Results from 3 Swiss Frailty studies

Transcription

1 Geneva - February 25 Results from 3 Swiss Frailty studies F. Herrmann, MD MPH Department of Rehabilitation & Geriatrics Geneva University Hospitals Thônex - Switzerland

2 Studies Frailty judgment by the hospital team members: agreement degree and survival prediction. Herrmann F, Osiek A, Cos M, Robine JM, Michel JP Accepted in J Am Geriatr Soc Mai 25 * Prevalence estimates of frailty in the Swiss elderly. Herrmann F, Grandjean. R, Michel JP * Frailty in older adults: devising a mini-frailty-state score. Auckenthaler A, Chevalley T, Michel JP, Herrmann F *3rd Congress EUGMS (European Union Geriatric medicine Society), Wien, Austria, Studies Frailty judgment by the hospital team members: agreement degree and survival prediction. Accepted in J Am Geriatr Soc 24 * Prevalence estimates of frailty in the Swiss elderly. * Frailty in older adults: devising a mini-frailty-state score. * 3 rd Congress EUGMS (European Union Geriatric medicine Society), Wien, Austria,

3 Frailty judgment by the hospital team members: agreement degree and survival prediction F. Herrmann, A. Osiek, R. Grandjean, J.-M. Robine, J.-P. Michel Department of Rehabilitation & Geriatrics Geneva University Hospitals Thônex - Switzerland Method At admission to a geriatric hospital Nurses Residents Chief residents (staff) independently categorize, according to their own perception, elderly patients across 6 dimensions of frailty (with no definition provided)

4 Dimensions of frailty Physical Nutritional Sensorial Cognitive Psycho-emotional Social Physical

5 Nutritional Sensorial

6 Cognitive Psycho-emotional

7 Social Scaling of frailty dimension 5 Levels 3 Levels 2 Levels Code Very robust Robust Robust 2 Robust 3 Neither frail, nor robust nfnr 4 Frail Frail Frail 5 Very frail

8 Scaling of frailty dimension 5 Levels 3 Levels 2 Levels Code Very robust Robust Robust 2 Robust 3 Neither frail, nor robust nfnr 4 Frail Frail Frail 5 Very frail Study population 52 consecutive admissions Geneva s geriatric hospital ( ) 366 women, mean age 84.8 ± 6.7yrs 46 men, mean age 82.4 ± 7.2 yrs

9 Population characteristics N Mean SD Median % Age Weight [kg] Height [m] BMI [kg/m 2 ] CRP [mg/l] Albumin [g/l] Nb of drugs MMS Population characteristics Men Women T-test Wilcoxon Covariable N Mean SD Median N Mean SD Median p p Age Weight [kg] Height [m] BMI [kg/m 2 ] CRP [mg/l] Albumin [g/l] Nb of drugs MMS

10 Pourcentage de patients pour lesquels les valeurs du BMI, de la CRP, de l'albumine, du MMS et du score de Charlson mesurées ou calculées sont pathologiques Homme Femme Total N % N % N % p BMI ( <2 et >25 ) CRP ( > ) Albumine ( <3 ) MMS ( <23 ) Charlson ( > ) Mean frailty scores (5 levels) Paired T- test Nurse Resident Staff Repeated measure N vs S N vs R R vs S Anova Fragilité N Mean SD N Mean SD N Mean SD p p p p Physique Nutritionnelle Sensorielle Cognitive Psycho-affective Sociale

11 Prevalence (%) of frailty according to dimensions and professional background (N= 52) Dimensions Nurse Resident Staff Agreement among all Kappa Physical Nutritional Sensorial Cognitive Psycho-emotional Social Reliability Cronbach's alpha Prevalence (%) of frailty according to dimensions and professional background (N= 52) Dimensions Nurse Resident Staff Agreement among all Kappa Physical Nutritional Sensorial Cognitive Psycho-emotional Social Reliability Cronbach's alpha

12 Agreement level & Kappa Agreement Almost perfect Substantial Moderate Fair Slight Poor Accord Excellent Bon Modéré Médiocre Mauvais Très mauvais Kappa <. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 977;33():59-74.) Mesure de la cohérence interne des différentes échelles à l'aide de l'alpha de Cronbach Score fragilité estimée 5 niveaux Score fragilité estimée 3 niveaux Score fragilité en binaire Fragilité Spécialiste Infirmière Interne spécialiste Infirmière Interne spécialiste Infirmière Interne Physique Nutritionnelle Sensorielle Cognitive psychoaffective Sociale test échelle total

13 Predictors of frailty Logistic regression for physical frailty Dimension Physical Nutritional Sensorial Cognitive Psychoemotional Social Covariate OR p OR p OR p OR p OR p OR p Age Sex MMS Albumin [g/l] BMI [kg/m 2 ] Charlson CRP [mg/l]

14 Logistic regression for predicting frailty when the 3 professionals agree Dimension Physical Nutritional Sensorial Covariate OR p OR p OR p Age Sex MMS Albumin [g/l] BMI [kg/m 2 ] Charlson CRP [mg/l] Dimension Cognitive Psycho-emotional Social Covariate OR p OR p OR p Age Sex MMS Albumin [g/l] BMI [kg/m 2 ] Charlson CRP [mg/l] Logistic regression for predicting frailty when the 3 professionals agree Dimension Physical Nutritional Sensorial Covariate OR p OR p OR p Age Sex MMS Albumin [g/l] BMI [kg/m 2 ] Charlson CRP [mg/l] Dimension Cognitive Psycho-emotional Social Covariate OR p OR p OR p Age Sex MMS Albumin [g/l] BMI [kg/m 2 ] Charlson CRP [mg/l]

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16 Survival analysis 487 patient s first admissions Survival assessed with the State population office database on (5 months follow-up) Cox regression models Univariate Adjusted for age, gender, BMI, CRP, Albumin, # of drugs (with imputed missing values) Risk of death (Crude Hazard ratio) during the 5 months follow-up according to frailty dimensions and professional background (N= 487) Dimensions Nurse Resident Staff Physical Nutritional Sensorial Cognitive Psycho-emotional Social Sum of dimensions P <.5

17 Survival analysis Univariate Cox models Physical, nutritional and sensorial frailty significant for nurses and residents Cognitive frailty significant only for staff physicians Risk of death (Adjusted Hazard ratio*) during the 5 months follow-up according to frailty dimensions and professional background (N= 487) Dimensions Nurse Resident Staff Physical Nutritional Sensorial Cognitive Psycho-emotional Social Sum of dimensions.4.4. P <.5 *Age, gender, albumin, BMI, CRP, # of drugs

18 Kaplan-Meyer survival curve Nurse s s sensorial frailty % Survival Log rank test: p =.4 Robust Frail Follow-up post admission [months] Kaplan-Meyer survival curve Resident s s physical frailty % Survival Log rank test: p =.6 Robust Frail Follow-up post admission [months]

19 Survival analysis Multivariate Cox models adjusted for : Age, gender, albumin : always significant BMI, CRP, # of drugs : never significant Sensorial frailty significant for nurses only Physical frailty significant for residents only No significant dimensions for staff physicians Sum of 6 dimensions significant for all professionnals Each frailty dimension increases the risk of death by 4% for nurses and residents, % for staff physicians Conclusion The 3 health care professional categories do not share the same perception of the term frailty. Therefore the clinical use of this term should be avoided until a better consensus emerges Which could differ whether the evaluation involves an individual or a population and also according to the settings.

20 Conclusion Studensky s ongoing validation project as well as other attempts should provide a better detection of physical or global frailty to try to prevent its consequences Studenski S et al. Clinical Global Impression of Change in Physical Frailty: development of a measure based on clinical judgment. J Am Geriatr Soc 24; 52:56-6. Wien, Austria, September 24 Prevalence of frailty in Switzerland: Concepts and figures F. Herrmann, R. Grandjean, J.-P. Michel Department of Rehabilitation & Geriatrics Geneva University Hospitals Thônex - Switzerland

21 Aims Estimate the prevalence of frailty in the Swiss community-dwelling population, starting from theoretical definitions of frailty Look for association between frailty and health services utilization Methods Secondary analysis 2 nd wave Swiss Health Survey ( / / 22-3) Swiss Federal Statistical Office National cross-sectional survey Vonlanthen C. OFS 997

22 Methods Representative sample (69% response rate) 3'4 community residents aged 4+ 2'77 persons interviewed '792 written questionnaire (83%) 5'934 respondents aged 55+ 3'59 respondents aged 65+ Abelin T.OFS 2 Frailty theoretical models Physical (Fried) Social vulnerability model (Lalive) Loss of functional autonomy (Katz)

23 Results: Fried s frailty (>2/5( >2/5) Fried Proxy in the Swiss study Unintentional weight loss Protein intake (5 kg in past year) Self-reported exhaustion Psychological health Weakness (grip strength) Feeling weak Slow walking speed Ability to walk 2 m Low physical activity Weekly reported activites L. P. Fried et al., J Gerontol A Biol Sci Med Sci 56, M46-56; 2 Max Bircher-Benner eating his Müesli isst sein Müesli Schweizerische Ärztezeitung / Bulletin des médecins suisses 356,

24 Fried s frailty prevalence original study (65+) 7.2% Frailty Criteria Men Women Total P N sample L. P. Fried et al., J Gerontol A Biol Sci Med Sci 56, M46-56; 2 Fried s frailty prevalence Swiss study (65+) 6.% Frailty Criteria Men Women Total P N sample N population

25 Fried s frailty prevalence Swiss study (55+) 5.5% Frailty Criteria Men Women Total P N sample N population Results: Chin s s frailty (>2/2( >2/2) Chin Unintentional weight loss (5 kg in past year) Proxy in the Swiss study Protein intake Low physical activity Weekly reported activities A. Chin et al., J Clin Epidemiol 52, 5-2, 999

26 Results: social frailty (>3/9( >3/9).4% Sensorial impairments limiting communication Subject to Violence Civil status Loneliness Mental health Associative life Activity limitations Income Physical activity Significant gender effect W 5.%, M 6.7% (P <.) C. Lalive d Epiney et al., Med & Hyg 23 Results: ADL (2/2( 2/2) 3.8% At least one dependencies on Katz s ADL and No informal social network (receiving help from neither family nor friends) Significant gender effect W 4.9%, M 2.5% (P <.) Carlson JE et al., Am J Phys Med Rehabil 998

27 Frailty prevalence by age % et + Fried Social ADL Frailty prevalence by age % Fried Chin Social ADL et +

28 Frailty prevalence Fried Social ADL (Katz) N pop. % Total Frailty prevalence (N by age group) Fried Social ADL (Katz) et + Total

29 Frailty prevalence (% by age group) Fried Social ADL (Katz) et + Total Predictors of ressources consomption Frailty Yes No Age >= 7 Yes No Gender male Yes No

30 Frequence prediction of the number of medical consultations in previous year (Swiss Fried) probabilité prédite probabilité observée.2.2 Frequence density probabilité 4 probabilité visites chez le le médecin Nb medical consultation Frequence prediction of the number of medical consultations in previous year (Swiss Fried).2 probabilité prédite probabilité observée probabilité Frequence density. Data fited with Negative Binomial regression visites chez le médecin Nb medical consultation

31 Fried s frailty model impact on number of medicalm consultations (Negative Binomial regression) IRR [95% CI] P Fried s Frailty Age >= Gender male Frequence prediction of the number hospitalisation days in previous year (Swiss Fried) probabilité observée.8 Frequence density 4 probabilité jours d'hospitalisation Nb hospitalisation

32 Frequence prediction of the number hospitalisation days in previous year (Swiss Fried) probabilité prédite probabilité observée probabilité Frequence density Data fited with Negative Binomial regression jours d'hospitalisation Nb hospitalisation Frequence prediction of the number of drugs classes in the last 7 days (Swiss Fried).6 probabilité observée Frequence density 4 probabilité nombre de catégories de médicaments 7 derniers jours Nb drug classes

33 Frequence prediction of the number of drugs classes in the last 7 days (Swiss Fried).6 probabilité prédite probabilité observée probabilité Frequence density.4.2 Data fited with Negative Binomial regression 5 nombre de catégories de médicaments 7 derniers jours Nb drug classes Frequence prediction of the number of drugs classes in the last 7 days (Swiss Fried) probabilité prédite probabilité observée probabilité Frequence density.5 Negative Binomial regression hospitalisations Nb drug classes

34 Impact on health care resources consumption (Negative( Binomial regressions) Consultations Hospitalisations [days] Drugs classes Frailty model IRR IRR IRR Fried Chin Social ADL limitations P <., adjusting for age and gender Impact on health care resources consumption (Negative( Binomial regression) Nb Medical consultations Hospitalisations [days] Drugs classes Frailty model IRR [95% CI] P>z IRR [95% CI] P>z IRR [95% CI] P>z Fried Frailty Age >= Gender male Social Frailty Age >= Gender male ADL limitations Frailty Age >= Gender male

35 Impact on health care resources consumption (Negative( Binomial regression) Nb Medical consultations Hospitalisations [days] Drugs classes Frailty model IRR [95% CI] P>z IRR [95% CI] P>z IRR [95% CI] P>z Fried Frailty Age >= Gender male Chin Frailty Age >= Gender male Social Frailty Age >= Gender male ADL limitations Frailty Age >= Gender male Number of medical consultations in previous year (Swiss Fried) probabilité prédite probabilité observée.2.5 Negative Binomial regression probabilité Probability visites chez le médecin Nb medical consultation

36 Fried s frailty model impact on number of medicalm consultations (Negative Binomial regression) IRR [95% CI] P Fried s Frailty Age >= Gender male Number of hospitalisation days in previous year (Swiss Fried) probabilité prédite probabilité observée Negative Binomial regression probabilité Probability hospitalisations Nb hospitalisation

37 Number of drugs classes in the last 7 days (Swiss Fried) probabilité prédite probabilité observée Negative Binomial regression probabilité Probability hospitalisations Nb medical consultation Impact on health care resources consumption (Negative( Binomial regressions) Consultations Hospitalisations [days] Drugs classes Frailty model IRR IRR IRR Fried Social ADL limitations P <., adjusting for age and gender

38 Frailty Prevalent concept in geriatrics Distinct clinical syndrome: Shrinking (unintentional loss of weight and sarcopenia) Weakness Poor endurance Exhaustion Slowness and low activity Constellation of many conditions J.-P. Michel, in Vulnerability and aging (Elsevier, Paris, 22) pp Frailty Useful concept because it corresponds to an unsteady "dynamic state of equilibrium" between: Health Independence Caring network Resourcefulness Illness Loss of autonomy Loneliness Lack of means J.-P. Michel, in Vulnerability and aging (Elsevier, Paris, 22) pp

39 Theoretical trajectories of dying J. R. Lunney et al., J Am Geriatr Soc 5, 8-2 (22) J. R. Lunney et al., Jama 289, (23) ADL dependencies for each month cohort Frailty: nursing home stay anytime during the 6 years follow-up J. R. Lunney et al., Jama 289, (23)

40 Conclusion Frailty y correspond to a progressive reduction of functional reserve Lack of adequate response to disturbing events Identifiable (weight loss, sarcopenia,, falls, confusion, ) Induce demand of care Need for early markers Conclusion Lack of a unique definition Prevalence increase with age, whatever the definition Gender effects depends on the definition Association with health care use

41 Acknowledgement Unité des Services de Santé,, IUMSP, Lausanne B. Santos-Eggimann N. Chavaz Cirilli J. Junod A. Clerc-Bérod rod Supported in part by Swiss National Science Foundation grant # / / Geneva - February 25 Frailty in older adults: devising a mini-frailty frailty-state score F. Herrmann, A. Auckenthaler, T. Chevalley, J.P. Michel Department of Rehabilitation & Geriatrics Geneva University Hospitals Thônex - Switzerland

42 Introduction Frailty is recognized as a powerful concept that is of special interest when trying to sort out the risk of adverse health outcomes, in older population. There is still no agreement on a frailty definition or measure instrument. 2 - Brown M, et al. Physical and performance measures for the identification of mild to moderate frailty. J Gerontol A Biol Sci Med Sci 2;55:M Rockwood K et al. Frailty in elderly people: an evolving concept.. CMAJ 994;5: Methods Data come from a cohort of 45 patients, 65+, who underwent a comprehensive geriatric assessment at the emergency ward in 996.

43 Methods Variables selection based on univariate logistic regression Stepwise-backward logistic regression models (with a predefined threshold set at p.2). Results: Population characteristics Age MMSE Charlson MIF Gender N Mean (SD) N Mean (SD) N Mean (SD) N Mean (SD) Women (7.3) (4.3) (.2) (6.) Men (7.7) (3.5) (.4) (4.9) p <..8 <. <. Total (7.6) (4.) 94. (.3) (5.7) [%]

44 Results: Prediction of death and institutionalization Death Institutionalization OR P OR P OR P OR P OR P OR P Variables n = 789 age yrs age Gender male Comorbidity score* Wash and bath oneself Dress and undress lb***.8.8 Low er limbs mobility Use stairs Home alone.5.6 Get out of bed Prepare ow n meal Shopping Use public transport Needs assistance Time orientation (MMSE) Grooming Follow -up tim e [month] Results: Prediction of emergency visit and hospitalisation Variables N = 789 Emergency visit Hospitalization OR P OR P OR P OR P OR P OR P age yrs age Gender male.3.9 Comorbidity score* Wash and bath oneself Dress and undress lb***.78.6 Low er limbs mobility Use stairs Home alone.4.4 Get out of bed Prepare ow n meal Shopping Use public transport Needs assistance Time orientation (MMSE) Grooming Follow -up time

45 Results: Predictive characteristics of adverse outcomes models [range%] Death Institutionalization Emergency Hospitalization visits R 2 [ 6-2] [ 3-4] [ - 2] [2-3] Area under [ 67-73] [ 76-79] [ 56-6] [ 6-6] ROC R-square (R2) and area under the receiver operator curve (ROC) show that individual outcome models had poor predictive capacity throughout the follow-up periods. Conclusions The studied variables are not good indicators of frailty when the syndrome is defined as any of 4 adverse outcomes: Death Emergency room visit Institutionalization Hospital admission.