The development and validation of a comorbidity index for prostate cancer among Black men

Charlson Comorbidity Index: A Critical Review of Clinimetric Properties

The present critical review was conducted to evaluate the clinimetric properties of the Charlson Comorbidity Index (CCI), an assessment tool designed specifically to predict long-term mortality, with regard to its reliability, concurrent validity, sensitivity, incremental and predictive validity. The original version of the CCI has been adapted for use with different sources of data, ICD-9 and ICD-10 codes. The inter-rater reliability of the CCI was found to be excellent, with extremely high agreement between self-report and medical charts. The CCI has also been shown either to have concurrent validity with a number of other prognostic scales or to result in concordant predictions. Importantly, the clinimetric sensitivity of the CCI has been demonstrated in a variety of medical conditions, with stepwise increases in the CCI associated with stepwise increases in mortality. The CCI is also characterized by the clinimetric property of incremental validity, whereby adding the CCI to other measures increases the overall predictive accuracy. It has been shown to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit (ICU), trauma, and cancer patients. It may also predict in-hospital mortality, although in some instances, such as ICU or trauma patients, the CCI did not perform as well as other instruments designed specifically for that purpose. The CCI thus appears to be clinically useful not only to provide a valid assessment of the patient's unique clinical situation, but also to demarcate major diagnostic and prognostic differences among subgroups of patients sharing the same medical diagnosis.

The burden of chronic diseases among Australian cancer patients: Evidence from a longitudinal exploration, 2007-2017

INTRODUCTION

Cancer is a major public health concern in terms of morbidity and mortality worldwide. Several types of cancer patients suffer from chronic comorbid conditions that are a major clinical challenge for treatment and cancer management. The main objective of this study was to investigate the distribution of the burden of chronic comorbid conditions and associated predictors among cancer patients in Australia over the period of 2007-2017.

METHODS

The study employed a prospective longitudinal design using data from the Household, Income and Labour Dynamics in Australia survey. The number of chronic comorbid conditions was measured for each respondent. The longitudinal effect was captured using a fixed-effect negative binomial regression model, which predicted the potential factors that played a significant role in the occurrence of chronic comorbid conditions.

RESULTS

Sixty-one percent of cancer patients experienced at least one chronic disease over the period, and 21% of patients experienced three or more chronic diseases. Age (>65 years old) (incidence rate ratio, IRR = 1.15; 95% confidence interval, CI: 1.05, 1.40), inadequate levels of physical activity (IRR = 1.25; 95% CI: 1.09, 1.59), patients who suffered from extreme health burden (IRR = 2.30; 95% CI: 1.73, 3.05) or moderate health burden (IRR = 1.90; 95% CI: 1.45, 2.48), and patients living in the poorest households (IRR = 1.21; 95% CI: 1.11, 1.29) were significant predictors associated with a higher risk of chronic comorbid conditions.

CONCLUSIONS

A large number of cancer patients experience an extreme burden of chronic comorbid conditions and the different dimensions of these in cancer survivors have the potential to affect the trajectory of their cancer burden. It is also significant for health care providers, including physical therapists and oncologists, who must manage the unique problems that challenge this population and who should advocate for prevention and evidence-based interventions.

A new population-based risk stratification tool was developed and validated for predicting mortality, hospital admissions, and health care costs

OBJECTIVES

The aim of this study was to develop a new population-based risk stratification tool (Chronic Related Score [CReSc]) for predicting 5-year mortality and other outcomes.

STUDY DESIGN AND SETTING

The score included 31 conditions selected from a list of 65 candidates whose weights were assigned according to the Cox model coefficients. The model was built from a sample of 5.4 million National Health Service (NHS) beneficiaries from the Italian Lombardy Region and applied to the remaining 2.7 million NHS beneficiaries. Predictive performance was assessed by discrimination and calibration. CReSc ability in predicting secondary endpoints (i.e., hospital admissions and health care costs) was investigated. Finally, the relationship between CReSc and income was considered.

RESULTS

Among individuals aged 50-85 years, CReSc performance showed (1) an area under the receiver operating characteristic curve of 0.730, (2) an improved reclassification from 44% to 52% with respect to other scores, and (3) a remarkable calibration. A trend toward increasing rates of all the considered endpoints as CReSc increases was observed. Compared with individuals on low-intermediate income, NHS beneficiaries on high income showed better CReSc profile.

CONCLUSION

We developed a risk stratification tool able to predict mortality, costs, and hospital admissions. The application of CReSc may generate clinically and operationally important effects.

Prostate Cancer Disparities by Race and Ethnicity: From Nucleotide to Neighborhood

Prostate cancer (CaP) incidence, morbidity, and mortality rates vary substantially by race and ethnicity, with African American men experiencing among the highest CaP rates in the world. The causes of these disparities are multifactorial and complex, and likely involve differences in access to screening and treatment, exposure to CaP risk factors, variation in genomic susceptibility, and other biological factors. To date, the proportion of CaP that can be explained by environmental exposures is small and differences in the role factors play by race or ethnicity is poorly understood. In the absence of additional data, it is likely that environmental factors do not contribute greatly to CaP disparities. In contrast, CaP has one of the highest heritabilities of all major cancers and many CaP susceptibility genes have been identified. Some CaP loci, including the risk loci found at chromosome 8q24, have consistent effects in all racial/ethnic groups studied to date. However, replication of many susceptibility loci across race or ethnicity remains limited. It is likely that inequities in health care access strongly influences CaP disparities. CaP is a disease with a complex multifactorial etiology, and therefore any approach attempting to address racial/ethnic disparities in CaP must consider the many sources that influence risk, outcomes, and disparities.

Developing and validating a novel multisource comorbidity score from administrative data: a large population-based cohort study from Italy

OBJECTIVE

To develop and validate a novel comorbidity score (multisource comorbidity score (MCS)) predictive of mortality, hospital admissions and healthcare costs using multiple source information from the administrative Italian National Health System (NHS) databases.

METHODS

An index of 34 variables (measured from inpatient diagnoses and outpatient drug prescriptions within 2 years before baseline) independently predicting 1-year mortality in a sample of 500 000 individuals aged 50 years or older randomly selected from the NHS beneficiaries of the Italian region of Lombardy (training set) was developed. The corresponding weights were assigned from the regression coefficients of a Weibull survival model. MCS performance was evaluated by using an internal (ie, another sample of 500 000 NHS beneficiaries from Lombardy) and three external (each consisting of 500 000 NHS beneficiaries from Emilia-Romagna, Lazio and Sicily) validation sets. Discriminant power and net reclassification improvement were used to compare MCS performance with that of other comorbidity scores. MCS ability to predict secondary health outcomes (ie, hospital admissions and costs) was also investigated.

RESULTS

Primary and secondary outcomes progressively increased with increasing MCS value. MCS improved the net 1-year mortality reclassification from 27% (with respect to the Chronic Disease Score) to 69% (with respect to the Elixhauser Index). MCS discrimination performance was similar in the four regions of Italy we tested, the area under the receiver operating characteristic curves (95% CI) being 0.78 (0.77 to 0.79) in Lombardy, 0.78 (0.77 to 0.79) in Emilia-Romagna, 0.77 (0.76 to 0.78) in Lazio and 0.78 (0.77 to 0.79) in Sicily.

CONCLUSION

MCS seems better than conventional scores for predicting health outcomes, at least in the general population from Italy. This may offer an improved tool for risk adjustment, policy planning and identifying patients in need of a focused treatment approach in the everyday medical practice.

Diabetes and mortality in patients with prostate cancer: a meta-analysis

BACKGROUND

There are conflicting results as to the association between pre-existing diabetes and the risk of mortality in patients with prostate cancer. The purpose of this study is to estimate the influence of pre-existing diabetes on prostate cancer-specific mortality and all-cause mortality.

METHODS

We searched PubMed and Embase to identify studies that investigated the association between pre-existing diabetes and risk of death among men with prostate cancer. Pooled risk estimates and 95 % confidence intervals were calculated using fixed-effects models or random-effects models. Heterogeneity tests were conducted between studies. Publication bias was analyzed by using the Egger's test, Begg's test, and the trim and fill method.

RESULTS

Of the 733 articles identified, 17 cohort studies that had 274,677 male patients were included in this meta-analysis. Pre-existing diabetes was associated with a 29 % increase in prostate cancer-specific mortality [relative risk (RR) 1.29, 95 % CI 1.22-1.38, I(2) = 66.68 %], and with a 37 % increase in all-cause mortality (RR 1.37, 95 % CI 1.29-1.45, p < 0.01, I(2) = 90.26 %). Additionally, in a subgroup analysis that was a type specific analysis focusing on type 2 diabetes and was conducted only with three cohort studies, pre-existing type 2 diabetes was associated with all-cause mortality (RR 2.01, 95 % CI 1.37-2.96, I(2) = 95.55 %) and no significant association with prostate cancer-specific mortality was detected (RR 1.17, 95 % CI 0.96-1.42, I(2) = 75.59 %). There was significant heterogeneity between studies and no publication bias was found.

CONCLUSIONS

This meta-analysis suggests diabetes may result in a worse prognosis for men with prostate cancer. Considering heterogeneity between studies, additional studies should be conducted to confirm these findings, and to allow generalization regarding the influence that each type of diabetes has on prostate cancer mortality.

The impact of comorbidity on cancer and its treatment

Answer questions and earn CME/CNE Comorbidity is common among cancer patients and, with an aging population, is becoming more so. Comorbidity potentially affects the development, stage at diagnosis, treatment, and outcomes of people with cancer. Despite the intimate relationship between comorbidity and cancer, there is limited consensus on how to record, interpret, or manage comorbidity in the context of cancer, with the result that patients who have comorbidity are less likely to receive treatment with curative intent. Evidence in this area is lacking because of the frequent exclusion of patients with comorbidity from randomized controlled trials. There is evidence that some patients with comorbidity have potentially curative treatment unnecessarily modified, compromising optimal care. Patients with comorbidity have poorer survival, poorer quality of life, and higher health care costs. Strategies to address these issues include improving the evidence base for patients with comorbidity, further development of clinical tools to assist decision making, improved integration and coordination of care, and skill development for clinicians. CA Cancer J Clin 2016;66:337-350. © 2016 American Cancer Society.

Diabetes mellitus is associated with elevated risk of mortality amongst patients with prostate cancer: a meta-analysis of 11 cohort studies

PURPOSE

Diabetes mellitus is associated with a decreased risk of prostate cancer. However, previous studies examining the associations between diabetes mellitus and prostate cancer prognosis have produced mixed results. Here, we aim to summarize the effect of diabetes mellitus on prostate cancer prognosis.

METHODS

We searched the database of PubMed from inception through 31 March 2014 for articles evaluating the effect of diabetes on outcome in prostate cancer patients, and a meta-analysis was conducted.

RESULTS

A total of 11 cohort studies were included in this meta-analysis, of which seven studies were carried out to investigate whether diabetes mellitus is associated with all-cause mortality amongst those with prostate cancer, seven studies to investigate whether diabetes mellitus is associated with prostate cancer-specific mortality and two studies to investigate the relationship of diabetes mellitus and nonprostate cancer mortality. The meta-analysis results suggested that diabetes mellitus could significantly affect the incidence of all-cause mortality amongst those with prostate cancer (hazard ratio = 1.50, 95% confidence interval = 1.25-1.79). Besides, diabetes mellitus was also associated with prostate cancer-specific mortality (hazard ratio = 1.26, 95% confidence interval = 1.20-1.33) and nonprostate cancer mortality (hazard ratio = 1.83, 95% confidence interval = 1.33-2.52) separately. There was no obvious publication bias amongst the studies included.

CONCLUSION

The results of this meta-analysis reveal an association of diabetes mellitus with adverse prognosis amongst those with prostate cancer. The biological basis of the association of diabetes mellitus with prostate cancer incidence and prognosis remains unclear. Doctors could pay more attention to prostate patients with pre-existing diabetes mellitus, and more aggressive treatment regimens should be considered.

Can valid and practical risk-prediction or casemix adjustment models, including adjustment for comorbidity, be generated from English hospital administrative data (Hospital Episode Statistics)? A national observational study

Background

NHS hospitals collect a wealth of administrative data covering accident and emergency (A&E) department attendances, inpatient and day case activity, and outpatient appointments. Such data are increasingly being used to compare units and services, but adjusting for risk is difficult.

Objectives

To derive robust risk-adjustment models for various patient groups, including those admitted for heart failure (HF), acute myocardial infarction, colorectal and orthopaedic surgery, and outcomes adjusting for available patient factors such as comorbidity, using England’s Hospital Episode Statistics (HES) data. To assess if more sophisticated statistical methods based on machine learning such as artificial neural networks (ANNs) outperform traditional logistic regression (LR) for risk prediction. To update and assess for the NHS the Charlson index for comorbidity. To assess the usefulness of outpatient data for these models.

Main outcome measures

Mortality, readmission, return to theatre, outpatient non-attendance. For HF patients we considered various readmission measures such as diagnosis-specific and total within a year.

Methods

We systematically reviewed studies comparing two or more comorbidity indices. Logistic regression, ANNs, support vector machines and random forests were compared for mortality and readmission. Models were assessed using discrimination and calibration statistics. Competing risks proportional hazards regression and various count models were used for future admissions and bed-days.

Results

Our systematic review and empirical analysis suggested that for general purposes comorbidity is currently best described by the set of 30 Elixhauser comorbidities plus dementia. Model discrimination was often high for mortality and poor, or at best moderate, for other outcomes, for examplec = 0.62 for readmission andc = 0.73 for death following stroke. Calibration was often good for procedure groups but poorer for diagnosis groups, with overprediction of low risk a common cause. The machine learning methods we investigated offered little beyond LR for their greater complexity and implementation difficulties. For HF, some patient-level predictors differed by primary diagnosis of readmission but not by length of follow-up. Prior non-attendance at outpatient appointments was a useful, strong predictor of readmission. Hospital-level readmission rates for HF did not correlate with readmission rates for non-HF; hospital performance on national audit process measures largely correlated only with HF readmission rates.

Conclusions

Many practical risk-prediction or casemix adjustment models can be generated from HES data using LR, though an extra step is often required for accurate calibration. Including outpatient data in readmission models is useful. The three machine learning methods we assessed added little with these data. Readmission rates for HF patients should be divided by diagnosis on readmission when used for quality improvement.

Future work

As HES data continue to develop and improve in scope and accuracy, they can be used more, for instance A&E records. The return to theatre metric appears promising and could be extended to other index procedures and specialties. While our data did not warrant the testing of a larger number of machine learning methods, databases augmented with physiological and pathology information, for example, might benefit from methods such as boosted trees. Finally, one could apply the HF readmissions analysis to other chronic conditions.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Identifying important comorbidity among cancer populations using administrative data: Prevalence and impact on survival

AIMS

Our study sought to optimize the identification and investigate the impact of comorbidity in cancer patients using routinely collected hospitalization data.

METHODS

We undertook an iterative process of classification of important clinical conditions involving evaluation of relevant literature and consultation with clinicians. Patients diagnosed with colon, rectal, breast, ovarian, uterine, stomach, liver, renal or bladder cancers (n = 14,096) between 2006 and 2008 were identified from the New Zealand Cancer Registry. Conditions were identified using data on diagnoses from hospital admissions for 5 years prior to cancer diagnosis. Patients were followed up until end of 2009 using routine mortality data. Prevalence estimates for each condition by site were calculated. All-cause mortality impact of common conditions was investigated using Cox regression models adjusted for age and stage at diagnosis.

RESULTS

Patients with liver and stomach cancers tended to have higher comorbidity and those with breast cancer, lower comorbidity than other cancer patients. Of the 50 conditions, the most common were hypertension (prevalence 8.0-20.9%), cardiac conditions (2.1-13.5%) and diabetes with (2.3-13.3%) and without (2.9-12.9%) complications. Comorbidity was associated with higher all-cause mortality but the impact varied by condition and across cancer site, with impact less for cancers with poor prognoses. Conditions most consistently associated with adverse outcomes across all cancer sites were renal disease, coagulopathies and congestive heart failure.

CONCLUSION

Comorbidity is highly prevalent in cancer populations, but prevalence and impact of conditions differ markedly by cancer type.

Systematic review of comorbidity indices for administrative data

BACKGROUND

Adjustment for comorbidities is common in performance benchmarking and risk prediction. Despite the exponential upsurge in the number of articles citing or comparing Charlson, Elixhauser, and their variants, no systematic review has been conducted on studies comparing comorbidity measures in use with administrative data. We present a systematic review of these multiple comparison studies and introduce a new meta-analytical approach to identify the best performing comorbidity measures/indices for short-term (inpatient + ≤ 30 d) and long-term (outpatient+>30 d) mortality.

METHODS

Articles up to March 18, 2011 were searched based on our predefined terms. The bibliography of the chosen articles and the relevant reviews were also searched and reviewed. Multiple comparisons between comorbidity measures/indices were split into all possible pairs. We used the hypergeometric test and confidence intervals for proportions to identify the comparators with significantly superior/inferior performance for short-term and long-term mortality. In addition, useful information such as the influence of lookback periods was extracted and reported.

RESULTS

Out of 1312 retrieved articles, 54 articles were eligible. The Deyo variant of Charlson was the most commonly referred comparator followed by the Elixhauser measure. Compared with baseline variables such as age and sex, comorbidity adjustment methods seem to better predict long-term than short-term mortality and Elixhauser seems to be the best predictor for this outcome. For short-term mortality, however, recalibration giving empirical weights seems more important than the choice of comorbidity measure.

CONCLUSIONS

The performance of a given comorbidity measure depends on the patient group and outcome. In general, the Elixhauser index seems the best so far, particularly for mortality beyond 30 days, although several newer, more inclusive measures are promising.

Does pre-existing diabetes affect prostate cancer prognosis? A systematic review

To summarize the influence of pre-existing diabetes on mortality and morbidity in men with prostate cancer. We searched MEDLINE and EMBASE from inception through 1 October 2008. Search terms were related to diabetes, cancer and prognosis. Studies were included if they reported an original data analysis of prostate cancer prognosis, compared outcomes between men with and without diabetes and were in English. Titles, abstracts and articles were reviewed independently by two authors. Conflicts were settled by consensus or third review. We abstracted data on study design, analytic methods, outcomes and quality. We summarized mortality and morbidity outcomes qualitatively and conducted a preliminary meta-analysis to quantify the risk of long-term (>3 months), overall mortality. In total, 11 articles were included in the review. Overall, one of four studies found increased prostate cancer mortality, one of two studies found increased nonprostate cancer mortality and one study found increased 30-day mortality. Data from four studies could be included in a preliminary meta-analysis for long-term, overall mortality and produced a pooled hazard ratio of 1.57 (95% CI: 1.12-2.20). Diabetes was also associated with receiving radiation therapy, complication rates, recurrence and treatment failure. Our analysis suggests that pre-existing diabetes affects the treatment and outcomes of men with prostate cancer.

Cause of death in older men after the diagnosis of prostate cancer

OBJECTIVES

To compare survival and cause of death in men aged 65 and older diagnosed with prostate cancer and with survival and cause of death in a noncancer control population.

DESIGN

Retrospective cohort from a population-based tumor registry linked to Medicare claims data.

SETTING

Eleven regions of the Surveillance, Epidemiology and End Results (SEER) Tumor Registry.

PARTICIPANTS

Men aged 65 to 84 (N=208,601) diagnosed with prostate cancer from 1988 through 2002 formed the basis for different analytical cohorts.

MEASUREMENTS

Survival as a function of stage and tumor grade (low, Gleason grade<7; moderate, grade=7; and high, grade=8-10) was compared with survival in men without any cancer using Cox proportional hazards regression. Cause of death according to stage and tumor grade were compared using chi-square statistics.

RESULTS

Men with early-stage prostate cancer and with low- to moderate-grade tumors (59.1% of the entire sample) experienced a survival not substantially worse than men without prostate cancer. In those men, cardiovascular disease and other cancers were the leading causes of death.

CONCLUSION

The excellent survival of older men with early-stage, low- to moderate-grade prostate cancer, along with the patterns of causes of death, implies that this population would be well served by an ongoing focus on screening and prevention of cardiovascular disease and other cancers.

Long-term all-cause mortality in cancer patients with preexisting diabetes mellitus: a systematic review and meta-analysis

CONTEXT

Diabetes mellitus appears to be a risk factor for some cancers, but the effect of preexisting diabetes on all-cause mortality in newly diagnosed cancer patients is less clear.

OBJECTIVE

To perform a systematic review and meta-analysis comparing overall survival in cancer patients with and without preexisting diabetes.

DATA SOURCES

We searched MEDLINE and EMBASE through May 15, 2008, including references of qualifying articles.

STUDY SELECTION

English-language, original investigations in humans with at least 3 months of follow-up were included. Titles, abstracts, and articles were reviewed by at least 2 independent readers. Of 7858 titles identified in our original search, 48 articles met our criteria.

DATA EXTRACTION

One reviewer performed a full abstraction and other reviewers verified accuracy. We contacted authors and obtained additional information for 3 articles with insufficient reported data.

RESULTS

Studies reporting cumulative survival rates were summarized qualitatively. Studies reporting Cox proportional hazard ratios (HRs) or Poisson relative risks were combined in a meta-analysis. A random-effects model meta-analysis of 23 articles showed that diabetes was associated with an increased mortality HR of 1.41 (95% confidence interval [CI], 1.28-1.55) compared with normoglycemic individuals across all cancer types. Subgroup analyses by type of cancer showed increased risk for cancers of the endometrium (HR, 1.76; 95% CI, 1.34-2.31), breast (HR, 1.61; 95% CI, 1.46-1.78), and colorectum (HR, 1.32; 95% CI, 1.24-1.41).

CONCLUSIONS

Patients diagnosed with cancer who have preexisting diabetes are at increased risk for long-term, all-cause mortality compared with those without diabetes.

A Refined Comorbidity Measurement Algorithm for Claims-Based Studies of Breast, Prostate, Colorectal, and Lung Cancer Patients

PURPOSE

We evaluated (i) how combining comorbid conditions identified from Medicare inpatient or physician claims into a single comorbidity index compared with three other comorbidity indices and (ii) the need for comorbid condition weights that are specific to different cancer sites.

METHODS

This observational study used the SEER-Medicare linked database, from which four cohorts of cancer patients were derived: breast (n = 26,377), prostate (n = 53,503), colorectal (n = 26,460), and lung (n = 33,975). We calculated two established (Charlson; NCI) and two new (NCI Combined; Uniform Weights) comorbidity indices, and used Cox proportional hazards models to assess their predictive ability. We also used a pooled dataset to examine the inclusion of cancer site-specific condition weights.

RESULTS

The four comorbidity indices all significantly predicted mortality, but the NCI and new NCI Combined indices showed the greatest contribution to model fit. The new NCI Combined index is simpler to use and statistically more efficient than the NCI index. Modeling further demonstrated the utility of cancer site-specific weights.

CONCLUSIONS

Our results support the need for cancer site-specific comorbidity measures that employ empirically-derived condition weights. The new NCI Combined index is a refined, easier to implement comorbidity measurement algorithm appropriate for investigators using administrative claims databases to study four commonly-occurring cancers.

In search of the perfect comorbidity measure for use with administrative claims data: does it exist?

BACKGROUND

Numerous measures of comorbidity have been developed for health services research with administrative claims.

OBJECTIVE

We sought to compare the performance of 4 claims-based comorbidity measures.

RESEARCH DESIGN AND SUBJECTS

We undertook a retrospective cohort study of 5777 Medicare beneficiaries ages 66 and older with stage III colon cancer reported to the Surveillance, Epidemiology, and End Results Program between January 1, 1992 and December 31, 1996.

MEASURES

Comorbidity measures included Elixhauser's set of 30 condition indicators, Klabunde's outpatient and inpatient indices weighted for colorectal cancer patients, Diagnostic Cost Groups, and the Adjusted Clinical Group (ACG) System. Outcomes included receipt of adjuvant chemotherapy and 2 year noncancer mortality.

RESULTS

For all measures, greater comorbidity significantly predicted lower receipt of chemotherapy and higher noncancer death. Nested logistic regression modeling suggests that using more claims sources to measure comorbidity generally improves the prediction of chemotherapy receipt and noncancer death, but depends on the measure type and outcome studied. All 4 comorbidity measures significantly improved the fit of baseline regression models for both chemotherapy receipt (baseline c-statistic 0.776; ranging from 0.779 after adding ACGs and Klabunde to 0.789 after Elixhauser) and noncancer death (baseline c-statistic 0.687; ranging from 0.717 after adding ACGs to 0.744 after Elixhauser).

CONCLUSIONS

Although some comorbidity measures demonstrate minor advantages over others, each is fairly robust in predicting both chemotherapy receipt and noncancer death. Investigators should choose among these measures based on their availability, comfort with the methodology, and outcomes of interest.

Outcomes Research: A Methodologic Review

OBJECTIVES

We explored the history and conceptual trends of outcomes research.

METHODS

We described different aspects of this field, after dividing it into conceptually distinct strata.

RESULTS

Outcomes research can be divided into macro, meso and micro levels. Each level is further subdivided. Macro-level research targets cost and health care utilization, as well as racial, ethnic and geopolitical population health determinants. Meso-level studies address effectiveness, variability, disease impact, clinical modeling and program evaluation studies. Finally, micro-level studies address all aspects of direct patient-clinician decision-making.

CONCLUSIONS

An explosion of outcomes research has occurred in the past decades. Wide access to information technology, data sharing and collaborative efforts between researchers represent some of the ingredients that did and will continue to fuel that growth.

Charlson scores based on ICD-10 administrative data were valid in assessing comorbidity in patients undergoing urological cancer surgery

BACKGROUND AND OBJECTIVES

Adjustment for comorbidity is an essential component of any observational study comparing outcomes. We evaluated the validity of the Charlson comorbidity score based on ICD-10 codes in patients undergoing urological cancer surgery within an English administrative database.

STUDY DESIGN AND SETTING

Patients who underwent radical urological cancer surgery between 1998 and 2002 in the English National Health Service were identified from the Hospital Episode Statistics database (N = 20,138). ICD-9-CM codes defining comorbid diseases according to the Deyo and Dartmouth-Manitoba adaptations of the Charlson comorbidity score were translated into ICD-10 codes.

RESULTS

Charlson scores derived by the ICD-10 translation of the Deyo and Dartmouth-Manitoba adaptations were identical in 16,623 patients (83%; kappa = .63). For both adaptations, ICD-10 scores increased with age, were higher in patients admitted on an emergency basis, and predicted short-term outcome. Addition of either the ICD-10 Charlson Deyo or Dartmouth-Manitoba score to risk models containing age and sex to predict in-hospital mortality resulted in a better model fit but only in small improvements of the predictive power.

CONCLUSION

The ICD-10 translations of the Deyo and Dartmouth-Manitoba adaptations performed similarly in risk models predicting hospital mortality following urological cancer surgery. Adjustment for comorbidity over and above age and sex alone does not seem to provide a large improvement.

Comorbidity indices in hematopoietic stem cell transplantation: a new report card

Comorbid conditions have not been studied systematically for impact upon patient outcome in the setting of hematopoietic stem cell transplantation (HSCT). Patients formerly excluded from myeloablative transplant due to comorbid illnesses now receive reduced-intensity conditioning regimens; hence, the incidence of comorbid conditions in HSCT recipients is expected to increase. Comorbid grading systems developed without regard for oncology patients have been applied in retrospective fashion to HSCT patients. Two commonly used scales (Charlson Comorbidity Index and the Adult Comorbidity Inventory-27) fail to include critical information: tumor and histologic type/stage, extent of prior treatment, donor stem cell source and cell type and preparative regimen. Further, data are reported in retrospective rather than prospective fashion. Despite limitations, however, such grading systems exhibit ease and utility for evaluation and may have predictive value for patient outcome. Modifying such approaches to include additional factors and appropriate weighting of components may enable an improved comparison of techniques and study results. These scoring systems may elucidate predictors of outcome and disease natural history and enhance statistical efficiency methods of HSCT. Refined scoring could be used effectively to assign patients to differing transplant conditioning regimens, that is, myeloablative vs reduced intensity. Prospective validation of such grading systems is encouraged.