The Australian Injury Comorbidity Index to Predict Mortality

International Classification of Disease based Injury Severity Score (ICISS): a comparison of methodologies applied to linked data from New South Wales, Australia

BACKGROUND

The International Classification of Disease Injury Severity Score (ICISS) provides an efficient method to determine injury severity in hospitalised injury patients. Injury severity metrics are of particular interest for the tracking of road transport injury rates and trends. The aims of this study were to calculate ICISS using linked morbidity and mortality datasets and to compare predictive ability of various methods and metrics.

METHODS

This was a retrospective analysis of Admitted Patient Data Collection records from New South Wales, Australia, linked with mortality data. Using a split sample approach, design data (2008-2014; n=1 035 174 periods of care) was used to derive survival risk ratios and calculate various ICISS scales based on in-hospital death and 3-month death. These scales were applied to testing data (2015-2017; n=575 306). Logistic regression modelling was used to determine model discrimination and calibration.

RESULTS

There were 12 347 (1.19%) in-hospital deaths and 29 275 (2.83%) 3-month deaths in the design data. Model discrimination ranged from acceptable to excellent (area under the curve 0.75-0.88). Serious injury (ICISS≤0.941) rates in the testing data varied, with a range of 10%-31% depending on the methodology. The 'worst injury' ICISS was always superior to 'multiplicative injury' ICISS in model discrimination and calibration.

CONCLUSIONS

In-hospital death and 3-month death were used to generate ICISS; the former is recommended for settings with a focus on short-term threat to life, such as in trauma care settings. The 3-month death approach is recommended for outcomes beyond immediate clinical care, such as injury compensation schemes.

Trends in mortality outcomes of hospital-admitted injury in Victoria, Australia 2001-2021

Due to advancements in trauma treatment methods, it is expected that survivability of hospital-admitted injuries gradually improves over time. However, measurement of trends in all-cause injury survivability is complicated by changes in case mix, demographics and hospital admission policy. The aim of this study is to determine trends in hospital-admitted injury survivability in Victoria, Australia, taking case-mix and patient demographics into account, and to explore the potential impact of changes in hospital admission practices. Injury admission records (ICD-10-AM codes S00-T75 and T79) between 1 July 2001 and 30 June 2021 were extracted from the Victorian Admitted Episodes Dataset. ICD-based Injury Severity Score (ICISS) calculated from Survival Risk Ratios for Victoria was used as an injury severity measure. Death-in-hospital was modelled as a function of financial year, adjusting for age group, sex and ICISS, as well as admission type and length of stay. There were 19,064 in-hospital deaths recorded in 2,362,991 injury-related hospital admissions in 2001/02-2020/21. Rates of in-hospital death decreased from 1.00% (866/86,998) in 2001/02 to 0.72% (1115/154,009) in 2020/21. ICISS was a good predictor of in-hospital death with an area-under-the-curve of 0.91. In-hospital death was associated with financial year (Odds Ratio 0.950 [95%CI 0.947, 0.952]), in logistic regression modelling adjusted for ICISS, age and sex. In stratified modelling, decreasing injury death trends were observed in each of the top 10 injury diagnoses (together constituting > 50% of cases). Admission type and length of stay were added to the model: these did not alter the effect of year on in-hospital death. In conclusion, a 28% reduction in rates of in-hospital deaths in Victoria was observed over the 20-year study period, in spite of aging of the injured population. This amounts to 1222 additional lives saved in 2020/21 alone. Survival Risk Ratios therefore change markedly over time. A better understanding of the drivers of positive change will help to further reduce the injury burden in Victoria.

Emergency presentations of older patients living with frailty: Presenting symptoms compared with non-frail patients

BACKGROUND AND OBJECTIVE

Symptoms may differ between frail and non-frail patients presenting to Emergency Departments (ED). However, the association between frailty status and type of presenting symptoms has not been investigated. We aimed to systematically analyse presenting symptoms in frail and non-frail older emergency patients and hypothesized that frailty may be associated with nonspecific complaints (NSC), such as generalised weakness.

METHODS

Secondary analysis of a prospective, single centre, observational all-comer cohort study conducted in the ED of a Swiss tertiary care hospital. All presentations of patients aged 65 years and older were analysed. At triage, presenting symptoms and frailty were systematically assessed using a questionnaire. Patients with a Clinical Frailty Scale (CFS) > 4 were considered frail. Presenting symptoms, stratified by frailty status, were analysed. The association between frailty and generalised weakness was tested by logistic regression.

RESULTS

Overall, 2'416 presentations of patients 65 years and older were analysed. Mean age was 78.9 (SD 8.4) years, 1'228 (50.8%) patients were female, and 885 (36.6%) patients were frail (CFS > 4). Generalised weakness, dyspnea, localised weakness, speech disorder, loss of consciousness and gait disturbance were recorded more often in frail patients, whereas chest pain was reported more often by non-frail patients. Generalised weakness was reported as presenting symptom in 166 (18.8%) frail patients and in 153 (10.0%) non-frail patients. Frailty was associated with generalised weakness after adjusting for age, gender and elevated National Early Warning Score 2 (NEWS) ≥ 3 (OR 1.19, CI 1.10-1.29, p < 0.001).

CONCLUSION

Presenting symptoms differ in frail and non-frail patients. Frailty is associated with generalised weakness at ED presentation.

The clinical frailty scale predicts 1-year mortality in emergency department patients aged 65 years and older

OBJECTIVE

To validate the Clinical Frailty Scale (CFS) for prediction of 1-year all-cause mortality in the emergency department (ED) and compare its performance to the Emergency Severity Index (ESI).

METHODS

Prospective cohort study at the ED of a tertiary care center in Northwestern Switzerland. All patients aged ≥65 years were included from March 18 to May 20, 2019, after informed consent. Frailty status was assessed using CFS, excluding level 9 (palliative). Acuity level was assessed using ESI. Both CFS and ESI were adjusted for age, sex and presenting condition in multivariable logistic regression. Prognostic performance was assessed for discrimination and calibration separately. Estimates were internally validated by Bootstrapping. Restricted mean survival time (RMST) was determined for all levels of CFS.

RESULTS

In the final study population of 2191 patients, 1-year all-cause mortality was 17% (n = 372). RMST values ranged from 219 days for CFS 8 to 365 days for CFS 1. The adjusted CFS model had an area under receiver operating characteristic of 0.767 (95% confidence interval [CI]: 0.741-0.793), compared to 0.703 (95% CI: 0.673-0.732) for the adjusted ESI model.

CONCLUSION

The CFS predicts 1-year all-cause mortality for older ED patients and predicts survival time in a graded manner. The CFS is superior to the ESI when adjusted for age, sex, and presenting condition.

Australian Injury Comorbidity Indices (AICIs) to predict burden and readmission among hospital-admitted injury patients

BACKGROUND

Existing comorbidity measures predict mortality among general patient populations. Due to the lack of outcome specific and patient-group specific measures, the existing indices are also applied to non-mortality outcomes in injury epidemiology. This study derived indices to capture the association between comorbidity, and burden and readmission outcomes for injury populations.

METHODS

Injury-related hospital admissions data from July 2012 to June 2014 (161,334 patients) for the state of Victoria, Australia were analyzed. Various multivariable regression models were run and results used to derive both binary and weighted indices that quantify the association between comorbidities and length of stay (LOS), hospital costs and readmissions. The new and existing indices were validated internally among patient subgroups, and externally using data from the states of New South Wales and Western Australia.

RESULTS

Twenty-four comorbidities were significantly associated with overnight stay, twenty-seven with LOS, twenty-eight with costs, ten with all-cause and eleven with non-planned 30-day readmissions. The number of and types of comorbidities, and their relative impact were different to the associations established with the existing Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Measure (ECM). The new indices performed equally well to the long-listed ECM and in certain instances outperformed the CCI.

CONCLUSIONS

The more parsimonious, up to date, outcome and patient-specific indices presented in this study are better suited for use in present injury epidemiology. Their use can be trialed by hospital administrations in resource allocation models and patient classification models in clinical settings.

Validation of the Clinical Frailty Scale for Prediction of Thirty-Day Mortality in the Emergency Department

STUDY OBJECTIVE

We validate the Clinical Frailty Scale by examining its independent predictive validity for 30-day mortality, ICU admission, and hospitalization and by determining its reliability. We also determine frailty prevalence in our emergency department (ED) as measured with the Clinical Frailty Scale.

METHODS

This was a prospective observational study including consecutive ED patients aged 65 years or older, from a single tertiary care center during a 9-week period. To examine predictive validity, association with mortality was investigated through a Cox proportional hazards regression; hospitalization and ICU transfer were investigated through multivariable logistic regression. We assessed reliability by calculating Cohen's weighted κ for agreement of experts who independently assigned Clinical Frailty Scale levels, compared with trained study assistants. Frailty was defined as a Clinical Frailty Scale score of 5 and higher.

RESULTS

A total of 2,393 patients were analyzed in this study, of whom 128 died. Higher frailty levels were associated with higher hazards for death independent of age, sex, and condition (medical versus surgical). The area under the curve for 30-day mortality prediction was 0.81 (95% confidence interval [CI] 0.77 to 0.85), for hospitalization 0.72 (95% CI 0.70 to 0.74), and for ICU admission 0.69 (95% CI 0.66 to 0.73). Interrater reliability between the reference standard and the study team was good (weighted Cohen's κ was 0.74; 95% CI 0.64 to 0.85). Frailty prevalence was 36.8% (n=880).

CONCLUSION

The Clinical Frailty Scale appears to be a valid and reliable instrument to identify frailty in the ED. It might provide ED clinicians with useful information for decisionmaking in regard to triage, disposition, and treatment.

Is there an agreement between self-reported medical diagnosis in the CARTaGENE cohort and the Québec administrative health databases?

BACKGROUND

Population health studies often use existing databases that are not necessarily constituted for research purposes. The question arises as to whether different data sources such as in administrative health data (AHD) and self-report questionnaires are equivalent and lead to similar information.

OBJECTIVES

The main objective of this study was to assess the level of agreement between self-reported medical conditions and medical diagnosis captured in AHD. A secondary objective was to identify predictors of agreement among medical conditions between the two data sources. Therefore, the purposes of the study were to explore the extent to which these two methods of commonly used public health data collection provide concordant records and identify the main predictors of statistical variations.

METHODS

Data were extracted from CARTaGENE, a population-based cohort in Québec, Canada, which was linked to the provincial health insurance records of the same individuals, namely the MED-ÉCHO database from the Régie de l'assurance maladie du Québec (RAMQ) and the fee-for-service billing records provided by the physician, for the time period 1998-2012. Agreement statistics (kappa coefficient) along with sensitivity, specificity and predictive positive value were calculated for 19 chronic conditions and 12 types of cancers. Logistic regressions were used to identify predictors of concordance between self-report and AHD from significant covariates (sex, age groups, education, region, income, heavy utilization of health care system and Charlson comorbidity index).

RESULTS

Agreement between self-reported data and AHD across diseases ranged from kappa of 0.09 for chronic renal failure to 0.86 for type 2 diabetes. Sensitivity of self-reported data was higher than 50% for 14 out of the 31 medical conditions studied, especially for myocardial infarction (88.62%), breast cancer (86.28%), and diabetes (85.06%). Specificity was generally high with a minimum value of 89.70%. Lower concordance between data sources was observed for higher frequency of health care utilization and higher comorbidity scores.

CONCLUSION

Overall, there was moderate agreement between the two data sources but important variations were found depending on the type of disease. This suggests that CARTaGENE's participants were generally able to correctly identify the kind of diseases they suffer from, with some exceptions. These results may help researchers choose adequate data sources according to specific study objectives. These results also suggest that Québec's AHD seem to underestimate the prevalence of some chronic conditions, which might result in inaccurate estimates of morbidity with consequences for public health surveillance.