{ "cells": [ { "cell_type": "markdown", "id": "c030abb8", "metadata": {}, "source": [ "# Exercice 4 : Rein artificiel\n", "\n", "Un rein artificiel est un procédé biomédial qui permet d'épurer le sang de personnes ayant une insuffisance rénale : on parle de dialyse rénale. Le principe est de mettre en contact le sang du patient (avec une circulation extracorporelle) contenant les molécules à éliminer (urée, toxines, ...) avec une solution (le dialysat) ne contenant pas ces molécules à travers une membrane . Les molécules à éliminer diffusent alors des zones concentrées (le sang) vers le dialysat et sont donc éliminées du sang. Le procédé comporte deux entrées et deux sorties et peut se représenter avec le schéma suivant :" ] }, { "cell_type": "code", "execution_count": 44, "id": "a991ee53", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "xs, ys = 40, 40 #position du melangeur\n", "lx, ly = 40, 20 #taille du melangeur\n", "la, hla, hwa=25, 5,2 #taille des fleches\n", "\n", "#tracé du flowsheet\n", "def plot_flowsheet_dialyseur():\n", " fig, ax = plt.subplots(figsize=(8, 8)) \n", " ax.set_aspect( 1 )\n", " plt.axis('off')\n", " plt.plot([2.5,5,5,2.5,5,10,5,5,7],[70,80,90,80,90,80,90,80,70],'k') #bonhomme\n", " tete=plt.Circle((5,93),3, color='k')\n", " ax.add_patch(tete)\n", " plt.plot([xs+lx+la+hla,xs+lx+la+hla,xs-la-hla-2,xs-la-hla-2],[ys+3*ly/4,85,85,ys+3*ly/4],'r') #circuit extracorporel\n", " plt.arrow(xs-la+hla-2,85,-hla,0, head_width=hwa, head_length=hla, fc='r', ec='r')\n", " plt.plot([xs,xs+lx,xs+lx,xs,xs],[ys,ys,ys+ly,ys+ly,ys],'k') #dialyseur\n", " plt.plot([xs,xs+lx],[ys+ly/2,ys+ly/2],'k--',linewidth=2)\n", " plt.arrow(xs,ys+ly/4,-la,0, head_width=hwa, head_length=hla, fc='b', ec='b')\n", " plt.arrow(xs-la-hla-2,ys+3*ly/4,la+2,0, head_width=hwa, head_length=hla, fc='r', ec='r')\n", " plt.arrow(xs+lx+la+hla,ys+ly/4,-la,0, head_width=hwa, head_length=hla, fc='b', ec='b')\n", " plt.arrow(xs+lx,ys+3*ly/4,la,0, head_width=hwa, head_length=hla, fc='r', ec='r')\n", " plt.text(50,51,'Dialyseur',c='k', size=15)\n", " plt.text(xs-la-hla,ys+3*ly/4+2, 'Entrée Sang', size=15, color='r')\n", " plt.text(xs+lx+5,ys+3*ly/4+2, 'Sortie Sang', size=15, color='r')\n", " plt.text(xs-la-hla,ys+ly/4-6, 'Sortie dialysat', size=15, color='b')\n", " plt.text(xs+lx+5,ys+ly/4-6, 'Entrée dialysat', size=15, color='b')\n", " \n", "plot_flowsheet_dialyseur()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "370e7428", "metadata": {}, "source": [ "Lors du fonctionnement du rein artificiel, on mesure les débits et les concentrations en urée suivante :" ] }, { "cell_type": "code", "execution_count": 45, "id": "68a10036", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "F_ES=200 #ml/min\n", "F_SS=200 #ml/min\n", "F_ED=600 #ml/min\n", "F_SD=600 #ml/min\n", "c_ES=5 #g/L\n", "c_ED=0 #g/L\n", "c_SD=1.5 #g/L\n", "\n", "plot_flowsheet_dialyseur()\n", "plt.text(xs-la-hla,ys+3*ly/4+2, 'Entrée Sang', size=15, color='r')\n", "plt.text(xs-la-hla,ys+3*ly/4+14, r\"$F^{ES}$\"+'={} ml/min'.format(F_ES), size=15)\n", "plt.text(xs-la-hla,ys+3*ly/4+8, r\"$c^{ES}_{mU}$\"+'={} g/l'.format(c_ES), size=15)\n", "plt.text(xs+lx+5,ys+3*ly/4+2, 'Sortie Sang', size=15, color='r')\n", "plt.text(xs+lx+5,ys+3*ly/4+14, r\"$F^{SS}$\"+'={} ml/min'.format(F_SS), size=15)\n", "plt.text(xs+lx+5,ys+3*ly/4+8, r\"$c^{SS}_{mU}$\"+'=?', size=15) \n", "plt.text(xs-la-hla,ys+ly/4-6, 'Sortie dialysat', size=15, color='b')\n", "plt.text(xs-la-hla,ys+ly/4-12, r\"$F^{SD}$\"+'={} ml/min'.format(F_SD), size=15)\n", "plt.text(xs-la-hla,ys+ly/4-18, r\"$c^{SD}_{mU}$\"+'={} g/l'.format(c_SD), size=15)\n", "plt.text(xs+lx+5,ys+ly/4-6, 'Entrée dialysat', size=15, color='b')\n", "plt.text(xs+lx+5,ys+ly/4-12, r\"$F^{ED}$\"+'={} ml/min'.format(F_ED), size=15)\n", "plt.text(xs+lx+5,ys+ly/4-18, r\"$c^{ED}_{mU}$\"+'={} g/l'.format(c_ED), size=15)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ae6db868", "metadata": {}, "source": [ "1- Calculer la concentration en urée dans le sang qui sort du rein artificiel et qui retourne dans le corps du patient\n", "\n", "> On applique un bilan sur les débits partiels en urée. Pour cela on écrit que la somme des débits entrants en urée est égale à la somme des débits sortants en urée :\n", "> $$\\sum{F^{E}~c^{E}_{U}}=\\sum{F^{S}~c^{S}_{U}}$$\n", "> $$F^{ES}~c^{ES}_{U}+F^{ED}~c^{ED}_{U}=F^{SS}~c^{SS}_{U}+F^{SD}~c^{SD}_{U}$$\n", ">$$c^{SS}_{U}=\\frac{F^{ES}~c^{ES}_{U}+F^{ED}~c^{ED}_{U}-F^{SD}~c^{SD}_{U}}{F^{SS}}$$\n" ] }, { "cell_type": "code", "execution_count": 46, "id": "eeb49aef", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La concentration en urée en sortie du dialyseur est de 0.5 g/l\n" ] } ], "source": [ "c_SS=(F_ES*c_ES+F_ED*c_ED-F_SD*c_SD)/F_SS\n", "\n", "print('La concentration en urée en sortie du dialyseur est de {} g/l'.format(c_SS))" ] }, { "cell_type": "markdown", "id": "6b65ef04", "metadata": {}, "source": [ "2- En déduire le débit d urée qui est éliminé du sang du patient (vous pourrez faire deux bilans différents pour trouver cette valeur)\n", "\n", "> Le débit d'urée éliminé, j, peut être calculé en déterminant la quantité éliminée du sang $F^{ES}~c^{ES}_{U}-F^{SS}~c^{SS}_{U}$ ou la quantité récupérée dans le dialysat $F^{SD}~c^{SD}_{U}-F^{ED}~c^{ED}_{U}$ :\n", "$$j_U=F^{ES}~c^{ES}_{U}-F^{SS}~c^{SS}_{U}=F^{SD}~c^{SD}_{U}-F^{ED}~c^{ED}_{U}$$" ] }, { "cell_type": "code", "execution_count": 47, "id": "9f1d1ff7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le débit d urée est 0.9 g/min\n" ] } ], "source": [ "j1=(F_ES*c_ES-F_SS*c_SS)*0.001\n", "j2=(F_SD*c_SD-F_ED*c_ED)*0.001\n", "\n", "if round(j1,3) != round(j2,3):\n", " print ('PROBLEME de calcul')\n", "\n", "print('Le débit d urée est {} g/min'.format(j1,j2))" ] }, { "cell_type": "markdown", "id": "be69a960", "metadata": {}, "source": [ "3- La clairance est définit comme le rapport de ce débit sur la concentration en urée dans le sang du patient en entrée de dialyseur. Calculer la clairance du rein artificiel. \n", "\n", "> La clairance est définie comme \n", "$$K=\\frac{j_U}{c^{ES}_{U}}$$\n", "\n" ] }, { "cell_type": "code", "execution_count": 48, "id": "871442ea", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La clairance est de 180.0 ml/min\n" ] } ], "source": [ "K=j1/c_ES #L/min\n", "print('La clairance est de {} ml/min'.format(K*1000))" ] }, { "cell_type": "markdown", "id": "94661ac8", "metadata": {}, "source": [ "La clairance du rein artificiel peut être comparée au débit de filtration glomérulaire, DFG, du patient qui est définit comme le débit de sang épuré. Le DFG d'un patient sain est de l'ordre de 120 ml/min. \n", "\n", "4. Comparer la clairance du rein artificiel au DFG naturel du rein.\n", "\n", "> La clairance du rein artificiel est supérieure à celui d'un rein normal. C'est normal puisque les séances de dialyse se déroulent sur une période de 4 ans et plusieurs jours par semaine. Le rein artificiel doit donc permettre une épuration du sang plus rapide que celle qui se produit naturellement 24h/24.\n", "\n" ] }, { "cell_type": "markdown", "id": "5a7c85f3", "metadata": {}, "source": [ "# Pour aller plus loin\n", "\n", "### Estimation du temps de dialyse \n", "\n", "Un patient de 80 kg arrive avec une teneur en urée, $c_0$ de 3 g/L. La dialyse doit lui permettre de repartir avec une concentration en urée, $c_f$ de 0,8 g/L.\n", "\n", "Par un bilan sur le sang du patient, il est possible de démontrer (en master !) que la concentration en urée dans le sang du patient, $c$, évolue au cours du temps $t$ selon :\n", "$$\\frac{c}{c_0}=e^{-\\frac{K}{V}t}$$\n", "où $K$ est la clairance du rein artificiel et $V$ est le volume d'eau corporelle.\n", "En intégrant cette relation, on peut définir le temps de dialyse, $t_D$ permettant d'atteindre la concentration $c_f$ en urée :\n", "$$t_D=V\\frac{ln(c_0/c_f)}{K}$$\n", "\n", "Calculer le temps de dialyse nécessaire.\n", "\n" ] }, { "cell_type": "code", "execution_count": 49, "id": "2a5c1329", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le temps de dialyse estimé est de 5.68 h\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "K1=K/(1000*60) #clairance en m3/s\n", "C0=3\n", "Cf=0.8 \n", "P=80 #kg\n", "# Volume d’eau corporelle à la fin de séance (env 58% poids du corps)\n", "ECTF=0.58*P/1000 #m3\n", "td=ECTF*np.log(C0/Cf)/K1\n", "print ('Le temps de dialyse estimé est de {:.2f} h'.format(td/3600))\n", "t=np.linspace(0,6,100)\n", "plt.plot(t,C0*np.exp(-K1*t*3600/ECTF))\n", "plt.plot([0,td/3600],[Cf,Cf],'r--')\n", "plt.plot([td/3600,td/3600],[0,Cf],'r--')\n", "plt.xlabel('t (h)')\n", "plt.ylabel('c en urée (g/L)')\n", "plt.ylim(0,C0)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "20247adc", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0c320705", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }