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py_qrisk.c
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py_qrisk.c
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/*
* Copyright 2014-5 ClinRisk Ltd.
*
* This file is part of QRISK2-2015 (http://qrisk.org, http://qrisk.org/QRISK2-2015-lgpl-source.tgz).
*
* QRISK2-2015 is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* QRISK2-2015 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with QRISK2-2015. If not, see <http://www.gnu.org/licenses/>.
*
* Additional terms
*
* The following disclaimer must be held together with any risk score score generated by this code.
* If the score is displayed, then this disclaimer must be displayed or otherwise be made easily accessible, e.g. by a prominent link alongside it.
* The initial version of this file, to be found at http://qrisk.org/QRISK2-2015-lgpl-source.tgz, faithfully implements QRISK2-2015.
* ClinRisk Ltd. have released this code under the GNU Lesser General Public License to enable others to implement the algorithm faithfully.
* However, the nature of the GNU Lesser General Public License is such that we cannot prevent, for example, someone accidentally
* altering the coefficients, getting the inputs wrong, or just poor programming.
* ClinRisk Ltd. stress, therefore, that it is the responsibility of the end user to check that the source that
* they receive produces the same results as the original code posted at http://qrisk.org/QRISK2-2015-lgpl-source.tgz.
* Inaccurate implementations of risk scores can lead to wrong patients being given the wrong treatment.
*
* End of additional terms
*/
#include <math.h>
#include <string.h>
#include <Python.h>
static PyObject* calcFemRaw(PyObject* self, PyObject* args){
int surv = 10;
int age, b_AF, b_ra, b_renal, b_treatedhyp, b_type1, b_type2, ethrisk, fh_cvd, smoke_cat;
double bmi, rati, sbp, town;
if (!PyArg_ParseTuple(args, "iiiiiiidiiddid", &age, &b_AF, &b_ra, &b_renal, &b_treatedhyp, &b_type1, &b_type2, &bmi, ðrisk, &fh_cvd, &rati, &sbp, &smoke_cat, &town)){
return NULL;
}
double survivor[16] = {
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0.989747583866119,
0,
0,
0,
0,
0
};
/* The conditional arrays */
double Iethrisk[10] = {
0,
0,
0.2574099349831925900000000,
0.6129795430571779400000000,
0.3362159841669621300000000,
0.1512517303224336400000000,
-0.1794156259657768100000000,
-0.3503423610057745400000000,
-0.2778372483233216800000000,
-0.1592734122665366000000000
};
double Ismoke[5] = {
0,
0.2119377108760385200000000,
0.6618634379685941500000000,
0.7570714587132305600000000,
0.9496298251457036000000000
};
/* Applying the fractional polynomial transforms */
/* (which includes scaling) */
double dage = age;
dage=dage/10;
double age_2 = dage;
double age_1 = pow(dage,.5);
double dbmi = bmi;
dbmi=dbmi/10;
double bmi_2 = pow(dbmi,-2)*log(dbmi);
double bmi_1 = pow(dbmi,-2);
/* Centring the continuous variables */
age_1 = age_1 - 2.086397409439087;
age_2 = age_2 - 4.353054523468018;
bmi_1 = bmi_1 - 0.152244374155998;
bmi_2 = bmi_2 - 0.143282383680344;
rati = rati - 3.506655454635620;
sbp = sbp - 125.040039062500000;
town = town - 0.416743695735931;
/* Start of Sum */
double a=0;
/* The conditional sums */
a += Iethrisk[ethrisk];
a += Ismoke[smoke_cat];
/* Sum from continuous values */
a += age_1 * 4.4417863976316578000000000;
a += age_2 * 0.0281637210672999180000000;
a += bmi_1 * 0.8942365304710663300000000;
a += bmi_2 * -6.5748047596104335000000000;
a += rati * 0.1433900561621420900000000;
a += sbp * 0.0128971795843613720000000;
a += town * 0.0664772630011438850000000;
/* Sum from boolean values */
a += b_AF * 1.6284780236484424000000000;
a += b_ra * 0.2901233104088770700000000;
a += b_renal * 1.0043796680368302000000000;
a += b_treatedhyp * 0.6180430562788129500000000;
a += b_type1 * 1.8400348250874599000000000;
a += b_type2 * 1.1711626412196512000000000;
a += fh_cvd * 0.5147261203665195500000000;
/* Sum from interaction terms */
a += age_1 * (smoke_cat==1) * 0.7464406144391666500000000;
a += age_1 * (smoke_cat==2) * 0.2568541711879666600000000;
a += age_1 * (smoke_cat==3) * -1.5452226707866523000000000;
a += age_1 * (smoke_cat==4) * -1.7113013709043405000000000;
a += age_1 * b_AF * -7.0177986441269269000000000;
a += age_1 * b_renal * -2.9684019256454390000000000;
a += age_1 * b_treatedhyp * -4.2219906452967848000000000;
a += age_1 * b_type1 * 1.6835769546040080000000000;
a += age_1 * b_type2 * -2.9371798540034648000000000;
a += age_1 * bmi_1 * 0.1797196207044682300000000;
a += age_1 * bmi_2 * 40.2428166760658140000000000;
a += age_1 * fh_cvd * 0.1439979240753906700000000;
a += age_1 * sbp * -0.0362575233899774460000000;
a += age_1 * town * 0.3735138031433442600000000;
a += age_2 * (smoke_cat==1) * -0.1927057741748231000000000;
a += age_2 * (smoke_cat==2) * -0.1526965063458932700000000;
a += age_2 * (smoke_cat==3) * 0.2313563976521429400000000;
a += age_2 * (smoke_cat==4) * 0.2307165013868296700000000;
a += age_2 * b_AF * 1.1395776028337732000000000;
a += age_2 * b_renal * 0.4356963208330940600000000;
a += age_2 * b_treatedhyp * 0.7265947108887239600000000;
a += age_2 * b_type1 * -0.6320977766275653900000000;
a += age_2 * b_type2 * 0.4023270434871086800000000;
a += age_2 * bmi_1 * 0.1319276622711877700000000;
a += age_2 * bmi_2 * -7.3211322435546409000000000;
a += age_2 * fh_cvd * -0.1330260018273720400000000;
a += age_2 * sbp * 0.0045842850495397955000000;
a += age_2 * town * -0.0952370300845990780000000;
/* Calculate the score itself */
double score = 100.0 * (1 - pow(survivor[surv], exp(a)) );
//return score;
return PyFloat_FromDouble(score);
}
/*
* Bind Python function names to our C functions
*/
static PyMethodDef py_qriskMethods[] = {
{"calcFemRaw", calcFemRaw, METH_VARARGS},
{NULL, NULL}
};
/*
* Python calls this to let us initialize our module
*/
void initpy_qrisk()
{
(void) Py_InitModule("py_qrisk", py_qriskMethods);
}