The Charlson Comorbidity Index in Registry-based Research

2017 ◽  
Vol 56 (05) ◽  
pp. 401-406 ◽  
Author(s):  
Jesper Lagergren ◽  
Nele Brusselaers
Keyword(s):  
Royal College ◽  
Charlson Comorbidity Index ◽  
International Classification Of Diseases ◽  
General Purpose ◽  
Morbidity Score ◽  
Co Morbidity ◽  
Classification Of Diseases ◽  
Comorbidity Scores ◽  
Comorbidity Index ◽  
Icd Coding

SummaryBackground: Comorbidities may have an important impact on survival, and comorbidity scores are often implemented in studies assessing prognosis. The Charlson Comorbidity index is most widely used, yet several adaptations have been published, all using slightly different conversions of the International Classification of Diseases (ICD) coding.Objective: To evaluate which coding should be used to assess and quantify comorbidity for the Charlson Comorbidity Index for registry-based research, in particular if older ICD versions will be used.Methods: A systematic literature search was used to identify adaptations and modifications of the ICD-coding of the Charlson Comorbidity Index for general purpose in adults, published in English. Back-translation to ICD version 8 and version 9 was conducted by means of the ICD-code converter of Statistics Sweden.Results: In total, 16 studies were identified reporting ICD-adaptations of the Charlson Comorbidity Index. The Royal College of Surgeons in the United Kingdom combined 5 versions into an adapted and updated version which appeared appropriate for research purposes. Their ICD-10 codes were back-translated into ICD-9 and ICD-8 according to their proposed adaptations, and verified with previous versions of the Charlson Comorbidity Index.Conclusion: Many versions of the Charlson Comorbidity Index are used in parallel, so clear reporting of the version, exact ICD- coding and weighting is necessary to obtain transparency and reproducibility in research. Yet, the version of the Royal College of Surgeons is up-to-date and easy-to-use, and therefore an acceptable co-morbidity score to be used in registry-based research especially for surgical patients.

Development and Validation of a Pediatric Comorbidity Index

2020 ◽  
Author(s):  
Jenny W Sun ◽  
Florence T Bourgeois ◽  
Sebastien Haneuse ◽  
Sonia Hernández-Díaz ◽  
Joan E Landon ◽  
...  
Keyword(s):  
Pediatric Patients ◽  
Pediatric Population ◽  
International Classification Of Diseases ◽  
Epidemiologic Studies ◽  
Internal Validation ◽  
Confounding Bias ◽  
Classification Of Diseases ◽  
Utilization Data ◽  
Comorbidity Scores ◽  
Comorbidity Index

Abstract Comorbidity scores are widely used to help address confounding bias in nonrandomized studies conducted within healthcare databases, but existing scores were developed to predict all-cause mortality in adults and may not be appropriate for use in pediatric studies. We developed and validated a pediatric comorbidity index, using healthcare utilization data from the tenth revision of the International Classification of Diseases. Within the MarketScan database, pediatric patients (<18 years) continuously enrolled between October 1, 2015-September 30, 2017 were identified. Logistic regression was used to predict the 1-year risk of hospitalization based on 27 predefined conditions and empirically-identified conditions derived from the most prevalent diagnoses among patients with the outcome. A single numerical index was created by assigning weights to each condition based on its beta coefficient. We conducted internal validation of the index and compared its performance to existing adult scores. The pediatric comorbidity index consisted of 24 conditions and achieved a c-statistic of 0.718 (95% confidence interval [CI] 0.714, 0.723). The index oasutperformed existing adult scores in a pediatric population (c-statistics ranging from 0.522 to 0.640). The pediatric comorbidity index provides a summary measure of disease burden and can be used for risk adjustment in epidemiologic studies of pediatric patients.

Epidemiology of Parkinson's Disease: A Population-Based Study in Primary Care in Italy

2016 ◽  
Vol 47 (1) ◽  
pp. 38-45 ◽  
Author(s):  
Elisabetta Pupillo ◽  
Claudio Cricelli ◽  
Francesco Mazzoleni ◽  
Iacopo Cricelli ◽  
Alessandro Pasqua ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Charlson Comorbidity Index ◽  
International Classification Of Diseases ◽  
Population Based ◽  
Italian Population ◽  
Hazard Ratios ◽  
Classification Of Diseases ◽  
Comorbidity Index ◽  
Clinical Conditions

Background: There are no studies on prevalence, incidence and comorbidities of Parkinson's disease (PD) in the Italian population. Methods: The database of 700 Italian general practitioners (population, 923,356) was investigated. All patients with International Classification of Diseases Ninth Revision - Clinical Modification (ICD-9-CM) diagnosis of PD during the period 2002-2012 were included. Parkinsonisms were excluded. Clinical conditions preceding PD were identified through ICD-9-CM codes. The Charlson Comorbidity Index was used. PD crude and standardized prevalence and annual incidence were calculated. Crude and adjusted hazard ratios were calculated for comorbidities. Results: A total of 2,204 patients (1,140 men, 1,064 women, age 22-95 years) were included. The crude prevalence of PD was 239/100,000. Prevalence increased exponentially with age. Standardized prevalence was 233 (95% CI 232-235). One hundred ninety-four patients were newly diagnosed, giving a crude incidence of 22/100,000 and a standardized incidence of 23.1/100,000 (95% CI 22.9-23.2). Incidence increased steadily until age 75-84 years and then decreased. Older age, cardiovascular and gastrointestinal disorders, diabetes, and restless-legs syndrome were associated with increased PD risk and smoking and hypersomnia with decreased PD risk. The Charlson Comorbidity Index was associated with PD risk with a documented gradient. Conclusions: Prevalence and incidence of PD in Italy are in line with studies with the highest case ascertainment. PD risk varies with the number and type of comorbidities.

scholarly journals Patient Outcomes following Subarachnoid Hemorrhage between the Medical Center and Regional Hospital: Whether All Patients Should Be Transferred to Medical Centers

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Tsung-Ying Lin ◽  
Chieh Hsin Wu ◽  
Wei-Che Lee ◽  
Chao-Wen Chen ◽  
Liang-Chi Kuo ◽  
...  
Keyword(s):  
Health Insurance ◽  
Subarachnoid Hemorrhage ◽  
Hospital Volume ◽  
Medical Center ◽  
International Classification Of Diseases ◽  
Research Database ◽  
Medical Centers ◽  
Classification Of Diseases ◽  
Comorbidity Index

Subarachnoid hemorrhage (SAH) is a critical illness that may result in patient mortality or morbidity. In this study, we investigated the outcomes of patients treated in medical center and nonmedical center hospitals and the relationship between such outcomes and hospital and surgeon volume. Patient data were abstracted from the National Health Insurance Research Database of Taiwan in the Longitudinal Health Insurance Database 2000, which contains all claims data of 1 million beneficiaries randomly selected in 2000. The International Classification of Diseases, Ninth Revision, subarachnoid hemorrhage (430) was used for the inclusion criteria. We identified 355 patients between 11 and 87 years of age who had subarachnoid hemorrhage. Among them, 32.4% (115/355) were men. The median Charlson comorbidity index (CCI) score was 1.3 (SD ± 0.6). Unadjusted logistic regression analysis demonstrated that low mortality was associated with high hospital volume (OR = 3.21; 95% CI: 1.18–8.77). In this study, we found no statistical significances of mortality, LOS, and total charges between medical centers and nonmedical center hospitals. Patient mortality was associated with hospital volume. Nonmedical center hospitals could achieve resource use and outcomes similar to those of medical centers with sufficient volume.

18763 Validation of case identification for alopecia areata using International Classification of Diseases (ICD) coding

2020 ◽  
Vol 83 (6) ◽  
pp. AB220
Author(s):  
Sara J. Li ◽  
Jonathan Lavian ◽  
Eunice Lee ◽  
Lindsey Bordone ◽  
Fernanda Caroline da Graca Polubriaginof ◽  
...  
Keyword(s):  
Alopecia Areata ◽  
International Classification Of Diseases ◽  
International Classification ◽  
Case Identification ◽  
Classification Of Diseases ◽  
Icd Coding

Evaluation of the Charlson comorbidity index to predict outcome after esophagectomy in older patients.

2011 ◽  
Vol 29 (4_suppl) ◽  
pp. 27-27
Author(s):  
A. Kothari ◽  
T. Bretl ◽  
T. Weigel
Keyword(s):  
Surgical Outcomes ◽  
Charlson Comorbidity Index ◽  
Length Of Hospital Stay ◽  
Elderly Age ◽  
Co Morbidity ◽  
Risk Patients ◽  
Comorbidity Index ◽  
Morbidity Index ◽  
Index Future ◽  
Morbid Conditions

27 Background: Esophagectomy remains a preferred treatment for several neoplastic and non-neoplastic conditions; however it is often avoided in elderly patients with several co-morbid conditions. Several centers endorse the use of the Charlson comorbidity index to predict surgical outcomes in high risk patients. To date, this standard measure of co-morbidity has not been used to predict surgical outcomes following esophagectomy in elderly (age ≥70) patients. Methods: We reviewed data from an IRB-approved, prospectively maintained thoracic surgery database over a three-year period (March, 2006 – March, 2009). We compared incidence of post-operative events, total length of stay, 30-day mortality, rate of readmission, and calculated Charlson comorbidity indices (CCI) for all patients. A validated electronic application was used to calculate CCI based on patient age, BMI, substance use, malignancy, and co-morbid diseases (CV, respiratory, GI, endocrine, inflammatory, psychiatric, neurologic, and immunologic). Results: There were 75 patients below the age of 70 and 41 patients ≥ 70 years old who underwent esophagectomy over the 3-year period studied. Patients over the age of 70 had a significantly higher CCI (5.02) than patients under the age of 70 (3.19, p < 0.05). However, the 30 day mortality in patients ≥ 70 (0.0%) and under 70 (2.3%) was not significantly different between groups (p = 0.33). There was no difference in median length of hospital stay (7 days vs. 7 days, p = 0.95) and rate of readmission (7.5% vs. 9.3%, p = 0.74) when comparing patients ≥ 70 and < 70 years old, respectively. Patients ≥ 70 had a significantly lower incidence of complications than patients under the age of 70 (34.1% vs. 60.0%, p < 0.05). Conclusions: Patients ≥ 70 years old had higher Charlson comorbidity indices than patients < 70 years old, however surgical outcomes in both groups following esophagectomy were similar. In this population, CCI may not be a valid tool for measuring surgical risk perhaps due to the inclusion of age in the index. Future study will focus on the development of a co-morbidity index which can predict outcomes following esophagectomy and is not biased by age. No significant financial relationships to disclose.

P01-246-3Q29 case-control association study of co-morbid migraine in bipolar affective disorder

2011 ◽  
Vol 26 (S2) ◽  
pp. 247-247
Author(s):  
M. Schmoeger ◽  
S. Cohen-Woods ◽  
G. Hosang ◽  
M. Schloegelhofer ◽  
I. Craig ◽  
...  
Keyword(s):  
Association Study ◽  
Affective Disorder ◽  
Bipolar Affective Disorder ◽  
International Classification Of Diseases ◽  
Case Control ◽  
Genome Wide ◽  
A Genome ◽  
Co Morbidity ◽  
Classification Of Diseases ◽  
Operational Criteria

According to Oedegaard et al. (2010) the co-morbidity of migraine and bipolar disorder (BPD) is well documented in numerous epidemiological and clinical studies, and there are clear pathophysiological similarities. Interestingly, in a genome-wide scan, Lea et al. (2005) identified a susceptibility locus for a severe heritable form of common migraine on chromosome 3q29. With respect to BPD, a susceptibility region on chromosome 3q29 was identified in a genome-wide linkage scan (Bailer et al. 2002) and follow-up linkage analysis (Schosser et al. 2004). These findings were also supported by further fine-mapping of this region (Schosser et al. 2007). Since 3q29 is among the chromosomal regions implicated in migraine and bipolar linkage studies, the aim of the current study is to test for 3q29 association of migraine in sample of patients with BPD. The sample consists of 463 patients with a diagnosis of BPD (34.63% men, 65.37% women; mean age ± SD: 48.01 ± 11.26), as defined by the Diagnostic and Statistical Manual 4th edition operational criteria (DSM-IV) and the International Classification of Diseases 10th edition operational criteria (ICD-10), derived from the Bipolar Affective Disorder Case Control Study (BACCS). A total of 51 SNPs in the region of the 3q29 were genotyped using Sequenom MassARRAY® iPLEX Gold and tested for association with migraine. The results of this association study investigating the 3q29 region in a sample of patients with BPD will be presented.

scholarly journals Psychosis prevalence and physical, metabolic and cognitive co-morbidity: data from the second Australian national survey of psychosis

2014 ◽  
Vol 44 (10) ◽  
pp. 2163-2176 ◽  
Author(s):  
V. A. Morgan ◽  
J. J. McGrath ◽  
A. Jablensky ◽  
J. C. Badcock ◽  
A. Waterreus ◽  
...  
Keyword(s):  
Psychotic Disorders ◽  
Good Health ◽  
International Classification Of Diseases ◽  
Two Phase ◽  
Population Mean ◽  
The Metabolic Syndrome ◽  
Co Morbidity ◽  
Premorbid Intelligence ◽  
Morbidity Data ◽  
Classification Of Diseases

BackgroundThere are insufficient data from nationwide surveys on the prevalence of specific psychotic disorders and associated co-morbidities.MethodThe 2010 Australian national psychosis survey used a two-phase design to draw a representative sample of adults aged 18–64 years with psychotic disorders in contact with public treatment services from an estimated resident population of 1 464 923 adults. This paper is based on data from 1642 participants with an International Classification of Diseases (ICD)-10 psychotic disorder. Its aim is to present estimates of treated prevalence and lifetime morbid risk of psychosis, and to describe the cognitive, physical health and substance use profiles of participants.ResultsThe 1-month treated prevalence of psychotic disorders was 3.10 cases per 1000 population aged 18–64 years, not accounting for people solely accessing primary care services; lifetime morbid risk was 3.45 per 1000. Mean premorbid intelligence quotient was approximately 0.5 s.d.s below the population mean; current cognitive ability (measured with a digit symbol coding task) was 1.6 s.d.s below the population mean. For both cognitive tests, higher scores were significantly associated with better independent functioning. The prevalence of the metabolic syndrome was high, affecting 60.8% of participants, and pervasive across diagnostic groups. Of the participants, two-thirds (65.9%) were current smokers, 47.4% were obese and 32.4% were sedentary. Of the participants, half (49.8%) had a lifetime history of alcohol abuse/dependence and 50.8% lifetime cannabis abuse/dependence.ConclusionsOur findings highlight the need for comprehensive, integrative models of recovery to maximize the potential for good health and quality of life for people with psychotic illness.

scholarly journals Effect of Sex on Clinical Outcomes in Patients with Coronavirus Disease: A Population-Based Study

2020 ◽  
Vol 10 (1) ◽  
pp. 38
Author(s):  
Kyu Hyang Cho ◽  
Sang Won Kim ◽  
Jong Won Park ◽  
Jun Young Do ◽  
Seok Hui Kang
Keyword(s):  
Clinical Outcomes ◽  
Kidney Injury ◽  
International Classification Of Diseases ◽  
Population Based ◽  
Charlson Comorbidity Index Score ◽  
Population Based Study ◽  
Classification Of Diseases ◽  
Insurance Claims Data ◽  
Comorbidity Index ◽  
The Impact

Background: This study aimed to evaluate the association between sex and clinical outcomes in patients with coronavirus disease (COVID-19) using a population-based dataset. Methods: In this retrospective study, insurance claims data from the Korea database were used. Patients who tested positive for COVID-19 were included in the study. All diseases were defined according to the International Classification of Diseases 10th revision. During follow-up, the clinical outcomes, except mortality, were assessed using the electrical codes from the dataset. The clinical outcomes noted were: hospitalization, the use of inotropics, high flow nasal cannula, conventional oxygen therapy, mechanical ventilation, extracorporeal membrane oxygenation, development of acute kidney injury, cardiac arrest, myocardial infarction, acute heart failure, pulmonary embolism, and disseminated intravascular coagulation after the diagnosis of COVID-19. Results: A total of 7327 patients were included; of these, 2964 patients (40.5%) were men and 4363 patients (59.5%) were women. There were no significant differences in the Charlson comorbidity index score between men and women in the same age group. The incidence of mortality and clinical outcomes was higher among men than among women. The mortality rate was the highest for the populations aged 50–64 or ≥65 years. The subgroup analyses for age, diabetes mellitus, or hypertension showed favorable results for patient survival or clinical outcomes for women compared to men. Conclusion: Our population-based study showed that female patients with COVID-19 were associated with favorable outcomes. Furthermore, the impact of sex was more evident in patients aged 50–64 or ≥65 years.

scholarly journals An explainable CNN approach for medical codes prediction from clinical text

2021 ◽  
Vol 21 (S9) ◽  
Author(s):  
Shuyuan Hu ◽  
Fei Teng ◽  
Lufei Huang ◽  
Jun Yan ◽  
Haibo Zhang
Keyword(s):  
International Classification Of Diseases ◽  
Text Documents ◽  
Clinical Text ◽  
Clinical Notes ◽  
Low Level ◽  
Classification Of Diseases ◽  
Convolution Filter ◽  
Automatic Coding ◽  
Mimic Iii ◽  
Icd Coding

Abstract Background Clinical notes are unstructured text documents generated by clinicians during patient encounters, generally are annotated with International Classification of Diseases (ICD) codes, which give formatted information about the diagnosis and treatment. ICD code has shown its potentials in many fields, but manual coding is labor-intensive and error-prone, lead to researches of automatic coding. Two specific challenges of this task are (1) given an annotated clinical notes, the reasons behind specific diagnoses and treatments are  implicit; (2) explainability is important for practical automatic coding method, the method should not only explain its prediction output but also have explainable internal mechanics. This study aims to develop an explainable CNN approach to address these two challenges. Method Our key idea is that for the automatic ICD coding task, the presence of informative snippets in the clinical text that correlated with each code plays an important role in the prediction of codes, and an informative snippet can be considered as a local and low-level feature. We infer that there exists a correspondence between a convolution filter and a local and low-level feature. Base on the inference, we come up with the Shallow and Wide Attention convolutional Mechanism (SWAM) to improve the CNN-based models’ ability to learn local and low-level features for each label. Results We evaluate our approach on MIMIC-III, an open-access dataset of ICU medical records. Our approach substantially outperforms previous results on top-50 medical code prediction on MIMIC-III dataset, the precision of the worst-performing 10% labels in previous works is increased from 0% to 53% on average. We attribute this improvement to SWAM, by which the wide architecture with attention mechanism gives the model ability to more extensively learn the unique features of different codes, and we prove it by an ablation experiment. Besides, we perform manual analysis of the performance imbalance between different codes, and preliminary conclude the characteristics that determine the difficulty of learning specific codes. Conclusions Our main contributions can be summarized into the following three: (1) We present local and low-level features, a.k.a. informative snippets play an important role in the automatic ICD coding task, and the informative snippets extracted from the clinical text provide explanations for each code. (2) We propose that there exists a correspondence between a convolution filter and a local and low-level feature. A combination of wide and shallow convolutional layer and attention layer can help the CNN-based models better learn local and low-level features. (3) We improved the precision of the worst-performing 10% labels from 0 to 53% on average.

scholarly journals Performance of Charlson and Elixhauser Comorbidity Index to Predict in-Hospital Mortality in Patients with Stroke in Sumadija and Western Serbia

2021 ◽  
Author(s):  
Biljana Bajic ◽  
Igor Galic ◽  
Natasa Mihailovic ◽  
Svetlana Ristic ◽  
Svetlana Radevic ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Area Under The Curve ◽  
Scoring Systems ◽  
International Classification Of Diseases ◽  
Administrative Databases ◽  
Stroke Patients ◽  
Discriminative Performance ◽  
Increased Risk ◽  
Classification Of Diseases ◽  
Comorbidity Index

Background: Comorbidities are major predictors of in-hospital mortality in stroke patients. The Charlson comorbidity index (CCI) and the Elikhauser comorbidity index (ECI) are scoring systems for classifying comorbidities. We aimed to compare the performance of the CCI and ECI to predict in-hospital mortality in stroke patients. Methods: We included patients hospitalized for stroke in the Clinical Center of Kragujevac, Serbia for the last 7 years. Hospitalizations caused by stroke, were identified by the International Classification of Diseases-10 (ICD-10) codes I60.0 - I69.9. All patients were divided into two cohorts: Alive cohort (n=3297) and Mortality cohort (n=978). Results: There were significant associations between higher CCIS and increased risk of in-hospital mortality (HR = 1.07, 95% CI = 1.01–1.12) and between higher ECIS and increased risk of in-hospital mortality (HR = 1.04, 95% CI = 0.99–1.09). Almost 2/3 patients (66.9%) had comorbidities included in the CCI score and 1/3 patients (30.2%) had comorbidities included in the ECI score. The statistically significant higher CCI score (t = -3.88, df = 1017.96, P <0.01) and ECI score (t = -6.7, df = 1447.32, P <0.01) was in the mortality cohort. Area Under the Curve for ECI score was 0.606 and for CCI score was 0.549. Conclusion: Both, the CCI and the ECI can be used as scoring systems for classifying comorbidities in the administrative databases, but the model’s ECI Score had a better discriminative performance of in-hospital mortality in the stroke patients than the CCI Score model.

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