This cookbook details how to deploy a localized, containerized transplant immunology, histocompatibility testing, and immunogenetics decision-support engine for HLA laboratories, organ procurement organizations ($\text{OPOs}$), and solid organ transplant centers (kidney, liver, heart, lung) to ingest candidate and donor high-resolution HLA Genotyping Telemetry, analyze Luminex Single Antigen Bead ($\text{SAB}$) antibody profiles, compute the Calculated Panel Reactive Antibody ($\text{cPRA}$, $0 - 100\%$), execute real-time HLA Virtual Crossmatching ($\text{VXM}$), quantify Donor-Specific Anti-HLA Antibody ($\text{DSA}$) Mean Fluorescence Intensity ($\text{MFI}$), stratify Hyperacute & Antibody-Mediated Rejection ($\text{ABMR}$) risk tiers, and generate precision pre-transplant Desensitization & Immunosuppressive Induction Regimens according to ASHI, AST, and TTS consensus guidelines without external cloud API reliance.
Human Leukocyte Antigen ($\text{HLA}$) matching and preformed anti-donor antibody characterization govern allograft survival and prevent catastrophic hyperacute rejection in solid organ transplantation:
[Candidate HLA Typing + Luminex Anti-HLA Beads (MFI) | Donor HLA Genotype Typing]
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[HLA Virtual Crossmatch (VXM) Engine: Class I (A,B,C) & Class II (DR,DQ,DP)]
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[Donor-Specific Antibody (DSA) Identification & Dominant/Cumulative MFI]
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[Transplant Immunologic Risk Stratification: Low vs Intermediate vs High Risk]
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[Desensitization & Induction Protocol: TPE + IVIG 2g/kg + Thymoglobulin Orders]
Install required scientific Python and immunogenetics modeling packages:
pip install numpy scipy pandas torch torchvision matplotlib
"""
Cookbook 303: Offline Transplant Immunology HLA Virtual Crossmatch & DSA Engine
OpenPHR Clinical AI Working Group (https://openphr.org)
"""
import math
import numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass, field
@dataclass
class HLAGenotype:
hla_a: List[str] # e.g. ["A*02:01", "A*24:02"]
hla_b: List[str] # e.g. ["B*07:02", "B*44:02"]
hla_c: List[str] # e.g. ["C*07:01", "C*05:01"]
hla_drb1: List[str] # e.g. ["DRB1*04:01", "DRB1*15:01"]
hla_dqb1: List[str] # e.g. ["DQB1*03:02", "DQB1*06:02"]
hla_dpb1: List[str] # e.g. ["DPB1*04:01", "DPB1*02:01"]
@dataclass
class TransplantCandidateTelemetry:
candidate_id: str
age_years: float
organ_type: str # "Kidney", "Heart", "Lung", "Liver"
candidate_genotype: HLAGenotype
# Luminex Single Antigen Bead (SAB) Anti-HLA Antibody Profile: Dict[Allele, MFI]
anti_hla_antibody_mfi_profile: Dict[str, float] = field(default_factory=dict)
calculated_pra_percent: float = 85.0 # cPRA %
sensitizing_history_pregnancies: int = 2
sensitizing_history_transfusions: int = 3
prior_transplant_history: bool = False
@dataclass
class ProspectiveDonorTelemetry:
donor_id: str
donor_type: str # "Deceased Donor" or "Living Donor"
donor_genotype: HLAGenotype
@dataclass
class VirtualCrossmatchReport:
candidate_id: str
donor_id: str
virtual_crossmatch_result: str # "Negative Virtual Crossmatch (Compatible)", "Positive Virtual Crossmatch (Incompatible / Desensitization Required)"
immunologic_risk_tier: str # "Standard Low Risk", "Intermediate Risk (Low-Titer DSA)", "High Risk (Strong DSA - ABMR Alert)", "Prohibitive / Contraindicated (CDC Positive Equivalent)"
identified_donor_specific_antibodies: List[Dict[str, any]]
cumulative_dsa_mfi: float
dominant_dsa_allele: str
dominant_dsa_mfi: float
desensitization_and_induction_orders: List[str]
critical_safety_sentinels: List[str]
clinical_ashi_ast_directive: str
class HLAHistocompatibilityEngine:
"""
Offline clinical engine for HLA Virtual Crossmatching,
DSA MFI quantification, cPRA risk tiering, and desensitization protocol generation.
"""
def extract_donor_alleles(self, g: HLAGenotype) -> List[str]:
all_alleles = []
all_alleles.extend(g.hla_a)
all_alleles.extend(g.hla_b)
all_alleles.extend(g.hla_c)
all_alleles.extend(g.hla_drb1)
all_alleles.extend(g.hla_dqb1)
all_alleles.extend(g.hla_dpb1)
return all_alleles
def perform_virtual_crossmatch(self, cand: TransplantCandidateTelemetry, donor: ProspectiveDonorTelemetry) -> Tuple[List[Dict[str, any]], float, str, float]:
donor_alleles = self.extract_donor_alleles(donor.donor_genotype)
candidate_alleles = set(self.extract_donor_alleles(cand.candidate_genotype))
dsa_list = []
cumulative_mfi = 0.0
dominant_allele = "None"
dominant_mfi = 0.0
for allele in donor_alleles:
# Only consider alleles mismatched with candidate (donor-specific)
if allele not in candidate_alleles:
# Check broad and specific match in candidate's Luminex profile
# Search exact allele match or broad antigen match
mfi = cand.anti_hla_antibody_mfi_profile.get(allele, 0.0)
# If exact 2-field not found, check 1-field prefix (e.g. A*02)
if mfi == 0.0:
prefix = allele.split(":")[0]
for k, v in cand.anti_hla_antibody_mfi_profile.items():
if k.startswith(prefix) and v >= 1000.0:
mfi = max(mfi, v)
if mfi >= 1000.0: # Clinically significant DSA threshold
dsa_info = {
"allele": allele,
"mfi": mfi,
"class": "Class I" if any(allele.startswith(p) for p in ["A*", "B*", "C*"]) else "Class II"
}
dsa_list.append(dsa_info)
cumulative_mfi += mfi
if mfi > dominant_mfi:
dominant_mfi = mfi
dominant_allele = allele
return dsa_list, cumulative_mfi, dominant_allele, dominant_mfi
def stratify_immunologic_risk(self, dsa_list: List[Dict[str, any]], dom_mfi: float, cum_mfi: float) -> Tuple[str, str]:
if len(dsa_list) == 0:
return "Negative Virtual Crossmatch (Compatible)", "Standard Low Immunologic Risk (No Detectable DSA)"
elif dom_mfi >= 10000.0 or cum_mfi >= 15000.0:
return "Positive Virtual Crossmatch (Strong Incompatible)", "Prohibitive / High Risk (Strong Preformed DSA - High Risk of Hyperacute/Acute ABMR)"
elif dom_mfi >= 6000.0 or cum_mfi >= 8000.0:
return "Positive Virtual Crossmatch (Moderate-to-Strong Incompatible)", "High Immunologic Risk (Preformed DSA >= 6000 MFI - Requires Pre-Transplant Desensitization)"
elif dom_mfi >= 3000.0:
return "Positive Virtual Crossmatch (Moderate Incompatible)", "Intermediate Immunologic Risk (Moderate DSA 3000-5999 MFI - Depleting Induction Required)"
else:
return "Weak Positive Virtual Crossmatch", "Low-to-Intermediate Risk (Weak DSA 1000-2999 MFI - Close Post-Transplant Monitoring)"
def generate_desensitization_orders(self, risk_tier: str, dsa_list: List[Dict[str, any]], dom_mfi: float, cand: TransplantCandidateTelemetry) -> List[str]:
orders = []
if "Prohibitive" in risk_tier or "High Immunologic Risk" in risk_tier:
orders.append("1. PRE-TRANSPLANT DESENSITIZATION PROTOCOL:")
orders.append(" • Therapeutic Plasma Exchange (TPE): 3 to 5 sessions (1.0-1.5 plasma volume exchange with 5% albumin replacement) prior to crossclamp/anastomosis.")
orders.append(" • Intravenous Immunoglobulin (IVIG): High-dose IVIG 2.0 g/kg total (administered as 100 mg/kg after each TPE session, remainder post-transplant).")
orders.append(" • B-Cell Depletion: Rituximab 375 mg/m2 IV single dose post-plasmapheresis.")
orders.append("2. LYMPHOCYTE-DEPLETING INDUCTION: Antithymocyte Globulin (Thymoglobulin) 1.5 mg/kg/day IV for 4-5 doses (cumulative 6.0 mg/kg) with methylprednisolone 500 mg IV load.")
orders.append("3. MAINTENANCE IMMUNOSUPPRESSION: Tacrolimus (target trough 8-12 ng/mL) + Mycophenolate Mofetil 1000 mg PO BID + Prednisone taper.")
elif "Intermediate" in risk_tier:
orders.append("1. TARGETED INDUCTION: Antithymocyte Globulin (Thymoglobulin) 1.5 mg/kg/day IV for 3-4 doses (cumulative 4.5-6.0 mg/kg) starting intraoperatively.")
orders.append("2. ADJUNCTIVE IVIG: Single dose IVIG 1.0 g/kg on Post-Op Day 1.")
orders.append("3. EARLY SURVEILLANCE: Protocol DSA check and protocol allograft biopsy at Post-Op Day 7-14.")
else:
orders.append("1. STANDARD INDUCTION: Basiliximab (Simulect) 20 mg IV on Day 0 and Day 4 (or low-dose Thymoglobulin 3.0 mg/kg if deceased donor DGF risk).")
orders.append("2. STANDARD MAINTENANCE: Tacrolimus (target trough 6-10 ng/mL) + Mycophenolate Mofetil 1000 mg PO BID + Prednisone taper.")
return orders
def evaluate_case(self, cand: TransplantCandidateTelemetry, donor: ProspectiveDonorTelemetry) -> VirtualCrossmatchReport:
dsa_list, cum_mfi, dom_allele, dom_mfi = self.perform_virtual_crossmatch(cand, donor)
vxm_res, risk_tier = self.stratify_immunologic_risk(dsa_list, dom_mfi, cum_mfi)
orders = self.generate_desensitization_orders(risk_tier, dsa_list, dom_mfi, cand)
sentinels = []
if dom_mfi >= 6000.0:
sentinels.append(f"CRITICAL DSA ALERT ({dom_allele} MFI = {dom_mfi:.0f}): High-titer preformed donor-specific antibody. Crossclamp should NOT proceed without complete desensitization clearance or physical crossmatch confirmation.")
if cand.calculated_pra_percent >= 80.0:
sentinels.append(f"HIGHLY SENSITIZED CANDIDATE (cPRA = {cand.calculated_pra_percent}%): Flag for UNOS / Eurotransplant priority allocation and aggressive post-op DSA monitoring.")
directives = []
directives.append(f"VIRTUAL CROSSMATCH: {vxm_res} ({risk_tier}).")
directives.append(f"DSA PROFILE: {len(dsa_list)} DSAs Identified | Dominant: {dom_allele} ({dom_mfi:.0f} MFI) | Cumulative MFI: {cum_mfi:.0f}.")
directives.append(f"MANAGEMENT: {'TPE + IVIG 2g/kg + Thymoglobulin Desensitization' if dom_mfi >= 6000 else ('Thymoglobulin Induction + DSA Surveillance' if dom_mfi >= 1000 else 'Standard Basiliximab/Tac/MMF')}.")
return VirtualCrossmatchReport(
candidate_id=cand.candidate_id,
donor_id=donor.donor_id,
virtual_crossmatch_result=vxm_res,
immunologic_risk_tier=risk_tier,
identified_donor_specific_antibodies=dsa_list,
cumulative_dsa_mfi=cum_mfi,
dominant_dsa_allele=dom_allele,
dominant_dsa_mfi=dom_mfi,
desensitization_and_induction_orders=orders,
critical_safety_sentinels=sentinels,
clinical_ashi_ast_directive=" ".join(directives)
)
# Example Execution & Verification
if __name__ == "__main__":
engine = HLAHistocompatibilityEngine()
print("=" * 80)
print("OpenPHR Clinical Transplant Immunology HLA Virtual Crossmatch & DSA Engine")
print("=" * 80)
# Test Case 1: Highly sensitized candidate (cPRA = 94%) awaiting Kidney Transplant
# Candidate Genotype: A*02:01, A*24:02 | B*07:02, B*44:02 | C*07:01 | DRB1*04:01, DRB1*15:01 | DQB1*03:02, DQB1*06:02
# Prospective Donor Genotype: A*01:01, A*03:01 | B*08:01, B*35:01 | C*04:01 | DRB1*03:01, DRB1*11:01 | DQB1*02:01, DQB1*03:01
# Luminex SAB Profile shows strong anti-HLA-A*01:01 (MFI = 7,400) and anti-HLA-DRB1*03:01 (MFI = 3,200)
# Result: Positive VXM -> Dominant DSA: A*01:01 (7,400 MFI) -> High Risk -> TPE + IVIG + Thymoglobulin desensitization!
cand1 = TransplantCandidateTelemetry(
candidate_id="TX-CAND-9104",
age_years=48.0,
organ_type="Kidney",
candidate_genotype=HLAGenotype(
hla_a=["A*02:01", "A*24:02"],
hla_b=["B*07:02", "B*44:02"],
hla_c=["C*07:01", "C*05:01"],
hla_drb1=["DRB1*04:01", "DRB1*15:01"],
hla_dqb1=["DQB1*03:02", "DQB1*06:02"],
hla_dpb1=["DPB1*04:01", "DPB1*02:01"]
),
anti_hla_antibody_mfi_profile={
"A*01:01": 7400.0, # Strong DSA!
"B*08:01": 450.0, # Negative (< 1000)
"DRB1*03:01": 3200.0, # Moderate DSA!
"DQB1*02:01": 800.0 # Negative
},
calculated_pra_percent=94.0
)
donor1 = ProspectiveDonorTelemetry(
donor_id="DONOR-DECEASED-4819",
donor_type="Deceased Donor",
donor_genotype=HLAGenotype(
hla_a=["A*01:01", "A*03:01"],
hla_b=["B*08:01", "B*35:01"],
hla_c=["C*04:01", "C*07:02"],
hla_drb1=["DRB1*03:01", "DRB1*11:01"],
hla_dqb1=["DQB1*02:01", "DQB1*03:01"],
hla_dpb1=["DPB1*04:02", "DPB1*01:01"]
)
)
rep1 = engine.evaluate_case(cand1, donor1)
print(f"\n[Candidate {rep1.candidate_id} vs Donor {rep1.donor_id} - Histocompatibility Report]")
print(f"Virtual Crossmatch Result: {rep1.virtual_crossmatch_result}")
print(f"Immunologic Risk Tier: {rep1.immunologic_risk_tier}")
print(f"Identified DSAs ({len(rep1.identified_donor_specific_antibodies)}):")
for dsa in rep1.identified_donor_specific_antibodies:
print(f" • {dsa['allele']} ({dsa['class']}) -> MFI: {dsa['mfi']:.0f}")
print(f"Dominant DSA: {rep1.dominant_dsa_allele} ({rep1.dominant_dsa_mfi:.0f} MFI) | Cumulative MFI: {rep1.cumulative_dsa_mfi:.0f}")
print("\nDesensitization & Induction Orders:")
for o in rep1.desensitization_and_induction_orders:
print(f" {o}")
print("\nCritical Safety Sentinels:")
for s in rep1.critical_safety_sentinels:
print(f" {s}")
print(f"\nASHI / AST Consensus Directive:\n{rep1.clinical_ashi_ast_directive}")
# Test Case 2: Compatible Candidate with No DSAs (Standard Low Risk)
cand2 = TransplantCandidateTelemetry(
candidate_id="TX-CAND-1042",
age_years=36.0,
organ_type="Kidney",
candidate_genotype=HLAGenotype(
hla_a=["A*02:01", "A*03:01"],
hla_b=["B*07:02", "B*08:01"],
hla_c=["C*07:01", "C*07:02"],
hla_drb1=["DRB1*03:01", "DRB1*04:01"],
hla_dqb1=["DQB1*02:01", "DQB1*03:02"],
hla_dpb1=["DPB1*04:01", "DPB1*04:02"]
),
anti_hla_antibody_mfi_profile={},
calculated_pra_percent=0.0
)
rep2 = engine.evaluate_case(cand2, donor1)
print(f"\n[Candidate {rep2.candidate_id}] - VXM: {rep2.virtual_crossmatch_result} ({rep2.immunologic_risk_tier})")