Charlson Comorbidity Index Score and Risk of Severe Outcome and Death in Danish COVID-19 Patients

Christensen, Daniel; Strange, Jarl Emanuel; Gislason, Gunnar; Torp‐Pedersen, Christian; Gerds, Thomas Alexander; Fosbøl, Emil Loldrup; Phelps, Matthew

doi:10.1007/s11606-020-05991-z

Cited by 96 publications

(99 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, we found that predictive ability of the score obtained by weighting risk factors of SARS-CoV-2 infection, was not better than generic scores of comorbidity and comedication. This expands upon previous findings of individual comorbidities as independent risk factors for SARS-CoV-2 infection [50,51], and confirms our substantial inability to predict the risk of SARS-CoV-2…”

Section: Discussionsupporting

confidence: 89%

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

Orlando

Rea

Savaré

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundThe novel coronavirus (SARS-CoV-2) pandemic spread rapidly worldwide increasing exponentially in Italy. To date, there is lack of studies describing clinical characteristics of the population most at risk of infection. Hence, we aimed to identify clinical predictors of SARS-CoV-2 infection risk and to develop and validate a score predicting SARS-CoV-2 infection risk comparing it with unspecific surrogates.MethodsRetrospective case/control study using administrative health-related database was carried out in Southern Italy (Campania region) among beneficiaries of Regional Health Service aged over than 30 years. For each subject with Covid-19 confirmed diagnosis (case), up to five controls were randomly matched for gender, age and municipality of residence. Odds ratios and 90% confidence intervals for associations between candidate predictors and risk of infection were estimated by means of conditional logistic regression. SARS-CoV-2 Infection Score (SIS), was developed by generating a total aggregate score obtained from assignment of a weight at each selected covariate using coefficients estimated from the model. Finally, the score was categorized by assigning increasing values from 1 to 4. SIS was validated by comparison with specific and unspecific predictors of SARS-CoV-2 infection.ResultsSubjects suffering from diabetes, anaemias, Parkinson’s disease, mental disorders, cardiovascular and inflammatory bowel and kidney diseases showed increased risk of SARS-CoV-2 infection. Similar estimates were recorded for men and women and younger and older than 65 years. Fifteen conditions significantly contributed to the SIS. As SIS value increases, risk progressively increases, being odds of SARS-CoV-2 infection among people with the highest SIS value (SIS=4), 1.74 times higher than those unaffected by any SIS contributing conditions (SIS=1).ConclusionThis study identified conditions and diseases making individuals more vulnerable to SARS-CoV-2 infection. Our results are a decision-maker support tool for identifying population most at risk allowing adoption of preventive measures to minimize a potential new relapse damage.

show abstract

Section: Discussionsupporting

confidence: 89%

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

Orlando

Rea

Savaré

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, we found that predictive ability of the score obtained by weighting risk factors of SARS-CoV-2 infection did not overcome that of some generic scores of comorbidities and comedications. This expands upon previous findings of individual comorbidities as independent risk factors for SARS-CoV-2 infection [ 57 , 58 ], and confirms our substantial inability to predict the risk of SARS-CoV-2 infection. This can be explained by several limitations of our approach, which generate estimates biased towards the null.…”

Section: Discussionsupporting

confidence: 89%

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy

et al. 2021

View full text Add to dashboard Cite

Background The novel coronavirus (SARS-CoV-2) pandemic spread rapidly worldwide increasing exponentially in Italy. To date, there is lack of studies describing clinical characteristics of the people at high risk of infection. Hence, we aimed (i) to identify clinical predictors of SARS-CoV-2 infection risk, (ii) to develop and validate a score predicting SARS-CoV-2 infection risk, and (iii) to compare it with unspecific scores. Methods Retrospective case-control study using administrative health-related database was carried out in Southern Italy (Campania region) among beneficiaries of Regional Health Service aged over than 30 years. For each person with SARS-CoV-2 confirmed infection (case), up to five controls were randomly matched for gender, age and municipality of residence. Odds ratios and 90% confidence intervals for associations between candidate predictors and risk of infection were estimated by means of conditional logistic regression. SARS-CoV-2 Infection Score (SIS) was developed by generating a total aggregate score obtained from assignment of a weight at each selected covariate using coefficients estimated from the model. Finally, the score was categorized by assigning increasing values from 1 to 4. Discriminant power was used to compare SIS performance with that of other comorbidity scores. Results Subjects suffering from diabetes, anaemias, Parkinson’s disease, mental disorders, cardiovascular and inflammatory bowel and kidney diseases showed increased risk of SARS-CoV-2 infection. Similar estimates were recorded for men and women and younger and older than 65 years. Fifteen conditions significantly contributed to the SIS. As SIS value increases, risk progressively increases, being odds of SARS-CoV-2 infection among people with the highest SIS value (SIS = 4) 1.74 times higher than those unaffected by any SIS contributing conditions (SIS = 1). Conclusion Conditions and diseases making people more vulnerable to SARS-CoV-2 infection were identified by the current study. Our results support decision-makers in identifying high-risk people and adopting of preventive measures to minimize the spread of further epidemic waves.

show abstract

“…A total of 20 studies were included in the qualitative and quantitative synthesis ( Fig. 1 , Table 1 ) [ 2 , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] ]. One study which we included described an OR value.…”

Section: Resultsmentioning

confidence: 99%

“…As of July 17, 2020, the World Health Organisation (WHO) has declared more than 13 million people as a positive confirmed COVID-19 case that results in more than 580.000 deaths [ 1 ]. Through descriptive observational studies, it is well established that patients with comorbidities are disproportionately affected by COVID-19 and associated with worse clinical outcomes [ [2] , [3] , [4] , [5] ].…”

Section: Introductionmentioning

confidence: 99%

Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis

Kuswardhani

Henrina

Pranata

et al. 2020

Diabetes & Metabolic Syndrome: Clinical Research & Revi

214

161

View full text Add to dashboard Cite

Background and aims The ongoing COVID-19 pandemic is disproportionately affecting patients with comorbidities. Therefore, thorough comorbidities assessment can help establish risk stratification of patients with COVID-19, upon hospital admission. Charlson Comorbidity Index (CCI) is a validated, simple, and readily applicable method of estimating the risk of death from comorbid disease and has been widely used as a predictor of long-term prognosis and survival. Methods We performed a systematic review and meta-analysis of CCI score and a composite of poor outcomes through several databases. Results Compared to a CCI score of 0, a CCI score of 1–2 and CCI score of ≥3 was prognostically associated with mortality and associated with a composite of poor outcomes. Per point increase of CCI score also increased mortality risk by 16%. Moreover, a higher mean CCI score also significantly associated with mortality and disease severity. Conclusion CCI score should be utilized for risk stratifications of hospitalized COVID-19 patients.

show abstract

Charlson Comorbidity Index Score and Risk of Severe Outcome and Death in Danish COVID-19 Patients

Cited by 96 publications

References 2 publications

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy

Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy

Charlson comorbidity index and a composite of poor outcomes in COVID-19 patients: A systematic review and meta-analysis

Contact Info

Product

Resources

About