A cardiovascular risk prediction model for older people: Development and validation in a primary care population

Classification rule for ten year MACE Risk in primary care tarragona older adults with type2 diabetes: a CHAID decision-tree analysis

BACKGROUND

Cardiovascular disease is the leading cause of mortality among individuals with Type 2 Diabetes Mellitus (T2DM). This study developed a simple tool to predict the 10-year risk of major adverse cardiovascular events (MACE) in T2DM patients over 60 years within primary care.

METHODS

A retrospective cohort study was conducted on patients with T2DM who were over 60 years old in Tarragona, spanning from 01/01/2009-31/12/2018. Primary outcome was MACE, which included acute myocardial infarction (AMI), stroke, and cardiovascular death, all of which were identified using ICD-9 diagnostic codes. Other variables were age, sex, comorbidities, risk factors, as well as clinical and laboratory parameters. A Chi-Square Automatic Interaction Detector (CHAID) decision tree classification was utilized to assess the 10-year risk of developing a new MACE.

RESULTS

Five thousand five hundred fifty-four patients with T2DM were identified. Among the 4,666 with T2DM and without previous MACE, 779 patients went on to develop a new MACE. The CHAID model categorizes individuals into three risk groups based on the primary predictor variable, which is age. For patients under the age of 71 with hypertension, having HDL-c levels less than 39 mg/dL increases the risk of developing a new MACE to 19.9%. Among individuals aged 71 to 75 years, having fasting glucose levels greater than 177 mg/dL elevates the risk to 27.2%.

CONCLUSION

Classification trees based on CHAID allow for the development of decision rules and simplify the stratification of cardiovascular risk in patients with T2DM, making it a valuable tool for risk assessment within a primary care setting.

Scoping review of the use of multimorbidity variables in cardiovascular disease risk prediction

BACKGROUND

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Many countries use pooled cohort equations or similar risk prediction models to assess atherosclerotic CVD risk to guide preventive measures. There is evidence that clinical CVD risk prediction equations are less accurate for adults with higher levels of multimorbidity (the co-occurrence of multiple long-term conditions). Operating within a single disease paradigm may not be appropriate for adults with multimorbidity who may be at higher risk of both CVD and non-CVD death. This scoping review was conducted to gather evidence on the inclusion of multimorbidity measures in CVD risk models to assess their methodology and identify evidence gaps in the literature.

METHODS

The review covers literature from 1 January 2012 to 23 September 2022, using the Arksey and O'Malley framework. We searched MEDLINE, Embase, and Cochrane databases published during this period and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines.

RESULTS

This review identified fourteen studies reporting multivariable prognostic CVD models that included a multimorbidity variable. Of these, four studies specifically looked at the added benefit of a multimorbidity variable in a CVD risk model. Only one of these studies was conducted in a primary prevention cohort (i.e., people were free of CVD at baseline). This scoping review revealed several primary evidence gaps, notably the limited literature on the topic, the model performance in ethnic subpopulations, and the comparative assessment of alternative multimorbidity variables beyond the Charlson Comorbidity Index.

CONCLUSIONS

Few studies have assessed the impact of incorporating multimorbidity indices in primary and secondary prevention cohorts. Future research is needed to evaluate the incremental value of multimorbidity indices in cardiovascular disease risk prediction models to inform risk stratification and management strategies in people with multimorbidity.

Development and validation of a five-year cardiovascular risk assessment tool for Asian adults aged 75 years and older

BACKGROUND

To identify cardiovascular (CV) risk factors in Asian elderly aged 75 years and older and subsequently develop and validate a sex-specific five-year CV risk assessment tool for this population.

METHODS

This study included 12,174 patients aged ≥ 75 years without a prior history of cardiovascular disease at a single hospital in Taiwan. Electronic health records were linked to the National Health Insurance Research Database and the National Death Registry to ensure comprehensive health information. Eligible patients were randomly divided into derivation (80%) and validation (20%) cohorts. A sex-specific CV risk assessment tool was developed to predict major adverse cardiovascular events (MACE) using Cox regression modeling.

RESULTS

During a median follow-up period of 8.6 years for men and 8.5 years for women in the derivation cohort, MACE occurred in 3.62% of men and 3.02% of women. Predictors for men comprised advanced age, smoking, non-HDL-C levels > 160 mg/dL, metastatic cancer, and aspirin usage. Predictors for women included advanced age, smoking, atrial fibrillation, cancer, dementia, osteoarthritis, systemic lupus erythematosus, use of antihypertensives, and use of oral anticoagulants. In the validation cohort, the sex-specific risk assessment tool demonstrated fair discriminative power (AUC: men, 0.64; women, 0.68). Model calibration demonstrated good performance for women but was less optimal for men.

CONCLUSIONS

This sex-specific CV risk assessment tool shows fair discriminative capability in estimating risk of cardiovascular disease among elderly Asians, potentially enabling targeted interventions in this vulnerable population.

An ECG-based machine-learning approach for mortality risk assessment in a large European population

AIMS

Through a simple machine learning approach, we aimed to assess the risk of all-cause mortality after 5 years in a European population, based on electrocardiogram (ECG) parameters, age, and sex.

METHODS

The study included patients between 40 and 90 years old who underwent ECG recording between January 2008 and October 2022 in the metropolitan area of Modena, Italy. Exclusion criteria established a patient cohort without severe ECG abnormalities, namely, tachyarrhythmias, bradyarrhythmias, Wolff-Parkinson-White syndrome, second- or third- degree AV block, bundle-branch blocks, more than three premature beats, poor signal quality, and presence of pacemakers and implantable cardioverter- defibrillators. Mortality was assessed using a set of logistic regression models, differentiated by age group, to which the Akaike Information Criterion was applied. Model fitting was evaluated using confusion matrix-related performance metrics, the area under the receiver operating characteristic (ROC) curve (AUC), and the predictive significance against the no-information rate (NIR).

RESULTS

53692 patients were enrolled, of whom 14353 (26.73 %) died within 5 years of ECG registration. The logistic regression model distinguished between those who died and those who survived based on the predicted mortality probability for all age groups, obtaining a significant difference between the predicted mortality and the NIR in 14 of the 55 age groups. Good accuracy and performance metrics were observed, resulting in an average AUC of 0.779.

CONCLUSIONS

The proposed model showed a good predictive performance in patients without severe ECG abnormalities. Therefore, this study highlights the potential of ECGs as prognostic rather than diagnostic tools.

Community-based complex interventions to sustain independence in older people, stratified by frailty: a systematic review and network meta-analysis

Background

Sustaining independence is important for older people, but there is insufficient guidance about which community health and care services to implement.

Objectives

To synthesise evidence of the effectiveness of community services to sustain independence for older people grouped according to their intervention components, and to examine if frailty moderates the effect.

Review design

Systematic review and network meta-analysis.

Eligibility criteria

Studies: Randomised controlled trials or cluster-randomised controlled trials. Participants: Older people (mean age 65+) living at home. Interventions: community-based complex interventions for sustaining independence. Comparators: usual care, placebo or another complex intervention.

Main outcomes

Living at home, instrumental activities of daily living, personal activities of daily living, care-home placement and service/economic outcomes at 1 year.

Data sources

We searched MEDLINE (1946-), Embase (1947-), CINAHL (1972-), PsycINFO (1806-), CENTRAL and trial registries from inception to August 2021, without restrictions, and scanned reference lists.

Review methods

Interventions were coded, summarised and grouped. Study populations were classified by frailty. A random-effects network meta-analysis was used. We assessed trial-result risk of bias (Cochrane RoB 2), network meta-analysis inconsistency and certainty of evidence (Grading of Recommendations Assessment, Development and Evaluation for network meta-analysis).

Results

We included 129 studies (74,946 participants). Nineteen intervention components, including 'multifactorial-action' (multidomain assessment and management/individualised care planning), were identified in 63 combinations. The following results were of low certainty unless otherwise stated. For living at home, compared to no intervention/placebo, evidence favoured: multifactorial-action and review with medication-review (odds ratio 1.22, 95% confidence interval 0.93 to 1.59; moderate certainty) multifactorial-action with medication-review (odds ratio 2.55, 95% confidence interval 0.61 to 10.60) cognitive training, medication-review, nutrition and exercise (odds ratio 1.93, 95% confidence interval 0.79 to 4.77) and activities of daily living training, nutrition and exercise (odds ratio 1.79, 95% confidence interval 0.67 to 4.76). Four intervention combinations may reduce living at home. For instrumental activities of daily living, evidence favoured multifactorial-action and review with medication-review (standardised mean difference 0.11, 95% confidence interval 0.00 to 0.21; moderate certainty). Two interventions may reduce instrumental activities of daily living. For personal activities of daily living, evidence favoured exercise, multifactorial-action and review with medication-review and self-management (standardised mean difference 0.16, 95% confidence interval -0.51 to 0.82). For homecare recipients, evidence favoured the addition of multifactorial-action and review with medication-review (standardised mean difference 0.60, 95% confidence interval 0.32 to 0.88). Care-home placement and service/economic findings were inconclusive.

Limitations

High risk of bias in most results and imprecise estimates meant that most evidence was low or very low certainty. Few studies contributed to each comparison, impeding evaluation of inconsistency and frailty. Studies were diverse; findings may not apply to all contexts.

Conclusions

Findings for the many intervention combinations evaluated were largely small and uncertain. However, the combinations most likely to sustain independence include multifactorial-action, medication-review and ongoing review of patients. Some combinations may reduce independence.

Future work

Further research is required to explore mechanisms of action and interaction with context. Different methods for evidence synthesis may illuminate further.

Study registration

This study is registered as PROSPERO CRD42019162195.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR128862) and is published in full in Health Technology Assessment; Vol. 28, No. 48. See the NIHR Funding and Awards website for further award information.

The Reasons for the Low Uptake of New Antidiabetic Drugs with Cardiovascular Effects-A Family Doctor Perspective

Chronic diseases, such as type 2 diabetes (T2D), are difficult to manage because they demand continuous therapeutic review and monitoring. Beyond achieving the target HbA1c, new guidelines for the therapy of T2D have been introduced with the new groups of antidiabetics, glucagon-like peptide-1 receptor agonists (GLP-1ra) and sodium-glucose cotransporter-2 inhibitors (SGLT2-in). Despite new guidelines, clinical inertia, which can be caused by physicians, patients or the healthcare system, results in T2D not being effectively managed. This opinion paper explores the shift in T2D treatment, challenging assumptions and evidence-based recommendations, particularly for family physicians, considering the patient's overall situation in decision-making. We looked for the possible reasons for clinical inertia and the poor application of guidelines in the management of T2D. Guidelines for antidiabetic drugs should be more precise, providing case studies and clinical examples to define clinical contexts and contraindications. Knowledge communication can improve confidence and should include clear statements on areas of decision-making not supported by evidence. Precision medicine initiatives in diabetes aim to identify subcategories of T2D patients (including frail patients) using clustering techniques from data science applications, focusing on CV and poor treatment outcomes. Clear, unconditional recommendations for personalized T2D management may encourage drug prescription, especially for family physicians dealing with diverse patient contexts and clinical settings.

A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population

BACKGROUND

Cardiovascular disease (CVD) has evolved into a serious public health issue that demands the use of suitable methods to estimate the risk of the disease. As a result, in a sample of individuals who completed a 3-year low-sodium salt or conventional salt intervention in a hypertensive environment, we constructed a 13-year cardiovascular (CV) event risk prediction model with a 10-year follow-up.

METHODS

A Cox proportional hazards model was used to build a prediction model based on data from 306 participants who matched the inclusion criteria. Both the discriminating power and the calibration of the prediction models were assessed. The discriminative power of the prediction model was measured using the area under the curve (AUC). Brier scores and calibration plots were used to assess the prediction model's calibration. The model was internally validated using the tenfold cross-validation method. The nomogram served as a tool for visualising the model.

RESULTS

Among the 306 total individuals, there were 100 cases and 206 control. In the model, there were six predictors including age, smoking, LDL-C (low-density lipoprotein cholesterol), baseline SBP (systolic blood pressure), CVD (cardiovascular history), and CNV (genomic copy number variation) nsv483076. The fitted model has an AUC of 0.788, showing strong model discrimination, and a Brier score of 0.166, indicating that it was well-calibrated. According to the results of internal validation, the prediction model utilised in this study had a good level of repeatability. According to the model integrating the interaction of CNVs and baseline blood pressure, the effect of baseline SBP on CV events may be greater when nsv483076 was normal double copies than when nsv483076 was copy number variation.

CONCLUSIONS

The efficacy of risk prediction models for CV events that include environmental and genetic components is excellent, and they may be utilised as risk assessment tools for CV events in specific groups to offer a foundation for tailored intervention strategies.

Prediction of cardiovascular events from systolic or diastolic blood pressure

Over time, a focus on blood pressure has transferred from diastolic pressure to systolic pressure. Formal analyses of differences in predictive value are scarce. Our goal of the study was whether office SBP adds prognostic information to office DBP and whether both 24-h ambulatory SBP and 24-h ambulatory DBP is specifically important. The authors examined 2097 participants from a population cohort recruited in Copenhagen, Denmark. Cause-specific Cox regression was performed to predict 10-year person-specific absolute risks of fatal and non-fatal cardiovascular (CV) events. Also, the time-dependent area under the receiver operator curve (AUC) was utilized to evaluate discriminative ability. The calibration plots of the models (Hosmer-May test) were calculated as well as the Brier score which combines (discrimination and calibration). Adding both 24-h ambulatory SBP and 24-h ambulatory diastolic blood pressure did not significantly increase AUC for CV mortality and CV events. Moreover, adding both office SBP and office DBP did not significantly improve AUC for both CV mortality and CV events. The difference in AUC (95% confidence interval; p-value) was .26% (-.2% to .73%; .27) for 10-year CV mortality and .69% (-.09% to 1.46%; .082) for 10-year risk of CV events. The difference in AUC was .12% (-.2% to .44%; .46) for 10-year CV mortality and .04% (-.35 to .42%; .85) for 10-year risk of CV events. Moreover, for both CV mortality and CV events, office SBP did not improve prognostic information to office DBP. In addition, the Brier scores of office BP in both CV mortality and CV events were .078 and .077, respectively. Furthermore, the Brier scores were .077 and .078 in CV mortality and CV events of 24-h ambulatory. For the average population as those participating in a population survey, the 10-year discriminative ability for long-term predictions of CV death and CV events is not improved by adding systolic to diastolic blood pressure. This finding is found for ambulatory as well as office blood pressure.

External validation of the BEST-J score and a new risk prediction model for ESD delayed bleeding in patients with early gastric cancer

BACKGROUND

Delayed bleeding is an important adverse event after gastric endoscopic submucosal dissection (ESD). We aimed to externally validate the Bleeding after ESD Trend from Japan (BEST-J) score and subsequently develop a risk prediction model for bleeding in Chinese patients with early gastric cancer (EGC) after ESD.

METHODS

The clinical data of patients who underwent ESD for EGC in Beijing Friendship Hospital from June 2013 to December 2019 were collected retrospectively. The BEST-J score was evaluated according to the clinical data. Through univariate and multivariate logistic regression analyses of the clinical data, the factors affecting delayed bleeding were identified, and a new risk prediction model for bleeding was established. Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of the two prediction models.

RESULTS

A total of 444 patients with EGC undergoing ESD were included, of whom 27 patients had delayed bleeding (6.1%). Multivariate logistic regression analysis showed that a history of smoking (P = 0.029), tumor size > 20 mm (P = 0.022), intraoperative use of hemoclips (P = 0.025), resection of multiple tumors (P = 0.027), and prolongation of activated partial thromboplastin time (APTT) (P = 0.020) were independent influencing factors for delayed bleeding. ROC curve analysis showed that the areas under the curves (AUCs) were different between the BEST-J score and the newly built prediction model (0.624 vs. 0.749, P = 0.012).

CONCLUSIONS

The BEST-J score has moderately good discrimination for Chinese patients with EGC. However, for patients with EGC without severe comorbidities, the new risk prediction model may predict delayed bleeding better than the BEST-J score.

Association between brachial-ankle pulse wave velocity and cardiovascular and cerebrovascular disease in different age groups

BACKGROUND

To investigate the association between brachial-ankle pulse wave velocity (baPWV) and cardiovascular and cerebrovascular disease (CVD) in different age groups.

METHODS

A total of 39 417 people, receiving Kailuan physical examination, completing baPWV examination from 2010 to 2017, with no history of CVD and atrial fibrillation, were selected as the observation objects. The population was categorized into one age group per 10 years, namely the <50, 50-59, 60-69, 70-79, and ≥80-year-old groups, and the total population, and each group was further assigned into three classes according to the triple quartiles of baPWV. Kaplan-Meier method helped to calculate the cumulative incidence of CVD in different age groups. The effect of baPWV on CVD in different age groups was evaluated using the Cox proportional hazards regression model.

RESULTS

Kaplan-Meier survival curve indicated statistical significance (p < .05) in the cumulative incidence of CVD among the whole population, <50, 50-59, and 60-69-year-old groups, while the cumulative incidence of end-point events among the baPWV subgroups of 70-79 and ≥80-year-old groups exhibited no statistical significance (p > .05). Compared with baPWV in the Q1 group, hazard ratio value (95% confidence interval [CI]) of CVD in the Q3 group was 4.14 (95% CI: 2.98-5.75) in the total population, 2.98 (95% CI: 1.08-8.21) in <50-year-old population, 4.49 (95% CI: 2.89-7.00) in 50-59-year-old population, 2.78 (95% CI: 1.76-4.39) in 60-69-year-old population, 1.39 (95% CI: 0.86-2.24) in 70-79-year-old population, and 1.15 (95% CI: 0.55-2.41) in ≥80-year-old population.

CONCLUSION

CVD risk attributed to increased arterial stiffness reduces with age.

Improved Prediction of Older Adult Discharge After Trauma Using a Novel Machine Learning Paradigm

BACKGROUND

The ability to reliably predict outcomes after trauma in older adults (age ≥ 65 y) is critical for clinical decision making. Using novel machine-learning techniques, we sought to design a nonlinear, competing risks paradigm for prediction of older adult discharge disposition following injury.

MATERIALS AND METHODS

The National Trauma Databank (NTDB) was used to identify patients 65+ y between 2007 and 2014. Training was performed on an enriched cohort of diverse patients. Factors included age, comorbidities, length of stay, and physiologic parameters to predict in-hospital mortality and discharge disposition (home versus skilled nursing/long-term care facility). Length of stay and discharge status were analyzed via competing risks survival analysis with Bayesian additive regression trees and a multinomial mixed model.

RESULTS

The resulting sample size was 47,037 patients. Admission GCS and age were important in predicting mortality and discharge disposition. As GCS decreased, patients were more likely to die (risk ratio increased by average of 1.4 per 2-point drop in GCS, P < 0.001). As GCS decreased, patients were also more likely to be discharged to a skilled nursing or long-term care facility (risk ratio decreased by 0.08 per 2-point decrease in GCS, P< 0.001). The area under curve for prediction of discharge home was improved in the competing risks model 0.73 versus 0.43 in the traditional multinomial mixed model.

CONCLUSIONS

Predicting older adult discharge disposition after trauma is improved using machine learning over traditional regression analysis. We confirmed that a nonlinear, competing risks paradigm enhances prediction on any given hospital day post injury.

Analysis of Global Oxidative Status Using Multimarker Scores Reveals a Specific Association Between Renal Dysfunction and Diuretic Therapy in Older Adults

Aging and chronic kidney disease (CKD) are important interrelated cardiovascular risk (CVR) factors linked to oxidative stress, but this relationship has not been well studied in older adults. We assessed the global oxidative status in an older population with normal to severely impaired renal function. We determined the oxidative status of 93 older adults (mean age 85 years) using multimarker scores. OxyScore was computed as index of systemic oxidative damage by analyzing carbonyl groups, oxidized low-density lipoprotein, 8-hydroxy-2'-deoxyguanosine, and xanthine oxidase activity. AntioxyScore was computed as index of antioxidant defense by analyzing catalase and superoxide dismutase (SOD) activity and total antioxidant capacity. OxyScore and AntioxyScore were higher in subjects with estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 than in peers with eGFR >60 mL/min/1.73 m2, with protein carbonyls, catalase, and SOD activity as major drivers. Older adults with a recent cardiovascular event had similar OxyScore and AntioxyScore as peers with eGFR >60 mL/min/1.73 m2. Multivariate linear regression analysis revealed that both indices were associated with decreased eGFR independently of traditional CVR factors. Interestingly, AntioxyScore was also associated with diuretic treatment, and a more pronounced increase was seen in subjects receiving combination therapy. The associations of AntioxyScore with diuretic treatment and eGFR were mutually independent. In conclusion, eGFR is the major contributor to the imbalance in oxidative stress in this older population. Given the association between oxidative stress, CKD, and CVR, the inclusion of renal function parameters in CVR estimators for older populations, such as the SCORE-OP, might improve their modest performance.

Are competing-risk models superior to standard Cox models for predicting cardiovascular risk in older adults? Analysis of a whole-of-country primary prevention cohort aged ≥65 years

BACKGROUND

Both cardiovascular disease (CVD) risk and deaths from non-CVD causes, which may preclude a CVD event, increase with age. We evaluated whether accounting for the competing risk of non-CVD death improves the performance of CVD risk-prediction equations in older adults.

METHODS

All New Zealanders aged ≥65 years in 2012 without a prior CVD hospitalization were identified by anonymized linkage of eight routinely collected national health data sets. Sex-specific equations estimating the 5-year risk of a fatal or non-fatal CVD event were constructed using standard Cox and Fine-Gray (competing-risk) approaches. The pre-specified predictors were: age, ethnicity, deprivation level, diabetes, atrial fibrillation and baseline preventive pharmacotherapy. Model performance was evaluated by assessing calibration and discrimination in the overall cohort and in ethnic and age-specific subgroups.

RESULTS

Among 360 443 people aged ≥65 years with 1 615 412 person-years of follow-up, 14.6% of men and 12.1% of women had a first CVD event, whereas 8.5% of men and 7.6% of women died from a non-CVD cause. Standard Cox models overestimated the mean predicted the 5-year CVD risk by ∼1% overall and by 5-6% in the highest risk deciles. The mean predicted CVD risk from the Fine-Gray models approximated the observed risk overall, although slight underestimation occurred in some subgroups. Discrimination was similar for both models.

CONCLUSIONS

In a whole-of-country primary prevention cohort aged ≥65 years, standard Cox models overestimated the 5-year CVD risk whereas the Fine-Gray models were generally better calibrated. New CVD risk equations that take competing risks into account should be considered for older people.

Predictive value of traditional risk factors for cardiovascular disease in older people: A systematic review

With increasing age, associations between traditional risk factors (TRFs) and cardiovascular disease (CVD) shift. It is unknown which mid-life risk factors remain relevant predictors for CVD in older people. We systematically searched PubMed and EMBASE on August 16th 2019 for studies assessing predictive ability of >1 of fourteen TRFs for fatal and non-fatal CVD, in the general population aged 60+. We included 12 studies, comprising 11 unique cohorts. TRF were evaluated in 2 to 11 cohorts, and retained in 0-70% of the cohorts: age (70%), diabetes (64%), male sex (57%), systolic blood pressure (SBP) (50%), smoking (36%), high-density lipoprotein cholesterol (HDL) (33%), left ventricular hypertrophy (LVH) (33%), total cholesterol (22%), diastolic blood pressure (20%), antihypertensive medication use (AHM) (20%), body mass index (BMI) (0%), hypertension (0%), low-density lipoprotein cholesterol (0%). In studies with low to moderate risk of bias, systolic blood pressure (SBP) (80%), smoking (80%) and HDL cholesterol (60%) were more often retained. Model performance was moderate with C-statistics ranging from 0.61 to 0.77. Compared to middle-aged adults, in people aged 60+ different risk factors predict CVD and current prediction models perform only moderate at best. According to most studies, age, sex and diabetes seem valuable predictors of CVD in old-age. SBP, HDL cholesterol and smoking may also have predictive value. Other blood pressure and cholesterol related variables, BMI, and LVH seem of very limited or no additional value. Without competing risk analysis, predictors are overestimated.

Frailty Phenotype: Evidence of Both Physical and Mental Health Components in Community-Dwelling Early-Old Adults

Background

Demographic aging results in increased incidence of old-age disability. Frailty is a major factor contributing to old-age disability. The aim of this study was to investigate the prevalence of the frailty phenotype as defined by Fried et al and to estimate the need for associated preventative interventions in early-old community-dwelling inhabitants of the southern industrial region of Poland, as well as to investigate the defining components of the frailty phenotype.

Methods

The study group consisted of 160 individuals with an average age of 66.8 ± 4.2 years (\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$\bar x$$ \end{document} ± SD), 71 (44.4%) of study participants were women. The cohort was randomized out of over 843 thousand community-dwelling Upper Silesian inhabitants aged 60–74 years, who agreed to participate in this project. A comprehensive geriatric assessment (CGA), frailty phenotype test (as described by Fried et al) blood tests and bioimpedance body structure analysis was completed for study participants. Functional assessment included Barthel Index of Activities of Daily Living (Barthel Index), Instrumental Activities of Daily Living Scale (IADL), Mini-Mental State Examination (MMSE), the Timed Up and Go (TUG) test, Tinetti Performance-Oriented Mobility Assessment (POMA), and Geriatric Depression Scale – Short Form (GDS-SF).

Results

Prefrailty was diagnosed in 24.4% of the subjects (95% Confidence Interval (CI) = 17.7–31.0%; 31% in women and 19.1% in men, P=0.082) and frailty in 2.5% subjects (95% CI 0.1–4.9%; more frequently in women: 4.2% versus 1.1% in men, P=0.046). Having one or more positive frailty criteria was positively associated with depression (odds ratio (OR)=2.85, 95% CI=1.08–7.54, P=0.035) and negatively associated with MMSE score (OR=0.72, 95% CI=0.56–0.93, P=0.012) and fat-free mass (OR=0.96, 95% CI=0.92–0.99, P=0.016) in multivariate logistic regression analysis adjusted for age, sex, disease prevalence, number of medications, functional tests (Barthel Index, IADL, MMSE, GDS-SF), BMI, bioimpedance body composition score, and blood tests.

Conclusion

At least 25% of the early-old community-dwelling population would benefit from a frailty prevention program. The frailty phenotype reflects both physical and mental health in this population.

A cardiovascular risk prediction model for older people: Development and validation in a primary care population

Cardiovascular risk prediction is mainly based on traditional risk factors that have been validated in middle‐aged populations. However, associations between these risk factors and cardiovascular disease (CVD) attenuate with increasing age. Therefore, for older people the authors developed and internally validated risk prediction models for fatal and non‐fatal CVD, (re)evaluated the predictive value of traditional and new factors, and assessed the impact of competing risks of non‐cardiovascular death. Post hoc analyses of 1811 persons aged 70‐78 year and free from CVD at baseline from the preDIVA study (Prevention of Dementia by Intensive Vascular care, 2006‐2015), a primary care‐based trial that included persons free from dementia and conditions likely to hinder successful long‐term follow‐up, were performed. In 2017‐2018, Cox‐regression analyses were performed for a model including seven traditional risk factors only, and a model to assess incremental predictive ability of the traditional and eleven new factors. Analyses were repeated accounting for competing risk of death, using Fine‐Gray models. During an average of 6.2 years of follow‐up, 277 CVD events occurred. Age, sex, smoking, and type 2 diabetes mellitus were traditional predictors for CVD, whereas total cholesterol, HDL‐cholesterol, and systolic blood pressure (SBP) were not. Of the eleven new factors, polypharmacy and apathy symptoms were predictors. Discrimination was moderate (concordance statistic 0.65). Accounting for competing risks resulted in slightly smaller predicted absolute risks. In conclusion, we found, SBP, HDL, and total cholesterol no longer predict CVD in older adults, whereas polypharmacy and apathy symptoms are two new relevant predictors. Building on the selected risk factors in this study may improve CVD prediction in older adults and facilitate targeting preventive interventions to those at high risk.