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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 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. CONCLUSIONS:Associations between outcomes and comorbidities vary based on the type of outcome measure. The new comorbidity indices developed in this study provide a relevant, parsimonious and up-to-date method to capture the effect of comorbidity on in-hospital complications among admitted injury patients and is better suited for use in that context compared to the CCI and ECM.

SUBMITTER: Fernando DT

PROVIDER: S-EPMC7485849 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Fernando Dasamal Tharanga DT Berecki-Gisolf Janneke J Newstead Stuart S Ansari Zahid Z

PloS one 20200911 9

<h4>Background</h4>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 pop ...[more]

PMID: 32915808

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Project description:BackgroundA wide range of outcome measures can be calculated for hospital-treated injury patients. These include mortality, use of critical care services, complications, length of stay, treatment costs, readmission and nursing care after discharge. Each address different aspects and phases of injury recovery and can yield vastly different results. This study aims to: (1) measure and report this range of outcomes in hospital-treated injury patients in a defined population; and (2) describe the associations between injury characteristics, socio-demographics and comorbidities and the various outcomes.MethodsA retrospective analysis was conducted of injury-related hospital admissions from July 2012 to June 2014 (152,835 patients) in Victoria, Australia. The admission records were linked within the dataset, enabling follow-up, to assess the outcomes of in-hospital death, burden, complications and 30-day readmissions. Associations between factors and outcomes were determined using univariate regression analysis.ResultsThe proportion of patients who died in hospital was 0.9%, while 26.8% needed post-discharge care. On average patients had 2.4 complications (confidence interval (CI) 2.4-2.5) related to their initial injury, the mean cost of treating a patient was Australian dollars 7013 (CI 6929-7096) and the median length of stay was one day (inter quartile range 1-3). Intensive-care-unit-stay was recorded in 3% of the patients. All-cause 30-day readmissions occurred in 12.3%, non-planned 30-day readmissions in 7.9%, while potentially avoidable 30-day readmissions were observed in 3.2% of the patients. Increasing age was associated with all outcomes. The need for care post-discharge from hospital was highest among children and the oldest age group (85 years and over). Injury severity was associated with all adverse outcomes. Increasing number of comorbidities increased the likelihood of all outcomes. Overall, outcomes are shown to differ by age, gender, comorbidities, body region injured, injury type and injury severity, and to a lesser extent by socio-economic areas.ConclusionsOutcomes and risk factors differ depending on the outcome measured, and the method used for measuring the outcome. Similar outcomes measured in different ways produces varying results. Data linkage has provided a valuable platform for a comprehensive overview of outcomes, which can help design and target secondary and tertiary preventive measures.

Project description:BackgroundUnderstanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals.MethodsRetrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012-2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types.ResultsOverall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865-0.868) and van Walraven's weights (0.863, 95% CI, 0.862-0.864) had substantial advantage over Charlson's weights (0.850, 95% CI, 0.849-0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights.ConclusionsAll weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

Project description:INTRODUCTION:The surgical comorbidity assessment is important for patient risk stratification, counseling, and research. In adults, risk assessment indices, such as the Charlson Co-morbidity Score (CCS) or Van Walraven Index (VWI), are well established. In pediatrics, however, risk assessment indices are scarce. Recently, a pediatric-specific risk assessment index, the Rhee index, was developed to discriminate mortality for pediatric general surgery patients. Currently, there is no validated risk assessment tool in pediatric urology. OBJECTIVE:We compared the performance of the CCS, VWI, and Rhee Index in discriminating postoperative complications and readmissions to the emergency room/inpatient unit after pediatric urological procedures. METHODS:We analyzed the Nationwide Readmissions Database (NRD), State Inpatient Databases (SID), and State Emergency Department Databases (SEDD). We included patients (<18 years) who underwent the following urological procedures: ureteroneocystostomy, ureteroureterostomy, radical/partial nephrectomy, pyeloplasty, appendicovesicostomy, enterocystoplasty, vesicostomy, and bladder neck sling. Complications were identified based on definitions in the National Surgical Quality Improvement Program (NSQIP). Thirty-day emergency room admission and inpatient readmissions were extracted. Comorbidity scores were calculated using each of the three indices. We compared the performance of each index in discriminate primarily postoperative complications in the NRD and both admission types in the SID/SEDD by constructing a receiver operating characteristics (ROC). AUCs were compared using the Delong method. This protocol was reviewed by our Institutional Review Board and deemed to be exempt. RESULTS:We identified a total of 8006 patients in NRD and 6236 patients in SID/SEDD. The Rhee index had the best performance for discriminating postoperative complications (AUC = 0.67, 95% CI 0.64-0.70) compared to CCS (AUC = 0.62, 95% CI 0.60-0.65) and VWI (AUC = 0.62, 95% CI 0.59-0.65); p < 0.01. The CCS had the best performance for discriminating 30-day inpatient readmissions (AUC = 0.63, 95% CI 0.61-0.66) than VWI (AUC = 0.54, 95% CI 0.52-0.57), and Rhee Index (AUC = 0.56, 95% CI 0.54-0.59); p < 0.0001. All three indices had similarly poor discrimination for 30-day ER admissions: CCS (AUC = 0.52), VWI (AUC = 0.51), and Rhee Index (AUC = 0.50); p = 0.5 (see Table). DISCUSSION:The Rhee Index had the best performance for discriminating postoperative complications, while the CCS was superior for discriminating inpatient readmissions among the three indices. Limitations to our study include inpatient-only procedures, inability to identify complications managed in clinics, omission of secondary operations, accounting for parental anxiety, and the generalizability of SID. CONCLUSIONS:The three comorbidity indices evaluated are poor discriminators for postoperative complications, 30-day inpatient readmissions or 30-day ER admissions. A new index is needed for pediatric urology patients.

Project description:BACKGROUND:Predicting risk of premature death is one of the most basic tasks in medicine and public health, but has proven to be difficult over the long term even with the best prognostic models. One popular strategy has been to improve prognostic models with candidate genes and other novel biomarkers. However, the gains in predictive power have been modest and the costs have been high, leading to a demand for cost-effective alternatives. We conducted a proof-of-principle investigation to examine whether simple, cheap, and noninvasive paper-and-pencil measures of social class and personality phenotype could improve the performance of one of the most widely used prediction models for all-cause mortality, the Charlson Comorbidity Index (CCI). METHODS:We used data from baseline and 25-year mortality follow-up of the UK Health and Lifestyle Study cohort. In a subset of the cohort, we first identified 5 psychosocial factors highly predictive of mortality: income, education, type A personality, communalism (preference for the company of others), and "lie" scale (a measure of denial, putatively associated with ill health). We then examined the predictive performance of the CCI with and without these measures in a validation subsample. RESULTS:Across 5-, 10-, 15-, 20-, and 25-year time horizons, the psychosocially augmented CCI showed substantially better discrimination [area under the receiver-operating curves (95% confidence interval) from 0.83 (0.81-0.85) to 0.84 (0.83-0.86)] than the CCI [area under the receiver-operating curves from 0.74 (0.71-0.76) to 0.77 (0.76-0.79)]. These translated into net reclassification improvements from 27% (23%-31%) to 35% (32%-38%) of survivors and from 23% (17%-30%) to 34% (17%-30%) of decedents; and 23%-42% reductions in the Number Needed to Screen. Calibration improved at all time horizons except 25 years, where it was decreased. CONCLUSION:Widespread attempts to improve prognostic models might consider not only novel biomarkers, but also psychosocial questionnaire measures.

Project description:BackgroundSeveral different indices summarize patient comorbidity using health care data. An accurate index can be used to describe the risk profile of patients, and as an adjustment factor in analyses. How well these indices perform in persons with chronic kidney disease (CKD) is not well known.ObjectiveAssess the performance of 5 comorbidity indices at predicting mortality in 3 different patient groups with CKD: incident kidney transplant recipients, maintenance dialysis patients, and individuals with low estimated glomerular filtration rate (eGFR).DesignPopulation-based retrospective cohort study.SettingOntario, Canada, between 2004 and 2014.PatientsIndividuals at the time they first received a kidney transplant, received maintenance dialysis, or were confirmed to have an eGFR less than 45 mL/min per 1.73m2.MeasurementsFive comorbidity indices: Charlson comorbidity index, end-stage renal disease-modified Charlson comorbidity index, Johns Hopkins' Aggregated Diagnosis Groups score, Elixhauser score, and Wright-Khan index. Our primary outcome was 1-year all-cause mortality.MethodsComorbidity indices were estimated using information in the prior 2 years. Each group was randomly divided 100 times into derivation and validation samples. Model discrimination was assessed using median c-statistics from logistic regression models, and calibration was evaluated graphically.ResultsWe identified 4111 kidney transplant recipients, 23 897 individuals receiving maintenance dialysis, and 181 425 individuals with a low eGFR. Within 1 year, 108 (2.6%), 4179 (17.5%), and 17 898 (9.9%) in each group had died, respectively. In the validation sample, model discrimination was inadequate with median c-statistics less than 0.7 for all 5 comorbidity indices for all 3 groups. Calibration was also poor for all models.LimitationsThe study used administrative health care data so there is the potential for misclassification. Indices were modeled as continuous scores as opposed to indicators for individual conditions to limit overfitting.ConclusionsExisting comorbidity indices do not accurately predict 1-year mortality in patients with CKD. Current indices could be modified with additional risk factors to improve their performance in CKD, or a new index could be developed for this population.

Dataset Information

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

Publications

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

Similar Datasets

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