Performance of comorbidity indices in measuring outcomes after acute myocardial infarction in Australian indigenous and non-indigenous patients

Comparison of Indexes to Measure Comorbidity Burden and Predict All-Cause Mortality in Rheumatoid Arthritis

Objectives: To examine the comorbidity burden in patients with rheumatoid arthritis (RA) patients using a nationwide population-based cohort by assessing the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), Multimorbidity Index (MMI), and Rheumatic Disease Comorbidity Index (RDCI) scores and to investigate their predictive ability for all-cause mortality. Methods: We identified 24,767 RA patients diagnosed from 1998 to 2008 in Taiwan and followed up until 31 December 2013. The incidence of comorbidities was estimated in three periods (before, during, and after the diagnostic period). The incidence rate ratios were calculated by comparing during vs. before and after vs. before the diagnostic period. One- and 5-year mortality rates were calculated and discriminated by low and high-score groups and modified models for each index. Results: The mean score at diagnosis was 0.8 in CCI, 2.8 in ECI, 0.7 in MMI, and 1.3 in RDCI, and annual percentage changes are 11.0%, 11.3%, 9.7%, and 6.8%, respectively. The incidence of any increase in the comorbidity index was significantly higher in the periods of “during” and “after” the RA diagnosis (incidence rate ratios for different indexes: 1.33–2.77). The mortality rate significantly differed between the high and low-score groups measured by each index (adjusted hazard ratios: 2.5–4.3 for different indexes). CCI was slightly better in the prediction of 1- and 5-year mortality rates. Conclusions: Comorbidities are common before and after RA diagnosis, and the rate of accumulation accelerates after RA diagnosis. All four comorbidity indexes are useful to measure the temporal changes and to predict mortality.

Factors affecting hospital readmission rates following an acute coronary syndrome: A systematic review

Aim

To synthesise quantitative evidence on factors that impact hospital readmission rates following ACS with comorbidities.

Design

Systematic review and narrative synthesis.

Data sources

A search of eight electronic databases, including Embase, Medline, PsycINFO, Web of Science, CINAHL, Cochrane Library, Scopus and the Joanna Briggs Institute (JBI).

Review methods

The search strategy included keywords and MeSH terms to identify English language studies published between 2001 and 2020. The quality of included studies was assessed by two independent reviewers, using Joanna Briggs Institute (JBI) critical appraisal tools.

Results

Twenty‐four articles were included in the review. All cause 30‐day readmission rate was most frequently reported and ranged from 4.2% to 81%. Reported factors that were associated with readmission varied across studies from socio‐demographic, behavioural factors, comorbidity factors and cardiac factors. Findings from some of the studies were limited by data source, study designs and small sample size.

Conclusion

Strategies that integrate comprehensive discharge planning and individualised care planning to enhance behavioural support are related to a reduction in readmission rates. It is recommended that nurses are supported to influence discharge planning and lead the development of nurse‐led interventions to ensure discharge planning is both coordinated and person‐centred.

The modified Healthy Aging Index is associated with mobility limitations and falls in a community-based sample of oldest old

BACKGROUND AND AIMS

The Healthy Aging Index (HAI) is useful in capturing the health status of multiple organ systems in older adults. Previous studies have mainly focused on the association of HAI with mortality and disability. We constructed a modified HAI (mHAI) to examine its association with mobility limitations and falls in a community-based sampling of older Chinese adults.

METHODS

We investigated 399 community-dwelling older adults aged 80 years or older, and constructed the mHAI with five non-invasive tests (systolic blood pressure, the Montreal Cognitive Assessment test, glucose concentrations, cystatin C levels, and self-reported respiratory problems).

RESULTS

The mean mHAI score for the participants in our study was 3.6. After multivariate adjustment, per unit increase in mHAI score was associated with self-reported difficulty in stooping, kneeling, or crouching (odds ratio [OR] = 1.16, 95% confidence interval [CI] 1.00-1.34), and walking 400 m (OR = 1.21, 95% CI 1.03-1.42). Per unit increase in mHAI score was also associated with poor balance (OR = 1.29, 95% CI 1.07-1.55), lower extremity strength limitation (OR = 1.30, 95% CI 1.10-1.52), low handgrip strength (OR = 1.25, 95% CI 1.08-1.46), and slow gait speed (OR = 1.21, 95% CI 1.02-1.42). The association between mHAI and falls was also significant (per unit of mHAI OR = 1.21, 95% CI 1.04-1.40).

CONCLUSION

The mHAI can be used as a simple assessment tool to determine mobility status in older adults and identify those at high risk for falls.

Cognitive impairment in patients with atrial fibrillation: Implications for outcome in a cohort study

BACKGROUND

The impact of cognitive status on outcomes of patients with atrial fibrillation (AF) is not well defined.

AIMS

To assess the prevalence of cognitive impairment in AF patients and evaluate its association with: i) all-cause mortality; ii) a composite endpoint of death, stroke/systemic embolism, hemorrhages, acute coronary syndrome, pulmonary embolism, new/worsening heart failure.

METHODS

In a cohort study, cognitive status was assessed at baseline by the Mini Mental State examination adjusted for age and education (aMMSE). aMMSE <24 was considered indicative of cognitive impairment.

RESULTS

The cohort included 437 patients (61.3% male, mean age 73.4 ± 11.7 years). Sixty-three patients (14.4%) had cognitive impairment at baseline aMMSE. Permanent AF (odds ratio [OR] 1.750; 95%CI 1.012-3.025; p = .045), haemoglobin levels (OR 0.827; 95%CI 0.707-0.967; p = .017) and previous treatment with antiplatelet drugs only, without oral anticoagulation, (OR 4.352; 95%CI 1.583-11.963; p = .004) were independently associated with cognitive impairment at baseline. After a median follow-up of 887 days (interquartile range 731-958) 30 patients died (7.1%), and 97 (22.9%) reached the composite endpoint. After adjustment for Elixhauser Comorbidy Measure, aMMSE <24 was significantly associated with all-cause mortality (hazard ratio [HR] 2.473, 95%CI 1.062-5.756, p = .036) and with the composite endpoint (HR 1.852, 95%CI 1.106-3.102, p = .019).

CONCLUSIONS

In patients with AF, cognitive impairment (aMMSE <24) is associated with worse outcomes, and the association of adverse outcomes with previous treatment with antiplatelet drugs only, without oral anticoagulation, highlights the potential role of appropriate antithrombotic treatment for improving patient prognosis.

A Review of Coronary Artery Bypass Grafting in the Indigenous Australian Population

INTRODUCTION

Indigenous Australians experience poorer health outcomes than non-Indigenous Australians. Ischaemic heart disease is a leading contributor to the mortality gap which exists between Indigenous and non-Indigenous Australians.

METHODS

We reviewed the literature in regards to Indigenous Australians undergoing coronary artery bypass grafting (CABG) for management of ischaemic heart disease.

RESULTS

Younger patients with higher rates of preventable risk factors constitute the Indigenous Australian CABG population. Indigenous Australian females are over-represented in series to date. High rates of left ventricular dysfunction are seen in the Indigenous CABG cohorts potentially reflecting barriers to medical care or the influence of high rates of diabetes observed in the Indigenous Australian population. The distribution of coronary artery disease appears to differ between Indigenous Australian and non-Indigenous CABG cohorts likely reflecting a difference in the referral patterns of the two population groups with diabetes again likely influencing management decisions. Reduced utilisation of arterial conduits in Indigenous Australian cohorts has been identified in a number of series. This is of particular concern given the younger age structure of the Indigenous Australian cohorts. Indigenous Australian patients suffer excess morbidity and mortality in the longer term after undergoing CABG. Ventricular dysfunction and excess comorbidities in the Indigenous Australian CABG population appear largely responsible for this.

CONCLUSION

Excess morbidity and mortality endured by Indigenous Australians in the longer term following CABG appears largely contributed to by higher rates of ventricular dysfunction and comorbidities in the Indigenous Australian CABG population. Maximising internal mammary artery use and continued focus on strategies to reduce the impact of diabetes, renal impairment and heart failure in the Indigenous Australian population is essential to reduce the mortality gap experienced by Indigenous Australians secondary to ischaemic heart disease.

Invasive management of acute coronary syndrome: Interaction with competing risks

BACKGROUND

The aim of this study was to characterise the interaction between ACS- and non-ACS-risk on the benefits of invasive management in patients presenting with acute coronary syndrome (ACS).

METHODS

Consecutive patients admitted to a tertiary hospital's Cardiac Care Unit in the months of July-December, 2003-2011 with troponin elevation (>30 ng/L) were included. "ACS-specific-risk" was estimated using the GRACE score and "non-ACS-risk" was estimated using the Charlson-Comorbidity-Index (CCI). Inverse-probability-of-treatment weighting was used to adjust for baseline differences between patients who did or did not receive invasive management. A multivariable flexible parametric model was used to characterise the time-varying hazard.

RESULTS

In total, 3057 patients were included with a median follow-up of 9.0 years. Based on CCI, 1783 patients were classified as 'low-non-ACS risk' (CCI ≤ 1; invasive management 81%; 12-month mortality 5%), 820 as 'medium-non-ACS risk' (CCI 2-3; invasive management 68%; 12-month mortality 13%), and 468 as 'high-non-ACS risk' (CCI ≥ 4; invasive management 47%; 12-month mortality 29%). After adjustment, invasive management was associated with a significant reduction in one-year overall-mortality in the 'low-risk' and 'medium-risk' groups (HR = 0.38, 95%CI:0.26-0.56; HR = 0.46, 95%CI:0.32-0.67); but not in the 'high-risk' group (HR = 1.02, 95%CI:0.67-1.56). The absolute benefit of invasive management was greatest with higher baseline ACS-risk, with a non-linear interaction between ACS- and non-ACS-risk.

CONCLUSIONS

There is a complex interaction between ACS- and non-ACS-risk on the benefit of invasive management. These results highlight the need to develop robust methods to objectively quantify risk attributable to non-ACS comorbidities in order to make informed decisions regarding the use of invasive management in individuals with numerous comorbidities.

Comorbidity and cervical cancer survival of Indigenous and non-Indigenous Australian women: A semi-national registry-based cohort study (2003-2012)

BACKGROUND

Little is known about the impact of comorbidity on cervical cancer survival in Australian women, including whether Indigenous women's higher prevalence of comorbidity contributes to their lower survival compared to non-Indigenous women.

METHODS

Data for cervical cancers diagnosed in 2003-2012 were extracted from six Australian state-based cancer registries and linked to hospital inpatient records to identify comorbidity diagnoses. Five-year cause-specific and all-cause survival probabilities were estimated using the Kaplan-Meier method. Flexible parametric models were used to estimate excess cause-specific mortality by Charlson comorbidity index score (0,1,2+), for Indigenous women compared to non-Indigenous women.

RESULTS

Of 4,467 women, Indigenous women (4.4%) compared to non-Indigenous women had more comorbidity at diagnosis (score ≥1: 24.2% vs. 10.0%) and lower five-year cause-specific survival (60.2% vs. 76.6%). Comorbidity was associated with increased cervical cancer mortality for non-Indigenous women, but there was no evidence of such a relationship for Indigenous women. There was an 18% reduction in the Indigenous: non-Indigenous hazard ratio (excess mortality) when comorbidity was included in the model, yet this reduction was not statistically significant. The excess mortality for Indigenous women was only evident among those without comorbidity (Indigenous: non-Indigenous HR 2.5, 95%CI 1.9-3.4), indicating that factors other than those measured in this study are contributing to the differential. In a subgroup of New South Wales women, comorbidity was associated with advanced-stage cancer, which in turn was associated with elevated cervical cancer mortality.

CONCLUSIONS

Survival was lowest for women with comorbidity. However, there wasn't a clear comorbidity-survival gradient for Indigenous women. Further investigation of potential drivers of the cervical cancer survival differentials is warranted.

IMPACT

The results highlight the need for cancer care guidelines and multidisciplinary care that can meet the needs of complex patients. Also, primary and acute care services may need to pay more attention to Indigenous Australian women who may not obviously need it (i.e. those without comorbidity).

A Modified Healthy Aging Index and Its Association with Mortality: The National Health and Nutrition Examination Survey, 1999-2002

BACKGROUND

Comorbidity indices that are based on clinically recognized disease do not capture the full spectrum of health. The Healthy Aging Index (HAI) was recently developed to describe a wider range of health and disease across multiple organ systems. We characterized the distribution of a modified HAI (mHAI) by sociodemographics in a representative sample of the U.S. population. We also examined the association of the mHAI with mortality across individuals with different levels of clinically recognizable comorbidities.

METHODS

Data are from the National Health and Nutrition Examination Survey (1999-2000, 2001-2002) on 2,451 adults aged 60 years or older. Five mHAI components (systolic blood pressure, Digit Symbol Substitution Test, cystatin C, glucose, and respiratory problems) were scored 0 (healthiest), 1, or 2 (unhealthiest) by sex-specific tertiles or clinically relevant cutoffs and summed to construct the mHAI.

RESULTS

The mean mHAI score was 4.3; 20.6% had a score of 0-2. 33.2% had a score of 3-4, 31.0% had a score of 5-6, and 15.2% had a score of 7-10. Mean mHAI scores were lower in adults who were younger, non-Hispanic whites, more educated, and married/living with partner. After multivariate adjustment, per unit higher of the mHAI was associated with higher all-cause mortality (HR = 1.19, 95% CI = 1.11-1.27) and higher cardiovascular mortality (HR = 1.23, 95% CI = 1.11-1.35). Within each comorbidity category (0, 1, 2, 3, 4+), the mHAI was still widely distributed and further stratified mortality.

CONCLUSIONS

Substantial variation exists in the mHAI across sociodemographic subgroups. The mHAI could provide incremental value for mortality risk prediction beyond clinically diagnosed chronic diseases among elders.

Trends in cancer incidence and survival for Indigenous and non-Indigenous people in the Northern Territory

OBJECTIVE

To assess trends in cancer incidence and survival for Indigenous and non-Indigenous Australians in the Northern Territory.

DESIGN

Retrospective analysis of population-based cancer registration data.

SETTING

New cancer diagnoses in the NT, 1991-2012.

MAIN OUTCOME MEASURES

Age-adjusted incidence rates; rate ratios comparing incidence in NT Indigenous and non-Indigenous populations with that for other Australians; 5-year survival; multivariable Poisson regression of excess mortality.

RESULTS

The incidence of most cancers in the NT non-Indigenous population was similar to that for other Australians. For the NT Indigenous population, the incidence of cancer at several sites was much higher (v other Australians: lung, 84% higher; head and neck, 325% higher; liver, 366% higher; cervix, 120% higher). With the exception of cervical cancer (65% decrease), incidence rates in the Indigenous population did not fall between 1991-1996 and 2007-2012. The incidence of several other cancers (breast, bowel, prostate, melanoma) was much lower in 1991-1996 than for other Australians, but had increased markedly by 2007-2012 (breast, 274% increase; bowel, 120% increase; prostate, 116% increase). Five-year survival was lower for NT Indigenous than for NT non-Indigenous patients, but had increased for both populations between 1991-2000 and 2001-2010.

CONCLUSION

The incidence of several cancers that were formerly less common in NT Indigenous people has increased, without a concomitant reduction in the incidence of higher incidence cancers (several of which are smoking-related). The excess burden of cancer in this population will persist until lifestyle risks are mitigated, particularly by reducing the extraordinarily high prevalence of smoking.

Adverse outcome after incident stroke hospitalization for Indigenous and non-Indigenous Australians in the Northern Territory

BACKGROUND

Survival after a stroke is lower for Indigenous than other stroke patients in Australia. It is not known whether recurrence is more common for Indigenous patients, or whether their higher prevalence of comorbidity affects their lower survival.

AIMS

This study aimed to investigate the stroke recurrence and role of comorbidities in adverse stroke outcomes (recurrence and death) for Indigenous compared with other Australians.

METHODS

A retrospective cohort study of first hospitalization for stroke (n = 2105) recorded in Northern Territory hospital inpatient data between 1996 and 2011 was conducted. For the multivariable analyses of adverse outcomes, logistic regression was used for case fatality and competing risk analysis for recurrent stroke and long-term death. Comorbidities (identified from inpatient diagnosis data) were analyzed using the Charlson Comorbidity Index (modified for stroke outcomes).

RESULTS

Prevalence of comorbidities, case fatality, incidence of re-hospitalization for recurrent stroke, and long-term death rate were higher for Indigenous than non-Indigenous stroke patients. Adjustment for comorbidity in multivariable analyses considerably reduced Indigenous patients' excess risk for case fatality (odds ratio: 1·25, 0·88-1·78) and long-term death (standard hazard ratio: 1·27, 1·01-1·61) (but not recurrence), implying that their excess risk of death was in part due to higher comorbidity prevalence.

CONCLUSION

Indigenous stroke patients have higher prevalence of comorbidities than non-Indigenous stroke patients, which explained part of the disparity in both case fatality and long-term survival but did not explain the disparity in stroke recurrence at all.

Better health outcomes at lower costs: the benefits of primary care utilisation for chronic disease management in remote Indigenous communities in Australia's Northern Territory

Background

Indigenous residents living in remote communities in Australia’s Northern Territory experience higher rates of preventable chronic disease and have poorer access to appropriate health services compared to other Australians. This study compared health outcomes and costs at different levels of primary care utilisation to determine if primary care represents an efficient use of resources for Indigenous patients with common chronic diseases namely hypertension, diabetes, ischaemic heart disease, chronic obstructive pulmonary disease and renal disease.

Methods

This was an historical cohort study involving a total of 14,184 Indigenous residents, aged 15 years and over, who lived in remote communities and used a remote clinic or public hospital from 2002 to 2011. Individual level demographic and clinical data were drawn from primary care and hospital care information systems using a unique patient identifier. A propensity score was used to improve comparability between high, medium and low primary care utilisation groups. Incremental cost-effectiveness ratios and acceptability curves were used to analyse four health outcome measures: total and, avoidable hospital admissions, deaths and years of life lost.

Results

Compared to the low utilisation group, medium and high levels of primary care utilisation were associated with decreases in total and avoidable hospitalisations, deaths and years of life lost. Higher levels of primary care utilisation for renal disease reduced avoidable hospitalisations by 82-85%, deaths 72-75%, and years of life lost 78-81%. For patients with ischaemic heart disease, the reduction in avoidable hospitalisations was 63-78%, deaths 63-66% and years of life lost 69-73%. In terms of cost-effectiveness, primary care for renal disease and diabetes ranked as more cost-effective, followed by hypertension and ischaemic heart disease. Primary care for chronic obstructive pulmonary disease was the least cost-effective of the five conditions.

Conclusion

Primary care in remote Indigenous communities was shown to be associated with cost-savings to public hospitals and health benefits to individual patients. Investing $1 in primary care in remote Indigenous communities could save $3.95-$11.75 in hospital costs, in addition to health benefits for individual patients. These findings may have wider applicability in strengthening primary care in the face of high chronic disease prevalence globally.

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Objective Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations. Methods The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation. Results The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78; 95% confidence interval (CI) 0.71–0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72; 95% CI 0.66–0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission. Conclusions Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions. What is known about the topic? Many clinical and demographic risk factors are known for hospital readmissions following acute myocardial infarction, including multivessel disease, high baseline heart rate, hypertension, diabetes, obesity, chronic obstructive pulmonary disease and psychiatric morbidity. However, combining these risk factors into indices for predicting readmission had limited success. A recent study reported a C-statistic of 0.73 for predicting 30-day readmissions. In a recent American study, a simple seven-factor score was shown to predict hospital readmissions among medical patients. What does this paper add? This paper presents a way to predict readmissions following myocardial infarction using routinely collected administrative data. The model performed better than the recently described HOSPITAL score and a model derived from Elixhauser comorbidities. Moreover, the model uses only data generally available in most hospitals. What are the implications for practitioners? Routine hospital data available at discharges can be used to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.

Deriving comorbidities from medical records using natural language processing

Extracting comorbidity information is crucial for phenotypic studies because of the confounding effect of comorbidities. We developed an automated method that accurately determines comorbidities from electronic medical records. Using a modified version of the Charlson comorbidity index (CCI), two physicians created a reference standard of comorbidities by manual review of 100 admission notes. We processed the notes using the MedLEE natural language processing system, and wrote queries to extract comorbidities automatically from its structured output. Interrater agreement for the reference set was very high (97.7%). Our method yielded an F1 score of 0.761 and the summed CCI score was not different from the reference standard (p=0.329, power 80.4%). In comparison, obtaining comorbidities from claims data yielded an F1 score of 0.741, due to lower sensitivity (66.1%). Because CCI has previously been validated as a predictor of mortality and readmission, our method could allow automated prediction of these outcomes.