Effect of comorbidity on injury outcomes: a review of existing indices

Fernando, Dasamal Tharanga; Berecki‐Gisolf, Janneke; Newstead, Stuart; Ansari, Zahid

doi:10.1016/j.annepidem.2019.06.004

Cited by 14 publications

(5 citation statements)

References 61 publications

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“…This study demonstrated the variation in associations between comorbidity and outcomes, depending on the outcome measure, confirming suggestions from previous studies which recommended study- and outcome-specific comorbidity indices [ 35 – 39 ]. These indices were derived using a population-based database; the indices are current and can be used for general injury patients.…”

Section: Discussionsupporting

confidence: 88%

The Australian Injury Comorbidity Indices (AICIs) to predict in-hospital complications: A population-based data linkage study

et al. 2020

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Background Hospital-admitted patients are at risk of experiencing certain adverse outcomes during their hospital-stay. Patients may need to be admitted to the intensive care unit or be placed on the ventilator while there is also a possibility for complications to develop. Pre-existing comorbidity could increase the risk of these outcomes. The Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Measure (ECM), originally derived for mortality outcomes among general medical populations, are widely used for assessing these in-hospital complications even among specific injury populations. This study derived indices to specifically capture the effect of comorbidity on intensive care unit and ventilator use as well as hospital-acquired complications for injury patients. Methods Retrospective data on injury hospital-admissions from July 2012 to June 2014 (161,334 patients) for the state of Victoria, Australia was analysed. Results from multivariable regression analysis were used to derive the Australian Injury Comorbidity Indices (AICIs) for intensive care unit and ventilator hours and hospital-acquired complications. The AICIs, CCI and ECM were validated on data from Victoria and two other Australian states. Results Five comorbidities were significantly associated with intensive care unit hours, two with ventilator hours and fifteen with hospital-acquired complications for hospitalised injury patients. Not all diseases listed in the CCI or ECM were found to be associated with these outcomes. The AICIs performed equally well in terms of predictive ability to the long-listed ECM and in most instances outperformed the CCI.

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Section: Discussionsupporting

confidence: 88%

The Australian Injury Comorbidity Indices (AICIs) to predict in-hospital complications: A population-based data linkage study

et al. 2020

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“…Other studies have also found similar AUROC values for ASA classification (0.584, 95% CI 0.578-0.589), mCCI (0.567, 95% CI 0.561-0.573), and mFI (0.534, 95% CI 0.529-0.539). 9 , 10 , 20 , 21 In this data set, the mean complication rate was 18.6%. In our data set, there was a stepwise increase in complication rate with increasing CRS ( Table IV ).…”

Section: Resultsmentioning

confidence: 84%

“…This demonstrates a similar discriminative ability to the most common comorbidity indices currently utilized, including ASA, mCCI, and mFI. 9 , 10 , 20 , 21 …”

Section: Discussionmentioning

confidence: 99%

A novel comorbidity risk score for predicting postoperative 30-day complications in total shoulder arthroplasty and elucidation of potential racial disparities

Turk¹,

Li²,

Saini³

et al. 2022

JSES International

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“…The number of comorbidities for each enrollee was calculated using the Elixhauser Index for adults (ages 18–64) and the Pediatric Complex Chronic Conditions Classification System for children (ages 0–17) [ 10 , 11 ]. Both indices have been used to identify preexisting conditions among large administrative data sources [ 12–17 ].…”

Section: Methodsmentioning

confidence: 99%

Regional Variation in Outpatient Antibiotic Prescribing for Acute Respiratory Tract Infections in a Commercially Insured Population, United States, 2017

Bizune

Tsay

Palms

et al. 2023

Open Forum Infectious Diseases

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Background Studies have shown that the Southern United States has higher rates of outpatient antibiotic prescribing rates compared with other regions in the country, but the reasons for this variation are unclear. We aimed to determine whether the regional variability in outpatient antibiotic prescribing for respiratory diagnoses can be explained by differences in prescriber clinical factors found in a commercially insured population. Methods We analyzed the 2017 IBM MarketScan Commercial Database of commercially insured individuals aged <65 years. We included visits with acute respiratory tract infection (ARTI) diagnoses from retail clinics, urgent care centers, emergency departments, and physician offices. ARTI diagnoses were categorized based on antibiotic indication. We calculated risk ratios and 95% CIs stratified by ARTI tier and region using log-binomial models controlling for patient age, comorbidities, care setting, prescriber type, and diagnosis. Results Of the 14.9 million ARTI visits, 40% received an antibiotic. The South had the highest proportion of visits with an antibiotic prescription (43%), and the West the lowest (34%). ARTI visits in the South are 34% more likely receive an antibiotic for rarely antibiotic-appropriate ARTI visits when compared with the West in multivariable modeling (relative risk, 1.34; 95% CI, 1.33–1.34). Conclusions It is likely that higher antibiotic prescribing in the South is in part due to nonclinical factors such as regional differences in clinicians' prescribing habits and patient expectations. There is a need for future studies to define and characterize these factors to better inform regional and local stewardship interventions and achieve greater health equity in antibiotic prescribing.

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Effect of comorbidity on injury outcomes: a review of existing indices

Cited by 14 publications

References 61 publications

The Australian Injury Comorbidity Indices (AICIs) to predict in-hospital complications: A population-based data linkage study

The Australian Injury Comorbidity Indices (AICIs) to predict in-hospital complications: A population-based data linkage study

A novel comorbidity risk score for predicting postoperative 30-day complications in total shoulder arthroplasty and elucidation of potential racial disparities

Regional Variation in Outpatient Antibiotic Prescribing for Acute Respiratory Tract Infections in a Commercially Insured Population, United States, 2017

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