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

Monterde, David; Carot-Sans, Gerard; Cainzos‐Achirica, Miguel; Abilleira, Sònia; Coca, Marc; Vela, Emili; Clèries, Montse; Valero-Bover, Damià; Comín‐Colet, Josep; Garcı́a-Eroles, Luis; Sust, Pol Pérez; Arrufat, Miquel; Lejardi, Yolanda; Piera-Jiménez, Jordi

doi:10.2147/rmhp.s326132

Cited by 18 publications

(12 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Models presented to date identify the comorbidity burden among a factor contributing to the risk of severe illness; however, multimorbidity is typically measured by the presence (and/or unweighted counts) of diagnoses from a predefined list of chronic conditions. Alternatively, exhaustive measures of multimorbidity based on weighted counts of all chronic conditions (and relevant and recent acute diagnoses) may provide a more accurate perspective of patients’ health risk 15 , 16 .…”

Section: Introductionmentioning

confidence: 99%

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

Vela

Carot-Sans

Clèries

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Vela

Carot-Sans

Clèries

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lastly, our study considered only 14 disease and health conditions but comorbidity burden taken as a whole has been reported in the literature to play a significant role in risk for COVID-19 positivity (among other COVID-19 related outcomes) 34 . Moreover, cumulative comorbidity burden may mediate some of the age associations [35][36][37] . Future work should consider the correlated effects of comorbidity burden towards the stabilization (or lack thereof) of the laboratory parameters (blood biomarkers).…”

Section: Discussionmentioning

confidence: 99%

Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK

2023

View full text Add to dashboard Cite

Background Risk for COVID-19 positivity and hospitalization due to diverse environmental and sociodemographic factors may change as the pandemic progresses. Methods We investigated the association of 360 exposures sampled before COVID-19 outcomes for participants in the UK Biobank, including 9268 and 38,837 non-overlapping participants, sampled at July 17, 2020 and February 2, 2021, respectively. The 360 exposures included clinical biomarkers (e.g., BMI), health indicators (e.g., doctor-diagnosed diabetes), and environmental/behavioral variables (e.g., air pollution) measured 10–14 years before the COVID-19 time periods. Results Here we show, for example, “participant having son and/or daughter in household” was associated with an increase in incidence from 20% to 32% (risk difference of 12%) between timepoints. Furthermore, we find age to be increasingly associated with COVID-19 positivity over time from Risk Ratio [RR] (per 10-year age increase) of 0.81 to 0.6 (hospitalization RR from 1.18 to 2.63, respectively). Conclusions Our data-driven approach demonstrates that time of pandemic plays a role in identifying risk factors associated with positivity and hospitalization.

show abstract

“…The baseline characteristics of study patients (i.e., before admission) included age, sex, morbidity burden measured using the AMG score, hospitalizations, emergency room admissions, and expenditure within the past year. Information regarding the HaH episode included the length of stay (LoS) and the complexity of hospitalization, measured using two case-mix tools: the Case Mix Index-APR-DRG v35 (CMI), 28 broadly used for payment purposes, and the Queralt index, 29,30 recently developed by the Catalan Institute of Health and showing higher performance for predicting general hospitalization endpoints.…”

Section: Methodsmentioning

confidence: 99%

Five years of Hospital at Home adoption in Catalonia: impact and challenges

González-Colom

Carot-Sans

Vela

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Hospital at home (HaH), either admission avoidance (AA) or early supported discharge (ESD), was increasingly implemented in Catalonia (7.7 M, Spain) for selected patients, achieving regional adoption within the 2011-2015 Health Plan. This study aimed to assess population-wide HaH outcomes over five years (2015-2019) in a consolidated regional HaH program and provide context-independent recommendations for service quality assurance. Methods: A mixed-methods approach was adopted, combining population-based retrospective analyses of registry information with qualitative research. AA and ESD were separately compared with conventional hospitalization groups using propensity score matching techniques. In the analysis, we evaluated the 12-month period before the acute episode, the admission, and use of healthcare resources at 30 and 90 days after discharge. A panel of experts discussed the results and provided recommendations for monitoring HaH services. Results: The adoption of AA steadily increased from 5,185 to 8,086 episodes/year (total episodes 31,901; mean age 73 (SD 17) years; 79% high-risk patients), whereas ESD remained stable over the study period, averaging 5,329 episodes per year (total episodes 26,646; mean age 68 (SD 16) years; 71% high-risk patients). Mortality rates were similar in HaH and conventional hospitalization within the episode (AA: 0.31% vs. 0.45%; ESD: 0.18% vs. 0.45%) and at 30-days (AA: 3.94% vs. 3.24%; ESD: 4.50% vs. 4.07%). Likewise, the frequency of patients requiring hospital re-admissions or ER visits 30 days after discharge was similar in HaH (AA and ESD) and the corresponding controls. The 27 healthcare providers assessed showed high variability in patients age, multimorbidity, severity of episodes, recurrences, and length of stay of AA episodes. Recommendations aiming at enhancing service delivery were produced. Conclusions: Besides confirming safety and value generation of AA, we found that this service is delivered in a case-mix of diferent scenarios, encouraging provider-profiled monitoring of the service, particularly for ESD modalities. Keywords: Hospital at Home, Admission Avoidance, Early Supported Discharge, Implementation Science, Integrated Care.

show abstract

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

Cited by 18 publications

References 37 publications

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

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

Prioritization of COVID-19 risk factors in July 2020 and February 2021 in the UK

Five years of Hospital at Home adoption in Catalonia: impact and challenges

Contact Info

Product

Resources

About