RiskStra*fica*onforHealth‐CareAssociatedC.diff

JennaWiens,JohnV.Gu?agandEricHorvitz*Massachuse?sIns*tuteofTechnology

*MicrosoIResearch

LearningEvolvingPa*entRiskProcessesforC.diffColoniza*on

1

Clostridiumdifficile(C.diff)

•  Bacteriatakesoverthegutwhennormalfloragetswipedout

•  Transmi?edthroughthemouth•  Causesseverediarrhea,intes*naldiseases•  Treatment:metronidazole,oralvancomycin

•  20%ofcasesrelapsewithin60‐days

Source:h?p://www.helladelicious.com/wp‐content/uploads/2012/05/Gut‐Flora‐Bacteria.jpgh?p://newsimg.bbc.co.uk/media/images/41223000/jpg/_41223637_clostridium203spl.jpg

(PepinJetal.,2005)

2

Prevalence

•  Hospital‐acquired:178,000/year

•  Onparwithnumber

ofnewcasesofinvasivebreastcancerintheUSeachyear

(McDonaldetal.,2006)

(CDC,2011)(AmericanCancerSociety,2009)

3

Represen*ngandreasoningabouttemporalprocessespromisestoenhancetheaccuracyofinferencesaboutrisk.

PATIENT RISK

•  Collectedatthe*meofadmission

•  e.g.,admissioncomplaint,previousadmissions,homemeds

•  Changesduringthe

hospitaliza*on•  e.g.,currentmeds,current

procedures,currentloca8on,hospitalcondi8ons

RiskFactors

4

TypicalApproachinClinicalLiterature

Time(days)

IndexEvent

Es*m

ated

Risk

Riskbasedonpa*ent’sstateat*meofadmission

Riskbasedonpa*ent’sstatexdaysbeforeindexevent

(Dubberkeetal.,2011)

(Tanneretal.,2009)

5

OurApproach

Time(days)

IndexEvent

P(IndexEvent)

EvolvingRiskProfile

Es*m

ated

Risk

6

Es*m

ated

risk High

Low

High

Low

RiskProcessesHypothesis:extrac*ngandanalyzingevolvingpa*entriskcanleadtoamoreaccuratemodelforpredic*nginfec*ons

RiskProcess:describestheevolu*onofriskoverthecourseofahospitaladmission

Posi*vePa*ents

7

InferringRiskProcesses

•  Challenges:•  Nogroundtruthaboutrisk

o Retrospec*vedatanotallpa*entsgettestedo Actualriskonanydayisunknowable

•  Thousandsofcorrelatedvariables

Time(days)

Risk

8

TheData

•  DatabasefromalargeurbanhospitalintheUS•  In‐pa*entstaysfromasingleyear•  Inclusioncriteria(seepaperfordetails)– Eliminateeasilyiden*fiablecases

Popula*on:•  ~10,000hospitaladmissions•  ~200Posi*veC.diffcases

9

ExperimentalSetup

•  Training&Tes*ng– Randomlysubsampledthenega*veclass– Splitdataintostra*fiedtrainingandtestsets70/30.– Trainingset1,251admissions(127posi*ve)– Tes*ngset532admissions(50posi*ve)

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TimeInvariant TimeVarying•  prev.ICD9codes •  pa*ent’sage •  labresults•  homemedica*ons •  pa*ent’smaritalstatus •  procedures•  prev.admissionmedica*ons •  pa*ent’ssex •  loca*onroom•  pa*ent’scity •  expectedsurgery •  loca*onunit•  a?endingMD •  ERadmission •  medica*ons•  Hospitalservice •  dialysis •  vitals•  admissionsource •  diabe*c •  dayofadmission•  financialclasscode •  historyofC.diff •  unitCP•  admissioncomplaint •  num.hospitalvisits(90days) •  hospitalwideCP•  admissionprocedure •  avg.,max.,totallos(90days)

•  pa*ent’srace

Features

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Features:>10,000variablesforeachdayofeveryhospitaladmission

TimeInvariant TimeVarying•  prev.ICD9codes •  pa*ent’sage •  labresults•  homemedica*ons •  pa*ent’smaritalstatus •  procedures•  prev.admissionmedica*ons •  pa*ent’ssex •  loca*onroom•  pa*ent’scity •  expectedsurgery •  loca*onunit•  a?endingMD •  ERadmission •  medica*ons•  Hospitalservice •  dialysis •  vitals•  admissionsource •  diabe*c •  dayofadmission•  financialclasscode •  historyofC.diff •  unitCP•  admissioncomplaint •  num.hospitalvisits(90days) •  hospitalwideCP•  admissionprocedure •  avg.,max.,totallos(90days)

•  pa*ent’srace

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Represen*ngahospitalstay: pn

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OurApproachtoRiskStra*fica*on

RiskStra*fy

SVM

…

… …

p1p2

pn

r1

rn

r2

r = [r1, r2,..., r

n]

y

P

14

OurApproachtoRiskStra*fica*on

RiskStra*fy

SVM

…

… …

p1p2

pn

r1

rn

r2

r = [r1, r2,..., r

n]

ySVMrequireslabels.

P

15

LabelingtheData

DaysfromAdmission

IndexEvent

GroundTruth

Risk

16

LabelingtheData

Posi*veTest

GroundTruth

Risk

DaysfromAdmission

17

LabelingtheData

Posi*veTest

Risk

X

DaysfromAdmission

+1

‐1

18

LabelingtheData

Posi*veTest

Wehopetoiden*fyhighriskpa*entsasearlyaspossible.

Risk

DaysfromAdmission

+1

‐1

Weassigneachdayofadmissioninwhichapa*enteventuallytestsposi*veasposi*ve(highrisk)…

…andnega*ve(lowrisk)otherwise.

19

LearningtheDecisionBoundary

Weexpectapa*ent’sriskfluctuatesnoiseinthetraininglabels

20

LearningtheDecisionBoundary

Note:Simplifiedillustra*on.Welearnalinearhyperplaneinthehighdimensionalfeaturespace. 21

DailyRisk‐>SVMCon*nuousPredic*ons

WeconsiderthedistanceeachfeaturevectorliesfromtheSVMdecisionboundarythisresultsinacon7nuouspredic*onforeachday.

!3 !2 !1 0 1 2 30

500

1000

1500

SVM Predictions

Fre

quency

NegativePositive

(Trainingdata)

rd = w• pd ! b

22

OurApproachtoRiskStra*fica*on

RiskStra*fy

SVM

…

… …

p1p2

pn

r�

rn

r2

r = [r1, r2,..., r

n]

y

P

23

ExampleRiskProcesses

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UsingRiskProcessesforRiskStra*fica*on

•  Instantaneousapproach:– Analogoustotypicalriskstra*fica*onapproaches– Considersvalueofriskprocessonlyondayofpredic*on

•  Cumula*veapproach:–  Combinees*matesfromallpreviousdays

E.g.,constant,linear,andquadra*cweightedaverages

(Dubberkeetal.,2011)

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Evalua*ngInstantaneousApproach

•  Considerinstantaneouses*mateforpa*entriskataconstantdistancebeforetheindexevente.g.,2days

Computeclassifierperformancebysweepingthedecisionthresholdfrommintomax.

1 2 3 4 5 6−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

Days from Admission

Ris

k

True LabelPredicted RiskThreshold

Pa*enttestsposi*veforC.diffonday8

Es*m

ated

Risk

(r)

DaysfromAdmissiontoHospital

PatientRisk = rindex!2

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Evalua*ngCumula*veApproach

•  Combinees*matesforpa*entriskfromthe*meofadmissionuptoaconstantdistancefromtheindexevente.g.,2days

Computeclassifierperformancebysweepingthedecisionthresholdfrommintomax.

1 2 3 4 5 6−0.6

−0.4

−0.2

0

0.2

0.4

0.6

0.8

1

Days from Admission

Ris

k

True LabelPredicted RiskThreshold

Pa*enttestsposi*veforC.diffonday8

Es*m

ated

Risk

(r)

DaysfromAdmissiontoHospital

PatientRisk ' = f ([r1, r2,..., rindex!2 ])

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DefiningtheIndexEvent

•  Posi*veExamplesdayofposi*vetestresult– Weconsideronlydatacollecteduptotwodaysbeforeaposi*vetestresult

•  Nega*veExamplesmidpointofadmission– Consideringdischargeastheindexeventcanleadtodecep*velygoodresults

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Results

Approach Tes7ngAUROC(95%CI)

Constantweightedavg. 0.7518(0.69‐0.81)

Linearweightedavg. 0.7444(0.67‐0.80)

Quadra*cweightedavg. 0.7360(0.67‐0.80)

Instantaneous 0.6870(0.61‐0.77)

Cumula*

ve

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Results

1st 2nd 3rd 4th 5th0

0.025

0.05

0.075

0.1

0.125

0.15

0.175

0.2

0.225

0.25

Quintile

Pro

babili

ty o

f P

osi

tive T

est

Pa*entsinthe5thquin*leareat>20‐foldgreaterriskthanthoseinthe1stquin*le!

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Conclusion•  Firststepinanalyzinghowpa*entriskforacquiringC.diffmayevolveduringahospitaliza*on–  Improvementoverexis*ngmethods

•  Nextsteps:–  Findpa?ernsofriskthatleadtoworse/be?eroutcomes–  Inves*gateapplica*oninothercontexts(e.g.,otherHAIs,in‐hospitalmortality,LOS)

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Acknowledgements

•  NaturalSciencesandEngineeringResearchCouncilofCanada(NSERC)

•  Na*onalScienceFounda*on(NSF)•  QuantaComputerInc.

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WorksCited•  McDonaldLC,etal.Clostridiumdifficileinfec*oninpa*ents

dischargedfromUSshort‐stayhospitals,1996‐2003.EmergInfectDis2006;12:409‐15.

•  PepinJ,etal.IncreasingriskofrelapseaIertreatmentofClostridiumdifficilecoli*esinQuebec,Canada.ClinInfectDis.2005Jun1;40(11):1591‐7.

•  AmericanCancerSociety.BreastCancerFacts&Figures2009‐2010.Atlanta:AmericanCancerSociety,Inc.

•  Tanner,J.etal.Waterlowscoretopredictpa*entsatriskofdevelopingclostridiumdifficile‐associateddisease.Journalofhospitalinfec*on,71(3):239‐244,2009.

•  Dubberke,E.etal.,Developmentandvalida*onofaclostridiumdifficileinfec*onriskpredic*onmodel.InfectControlHospEpidemiol32(4):360‐366,2011.

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