perc_calculator.py•10 kB
"""
PERC (Pulmonary Embolism Rule-out Criteria) Calculator
"""
from typing import Dict, Any
from medcalc import (
BaseCalculator,
CalculatorInfo,
Parameter,
ParameterType,
ValidationResult,
CalculationResult,
register_calculator
)
@register_calculator("perc")
class PERCCalculator(BaseCalculator):
"""PERC肺栓塞排除标准计算器实现"""
def get_info(self) -> CalculatorInfo:
return CalculatorInfo(
id=48,
name="PERC Rule (Pulmonary Embolism Rule-out Criteria)",
category="pulmonology",
description="PERC rule for excluding pulmonary embolism in low-risk patients",
parameters=[
Parameter(
name="age",
type=ParameterType.NUMERIC,
required=True,
min_value=0,
max_value=120,
description="Patient age in years"
),
Parameter(
name="heart_rate",
type=ParameterType.NUMERIC,
required=True,
unit="bpm",
min_value=30,
max_value=300,
description="Heart rate in beats per minute"
),
Parameter(
name="oxygen_saturation",
type=ParameterType.NUMERIC,
required=True,
unit="%",
min_value=50,
max_value=100,
description="Oxygen saturation on room air (%)"
),
Parameter(
name="unilateral_leg_swelling",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Unilateral leg swelling"
),
Parameter(
name="hemoptysis",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Hemoptysis (coughing up blood)"
),
Parameter(
name="recent_surgery_or_trauma",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Recent surgery or trauma (within 4 weeks, requiring general anesthesia)"
),
Parameter(
name="previous_pe",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Prior pulmonary embolism"
),
Parameter(
name="previous_dvt",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Prior deep vein thrombosis"
),
Parameter(
name="hormone_use",
type=ParameterType.BOOLEAN,
required=False,
default=False,
description="Hormone use (oral contraceptives, hormone replacement, or estrogenic hormones)"
)
]
)
def validate_parameters(self, parameters: Dict[str, Any]) -> ValidationResult:
"""验证参数"""
errors = []
# 获取参数定义
info = self.get_info()
param_defs = {p.name: p for p in info.parameters}
# 验证必填参数
for param in info.parameters:
if param.required and param.name not in parameters:
errors.append(f"Missing required parameter: {param.name}")
elif param.name in parameters:
value = parameters[param.name]
# 验证数值范围
if param.type.value == "numeric" and value is not None:
try:
value = float(value)
if param.min_value is not None and value < param.min_value:
errors.append(f"{param.name}: {value} is below minimum {param.min_value}")
if param.max_value is not None and value > param.max_value:
errors.append(f"{param.name}: {value} is above maximum {param.max_value}")
except (TypeError, ValueError):
errors.append(f"{param.name}: Invalid numeric value {value}")
return ValidationResult(
is_valid=len(errors) == 0,
errors=errors
)
def calculate(self, parameters: Dict[str, Any]) -> CalculationResult:
"""计算PERC标准"""
# 获取参数值 - 直接从字典获取，使用默认值
age = parameters.get("age")
heart_rate = parameters.get("heart_rate")
oxygen_saturation = parameters.get("oxygen_saturation")
unilateral_leg_swelling = parameters.get("unilateral_leg_swelling", False)
hemoptysis = parameters.get("hemoptysis", False)
recent_surgery_or_trauma = parameters.get("recent_surgery_or_trauma", False)
previous_pe = parameters.get("previous_pe", False)
previous_dvt = parameters.get("previous_dvt", False)
hormone_use = parameters.get("hormone_use", False)
criteria_count = 0
explanation_parts = []
# 1. 年龄 ≥ 50岁
if age >= 50:
criteria_count += 1
explanation_parts.append(f"Age {age} ≥ 50 years: +1 criterion")
else:
explanation_parts.append(f"Age {age} < 50 years: 0 criteria")
# 2. 心率 ≥ 100 bpm
if heart_rate >= 100:
criteria_count += 1
explanation_parts.append(f"Heart rate {heart_rate} ≥ 100 bpm: +1 criterion")
else:
explanation_parts.append(f"Heart rate {heart_rate} < 100 bpm: 0 criteria")
# 3. 室内空气下血氧饱和度 < 95%
if oxygen_saturation < 95:
criteria_count += 1
explanation_parts.append(f"O2 saturation {oxygen_saturation}% < 95%: +1 criterion")
else:
explanation_parts.append(f"O2 saturation {oxygen_saturation}% ≥ 95%: 0 criteria")
# 4. 单侧腿部肿胀
if unilateral_leg_swelling:
criteria_count += 1
explanation_parts.append("Unilateral leg swelling: +1 criterion")
else:
explanation_parts.append("No unilateral leg swelling: 0 criteria")
# 5. 咯血
if hemoptysis:
criteria_count += 1
explanation_parts.append("Hemoptysis: +1 criterion")
else:
explanation_parts.append("No hemoptysis: 0 criteria")
# 6. 近期手术或外伤
if recent_surgery_or_trauma:
criteria_count += 1
explanation_parts.append("Recent surgery or trauma (within 4 weeks): +1 criterion")
else:
explanation_parts.append("No recent surgery or trauma: 0 criteria")
# 7. 既往PE或DVT史
if previous_pe or previous_dvt:
criteria_count += 1
history_types = []
if previous_pe:
history_types.append("PE")
if previous_dvt:
history_types.append("DVT")
explanation_parts.append(f"Prior {'/'.join(history_types)}: +1 criterion")
else:
explanation_parts.append("No prior PE or DVT: 0 criteria")
# 8. 激素使用
if hormone_use:
criteria_count += 1
explanation_parts.append("Hormone use: +1 criterion")
else:
explanation_parts.append("No hormone use: 0 criteria")
# 生成解释
explanation = "PERC Rule (Pulmonary Embolism Rule-out Criteria) assessment:\n\n"
explanation += "\n".join([f"• {part}" for part in explanation_parts])
explanation += f"\n\nTotal PERC criteria met: {criteria_count}/8"
# 添加解释和建议
if criteria_count == 0:
perc_negative = True
recommendation = "PERC rule negative - PE can be ruled out without further testing in low-risk patients"
pe_probability = "<1.8%"
else:
perc_negative = False
recommendation = "PERC rule positive - Further evaluation for PE is warranted (consider D-dimer, imaging)"
pe_probability = "Cannot be ruled out"
explanation += f"\n\nPERC Rule: {'Negative' if perc_negative else 'Positive'}"
explanation += f"\nPE Probability: {pe_probability}"
explanation += f"\nRecommendation: {recommendation}"
# 添加使用注意事项
explanation += "\n\nNote: PERC rule should only be applied to patients with low clinical probability of PE"
return CalculationResult(
value=criteria_count,
unit="criteria",
explanation=explanation,
metadata={
"perc_negative": perc_negative,
"pe_probability": pe_probability,
"recommendation": recommendation,
"criteria_breakdown": {
"age_50_or_older": age >= 50,
"heart_rate_100_or_higher": heart_rate >= 100,
"oxygen_sat_less_than_95": oxygen_saturation < 95,
"unilateral_leg_swelling": unilateral_leg_swelling,
"hemoptysis": hemoptysis,
"recent_surgery_trauma": recent_surgery_or_trauma,
"prior_pe_dvt": previous_pe or previous_dvt,
"hormone_use": hormone_use
}
}
)