Transcript of Routine use of the “ADE Scorecards”, an application for automated ADE detection, in a general...
- Slide 1
- Routine use of the ADE Scorecards, an application for automated
ADE detection, in a general hospital Emmanuel Chazard a, Michel
Luyckx b, Jean-Baptiste Beuscart c, Laurie Ferret a, Rgis Beuscart
a a Public Health Department, Lille University Hospital; UDSL EA
2694; Univ Lille Nord de France; F-59000 Lille, France. b Hospital
Pharmacy, Lille University Hospital; UDSL EA 4481; Univ Lille Nord
de France; F-59000 Lille, France. c Geriatrics Department, Lille
University Hospital; UDSL EA 2694; Univ Lille Nord de France;
F-59000 Lille, France.
- Slide 2
- Introduction I. Adverse Drug Events II. The PSIP European
Project III. The ADE Scorecards 2013-08-222 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 3
- 2013-08-22 Adverse drug events ADEs = Adverse Drug Events
Several definitions. Institute of Medicine (2007): An injury
resulting from the use of a drug An injury due to medication
management rather than the underlying condition of the patient
Epidemiological data: 98,000 deaths per year in the US An ADE would
occur in 5-9% of inpatient stays Two fields of research:
Retrospective ADE detection Prospective prevention of ADEs by CDSS
(clinical decision support systems) 3 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 4
- 2013-08-22 Funded by the European Research Council, 7th
framework program (agreement N216130) The PSIP European Project
Patient Safety Through Intelligent Procedures in Medication 4
Routine use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital. Hospitals: Lille University
Hospital (F) Rouen University Hospital (F) Denain general hospital
(F) Hospitals from the Region Hovedstaden (Dk) Industrial partners:
Oracle (Europe) IBM Denmark (Dk) Medasys (F) Vidal SA (F) KITE
solutions (I) Ideea Advertising (Ro) Academic partners: Aristotle
Thessaloniki University (Gr) Aalborg University (Dk) UMIT Innsbruck
University (Au)
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- 2013-08-22 The ADE Scorecards, a tool for automated ADE
detection in EHR 5 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general hospital. EHR
Database ADE de- tection rules Computation step List of potential
ADE cases Contextualized statistics Web-based display tool
-Statistics on ADEs -Description of rules -Cases review Routinely-
collected data (diags, lab, drugs, etc.). /!\ ADEs are not flagged.
236 validated complex rules Statistics and automated filters are
site- dependant Real past inpatient stays Multilingual (En, Fr, Dk,
Bu)
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- 2013-08-226 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
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- 2013-08-227 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
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- 2013-08-228 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
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- 2013-08-229 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital. Number of cases
per month Histogram of appearance delay
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- 2013-08-2210 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
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- 2013-08-2211 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
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- 2013-08-2212 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
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- 2013-08-2213 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
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- 2013-08-2214 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
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- 2013-08-2215 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital. 83
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- 16 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.2013-08-22
- Slide 17
- The ADE Scorecards, a tool for automated ADE detection in EHR
Usage: Installed in 5 hospitals (2 Danish, 2 French and 1
Bulgarian) Routinely used by the physicians and pharmacists of a
French general hospital during three years Objective: show the
results of its use in real-life situation 17 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 18
- Material and methods I. Quantitative analysis II. Qualitative
analysis 2013-08-2218 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
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- Quantitative & qualitative analysis Analyzed data: January
2007 to August 2012 Description: Patients, medical background (95%
confidence intervals) Comparisons: Between medical departments
(displayed here: Gynecology-Obstetrics vs. Cardiology) Chi-2 tests
& Students t-tests (with =5%) Focus on 2 outcomes:
Hyperkalemia: defined as K + >5.5mmol/l may induce lethal
cardiac rhythm troubles INR increase: defined as INR5
(INR=international normalized ratio) due to VKA overdose or
interaction (VKA=vitamin K antagonist) may induce a severe
hemorrhage Observation of the daily use by a human-factors
specialist and a pharmacist 2013-08-2219 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 20
- Results I. Characteristics of the patients II. Potential ADEs
with INR increase III. Potential ADEs with hyperkalemia
2013-08-2220 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.
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- Characteristics of the patients ( n=73,836 inpatient stays)
2013-08-2221 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.
ParameterOverallCardiologyGyn. Obs.p value (all) Age
(years)60.267.628 p5 => risk of hemorrhage The tree attempts to
explain INR>5
- Slide 35
- 2013-08-22 Results Example of decision tree (2) 1. VKA &
butyrophenone discontinuation P=0.4 2. VKA & no butyrophenone
discontinuation & hypoalbuminemia P=0.5 3. VKA & no
butyrophenone discontinuation & no hypoalbuminemia P=0.05 4. no
VKA P=0 We obtain 4 rules. 2 of them are associated with increased
risk of hemorrhage => 2 ADE detection rules 4 3 2 1 35 Routine
use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital.
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- 2013-08-22 Results Example of decision tree (3) Butyrophenone:
neuroleptic drugs, may accelerate the intestinal transit VKA Step
1: normal VKA intake INR VKA Step 2: butyrophenone => transit
acceleration => too low INR Buty. INR VKA Step 3: increased
intake of VKA => normal INR Buty. INR VKA Step 4: buty.
discontinuation => normal transit => too high INR INR 36
Routine use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital.
- Slide 37
- 2013-08-22 Results Example of decision tree (4) Albumine =
plasmatic protein to which VKA bind. Only the non- bound part is
biologically active. Serum albuminVKA Normal state: 99% of the VKA
bind to albumin. Only 1% of VKA are biologically active. The intake
is based on it. Hypoalbuminemia: decrease of the bound fraction,
increase of the non-bound fraction => too high INR (with
constant intake) 37 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
- Slide 38
- 2013-08-22 Complete 2010 year of one hospital Number of stays
:14,747 Number of hyperkalemia cases :117 (7.93) exhaustive review
Result Precision39/75=52.0% Recall39/41=95.1% Harmonic mean67.2%
Number of reported cases0/41=0% 52.0% 95.1% Results Evaluation of
the ADE retrospective detection 38 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 39
- 2013-08-22 Retrospective detection of ADEs Retrospective
identification of past ADEs, although no explicit signal exists in
the data Inpatient stay Prescription Hospital information system,
databases 39 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.
- Slide 40
- 2013-08-22 Prospective prevention of ADEs Inpatient stay
Hospital information system Prescription Alert method Change of the
drug prescription Alert generation, before the ADE occurs, in order
to prevent it. 40 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
- Slide 41
- 2013-08-22 Statistiques sur les EIM dans la base de donnes PSIP
*: le nombre dvnements est rapport au nombre total de sjours, alors
que les nombres suivants sont rapports aux sjours de plus de 2
jours uniquement **: ces nombres sont extrapols depuis un
chantillon 41 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.
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- 2013-08-2242 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
- Slide 43
- 2013-08-22 Pour chaque rgle, les statistiques contextualises
sont calcules dans chaque service X all departments X surgery X
gyneco-obstetrics X medicine A X medicine B X pneumology Y all
departments Y apoplexy Y cardio & endocrinology Y geriatrics Y
gynecology Y intensive care unit Y internal medicine Y obstetrics Y
orthopedics Y rheumatology Y urology Z all departments W all
departments X all dpts 43 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
- Slide 44
- Prvention prospective des EIM 44 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.2013-08-22
- Slide 45
- Prvention prospective des EIM Approche de PSIP Ex : AVK &
IPP risque hmorragique Implmentation classique : Implmentation PSIP
: Service AService BService C AVK&IPP alerte Service AService
BService C Probabilit mesure 10% AVK&IPP alerte Probabilit
mesure 0.01% AVK&IPP silence Circonstance inconnue AVK&IPP
alerte 45 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.
- Slide 46
- 2013-08-22 Prvention prospective des EIM Approche de PSIP
Dveloppement de plusieurs systmes daide la dcision : Prototype IBM
Prototype Medasys Simulation web dordonnance Caractristiques
majeures : Alertes filtres statistiquement, contextualises moins
dalertes, plus pertinentes Rgles raffines (segmentation
statistique) prdiction du risque plus pertinente Mthodes dalerte
moins interruptives, plus acceptables 46 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 47
- Data reuse in insurance companies Routine activities: Reuse of
the data: 2013-08-22 Transactional activities. The company: How
much should Mr Smith pay for his car insurance? Nearly-custom
database Model for predicting individual risk Statistical analysis
Daily feeding and updating Accidents database (outcomes)
Contributions data (incomes) Demographic data Data transformation
Recruits and follows customers Banks insurance premiums Pays out
claims Personalized insurance premiums Decision 47 Routine use of
the ADE Scorecards, an application for automated ADE detection, in
a general hospital.
- Slide 48
- big data is generally a property of the routinely collected
data that can be reused Big can be understood through 5 dimensions:
idagegenderdiagnosis Definition of big data 2013-08-22
idagegenderdiagnosis 12323MI10 12578MK37 24513FM61.2 27824MI41
32465FI48 35034FF20.2 1-Many records 2-Many variables IdParVal
123K+4.5 123K+4.8 123K+5.2 3-Many possible values for qualitative
variables 4-Many tables & relationships 5-Variables with
repeated measurements 48 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
- Slide 49
- Challenges in data reuse Where is the secret of a successful
data reuse? 2013-08-22 Scientific question Nearly-custom database
Results Statistical analysis Transactional database 3 Transactional
database 2 Transactional database 1 Data transformation
Interpretation Knowledge Here? Not really Data mining techniques (
statistical methods) are used, but not specific. Here? Not really
Data mining techniques ( statistical methods) are used, but not
specific. Here? Partially Significant tests are nearly always
observed in Big Data: correct the risk, consider the effect size.
Cf. post. Here? Partially Significant tests are nearly always
observed in Big Data: correct the risk, consider the effect size.
Cf. post. Here? Yes, mainly! The decisions that are taken for the
data transformation process have a critical effect. Here? Yes,
mainly! The decisions that are taken for the data transformation
process have a critical effect. 49 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 50
- Data reuse of EHR (Electronic Health Records) 2013-08-22 Demo.
& Admin Age80 Man?0 Dead?0 Length of Stay9 () Diagnoses
E119Diabetes I251Athrosclrosis I10Arterial hypertension
N300Cystitis Lab results NPU03230 Potassium Administered drugs
Free-text reports Medical devices?? Procedures ZZBQ002Thorax
radiography 50 Routine use of the ADE Scorecards, an application
for automated ADE detection, in a general hospital.
- Slide 51
- Data quality assessment 2013-08-2251 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 52
- Data quality assessment Getting reliable data is a challenge
=> an iterative quality control is mandatory Extraction format,
basic requirements (tables, fields) Single value validity. E.g.:
Incorrect type: Age=old Impossible value: age=141,
diagnosis=HHFA001 Out-of-terminology value: diagnosis=B99.0
Univariate validity. E.g.: Each value is possible, but Mean(age)=85
Contextual: mean(age)=21 in Pediatrics Bivariate validity. E.g.:
Length of stay=2, admission=2013-05-14, discharge=2013-05-14 Age=21
in a Geriatrics Unit Age=21 with diagnosis=Alzheimer disease
2013-08-2252 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.
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- Data transformation, data aggregation 2013-08-2253 Routine use
of the ADE Scorecards, an application for automated ADE detection,
in a general hospital.
- Slide 54
- Data transformation: what for? 2013-08-22 Mandatory before
using statistical methods Enables to transform data into
information: how would a human comment/summarize the raw data?
Suppresses 3 over 5 dimensions of bigness that are not compatible
with statistical analysis 54 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
- Slide 55
- Data transformation, example 1: suppression repeated
measurements (1) 2013-08-22 Potassium: time0 Id
stayDateParameterValue 1230Potassium4 1231Potassium4 123 0Sodium140
123 5280Potassium3.2 528 Sodium: time0 Potassium: time0 Stay n123
Stay n528 Several parameters may be measured several times during a
given inpatient stay. => One curve per {id_patient*parameter} 55
Routine use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital.
- Slide 56
- Data transformation, example 1: suppression repeated
measurements (2) Objective: 1 table with 1 row per stay Example of
simple transformation (without a priori knowledge): 2013-08-22
Potassium: time0 Sodium: time0 Potassium: time0 Stay n123 Stay n528
Id stay Potassium.Min Potassium.Max Sodium.Min Sodium.Max
1233.86.2136142 5282.83.2NA 56 Routine use of the ADE Scorecards,
an application for automated ADE detection, in a general
hospital.
- Slide 57
- Data transformation, example 1: suppression repeated
measurements (3) Another example of transformation, with knowledge
Uses the range of normal values according to the parameters,
summarizes the anomalies 2013-08-22 Id stay Hyperkalemia
Hypokalemia Hypernatremia Hyponatremia 1231000 52800NA Potassium:
time0 Sodium: time0 Potassium: time0 Stay n123 Stay n528 57 Routine
use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital.
- Slide 58
- Data transformation, example 2: interpretation of a qualitative
variable (1) 2013-08-22 Id stayProcedure.codeProcedure.wording
123LMMC004Bilateral treatment of inguinal hernia without
prosthesis, by video surgery 123GLHF001Arterial blood collection
for blood gas and pH sampling 528LMMC020Treatment of abdominal
hernia with prosthesis, by laparoscopy 528ZZBQ002Thorax radiography
Each stay may have 0, 1 or several procedures. The terminology used
(CCAM) has more than 5,000 possible codes. In this case, we only
interest on hernia treatment. 58 Routine use of the ADE Scorecards,
an application for automated ADE detection, in a general
hospital.
- Slide 59
- Data transformation, example 2: interpretation of a qualitative
variable (2) 2013-08-22 Id stayProcedure.code 123LMMC004 123GLHF001
528LMMC020 123ZZBQ002 New qualitative variables are created: Each
variable has few possible values A mapping is necessary (with
overlaps) Requires a strong medical knowledge about codes Id stay
H.type H.prosthesis H.approach H.bilateral
123Inguinal0Videosurgery1 528Abdominal1Laparoscopy0 59 Routine use
of the ADE Scorecards, an application for automated ADE detection,
in a general hospital.
- Slide 60
- Data transformation, example 2: interpretation of a qualitative
variable (3) Example of mapping used for hernia FROM (raw data): 18
codes TO (information): Type (n=3) Prosthesis (n=2) Approach (n=6)
Bilateral (n=2) 2013-08-2260 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
- Slide 61
- Problems of interpretation with big data I. Many variables
(columns) => type I error correction II. Many records (rows)
=> effect size => over fitting of the models 2013-08-2261
Routine use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital.
- Slide 62
- 1- Type I error inflation with numerous statistical tests
Reminder: principle of statistical tests (1) Example : we want to
test whether variables A & B are independent. A null hypothesis
is supposed: H 0 : A & B are independent Alternative hypothesis
H 1 : A & B are not independent. A test statistic is computed:
Its behavior under H 1 is unknown! Under H 0, this test statistics
follows a known distribution 2013-08-22 Example : A & B are
qualitative Test = Khi test Test statistic = 62 Routine use of the
ADE Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 63
- Example : The Test statistic follows a distribution The reject
zone is represented in red: Under H 0, this test statistics follows
a known distribution Zone of reject of H 0 : extreme values of the
test statistic, corresponding to a chosen probability (generally
5%) 2013-08-22 Distribution and zone of reject of H 0 in a Khi test
1- Type I error inflation with numerous statistical tests Reminder:
principle of statistical tests (2) 95%5% 63 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 64
- 1- Type I error inflation with numerous statistical tests
Reminder: principle of statistical tests (3) Use of the test: we
suppose that H 0 is true. If the result of the test is an
improbable value (associated probability p < ), we decide that H
0 is not true: we reject H 0. But, if H 0 is really true, there
exists a risk to reject it wrongly: the risk = the Type 1 error
(generally 5%) 2013-08-2264 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
- Slide 65
- 1- Type I error inflation with numerous statistical tests What
if several tests are realized? Scenario with several tests: k
independent tests are realized on a dataset A Signal is observed if
at least one of the k tests is significant (p inflation of the
risk. 2013-08-2265 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
- Slide 66
- 1- Type I error inflation with numerous statistical tests What
if several tests are realized? => inflation of the risk.
2013-08-22 Number of tests (0-100) Total alpha risk if every test
is computed with a 5% individual alpha risk 66 Routine use of the
ADE Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 67
- 1- Type I error inflation with numerous statistical tests How
to correct this inflation? In order to get the total wished, the
individual threshold indiv must be modified: idks correction: Use
indiv =1-(1- total ) 1/k Reminder: a b = e b.ln(a) Bonferronis
correction, very popular: For usual values of total (1 to 10%), use
simply: indiv =( total )/k in practice: indiv =(0.05)/k Threshold
very close (and inferior to) idks threshold: more conservative,
easy to compute Usable even when the k tests are not independant!
2013-08-2267 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.
- Slide 68
- 1- Type I error inflation with numerous statistical tests
Example of use (1) A dataset of inpatient stays contains X binary
variables (e.g. administration of Spironolactone, hyperkalemia,
etc.). We want to discover associations by testing every couple of
variables. For X=2, 10, 100, 1000, 10000: How many tests are
performed? What is the value of total if indiv =0.05? What indiv
should be used to obtain total =0.05? idk:Bonferroni: 2013-08-2268
Routine use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital.
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- Number of variables Number of tests total if indiv =5% Sidaks
indiv Bonferronis indiv 210.055.000E-02
10450.90061.139E-031.111E-03 10049501.01.036E-051.010E-05 1000499
5001.01.027E-071.001E-07 1000049 995 0001.01.026E-091.000E-09 1-
Type I error inflation with numerous statistical tests Example of
use (2) A dataset of inpatient stays contains X binary variables
(e.g. administration of Spironolactone, hyperkalemia, etc.). We
want to discover associations by testing every couple of variables.
For X=2, 10, 100, 1000, 10000: 2013-08-2269 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 70
- 2- Significance versus size of the effect Formulation of the
result of a statistical test A decision variable is first computed.
Then it is tested. Two equivalent ways to formulate the result
(examples with =5%): The p value (significance) : We test whether
the decision variable is different from the value expected under H
0 (generally 0 or 1) H 0 is rejected if p < 5% The confidence
interval: The test returns the 95% confidence interval of the
decision variable H 0 is rejected if this confidence interval does
not contain the value expected under H 0 (generally 0 or 1)
2013-08-2270 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.
- Slide 71
- 2- Significance versus size of the effect Example: comparing
proportions We want to compare 2 proportions with =5%. Examples of
equivalent ways: Way #1: = 1 - 2 is the effect size H 0 : =0 Test
=> p value: H 0 is rejected if p < 0.05 Way #2: Relative risk
RR= P(A/B) / P(A/B) is the effect size H 0 : RR=1 Test =>
confidence interval CI 0.95 : H 0 is rejected if CI 0.95 does not
contain 1 2013-08-22 0 Significance Effect size 1 RR Confidence
interval of RR Significance Effect size 71 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 72
- 2- Significance versus size of the effect Example of
proportions comparison In a hospital database (n=500,000 patients)
we want to test whether the intake of Drug X is associated with the
occurrence of cardiac insufficiency Relative risk with 95%
confidence interval: 1.071 [1.008 ; 1.137] => statistically
significant, but weak effect size (+7.1%) 2013-08-22 CI+CI- (%CI+)
drug+1,07024,000 4.2% drug-18,930456,000 3.9% 72 Routine use of the
ADE Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 73
- 3- Overfitting of predictive models Why does it happen in Big
Data? In Big Data and Data Reuse, it seems easy to discover good
predictive models: data-driven approaches: without hypothesis
Absence of Bonferronis correction Big sample sizes => high
significance (even if poor size effect) Overfitting of the model:
The models seems to be good in the sample: it is optimized for this
particular sample But it could be fortuitous: perhaps another model
would have been discovered on another sample 2013-08-2273 Routine
use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital.
- Slide 74
- 3- Overfitting of predictive models Which solutions? Those
solutions should be applied together when possible: Apply
Bonferronis correction Consider the effect size as a stop criterion
in some algorithmes Trade-off between the complexity of the model
and the goodness of fit of the model using criterion such as Akaike
Information Criterion Build the model and test it on 2 different
databases 2013-08-2274 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
- Slide 75
- 3- Overfitting of predictive models Which solutions? Example of
procedure for predictive models: 2013-08-22 Original database
Random sampling Learning setEvaluation set 20%80% Predictive model
Statistical procedure Evaluation (prediction error computation)
Learning phase Evaluation phase Final predictive model Statistical
procedure 100% Final learning 75 Routine use of the ADE Scorecards,
an application for automated ADE detection, in a general
hospital.
- Slide 76
- Data reuse of HER Some data sources Drug (distinguish drug
prescription and drug administration). Dont forget to add some
sources: Some procedures to map, e.g.: Scanner with iodine =>
iodine Surgery with anesthesia => anesthetic drug Perfusion,
that are often provided without terminology code The drugs the
patient brings with him Medical devices (implanted or not): Raw
data: hudge amount Aggregated data Results of the interpretation
automatically done by the device 2013-08-2276 Routine use of the
ADE Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 77
- Data reuse of HER Terminologies Vocabulary, support of semantic
interoprability: Couples of codes and wordings Codes are sometimes
included in a hierarchy Enable to associate each concept to a code
Contrary to free-text, unambiguous and usable for statistical
analysis Example : rysiple = rsiple = dermo-hypodermite = A46 in
ICD10 77 Routine use of the ADE Scorecards, an application for
automated ADE detection, in a general hospital.2013-08-22
- Slide 78
- Data reuse of HER Drug terminologies Commercial name Ex :
Dafalgan CID Common international denomination Ex : Paractamol /
Acetaminophen Various terminology Among them, the ATC
classification ATC = Anatomical Therapeutic and Chemical
classification Codes, wordings and hierarchy (principally based on
the therapeutic indication) 78 Routine use of the ADE Scorecards,
an application for automated ADE detection, in a general
hospital.
- Slide 79
- 2013-08-22 Example of the Aspirin in the ATC classification 79
Routine use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital.
- Slide 80
- 2013-08-22 Laboratory results Biochemistry, hematology,
bacteriology, virology, immunology Examples terminologies: LOINC
(recommended) IUPAC But most of the time, each laboratory produces
its own terminology: No semantic interoperability: Impossible to
pool 2 hospital databases In the same hospital, data are sometimes
heterogeneous according to the date of the measure Need for
producing ad hoc mappings 80 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.
- Slide 81
- 2013-08-22 Introduction Le projet europen PSIP 81 Routine use
of the ADE Scorecards, an application for automated ADE detection,
in a general hospital.
- Slide 82
- 2013-08-22 Patient XXX Age80 Homme ?0 Dcd ?0 Dure sjour9 ()
Diagnostics E119Diabte I251Athrosclrose I10HTA N300Cystite NPU03230
Potassium Mdicaments administrs Courriers et compte-rendus
Administered drugs Dtection rtrospective des EIM Modle de donnes 82
Routine use of the ADE Scorecards, an application for automated ADE
detection, in a general hospital.
- Slide 83
- 2013-08-22 Vue gnrale des rgles dEIM 83 Routine use of the ADE
Scorecards, an application for automated ADE detection, in a
general hospital.
- Slide 84
- 2013-08-22 Nombre croissant de sjours analyss 84 Routine use of
the ADE Scorecards, an application for automated ADE detection, in
a general hospital.
- Slide 85
- 2013-08-22 Facult des rgles dtecter rtrospectivement des EIM
Revue de cas experte : valuation de la prcision des rgles Cas
valids / cas revus Prcision Intervalle de confiance 95%
Hyperkalimie (K+>5.3mmol/l) 54 / 10153.5% [43.7;63.2] Surdosage
en AVK (INR>4.9) 5 / 955.6% [23.1;88.0] Insuffisance rnale
(creat.>135 mol/l or ure>16.6 mmol/l) 35 / 7546.7%
[35.4;58.0] Autres types dvnements14 / 5326.4% [14.5;38.3] TOTAL108
/ 23845.4% [39.1;51.7] 85 Routine use of the ADE Scorecards, an
application for automated ADE detection, in a general
hospital.