The Relative Ability of Comorbidity Ascertainment Methodologies to Predict In-Hospital Mortality Among Hospitalized Community-acquired Pneumonia Patients

Abstract

Background– Despite widespread use of comorbidities for population health descriptions and risk adjustment, the ideal method for ascertaining comorbidities is not known. We sought to compare the relative value of several methodologies by which comorbidities may be ascertained. Methods– This is an observational study of 1,596 patients admitted to the University of Chicago for community-acquired pneumonia from 1998–2012. We collected data via chart abstraction, administrative data, and patient report, then performed logistic regression analyses, specifying comorbidities as independent variables and in-hospital mortality as the dependent variable. Finally, we compared area under the curve (AUC) statistics to determine the relative ability of each method of comorbidity ascertainment to predict in-hospital mortality. Results– Chart review (area under curve [AUC] 0.72) and administrative data (Charlson AUC 0.83, Elixhauser AUC 0.84) predicted in-hospital mortality with greater fidelity than patient report (AUC 0.61). However, multivariate logistic regression analyses demonstrated that individual comorbidity derivation via chart review had the strongest relationship with in-hospital mortality. This is consistent with prior literature suggesting that administrative data has inherent, paradoxical biases with important implications for risk adjustment based solely on administrative data. Conclusions– Although comorbidities derived through administrative data did produce an AUC greater than chart review, our analyses suggest a coding bias in several comorbidities with a paradoxically protective effect. Therefore, chart review, while labor and resource intensive, may be the ideal method for ascertainment of clinically-relevant comorbidities.

Underlying Medical Conditions and Severe Illness Among 540,667 Adults Hospitalized With COVID-19, March 2020–March 2021

“…Hypertension and disorders of lipid metabolism (the most prevalent conditions), and obesity and diabetes with complication (strong risk factors for death, IMV, and ICU admission) are associated with well-described hormonal and inflammatory pathways, also previously shown to be risk factors for severe COVID-19 illness (16). High baseline prevalence of obesity and diabetes, combined with their association with severe COVID-19 illness, sug-gest that these 2 conditions could have an outsized impact on the population with COVID-19.…”

“…Our sensitivity analysis of a subset of patients with pre-COVID encounters identified a higher relative risk of death associated with uncomplicated diabetes present before the first COVID hospitalization. Coding bias (uncomplicated diabetes may be less frequently coded in hospitalizations with severe outcomes) ( 17 ) or reverse causality (diabetes complications arising from COVID-19 illness or treatment) ( 18 ) could explain this finding.…”

“…For example, obesity was diagnosed in 33.0% of the patients, which is possibly an underestimate of this condition, given the national prevalence of 42.4% in 2017–2018 ( 30 ) and the prevalence of 50.8% among patients with available height and weight data in PHD-SR ( 14 ). Fifth, prior literature shows evidence of both increased documentation ( 31 ) and underdiagnosis of certain chronic conditions among patients with more severe illness ( 17 ). Sixth, the interrelation of the conditions made it difficult to obtain independent associations, which could explain why certain conditions (disorders of lipid metabolism, sleep–wake disorders, esophageal disorders, and depressive disorders) had a positive association with COVID-19 illness when not adjusted for other conditions and a negative association when adjusted for other conditions.…”

Differences in the Predictive value of Elixhauser Comorbidity Index and the Charlson Comorbidity indices in patients with hand infections

“…Regarding mortality, the CCI demonstrated superior discriminative ability compared to the ECI. Some studies report similar outcomes in a variety of specialties 44,45 while other studies report the opposite. 20,28,46,47 Due to this lack of consensus, a single, unifying opinion about superiority of one index over another seems to be impractical.…”

Early Reoperation Rates and Its Risk Factors after Instrumented Spinal Fusion Surgery for Degenerative Spinal Disease: A Nationwide Cohort Study of 65,355 Patients

“…Preexisting medical comorbidities appearing within 1 year before the index surgery were identified according to the ICD-10 codes ( Supplementary Table S1 ) and evaluated using the Charlson comorbidity index (CCI). The CCI score is the sum of the weighted scores for each comorbidity and shows good agreement with the ICD-10 codes [ 16 , 17 ]. Data regarding transfusion (allogenous or autologous; Supplementary Table S2 ) and steroid use ( Supplementary Table S3 ) during the index surgery were also retrieved.…”