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Published on in Vol 26 (2024)

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Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56042, first published .
Creating a Modified Version of the Cambridge Multimorbidity Score to Predict Mortality in People Older Than 16 Years: Model Development and Validation

Creating a Modified Version of the Cambridge Multimorbidity Score to Predict Mortality in People Older Than 16 Years: Model Development and Validation

Creating a Modified Version of the Cambridge Multimorbidity Score to Predict Mortality in People Older Than 16 Years: Model Development and Validation

Authors of this article:

Debasish Kar1, 2 Author Orcid Image ;   Kathryn S Taylor1 Author Orcid Image ;   Mark Joy1 Author Orcid Image ;   Sudhir Venkatesan3 Author Orcid Image ;   Wilhelmine Meeraus4 Author Orcid Image ;   Sylvia Taylor4 Author Orcid Image ;   Sneha N Anand1 Author Orcid Image ;   Filipa Ferreira1 Author Orcid Image ;   Gavin Jamie1 Author Orcid Image ;   Xuejuan Fan1 Author Orcid Image ;   Simon de Lusignan1, 5 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    1Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom

    2Peninsula Medical School, University of Plymouth, Plymouth, United Kingdom

    3Medical & Payer Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca PLC, Cambridge, United Kingdom

    4Medical Evidence, Vaccines and Immune Therapies, AstraZeneca PLC, Cambridge, United Kingdom

    5Royal College of General Practitioners of the United Kingdom, London, United Kingdom

    *these authors contributed equally

    Corresponding Author:

    Kathryn S Taylor, PhD

    Nuffield Department of Primary Care Health Sciences

    University of Oxford

    Radcliffe Primary Care Building, Radcliffe Observatory Quarter

    Woodstock Road

    Oxford, OX2 6GG

    United Kingdom

    Phone: 44 1865617855

    Email: kathryn.taylor@phc.ox.ac.uk


    Background: No single multimorbidity measure is validated for use in NHS (National Health Service) England’s General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR), the nationwide primary care data set created for COVID-19 pandemic research. The Cambridge Multimorbidity Score (CMMS) is a validated tool for predicting mortality risk, with 37 conditions defined by Read Codes. The GDPPR uses the more internationally used Systematized Nomenclature of Medicine clinical terms (SNOMED CT). We previously developed a modified version of the CMMS using SNOMED CT, but the number of terms for the GDPPR data set is limited making it impossible to use this version.

    Objective: We aimed to develop and validate a modified version of CMMS using the clinical terms available for the GDPPR.

    Methods: We used pseudonymized data from the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC), which has an extensive SNOMED CT list. From the 37 conditions in the original CMMS model, we selected conditions either with (1) high prevalence ratio (≥85%), calculated as the prevalence in the RSC data set but using the GDPPR set of SNOMED CT codes, divided by the prevalence included in the RSC SNOMED CT codes or (2) conditions with lower prevalence ratios but with high predictive value. The resulting set of conditions was included in Cox proportional hazard models to determine the 1-year mortality risk in a development data set (n=500,000) and construct a new CMMS model, following the methods for the original CMMS study, with variable reduction and parsimony, achieved by backward elimination and the Akaike information stopping criterion. Model validation involved obtaining 1-year mortality estimates for a synchronous data set (n=250,000) and 1-year and 5-year mortality estimates for an asynchronous data set (n=250,000). We compared the performance with that of the original CMMS and the modified CMMS that we previously developed using RSC data.

    Results: The initial model contained 22 conditions and our final model included 17 conditions. The conditions overlapped with those of the modified CMMS using the more extensive SNOMED CT list. For 1-year mortality, discrimination was high in both the derivation and validation data sets (Harrell C=0.92) and 5-year mortality was slightly lower (Harrell C=0.90). Calibration was reasonable following an adjustment for overfitting. The performance was similar to that of both the original and previous modified CMMS models.

    Conclusions: The new modified version of the CMMS can be used on the GDPPR, a nationwide primary care data set of 54 million people, to enable adjustment for multimorbidity in predicting mortality in people in real-world vaccine effectiveness, pandemic planning, and other research studies. It requires 17 variables to produce a comparable performance with our previous modification of CMMS to enable it to be used in routine data using SNOMED CT.

    J Med Internet Res 2024;26:e56042

    doi:10.2196/56042

    Keywords

    pandemics; COVID-19; multimorbidity; prevalence; predictive model; discrimination; calibration; systematized nomenclature of medicine; computerized medical records; systems 


    People with multimorbidity, defined by those with 2 or more long-term conditions (LTCs) [1-6], have complex needs and impose increasing demands on primary care services given the aging population. Multimorbidity is associated with reduced life expectancy [7], lower quality of life [8], and an increased risk of hospitalization and death due to COVID-19 [9]. In clinical trials, vaccination against COVID-19 showed reduced risk of hospitalization and death in all groups [10,11]. However, in real-world studies, people with multimorbidity benefited less from vaccination [12] and were at increased risk of mortality, morbidity, and hospitalization, compared to those without multimorbidity [13]. People with 5 or more LTCs had a more than 4-fold higher risk of severe COVID-19 outcomes than those with less than 5 LTCs [12].

    Developing a single comorbidity measure is challenging [14]. The Charlson Comorbidity Index (CCI) is a commonly used tool to predict mortality over time [15]. However, CCI is based on hospital data, therefore, its applicability to primary care data is limited and not readily implementable [16]. The Cambridge Multimorbidity Score (CMMS) addressed this limitation and is an established measure of multimorbidity in primary care data. The original CMMS used 37 LTCs from routine primary care data in computerized medical records to predict the risk of primary care consultations, unplanned hospital admissions, and mortality [17]. It was developed and validated using the Clinical Practice Research Datalink [18] from the codes of Read version 2. However, Read version 2 is no longer used in England and is not updated since 2018 [19]. Additionally, the original CMMS model excluded people younger than 21 years of age, which somewhat restricted its applicability to the general population.

    To overcome these limitations, we have already developed and validated a modified CMMS, replacing Read version 2 with the Systematized Nomenclature of Medicine clinical terms (SNOMED CT) [20] and using pseudonymized data from the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) sentinel network of individuals aged 16 years or older [21]. Established in 1967, the RSC is an internationally renowned source of primary care data [22]. It has been used for influenza and respiratory disease monitoring for the last 50 years [23]. During the COVID-19 pandemic, with linkage to existing NHS (National Health service) England data sets, RSC data were also used to understand its epidemiology and assess vaccine effectiveness and safety [24-27]. This modified version of the CMMS was used to assess the real-world effectiveness of the Oxford-AstraZeneca COVID-19 vaccine in England (RAVEN) study, which was run on the RSC using linked data from NHS England [28].

    The RAVEN study also used primary care data from the larger and nationwide General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) data source maintained by NHS England, providing pseudonymized data for over 54 million people in England [29]. GDPPR is linked at the individual patient level to hospital, death, vaccine exposure, and test results. However, while substantial, its primary care data collection is incomplete. The primary care data were created from the existing list of conditions comprising 56,319 different SNOMED CT codes. This is a large number, but it is less than 20% of all SNOMED CT codes. These covered some clinical conditions well (eg, diabetes) and some less so (eg, psychoactive substance disorder). This study aimed to develop and validate a modified version of the CMMS, which could be used for the population aged 16 years and older in this new English NHS nationwide data set (GDPPR).


    UK Primary Care Data

    In the United Kingdom, each patient registers with a single general practitioner practice. Information about their primary care consultations, prescriptions, investigation results, and certified sickness and mortality data are recorded in computerized medical records systems. Each patient has a unique identifier, the NHS number, which allows data linkage with other data sets, including the hospital data, Hospital Episode Statistics, death certificate data provided by the Office for National Statistics and the NHS prescribing data set [30].

    Data Sources

    We used pseudonymized data from the RSC to construct and validate a revised version of CMMS based on the limited set of SNOMED CT codes (we refer to this as the GDPPR-modified CMMS). RSC data are stored in the Oxford RCGP Digital Informatics Hub (ORCHID) trusted research environment. The RCGP RSC extracts data from just under 2000 general practices in England [31] and provides a data set that is representative of individuals in England.

    We applied the same analytical approach and the same inclusion criteria as in our previous study, where we developed and validated a new CMMS for RSC using the more extensive list of SNOMED CT codes (we refer to this as the RSC-modified CMMS) [21]. We included people aged 16 years or older on the index date registered with a practice for 12 months or longer. Three separate data sets were sampled from the RSC as described previously (Figures S1 and S2 in Multimedia Appendix 1 [21]) and they are (1) derivation data set (n=500,000); (2) validation data set 1 (n=250,000) with the same study start and study end date as the derivation set (synchronous outcome); and (3) validation data set 2 (n=250,000) with 12-month outcome at a different time point to the derivation data set (asynchronous outcome), and 60-month outcome occurring at the same time point as the 12-month outcome of the derivation data set (synchronous outcome), as illustrated in Figure 1 [21]. These 3 data sets were generally comparable in terms of age, sex, number of conditions, and follow-up time.

    ‎
    Figure 1. Study design for the development and validation of the GDPPR-version of the CMMS, involving 3 cohorts, with index dates set at 12 months after their respective study start dates, and synchronous and asynchronous outcomes at 1 year and 5 years (adapted from Tsang et al [21], which is published under Creative Commons Attribution 4.0 International License). CMMS: Cambridge Multimorbidity Score; GDPPR: General Practice Extraction Service Data for Pandemic Planning and Research.

    Curating and Selecting Individual CMMS Component Variables

    In selecting conditions for a new modified CMMS model, we first considered all 37 conditions that were included in the original CMMS development and validation and used the same definitions and prescriptions [17]. Following the approach we previously developed for the RSC-modified CMMS with SNOMED CT [21], we carefully curated the conditions within the limited set of SNOMED CT codes in the GDPPR. We performed a confirmatory study concerning SNOMED CT coverage in the GDPPR by carrying out a statistical and clinical matching of the conditions between the GDPPR and RSC (Figure 2). A matching percentage for each condition was defined as the prevalence ratio, that is, the prevalence in the RSC data set but included in the GDPPR set of SNOMED CT codes divided by the prevalence included in the RSC set of SNOMED CT codes. Therefore, a ratio less than 100% indicated a higher prevalence of that condition within the RSC data set using its set of RSC SNOMED CT codes, compared to the prevalence using the GDPPR SNOMED CT list on the same data set. We set an 85% threshold for inclusion in the development of the GDPPR-modified CMMS model unless there was a clinical reason to accept a lower threshold.

    As the RSC provides a data set that is representative of England, we assumed that the actual prevalence of each condition in the RSC data set is similar to that in the GDPPR data set, and therefore, the RCS data set provided a suitable environment to develop the GDPPR-modified CMMS. We developed this GDPPR-modified CMMS in the RSC data set because it offered a complete set of SNOMED CT codes for each clinical concept, and we could replicate the reduced data set within GDPPR and then compare the case finding with each approach.

    ‎
    Figure 2. Selection of candidate CMMS conditions for the GDPPR-modified CMMS from the 37 conditions included in the original CMMS and inclusion of conditions based on prevalence defined by both the SNOMED CT lists available in the GDPPR and RSC, or knowledge that the condition is known to have high predictive value. CMMS: Cambridge Multimorbidity Score; GDPPR: General Practice Extraction Service Data for Pandemic Planning and Research; RSC: Research and Surveillance Centre; SNOMED CT: Systematized Nomenclature of Medicine clinical terms.

    Statistical Analyses

    Using the previously described method [21], we used time-to-mortality Cox proportional hazards models based on the development data set. We first included the conditions as binary indicators with sex and age (in decades) and a quadratic age term as covariates. We then carried out a variable reduction process via backward elimination and using the Akaike information criteria as the stopping criterion [32]. The goal was to be parsimonious with the number of variables needed for implementation. This was carried out using the “fastbw” function from the rms R package. Model performance evaluation was based on discrimination and calibration. Discrimination was assessed by the pseudo R2, Somers D, and Harrell C [33]. Model calibration was evaluated by plotting a calibration curve and recalibration was carried out by resampling using cross-validation to correct for optimism or overfitting. This was implemented using the “calibrate” function in the rms R package. The model was developed, performance was evaluated on the derivation data set, and we then evaluated the performance of the models on the 2 validation data sets. All data preparation and analyses were conducted in R (version 4.1.0; R Core Team) [34], using the following R packages lme4 (version 1.1-27) [35], lubridate (version 1.7.10) [36,37], randomizr (version 0.20.0) [37], rms (version 6.2-0) [38], survival (version 3.2-11) [39,40], tableone (version 0.12.0) [41], and tidyverse (version 1.3.1) [42].

    Ethical Considerations

    This development of the CMMS for use in GDPPR was performed as part of the RAVEN study which received ethical approval (Integrate Research Application Service number 300259) and was approved by the Health Research Authority’s Bromley Research Ethics Committee reference 21/HRA/1971, on October 8, 2021. NHS England hosts the national safe haven for patient data. The legal basis for this is Regulation 3 of the Health Service (Control of Patient Information Regulations) 2002 [43]. Pseudonymized data extracted from the practices are kept in a secured server at ORCHID, which is an NHS England policy-compliant trusted research environment (organization code EE133863-MSD-NDPCHS).


    Of the 37 conditions in the original CMMS model, 22 were included in developing the GDPPR-modified CMMS (Table 1). This involved 5 conditions based on clinical judgement—alcohol problems, chronic liver disease and viral hepatitis, stroke and transient ischemic attack, thyroid disorders, and dementia—which were included for clinical reasons alone (Figure 2), as they were likely to have a good predictive value, notwithstanding their lower prevalence ratios. All but thyroid disorders remained in the model after variable reduction in the final 17-condition model (Table 1).

    Table 1. Inclusion of the original CMMSa set of 37 medical conditions in the 2 modified versions of the CMMS, for use in the RSCb and for use in the GDPPRc, using the SNOMED CTd list available for the 2 respective data sets, ordered by prevalence of these conditions in the RSC data set.
    ConditionPatients in RSC data set as defined by RSC SNOMED CT codes (n=7,555,767), nPrevalence ratio (%)Final RSC-modified modele (n=21)Initial GDPPR- modified model (n=22)Final GDPPR- modified modele (n=17)
    Diabetes412,960100.0✓✓✓
    Chronic kidney disease260,270100.0✓✓✓
    Chronis sinusitis103,638100.0
    		✓
    Bronchiectasis34,05099.9
    		✓
    Atrial fibrillation198,94999.7✓✓✓
    Asthma currently treated542,00199.0
    		✓
    Chronic obstructive pulmonary disease147,19199.0✓✓✓
    Schizophrenia or bipolar disease48,97597.9✓✓✓
    Epilepsy54,96495.9✓✓✓
    Parkinsonism17,15695.8✓✓✓
    Constipation123,99995.7✓✓✓
    Hypertension1,280,95894.7
    		✓
    Learning disability44,10093.4✓✓✓
    Heart failure95,82893.0✓✓✓
    Cancer in the last 5 years168,57791.1✓✓✓
    Peripheral vascular disease38,94687.3✓✓✓
    Coronary heart disease322,33884.7
    		✓✓
    Dementia80,15684.2✓✓✓
    Thyroid disorders420,68183.3
    		✓
    Stroke and transient ischemic attack182,97981.0
    		✓✓
    Migraine33,71968.4


    Connective tissue disorder or rheumatoid arthritis153,35051.8


    Chronic liver disease and viral hepatitis52,26549.3✓✓✓
    Painful conditions810,45843.6✓

    Alcohol problems190,43933.0✓✓✓
    Psoriasis or eczema63,55626.5


    Inflammatory bowel disease50,37025.6


    Anxiety or depression919,96225.1✓

    Disorders of prostate201,55925.0✓

    Blindness and low vision88,39817.5


    Diverticular disease of intestine229,53617.2


    Anorexia52,74216.7


    Hearing loss581,80412.9


    Peptic ulcer disease98,4879.5


    Irritable bowel syndrome407,8800.0✓

    Psychoactive substance misuse92,9440.0✓

    Multiple sclerosis15,7170.0✓

    aCMMS: Cambridge Multimorbidity Score.

    bRSC: Research and Surveillance Centre.

    cGDPPR: General Practice Extraction Service Data for Pandemic Planning and Research.

    dSNOMED CT: Systematized Nomenclature of Medicine clinical terms.

    eAfter variable reduction.

    There were few differences in the development and validation data sets in terms of age, sex, number of conditions and follow-up time (Table 2).

    The prevalence of the 22 conditions in the model derivation data set is presented in Table 3. These prevalences and their rankings were generally similar to those reported in the original CMMS study [17], and our previous study [21]. There was only 1 exception, coronary heart disease, which ranked lower (7163/500,000, 1.4%; original CMMS study—15,887/300,000, 4.8%; and previous RSC study—15,887/300,000, 5.3%).

    The model performance of the 22-condition and 17-condition models was almost identical and similar to those in the original CMMS study and the RSC-modified CMMS (Tables 4 and 5).

    Table 2. Descriptive statistics of 3 data sets sampled from the RSCa for deriving and validating a modified version of the CMMSb for use in the GDPPRc data set, within the constraints of its limited set of SNOMED CTd codes.

    		Derivation (2019)Validation 1 (2019)Validation 2 (2015)
    Male, n (%)247,807 (49.6)124,514 (49.8)123,541 (49.4)
    Age at index date in years

    		Mean (SD)49.03 (19.29)48.11 (19.09)46.0 (19.34)

    		Range16-9516-9516-95

    		65-84, n (%)103,587 (22)48,016 (0.21)46,834 (0.20)

    		85 or older, n (%)16,387 (4)7,513 (0.03)7,608 (0.03)
    Number of conditions

    		Mean (SD)0.72 (1.19)0.69 (1.17)0.70 (1.0)

    		Range0-110-110-11

    		0, n (%)309,089 (62)157,981 (63)161,941 (65)

    		1, n (%)101,547 (20)49,463 (20)47,530 (19)

    		2 or more, n (%)89,364 (18)42,556 (17)40,529 (16)
    Number of deaths in follow-up, n510423922408/11,948
    Mean follow-up timee (days), n352.8351.9350.4/1538.0
    Total person yearse,f, n482,885.5240,859.6239,859.2/1,052,567
    Mortality rate (per 1000 person years)e, n10.579.9310.04/11.35

    aRSC: Research and Surveillance Centre.

    bCMMS: Cambridge Multimorbidity Score.

    cGDPPR: General Practice Extraction Service Data for Pandemic Planning and Research.

    dSNOMED CT: Systematized Nomenclature of Medicine clinical terms.

    e1-year follow-up for validation 1 and 1- and 5-year follow-up for validation 2.

    fCalculated as number of person-days divided by 365.25.

    Table 3. Prevalence in individuals in the model derivation data set of the 22 candidate conditions in the GDPPRa-modified CMMSb model before variable reduction and weights for the final set of 17 conditions after variable reduction, with conditions ordered by prevalence.
    ConditionValue (n=500,000), n (%)Weight
    Hypertension98,849 (19.8)N/Ac
    Asthma currently treated36,951 (7.4)N/A
    Diabetes33,312 (6.7)0.2623
    Thyroid disorders28,891 (5.8)N/A
    Chronic kidney disease23,145 (4.6)0.1286
    Atrial fibrillation15,041 (3.0)0.2779
    Cancer in the last 5 years13,059 (2.6)1.1876
    Chronic obstructive pulmonary disease12,734 (2.5)0.6638
    Alcohol problems12,132 (2.4)0.5670 
    Stroke and transient ischemic attack12,118 (2.4)0.2299
    Constipation8698 (1.7)0.5889
    Chronis sinusitis8195 (1.6)N/A
    Coronary heart disease21,897 (1.4)0.1201
    Heart failure7163 (1.4)0.5022
    Dementia5884 (1.2)0.9815
    Epilepsy4114 (0.8)0.6714
    Schizophrenia or bipolar disorder3819 (0.8)0.5621
    Learning disability2857 (0.6)1.0992
    Peripheral vascular disease2963 (0.6)0.3519
    Bronchiectasis2563 (0.5)N/A
    Chronic liver disease and viral hepatitis1890 (0.4)1.0844
    Parkinsonism1409 (0.3)0.5339

    aGDPPR: General Practice Extraction Service Data for Pandemic Planning and Research.

    bCMMS: Cambridge Multimorbidity Score.

    cN/A: not applicable.

    Table 4. Model discrimination for the final RSCa-modified and GDPPRb-modified versions of the CMMSc, after variable reduction, and compared with a full 37-condition model using RSC data and SNOMED CTd.

    		37-condition model [21]Final RSC-modified 21-condition model [21]Final GDPPR-modified 17-condition model
    Pseudo R20.1530.1530.140
    Somers D0.8510.8510.833
    Harrell C(SE)

    		Derivation0.925 (0.002)0.926 (0.002)0.916 (0.002)

    		Validation 10.920 (0.004)0.921 (0.004)0.922 (0.003)

    		Validation 2, 1-year follow-up0.920 (0.003)0.920 (0.003)0.915 (0.003)

    		Validation 2, 5-year follow-up0.907 (0.002)0.907 (0.002)0.902 (0.001)

    aRSC: Research and Surveillance Centre.

    bGDPPR: General Practice Extraction Service Data for Pandemic Planning and Research.

    cCMMS: Cambridge Multimorbidity Score.

    dSNOMED CT: Systematized Nomenclature of Medicine clinical terms.

    Table 5. Hazard ratios (95 CIs) of the predictors for the final RSCa-modified and GDPPRb-modified versions of the CMMSc, after variable reduction, and compared with a full 37-condition model using RSC data and SNOMED CTd.

    		37-condition model HRe (95% CI) [21]RSC-modified 21-condition model HR (95% CI) [21]GDPPR-modified 17-condition model HR (95% CI)
    Age (10 years)1.22 (1.02-1.47)N/Af1.02 (1.01-1.04)
    [Age_(10 years)]21.05 (1.03-1.06)1.06 (1.06-1.06)1.00 (1.00-1.00)
    Sex (Male)1.33 (1.23-1.45)1.34 (1.24-1.46)1.14 (1.08-1.21)
    Cancer in the last 5 years3.31 (2.99-3.67)3.33 (3.00-3.69)3.28 (3.06-3.52)
    Dementia2.57 (2.33-2.84)2.55 (2.32-2.82)2.67 (2.47-2.88)
    Alcohol problems2.17 (1.84-2.55)2.21 (1.88-2.60)1.76 (1.53-2.03)
    Multiple sclerosis2.13 (1.32-3.44)2.14 (1.33-3.46)N/A
    Chronic liver disease and viral hepatitis1.98 (1.57-2.49)1.99 (1.58-2.50)2.96 (2.38-3.68)
    Chronic obstructive pulmonary disease1.96 (1.76-2.18)2.02 (1.83-2.23)1.94 (1.80-2.10)
    Learning disability1.88 (1.14-3.10)1.89 (1.15-3.11)3.00 (2.18-4.13)
    Parkinsonism1.71 (1.39-2.11)1.73 (1.40-2.13)1.71 (1.43-2.04)
    Heart failure1.66 (1.49-1.85)1.66 (1.49-1.84)1.65 (1.51-1.80)
    Epilepsy1.59 (1.25-2.02)1.61 (1.27-2.04)1.96 (1.62-2.37)
    Schizophrenia or bipolar disorder1.59 (1.22-2.06)1.62 (1.25-2.10)1.75 (1.42-2.17)
    Psychoactive substance abuse1.57 (1.20-2.04)1.57 (1.20-2.04)N/A
    Painful condition1.55 (1.42-1.68)1.56 (1.44-1.69)N/A
    Constipation1.47 (1.33-1.62)1.47 (1.33-1.62)1.80 (1.67-1.95)
    Atrial fibrillation1.39 (1.27-1.53)1.40 (1.27-1.53)1.32 (1.23-1.42)
    Peripheral vascular disease1.39 (1.07-1.81)1.40 (1.08-1.82)1.42 (1.24-1.63)
    Anxiety or depression1.38 (1.27-1.50)1.38 (1.27-1.50)N/A
    Diabetes1.31 (1.20-1.43)1.34 (1.23-1.46)1.30 (1.21-1.39)
    Psoriasis or eczema1.27 (1.03-1.57)N/AN/A
    Chronic kidney disease1.24 (1.14-1.35)1.24 (1.14-1.35)1.14 (1.06-1.21)
    Anorexia or bulimia1.22 (0.66-2.28)N/AN/A
    Peptic ulcer1.13 (0.98-1.30)N/AN/A
    Bronchiectasis1.11 (0.87-1.41)N/AN/A
    Stroke and transient ischemic attack1.11 (1.00-1.24)N/A1.26 (1.16-1.36)
    Asthma currently treated1.05 (0.93-1.18)N/AN/A
    Hypertension1.04 (0.96-1.13)N/AN/A
    Thyroid disorder1.03 (0.92-1.14)N/AN/A
    Coronary heart disease1.00 (0.91-1.09)N/A1.13 (1.05-1.21)
    Chronic sinusitis0.98 (1.57-2.49)N/AN/A
    Rheumatoid arthritis0.98 (0.85-1.12)N/AN/A
    Blindness and low vision0.96 (0.84-1.11)N/AN/A
    Diverticular disease of intestine0.92 (0.82-1.02)N/AN/A
    Hearing loss0.92 (0.95-1.00)N/AN/A
    Disorder of the prostate0.83 (0.74-0.93)0.83 (0.74-0.93)N/A
    Irritable bowel syndrome0.83 (0.71-0.95)0.82 (0.71-0.94)N/A
    Inflammatory bowel disease0.65 (0.43-0.97)N/AN/A
    Migraine0.59 (0.25-1.42)N/AN/A

    aRSC: Research and Surveillance Centre.

    bGDPPR: General Practice Extraction Service Data for Pandemic Planning and Research.

    cCMMS: Cambridge Multimorbidity Score.

    dSNOMED CT: Systematized Nomenclature of Medicine clinical terms.

    eHR: hazard ratio.

    fN/A: not applicable.

    For 1-year mortality, discrimination was high in both the derivation and validation data sets (Harrell C=0.92) and for 5-year mortality, it was slightly lower (Harrell C=0.90). The model calibration displayed underprediction at lower risks (<60%), and the calibration improved with the adjustment for optimism or overfitting (Figure 3).

    ‎
    Figure 3. Calibration curve for the final 17 condition GDPPR-modified CMMS model. Black: observed; blue: optimism corrected; gray: ideal. Mean (SE) is 0.07 (0.9). Quantile is 0.009. CMMS: Cambridge Multimorbidity Score; GDPPR: General Practice Extraction Service Data for Pandemic Planning and Research.

    Principal Findings

    In this study, we developed and validated a modified version of a single measure of multimorbidity, CMMS, for use within a national data set created during the pandemic from all existing primary care data collections, GDPPR, and using its limited set of SNOMED CT codes. The initial model included 22 conditions from the set of 37 in the original CMMS model and the reduced 17-condition model showed an identical performance in predicting mortality to the 22-condition model and a similar performance compared to the original 37-condition CMMS and the previous modification which was based on an extensive SNOMED CT data set.

    Interpretations and Implications

    The GDPPR database remains available and is listed in the NHS Data Model and Dictionary [44]. It is now one of the data collections available through the NHS England Secure Data Environment [45], which was created as part of NHS England’s Data Saves Lives policy, following the Goldacre report [46].

    This new single measure of multimorbidity will help us measure vaccine effectiveness using GDPPR, NHS England’s nationwide database. We will use this GDPPR-modified CMMS score in the RAVEN study to build on the existing evidence base [47,48]. Several observational studies have shown that the effectiveness of vaccination could be suboptimal in people with multimorbidity [49], and thus it is important to be able to explore and adjust for multimorbidity.

    This tool may also be useful in a wider range of studies of people with multimorbidity, including vaccine and post-authorization safety studies. The risk of hospitalization, admission to intensive care unit beds, and mortality in people with multimorbidity are significantly higher than in the general population [50,51]. In an observational study of hospitalized patients in the United Kingdom with COVID-19, the crude mortality in people with multimorbidity, compared to single comorbidity, after adjusting for the demographic factors, was more than double (1492/3961, 37.7% vs 341/1971, 17.3%) [52]. It was estimated to reduce 63.5% (1905/3000) of deaths by prioritizing people with multimorbidity [53]. Therefore, people with multimorbidity were prioritized for vaccine rollout [54].

    Our previous study showed that reducing the original CMMS variables from 37 to 21 did not compromise mortality predictability in people with multimorbidity [21]. In this study, we have demonstrated that the number of conditions can be reduced to 17 to match the data available in GDPPR and still can be a very good predictor of mortality.

    Comparison With Prior Work

    We focused on mortality and this is the outcome most often reported in development studies of comorbidity indices [55]. There are many published comorbidity indices and they vary according to their time of development (and thus the number of modifications), derivation population, conditions (predictors), prediction horizon, outcome predicted, and data source [52]. Historically, the mortality indices have been designed for people in hospitals, using secondary care coding systems and they have provided predictions of in-hospital mortality and mortality between 6 months and 5 years [55]. The GDPPR-modified CMMS is the latest adaptation to the original CMMS [17] for predicting mortality in primary care. The predictions for the CMMS and its modified versions are for the same prediction horizons of 1 year and 5 years. The previous modification adapted the CMMS to conditions defined by the internationally recognized SNOMED CT coding system [21], as the original CMMS was based on a population in the United States and conditions defined by the Read clinical terminology, which is no longer used in England. Both modifications of the CMMS have been developed on English populations with a lower minimum age compared to that for the original version (16 years as opposed to 21 years).

    This GDPPR-modified version produces a multimorbidity index for mortality based on conditions defined by the limited SNOMED CT list of the GDPPR. The RSC provided the development data set for both modifications of the CMMS. The RCS has a complete set of SNOMED CT codes. Its data set includes people registered in a fraction of English general practices, and the assumption of this study is that the underlying prevalence of each CMMS condition in people in the RSC data set is the same as those in the larger GDPPR data set. While the RSC is recruited to be nationally representative, there may inevitably be differences [24].

    The GDPPR includes the English primary care data used by the British Heart Foundation’s Data Science Centre for their COVID-19 and cardiovascular diseases Consortium’s work. Our version of CMMS could be deployed by this and other groups using GDPPR or underlying primary care (General Practice Extraction Service) data [56].

    Strengths

    The main strength of this study is that it built on our expertise in developing a version of CMMS that could be applied to routine clinical data recorded using SNOMED CT. This terminology is used internationally [21]. We overcame the limitations of the relatively limited number of SNOMED clinical terms in GDPPR and demonstrated that a 17-condition CMMS could run in the GDDPR data set of 54 million individuals.

    Limitations

    There are several limitations of this study. We only predicted the mortality risk and did not predict the hospitalization or intensive care unit admission risk. The conditions were a subset of those included in the original CMMS study, which arose from a review of multimorbidity literature at the time of its development. The nature of disease and treatments, as to population characteristics, will change over time. Hence, the new CMMS versions will need to be updated regularly, and this may involve adding conditions that were not included in the original CMMS. Although we split our initial data set randomly into development and validation data sets, we have performed simple temporal external validation [57]. A more robust form of external validation would involve investigating the generalizability to other countries (geographical validation) or other settings (domain validation), but neither is relevant in this case as we are considering a national data set, and we have developed the new CMMS using what we assume to be a representative sample of the adult English population. The generalizability could be tested further on other primary care data such as Clinical Practice Research Datalink [18], which provides a database of anonymized health records for another sample of English general practitioner practices.

    Conclusions

    This latest modification of the CMMS provides a new validated single multimorbidity measure, which was generated through a combination of unique access to data and expertise in validation. The RSC provided nationally representative and comprehensive primary care data. The study team had experience in developing a validated CMMS version to use within SNOMED CT. This combination meant that it was possible to develop and validate a new version of CMMS for use in the national English data set, the GDPPR. Our previous study showed that reducing the original CMMS variables from 37 to 21 did not compromise mortality predictability in people with multimorbidity [21]. In this study, we have demonstrated that the number of conditions can be reduced to 17 to match the data available in GDPPR and still can be a very good predictor of mortality. Therefore, researchers using this national database, or looking for a further reduced CMMS measure, can use this 17-component single measure of comorbidity.

    The approach used in this study could also be applied in other contexts. Our approach has been to replicate a validated multimorbidity measure in a smaller, but complete and high data quality sentinel network database, the RSC. Within the RSC we could ensure the model performs as well as the one run on complete data [21]. Additionally, developing and validating this reduced CMMS model in the RSC required less processing time. This may make this reduced version more attractive to other users, should processing time be at a premium.

    Acknowledgments

    We would like to acknowledge that Ruby Tsang prepared the R script that we modified, Rashmi Wimalaratna helped with the data curation, and Julian Sherlock was also involved in preparing the data. This study was conducted to support the real-world effectiveness of the Oxford-AstraZeneca COVID-19 vaccine in England (RAVEN) study which was funded by AstraZeneca. The sponsors were involved in the Systematized Nomenclature of Medicine (SNOMED) matching process (SV), and in reviewing and approving the paper (SV, WM, and ST).

    Data Availability

    The data sets generated and analyzed during this study are not publicly available as they have to be analyzed within a secure network, after appropriate training and subject to ethical approval, and this can be arranged by the corresponding author on reasonable request.

    Authors' Contributions

    SdL conceived this research project and played a supervisory role. XF was involved in extracting data from the Research and Surveillance Centre. DK curated the variables. DK compared the SNOMED CT lists and DK and SdL conducted the clinical sign off of conditions to be included in the analysis. KST, SV, and MJ were involved in the statistical analyses. SNA and FF were responsible for managing the project. DK and KST drafted the paper. All authors have reviewed and approved the final paper.

    Conflicts of Interest

    SdL is the principal investigator for real-world effectiveness of the Oxford-AstraZeneca COVID-19 vaccine in England (RAVEN; EUPAS43571) funded by AstraZeneca. SdL is also the Director of the Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), which is included in his academic role at the University of Oxford. He has received research funding through his University from AstraZeneca, GlaxoSmithKline (GSK), Lily, Moderna, Medical Science Division (MSD), Sanofi, Seqirus, and Takeda. He has also served as an advisory board member for AstraZeneca, GSK, Sanofi, Seqirus, and Pfizer. SV, WM and ST are employees of AstraZeneca and may own stock/shares. ST reports ownership of GSK stocks/shares.

    Multimedia Appendix 1

    Flowcharts showing the selection of practices and individuals for inclusion in the analysis (adapted from Tsang et al [21], which is published under Creative Commons Attribution 4.0 International License).

    DOCX File , 118 KB
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    ‎
    CCI: Charlson Comorbidity Index
    CMMS: Cambridge Multimorbidity Score
    GDPPR: General Practice Extraction Service Data for Pandemic Planning and Research
    LTC: long-term condition
    NHS: National Health Service
    ORCHID: Oxford Royal College of General Practitioners Digital Informatics Hub
    RAVEN: real-world effectiveness of the Oxford-AstraZeneca COVID-19 vaccine in England
    RCGP: Royal College of General Practitioners
    RSC: Research and Surveillance Centre
    SNOMED CT: Systematized Nomenclature of Medicine clinical terms

    Edited by A Mavragani; submitted 03.01.24; peer-reviewed by P-H Liao, A Azcoaga-Lorenzo, C Okusi; comments to author 29.02.24; revised version received 17.05.24; accepted 20.05.24; published 26.08.24.

    Copyright

    ©Debasish Kar, Kathryn S Taylor, Mark Joy, Sudhir Venkatesan, Wilhelmine Meeraus, Sylvia Taylor, Sneha N Anand, Filipa Ferreira, Gavin Jamie, Xuejuan Fan, Simon de Lusignan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.08.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.