Comparative ability of comorbidity classification methods for administrative data to predict outcomes in patients with chronic obstructive pulmonary disease

Epidemiology and association of neighbourhood marginalisation on violent knife assaults in Ontario: a population-based case-control study

BACKGROUND

Violent knife assaults ('stabbings') are underappreciated as a source of morbidity and mortality. The two objectives of this study were to describe the epidemiology of stabbing injuries in the population of Ontario, Canada and to assess the associations between two measures of neighbourhood marginalisation-material deprivation and housing instability, and the risk of stabbing injury.

METHODS

We conducted a population-based case-control study over 2004-18 using linked administrative data. Cases suffered a stabbing injury resulting in an emergency department visit, hospitalisation or death. Four age and sex-matched controls were matched to each case. Multivariate logistic regression was used to assess the associations between neighbourhood material deprivation as well as housing instability and the risk of injury. Mean annualised injury incidences were estimated using the number of cases identified divided by the total population of Ontario for that year.

RESULTS

We identified 26 657 individuals with a stabbing injury, of which 724 (2.7%) were fatal. The mean annualised incidence was 13.4 per 100 000 (95% CI: 12.7 to 15.9). Victims were disproportionately young (median age 25 years; IQR: 20-37 years) males (84.1%), from large urban centres (77.5%), and in the lowest income quintile (39.3%). In multivariate models, neighbourhood material deprivation (OR 1.45, 95% CI: 1.43 to 1.47) and housing instability (OR 1.4, 95% CI: 1.22 to 1.26) were associated with risk of injury.

CONCLUSIONS

Stabbing injuries are a substantial public health problem that affects individuals of all ages and demographics but disproportionately affects younger men in urban settings. There is a weak association between residence in marginalised neighbourhoods and the risk of stabbing injury. Future studies should aim to better understand the nature of this association and consider opportunities for public health interventions to reduce the burden of violent knife injuries.

Association of Delirium with Long-Term Mortality in Critically Ill Patients with COPD Who Survived to Discharge: A Retrospective Cohort Study

BACKGROUND

Critically ill patients with chronic obstructive pulmonary disease (COPD) face significant mortality after hospital discharge. Delirium is common in patients with COPD, but its impact on long-term mortality in critically ill COPD patients who survive to discharge remains uncertain.

METHODS

Critically ill patients with COPD who survived to discharge were selected from the Medical Information Mart for Intensive Care IV database. Delirium was assessed using the Confusion Assessment Method for Intensive Care Unit. The primary outcome was 365- and 180-day mortality after discharge. The secondary outcomes included 90- and 30-day mortality following discharge, length of intensive care unit (ICU) and hospital stays, and nursing care needs after hospital discharge.

RESULTS

Of the 2621 survivors of critically ill COPD patients, 982 had suffered delirium during their ICU stay and 709 died within 365 days after hospital discharge. Delirium was significantly associated with 365-day mortality after hospital discharge (adjusted hazard ratio [HR] 1.22; 95% confidence interval [CI] 1.02-1.47). The results were consistent for 180-, 90-, and 30-day post-discharge mortality (adjusted HR [95% CI]: 1.35 [1.09-1.66], 1.48 [1.16-1.89], and 1.68 [1.21-2.32], respectively). Additionally, patients with delirium had longer ICU and hospital stay (adjusted β 2.75; 95% CI 2.35-3.16 and 4.25; 95% CI 3.51-4.98, respectively) and increased nursing care needs after hospital discharge (adjusted odds ratio, 1.56; 95% CI 1.13-2.14).

CONCLUSION

ICU delirium was an independent risk factor for both long-term and short-term mortality in critically ill patients with COPD who survived to discharge.

Prognostic models and factors identifying end-of-life in non-cancer chronic diseases: a systematic review

BACKGROUND

Precise prognostic information, if available, is very helpful for guiding treatment decisions and resource allocation in patients with non-cancer non-communicable chronic diseases (NCDs). This study aimed to systematically review the existing evidence, examining prognostic models and factors for identifying end-of-life non-cancer NCD patients.

METHODS

Electronic databases, including Medline, Embase, CINAHL, Cochrane Library, PsychINFO and other sources, were searched from the inception of these databases up until June 2023. Studies published in English with findings mentioning prognostic models or factors related to identifying end-of-life in non-cancer NCD patients were included. The quality of studies was assessed using the Quality in Prognosis Studies tool.

RESULTS

The analysis included data from 41 studies, with 16 focusing on chronic obstructive pulmonary diseases (COPD), 10 on dementia, 6 on heart failure and 9 on mixed NCDs. Traditional statistical modelling was predominantly used for the identified prognostic models. Common predictors in COPD models included dyspnoea, forced expiratory volume in 1 s, functional status, exacerbation history and body mass index. Models for dementia and heart failure frequently included comorbidity, age, gender, blood tests and nutritional status. Similarly, mixed NCD models commonly included functional status, age, dyspnoea, the presence of skin pressure ulcers, oral intake and level of consciousness. The identified prognostic models exhibited varying predictive accuracy, with the majority demonstrating weak to moderate discriminatory performance (area under the curve: 0.5-0.8). Additionally, most of these models lacked independent external validation, and only a few underwent internal validation.

CONCLUSION

Our review summarised the most relevant predictors for identifying end-of-life in non-cancer NCDs. However, the predictive accuracy of identified models was generally inconsistent and low, and lacked external validation. Although efforts to improve these prognostic models should continue, clinicians should recognise the possibility that disease heterogeneity may limit the utility of these models for individual prognostication; they may be more useful for population level health planning.

A Frailty Index to Predict Mortality, Resource Utilization and Costs in Patients Undergoing Coronary Artery Bypass Graft Surgery in Ontario

Background

People living with frailty are vulnerable to poor outcomes and incur higher health care costs after coronary artery bypass graft (CABG) surgery. Frailty-defining instruments for population-level research in the CABG setting have not been established. The objectives of the study were to develop a preoperative frailty index for CABG (pFI-C) surgery using Ontario administrative data; assess pFI-C suitability in predicting clinical and economic outcomes; and compare pFI-C predictive capabilities with other indices.

Methods

A retrospective cohort study was conducted using health administrative data of 50,682 CABG patients. The pFI-C comprised 27 frailty-related health deficits. Associations between index scores and mortality, resource use and health care costs (2022 Canadian dollars [CAD]) were assessed using multivariable regression models. Capabilities of the pFI-C in predicting mortality were evaluated using concordance statistics; goodness of fit of the models was assessed using Akakie Information Criterion.

Results

As assessed by the pFI-C, 22% of the cohort lived with frailty. The pFI-C score was strongly associated with mortality per 10% increase (odds ratio [OR], 3.04; 95% confidence interval [CI], [2.83,3.27]), and was significantly associated with resource utilization and costs. The predictive performances of the pFI-C, Charlson, and Elixhauser indices and Johns Hopkins Aggregated Diagnostic Groups were similar, and mortality models containing the pFI-C had a concordance (C)-statistic of 0.784. Cost models containing the pFI-C showed the best fit.

Conclusions

The pFI-C is predictive of mortality and associated with resource utilization and costs during the year following CABG. This index could aid in identifying a subgroup of high-risk CABG patients who could benefit from targeted perioperative health care interventions.

General Comorbidity Indicators Contribute to Fracture Risk Independent of FRAX: Registry-Based Cohort Study

CONTEXT

FRAX® estimates 10-year fracture probability from osteoporosis-specific risk factors. Medical comorbidity indicators are associated with fracture risk but whether these are independent from those in FRAX is uncertain.

OBJECTIVE

We hypothesized Johns Hopkins Aggregated Diagnosis Groups (ADG®) score or recent hospitalization number may be independently associated with increased risk for fractures.

METHODS

This retrospective cohort study included women and men age ≥ 40 in the Manitoba BMD Registry (1996-2016) with at least 3 years prior health care data and used linked administrative databases to construct ADG scores along with number of hospitalizations for each individual. Incident Major Osteoporotic Fracture and Hip Fracture was ascertained during average follow-up of 9 years; Cox regression analysis determined the association between increasing ADG score or number of hospitalizations and fractures.

RESULTS

Separately, hospitalizations and ADG score independently increased the hazard ratio for fracture at all levels of comorbidity (hazard range 1.2-1.8, all P < 0.05), irrespective of adjustment for FRAX, BMD, and competing mortality. Taken together, there was still a higher than predicted rate of fracture at all levels of increased comorbidity, independent of FRAX and BMD but attenuated by competing mortality. Using an intervention threshold of major fracture risk >20%, application of the comorbidity hazard ratio multiplier to the patient population FRAX scores would increase the number of treatment candidates from 8.6% to 14.4%.

CONCLUSION

Both complex and simple measures of medical comorbidity may be used to modify FRAX-based risk estimates to capture the increased fracture risk associated with multiple comorbid conditions in older patients.

Outpatient mental health service use in major trauma survivors: A population-based cohort study from Ontario, Canada

BACKGROUND

Mental health is recognized as an important outcome in major trauma survivors. There are limited data on how and when survivors access mental health services. Our objectives were to determine the frequency, timing, and factors associated with outpatient mental health service utilization in major trauma survivors.

METHODS

We conducted a population-based cohort study using linked administrative data on patients treated for major trauma in Ontario between 2009 and 2017. All trauma survivors were included in the cohort, and composite rates of outpatient mental health service use (family doctor, psychiatrist, or emergency department visits) were compared in the 3 years preinjury and postinjury. We used an exposure crossover study design and negative binomial regression with generalized estimating equations for the analysis. Multivariate logistic regression analyses were used to identify patient characteristics associated with mental health service use during the follow-up period.

RESULTS

There were 37,260 patients included in the cohort, predominantly men (70.8%) from urban areas (84.8%) suffering blunt trauma (89.7%). Among the patients, 47.4% used mental health services during the preinjury period and 61.8% during the postinjury period (absolute increase of 14.4%). Compared with the 3 years preinjury, the rate of mental health service use increased by 80% (rate ratio, 1.8; 95% confidence interval, 1.75-1.77) in the 3 years following injury. Most survivors (62.2%) used services within the first year following discharge. Postinjury mental health service use (adjusted odds ratio, 3.4; 95% confidence interval, 2.2-3.7) was most strongly associated with preinjury mental health service use.

CONCLUSION

Preinjury and postinjury outpatient mental health service use is common in trauma patients, with preinjury service use predictive of ongoing service use. Mental health concerns should be an expected outcome of major trauma and efforts made to implement and expand access to services for injury survivors across trauma systems.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level IV.

Determining the influence of the primary and specialist network of care on patient and system outcomes among patients with a new diagnosis of chronic obstructive pulmonary disease (COPD)

Introduction

Care for patients with chronic obstructive pulmonary disease (COPD) is provided by both family physicians (FP) and specialists. Ideally, patients receive comprehensive and coordinated care from this provider team. The objectives for this study were: 1) to describe the family and specialist physician network of care for Ontario patients newly diagnosed with COPD and 2) to determine the associations between selected characteristics of the physician network and unplanned healthcare utilization.

Methods

We conducted a retrospective cohort study using Ontario health administrative data housed at ICES (formerly the Institute for Clinical Evaluative Sciences). Ontario patients, ≥ 35 years, newly diagnosed with COPD were identified between 2005 and 2013. The FP and specialist network of care characteristics were described, and the relationship between selected characteristics (i.e., continuity of care) with unplanned healthcare utilization during the first 5 years after COPD diagnosis were determined in multivariate models.

Results

Our cohort consisted of 450,837 patients, mean age 61.5 (SD 14.6) years. The FP was the predominant provider of care for 86.4% of the patients. Using the Bice-Boxerman’s Continuity of Care Index (COCI), a measure reflecting care across different providers, 227,082 (50.4%) were categorized in a low COCI group based on a median cut-off. In adjusted analyses, patients in the low COCI group were more likely to have a hospital admission (OR = 2.27, 95% CI 2.20,2.22), 30-day readmission (OR = 2.44, 95% CI 2.39, 2.49) and ER visit (OR = 2.27, 95% CI 2.25, 2.29).

Conclusion

Higher indices of continuity of care are associated with reduced unplanned hospital use for patients with COPD. Primary care-based practice models to enhance continuity through coordination and integration of both primary and specialist care have the potential to enhance the health experience for patients with COPD and should be a health service planning priority.

Evaluation of risk adjustment performance of diagnosis-based and medication-based comorbidity indices in patients with chronic obstructive pulmonary disease

Objectives

This study assessed risk adjustment performance of six comorbidity indices in two categories of comorbidity measures: diagnosis-based comorbidity indices and medication-based ones in patients with chronic obstructive pulmonary disease (COPD).

Methods

This was a population–based retrospective cohort study. Data used in this study were sourced from the Taiwan National Health Insurance Research Database. The study population comprised all patients who were hospitalized due to COPD for the first time in the target year of 2012. Each qualified patient was individually followed for one year starting from the index date to assess two outcomes of interest, medical expenditures within one year after discharge and in-hospital mortality of patients. To assess how well the added comorbidity measures would improve the fitted model, we calculated the log-likelihood ratio statistic G2. Subsequently, we compared risk adjustment performance of the comorbidity indices by using the Harrell c-statistic measure derived from multiple logistic regression models.

Results

Analytical results demonstrated that that comorbidity measures were significant predictors of medical expenditures and mortality of COPD patients. Specifically, in the category of diagnosis-based comorbidity indices the Elixhauser index was superior to other indices, while the RxRisk-V index was a stronger predictor in the framework of medication-based codes, for gauging both medical expenditures and in-hospital mortality by utilizing information from the index hospitalization only as well as the index and prior hospitalizations.

Conclusions

In conclusion, this work has ascertained that comorbidity indices are significant predictors of medical expenditures and mortality of COPD patients. Based on the study findings, we propose that when designing the payment schemes for patients with chronic diseases, the health authority should make adjustments in accordance with the burden of health care caused by comorbid conditions.

Age- and gender-based comorbidity categories in general practitioner and pulmonology patients with COPD

Chronic obstructive pulmonary disease (COPD) is a debilitating medical condition often accompanied by multiple chronic conditions. COPD is more frequent among older adults and affects both genders. The aim of the current cross-sectional survey was to characterize chronic comorbidities stratified by gender and age among patients with COPD under the care of general practitioners (GP) and pulmonologists, using real-world patient data. A total of 7966 COPD patients (women: 45%) with more than 5 years of the observation period in the practice were examined using 60 different Chronic comorbid conditions (CCC) and Elixhauser measures. More than 9 in 10 patients had at least one, and 51.7% had more than three comorbidities. No gender difference was found in the number of comorbidities. However, men had higher Elixhauser-van Walraven index scores than women, and the types of comorbidities differed by gender. An increasing number of comorbidities was seen with aging but the patients in their 30s and 40s also had a high number of comorbidities. Moreover, GP patients had a higher number and a wider array of documented comorbidities than pulmonology patients did. Psychological comorbidities were common in all patients, but particularly among younger patients. These findings around gender- and age-stratified comorbidities under the care of GPs and pulmonologists have implications for the choice of data provenience for decision-making analysis and treatment selection and success.

Emergency department return visits and hospital admissions in trauma team assessed patients initially discharged from the emergency department: a population-based cohort study

BACKGROUND

Many injured patients are transported directly to trauma centers, found to be minimally injured, and discharged directly home from the emergency department (ED). Our objectives were to characterize the short-term outcomes in this discharged patient population and to identify patient factors predictive of ED return visits.

METHODS

We conducted a retrospective population-based cohort study using linked administrative datasets involving patients assessed at trauma centers in Ontario, Canada between April 1, 2009 and March 31, 2020. Patients who were assessed by a trauma team and discharged directly home from ED were included. The primary outcome was the percentage of patients with an ED return visit within 14-days. We used multivariate logistic regression analyses to identify patient characteristics predictive of at least one ED return visit.

RESULTS

There were 5550 patients included in the study. 1004 (18.1%) of patients had at least one ED return visit but only 100 patients (1.8%) were admitted to hospital following initial discharge. Common reasons for ED return visits included wound care concerns (17.2%), head injury complaints (15.6%), and substance misuse (6.8%). Rural residence (OR 1.83, 95% CI: 1.45 - 2.29), history of anxiety disorder (OR 2.05, 95% CI: 1.54 - 2.73), high baseline ED usage (OR 2.58, 95% CI: 2.03 - 3.28), penetrating injury (OR 1.42, 95% CI: 1.20 - 1.68), and extremity fracture (OR 1.52, 95% CI: 1.24 - 1.88) predicted return visits.

CONCLUSION

Patients discharged directly have high rates of ED return visits but low rates of hospital admission or delayed surgical intervention. Trauma services should expand quality assurance initiatives to capture return visits, understand any gaps in clinical service provision, and aim to minimize unnecessary ED return visits.

LEVEL OF EVIDENCE

Level VI prognostic study.

Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on health care. To optimize the allocation of precious preventive care management resources and improve the outcomes for high-risk patients with COPD, we recently built the most accurate model to date to predict severe COPD exacerbations, which need inpatient stays or emergency department visits, in the following 12 months. Our model is a machine learning model. As is the case with most machine learning models, our model does not explain its predictions, forming a barrier for clinical use. Previously, we designed a method to automatically provide rule-type explanations for machine learning predictions and suggest tailored interventions with no loss of model performance. This method has been tested before for asthma outcome prediction but not for COPD outcome prediction.

Objective

This study aims to assess the generalizability of our automatic explanation method for predicting severe COPD exacerbations.

Methods

The patient cohort included all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019. In a secondary analysis of 43,576 data instances, we used our formerly developed automatic explanation method to automatically explain our model’s predictions and suggest tailored interventions.

Results

Our method explained the predictions for 97.1% (100/103) of the patients with COPD whom our model correctly predicted to have severe COPD exacerbations in the following 12 months and the predictions for 73.6% (134/182) of the patients with COPD who had ≥1 severe COPD exacerbation in the following 12 months.

Conclusions

Our automatic explanation method worked well for predicting severe COPD exacerbations. After further improving our method, we hope to use it to facilitate future clinical use of our model.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study

Background

Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes.

Objective

The aim of this study is to develop a more accurate model to predict severe COPD exacerbations.

Methods

We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD.

Results

The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347).

Conclusions

Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes.

International Registered Report Identifier (IRRID)

RR2-10.2196/13783

Chronic disease surveillance in Alberta's tomorrow project using administrative health data

Introduction

Alberta’s Tomorrow Project (ATP) is the largest population-based prospective cohort study of cancer and chronic diseases in Alberta, Canada. The ATP cohort data were primarily self-reported by participants on lifestyle behaviors and disease risk factors at the enrollment, which lacks sufficient and accurate data on chronic disease diagnosis for longer-term follow-up.

Objectives

To characterize the occurrence rate and trend of chronic diseases in the ATP cohort by linking with administrative healthcare data.

Methods

A set of validated algorithms using ICD codes were applied to Alberta Health (AH) administrative data (October 2000-March 2018) linked to the ATP cohort to determine the prevalence and incidence of common chronic diseases.

Results

There were 52,770 ATP participants (51.2±9.4 years old at enrollment and 63.7% females) linked to the AH data with average follow-up of 10.1±4.4 years. In the ATP cohort, hypertension (18.5%), depression (18.1%), chronic pain (12.8%), osteoarthritis (10.1%) and cardiovascular diseases (8.7%) were the most prevalent chronic conditions. The incidence rates varied across diseases, with the highest rates for hypertension (22.1 per 1000 person-year), osteoarthritis (16.2 per 1000 person-year) and ischemic heart diseases (13.0 per 1000 person-year). All chronic conditions had increased prevalence over time (p < for trend tests), while incidence rates were relatively stable. The proportion of participants with two or more of these conditions (multi-morbidity) increased from 3.9% in 2001 to 40.3% in 2017.

Conclusions

This study shows an increasing trend of chronic diseases in the ATP cohort, particularly related to cardiovascular diseases and multi-morbidity. Using administrative health data to monitor chronic diseases for large population-based prospective cohort studies is feasible in Alberta, and our approach could be further applied in a broader research area, including health services research, to enhance research capacity of these population-based studies in Canada.

The association between asthma and perinatal mental illness: a population-based cohort study

BACKGROUND

Asthma is a risk factor for mental illness, but few studies have explored this association around the time of pregnancy. We studied the association between asthma and perinatal mental illness and explored the modifying effects of social and medical complexities.

METHODS

In a population-based cohort of 846 155 women in Ontario, Canada, with a singleton live birth in 2005-2015 and no recent history of mental illness, modified Poisson regression models were constructed to examine the association between asthma diagnosed before pregnancy and perinatal mental illness, controlling for socio-demographics and medical history. We explored the modifying effects of social and medical complexities using relative excess risk due to interaction. Additional analyses examined the association between asthma and perinatal mental illness by timing and type of mental illness.

RESULTS

Women with asthma were more likely than those without asthma to have perinatal mental illness [adjusted relative risk (aRR) 1.14; 95% (confidence interval) CI: 1.13, 1.16]. Asthma was associated with increased risk of diagnosis of mental illness prenatally (aRR 1.11; 95% CI: 1.08, 1.13) and post-partum (aRR 1.17; 95% CI: 1.15, 1.19) and specifically diagnoses of mood and anxiety disorders (aRR 1.14; 95% CI: 1.13, 1.16), psychotic disorders (aRR 1.20; 95% CI: 1.10, 1.31) and substance- or alcohol-use disorders (aRR 1.24; 95% CI: 1.14, 1.36). There was no effect modification related to social or medical complexity for these outcomes.

CONCLUSIONS

Women with asthma predating pregnancy are at slightly increased risk of mental illness in pregnancy and post-partum. A multidisciplinary management strategy may be required to ensure timely identification and treatment.

A narrative review of using prescription drug databases for comorbidity adjustment: A less effective remedy or a prescription for improved model fit?

BACKGROUND

The use of claims data for identifying comorbid conditions in patients for research purposes has been widely explored. Traditional measures of comorbid adjustment included diagnostic data (e.g., ICD-9-CM or ICD-10-CM codes), with the Charlson and Elixhauser methodology being the two most common approaches. Prescription data has also been explored for use in comorbidity adjustment, however early methodologies were disappointing when compared to diagnostic measures.

OBJECTIVE

The objective of this methodological review is to compare results from newer studies using prescription-based data with more traditional diagnostic measures.

METHODS

A review of studies found on PubMed, Medline, Embase or CINAHL published between January 1990 and December 2020 using prescription data for comorbidity adjustment. A total of 50 studies using prescription drug measures for comorbidity adjustment were found.

CONCLUSIONS

Newer prescription-based measures show promise fitting models, as measured by predictive ability, for research, especially when the primary outcomes are utilization or drug expenditure rather than diagnostic measures. More traditional diagnostic-based measures still appear most appropriate if the primary outcome is mortality or inpatient readmissions.

Case-Ascertainment Models to Identify Adults with Obstructive Sleep Apnea Using Health Administrative Data: Internal and External Validation

Background

There is limited evidence on whether obstructive sleep apnea (OSA) can be accurately identified using health administrative data.

Study Design and Methods

We derived and validated a case-ascertainment model to identify OSA using linked provincial health administrative and clinical data from all consecutive adults who underwent a diagnostic sleep study (index date) at two large academic centers (Ontario, Canada) from 2007 to 2017. The presence of moderate/severe OSA (an apnea–hypopnea index≥15) was defined using clinical data. Of 39 candidate health administrative variables considered, 32 were tested. We used classification and regression tree (CART) methods to identify the most parsimonious models via cost-complexity pruning. Identified variables were also used to create parsimonious logistic regression models. All individuals with an estimated probability of 0.5 or greater using the predictive models were classified as having OSA.

Results

The case-ascertainment models were derived and validated internally through bootstrapping on 5099 individuals from one center (33% moderate/severe OSA) and validated externally on 13,486 adults from the other (45% moderate/severe OSA). On the external cohort, parsimonious models demonstrated c-statistics of 0.75–0.81, sensitivities of 59–60%, specificities of 87–88%, positive predictive values of 79%, negative predictive values of 73%, positive likelihood ratios (+LRs) of 4.5–5.0 and –LRs of 0.5. Logistic models performed better than CART models (mean integrated calibration indices of 0.02–0.03 and 0.06–0.12, respectively). The best model included: sex, age, and hypertension at the index date, as well as an outpatient specialty physician visit for OSA, a repeated sleep study, and a positive airway pressure treatment claim within 1 year since the index date.

Interpretation

Among adults who underwent a sleep study, case-ascertainment models for identifying moderate/severe OSA using health administrative data had relatively low sensitivity but high specificity and good discriminative ability. These findings could help study trends and outcomes of OSA individuals using routinely collected health care data.

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis

Background

Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

Objective

To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

Methods

We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation.

Results

We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

Conclusions

Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources.

International Registered Report Identifier (IRRID)

PRR1-10.2196/27065

Injury-related deaths in the Ontario provincial trauma system: a retrospective population-based cohort analysis

BACKGROUND

Although Ontario has an established trauma system, it experiences a substantial burden of morbidity and mortality from injury. Our objective was to describe patterns of fatal injury in Ontario, with a focus on location of death (out of hospital, trauma or non-trauma centre) and receipt of surgical intervention before death.

METHODS

We conducted a retrospective population-based cohort study using linked administrative data on fatal injuries in children and adults (no age restrictions) in Ontario between 2000 and 2016. We identified injury-related deaths in the Ontario Registrar General Death database. We developed descriptive statistics for injury characteristics and causes of death. We calculated the fatal injury incidence rate for each year of the study. The primary outcome was cause of death; the secondary outcome was receipt of surgical intervention.

RESULTS

The analysis included 19 408 people. The mean annual incidence of fatal injury averaged 8.7 (95% confidence interval 7.7-9.6) per 100 000. The most common mechanisms of injury were motor vehicle collisions (12 065, 62.2%), followed by gunshot wounds (3134, 16.1%) and falls (2387, 12.3%). Deaths frequently occurred out of hospital (72.6%), rather than at a trauma centre (14.2%) or non-trauma centre (13.2%). Patients treated at trauma centres were significantly more likely to receive a surgical intervention (standardized difference 0.6) than those treated at non-trauma centres.

INTERPRETATION

Most injury deaths in Ontario occur in the out-of-hospital setting or are managed at non-trauma centres; many patients receive no surgical intervention before death. There are likely opportunities to improve access to specialized injury care in Ontario's trauma system.

Comparative Performance of Comorbidity Measures in Predicting Health Outcomes in Patients with Chronic Obstructive Pulmonary Disease

Purpose

Multiple studies have suggested that comorbidities pose negative impacts on the survival of patients with chronic obstructive pulmonary disease (COPD); few have applied comorbidity measures driven from health insurance claims databases to predict various health outcomes. We aimed to examine the performance of commonly used comorbidity measures based on diagnosis and pharmacy dispensing claims information in predicting future death and hospitalization in COPD patients.

Methods

We identified COPD patients in a population-based Taiwanese database. We built logistic regression models with age, sex, and baseline comorbidities measured by either diagnosis or pharmacy claims information as predictors of subsequent-year death or hospitalization in a random 50% sample and validated the discrimination in the other 50%. The diagnosis-based comorbidity measures included the Charlson Index and the Elixhauser comorbidity measure; the pharmacy-based comorbidity measures included the updated Chronic Disease Score (CDS) and the Pharmacy-Based Comorbidity Index (PBDI).

Results

We identified 428,251 eligible patients. For overall death, the Elixhauser comorbidity measure showed the best predictive performance (c-statistic=0.832), followed by the PBDI (c-statistic=0.822), the Charlson Index (c-statistic=0.815), and the updated CDS (c-statistic=0.808). For overall hospitalization, the PBDI (c-statistics=0.730) and the Elixhauser comorbidity measure (c-statistics=0.724) outperformed the updated CDS (c-statistics=0.714) and the Charlson Index (c-statistics=0.710). For hospitalization due to cardiovascular, cerebrovascular, or respiratory diseases, the comorbidity models showed similar predictive ranks and demonstrated c-statistics higher than 0.75. However, none of the models could adequately predict hospitalization due to other reasons (c-statistics < 0.60).

Conclusion

Our study comprehensively compared the predictive performance of comorbidity measures. The Elixhauser comorbidity measure and the PBDI are useful tools for describing comorbid conditions and predicting health outcomes in COPD patients.

Algorithms to identify COPD in health systems with and without access to ICD coding: a systematic review

Background

Chronic obstructive pulmonary disease (COPD) causes significant morbidity and mortality worldwide. Estimation of incidence, prevalence and disease burden through routine insurance data is challenging because of under-diagnosis and under-treatment, particularly for early stage disease in health care systems where outpatient International Classification of Diseases (ICD) diagnoses are not collected. This poses the question of which criteria are commonly applied to identify COPD patients in claims datasets in the absence of ICD diagnoses, and which information can be used as a substitute. The aim of this systematic review is to summarize previously reported methodological approaches for the identification of COPD patients through routine data and to compile potential criteria for the identification of COPD patients if ICD codes are not available.

Methods

A systematic literature review was performed in Medline via PubMed and Google Scholar from January 2000 through October 2018, followed by a manual review of the included studies by at least two independent raters. Study characteristics and all identifying criteria used in the studies were systematically extracted from the publications, categorized, and compiled in evidence tables.

Results

In total, the systematic search yielded 151 publications. After title and abstract screening, 38 publications were included into the systematic assessment. In these studies, the most frequently used (22/38) criteria set to identify COPD patients included ICD codes, hospitalization, and ambulatory visits. Only four out of 38 studies used methods other than ICD coding. In a significant proportion of studies, the age range of the target population (33/38) and hospitalization (30/38) were provided. Ambulatory data were included in 24, physician claims in 22, and pharmaceutical data in 18 studies. Only five studies used spirometry, two used surgery and one used oxygen therapy.

Conclusions

A variety of different criteria is used for the identification of COPD from routine data. The most promising criteria set in data environments where ambulatory diagnosis codes are lacking is the consideration of additional illness-related information with special attention to pharmacotherapy data. Further health services research should focus on the application of more systematic internal and/or external validation approaches.

Mental health outcomes after major trauma in Ontario: a population-based analysis

BACKGROUND

Major injury continues to be a common source of morbidity and mortality; improving the functional recovery of survivors of major trauma requires a better understanding of the mental health outcomes that may occur in this population. We assessed the association between major trauma and the development of a new mental health diagnosis or death by suicide.

METHODS

We completed a population-based, self-controlled, longitudinal cohort analysis using linked administrative data on patients treated for major trauma in Ontario between 2005 and 2010. All survivors were included and composite rates of mental health diagnoses during inpatient admissions were compared between the 5 years after injury and the 5 years before injury, using Poisson regression with generalized estimating equations. The incidence of suicide was calculated for the 5 years after injury. Risk factors for suicide were calculated using Cox proportional hazard regression analyses.

RESULTS

The analysis included 19 338 patients, predominantly men (70.7%) from urban areas (82.6%), with unintentional (89%), blunt injuries (93.4%). Overall, trauma was associated with a 40% increase in the postinjury rate of mental health diagnoses (incidence rate ratio [IRR] 1.4, 95% [confidence interval] CI 1.1 to 1.8). The suicide rate was 70 per 100 000 patients per year, substantially higher than the population average. Risk factors for completing suicide were prior inpatient diagnosis of mood disorder (hazard ratio [HR] 4.3, 95% CI 2.1 to 8.8) and self-inflicted injury (HR 7.8, 95% CI 3.9 to 15.4).

INTERPRETATION

Survivors of major trauma are at a heightened risk of developing mental health conditions or death by suicide in the years after their injury. Patients with pre-existing mental health disorders or who are recovering from a self-inflicted injury are at particularly high risk.

Comorbidity and thirty-day hospital readmission odds in chronic obstructive pulmonary disease: a comparison of the Charlson and Elixhauser comorbidity indices

Background

Readmissions following exacerbations of chronic obstructive pulmonary disease (COPD) are prevalent and costly. Multimorbidity is common in COPD and understanding how comorbidity influences readmission risk will enable health systems to manage these complex patients.

Objectives

We compared two commonly used comorbidity indices published by Charlson and Elixhauser regarding their ability to estimate readmission odds in COPD and determine which one provided a superior model.

Methods

We analyzed discharge records for COPD from the Nationwide Readmissions Database spanning 2010 to 2016. Inclusion and readmission criteria from the Hospital Readmissions Reduction Program were utilized. Elixhauser and Charlson Comorbidity Index scores were calculated from published methodology. A mixed-effects logistic regression model with random intercepts for hospital clusters was fit for each comorbidity index, including year, patient-level, and hospital-level covariates to estimate odds of thirty-day readmissions. Sensitivity analyses included testing age inclusion thresholds and model stability across time.

Results

In analysis of 1.6 million COPD discharges, readmission odds increased by 9% for each half standard deviation increase of Charlson Index scores and 13% per half standard deviation increase of Elixhauser Index scores. Model fit was slightly better for the Elixhauser Index using information criteria. Model parameters were stable in our sensitivity analyses.

Conclusions

Both comorbidity indices provide meaningful information in prediction readmission odds in COPD with slightly better model fit in the Elixhauser model. Incorporation of comorbidity information into risk prediction models and hospital discharge planning may be informative to mitigate readmissions.

Does the frequency of routine follow-up after curative treatment for head-and-neck cancer affect survival?

Background

Routine follow-up is a cornerstone of oncology practice, but evidence to support most aspects of follow-up is lacking. Our objective was to investigate the relationship between frequency of routine follow-up and survival.

Methods

This population-based study used electronic health care data relating to 5310 patients from Ontario diagnosed with squamous-cell head-and-neck cancer during 2007-2012. Treatments included surgery (24.6%), radiotherapy with or without chemotherapy (52.4%), and combined surgery and radiotherapy (23%). We determined the oncologist who was following each patient after treatment; calculated the average follow-up visits to the oncologist during the subsequent 2.5 years for all patients who were doing well; and used Kaplan-Meier and multiple variable regression analysis to compare, by treatment, overall survival for patients in the high, typical, and low follow-up oncologist groups.

Results

Many oncologists saw patients 40%-80% more often than other oncologists did. No relationship of appointment frequency with survival was observed for patients in any treatment group.

Conclusions

The practice of routine follow-up varies and is costly both to a health care system and to patients. Without evidence about the effectiveness of current policies, further research is required to investigate new or optimal practices.

Do doctors who order more routine medical tests diagnose more cancers? A population-based study from Ontario Canada

Background

The overuse of medical tests leads to higher costs, wasting of resources, and the potential for overdiagnosis of disease. This study was designed to determine whether the patients of family doctors who order more routine medical tests are diagnosed with more cancers.

Method

A retrospective population‐based cross‐sectional study using administrative health care data in Ontario Canada. We investigated the ordering of 23 routine laboratories and imaging tests 2008‐20012 by 6849 Ontario family physicians on their 4.9 million rostered adult patients. We compared physicians’ test utilization and calculated case‐mix adjusted observed to expected (O:E) utilization ratios to categorize physicians as Typical, Higher or Lower testers. Age‐sex standardized rates (cases/10 000 patient years) and Rate Ratios were determined for cancers of the thyroid, prostate, breast, lymphoma, kidney, melanoma, uterus, ovary, lung, esophagus, and pancreas for each tester group.

Results

There was wide variation in the use of the 23 tests by Ontario physicians. 26% and 24% of physicians were deemed Higher Testers for laboratory and imaging tests, while 41% and 38% were Typical Testers. The patients of higher test users were diagnosed with more cancers of thyroid (laboratory [RR 1.61, 95% CI 1.39‐1.87] and imaging [RR 2.08, 95% CI 0.88‐2.30]) and prostate (laboratory [RR 1.10, 95% CI 1.03‐1.18] and imaging [RR 1.05, 95% CI 1.00‐1.10]).

Conclusion

There is a wide variation in the ordering of routine and common medical tests among Ontario family doctors. The patients of higher testers were diagnosed with more thyroid and prostate cancers.

Primary care utilization in people who experience imprisonment in Ontario, Canada: a retrospective cohort study

Background

Access to primary care is an important determinant of health, and data are sparse on primary care utilization for people who experience imprisonment. We aimed to describe primary care utilization for persons released from prison, and to compare utilization with the general population.

Methods

We linked correctional data for all persons released from provincial prison in Ontario, Canada in 2010 with health administrative data. We matched each person by age and sex with four general population controls. We compared primary care utilization rates using generalized estimating equations. We adjusted rate ratios for aggregated diagnosis groups, to explore this association independent of comorbidity. We examined the proportion of people using primary care using chi squared tests and time to first primary care visit post-release using the Kaplan-Meier method.

Results

Compared to the general population controls, the prison release group had significantly increased relative rates of primary care utilization: at 6.1 (95% CI 5.9-6.2) in prison, 3.7 (95% CI 3.6-3.8) in the week post-release and between 2.4 and 2.6 in the two years after prison release. All rate ratios remained significantly increased after adjusting for comorbidity. In the month after release, however, 66.3% of women and 75.5% of men did not access primary care.

Conclusions

Primary care utilization is high in prison and post-release for people who experience imprisonment in Ontario, Canada. Increased use is only partly explained by comorbidity. The majority of people do not access primary care in the month after prison release. Future research should identify reasons for increased use and interventions to improve care access for persons who are not accessing care post-release.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-3660-2) contains supplementary material, which is available to authorized users.

Comparing Five Comorbidity Indices to Predict Mortality in Chronic Kidney Disease: A Retrospective Cohort Study

Background:

Several 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.

Objective:

Assess 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).

Design:

Population-based retrospective cohort study.

Setting:

Ontario, Canada, between 2004 and 2014.

Patients:

Individuals 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.

Measurements:

Five 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.

Methods:

Comorbidity 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.

Results:

We 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.

Limitations:

The 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.

Conclusions:

Existing 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.

Postoperative Rehabilitation May Reduce the Risk of Readmission After Groin Hernia Repair

Thirty-day readmission after surgery has been proposed as a quality-of-care indicator. We explored the effect of postoperative rehabilitation on readmission risk after groin hernia repair. We used the French National Discharge Database to identify all index hospitalizations for groin hernia repair in 2011. Readmissions within 30 days of discharge were clinically classified in terms of their relationship to the index stay. We used logistic regression to adjust the risk of readmission for patient, procedure and hospital factors. Among 122,952 index hospitalizations for inguinal hernia repair, 3,357 (2.7%) related 30-day readmissions were recorded. Reiterated analyses indicated that readmission risk was consistently associated with patient complexity: age (per year after 60 years, OR 1.03, 95% CI 1.02-1.03, P < 0.001), hospitalization within the previous year (OR 1.56, 95% CI 1.44-1.69, P < 0.001), and increasing severity and combination of co-morbidities. Postoperative rehabilitation was identified as a protective factor (OR 0.56, 95% CI 0.46-0.69, P < 0.001). Older patients and those with greater comorbidity are at elevated risk of readmission after inguinal hernia repair. Postoperative rehabilitation may reduce this risk. Further studies are warranted to confirm the protective effect of postoperative rehabilitation.

Health Services Burden of Undiagnosed and Overdiagnosed COPD

BACKGROUND

Misdiagnosis of COPD is common. The goal of this study was to quantify the health services burden of undiagnosed and overdiagnosed COPD in a real-world, North American population.

METHODS

A population-based cohort study was conducted. Presence of COPD using spirometry was ascertained in randomly selected adults aged ≥ 40 years from Ontario, Canada, who participated in the Canadian Obstructive Lung Disease study. The presence of physician-diagnosed COPD was ascertained for the same subjects by using linked health administrative data. Participants were then categorized into four groups: correctly diagnosed, undiagnosed, overdiagnosed, and no COPD according to either criteria. Age- and sex-standardized rates of hospitalizations, ED visits, and ambulatory care visits in each group were determined and compared.

RESULTS

Of 1,403 participants, 13.7% had undiagnosed COPD, 5.1% were overdiagnosed, and 3.7% had correctly diagnosed COPD. Subjects with overdiagnosed COPD had significantly higher rates of hospitalizations, ED visits, and ambulatory care visits, and subjects with moderate to severe undiagnosed COPD had higher rates of hospitalizations, than subjects in the non-COPD population.

CONCLUSIONS

Undiagnosed and overdiagnosed COPD contribute to significant health care burden. Given that misdiagnosed COPD was fivefold more common than correctly diagnosed COPD, these findings point to a substantial misdiagnosis-associated burden of disease that might be prevented, at least in part, with a correct diagnosis.

A Systematic Review of Comorbidity Measurement Methods for Patients With Nontraumatic Brain Injury in Inpatient Rehabilitation Settings

This review summarizes comorbidity measurements used on patients with nontraumatic brain injury in inpatient rehabilitation and describes findings on measurement validation and comorbidity profiles. MEDLINE and MEDLINE In-Process, EMBASE, PsycINFO, the Cochrane Database of Systematic Reviews, Health, and Psychosocial Measurement Instruments were searched. Two reviewers screened results according to predefined inclusion and exclusion criteria. Population, statistical methods, comorbidity measurement, justification of its use, and results involving comorbidity were extracted using a standard table. Of 9476 articles retrieved, 16 were included. Comorbidity has been measured using various methods including the following: number and type within various classification systems, such as the International Disease Classification system, the Charlson comorbidity index, Centers for Medicare and Medicaid Services comorbidity tiers and patient comorbidity and complexity level values and subsets of diagnoses within nonadministrative data studies. No studies have assessed the predictive ability of the comorbidity measurements for inpatient rehabilitation outcomes in this population. Because comorbidities are common among the nontraumatic brain injury population, the predictive validity of comorbidity measurements should be assessed to determine the most appropriate measure to predict or risk adjust rehabilitation outcomes, which has implications for the development of clinical guidelines, and to inform health service research, planning, and delivery.

Health-economic evaluation of home telemonitoring for COPD in Germany: evidence from a large population-based cohort

INTRODUCTION

Telemonitoring for COPD has gained much attention thanks to its potential of reducing morbidity and mortality, healthcare utilisation and costs. However, its benefit with regard to clinical and economic outcomes remains to be clearly demonstrated.

OBJECTIVE

To analyse the effect of Europe's largest COPD telemonitoring pilot project on direct medical costs, health resource utilisation and mortality at 12 months.

METHODS

We evaluated a population-based cohort using administrative data. Difference-in-difference estimators were calculated to account for time-invariant unobservable heterogeneity after removing dissimilarities in observable characteristics between the telemonitoring and control group with a reweighting algorithm.

RESULTS

The analysis comprised 651 telemonitoring participants and 7047 individuals in the standard care group. The mortality hazards ratio was lower in the intervention arm (HR 0.51, 95 % CI 0.30-0.86). Telemonitoring cut total costs by 895 € (p < 0.05) compared to COPD standard care, mainly driven by savings in COPD-related hospitalisations in (very) severe COPD patients (-1056 €, p < 0.0001). Telemonitoring enrolees used healthcare (all-cause and COPD-related) less intensely with shorter hospital stays, fewer inpatient stays and smaller proportions of people with emergency department visits and hospitalisations (all p < 0.0001). Reductions in mortality, costs and healthcare utilisation were greater for (very) severe COPD cases.

CONCLUSION

This is the first German study to demonstrate that telemonitoring for COPD is a viable strategy to reduce mortality, healthcare costs and utilisation at 12 months. Contrary to widespread fear, reducing the intensity of care does not seem to impact unfavourably on health outcomes. The evidence offers strong support for introducing telemonitoring as a component of case management.

Prognostic variables and scores identifying the end of life in COPD: a systematic review

INTRODUCTION

COPD is a major cause of mortality, and the unpredictable trajectory of the disease can bring challenges to end-of-life care. We aimed to investigate known prognostic variables and scores that predict prognosis in COPD in a systematic literature review, specifically including variables that contribute to risk assessment of patients for death within 12 months.

METHODS

We conducted a systematic review on prognostic variables, multivariate score or models for COPD. Ovid MEDLINE, EMBASE, the Cochrane database, Cochrane CENTRAL, DARE and CINAHL were searched up to May 1, 2016.

RESULTS

A total of 5,276 abstracts were screened, leading to 516 full-text reviews, and 10 met the inclusion criteria. No multivariable indices were developed with the specific aim of predicting all-cause mortality in stable COPD within 12 months. Only nine indices were identified from four studies, which had been validated for this time period. Tools developed using expert knowledge were also identified, including the Gold Standards Framework Prognostic Indicator Guidance, the RADboud Indicators of Palliative Care Needs, the Supportive and Palliative Care Indicators Tool and the Necesidades Paliativas program tool.

CONCLUSION

A number of variables contributing to the prediction of all-cause mortality in COPD were identified. However, there are very few studies that are designed to assess, or report, the prediction of mortality at or less than 12 months. The quality of evidence remains low, such that no single variable or multivariable score can currently be recommended.

The value of complementing administrative data with abstracted information on smoking and obesity: A study in kidney cancer

INTRODUCTION

Variables, such as smoking and obesity, are rarely available in administrative databases. We explored the added value of including these data in an administrative database study evaluating the association of statin use with survival in kidney cancer.

METHODS

We linked administrative data with chart-abstracted data on smoking and obesity for 808 patients undergoing nephrectomy for kidney cancer. Base models consisted of variables from administrative databases (age, sex, year of surgery, and different measures of comorbidity [to compare their sensitivity to smoking and obesity data]); extended models added chart-abstracted data. We compared coefficients for statin use with overall (OS) and cancer-specific survival (CSS), and used the c-statistic and net reclassification improvement (NRI) to compare predications of five-year survival obtained from Cox proportional hazard models.

RESULTS

The coefficient for statin use changed minimally following addition of abstracted data (<6% for OS, <2% for CSS). Base models performed similarly for OS, with c-statistics of 0.75 (95% confidence interval [CI] 0.72-0.79) for Charlson score and 0.73 (95% CI 0.69-0.78) for John Hopkins Aggregated Diagnosis Groups score. After including abstracted data, c-statistics modestly improved (change <0.02); CSS demonstrated similar findings. NRIs were 0.210 (95% CI 0.062-0.297) and 0.186 (-0.031-0.387) when using the Charlson score, and 0.207 (0.068-0.287) and 0.197 (0.007-0.399) when using the Aggregated Diagnosis Groups score, for OS and CSS, respectively.

CONCLUSIONS

The inclusion of data on smoking and obesity marginally influences survival models in kidney cancer studies using administrative data.

Relationship Between Cardiac Rehabilitation Participation and Health Service Expenditures Within a Universal Health Care System

OBJECTIVES

To examine the relationship between cardiac rehabilitation participation and health service expenditures in Ontario, Canada.

PATIENTS AND METHODS

A total of 6284 patients referred to cardiac rehabilitation between April 1, 2003, and December 31, 2010, were linked to 6284 matched cardiac rehabilitation eligible nonreferred controls and followed over a 3-year period across multiple linked administrative databases to identify health service utilization expenditures and mortality. All patients had previous cardiac hospitalizations within the preceding year. Four cardiac rehabilitation eligible groups of patients were balanced using propensity score weights: (1) no referral; (2) no participation; (3) low participation levels (ie, attending <67% of prescheduled classes); and (4) high participation levels (ie, attending ≥67% prescheduled classes). Each group of patients was balanced in age, sex, geography, socioeconomic status, previous hospitalizations, ambulatory care conditions, cardiovascular risk factors, comorbidities, and previous health care expenditures. Generalized linear models were used to examine differences in health service expenditures (from all sources including hospitalizations, physician visits, diagnostic tests, and drugs for those older than 65 years) per "eligible day alive" over the 3-year period.

RESULTS

Compared with the nonreferred population, health service expenditures followed a dose-response relationship and were lowest in patients who had the highest cardiac rehabilitation programmatic participation levels (P<.001). Cost differences across groups separated early, remained divergent, and applied to all components of health care expenditures (P<.001). Sensitivity analyses confirmed that the findings were not secondary to reverse causality.

CONCLUSION

Participation in cardiac rehabilitation is associated with lower long-term health service utilization expenditures within a publicly funded health care system.

Improving medication safety: Development and impact of a multivariate model-based strategy to target high-risk patients

BACKGROUND

Preventive strategies to reduce clinically significant medication errors (MEs), such as medication review, are often limited by human resources. Identifying high-risk patients to allow for appropriate resource allocation is of the utmost importance. To this end, we developed a predictive model to identify high-risk patients and assessed its impact on clinical decision-making.

METHODS

From March 1st to April 31st 2014, we conducted a prospective cohort study on adult inpatients of a 1,644-bed University Hospital Centre. After a clinical evaluation of identified MEs, we fitted and internally validated a multivariate logistic model predicting their occurrence. Through 5,000 simulated randomized controlled trials, we compared two clinical decision pathways for intervention: one supported by our model and one based on the criterion of age.

RESULTS

Among 1,408 patients, 365 (25.9%) experienced at least one clinically significant ME. Eleven variables were identified using multivariable logistic regression and used to build a predictive model which demonstrated fair performance (c-statistic: 0.72). Major predictors were age and number of prescribed drugs. When compared with a decision to treat based on the criterion of age, our model enhanced the interception of potential adverse drug events by 17.5%, with a number needed to treat of 6 patients.

CONCLUSION

We developed and tested a model predicting the occurrence of clinically significant MEs. Preliminary results suggest that its implementation into clinical practice could be used to focus interventions on high-risk patients. This must be confirmed on an independent set of patients and evaluated through a real clinical impact study.

Influence of Using Different Databases and 'Look Back' Intervals to Define Comorbidity Profiles for Patients with Newly Diagnosed Hypertension: Implications for Health Services Researchers

OBJECTIVE

To determine the data sources and 'look back' intervals to define comorbidities.

DATA SOURCES

Hospital discharge abstracts database (DAD), physician claims, population registry and death registry from April 1, 1994 to March 31, 2010 in Alberta, Canada.

STUDY DESIGN

Newly-diagnosed hypertension cases from 1997 to 2008 fiscal years were identified and followed up to 12 years. We defined comorbidities using data sources and duration of retrospective observation (6 months, 1 year, 2 years, and 3 years). The C-statistics for logistic regression and concordance index (CI) for Cox model of mortality and cardiovascular disease hospitalization were used to evaluate discrimination performance for each approach of defining comorbidities.

PRINCIPAL FINDINGS

The comorbidities prevalence became higher with a longer duration. Using DAD alone underestimated the prevalence by about 75%, compared to using both DAD and physician claims. The C-statistic and CI were highest when both DAD and physician claims were used, and model performance improved when observation duration increased from 6 months to one year or longer.

CONCLUSION

The comorbidities prevalence is greatly impacted by the data source and duration of retrospective observation. A combination of DAD and physicians claims with at least one year observation duration improves predictions for cardiovascular disease and one-year mortality outcome model performance.

Early and Long-term Outcomes of Older Adults after Acute Care Encounters for Chronic Obstructive Pulmonary Disease Exacerbation

RATIONALE

Older patients are at high risk of death and rehospitalization after hospitalizations for acute exacerbations of chronic obstructive pulmonary disease (AE-COPD). Emergency department visits comprise a substantial portion of acute care encounters in this patient population. The risks of mortality and repeat acute care encounters, including both hospital readmission and repeat emergency department visits, after AE-COPD among older adults are not well understood.

OBJECTIVES

To examine early and long-term rates of death and repeat acute care encounters after hospitalization or emergency department visit for AE-COPD in Medicare fee-for-service beneficiaries and to identify patient characteristics, including medical comorbid conditions, associated with these outcomes.

METHODS

A retrospective analysis was conducted using a nationally representative 5% sample of Medicare fee-for-service claims data from the U.S. Centers for Medicare and Medicaid Services to identify Medicare beneficiaries 65 years or older who had an acute care episode for an AE-COPD between January 1, 2006, and December 31, 2010 (n = 52,741). Outcomes of interest were all-cause mortality, repeat acute care encounters for any cause, and repeat acute care encounters for AE-COPD at 30 days, 1 year, and 3 years.

MEASUREMENTS AND MAIN RESULTS

Acute care encounters, including hospitalizations and emergency department visits for AE-COPD, were associated with substantial subsequent mortality risk, with 4.6, 24.4, and 48.2% dying by 30 days, 1 year, and 3 years, respectively. The risk of repeat hospitalization or emergency department visit was similarly high, with 1 in 4 patients having a repeat acute care encounter within 30 days of discharge, increasing to 9 in 10 in the next 3 years. Several comorbid conditions and other patient factors, including heart failure, malnutrition, dual eligibility for Medicare and Medicaid, and prior supplemental oxygen use, were independently associated with increased risk of repeat acute care encounter.

CONCLUSIONS

Repeat hospitalizations and emergency department visits and death are common in older fee-for-service Medicare beneficiaries seen in acute care for AE-COPD. Our results suggest that addressing important comorbid conditions, such as heart failure or malnutrition, and targeting resources to oxygen-dependent or dual Medicare- and Medicaid-eligible patients may help modify these outcomes.

Predicting frequent COPD exacerbations using primary care data

PURPOSE

Acute COPD exacerbations account for much of the rising disability and costs associated with COPD, but data on predictive risk factors are limited. The goal of the current study was to develop a robust, clinically based model to predict frequent exacerbation risk.

PATIENTS AND METHODS

Patients identified from the Optimum Patient Care Research Database (OPCRD) with a diagnostic code for COPD and a forced expiratory volume in 1 second/forced vital capacity ratio <0.7 were included in this historical follow-up study if they were ≥40 years old and had data encompassing the year before (predictor year) and year after (outcome year) study index date. The data set contained potential risk factors including demographic, clinical, and comorbid variables. Following univariable analysis, predictors of two or more exacerbations were fed into a stepwise multivariable logistic regression. Sensitivity analyses were conducted for subpopulations of patients without any asthma diagnosis ever and those with questionnaire data on symptoms and smoking pack-years. The full predictive model was validated against 1 year of prospective OPCRD data.

RESULTS

The full data set contained 16,565 patients (53% male, median age 70 years), including 9,393 patients without any recorded asthma and 3,713 patients with questionnaire data. The full model retained eleven variables that significantly predicted two or more exacerbations, of which the number of exacerbations in the preceding year had the strongest association; others included height, age, forced expiratory volume in 1 second, and several comorbid conditions. Significant predictors not previously identified included eosinophilia and COPD Assessment Test score. The predictive ability of the full model (C statistic 0.751) changed little when applied to the validation data set (n=2,713; C statistic 0.735). Results of the sensitivity analyses supported the main findings.

CONCLUSION

Patients at risk of exacerbation can be identified from routinely available, computerized primary care data. Further study is needed to validate the model in other patient populations.

Observing temporal trends in cardiac rehabilitation from 1996 to 2010 in Ontario: characteristics of referred patients, programme participation and mortality rates

OBJECTIVES

We sought to describe temporal trends in the sociodemographic and clinical characteristics of participants referred to cardiac rehabilitation (CR), and its effect on programme participation and all-cause mortality over 14 years.

SETTING

A large CR centre in Toronto, Canada.

PARTICIPANTS

Consecutive patients between 1996 and 2010.

PRIMARY AND SECONDARY OUTCOME MEASURES

Referrals received were deterministically linked to administrative data, to complement referral form abstraction. Out-of-hospital deaths were identified using vital statistics. Patients were tracked until 2012, and mortality was ascertained. Percentage attendance at prescribed sessions was also assessed.

RESULTS

There were 29,171 referrals received, of which 28,767 (98.6%) were successfully linked, of whom 22,795 (79.2%) attended an intake assessment. The age of the referred population steadily increased, with more females, less affluent and more single patients referred over time (p<0.001). More patients were referred following percutaneous coronary intervention and less following coronary artery bypass graft surgery (p<0.001). The number of comorbidities decreased (p<0.001). Hypertension increased over time (p<0.001), yet the control of cholesterol steadily improved over time. The proportion of smokers decreased over time (p<0.001). Participation in CR significantly declined, and there were no significant changes in mortality. 3-year mortality rates were less than 5%.

CONCLUSIONS

Characteristics of referred patients tended to reflect broader trends in risk factors and cardiovascular disease burden. Physicians appear to be referring more sociodemographically diverse patients to CR; however, programmes may need to better adapt to engage these patients to fully participate. More complex patients should be referred, using explicit criteria-based referral processes.

A systematic review protocol for measuring comorbidity in inpatient rehabilitation for non-traumatic brain injury

BACKGROUND

Comorbidity can affect health-care utilization and outcomes, and the results and interpretation of risk adjustment studies that attempt to predict rehabilitation utilization and outcomes are influenced by the choice of comorbidity measurement. Although the identification of an appropriate measurement has been conducted in some populations and outcomes, this information is currently lacking for the non-traumatic brain injury (nTBI) population in inpatient rehabilitation settings. As such, this is a systematic review protocol to survey the methods used to measure comorbidities in the rehabilitation setting for patients with nTBI.

METHODS/DESIGN

MEDLINE, MEDLINE In-Process, Embase, The Cochrane Database of Systematic Reviews, PsycINFO, and Health and Psychosocial Instruments will be systematically searched using the concepts 'nTBI,' 'comorbidity,' and 'rehabilitation.' Grey matters and the reference list of eligible articles will also be searched. Study selection will be performed independently by two reviewers based on predetermined eligibility criteria through two rounds of screening using, first, the title and abstract, followed by full-text. Extracted information will include study purpose, design, and setting; data source and type; outcomes variables; statistical methods; comorbidity measurement method, rationale, justification, or validation; and results involving comorbidity. The data will be tabulated and narratively synthesized. Meta-analyses will be performed if appropriate.

SYSTEMATIC REVIEW REGISTRATION

This protocol has not been registered with PROSPERO.

DISCUSSION

This protocol provides a systematic method for surveying current practice as well as monitoring the progress on comorbidity measurement methodology and effects of comorbidity on rehabilitation outcomes for patients with nTBI. The selection of an appropriate comorbidity measurement method has implications for the interpretation of both descriptive and risk adjustment studies, and thus, the validity of evidence used to inform planning and delivery of services.

A needs assessment to determine the need for respiratory therapy in complex continuing care: A methods paper

BACKGROUND

There is an emerging demand for complex continuing care for patients who are too ill to safely return home, but for whom hospitalization in an acute care environment is unnecessary or inappropriate. Despite the need and medical complexity of these patients, few respiratory therapists are practising in this environment, and little evidence exists to guide the implementation of respiratory therapy services in this setting.

OBJECTIVE

In response to a perceived need for greater respiratory services at Saint Vincent Hospital (Ottawa, Ontario), a needs assessment was undertaken to assess the prevalence of respiratory diseases and for increased respiratory therapist coverage at this complex continuing care hospital.

METHODS

An initial literature review was conducted to guide the assessment, and identified only one tool of relevance, which was obtained and formed the basis of the further development of tools for collecting data at the hospital level and on patient care units at the facility. This needs assessment tool was expanded to include priority areas of relevance that fall within the scope of practice of respiratory therapists, and was supplemented by the analysis of administrative databases and qualitative data gathered through unit walkthroughs and unstructured key informant interviews. A health systems framework was used to structure recommendations for the development of interventions and programs for this patient population.

RESULTS

The burden of respiratory disease was significant, and included a high prevalence of inhaled medication and oxygen use, and a significant workload that could be attributed to addressing the respiratory needs of patients.

CONCLUSION

A range of tools and methods are needed to conduct needs assessments for respiratory therapy in complex continuing care. Using multiple data sources, a significant burden of respiratory diseases was present at the Saint Vincent Hospital; further studies in other complex continuing care hospitals are needed to understand the significance of these findings among this patient population more generally.

COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling

Earlier studies on hospitalization risk are largely based on regression models. To our knowledge, network modeling of multiple comorbidities is novel and inherently enables multidimensional scoring and unbiased feature reduction. Network modeling was conducted using an independent validation design starting from 38,695 patients, 1,446,581 visits, and 430 distinct clinical facilities/hospitals. Odds ratios (OR) were calculated for every pair of comorbidity using patient counts and compared their tendency with hospitalization rates and ED visits. Network topology analyses were performed, defining significant comorbidity associations as having OR≥5 & False-Discovery-Rate≤10(-7). Four COPD-associated comorbidity sub-networks emerged, incorporating multiple clinical systems: (i) metabolic syndrome, (ii) substance abuse and mental disorder, (iii) pregnancy-associated conditions, and (iv) fall-related injury. The latter two have not been reported yet. Features prioritized from the network are predictive of hospitalizations in an independent set (p<0.004). Therefore, we suggest that network topology is a scalable and generalizable method predictive of hospitalization.

Short- and medium-term prognosis in patients hospitalized for COPD exacerbation: the CODEX index

BACKGROUND

No valid tools exist for evaluating the prognosis in the short and medium term after hospital discharge of patients with COPD. Our hypothesis was that a new index based on the CODEX (comorbidity, obstruction, dyspnea, and previous severe exacerbations) index can accurately predict mortality, hospital readmission, and their combination for the period from 3 months to 1 year after discharge in patients hospitalized for COPD.

METHODS

A multicenter study of patients hospitalized for COPD exacerbations was used to develop the CODEX index, and a different patient cohort was used for validation. Comorbidity was measured using the age-adjusted Charlson index, whereas dyspnea, obstruction, and severe exacerbations were calculated according to BODEX (BMI, airfl ow obstruction, dyspnea, and previous severe exacerbations) thresholds. Information about mortality and readmissions for COPD or other causes was collected at 3 and 12 months after hospital discharge.

RESULTS

Two sets of 606 and 377 patients were included in the development and validation cohorts, respectively. The CODEX index was associated with mortality at 3 months ( P < .0001; hazard ratio [HR], 1.5; 95% CI, 1.2-1.8) and 1 year ( P < .0001; HR, 1.3; 95% CI, 1.2-1.5 ), hospital readmissions in the same periods, and their combination (all P < .0001). All CODEX C statistics were superior to those of the BODEX, DOSE (dyspnea, airfl ow obstruction, smoking status, and exacerbation frequency), and updated ADO (age, dyspnea, and airfl ow obstruction) indexes.

CONCLUSIONS

The CODEX index was a useful predictor of survival and readmission at both 3 months and 1 year after hospital discharge for a COPD exacerbation, with a prognostic capacity superior to other previously published indexes.

Exploration of ICD-9-CM coding of chronic disease within the Elixhauser Comorbidity Measure in patients with chronic heart failure

INTRODUCTION

International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes capture comorbidities that can be used to risk adjust nonrandom patient groups. We explored the accuracy of capturing comorbidities associated with one risk adjustment method, the Elixhauser Comorbidity Measure (ECM), in patients with chronic heart failure (CHF) at one Veterans Affairs (VA) medical center. We explored potential reasons for the differences found between the original codes assigned and conditions found through retrospective review.

METHODS

This descriptive, retrospective study used a cohort of patients discharged with a principal diagnosis coded as CHF from one VA medical center in 2003. One admission per patient was used in the study; with multiple admissions, only the first admission was analyzed. We compared the assignment of original codes assigned to conditions found in a retrospective, manual review of the medical record conducted by an investigator with coding expertise as well as by physicians. Members of the team experienced with assigning ICD-9-CM codes and VA coding processes developed themes related to systemic reasons why chronic conditions were not coded in VA records using applied thematic techniques.

RESULTS

In the 181-patient cohort, 388 comorbid conditions were identified; 305 of these were chronic conditions, originally coded at the time of discharge with an average of 1.7 comorbidities related to the ECM per patient. The review by an investigator with coding expertise revealed a total of 937 comorbidities resulting in 618 chronic comorbid conditions with an average of 3.4 per patient; physician review found 872 total comorbidities with 562 chronic conditions (average 3.1 per patient). The agreement between the original and the retrospective coding review was 88 percent. The kappa statistic for the original and the retrospective coding review was 0.375 with a 95 percent confidence interval (CI) of 0.352 to 0.398. The kappa statistic for the retrospective coding review and physician review was 0.849 (CI, 0.823-0.875). The kappa statistic for the original coding and the physician review was 0.340 (CI, 0.316-0.364). Several systemic factors were identified, including familiarity with inpatient VA and non-VA guidelines, the quality of documentation, and operational requirements to complete the coding process within short time frames and to identify the reasons for movement within a given facility.

CONCLUSION

Comorbidities within the ECM representing chronic conditions were significantly underrepresented in the original code assignment. Contributing factors potentially include prioritization of codes related to acute conditions over chronic conditions; coders' professional training, educational level, and experience; and the limited number of codes allowed in initial coding software. This study highlights the need to evaluate systemic causes of underrepresentation of chronic conditions to improve the accuracy of risk adjustment used for health services research, resource allocation, and performance measurement.