Impact of stratification on the effectiveness of a comprehensive patient-centered strategy for multimorbid patients

Chronic disease incidence explained by stepwise models and co-occurrence among them

Multimorbidity (MM) is the co-occurrence of two or more chronic diseases. We provided a dynamic approach revealing the MM complexity constructing a multistep incidence-age model for all patients with MM between 2014 and 2021 in the Basque Health System, Spain. The multistep model, with eight steps for males and nine for females, is a very well-fitting representation of MM. To gain insight into the MM components, we modeled the 19 diseases used to calculate the Charlson Comorbidity Index (CCI). We observed that the CCI diseases formed a complex interaction network. Hierarchical clustering of the incidence-age profiles clustered the CCI diseases into low- and high-risk of dying pathologies. Diseases with a higher number of steps are better represented by a multistep model. Anatomically, diseases associated with the central nervous system have the highest number of steps, followed by those associated with the kidney, heart, peripheral vasulature, pancreas, joints, cerebral vasculature, lung, stomach, and liver.

Promising algorithms to perilous applications: a systematic review of risk stratification tools for predicting healthcare utilisation

OBJECTIVES

Risk stratification tools that predict healthcare utilisation are extensively integrated into primary care systems worldwide, forming a key component of anticipatory care pathways, where high-risk individuals are targeted by preventative interventions. Existing work broadly focuses on comparing model performance in retrospective cohorts with little attention paid to efficacy in reducing morbidity when deployed in different global contexts. We review the evidence supporting the use of such tools in real-world settings, from retrospective dataset performance to pathway evaluation.

METHODS

A systematic search was undertaken to identify studies reporting the development, validation and deployment of models that predict healthcare utilisation in unselected primary care cohorts, comparable to their current real-world application.

RESULTS

Among 3897 articles screened, 51 studies were identified evaluating 28 risk prediction models. Half underwent external validation yet only two were validated internationally. No association between validation context and model discrimination was observed. The majority of real-world evaluation studies reported no change, or indeed significant increases, in healthcare utilisation within targeted groups, with only one-third of reports demonstrating some benefit.

DISCUSSION

While model discrimination appears satisfactorily robust to application context there is little evidence to suggest that accurate identification of high-risk individuals can be reliably translated to improvements in service delivery or morbidity.

CONCLUSIONS

The evidence does not support further integration of care pathways with costly population-level interventions based on risk prediction in unselected primary care cohorts. There is an urgent need to independently appraise the safety, efficacy and cost-effectiveness of risk prediction systems that are already widely deployed within primary care.

Controversies regarding transplantation of mesenchymal stem cells

Mesenchymal stem cells (MSCs) have tantalized regenerative medicine with their therapeutic potential, yet a cloud of controversies looms over their clinical transplantation. This comprehensive review navigates the intricate landscape of MSC controversies, drawing upon 15 years of clinical experience and research. We delve into the fundamental properties of MSCs, exploring their unique immunomodulatory capabilities and surface markers. The heart of our inquiry lies in the controversial applications of MSC transplantation, including the perennial debate between autologous and allogeneic sources, concerns about efficacy, and lingering safety apprehensions. Moreover, we unravel the enigmatic mechanisms surrounding MSC transplantation, such as homing, integration, and the delicate balance between differentiation and paracrine effects. We also assess the current status of clinical trials and the ever-evolving regulatory landscape. As we peer into the future, we examine emerging trends, envisioning personalized medicine and innovative delivery methods. Our review provides a balanced and informed perspective on the controversies, offering readers a clear understanding of the complexities, challenges, and potential solutions in MSC transplantation.

Validating and Improving Adjusted Clinical Group's Future Hospitalization and High-Cost Prediction Models for Dutch Primary Care

The rise in health care costs, caused by older and more complex patient populations, requires Population Health Management approaches including risk stratification. With risk stratification, patients are assigned individual risk scores based on medical records. These patient stratifications focus on future high costs and expensive care utilization such as hospitalization, for which different models exist. With this study, the research team validated the accuracy of risk prediction scores for future hospitalization and high health care costs, calculated by the Adjusted Clinical Group (ACG)'s risk stratification models, using Dutch primary health care data registries. In addition, they aimed to adjust the US-based predictive models for Dutch primary care. The statistical validity of the existing models was assessed. In addition, the underlying prediction models were trained on 95,262 patients' data from de Zoetermeer region and externally validated on data of 48,780 patients from Zeist, Nijkerk, and Urk. Information on age, sex, number of general practitioner visits, International Classification of Primary Care coded information on the diagnosis and Anatomical Therapeutic Chemical Classification coded information on the prescribed medications, were incorporated in the model. C-statistics were used to validate the discriminatory ability of the models. Calibrating ability was assessed by visual inspection of calibration plots. Adjustment of the hospitalization model based on Dutch data improved C-statistics from 0.69 to 0.75, whereas adjustment of the high-cost model improved C-statistics from 0.78 to 0.85, indicating good discrimination of the models. The models also showed good calibration. In conclusion, the local adjustments of the ACG prediction models show great potential for use in Dutch primary care.

Development and validation of predictive models for unplanned hospitalization in the Basque Country: analyzing the variability of non-deterministic algorithms

BACKGROUND

The progressive ageing in developed countries entails an increase in multimorbidity. Population-wide predictive models for adverse health outcomes are crucial to address these growing healthcare needs. The main objective of this study is to develop and validate a population-based prognostic model to predict the probability of unplanned hospitalization in the Basque Country, through comparing the performance of a logistic regression model and three families of machine learning models.

METHODS

Using age, sex, diagnoses and drug prescriptions previously transformed by the Johns Hopkins Adjusted Clinical Groups (ACG) System, we predict the probability of unplanned hospitalization in the Basque Country (2.2 million inhabitants) using several techniques. When dealing with non-deterministic algorithms, comparing a single model per technique is not enough to choose the best approach. Thus, we conduct 40 experiments per family of models - Random Forest, Gradient Boosting Decision Trees and Multilayer Perceptrons - and compare them to Logistic Regression. Models' performance are compared both population-wide and for the 20,000 patients with the highest predicted probabilities, as a hypothetical high-risk group to intervene on.

RESULTS

The best-performing technique is Multilayer Perceptron, followed by Gradient Boosting Decision Trees, Logistic Regression and Random Forest. Multilayer Perceptrons also have the lowest variability, around an order of magnitude less than Random Forests. Median area under the ROC curve, average precision and positive predictive value range from 0.789 to 0.802, 0.237 to 0.257 and 0.485 to 0.511, respectively. For Brier Score the median values are 0.048 for all techniques. There is some overlap between the algorithms. For instance, Gradient Boosting Decision Trees perform better than Logistic Regression more than 75% of the time, but not always.

CONCLUSIONS

All models have good global performance. The only family that is consistently superior to Logistic Regression is Multilayer Perceptron, showing a very reliable performance with the lowest variability.

Information and Communication Technology Based Integrated Care for Older Adults: A Scoping Review

Background

Integrated care is an important initiative to respond positively to the ageing of society and information and communication technology(ICT) plays an important role in facilitating the integration of functional and normative health and social care. The scoping review aims to synthesize evidence on the experience and practice of ICT-based implementation of integrated care for older adults.

Methods

This study followed the research framework developed by Arksey and O'malley for the scoping review and systematically searched for relevant studies published between 1 January 2000 and 30 March 2022 from nine electronic databases, three specialist journals, three key institutional websites, 11 integrated care project websites, google scholar and references of the studies to be included. Two reviewers independently screened and extracted data and used thematic analysis to sort out and summarize the core elements, hindrances and facilitators of ICT-based integrated care.

Results

A total of 77 studies were included in this study, including 36 ICT-based practice models of integrated care with seven core elements of implementation including single entry point, comprehensive geriatric assessment, personalized care planning, multidisciplinary case conferences, coordinated care, case management and patient empowerment, which generally had a positive effect on improving quality of life, caregiver burden and primary care resource utilization for older adults, but effectiveness evaluations remained Heterogeneity exists. The barriers and facilitators to ICT-based implementation of integrated care were grouped into four themes: demand-side factors, provider factors, technology factors and system factors.

Conclusion

The implementation of ICT-based integrated care for the elderly is expected to improve the health status of both the supply and demand of services, but there is still a need to strengthen the supply of human resources, team training and collaboration, ICT systems and financial support in order to promote the wider use of ICT in integrated care.

The Impact of Wearable Devices on Physical Activity for Chronic Disease Patients: Findings from the 2019 Health Information National Trends Survey

BACKGROUND

Wearable devices are shown to be an advanced tool for chronic disease management, but their impacts on physical activity remain uninvestigated. This study aims to examine the effect of wearable devices on physical activity in general people and chronic patients.

METHODS

Our sample was from the third cycle of the fifth iteration of the Health Information National Trends Survey (HINTS), which includes a total of 5438 residents. Genetic matching was used to evaluate the effect of wearable devices on physical activity in different populations.

RESULTS

(1) Both using wearable devices and using them with high frequency will improve physical activity for the whole population. (2) Wearable devices may have greater positive effects on physical activity for chronic patients. (3) Especially in patients with hypertension, high-frequency use of wearable devices can significantly improve the duration and frequency of physical activity.

CONCLUSIONS

Wearable devices lead to more physical activity, and the benefit is more noticeable for chronic patients, particularly those with hypertension.

Supply-side solutions targeting demand-side characteristics: causal effects of a chronic disease management program on adherence and health outcomes

We estimate the effects of a chronic disease management program (CDMP) which adapts various supply-side interventions to specific demand-side conditions (disease-staging) for patients with chronic kidney disease (CKD). Using a unique dataset on the entire population of the Emilia-Romagna region of Italy with hospital-diagnosed CKD, we estimate the causal effects of the CDMP on adherence indicators and health outcomes. As CKD is a progressive disease with clearly-defined disease stages and a treatment regimen that can be titrated by disease severity, we calculate dynamic, severity-specific, indicators of adherence as well as several long-term health outcomes. Our empirical work produces statistically significant and sizeable causal effects on many adherence and health outcome indicators across all CKD patients. More interestingly, we show that the CDMP produces larger effects on patients with early-stage CKD, which is at odds with some of the literature on CDMP that advocates intensifying interventions for high-cost (or late-stage) patients. Our results suggest that it may be more efficient to target early-stage patients to slow the deterioration of their health capital. The results contribute to a small, recent literature in health economics that focuses on the marginal effectiveness of CDMPs after controlling either for supply- or demand-side sources of heterogeneity.

An Integrated Care Platform System (C3-Cloud) for Care Planning, Decision Support, and Empowerment of Patients With Multimorbidity: Protocol for a Technology Trial

Background

There is an increasing need to organize the care around the patient and not the disease, while considering the complex realities of multiple physical and psychosocial conditions, and polypharmacy. Integrated patient-centered care delivery platforms have been developed for both patients and clinicians. These platforms could provide a promising way to achieve a collaborative environment that improves the provision of integrated care for patients via enhanced information and communication technology solutions for semiautomated clinical decision support.

Objective

The Collaborative Care and Cure Cloud project (C3-Cloud) has developed 2 collaborative computer platforms for patients and members of the multidisciplinary team (MDT) and deployed these in 3 different European settings. The objective of this study is to pilot test the platforms and evaluate their impact on patients with 2 or more chronic conditions (diabetes mellitus type 2, heart failure, kidney failure, depression), their informal caregivers, health care professionals, and, to some extent, health care systems.

Methods

This paper describes the protocol for conducting an evaluation of user experience, acceptability, and usefulness of the platforms. For this, 2 “testing and evaluation” phases have been defined, involving multiple qualitative methods (focus groups and surveys) and advanced impact modeling (predictive modeling and cost-benefit analysis). Patients and health care professionals were identified and recruited from 3 partnering regions in Spain, Sweden, and the United Kingdom via electronic health record screening.

Results

The technology trial in this 4-year funded project (2016-2020) concluded in April 2020. The pilot technology trial for evaluation phases 3 and 4 was launched in November 2019 and carried out until April 2020. Data collection for these phases is completed with promising results on platform acceptance and socioeconomic impact. We believe that the phased, iterative approach taken is useful as it involves relevant stakeholders at crucial stages in the platform development and allows for a sound user acceptance assessment of the final product.

Conclusions

Patients with multiple chronic conditions often experience shortcomings in the care they receive. It is hoped that personalized care plan platforms for patients and collaboration platforms for members of MDTs can help tackle the specific challenges of clinical guideline reconciliation for patients with multimorbidity and improve the management of polypharmacy. The initial evaluative phases have indicated promising results of platform usability. Results of phases 3 and 4 were methodologically useful, yet limited due to the COVID-19 pandemic.

Trial Registration

ClinicalTrials.gov NCT03834207; https://clinicaltrials.gov/ct2/show/NCT03834207

International Registered Report Identifier (IRRID)

RR1-10.2196/21994

Organisational models in primary health care to manage chronic conditions: A scoping review

Chronic diseases are increasing incessantly, and more efforts are needed in order to develop effective organisational models in primary health care, which may address the challenges posed by the consequent multimorbidity. The aim of this study was to assess and map methods, interventions and outcomes investigated over the last decade regarding the effectiveness of chronic care organisational models in primary care settings. We conducted a scoping review including systematic reviews, clinical trials, and observational studies, published from 2010 to 2020, that evaluated the effectiveness of organisational models for chronic conditions in primary care settings, including home care, community, and general practice. We included 67 international studies out of the 6,540 retrieved studies. The prevalent study design was the observational design (25 studies, 37.3%), and 62 studies (92.5%) were conducted on the adult population. Four main models emerged, called complex integrated care models. These included models grounded on the Chronic Care Model framework and similar, case or care management, and models centred on involvement of pharmacists or community health workers. Across the organisational models, self-management support and multidisciplinary teams were the most common components. Clinical outcomes have been investigated the most, while caregiver outcomes have been detected in the minority of cases. Almost one-third of the included studies reported only significant effects in the outcomes. No sufficient data were available to determine the most effective models of care. However, more complex models seem to lead to better outcomes. In conclusion, in the development of more comprehensive organisational models to manage chronic conditions in primary health care, more efforts are needed on the paediatric population, on the inclusion of caregiver outcomes in the effectiveness evaluation of organisational models and on the involvement of social community resources. As regarding the studies investigating organisational models, more detailed descriptions should be provided with regard to interventions, and the training, roles and responsibilities of health and lay figures in delivering care.

A systematic review of risk stratification tools internationally used in primary care settings

BACKGROUND AND AIMS

In our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub-populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance.

METHODS

We performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co-operation and Development countries published in English-language journals were included. Search engines were utilized with keywords, for example, "primary care," "risk stratification," and "model." Risk stratification tools were compared based on different measures: area under the curve (AUC) and C-statistics for dichotomous outcomes and R 2 for continuous outcomes.

RESULTS

The search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C-statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7).

CONCLUSION

Based on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes.

Assessment of the Adjusted Clinical Groups system in Dutch primary care using electronic health records: a retrospective cross-sectional study

BACKGROUND

Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners' (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP healthcare utilization.

METHODS

To reach our aim, two variables of the ACG System, the Aggregated Diagnosis Groups (ADG) and the mutually exclusive ACG categories were explored. The population for this pilot analysis consisted of 23,618 persons listed with five participating general practices within one region in the Netherlands. ACG analyses were performed based on historical Electronic Health Records data from 2014 consisting of primary care diagnoses and pharmaceutical data. Logistic regression models were estimated and AUC's were calculated to explore the diagnostic value of the models including ACGs and ADGs separately with GP healthcare utilization as the dependent variable. The dependent variable was categorized using four different cut-off points: zero, one, two and three visits per year.

RESULTS

The ACG and ADG models performed as well as models using International Classification of Primary Care chapters, regarding the association with GP utilization. AUC values were between 0.79 and 0.85. These models performed better than the base model (age and gender only) which showed AUC values between 0.64 and 0.71.

CONCLUSION

The results of this study show that the ACG system is a useful tool to stratify Dutch primary care populations with GP healthcare utilization as the outcome variable.

Dynamic evaluation of the comparative effectiveness of an integrated program for heart failure care

RATIONALE, AIMS AND OBJECTIVES

An integrated care program for heart failure (HF) was developed in the Basque Country in 2013. The objective of this research was to evaluate its effectiveness through the number of hospital admissions in three integrated healthcare organizations (IHOs), taking into account the longitudinal nature of the disease and the intensity of the implementation.

METHODS

A retrospective observational study was carried out, based on data entered in administrative and clinical databases between 2014 and 2018 for a total population of 230 000. In addition to conventional statistical analyses, Andersen-Gill models for recurrent events were used, incorporating dynamic variables that allowed assessment of the intervention's intensity before each hospitalization.

RESULTS

A total of 6768 patients were analysed. Age (hazard ratio [HR] = 1.016; 95% confidence interval [CI] 1.011-1.022), the Charlson index (HR = 1.067, 95% CI 1.047-1.087), and the number of previous hospitalizations (HR = 1.632, 95% CI 1.557-1.712) were risk factors for readmission. Differences between IHOs were also statistically significant. Greater intervention intensity was associated with a lower hospitalization rate (HR = 0.995, 95% CI 0.990-1.000). As indicated by the interaction between intervention intensity and IHO, differences between IHOs disappeared when intensity rose. No inequities in hospitalization were found as a function of deprivation index or sex. Nonetheless, inequity in the implementation of the program by sex was clear, women with HF receiving less intense intervention than men with the same level of comorbidity and age.

CONCLUSIONS

The extent of program implementation measured by intervention intensity is a main driver of the effectiveness of an educational and monitoring program for HF. The evaluation of HF program effectiveness on readmissions must take into account the entire natural history of the disease. Implementation intensity explains differences between IHOs.

Identifying subgroups of high-need, high-cost, chronically ill patients in primary care: A latent class analysis

Introduction

Segmentation of the high-need, high-cost (HNHC) population is required for reorganizing care to accommodate person-centered, integrated care delivery. Therefore, we aimed to identify and characterize relevant subgroups of the HNHC population in primary care by using demographic, biomedical, and socioeconomic patient characteristics.

Methods

This was a retrospective cohort study within a Dutch primary care group, with a follow-up period from September 1, 2014 to August 31, 2017. Chronically ill patients were included in the HNHC population if they belonged to the top 10% of care utilizers and/or suffered from multimorbidity and had an above-average care utilization. In a latent class analysis, forty-one patient characteristics were initially used as potential indicators of heterogeneity in HNHC patients’ needs.

Results

Patient data from 12 602 HNHC patients was used. A 4-class model was considered statistically and clinically superior. The classes were named according to the characteristics that were most dominantly present and distinctive between the classes (i.e. mainly age, household position, and source of income). Class 1 (‘older adults living with partner’) included 39.3% of patients, class 2 (‘older adults living alone’) included 25.5% of patients, class 3 (‘middle-aged, employed adults with family’) included 23.3% of patients, and class 4 (‘middle-aged adults with social welfare dependency’) included 11.9% of patients. Diabetes was the most common condition in all classes; the second most prevalent condition differed between osteoarthritis in class 1 (21.7%) and 2 (23.8%), asthma in class 3 (25.3%), and mood disorders in class 4 (23.1%). Furthermore, while general practitioner (GP) care utilization increased during the follow-up period in the classes of older adults, it remained relatively stable in the middle-aged classes.

Conclusions

Although the HNHC population is heterogeneous, distinct subgroups with relatively homogeneous patterns of mainly demographic and socioeconomic characteristics can be identified. This calls for tailoring care and increased attention for social determinants of health.

Gender and socioeconomic inequalities in the implementation of the Basque programme for multimorbid patients

Background

The aim of our study was to increase awareness of the relevance of the implemented programmes to inequity of access and inequality of health by analyzing the impact of a patient-centred strategy for multimorbid patients.

Methods

This retrospective study compared the 2014 multimorbid patient group (intervention group) with its 2012 analogue (control group), before the Department of Health of the Basque Country launched the strategy for managing disease chronicity. Inequalities in healthcare access were represented by differences in the inclusion of patients in the programme and in contacts with primary care (PC) services by gender and socioeconomic status (measured by deprivation index by census track). Likewise, differences in hospital care represented inequalities in health outcomes. Generalized linear models were used to analyze relationships among variables. A propensity score by a genetic matching approach was used to minimize possible selection bias.

Results

At baseline, women had less probability of being eligible for the programme. No clear patterns were seen in resource consumption in PC. The probability of hospitalization was higher for men and increased according to socioeconomic status. The implementation of the programme yielded more contacts with PC services in all groups and a reduction in hospitalizations, especially among men and the most socioeconomically deprived patients.

Conclusion

The patient-centred, integrated-care intervention launched by the Department of Health of the Basque Country might have reduced some gender and socioeconomic inequalities in health outcomes, as it avoided more hospitalizations in subgroups that presented with more episodes of decompensation in the reference year.

Impact of stratification on the effectiveness of a comprehensive patient-centered strategy for multimorbid patients

OBJECTIVE

The objective of this work was to assess the effectiveness of a population-level patient-centered intervention for multimorbid patients based on risk stratification for case finding in 2014 compared with the baseline scenario in 2012.

DATA SOURCE

Clinical and administrative databases.

STUDY DESIGN

This was an observational cohort study with an intervention group and a historical control group. A propensity score by a genetic matching approach was used to minimize bias. Generalized linear models were used to analyze relationships among variables.

DATA COLLECTION

We included all eligible patients at the beginning of the year and followed them until death or until the follow-up period concluded (end of the year). The control group (2012) totaled 3558 patients, and 4225 patients were in the intervention group (2014).

PRINCIPAL FINDING

A patient-centered strategy based on risk stratification for case finding and the implementation of an integrated program based on new professional roles and an extensive infrastructure of information and communication technologies avoided 9 percent (OR: 0.91, CI: 0.86-0.96) of hospitalizations. However, this effect was not found in nonprioritized groups whose probability of hospitalization increased (OR: 1.19, CI = 1.09-1.30).

CONCLUSIONS

In a before-and-after analysis using propensity score matching, a comprehensive, patient-centered, integrated care intervention was associated with a lower risk of hospital admission among prioritized patients, but not among patients who were not prioritized to receive the intervention.