Evaluation of a patient-centered integrated care program for individuals with frequent hospital readmissions and multimorbidity

Age-friendly healthcare: integrating the 4Ms to enable age-friendly cancer care

PURPOSE OF REVIEW

There is a growing movement towards person-centred, age-friendly healthcare in the care of older adults, including those with cancer. The Age-Friendly Health Systems (AFHS) initiative uses the 4Ms framework to enable this change. This review documents the utility and implications of 4Ms implementation across different settings, with a particular focus on cancer care.

RECENT FINDINGS

The AFHS initiative 4Ms framework uses a set of core, evidence-based guidelines (focussing on What Matters, Medication, Mentation and Mobility) to improve person-centred care. The successful implementation of the 4Ms has been documented in many different healthcare settings including orthopaedics primary care, and cancer care. Implementation of the 4Ms framework into existing workflows complements the use of geriatric assessment to improve care of older adults with cancer. Models for implementation of the 4Ms within a cancer centre are described. Active engagement and education of healthcare providers is integral to success. Solutions to implementing the What Matters component are addressed.

SUMMARY

Cancer centres can successfully implement the 4Ms framework into existing workflows through a complex change management process and development of infrastructure that engages healthcare providers, facilitating cultural change whilst employing quality improvement methodology to gradually adapt the status quo to age-friendly processes.

Demographic transition and hospital admissions in Spanish centenarians, 2004-2020: Geographical variations and sex-related differences

BACKGROUND

This study aims to describe the distribution and temporal trends of the centenarian population and their hospital admissions in Spain over the past two decades, focusing on regional and sex-based differences.

METHODS

A retrospective study was conducted using data from the Spanish National Health System's Hospital Discharge Records-Minimum Basic Data Set. The analysis included all hospitalized patients ≥100 years between January 2004 and December 2020. The crude annual centenarian population and admission rates were calculated. Joinpoint regression analysis and cross-correlation analysis were used to identify trends and associations.

RESULTS

From 2004 to 2020, the centenarian population in Spain increased by 89.0 %, with a larger increase observed in women (86.6 %) than men (32.9 %). Significant geographic variability was found, with rates from 1.1 to 5.2 × 10,000 inhabitants per year across different regions. Joinpoint analysis identified three trends: a decline from 2004 to 2008, an increase from 2008 to 2015, and a slower increase from 2015 to 2020. Hospital admissions of centenarians increased by 121.5 %, with a larger increase in women than men (212.1% vs 90.7 %); women represented 75.4 % of admissions. The proportion of centenarian admissions to total hospitalizations showed an upward trend until 2015 and then stabilized; it also varied among regions.

CONCLUSION

There was a significant increase in the centenarian population and hospital admissions of centenarians in Spain. There are regional disparities in their distribution, with women representing a larger proportion of centenarians and hospital admissions. Understanding these trends and differences is crucial for implementing interventions that ensure adequate healthcare for centenarians.

Competences of internal medicine specialists for the management of patients with multimorbidity. EFIM multimorbidity working group position paper

Patients with multimorbidity increasingly impact healthcare systems, both in primary care and in hospitals. This is particularly true in Internal Medicine. This population associates with higher mortality rates, polypharmacy, hospital readmissions, post-discharge syndrome, anxiety, depression, accelerated age-related functional decline, and development of geriatric syndromes, amongst others. Internists and Hospitalists, in one of their roles as Generalists, are increasingly asked to attend to these patients, both in their own Departments as well as in surgical areas. The management of polypathology and multimorbidity, however, is often complex, and requires specific clinical skills and corresponding experience. In addition, patients' needs, health-care environment, and routines have changed, so emerging and re-emerging specific competences and approaches are required to offer the best coordinated, continuous, and comprehensive integrated care to these populations, to achieve optimal health outcomes and satisfaction of patients, their relatives, and staff. This position paper proposes a set of emerging and re-emerging competences for internal medicine specialists, which are needed to optimally address multimorbidity now and in the future.

Machine learning and LACE index for predicting 30-day readmissions after heart failure hospitalization in elderly patients

Machine learning (ML) techniques may improve readmission prediction performance in heart failure (HF) patients. This study aimed to assess the ability of ML algorithms to predict unplanned all-cause 30-day readmissions in HF elderly patients, and to compare them with conventional LACE (Length of hospitalization, Acuity, Comorbidities, Emergency department visits) index. All patients aged ≥ 65 years discharged alive between 2010 and 2019 after a hospitalization for acute HF were included in this retrospective cohort study. We applied MICE (Multivariate Imputation via Chained Equations) method to obtain a balanced, fully valued dataset and LASSO (Least Absolute Shrinkage and Selection Operator) algorithm to get the most significant features. Training (80% of records) and test (20%) cohorts were randomly selected. Study population: 3079 patients, 394 (12.8%) presented at least one readmission within 30 days, and 2685 (87.2%) did not. In the test cohort AUCs (IC95%) of XGBoost, Ada Boost Classifier, Random forest, and Gradient Boosting, and LACE Index were: 0.803 (0.734-0.872), 0.782 (0.711-0.854), 0.776 (0.703-0.848), 0.786 (0.715-0.857), and 0.504 (0.414-0.594), respectively, for predicting readmissions. A SHAP analysis was performed to offer a breakdown of the ML variables associated with readmission. Positive and negative predicting values estimates of the different ML models and LACE index were also provided, for several values of readmission rate prevalence. Among elderly patients, the rate of all-cause unplanned 30-day readmissions after hospitalization due to an acute HF was high. ML models performed better than the conventional LACE index for predicting readmissions. ML models can be proposed as promising tools for the identification of subjects at high risk of hospitalization in this clinical setting, enabling care teams to target interventions for improving overall clinical outcomes.