The Best Use of the Charlson Comorbidity Index With Electronic Health Care Database to Predict Mortality

Bannay, A.; Chaignot, C.; Blotière, Pierre-Olivier; Basson, Mickaël; Weill, Alain; Ricordeau, P.; Alla, François

doi:10.1097/mlr.0000000000000471

Cited by 341 publications

(267 citation statements)

References 20 publications

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“…The differences between crude and matched results indicate the degree to which biases were corrected. Adding the Charlson comorbidity index, a predictor of 1‐year mortality in the database , did not change the results, especially on mortality. The hdPS distribution curves overlapped over the whole population range, so that results in the matched population might safely be extrapolated to the whole population, as indicated by the similarity between results in matched populations and adjusted results in whole populations.…”

Section: Discussionmentioning

confidence: 83%

Effectiveness and safety of 110 or 150 mg dabigatran vs. vitamin K antagonists in nonvalvular atrial fibrillation

Blin

Dureau-Pournin

Cottin

et al. 2018

Brit J Clinical Pharma

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Aims We compared the 1‐year safety and effectiveness of dabigatran 110 mg (D110) or 150 mg (D150) twice daily to vitamin K antagonists (VKA) in patients with nonvalvular atrial fibrillation. Methods New user cohort study of patients dispensed D110 or D150 vs. VKA in 2013 for nonvalvular atrial fibrillation, followed 1 year in the French Système National des Données de Santé (66 million persons). D110 and D150 users were matched 1:1 with VKA users on sex, age, date of first drug dispensing and high‐dimensional propensity score. Hazard ratios [HR (95% confidence intervals)] for stroke and systemic embolism (SSE), major bleeding (MB) and death were computed using Cox proportional hazards or Fine and Gray models during exposure. Results In 14 442 matched D110 and VKA patients, mean age 79, 49% male, 91% with CHA2DS2‐VASc ≥2 and 8% with HAS‐BLED score >3, incidence rates of SSE were 1.9% and 2.6% person‐years [HR 0.69 (0.56–0.84)], MB 1.8% and 2.9% [0.62 (0.51–0.76)], death 7.2% and 8.6% [0.84 (0.76–0.94)]. In 8389 matched D150 and VKA patients, mean age 67, 67% male, 65% with CHA2DS2‐VASC ≥2; < 5% HAS‐BLED >3, incidence rates were for SSE 1.4% and 1.9% [0.76 (0.56–1.04)], MB 0.6% and 1.9% [0.30 (0.20–0.46)], death 1.6% and 3.6% [0.46 (0.35–0.59)]. Numbers needed to treat to observe one fewer death were 78 for D110, 88 for D150. Conclusion In real life D110 and D150 were at least as effective, and safer than VKA.

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Section: Discussionmentioning

confidence: 83%

Effectiveness and safety of 110 or 150 mg dabigatran vs. vitamin K antagonists in nonvalvular atrial fibrillation

Blin

Dureau-Pournin

Cottin

et al. 2018

Brit J Clinical Pharma

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“…Furthermore, comorbidity assessment should finally become an integral part of risk stratification in clinical trials . Finally, to facilitate the implementation of comorbidity assessment in clinical routine of head and neck cancer treatment, modern electronic health care database systems should be used to make comorbidity assessment more effective , and modern tools like electronic applications for rapid calculation of comorbidity scores should be used .…”

Section: Discussionmentioning

confidence: 99%

Role of comorbidity on outcome of head and neck cancer: a population‐based study in Thuringia, Germany

et al. 2016

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To examine the impact of comorbidity on overall survival (OS) in a population‐based study of patients with head and neck cancer who were treated between 2009 and 2011. Data of 1094 patients with primary head and neck carcinomas without distant metastasis from the Thuringian cancer registries were evaluated concerning the influence of patient's characteristics and comorbidity on OS. Data on comorbidity prior to head and neck cancer diagnosis was adapted to the Charlson Comorbidity (CCI), age‐adjusted CCI (ACCI), head and neck CCI (HNCCI), simplified comorbidity score (SCS), and to the Adult Comorbidity Evaluation–27 (ACE‐27). Most patients were male (80%; median age: 60 years; 50% stage IV tumors). Smoking, alcohol abuse, and anemia were registered for 38%, 33%, and 23% of the patients, respectively. Predominant therapy was surgery + radiochemotherapy (30%), surgery (29%), and surgery + radiotherapy (21%). Mean CCI, ACCI, HNCCI, SCS and ACE‐27 were 1.0 ± 1.5, 2.6 ± 2.1, 0.6 ± 0.8, 4.4 ± 4.2, and 0.9 ± 0.9, respectively. Median follow‐up was 25.7 months. Multivariable analyses showed that higher age, higher UICC stage, no therapy, including surgery or radiotherapy, alcohol abuse, and anemia, higher comorbidity were independent risk factors for worse OS (all P < 0.05). According to the discriminatory power analysis none of the five comorbidity scores was superior to the other scores to prognosticate OS. This population‐based study showed that comorbidity is frequent in German patients with head and neck cancer and is an important risk factor for poor OS. Comorbidity should be routinely assessed and taken into account in prospective clinical trials.

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“…[10, 22–26] There is a wealth of literature on comorbidity scores [23, 27, 28] and on adapting them to different data sets [29–31], varying numbers of comorbidities included for consideration, and varying subsets of the population [32–34] or diseases of interest. [35, 36] The literature mostly focusses on how administrative data compares to medical records [22] in terms of identifying relevant comorbidities, and if a particular score or modified score is a good predictor of mortality.…”

Section: Discussionmentioning

confidence: 99%

“…[10] The widespread use of this index could be explained by the fact that it is not designed for patients with a particular disease and is recommended when overall mortality is the outcome of interest. [10] It does not require extensive information, which makes it appealing to researchers who access administrative data rather than individual clinical notes. [9] However, no gold standard approach to measure comorbidity in the context of cancer exists, and the source of data to ascertain comorbidities varies.…”

Section: Introductionmentioning

confidence: 99%

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Reproducibility, reliability and validity of population-based administrative health data for the assessment of cancer non-related comorbidities

et al. 2017

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BackgroundPatients with comorbidities do not receive optimal treatment for their cancer, leading to lower cancer survival. Information on individual comorbidities is not straightforward to derive from population-based administrative health datasets. We described the development of a reproducible algorithm to extract the individual Charlson index comorbidities from such data. We illustrated the algorithm with 1,789 laryngeal cancer patients diagnosed in England in 2013. We aimed to clearly set out and advocate the time-related assumptions specified in the algorithm by providing empirical evidence for them.MethodsComorbidities were assessed from hospital records in the ten years preceding cancer diagnosis and internal reliability of the hospital records was checked. Data were right-truncated 6 or 12 months prior to cancer diagnosis to avoid inclusion of potentially cancer-related comorbidities. We tested for collider bias using Cox regression.ResultsOur administrative data showed weak to moderate internal reliability to identify comorbidities (ICC ranging between 0.1 and 0.6) but a notably high external validity (86.3%). We showed a reverse protective effect of non-cancer related Chronic Obstructive Pulmonary Disease (COPD) when the effect is split into cancer and non-cancer related COPD (Age-adjusted HR: 0.95, 95% CI:0.7–1.28 for non-cancer related comorbidities). Furthermore, we showed that a window of 6 years before diagnosis is an optimal period for the assessment of comorbidities.ConclusionTo formulate a robust approach for assessing common comorbidities, it is important that assumptions made are explicitly stated and empirically proven. We provide a transparent and consistent approach useful to researchers looking to assess comorbidities for cancer patients using administrative health data.

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The Best Use of the Charlson Comorbidity Index With Electronic Health Care Database to Predict Mortality

Abstract: Our study is the first to adapt the Charlson index to a large health care database including >6 million of inpatients. When mortality is the outcome, we recommended using the age-adjusted Charlson index as 4-level score to take into account comorbidities.

Cited by 341 publications

References 20 publications

Effectiveness and safety of 110 or 150 mg dabigatran vs. vitamin K antagonists in nonvalvular atrial fibrillation

Effectiveness and safety of 110 or 150 mg dabigatran vs. vitamin K antagonists in nonvalvular atrial fibrillation

Role of comorbidity on outcome of head and neck cancer: a population‐based study in Thuringia, Germany

Reproducibility, reliability and validity of population-based administrative health data for the assessment of cancer non-related comorbidities

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