Innovation for Tomorrow: what the future holds Falls Prediction

Transcription

1 Innovation for Tomorrow: what the future holds Falls Prediction Lorenzo Chiari 1,2 1 Personal Health Systems Lab, Dept. Electrical, Electronic and Information Engineering «Guglielmo Marconi» 2 Health Sciences and Technologies - Interdepartmental Center for Industrial Research University of Bologna, Italy {lorenzo.chiari@unibo.it}

2 Can we predict (future) falls? Lorenzo Chiari 1,2 1 Personal Health Systems Lab, Dept. Electrical, Electronic and Information Engineering «Guglielmo Marconi» 2 Health Sciences and Technologies - Interdepartmental Center for Industrial Research University of Bologna, Italy {lorenzo.chiari@unibo.it}

3 Acknowledgements Pierpaolo Palumbo 1, Luca Palmerini 1, Sabato Mellone 1 1 Personal Health Systems Lab, Dept. of Electrical, Electronic and Information Engineering «Guglielmo Marconi» - University of Bologna, Italy Carlo Tacconi, Alice Coni 2 2 Health Sciences and Technologies - Interdepartmental Center for Industrial Research University of Bologna, Italy Federico Chesani 3, Luca Cattelani 3 3 Dept of Computer Science and Engineering, University of Bologna, Italy Stefania Bandinelli 4, Marco Colpo 4 4 Laboratory of Clinical Epidemiology, Azienda Sanitaria di Firenze, Italy

4 Acknowledgements farseeingresearch.eu

5 Whatever the media are dealing with today, the university has already dealt with it twenty years ago, and what the university deals with today will be reported by the media in twenty years. Attending university means being decades ahead of your time. It's the same reason why literacy prolongs life. Who does not read has only his life, which, I assure you, is very little. On the contrary, when we shall die we will remember to have crossed the Rubicon with Caesar, fought with Napoleon at Waterloo, traveled with Gulliver and met dwarves and giants. A small compensation for the lack of immortality. Umberto Eco ( )

6 The present

7 The present RISK FACTORS FALL RISK ASSESSMENT FALL PREVENTION - Tools for falls prediction are needed as strategic components for fall prevention - Clinical risk factors

8 The guidelines (ask & evaluate) Screening for high risk Assessment of multiple risk factors for those at high risk Implementation of a tailored intervention J Am Geriatr Soc. 2011

9 Predictive tools: a bit of history Traditional tools (1 st generation) - Often based on subjective evaluations - No use of statistics, no probabilistic meaning 2 nd generation tools - (e.g. PPA) Validation of traditional tools Sensor-based tools (3 rd generation) -Proof of concept 1986 POMA 1991 TUG 2008 Lamb s screening tree 2013 Howcroft s review on sensor-based tools 1986 Get- Up and Go Test 1994 Guralnik s SPPB 2003 Lord s Physiological Profile Assessment PPA 2010 Deandrea s review on fall risk factors time Consciousness in statistics - development and validation Advent and diffusion of high-throughput technology: -inertial sensors Advances in statistical learning for highdimensional problems

10 Where are we with falls prediction? Guidelines were tested on older disabled women and in communitydwelling older adults and found to be suboptimal with respect to other screening tool and of moderate clinical utility (Muir et al, J Geriatr Phys Ther, 2010). Predictive accuracy of current versions of these screening algorithms not currently reported. Many other screening tools have been proposed in the literature but few of them have been tested outside the derivation cohort. History of falls is a strong risk indicator for future falls (Ganz et al, JAMA, 2007), although it alone does not suffice for primary prevention. SPPB is a tool to assess physical performance, commonly included in comprehensive geriatric assessments. Its association with falls and injurious falls is documented (Veronese et al, Rejuvenation Res. 2014) but its prognostic performance is not reported.

11 Where are we with falls prediction? TUG has been judged inadequate in several studies (e.g. Barry et al, BMC Geriatr. 2014). Gait speed is an indicator of health state in geriatric populations. Its prognostic value for future falls has been shown to be equivalent to total time to perform the TUG (Viccaro et al, J Am Geriatr Soc. 2011). Palumbo, PhD Thesis, 2015

12 Clinical knowledge Clinical risk factors (CRF) for falls are well known What if we fully exploit the knowledge generated by CRF?

13 A possible operationalization of established 2nd generation tools Screening for high risk Assessment of multiple risk factors for those at high risk Implementation of a tailored intervention J Am Geriatr Soc. 2011

14 The FRAT-UP tool Assessment tool for evaluating the fall risk within a year Aimed to general practitioners and health organizations (per-subject evaluation vs. population wide) (So far) focused on community-dwelling older people Based on 26 risk factors available in the literature Exploits available clinical information about the subject Freely available as a web-based application at the url ffrat.farseeingresearch.eu Cattelani et al., J. Med. Int. Res., 2015

15 User interface -Allows the use of statistical prevalence of the risk factor -Support for dichotomic, scalar and synergy risk factors

16 CRF Coverage of guidelines domains AGS/BGS FRAT-up Data-driven model (variables available for selection) Relevant medical history, physical examination, cognitive and functional assessment Diabetes MMSE Self-perceived health status Widely covered History of falls Yes Yes Yes Medications number of medications, sedatives, antihypertensives, Domain accurately covered. antiepileptics, L. Chiari University of Bologna I-DON T-FALL Gait problems assessed WORKSHOP Gait speed in different ON FALL via the Revised walking tests, FICSIT, SPPB, etc. PREVENTION sub-score, walking AND aid MANAGEMENT - use Roma, 25 September 2015 Gait, balance, and mobility Visual acuity Other neurological impairment Visual acuity, contrast sensitivity, visual stereognosis Parkinson s disease Visual acuity, contrast sensitivity, visual stereognosis Parkinson s disease, subclinical/non overt neurological signs Muscle strength No Grip strength, lower limb muscle strength Heart rate and rhythm No Yes Postural hypotension No No (There is information about blood pressure before and after standing up but not explicity the difference ) Feet and footware No Information about shoes Environmental hazards No No No Data-driven model (variables most frequently selected) Self-perceived health status, CESD, familiarity to diabetes, Number of medications, drug for dementia, antihypertensives, antidepressants Gait speed, cautious attitude while walking

17 Clinical risk factors: can we do better? Palumbo et al., PLOS One, 2015

18 Validation on epidemiologic DBs AUC (95% CI) ActiFE ELSA InCHIANTI TILDA FRAT-up ( ) ( ) ( ) ( ) Model fitted on ActiFE ( ) ( ) ( ) ( ) Model fitted on ELSA ( ) ( ) ( ) ( ) Model fitted on InCHIANTI ( ) ( ) ( ) ( ) Model fitted on TILDA ( ) ( ) ( ) ( ) Palumbo et al. 2016, in preparation

19 Predictive tools: FRAT-UP Palumbo, PhD Thesis, 2015

20 A JUMP INTO THE FUTURE (THE FUTURE IS NOW)

21 Sensor-based assessment Instrumented Functional Tests Timed Up and Go Balance L. Chiari University of Bologna I-DON T-FALL WORKSHOP ON FALL PREVENTION AND MANAGEMENT - Roma, 25 September 2015 Repeated Chair Standing Gait Sensor-based multidimensional assessment

22 Sensor-based assessment Instrumented Physical Activity Monitoring L. Chiari University of Bologna I-DON T-FALL WORKSHOP ON FALL PREVENTION AND MANAGEMENT - Roma, 25 September 2015 Sensor-based multidimensional assessment (incl. exposure!)

23 State of the art - A number of studies using the former approaches has been done now (e.g. Marschollek et al, 2011; Schwesig et al. 2013; Schwenk et al, 2014; van Schooten et al, 2015) L. Chiari University of Bologna I-DON T-FALL WORKSHOP ON FALL PREVENTION AND MANAGEMENT - Roma, 25 September 2015 which assessed fall risk prospectively. - Results are very promising - Some commercial solutions are on the market but - Datasets are relatively small, yet - They lack of external validation

24 Preliminary results Sensor-based assessment only Factor analysis on data from an instrumented TUG global performance smoothness of sit to walk transition (StW) lateral weight shift control during the turn to sit transition (TtS) lateral weight shift L. control Chiari during University StW of Bologna forward weight I-DON T-FALL shifting control WORKSHOP during StW ON FALL smoothness of PREVENTION TtS AND MANAGEMENT - Roma, 25 September 2015 Confounders: age, gender, MMSE, BMI, SPPB, CES-D, #Drugs Logit multiple regression model All factors are associated with falls at 6 months with AUC=0.74 (p=0,01) All factors are associated with falls at 12 months with AUC= (p=0,01) Single factor, Smoothness of StW is associated with falls at 12 months with AUC=0.708 (p=0,01) Colpo et al, Proc. GSA, 2015 Colpo et al, Proc SIGG, 2015

25 Prognostic tools: we can do better! L. Chiari University of Bologna I-DON T-FALL WORKSHOP ON FALL PREVENTION AND MANAGEMENT - Roma, 25 September 2015

26 The added value of a measurement Gait features computed from longterm recordings of PA

27 Looking into the future (As soon as reliable and validated tools become available) - Push the process back from secondary to primary prevention - Predicting time to first fall (e.g. Campanini et al, EUFF 2016) - Better stratified risk phenotyping tailored prevention - Link fall risk with exposure - Dynamic risk assessment Open issues: - Big data, data fusion, data integration - Standardization (e.g. see Redmond et al, 2014; Klenk et al, 2013) - Assessment of impact in clinical practice (e.g. see Meyer et al, 2009)

28 A future scenario?

29 Forecast