#!/usr/bin/env python3
"""
TMP-SMX-HK — Trimethoprim-sulfamethoxazole-associated hyperkalemia risk-context stratification.

Transparent clinical skill for estimating concern before or during TMP-SMX therapy.

Authors: Dr. Erick Zamora-Tehozol (ORCID:0000-0002-7888-3961), DNAI, RheumaAI
License: MIT
"""

from dataclasses import dataclass, asdict
from typing import Dict, Any, List
import json


@dataclass
class TmpSmxHkInput:
    age: int
    indication: str
    current_or_planned_tmp_smx: bool = True
    treatment_dose: bool = False
    prophylaxis_only: bool = False
    egfr_below_60: bool = False
    egfr_below_30: bool = False
    baseline_potassium_5_or_more: bool = False
    acei_arb: bool = False
    spironolactone_or_eplerenone: bool = False
    other_k_retaining_drugs: bool = False
    diabetes: bool = False
    heart_failure: bool = False
    dehydration_or_aki: bool = False
    rising_potassium_during_therapy: bool = False
    ecg_changes_or_weakness: bool = False


def exposure_component(inp: TmpSmxHkInput) -> float:
    score = 0.0
    if inp.current_or_planned_tmp_smx:
        score += 1.0
    if inp.treatment_dose:
        score += 1.4
    if inp.prophylaxis_only:
        score -= 0.4
    return max(score, 0.0)


def renal_component(inp: TmpSmxHkInput) -> float:
    score = 0.0
    if inp.egfr_below_60:
        score += 1.4
    if inp.egfr_below_30:
        score += 2.0
    if inp.baseline_potassium_5_or_more:
        score += 1.8
    if inp.age >= 75:
        score += 1.0
    elif inp.age >= 65:
        score += 0.5
    if inp.diabetes:
        score += 0.8
    return score


def medication_component(inp: TmpSmxHkInput) -> float:
    score = 0.0
    if inp.acei_arb:
        score += 1.0
    if inp.spironolactone_or_eplerenone:
        score += 2.6
    if inp.other_k_retaining_drugs:
        score += 1.0
    return score


def acute_signal_component(inp: TmpSmxHkInput) -> float:
    score = 0.0
    if inp.heart_failure:
        score += 0.8
    if inp.dehydration_or_aki:
        score += 1.8
    if inp.rising_potassium_during_therapy:
        score += 2.6
    if inp.ecg_changes_or_weakness:
        score += 2.4
    return score


def total_score(inp: TmpSmxHkInput) -> float:
    score = (
        exposure_component(inp)
        + renal_component(inp)
        + medication_component(inp)
        + acute_signal_component(inp)
    )
    if inp.current_or_planned_tmp_smx and inp.spironolactone_or_eplerenone:
        score += 2.2
    if inp.current_or_planned_tmp_smx and inp.acei_arb and inp.egfr_below_60:
        score += 1.2
    if inp.egfr_below_30 and inp.baseline_potassium_5_or_more:
        score += 1.6
    if inp.rising_potassium_during_therapy and inp.ecg_changes_or_weakness:
        score += 1.8
    if inp.prophylaxis_only and not inp.egfr_below_60 and not inp.spironolactone_or_eplerenone and not inp.baseline_potassium_5_or_more:
        score -= 0.8
    return round(max(score, 0.0) * 5.0, 1)


def classify(score: float) -> str:
    if score >= 70:
        return "CONTRAINDICATED / CRITICAL"
    if score >= 40:
        return "VERY HIGH"
    if score >= 20:
        return "HIGH"
    return "LOW"


def recommendations(inp: TmpSmxHkInput, score: float) -> List[str]:
    out: List[str] = []
    if score < 20:
        out.append("Risk context is low enough for cautious TMP-SMX use with routine potassium follow-up if clinically indicated.")
    elif score < 40:
        out.append("High-risk context: review indication, renal function, baseline potassium, and concurrent potassium-raising medications before continuing TMP-SMX.")
    elif score < 70:
        out.append("Very high concern: avoid TMP-SMX when alternatives are acceptable, or intensify potassium/creatinine monitoring immediately if treatment is essential.")
    else:
        out.append("TMP-SMX exposure is contraindicated or clinically critical until hyperkalemia is excluded or stabilized.")
        out.append("Arrange urgent clinician-level evaluation with serum potassium, creatinine, ECG review, and medication reconciliation.")

    if inp.spironolactone_or_eplerenone:
        out.append("Mineralocorticoid receptor antagonist co-exposure is a major amplifier of TMP-SMX hyperkalemia risk.")
    if inp.acei_arb:
        out.append("Renin-angiotensin system blockade further narrows potassium safety margins during TMP-SMX therapy.")
    if inp.egfr_below_30:
        out.append("Advanced CKD reduces renal potassium excretion and sharply increases concern.")
    return out


def alerts(inp: TmpSmxHkInput, score: float) -> List[str]:
    out: List[str] = []
    if inp.rising_potassium_during_therapy:
        out.append("Rising potassium during TMP-SMX therapy should be treated as a true drug-safety signal, not a trivial fluctuation.")
    if inp.ecg_changes_or_weakness:
        out.append("Weakness or ECG changes raise urgency because clinically significant hyperkalemia may already be present.")
    if inp.dehydration_or_aki:
        out.append("Intercurrent AKI or dehydration can rapidly magnify trimethoprim-related potassium retention.")
    if score >= 40:
        out.append("This tool supports transparent triage only; definitive hyperkalemia diagnosis and treatment require direct clinical evaluation and laboratory review.")
    return out


def run_tmp_smx_hk(inp: TmpSmxHkInput) -> Dict[str, Any]:
    score = total_score(inp)
    return {
        "input_summary": asdict(inp),
        "exposure_component": round(exposure_component(inp), 2),
        "renal_component": round(renal_component(inp), 2),
        "medication_component": round(medication_component(inp), 2),
        "acute_signal_component": round(acute_signal_component(inp), 2),
        "total_score": score,
        "risk_class": classify(score),
        "recommended_actions": recommendations(inp, score),
        "alerts": alerts(inp, score),
        "limitations": [
            "Evidence-informed heuristic model, not a prospectively calibrated probability calculator for TMP-SMX-associated hyperkalemia.",
            "Weights are derived from pharmacologic mechanism, observational outcome studies, and bedside safety logic rather than multivariable derivation in a single cohort.",
            "The tool cannot distinguish every alternative cause of hyperkalemia, including adrenal insufficiency, tumor lysis, or unreported supplements.",
            "Medication lists may miss potassium-containing salt substitutes or over-the-counter contributors.",
            "Use only as a transparent decision-support aid alongside clinician judgment, direct examination, and formal electrolyte management."
        ]
    }


if __name__ == '__main__':
    demos = [
        (
            'PJP prophylaxis in a younger patient without kidney disease',
            TmpSmxHkInput(age=39, indication='PJP prophylaxis', prophylaxis_only=True),
        ),
        (
            'Older CKD patient on ACEi and spironolactone receiving treatment-dose TMP-SMX',
            TmpSmxHkInput(age=78, indication='Urinary tract infection', treatment_dose=True, egfr_below_60=True, acei_arb=True, spironolactone_or_eplerenone=True, diabetes=True),
        ),
        (
            'Advanced CKD patient with rising potassium and ECG concern during TMP-SMX therapy',
            TmpSmxHkInput(age=81, indication='Skin/soft tissue infection', treatment_dose=True, egfr_below_60=True, egfr_below_30=True, baseline_potassium_5_or_more=True, acei_arb=True, spironolactone_or_eplerenone=True, other_k_retaining_drugs=True, heart_failure=True, dehydration_or_aki=True, rising_potassium_during_therapy=True, ecg_changes_or_weakness=True),
        ),
    ]

    print('=' * 78)
    print('TMP-SMX-HK — TMP-SMX-Associated Hyperkalemia Risk-Context Stratification')
    print('Authors: Dr. Erick Zamora-Tehozol, DNAI, RheumaAI')
    print('=' * 78)
    for label, demo in demos:
        result = run_tmp_smx_hk(demo)
        print(f'\n--- {label} ---')
        print(json.dumps(result, indent=2))