{"paper_id":"4a27d73e-d078-40c0-a3e4-0bbac4177b9e","body_text":"[1]  \n \nFeasibility of linking markers of dementia-related health in primary care medical \nrecords to cognitive function assessed in a specialist dementia service \n \nMichelle Marshall1*, Paul Campbell1,2, James Bailey1, Carolyn A Chew-Graham1,2, Peter \nCroft1, Martin Frisher3, Richard Hayward1, Rashi Negi2, Trishna Rathod-Mistry14, Swaran \nSingh5, Louise Robinson6, Athula Sumathipala1,2, Nwe Thein2, Kate Walters7, Scott Weich8, \nKelvin P Jordan1,9 \n \nAffiliations: \n1School of Medicine, Keele University, Keele, Staffordshire, ST5 5BG, UK  \n2Midlands Partnership NHS Foundation Trust, Department of Research and Innovation, St. \nGeorge's Hospital, Corporation Street, Stafford, ST16 3SR, UK \n3School of Pharmacy and Bioengineering, Keele University, Keele, Staffordshire, ST5 5BG, \nUK \n4Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and \nMusculoskeletal Sciences, University of Oxford, Oxford, OX3 7FY, UK. \n5Division of Mental Health and Wellbeing, Warwick Medical School, University of Warwick, \nCoventry CV4 7AL, UK \n6Population Health Sciences Institute, Campus for Ageing and Vitality, Newcastle University, \nNewcastle upon Tyne, NE4 5PL, UK \n7Research Department of Primary Care & Population Health, University College London, \nRoyal Free Campus, Rowland Hill St, London NW3 2PF, UK \n8Mental Health Research Unit, School of Health and Related Research (ScHARR), University \nof Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK \n9Centre for Prognosis Research, Keele University, Keele, Staffordshire, ST5 5BG, UK \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n[2]  \n \n*Corresponding author: Michelle Marshall, School of Medicine, Keele University, \nStaffordshire ST5 5BG, UK. Tel. +44 1782 734872; Fax: +44 1782 734719. Email: \nm.marshall@keele.ac.uk \n \nORCID iD: 0000-0001-8163-6948 \n \nWord count: 3042 \n \nFigure number: 1 \n \nTable number: 5 & 1 supplementary table \n \nKeywords: Feasibility study, Dementia, Electronic Health Records, Primary Care, Validation, \nDisease Progression \n  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[3]  \n \nAbstract   \nObjectives  \nTo assess the feasibility of linking and comparing markers of dementia-related health \nrecorded in primary care electronic health records (EHR) to assessments of cognitive function \nundertaken in a specialist dementia service.  \nMethods \nOne thousand patients in a UK secondary care specialist dementia service were invited to take \npart. Primary care EHR were requested from 72 general practices of consenting patients. \nSixty-three previously established individual markers within 13 broader domains of dementia-\nrelated health were then extracted from primary care EHR and compared to cognitive \nassessments scores recorded in the dementia service EHR.  \nResults \n258 (26%) patients consented to take part. At least one cognitive assessment score was \nrecorded for 242 (94%) patients, but primary and secondary care EHR records could only be \nlinked in 93 patients. 56 of these 93 patients had two cognitive assessments scores at least 12 \nmonths apart. In the patients with data available for analysis individuals with a higher number \nof markers and domains recorded in their primary care records had lower mean cognitive \nassessment scores (range 1.6-2.1 points), and after adjustment for earlier cognitive scores \n(range 2.0-2.5 points), indicating poorer cognitive function, although differences were not \nstatistically significant. \nConclusion \nThis feasibility study highlights the challenges in obtaining consent and linking primary and \nsecondary care EHR in dementia, and in extracting cognitive function scores from dementia \nservice EHR.   \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[4]  \n \nIntroduction \nThe number of people with dementia is increasing as the population ages and dementia has a \nlarge impact on the lives of individuals with the condition as well as their families and \ncaregivers.\n1,2 Strategies have been proposed to prolong independence, reduce hospital \nadmissions, delay nursing home admissions, and prevent early mortality for people with \ndementia.3-6 Information on the course of dementia prior to these long-term outcomes could \nimprove prognosis at an individual patient level, aid planning and monitoring of care for \ndementia, and allow evaluation of earlier outcomes in research studies including clinical trials \nin dementia.1,7 \n \nIn many countries primary care is the first point of contact and location of management of \ncommon health conditions including dementia. Primary care can play a key role in addressing \nstrategies to improve outcomes for dementia. One potential resource for monitoring the course \nof dementia in primary care are Electronic Health Records (EHR). Primary care EHR contain \ninformation that is routinely recorded in patient encounters. This typically includes coded \nreasons for consultations, prescriptions, referrals, investigations and tests. In the UK, over \n95% of the population are registered with a general practitioner (GP) and the place where \nmost routine chronic disease management including dementia occurs, and so these records are \na useful resource for studying how illnesses progress. However, to date, primary care EHR \nhave not been used to research the course of dementia after diagnosis. There is evidence that \nthis might be possible as key comorbidities and signs and symptoms likely to be recorded in \nprimary care have been associated with dementia and could be indicative of disease \nprogression and severity (e.g. malnutrition, fall trauma, neuropsychiatric disorders, sleep \ndisorders.8,9 These signs and symptoms are likely to occur prior to more recognised long-term \noutcomes such as hospital or care home admission, and earlier mortality. Therefore, there is \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[5]  \n \nthe potential for primary care EHR to be a source of population-wide data on course and \nprognosis of dementia for research and monitoring and for targeted anticipatory care of \nindividuals. \n \nWe have previously established a set of potential primary care EHR markers (categorised into \ndifferent domains) of dementia progression,\n10 and shown that these are associated with future  \noutcomes such as mortality and hospital admission.11 In particular, we found that the number \nof different domains accumulated in the primary care records in a 12-month period was \nassociated with the occurrence of these future outcomes.11 However, an important gap in \ndeveloping these EHR markers as the basis for epidemiological and intervention studies is to \nestablish their construct validity as markers of actual dementia severity and progression.  \n \nIn order to address this gap, we have undertaken a feasibility study obtaining and linking \nprimary and secondary care EHR in patients sampled from a secondary care setting where \nobjective assessments of cognitive function had been performed and recorded as part of \nclinical care. In patients consenting to accessing and linking their records, we compared the \nresults of these assessments with data extracted independently from the primary care EHR of \nthe individuals in this sample. We also assessed the challenges of performing this type of \nstudy.  \n \n \nMaterials & methods \nStudy population \nThe CoMed study recruited patients from a secondary care dementia service within South \nStaffordshire and Shropshire, UK, delivered by the Midlands Partnership NHS Foundation \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[6]  \n \nTrust. Written informed consent was obtained from patients with dementia (or personal \nconsultee's advice for those not able to give consent) to access and link their secondary care \ndementia service and primary care medical records for research purposes. Ethical approval \nwas obtained by the UK National Research Ethics Service, Wales 7 Committee (REC \nreference: 18/WA/0423). \n \nEligible participants met the following selection criteria. \nInclusion criteria: \n• Aged 18 years and over  \n• Confirmed diagnosis of dementia recorded in the dementia service medical records \n• Assessment by the dementia service in the previous 12 months \n• Living in the UK regions covered by three local Clinical Commissioning Groups \n(CCGs)   \nExclusion criteria: \n• Lists of potentially eligible patients were screened by clinical care teams to exclude \nthose where contact would likely cause undue distress or harm e.g. palliative care or \nsignificant life event \n• A recorded indication in their dementia service medical records that they did not wish \nto take part in research.  \n \nOne thousand eligible patients were randomly selected and mailed a study information pack \nby post inviting them to take part in the study, i.e. consent to access and linkage of their \nprimary care and dementia service records, with a reminder sent after two weeks if no \nresponse.  \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[7]  \n \nData collection from medical records \nIn those consenting to take part in the study, cognitive assessment scores in the 10 years prior \nto the date of consent were retrieved from the electronic dementia service medical records. \nCognitive assessments used by the dementia service included the Mini Mental State \nExamination (MMSE), Addenbrooke’s Cognitive Examination - III (ACE-III) and Mini \nAddenbrooke’s Cognitive Examination (MACE).12-14 Higher scores for each test reflect better \ncognitive function. ACE III (range 0-100) and MACE scores (0-30) were converted into \nstandardised MMSE scores (0-30) using previously established conversion methods.15,16  \n \nPrimary care EHR were requested from each consenting patient’s general practice for the 10 \nyears prior to the date of consent was provided. This included all recorded electronic Read \ncodes (a hierarchical coding system used in UK primary care for recording morbidity and \nprocesses of care) and prescriptions. EHR were requested in the form of an electronic \ndownload at the general practice and transferred to the researchers via NHS email.  \n \nMarkers of dementia progression \nA list of potential primary care markers (Read coded and prescribed medication) of dementia-\nrelated health nested into domains has been established previously. Full methodology is \ndetailed elsewhere;\n10 but included systematic literature searches, consensus exercises \nincluding GPs, psychiatrists, epidemiologists and EHR researchers, and analysis of a regional \nprimary care EHR database. Sixty-three potential markers of dementia-related health were \ngrouped into 13 domains (Supplementary Table 1): Care, Home Pressures, Severe \nNeuropsychiatric, Neuropsychiatric, Cognitive Function, Daily Functioning, Safety, \nComorbidity, Symptoms, Diet/Nutrition, Imaging, Increased Multimorbidity (based on \npolypharmacy), and Change in Dementia-Related Drug. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[8]  \n \nAnalysis \nIn consenting patients, the number and proportion of patients with linked primary and \nsecondary care EHR was determined. Then i) the number of patients with at least one \ncognitive assessment score recorded was established for the cross-sectional analysis and ii) \nthe number of patients with two cognitive assessments scores at least 12 months apart was \nestablished for the longitudinal analysis. \n \nWe compared the results of cognitive assessments undertaken as part of clinical care in the \nsecondary care dementia service with data extracted independently from the primary care \nEHR.  \n \nFor the cross-sectional analyses all consenting participants who had at least one assessment \nrecorded in the dementia service were included. Domains and markers were identified in their \nprimary care EHR for the 12 months before each patient’s most recent cognitive assessment \n(the “end” score). Patients were then grouped based on the tertile number of domains and of \nmarkers recorded in the primary care EHR over that 12-month period. The mean standardised \nMMSE score for each group and mean differences in scores between groups were calculated \nusing the most recent cognitive assessment score in the dementia service medical records. The \nrelationships between cognitive assessment scores and recording of individual domains were \nalso determined. \n \nFor the longitudinal analyses the sub-group of consenting participants who had at least two \nassessments recorded in the dementia service at least 12 months apart were analysed. Records \nof domains and markers were identified in primary care records between the dates of a \npatient’s earliest (start) and most recent (end) cognitive assessment at the dementia service. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[9]  \n \nPatients were again grouped based on the tertile number of domains and of markers recorded \nin the primary care EHR over that period. Mean standardised MMSE end score and mean \ndifferences in scores between groups (with 95% confidence intervals) were derived adjusting \nfor the earliest recorded score (the “start” score) using analysis of covariance. Finally, the \nrelationships between most recent cognitive assessment score and recording of individual \ndomains, adjusted for earliest cognitive assessment score, were also determined. \n \n \nResults \nOf the 1000 patients invited to take part, 258 (26%) consented (Figure 1). Two-hundred and \nforty-two (94%) patients had one or more cognitive assessment scores recorded in their \ndementia service medical records. Primary care EHR were obtained and linked to dementia \nservice medical records for 93 (38%) of these 242 patients from 34/72 (47%) GP practices \nand they formed the main sample in which the cross-sectional analysis was undertaken. There \nwas no response from 30 (42%) practices covering 121 (50%) patients. There were 8 (11%) \nGP practices that did make contact but who did not contribute EHR data. The main reasons \nfor this were being too busy (4 GP practices; 12 patients), incompatible systems for electronic \ndownload (2 GP practice; 8 patients) and inability/against practice protocol to send electronic \ndata (2 GP practices; 8 patients). Age and gender distributions were comparable between \nthose with and without primary care EHR information, but the diagnosis duration to \nMMSE  \nend sco re  was shorter and the end (most recent) median MMSE score was slightly higher \nindicating better cognitive function in those with linked primary care information (Table 1).  \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[11]  \n \nTable 1. Descriptive characteristics of study participants overall, and in those with and without \nlinked primary care EHR \n All patie n ts wi t h ≥1 \nMM S E scor e  \nPa t ie n ts  w i t h  ≥1  \nMMSE scor e b ut  no \nprimary c are EHR  \nPa t ien t s  w it h ≥ 1  \nMMSE sc ore &  \nprimary care E H R  \nN u mber o f par ticip an t s \n \n242 149 93 \nAge: Me a n (S D)  \n \n78.9 (8.4 ) 78.4 ( 9 .0 )  79.6  (7.4 )  \nFema le Se x: n  ( %) \n \n140 (58)  88 (59)  52 (5 6)  \nDiagno si s dura tion t o  \nMMSE end  s c ore *; day s :  \nMedian (I Q R )  \n \n \n350 (-2, 7 06)  \n \n399 (40 , 784)  \n \n260 (- 5 , 582)  \nMMSE end  s c ore *: M ean (S D) \n               Medi an (I Q R) \n23.2 (11. 0 )  \n23.2 (20. 3, 25.8 )  \n23.1 ( 1 3.5 )  \n22.8 ( 1 9.2, 25 .1 )  \n23.2  (4.6 )  \n24.4  (20.9, 2 6.7 )  \n* M MSE  e n d score = m o st rec ently recorded sc o re . \n \nIn the cross-sectional analysis, individuals with the highest numbers of markers (≥5 ) and \ndomains (≥4 ) recorded in their primary care records in the 12 months before their most recent \ndementia service assessment had lower mean MMSE end scores by 2.1 (markers) and 1.6 \n(domains) points, respectively, than those with the fewest (0-2 markers or domains) indicating \npoorer cognitive function (Table 2). However, differences were not statistically significant. \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[12]  \n \nTable 2. Relationship between MMSE score and the number of domains and ma rkers (n=93) \n Number  n \npa t i en ts  \nMedia n MM SE \ne n d  s cor e (IQ R) \nMea n MMSE \ne nd sco re (S D )  \nMean di ff er e nc e in            \n \nMMSE e nd score   \n(95%  CI)  \nDomain s a 0-2  \n3 \n≥4 \n37 \n25 \n31 \n24.4  (21.8, 2 6.2 )  \n26.2  (22.1, 2 7.8 )  \n23.8  (18.0, 2 5.6 )  \n23 . 6  ( 3 . 6) \n24 . 0  ( 4 . 8) \n22 . 0  ( 4 . 6) \nRef  \n0.4 ( -1 . 9, 2.8 )  \n-1.6 ( -3 .8 , 0.6)  \nMarker s a 0-2  \n3-4  \n≥5 \n29 \n38 \n26 \n24.4  (21.2, 2 6.4 )  \n25.1  (21.9, 2 7.2 )  \n23.5  (18.0, 2 5.4 )  \n23 . 6  ( 3 . 9) \n24 . 0  ( 4 . 2) \n21 . 6  ( 4 . 6) \nRef  \n0.4 ( -1 . 8, 2.6 )  \n-2.1 ( -4 .5 , 0.3)  \na  Record e d in t he 1 2m  before most rec e nt M M S E asses sment d a te. MMSE  e nd score = most rec ently  re cord e d  score .  \n \nFifty-six patients had two cognitive assessments scores recorded in the dementia service \nmedical records a minimum of 12 months apart and had primary care information obtained. \nThese patients formed the sub-sample in which the longitudinal analysis was undertaken. \nMedian time between start and end assessments was 783 (IQR 555, 1116) days. Mean \ndifferences in most recent MMSE scores after adjustment for earliest MMSE score, \ncomparing those with the most recorded markers (\n≥7 )  and domains (≥6 ) to those with the \nfewest, were 2.0 and 2.5 points, respectively (Table 3). This suggest more cognitive function \ndecline in those with more recorded markers and domains, however the differences were not \nstatistically significant. \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[13]  \n \nTable 3. Relationship between MMSE score over time and the number of domains and markers \n(n=56) \n Number  n  \npatien t s \nMean \nMM S E end \nsc o r e  \nUnadj usted m e an \ndi ffe re nce i n MMSE \ne nd sco re  \n(95 % CI)  \nA d justed mean \ndiff ere nce in MMSE \nend sco re\na   \n(95% CI)  \nDomain s b 1-3  \n4-5  \n≥6 \n20 \n26 \n10 \n24.5 \n23.0 \n21.1 \nRe f \n-1. 6 ( -4 .5, 1.3 ) \n-3. 5 ( -7 .3, 0.3 ) \nRef  \n-1.0 ( -3 .4, 1 .3)  \n-2.5 ( -5 .5, 0 .6)  \nMarker s b 1-4  \n5-6  \n≥7 \n22 \n16 \n18 \n24.2 \n23.4 \n21.8 \nRe f \n-0. 8 ( -4 .1, 2.4 ) \n-2. 4 ( -5 .5, 0.8 ) \nRef  \n-0.4 ( -3 .0, 2 .2)  \n-2.0 ( -4 .5, 0 .5)  \na A dju ste d f or M MSE sta rt (e arliest) score; b  R e cord e d  betwe en da tes o f  sta rt a nd en d score. End  sc o re  =  most rece n tly \nre cord e d  score.  \n \nIndividuals in the cross-sectional sample (n=93) who had markers recorded in the domains of \nDaily Functioning, Safety, Care, and Diet/Nutrition in the 12 months before their dementia \nservice assessment had lower mean MMSE end scores by 3.5 to 7.6 points, indicating poorer \ncognitive function compared to individuals that did not have markers recorded from these \ndomains (Table 4).  However, the number of people recorded with these domains was low. \n \nIn the longitudinal analysis (n=56), reduced mean scores on the most recent MMSE \nassessment persisted for patients with recorded markers in the domains of Daily Functioning, \nSafety, Care, and Diet/Nutrition after adjustment for earliest recorded MMSE score by 1.7 to \n3.3 points, showing they had more cognitive function decline compared to individuals that did \nnot have markers in these domains (Table 5). However, the number of patients with these \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[14]  \n \ndomains were again low and differences were only statistically significant for the Safety \ndomain. \n \nTable 4. Relationship between MMSE score and individual domains (n=93) \n n patien t s \nwith \nre c o r d e d  \ndomain  \nMean M MS E e nd sc o r e  \n \nMean d i ffe r e nc e (95 % CI )  \nDomain a  Domain \nabs ent  \nDomain \np r es ent  \n \nCar e  13 23.7  20.2 -3.5 ( -6 .2,  0.9)  \nHome Pr es s u re s  0 23.2  c  c  \nSev ere N euro ps y chia tric  2 23.3  c  c  \nN e ur o p s y chia tric  42 23.1  23.3 0.3 (-1 .6 , 2.2 )  \nCognitiv e Fun c tion  35 22.4  24.5 2.1 (0. 2, 4 .0)  \nDaily Func t i oning  5 23.6  16.1 -7.6 ( -11.4 , -3 .7)  \nSafe ty  7 23.5  19.0 -4.6 ( -8 .0,  -1 .1)  \nComorbidi ty  54 22.6  23.6 1.0 (-0 .9 , 2.9 )  \nSymp t om s 26 23.3  23.0 -0.3 ( -2 .4,  1.8)  \nDiet /Nutri t i on  17 23.9  20.2 -3.7 ( -6 .0,  -1 .3)  \nImagi ng 17 23.2  23.0 -0.2 ( -2 .7,  2.2)  \nI n c r e a s e d  M u lt im or bi d i t y b 40 23.5  22.8 -0.6 ( -2 .5,  1.3)  \nChang e in Demen t ia -r e l at ed Dr ug b 21 23.1  23.6 0.6 (-1 .7 , 2.8 )  \na  Record e d in 12 m onths  befo re  most rece n t MM S E ass e s sment da te;  b  C om pare d  t o  prev i ous 1 2 m o n th s ; c Not  presented \na s p re vale n c e  was less  tha n 5 p e ople.  M MSE  e n d  s co r e = most rec entl y  re cord e d  sc o re .  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[15]  \n \nTable 5. Relationship between MMSE score over time and individual domains (n=56) \n N  \npatien t s \nwith \nre c o r d e d  \ndomain  \nMean MMSE  e nd \nsc o r e  \nUn a d j u s t e d  m e a n  \ndiff ere nce\n (95% C I)  \nA d justed m ean \ndiff ere nce\nc  ( 95 %  \nCI )  \nDomain a  D o m a i n  \nabs ent  \nDomain  \npre sen t  \n  \nCar e  15 24.0 20. 9 -3.1 ( -6 .0, -0 . 2)  - 2 .2 ( -4 .6, 0 .1)  \nHome Pr es s u re s  0 23.2 d     d  d  \nSev ere N euro ps y chia tric  2 23.4 d  d  d  \nN e ur o p s y chia tric  33 22.8 23. 5 0.7 (-2 .0, 3.4 )  0.8 (-1 .4, 3 .0 )  \nCognitiv e Fun c tion  18 22.7 24. 3 1.7 (-1 .2, 4.5 )  0.6 (-1 .7, 3 .0 )  \nDaily Func t i oning  6 23.6 19. 5 -4.2 ( -8 .3, 0 .0)  - 1 .7 ( -5 .3, 1 .8)  \nSafe ty  10 23.8 20. 3 -3.5 ( -6 .9, -0 . 2)  - 3 .3 ( -6 .0, -0 .7 ) \nComorbidi ty  43 21.2 23. 8 2.6 (-0 .5, 5.7 )  0.5 (-2 .1, 3 .2 )  \nSymp t om s 29 23.7 22. 7 -1.0 ( -3 .7, 1 .6)  - 0 .1 ( -2 .3, 2 .0)  \nDiet /Nutri t i on  17 24.1 21. 1 -2.9 ( -5 .7, -0 . 1)  - 1 .9 ( -4 .2, 0 .4)  \nImagi ng 16 22.8 24. 2 1.4 (-1 .6, 4.3 )  1.5 (-0 .9, 3 .8 )  \nIncrea sed M u lt i mo r b idity\nb 23 24.1 21. 9 -2.1 ( -4 .8, 0 .5)  - 0 .8 ( -3 .0, 1 .5)  \nChang e in Demen t ia -r e l at ed Dr ug b 16 23.2 23. 3 0.1 (-2 .9, 3.1 )  - 0 .5 ( -2 .8, 1 .9)  \na R e cord e d  between date s o f ea rli e st an d m ost rec ent re cord e d  MM S E  score.;  b  Co m p a red t o  p re vious 1 2  m o n ths; c  \nAdjusted fo r ea rli e st recorded MMSE  sc o re ; d No t p re s e n te d  as  prev alence  wa s l e ss than  5 peo pl e . MM S E end  sc o re =m o st \nre c ently rec o rded score.  \n \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[16]  \n \nDiscussion \nThis study aimed to pilot linking and comparing potential markers of dementia progression \nroutinely recorded in primary care against cognitive assessments undertaken in a specialist \ndementia service. There were difficulties in obtaining primary care information which meant \nthat linked primary and secondary care EHR could only be obtained in just over a third of \nconsenting patients. Further to this, fewer than expected patients had repeated cognitive \nassessment scores that were at least 12 months apart recorded in the dementia service medical \nrecords. While the study was underpowered, those with a higher number of domains and \nmarkers recorded in primary care had trends towards poorer cognitive function as assessed in \nthe dementia service which suggests the domains and markers are associated with greater \ndisease progression. These differences were not statistically significant, but the findings do \nconcord with our previous validation study which showed that the number of recorded \ndomains early after diagnosis were strongly associated with long term outcomes of hospital \nadmission, palliative care and mortality.\n11  \n \nThis study used information routine collected as part of primary care to investigate a \nrigorously developed set of domains and markers. This approach reduced the burden on the \npatient with dementia and their caregiver who were asked only for consent to access and link \nmedical records. Previous dementia studies have recruited by post with response rates from \nUK and other countries ranging between 22.3 (UK) and 31.3% (Norway);17,18 this method was \nselected as it was felt it would be less time consuming than recruiting through dementia \nservice clinics and would not add extra burden to clinicians or patients in the consultations in \nwhich there are already often time pressures. While our response rate of 25.2% is generally in \nline with the previous dementia studies recruiting by post, and comparable to other large \nsurveys, the response to our study was low.17-19 Information including study packs handed out \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[17]  \n \nby dementia service staff at clinical appointments could potentially increase response. A study \npopulation of 258 could have given us sufficient power to undertake the planned analysis \nhowever, there was difficulty in obtaining information from general practices. This was \ndespite providing instructions and technical support for undertaking the electronic download \nof GP record data. This is likely to reflect time pressures but may also relate to lack of \nexperience with research and concern over releasing records in more research-naïve practices. \nThis reduced the power of our study, and particularly limited our assessment of individual \ndomains.  \n \nIn this study there were also a number of challenges related to the lack of standardisation and \ncompletion of cognitive assessment measures utilised within the secondary care dementia \nservice.\n4 Firstly, mapping ACE-III and MACE to the MMSE was possible based on published \nformulae, however this method has not been extensively validated and some patients had \nvalues outside the range of the MMSE once standardised. Secondly, many patients had only \none (or no) cognitive assessment score and this may reflect that in clinical practice it is the \nindividual areas covered by the measures that are more important (i.e. the indicators of \nspecific components of memory function), rather than the full scores themselves. Added to \nthat is the potential for patients to be unwilling or not able to complete the full test. This study \nhas also focussed on those with diagnosed dementia. While advances have been made to \nimprove the timely detection of dementia within primary care;20 a significant proportion (~ \n40%) will be undetected and undiagnosed, and those with more severe dementia may be more \nlikely to be diagnosed.21,22 \n \nDespite the reduced sample size, the domains of Care, Daily Functioning, Safety, and \nDiet/Nutrition showed trends of poorer cognitive assessment scores in those with markers \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[18]  \n \nfrom these domains recorded in primary care. The mean differences seen in MMSE scores \nbetween individuals with and without markers recorded in these domains were clinically \nsignificant when compared to recommended levels of important differences (between 1 and 3 \nMMSE points).\n23 Importantly, these domains were also found to be associated with longer \nterm outcomes of hospital admission, palliative care and mortality in a previous validation \nstudy in a UK primary care database.11 However, there was a low number of people with \nthese domains recorded and so this current study should be viewed as exploratory with its \nfindings requiring confirmation in larger studies. \n \nOur findings, in this and our previous study, suggest particular domains and markers impact \non outcomes for those with dementia.\n11 This has also been found in previous non-EHR \nresearch internationally. For example, changes in care including shared decision making and \nadvanced care planning have been found to be associated with care home admission and \npalliative care,24,25 and caregiver coping and stress are associated with mortality in the person \nwith dementia.26 Common markers in the Safety domain are falls and fractures and are likely \nto reflect the increased vulnerability in this population. Falls have previously been associated \nwith increased rates of hospitalisation and mortality.27-29 Nutrition has previously been \nassociated with the longer-term outcome of mortality in people living with dementia.30,31 This \nsupports the creditability that these domains may be valid indicators of poorer short-term and \nlong-term outcomes in dementia.  \n \nA potentially unexpected finding was that individuals who had one or more primary care \nmarkers recorded in the cognitive function domain had higher (better) MMSE scores in the \ncross-sectional analysis, although this was not apparent when adjusted for earlier MMSE \nscore in the longitudinal analysis. It is possible cognitive function markers are recorded in \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[19]  \n \nprimary care more commonly in the earlier stages of the disease as part of the diagnosis \nprocess.  \n \nThe challenges in gaining consent and collating EHR information in this study can inform \nsimilar future research, including the larger studies needed to confirm the findings of this \nstudy. Improved collection of primary care information may be possible by reversing the \napproach taken here and determining the initial study population at general practices, who \nagree a priori to release records of consenting patients and are located within the dementia \nservice’s catchment area, rather than at the dementia service. This would mean gaining \nagreement from a large number of practices and hence be more resource intensive. It may also \nhelp to target GP practices that have previously been involved in research as they will have \ngreater understanding and appreciation of being involved in research, and potentially be more \nsupportive. In the future, improved integration of records between primary and secondary care \nmay also help studies like this. However, future research studies may also need to consider \npatient-reported data collected by either survey or interview and linked to EHR in order to \nexplore in-depth the association between dementia assessments, patient and carer information, \nand EHR data. \n \nDespite the challenges, the findings from this study of linked primary care and secondary care \ndementia service medical records and our previous study using a national EHR database \nsuggest these markers and domains recorded in primary care do reflect disease progression in \ndementia. Further research is needed to assess how these markers and domains may be used \nby healthcare professionals to characterise, monitor, and predict the future course of patients \nfollowing a diagnosis of dementia.  \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[20]  \n \nAcknowledgements  \nThe study team would also like to acknowledge the Patient and Public Involvement and \nEngagement Dementia Group within the School of Medicine, Keele University, for their input \ninto the development and interpretation of results for the Course of Dementia using Medical \nRecords (CoMed) study and also the overall Measurement of Dementia Disease Progression \nIn Primary care (MEDDIP) programme of work, which encompasses this current study. The \nstudy team would also like to thank the Midlands Partnership NHS Foundation Trust and the \nproject steering group for the MEDDIP study for their support. \n \nFunding: This work was supported by The Dunhill Medical Trust under Grant [RPGF \n1711/11] as part of The MEasurement of Dementia DIsease progression in Primary care \n(MEDDIP) study. KPJ and CCG are also supported by matched funding awarded to the NIHR \nApplied Research Collaboration (West Midlands). The views and opinions expressed are \nthose of the authors and not necessarily the views of The Dunhill Medical Trust, NHS, the \nNIHR or the Department of Health and Social Care.  \n \nDeclaration of interest statement: The authors have no conflicts of interest to declare. \n \nAuthor contributions: Study was derived and planned by MM, PCa, CAC-G, PCr, MF, SS, \nAS, KW, SW, and KPJ. MM and KPJ performed analysis. All authors contributed to \ninterpretation of findings. MM and KPJ drafted the paper and all authors commented on \nsubsequent draft versions and approved the final version. \n \nEthical conduct of research statement: Written informed consent was obtained from \npatients with dementia (or personal consultee's advice for those not able to give consent) to \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[21]  \n \naccess and link their secondary care dementia service and primary care medical records for \nresearch purposes. Ethical approval was obtained by the UK National Research Ethics \nService, Wales 7 Committee (REC reference: 18/WA/0423). \n \nData availability statement: It is possible for external researchers to request access to the \nsummarised (aggregated) data from this study through a formal data request process. \nResearchers wanting to apply to access the data from the School of Medicine, Keele \nUniversity, should email medicine.datasharing@keele.ac.uk or contact the Principal \nInvestigator of the study, Dr Michelle Marshall (m.marshall@keele.ac.uk), for further \ninformation.  \n  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[22]  \n \nReferences \n1. Prince M, Comas-Herrera A, Knapp M, et al. World Alzheimer report 2016: Improving \nhealthcare for people living with dementia. Coverage, quality and costs now and in the future. \nAlzheimer’s Disease International (2016). \n2. Department of Health. Prime Minister’s challenge on dementia 2020. Department of Health \n(2015, accessed 01 September 2021). \nhttps://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/\n414344/pm-dementia2020.pdf  \n3. World Health Organisation. Global action on the public health response to dementia 2017-2025. \nWorld Health Organisation (2017).  \n4. National Institute for Health and Care Excellence (UK). Dementia assessment, management and \nsupport for people living with dementia and their carers (NG97). National Institute for Health and \nCare Excellence (UK) (2018).  \n5. Department of Health. Living well with dementia: A national dementia strategy. World Health \nOrganisation. (2009). \n6. Abdi Z, Burns A. Championing of dementia in England. Alzheimers Res Ther 2012; 4: 36.  \n7. Prince M, Wimo A, Guerchet M, et al. World Alzheimer report 2015: The global impact of \ndementia. An analysis of prevalence, incidence, cost and trends. Alzheimer’s Disease \nInternational (2015). \n8. Alzheimer’s Association. Alzheimer's disease facts and figures. Alzheimers Dement 2015; 11: \n332-384.  \n9. Shepherd H, Livingston G, Chan J, et al. Hospitalisation rates and predictors in people with \ndementia: a systematic review and meta-analysis. BMC Med 2019; 17: 130.  \n10. Campbell P, Rathod-Mistry T, Marshall M, et al. Markers of dementia-related health in primary \ncare electronic health records. Aging Ment Health 2021; 25: 1452-1462.  \n11. Rathod-Mistry T, Marshall M, Campbell P, et al. Indicators of dementia disease progression in \nprimary care: an electronic health record cohort study. Eur J Neurol 2021; 28: 1499-1510. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[23]  \n \n12. Folstein MF, Folstein SE, McHugh PR. \"Mini-mental state\". A practical method for grading \nthe cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: 189-198.  \n13. Mathuranath PS, Nestor PJ, Berrios GE, et al. A brief cognitive test battery to differentiate \nAlzheimer's disease and frontotemporal dementia. Neurology 2020; 55: 1613-1620.  \n14. Hsieh S, McGrory S, Leslie F, et al.  The Mii-Addenbrooke's Cognitive Examination: a new \nassessment tool for dementia. Dement. Geriatr Cogn Disord 2015; 39: 1-11.  \n15. Matías-Guiu JA, Pytel V, Cortés-Martínez A, et al. 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Eichler T, Thyrian JR, Hertel J, et al. Rates of formal diagnosis in people screened positive for \ndementia in primary care: results of the DelpHi-Trial. J Alzheimers Dis 2014; 42: 451-458.  \n21. Lang L, Clifford A, Wei L, et al. Prevalence and determinants of undetected dementia in the \ncommunity: a systematic literature review and a meta-analysis. BMJ Open 2017; 7: e011146.  \n22. Aldus CF, Arthur A, Dennington-Price A, et al. Undiagnosed dementia in primary care: a record \nlinkage study. Health Serv Deliv Res 2020; 8: 20. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[24]  \n \n23. Andrews JS, Desai U, Kirson NY, et al. Disease severity and minimal clinically important \ndifferences in clinical outcome assessments for Alzheimer's disease clinical trials. Alzheimers \nDement 2019; 5: 354-363.   \n24. Christensen MD, White HK. Dementia assessment and management. J Am Med Dis Assoc  2006; \n7: 109-118.  \n25. Thygesen E, Saevareid HI, Lindstrom TC, et al. Predicting needs for nursing home admission - \ndoes sense of coherence delay nursing home admission in care dependent older people? A \nlongitudinal study. Int J Older People Nurs 2099; 4: 12-21.  \n26. Lwi SJ, Ford BQ, Casey JJ, et al. Poor caregiver mental health predicts mortality of patients with \nneurodegenerative disease. Proc Natl Acad Sci USA 2017; 114: 7319-7324.  \n27. Kulmala J, Nykänen I, Mänty M, et al. Association between frailty and dementia: a population-\nbased study. Gerontology 2014; 60: 16-21.  \n28. Koutsavlis AT, Wolfson C. Elements of mobility as predictors of survival in elderly patients \nwith dementia: findings from the Canadian Study of Health and Aging. Chronic Dis Can 2000; \n21: 93-103.  \n29. Mitchell R, Draper B, Brodaty H, et al. An 11-year review of hip fracture hospitalisations, \nhealth outcomes, and predictors of access to in-hospital rehabilitation for adults ≥/i2 65 years \nliving with and without dementia: a population-based cohort study. Osteoporos Int  2020; 31: \n465-474.  \n30. de Sousa OV, Mendes J, Amaral TF. Nutritional and Functional Indicators and Their Association \nWith Mortality Among Older Adults With Alzheimer's Disease. Am J Alzheimers Dis Other \nDemen 2020; 35: 1533317520907168.  \n31. Connors MH, Ames D, Boundy K, et al. Predictors of Mortality in Dementia: The PRIME Study. \nJ Alzheimers Dis 2016; 52: 967-974. \n  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[25]  \n \nSupplementary Table 1. List of the markers within each domain and examples \nDomain  Marke r Examp les \nCar e  Addition a l Help  H ome  help, day c ar e  \nCa rer  Evide nce h as  a ca r e r  i n r ecord s  \nS hared Deci s ion M a king  Shared d e cisi on ma king \nAdv anced  Dire ctive  A d vanc ed c are pl ann ing  \nHome Pr es su r e s  Home P res su r e s M a r i ta l probl ems , fami ly ber ea vemen t/ row  \nSe vere N euro psychi a tr ic         S evere M enta l Illne s s (co ded )  Psy c h o s i s ,  sc h i z o p h r e n i a  \nS evere M enta l Illne s s \n( m e d ic a t io n ) \nA n t i -p syc hotic d r ug  \nSe c t i o ne d S e ct io ne d  F o r m  co m p l et e d/ fe e  pa i d \nC r is is  M en t al  c r i s is  p l an ,  r e f e r r a l  t o  c r is is  t eam  \nS uicida l Suic idal, high/medi um suic id e ri sk \nNeuro ps y chia t ric  Depr e s s io n, Anxie t y , Str e ss \n(c oded )\n \nD e pre s sion, an xie ty, str ess  \n Depr e s s io n, Anxie t y , Str e ss \n( m e d ic a t io n ) \nA n t i -de p r e ssan t drug  \n Agg r e ssive B ehavi our A g gres s iv e / a bu s i ve b eha viour  \n S leep Proble m s  (c oded )  Insomn ia, nig htma re s \n S leep Proble m s  (m e dica t i on ) H y pnotic/anx iolytic  drug  \n Beh aviour al  I ssue s  Behavi oura l probl em, disi nhibit ed be havi ou r \n L ow Mood Low mood, tear ful, wor r i ed, la ck o f \nconc entra t i on  \n Wande r in g W a nde rs  d u r in g day / nig h t \nCog nitive F unc tion  Cog nition  Cognitiv e dec lin e, me n t a lly vague  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[26]  \n \nMemory Los s M e mory lo ss, amne sia , p oor  memo ry \nCon fu sion  Con fu s io n, del irium,  di sor i e nta te d  \nApha sia  A p ha s i a, spe ec h th erapy /de fec t, stamme r \nDaily  Func tioning  \n \nBed bound  Bedbound , b ed -r i dd en  \nWheel cha ir  Pr o vi sion o f/ind epen de n t  in whe elch air  \nS evere mob ili ty li mitatio n  H ous eboun d , cha irboun d, zimmer frame  \nMobil ity – L e ss S ever e \nL imitation  \nM o bility po or, walk ing stick , gai t  a bnorm ality \nPre s s ur e Sor e  Pr e ssur e s o re,  decub i t u s ul cer  \nDriving  U nfit to driv e, adv ise d abou t driv ing \nDif fic ulty in  Eating  Eating pr obl em, d epe n dent for eati ng \nDif fic ulty Handli n g Fin ance  N ee d s  h e lp hand ling fi na ncia l a ff air s  \nPe rson al Ca re Li mita tion  D e pe nden t fo r dre ssing / toil e t/ba thing  \nS t a irs L imita tion  D i ffic ulty ma nagi ng st a i rs, ne ed help on sta irs  \nS af et y Fa l l  R e c o r d ed  f al l \nF r a ctur e  Recorde d f r ac ture  (exc l. skull)  \nIntr ac r a nial In jur y  Skull  fr a ctu re, c oncu s s i on  \nS afe ty A sse s s m ent  Falls ri sk a s se ssmen t, hom e  sa fe t y  adv ice  \nComorbidi t y  Ca r di ova scula r M y oca r di al in farc t i on, i schae mic he art di s e ase  \nS t roke  Str o ke, c ereb ral in fa r c ti on  \nPar ki ns on’ s  D is e ase Pa r k i ns o n’ s  d ise as e \nMotor Neu rone Di s e a se  M o t or Neu rone di s e a s e  \nDiabe t e s D i abe te s melli tu s ( type I  or  I I)  \nEpi lepsy  Epilepsy,  gra nd ma l/ p e t i ti mal, fi t fr equ e nc y \nAs t h ma / COPD A s thma, C OP D, c hro nic bronc hi ti s \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[27]  \n \nMusculo ske let al Pa in  O s teo art hr iti s , regi onal p a in, rh eumato id  \nart hri ti s  \nAnae mia  Ir o n de fici ency  ana e mia, Vitam in B12 d e fi cienc y \nOcula r Cat aract , re tinopa t h y, gl aucoma , blind n e ss  \nHype r te nsi on  Essen tial  hyper ten sio n, hy per t e n s iv e di s ea se  \nCa ndidia si s  Candidia s i s , thru s h  \nSy mptoms Dizz in e ss  D i z zine s s , v e rt i go, hy po ten s i on , giddi ne ss  \nInc ontin enc e  Incontin e nt  o f urin e/fa ece s, urge ncy m icturiti on  \nCon stip ation / IBS  Con s tip a t i on , irri t a ble b ow el synd rome (I BS)  \nDiarr hoea  D i arrhoe a , loo se st ool s \nUrinary  Rete ntion o f urin e,  ha ematu ria,  dy sur i a  \nNeurol ogi cal  Fit (no e pi lep sy rec o r d), bl ack out  \nChe s t p ai n (no n-\nca r d i o v as cu l a r) \nCo s toc h ondri tis, mus c ul osk elet al / u ns pec ified \nches t pain  \nOral He alth  Stomati ti s, poo r oral hy gien e , sore mou th \nS wallo wing D i ffic ulty swa llowi ng liqu id s / s o lid s,  dysp hagi a \nHearing  Lo ss D e a fne ss, h e aring lo s s / impa irme nt  \n“F e e ls  U n wel l ”  R e c o r d ed  ‘ F e el s  u n wel l ’ \nDiet / N utri tion  Poo r  Die t  A d vice  re die t, hi gh fa t die t , die tici an r ef erral  \nNutr i t i on  Vitamin /iro n de fic iency , o st e omalac ia  \nWeig ht  Lo s s W e ight dec r e a s i ng / l o ss, unde rw eight  \nDiet ary Supplem e nt D i eta r y  s upplemen t  \nIma ging Ima ging X-r a y, MR I, E C G , D XA , angiog r a m, C AT sc an  \nInc r e a se d Multimo rbidity  Inc rea s e in Po lypha rmac y Increa s e in  count o f di f f e r e n t  d r ug s  pre sc ribe d  \nDemen tia -rel a te d Drug  Cha nge in  D e men tia -rel at ed N ew  or cha nged d e menti a drug  pr e s c r i b ed  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint \n\n[28]  \n \nDrug  \nT a bl e  mo difi e d fro m  Tab le 1 in Ca mpbel l  P , Ra th od-M istry T , Ma rshall  M, B ailey  J, Chew - Grah am CA, C roft  P, Fr ish e r M,  \nHayward  R ,  Negi  R,  S i ngh S,  T a nt alo-B ake r S ,  T a raf d ar S , Ba batun d e OO , R o b i n s o n L , Sumath ip a l a  A ,  T h e i n N, Walters K, \nWeich S,  Jor d an KP .  ( 2 0 21) M a rk er s of  deme n tia-rela te d  hea lth  i n prima r y ca re  e lectron i c hea lth  re cord s. Aging &  Mental \nHealth . 25,  145 2 -1 4 6 2 (2021).  Reprod uced wi t h p e rmissio n from Taylor  &  Fran c i s  Gr ou p . \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprint this version posted October 13, 2022. ; https://doi.org/10.1101/2022.10.11.22279756doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}