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EP-4740213-A1 - SYSTEMS AND METHODS FOR AI BASED INFECTION CLASSIFICATION

EP4740213A1EP 4740213 A1EP4740213 A1EP 4740213A1EP-4740213-A1

Abstract

Methods, systems, and computer storage media may be used to detect and differentiate bacterial and viral infections. Cellular population parameters such as the monocyte distribution width (MDW) can be acquired for an individual. Clinical measurements such as a complete blood count (CBC), a measure of procalcitonin (PCT) or a measure of C-reactive protein (CRP)can be obtained for an individual. A trained model can be used to compute an output value from the parameters. The output value can be compared to a threshold value to determine a probability associated with a bacterial or viral infection. A report can be generated from the probability and sent to the appropriate party.

Inventors

  • LIAO, Jiexi
  • TSALIK, EPHRAIM

Assignees

  • Cepheid

Dates

Publication Date
20260513
Application Date
20240703

Claims (20)

  1. 1. A method of detecting and differentiating bacterial and viral infections comprising: acquiring a hematological cell parameter for a sample from an individual; obtaining, a set of clinical measurements for an organism associated with the sample which includes at least one of an erythrocyte sedimentation rate (ESR), anion gap, a measure of procalcitonin (PCT), a measure of C-reactive protein (CRP), a measure of alkaline phosphatase (ALK), a measure of aspartate aminotransferase (AST), a measure of calcium, or oxygen (O2) saturation; generating an input data set for the organism including the hematological cell parameter and the clinical measurements; providing the input data set as input to a trained model configured to generate an output value; comparing the output value of the trained model to a predetermined threshold value to determine a probability associated with a bacterial infection or a viral infection.
  2. 2. The method of claim 1, wherein the sample is obtained from blood, serum, plasma, urine, or other body fluid drawn from a human.
  3. 3. The method of claim 1 or 2, wherein when an output value exceeds the predetermined threshold value, the method further comprises modifying a report to include an indication of suspected bacterial infection.
  4. 4. The method of any one of claims 1-3, wherein when an output value is less than or equal to a predetermined threshold value, the method further comprises modifying a report to include an indication of suspected viral infection.
  5. 5. The method of any one of claims 1-4, further comprising: communicatively coupling with a database; and retrieving, from the database values associated with the hematological cell parameter, the erythrocyte sedimentation rate (ESR), the anion gap, the measure of PCT, the measure of CRP, the measure of ALK, the measure of AST, the measure of calcium, or the O2 saturation for the organism.
  6. 6. The method of claim 1 or 2, wherein the trained model is trained using a log loss function to optimize true positives for a bacterial.
  7. 7. The method of any one of claims 1-6, wherein the hematological cell parameter is selected from a cell population parameter, complete blood count (CBC), white blood cell count, mean platelet volume (MPV), ratios thereof, or a combination thereof.
  8. 8. The method of claim 7, wherein the hematological cell parameter is a cell population parameter.
  9. 9. The method of claim 7 or 8, wherein the cell population parameter is selected from a monocyte cell population parameter, a granulocyte cell population parameter, a lymphocyte cell population parameter, a red blood cell population parameter, white blood cell count, platelet count, mean platelet volume (MPV), ratios thereof, or a combination thereof.
  10. 10. The method of claim 8 or 9, wherein the cell population parameter comprises a monocyte cell population parameter.
  11. 11. The method of claim 10, wherein the monocyte cell population parameter is selected from monocyte distribution width (MDW), monocyte count, standard deviation in monocyte volume, mean monocyte volume, reactive monocytes, ratios thereof, or a combination thereof, preferably wherein the monocyte cell population parameter is MDW.
  12. 12. The method of any one of claims 8-11, wherein cell population parameter comprises a granulocyte cell population parameter.
  13. 13. The method of claim 12, wherein the granulocyte cell population parameter comprises eosinophil count, neutrophil count, basophil count, neutrophil volume distribution width, standard deviation in the volume of neutrophils, ratios thereof, or a combination thereof.
  14. 14. The method of any one of claims 8-13, wherein cell population parameter comprises a red blood cell population parameter.
  15. 15. The method of claim 14, wherein the red blood cell population parameter comprises red blood cell count, red cell distribution width (RDW), a measure of hemoglobin, a measure of hematocrit level, ratios thereof, or a combination thereof.
  16. 16. The method of any one of claims 8-15, wherein cell population parameter comprises a lymphocyte cell population parameter.
  17. 17. The method of claim 16, wherein the lymphocyte cell population parameter comprises lymphocyte count, standard deviation in lymphocyte volume, mean lymphocyte volume, reactive lymphocyte, ratios thereof, or a combination thereof.
  18. 18. The method of any one of claims 8-17, wherein cell population parameter comprises a neutrophil to lymphocyte ratio (NLR), eosinophil to lymphocyte ratio (ELR), lymphocyte to monocyte ratio (LMR), eosinophil-to-monocyte ratio (EMR), platelet-to-lymphocyte ratio (PLR), platelet-to-neutrophil ratio, monocyte to lymphocyte ratio (MLR), mean platelet volume-to- platelet count (MPV/PC) ratio, basophil to lymphocyte (BLR) ratio, or combinations thereof.
  19. 19. The method of claim 18, wherein the cell population parameter comprises a neutrophil to lymphocyte ratio (NLR).
  20. 20. The method of any one of claims 7-19, wherein the hematological cell parameter comprises complete blood count (CBC).

Description

SYSTEMS AND METHODS FOR Al BASED INFECTION CLASSIFICATION CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application is an international application which claims priority to and benefit of U.S. Provisional Application Serial No. 63/525,544 filed July 7, 2023, the contents of which are incorporated by reference in their entirety for all purposes. BACKGROUND [0002] Differentiating bacterial and viral infections remains a challenge in the medical community. Often symptoms of a bacterial infection will parallel those of a viral infection and vice versa. Nevertheless, correctly diagnosing a bacterial or viral infection can be critical and sometimes even lifesaving to an individual. In the case of misdiagnosis of a bacterial infection as a virus can lead to a considerably delay in administering antibacterials. Erroneous diagnosis of this type can result in a medical emergency which could be lethal such as when an individual experiences a problematic case of sepsis. However, if the correct diagnosis is made in a timely fashion, a patient can be treated with empiric antibacterial therapy and generally quickly recovers. Conversely, misdiagnosis of a bacterial infection, when an individual’s symptoms are actually the result of a viral infection, can lead to complications as well. In such cases, a medical professional may erroneously prescribe an antibacterial when a viral infection is actually the root cause of the patient’s symptoms. An unnecessary antibacterial prescription can lead to loss and a disruption of the gut microflora which can result in opportunistic bacterial infections, such as Clostridioides difficile, to uncontrollably propagate causing painful inflammation and a potentially life-threatening situation. Additionally, unnecessary exposure can also lead to antimicrobial resistance within bacterial populations. [0003] While methodologies to aid in correctly diagnosing bacterial and viral infections have been produced, development and advancements of technology in this area are needed to improve the quality, cost, and efficiency of medical care. Current approaches utilize procalcitonin (PCT), c- reactive protein (CRP), white blood cell (WBC), and neutrophil counts in a clinical setting to make early diagnosis of bacterial infections. However, studies suggest that these parameters do not reliably differentiate between a bacterial versus viral infection; meta-analysis of PCT in pneumonia found a 55% sensitivity and 76% specificity with AUROCs that vary from 0.60-0.85 (see PMID 31241140). Consequently, systems and methods that more accurately aid medical professionals in differentiating between bacterial and viral infections are desirable within the medical community. BRIEF SUMMARY [0004] In various aspects, systems and methods are provided for training a model, and using the trained model to probabilistically assess an infection for an individual as more likely to be bacterial or more likely to be viral. The prediction probability can be derived by measuring and inputting parameters such as hematological cell parameters (e.g., cell population parameters), host biomarkers (e.g., proteins, RNAs, or metabolites), demographic information, and/or clinical measurements, including those that utilize hematological or metabolic parameters of an individual into a trained model. In aspects, parameters used as an input to the trained model may include a eosinophil count, neutrophil count, monocyte distribution width (MDW), red blood cell (RBC) count, basophil count, lymphocyte count, a measure of hemoglobin, white blood cell (WBC) count, red cell distribution width (RDW), monocyte count, a measure of hematocrit level, a measure of mean platelet volume (MPV), platelet count, monocyte cell population parameter, a granulocyte cell population parameter, a lymphocyte cell population parameter, a red blood cell population parameter, white blood cell count, platelet count, complete blood count (CBC), erythrocyte sedimentation rate (ESR), a neutrophil to lymphocyte ratio (NLR), eosinophil to lymphocyte ratio (ELR), lymphocyte to monocyte ratio (LMR), eosinophil-to-monocyte ratio (EMR), platelet-to-lymphocyte ratio (PLR), platelet-to- neutrophil ratio, monocyte to lymphocyte ratio (MLR), mean platelet volume-to-platelet count (MPV/PC) ratio, basophil to lymphocyte (BLR) ratio, anion gap, alkaline phosphatase (ALK), aspartate aminotransferase (AST), calcium, oxygen (O2) saturation, vimentin (VIM), TNF-related apoptosis-inducing ligand (TRAIL), procalcitonin (PCT), C Reactive Protein (CRP), interferon y- induced protein (IP- 10), Myxovirus resistance A (MxA), Cluster of Differentiation 64 (CD64), human neutrophile lipocalin (HNL), blood urea nitrogen (BUN), lactate, heparin-binding protein (HBP), adrenomedullin (ProADM), bioavailable adrenomedullin (bio-ARM), midregional adrenomedullin (MR-proADM), interleukin 6 (IL-6), ABL Proto-Oncogene 1 (ABL1), Interferon Regulatory Factor 9 (IRF9), Integrin Subunit Alpha M (ITGAM),