caprini_calculator.py•19 kB
"""
Caprini Score Calculator
"""
from typing import Dict, Any
from medcalc import (
BaseCalculator,
CalculatorInfo,
Parameter,
ParameterType,
ValidationResult,
CalculationResult,
register_calculator
)
@register_calculator("caprini")
class CapriniCalculator(BaseCalculator):
"""Caprini静脉血栓栓塞评分计算器实现"""
def get_info(self) -> CalculatorInfo:
return CalculatorInfo(
id=36,
name="Caprini Score",
category="cardiology",
description="Caprini risk assessment model for venous thromboembolism",
parameters=[
Parameter(
name="age",
type=ParameterType.NUMERIC,
required=True,
min_value=0,
max_value=120,
description="Patient age in years"
),
Parameter(
name="sex",
type=ParameterType.CHOICE,
required=True,
choices=["Male", "Female"],
description="Patient sex"
),
Parameter(
name="surgery_type",
type=ParameterType.CHOICE,
required=False,
choices=["none", "minor", "major", "laparoscopic", "arthroscopic", "elective_major_lower_extremity_arthroplasty"],
default="none",
description="Type of surgery"
),
Parameter(
name="major_surgery",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Major surgery in the last month"
),
Parameter(
name="chf",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Congestive heart failure in the last month"
),
Parameter(
name="sepsis",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Sepsis in the last month"
),
Parameter(
name="pneumonia",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Pneumonia in the last month"
),
Parameter(
name="immobilizing_plaster_cast",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Immobilizing plaster cast in the last month"
),
Parameter(
name="hip_pelvis_leg_fracture",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Hip, pelvis, or leg fracture in the last month"
),
Parameter(
name="stroke",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Stroke in the last month"
),
Parameter(
name="multiple_trauma",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Multiple trauma in the last month"
),
Parameter(
name="acute_spinal_cord_injury",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Acute spinal cord injury causing paralysis in the last month"
),
Parameter(
name="varicose_veins",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Varicose veins"
),
Parameter(
name="current_swollen_legs",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Current swollen legs"
),
Parameter(
name="current_central_venous",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Current central venous access"
),
Parameter(
name="previous_dvt",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Previous DVT documented"
),
Parameter(
name="previous_pe",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Previous pulmonary embolism documented"
),
Parameter(
name="family_history_thrombosis",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Family history of thrombosis"
),
Parameter(
name="positive_factor_v",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Positive Factor V Leiden"
),
Parameter(
name="positive_prothrombin",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Positive prothrombin 20210A"
),
Parameter(
name="serum_homocysteine",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Elevated serum homocysteine"
),
Parameter(
name="positive_lupus_anticoagulant",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Positive lupus anticoagulant"
),
Parameter(
name="elevated_anticardiolipin_antibody",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Elevated anticardiolipin antibody"
),
Parameter(
name="heparin_induced_thrombocytopenia",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Heparin-induced thrombocytopenia"
),
Parameter(
name="congenital_acquired_thrombophilia",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Other congenital or acquired thrombophilia"
),
Parameter(
name="mobility",
type=ParameterType.CHOICE,
required=False,
choices=["normal", "bed_rest", "confined_bed_72h"],
default="normal",
description="Mobility status: normal (0), bed_rest = 'on bed rest' (1), confined_bed_72h = 'confined to bed >72 hours' (2)"
),
Parameter(
name="inflammatory_bowel_disease",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="History of inflammatory bowel disease"
),
Parameter(
name="acute_myocardial_infarction",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Acute myocardial infarction"
),
Parameter(
name="copd",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Chronic obstructive pulmonary disease"
),
Parameter(
name="malignancy",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Present or previous malignancy"
),
Parameter(
name="bmi",
type=ParameterType.NUMERIC,
required=True,
unit="kg/m²",
min_value=10,
max_value=80,
description="Body mass index in kg/m²"
)
]
)
def validate_parameters(self, parameters: Dict[str, Any]) -> ValidationResult:
"""验证参数"""
errors = []
# 验证必需参数
if "age" not in parameters or parameters["age"] is None:
errors.append("Age is required")
elif not isinstance(parameters["age"], (int, float)) or parameters["age"] < 0 or parameters["age"] > 120:
errors.append("Age must be between 0 and 120")
if "sex" not in parameters or parameters["sex"] is None:
errors.append("Sex is required")
elif parameters["sex"] not in ["Male", "Female"]:
errors.append("Sex must be 'Male' or 'Female'")
if "bmi" not in parameters or parameters["bmi"] is None:
errors.append("BMI is required")
elif not isinstance(parameters["bmi"], (int, float)) or parameters["bmi"] < 10 or parameters["bmi"] > 80:
errors.append("BMI must be between 10 and 80")
return ValidationResult(
is_valid=len(errors) == 0,
errors=errors
)
def calculate(self, parameters: Dict[str, Any]) -> CalculationResult:
"""计算Caprini评分"""
# 获取参数值 - 直接从字典获取，使用默认值
age = parameters.get("age")
sex = parameters.get("sex")
surgery_type = parameters.get("surgery_type", "none")
major_surgery = parameters.get("major_surgery", False)
chf = parameters.get("chf", False)
sepsis = parameters.get("sepsis", False)
pneumonia = parameters.get("pneumonia", False)
immobilizing_plaster_cast = parameters.get("immobilizing_plaster_cast", False)
hip_pelvis_leg_fracture = parameters.get("hip_pelvis_leg_fracture", False)
stroke = parameters.get("stroke", False)
multiple_trauma = parameters.get("multiple_trauma", False)
acute_spinal_cord_injury = parameters.get("acute_spinal_cord_injury", False)
varicose_veins = parameters.get("varicose_veins", False)
current_swollen_legs = parameters.get("current_swollen_legs", False)
current_central_venous = parameters.get("current_central_venous", False)
previous_dvt = parameters.get("previous_dvt", False)
previous_pe = parameters.get("previous_pe", False)
family_history_thrombosis = parameters.get("family_history_thrombosis", False)
positive_factor_v = parameters.get("positive_factor_v", False)
positive_prothrombin = parameters.get("positive_prothrombin", False)
serum_homocysteine = parameters.get("serum_homocysteine", False)
positive_lupus_anticoagulant = parameters.get("positive_lupus_anticoagulant", False)
elevated_anticardiolipin_antibody = parameters.get("elevated_anticardiolipin_antibody", False)
heparin_induced_thrombocytopenia = parameters.get("heparin_induced_thrombocytopenia", False)
congenital_acquired_thrombophilia = parameters.get("congenital_acquired_thrombophilia", False)
mobility = parameters.get("mobility", "normal")
inflammatory_bowel_disease = parameters.get("inflammatory_bowel_disease", False)
acute_myocardial_infarction = parameters.get("acute_myocardial_infarction", False)
copd = parameters.get("copd", False)
malignancy = parameters.get("malignancy", False)
bmi = parameters.get("bmi")
score = 0
explanation_parts = []
# 1. 年龄评分
if age <= 40:
age_points = 0
elif 41 <= age <= 60:
age_points = 1
elif 61 <= age <= 74:
age_points = 2
else: # >= 75
age_points = 3
score += age_points
explanation_parts.append(f"Age {age} years: +{age_points} points")
# 2. 性别评分
if sex == "Female":
score += 1
explanation_parts.append("Female sex: +1 point")
else:
explanation_parts.append("Male sex: 0 points")
# 3. 手术类型评分
surgery_points = {
"none": 0,
"minor": 1,
"major": 2,
"laparoscopic": 2,
"arthroscopic": 2,
"elective_major_lower_extremity_arthroplasty": 5
}
surgery_score = surgery_points[surgery_type]
score += surgery_score
if surgery_score > 0:
explanation_parts.append(f"Surgery type ({surgery_type}): +{surgery_score} points")
# 4. 近期事件（≤1个月）
recent_events = [
(major_surgery, "Major surgery", 1),
(chf, "Congestive heart failure", 1),
(sepsis, "Sepsis", 1),
(pneumonia, "Pneumonia", 1),
(immobilizing_plaster_cast, "Immobilizing plaster cast", 2),
(hip_pelvis_leg_fracture, "Hip/pelvis/leg fracture", 5),
(stroke, "Stroke", 5),
(multiple_trauma, "Multiple trauma", 5),
(acute_spinal_cord_injury, "Acute spinal cord injury", 5)
]
for condition, name, points in recent_events:
if condition:
score += points
explanation_parts.append(f"{name} (≤1 month): +{points} points")
# 5. 静脉疾病或凝血障碍
venous_conditions = [
(varicose_veins, "Varicose veins", 1),
(current_swollen_legs, "Current swollen legs", 1),
(current_central_venous, "Current central venous access", 2),
(previous_dvt, "Previous DVT", 3),
(previous_pe, "Previous PE", 3),
(family_history_thrombosis, "Family history of thrombosis", 3),
(positive_factor_v, "Positive Factor V Leiden", 3),
(positive_prothrombin, "Positive prothrombin 20210A", 3),
(serum_homocysteine, "Elevated serum homocysteine", 3),
(positive_lupus_anticoagulant, "Positive lupus anticoagulant", 3),
(elevated_anticardiolipin_antibody, "Elevated anticardiolipin antibody", 3),
(heparin_induced_thrombocytopenia, "Heparin-induced thrombocytopenia", 3),
(congenital_acquired_thrombophilia, "Other thrombophilia", 3)
]
for condition, name, points in venous_conditions:
if condition:
score += points
explanation_parts.append(f"{name}: +{points} points")
# 6. 活动能力
mobility_points = {
"normal": 0,
"bed_rest": 1,
"confined_bed_72h": 2
}
mobility_score = mobility_points[mobility]
score += mobility_score
if mobility_score > 0:
explanation_parts.append(f"Mobility ({mobility}): +{mobility_score} points")
# 7. 其他病史
other_conditions = [
(inflammatory_bowel_disease, "Inflammatory bowel disease", 1),
(acute_myocardial_infarction, "Acute myocardial infarction", 1),
(copd, "COPD", 1),
(malignancy, "Malignancy", 2)
]
for condition, name, points in other_conditions:
if condition:
score += points
explanation_parts.append(f"{name}: +{points} points")
# 8. BMI评分（匹配原始代码：≥25加2分，虽然标准应该是1分）
if bmi >= 25:
score += 2
explanation_parts.append(f"BMI {bmi} ≥25 kg/m²: +2 points (matches original code)")
else:
explanation_parts.append(f"BMI {bmi} <25 kg/m²: 0 points")
# 生成解释
explanation = "Caprini Score calculation:\n\n"
explanation += "\n".join([f"• {part}" for part in explanation_parts])
explanation += f"\n\nTotal Caprini Score: {score} points"
# 添加风险分层和预防建议
if score == 0:
risk_level = "Very low risk"
vte_risk = "<0.5%"
recommendation = "Early ambulation"
elif score == 1 or score == 2:
risk_level = "Low risk"
vte_risk = "~1.5%"
recommendation = "Mechanical prophylaxis (compression stockings/IPC)"
elif score == 3 or score == 4:
risk_level = "Moderate risk"
vte_risk = "~3%"
recommendation = "Mechanical prophylaxis ± pharmacologic prophylaxis"
elif score >= 5:
risk_level = "High risk"
vte_risk = ">6%"
recommendation = "Mechanical + pharmacologic prophylaxis"
explanation += f"\n\nRisk Level: {risk_level}"
explanation += f"\nVTE Risk: {vte_risk}"
explanation += f"\nRecommendation: {recommendation}"
return CalculationResult(
value=score,
unit="points",
explanation=explanation,
metadata={
"risk_level": risk_level,
"vte_risk": vte_risk,
"recommendation": recommendation,
"age_points": age_points,
"sex_points": 1 if sex == "Female" else 0,
"surgery_points": surgery_score,
"mobility_points": mobility_score,
"bmi_points": 2 if bmi >= 25 else 0
}
)