Applicability of the adjusted morbidity groups algorithm for healthcare programming: results of a pilot study in Italy.

BACKGROUND: Population-based Health Risk Assessment (HRA) tools are strategic for the implementation of integrated care. Various HRA algorithms have been developed in the last decades worldwide. Their full adoption being limited by technical, functional, and economical factors. This study aims to apply the Adjusted Morbidity Groups (AMG) algorithm in the context of an Italian Region, and evaluate its performance to support decision-making processes in healthcare programming. METHODS: The pilot study used five Healthcare Administrative Databases (HADs) covering the period 2015-2021. An iterative semi-automated procedure was developed to extract, filter, check and merge the data. A technical manual was developed to describe the process, designed to be standardized, reproducible and transferable. AMG algorithm was applied and descriptive analysis performed. A dashboard structure was developed to exploit the results of the tool. RESULTS: AMG produced information on the health status of Marche citizens, highlighting the presence of chronic conditions from age 45 years. Persons with high and very high level of complexity showed elevated mortality rates and an increased use of healthcare resources. A visualization dashboard was intended to provide to relevant stakeholders accessible, updated and ready-to-use aggregated information on the health status of citizens and additional insight on the use of the healthcare services and resources by specific groups of citizens. CONCLUSION: The flexibility of the AMG, together with its ability to support policymakers and clinical sector, could favour its implementation in different scenarios across Europe. A clear strategy for the adoption of HRA tools and related key elements and lessons learnt for a successful transferability at the EU level were defined. HRA strategies should be considered a pillar of healthcare policies and programming to achieve person-centred care and promote the sustainability of the EU healthcare systems.

Healthcare risk stratification model for emergency departments based on drugs, income and comorbidities: the DICER-score.

BACKGROUND: During the last decade, the progressive increase in age and associated chronic comorbidities and polypharmacy. However, assessments of the risk of emergency department (ED) revisiting published to date often neglect patients' pharmacotherapy plans, thus overseeing the Drug-related problems (DRP) risks associated with the therapy burden. The aim of this study is to develop a predictive model for ED revisit, hospital admission, and mortality based on patient's characteristics and pharmacotherapy. METHODS: Retrospective cohort study including adult patients visited in the ED (triage 1, 2, or 3) of multiple hospitals in Catalonia (Spain) during 2019. The primary endpoint was a composite of ED visits, hospital admission, or mortality 30 days after ED discharge. The study population was randomly split into a model development (60%) and validation (40%) datasets. The model included age, sex, income level, comorbidity burden, measured with the Adjusted Morbidity Groups (GMA), and number of medications. Forty-four medication groups, associated with medication-related health problems, were assessed using ATC codes. To assess the performance of the different variables, logistic regression was used to build multivariate models for ED revisits. The models were created using a "stepwise-forward" approach based on the Bayesian Information Criterion (BIC). Area under the curve of the receiving operating characteristics (AUCROC) curve for the primary endpoint was calculated. RESULTS: 851.649 patients were included; 134.560 (15.8%) revisited the ED within 30 days from discharge, 15.2% were hospitalized and 9.1% died within 30 days from discharge. Four factors (sex, age, GMA, and income level) and 30 ATC groups were identified as risk factors and combined into a final score. The model showed an AUCROC values of 0.720 (95%CI:0.718-0.721) in the development cohort and 0.719 (95%CI.0.717-0.721) in the validation cohort. Three risk categories were generated, with the following scores and estimated risks: low risk: 18.3%; intermediate risk: 40.0%; and high risk: 62.6%. CONCLUSION: The DICER score allows identifying patients at high risk for ED revisit within 30 days based on sociodemographic, clinical, and pharmacotherapeutic characteristics, being a valuable tool to prioritize interventions on discharge.

Prevention of Unplanned Hospital Admissions in Multimorbid Patients Using Computational Modeling: Observational Retrospective Cohort Study.

BACKGROUND: Enhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection. OBJECTIVE: The study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes. METHODS: Gradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles. RESULTS: Performance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%). CONCLUSIONS: The results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation.

Performance of quantitative measures of multimorbidity: a population-based retrospective analysis.

BACKGROUND: Multimorbidity measures are useful for resource planning, patient selection and prioritization, and factor adjustment in clinical practice, research, and benchmarking. We aimed to compare the explanatory performance of the adjusted morbidity group (GMA) index in predicting relevant healthcare outcomes with that of other quantitative measures of multimorbidity. METHODS: The performance of multimorbidity measures was retrospectively assessed on anonymized records of the entire adult population of Catalonia (North-East Spain). Five quantitative measures of multimorbidity were added to a baseline model based on age, gender, and socioeconomic status: the Charlson index score, the count of chronic diseases according to three different proposals (i.e., the QOF, HCUP, and Karolinska institute), and the multimorbidity index score of the GMA tool. Outcomes included all-cause death, total and non-scheduled hospitalization, primary care and ER visits, medication use, admission to a skilled nursing facility for intermediate care, and high expenditure (time frame 2017). The analysis was performed on 10 subpopulations: all adults (i.e., aged > 17 years), people aged > 64 years, people aged > 64 years and institutionalized in a nursing home for long-term care, and people with specific diagnoses (e.g., ischemic heart disease, cirrhosis, dementia, diabetes mellitus, heart failure, chronic kidney disease, and chronic obstructive pulmonary disease). The explanatory performance was assessed using the area under the receiving operating curves (AUC-ROC) (main analysis) and three additional statistics (secondary analysis). RESULTS: The adult population included 6,224,316 individuals. The addition of any of the multimorbidity measures to the baseline model increased the explanatory performance for all outcomes and subpopulations. All measurements performed better in the general adult population. The GMA index had higher performance and consistency across subpopulations than the rest of multimorbidity measures. The Charlson index stood out on explaining mortality, whereas measures based on exhaustive definitions of chronic diagnostic (e.g., HCUP and GMA) performed better than those using predefined lists of diagnostics (e.g., QOF or the Karolinska proposal). CONCLUSIONS: The addition of multimorbidity measures to models for explaining healthcare outcomes increase the performance. The GMA index has high performance in explaining relevant healthcare outcomes and may be useful for clinical practice, resource planning, and public health research.

[Clinical validation of 2 morbidity groups in the primary care setting]. / Validación clínica de 2 agrupadores de morbilidad en el ámbito de atención primaria.

INTRODUCTION: Adjusted Morbidity Groups (GMAs) and the Clinical Risk Groups (CRGs) are population morbidity based stratification tools which classify patients into mutually exclusive categories. OBJETIVE: To compare the stratification provided by the GMAs, CRGs and that carried out by the evaluators according to the levels of complexity. DESIGN: Random sample stratified by morbidity risk. LOCATION: Catalonia. PARTICIPANTS: Forty paired general practitioners in the primary care, matched pairs. INTERVENTIONS: Each pair of evaluators had to review 25 clinical records. MAIN OUTPUTS: The concordance by evaluators, and between the evaluators and the results obtained by the 2 morbidity tools were evaluated according to the kappa index, sensitivity, specificity, and positive and negative predicted values. RESULTS: The concordance between general practitioners pairs was around the kappa value 0.75 (mean value=0.67), between the GMA and the evaluators was similar (mean value=0.63), and higher than for the CRG (mean value=0.35). The general practitioners gave a score of 7.5 over 10 to both tools, although for the most complex strata, according to the professionals' assignment, the GMA obtained better scores than the CRGs. The professionals preferred the GMAs over the CRGs. These differences increased with the complexity level of the patients according to clinical criteria. Overall, less than 2% of serious classification errors were found by both groupers. CONCLUSION: The evaluators considered that both grouping systems classified the studied population satisfactorily, although the GMAs showed a better performance for more complex strata. In addition, the clinical raters preferred the GMAs in most cases.

Evaluation of integrated care services in Catalonia: population-based and service-based real-life deployment protocols.

BACKGROUND: Comprehensive assessment of integrated care deployment constitutes a major challenge to ensure quality, sustainability and transferability of both healthcare policies and services in the transition toward a coordinated service delivery scenario. To this end, the manuscript articulates four different protocols aiming at assessing large-scale implementation of integrated care, which are being developed within the umbrella of the regional project Nextcare (2016-2019), undertaken to foster innovation in technologically-supported services for chronic multimorbid patients in Catalonia (ES) (7.5 M inhabitants). Whereas one of the assessment protocols is designed to evaluate population-based deployment of care coordination at regional level during the period 2011-2017, the other three are service-based protocols addressing: i) Home hospitalization; ii) Prehabilitation for major surgery; and, iii) Community-based interventions for frail elderly chronic patients. All three services have demonstrated efficacy and potential for health value generation. They reflect different implementation maturity levels. While full coverage of the entire urban health district of Barcelona-Esquerra (520 k inhabitants) is the main aim of home hospitalization, demonstration of sustainability at Hospital Clinic of Barcelona constitutes the core goal of the prehabilitation service. Likewise, full coverage of integrated care services addressed to frail chronic patients is aimed at the city of Badalona (216 k inhabitants). METHODS: The population-based analysis, as well as the three service-based protocols, follow observational and experimental study designs using a non-randomized intervention group (integrated care) compared with a control group (usual care) with a propensity score matching method. Evaluation of cost-effectiveness of the interventions using a Quadruple aim approach is a central outcome in all protocols. Moreover, multi-criteria decision analysis is explored as an innovative method for health delivery assessment. The following additional dimensions will also be addressed: i) Determinants of sustainability and scalability of the services; ii) Assessment of the technological support; iii) Enhanced health risk assessment; and, iv) Factors modulating service transferability. DISCUSSION: The current study offers a unique opportunity to undertake a comprehensive assessment of integrated care fostering deployment of services at regional level. The study outcomes will contribute refining service workflows, improving health risk assessment and generating recommendations for service selection. TRIALS REGISTRATION: NCT03130283 (date released 04/06/2018), NCT03768050 (date released 12/05/2018), NCT03767387 (date released 12/05/2018).

[Validity of adjusted morbidity groups with respect to clinical risk groups in the field of primary care]. / Validez de los grupos de morbilidad ajustados respecto a los clinical risk groups en el ámbito de la atención primaria.

OBJECTIVE: To compare the performance in terms of goodness of fit and explanatory power of 2morbidity groupers in primary care (PC): adjusted morbidity groups (AMG) and clinical risk groups (CRG). DESIGN: Cross-sectional study. LOCATION: PC in the Catalan Institute for the Health (CIH), Catalonia, Spain. PARTICIPANTS: Population allocated in primary care centers of the CIH for the year 2014. MAIN MEASUREMENTS: Three indicators of interest are analyzed such as urgent hospitalization, number of visits and spending in pharmacy. A stratified analysis by centers is applied adjusting generalized lineal models from the variables age, sex and morbidity grouping to explain each one of the 3variables of interest. The statistical measures to analyze the performance of the different models applied are the Akaike index, the Bayes index and the pseudo-variability explained by deviance change. RESULTS: The results show that in the area of the primary care the explanatory power of the AMGs is higher to that offered by the CRGs, especially for the case of the visits and the pharmacy. CONCLUSIONS: The performance of GMAs in the area of the CIH PC is higher than that shown by the CRGs.

[Adjusted morbidity groups: A new multiple morbidity measurement of use in Primary Care]. / Los grupos de morbilidad ajustados: nuevo agrupador de morbilidad poblacional de utilidad en el ámbito de la atención primaria.

The Adjusted Morbidity Groups (GMA) is a new morbidity measurement developed and adapted to the Spanish healthcare System. It enables the population to be classified into 6 morbidity groups, and in turn divided into 5 levels of complexity, along with one healthy population group. Consequently, the population is divided into 31 mutually exclusive categories. The results of the stratification in Catalonia are presented. GMA is a method for grouping morbidity that is comparable to others in the field, but has been developed with data from the Spanish health system. It can be used to stratify the population and to identify target populations. It has good explanatory and predictive results in the use of health resources indicators. The Spanish Ministry of Health is promoting the introduction of the GMA into the National Health System.

Retrospective analysis of hospitalization costs using two payment systems: the diagnosis related groups (DRG) and the Queralt system, a newly developed case-mix tool for hospitalized patients.

BACKGROUND: Hospital services are typically reimbursed using case-mix tools that group patients according to diagnoses and procedures. We recently developed a case-mix tool (i.e., the Queralt system) aimed at supporting clinicians in patient management. In this study, we compared the performance of a broadly used tool (i.e., the APR-DRG) with the Queralt system. METHODS: Retrospective analysis of all admissions occurred in any of the eight hospitals of the Catalan Institute of Health (i.e., approximately, 30% of all hospitalizations in Catalonia) during 2019. Costs were retrieved from a full cost accounting. Electronic health records were used to calculate the APR-DRG group and the Queralt index, and its different sub-indices for diagnoses (main diagnosis, comorbidities on admission, andcomplications occurred during hospital stay) and procedures (main and secondary procedures). The primary objective was the predictive capacity of the tools; we also investigated efficiency and within-group homogeneity. RESULTS: The analysis included 166,837 hospitalization episodes, with a mean cost of € 4,935 (median 2,616; interquartile range 1,011-5,543). The components of the Queralt system had higher efficiency (i.e., the percentage of costs and hospitalizations covered by increasing percentages of groups from each case-mix tool) and lower heterogeneity. The logistic model for predicting costs at pre-stablished thresholds (i.e., 80th, 90th, and 95th percentiles) showed better performance for the Queralt system, particularly when combining diagnoses and procedures (DP): the area under the receiver operating characteristics curve for the 80th, 90th, 95th cost percentiles were 0.904, 0.882, and 0.863 for the APR-DRG, and 0.958, 0.945, and 0.928 for the Queralt DP; the corresponding values of area under the precision-recall curve were 0.522, 0.604, and 0.699 for the APR-DRG, and 0.748, 0.7966, and 0.834 for the Queralt DP. Likewise, the linear model for predicting the actual cost fitted better in the case of the Queralt system. CONCLUSIONS: The Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.

Toward Adoption of Health Risk Assessment in Population-Based and Clinical Scenarios: Lessons From JADECARE.

Introduction: Health risk assessment (HRA) strategies are cornerstone for health systems transformation toward value-based patient-centred care. However, steps for HRA adoption are undefined. This article analyses the process of transference of the Adjusted Morbidity Groups (AMG) algorithm from the Catalan Good Practice to the Marche region (IT) and to Viljandi Hospital (EE), within the JADECARE initiative (2020-2023). Description: The implementation research approach involved a twelve-month pre-implementation period to assess feasibility and define the local action plans, followed by a sixteen-month implementation phase. During the two periods, a well-defined combination of experience-based co-design and quality improvement methodologies were applied. Discussion: The evolution of the Catalan HRA strategy (2010-2023) illustrates its potential for health systems transformation, as well as its transferability. The main barriers and facilitators for HRA adoption were identified. The report proposes a set of key steps to facilitate site customized deployment of HRA contributing to define a roadmap to foster large-scale adoption across Europe. Conclusions: Successful adoption of the AMG algorithm was achieved in the two sites confirming transferability. Marche identified the key requirements for a population-based HRA strategy, whereas Viljandi Hospital proved its potential for clinical use paving the way toward value-based healthcare strategies.

Serum potassium abnormalities, renin-angiotensin-aldosterone system inhibitor discontinuation, and clinical outcomes in patients with chronic cardiovascular, metabolic, and renal conditions: A population-based analysis.

BACKGROUND: Renin-angiotensin-aldosterone system inhibitors (RAASIs) play a crucial role in the treatment of several chronic cardiovascular conditions. Nonetheless, hyperkalemia, a frequent side effect, often leads to the discontinuation of RAASIs. The implications of hyperkalemia-driven changes in RAASI medications are poorly understood. METHODS: Population-based, observational, retrospective cohort study. Two large healthcare databases were utilized to identify 77,089 individuals aged 55 years and older with chronic conditions who were prescribed RAASIs between 2015 and 2017 in Southern Barcelona, Spain. We assessed the interplay between serum potassium abnormalities, RAASI management, and their associations with clinical outcomes, adjusting for potential confounders including socioeconomic factors, medical conditions, and potassium levels. RESULTS: The one-year prevalence of hyperkalemia (defined as serum potassium, K+ >5.0 mmol/L) was 17.8 %. RAASI were down-titrated in 16.1 % of these 13,673 patients with K+ levels. Factors linked to a higher likelihood of reducing/discontinuing RAASI after developing hyperkalemia included older age, impaired kidney function, higher potassium levels, and previous hospitalizations. Dose reduction/discontinuation of RAASI after developing hyperkalemia was associated with an increased risk of hospitalization (adjusted hazard ratio [HR] 1.16, 95 % confidence interval [CI] 1.10-1.21) and with increased mortality (HR 1.60, 95 % CI 1.56-1.84). CONCLUSION: In this large, observational study, hyperkalemia was linked to a greater likelihood of discontinuing RAASIs. Down-titration of RAASI was independently associated with unfavorable clinical outcomes such as hospitalization and specially mortality. Although the observational nature of the study, these findings underscore the importance of preventing circumstances that may lead to RAASI down-titration, such as hyperkalemia, as well as preventing hospitalizations and mortality, to ensure RAASI benefits.

Is Age the Most Important Risk Factor in COVID-19 Patients? The Relevance of Comorbidity Burden: A Retrospective Analysis of 10,551 Hospitalizations.

Purpose: To assess the contribution of age and comorbidity to the risk of critical illness in hospitalized COVID-19 patients using increasingly exhaustive tools for measuring comorbidity burden. Patients and Methods: We assessed the effect of age and comorbidity burden in a retrospective, multicenter cohort of patients hospitalized due to COVID-19 in Catalonia (North-East Spain) between March 1, 2020, and January 31, 2022. Vaccinated individuals and those admitted within the first of the six COVID-19 epidemic waves were excluded from the primary analysis but were included in secondary analyses. The primary outcome was critical illness, defined as the need for invasive mechanical ventilation, transfer to the intensive care unit (ICU), or in-hospital death. Explanatory variables included age, sex, and four summary measures of comorbidity burden on admission extracted from three indices: the Charlson index (17 diagnostic group codes), the Elixhauser index and count (31 diagnostic group codes), and the Queralt DxS index (3145 diagnostic group codes). All models were adjusted by wave and center. The proportion of the effect of age attributable to comorbidity burden was assessed using a causal mediation analysis. Results: The primary analysis included 10,551 hospitalizations due to COVID-19; of them, 3632 (34.4%) experienced critical illness. The frequency of critical illness increased with age and comorbidity burden on admission, irrespective of the measure used. In multivariate analyses, the effect size of age decreased with the number of diagnoses considered to estimate comorbidity burden. When adjusting for the Queralt DxS index, age showed a minimal contribution to critical illness; according to the causal mediation analysis, comorbidity burden on admission explained the 98.2% (95% CI 84.1-117.1%) of the observed effect of age on critical illness. Conclusion: Comorbidity burden (when measured exhaustively) explains better than chronological age the increased risk of critical illness observed in patients hospitalized with COVID-19.

Development and validation of a population-based risk stratification model for severe COVID-19 in the general population.

The shortage of recently approved vaccines against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the need for evidence-based tools to prioritize healthcare resources for people at higher risk of severe coronavirus disease (COVID-19). Although age has been identified as the most important risk factor (particularly for mortality), the contribution of underlying comorbidities is often assessed using a pre-defined list of chronic conditions. Furthermore, the count of individual risk factors has limited applicability to population-based "stratify-and-shield" strategies. We aimed to develop and validate a COVID-19 risk stratification system that allows allocating individuals of the general population into four mutually-exclusive risk categories based on multivariate models for severe COVID-19, a composite of hospital admission, transfer to intensive care unit (ICU), and mortality among the general population. The model was developed using clinical, hospital, and epidemiological data from all individuals among the entire population of Catalonia (North-East Spain; 7.5 million people) who experienced a COVID-19 event (i.e., hospitalization, ICU admission, or death due to COVID-19) between March 1 and September 15, 2020, and validated using an independent dataset of 218,329 individuals with COVID-19 confirmed by reverse transcription-polymerase chain reaction (RT-PCR), who were infected after developing the model. No exclusion criteria were defined. The final model included age, sex, a summary measure of the comorbidity burden, the socioeconomic status, and the presence of specific diagnoses potentially associated with severe COVID-19. The validation showed high discrimination capacity, with an area under the curve of the receiving operating characteristics of 0.85 (95% CI 0.85-0.85) for hospital admissions, 0.86 (0.86-0.97) for ICU transfers, and 0.96 (0.96-0.96) for deaths. Our results provide clinicians and policymakers with an evidence-based tool for prioritizing COVID-19 healthcare resources in other population groups aside from those with higher exposure to SARS-CoV-2 and frontline workers.

Impact on clinical outcomes and health costs of deranged potassium levels in patients with chronic cardiovascular, metabolic, and renal conditions.

INTRODUCTION AND OBJECTIVES: Potassium derangements are frequent among patients with chronic cardiovascular conditions. Studies on the associations between potassium derangements and clinical outcomes have yielded mixed findings, and the implications for health care expenditure are unknown. We assessed the population-based associations between hyperkalemia, hypokalemia and clinical outcomes and health care costs, in patients with chronic heart failure, chronic kidney disease, diabetes mellitus, hypertension, and ischemic heart disease. METHODS: Population-based, longitudinal study including up to 36 269 patients from a health care area with at least one of the above-mentioned conditions. We used administrative, hospital and primary care databases. Participants were followed up between 2015 and 2017, were aged ≥ 55 years and had at least 1 potassium measurement. Four analytic designs were used to evaluate prevalent and incident cases and the use of renin-angiotensin-aldosterone system inhibitors. RESULTS: Hyperkalemia was twice as frequent as hypokalemia. On multivariable-adjusted analyses, hyperkalemia was robustly and significantly associated with an increased risk of all-cause death (HR from Cox regression models ranging from 1.31-1.68) and with an increased odds of a yearly health care expenditure >85th percentile (OR, 1.21-1.29). Associations were even stronger in hypokalemic patients (HR for all-cause death, 1.92-2.60; OR for health care expenditure> percentile 85th, 1.81-1.85). CONCLUSIONS: Experimental studies are needed to confirm whether the prevention of potassium derangements reduces mortality and health care expenditure in these chronic conditions. Until then, our findings provide observational evidence on the potential importance of maintaining normal potassium levels.

Performance of Three Measures of Comorbidity in Predicting Critical COVID-19: A Retrospective Analysis of 4607 Hospitalized Patients.

BACKGROUND: Comorbidity burden has been identified as a relevant predictor of critical illness in patients hospitalized with coronavirus disease 2019 (COVID-19). However, comorbidity burden is often represented by a simple count of few conditions that may not fully capture patients' complexity. PURPOSE: To evaluate the performance of a comprehensive index of the comorbidity burden (Queralt DxS), which includes all chronic conditions present on admission, as an adjustment variable in models for predicting critical illness in hospitalized COVID-19 patients and compare it with two broadly used measures of comorbidity. MATERIALS AND METHODS: We analyzed data from all COVID-19 hospitalizations reported in eight public hospitals in Catalonia (North-East Spain) between June 15 and December 8 2020. The primary outcome was a composite of critical illness that included the need for invasive mechanical ventilation, transfer to ICU, or in-hospital death. Predictors including age, sex, and comorbidities present on admission measured using three indices: the Charlson index, the Elixhauser index, and the Queralt DxS index for comorbidities on admission. The performance of different fitted models was compared using various indicators, including the area under the receiver operating characteristics curve (AUROCC). RESULTS: Our analysis included 4607 hospitalized COVID-19 patients. Of them, 1315 experienced critical illness. Comorbidities significantly contributed to predicting the outcome in all summary indices used. AUC (95% CI) for prediction of critical illness was 0.641 (0.624-0.660) for the Charlson index, 0.665 (0.645-0.681) for the Elixhauser index, and 0.787 (0.773-0.801) for the Queralt DxS index. Other metrics of model performance also showed Queralt DxS being consistently superior to the other indices. CONCLUSION: In our analysis, the ability of comorbidity indices to predict critical illness in hospitalized COVID-19 patients increased with their exhaustivity. The comprehensive Queralt DxS index may improve the accuracy of predictive models for resource allocation and clinical decision-making in the hospital setting.

Comparative Study of Postural Garment Versus Exercises for Patients With Nonspecific Cervical Pain: Protocol for a Randomized Crossover Trial.

BACKGROUND: There is a high prevalence of work-related musculoskeletal disorders among health care professionals. Posture is an essential point to be addressed for health care professionals with musculoskeletal disorders. Cervical pain can result from several conditions. Treatment should include posture modification and home exercise. OBJECTIVE: This study aims to compare a new postural garment (Posture Plus Force; Medi, Bayreuth, Germany) with exercises for women with nonspecific cervical pain. The investigators focus on nurses and allied health professionals due to the importance of posture in work-related musculoskeletal disorders. METHODS: This randomized crossover clinical trial has a 3-month treatment sequence and a 3-month washout period. Participants will include nurses and allied health professionals 21 to 55 years of age with cervical pain. Participants are allocated at random to two intervention groups: a postural garment (Posture Plus Force) to be worn for 2 to 4 hours per day for 90 days (P+ group) and five physiotherapy sessions (20 minutes each) to learn stretching and strengthening exercises with instructions to continue at home on a daily basis for 90 days (Ex group). The participants in each group will crossover interventions after a 3-month washout period. The primary outcomes are postural control and pain intensity. A static posturography will be performed with a scan (SpinalMouse; Idiag AG, Fehraltorf, Switzerland). The visual analogue scale is a psychometric measuring instrument designed to document cervical pain severity in individual participants. The secondary outcomes are cervical pain-related disability, catastrophizing, the global perceived effect of treatment, and the evaluation of garment comfort. Physical activity is assessed with the International Physical Activity Questionnaire. Assessment of primary and secondary outcomes is performed at T0 (pre-intervention), T1 (immediately after garment fitting for P+ group), T30, T60, and T90. The same measurements are recorded after the washout period and during the second intervention following the same sequence. All patients are provided with a logbook for compliance recording, over the counter drug use, pain evaluation, and sick leave. Statistical analysis is conducted following intention-to-treat principles and the treatment effects calculated using linear mixed models. RESULTS: The study design has been approved by the Ethics Commission of Hospital N Sra de Meritxell, Andorra in March 2017. A total of 32 participants are already enrolled in the study. An extension of the study is planned in a Spanish university hospital to achieve a larger sample. Study results are expected to be published during 2020. CONCLUSIONS: The Postural garment is expected to improve cervical pain by enhancing posture. TRIAL REGISTRATION: ClinicalTrials.gov NCT03560492; https://clinicaltrials.gov/ct2/show/NCT03560492. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14807.

Real-World Epidemiology of Potassium Derangements Among Chronic Cardiovascular, Metabolic and Renal Conditions: A Population-Based Analysis.

BACKGROUND: The aims of the present analysis are to estimate the prevalence of five key chronic cardiovascular, metabolic and renal conditions at the population level, the prevalence of renin-angiotensin-aldosterone system inhibitor (RAASI) medication use and the magnitude of potassium (K+) derangements among RAASI users. METHODS AND RESULTS: We used data from more than 375,000 individuals, 55 years of age or older, included in the population-based healthcare database of the Catalan Institute of Health between 2015 and 2017. The conditions of interest were chronic heart failure (CHF), chronic kidney disease (CKD), diabetes mellitus, ischemic heart disease and hypertension. RAASI medications included angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, mineralocorticoid receptor antagonists (MRAs) and renin inhibitors. Hyperkalemia was defined as K+ levels >5.0 mEq/L and hypokalemia as K+ <3.5 mEq/L. The prevalence of chronic cardiovascular, metabolic and renal conditions was high, and particularly that of hypertension (prevalence ranging from 48.2% to 48.9%). The use of at least one RAASI medication was almost ubiquitous in these patients (75.2-77.3%). Among RAASI users, the frequency of K+ derangements, mainly of hyperkalemia, was very noticeable (12% overall), particularly in patients with CKD or CHF, elderly individuals and users of MRAs. Hypokalemia was less frequent (1%). CONCLUSION: The high prevalence of K+ derangements, and particularly hyperkalemia, among RAASI users highlights the real-world relevance of K+ derangements, and the importance of close monitoring and management of K+ levels in routine clinical practice. This is likely to benefit a large number of patients, particularly those at higher risk.

Performance of Comprehensive Risk Adjustment for the Prediction of In-Hospital Events Using Administrative Healthcare Data: The Queralt Indices.

BACKGROUND: Accurate risk adjustment is crucial for healthcare management and benchmarking. PURPOSE: We aimed to compare the performance of classic comorbidity functions (Charlson's and Elixhauser's), of the All Patients Refined Diagnosis Related Groups (APR-DRG), and of the Queralt Indices, a family of novel, comprehensive comorbidity indices for the prediction of key clinical outcomes in hospitalized patients. MATERIAL AND METHODS: We conducted an observational, retrospective cohort study using administrative healthcare data from 156,459 hospital discharges in Catalonia (Spain) during 2018. Study outcomes were in-hospital death, long hospital stay, and intensive care unit (ICU) stay. We evaluated the performance of the following indices: Charlson's and Elixhauser's functions, Queralt's Index for secondary hospital discharge diagnoses (Queralt DxS), the overall Queralt's Index, which includes pre-existing comorbidities, in-hospital complications, and principal discharge diagnosis (Queralt Dx), and the APR-DRG. Discriminative ability was evaluated using the area under the curve (AUC), and measures of goodness of fit were also computed. Subgroup analyses were conducted by principal discharge diagnosis, by age, and type of admission. RESULTS: Queralt DxS provided relevant risk adjustment information in a larger number of patients compared to Charlson's and Elixhauser's functions, and outperformed both for the prediction of the 3 study outcomes. Queralt Dx also outperformed Charlson's and Elixhauser's indices, and yielded superior predictive ability and goodness of fit compared to APR-DRG (AUC for in-hospital death 0.95 for Queralt Dx, 0.77-0.93 for all other indices; for ICU stay 0.84 for Queralt Dx, 0.73-0.83 for all other indices). The performance of Queralt DxS was at least as good as that of the APR-DRG in most principal discharge diagnosis subgroups. CONCLUSION: Our findings suggest that risk adjustment should go beyond pre-existing comorbidities and include principal discharge diagnoses and in-hospital complications. Validation of comprehensive risk adjustment tools such as the Queralt indices in other settings is needed.