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.

A Web-Based App for Emotional Management During the COVID-19 Pandemic: Platform Development and Retrospective Analysis of its Use Throughout Two Waves of the Outbreak in Spain.

BACKGROUND: Quarantines and nationwide lockdowns implemented for containing the spread of the COVID-19 pandemic may lead to distress and increase the frequency of anxiety and depression symptoms among the general population. During the nationwide lockdown of the first wave of the COVID-19 outbreak in Spain, we developed and launched a web-based app to promote emotional self-care in the general population and facilitate contact with health care professionals. OBJECTIVE: This study aimed to describe a web-based app and analyze its utilization pattern throughout 2 successive waves of the COVID-19 outbreak in Spain. METHODS: Our web-based app targeted all individuals aged 18 years or more and was designed by adapting the contents of a mobile app for adjuvant treatment of posttraumatic stress disorder (ie, the PTSD Coach app) to the general population and the pandemic or lockdown scenario. We retrospectively assessed the utilization pattern of the web-based app using data systematically retrieved from Google Analytics. Data were grouped into 3 time periods, defined using Joinpoint regression analysis of COVID-19 incidence in our area: first wave, between-wave period, and second wave. RESULTS: The resulting web-based app, named gesioemocional.cat, maintains the navigation structure of the PTSD Coach app, with three main modules: tools for emotional self-care, a self-assessment test, and professional resources for on-demand contact. The self-assessment test combines the Patient Health Questionnaire-2 and the 7-item Generalized Anxiety Disorder scale and offers professional contact in the advent of a high level of depression and anxiety; contact is prioritized in accordance with a screening questionnaire administered at the time of obtaining individual consent to be contacted. The tools for emotional self-care can be accessed either on-demand or symptom-driven. The utilization analysis showed a high number of weekly accesses during the first wave. In this period, press releases regarding critical events of the pandemic progression and government decisions on containment measures were followed by a utilization peak, irrespective of the sense (ie, positive or negative) of the information. Positive information pieces (eg, relaxation of containment measures due to a reduction of COVID-19 cases) resulted in a sharp increase in utilization immediately after information release, followed by a successive decline in utilization. The second wave was characterized by a lower and less responsive utilization of the web-based app. CONCLUSIONS: mHealth tools may help the general population cope with stressful conditions associated with the pandemic scenario. Future studies shall investigate the effectiveness of these tools among the general population-including individuals without diagnosed mental illnesses-and strategies to reach as many people as possible.

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.

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.

Turning the Crisis Into an Opportunity: Digital Health Strategies Deployed During the COVID-19 Outbreak.

Digital health technologies offer significant opportunities to reshape current health care systems. From the adoption of electronic medical records to mobile health apps and other disruptive technologies, digital health solutions have promised a better quality of care at a more sustainable cost. However, the widescale adoption of these solutions is lagging behind. The most adverse scenarios often provide an opportunity to develop and test the capacity of digital health technologies to increase the efficiency of health care systems. Catalonia (Northeast Spain) is one of the most advanced regions in terms of digital health adoption across Europe. The region has a long tradition of health information exchange in the public health care sector and is currently implementing an ambitious digital health strategy. In this viewpoint, we discuss the crucial role digital health solutions play during the coronavirus disease (COVID-19) pandemic to support public health policies. We also report on the strategies currently deployed at scale during the outbreak in Catalonia.

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.

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

INTRODUCCIÓN: Los grupos de morbilidad ajustados (GMA) y los clinical risk groups (CRG) son herramientas de estratificación poblacional basada en la morbilidad que permiten clasificar a los pacientes en categorías mutuamente excluyentes. OBJETIVO: Comparar la estratificación, según niveles de complejidad, proporcionada por los GMA con la de los CRG y con la realizada ad-hoc por los evaluadores. DISEÑO: Muestra aleatoria por estratos de riesgo de morbilidad. Emplazamiento: Cataluña. PARTICIPANTES: Cuarenta médicos de atención primaria emparejados 2 a 2. INTERVENCIONES: Cada pareja de evaluadores tuvo que validar 25 historias. Mediciones principales: Se evaluó la concordancia entre evaluadores, y entre los evaluadores y los resultados obtenidos por los 2 agrupadores de morbilidad con el índice kappa, la sensibilidad, la especificidad, el valor predictivo positivo y el negativo. RESULTADOS: La concordancia entre evaluadores se situó alrededor del valor kappa 0,75 (valor medio = 0,67), entre los GMA y los evaluadores fue similar (valor medio = 0,63), y más elevada que para los CRG (valor medio = 0,35). Los profesionales otorgaron una puntuación de 7,5 a la bondad de ambos agrupadores, aunque para los estratos de mayor complejidad, según asignación de los profesionales, los GMA obtuvieron mejores puntuaciones que los CRG. Los profesionales prefirieron mayoritariamente los GMA frente a los CRG. Estas diferencias se incrementaron con el aumento de la complejidad de los pacientes según el criterio clínico. Globalmente, se encontró menos de un 2% de errores graves de clasificación proporcionada por ambos agrupadores. CONCLUSIÓN: Los profesionales consideraron que ambos agrupadores clasificaban adecuadamente a la población, aunque los GMA tienen un mejor comportamiento en los estratos que los profesionales identifican como de mayor complejidad. Además, en la mayoría de los casos, los evaluadores clínicos prefirieron los GMA

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

[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.

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

Objetivo: Comparar el rendimiento referente a la bondad de ajuste y el poder explicativo de 2agrupadores de morbilidad en el ámbito de la atención primaria (AP): los grupos de morbilidad ajustados (GMA) y los clinical risk groups (CRG). Diseño: Estudio transversal. Emplazamiento: Ámbito de la AP del Instituto Catalán de la Salud (ICS), Cataluña, España. Participantes: Población asignada a centros de AP del ICS para el año 2014. Mediciones principales: Se analizan 3 indicadores de interés, como son el ingreso urgente, el número de visitas y el gasto en farmacia. Se aplica un análisis estratificado por centros ajustando modelos lineales generalizados a partir de las variables edad, sexo y agrupador de morbilidad para explicar cada una de las 3 variables de interés. Las medidas estadísticas para analizar el rendimiento de los distintos modelos aplicados son el índice de Akaike, el índice de Bayes y la seudovariabilidad explicada mediante cambio de deviance. Resultados: Los resultados muestran que en el ámbito de la AP del ICS el poder explicativo de los GMA es superior al ofrecido por los CRG, especialmente para el caso de las visitas y el gasto en farmacia. Conclusiones: El rendimiento de los GMA en el ámbito de la AP del ICS es superior al mostrado por los CRG

Objective: To compare the performance in terms of goodness of fit and explanatory power of 2 morbidity 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 3 variables 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

[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.

The GABAergic septohippocampal pathway in control and reeler mice: target specificity and termination onto Reelin-expressing interneurons.

The septohippocampal pathway contains two separate components: the cholinergic and the GABAergic. Whereas cholinergic fibers terminate on many hippocampal cell types, GABAergic septohippocampal fibers selectively contact the cell bodies of hippocampal interneurons. We examined whether the GABAergic septohippocampal system was altered in reeler mice. First, we found that both components of the septohippocampal pathway in mice present a distribution and target-cell specificity similar to that described in rats. We also show that GABAergic septohippocampal axons terminate on subpopulations of interneurons expressing reelin, which may implicate this extracellular matrix protein in the targeting of septohippocampal axons. We thus examined the septohippocampal pathway in reeler mice defective in Reelin. In contrast to wild-type animals, reeler mice displayed an ectopic location of both cholinergic and GABAergic fibers, which accumulate close to the hippocampal fissure. Despite their altered distribution, GABAergic septal axons maintain their target-cell selectivity innervating exclusively the perisomatic region of hippocampal interneurons. Thus, as in wild type, GABAergic septal fibers formed complex baskets around the cell body of GAD-positive hippocampal neurons in reeler mice. In addition, we found that reeler hippocampi have an altered distribution of hippocampal interneurons expressing PARV or CALB, many of which are located close to the hippocampal fissure. We thus conclude that although reeler mice have an altered distribution of hippocampal interneurons, GABAergic septohippocampal axons nevertheless terminate on their specific target interneurons. Thus, whereas target layer termination of septal fibers is severely impaired in reeler mice, our data indicate that the cell-specific targeting of GABAergic septohippocampal axons is governed by Reelin-independent signals.