Calc Reynolds

{
  "count": 2,
  "items": [
    {
      "age": 55,
      "b": 22.4031,
      "citation": {
        "authors": "Ridker PM, Buring JE, Rifai N, Cook NR",
        "doi": "10.1001/jama.297.6.611",
        "id": "ridker-2007",
        "pmid": "17299196",
        "source": "JAMA. 2007;297(6):611-619",
        "title": "Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score"
      },
      "diabetes": false,
      "disclaimer": "Not a medical device. Deterministic published-formula or published-code output for autonomous agents. A licensed clinician remains responsible for patient care and billing submissions.",
      "family_mi_before_60": false,
      "formula": "reynolds",
      "formula_expression": "10-year CVD risk (%) = [1 - 0.98634^(exp(B - 22.325))] * 100; B = 0.0799*age + 3.137*ln(SBP) + 0.180*ln(hsCRP) + 1.382*ln(total cholesterol) - 1.172*ln(HDL-C) + 0.134*HbA1c(%) [if diabetic] + 0.818 [if current smoker] + 0.438 [if family history of premature MI]; Ridker JAMA 2007 Model B women; natural log; cholesterol and HDL mg/dL; hsCRP mg/L; SBP mm Hg; not men; not ATP-III; not 2013 PCE",
      "hdl_mg_dl": 50,
      "hscrp_mg_l": 2,
      "risk_percent": 1.5,
      "sbp_mm_hg": 125,
      "smoker": false,
      "total_cholesterol_mg_dl": 200
    },
    {
      "age": 55,
      "b": 22.4031,
      "citation": {
        "authors": "Ridker PM, Buring JE, Rifai N, Cook NR",
        "doi": "10.1001/jama.297.6.611",
        "id": "ridker-2007",
        "pmid": "17299196",
        "source": "JAMA. 2007;297(6):611-619",
        "title": "Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score"
      },
      "diabetes": false,
      "disclaimer": "Not a medical device. Deterministic published-formula or published-code output for autonomous agents. A licensed clinician remains responsible for patient care and billing submissions.",
      "family_mi_before_60": false,
      "formula": "reynolds",
      "formula_expression": "10-year CVD risk (%) = [1 - 0.98634^(exp(B - 22.325))] * 100; B = 0.0799*age + 3.137*ln(SBP) + 0.180*ln(hsCRP) + 1.382*ln(total cholesterol) - 1.172*ln(HDL-C) + 0.134*HbA1c(%) [if diabetic] + 0.818 [if current smoker] + 0.438 [if family history of premature MI]; Ridker JAMA 2007 Model B women; natural log; cholesterol and HDL mg/dL; hsCRP mg/L; SBP mm Hg; not men; not ATP-III; not 2013 PCE",
      "hdl_mg_dl": 50,
      "hscrp_mg_l": 2,
      "risk_percent": 1.5,
      "sbp_mm_hg": 125,
      "smoker": false,
      "total_cholesterol_mg_dl": 200
    }
  ],
  "path": "/api/calc/reynolds"
}

# bash or zsh: see the payment challenge. Nothing is charged.
curl -sS -g -i -X POST 'https://api.magentlab.com/api/calc/reynolds' \
  -H 'content-type: application/json' \
  --data-raw '{"items":[{"a1c_percent":7.5,"age":55,"diabetes":false,"family_mi_before_60":false,"hdl_mg_dl":50,"hscrp_mg_l":2,"sbp_mm_hg":125,"smoker":false,"total_cholesterol_mg_dl":200},{"a1c_percent":7.5,"age":55,"diabetes":false,"family_mi_before_60":false,"hdl_mg_dl":50,"hscrp_mg_l":2,"sbp_mm_hg":125,"smoker":false,"total_cholesterol_mg_dl":200}]}'

# x402 v2 sends the challenge base64-encoded in the PAYMENT-REQUIRED header (v1 sends it in the body).
# To read the header:
curl -sS -g -o /dev/null -D - -X POST 'https://api.magentlab.com/api/calc/reynolds' \
  -H 'content-type: application/json' \
  --data-raw '{"items":[{"a1c_percent":7.5,"age":55,"diabetes":false,"family_mi_before_60":false,"hdl_mg_dl":50,"hscrp_mg_l":2,"sbp_mm_hg":125,"smoker":false,"total_cholesterol_mg_dl":200},{"a1c_percent":7.5,"age":55,"diabetes":false,"family_mi_before_60":false,"hdl_mg_dl":50,"hscrp_mg_l":2,"sbp_mm_hg":125,"smoker":false,"total_cholesterol_mg_dl":200}]}' \
  | grep -i '^payment-required:' | cut -d' ' -f2 | tr -d '\r' | base64 --decode

// npm i @x402/fetch @x402/evm viem
import { wrapFetchWithPayment, x402Client } from "@x402/fetch";
import { registerExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";

// Pays at most $0.007 per call.
const client = new x402Client().setSpendControls({ maxAmountPerPayment: "$0.007" });
registerExactEvmScheme(client, { signer: privateKeyToAccount(process.env.EVM_PRIVATE_KEY) });
const pay = wrapFetchWithPayment(fetch, client);

// Not sure it's safe to pay? ToolVet checks it first for $0.005. Pay for that from a separate
// client: this one is capped at $0.007 per payment. GET https://toolvet.app/api/v1/check?url=https%3A%2F%2Fapi.magentlab.com%2Fapi%2Fcalc%2Freynolds&method=POST
const res = await pay("https://api.magentlab.com/api/calc/reynolds", {
  method: "POST",
  headers: { "content-type": "application/json" },
  body: JSON.stringify({
    "items": [
      {
        "a1c_percent": 7.5,
        "age": 55,
        "diabetes": false,
        "family_mi_before_60": false,
        "hdl_mg_dl": 50,
        "hscrp_mg_l": 2,
        "sbp_mm_hg": 125,
        "smoker": false,
        "total_cholesterol_mg_dl": 200
      },
      {
        "a1c_percent": 7.5,
        "age": 55,
        "diabetes": false,
        "family_mi_before_60": false,
        "hdl_mg_dl": 50,
        "hscrp_mg_l": 2,
        "sbp_mm_hg": 125,
        "smoker": false,
        "total_cholesterol_mg_dl": 200
      }
    ]
  }),
});
console.log(res.status, await res.text());

# pip install "x402[requests,evm]"
import os
from eth_account import Account
from x402 import x402ClientSync
from x402.http.clients import x402_requests
from x402.mechanisms.evm import EthAccountSigner
from x402.mechanisms.evm.exact.register import register_exact_evm_client

# Pays at most $0.007 per call.
client = x402ClientSync().set_spend_controls({"max_amount_per_payment": "$0.007"})
register_exact_evm_client(client, EthAccountSigner(Account.from_key(os.environ["EVM_PRIVATE_KEY"])))

with x402_requests(client) as session:
    res = session.post(
        "https://api.magentlab.com/api/calc/reynolds",
        json={
            "items": [
                {
                    "a1c_percent": 7.5,
                    "age": 55,
                    "diabetes": False,
                    "family_mi_before_60": False,
                    "hdl_mg_dl": 50,
                    "hscrp_mg_l": 2,
                    "sbp_mm_hg": 125,
                    "smoker": False,
                    "total_cholesterol_mg_dl": 200,
                },
                {
                    "a1c_percent": 7.5,
                    "age": 55,
                    "diabetes": False,
                    "family_mi_before_60": False,
                    "hdl_mg_dl": 50,
                    "hscrp_mg_l": 2,
                    "sbp_mm_hg": 125,
                    "smoker": False,
                    "total_cholesterol_mg_dl": 200,
                },
            ],
        },
    )
    print(res.status_code, res.text)