Can information on functional and cognitive status improve short-term mortality risk prediction among community-dwelling older people? A cohort study using a UK primary care database

[BeoNet-Halle-development of a multifunctional database for the automated extraction of healthcare data from general practitioner and specialist practices]

The Beobachtungspraxennetzwerk Halle (BeoNet-Halle) is an innovative database of outpatient care that has been collecting patient data from participating primary care and specialty practices throughout Germany since 2020 and making it available for research and care. The database is set up and maintained by the Institute of Medical Epidemiology, Biometrics and Informatics and the Institute of General Practice and Family Medicine of the Martin Luther University Halle-Wittenberg. Furthermore, the Data Integration Center of the University Medical Center Halle is involved in the project. In principle, anonymized and pseudonymized patient data from all commercially available practice management systems should flow into the databases.In this article, we describe the structure and methods of the multi-purpose database BeoNet and quantify the current data stock. The workflow of collection, transfer, and storage of broad consents is described and advantages and limitations of the database are discussed.BeoNet-Halle currently contains anonymized data of approximately 73,043 patients from five physician practices. Furthermore, it includes data from more than 2,653,437 ICD-10 diagnoses, 1,403,726 prescriptions, and 1,894,074 laboratory results. Pseudonymized data were successfully exported from 481 patients.BeoNet-Halle enables an almost seamless representation of the care provided in the participating practices. In the future, the database will map patient treatment pathways across practices and provide high-quality care data to contribute to health policy decision-making and optimization of care processes.

Mortality Risk Models for Persons with Dementia: A Systematic Review

BACKGROUND

Persons with dementia have higher mortality than the general population. Objective, standardized predictions of mortality risk in persons with dementia could help with planning resources for care close to the end of life.

OBJECTIVE

To systematically review prediction models for risk of death in persons with dementia.

METHODS

The Medline and PsycInfo databases were searched on November 29, 2020, for prediction models estimating the risk of death in persons with dementia. Study quality was assessed using the Prediction model Risk Of Bias ASsessment Tool.

RESULTS

The literature search identified 2,828 studies, of which 18 were included. These studies described 16 different prediction models with c statistics mostly ranging from 0.67 to 0.79. Five models were externally validated, of which four were applicable. There were two models that were both applicable and had reasonably low risk of bias. One model predicted risk of death at six months in persons with advanced dementia residing in a nursing home. The other predicted risk of death at three years in persons seen in primary care practice or a dementia specialty clinic, derived from a nationwide registry in Sweden but not externally validated.

CONCLUSION

Valid, applicable models with low risk of bias were found in two settings: advanced dementia in a nursing home and outpatient practices. The outpatient model requires external validation. Better models are needed for persons with mild to moderate dementia in nursing homes, a common demographic. These models may be useful for educating persons living with dementia and care partners and directing resources for end of life care.Registration:The study protocol is registered on PROSPERO as RD4202018076.

Cause-specific mortality prediction in older residents of São Paulo, Brazil: a machine learning approach

BACKGROUND

Populational ageing has been increasing in a remarkable rate in developing countries. In this scenario, preventive strategies could help to decrease the burden of higher demands for healthcare services. Machine learning algorithms have been increasingly applied for identifying priority candidates for preventive actions, presenting a better predictive performance than traditional parsimonious models.

METHODS

Data were collected from the Health, Well Being and Aging (SABE) Study, a representative sample of older residents of São Paulo, Brazil. Machine learning algorithms were applied to predict death by diseases of respiratory system (DRS), diseases of circulatory system (DCS), neoplasms and other specific causes within 5 years, using socioeconomic, demographic and health features. The algorithms were trained in a random sample of 70% of subjects, and then tested in the other 30% unseen data.

RESULTS

The outcome with highest predictive performance was death by DRS (AUC-ROC = 0.89), followed by the other specific causes (AUC-ROC = 0.87), DCS (AUC-ROC = 0.67) and neoplasms (AUC-ROC = 0.52). Among only the 25% of individuals with the highest predicted risk of mortality from DRS were included 100% of the actual cases. The machine learning algorithms with the highest predictive performance were light gradient boosted machine and extreme gradient boosting.

CONCLUSION

The algorithms had a high predictive performance for DRS, but lower for DCS and neoplasms. Mortality prediction with machine learning can improve clinical decisions especially regarding targeted preventive measures for older individuals.

Primary Care Prognostic (PCP) Index of 11-Year Mortality Risk: Development and Validation of a Brief Prognostic Tool

BACKGROUND

Healthcare providers use a life expectancy of at least 5 to 10 years in shared clinical decision-making with older adults about cancer screening, major surgeries, and disease prevention interventions. At present, few prognostic indexes predict long-term mortality beyond 10 years or are suited for use in primary care settings.

OBJECTIVE

We developed and validated an 8-item multidimensional index predicting 11-year mortality for use in primary care.

DESIGN, SETTING, AND PARTICIPANTS

Using data from the Singapore Longitudinal Ageing Studies (SLAS), we developed a Primary Care Prognostic (PCP) Index for predicting 11-year mortality risk in a development cohort (n = 1550) and validated it in a geographically different cohort (n = 928).

MAIN MEASURES

The PCP Index was derived from eight indicators (body mass loss, weakness, slow gait, comorbidity, polypharmacy, IADL/BADL dependency, low albumin, low total cholesterol, out of 25 candidate indicators) using stepwise Cox proportional hazard models.

KEY RESULTS

In the developmental cohort, the mortality hazard ratio increased by 53% per PCP point score increase, independent of age and sex. Across risk categories, absolute risks of mortality increased from 5% (score 0) to 67.9% (scores 7-9), with area under curve (AUC = 0.77 (95% CI 0.73-0.80)). The PCP Index also predicted mortality in the validation cohort, with AUC = 0.70 (95% CI 0.64-0.75).

CONCLUSIONS

The PCP Index using simple clinical assessments and point scoring is a potentially useful prognostic tool for predicting long-term mortality and is well suited for risk stratification and shared clinical decision-making with older adults in primary care.

Using the Multidimensional Prognostic Index (MPI) to improve cost-effectiveness of interventions in multimorbid frail older persons: results and final recommendations from the MPI_AGE European Project

MPI_AGE is a European Union co-funded research project aimed to use the Multidimensional Prognostic Index (MPI), a validated Comprehensive Geriatric Assessment (CGA)-based prognostic tool, to develop predictive rules that guide clinical and management decisions in older people in different European countries. A series of international studies performed in different settings have shown that the MPI is useful to predict mortality and risk of hospitalization in community-dwelling older subjects at population level. Furthermore, studies performed in older people who underwent a CGA before admission to a nursing home or receiving homecare services showed that the MPI successfully identified groups of persons who could benefit, in terms of reduced mortality, of specific therapies such as statins in diabetes mellitus and coronary artery disease, anticoagulants in atrial fibrillation and antidementia drugs in cognitive decline. A prospective trial carried out in nine hospitals in Europe and Australia demonstrated that the MPI was able to predict not only in-hospital and long-term mortality, but also institutionalization, re-hospitalization and receiving homecare services during the one-year follow-up after hospital discharge. The project also explored the association between MPI and mortality in hospitalized older patients in need of complex procedures such as transcatheter aortic valve implantation or enteral tube feeding. Evidence from these studies has prompted the MPI_AGE Investigators to formulate recommendations for healthcare providers, policy makers and the general population which may help to improve the cost-effectiveness of appropriate health care interventions for older patients.

Analgesic drug use in elderly persons: A population-based study in Southern Italy

Introduction

Analgesics such as non-steroidal anti-inflammatory drugs (NSAIDs), weak and strong opioids are commonly used among elderly persons. The aim of this study was to describe the demographic and clinical characteristics of elderly analgesic users and to measure the frequency of analgesic use, including the frequency of potentially inappropriate analgesic use.

Methods

The Arianna database was used to carry out this study. This database contains prescription data with associated indication of use for 1,076,486 inhabitants registered with their general practitioners (GPs) in the Caserta Local Health Unit (Caserta district, Campania region in Italy). A cohort of persons aged ≥65 years old with >1 year of database history having at least one analgesic drug (NSAIDs, strong or weak opioids) between 2010 and 2014 were identified. The date of the first analgesic prescription in the study period was considered the index date (ID).

Results

From a source population of 1,076,486 persons, 116,486 elderly persons were identified. Of these, 94,820 elderly persons received at least one analgesic drug: 36.6% were incident NSAID users (N = 36,629), while 13.2% were incident weak opioid users (N = 12,485) and 8.1% were incident strong opioid users (N = 7,658). In terms of inappropriate analgesic use, 9.2% (N = 10,763) of all elderly users were prescribed ketorolac/indomethacin inappropriately, since these drugs should not be prescribed to elderly persons. Furthermore, at least half all elderly persons with chronic kidney disease or congestive heart failure were prescribed NSAIDs, while these drugs should be avoided.

Conclusion

Analgesics are commonly used inappropriately among elderly persons, suggesting that prescribing practice in the catchment area may yet be improved.

Functional Status of Older Adults with Dementia at the End of Life: Is there Still Anything to do?

Aims:

There is a lack of data on physical functional status near death of patients with different types of dementia that can contribute to decisions about what kind of care is needed. The aim of this study was to investigate the course of functional status along with the documented reasons for death in participants with dementia who had regularly been followed at a geriatric outpatient unit.

Setting and Design:

A retrospective observational cohort study was done using the database of a geriatric outpatient clinic.

Subjects and Methods:

Sociodemographic and medical records of patients with Alzheimer's disease (AD), vascular dementia, mixed dementia, and dementia with Lewy bodies (DLB)/Parkinson's disease dementia (PDD) who had received routine care in a geriatrics outpatient setting for a minimum of 12 months before death were analyzed. Scores for activities of daily living and documented probable causes of death were recorded.

Results:

Of the 258 participants, 111 (42 female and 69 male) were included in this study. AD was the leading cause of dementia (51.8%). The median duration of survival with dementia was 4 years. The leading causes of death were cardiovascular disease (CVD) (27.0%) and dementia (27.0%) followed by infections (21.6%) and stroke (10.8%). Disability was the highest in patients with DLB/PDD.

Conclusions:

This study found relatively shorter survival after the diagnosis of dementia when compared to other populations. CVD still appeared as a major cause of that in this particular disease. Most debilitating type of dementia was DLB/PDD.

All-cause mortality and antipsychotic use among elderly persons with high baseline cardiovascular and cerebrovascular risk: a multi-center retrospective cohort study in Italy

BACKGROUND

Little is known about the comparative risk of death with atypical or conventional antipsychotics (APs) among persons with cardiovascular or cerebrovascular disease (CCD).

RESEARCH DESIGN AND METHODS

A cohort study was conducted using five Italian claims databases. New atypical AP users with CCD aged ≥65 (reference) were matched to new conventional AP users. Mortality per 100 person-years (PYs) and hazard ratios (HR), estimated using Cox models, were reported. Incidence and risk of death were estimated for persons having drug-drug interactions. Outcome occurrence was evaluated 180 days after AP initiation.

RESULTS

Overall 24,711 and 27,051 elderly new conventional and atypical AP users were identified. The mortality rate was 51.3 and 38.5 deaths per 100 PYs for conventional and atypical AP users. Mortality risk was 1.33 (95%CI: 1.27-1.39) for conventional APs. There was no increased mortality risk with single drug-drug interactions (DDIs) vs. no DDI. AP users with ≥1 DDI had a 29% higher mortality risk compared to no DDI in the first 90 days of treatment (HR: 1.29 (95% CI: 1.00-1.67)).

CONCLUSIONS

Conventional APs had a higher risk of death than atypical APs among elderly persons with CCD. Having ≥1 DDI was associated with an increased risk of death.

Drugs that increase the risk of community-acquired pneumonia: a narrative review

INTRODUCTION

Community-acquired pneumonia (CAP), a major cause of morbidity and mortality, is the leading infectious cause of death in the developed world. Population-based studies and systematic reviews have identified a large number of risk factors for the development of pneumonia in adults. In addition to age, lifestyle habits, and comorbidities, some forms of pharmacotherapy may also increase the risk for CAP.

AREAS COVERED

MEDLINE, CENTRAL, and Web of Science were used in 2017 to search for case-control, cohort studies, as well as randomized controlled trials and meta-analysis that involved outpatient proton pump inhibitors (PPIs), inhaled corticosteroids (ICSs), antipsychotics, oral antidiabetics, and CAP diagnosis in patients aged >18 years.

EXPERT OPINION

Our review confirmed that the use of ICSs, PPIs or antipsychotic drugs was independently associated with an increased risk for CAP. We also identified a positive association between specific oral antidiabetics and the development of pneumonia.