2020
DOI: 10.1371/journal.pone.0238182
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The Australian Injury Comorbidity Indices (AICIs) to predict in-hospital complications: A population-based data linkage study

Abstract: 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 us… Show more

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Cited by 4 publications
(2 citation statements)
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“…The optimal metric to quantify the burden of preexisting medical conditions among trauma patients is a topic of ongoing research and debate, and the limitations of CCI in trauma patients are well described (34)(35)(36). analysis demonstrated that several preexisting medical conditions included in CCI were not associated with risk of UIR in either the adult or elderly patient cohorts.…”
Section: Discussionmentioning
confidence: 99%
“…The optimal metric to quantify the burden of preexisting medical conditions among trauma patients is a topic of ongoing research and debate, and the limitations of CCI in trauma patients are well described (34)(35)(36). analysis demonstrated that several preexisting medical conditions included in CCI were not associated with risk of UIR in either the adult or elderly patient cohorts.…”
Section: Discussionmentioning
confidence: 99%
“…Presence of hypertension 15 , dyslipidemia 15 , atrial fibrillation (AF) 4 , 19 , 20 , hospitalization for heart failure (hHF) 4 , 21 , 22 , dementia 23 , 24 , and end‐stage renal disease (ESRD) 14 were defined according to previous studies. Charlson Comorbidity Index (CCI) was calculated according to the established method 25 , 26 based on the diagnostic codes provided in previous reports 27 , 28 . Low‐income level was assigned to the lowest 20% of the entire population based on monthly household income.…”
Section: Methodsmentioning
confidence: 99%