framingham_calculator.py•9.95 kB
"""
Framingham Risk Score Calculator
"""
import math
from typing import Dict, Any
from medcalc import (
BaseCalculator,
CalculatorInfo,
Parameter,
ParameterType,
ValidationResult,
CalculationResult,
register_calculator
)
from medcalc.utils import round_number
@register_calculator("framingham")
class FraminghamCalculator(BaseCalculator):
"""Framingham冠心病风险评分计算器实现"""
def get_info(self) -> CalculatorInfo:
return CalculatorInfo(
id=46,
name="Framingham Risk Score",
category="cardiology",
description="Framingham 10-year coronary heart disease risk assessment",
parameters=[
Parameter(
name="age",
type=ParameterType.NUMERIC,
required=True,
min_value=30,
max_value=79,
description="Patient age in years (30-79)"
),
Parameter(
name="sex",
type=ParameterType.CHOICE,
required=True,
choices=["Male", "Female"],
description="Patient sex"
),
Parameter(
name="total_cholesterol",
type=ParameterType.NUMERIC,
required=True,
unit="mg/dL",
min_value=100,
max_value=400,
description="Total cholesterol in mg/dL"
),
Parameter(
name="hdl_cholesterol",
type=ParameterType.NUMERIC,
required=True,
unit="mg/dL",
min_value=20,
max_value=100,
description="HDL cholesterol in mg/dL"
),
Parameter(
name="systolic_bp",
type=ParameterType.NUMERIC,
required=True,
unit="mmHg",
min_value=90,
max_value=200,
description="Systolic blood pressure in mmHg"
),
Parameter(
name="bp_medication",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Currently taking blood pressure medication"
),
Parameter(
name="smoker",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Current smoker"
),
Parameter(
name="diabetes",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Diabetes mellitus"
)
]
)
def validate_parameters(self, parameters: Dict[str, Any]) -> ValidationResult:
"""验证参数"""
errors = []
info = self.get_info()
# 基础验证 - 检查所有必需参数
for param in info.parameters:
if param.required and param.name not in parameters:
errors.append(f"Missing required parameter: {param.name}")
# 如果参数存在，验证其值
if param.name in parameters:
value = parameters[param.name]
# 数值范围验证
if param.type == ParameterType.NUMERIC:
try:
num_value = float(value)
range_error = self.validate_numeric_range(num_value, param)
if range_error:
errors.append(range_error)
except (ValueError, TypeError):
errors.append(f"Parameter {param.name} must be a number")
# 选择项验证
elif param.type == ParameterType.CHOICE:
if param.choices and value not in param.choices:
errors.append(f"Parameter {param.name} must be one of: {param.choices}")
# 年龄范围验证
age = parameters.get("age")
if age is not None and (age < 30 or age > 79):
errors.append("Age must be between 30 and 79 years for Framingham risk calculation")
return ValidationResult(
is_valid=len(errors) == 0,
errors=errors
)
def calculate(self, parameters: Dict[str, Any]) -> CalculationResult:
"""计算Framingham风险评分"""
# 获取参数定义
info = self.get_info()
param_defs = {p.name: p for p in info.parameters}
# 获取参数值
age = self.get_param_value(parameters, "age", param_defs["age"])
sex = self.get_param_value(parameters, "sex", param_defs["sex"])
total_cholesterol = self.get_param_value(parameters, "total_cholesterol", param_defs["total_cholesterol"])
hdl_cholesterol = self.get_param_value(parameters, "hdl_cholesterol", param_defs["hdl_cholesterol"])
systolic_bp = self.get_param_value(parameters, "systolic_bp", param_defs["systolic_bp"])
bp_medication = self.get_param_value(parameters, "bp_medication", param_defs["bp_medication"])
smoker = self.get_param_value(parameters, "smoker", param_defs["smoker"])
diabetes = self.get_param_value(parameters, "diabetes", param_defs["diabetes"])
# 转换布尔值为数值
bp_med = 1 if bp_medication else 0
smoking = 1 if smoker else 0
dm = 1 if diabetes else 0
# 计算年龄限制（用于年龄×吸烟交互项）
if sex == "Male":
age_smoke = min(age, 70)
else:
age_smoke = min(age, 78)
# 计算风险评分
if sex == "Male":
# 男性Framingham风险评分公式
risk_score = (
52.00961 * math.log(age) +
20.014077 * math.log(total_cholesterol) +
-0.905964 * math.log(hdl_cholesterol) +
1.305784 * math.log(systolic_bp) +
0.241549 * bp_med +
12.096316 * smoking +
-4.605038 * (math.log(age) * math.log(total_cholesterol)) +
-2.84367 * (math.log(age_smoke) * smoking) +
-2.93323 * (math.log(age) * math.log(age)) +
-172.300168
)
# 男性10年CHD风险
risk_percentage = (1 - 0.9402 ** math.exp(risk_score)) * 100
else:
# 女性Framingham风险评分公式
risk_score = (
31.764001 * math.log(age) +
22.465206 * math.log(total_cholesterol) +
-1.187731 * math.log(hdl_cholesterol) +
2.552905 * math.log(systolic_bp) +
0.420251 * bp_med +
13.07543 * smoking +
-5.060998 * (math.log(age) * math.log(total_cholesterol)) +
-2.996945 * (math.log(age_smoke) * smoking) +
-146.5933061
)
# 女性10年CHD风险
risk_percentage = (1 - 0.98767 ** math.exp(risk_score)) * 100
# 四舍五入到小数点后1位
risk_percentage = round_number(risk_percentage, 1)
# 生成解释
explanation_parts = [
f"Patient: {age}-year-old {sex.lower()}",
f"Total cholesterol: {total_cholesterol} mg/dL",
f"HDL cholesterol: {hdl_cholesterol} mg/dL",
f"Systolic blood pressure: {systolic_bp} mmHg",
f"Blood pressure medication: {'Yes' if bp_medication else 'No'}",
f"Current smoker: {'Yes' if smoker else 'No'}",
f"Diabetes: {'Yes' if diabetes else 'No'}"
]
explanation = "Framingham 10-year Coronary Heart Disease Risk calculation:\n\n"
explanation += "\n".join([f"• {part}" for part in explanation_parts])
explanation += f"\n\nCalculated risk score: {round_number(risk_score, 3)}"
explanation += f"\n10-year CHD risk: {risk_percentage}%"
# 添加风险分层
if risk_percentage < 10:
risk_category = "Low risk"
recommendation = "Lifestyle modifications, reassess in 4-6 years"
elif risk_percentage < 20:
risk_category = "Intermediate risk"
recommendation = "Consider additional risk factors, lifestyle modifications, possible statin therapy"
else:
risk_category = "High risk"
recommendation = "Aggressive risk factor modification, statin therapy recommended"
explanation += f"\n\nRisk Category: {risk_category}"
explanation += f"\nRecommendation: {recommendation}"
return CalculationResult(
value=risk_percentage,
unit="%",
explanation=explanation,
metadata={
"risk_score": round_number(risk_score, 3),
"risk_category": risk_category,
"recommendation": recommendation,
"sex": sex,
"age_smoke_interaction": age_smoke,
"risk_factors": {
"age": age,
"total_cholesterol": total_cholesterol,
"hdl_cholesterol": hdl_cholesterol,
"systolic_bp": systolic_bp,
"bp_medication": bp_medication,
"smoker": smoker,
"diabetes": diabetes
}
}
)