automated data aggregation for time-series ... - efmi stc · pdf filestudy case on anaesthesia...

Antoine LAMERa, b, c, Mathieu JEANNEa,b, Grégoire FICHEURc and Romaric MARCILLY b

a Univ. Lille, CHU Lille, Pôle d’anesthésie-réanimation, F-59000 Lille, Franceb Univ. Lille, Inserm, CHU Lille, CIC 1403 - Centre d’Investigation Clinique Innovations Technologiques, F-59000 Lille,Francec Univ. Lille, CHU Lille, EA 2694 - Santé publique : épidémiologie et qualité des soins, F-59000 Lille, France

19/05/2016

Automated data aggregation for time-series analysis:

study case on anaesthesia data warehouse

Automated data aggregation for time-series analysis: study case on anaesthesia data warehouse 2

1 - Introduction

Operational databases daily collect high volumes of data :

• patient care or legal feature

• but also research puposes or assessment of quality of care

E.g. anesthesia databases :

• time-series data during the anesthesia procedure

• statistical link between adverse events and patient outome


1 - Introduction

Hospital stay length

Mortality

Tachycardia

Hypertension

Hypotension

Low BIS

Low minimum alveolarconcentration

Reich et al. 2002, Kertai et al. 2012, Sessler et al. 2014

Adverse events: Patient outcome:


1 - Introduction

Difficulties :

• Variability in documentation

• Heterogeneity of data structures between systems

• Transactional system not suitable for query of large volume of data

Nunez (2004), Dentler et al. 2013

Vital sign

Intervention

~ 2000 measurements per intervention

Events

Intervention

Transfer in

recovery

room

Arrival in

operative

room

InductionEnd of

anesthesiaIncision

End of

surgery

~ 100 events per intervention


1 - Introduction

Objective : Transform high volume of data into usable information

1 ) Study periods

2 ) Aggregated measures

3) Abnormal values of vital parameters

4) Drug administration

Aggregation engines :


2 – Methods

AIMS

Hospital

stay

Biology

Source systems Data Marts

Data

Warehouse

Data

preparation

• Extract• Transform• Load

Aggregation

engines


2 – Methods

Aggregation engine:


2 – Methods

Aggregation engine:

INTERVENTION_ID PARAMETER VALUE DATE

125823 MAP 75 10:21:34

125823 MAP 69 10:26:41

125823 MAP 66 10:32:12

125823 MAP 59 10:38:04

… … … …

MAP = Mean Arterial Pressure

INTERVENTION_ID Mean MAP during

anesthesia

Mean MAP duringsurgery

Mean MAP during

induction

MAP < 60 Duration < 60 …

125823 65 68 58 Yes 5.43 …

… … … … … … …


2 – Methods

Events

Intervention

Induction

End of anesthesiaAtropinePropofol

Anesthesia

[-10 ; 0] [0 ; 10]

End-tidal volume > 0

Study periods:


2 – Methods

Aggregated measures:

Heart rate

Intervention

Aggregated measures

Intervention

Anesthesia:

Mean = 87

Min = 54

Max = 121

Aggregation function over vital parameter data in a study period


2 – Methods

Abnormal values of vital parameters:

Vital sign

Intervention

Threshold

Abnormal values

Intervention

Threshold

Episode 1 Episode 2

INTERVENTION SEUIL START END

12490 MAP < 50 10:23:43 10:32:10

101349 23 08:21:10 08:24:26

101349 23 08:45:49 08:54:10

INTERVENTION THRESHL DURATION MISSING DATA

12490 5 00:08:27 00:01:12

101349 23 00:11:37 00:00:00


2 – Methods

Abnormal values of vital parameters:

Legend:

Recording Missing Data

Measures Comparison

THEN

ELSE

THEN ELSE

THEN

ELSE

Recording Missing Data

ELSE

THEN

Measure Selection

Local Variables Storage

1

Case Initialization

3

2

1

3

1

1

Adding Missing Data

IF MI > MXI

Episode Closing

IF Episode ongoing

Adding Missing Data

IF MI > MXI

Episode Opening

IF Measure outside threshold

Adding Missing Data

MI > MXI

Episode Closing

Episode ongoing

IF MI > MXI

IF Last measure

IF New case

IF New caseCondition

Action

Measure Selection

1Situation

Episode Closing

IF Episode ongoingAction performedwhen condition is met

Adding Missing Data

2

2

3

Episode Closing

IF Episode ongoing

Episode Opening



Episode Closing

IF Episode ongoing

Episode Opening

IF No episode ongoing


2 – Methods

Drug administration:

Drugs

Intervention

Topalgic

100 mg

Propofol

200 mg

Sufentanil

15 µg

Paracétamol

1g

Sufentanil

10 µg

Hypnovel

1 mg

Total administered dose

Operative room:

Hypnovel 1 mg

Anesthesia:

Propofol 200 mg

Remifentanil Ø

Sufentanil : 25µg

Study period

Aggregation of drug doses during a study period


2 – Methods

Study cases:

What are the variations of heart rate around the administration of atropine ?

What are the occurrence rate, the depth and the duration of episodes of hypotension after induction of anaesthesia ?

What is the total amount of ephedrine administered to manage blood pressure following the start of anaesthesia ?


3 – Results

Raw data (2010-2014):

Data Number of rows

Patients 175 214

Interventions 276 812

Events and drugs 43 314 015

Mesures 1 545 582 585

Hospital stay 2 377 129

Usable information:

Data Number of columns

Study periods 40

Aggregated measures 1000

Abnormal values of vital parameters 300

Drug administration 160


3 – Results

T0 + 15T0 T0 + 30 T0 + 45

Atropine

Heart rate

TimeT0 - 10

77 (17) 76 (17) 75 (17)53 (9) 87 (20)

Study period and aggregated measures:

Evolution of heart rate around administration of Atropine (17118 interventions)

T0 + 15T0 T0 + 30 T0 + 45

Atropine

Heart rate

TimeT0 - 10


3 – Results

Minimal Threshold

(mmHg)Nb of interventions (%)

Median time between

induction and start of first

episode

< 50 10960 (13.53) 12.67

< 55 10060 (12.42) 13.72

< 60 13197 (16.29) 13.80

< 65 13524 (16.69) 13.17

< 70 11155 (13.77) 12.65

< 75 7849 (9.69) 12.58

- 14269 (17.61) -

Study period and abnormal values of vital parameters:

Minimal threshold of MAP after induction (81 014 interventions)


3 – Results

Study period, abnormal values of vital parameters and drugadministration:

Threshold (mmHg)

Interventions with

administration of ephedrine

following induction (%)

Ephedrine (mg) (median

[interquartile])

< 50 6600 (60.22) 9 [9 ; 15]

< 55 4525 (44.98) 9 [9 ; 12]

< 60 3812 (28.89) 9 [6 ; 9]

< 65 1854 (13.71) 9 [6 ; 9]

< 70 672 (6.02) 9 [6 ; 9]

< 75 249 (3.17) 9 [6 ; 9]

- 1974 (13.83) 12 [9 ; 18]

Total 17712 (24.30) 9 [9 ; 12]

Ephedrine following induction


4 – Conclusion and Discussion

• Development is time consuming but efficient.

• Adaptable to other time-series data (e.g. intensive care).

• Computed indicators are repeatable over time for quality ofcare assessment.

• Transformation of raw data into information directly usable.

20

Thank you for your attention

automated data aggregation for time-series ... - efmi stc · pdf filestudy case on anaesthesia...

Documents