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Machine lea ning models in p edic ing heal h ca e cos s in pa ien s wi h a ecen acu e
co ona y synd ome : A p ospec i e pilo s udy
© 2023 Hea Rhy hm Socie y
Published e sion
Hau ala, A o J.; Sha azipou , Babooshka; A sa , Beki ; Tulppo, Mikko P.;
Mie inen, Kaisa
Hau ala, A. J., Sha azipou , B., A sa , B., Tulppo, M. P., & Mie inen, K. (2023). Machine lea ning
models in p edic ing heal h ca e cos s in pa ien s wi h a ecen acu e co ona y synd ome : A
p ospec i e pilo s udy. Ca dio ascula Digi al Heal h Jou nal, 4(4), 137-142.
h ps://doi.o g/10.1016/j.c dhj.2023.05.001
2023
Machine lea ning models in p edic ing heal h ca e
cos s in pa ien s wi h a ecen acu e co ona y
synd ome: A p ospec i e pilo s udy
A o J. Hau ala, PhD,*Babooshka Sha azipou , PhD,
†
Beki A sa , PhD,
†
Mikko P. Tulppo, PhD,
‡
Kaisa Mie inen, PhD
†
F om he *Facul y o Spo and Heal h Sciences, Uni e si y o Jy askyla, Jy askyla, Finland,
†
Facul y o
In o ma ion Technology, Uni e si y o Jy askyla, Jy askyla, Finland, and
‡
Resea ch Uni o
Biomedicine and In e nal Medicine, Medical Resea ch Cen e Oulu, Oulu Uni e si y Hospi al,
Uni e si y o Oulu, Oulu, Finland.
BACKGROUND Heal h ca e budge s a e limi ed, equi ing he
op imal use o esou ces. Machine lea ning (ML) me hods may
ha e an eno mous po en ial o e ec i e use o heal h ca e e-
sou ces.
OBJECTIVE We assessed he applicabili y o selec ed ML ools o
e alua e he con ibu ion o known isk ma ke s o p ognosis o
co ona y a e y disease o p edic heal h ca e cos s o all easons
in pa ien s wi h a ecen acu e co ona y synd ome (n 565, aged
65 69 yea s) o 1-yea ollow-up.
METHODS Risk ma ke s we e assessed a baseline, and heal h ca e
cos s we e collec ed om elec onic heal h egis ies. The C oss-
decomposi ion algo i hms we e used o ank he conside ed isk
ma ke s based on hei impac s on a iances. Then eg ession anal-
ysis was pe o med o p edic cos s by en e ing he fi s op- anking
isk ma ke and adding he nex -bes ma ke s, one by one, o build
up al oge he 13 p edic i e models.
RESULTS The a e age annual heal h ca e cos s we e V2601 6
V5378 pe pa ien . The Dep ession Scale showed he highes p e-
dic i e alue ( 50.395), accoun ing o 16% o he cos s
(P5.001). When he nex 2 anked ma ke s (LDL choles e ol, 5
0.230; and le en icula ejec ion ac ion, 5-0.227, espec-
i ely) we e added o he model, he p edic i e alue was 24% o
he cos s (P5.001).
CONCLUSION Highe dep ession sco e is he p ima y a iable o e-
cas ing heal h ca e cos s in 1-yea ollow-up among acu e co ona y
synd ome pa ien s. The ML ools may help decision-making when
planning op imal u iliza ion o ea men s a egies.
KEYWORDS Co ona y a e y disease; Co ona y hea disease; A ifi-
cial in elligence; Heal h ca e cos s; Economic e alua ion
(Ca dio ascula Digi al Heal h Jou nal 2023;4:137–142) ©2023
Hea Rhy hm Socie y. This is an open access a icle unde he CC
BY license (h p://c ea i ecommons.o g/licenses/by/4.0/).
In oduc ion
Ca dio ascula disease incidence and mo ali y a es a e
declining in many coun ies in Eu ope bu s ill emain a majo
cause o mo bidi y and mo ali y
1
wi h a significan impac
on heal h ca e cos s. The economic bu den o ca dio ascula
diseases in he Eu opean Union egion was e alua ed o be
V169 billion annually and 62% o hese cos s we e ela ed
o heal h ca e.
2
The da a om he Uni ed S a es show ha
expendi u e on ca dio ascula disease and ca dio ascula
isk ac o s in 2016 was $320 billion. Heal h se ices o
ischemic hea disease ($80 billion) and ea men o hype -
ension ($71 billion) we e he main causes o he cos s, ol-
lowed by ea men o hype lipidemia.
3
Based on he
EUROASPIRE su ey,
4
cos s o op imized ailo ed p e en-
ion such as smoking cessa ion, die and exe cise, be e man-
agemen o ele a ed blood p essu e and/o low-densi y
lipop o ein (LDL) choles e ol, and sa ings o a oided e en s
we e es ima ed based on coun y-specific da a. The esul s
showed ha op imizing seconda y p e en ion is clea ly
cos -e ec i e compa ed wi h he cu en gene al guideline–
o ien ed p e en ion.
4
Heal h ca e p o ide s wo ldwide a e equi ed o se p io -
i ies and alloca e esou ces wi hin he cons ain o limi ed
unding. Howe e , decision make s may no be well equip-
ped o make explici a ioning decisions and may o en ely
on his o ical o poli ical esou ce alloca ion p ocesses.
5
The e o e, economic e alua ion o heal h ca e o ope a ional
planning and decision-making is i al o alloca ion o e-
sou ces o e ec i e ea men s ha p o ide pa ien s he g ea -
es possible heal h benefi s a easonable cos s. Since heal h
ca e sys ems, ca e p ac ices, and ela i e p ices o heal h
ca e in es men s a y om coun y o coun y, i is impo an
o ha e coun y-specific da a o suppo decision-making.
6
ClinicalT ials.go iden ifie : Reco d NCT01916525. Add ess ep in
eques s and co espondence: D A o J. Hau ala, Facul y o Spo s and
Heal h Sciences, Uni e si y o Jy askyla, PO Box 35, FI-40014 Uni e si y
o Jy askyla, Finland. E-mail add ess: a o.j.hau ala@jyu.fi.
2666-6936/© 2023 Hea Rhy hm Socie y. This is an open access a icle unde he CC BY license
(h p://c ea i ecommons.o g/licenses/by/4.0/).
h ps://doi.o g/10.1016/j.c dhj.2023.05.001
Machine lea ning (ML) and a ificial in elligence me hods
may ha e a conside ably high po en ial o bo h e ec i e and
cos -e ec i e use o heal h ca e esou ces when implemen ed
in clinical p ac ice.
7,8
Fo example, Schwalm and colleagues
9
showed ecen ly ha an ML p edic ion model used as an on-
line decision suppo ool by e e ing physicians could
imp o e he diagnos ic yield o in asi e co ona y angiog aphy
in s able co ona y a e y disease (CAD) pa ien s. The decision-
make planning o op imal use o heal h ca e esou ces may
benefi om he p edic ion model o he mos impo an isk
ac o o combina ion o hose con ibu ing mos o heal h
ca e cos s. The ea u e impo ance analysis is widely used in
p edic i e modeling, ep esen ing he significance o he inpu
ea u es a he a ge a iables p edic ion by calcula ing p edic-
i e sco es.
10
We conduc ed an analysis using selec ed ea u e
impo ance analysis ools o assess he con ibu ion o well-
add essed causal and modifiable isk ma ke s o p ognosis
o CAD a baseline o p edic heal h ca e cos s in pa ien s
wi h a ecen acu e co ona y synd ome (ACS) o 1-yea
ollow-up in he Finnish heal h ca e sys em.
Me hods
S udy popula ion
This s udy is pa o he EFEX-CARE (E ec i eness o Ex-
e cise Ca diac Rehabili a ion) s udy ha has been egis e ed
a ClinicalT ials.go (Iden ifie Reco d NCT01916525). The
pa ien s in he EFEX-CARE s udy ha e been ec ui ed om a
consecu i e se ies o ACS pa ien s in he Di ision o Ca di-
ology o he Oulu Uni e si y Hospi al. They all unde wen
co ona y angiog aphy o confi m he CAD. The s udy popu-
la ion o he EFEX-CARE s udy has been p e iously
desc ibed in de ail,
11
bu o pu i b iefly, exclusion c i e ia
included NYHA class III, scheduled o eme gency p oced-
u e o bypass su ge y, uns able angina pec o is, se e e pe-
iphe al a he oscle osis, diabe ic e inopa hy o neu opa hy,
o inabili y o independen daily physical ac i i y, eg, owing
o musculoskele al p oblems. In his s udy, we epo heal h
ca e cos s o 1 yea ollow-up and isk ma ke da a a base-
line measu ed abou 2–3 weeks a e hei hospi al discha ge
o he pa ien s ea ed acco ding o usual ca e. Al oge he , all
da a needed o analysis we e a ailable o 65 pa ien s. The
s udy epo ed in his pape adhe ed o he CONSORT guide-
lines and was ca ied ou acco ding o he Decla a ion o Hel-
sinki; he local commi ee o esea ch e hics o he No he n
Os obo hnia Hospi al Dis ic app o ed he p o ocol. All he
subjec s ga e w i en in o med consen .
Assessmen o pa ien cha ac e is ics, isk
ma ke s, and heal h ca e cos s
Body weigh and heigh we e measu ed o assess body
composi ion. Blood p essu e was measu ed in a supine posi-
ion a e a 10-minu e es ing pe iod acco ding o he cu en
guideline. Sel - a ed dep ession was assessed by using he
Dep ession Scale (DEPS) ques ionnai e.
12
The hospi al eg-
is y and s anda d ques ionnai es we e used o ga he he
da a ega ding smoking s a us, alcohol use diso de s iden ifi-
ca ion (AUDIT-C),
13
medica ion, his o y o acu e myoca -
dial in a c ion, and e ascula iza ion. Assessmen o le
en icula sys olic unc ion was pe o med using 2-D echo-
ca diog aphy (Vi id 7; GE Heal hca e, Wauwa osa, WI).
Blood samples om as ing s age we e ob ained o analysis
o plasma glucose and glyca ed hemoglobin (HbA1c), blood
lipids, insulin, and high-sensi i i y C- eac i e p o ein a e a
12-hou o e nigh as using consis en me hods (Oulu
Uni e si y Hospi al, Oulu, Finland). An inc emen al
symp om-limi ed maximal exe cise es was pe o med a
he Oulu Uni e si y Hospi al on a bicycle e gome e (Mona k
E gomedic 839 E; Mona k Exe cise AB, Vansb o, Sweden)
o assessmen o maximal physical exe cise capaci y (me a-
bolic equi alen s). The 15D ques ionnai e was used o eco d
heal h- ela ed quali y o li e
14
and i was comple ed by he pa-
ien s a he hospi al be o e hospi al discha ge. In he es ima-
ion o heal h ca e cos s, bo h specialized and p ima y heal h
ca e se ices, as well as he cos s o occupa ional heal h ca e
se ices, we e conside ed. Social secu i y ID numbe s we e
used o de e mine isi s o ambula o y ca e, numbe o ea -
men days, and use o ex e nal se ices o calcula e heal h
ca e cos s a ising om he use o heal h se ices on he
pa o specialized heal h ca e. The exac cos s we e
measu ed based on in oicing (using Diagnosis Rela ed
G oups classifica ion). In o ma ion on he use o p ima y
heal h ca e and cos s ela ed o i was ob ained om elec-
onic heal h egis ies by using unique social secu i y ID
numbe s o de e mine isi s o he doc o , o he significan
examina ions such as la ge adiog aphs, and in-wa d ea -
men days. Fu he mo e, he use o home ca e and possible
ins i u ional ca e (eg, assis ed ca e home, e c) was de e mined
om egis ies. The epo o he Social Insu ance Ins i u e o
Finland (KELA)
15
was used o es ima e occupa ional heal h
ca e se ice cos s. Finally, all cos s we e managed as 2015
alues. Because o he 1-yea ime ho izon o he analysis,
no discoun ing was applied.
KEY FINDINGS
Ad anced da a analy ics and machine lea ning ools can
po en ially be used o p edic heal h ca e cos s in eal-
wo ld clinical se ings.
Ou pilo s udy showed o he fi s ime, using machine
lea ning ools, ha dep ession, exp essed as highe
dep ession sco es, is he p ima y heal h measu emen
o ecas ing heal h ca e cos s o all easons, ollowed
by low-densi y lipop o ein choles e ol and le en ic-
ula ejec ion ac ion, in 1-yea ollow-up among acu e
co ona y synd ome pa ien s.
Applica ions o machine lea ning and a ificial in elli-
gence me hods o heal h ca e cos s can p o ide in o -
ma ion ha may be help ul o decision-making when
planning op imal u iliza ion o ea men s a egies
and esou ces in heal h ca e se ings.
138 Ca dio ascula Digi al Heal h Jou nal, Vol 4, No 4, Augus 2023
De elopmen o p edic i e models
In p edic i e modeling, he significance o he inpu ea u es a
he a ge a iable p edic ions is ep esen ed by some sco es
calcula ed h ough a so-called ea u e impo ance analysis.
10
In o he wo ds, hese sco es demons a e he impo ance o a
ea u e/ a iable o a p edic ion. Indeed, ea u e impo ance
analysis is o en used, as ea u e selec ion, o educe he num-
be o inpu a iables, bo h o educe he compu a ional cos o
modeling and, in some cases, o imp o e he model’spe o -
mance. These ea u e impo ance sco es p o ide insigh in o
he da a and models and play a c ucial ole in imp o ing he
e ficiency o he p edic i e models h ough ea u e selec-
ion
16,17
and dimensionali y educ ion.
18,19
Va ious ea u e
impo ance me hods ha e been de eloped in he li e a u e,
eg, based on s a is ical co ela ions and a iances. Howe e ,
he choice o me hods depends on a iables and he ype o
da a. The e o e, i is ecommended o e alua e a ious ech-
niques o find sui able ones.
We conduc ed a ea u e impo ance analysis on ou da a-
se o check he significance o selec ed isk ac o s in p edic -
ing all heal h ca e cos s. A e we es ed a ious ea u e
impo ance me hods, he ollowing ones p o ed hei s abili y
in se e al es s on andomly selec ed subg oups o samples
om he da ase : C oss decomposi ion
20
; pa ial leas squa es
(PLS) canonical analysis (PLSC), PLS based on singula
alue decomposi ion (PLSSVD), PLS eg ession (PLSRe-
g ession), and canonical co ela ion analysis (CCA) algo-
i hms ank he conside ed isk ac o s based on hei
impac s on a iances (ie, show which isk ac o leads o
he highes a iance in cos s). PLSReg ession anks consid-
e ed isk ac o s based on absolu e alues o he co ela ions
be ween he isk ac o and he cos s. Analysis o a iance
(ANOVA) es has also been used o ea u e selec ion o
ank conside ed isk ac o s based on hei P alues. The
used me hods eflec he in insic p edic i e alue o he
isk ac o s and a e no dependen on a pa icula p edic i e
model ha makes hem mo e sui able in ou case. PLS es i-
ma o s a e pa icula ly sui ed when he e is mul icollinea i y
among he isk ac o s.
21
A e anking o isk ac o s o heal h ca e cos s, a linea
eg ession analysis was pe o med o p edic cos s by
en e ing he fi s op- anking isk ma ke and adding he
nex -bes ma ke s, one by one, o build up al oge he 13 p e-
dic i e models. Desc ip i e s a is ical analyses we e conduc -
ed using means, s anda d de ia ions (SDs), and p opo ions,
as app op ia e. SPSS so wa e (SPSS 26; SPSS Inc, Chicago,
IL) was used o p edic i e da a analyses. S a is ical signifi-
cance was defined as a P alue ,.05 o all es s.
Resul s
Baseline demog aphics, clinical cha ac e is ics, and medica-
ion use o he s udy pa icipan s a e illus a ed in Table 1.
The o al a e age cos pe ACS pa ien o all easons was
V2601 65378 o a 1-yea ollow-up.
The anking o isk ac o s o p edic ion o he heal h ca e
cos is p esen ed in Figu e 1. The colo code on he igh side
ep esen s he isk ac o s anking (1–13) in each ea u e selec-
ion me hod. The lowe ank alue (da ke colo in he hea -
map) deno es he highe impo ance o he isk ac o . The
numbe s in pa en heses (1–13) show he agg ega ed ank o
each isk ac o o e hei anking ound in a ious me hods.
Table 2 shows he final p edic i e models and hei con i-
bu ions o he cos s. Fu he mo e, he di ec ion o each isk
ma ke con ibu ion (nega i e o posi i e) is shown as co ela-
ion alues. The DEPS showed he highes p edic i e alue
Table 1 Baseline demog aphics, clinical cha ac e is ics, heal h
ca e cos s, and medica ion use o he s udy g oup (n 565)
Va iable ACS pa ien s
Men 46 (71%)
Pa ien s wi h T2D 11 (17%)
Age, yea s 65 69
Weigh , kg 83 614
BMI, kg/m
2
28.0 64.3
Sys olic BP, mm Hg 137 622
Dias olic BP, mm Hg 78 611
Maximal exe cise capaci y, MET 5.6 61.7
Quali y o li e, 15D scale 0.90 60.08
AUDIT-C o alcohol use 2.9 62.4
Dep ession scale 4.6 65.3
Cu en smoke 8 (12%)
To al a e age heal h ca e cos pe pa ien
Cos o all easons (V) 2601 65378
His o y o AMI
NSTEMI 45 (51%)
STEMI 22 (34%)
Re ascula iza ion
PCI 55 (85%)
Ea lie CABG 8 (12%)
Ca diac unc ion
LVEF, % 62 67
CCS class 1.6 60.6
Labo a o y analyses
HbA1c, % 6.0 60.8
Fas ing plasma glucose, mmol/L 6.0 61.0
To al choles e ol, mmol/L 3.8 60.7
HDL choles e ol, mmol/L 1.2 60.3
LDL choles e ol, mmol/L 2.1 60.7
T iglyce ides, mmol/L 1.3 60.6
hs-CRP, mg/L 2.7 66.0
Medica ion
Be a-blocke s 56 (86%)
ACEI o ARB 54 (83%)
Lipids 64 (98%)
An icoagulan s 64 (98%)
Calcium an agonis s 17 (26%)
Ni a es 18 (28%)
Diu e ics 15 (23%)
Values a e means 6SD o numbe (pe cen age) o subjec s.
15D 5heal h- ela ed quali y o li e ques ionnai e; ACEI 5angio ensin-
con e ing enzyme inhibi o ; ACS 5acu e co ona y synd ome; AMI 5acu e
myoca dial in a c ion; ARB 5angio ensin ecep o blocke ; AUDIT-C 5
Alcohol Use Diso de s Iden ifica ion Tes ; BMI 5body mass index; BP 5
blood p essu e; CABG 5co ona y a e y bypass g a ; CCS 5Canadian Ca -
dio ascula Socie y g ading o angina pec o is; HbA1c 5glyca ed hemoglo-
bin; HDL 5high-densi y lipop o ein; hs-CRP 5high-sensi i i y C- eac i e
p o ein; LDL 5low-densi y lipop o ein; LVEF 5le en icula ejec ion ac-
ion; MET 5me abolic equi alen ; NSTEMI 5non-ST-segmen ele a ion
myoca dial in a c ion; PCI 5pe cu aneous co ona y in e en ion; STEMI
5ST-segmen ele a ion myoca dial in a c ion; T2D 5 ype 2 diabe es.
Hau ala e al P edic ion Models o Heal h Ca e Cos s 139
( 50.395), accoun ing o 16% o he cos s (P5.001). Those
pa ien s who showed highe sco es o dep ession had highe
heal h ca e cos s. When he nex 2 anked ma ke s (LDL
choles e ol, 50.230; and le en icula ejec ion ac ion
[LVEF], 5-0.227, espec i ely) we e added o he model,
he p edic i e alue was 24% o he cos s (P5.001). Finally,
ha ing all 13 isk ma ke s (including, eg, smoking, sys olic
blood p essu e, and diabe es) in he model, hey p edic ed
30% o he cos s (P5.094).
Discussion
The p esen s udy demons a ed ha selec ed ML ools a e
applicable o p edic heal h ca e cos s o all easons in 1-
yea ollow-up when assessing he con ibu ion o well-
add essed causal and modifiable isk ma ke s o p ognosis
o CAD collec ed a baseline in pa ien s wi h a ecen ACS.
We ound ha dep ession exp essed as he highe DEPS
sco e is he p ima y con ibu ing ac o o heal h ca e cos s
in 1-yea ollow-up, ollowed by a highe LDL choles e ol
le el and lowe alues o LVEF. These esul s may be use ul
o decision-making when planning and ocusing on op imal
u iliza ion o heal h ca e esou ces. Addi ionally, ou findings
may highligh he po en ial use o sophis ica ed ML da a an-
aly ics ools in eal-wo ld clinical se ings when making eco-
nomic analyses o suppo decision-making.
A baseline, he mos dominan p edic o s o all heal h
ca e se ice cos s in 1-yea ollow-up we e ela ed o a highe
le el o dep ession, a highe le el o LDL choles e ol, a lowe
le el o ejec ion ac ion, a lowe le el o exe cise capaci y,
and a lowe le el o heal h- ela ed quali y o li e, accoun ing
o abou 25% o he cos s in s able ACS pa ien s ea ed ac-
co ding o he cu en guidelines. All hose isk ac o s a e
well add essed as impo an causal and modifiable ac o s
o he p ognosis o disease.
22
In e es ingly, psychosocial
isk ac o s, such as dep ession, ha e shown hei impo ance
in a ec ing ca dio ascula p ognosis, ea men adhe ence,
quali y o li e, and sudden ca diac dea h.
23,24
I is no able
ha in he p esen s udy, he highes le el o co ela ion be-
ween heal h ca e cos s and he dep ession sco e migh be
conside ed as a mode a e associa ion ( 50.395), since a
high le el o co ela ion usually exceeds alues o .0.5
and could be in e p e ed as a s ong associa ion. Howe e ,
Figu e 1 Rank agg ega ion o he isk ac o s calcula ed by di e en me hods (each column ep esen s a ea u e selec ion me hod). The lowe ank alue
(da ke colo in he hea map) deno es he highe impo ance o he isk ac o . CCA 5canonical co ela ion analysis; LDL 5low-densi y lipop o ein; PLSC
5pa ial leas squa es canonical analysis; PLSR 5pa ial leas squa es eg ession; PLSSVD 5pa ial leas squa es based on singula alue decomposi ion;
F alue 5 alue om he analysis o a iance.
Table 2 Linea eg ession analysis models o p edic ion o heal h
ca e cos s
Risk ma ke s R co ela ion Model R
2
P alue
Dep ession Scale 0.395 1 0.156 .001
LDL choles e ol 0.230 2 0.190 .001
Ejec ion ac ion -0.227 3 0.240 .001
Maximal exe cise capaci y -0.142 4 0.245 .002
Quali y o li e, 15D -0.106 5 0.251 .004
Age 0.092 6 0.273 .004
Sex 0.090 7 0.273 .008
Smoking -0.089 8 0.274 .016
Sys olic blood p essu e -0.070 9 0.276 .026
AUDIT-C o alcohol use -0.050 10 0.285 .035
Diabe es -0.048 11 0.298 .042
HbA1c -0.027 12 0.299 .064
Body mass index -0.022 13 0.300 .094
The models we e defined acco ding o anking analysis o well-add essed
causal and modifiable isk ma ke s o p ognosis o co ona y a e y disease a
baseline. Model 1 includes op- anking isk ma ke Dep ession Scale. Models
om 2 o 13 we e defined by en e ing he second-highes pa ame e (LDL
choles e ol) o he model, hen he nex -highes isk ma ke s we e added
one by one o he defined models (3, ejec ion ac ion; 4, maximal exe cise
capaci y; 5, quali y o li e; 6, age; 7, sex; 8, smoking; 9, sys olic blood p es-
su e; 10, AUDIT-C o alcohol use; 11, diabe es; 12, HbA1c; and 13, body
mass index). Abb e ia ions as in Table 1.
140 Ca dio ascula Digi al Heal h Jou nal, Vol 4, No 4, Augus 2023

dep essi e symp oms ha e been shown o be s ongly associ-
a ed wi h highe le els o s ess, low social suppo , unem-
ploymen , low amily income, and unheal hy li es yle such
as low physical ac i i y, low ui and ege able in ake, and
excessi e sal consump ion in CAD pa ien s.
25
Symp oms
o dep ession a e highly p e alen in s able CAD pa ien s,
and hei long- e m ajec o ies a e sugges ed o be he single
bigges d i e o heal h ca e cos s.
26
The e o e, managemen
o dep ession symp oms migh be one o he p ima y ocuses
o policymake s and decision make s in planning ea men
and esou ces o s able CAD pa ien s.
23
As men ioned abo e, dep ession is a common como bidi y
in CAD pa ien s and nume ous po en ial mechanisms ha e
been pos ula ed o he ela ionship be ween dep ession and
CAD. I has been documen ed ha se e al clinical ac o s
can be d i ing dep ession concu en ly and hus may con ound
esul s when aiming o in e p e and define a causal isk ac o
o dep ession. This kind o analysis may equi e e idence ha
educ ion o he isk ac o educes isk.
27,28
The DEPS scale
we used in his s udy is a 10-i em sel - epo scale ha assesses
he se e i y o dep essi e symp oms. Rega ding he clinically
meaning ul alue o he DEPS scale, i has been sugges ed ha
a sco e o 10 o highe on he DEPS scale is a use ul cu o o
iden i ying clinically significan dep essi e symp oms.
29
Fu he mo e, i is impo an o no e ha a DEPS sco e o 10
o highe should no be used as he sole basis o diagnosing
dep ession. A comp ehensi e clinical e alua ion, including a
ho ough his o y and physical examina ion, is necessa y o
make an accu a e diagnosis and de elop an app op ia e ea -
men plan o dep ession.
E en hough no in he scope o he p esen s udy, we pe -
o med u he analysis using he DEPS sco e o 10 o find
ou i he measu es we ha e assessed, including medica ion
(p esen ed in Table 1), a e associa ed wi h he DEPS scale.
Se en pa ien s had a alue o 10 o highe . The only pa am-
e e associa ed wi h he DEPS scale was quali y o li e, as-
sessed wi h 15D ques ionnai e ( 5-0.396, P5.001).
In he p esen s udy, he selec ed ea u e impo ance
me hods showed hei applicabili y o ank well-known isk
ma ke s o find he mos p e e ed fi s -o de a ge s o isk
ma ke s o con ibu e o heal h ca e cos s. We also used
and e alua ed some o he ele an isk ma ke s o he
ACS popula ion in he de elopmen p ocess. Fo example,
since high-sensi i i y C- eac i e p o ein has been shown o
be an independen p edic o o ad e se ca dio ascula
e en s among CAD pa ien s,
30
we es ed i i con ibu es o
he o de o leading p edic i e isk ma ke s. We ound ha
including high-sensi i i y C- eac i e p o ein in he analysis
as an ex a isk ma ke will no change he esul s. Fu he -
mo e, we assessed i he o de o he leading p edic i e isk
ma ke will change i we emo e, one by one, he isk
ma ke s anked om 6 o 13. Despi e exclusion o he
isk ma ke s om he ea u e impo ance analysis, he o de
o he 5 leading ma ke s emained he same. The e o e, we
belie e ha in addi ion o selec ed ea u e impo ance anal-
ysis ools, he selec ed isk ma ke s included we e ele an
and alid o he pe o med analysis.
The use o heal h ca e se ices in he p esen s udy was
de i ed om hospi al eco ds ins ead o , o example, om pa-
ien sel - epo s, he eby elimina ing ecall bias. Secondly, he
cha ac e is ics o pa ien s a he baseline we e widely assessed,
including clinical s a us, medica ion, and comp ehensi e lab-
o a o y analysis. We eel ha hese a e he s eng hs o his
s udy. A limi a ion o his pilo s udy is ha he pa ien sample
in he EFEX-CARE s udy is small and may be pa ly selec ed,
which could limi he gene alizabili y o a b oade popula ion
o ACS pa ien s wi h significan como bidi ies. We showed
ha a e combining all he 13 s udied ma ke s, only 30% o
he cos s could be p edic ed by his model. This could be in e -
p e ed as a ela i ely low a e. Howe e , he p op ie a y na u e
o economic da a, and he ac ha elemen s o heal h ca e
cos s a e coming om di e en en i ies, may a leas pa ly
explain ou esul s. Fo example, we we e able o analyze
di ec heal h ca e cos s, bu no indi ec cos s such as he ex-
penses incu ed om he cessa ion o educ ion o wo k p o-
duc i i y. The o he ques ion ha emains open is whe he i
would be possible o aise he p edic i e alue by adding
mo e a iables; his could be a a ge o u u e esea ch. Addi-
ionally, al hough we ca e ully assessed a ious ea u e impo -
ance me hods o p o e hei s abili y, he ela i ely low
numbe o samples and mul icollinea i y among he isk ac-
o s may aise some cau ion in o e all in e p e a ion and
gene alizabili y in o e all in e p e a ion o he esul s. Howe -
e , he p oposed me hodology is gene ic enough o be applied
in any field o se ing o medical and heal h ca e in which isk
p ofiles o pa ien s exis and heal h ca e cos s o ce ain pe-
iods a e assessed.
Because heal h ca e budge s a e limi ed wo ldwide, he e is
a c ucial need o s a egies o heal h ca e sys ems ha p o e o
be cos -e ec i e. The need o ca e s a egies should a he
same ime be low cos and gi e he bes e ec o ca e. How-
e e , di ec assessmen o cos s is no easonable in di e en
coun ies because o di e ences in social and heal h ca e se -
ices na ionally. The e o e, he esul s o he p esen s udy
may be use ul o policymake s especially in he Finnish
heal h ca e sys em when planning and deciding how limi ed
heal h ca e esou ces should be used in he op imal way.
Conclusion
Ou s udy showed ha dep ession, exp essed as highe
dep ession sco es, is he p ima y ac o o ecas ing heal h
ca e cos s o all easons, ollowed by LDL choles e ol and
LVEF, in 1-yea ollow-up among ACS pa ien s. These e-
sul s a e help ul o decision-making when planning op imal
u iliza ion o ea men s a egies and esou ces in di e en
heal h ca e se ings. Fu he mo e, ou findings confi m he
po en ial use o ad anced da a analy ics and ML ools in
eal-wo ld clinical se ings.
Acknowledgmen s
The au ho s would like o hank he EFEX-CARE s udy
g oup o hei excellen wo k and assis ance h oughou
he EFEX-CARE s udy. This esea ch is ela ed o he
Hau ala e al P edic ion Models o Heal h Ca e Cos s 141
hema ic esea ch a ea o Decision Analy ics u ilizing Causal
Models and Mul iobjec i e Op imiza ion (DEMO, jyu.fi/
demo) o he Uni e si y o Jy askyla.
Funding Sou ces
This s udy was pa ly unded by he Academy o Finland,
Finland (g an no. 322221).
Disclosu es
The au ho s ha e no conflic s o disclose.
Au ho ship
All au ho s a es hey mee he cu en ICMJE c i e ia o
au ho ship.
Pa ien Consen
All pa ien s p o ided w i en in o med consen .
E hics S a emen
The au ho s designed he s udy and ga he ed and analyzed
he da a acco ding o he Helsinki Decla a ion guidelines
on human esea ch. The esea ch p o ocol used in his s udy
was e iewed and app o ed by he ins i u ional e iew boa d.
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