Cardiovascular Event Risk Assessment Fusion of Individual Risk Assessment Tools Applied to the Portuguese Population

Transcription

1 Cardovascuar Event Rsk Assessment Fuson of Indvdua Rsk Assessment Toos Apped to the Portuguese Popuaton S. Paredes, T. Rocha, P. de Carvaho, J. Henrques, J. Moras*, J. Ferrera, M. Mendes Abstract Cardovascuar dsease (CVD) s the word s argest ker, responsbe for 7. mon deaths per year. Thus, the mprovement of the prognoss of ths dsease s an mportant factor to defeat the current statstcs. Athough there are severa rsk toos avaabe to assess the rsk of occurrence of a cardovascuar event wthn a gven perod of tme, these toos present some major drawbacks. In partcuar, each ndvdua too consders a reduced number of rsk factors, does not permt to ncorporate addtona cnca knowedge and presents dffcutes n copng wth mssng rsk factors. In order to overcome the dentfed weaknesses, a fexbe framework s proposed here, based on the fuson of a set of dstnct rsk assessment toos. The methodoogy s based on two man hypotheses: ) t s possbe to derve a common representaton for the ndvdua rsk assessment toos, ) t s possbe to combne (fuson) the obtaned ndvdua modes, n order to acheve the referred goas. Addtonay, through the mpementaton of optmzaton technques, an ncreasng n the goba rsk predcton performance s aso nvestgated. The vadaton of the strategy s carred out through the combnaton of three current rsk assessment toos (GRACE, TIMI, PURSUIT) deveoped to predct the rsk of an event n coronary artery dsease (CAD) patents. The combnaton of these toos s vadated wth two rea patents testng datasets: ) Santa Cruz Hospta, Lsbon/Portuga, N=460 ACS-NSTEMI patents; ) Santo André Hospta, Lera/Portuga, N=99 ACS- NSTEMI patents. Consderng the obtaned resuts wth the avaabe datasets t s possbe to state that the nta goas of ths work were acheved. Ths evdence makes ths work a vad contrbuton for the mprovement of the rsk assessment apped to cardovascuar dseases. I. INTRODUCTION The cardovascuar dsease 2 (CVD) dsease s the word s argest ker, responsbe for 7. mon deaths per year []. In fact, each year, cardovascuar dsease causes over 4.3 Ths work was supported by HeartCyce EU project (FP ) and CISUC (Center for Informatcs and Systems of Unversty of Combra). Insttuto Potécnco de Combra, Departamento de Engenhara Informátca e de Sstemas, Rua Pedro Nunes, Combra. {sparedes@sec.pt, teresa@sec.pt} CISUC, Departamento de Engenhara Informátca, Unversdade de Combra, Póo II, Combra,{carvaho@de.uc.pt, jh@de.uc.pt} *Servço de Cardooga, Hospta Santo André, EPE Rua das Ohavas, Lera, {joaomoras@hsaera.mn-saude.pt} Servço de Cardooga, Hospta Santa Cruz, Lsboa, {jorge_ferrera@netcabo.pt, mgue.mendes.md@sapo.pt } ACS-NSTEMI Acute Coronary Syndrome wth non-st segment eevaton 2 Cardovascuar dsease s caused by dsorders of the heart and bood vesses, ncudng coronary heart dsease (heart attacks), cerebrovascuar dsease (stroke), rased bood pressure (hypertenson), perphera artery dsease, rheumatc heart dsease, congenta heart dsease and heart faure. mon deaths n Europe and amost 2.0 mon n the European Unon. Consequenty, CVD s the man cause of ness and death n Europe, responsbe for 23% of the tota dsease burden [2]. Moreover, CVD aone represents approxmatey 92 bon /year to heath costs n the European Unon [3]. Furthermore, the popuaton of the EU and the western word s agng. The number of edery peope aged w ncrease approxmatey by 37% by 2030 [4]. It s recognzed that ths demographc change n the popuaton w resut n unaffordabe heath costs. In ths context, the correct dagnoss and prognoss of cardovascuar dsease assumes a partcuar mportance n tryng to reduce these statstcs. The assessment of the rsk of occurrence of an event,.e. the evauaton of the probabty of occurrence of an event gven the patent s past and current exposure to rsk factors, s crtca to mprove prognoss. Ths way, t s possbe to ncrease the quaty of preventve heath care, as ths assessment data w hep physcans to dentfy and adapt the treatment/care pan to an ndvdua patent [5][6]. Severa rsk score toos 3 were deveoped to assess the probabty of occurrence of a CVD event wthn a certan perod of tme (months/years). Avaabe rsk score toos dffer on the assessed perod of tme (months/years), dsease (coronary artery dsease, heart faure,...), predcted events (death/non-fata), rsk factors consdered n the mode, patent s condtons (ambuatory patents, hosptazed patents, cardac transpant canddates, ). These toos are very usefu athough they present some mportant weaknesses: ) they gnore the nformaton provded by other rsk assessment toos that were prevousy deveoped, ) each ndvdua too consders a reduced number of rsk factors, ) they have dffcuty n copng wth mssng rsk factors, v) they do not aow the ncorporaton of addtona cnca knowedge, v) some toos do not assure the cnca nterpretabty of the respectve parameters. The proposed approach ams to defeat these faws and smutaneousy consder the vauabe nformaton provded by these toos. Therefore, rather than to derve a new mode, the proposed methodoogy ntends to create a fexbe goba framework (goba mode) based on the combnaton of avaabe rsk assessment toos. The combnaton of ndvdua toos w aso permt to overcome of the addtona dffcuty of seectng the best too to use n the day cnca practce. Actuay, one of these statstca toos s typcay seected as the standard 3 In order to carfy, rsk assessment modes that have been statstcay vadated and are avaabe n terature are gong to be desgnated through ths work as rsk assessment toos. 925

2 mode to be apped n a gven nsttuton. However, the choce of the rsk assessment too can be dffcut snce there mght not be a consensus about the best too to use. The deveoped methodoogy [7] s based on two man hypotheses: ) It s possbe to mpement a common representaton of ndvdua rsk assessment toos. In fact, current rsk assessment toos are dversey represented (charts, equatons, scores, ). Ths does not factate ther ntegraton/combnaton. The abty to dea wth mssng rsk factor aong wth the fexbty to ncorporate addtona cnca knowedge (new rsk factors) are further aspects that nfuenced the seecton of the cassfer to mpement ths frst step. Moreover, ts parameters/rues must be cncay nterpretabe; ) It s possbe to mpement a combnaton of the obtaned ndvdua modes. The abty of combnng avaabe knowedge from varous sources s usefu snce t creates a fexbe goba framework whch orgnates the mentoned benefts. Addtonay, the parameters of the goba mode resutng from ths combnaton can be adjusted by s of optmzaton methodooges (such as genetc agorthms), n order to ncrease the CVD rsk predcton performance. Ths approach was vadated wth current rsk assessment toos specfc for secondary preventon on coronary artery dsease (CAD) patents, n partcuar for assessng the rsk of death/myocarda nfarcton wthn a short perod of tme (days/months). Here, of partcuar reevance are the statstca rsk assessment toos GRACE, TIMI (no STeevaton) and PURSUIT [8][9][0]. Ths vadaton was supported by two rea patents testng datasets: ) Santa Cruz Hospta, Lsbon/Portuga, N=460 ACS-NSTEMI patents; ) Santo André Hospta, Lera/Portuga, N=99 ACS- NSTEMI patents. The paper s organzed as foows: n secton II an outne of the deveoped methodoogy s presented. In secton III the resuts of the vadaton procedure wth the two datasets are dscussed. Secton IV summarzes the man concusons and the man research paths to be foowed up n the near future. II. METHODOLOGY Fgure presents the proposed combnaton methodoogy: and aso deang wth mssng rsk factors. Moreover, ths common representaton must assure the cnca nterpretabty of the mode. The second step of the methodoogy s the combnaton of the ndvdua modes. In ths phase the ndvdua modes that were orgnated from the prevous step (common representaton of ndvdua rsk assessment toos) are combned. The goba mode that resuts from ths combnaton scheme must be derved based on the avaabe nput rsk factors and the ndvdua modes seecton crtera. For nstance, f one ndvdua mode does not have any of ts nput vaues avaabe, then that mode shoud not be consdered for ntegraton n the combnaton scheme. Ths approach aows a very fexbe mode whch s abe to consder a varabe number of nput rsk factors, t enabes to ncorporate emprca cnca knowedge and t avods the necessty of choosng a partcuar mode as a standard mode for the cnca practce. However, the cnca reevance of a CVD rsk predcton system depends drecty of ts performance. Optmzaton technques, namey genetc agorthms, are adopted n ths stage to ncrease the goba mode s performance (maxmze senstvty and specfcty). The thrd phase s vadaton that s determnant to evauate the potenta cnca mportance of the proposed methodoogy, namey usng senstvty and specfcty metrcs. Ths phase s performed based on rea data and t ntends to be as ncusve as possbe. A. Common Representaton of Indvdua Toos Current ndvdua rsk score modes are descrbed by dfferent equatons/scores/charts [8][9][0]. So, n order to ease ther combnaton, a the ndvdua rsk score toos were represented based on a smar structure,.e. the same cassfer. The naïve Bayes cassfer was the seected cassfer. In fact, the naïve Bayes has a compettve performance wth the remanng cassfers, s smpe and can dea wth mssng rsk factors. Besdes these features, naïve Bayes assures the nterpretabty of the mode whch s one of the man goas of ths approach. Fnay, the structure of naïve Bayes smpfes the ncorporaton of emprca cnca knowedge [][2]. Fgure 2 depcts the structure of a naïve Bayes cassfer. Fgure Proposed Methodoogy The frst step of ths approach conssts n the creaton of a common representaton, based on a machne earnng cassfcaton methodoogy that can be apped to a the seected ndvdua toos 4. The cassfer must be seected consderng both the combnaton of these ndvdua modes 4 The seecton of current avaabe rsk assessment toos must be done accordng to the specfc CVD rsk assessment context, e.g. secondary preventon, coronary artery dsease patents, month,... Fgure 2 Naïve Bayes Structure The varabe X = [ X... X p ] s a vector of random varabes and C s a random varabe that denotes the cass of an nstance, where c s a partcuar cass abe. A vector x = [ x... x p ] represents a partcuar nstance that contans the 926

3 observed vaues of the dfferent p attrbutes,.e., X = x s the same as X = x... Xp = xp. In the context of ths work, X = [ X... X p ] s a set of observatons (rsk factors) such as cnca examnaton and aboratory measurements and C s the hypothess (e.g. rsk eve s Hgh ). The nference mechansm s descrbed by equaton () PC ( X) = PC ( X,..., X ) = α PC ( ) PX ( C) () p p = The term PC ( X ) s the probabty that the hypothess s correct after observatons have occurred (e.g., the probabty that rsk s Hgh gven the resuts of a cnca examnaton, measurements, ). PC ( ) s the probabty that the hypothess s correct before seeng any observaton (n ths exampe, the prevaence of the rsk eve). α s a normazaton constant. PX ( C ) s a kehood expressng the probabty of the observaton X beng made f the hypothess s correct (equvaent to the senstvty of the cnca examnaton). Ths partcuar Bayesan nference mechansm (naïve Bayes) assumes that attrbutes X = [ X... X p ], are condtonay ndependent, gven the vaue of hypothess [3]. It s recognzed that the voaton of the assumpton of ndependence may affect the performance of naïve Bayes cassfer [2][3][4]. In the present combnaton scheme the seecton of rsk factors consdered by the ndvdua toos resuts from a statstca anayss process. Ths procedure usuay starts wth a arge set of canddate rsk factors, where the most reevant, typcay not correated, are seected. Therefore, the eventua voaton of the attrbute ndependence s controed as the attrbutes ndependence s addressed n the statstca dervaton of each ndvdua rsk assessment too. Moreover, the present methodoogy addresses ths potenta ack of performance through the mpementaton of an optmzaton procedure, that s carred out n the modes combnaton phase, by s of a genetc agorthm approach. The structure of naïve Bayes cassfer s competey defned (Fgure 2) as a resut the constructon of the cassfer s restrcted to parameters earnng. Thus, the mode has to earn from the tranng data set, the condtona probabty PX ( C ) of each attrbute X gven the cass C as we as the pror probabty PC ( ) of the cass C. Then, the process of representng an ndvdua rsk assessment too as a naïve Bayes cassfer can be systematzed as foows: A tranng dataset ( N nstances x = [ x... x p ] composed of p attrbutes) s generated. Ths tranng dataset s apped to a gven rsk assessment too n order to obtan a compete abeed dataset J = {( x, c),...,( x N, cn)}. Based on J and through the maxmum kehood estmaton (2) t s possbe to dervate a naïve Bayes cassfer that resembes the behavor of that specfc rsk assessment too. The pror probabty PC ( ) resuts drecty from dstrbuton of the cass vaues. The condtona probabtes can be cacuated through the foowng expresson: = = = N PX ( x C c) N ( X = x C= c) ( C= c) It s mportant to refer that ths probabtes estmaton s reabe ony when the attrbutes are quatatve. Hence the dscretzaton of numerc attrbutes may have a great mpact n the constructon of the condtona probabtes tabes and therefore n the performance of the cassfer. The Equa Wdth Dscretzaton (EWD) was the seected dscretzaton method to aow the appcaton of the maxmum kehood estmaton to numerc attrbutes [5]. Ths process must be repeated to each one of the ndvdua rsk assessment toos that ntegrate the combnaton scheme. B. Indvdua Mode s Combnaton ) Combnaton strategy The modes combnaton phase s responsbe for the combnaton of the naïve Bayes cassfers that resembe the behavor of each one of the rsk assessment toos that ntegrate the combnaton scheme. Ths procedure, s descrbed n Fgure 3. Fgure 3 Combnaton Scheme Severa ndvdua modes M M = { M,..., M} are consdered to ntegrate the combnaton scheme, where each cassfer s characterzed by a specfc condtona j probabty tabe PX ( C, ) and by ther respectve pror probabty of output cass PC ( j ). PX ( C) j represents the condtona probabty tabe of attrbute of mode j, PC ( j ) s the pror probabty dstrbuton of mode j j (2) 927

4 regardng a specfc number of mutuay excusve casses, j x x = [ x... x p ] s the nput nstance consdered by the mode j (subset of the p rsk factors that are consdered by the ndvdua mode j ). The combnaton scheme mpements the drect combnaton of the ndvdua modes parameters (modes fuson) accordng to the foowng equaton: wj = j Φ j Φ j= j= b b j wj = j ϑ j ϑ j= j= PC ( ) PC ( ) ; = w PX ( C) PX ( C) ; = w Where s the number of ndvdua modes, b s the number of ndvdua modes that contan the attrbute X X, C j denotes each ndvdua mode that contans X, wj s the weght of mode j. Ths combnaton scheme s very fexbe whch permts the mpementaton of a combnaton strategy that depends on the characterstcs of each specfc combnaton, namey t: ) permts to assgn to each ndvdua mode a dfferent weght that s proportona to the respectve performance; ) aows dsabng a specfc mode. In ths way, dfferent ndvdua mode seecton crtera to ntegrate the combnaton scheme may be mpemented; ) aows the ncorporaton of addtona features to mprove rsk predcton. For nstance, the cnca partners that coaborated n ths work dentfed the possbty of assgn dfferent weghts to the dfferent attrbutes/rsk factors. The mode seecton crteron hghy nfuences the goba cassfcaton performance. Accordng to the mpemented condton, the nformaton of a gven mode was consdered f there was avaabe at east one of ts nputs. Moreover, some rsk factors may be consdered by more than one mode, whe other nputs beong ony to a snge mode. Therefore, the cassfcaton of the goba mode s dependent on the avaabty of nput rsk factors as we as on the seecton crtera to defne the ndvdua modes that shoud be ncuded n the combnaton scheme. Addtonay, to aow the combnaton of dfferent ndvdua modes the foowng condton has to be verfed: Indvdua modes have the same number of output eves (e.g. ow/ntermedate, hgh ). Ths restrcton ensures that modes share the same rsk assessment goa 5. Ths methodoogy aso makes the ncorporaton of cnca expertse a straghtforward operaton. In fact, a new mode can be drecty created by the physcan based on a CPT defnton, and easy ncorporated n the combnaton scheme. Ths s an mportant characterstc of ths method. (3) 2) Optmzaton An addtona optmzaton step can be performed to mprove the performance of the goba mode. Condtona probabty tabes PX ( C) of the goba mode can be optmzed by s of an optmzaton strategy, such as genetc agorthms (GA). Ths agorthm focuses on the parameters ( PX ( C); PC ( )) of the goba mode that was created through (3). An evauaton functon must be defned n order to assgn a quaty measure to each canddate souton. As a resut from severa experments, the seected evauaton functon s composed of two functons ( f, f 2 mutobjectve optmzaton 6 ) snce the optmzaton attempts to maxmze smutaneousy the specfcty and the senstvty of the goba mode. The objectve functons are gven by (4) n order to transform the maxmzaton of specfcty and senstvty nto a mnmzaton probem. TP TN f= ; f2 = TP + FN TN + FP : True postve; : Fase postve; : True negatve; : Fase negatve The optmzaton procedure s restrcted to the neghbourhood of the nta vaues n order to assure the cnca nterpretabty of the fna mode. For nstance, consderng three possbe categores for the 2 3 attrbute X, { x, x, x } and two mutuay excusve rsk casses { cc, 2} for the output C, the respectve condtona probabty tabe s defned by a matrx, as shown n equaton. Varabes δ kj denote the varatons on the probabty of the category k of attrbute C. Vadaton X gven the cass j. The vadaton procedure focused on the evauaton of the performance of the goba mode that s orgnated through the combnaton of current rsk assessment toos. In ths partcuar case, some current toos sutabe to predct rsk n coronary artery dsease (CAD) patents have been seected. Two dfferent datasets made avaabe by two Portuguese hosptas were used as testng datasets, whe the tranng (4) (5) 5 It s mportant to emphasze that ths restrcton does not obstructs the cnca appcaton of the proposed methodoogy. In fact, from the cnca perspectve the man goa s the dentfcaton of the hgh rsk patents. 6 Mutobjectve optmzaton s apped when a snge objectve wth severa constrants does not adequatey represent the optmzaton probem. In mutobjectve optmzaton there s a vector of objectve functons, where a tradeoff between objectves must be found. 928

5 dataset requred to generate the parameters to represent the ndvdua Bayesan cassfers was derved based on proper vaues avaabe n terature. The vadaton phase was composed of three man steps: ) performance assessment of ndvdua toos. Here, ndvdua toos were tested wth both popuatons. Ths data provded some addtona knowedge to adjust the weghts of ndvdua toos; ) performance assessment of the goba mode. The performance of the goba mode was evauated when dfferent weghts were assgned to the ndvdua modes. For a testng datasets, the Bayesan goba mode has been compared wth a votng mode 7 as we as wth each one of the ndvdua toos. In order to ncrease the statstca sgnfcance of the obtaned resuts, bootstrappng vadaton was empoyed whch aowed the dervaton of confdence ntervas of the formuas assessed. Parametrc statstca sgnfcance tests (Student s t-test, Levene s test) were executed to ncrease the reabty of the concusons extracted from ths comparson; ) mssng rsk factors. The abty of the Bayesan goba mode to dea wth mssng rsk factors was aso assessed. Each varabe has been successvey removed. For each varabe, the performance of three dfferent modes was evauated: ) Bayesan goba mode before optmzaton; ) Bayesan goba mode after optmzaton; ) Votng mode 8 [4]. The dfferent modes performance were compared based on parametrc statstca sgnfcance tests (Student t-test, Levene s test) that were compemented wth an anayss of varance. Aso n ths stuaton the vadaton was based on the bootstrappng vadaton wth n bootstrap sampes. A. Testng Datasets III. RESULTS ) Santa Cruz Hospta Dataset Ths dataset contans data from N=460 consecutve patents that were admtted n the Santa Cruz Hospta, Lsbon, wth Acute Coronary Syndrome wth non-st segment eevaton (ACS-NSTEMI) between March 999 and Juy 200. Tabe I contans the number of events consderng two dfferent endponts (death/myocarda nfarcton) and two dfferent perods (one month/one year). TABLE I ENDPOINTS OF SANTA CRUZ HOITAL DATAT Tme Event n % Tota 30 D days MI % D year MI D: Death; MI: Myocarda Infarcton 7 In votng, the cassfcaton produced by a cassfer s consdered as a vote for a partcuar cass vaue. The cass wth the hghest number of votes s seected as the fna cassfcaton. 8 If the mssng varabes were contnuous the repacement has been done based on the respectve vaues, n the case of Booean varabes ther vaue were successvey repaced by 0 and vaues. Tabe II presents the man cnca characterstcs of such patents (a detaed anayss can be found n Gonçaves et. a. [6]). Contnuous varabes wth a norma dstrbuton are expressed as vaue and standard devaton. Dscrete varabes are presented as frequences and percent vaues. TABLE II CLINICAL CHARACTERISTICS OF PATIENTS THAT INTEGRATE THE DATAT Mode Event Age (years) 63.4 ± 0.8 Sex (Mae/Femae) 36 (78.5%) / 99 (2.5%) Rsk Factors: Dabetes (0/) 352 (76.5%) / 08 (23.5%) Hyperchoesteroema (0/) 80 (39.%) / 280 (60.9%) Hypertenson (0/) 76 (38.3%) / 284 (6.7%) Smokng (0/) 362 (78.7 %) / 98 (2.3%) Prevous Hstory / Known CAD Myocarda Infarcton (0/) Myocarda Revascuarzaton (0/) PTCA CABG 249 (54.0%) / 2 (46.0%) 239 (5.9%) / 22 (48.%) 46 (3.7%) 03 (22.4%) Sbp (mmhg) 42.4 ± 26.9 Hr (bpm) 75.3 ± 8. Creatnne (mg/d).37 ±.26 Enroment [0 UA, MI] 80 (39. %) / 280 (60.9%) Kp /2/3/4 395 (85.9%) / 3 (6.8%) / 33 (7.3 %) / 0% CCS [0 I/II; CSS III/IV] 0 (24.0%) / 350 (76.0%) ST Segment Devaton (0/) 26 (47.0%) / 244 (53.0%) Sgns of Heart Faure(0/) 395 (85.9%) / 65 (4.%) Tn I > 0. ng/m (0/) 33 (68.0%) / 47 (32.0%) Cardac Arrest Admsson (0/) 460 (00%) / 0% Asprn (0/) 84 (40.0%) / 276 (60.0%) Angna (0/) 9 (4.0%) / 44 (96.0%) 2) Santo André Hospta Dataset Tabe III presents the man cnca characterstcs of patents data coected n Santo André Hospta, Lera, Portuga. TABLE III CLINICAL CHARACTERISTICS OF PATIENTS THAT INTEGRATE THE DATAT Mode Event Age (years) 68.0 ±.8 Sex (Mae/Femae) 68 (68.7%) / 3 (3.3%) Rsk Factors: Dabetes DMIT (0/) Dabetes DMNIT (0/) Hyperchoesteroema (0/) Hypertenson (0/) Smokng (0/) 9(9.9%) / 8 (8.%) 70 (70.7%) / 29 (29.3%) 59 (59.6%)/ 40 (40.4%) 26 (26.3%) / 73 (73.7%) 83 (83.8) / 6 (6.2%) Prevous Hstory / Known CAD 66 (66.7%) / 33 (33.3%) Sbp (mmhg) 45.7 ± 32. Hr (bpm) 83.2 ± 20.2 Creatnne (mg/d). ± 0.42 Enroment [0 UA, MI] 6 (6.%) / 93 (93.9%) Kp /2/3/4 70 (70.7%) / 2 (2.2%) / 7 (7.%) / (%) CCS [0 I/II; CSS III/IV] 78 (78.8%) / 2 (2.2%) ST Segment Devaton (0/) 98 (99%) / (%) Sgns of Heart Faure(0/) 70 (70.7%) / 29 (29.3%) Tn I > 0. ng/m (0/) 7 (7.%) / 92 (92.9%) Cardac Arrest Admsson (0/) 98 (99%) / (%) Asprn (0/) 7 (7.7%) / 28 (28.3%) Angna (0/) 33 (33.3%) / 66 (66.7%) 929

6 The avaabe dataset contans data from N=99 patents that were admtted n the Hospta wth Acute Coronary Syndrome (ACS-NSTEMI) durng B. Tranng Data Set The approach proposed by Twardy et. a. [7] was foowed to the generaton of the tranng data set. Contnuous varabes were normay dstrbuted. Vaues for and standard devaton were taken from the terature [6]. Dscrete varabes are bnary and were generated through a random process. The tranng data set was created x = [ x... x p ] for a N : wth. Ths tranng dataset was apped to the seected rsk assessment toos (Tabe IV) n order to obtan the respectve output cass J = {( x, c ),...,( x, c )}. N N C. Indvdua Rsk Assessment Toos Tabe IV presents the seected ndvdua rsk assessment toos to predct death/mi for CAD patents wthn a short perod. TABLE IV SHORT-TERM RISK ASSSMENT MODELS Mode Event Tme Prev. Type GRACE D [7] MI 6 m S PURSUIT D [8] MI 30 d S TIMI D MI [9] UR 4 d S Rsk Factors Age, SBP, CAA HR, Cr, STD, ECM, CHF Age, Sex, SBP, CCS, HR, STD, ERL, HF Age, STD, ECM, KCAD, AS, AG, RF D: Death; MI: Myocarda Infarcton; UR: Urgent revascuarzaton m: months; d: days; S: Secondary Preventon; Cr-Creatnne, HR Heart Rate, CAA Cardac Arrest at Admsson, CHF Congestve Heart Faure, STD - ST Segment. Depresson, ECE - Eevated Cardac Markers/Enzymes, KCAD- Known CAD, ERL Enroment (MI/UA), HF Heart Faure, CCS Angna cassfcaton, AS - Use of asprn n the prevous 7 days, AG - 2 or more angna events n past 24 hrs, RF - 3 or more cardac rsk factors D. Indvdua Rsk Assessment Toos Performance The proposed combnaton scheme requres that ndvdua modes have the same number of output eves. Ths work defnes the rsk stratfcaton n two categores: { ow/ntermedate rsk, hgh rsk }.Therefore, the hgh rsk category n the orgna modes matches the new hgh rsk. The remanng orgna categores were grouped nto ow/ntermedate rsk category. Tabe V shows the performance of the three ndvdua modes when a perod of 30 days s consdered. As observed the three modes present a very dfferent abty to predct the endpont n the three dfferent testng stuatons. GRACE was the rsk assessment too wth the best performance and dscrmnaton capabty n the three test stuatons. TIMI and PURSUIT presented a poor performance, so they are not as sutabe as GRACE to the endpont predcton n the consdered datasets. TABLE V PERFORMANCE OF LECTED INDIVIDUAL RISK ASSSMENT TOOLS Mode % Santa Cruz Santa Cruz Santo André 30 days/d/mi 30 days/d 30 days/d GRACE Acc AUC PURSUIT Acc AUC * TIMI Acc AUC * : Senstvty (%); : Specfcty (%); ACC: Accuracy (%); AUC: area under the Recever Operatng Characterstc E. Indvdua Modes Combnaton The Bayesan goba mode was derved accordng to the methodoogy expaned n II. The goba votng mode was mpemented consderng the votes (0/) of the three ndvdua modes. Orgna Boot Sampes n=000 Orgna Boot Sampes n=000 Orgna Boot Sampes n=000 TABLE VI PERFORMANCES COMPARISON SANTA CRUZ, (DEATH/MI) % GRACE PURSUIT TIMI ByG Vot G AUC (60.2; 6.3) (4.9;43.) (33.0; 34.0) (60.;6.3) (48.0;49.2) (74.8; 75.) (74.;74.3) (73.5; 73.7) (66.9;67.2) (75.5;75.8) G (67.0; 67.6) (55.5;56.2) (48.9; 49.7) (63.3;63.9) (60.0;60.7) TABLE VII PERFORMANCES COMPARISON SANTA CRUZ, DEATH % GRACE PURSUIT TIMI ByG Vot G AUC (76.5;78.0) (37.4;39.2) (22.3;23.7) (60.7;62.5) (52.9;54.7) (73.6;73.9) (73.;73.4) (72.8;73.) (65.6;65.9) (74.5;74.8) G (74.9;75.6) (5.;52.5) (38.0;39.5) (62.7;63.6) (62.2;63.3) TABLE VIII PERFORMANCES COMPARISON SANTO ANDRÉ, DEATH % GRACE PURSUIT TIMI ByG Vot G AUC (59.8;62.8) (8.6;2.2) (20.3;22.9) (78.9;8.5) (40.0;43.) (59.9;60.8) (7.6;72.5) (92.7;93.5) (66.4;67.2) (73.7;74.5) G (57.7;59.7) (27.4;30.5) (33.4;36.9) (7.5;73.) (49.3;52.) 930

7 Based on the prevous tabes (VI, VII, VIII), t s possbe to concude that n some testng stuatons (Tabe VIII) the Bayesan goba mode presents a better performance than the other modes. However, GRACE too showed hgher dscrmnaton capabty when apped patents of Santa Cruz dataset. These resuts demonstrate that the proposed combnaton scheme shoud be compemented wth the adjustment of ts parameters (optmzaton procedure) n order to mprove ts performance. F. Optmzaton The proposed fuson methodoogy can be adjusted to a specfc popuaton. If a dataset s avaabe, an optmzaton can be performed mprovng the behavor of the goba Bayesan mode. Tabe IX presents the optmzaton resuts, obtaned through a genetc agorthm approach. Orgna Boot Sampes n=000 TABLE IX PERFORMANCES COMPARISON Santa Cruz 30 days/d/mi Santa Cruz 30 days/d Santo André 30 days/d ByG ByG AO ByG ByG AO ByG ByG AO G AUC G 60.6 (60.;6.3) 67.0 (66.9;67.2) 63.6 (63.3;63.9) 72.9 (72.4;73.4) 69. (69.0;69.2) 70.9 (70.6;7.) 6.6 (60.7;62.5) 65.8 (65.6;65.9) 63. (62.7;63.6) 77.3 (76.5;78.0) 70.6 (70.5,70.8) 73.6 (73.3;74.0) 80.3 (78.9;8.5) 66.8 (66.4;67.2 ) 72.3 (7.5;73.) 79.8 (78.6;8.0) 83.8 (83.3;84.2) 80.9 (80.0;8.6) : Senstvty; : Specfcty; D: Death; MI: Myocarda Infarcton; (;)=95% Confdence Interva; ByG Bayesan Goba Mode; ByG AO Bayesan Goba Mode After Optmzaton. It s possbe to concude that genetc agorthms optmzaton mproved the performance of the Bayesan goba mode. The optmzaton was performed n the neghborhood of the nta vaues, athough ths restrcton may reduce the effcency of the optmzaton agorthm, t assures that the optmzaton procedure does not gnore the knowedge provded by the orgna rsk assessment toos. G. Mssng Rsk Factors The abty of the dfferent cassfers to dea wth mssng rsk factors was assessed through the comparson of the Bayesan approach (before and after the optmzaton procedure) wth the votng mode. Repacement of mssng rsk factors n votng mode was done accordng to the varabes type, such as: ) bnary varabes were repaced successvey by vaues 0 and ; ) as Kp eve s ordna, t was repaced sequentay by vaues, 2 and 3; ) a snge mputaton method based on the vaue was apped to the remanng varabes that are contnuous. Three dfferent stuatons were evauated: ) one mssng rsk factor; ) two mssng rsk factors; ) three mssng rsk factors. G G G TABLE X MISSING RISK FACTORS - SANTA CRUZ, (DEATH/MI) Parameter Bayesan Bayesan After Opt. Votng (55.3;58.8) (62.9;67.7) (43.9;52.6) std. dev range [45.4;72.2] [48.5;75.7] [27.3;72.7] (63.;67.9) (63.4;68.4) (7.3;78.2) std. dev range [47.3;74.4] [48.0;75.8] [46.9; 94.8] (60.2;6.8) (64.2;66.0) (57.5;60.6) std. dev range [57.8; 67.6] [60.3; 7.2] [50.5; 69.0] TABLE XI MISSING RISK FACTORS - SANTA CRUZ, (DEATH) Parameter Bayesan Bayesan After Opt. Votng (58.;6.9) (6.0;65.) (45.2;54.) std. dev range [46.;76.9] [46.2;84.6] [23.0;76.9] (62.2,66.9) (66.8,70.8) (70.9,77.8) std. dev range [46.5, 72.9] [55.0, 78.0] [46.0, 93.7] (6.0;63.0) (63.5;66.8) (57.5;6.6) std. dev range [57.9;66.8] [58.7;75.] [46.2;70.6] TABLE XII MISSING RISK FACTORS - SANTO ANDRÉ, (DEATH/MI) Parameter Bayesan Bayesan After Opt. Votng (66.4;75.) (7.;79.) (38.4;52.3) std. dev range [20;80] [20;80] [20;00] (64.4;66.6) (78.;80.7) (70.9;76.) std. dev range [6.7;8.9] [75.5;8.9] [58.5;88.9] (6.0;63.0) (63.5;66.8) (57.5;6.6) std. dev range [57.9;66.8] [58.7;75.] [46.2;70.6] It s possbe to concude that n the majorty of the test cases the goba Bayesan mode after optmzaton presents the best performance (hghest senstvty/hghest specfcty). However, n some stuatons (Santa Cruz Dataset) the votng mode presented the hghest specfcty s vaue. Ths ack of performance of the Bayesan goba mode n some testng stuatons must be further nvestgated. IV. CONCLUSIONS Ths work addressed the combnaton (fuson) of CVD rsk assessment toos. As referred, the combnaton of these ndvdua rsk assessment toos can overcome the respectve 93

8 weaknesses, namey: ) consder smutaneousy the nformaton provded by the seected ndvdua current rsk assessment toos, ) ncrease the number of rsk factors to compute the rsk, ) mprove the capabty to dea wth mssng rsk factors, v) aow the ncorporaton of addtona cnca knowedge, v) assure the cnca nterpretabty of the respectve parameters. Besdes, t emnates the need of a consensus on the best mode to use n the cnca practce. Fnay, ths goba mode can be easy adjusted for a gven popuaton. The obtaned resuts are very promsng, suggestng the potenta of the Bayesan approach to fuse current rsk assessment toos n a cnca practce context. Future work w further nvestgate the capabty of ths combnaton strategy to dea wth mssng nformaton as we as the ncorporaton of addtona cnca knowedge. Vadaton consderng a sgnfcant number of patents as we as ts appcaton to other popuatons w gve addtona sgnfcance to the deveoped strategy. [5] Yang. (2009). Dscretzaton for naïve- Bayes earnng managng dscretzaton bas and varance. Machne Learnng, Vo. 74, pp [6] Gonçaves P., Ferrera J., Aguar C., Seabra-Gomes R., TIMI, PURSUIT and GRACE rsk scores: sustaned prognostc vaue and nteracton wth revascuarzaton n NSTE-ACS, European Heart Journa, Vo. 26, pp , [7] Twardy C., Nchoson A., Korb K., McNe J., Data Mnng cardovascuar Bayesan networks, Schoo of Computer Scence Software Engneerng, Monash Unv., Mebourne, Tec. report, V. REFERENCES [] Word Heath Organzaton, Cardovascuar Dseases (CVDs), fact sheet n (accessed December 200) [2] European Heart Network, Heathy Hearts for A, Annua Report 2009.Avaabe: (accessed March 20). [3] European Heart Network, Heathy Hearts for A, Annua Report 2008.Avaabe: (accessed March 20). [4] Commsson of the European Communtes, Confrontng demographc change: a new sodarty between the generatons - Green paper. Avaabe: (Accessed n December 200). [5] Bertrand, M. et a. Management of acute coronary syndromes n patents presentng wthout persstent ST-segment eevaton, European Heart Journa, Vo. 23, , [6] Graham, I. et. a., Gudenes on preventng cardovascuar dsease n cnca practce: executve summary, European Heart Journa, Vo.28, , [7] S. Paredes, T. Rocha, P. Carvaho, J. Henrques, M. Harrs, J. Moras, Long Term Cardovascuar Rsk Modes' Combnaton, Computer Methods and Programs n Bomedcne Journa, 20. [8] Tang. E, et. a., Goba Regstry of Acute Coronary Events(GRACE) hospta dscharge rsk scores accuratey predcts ong term mortaty post-acute coronary syndrome, AHJ, Vo. 54, pp [9] Antman, E. et. a., The TIMI rsk score for Unstabe Angna / Non-St Eevaton MI A method for Prognostcaton and Therapeutc Decson Makng, Journa of Amercan Medca Assocaton,Vo. 284, pp , [0] Boersma E., K. Peper, E. Steyerberg, Predctors of outcome n patents wth acute coronary syndromes wthout persstent ST-segment eevaton. Resuts from an nternatona tra of 946 patents, Crcuaton 0; , [] Kotsants, S, Supervsed Machne Learnng: A Revew of Cassfcaton Technques, Informatca Vo.3, , [2] Zheng, A comparatve study of sem-naïve Bayes methods n cassfcaton earnng. Proceedngs of the 4th Austraasan Data Mnng Conference, (pp. pp ). [3] Fredman N., Geger D., Godszmdt M., Bayesan network cassfers, Machne Learnng, Vo.29, 3-63, 997. [4] Tsymba A., S. Puuronen and D. Patterson, Ensembe feature seecton wth the smpe Bayesan cassfcaton, n Informaton Fuson, Vo.4, Issue 2, pp (Esever, 2003). 932