A comparison of comorbidities obtained from hospital administrative data and medical charts in older patients with pneumonia
TL;DR: The validity of using secondary diagnoses from administrative data as an alternative to medical charts for identification of comorbidities varies with the specific condition in question, and is influenced by factors such as age, number of comerbidities, hospital admission in the previous 90 days, severity of illness, length of hospitalization, and whether inhospital death occurred.
Abstract: The use of comorbidities in risk adjustment for health outcomes research is frequently necessary to explain some of the observed variations. Medical charts reviews to obtain information on comorbidities is laborious. Increasingly, electronic health care databases have provided an alternative for health services researchers to obtain comorbidity information. However, the rates obtained from databases may be either over- or under-reported. This study aims to (a) quantify the agreement between administrative data and medical charts review across a set of comorbidities; and (b) examine the factors associated with under- or over-reporting of comorbidities by administrative data. This is a retrospective cross-sectional study of patients aged 55 years and above, hospitalized for pneumonia at 3 acute care hospitals. Information on comorbidities were obtained from an electronic administrative database and compared with information from medical charts review. Logistic regression was performed to identify factors that were associated with under- or over-reporting of comorbidities by administrative data. The prevalence of almost all comorbidities obtained from administrative data was lower than that obtained from medical charts review. Agreement between comorbidities obtained from medical charts and administrative data ranged from poor to very strong (kappa 0.01 to 0.78). Factors associated with over-reporting of comorbidities were increased length of hospital stay, disease severity, and death in hospital. In contrast, those associated with under-reporting were number of comorbidities, age, and hospital admission in the previous 90 days. The validity of using secondary diagnoses from administrative data as an alternative to medical charts for identification of comorbidities varies with the specific condition in question, and is influenced by factors such as age, number of comorbidities, hospital admission in the previous 90 days, severity of illness, length of hospitalization, and whether inhospital death occurred. These factors need to be taken into account when relying on administrative data for comorbidity information.
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TL;DR: Identifying patient characteristics and conditions associated with mortality with COVID-19 is important for hypothesis generating for clinical trials and to develop targeted intervention strategies.
Abstract: Background At the beginning of June 2020, there were nearly 7 million reported cases of coronavirus disease 2019 (COVID-19) worldwide and over 400,000 deaths in people with COVID-19. The objective of this study was to determine associations between comorbidities listed in the Charlson comorbidity index and mortality among patients in the United States with COVID-19. Methods and findings A retrospective cohort study of adults with COVID-19 from 24 healthcare organizations in the US was conducted. The study included adults aged 18–90 years with COVID-19 coded in their electronic medical records between January 20, 2020, and May 26, 2020. Results were also stratified by age groups (<50 years, 50–69 years, or 70–90 years). A total of 31,461 patients were included. Median age was 50 years (interquartile range [IQR], 35–63) and 54.5% (n = 17,155) were female. The most common comorbidities listed in the Charlson comorbidity index were chronic pulmonary disease (17.5%, n = 5,513) and diabetes mellitus (15.0%, n = 4,710). Multivariate logistic regression analyses showed older age (odds ratio [OR] per year 1.06; 95% confidence interval [CI] 1.06–1.07; p < 0.001), male sex (OR 1.75; 95% CI 1.55–1.98; p < 0.001), being black or African American compared to white (OR 1.50; 95% CI 1.31–1.71; p < 0.001), myocardial infarction (OR 1.97; 95% CI 1.64–2.35; p < 0.001), congestive heart failure (OR 1.42; 95% CI 1.21–1.67; p < 0.001), dementia (OR 1.29; 95% CI 1.07–1.56; p = 0.008), chronic pulmonary disease (OR 1.24; 95% CI 1.08–1.43; p = 0.003), mild liver disease (OR 1.26; 95% CI 1.00–1.59; p = 0.046), moderate/severe liver disease (OR 2.62; 95% CI 1.53–4.47; p < 0.001), renal disease (OR 2.13; 95% CI 1.84–2.46; p < 0.001), and metastatic solid tumor (OR 1.70; 95% CI 1.19–2.43; p = 0.004) were associated with higher odds of mortality with COVID-19. Older age, male sex, and being black or African American (compared to being white) remained significantly associated with higher odds of death in age-stratified analyses. There were differences in which comorbidities were significantly associated with mortality between age groups. Limitations include that the data were collected from the healthcare organization electronic medical record databases and some comorbidities may be underreported and ethnicity was unknown for 24% of participants. Deaths during an inpatient or outpatient visit at the participating healthcare organizations were recorded; however, deaths occurring outside of the hospital setting are not well captured. Conclusions Identifying patient characteristics and conditions associated with mortality with COVID-19 is important for hypothesis generating for clinical trials and to develop targeted intervention strategies.
309 citations
Cites background from "A comparison of comorbidities obtai..."
...Recording of ICD codes in administrative data may vary by factors such as age, number of comorbidities, severity of illness, length of hospitalization, and whether in-hospital death occurred [21]....
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TL;DR: In this article, the authors identified frequent underlying conditions and their attributable risk of severe COVID-19 illness and used multivariable generalized linear models to estimate adjusted risk of intensive care unit admission, invasive mechanical ventilation, and death associated with frequent conditions and total number of conditions.
Abstract: INTRODUCTION: Severe COVID-19 illness in adults has been linked to underlying medical conditions. This study identified frequent underlying conditions and their attributable risk of severe COVID-19 illness. METHODS: We used data from more than 800 US hospitals in the Premier Healthcare Database Special COVID-19 Release (PHD-SR) to describe hospitalized patients aged 18 years or older with COVID-19 from March 2020 through March 2021. We used multivariable generalized linear models to estimate adjusted risk of intensive care unit admission, invasive mechanical ventilation, and death associated with frequent conditions and total number of conditions. RESULTS: Among 4,899,447 hospitalized adults in PHD-SR, 540,667 (11.0%) were patients with COVID-19, of whom 94.9% had at least 1 underlying medical condition. Essential hypertension (50.4%), disorders of lipid metabolism (49.4%), and obesity (33.0%) were the most common. The strongest risk factors for death were obesity (adjusted risk ratio [aRR] = 1.30; 95% CI, 1.27-1.33), anxiety and fear-related disorders (aRR = 1.28; 95% CI, 1.25-1.31), and diabetes with complication (aRR = 1.26; 95% CI, 1.24-1.28), as well as the total number of conditions, with aRRs of death ranging from 1.53 (95% CI, 1.41-1.67) for patients with 1 condition to 3.82 (95% CI, 3.45-4.23) for patients with more than 10 conditions (compared with patients with no conditions). CONCLUSION: Certain underlying conditions and the number of conditions were associated with severe COVID-19 illness. Hypertension and disorders of lipid metabolism were the most frequent, whereas obesity, diabetes with complication, and anxiety disorders were the strongest risk factors for severe COVID-19 illness. Careful evaluation and management of underlying conditions among patients with COVID-19 can help stratify risk for severe illness.
133 citations
TL;DR: Charlson comorbidity index scores from chart review and administrative data showed good agreement and predicted 30-day and 1-year mortality in ICU patients as well as the physiology-based SAPS II.
Abstract: Purpose This study compared the Charlson comorbidity index (CCI) information derived from chart review and administrative systems to assess the completeness and agreement between scores, evaluate the capacity to predict 30-day and 1-year mortality in intensive care unit (ICU) patients, and compare the predictive capacity with that of the Simplified Acute Physiology Score (SAPS) II model. Patients and methods Using data from 959 patients admitted to a general ICU in a Norwegian university hospital from 2007 to 2009, we compared the CCI score derived from chart review and administrative systems. Agreement was assessed using % agreement, kappa, and weighted kappa. The capacity to predict 30-day and 1-year mortality was assessed using logistic regression, model discrimination with the c-statistic, and calibration with a goodness-of-fit statistic. Results The CCI was complete (n=959) when calculated from chart review, but less complete from administrative data (n=839). Agreement was good, with a weighted kappa of 0.667 (95% confidence interval: 0.596-0.714). The c-statistics for categorized CCI scores from charts and administrative data were similar in the model that included age, sex, and type of admission: 0.755 and 0.743 for 30-day mortality, respectively, and 0.783 and 0.775, respectively, for 1-year mortality. Goodness-of-fit statistics supported the model fit. Conclusion The CCI scores from chart review and administrative data showed good agreement and predicted 30-day and 1-year mortality in ICU patients. CCI combined with age, sex, and type of admission predicted mortality almost as well as the physiology-based SAPS II.
107 citations
Cites result from "A comparison of comorbidities obtai..."
...Secondary codes from administrative data do not indicate whether the conditions were preexisting or appeared after an admission; hence, conditions based on laboratory tests may have a higher prevalence in administrative data than in medical charts.(13) An advantage of using administrative health data for estimating CCI is that it can easily be collected for a large number of subjects, in contrast to more resource-consuming chart reviews....
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TL;DR: Marital status, Medicaid insurance status, and race may indicate how a patient's social and economic resources can impact his or her risk of being readmitted to the hospital, regardless of demographics or discharge disposition.
Abstract: Background: Reducing hospital readmissions has become a priority in the development of policies aimed at patient safety and cost reduction. Evaluating the incidence of rehospitalization of orthopaedic surgical patients could help to identify targets for more efficient perioperative care. We addressed two questions: What is the incidence of thirty-day readmission for orthopaedic patients at an academic hospital? Can any risk factors for readmission be identified among rehospitalized patients?
Methods: This is a retrospective cohort study examining 3264 orthopaedic surgical admissions during two fiscal years from the hospital’s quality-improvement database. Cases of patients with unplanned readmission within thirty days were subjected to univariate and multivariate analysis to determine the odds ratio (OR) for readmission. Further descriptive analysis was performed with use of electronic medical record data from the cohort of readmitted patients.
Results: The estimated cumulative incidence of unplanned thirty-day readmissions was 4.2% (i.e., 138 of the 3261 patients who were eligible for the study). Multivariate analysis indicated that marital status of “widowed” significantly increased the risk of readmission (OR, 1.846; 95% confidence interval [CI], 1.070 to 3.184; p = 0.03). Race significantly increased the odds of readmission in patients identified as African-American (OR, 2.178; 95% CI, 1.077 to 4.408; p = 0.03), or American Indian or Alaskan Native race (OR, 3.550; 95% CI, 1.429 to 8.815; p = 0.006). The risk of readmission was significant at p < 0.10 (OR 1.547; 95% CI, 0.941 to 2.545; p = 0.09) for patients with Medicaid insurance. Any intensive care unit stay gave the highest OR of readmission (OR, 2.356; 95% CI, 1.361 to 4.079; p = 0.002) for all demographic groups. Mean length of hospital stay was significantly longer, 5.9 days in the unplanned readmission group compared with 3.6 days for non-readmitted patients (OR, 1.038; 95% CI, 1.014 to 1.062; p = 0.002). Chart review of readmitted patients showed that 102 readmissions (73.9%) were classified as surgical; of these, thirty-five readmission events (34.3%) were for infection at the surgical site.
Conclusions: Longer length of hospital stay or admission to the intensive care unit significantly increased the likelihood of thirty-day readmission, regardless of demographics or discharge disposition. Marital status, Medicaid insurance status, and race may indicate how a patient’s social and economic resources can impact his or her risk of being readmitted to the hospital.
Level of Evidence: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.
99 citations
TL;DR: Different individuals, with different combinations of conditions, are identified as multimorbid when different data sources are used, and caution should be applied when ascertaining morbidity from a single data source as the agreement between self-report and administrative data is generally poor.
Abstract: Background
Estimating multimorbidity (presence of two or more chronic conditions) using administrative data is becoming increasingly common. We investigated (1) the concordance of identification of chronic conditions and multimorbidity using self-report survey and administrative datasets; (2) characteristics of people with multimorbidity ascertained using different data sources; and (3) whether the same individuals are classified as multimorbid using different data sources.
Methods
Baseline survey data for 90,352 participants of the 45 and Up Study—a cohort study of residents of New South Wales, Australia, aged 45 years and over—were linked to prior two-year pharmaceutical claims and hospital admission records. Concordance of eight self-report chronic conditions (reference) with claims and hospital data were examined using sensitivity (Sn), positive predictive value (PPV), and kappa (κ).The characteristics of people classified as multimorbid were compared using logistic regression modelling.
Results
Agreement was found to be highest for diabetes in both hospital and claims data (κ = 0.79, 0.78; Sn = 79%, 72%; PPV = 86%, 90%). The prevalence of multimorbidity was highest using self-report data (37.4%), followed by claims data (36.1%) and hospital data (19.3%). Combining all three datasets identified a total of 46 683 (52%) people with multimorbidity, with half of these identified using a single dataset only, and up to 20% identified on all three datasets. Characteristics of persons with and without multimorbidity were generally similar. However, the age gradient was more pronounced and people speaking a language other than English at home were more likely to be identified as multimorbid by administrative data.
Conclusions
Different individuals, with different combinations of conditions, are identified as multimorbid when different data sources are used. As such, caution should be applied when ascertaining morbidity from a single data source as the agreement between self-report and administrative data is generally poor. Future multimorbidity research exploring specific disease combinations and clusters of diseases that commonly co-occur, rather than a simple disease count, is likely to provide more useful insights into the complex care needs of individuals with multiple chronic conditions.
56 citations
References
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TL;DR: The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death fromComorbid disease for use in longitudinal studies and further work in larger populations is still required to refine the approach.
39,961 citations
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01 Jan 1981TL;DR: In this paper, the basic theory of Maximum Likelihood Estimation (MLE) is used to detect a difference between two different proportions of a given proportion in a single proportion.
Abstract: Preface.Preface to the Second Edition.Preface to the First Edition.1. An Introduction to Applied Probability.2. Statistical Inference for a Single Proportion.3. Assessing Significance in a Fourfold Table.4. Determining Sample Sizes Needed to Detect a Difference Between Two Proportions.5. How to Randomize.6. Comparative Studies: Cross-Sectional, Naturalistic, or Multinomial Sampling.7. Comparative Studies: Prospective and Retrospective Sampling.8. Randomized Controlled Trials.9. The Comparison of Proportions from Several Independent Samples.10. Combining Evidence from Fourfold Tables.11. Logistic Regression.12. Poisson Regression.13. Analysis of Data from Matched Samples.14. Regression Models for Matched Samples.15. Analysis of Correlated Binary Data.16. Missing Data.17. Misclassification Errors: Effects, Control, and Adjustment.18. The Measurement of Interrater Agreement.19. The Standardization of Rates.Appendix A. Numerical Tables.Appendix B. The Basic Theory of Maximum Likelihood Estimation.Appendix C. Answers to Selected Problems.Author Index.Subject Index.
16,435 citations
10,442 citations
TL;DR: The present method addresses some of the limitations of previous measures and produces an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.
Abstract: Objectives.This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets.Methods.The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other
8,138 citations
"A comparison of comorbidities obtai..." refers methods in this paper
...For comorbidities, the set of 30 conditions listed by Elixhauser et al [34] was used, with the exception of Human Immunodeficiency Virus (HIV) infection because the medical charts of patients with HIV were not available for review....
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Book•
15 Aug 2005
TL;DR: In this paper, the authors present a linear variance-components model for expiratory flow measurements, which is based on the Mini Wright measurements, and a three-level logistic random-intercept model.
Abstract: Preface LINEAR VARIANCE-COMPONENTS MODELS Introduction How reliable are expiratory flow measurements? The variance-components model Modeling the Mini Wright measurements Estimation methods Assigning values to the random intercepts Summary and further reading Exercises LINEAR RANDOM-INTERCEPT MODELS Introduction Are tax preparers useful? The longitudinal data structure Panel data and correlated residuals The random-intercept model Different kinds of effects in panel models Endogeneity and between-taxpayer effects Residual diagnostics Summary and further reading Exercises LINEAR RANDOM-COEFFICIENT AND GROWTH-CURVE MODELS Introduction How effective are different schools? Separate linear regressions for each school The random-coefficient model How do children grow? Growth-curve modeling Two-stage model formulation Prediction of trajectories for individual children Complex level-1 variation or heteroskedasticity Summary and further reading Exercises DICHOTOMOUS OR BINARY RESPONSES Models for dichotomous responses Which treatment is best for toenail infection? The longitudinal data structure Population-averaged or marginal probabilities Random-intercept logistic regression Subject-specific vs. population-averaged relationships Maximum likelihood estimation using adaptive quadrature Empirical Bayes (EB) predictions Other approaches to clustered dichotomous data Summary and further reading Exercises ORDINAL RESPONSES Introduction Cumulative models for ordinal responses Are antipsychotic drugs effective for patients with schizophrenia? Longitudinal data structure and graphs A proportional-odds model A random-intercept proportional-odds model A random-coefficient proportional-odds model Marginal and patient-specific probabilities Do experts differ in their grading of student essays? A random-intercept model with grader bias Including grader-specific measurement error variances Including grader-specific thresholds Summary and further reading Exercises COUNTS Introduction Types of counts Poisson model for counts Did the German health-care reform reduce the number of doctor visits? Longitudinal data structure Poisson regression ignoring overdispersion and clustering Poisson regression with overdispersion but ignoring clustering Random-intercept Poisson regression Random-coefficient Poisson regression Other approaches to clustered counts Which Scottish countries have a high risk of lip cancer? Standardized mortality ratios Random-intercept Poisson regression Nonparametric maximum likelihood estimation Summary and further reading Exercises HIGHER LEVEL MODELS AND NESTED RANDOM EFFECTS Introduction Which method is best for measuring expiratory flow? Two-level variance-components models Three-level variance-components models Did the Guatemalan immunization campaign work? A three-level logistic random-intercept model Summary and further reading Exercises CROSSED RANDOM EFFECTS Introduction How does investment depend on expected profit and capital stock? A two-way error-components model How much do primary and secondary schools affect attainment at age 16? An additive crossed random-effects model Including a random interaction A trick requiring fewer random effects Summary and further reading Exercises APPENDIX A: Syntax for gllamm, eq, and gllapred APPENDIX B: Syntax for gllamm APPENDIX C: Syntax for gllapred APPENDIX D: Syntax for gllasim References Author Index Subject Index
4,086 citations
"A comparison of comorbidities obtai..." refers methods in this paper
...Hierarchical multinomial regression modeling was performed for these analyses using the STATA program for generalized linear latent and mixed models (GLLAMM) [37]....
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