Xiaobo Zhong1,2, Jung-Yi Lin1, Lihua Li1, A M Barrett3,4, Jashvant Poeran1,5,6, Madhu Mazumdar1,2. 1. Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA. 2. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA. 3. Department of Neurology, Emory University of Medicine, Decatur, Georgia, USA. 4. Center for Visual and Neurocognitive Rehabilitation, Atlanta VA Health Care System, Decatur, Georgia, USA. 5. Leni and Peter W. May Department of Orthopaedics, Icahn School of Medicine at Mount Sinai, New York, USA. 6. Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, USA.
Abstract
OBJECTIVE: To derive and validate a comorbidity-based delirium risk index (DRI) to predict postoperative delirium. DATA SOURCE/STUDY SETTING: Data of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20-25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation. STUDY DESIGN: Thirty-six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson-Deyo Index for goodness-of-fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness-of-fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries. DATA COLLECTION: Hip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD-9 codes. Postoperative delirium was defined by using ICD-9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium. PRINCIPAL FINDINGS: The derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson-Deyo Comorbidity Index with better goodness-of-fit and predictive performance. CONCLUSIONS: Delirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.
OBJECTIVE: To derive and validate a comorbidity-based delirium risk index (DRI) to predict postoperative delirium. DATA SOURCE/STUDY SETTING: Data of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20-25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation. STUDY DESIGN: Thirty-six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson-Deyo Index for goodness-of-fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness-of-fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries. DATA COLLECTION: Hip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD-9 codes. Postoperative delirium was defined by using ICD-9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium. PRINCIPAL FINDINGS: The derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson-Deyo Comorbidity Index with better goodness-of-fit and predictive performance. CONCLUSIONS: Delirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.
Authors: Hude Quan; Vijaya Sundararajan; Patricia Halfon; Andrew Fong; Bernard Burnand; Jean-Christophe Luthi; L Duncan Saunders; Cynthia A Beck; Thomas E Feasby; William A Ghali Journal: Med Care Date: 2005-11 Impact factor: 2.983
Authors: Jashvant Poeran; Crispiana Cozowicz; Nicole Zubizarreta; Sarah M Weinstein; Stacie G Deiner; Rosanne M Leipzig; Joseph I Friedman; Jiabin Liu; Madhu Mazumdar; Stavros G Memtsoudis Journal: Eur J Anaesthesiol Date: 2020-08 Impact factor: 4.330