The document discusses translational research activities at UIB from 2019-2022, including developing predictive models for mortality in ischemic stroke and colorectal cancer. It describes developing a contextual model confirming the relevance of stroke units, and approaching convolutional neural networks. Several projects are mentioned, including applying the Foster-Hartman method to estimate stroke mortality in Spain, and developing a mobile app to estimate stroke risk of mortality. The goal is to close the circle from patients to data to models back to improving patient care.

1. Translational Research at the UIB
Closing the circle:
Patients-Data-Models-Patients
2019-2022 Trajectory

2. Activities with Innovation/Translation Potential
 Application of the Foster-Hartman method (SMR) from the HUT
 Development of a contextual model that mathematically confirms the
relevance of the Stroke Units.
 Development of two predictive models:
 Mortality in non-reperfused ischaemic stroke (GRD 14)
 Mortality in interventional Colorectal Cancer (ICD 9MC 154-155)
 Approaching convolutional neural networks

3. IDi value chain

4. INNOVATION IN PREDICTIVE MODELS:
SMR METHODOLOGY

6. Implantación del método de Foster en España

10. Empirical model (SEM) of Stroke Mortality in Spanish
intra-hospital settings: the role of Individual and
Contextual factors
Juan Manuel García-Torrecillas. HU Torrecárdenas
Jesús de la Fuente Arias. University of Almeria/Navarra
Giulliana Solinas. University of Sassary
Mar Iglesias Espinosa. HU Torrecárdenas
Angélica Garzón-Umerenkova. Fundación Konrad Lorenz
Javier Fiz Pérez. Universitá Europea di Roma

11. Stroke and Contextual Models

13. +
-
-

14. From data to information
Robust Logistic Modeling and transference to an App in
estimating the risk of mortality in the emergency department
Juan Manuel García-Torrecillas.
Hospital Torrecárdenas, Almería
Jesús de la Fuente Arias. University of Almeria
Giulliana Solinas. University of Sassary
Gabrielle Giorgy. University of Roma
Grupo de Investigación en CCR.
#EpiCCR
Hospital Universitario Torrecárdenas
Proyecto FIS-ISCIII 16/01931
Cofinanciado Fondos FEDER

15. Third etiology of death in Occident
USA: 1 event every 40 seconds
Incidence (Europe): 95-290 ep/100.000 p-year
High rate of complications and sequels
Mortality Rate 36 episodes/100,000person-year
First etiology of death in Spanish women
Is an time-dependent disease
Importance of
individual and
contextual
factors
Importance of
specialted
personal in the
first atention
What about benefits of knowing Stroke´s risk of mortality?

16. Third etiology of malignant neoplasm
15% tumoral incidence at Spain
Second aetiology of death in Europe
25,000 new cases/year at Spain
High mortality but with a forecast of continuous
improvement
Significant differences between observed and
expected mortality (SMR)
Importance of
individual and
contextual
factors
Importance of
specialted
personal in the
first atention
What about CCR mortality?

17. PRINCIPAL
Detect associated factors to in
hospital mortality in patients
admited by isquémic Stroke/CCR
surgery treated
ESPECIFIC
Evaluate the Discriminative
ability (trough de AUC) and
Callibration (trough visual risk
centiles of Hosmer Lemeshow
Test)

19. All the episodes of DRG 14//CCR admited in Spanish hospitals during the
period 2008-2012-14
Source Information
CMBDH years 2008-14
-Heath and Social Politics Ministery
-Statistical Web of Health Ministery
- National Health Statistics
Diagnosis Codification
CIE 9- MC
AP-GRD versión 21.0
Criterios de Urrea et al
Specific GRD
186,245 episodes (Stroke)
258,927 episodes (CCR)
Observational anality study.
Historic Cohort, DRG (Ischemic Stroke and CCR)
Design and Episodes in the study

20. Explanatory Variables
In Hospital Mortality
Dependent Variable
Variables in the study
Age (years)
Year (2008-2012)
Sex (male-female)
AACC (Méth. Foster/SMR)
Brain topography
Clinical Comorbidities
Financials
Dates: Born-Admission-Discharge
Type of Admission: (UI-PI)/ Type of
discharge
CIE Diagnostic (14 places)
CIE Procedures (20 places)
Clinical Complications

21. Descriptive and Inference
Inferential Study
Descriptive
Exploratory
Cuantitative Variables
Means (SD), CI 95% normal method
Categórical Variables
Frecuency Tables; % & CI 95%
Cualitative Variables
t de Student (con Levene)
Categorical Variables
Pearson χ2
CI Examination
Bivariant Studies
OR (CI 95%)
Robust Logistic Regression
OR adjusted (CI 95%)
Discrimination: ROC. C-Statistic
Calibration: Hosmer-Lemeshow
Predictive Model
Constant
observation of
the non-
differential
classification
bias

22. Introducing Data Science with Classic Models
Clasification
Trees

23. Permutation Test
Random Forest
Anothe point of view
Selecting Variables

26. Descriptive and Evolution of Admissions
(Stroke example)
186,245 patients admitted (episodes)
Length of stay: 7,54 (4,54) days
Age: 73,92(12,54) years
NDA: 6,91 (2.96) diag.
NPA: 3,27 (2,45) proc.
Readmissions: 4,8%
Exitus: 6,9%
36900
37000
37100
37200
37300
37400
37500
2008_
2009_
2010_
2011_
2012_
Año
37084
37114
37399
37228
37420
Admissions/Year

27. Robust Logistic Regression
Method of logistic: Forced Enter
Resampling (Bootstraping)
-Apparent paradoxical effect of HTA
-Effect of probable under-reporting of Dyslipidemia
C=0,7746

28. Calibration with Risk Decils
H-L STROKE. GRD 15
H-L CRC. CIE 9 154-155

29. FX1 = e EXP (-7,852 + 0,067*edad + 0,189*Sexoa + 0,679*Reingreso + 0,287*Cardiopatía isquémica -
0,336*HTA + 0,090*Diabetes + 0,428*Fibrilación Auricular - 0,441*Dislipemia + 0,397*Insuf Cardíaca +
1,039*Estenosis arteria basilar.

31. APPs for Ictus
- Quick
stimation of
risk
- Agile
- Easy

37. Neurotics (https://www.neurotics.es/)

38. Modelo y Score de CCR
Score de Riesgo

39. Inter-Model Comparison Internal Validation
CRC

40. Model and Score of Subdural H
Conservative Treatment

43. Colaboration UPM-HUT

44. AI-Conv

45. Conclusions
 The use of CADB such as the CMBD allows quality benchmarking
in Spain using the Foster methodology.
 Hierarchical structural equation methodology confirms pre-existing
evidence of the undeniable benefit of stroke units.
 The predictive mortality model for non-lysed ischaemic stroke can
be used to further optimise the high standards of stroke care
already in place (Patent, PCT and PRI).

46.  External validation with Neurology will allow us to refine the stroke
prediction model and assess whether imaging tests are essential
for risk prediction or whether clinical tests can be equivalent.
 The first entirely Spanish risk score for estimating the risk of death
after surgery for CRC, entirely from Almeria, has been achieved
(Registered).
 The development of mobile applications in conjunction with the
TICs Service will allow the estimation of complex risks in the
shortest possible time.