2 Work-up for Hypereosinophilia

2.1 Key Definitions

Eosinophilia is defined as an absolute eosinophil count >0.5 × 10⁹/L in the peripheral blood.

Show the code

definitions <- tibble(
  Term = c("Eosinophilia", 
           "Hypereosinophilia (HE)", 
           "Hypereosinophilic Syndrome (HES)",
           "Primary (Neoplastic) HE",
           "Secondary (Reactive) HE"),
  Definition = c("Absolute eosinophil count >0.5 × 10^9/L",
                 "Persistent eosinophil count ≥1.5 × 10^9/L on 2 examinations",
                 "HE with organ damage attributable to eosinophilia",
                 "Clonal/neoplastic eosinophilia",
                 "Non-clonal eosinophilia due to underlying condition"),
  `Clinical Significance` = c("Requires evaluation",
                              "May indicate serious underlying condition",
                              "Requires urgent intervention",
                              "Associated with tyrosine kinase gene fusions",
                              "Associated with allergies, infections, malignancies")
)

definitions %>%
  kable(caption = "Key Definitions in Eosinophilia") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
  column_spec(1, bold = TRUE, width = "20%") %>%
  column_spec(2, width = "40%") %>%
  column_spec(3, width = "40%")

Key Definitions in Eosinophilia
Term	Definition	Clinical Significance
Eosinophilia	Absolute eosinophil count >0.5 × 10^9/L	Requires evaluation
Hypereosinophilia (HE)	Persistent eosinophil count ≥1.5 × 10^9/L on 2 examinations	May indicate serious underlying condition
Hypereosinophilic Syndrome (HES)	HE with organ damage attributable to eosinophilia	Requires urgent intervention
Primary (Neoplastic) HE	Clonal/neoplastic eosinophilia	Associated with tyrosine kinase gene fusions
Secondary (Reactive) HE	Non-clonal eosinophilia due to underlying condition	Associated with allergies, infections, malignancies

2.2 Initial Evaluation Flowchart

2.3 Causes of Secondary (Reactive) Eosinophilia

Show the code

secondary_causes <- tibble(
  Category = c(rep("Infections", 4),
               rep("Allergic/Hypersensitivity", 3),
               rep("Medications", 3),
               rep("Malignancies", 3),
               rep("Other Conditions", 3)),
  Examples = c(
    # Infections
    "Parasitic (Strongyloides, Toxocara, Trichinella)",
    "Viral (HIV, HSV, HTLV-2)",
    "Fungal (Coccidioides, Histoplasma)",
    "Bacterial/Mycobacterial",
    # Allergic
    "Asthma, Allergic rhinitis",
    "Atopic dermatitis, Eczema",
    "Drug reactions, DRESS syndrome",
    # Medications
    "Aspirin, NSAIDs",
    "Antimicrobials",
    "Antiepileptics",
    # Malignancies
    "Solid tumors (lung, breast, GI)",
    "Hodgkin lymphoma",
    "T-cell lymphomas",
    # Other
    "Adrenal insufficiency",
    "Autoimmune diseases",
    "Graft-versus-host disease"
  )
)

secondary_causes %>%
  group_by(Category) %>%
  summarise(Examples = paste(Examples, collapse = "; ")) %>%
  kable(caption = "Common Causes of Secondary Eosinophilia") %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  column_spec(1, bold = TRUE, width = "30%") %>%
  column_spec(2, width = "70%")

Common Causes of Secondary Eosinophilia
Category	Examples
Allergic/Hypersensitivity	Asthma, Allergic rhinitis; Atopic dermatitis, Eczema; Drug reactions, DRESS syndrome
Infections	Parasitic (Strongyloides, Toxocara, Trichinella); Viral (HIV, HSV, HTLV-2); Fungal (Coccidioides, Histoplasma); Bacterial/Mycobacterial
Malignancies	Solid tumors (lung, breast, GI); Hodgkin lymphoma; T-cell lymphomas
Medications	Aspirin, NSAIDs; Antimicrobials; Antiepileptics
Other Conditions	Adrenal insufficiency; Autoimmune diseases; Graft-versus-host disease

2.4 Diagnostic Workup for Primary Eosinophilia

2.4.1 General Diagnostic Studies

Show the code

workup_tests <- tibble(
  `Test Category` = c(
    "Hematologic",
    "Hematologic",
    "Hematologic",
    "Biochemical",
    "Biochemical",
    "Immunologic",
    "Immunologic",
    "Bone Marrow",
    "Bone Marrow",
    "Molecular/Cytogenetic",
    "Molecular/Cytogenetic"
  ),
  Test = c(
    "CBC with differential",
    "Peripheral blood smear",
    "Flow cytometry",
    "Comprehensive metabolic panel",
    "Serum tryptase",
    "Quantitative immunoglobulins",
    "T-cell immunophenotyping",
    "Bone marrow aspirate and biopsy",
    "Immunohistochemistry",
    "FISH for PDGFRA",
    "Myeloid mutation panel (NGS)"
  ),
  Purpose = c(
    "Quantify eosinophilia, assess other cell lines",
    "Evaluate for dysplasia, blasts, abnormal cells",
    "Detect abnormal T-cell populations",
    "Assess organ function",
    "Screen for mast cell disorders",
    "Evaluate for immunodeficiency",
    "Identify clonal T-cells",
    "Assess cellularity, fibrosis, mast cells",
    "CD117, CD25, CD30, tryptase staining",
    "Detect FIP1L1::PDGFRA fusion",
    "Identify clonal markers"
  )
)

workup_tests %>%
  datatable(
    caption = "Diagnostic Workup for Primary Eosinophilia",
    options = list(
      pageLength = 15,
      autoWidth = TRUE,
      columnDefs = list(
        list(width = '25%', targets = 0),
        list(width = '35%', targets = 1),
        list(width = '40%', targets = 2)
      )
    )
  )

2.4.2 Tyrosine Kinase Gene Fusion Testing Algorithm

Show the code

# Create comprehensive TK Fusion Testing Table
tk_fusion_table <- tibble(
  `Step` = c(1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4),
  `Karyotype Result` = c(
    "Initial Testing",
    "Normal",
    "Abnormal",
    "Normal",
    "Normal",
    "Abnormal",
    "Abnormal",
    "Abnormal",
    "Abnormal",
    "Abnormal",
    "Any",
    "Any",
    "Any",
    "Any",
    "Any",
    "Any",
    "Any"
  ),
  `Test Performed` = c(
    "Karyotype Analysis",
    "→ FISH for PDGFRA",
    "→ Assess chromosomal breakpoints",
    "FISH for PDGFRA",
    "RT-qPCR for FIP1L1::PDGFRA",
    "FISH for PDGFRB",
    "FISH for FGFR1",
    "FISH for JAK2",
    "FISH for FLT3",
    "FISH for ABL1",
    "PDGFRA testing",
    "PDGFRB testing",
    "FGFR1 testing",
    "JAK2 testing",
    "FLT3 testing",
    "ABL1 testing",
    "RT-qPCR (if FISH negative)"
  ),
  `Chromosomal Location` = c(
    "All chromosomes",
    "4q12",
    "Multiple",
    "4q12",
    "4q12",
    "5q31-33",
    "8p11-12",
    "9p24",
    "13q12",
    "9q34",
    "4q12",
    "5q31-33",
    "8p11-12",
    "9p24",
    "13q12",
    "9q34",
    "4q12"
  ),
  `Result` = c(
    "Normal or Abnormal",
    "If negative → RT-qPCR",
    "Identify specific breakpoints",
    "Positive or Negative",
    "Positive or Negative",
    "Positive or Negative",
    "Positive or Negative",
    "Positive or Negative",
    "Positive or Negative",
    "Positive or Negative",
    "PDGFRA Rearrangement",
    "PDGFRB Rearrangement",
    "FGFR1 Rearrangement",
    "JAK2 Rearrangement",
    "FLT3 Rearrangement",
    "ABL1 Rearrangement",
    "FIP1L1::PDGFRA fusion"
  ),
  `Clinical Significance` = c(
    "Initial screening",
    "Most common TK fusion",
    "Guide targeted testing",
    "Cryptic deletion",
    "Most sensitive for FIP1L1",
    "CMML-like presentation",
    "Aggressive course",
    "Poor prognosis",
    "Variable course",
    "ALL/AML association",
    "Excellent imatinib response",
    "Good imatinib response",
    "Consider pemigatinib",
    "Consider ruxolitinib",
    "Consider FLT3 inhibitors",
    "Consider dasatinib/nilotinib",
    "Confirms diagnosis"
  ),
  `Next Action` = c(
    "Branch based on result",
    "Proceed to RT-qPCR",
    "Test specific genes",
    "If positive → Confirm",
    "If positive → Confirm",
    "If positive → Confirm",
    "If positive → Confirm",
    "If positive → Confirm",
    "If positive → Confirm",
    "If positive → Confirm",
    "Start treatment",
    "Start treatment",
    "Start treatment + HCT eval",
    "Start treatment + HCT eval",
    "Start treatment + HCT eval",
    "Start treatment",
    "Start treatment"
  )
)

# Display as comprehensive table
tk_fusion_table %>%
  gt() %>%
  tab_header(
    title = "Tyrosine Kinase Fusion Testing Algorithm",
    subtitle = "Systematic Approach Based on Karyotype Results"
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#90EE90"),
      cell_text(weight = "bold")
    ),
    locations = cells_body(rows = Step == 1)
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFFFE0"),
    locations = cells_body(rows = Step == 2)
  ) %>%
  tab_style(
    style = cell_fill(color = "#E0E0FF"),
    locations = cells_body(rows = Step == 3)
  ) %>%
  tab_style(
    style = cell_fill(color = "#90EE90"),
    locations = cells_body(rows = Step == 4)
  ) %>%
  tab_footnote(
    footnote = "FISH = Fluorescence in situ hybridization; RT-qPCR = Quantitative reverse transcriptase PCR",
    locations = cells_column_labels(columns = `Test Performed`)
  ) %>%
  tab_footnote(
    footnote = "HCT = Hematopoietic cell transplant",
    locations = cells_column_labels(columns = `Next Action`)
  )

Step	Karyotype Result	Test Performed¹	Chromosomal Location	Result	Clinical Significance	Next Action²
Tyrosine Kinase Fusion Testing Algorithm
Systematic Approach Based on Karyotype Results
1	Initial Testing	Karyotype Analysis	All chromosomes	Normal or Abnormal	Initial screening	Branch based on result
2	Normal	→ FISH for PDGFRA	4q12	If negative → RT-qPCR	Most common TK fusion	Proceed to RT-qPCR
2	Abnormal	→ Assess chromosomal breakpoints	Multiple	Identify specific breakpoints	Guide targeted testing	Test specific genes
3	Normal	FISH for PDGFRA	4q12	Positive or Negative	Cryptic deletion	If positive → Confirm
3	Normal	RT-qPCR for FIP1L1::PDGFRA	4q12	Positive or Negative	Most sensitive for FIP1L1	If positive → Confirm
3	Abnormal	FISH for PDGFRB	5q31-33	Positive or Negative	CMML-like presentation	If positive → Confirm
3	Abnormal	FISH for FGFR1	8p11-12	Positive or Negative	Aggressive course	If positive → Confirm
3	Abnormal	FISH for JAK2	9p24	Positive or Negative	Poor prognosis	If positive → Confirm
3	Abnormal	FISH for FLT3	13q12	Positive or Negative	Variable course	If positive → Confirm
3	Abnormal	FISH for ABL1	9q34	Positive or Negative	ALL/AML association	If positive → Confirm
4	Any	PDGFRA testing	4q12	PDGFRA Rearrangement	Excellent imatinib response	Start treatment
4	Any	PDGFRB testing	5q31-33	PDGFRB Rearrangement	Good imatinib response	Start treatment
4	Any	FGFR1 testing	8p11-12	FGFR1 Rearrangement	Consider pemigatinib	Start treatment + HCT eval
4	Any	JAK2 testing	9p24	JAK2 Rearrangement	Consider ruxolitinib	Start treatment + HCT eval
4	Any	FLT3 testing	13q12	FLT3 Rearrangement	Consider FLT3 inhibitors	Start treatment + HCT eval
4	Any	ABL1 testing	9q34	ABL1 Rearrangement	Consider dasatinib/nilotinib	Start treatment
4	Any	RT-qPCR (if FISH negative)	4q12	FIP1L1::PDGFRA fusion	Confirms diagnosis	Start treatment
¹ FISH = Fluorescence in situ hybridization; RT-qPCR = Quantitative reverse transcriptase PCR
² HCT = Hematopoietic cell transplant

Show the code

# Alternative: Simplified Decision Table
tk_decision_table <- tibble(
  `Initial Karyotype` = c(
    "NORMAL KARYOTYPE",
    "",
    "",
    "",
    "ABNORMAL KARYOTYPE",
    "",
    "",
    "",
    ""
  ),
  `Gene/Location` = c(
    "PDGFRA (4q12)",
    "↓",
    "If FISH negative:",
    "",
    "PDGFRB (5q31-33)",
    "FGFR1 (8p11-12)",
    "JAK2 (9p24)",
    "FLT3 (13q12)",
    "ABL1 (9q34)"
  ),
  `Testing Approach` = c(
    "1. FISH for PDGFRA",
    "2. If negative → RT-qPCR",
    "RT-qPCR for FIP1L1::PDGFRA",
    "",
    "FISH for PDGFRB",
    "FISH for FGFR1",
    "FISH for JAK2",
    "FISH for FLT3",
    "FISH for ABL1"
  ),
  `Common Fusion` = c(
    "FIP1L1::PDGFRA",
    "",
    "",
    "",
    "ETV6::PDGFRB",
    "ZMYM2::FGFR1",
    "PCM1::JAK2",
    "ETV6::FLT3",
    "ETV6::ABL1"
  ),
  `Clinical Features` = c(
    "↑ Tryptase, male predominance",
    "",
    "",
    "",
    "CMML-like presentation",
    "Aggressive, T-cell lymphoma",
    "Poor prognosis",
    "Variable course",
    "ALL/AML association"
  ),
  `Treatment` = c(
    "Imatinib 100mg daily",
    "",
    "",
    "",
    "Imatinib 100-400mg daily",
    "Pemigatinib, early HCT",
    "Ruxolitinib, early HCT",
    "Gilteritinib, early HCT",
    "Dasatinib/Nilotinib"
  )
)

# Display simplified table
tk_decision_table %>%
  kable(caption = "TK Fusion Testing: Simplified Decision Guide") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
                full_width = FALSE) %>%
  column_spec(1, bold = TRUE, width = "20%") %>%
  column_spec(2, width = "15%") %>%
  column_spec(3, width = "20%") %>%
  column_spec(4, width = "15%") %>%
  column_spec(5, width = "20%") %>%
  column_spec(6, width = "20%") %>%
  row_spec(1, background = "#FFE6E6", bold = TRUE) %>%
  row_spec(5, background = "#E6E6FF", bold = TRUE) %>%
  pack_rows("Normal Karyotype Pathway", 1, 4, label_row_css = "background-color: #FFE6E6; font-weight: bold;") %>%
  pack_rows("Abnormal Karyotype Pathway", 5, 9, label_row_css = "background-color: #E6E6FF; font-weight: bold;")

TK Fusion Testing: Simplified Decision Guide
Initial Karyotype	Gene/Location	Testing Approach	Common Fusion	Clinical Features	Treatment
Normal Karyotype Pathway
NORMAL KARYOTYPE	PDGFRA (4q12)	1. FISH for PDGFRA	FIP1L1::PDGFRA	↑ Tryptase, male predominance	Imatinib 100mg daily
	↓	2. If negative → RT-qPCR
	If FISH negative:	RT-qPCR for FIP1L1::PDGFRA

Abnormal Karyotype Pathway
ABNORMAL KARYOTYPE	PDGFRB (5q31-33)	FISH for PDGFRB	ETV6::PDGFRB	CMML-like presentation	Imatinib 100-400mg daily
	FGFR1 (8p11-12)	FISH for FGFR1	ZMYM2::FGFR1	Aggressive, T-cell lymphoma	Pemigatinib, early HCT
	JAK2 (9p24)	FISH for JAK2	PCM1::JAK2	Poor prognosis	Ruxolitinib, early HCT
	FLT3 (13q12)	FISH for FLT3	ETV6::FLT3	Variable course	Gilteritinib, early HCT
	ABL1 (9q34)	FISH for ABL1	ETV6::ABL1	ALL/AML association	Dasatinib/Nilotinib

2.5 Target Organ Evaluation

2.5.1 Organ-Specific Testing

Show the code

organ_tests <- tibble(
  `Organ System` = c(
    "Cardiac",
    "Cardiac",
    "Pulmonary",
    "Pulmonary",
    "Gastrointestinal",
    "Neurologic",
    "Skin",
    "General"
  ),
  `Clinical Indication` = c(
    "Chest pain, dyspnea",
    "Elevated troponin/BNP",
    "Cough, dyspnea",
    "Abnormal chest X-ray",
    "Abdominal pain, diarrhea",
    "Peripheral neuropathy",
    "Rash, urticaria",
    "Constitutional symptoms"
  ),
  `Recommended Testing` = c(
    "ECG, Troponin, NT-proBNP",
    "Echocardiogram, Cardiac MRI",
    "Pulmonary function tests",
    "CT chest, Bronchoscopy with BAL",
    "Endoscopy with biopsy",
    "EMG, Nerve biopsy",
    "Skin biopsy",
    "CT/MRI imaging"
  )
)

organ_tests %>%
  kable(caption = "Organ-Specific Evaluation in Eosinophilia") %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  column_spec(1, bold = TRUE, width = "20%") %>%
  column_spec(2, width = "35%") %>%
  column_spec(3, width = "45%") %>%
  row_spec(c(1,2), background = "#ffe6e6") %>%  # Highlight cardiac
  row_spec(c(3,4), background = "#e6f3ff")      # Highlight pulmonary

Organ-Specific Evaluation in Eosinophilia
Organ System	Clinical Indication	Recommended Testing
Cardiac	Chest pain, dyspnea	ECG, Troponin, NT-proBNP
Cardiac	Elevated troponin/BNP	Echocardiogram, Cardiac MRI
Pulmonary	Cough, dyspnea	Pulmonary function tests
Pulmonary	Abnormal chest X-ray	CT chest, Bronchoscopy with BAL
Gastrointestinal	Abdominal pain, diarrhea	Endoscopy with biopsy
Neurologic	Peripheral neuropathy	EMG, Nerve biopsy
Skin	Rash, urticaria	Skin biopsy
General	Constitutional symptoms	CT/MRI imaging

2.6 Clinical Pearls

2.6.1 Key Diagnostic Considerations

Show the code

# Create a summary visualization
pearls_data <- tibble(
  Category = c("Urgent Evaluation", "FIP1L1::PDGFRA", "PDGFRB", "FGFR1", "JAK2"),
  `Key Features` = c(
    "Cardiac involvement, thrombosis",
    "Male predominance, ↑ tryptase, mast cells",
    "CMML-like presentation",
    "Aggressive course, lymphadenopathy",
    "Poor prognosis, rapid progression"
  ),
  `Response to Imatinib` = c(NA, "Excellent", "Excellent", "Poor", "Poor"),
  Frequency = c(NA, 40, 25, 20, 10)  # Approximate percentages
)

# Create a bubble chart for gene fusion frequencies
plot_ly(
  pearls_data %>% filter(!is.na(Frequency)),
  x = ~Category,
  y = ~Frequency,
  size = ~Frequency,
  color = ~`Response to Imatinib`,
  colors = c("Excellent" = "green", "Poor" = "red"),
  type = 'scatter',
  mode = 'markers',
  marker = list(sizemode = 'diameter', sizeref = 2),
  text = ~paste("Gene:", Category, 
                "<br>Frequency:", Frequency, "%",
                "<br>Imatinib Response:", `Response to Imatinib`)
) %>%
  layout(
    title = "Tyrosine Kinase Gene Fusions in Eosinophilia",
    xaxis = list(title = "Gene Fusion"),
    yaxis = list(title = "Approximate Frequency (%)")
  )

2.6.2 Treatment Algorithm Summary

Show the code

treatment_summary <- tibble(
  `Gene Fusion` = c("FIP1L1::PDGFRA", "PDGFRB", "FGFR1", "JAK2", "FLT3", "ABL1"),
  `First-Line Treatment` = c(
    "Imatinib 100mg daily",
    "Imatinib 100-400mg daily",
    "Pemigatinib or clinical trial",
    "Ruxolitinib or clinical trial",
    "Gilteritinib or clinical trial",
    "Dasatinib or Nilotinib"
  ),
  `Consider HCT` = c("Rarely", "If resistant", "Early referral", 
                     "Early referral", "Early referral", "Early referral"),
  Prognosis = c("Excellent", "Good", "Poor", "Poor", "Variable", "Variable")
)

treatment_summary %>%
  kable(caption = "Treatment Summary by Gene Fusion") %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  column_spec(1, bold = TRUE) %>%
  column_spec(4, color = case_when(
    treatment_summary$Prognosis == "Excellent" ~ "green",
    treatment_summary$Prognosis == "Good" ~ "blue",
    treatment_summary$Prognosis == "Poor" ~ "red",
    TRUE ~ "black"
  ))

Treatment Summary by Gene Fusion
Gene Fusion	First-Line Treatment	Consider HCT	Prognosis
FIP1L1::PDGFRA	Imatinib 100mg daily	Rarely	Excellent
PDGFRB	Imatinib 100-400mg daily	If resistant	Good
FGFR1	Pemigatinib or clinical trial	Early referral	Poor
JAK2	Ruxolitinib or clinical trial	Early referral	Poor
FLT3	Gilteritinib or clinical trial	Early referral	Variable
ABL1	Dasatinib or Nilotinib	Early referral	Variable

2.6.3 Interactive Workup Checklist

Show the code

# Create an interactive checklist
checklist_items <- c(
  "Rule out secondary causes of eosinophilia",
  "Assess for urgent cardiac/thrombotic complications",
  "Complete blood count with differential",
  "Peripheral blood smear review",
  "Serum tryptase level",
  "Quantitative immunoglobulins including IgE",
  "T-cell immunophenotyping if indicated",
  "Bone marrow aspirate and biopsy",
  "FISH for PDGFRA (or RT-PCR for FIP1L1::PDGFRA)",
  "Karyotype analysis",
  "Additional FISH based on karyotype results",
  "Myeloid mutation panel (NGS) if available",
  "Organ-specific evaluation as indicated"
)

checklist_df <- tibble(
  Step = 1:length(checklist_items),
  Task = checklist_items,
  Priority = c(rep("High", 2), rep("Essential", 8), rep("Additional", 3))
)

checklist_df %>%
  datatable(
    caption = "Eosinophilia Workup Checklist",
    options = list(
      pageLength = 15,
      dom = 'Bfrtip',
      buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
    ),
    extensions = 'Buttons'
  ) %>%
  formatStyle(
    'Priority',
    backgroundColor = styleEqual(
      c("High", "Essential", "Additional"),
      c("#ff9999", "#ffcc99", "#ccccff")
    )
  )

2.7 Patient Note

Patient referred for absolute eosinophil count of >0.5 × 10⁹/L.
Review of systems notable forpresence/absence of B symptoms, rash, cardiac/pulmonary symptoms].
Medication review completed with particular attention to drug-induced eosinophilia. Primary workup by referring team appropriately included parasitic evaluation and basic inflammatory markers.
Further hematologic evaluation initiated with peripheral blood flow showed eosinophilia, serum tryptase, vitamin B12 level, and FISH panel for FIP1L1-PDGFRA, BCR-ABL1, and myeloid NGS panel
Flow cytometry ordered to evaluate for aberrant T-cell populations.
If idiopathic hypereosinophilic syndrome suspected, will assess for end-organ damage with urinalysis, LFTs, and consideration of tissue biopsy.
Bone marrow biopsy to be performed if peripheral studies non-diagnostic or concerning for myeloid/lymphoid neoplasm with eosinophilia.