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Computer aided decision support system for trauma patient care

BHSAI

Jinbo Bi, Ph.D.

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Presented at Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences & Technology, May 17th, 2010

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GoalsGoals

Primary Goal: Develop fieldable decision-assist tools for real-time triage/diagnosis of trauma casualties

● Systematic analysis of real-world data

– Step-by-step, evidence-based analysis– Methodically increasing complexity

● Many challenges

– Imperfect measurements– Imperfect knowledge of outcomes

● Seek patterns identifying an individual

– Not interested in group averages

● Parsimonious approach

– No complicated techniques unless justified – All other things being equal, the simplest solution the best

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● 674 patients (~25 min of transport time/patient)

Continuous vital signs

– 3 waveforms: ECG, PPG, Respiratory

– 9 vital signs: HR, RR, SBP, DBP, SpO2, …

Hospital trauma registry

– 100 attribute data: demographics, blood

transfusion, life-saving interventions, etc.

Physiologic Data: Trauma-StudyPhysiologic Data: Trauma-Study

Vital signs used in decision-support algorithms

HRRRSBPDBPSpO2Propaq

DBP: diastolic blood pressure RR: respiratory rateECG: electrocardiogram SBP: systolic blood pressureHR: heart rate SpO2: blood oxygen saturationPPG: photoplethysmogram

McKenna et al., Comput Methods Programs Biomed (2007)

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Decision Classifier for Hemorrhage DiagnosisDecision Classifier for Hemorrhage Diagnosis

Issues: – Unreliable (noisy)

– Missing values

Hemorrhage class (78 patients)Received in-hospital blood (1 unit within 24 h)

ANDAny related documented injury:

– Laceration of solid organs– Explicit vascular injury – Limb amputation

Control class (596 patients)No in-hospital blood

Decision Classifier

HRRR

SpO2

SBPDBP

Control(No Hemorrhage)

Hemorrhage

?

Chen et al., J Biomed Inform (2008) Chen et al., Prehosp Emerg Care (2009)

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Reliable and Unreliable Signal

Respiratory waveform --- respiratory rates

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All available data meeting our QI constraitshemorrhage controlexcluded

Hospital LSI: Blood>0, OR ‘1st 24h Fluid – RBC’>0 AND bleeding description

Hospital LSI: Blood<=0, OR ‘1st 24h Fluid – RBC’<=0

Bleeding Description FIELDS KEYWORDS 'AIS_Area' 'spleen' 'AIS_Code' 'artery' 'AIS_Injury_Desc' 'liver lac' 'DX_Area' 'liver hematoma' 'DX_Code' 'kidney lac' 'DX_Description' 'kidney hematoma' 'Procedure_Area' 'renal lac' 'ICD_9_Description' 'renal hematoma' 'NLSI' 'renal avulsion' 'LSI' 'lung lac' 'Patient_History_Incident_Desc' 'hemomediastinum' 'Patient_History_NL_Threat_DX_Desc' 'hemothorax' 'Patient_History_Op_NLS_Proc_Desc' 'htx' 'Patient_History_MOI' 'retroperitoteum Hematoma' 'retroperitoneal hematoma' 'pneumohemothorax' 'amputation' 'leg avulsions' 'leg avulsion' 'Angio' 'Suture of Artery' 'Suture of Vein' 'MAJOR abdominal laceration' 'laceration to pancreas' 'MajorLaceration|OpenPelvicFx.' 'thoracotomy' 'bld.loss'

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Data sets

Characteristics Total set IllustrativePopulation size 627 492

Male 473(75%) 373(76%)Female 154(25%) 119(24%)

Mean age 38.8(15) 38.2(16)Blunt injury 555(89%) 435(88%)

Penetrating injury 65(10%) 51(10%)Mortality 59(9%) 35(7%)

PreH Intubated 128(20%) 90(18%)Major respiratory interv 62(10%) 53(11%)

Major hemorrhage 71(12%) 55(11%)

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Train classifiers

‘Best-Feature’ Classifier‘Best-Feature’ Classifier

Test classifiers with AUC

C1 C2 C3 C31… …

100times

Average AUC over 100 times for each C

Train a classifier using

best feature set C

Best feature set C

Test the classifier via

AUC

All available data

Trainingdata

Testdata

100times

Report avg and std of AUC

30%

70%

50%

50%

Least squares method

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‘Ensemble’ Classifier‘Ensemble’ Classifier

C1 C2 C3 C25… …

Average 25 classifiers

Train a classifier by averaging 25

classifiers

Ensemble classifier C by Averaging

Test the classifier via

AUC

All available data

Trainingdata

Testdata

100times

Report avg and std of AUC

30%

70%

Least squares method

Train classifiers

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More towards apple-apple comparisonMore towards apple-apple comparison

C1 C2 C3 C25… …

Select C based on max training accuracy

Train a classifier by averaging 25

classifiers

Ensemble classifier C by Max

Test the classifier via

AUC

All available data

Trainingdata

Testdata

100times

Report avg and std of AUC

30%

70%

Least squares method

Restructure ‘Best Feature’ ClassifierTrain classifiers

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Constructing Classifiers: ensemble of LS classifiersConstructing Classifiers: ensemble of LS classifiers

Data: – Reliable signal in 16 min

• Mean HR• Mean RR• Mean SBP• Mean DBP• Mean SpO2• PP = SBP-DBP• SI = HR/SBP

TrainingFor each combination of the vital sign features, e.g. HR, SBP, SI

– Identify set of patients who have the features in the combination available

– Train a least squares regression model by minimizing

– – Form a group of regression models, each

serves as a classification probability function

TestingFor each patient,

– Only use classifiers that use the features available for the patient

– Average over the regression models to make final probability prediction

– If the probability > a threshold, hemorrhage, or otherwise, a control case

Chen et al., J Biomed Inform (2008)

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Building Classifiers: ensemble of LS classifiersBuilding Classifiers: ensemble of LS classifiers

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TP

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)ROC by averaging 100 models

Train (AUC=0.85893)

Test (AUC=0.84796)

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Current System – two cutoff points

Illustration in feature space, each classifier is based on a linear function

Proposed System – two classifiers

Yellow alert

Red alert

Yellow alert

Red alert

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Stratified Training: two classifiers targeted at different sensitivity levels

For illustration only

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Constructing Classifiers: ensemble of weighted LSConstructing Classifiers: ensemble of weighted LS

Data: – Reliable signal in 16 min

• Mean HR• Mean RR• Mean SBP• Mean DBP• Mean SpO2• PP = SBP-DBP• SI = HR/SBP

TrainingFor each combination of the vital sign features, e.g. HR, SBP, SI

– Identify set of patients who have the features in the combination available

– Train regression models by minimizing

where the value of v is optimized to achieve the best Positive Predictive Values for red alert and yellow alert, respectively

– Form two groups of regression models, one for 50% sen, one for 85% sen

TestingFor each patient,

– Only use classifiers, from each group, that use the features available for the patient

– Average over the regression models to make final probability prediction

– If both classifiers are positive, red alert, or if only one classifier is positive, yellow alert, or otherwise, a green control case

Chen et al., J Biomed Inform (2008)

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Computational Results

Statistic One classifier (Red)

Two classifier (Red)

One classifier (Yellow)

Two classifier (Yellow)

Train AUC 0.87 (0.82, 0.92) 0.87 (0.81, 0.91) 0.87 (0.82, 0.92) 0.87 (0.82, 0.91)Test AUC 0.85 (0.80, 0.89) 0.85 (0.79, 0.89) 0.85 (0.80, 0.89) 0.85 (0.79, 0.89)

PPV 0.51 0.61 0.25 0.28Specificity 0.94 0.96 0.67 0.70

One classifier Two classifierGreen Yellow Red Green Yellow Red

Hemorrhage 11 28 39 11 28 39Control 401 158 37 419 152 25

McNemar Yellow stat = 4.98, p = 0.02, Red stat = 6.72, p = 0.01

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Computational Results: comparison (I)

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Computational Results: comparison (II)

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Conclusion and ObservationConclusion and Observation

● Basic vital signs can be useful to predict major hemorrhage, especially when reliable signals are considered

● When different sensitivity levels are desired, training classifiers targeted at the given sensitivity may improve the positive predictive values

● Weighted least squares by penalizing asymmetrically the false positive errors and false negative errors can potentially be a powerful tool for stratified training