Automated data aggregation for time-series ... - EFMI STC · PDF filestudy case on anaesthesia...
Transcript of Automated data aggregation for time-series ... - EFMI STC · PDF filestudy case on anaesthesia...
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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
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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
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Automated data aggregation for time-series analysis: study case on anaesthesia data warehouse 3
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:
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Automated data aggregation for time-series analysis: study case on anaesthesia data warehouse 4
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
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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 :
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2 – Methods
AIMS
Hospital
stay
Biology
Source systems Data Marts
Data
Warehouse
Data
preparation
• Extract• Transform• Load
Aggregation
engines
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2 – Methods
Aggregation engine:
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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 …
… … … … … … …
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2 – Methods
Events
Intervention
Induction
End of anesthesiaAtropinePropofol
Anesthesia
[-10 ; 0] [0 ; 10]
End-tidal volume > 0
Study periods:
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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
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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
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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
IF Measure outside threshold
IF Measure outside threshold
Episode Closing
IF Episode ongoing
Episode Opening
IF No episode ongoing
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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
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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 ?
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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
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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
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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)
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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
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Automated data aggregation for time-series analysis: study case on anaesthesia data warehouse 19
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.
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20
Thank you for your attention