Validez de distintos instrumentos de evaluación de fragilidad en ancianos atendidos en diferentes medios hospitalarios

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1 Validez de distintos instrumentos de evaluación de fragilidad en ancianos atendidos en diferentes medios hospitalarios Prof. Leocadio Rodríguez Mañas Sº de Geriatría Hospital Universitario de Getafe Madrid, España

2 TOPICOS 1. El nuevo escenario en la atención sanitaria 2. Fragilidad: Un nuevo concepto que responde a las necesidades 3. Fragilidad un reto clínico: problemas a resolver 4. Fragilidad cómo funcionan los instrumentos de medida? 5. Por dónde seguir? 6. Conclusiones

3 JAMDA, 2017 TRANSICION DEMOGRÁFICA TRANSICION EPIDEMIOLOGICA TRANSICION CLINICA

4 THE THIRD TRANSITION BREAKING THE CLINICAL INERTIA CURE DISEASE SURVIVAL TO DO LONG-TERM CARE FUNCTION QUALITY OF LIFE RISK TO BENEFIT RATIO (NOT TO DO) TIMELY INTERVENTIONS Rodriguez-Mañas et al., JAMDA 2017

5 ROBUSTNESS LOW FUNCTIONAL RESERVE DISABILITY-DEPENDENCY SEVERE DEPENDENCY DEATH The functional continuum Isolated Physiological Vulnerability MULTYSYSTEMIC IMPAIRMENT Multiple Non-reversible conditions Whitson HE et al., 2007

6 FRAGILIDAD y DM Modelos de fragilidad Fried LP Rockwood K Rockwood K. J Am Geriat Soc. 2006;54: Fried et al. J Gerontol Med Sci. 2001;56A:M146-M156

7 Lancet 2015; 385: e7-9

8 Prevention Risk manag. Empowerment OUR CHALLENGE LONGEVITY (AMOUNT OF LIFE) CHRONIC DISEASE HEALTH SYSTEMS + SOCIAL SYSTEMS Integrated Coordinated. Continued QUALITY OF LIFE (FUNCTION) TO MAINTAIN OUR APPROACH Management of chronic disease oriented to avoid frailty and preserve function Management of frailty, as the phenotypic expression of disease in older adults Management of frailty, as the main predictive factor of adverse outcomes Promoting integrated, coordinated and continued care

9 NECESITAMOS DIAGNOSTICAR LAS SITUACIONES QUE PRECEDEN LA DISCAPACIDAD PARA INTERVENIR A TIEMPO

10 Lancet, We need clinical pathways 2.- We need biomrakers 3.- We need treatments

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12 Our research projects as Coordinator FRAILTOOLS A comprehensive validation of tools to screen and diagnose frailty in different clinical and social settings to provide instruments for integrated care in older adults FRAILCLINIC is aimed at assessing the feasibility and effectiveness of programs designed to detect and manage frail older patients in high risk clinical settings The FRAILOMIC initiative is a large scale research project aiming to identify the factors that turn frailty into disability. A randomized clinical trial to evaluate the effectiveness of a multi-modal intervention in older people with type 2 diabetes on frailty and quality of life

13 Tools to assess frailty Variability in different populations Variability in risk prediction Clinical usefulness Feasibility Clinical settings Biomarkers in risk determination, diagnosis and prognosis

14 Variability in classification and risk prediction Fried ST-Fried FRAIL FI FTSE Groningen etc X % X % Y % X % Y % RISK A RISK A RISK A RISK B

15 Variability in different populations Lourenço R et al., Age and Ageing 2014

16 Raising misclassification with changing risks Bouzon et al., JAMDA 2017

17 * * * * * * Bouzon et al., JAMDA 2017

18 Bouzon et al., JAMDA 2017 Time to event

19 Feasibility and effectiveness of the implementation of programs to screen and manage frail older patients in different clinical settings Project Grants (HP-PJ) 2nd European Union Health Programme

20 Objective To assess the feasibility and effectiveness of programs designed to detect (observational phase) and manage (interventional phase) frail older patients in high risk clinical settings and to avoid functional impairment and other associated adverse outcomes: Emergency room Cardiology General surgery Oncology

21 Phases PHASES: Observational and interventional 1.Observational phase: Feasibility and effectiveness of frailty screening programs implemented in different clinical settings. 2.Interventional phase: Feasibility and effectiveness of the implementation of programs to screen and manage frail older patients in different clinical settings

22 Participating centres SPAIN ohospital Universitario de Getafe (Madrid, España) ohospital Universitario Monte Naranco (Asturias, España) ITALY oospedale San Raffaele (Roma, Italia) ouniversita Cattolica del Sacro Cuore (Roma, Italia) UNITED KINGDOM odiabetes Frail Ltd (DIFRAIL) oaston University

23 Observational phase Tools assessed for Frailty: Fried criteria FRAIL Scale Tilburg Frailty Indicator Gröningen Frailty Indicator CFS or Rockwood modified ISAR (Emergency room) Balducci criteria (Oncology) VES 13 (Oncology) G8 (Oncology) Time recruitment: 10 months

24 Participants Inclusion criteria: o Patients older than 75 years, assessed in several clinical settings (different from Geriatry); Emergency Room, Cardiology, Elective and Urgent Surgery and Oncology. Exclusion criteria: o Do not able to give informed consent. o Participants with Impairment cognitive moderate or severe according to MMSE scale (score 18 or lower) and/or the GDS scale (score 5 or higher ). o Those with physical disability according to Barthel Scale (lower to 40) o o o Participants with critical acute disease. Life expectancy less than six months Patients living in nursing homes

25 Sociodemographic characteristics Variables Emergency Room Cardiology Elective Surgery Urgent Surgery Oncology Agregate N Age (DT) 83,71(5,66) 80,65(4,30) 79,50(3,04) 82,58(5,06 ) Gender (% males) 78,9(3,19) 81(4,62) 36,75 54,55 61,04 47,69 64,00 52,81 White race 100,00 99,55 99, ,00 99,50 Civil Status (%) Single 6,84 3,64 2,60 7,69 4,00 4,46 Married or cohabiting 39,32 51,36 70,78 44,62 62,00 54,13 Widow 52,99 43,18 25,32 47,69 32,00 40,10 Necessity of principal caregiver Profesional 12,62 4,78 2,92 13,85 2,00 6,56 Caregiver

26 Frailty classification by tool and setting Setting Emergency Room Fried (%) FRAIL (%) Tilbg (%) Grng (%) Rockw (%) ISAR (%) Bald (%) G8 (%) VES 13 (%) Total (%) 50,51 40,71 68,14 74,34 47,46 78, ,00 Cardiology 61,39 41,36 65,55 62,32 42, ,61 Elective Surgery Urgent Surgery 24,67 15,48 30,32 30,72 5, ,27 53,33 41,54 37,50 50,77 18, ,32 Oncology 47,92 30,00 36,00 40,00 6, ,28 81,63 34,69 36,31 Agregate 47,43 33,67 51,27 53,23 28, ,78

27 Feasibility of scales Complet e (%) Emergen gy Room (%) Cardiology (%) Elective Surgery (%) Urgent Surgery (%) Oncology (%) Fried 68,64 60,17 76,92 87,74 12, FRAIL 98,52 94,07 99,55 99, I. F.Tilburg 92,45 85,59 90,05 96,13 98, I.F.Gröninge n 91,13 85,59 90,05 96,13 98, T (min ) Rockwood modificada 99, , ISAR* 93,22 93, G8** 98, Balducci** 98, Ves13** 98, *Specífic of Emergency Room ** Specific of Oncology

28 Feasibility Fried scale Setting N Weight lost Medido s (%) Fatigue Medidos (%) Physical Activity Medidos (%) Gait Speed Medidos (%) Hand Grip Full Scale Medidos (%) Medidos (%) Emergency Room ,31 98,31 98,31 62,71 91,53 60,17 5 Cardiology ,10 99,55 99,10 76,92 95,02 76,92 5 Elective Surgery Urgent Surgery ,00 99,35 100,00 88,39 98,71 87,74 7, ,46 98,46 95,38 12,31 98,46 12,31 9 Oncology ,00 100,00 100,00 66,00 100,00 66,00 4 Agregate ,18 99,18 98,85 69,29 96,06 68,64 5 T (m)

29 Causes of non implementation: Fried scale

30 Degree of concordance among scales: Global Tilbg Grng Rockw Fried FRAIL FRAIL Fried Rockw Grng Tilbg INTERPRETATION KAPPA (Landis & Koch, 1977) Kappa Index: Slight agreement Fair agreement Moderate agreement Substantial agreement Almost perfect agreement

31 Emergency Room Cardiology ISAR Tilbg FRAIL Fried Rockw Grng Tilbg ISAR 0,59 Tilbg Grng FRAIL Fried Rockw Grng Tilbg 0,62 Grng Rocw Rockw Fried Fried FRAIL FRAIL Tilbg Elective Surgery FRAIL Fried Rockw Grong Tilburg 0,65 VES 13 G8 Oncology FRAIL Fried Rock Grng Tilbg Bald G8 VES 13 Grng Rockw 0,11 Bald Tilbg Grng 0,66 0,07 Fried FRAIL Rockw Fried FRAIL 0,66 0,02

32 KAPPA INDEX AMONG SCALES: URGENT SURGERY Tilbg Grng Rockw Fried FRAIL FRAIL Fried Rockw Grng Tilbg INTERPRETATION KAPPA (Landis & Koch, 1977) Kappa Index: Slight agreement Fair agreement Moderate agreement Substantial agreement Almost perfect agreement In this setting, feasibility of Fried s criteria is vrey low (12.8%)

33 A comprehensive validation of tools to screen and diagnose frailty in different clinical and social settings to provide instruments for integrated care in older adults Project Grants (HP-PJ) 3rd European Union Health Programme

34 General objectives 1. To assess the usefulness as screening and diagnosis tools of some selected instruments to detect frailty in: Clinical Hospitals (Geriatric settings: Acute Care Unit and Outpatient Office) Primary Care Social Nursing homes 2. To provide sequential diagnostic algorithms

35 Participating centres SPAIN Getafe University Hospital Fundación para la Investigación Biomédica del Hospital Universitario de Getafe (FIBHUG) Servicio Madrileño de Salud (SERMAS) ITALY Università Cattolica del Sacro Cuore (UCSC) FRANCE Centre Hospitalier Universitaire de Toulouse (CHUT) POLAND Jagiellonian University Medical College (JUMC) UNITED KINGDOM DIFRAIL Aston University (since 1st December 2016) Overview of the Project

36 Methodological approach PARTICIPANTS AND SETTINGS: Sample size is established in 485 person by setting; total of 1940 persons (97 per setting) Inclusion criteria: People 75 years or older. Attended in 4 different settings: In-Hospital Geriatric wards Hospital outpatient offices Primary Care Nursing Homes Exclusion criteria: Subjects unwilling or unable to consent or unable to participate MMSE <20 points Terminal illness (life expectancy <6 months) In Hospital and Primary Care: Dependency in more than 2 IADL (Lawton) In Nursing Homes: Barthel Index < 40

37 Instruments assessed INSTRUMENTS/TOOLS: 1. Frailty Phenotype- Fried criteria 2. FRAIL scale 3. SHARE-FI 4. CHSA- Clinical Frailty Scale 5. The 35 item Rockwood Scale 6. Shorten version of the Frailty Trait Scale- FTS 7. The Gerontopôle Frailty Screening Tool- GFST

38 Implementation of the Project Preliminary Results (Spain) RECRUITMENT STATUS SETTING Recruited Goal To Go Geriatric Ward Outpatient Consultation General Practice Reached Nursing Homes TOTAL Recruitment started: February 2016 Recruitment scheduled until: February 2017 (month 22) - extend until July 2017?

39 Implementation of the Project Preliminary Results (Spain) Rate of fulfilment and time spent on administration of six different frailty assessment tools in four clinical and social settings SETTINGS SCALES Completion (%) GW OC GP NH Time (sec) Mean (SD) Completion (%) Time (sec) Mean (SD) Completion (%) Time (sec) Mean (SD) Completion (%) Time (sec) Mean (SD) Fried (90.5) (94.8) (93.4) (56.4) FRAIL (50) (405.6) (32.1) (17.7) Rockwood (85.5) (89.7) (103.7) (61.3) CFS (4.3) (11.9) (3.6) (14) SHARE-FI (46.4) (41.1) (35.5) (51.2) GFST (23.4) (85.7) (12.2) (12.4) GW: Geriatric Ward. OC: Outpatient Consultations. GP: Primary Care Centres. NH: Nursing Homes. CFS: Clinical Frailty Scale.

40 Si no nos vale, probar 2-step uno sencillo con sensibilidad y otro con especificidad y biomarcadores Garcia-Garcia FJ et al., JAMDA 2014

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42 Partners of FRAILOMIC

43 PHASE 1 PHASE 2 Prospective Cohorts with Follow-up of 3 yrs + Bio-banks Potential BM - Risk factors Validation Cohorts to Be Followed during 2.5 yrs + Samples from participants Validated - Risk factors Lab Biomarkers - Genomic - Proteomic - Metabolomic - Classical BM - Diagnoses - Prognostic factors Potential Lab BMs - Genomic - Proteomic - Metabolomic - Classical BM - Diagnoses - Prognostic factors Substudies in Special populations/conditions: Diabetes, CVD, CVRF, Nutrition, Exercise Best Fitted Models PHASE 3 ToolKits Dissemination Exploitation PHASE 4

44 5bii. University of Valencia Main Frailomic results found during We carried out metabolomics plasma nuclear magnetic resonance (NMR) spectral deconvolution and further analysis on the ETES cohort. We found associations with frailty for total NMR cholesterol, isoleucine, lactate, acetate, citrate, polyunsaturated fatty acids and total creatine. We are exploring other hypotheses like ph variations (determined by NMR), lipid mobility (peak line width) and metabolic intercorrelations.

45 6b. Academic Hospital of Parma Main Frailomic results found during STARTING FROM 14 ANALYTES: VCAM-1, ICAM-1, MMP-9, MMP-11, ACTIVIN-A, ADIPONECTIN, MYOSTATIN, GALECTIN-3, TROPONIN-T, PROBNP, PCT, ESTRADIOL, A.N.A., SUPAR. AFTER COVARIATES ANALYSIS, 7 BIOMARKERS DISPLAY SIGNIFICANT CORRELATIONS PROBNP, TROPONIN-T, SUPAR, VCAM-1, MMP-9, GALECTIN-3, ADIPONECTIN.

46 PROGNOSIS QUESTION 4 QUESTION 5 RISK QUESTION 3 DIAGNOSIS QUESTION 1 QUESTION 2 Ques<ons Questions Addressed to be answered in Detail by FRAILOMIC Ques<on: Ques<on: To include: Covariates To include: Covariates Ques<on: To include - - Covariates Ques<on: Ques<on: To include all Covariates To include: Covariates 17

47 LABORATORY DATA PROCESS STATUS Solving ID issues Normaliza( on Raw-processed data Co-variates to consider Batches Experimental data is: De-idenKfied Added to the database COHORT 1 LABS 1 COHORT 2 LABS 2 YOUHEALTH COHORT 3 LABS 3 Analysis COHORT 4 of data: Associa<ons and Models LABS To consider: mrna-ets (SERMAS and SG) will be considered for the Phase 2. Not enough overlap with exiskng pakents. Feature Selec<on: Step 2 AIM METHODS RESULTS FAST NOTES ON HARMONIZATION LAB DATA FOR EACH COHORT 15 QUESTIONS ADDRESSED STEP 1: SELECT VARIABLES STEP 2: IMPUTATION STEP 3: MINIMAL MODEL Pre-selection FEATURE SELECTION STEP 1 AIM STATISTICS RESULTS Imputation FEATURE SELECTION STEP 2 AIM STATISTICS RESULTS MINIMAL MODELS AIM STATISTICS RESULTS IniKal SelecKon: Experimental variables. Cohort variables Filter: Those variability = 0 Those with > 25% of NA JSS Journal of Statistical Software MMMMMM YYYY, Volume VV, Issue II. / MICE: Multivariate Imputation by Chained Equations in R Stef van Buuren 1 TNO Quality of Life, Leiden 2 University of Utrecht Karin Groothuis-Oudshoorn 1 Roessingh RD, Enschede 2 University Twente ass problems caused by perfect prediction. Special attention to transformations, sum scores, indicesand interactionsusing passive imputation, and totheproper setup of thepredictor matrix. MICEV2.0isfreely available from CRAN as an Rpackage mice. This article provides a hands-on, stepwise approach to using micefor solvingincompletedata problemsin real data. MXM package 250 ImputaKons Selected for each imputakon 37 Keywords: multipleimputation, chained equations, fully conditional speci cation, gibbssampler, predictor selection, passiveimputation, R Introduction Multipleimputation (Rubin 1987, 1996) isthemethod of choicefor complex incompletedata problems. Missing data that occur in more than one variable presents a special challenge. Two general approachesfor imputing multivariatedata haveemerged: joint modeling (JM)

48 Minimal model for diagnosis Feature Selec<on: Step 2 Q1: DIAGNOSIS ALL

49 ETS Q1_FINAL Use of Minimal model and Toledo cohort information variables in diagnosis (Q1) by PCA a

50 CONCLUSIONES 1.- La implantación definitiva de la fragilidad en la práctica clínica necesita disponer de procedimientos diagnósticos bien definidos 2.- Los instrumentos disponibles hoy dia presentan problemas para su aplicación en la práctica diaria que afectan a su factibilidad, variabilidad en diferentes medios clínicos y su pobre correlación. 3.- La combinación de criterios clínicos y de laboratorio pueden aportar una mayor precisión diagnóstica

51 Gracias por su atención!