, respectively.</li>
	<li>To obtain the precise mathematical expression (formula 1; WBC = 1.098 &#215; Neutrophils + 1.046 &#215; Lymphocytes + 0.1645 (RMSE: 1%)) of the three-dimensional plane in Figure 1, invoke the following command: 
	[fitresult, gof, output] = fit ([X, Y], Z, &#39;poly11&#39;) 
	NOTE: This formula quantitatively describes the relationship among white blood cells, neutrophils, and lymphocytes in sepsis patients, providing a concise and accurate representation of the immune data.</li>
	<li>To evaluate the goodness of fit, calculate the normalized root mean square error (NRMSE) using the following command: 
	gof.rmse / (max(Z) - min(Z)) 
	NOTE: The resulting NRMSE value of 1% indicates that the fitted plane (formula 1) closely approximates the actual sample distribution in the three-dimensional space. This low error level underscores the reliability and validity of the obtained mathematical expression in capturing the intricate relationships among the immune parameters in sepsis patients.</li>
</ol>
4. Immune condition in sepsis 
NOTE: Self-Organizing Feature Maps (SOFM) were employed for unsupervised clustering to identify immune conditions in sepsis patients.
<ol>
	<li>By invoking the Immune_Condition function, generate clusters of sample points on the three-dimensional plane represented by formula 1, as depicted in Figure 2.</li>
	<li>Use the interactive visualization features for Figure 2 as described in Step 2.2. 
	NOTE: Figure 2 showcases nine automated clusters, labeled Cluster1 through Cluster9, derived from SOFM&#39;s unsupervised machine learning approach. This clustering technique takes into account both the spatial topology and the density of the samples, enabling the identification of distinct immune conditions within the sepsis patient population.</li>
</ol>
5. Typical immune oscillation trajectories in sepsis 
<ol>
	<li>Based on Figure 2, use the hold on command to maintain the figure in an overlayable state, then use the following commands to create a three-dimensional plot of the typical patient&#39;s trajectory data. 
	hold on 
	for i=1: size(p,1)-1 
	pause (3); plot3(p (i: i+1,2), p (i: i+1,3), p (i: i+1,1),&#39;k&#39;,&#39;Linewidth&#39;,3); 
	end</li>
</ol>

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<li>Adelman, M. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+gut+microbiome%27s+role+in+the+development%2C+maintenance%2C+and+outcomes+of+sepsis%5BTitle%5D)+AND+%22Crit+Care%22%5BJournal%5D)">The gut microbiome's role in the development, maintenance, and outcomes of sepsis.</a> Crit Care. 24 (1), 278(2020).</li>
<li>Kumar, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Pulmonary+innate+immune+response+determines+the+outcome+of+inflammation+during+pneumonia+and+sepsis-associated+acute+lung+injury%5BTitle%5D)+AND+%22Front+Immunol%22%5BJournal%5D)">Pulmonary innate immune response determines the outcome of inflammation during pneumonia and sepsis-associated acute lung injury.</a> Front Immunol. 11, 1722(2020).</li>
<li>Nakamori, Y., Park, E. J., Shimaoka, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Immune+deregulation+in+sepsis+and+septic+shock%3A+Reversing+immune+paralysis+by+targeting+PD-1%2FPD-L1+pathway%5BTitle%5D)+AND+%22Front+Immunol%22%5BJournal%5D)">Immune deregulation in sepsis and septic shock: Reversing immune paralysis by targeting PD-1/PD-L1 pathway.</a> Front Immunol. 11, 624279(2021).</li>
<li>Van der Poll, T., Shankar-Hari, M., Wiersinga, W. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+immunology+of+sepsis%5BTitle%5D)+AND+%22Immunity%22%5BJournal%5D)">The immunology of sepsis.</a> Immunity. 54 (11), 2450-2464 (2021).</li>
<li>Chaturvedi, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((T-cell+activation+profiles+distinguish+hemophagocytic+lymphohistiocytosis+and+early+sepsis%5BTitle%5D)+AND+%22Blood%22%5BJournal%5D)">T-cell activation profiles distinguish hemophagocytic lymphohistiocytosis and early sepsis.</a> Blood. 137 (17), 2337-2346 (2021).</li>
<li>Reyes, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+immune-cell+signature+of+bacterial+sepsis%5BTitle%5D)+AND+%22Nat+Med%22%5BJournal%5D)">An immune-cell signature of bacterial sepsis.</a> Nat Med. 26 (3), 333-340 (2020).</li>
<li>Pant, A., Mackraj, I., Govender, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Advances+in+sepsis+diagnosis+and+management%3A+a+paradigm+shift+towards+nanotechnology%5BTitle%5D)+AND+%22J+Biomed+Sci%22%5BJournal%5D)">Advances in sepsis diagnosis and management: a paradigm shift towards nanotechnology.</a> J Biomed Sci. 28 (1), 6(2021).</li>
<li>Kohonen, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Essentials+of+the+self-organizing+map%5BTitle%5D)+AND+%22Neural+Netw%22%5BJournal%5D)">Essentials of the self-organizing map.</a> Neural Netw. 37, 52-65 (2013).</li>
<li>Yang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+type+1+interferons+in+coagulation+induced+by+gram-negative+bacteria%5BTitle%5D)+AND+%22Blood%22%5BJournal%5D)">The role of type 1 interferons in coagulation induced by gram-negative bacteria.</a> Blood. 135 (14), 1087-1100 (2020).</li>
<li>Zhang, Y. Y., Ning, B. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Signaling+pathways+and+intervention+therapies+in+sepsis%5BTitle%5D)+AND+%22Signal+Transduct+Target+Ther%22%5BJournal%5D)">Signaling pathways and intervention therapies in sepsis.</a> Signal Transduct Target Ther. 6 (1), 407(2021).</li>
<li>Baghela, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Predicting+sepsis+severity+at+first+clinical+presentation%3A+The+role+of+endotypes+and+mechanistic+signatures%5BTitle%5D)+AND+%22EBioMedicine%22%5BJournal%5D)">Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures.</a> EBioMedicine. 75, 103776(2022).</li>
<li>Yao, R. Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Publication+trends+of+research+on+sepsis+and+host+immune+response+during+1999-2019%3A+A+20-year+bibliometric+analysis%5BTitle%5D)+AND+%22Int+J+Biol+Sci%22%5BJournal%5D)">Publication trends of research on sepsis and host immune response during 1999-2019: A 20-year bibliometric analysis.</a> Int J Biol Sci. 16 (1), 27-37 (2020).</li>
<li>Owen, A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((TLR+agonists+as+mediators+of+trained+immunity%3A+Mechanistic+insight+and+immunotherapeutic+potential+to+combat+infection%5BTitle%5D)+AND+%22Front+Immunol%22%5BJournal%5D)">TLR agonists as mediators of trained immunity: Mechanistic insight and immunotherapeutic potential to combat infection.</a> Front Immunol. 11, 622614(2021).</li>
<li>Jung, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+fetal+inflammatory+response+syndrome%3A+the+origins+of+a+concept%2C+pathophysiology%2C+diagnosis%2C+and+obstetrical+implications%5BTitle%5D)+AND+%22Semin+Fetal+Neonatal+Med%22%5BJournal%5D)">The fetal inflammatory response syndrome: the origins of a concept, pathophysiology, diagnosis, and obstetrical implications.</a> Semin Fetal Neonatal Med. 25 (4), 101146(2020).</li>
<li>Bruno, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Transcriptional+and+functional+insights+into+the+host+immune+response+against+the+emerging+fungal+pathogen+Candida+auris%5BTitle%5D)+AND+%22Nat+Microbiol%22%5BJournal%5D)">Transcriptional and functional insights into the host immune response against the emerging fungal pathogen Candida auris.</a> Nat Microbiol. 5 (12), 1516-1531 (2020).</li>
<li>Barichello, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Biomarkers+for+sepsis%3A+more+than+just+fever+and+leukocytosis-a+narrative+review%5BTitle%5D)+AND+%22Crit+Care%22%5BJournal%5D)">Biomarkers for sepsis: more than just fever and leukocytosis-a narrative review.</a> Crit Care. 26 (1), 14(2022).</li>
</ol>

The software tool for Probabilistic Scatter Plots for Immune States V1.0 is developed and owned by Beijing Intelligent Entropy Science &amp; Technology Co., Ltd. All intellectual property rights of this software are held by the company. The authors declare no conflicts of interest.

This study presents an approach to understanding the immune condition in sepsis by leveraging advanced data visualization and machine learning techniques. By uncovering the mathematical relationship among key immune cell populations and identifying distinct immune states, the study provides a new perspective on the complex immune dynamics in sepsis and contributes to the development of more effective diagnostic and therapeutic strategies11,12. The key findings in...

Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, remains a significant challenge in critical care medicine1. Despite advances in understanding the pathophysiology of sepsis, the complex interplay between the immune system and pathogens continues to pose difficulties in diagnosing and treating this condition effectively2. Current clinical approaches often focus on monitoring infection indicators, organ function, cytokines, microbial detection, and gut microbiome3. However, there is a growing recognition of the crucial role played by immune cells, particularly white blood cells, lymphocytes, and neutrophils, in the progression and resolution of sepsis4.
During the course of sepsis, the immune system undergoes a complex series of changes, characterized by an initial hyperinflammatory phase followed by a prolonged immunosuppressive phase5. The early phase is marked by a surge in neutrophil counts and a concomitant decrease in lymphocyte populations, reflecting the activation of innate immune responses and the suppression of adaptive immunity6. As the condition progresses, neutrophil levels may oscillate or become exhausted while lymphocyte counts continue to decline, leading to a state of immunosuppression that renders patients vulnerable to secondary infections7. Understanding the dynamic interplay among these immune cell populations is crucial for accurately assessing the immune status of sepsis patients and devising targeted interventions.
Traditional approaches to analyzing immune cell counts in sepsis have relied on univariate or bivariate analyses, which fail to capture the complex relationships among multiple immune parameters8. Recent advances in data visualization and machine learning techniques have opened up new possibilities for exploring high-dimensional immunological data9. In particular, three-dimensional scatter plot visualization and self-organizing feature maps (SOFM)10 have shown promise in uncovering hidden patterns and identifying distinct immune states in various disease contexts.
This study aims to investigate the immune condition in sepsis patients by analyzing the quantitative relationships among white blood cells, lymphocytes, and neutrophils using advanced data visualization and clustering techniques. The hypothesis is that these immune cell populations are constrained within a three-dimensional space governed by an underlying mathematical relationship. By uncovering this relationship and identifying distinct immune states using SOFM, the study seeks to provide a framework for understanding the immune dynamic states in sepsis and facilitating clinical decision-making.
The approach involves collecting blood samples from 512 sepsis patients admitted to the intensive care unit (ICU) and 205 healthy individuals, totaling 717 samples. The study population included both male (54.3%) and female (45.7%) participants, with ages ranging from 35 to 100 years (mean age: 73.5 years). Three-dimensional scatter plot visualization and numerical fitting are applied to establish a mathematical model describing the interplay among white blood cells, lymphocytes, and neutrophils in both sepsis patients and healthy controls. SOFM is then employed to automatically cluster the sepsis sample data in the three-dimensional space, yielding different immune states. By comparing the immune profiles and spatial distributions of sepsis patients with those of healthy individuals within the constraint boundary represented by the mathematical model, the study aims to gain insights into the pathophysiological mechanisms underlying sepsis and identify potential targets for immunomodulatory therapies.
By providing a quantitative method for assessing the immune condition of sepsis patients, the approach could enable more precise staging of the disease and guide the selection of appropriate interventions. Furthermore, the identification of distinct immune states using SOFM may lay the foundation for future research on personalized immunotherapy approaches tailored to the specific immune profiles of individual patients.
In summary, this study presents an approach to understanding the immune condition in sepsis by leveraging advanced data visualization and machine learning techniques. By uncovering the mathematical relationship between key immune cell populations in sepsis patients and healthy controls and identifying distinct immune states in sepsis patients, the study provides a new perspective on the complex immune dynamics in sepsis. This approach enables a more precise assessment of the disease state (Different Clusters) and can guide the selection of appropriate interventions, ultimately contributing to developing more effective diagnostic and therapeutic strategies.

<table><tbody><tr><td>MATLAB</td><td>MathWorks&amp;nbsp;</td><td>2022B</td><td>Computing and visualization&amp;nbsp;</td></tr><tr><td>Probabilistic Scatter Plots for Immune States</td><td>&amp;nbsp;Intelligent&lt;br/&gt; &amp;nbsp;Entropy</td><td>Immune States V1.0</td><td>Beijing Intelligent Entropy Science &amp;amp; Technology Co Ltd.&lt;br/&gt; Modeling for CT/MRI fusion</td></tr></tbody></table>

a data-driven approach to quantifying immune states in sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

In sepsis, understanding the interplay among white blood cells, lymphocytes, and neutrophils is crucial for assessing the immune condition and optimizing treatment strategies. Blood samples were collected from 512 patients diagnosed with sepsis and 205 healthy controls, totaling 717 samples. Data visualization analysis and three-dimensional numerical fitting were performed to establish a mathematical model describing the relationships among white blood cells, lymphocytes, and neutrophils. Self-organizing feature map (SOFM) was employed to automatically cluster the sepsis sample data in the three-dimensional space represented by the model, yielding different immune states. 
Analysis revealed that white blood cell, lymphocyte, and neutrophil counts are constrained within a three-dimensional plane, as described by the equation: WBC = 1.098 &#215; Neutrophils + 1.046 &#215; Lymphocytes + 0.1645, yielding a prediction error (RMSE) of 1%. This equation is universally applicable to all samples despite differences in their spatial distributions. SOFM clustering identified nine distinct immune states within the sepsis patient population, representing different levels of immune status, oscillation periods, and recovery stages. 
The proposed mathematical model, represented by the equation above, reveals a basic constraint boundary on the immune cell populations in both sepsis patients and healthy controls. Furthermore, the SOFM clustering approach provides a comprehensive overview of the distinct immune states observed within this constraint boundary in sepsis patients. This study lays the foundation for future work on quantifying and categorizing the immune condition in sepsis, which may ultimately contribute to the development of more objective diagnostic and treatment strategies.

The progression of sepsis involves a complex interplay between the human immune system and invading pathogens. In clinical diagnosis and treatment, much attention is focused on infection indicators, organ function markers, cytokines, microbial detection, and even the gut microbiome. However, this study emphasizes the importance of three common immune indicators: white blood cells, neutrophils, and lymphocytes, which are not without basis. Research has demonstrated that the pathological pr...

This study investigates the immune condition in sepsis by analyzing the quantitative relationships among white blood cells, lymphocytes, and neutrophils in sepsis patients and healthy controls using data visualization analysis and three-dimensional numerical fitting to establish a mathematical model.

In sepsis, understanding the interplay among white blood cells, lymphocytes, and neutrophils is crucial for assessing the immune condition and optimizing ...

Our research quantifies immune states in sepsis by analyze relationships among WBC, lymphocytes, and neutrophils using data visualization and 3D numerical fitting. We aim to establish mathematical constraints and identify the distinct immune states to improve disease monitor and treatment strategies. Set development focus of mountain parameter immune monitor in stable sepsis.Our approach include sepsis mathematical model lines combined with machine learning to quantify immune states, advising beyond the traditional univariate, analyze infection indicators, and organ dysfunction markers. Key technology include three demonstration data, whether the relation's of the faded maps, FOFM for clouds servers. Numerical fading methods and interactive visualization in phases.Those enable qualitative analysis of neumocil relationship and state identification. Primarily challenge include standard density data collection across diverse patients populations. Value dating manual stint plus fiction and developing robust mathematical metals that accurately capture the dynamical nature of immune response in sepsis.For the rest of validating immune state class affections in larger cohorts. Investing data relationship with other markers and developing predictive metal for the disease progression based on immune trajectors. To begin, launch the Microsoft Excel program on a desktop computer.Navigate to the insert icon on the ribbon and click on more commands followed by get add-ins in the add-ins section. In the office add-ins dialogue box, search for MATLAB spreadsheet link in the search bar. Locate the MATLAB spreadsheet link for Excel Add-in in the search results and click on the go button.Activate the spreadsheet link and upload the code for the add-in. Then click on the MATLAB spreadsheet link tab to confirm the add-in is ready to use. To import data for sepsis quantification, open the spreadsheet containing the sepsis patient data, which includes white blood cell counts, lymphocyte counts, and neutrophil counts.Next, select the range of cells containing the white blood cell counts, lymphocyte counts and neutrophil counts. Click on the MATLAB spreadsheet link tab in the Excel ribbon and click the send data to MATLAB button. Then choose the appropriate MATLAB instance from the dropdown menu in the send data to MATLAB dialogue box if multiple instances are running.Specify the variable name for the data in the variable name field and press okay. To check the contents of the imported data in MATLAB, and press enter. To begin, launch the MATLAB application on a computer system.Call the function immune_scatter 3A in MATLAB, where A is a variable storing the immune data of sepsis patients. This will generate a GUI or graphical user interface displaying a three-dimensional scatter plot of the samples. To explore the 3D scatter plot, click and drag the plot to rotate it in three dimensional space.Right click and drag to pan horizontally or vertically, or use the mouse wheel or toolbar controls to zoom in or out of specific regions. Click on individual points to display their corresponding values for white blood cells, lymphocytes, and neutrophils. Next, in the MATLAB workspace, assign the neutrophil count, lymphocyte count, and white blood cell count to the variables X, Y, and Z respectively.Fit the data using the onscreen command to generate a polynomial model. To evaluate the goodness of fit, calculate the normalized root means square error using the shown command. Now use the immune condition function to generate clusters of sample points on the three dimensional plane.Visualize these clusters using the interactive features described earlier, including rotation, pan, zoom and data cursor. Next, use the hold on command to maintain the figure in an overlayable state. Then use the presented commands to generate a three dimensional plot of a typical patient's trajectory data.The three dimensional scatter scatterplot revealed that the sample points of white blood cells, neutrophils and lymphocytes lie on a plane with a very small error. The self-organizing feature map or SOFM analysis identified nine distinct immune states based on clustering of white blood cell, neutrophil and lymphocyte counts. Clusters one, two, and four represented heightened immune activity characterized by elevated counts of both neutrophils and lymphocytes, while clusters 3, 5, 6, and nine reflected immune oscillation states.Cluster eight was identified as a state of reduced immune activity, potentially signifying either immunosuppression or a recovery phase following infection, while cluster seven likely represented patients in the recovery stage, showing improved immune function.

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Discover a data-driven approach to quantifying immune states in sepsis using data visualization and three-dimensional numerical fitting. This computational method identifies distinct immune states to improve disease monitoring and treatment strategies.

A Data-Driven Approach to Quantifying Immune States in Sepsis

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