Predicting in-hospital deaths from coronary artery bypass graft surgery. Do different severity measures give different predictions?

Evaluating Mortality Risk Adjustment Among Children Receiving Extracorporeal Support for Respiratory Failure

This study evaluates whether three commonly used pediatric intensive care unit (PICU) severity of illness scores, pediatric risk of mortality score (PRISM) III, pediatric index of mortality (PIM) 2, and pediatric logistic organ dysfunction (PELOD), are the appropriate tools to discriminate mortality risk in children receiving extracorporeal membrane oxygenation (ECMO) support for respiratory failure. This study also evaluates the ability of the Pediatric Risk Estimate Score for Children Using Extracorporeal Respiratory Support (Ped-RESCUERS) to discriminate mortality risk in the same population, and whether Ped-RESCUERS' discrimination of mortality is improved by additional clinical and laboratory measures of renal, hepatic, neurologic, and hematologic dysfunction. A multi-institutional retrospective cohort study was conducted on children aged 29 days to 17 years with respiratory failure requiring respiratory ECMO support. Discrimination of mortality was evaluated with the area under the receiver operating curve (AUC); model calibration was measured by the Hosmer-Lemeshow goodness of fit test and Brier score. Admission PRISM-III, PIM-2, and PELOD were found to have poor ability to discriminate mortality with an AUC of 0.56 [0.46-0.66], 0.53 [0.43-0.62], and 0.57 [0.47-0.67], respectively. Alternatively, Ped-RESCUERS performed better with an AUC of 0.68 [0.59-0.77]. Higher alanine aminotransferase, ratio of the arterial partial pressure of oxygen the fraction of inspired oxygen, and lactic acidosis were independently associated with mortality and, when added to Ped-RESCUERS, resulted in an AUC of 0.75 [0.66-0.82]. Admission PRISM-III, PIM-2, and PELOD should not be used for pre-ECMO risk adjustment because they do not discriminate death. Extracorporeal membrane oxygenation population-derived scores should be used to risk adjust ECMO populations as opposed to general PICU population-derived scores.

Challenges in Outcome Reporting

Although measuring outcomes is an integral part of medical quality improvement, large-scale outcome reporting efforts face several challenges. Among these are difficulties in establishing consensus definitions for outcome measurement; classifying gray outcomes, such as postoperative respiratory failure; and adequately adjusting for patient comorbidities and severity of illness. Unintended consequences of outcome reporting can also distort care in undesirable ways, and clinician reluctance to care for high-risk patients may occur with reporting programs. Ultimately, clinicians need not compare outcomes to improve and should recognize that even outcomes that cannot be precisely quantitated can still be improved.

Evaluation of the National Minimum Data Set for Neurological Conditions in Older Adults

AIM

To evaluate the National Minimum Data Set (NMDS) against the International Resident Assessment Instrument-Home Care (interRAI-HC) in diagnosing dementia or Parkinson disease (PD).

METHOD

The NMDS data were matched with interRAI-HC for all older individuals in New Zealand. Dementia or PD was compared within 90 and 180 days and 1 to 4 years preceding and subsequent to the date of diagnosis in interRAI-HC. Consistency was measured through sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), weighted kappa (κ), and McNemar test.

RESULTS

For a diagnosis within 90 days, dementia showed 60.77% sensitivity, 95.33% specificity, 68.46% PPV, and 93.58% NPV. The PD showed 65.74% sensitivity, 99.52% specificity, 80.43% PPV, and 98.98% NPV. κ for dementia (κ = 0.59), PD (κ = 0.720), and McNemar test was significant ( P < .001) for all lengths of follow-up.

CONCLUSION

Substantial agreement between multiple sources of health data can be a valuable resource for decision-making in older people with neurological conditions.

What are the rates and causes of hospital readmission after total knee arthroplasty?

BACKGROUND

Total knee arthroplasty (TKA) and related interventions such as revision TKA and the treatment of infected TKAs are commonly performed procedures. Hospital readmission rates are used to measure hospital performance, but risk factors (both medical and surgical) for readmission after TKA, revision TKA, and treatment for the infected TKA have not been well characterized.

QUESTIONS/PURPOSES

We measured (1) the unplanned hospital readmission rate in primary TKA and revision TKA, including antibiotic-spacer staged revision TKA to treat infection. We also evaluated (2) the medical and surgical causes of readmission and (3) risk factors associated with unplanned hospital readmission.

METHODS

This retrospective cohort study included a total of 1408 patients (1032 primary TKAs, 262 revision TKAs, 113 revision of infected TKAs) from one institution. All hospital readmissions within 90 days of discharge were evaluated for timing and cause. Diagnoses at readmission were categorized as surgical or medical. Readmission risk was assessed using a Cox proportional hazards model that incorporated patient demographics and medical comorbidities.

RESULTS

The unplanned readmission rate for the entire cohort was 4% at 30 days and 8% at 90 days. At 90 days postoperatively, revision of an infected TKA had the highest readmission rate, followed by revision TKA, with primary TKA having the lowest rate. Approximately three-fourths of readmissions were the result of surgical causes, mostly infection, arthrofibrosis, and cellulitis, whereas the remainder of readmissions were the result of medical causes. Procedure type (primary TKA versus revision TKA or staged treatment for infected TKA), hospital stay more than 5 days, discharge destination, and a fluid/electrolyte abnormality were each associated with risk of unplanned readmission.

CONCLUSIONS

Patients having revision TKA, whether for infection or other causes, are more likely to have an unplanned readmission to the hospital than are patients having primary TKA. When assessing hospital performance for TKA, it is important to distinguish among these surgical procedures.

Comparing hospital mortality--how to count does matter for patients hospitalized for acute myocardial infarction (AMI), stroke and hip fracture

Background

Mortality is a widely used, but often criticised, quality indicator for hospitals. In many countries, mortality is calculated from in-hospital deaths, due to limited access to follow-up data on patients transferred between hospitals and on discharged patients. The objectives were to: i) summarize time, place and cause of death for first time acute myocardial infarction (AMI), stroke and hip fracture, ii) compare case-mix adjusted 30-day mortality measures based on in-hospital deaths and in-and-out-of hospital deaths, with and without patients transferred to other hospitals.

Methods

Norwegian hospital data within a 5-year period were merged with information from official registers. Mortality based on in-and-out-of-hospital deaths, weighted according to length of stay at each hospital for transferred patients (W30D), was compared to a) mortality based on in-and-out-of-hospital deaths excluding patients treated at two or more hospitals (S30D), and b) mortality based on in-hospital deaths (IH30D). Adjusted mortalities were estimated by logistic regression which, in addition to hospital, included age, sex and stage of disease. The hospitals were assigned outlier status according to the Z-values for hospitals in the models; low mortality: Z-values below the 5-percentile, high mortality: Z-values above the 95-percentile, medium mortality: remaining hospitals.

Results

The data included 48 048 AMI patients, 47 854 stroke patients and 40 142 hip fracture patients from 55, 59 and 58 hospitals, respectively. The overall relative frequencies of deaths within 30 days were 19.1% (AMI), 17.6% (stroke) and 7.8% (hip fracture). The cause of death diagnoses included the referral diagnosis for 73.8-89.6% of the deaths within 30 days. When comparing S30D versus W30D outlier status changed for 14.6% (AMI), 15.3% (stroke) and 36.2% (hip fracture) of the hospitals. For IH30D compared to W30D outlier status changed for 18.2% (AMI), 25.4% (stroke) and 27.6% (hip fracture) of the hospitals.

Conclusions

Mortality measures based on in-hospital deaths alone, or measures excluding admissions for transferred patients, can be misleading as indicators of hospital performance. We propose to attribute the outcome to all hospitals by fraction of time spent in each hospital for patients transferred between hospitals to reduce bias due to double counting or exclusion of hospital stays.

Effect of risk adjustment method on comparisons of health care utilization between complementary and alternative medicine users and nonusers

OBJECTIVES

Complementary and alternative medicine (CAM) providers are becoming more integrated into the United States health care system. Because patients self-select CAM use, risk adjustment is needed to make the groups more comparable when analyzing utilization. This study examined how the choice of risk adjustment method affects assessment of CAM use on overall health care utilization.

DESIGN AND SUBJECTS

Insurance claims data for 2000-2003 from Washington State, which mandates coverage of CAM providers, were analyzed. Three (3) risk adjustment methods were compared in patients with musculoskeletal conditions: Adjusted Clinical Groups (ACG), Diagnostic Cost Groups (DCG), and the Charlson Index. Relative Value Units (RVUs) were used as a proxy for expenditures. Two (2) sets of median regression models were created: prospective, which used risk adjustments from the previous year to predict RVU in the subsequent year, and concurrent, which used risk adjustment measures to predict RVU in the same year.

RESULTS

The sample included 92,474 claimants. Prospective models showed little difference in the effect of CAM use on RVU among the three risk adjustment methods, and all models had low predictive power (R(2) ≤0.05). In the concurrent models, coefficients were similar in direction and magnitude for all risk adjustment methods, but in some models the predicted effect of CAM use on RVU differed by as much as double between methods. Results of DCG and ACG models were similar and were stronger than Charlson models.

CONCLUSIONS

Choice of risk adjustment method may have a modest effect on the outcome of interest.

Nurse staffing and post-surgical complications using the present on admission indicator

We evaluated the relationship between registered nurse (RN) staffing and six post-surgical complications: pneumonia, septicemia, urinary tract infections, thrombophlebitis, fluid overload, and decubitus ulcers, in a dataset that contained the present on admission (POA) indicator. We analyzed a longitudinal panel of 283 acute care hospitals in California from 1996 to 2001. Using an adaptation of the Quality Health Outcomes Model, we found no statistically significant relationships between RN staffing and the complications. In addition, the signs of the relationships were opposite to those expected. That is, as staffing increased, so did some of the complications. We discuss potential reasons for these anomalous results, including the possibility that increases in RN staffing may result in earlier detection of complications. Other explanations include issues with risk adjustment, the lack of nurse level variables in the model, and issues with the POA indicator itself.

Predicted risk of mortality models: surgeons need to understand limitations of the University HealthSystem Consortium models

BACKGROUND

The University HealthSystem Consortium (UHC) mortality risk adjustment models are increasingly being used as benchmarks for quality assessment. But these administrative database models may include postoperative complications in their adjustments for preoperative risk. The purpose of this study was to compare the performance of the UHC with the Society of Thoracic Surgeons (STS) risk-adjusted mortality models for adult cardiac surgery and evaluate the contribution of postoperative complications on model performance.

STUDY DESIGN

We identified adult cardiac surgery patients with mortality risk estimates in both the UHC and Society of Thoracic Surgeons databases. We compared the predictive performance and calibration of estimates from both models. We then reestimated both models using only patients without any postoperative complications to determine the relative contribution of adjustments for postoperative events on model performance.

RESULTS

In the study population of 2,171 patients, the UHC model explained more variability (27% versus 13%, p < 0.001) and achieved better discrimination (C statistic = 0.88 versus 0.81, p < 0.001). But when applied in the population of patients without complications, the UHC model performance declined severely. The C statistic decreased from 0.88 to 0.49, a level of discrimination equivalent to random chance. The discrimination of the Society of Thoracic Surgeons model was unchanged (C statistic of 0.79 versus 0.81).

CONCLUSIONS

Although the UHC model demonstrated better performance in the total study population, this difference in performance reflects adjustments for conditions that are postoperative complications. The current UHC models should not be used for quality benchmarks.

Interhospital transfer of critically ill and injured children: an evaluation of transfer patterns, resource utilization, and clinical outcomes

OBJECTIVE

To describe patterns of transfer, resource utilization, and clinical outcomes associated with interhospital transfer of critically ill and injured children.

DESIGN

Secondary analysis of administrative claims data.

PARTICIPANTS

Children 0 to 18 years in the Michigan Medicaid program who underwent interhospital transfer for intensive care from January 1, 2002 to December 31, 2004. The 3 sources of transfer from referring hospitals were: emergency department (ED), ward, or intensive care unit (ICU).

MEASUREMENTS

Mortality and duration of hospital stay at the receiving hospitals.

RESULTS

Of 1643 interhospital transfer admissions to intensive care at receiving hospitals, 62%, 31%, and 7% were from the ED, ward, and ICU of referring hospitals, respectively. Nineteen percent had comorbid illness, while 11% had organ dysfunction at the referring hospital. After controlling for comorbid illness, patient age, and pretransfer organ dysfunction; compared with ED transfers, mortality in the receiving hospital was higher for ward transfers (odds ratio [OR], 1.76; 95% confidence interval [CI], 1.02-3.03) but not for ICU transfers. Also, compared with ED transfers, hospital stay was longer by 1.5 days for ward transfers and by 13.5 days for ICU transfers.

CONCLUSION

In this multiyear, statewide sample, mortality and resource utilization were higher among children who underwent interhospital transfer to intensive care after initial hospitalization, compared with those transferred directly from emergency to intensive care. Decision-making underlying initial triage and subsequent interhospital transfer of critically ill children warrants further study.

Predicting in-hospital mortality in patients with cirrhosis: results differ across risk adjustment methods

Risk-adjusted health outcomes are often used to measure the quality of hospital care, yet the optimal approach in patients with liver disease is unclear. We sought to determine whether assessments of illness severity, defined as risk for in-hospital mortality, vary across methods in patients with cirrhosis. We identified 258,731 patients with cirrhosis hospitalized in the Nationwide Inpatient Sample between 2002 and 2005. The performance of four common risk adjustment methods (the Charlson/Deyo and Elixhauser comorbidity algorithms, Disease Staging, and All Patient Refined Diagnosis Related Groups [APR-DRGs]) for predicting in-hospital mortality was determined using the c-statistic. Subgroup analyses were conducted according to a primary versus secondary diagnosis of cirrhosis and in homogeneous patient subgroups (hepatic encephalopathy, hepatocellular carcinoma, congestive heart failure, pneumonia, hip fracture, and cholelithiasis). Patients were also ranked according to the probability of death as predicted by each method, and rankings were compared across methods. Predicted mortality according to the risk adjustment methods agreed for only 55%-67% of patients. Similarly, performance of the methods for predicting in-hospital mortality varied significantly. Overall, the c-statistics (95% confidence interval) for the Charlson/Deyo and Elixhauser algorithms, Disease Staging, and APR-DRGs were 0.683 (0.680-0.687), 0.749 (0.746-0.752), 0.832 (0.829-0.834), and 0.875 (0.873-0.878), respectively. Results were robust across diagnostic subgroups, but performance was lower in patients with a primary versus secondary diagnosis of cirrhosis.

CONCLUSION

Mortality analyses in patients with cirrhosis require sensitivity to the method of risk adjustment. Because different methods often produce divergent severity rankings, analyses of provider-specific outcomes may be biased depending on the method used.

The variation in risk adjusted mortality of intensive care units

BACKGROUND

This study aimed to estimate risk adjusted mortality rate in the ICUs (Intensive care units) by APACHE (Acute Physiology And Chronic Health Evaluation) III for revealing the performance variation in ICUs.

METHODS

This study focused on 1,090 patients in the ICUs of 18 hospitals. For establishing risk adjusted mortality predictive model, logistic regression analysis was performed. APACHE III, surgery experience, admission route, and major disease categories were used as independent variables. The performance of each model was evaluated by c-statistic and goodness-of-fit test of Hosmer-Lemeshow. Using this predictive model, the performance of each ICU was tested as ratio of predictive mortality rate and observed mortality rate.

RESULTS

The average observed mortality rate was 24.1%. The model including APACHE III score, admission route, and major disease categories was signified as the fittest one. After risk adjustment, the ratio of predictive mortality rate and observed mortality rate was distributed from 0.49 to 1.55.

CONCLUSIONS

The variation in risk adjusted mortality among ICUs was wide. The effort to reduce this quality difference is needed.

The impact of different prognostic models and their customization on institutional comparison of intensive care units

OBJECTIVES

To evaluate the influence of choice of a prognostic model and the effect of customization of these models on league tables (i.e., rank-order listing) in which intensive care units (ICUs) are ranked by standardized mortality ratios using Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Model II (MPM24II).

DESIGN

Retrospective analysis of prospectively collected data on ICU admissions.

SETTING

Forty Dutch ICUs.

PATIENTS

A data set from a national registry of 86,427 patients from January 2002 to October 2006.

INTERVENTIONS

The league tables associated with the different models were compared to evaluate their agreement. Bootstrapping was used to quantify the uncertainty in the ranks for ICUs. First, for each ICU the median rank and its 95% confidence interval were identified for each model. Then, for a given pair of models, for each ICU the median difference in rank and its associated 95% confidence interval were computed. A difference in rank for an ICU for a given pair of models was considered relevant if it was statistically significant and if one of the models would categorize this ICU as a performance outlier (excellent performer or very poor performer) while the other did not.

MEASUREMENTS AND MAIN RESULTS

For 20 ICUs, there was a significant difference in rank (2-19 positions) between one or more pairs of models. Three ICUs were rated as performance outliers by one of the models, while the other excluded this possibility with 95% certainty. Furthermore, for ten ICUs, one or more pairs of models classified these ICUs as performance outliers while the other model did not do so with certainty. Regarding the agreement between the original models and their customized versions, in all cases the median change in rank was three positions or less and the models fully agreed with respect to which ICUs should be classified as performance outliers.

CONCLUSIONS

Institutional comparison based on case-mix adjusted league tables is sensitive to the choice of prognostic model but not to customization of these models. League tables should always display the uncertainty associated with institutional ranks.

Assessing and using comorbidity measures in elderly veterans with lower extremity amputations

BACKGROUND

Understanding comorbidity prevalence and the effects of comorbidities in older veterans with lower extremity amputations may aid in assessing patient outcomes, resource use, and facility-level quality of care.

OBJECTIVES

To determine the degree to which adding outpatient to inpatient administrative data sources yields higher comorbidity prevalence estimates and improved explanatory power of models predicting 1-year mortality and to compare the Charlson/Deyo and Elixhauser comorbidity measures.

METHODS

A retrospective cohort study applying frequencies, cross-tabulations, and logistic regression models was conducted, including data from 2,375 veterans with lower extremity amputations. Comorbidity prevalence according to the Charlson/Deyo and Elixhauser measures, 1-year mortality rates, and standardized mortality ratios (SMRs) were analyzed.

RESULTS

Comorbidity prevalence estimates increased sharply for both the Charlson/Deyo and Elixhauser measures with the addition of data from multiple settings. The Elixhauser compared to the Charlson/Deyo generally yielded higher estimates but did not improve explanatory power for mortality. Modeling expected versus actual deaths produced varying SMRs across geographic regions but was not dependent on which measure or data sources were used.

CONCLUSIONS

Merging outpatient with inpatient data may reduce the under coding of comorbidities but does not enhance mortality prediction. Compared to the Charlson/Deyo, the Elixhauser has a more complete coding scheme for comorbid conditions, such as diabetes mellitus and peripheral vascular disease, important to addressing lower extremity amputation etiology.

Risk adjusted resource utilization for AMI patients treated in Japanese hospitals

Though risk adjustment is necessary in order to make equitable comparisons of resource utilization in the treatment of acute myocardial infarction patients, there is little in the literature that can be practically applied without access to clinical records or specialized registries. The aim of this study is to show that effective models of resource utilization can be developed based on administrative data, and to demonstrate a practical application of the same models by comparing the risk-adjusted performance of the hospitals in our dataset. The study sample included 1748 AMI cases discharged from 10 large, private teaching hospitals in Japan, between 10 April 2001 and 30 June 2004. Explanatory variables included procedures (CABG and PCI), length of stay, outcome, patient demographics, diagnosis and comorbidity status. Multiple linear regression models constructed for the study were able to account for 66.5, 27.7, and 58.4% of observed variation in total charges, length of stay and charges per day, respectively. The performance of models constructed for this study was comparable to or better than performance reported by other studies that made use of explanatory variables extracted from clinical data. The use of administrative data in risk adjustment makes broad scale application of risk adjustment feasible.

Comparison of Clinical and Administrative Data Sources for Hospital Coronary Artery Bypass Graft Surgery Report Cards

Background— Regardless of statistical methodology, public performance report cards must use the highest-quality validated data, preferably from a prospectively maintained clinical database. Using logistic regression and hierarchical models, we compared hospital cardiac surgery profiling results based on clinical data with those derived from contemporaneous administrative data.

Methods and Results— Fiscal year 2003 isolated coronary artery bypass grafting surgery results based on an audited and validated Massachusetts clinical registry were compared with those derived from a contemporaneous state administrative database, the latter using the inclusion/exclusion criteria and risk model of the Agency for Healthcare Research and Quality. There was a 27.4% disparity in isolated coronary artery bypass grafting surgery volume (4440 clinical, 5657 administrative), a 0.83% difference in observed in-hospital mortality (2.05% versus 2.88%), corresponding differences in risk-adjusted mortality calculated by various statistical methodologies, and 1 hospital classified as an outlier only with the administrative data–based approach. The discrepancies in volumes and risk-adjusted mortality were most notable for higher-volume programs that presumably perform a higher proportion of combined procedures that were misclassified as isolated coronary artery bypass grafting surgery in the administrative cohort. Subsequent analyses of a patient cohort common to both databases revealed the smoothing effect of hierarchical models, a 9% relative difference in mortality (2.21% versus 2.03%) resulting from nonstandardized mortality end points, and 1 hospital classified as an outlier using logistic regression but not using hierarchical regression.

Conclusions— Cardiac surgery report cards using administrative data are problematic compared with those derived from audited and validated clinical data, primarily because of case misclassification and nonstandardized end points.

Administrative Versus Clinical Data for Coronary Artery Bypass Graft Surgery Report Cards

OBJECTIVE

The objective of this study was to compare the performance of a risk model for isolated coronary artery bypass graft (CABG) surgery based on administrative data with that of a clinical risk model in predicting mortality and identifying hospital performance outliers.

METHODS

Clinical data records from the California CABG Mortality Reporting Program for 38,230 isolated CABG patients undergoing surgery in 2000-2001 were linked to records in the California patient discharge data (PDD) abstract. Risk factors based on administrative data that mirrored clinical risk factors were developed using the condition present at admission indicator in the PDD to separate preoperative acute conditions from complications of surgery. Using logistic regression, risk model performance across data sources was compared along with hospital risk-adjusted mortality ranks and quality ratings.

RESULTS

The administrative data showed lower prevalence of risk factors when compared with the clinical data. The clinical risk model had somewhat better discrimination (C = 0.824) than the administrative model (C = 0.799). The clinical model yielded 17 outliers and the administrative model 16 with agreement on 12 hospitals' status. Performance of the administrative risk model was minimally affected by removal of information from prior admissions and removal of risk factors not confirmed in the clinical record.

CONCLUSIONS

Unique properties of the California administrative data, including the ability to distinguish acute preoperative risk factors from complications of surgery, permitted construction of an administrative risk model that predicts mortality on par with most published clinical models. Despite this, the administrative model identified slightly different hospital outliers, which may indicate somewhat biased assessments of hospital patient risk.

Operative mortality and procedure volume as predictors of subsequent hospital performance

CONTEXT

Despite growing interest in evidence-based hospital referral for selected surgical procedures, there remains considerable debate about which measures should be used to identify high-quality providers.

OBJECTIVES

To assess the usefulness of historical mortality rates and procedure volume as predictors of subsequent hospital performance with different procedures.

DESIGN, SETTING, AND PARTICIPANTS

Using data from the national Medicare population, we identified all U.S. hospitals performing one of 4 high-risk procedures between 1994 and 1997. Hospitals were ranked and grouped into quintiles according to 1) operative mortality (adjusted for patient characteristics) and 2) procedure volume.

MAIN OUTCOME MEASURES

Risk-adjusted operative mortality in 1998 to 1999.

RESULTS

Although historical mortality and volume both predicted subsequent hospital performance, the predictive value of each varied by procedure. For coronary artery bypass graft surgery, mortality rates in 1998 to 1999 differed by 3.3% across quintiles of historical mortality (3.6% to 6.9%, best to worst quintile, respectively), but only by 1.0% across volume quintiles (4.8% to 5.8%). In contrast, for esophagectomy, mortality rates in 1998 to 1999 differed by 12.5% across volume quintiles (7.5% to 20.0%, best to worst quintile, respectively), but only by 1.5% across quintiles of historical mortality (11.4% to 12.9%). Historical mortality and procedure volume had comparable value as predictors of subsequent performance for pancreatic resection and elective abdominal aortic aneurysm repair. Our findings were similar when we repeated the analysis using data from later years.

CONCLUSIONS

Historical measures of operative mortality or procedure volume identify hospitals likely to have better outcomes in the future. The optimal measure for selecting high-quality providers depends on the procedure.

Predictors of hospital charges for children admitted with asthma

OBJECTIVES

To examine patient and hospital characteristics associated with varying hospital charges for children admitted with asthma.

METHODS

We conducted a retrospective cohort study of children (1-18 years old) hospitalized with asthma using data from the 2000 Kids' Inpatient Database (KID; n = 54,029). Predictors of interest included hospital type (teaching and children's hospitals) and patient characteristics (insurance type and race).

RESULTS

After adjusting for patient and hospital characteristics, hospital charges were similar at teaching and nonteaching hospitals. Charges at children's hospitals were higher by 440 US dollars or 10% (95% CI, 352-528) compared with nonchildren's hospitals. Children with Medicaid had higher charges by 132 US dollars or 3% (95% CI, 57-264) compared to those with private insurance. Compared to White children, Black children had higher charges by 396 US dollars or 10% (95% CI, 352-484), Hispanic children by 924 US dollars or 21% (95% CI, 880-1,012), and Asian children by 572 US dollars or 13% (572 US dollars; 95% CI, 352-792).

CONCLUSIONS

Important differences exist in the charges incurred by children with asthma based on patient and hospital characteristics. Efforts to understand the reasons behind the differences may help eliminate unnecessary variation in costs for asthma care.

Adverse outcomes in Belgian acute hospitals: retrospective analysis of the national hospital discharge dataset

OBJECTIVE

The prevalence and variability of adverse outcome rates in Belgian acute hospitals is examined by using the national hospital discharge database.

DESIGN

setting, and participants. Retrospective analysis based on administrative data of all Belgian acute hospitals, covering the full medical (n = 1 024 743) and surgical (n = 633 027) in-patients population for the year 2000.

MAIN OUTCOME MEASURES

For 11 adverse outcomes and failure-to-rescue, the rates and variability among hospitals were studied. The all patient refined diagnostic-related groups (APR-DRG) method was used for risk adjustment.

RESULTS

The prevalence of adverse outcomes was 7.12% in the medical and 6.32% in the surgical group. Rates ranged from 6.25 (deep venous thrombosis) to 32.3 (urinary tract infection) outcomes per 1000 discharges in the medical group and from 3.39 (deep venous thrombosis) to 17.6 (urinary tract infection) outcomes per 1000 discharges in the surgical group. The failure-to-rescue rate was 240 and 211 per 1000 discharges, respectively. Except for pressure ulcers and hospital-acquired sepsis, the prevalence of adverse outcomes was significantly higher (P = 0.001) in the medical group. All adverse outcome rates varied substantially among the hospitals surveyed.

CONCLUSIONS

This study identifies the occurrence of adverse outcomes in a national population. It adds information to the growing body of knowledge in predominantly Anglo-Saxon countries about adverse outcomes. Striking variation exists in the risk-adjusted adverse outcome rates across Belgian acute hospitals, revealing a large potential for quality gains that encourage further action.

Hospital discharge abstract data on comorbidity improved the prediction of death among patients hospitalized with aspiration pneumonia

OBJECTIVE

To use diagnoses reported as present at admission in California hospital discharge abstract data to identify categories of comorbid disease and conditions related to aspiration pneumonia and to assess their association with hospital mortality.

STUDY DESIGN AND SETTING

The study population included all persons hospitalized in California from 1996 through 1999, with a principal diagnosis of aspiration pneumonia. Present at admission diagnoses representing comorbid diseases were separated from conditions closely related to aspiration pneumonia by a physician panel through a computer supported Delphi process. Multivariable logistic regression was used to assess the probability of hospital death after adjusting for these patient characteristics. The statistical performance of this method was compared to the performance of two independent methods for measuring comorbid disease. The practical significance of differences in statistical performance was assessed by comparing the estimated effects of age, race, and ethnicity after adjustments using each method.

RESULTS

Mortality risk adjustment using present at admission diagnoses resulted in substantially better statistical performance and in different measurements of the adjusted effects of age, race, and ethnicity.

CONCLUSION

Reporting present at admission diagnoses in hospital discharge data yields meaningful improvements in hospital mortality risk adjustment.

Comparative analysis of risk-adjusted bypass surgery stratification models in a community hospital

BACKGROUND

Quality improvement in health care, which relies on appropriate strategies to evaluate and compare provider performance, has spawned the propagation of many public report cards or provider profiles for cardiac surgery. These risk-adjusted prediction models allow for the computation of a provider's expected outcome event rate compared with observed outcome events. The aim of this study was to assess the accuracy and reliability of 5 risk-adjusted predictive models for mortality in an independent population of patients in a community hospital who underwent coronary artery bypass graft surgery.

METHODS

Five nonproprietary models were selected for evaluation (Parsonnet, Canadian, Cleveland, New York, and the Northern New England).

RESULTS

The C-statistic for the 5 models was 0.752, 0.693, 0.748, 0.735, and 0.722 for the Parsonnet, Canadian, Cleveland, New York, and Northern New England models respectively. The H-L c2 calibration statistics were 4.948, P =.763; 1.616, P =.899; 11.96, P =.035; 10.23, P =.249; and c2 = 12.14, P =.145 for the Parsonnet, Canadian, Cleveland, New York, and Northern New England models respectively.

CONCLUSIONS

Comparing hospital-specific or surgeon-specific mortality/morbidity rates will remain a challenge. This analysis reaffirms the concept of risk-adjusting outcomes and emphasizes the importance of the risk-adjustment process for CABG surgery in a community hospital.

Aplicação da mortalidade hospitalar após a realização de cirurgia de revascularização do miocárdio para monitoramento do cuidado hospitalar

Este estudo classificou os hospitais vinculados ao Sistema Único de Saúde (SUS) do Brasil com base no desempenho para a realização de cirurgia de revascularização do miocárdio, entre 1996 e 1998, com o uso da taxa de mortalidade hospitalar ajustada pelo risco de morrer. Foram estudados 76 hospitais (58,00% do total) que realizaram mais de 150 cirurgias no período, correspondentes a 38.962 cirurgias (92,10% do total), que foram classificados como desviantes altos ou baixos, de acordo com a razão entre o número observado e o esperado de óbitos para cada hospital. A taxa global de mortalidade hospitalar foi de 7,20%. Para o grupo de pacientes operados nos hospitais desviantes baixos, foi de 3,48%, e, de 13,96% para os desviantes altos. A metodologia tem utilidade para discriminar os hospitais brasileiros com relação à mortalidade pós cirurgia de revascularização do miocárdio e pode ser um instrumento útil para identificação daqueles que possam apresentar problemas de qualidade.

Length of stay for common pediatric conditions: teaching versus nonteaching hospitals

OBJECTIVE

Pediatric teaching hospitals provide particular expertise in caring for children with complex or severe illnesses, yet most patients within teaching hospitals have common pediatric conditions. No study has determined whether children with common conditions remain hospitalized at teaching institutions longer than at nonteaching institutions. The objective of this study was to compare length of stay (LOS) for common pediatric conditions between teaching and nonteaching hospitals.

METHODS

This study uses Massachusetts's hospital data for all discharged children ages 0 to 17 years for 1995 and 1996. Discharges were included when the principal diagnosis indicated asthma, bacterial pneumonia, convulsions, dehydration, failure to thrive, gastroenteritis, or urinary tract infections. Hospitals were classified as either teaching or nonteaching using the 1995-1996 American Hospital Association Guide. Children were identified as having a chronic condition when any discharge diagnosis was 1 of those on a previously published catalog of chronic childhood illnesses. The analysis tested the association of hospital type with LOS, controlling for chronic conditions, insurance type, age, race, diagnosis, mortality, and disposition using multivariate linear regression.

RESULTS

Of 17 890 discharges for a common pediatric condition during the study period, 52.3% were from teaching hospitals. Twelve percent of common condition discharges also had a chronic disease diagnosis; 75.1% of these were discharged from a teaching hospital. LOS from nonteaching hospitals was shorter than from teaching hospitals (2.42 days vs 3.20 days). Although LOS for stays with a chronic diagnosis were longer than those without (4.75 days vs 2.56 days), controlling for chronic illness and other covariates did not eliminate the difference between LOS for nonteaching hospitals versus teaching hospitals (1.65 days vs 2.23 days).

CONCLUSION

Pediatric patients with common conditions have a shorter LOS in nonteaching hospitals than those admitted to teaching hospitals by a little more than half a day. These results are unchanged when accounting for chronic conditions despite the expected results of preferential admissions to teaching hospitals for this group of patients. Additional studies should better characterize differences in patient populations, describe differences in processes, and identify differences in patient experience and outcomes to understand better the potential benefits of treating children with specific conditions at particular types of hospitals.

Automated intensive care unit risk adjustment: results from a National Veterans Affairs study

CONTEXT

Comparison of outcome among intensive care units (ICUs) requires risk adjustment for differences in severity of illness and risk of death at admission to the ICU, historically obtained by costly chart review and manual data entry.

OBJECTIVE

To accurately estimate patient risk of death in the ICU using data easily available in hospital electronic databases to permit automation.

DESIGN AND SETTING

Cohort study to develop and validate a model to predict mortality at hospital discharge using multivariate logistic regression with a split derivation (17,731) and validation (11,646) sample formed from 29,377 consecutive first ICU admissions to medical, cardiac, and surgical ICUs in 17 Veterans' Health Administration hospitals between February 1996 and July 1997.

MAIN OUTCOME MEASURES

Mortality at hospital discharge adjusted for age, laboratory data, diagnosis, source of ICU admission, and comorbid illness.

RESULTS

The overall hospital death rate was 11.3%. In the validation sample, the model separated well between survivors and nonsurvivors (area under the receiver operating characteristic curve = 0.885). Examination of the observed vs. the predicted mortality across the range of mortality showed the model was well calibrated.

CONCLUSIONS

Automation could broaden access to risk adjustment of ICU outcomes with only a small trade-off in discrimination. Broader use might promote valid evaluation of ICU outcomes, encouraging effective practices and improving ICU quality.

Differences among hospitals in delivery of care for heart failure

Larger, urban teaching hospitals with larger volumes and greater availability of advanced services provide better care for certain diseases. Because such advanced services have limited importance for routine heart failure management, no hospital type is "disadvantaged." Data on 1,180 congestive heart failure patients were studied to assess the quality of care provided by various types of hospitals. Overall, there was no particular type of hospital that performed consistently better or worse across the quality indicators studied. Substantial opportunities for improvement exist among all hospital types in Tennessee.

Do hospitals with lower mortality have higher patient satisfaction? A regional analysis of patients with medical diagnoses

Although patient satisfaction is a widely used indicator of quality, relationships between satisfaction and other indicators are poorly studied. The current study examined hospital-level correlations between patient satisfaction and severity-adjusted mortality for 29 hospitals in northeastern Ohio during 1993-1997. Satisfaction with 6 dimensions of care was measured using a validated survey of 42,255 randomly selected patients with medical diagnoses. Severity-adjusted mortality rates were determined for 200,562 consecutive patients with 6 high-volume medical diagnoses. Analyses found that satisfaction scores were inversely correlated with mortality rates. For the cumulative 5-year period, correlations were significant or of borderline significance for 5 of the 6 dimensions (coordination [R = -0.40; P = .03], discharge instructions [R = -0.39; P = .04], overall quality [R = -0.38; P = .04], information provided [R = -0.37; P = .05], and nursing [R = -0.35; P = .06]). The correlation was weakest for physician care (R = -0.07; P = .72). These findings indicate that hospitals with higher patient satisfaction also tended to have lower severity-adjusted mortality. Associations were strongest for dimensions of satisfaction measuring patient communication, coordination of care, and nursing care and weakest for physician care.

Relevance of outlier cases in case mix systems and evaluation of trimming methods

OBJECTIVES

To determine the most appropriate outlier trimming method when the main source of information for case mix classification is length of stay (LOS) because cost information is unavailable.

METHODS

Discharges (35,262) from two public hospitals were analysed. LOS and cost outliers were calculated using different trimming methods. The agreement between cost and LOS trimming was analysed.

RESULTS

The trimming method using the geometric mean with two standard deviations (GM2) showed the highest level of agreement between cost and LOS and revealed the greatest proportion of extreme costs. Nearly 5% of cases were outliers, containing 16% of total LOS. This was the best approximation to 18% of extreme cost because when GM2 was applied to LOS, 88% of outlier cost was revealed.

CONCLUSIONS

The methods were analysed because they are the most frequently used but the same methodology could be employed to compare other outlier determination methods. Outliers should be calculated because they ought to be valued differently from inlier cases.

Administrative data versus corrected administrative data

The purpose of this research was to provide insight into the use of existing administrative data and to identify changes that could be made to improve broad-based use of administrative data. Data were collected on patients hospitalized with pneumonia at a 715 bed hospital in North Carolina in 1996-1997. Patients were selected from administrative databases via diagnosis and charge codes. Outcome variables were length of stay and total hospital charges. Explanatory variables were age, sex, race, insurance type, season of year, admission source (emergency department or other), comorbidity score, care path designation, physician specialty and teaching appointment. These data were collected from administrative data and then from a limited chart review to correct the administrative data. We found no significant differences in economic outcomes between the administrative data and the corrected administrative data. Administrative data appear to be a reliable and cost-effective data source for quality assessment.

Different regional organisational models and the quality of health care: the case of coronary artery bypass graft surgery

OBJECTIVES

The Italian regions of Emilia-Romagna and Lombardy within the Italian National Health Service provide an opportunity to see if two different approaches to the organisation of care--one more hierarchical and planned, the other more competitive and market-like--influence its quality through examining the relationship between the number of coronary artery bypass grafts (CABGs) and the rate of in-hospital mortality using administrative data for the period 1996-1998.

METHODS

Descriptive statistics and logistic regression models were used.

RESULTS

The volume-outcome relation was statistically significant in both regions (odds ratio 0.71, P < 0.0001). Although CABG performance in Emilia-Romagna was slightly poorer than in Lombardy (OR 1.22, P < 0.05), the potential advantage in terms of the reduced risk of death for patients treated at high-volume versus low-volume hospitals was significantly greater. In Emilia-Romagna, the average performance advantage of high-volume units was more substantial in the case of private accredited hospitals than public hospitals (OR = 0.50, P < 0.0001 versus OR = 0.64, P < 0.0001). In Lombardy, the performance advantage of concentrating CABG procedures was greater in private research hospitals (OR = 0.67, P < 0.0001), whereas results were not statistically significant for the other types of hospital, indicating a good level of performance in both public and private hospitals even at low volumes. This also partially explained the lower mortality rate observed in that region.

CONCLUSIONS

The degree of hierarchical regionalisation versus market-like arrangements characterising the two systems produced contrasting effects in terms of the quality of CABG surgery. Lombardy's more competitive environment appeared to achieve better performance in terms of a slightly lower probability of adverse outcomes, in a system with no formal assessment of population need and very high per capita revascularisation rates. To improve performance in the more hierarchical system adopted in Emilia-Romagna would require considerable effort to increase CABG surgery in low-volume cardiac units, and to sharpen performance incentives.

Validity of the risk adjustment approach to compare outcomes

This paper focuses on the issue of the extent to which the present mainstream risk adjustment (RA) methodology for measuring outcomes is a valid and useful tool for quality-improvement activities. The method's predictive and attributional validity are discussed, considering the confounding and effect modification produced by medical care over risk variables' effect. For this purpose, the sufficient-cause model and the counterfactual approach to effect and interaction are tentatively applied to the relationships between risk (prognostic) variables, medical technology, and quality of care. The main conclusions are that quality of care modifies the antagonistic interaction between medical technologies and risk variables, related to different types of responders, as well as the confounding of the effect of risk variables produced by related medical technologies. Thus, confounding of risk factors in the RA method, which limits the latter's predictive validity, is related to the efficacy and complexity of associated medical technologies and to the quality mix of services. Attributional validity depends on the validity of the probabilities estimated for each subgroup of risk (predictive validity) and the percentage of higher-risk patients at each service.

Specialty Heart Hospital Care: A Comparative Study

The objective of this study was to compare measures of MedCath heart hospital patient severity, quality, and Medicare-related expenditures for comparable services to a group of comparison heart hospitals. This analysis is relevant to stakeholders' concerns over the emergence of the specialty care hospital industry. The study incorporates Medicare data for seven MedCath hospitals as compared with 1192 hospitals that performed open-heart surgery in federal fiscal year 2001. The authors developed cardiac-specific patient severity measures based on All Patient Refined-Diagnostic Related Groups and all subsequent analyses were standardized for differences in case mix between MedCath and comparison group hospitals. Study results indicate that MedCath hospitals have higher cardiac case mix severity, fare better in indicators for quality of care, and provide care at less expense to Medicare than comparison group heart hospitals. These results imply that "cherry picking" arguments and quality-of-care concerns of the specialty care hospital industry critics do not seem applicable for MedCath hospitals.

Comparing outcomes of coronary artery bypass surgery: Is the New York Cardiac Surgery Reporting System model sensitive to changes in case mix?

OBJECTIVE

To assess the validity of using the standardized mortality ratio (SMR), based on the New York State Cardiac Surgery Reporting System (CSRS) prediction model to compare coronary artery bypass grafting (CABG) outcomes between hospitals.

DESIGN

The study was designed as a retrospective study based on a database containing all patients undergoing isolated CABG surgery in New York State hospitals in 1996 (n = 20,078). In the first part of this study, a computer simulation was used to assess the impact of case mix variation on the SMR. A computer-intensive algorithm was used to create 5,000 random case mixes from the patients in the CSRS database. The SMR associated with each of the 5,000 case mixes was calculated using a resampling algorithm. The second part of this study was designed to determine whether the identity of quality outliers among all of the 32 hospitals in the CSRS database would change after adjusting for the effects of case mix on the SMR. The SMR associated with the case mix of each hospital in the CSRS database (the hospital case mix SMR) was obtained using a resampling algorithm. The hospital SMR (as well as 95% confidence interval) was then calculated using bootstrapping for each of the 32 hospitals within the CSRS database. An adjusted SMR was then derived for each hospital by dividing the hospital SMR by the case mix SMR for that hospital.

SETTING

Thirty-two hospitals in New York State performing CABG surgery.

INTERVENTIONS

None.

RESULTS

Changes in patient case mix are associated with statistically significant changes in the SMR. However, there was no difference in the identity of quality outliers in the New York State CSRS database when using either the SMR or the SMR adjusted for the effects of case mix.

CONCLUSION

Risk-adjusted measures of outcomes in CABG patients may be potentially biased by differences in case mix between institutions because of the influence of case mix on the process of risk adjustment. There was, however, no evidence of bias in the specific application of the CSRS model to the hospitals in the CSRS database.

Prediction of mortality for congestive heart failure patients: results from different wards of an Italian teaching hospital

Congestive heart failure (CHF) constitutes an important public health problem in Italy, evidenced by the high number of hospital admissions each year. Significant inter-hospital as well as interward differences in mortality rates for CHF patients that have been described may, in part, be explained by the differences in the severity of the illness of admitted patients. The goal of this study was to predict 30-day severity-adjusted mortality risk in patients with CHF admitted to wards of a teaching hospital in Siena, Italy, in 1997. A 30-day mortality was determined by linking hospital discharge files with the Tuscany Mortality Registry database. The 3M all patient refined diagnosis related group (APR-DRG) software was used as a risk assessment method. The relationships between death and the following variables were studied by univariate analyses: APR-severity risk, APR-mortality risk, age, sex, length of stay and, discharge ward. Multivariate analysis was also performed to verify the associations between death and those parameters found to be significant by univariate analysis. Unadjusted mortality proportions ranged from 4.3 to 44.0%. Logistic regression analysis demonstrated that APR-mortality risk, length of stay, and discharge ward were significantly and independently associated with 30-day mortality risk in patients with CHF. In summary, 30-day mortality risk varied significantly according to the ward of discharge in an Italian teaching hospital, even after adjustment for severity of illness.

Risk adjustment using administrative data: impact of a diagnosis-type indicator

OBJECTIVES

To determine the frequency with which commonly coded clinical variables are complications, as opposed to baseline comorbidities, and to compare the results of 2 risk-adjusted outcome analyses for coronary artery bypass graft surgery for which we either (a) ignored, or (b) used the available "diagnosis-type indicator."

DESIGN

Analysis of existing administrative data.

SETTING

Twenty-three Canadian hospitals.

PATIENTS

A total of 50,357 coronary artery bypass graft surgery cases.

MEASUREMENTS AND MAIN RESULTS

Among 21 clinical variables whose definitions involve the diagnosis-type indicator, 14 were predominantly (> or =97%) baseline risk factors when present. Seven variables were often complication diagnoses: renal disease (when present, 13% coded as complications), recent myocardial infarction (15%), peptic ulcer disease (15%), congestive heart failure (17%), cerebrovascular disease (26%), hemiplegia (34%), and severe liver disease (35%). The results of risk adjustment analyses predicting in-hospital mortality differed when the diagnosis-type indicator was either used or ignored, and as a result, adjusted hospital mortality rates and rankings changed, often dramatically, with rankings increasing for 10 hospitals, decreasing for 9 hospitals, and remaining the same for only 4 hospitals.

CONCLUSIONS

The results of analyses performed using the diagnosis-type indicator in Canadian administrative data differ considerably from analyses that ignore the indicator. The widespread introduction of such an indicator should be considered in other countries, because risk-adjustment analyses performed without a diagnosis-type indicator may yield misleading results.

Treatment with tissue plasminogen activator and inpatient mortality rates for patients with ischemic stroke treated in community hospitals

BACKGROUND AND PURPOSE

Most analyses of intravenous tissue plasminogen activator (IV tPA) use for acute stroke in routine practice have been limited by sample size and generally restricted to patients treated in large academic medical facilities. In the present study, we sought to estimate among community hospitals the use of IV tPA and to identify factors associated with the use of IV tPA and inpatient mortality.

METHODS

We evaluated a retrospective cohort of 23 058 patients with ischemic stroke from 137 community hospitals.

RESULTS

Three hundred sixty-two (1.6%) patients were treated with IV tPA, and 9.9% of those patients died during the hospitalization period. In 35.0% of the hospitals, no patients were treated with IV tPA, whereas 14.6% of hospitals treated approximately 3.0% with IV tPA. After control for multiple factors, younger patients, more severely ill patients (OR 2.02, 95% CI 1.36 to 3.01), and patients treated in rural hospitals (OR 1.80, 95% CI 0.99 to 3.26) were more likely to receive IV tPA, whereas black patients were less likely (OR 0.54, 95% CI 0.31 to 0.95). There also was a trend showing that women were less likely to receive IV tPA (OR 0.84, 95% CI 0.69 to 1.03). Factors associated with an increased odds of inpatient mortality included receipt of IV tPA among men (OR 2.81, 95% CI 1.72 to 4.58) and increased age. Black patients were 27% less likely to die during hospitalization (95% CI 0.60 to 0.90).

CONCLUSIONS

In this large, retrospective evaluation of community hospital practice, the use IV tPA and inpatient mortality rates among IV tPA-treated patients were consistent with those of other studies. The likelihood of receiving IV tPA varies by race, age, disease severity, and possibly gender. These factors may influence mortality rates.

Computerized assessment of complications after colorectal surgery: is it valid?

PURPOSE

Historically, complication rates after colorectal surgery have been stratified by disease process, type of operation, or anesthesia risk derived after an intensive review of the medical record. Newer computer applications purport to shorten this process and predict the probability of postoperative complications by distinguishing them from comorbidities that are commingled on uniform discharge codes. We analyzed CaduCIS software, which uses discharge codes, to determine whether its predictions of comorbidity and complications were comparable to what was interpreted on the medical record.

METHODS

Two-hundred seventy patients were analyzed according to the principal and secondary diagnoses coded on discharge. Coding inaccuracies of clinical occurrences were identified by physician review of each medical record. The actual incidences of 17 common preoperative comorbidities and 11 postoperative complications were compared with those predicted by CaduCIS.

RESULTS

The CaduCIS-predicted distribution of comorbidities was similar to the actual occurrences in 15 of 17 categories. The overall incidence of complications obtained by physician (actual) review was 47 percent, compared with 46 percent predicted by CaduCIS. However, there was a statistical difference between the CaduCIS-predicted and the actual complication rates in 5 of the 11 categories. The most common preoperative comorbidity and complication was cardiopulmonary (47 percent and 28 percent, respectively).

CONCLUSION

The overall complication rate interpreted from the medical record (47 percent) was accurately predicted by CaduCIS (46 percent). Predictions of 5 of 11 individual complications were underestimated because of charting and coding inaccuracies, not because of computerized errors. Because uniform discharge coding of commingled comorbidity and complications is increasingly used to rapidly compute surgical outcomes, colon and rectal surgeons need to ensure compatibility of the actual and coded medical records.

Sistema de Informações Hospitalares como ajuste de risco em índices de desempenho

OBJETIVO: Analisar o uso do Sistema de Informações Hospitalares do Sistema Único de Saúde (SIH/SUS) no ajuste de risco das taxas de mortalidade hospitalar e avaliar a utilidade do índice de comorbidade de Charlson (ICC) no ajuste de risco de indicadores de desempenho. MÉTODOS: Foram selecionadas 40.299 internações ocorridas no Município do Rio de Janeiro entre dezembro de 1994 e dezembro de 1996. A medida de gravidade foi testada pelo ICC, que atribui pesos a 17 condições clínicas presentes nos diagnósticos secundários, a fim de obter a carga de morbidade do paciente (gravidade) independentemente do diagnóstico principal. Utilizou-se a regressão logística para avaliar o impacto do ICC na estimativa da chance de morrer no hospital. RESULTADOS: Nas internações selecionadas, observou-se que o ICC foi igual ou superior a um em apenas 5,7 % dos casos. Quando se aplicou o ICC combinado à idade, o percentual de casos com pontuação diferente de zero aumentou substancialmente. Os modelos testados apresentaram reduzida sensibilidade. CONCLUSÕES: Apesar de a presença de comorbidade ser importante na predição do risco de morrer, essa variável pouco discriminou a gravidade dos casos na base de dados do SIH/SUS, o que é explicado pela qualidade da informação diagnóstica nessa base de dados, na qual a idade é o preditor mais importante do risco de morrer, afora o diagnóstico principal. Apesar das limitações ainda existentes na qualidade da informação diagnóstica disponível no SIH/SUS, sugere-se o uso do ICC combinado como medida para ajuste do risco de morrer nas taxas calculadas a partir desses dados.

Coronary artery bypass graft surgery: socioeconomic inequalities in access and in 30 day mortality. A population-based study in Rome, Italy

OBJECTIVES

To evaluate whether coronary artery bypass graft (CABG) surgery is equally provided among different socioeconomic status (SES) groups in accordance with need. To estimate the association between SES and mortality occurring 30 days after CABG surgery.

DESIGN

Individual socioeconomic index assigned with respect to the characteristics of the census tract of residence (level I = highest SES; level IV = lowest SES). Comparison of age adjusted hospital admission rates of ischaemic heart disease (IHD) and CABG surgery among four SES groups. Retrospective cohort study of all patients who underwent CABG surgery during 1996-97.

SETTING

Rome (2 685 890 inhabitants) and the seven cardiac surgery units in the city.

PARTICIPANTS

All residents in Rome aged 35 years or more. A cohort of 1875 CABG patients aged 35 years or more.

MAIN OUTCOME MEASURES

Age adjusted hospitalisation rates for CABG and IHD and rate of CABG per 100 IHD hospitalisations by SES group, taking level I as the reference group. Odds ratios of 30 day mortality after CABG surgery, adjusted for age, gender, illness severity at admission, and type of hospital where CABG was performed.

RESULTS

People in the lowest SES level experienced an excess in the age adjusted IHD hospitalisation rates compared with the highest SES level (an excess of 57% among men, and of 94% among women), but the rate of CABG per 100 IHD hospitalisations was lower, among men, in the most socially disadvantaged level (8.9 CABG procedures per 100 IHD hospital admissions in level IV versus 14.1 in level I rate ratio= 0.63; 95% CI 0.44, 0.89). The most socially disadvantaged SES group experienced a higher risk of 30 day mortality after CABG surgery (8. 1%) than those in the highest SES group (4.8%); this excess in mortality was confirmed even when initial illness severity was taken into account (odds ratio= 2.89; 95% CI 1.44, 5.80).

CONCLUSIONS

The universal coverage of the National Health Service in Italy does not guarantee equitable access to CABG surgery for IHD patients. Factors related to SES are likely to influence poor prognosis after CABG surgery.

Challenges in comparing risk-adjusted bypass surgery mortality results: results from the Cooperative Cardiovascular Project

OBJECTIVES

We sought to evaluate the predictive accuracy of four bypass surgery mortality clinical risk models and to examine the extent to which hospitals' risk-adjusted surgical outcomes vary depending on which risk-adjustment method is applied.

BACKGROUND

Cardiovascular "report cards" often compare risk-adjusted surgical outcomes; however, it is unclear to what extent the risk-adjustment process itself may affect these metrics.

METHODS

As part of the Cooperative Cardiovascular Project's Pilot Revascularization Study, we compared the predictive accuracy of four bypass clinical risk models among 3,654 Medicare patients undergoing surgery at 28 hospitals in Alabama and Iowa. We also compared the agreement in hospital-level risk-adjusted bypass outcome performance ratings depending on which of the four risk models was applied.

RESULTS

Although the four risk models had similar discriminatory abilities (C-index, 0.71 to 0.74), certain models tended to overpredict mortality in higher-risk patients. There was high correlation between a hospital's risk-adjusted mortality rates regardless of which of the four models was used (correlation between risk-adjusted rating, 0.93 to 0.97). In contrast, there was limited agreement in which hospitals were identified as "performance outliers" depending on which risk-adjustment model was used and how outlier status was defined.

CONCLUSIONS

A hospital's risk-adjusted bypass surgery mortality rating, relative to its peers, was consistent regardless of the risk-adjustment model applied, supporting their use as a means of provider performance feedback. Designation of performance outliers, however, can vary significantly depending on the benchmark and methods used for this determination.

The use of Medicaid claims as a supplementary source of information on quality of asthma care

This study examines the correspondence between medical records and Medicaid claims to determine whether claims are a valid source of data for monitoring quality of asthma care. A total of 460 claims for care encounters were matched to medical records of the encounters. While most of the diagnoses and procedures recorded on the claims were documented in medical records, claims failed to identify 29% of encounters with asthma diagnoses and 45% of nebulization procedures administered during encounters. About 30% of documented asthma prescriptions were not associated with filed claims, and about 30% of filed claims for asthma medication were not documented in medical records.

Results of report cards for patients with congestive heart failure depend on the method used to adjust for severity

BACKGROUND

The validity of outcome report cards may depend on the ways in which they are adjusted for risk.

OBJECTIVES

To compare the predictive ability of generic and disease-specific survival prediction models appropriate for use in patients with heart failure, to simulate outcome report cards by comparing survival across hospitals and adjusting for severity of illness using these models, and to assess the ways in which the results of these comparisons depend on the adjustment method.

DESIGN

Analysis of data from a prospective cohort study.

SETTING

A university hospital, a Veterans Affairs (VA) medical center, and a community hospital.

PATIENTS

Sequential patients presenting in the emergency department with acute congestive heart failure.

MEASUREMENTS

Unadjusted 30-day and 1-year mortality across hospitals and 30-day and 1-year mortality adjusted by using disease-specific survival prediction models (two sickness-at-admission models, the Cleveland Health Quality Choice model, the Congestive Heart Failure Mortality Time-Independent Predictive Instrument) and generic models (Acute Physiology and Chronic Health Evaluation [APACHE] II, APACHE III, the mortality prediction model, and the Chadson comorbidity index).

RESULTS

The community hospital's unadjusted 30-day survival rate (85.0%) and the VA medical center's unadjusted 1-year survival rate (60.9%) were significantly lower than corresponding rates at the university hospital (92.7% and 67.5%, respectively). No severity model had excellent ability to discriminate patients by survival rates (all areas under the receiver-operating characteristic curve < 0.73). Whether the VA medical center, the community hospital, both, or neither had worse survival rates on simulated report cards than the university hospital depended on the prediction model used for adjustment.

CONCLUSIONS

Results of simulated outcome report cards for survival in patients with congestive heart failure depend on the method used to adjust for severity.

Strategies for improving comorbidity measures based on Medicare and Medicaid claims data

Claims-based measures of comorbid illness severity have generally relied on the diagnoses listed for a single hospitalization. Unfortunately, such diagnostic information is often limited because patients have not been hospitalized during periods of interest, because of incomplete coding of diagnoses on claims forms, or because listed diagnoses represent complications of the hospitalization rather than pre-existing comorbid conditions. To address these limitations, we developed and tested four comorbidity index scores for patients with breast cancer, each based on different sources of health services claims from Medicare and Medicaid: hospitalization for breast cancer surgery; outpatient care prior to the hospitalization; other inpatient care prior to the hospitalization; and all sources combined. Varying the number and type of sources of diagnostic information yielded only very small improvements in the prediction of mortality at 1 and 3 years. Surprisingly, even simpler measures of comorbidity (crude number of diagnoses) and of prior health care utilization (total days spent in the hospital) performed at least as well in predicting mortality as did the more complex index scores which assigned points and weights for specific conditions. The greatest improvement in explanatory power was observed when another source of clinical information (cancer stage derived from a population-based cancer registry) was used to supplement claims information. Expanding the source of claims diagnoses and focusing on time periods prior to an index hospitalization are insufficient for substantially improving the explanatory power of claims-based comorbidity indices. Other improvements suggested by our results should include: increasing the completeness and accuracy of claims diagnoses; supplementing diagnoses with health care utilization information in claims data; and supplementing claims data with other sources of clinical information.

Does diagnostic information contribute to predicting functional decline in long-term care?

BACKGROUND

Compared with the acute-care setting, use of risk-adjusted outcomes in long-term care is relatively new. With the recent development of administrative databases in long-term care, such uses are likely to increase.

OBJECTIVES

The objective of this study was to determine the contribution of ICD-9-CM diagnosis codes from administrative data in predicting functional decline in long-term care.

RESEARCH DESIGN

We used a retrospective sample of 15,693 long-term care residents in VA facilities in 1996.

METHODS

We defined functional decline as an increase of > or =2 in the activities of daily living (ADL) summary score from baseline to semiannual assessment. A base regression model was compared to a full model enhanced with ICD-9-CM codes. We calculated validated measures of model performance in an independent cohort.

RESULTS

The full model fit the data significantly better than the base model as indicated by the likelihood ratio test (chi2 = 179, df = 11, P <0.001). The full model predicted decline more accurately than the base model (R2 = 0.06 and 0.05, respectively) and discriminated better (c statistics were 0.70 and 0.68). Observed and predicted risks of decline were similar within deciles between the 2 models, suggesting good calibration. Validated R2 statistics were 0.05 and 0.04 for the full and base models; validated c statistics were 0.68 and 0.66.

CONCLUSIONS

Adding specific diagnostic variables to administrative data modestly improves the prediction of functional decline in long-term care residents. Diagnostic information from administrative databases may present a cost-effective alternative to chart abstraction in providing the data necessary for accurate risk adjustment.

Case mix adjustment in nursing systems research: the case of resident outcomes in nursing homes

Case mix indicates, for a resident population, the degree of risk for developing favorable or unfavorable outcomes. In a study of 164 nursing homes, we explored two methods for combining resident assessment data into a case mix index (CMI). We compared a facility-level, composite CMI to a prevalence-based CMI comprised of 22 separate resident characteristics for their adequacy in explaining resident outcomes. The prevalence-based CMI consistently explained more variance in outcomes than the facility level, composite CMI. This study indicates a reasonable method for using administrative databases containing resident assessment data to adjust for the influence of case mix on nursing home resident outcomes.

Effect of the Asymptomatic Carotid Atherosclerosis Study on carotid endarterectomy in Florida

BACKGROUND AND PURPOSE

The value of carotid endarterectomy (CEA) has been defined by several recent multicenter trials. The clinical effect of these trials remains undetermined since the Asymptomatic Carotid Atherosclerosis Study (ACAS) Clinical Advisory (dated September 28, 1994).

METHODS

Patients undergoing CEA (ICD-9-CM 38.12) in nonfederal Florida hospitals were identified from the discharge database. Data were analyzed by federal fiscal year (FY, October 1 through September 30), comparing the years following the Advisory (FY95-FY96) to the preceding 3 years (FY92-FY94).

RESULTS

There was a 68.3% increase in the number of CEAs during FY95-FY96 (mean FY92-FY94, 7,343; mean FY95-FY96, 12,356). This exceeded increases in total hospital discharges (4.5%), surgical discharges (2.2%), and the state's population (4.7%). The increase in CEAs spanned all patient demographic groups (gender, race, and age), although the magnitude was not consistent (range, 57.8% increase for 55 to 64 age group; 92.9% increase for > 84 age group). Concomitantly, there was a significant decrease in mortality (1.2% versus 0.8%), cardiac complication rate (ICD-9-CM 997.1, 4.1% versus 3.0%) and percentage of patients discharged > 7 days postoperatively (8.9% versus 4.9%). Mean length of stay declined 28% (5.8 versus 4.1 days), and mean adjusted charges declined 7% ($19,456 versus $18,055). Although the average case was less costly, the increased volume resulted in an estimated $56 million increase in annual hospital payments.

CONCLUSIONS

The dramatic increase in the number of CEAs performed in the state of Florida after release of the ACAS Clinical Advisory suggests a causal relationship and mandates further cost-effectiveness analyses.