Dataset Information

Defining Multimorbidity in Older Surgical Patients.

ABSTRACT: BACKGROUND:There are numerous definitions of multimorbidity (MM). None systematically examines specific comorbidity combinations accounting for multiple testing when exploring large datasets. OBJECTIVES:Develop and validate a list of all single, double, and triple comorbidity combinations, with each individual qualifying comorbidity set (QCS) more than doubling the odds of mortality versus its reference population. Patients with at least 1 QCS were defined as having MM. RESEARCH DESIGN:Cohort-based study with a matching validation study. SUBJECTS:All fee-for-service Medicare patients between age 65 and 85 without dementia or metastatic solid tumors undergoing general surgery in 2009-2010, and an additional 2011-2013 dataset. MEASURES:30-day all-location mortality. RESULTS:There were 576 QCSs (2 singles, 63 doubles, and 511 triples), each set more than doubling the odds of dying. In 2011, 36% of eligible patients had MM. As a group, multimorbid patients (mortality rate=7.0%) had a mortality Mantel-Haenszel odds ratio=1.90 (1.77-2.04) versus a reference that included both multimorbid and nonmultimorbid patients (mortality rate=3.3%), and Mantel-Haenszel odds ratio=3.72 (3.51-3.94) versus only nonmultimorbid patients (mortality rate=1.6%). When matching 3151 pairs of multimorbid patients from low-volume hospitals to similar patients in high-volume hospitals, the mortality rates were 6.7% versus 5.2%, respectively (P=0.006). CONCLUSIONS:A list of QCSs identified a third of older patients undergoing general surgery that had greatly elevated mortality. These sets can be used to identify vulnerable patients and the specific combinations of comorbidities that make them susceptible to poor outcomes.

SUBMITTER: Silber JH

PROVIDER: S-EPMC6086592 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Defining Multimorbidity in Older Surgical Patients.

Silber Jeffrey H JH Reiter Joseph G JG Rosenbaum Paul R PR Zhao Qingyuan Q Small Dylan S DS Niknam Bijan A BA Hill Alexander S AS Hochman Lauren L LL Kelz Rachel R RR Fleisher Lee A LA

Medical care 20180801 8

<h4>Background</h4>There are numerous definitions of multimorbidity (MM). None systematically examines specific comorbidity combinations accounting for multiple testing when exploring large datasets.<h4>Objectives</h4>Develop and validate a list of all single, double, and triple comorbidity combinations, with each individual qualifying comorbidity set (QCS) more than doubling the odds of mortality versus its reference population. Patients with at least 1 QCS were defined as having MM.<h4>Researc ...[more]

PMID: 29995695

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Project description:OBJECTIVES:To systematically identify knowledge clusters and research gaps in the health-related preferences of older patients with multimorbidity by mapping current evidence. DESIGN:Evidence map (systematic review variant). DATA SOURCES:MEDLINE, EMBASE, PsycINFO, PSYNDEX, CINAHL and Science Citation Index/Social Science Citation Index/-Expanded from inception to April 2018. STUDY SELECTION:Studies reporting primary research on health-related preferences of older patients (mean age ?60 years) with multimorbidity (?2 chronic/acute conditions). DATA EXTRACTION:Two independent reviewers assessed studies for eligibility, extracted data and clustered the studies using MAXQDA-18 content analysis software. RESULTS:The 152 included studies (62% from North America, 28% from Europe) comprised 57 093 patients overall (range 9-9105). All used an observational design except for one interventional study: 63 (41%) were qualitative (59 cross-sectional, 4 longitudinal), 85 (57%) quantitative (63 cross-sectional, 22 longitudinal) and 3 (2%) used mixed methods. The setting was specialised care in 85 (56%) and primary care in 54 (36%) studies. We identified seven clusters of studies on preferences: end-of-life care (n=51, 34%), self-management (n=34, 22%), treatment (n=32, 21%), involvement in shared decision making (n=25, 17%), health outcome prioritisation/goal setting (n=19, 13%), healthcare service (n=12, 8%) and screening/diagnostic testing (n=1, 1%). Terminology (eg, preferences, views and perspectives) and concepts (eg, trade-offs, decision regret, goal setting) used to describe health-related preferences varied substantially between studies. CONCLUSION:Our study provides the first evidence map on the preferences of older patients with multimorbidity. Included studies were mostly conducted in developed countries and covered a broad range of issues. Evidence on patient preferences concerning decision-making on screening and diagnostic testing was scarce. Differences in employed terminology, decision-making components and concepts, as well as the sparsity of intervention studies, are challenges for future research into evidence-based decision support seeking to elicit the preferences of older patients with multimorbidity and help them construct preferences. TRIAL REGISTRATION NUMBER:Open Science Framework (OSF): DOI 10.17605/OSF.IO/MCRWQ.

Project description:INTRODUCTION:Interaction of conditions and treatments, complicated care needs and substantial treatment burden make patient-physician encounters involving multimorbid older patients highly complex. To optimally integrate patients' preferences, define and prioritise realistic treatment goals and individualise care, a patient-centred approach is recommended. However, the preferences of older patients, who are especially vulnerable and frequently multimorbid, have not been systematically investigated with regard to their health status. The purpose of this evidence map is to explore current research addressing health-related preferences of older patients with multimorbidity, and to identify the knowledge clusters and research gaps. METHODS AND ANALYSIS:To identify relevant research, we will conduct searches in the electronic databases MEDLINE, EMBASE, PsycINFO, PSYNDEX, CINAHL, Social Science Citation Index, Social Science Citation Index Expanded and the Cochrane library from their inception. We will check reference lists of relevant articles and carry out cited reference research (forward citation tracking). Two independent reviewers will screen titles and abstracts, check full texts for eligibility and extract the data. Any disagreement will be resolved and consensus reached with the help of a third reviewer. We will include both qualitative and quantitative studies, and address preferences from the patients' perspectives in a multimorbid population of 60 years or older. There will be no restrictions on the publication language. Data extraction tables will present study and patient characteristics, aim of study, methods used to identify preferences and outcomes (ie, type of preferences). We will summarise the data using tables and figures (ie, bubble plot) to present the research landscape and to describe clusters and gaps. ETHICS AND DISSEMINATION:Due to the nature of the proposed evidence map, ethics approval will not be required. Results from our research will be disseminated by means of specifically prepared materials for patients, at relevant (inter)national conferences and via publication in peer-reviewed journals.

Project description:BACKGROUND:Multimorbidity is a global health issue, particularly for older adults in the primary care setting. An adequate portrayal of its epidemiology is essential to properly identify and understand the health care needs of this population. This study aimed to compare the differences in the prevalence of selected chronic conditions and multimorbidity, including its associated characteristics, using health survey/self-reported (SR) information only, administrative (Adm) data only and the combined (either) sources. METHODS:This was a secondary analysis of survey data from the first cycle of the Longitudinal Survey on Senior's Health and Health Services Use linked to health-Adm data. The analytical sample consisted of 1625 community-dwelling older adults (≥65 years) recruited in the waiting rooms of primary health clinics in a selected administrative region of the province of Quebec. Seventeen chronic conditions were assessed according to two different data sources. We examined the differences in the observed prevalence of chronic conditions and multimorbidity and the agreement between data sources. RESULTS:The prevalence of each of the 17 chronic conditions ranged from 1.2 to 68.7% depending on the data source. The agreement between different data sources was highly variable, with kappa coefficients (κ) ranging from 0.05 to 0.73. Multimorbidity was very high in this population, with an estimated prevalence of up to 95.9%. In addition, we found that the association between sociodemographic and behavioural factors and the presence of multimorbidity varied according to the different data sources and thresholds. CONCLUSIONS:This is the first study to simultaneously investigate chronic conditions and multimorbidity prevalence among primary care older adults using combined SR and health-Adm data. Our results call attention to (1) the possibility of underestimating cases when using a single data source and (2) the potential benefits of integrating information from different data sources to increase case identification. This is an important aspect of characterizing the health care needs of this fast-growing population.

Project description:BackgroundThe current understanding of the magnitude and consequences of multimorbidity in Chinese older adults with coronary heart disease (CHD) is insufficient. We aimed to assess the association and population-attributable fractions (PAFs) between multimorbidity and mortality among hospitalized older patients who were diagnosed with CHD in Shenzhen, China.MethodsWe conducted a retrospective cohort study of older Chinese patients (aged ≥ 65 years) who were diagnosed with CHD. Cox proportional hazards models were used to estimate the associations between multimorbidity and all-cause and cardiovascular disease (CVD) mortality. We also calculated the PAFs.ResultsThe study comprised 76,455 older hospitalized patients who were diagnosed with CHD between January 1, 2016, and August 31, 2022. Among them, 70,217 (91.9%) had multimorbidity, defined as the presence of at least one of the predefined 14 chronic conditions. Those with cancer, hemorrhagic stroke and chronic liver disease had the worst overall death risk, with adjusted HRs (95% CIs) of 4.05 (3.77, 4.38), 2.22 (1.94, 2.53), and 1.85 (1.63, 2.11), respectively. For CVD mortality, the highest risk was observed for hemorrhagic stroke, ischemic stroke, and chronic kidney disease; the corresponding adjusted HRs (95% CIs) were 3.24 (2.77, 3.79), 1.91 (1.79, 2.04), and 1.81 (1.64, 1.99), respectively. All-cause mortality was mostly attributable to cancer, heart failure and ischemic stroke, with PAFs of 11.8, 10.2, and 9.1, respectively. As for CVD mortality, the leading PAFs were heart failure, ischemic stroke and diabetes; the corresponding PAFs were 18.0, 15.7, and 6.1, respectively.ConclusionsMultimorbidity was common and had a significant impact on mortality among older patients with CHD in Shenzhen, China. Cancer, heart failure, ischemic stroke and diabetes are the primary contributors to PAFs. Therefore, prioritizing improved treatment and management of these comorbidities is essential for the survival prognosis of CHD patients from a holistic public health perspective.

Project description:PurposeMultimorbidity is commonly defined and measured using condition counts. The UK National Institute for Health Care Excellence Guidelines for Multimorbidity suggest that a medication-orientated approach could be used to identify those in need of a multimorbidity approach to management.ObjectivesTo compare the accuracy of medication-based and diagnosis-based multimorbidity measures at higher cut-points to identify older community-dwelling patients who are at risk of poorer health outcomes.DesignA secondary analysis of a prospective cohort study with a 2-year follow-up (2010-2012).Setting15 general practices in Ireland.Participants904 older community-dwelling patients.ExposureBaseline multimorbidity measurements based on both medication classes count (MCC) and chronic disease count (CDC).OutcomesMortality, self-reported health related quality of life, mental health and physical functioning at follow-up.AnalysisSensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) adjusting for clustering by practice for each outcome using both definitions.ResultsOf the 904 baseline participants, 53 died during follow-up and 673 patients completed the follow-up questionnaire. At baseline, 223 patients had 3 or more chronic conditions and 89 patients were prescribed 10 or more medication classes. Sensitivity was low for both MCC and CDC measures for all outcomes. For specificity, MCC was better for all outcomes with estimates varying from 88.8% (95% CI 85.2% to 91.6%) for physical functioning to 90.9% (95% CI 86.2% to 94.1%) for self-reported health-related quality of life. There were no differences between MCC and CDC in terms of PPV and NPV for any outcomes.ConclusionsNeither measure demonstrated high sensitivity. However, MCC using a definition of 10 or more regular medication classes to define multimorbidity had higher specificity for predicting poorer health outcomes. While having limitations, this definition could be used for proactive identification of patients who may benefit from targeted clinical care.

Project description:BackgroundTreatment complexity rises in line with the number of drugs, single doses, and administration methods, thereby threatening patient adherence. Patients with multimorbidity often need flexible, individualised treatment regimens, but alterations during the course of treatment may further increase complexity. The objective of our study was to explore medication changes in older patients with multimorbidity and polypharmacy in general practice.MethodsWe retrospectively analysed data from the cluster-randomised PRIMUM trial (PRIoritisation of MUltimedication in Multimorbidity) conducted in 72 general practices. We developed an algorithm for active pharmaceutical ingredients (API), strength, dosage, and administration method to assess changes in physician-reported medication data during two intervals (baseline to six-months: ∆1; six- to nine-months: ∆2), analysed them descriptively at prescription and patient levels, and checked for intervention effects.ResultsOf 502 patients (median age 72 years, 52% female), 464 completed the study. Changes occurred in 98.6% of patients (changes were 19% more likely in the intervention group): API changes during ∆1 and ∆2 occurred in 414 (82.5%) and 338 (67.3%) of patients, dosage alterations in 372 (74.1%) and 296 (59.2%), and changes in API strength in 158 (31.5%) and 138 (27.5%) respectively. Administration method changed in 79 (16%) of patients in both ∆1 and ∆2. Simvastatin, metformin and aspirin were most frequently subject to alterations.ConclusionMedication regimens in older patients with multimorbidity and polypharmacy changed frequently. These are mostly due to discontinuations and dosage alterations, followed by additions and restarts. These findings cast doubt on the effectiveness of cross-sectional assessments of medication and support longitudinal assessments where possible.Trial registration1. Prospective registration: Trial registration number: NCT01171339 ; Name of registry: ClinicalTrials.gov; Date of registration: July 27, 2010; Date of enrolment of the first participant to the trial: August 12, 2010. 2. Peer reviewed trial registration: Trial registration number: ISRCTN99526053 ; Name of registry: Controlled Trials; Date of registration: August 31, 2010; Date of enrolment of the first participant to the trial: August 12, 2010.

Dataset Information

Defining Multimorbidity in Older Surgical Patients.

Publications

Defining Multimorbidity in Older Surgical Patients.

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