Deirdre Hennessy

A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients

BackgroundRisk adjustment and mortality prediction in studies of critical care are usually performed using acuity of illness scores, such as Acute Physiology and Chronic Health Evaluation II (APACHE II), which emphasize physiological derangement. Common risk adjustment systems used in administrative datasets, like the Charlson index, are entirely based on the presence of co-morbid illnesses. The purpose of this study was to compare the discriminative ability of the Charlson index to the APACHE II in predicting hospital mortality in adult multisystem ICU patients.MethodsThis was a population-based cohort design. The study sample consisted of adult (>17 years of age) residents of the Calgary Health Region admitted to a multisystem ICU between April 2002 and March 2004. Clinical data were collected prospectively and linked to hospital outcome data. Multiple regression analyses were used to compare the performance of APACHE II and the Charlson index.ResultsThe Charlson index was a poor predictor of mortality (C = 0.626). There was minimal difference between a baseline model containing age, sex and acute physiology score (C = 0.74) and models containing either chronic health points (C = 0.76) or Charlson index variations (C = 0.75, 0.76, 0.77). No important improvement in prediction occurred when the Charlson index was added to the full APACHE II model (C = 0.808 to C = 0.813).ConclusionThe Charlson index does not perform as well as the APACHE II in predicting hospital mortality in ICU patients. However, when acuity of illness scores are unavailable or are not recorded in a standard way, the Charlson index might be considered as an alternative method of risk adjustment and therefore facilitate comparisons between intensive care units.

Cervical Spine Clearance in Obtunded Blunt Trauma Patients: A Prospective Study

BACKGROUND : An acceptable algorithm for clearance of the cervical spine (C-spine) in the obtunded trauma patient remains controversial. Undetected C-spine injuries of an unstable nature can have devastating consequences. This has led to reluctance toward C-spine clearance in these patients. OBJECTIVE : To objectify the accuracy of computed tomography (CT) scanning compared with dynamic radiographs within a well established C-spine clearance protocol in obtunded trauma patients at a level I trauma center. METHODS : This was a prospective study of consecutive blunt trauma patients (18 years or older) admitted to a single institution between December 2004 and April 2008. To be eligible for study inclusion, patients must have undergone both a CT scan and dynamic plain radiographs of their C-spine as a part of their clearance process. RESULTS : Among 402 patients, there was one injury missed on CT but detected by dynamic radiographs. This resulted in a percentage of missed injury of 0.25%. Subsequent independent review of the CT scan revealed that in fact pathologic changes were present on the scan indicative of the injury. CONCLUSIONS : Our results indicate that CT of the C-spine is highly sensitive in detecting the vast majority (99.75%) of clinically significant C-spine injuries. We recommend that CT be used as the sole modality to radiographically clear the C-spine in obtunded trauma patients and do not support the use of flexion-extension radiographs as an ancillary diagnostic method.

Predictive risk algorithms in a population setting: an overview

Background The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health. Objective To describe the role of predictive risk algorithms in a population setting. Methods First, predictive risk algorithms and how clinicians use them are described. Second, the population uses of risk algorithms are described, highlighting the strengths of risk algorithms for health planning. Lastly, the way in which predictive risk algorithms are developed is discussed briefly and a guide for algorithm assessment in population health presented. Conclusion For the past 20 years, absolute and baseline risk has been a cornerstone of population health planning. The most accurate and discriminating method to generate such estimates is the use of multivariable risk algorithms. Routinely collected data can be used to develop algorithms with characteristics that are well suited to health planning and such data are increasingly available. The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health.

The association of income with health behavior change and disease monitoring among patients with chronic disease.

Background Management of chronic diseases requires patients to adhere to recommended health behavior change and complete tests for monitoring. While studies have shown an association between low income and lack of adherence, the reasons why people with low income may be less likely to adhere are unclear. We sought to determine the association between household income and receipt of health behavior change advice, adherence to advice, receipt of recommended monitoring tests, and self-reported reasons for non-adherence/non-receipt. Methods We conducted a population-weighted survey, with 1849 respondents with cardiovascular-related chronic diseases (heart disease, hypertension, diabetes, stroke) from Western Canada (n = 1849). We used log-binomial regression to examine the association between household income and the outcome variables of interest: receipt of advice for and adherence to health behavior change (sodium reduction, dietary improvement, increased physical activity, smoking cessation, weight loss), reasons for non-adherence, receipt of recommended monitoring tests (cholesterol, blood glucose, blood pressure), and reasons for non-receipt of tests. Results Behavior change advice was received equally by both low and high income respondents. Low income respondents were more likely than those with high income to not adhere to recommendations regarding smoking cessation (adjusted prevalence rate ratio (PRR): 1.55, 95%CI: 1.09–2.20), and more likely to not receive measurements of blood cholesterol (PRR: 1.72, 95%CI 1.24–2.40) or glucose (PRR: 1.80, 95%CI: 1.26–2.58). Those with low income were less likely to state that non-adherence/non-receipt was due to personal choice, and more likely to state that it was due to an extrinsic factor, such as cost or lack of accessibility. Conclusions There are important income-related differences in the patterns of health behavior change and disease monitoring, as well as reasons for non-adherence or non-receipt. Among those with low income, adherence to health behavior change and monitoring may be improved by addressing modifiable barriers such as cost and access.

Projections of preventable risks for cardiovascular disease in Canada to 2021: a microsimulation modelling approach

BACKGROUND Reductions in preventable risks associated with cardiovascular disease have contributed to a steady decrease in its incidence over the past 50 years in most developed countries. However, it is unclear whether this trend will continue. Our objective was to examine future risk by projecting trends in preventable risk factors in Canada to 2021. METHODS We created a population-based microsimulation model using national data on births, deaths and migration; socioeconomic data; cardiovascular disease risk factors; and algorithms for changes in these risk factors (based on sociodemographic characteristics and previous cardiovascular disease risk). An initial population of 22.5 million people, representing the Canadian adult population in 2001, had 13 characteristics including the risk factors used in clinical risk prediction. There were 6.1 million potential exposure profiles for each person each year. Outcome measures included annual prevalence of risk factors (smoking, obesity, diabetes, hypertension and lipid levels) and of co-occurring risks. RESULTS From 2003 to 2009, the projected risks of cardiovascular disease based on the microsimulation model closely approximated those based on national surveys. Except for obesity and diabetes, all risk factors were projected to decrease through to 2021. The largest projected decreases were for the prevalence of smoking (from 25.7% in 2001 to 17.7% in 2021) and uncontrolled hypertension (from 16.1% to 10.8%). Between 2015 and 2017, obesity was projected to surpass smoking as the most prevalent risk factor. INTERPRETATION Risks of cardiovascular disease are projected to decrease modestly in Canada, leading to a likely continuing decline in its incidence.

Implementation of ICD-10 in Canada: how has it impacted coded hospital discharge data?

BackgroundThe purpose of this study was to assess whether or not the change in coding classification had an impact on diagnosis and comorbidity coding in hospital discharge data across Canadian provinces.MethodsThis study examined eight years (fiscal years 1998 to 2005) of hospital records from the Hospital Person-Oriented Information database (HPOI) derived from the Canadian national Discharge Abstract Database. The average number of coded diagnoses per hospital visit was examined from 1998 to 2005 for provinces that switched from International Classifications of Disease 9th version (ICD-9-CM) to ICD-10-CA during this period. The average numbers of type 2 and 3 diagnoses were also described. The prevalence of the Charlson comorbidities and distribution of the Charlson score one year before and one year after ICD-10 implementation for each of the 9 provinces was examined. The prevalence of at least one of the seventeen Charlson comorbidities one year before and one year after ICD-10 implementation were described by hospital characteristics (teaching/non-teaching, urban/rural, volume of patients).ResultsNine Canadian provinces switched from ICD-9-CM to ICD-I0-CA over a 6 year period starting in 2001. The average number of diagnoses coded per hospital visit for all code types over the study period was 2.58. After implementation of ICD-10-CA a decrease in the number of diagnoses coded was found in four provinces whereas the number of diagnoses coded in the other five provinces remained similar. The prevalence of at least one of the seventeen Charlson conditions remained relatively stable after ICD-10 was implemented, as did the distribution of the Charlson score. When stratified by hospital characteristics, the prevalence of at least one Charlson condition decreased after ICD-10-CA implementation, particularly for low volume hospitals.ConclusionIn conclusion, implementation of ICD-10-CA in Canadian provinces did not substantially change coding practices, but there was some coding variation in the average number of diagnoses per hospital visit across provinces.

Measuring Burden of Unhealthy Behaviours Using a Multivariable Predictive Approach: Life Expectancy Lost in Canada Attributable to Smoking, Alcohol, Physical Inactivity, and Diet

Background Behaviours such as smoking, poor diet, physical inactivity, and unhealthy alcohol consumption are leading risk factors for death. We assessed the Canadian burden attributable to these behaviours by developing, validating, and applying a multivariable predictive model for risk of all-cause death. Methods A predictive algorithm for 5 y risk of death—the Mortality Population Risk Tool (MPoRT)—was developed and validated using the 2001 to 2008 Canadian Community Health Surveys. There were approximately 1 million person-years of follow-up and 9,900 deaths in the development and validation datasets. After validation, MPoRT was used to predict future mortality and estimate the burden of smoking, alcohol, physical inactivity, and poor diet in the presence of sociodemographic and other risk factors using the 2010 national survey (approximately 90,000 respondents). Canadian period life tables were generated using predicted risk of death from MPoRT. The burden of behavioural risk factors attributable to life expectancy was estimated using hazard ratios from the MPoRT risk model. Findings The MPoRT 5 y mortality risk algorithms were discriminating (C-statistic: males 0.874 [95% CI: 0.867–0.881]; females 0.875 [0.868–0.882]) and well calibrated in all 58 predefined subgroups. Discrimination was maintained or improved in the validation cohorts. For the 2010 Canadian population, unhealthy behaviour attributable life expectancy lost was 6.0 years for both men and women (for men 95% CI: 5.8 to 6.3 for women 5.8 to 6.2). The Canadian life expectancy associated with health behaviour recommendations was 17.9 years (95% CI: 17.7 to 18.1) greater for people with the most favourable risk profile compared to those with the least favourable risk profile (88.2 years versus 70.3 years). Smoking, by itself, was associated with 32% to 39% of the difference in life expectancy across social groups (by education achieved or neighbourhood deprivation). Conclusions Multivariable predictive algorithms such as MPoRT can be used to assess health burdens for sociodemographic groups or for small changes in population exposure to risks, thereby addressing some limitations of more commonly used measurement approaches. Unhealthy behaviours have a substantial collective burden on the life expectancy of the Canadian population.

Cardiovascular Disease Population Risk Tool (CVDPoRT): predictive algorithm for assessing CVD risk in the community setting. A study protocol

Introduction Recent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting. Methods and analysis The Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77 251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619 886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50 000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers. Ethics and dissemination This study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible electronically for population and individual uses. Trial registration number ClinicalTrials.gov NCT02267447.

Predicting stroke risk based on health behaviours: development of the stroke population risk tool (SPoRT).

Background Health behaviours, important factors in cardiovascular disease, are increasingly a focus of prevention. We appraised whether stroke risk can be accurately assessed using self-reported information focused on health behaviours. Methods Behavioural, sociodemographic and other risk factors were assessed in a population-based survey of 82 259 Ontarians who were followed for a median of 8.6 years (688 000 person-years follow-up) starting in 2001. Predictive algorithms for 5-year incident stroke resulting in hospitalization were created and then validated in a similar 2007 survey of 28 605 respondents (median 4.2 years follow-up). Results We observed 3 236 incident stroke events (1 551 resulting in hospitalization; 1 685 in the community setting without hospital admission). The final algorithms were discriminating (C-stat: 0.85, men; 0.87, women) and well-calibrated (in 65 of 67 subgroups for men; 61 of 65 for women). An index was developed to summarize cumulative relative risk of incident stroke from health behaviours and stress. For men, each point on the index corresponded to a 12% relative risk increase (180% risk difference, lowest (0) to highest (9) scores). For women, each point corresponded to a 14% relative risk increase (340% difference, lowest (0) to highest (11) scores). Algorithms for secondary stroke outcomes (stroke resulting in death; classified as ischemic; excluding transient ischemic attack; and in the community setting) had similar health behaviour risk hazards. Conclusion Incident stroke can be accurately predicted using self-reported information focused on health behaviours. Risk assessment can be performed with population health surveys to support population health planning or outside of clinical settings to support patient-focused prevention.

Risk of work loss due to illness or disability in patients with osteoarthritis: a population-based cohort study

OBJECTIVES To estimate the risk of work loss due to illness or disability in a cohort of employed persons with OA compared with matched non-OA individuals. METHODS We performed a population-based cohort analysis using the last six cycles of the Canadian longitudinal National Population Health Survey from 2000 to 2010. OA cases and up to four age- and sex-matched non-OA individuals were selected. Discrete time hazard regression models were used to estimate the hazard of work loss due to illness or disability. To analyse the effect of a self-reported OA measure on the outcome, we performed a sensitivity analyses for case selection. RESULTS From 7273 employed individuals between the ages of 20 and 70 years in the National Population Health Survey, 659 OA cases were selected and matched to 2144 non-OA individuals. The proportion of OA cases who experienced work loss due to illness or disability during the follow-up period was 12.6%, compared with 9.3% for non-OA individuals (P < 0.001). OA cases had a 90% [hazard ratio (HR) 1.90 (95% CI 1.36, 3.23)] higher hazard of work loss due to illness or disability compared with their matched non-OA individuals after adjusting for sociodemographic, health and work-related status. The adjusted HRs were 1.61 (95% CI 1.13, 2.30) and 2.04 (95% CI 1.74, 4.75) for females and males, respectively. CONCLUSION OA is independently associated with an increased risk of work loss due to illness or disability. Given the high prevalence of OA in the population of working age, future research may wish to investigate ways to improve occupational participation among OA patients.