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Hospital Mortality Measure for COPD
Chronic obstructive pulmonary disease (COPD) affects as many as 24 million individuals in the United States, is responsible for more than 700,000 annual hospital admissions, and is currently the nation's third leading cause of death, accounting for nearly $49.9 billion in medical spending in 2010.[1, 2] Reported in‐hospital mortality rates for patients hospitalized for exacerbations of COPD range from 2% to 5%.[3, 4, 5, 6, 7] Information about 30‐day mortality rates following hospitalization for COPD is more limited; however, international studies suggest that rates range from 3% to 9%,[8, 9] and 90‐day mortality rates exceed 15%.[10]
Despite this significant clinical and economic impact, there have been no large‐scale, sustained efforts to measure the quality or outcomes of hospital care for patients with COPD in the United States. What little is known about the treatment of patients with COPD suggests widespread opportunities to increase adherence to guideline‐recommended therapies, to reduce the use of ineffective treatments and tests, and to address variation in care across institutions.[5, 11, 12]
Public reporting of hospital performance is a key strategy for improving the quality and safety of hospital care, both in the United States and internationally.[13] Since 2007, the Centers for Medicare and Medicaid Services (CMS) has reported hospital mortality rates on the Hospital Compare Web site, and COPD is 1 of the conditions highlighted in the Affordable Care Act for future consideration.[14] Such initiatives rely on validated, risk‐adjusted performance measures for comparisons across institutions and to enable outcomes to be tracked over time. We present the development, validation, and results of a model intended for public reporting of risk‐standardized mortality rates for patients hospitalized with exacerbations of COPD that has been endorsed by the National Quality Forum.[15]
METHODS
Approach to Measure Development
We developed this measure in accordance with guidelines described by the National Quality Forum,[16] CMS' Measure Management System,[17] and the American Heart Association scientific statement, Standards for Statistical Models Used for Public Reporting of Health Outcomes.[18] Throughout the process we obtained expert clinical and stakeholder input through meetings with a clinical advisory group and a national technical expert panel (see Acknowledgments). Last, we presented the proposed measure specifications and a summary of the technical expert panel discussions online and made a widely distributed call for public comments. We took the comments into consideration during the final stages of measure development (available at
Data Sources
We used claims data from Medicare inpatient, outpatient, and carrier (physician) Standard Analytic Files from 2008 to develop and validate the model, and examined model reliability using data from 2007 and 2009. The Medicare enrollment database was used to determine Medicare Fee‐for‐Service enrollment and mortality.
Study Cohort
Admissions were considered eligible for inclusion if the patient was 65 years or older, was admitted to a nonfederal acute care hospital in the United States, and had a principal diagnosis of COPD or a principal diagnosis of acute respiratory failure or respiratory arrest when paired with a secondary diagnosis of COPD with exacerbation (Table 1).
| ICD‐9‐CM | Description |
|---|---|
| |
| 491.21 | Obstructive chronic bronchitis; with (acute) exacerbation; acute exacerbation of COPD, decompensated COPD, decompensated COPD with exacerbation |
| 491.22 | Obstructive chronic bronchitis; with acute bronchitis |
| 491.8 | Other chronic bronchitis; chronic: tracheitis, tracheobronchitis |
| 491.9 | Unspecified chronic bronchitis |
| 492.8 | Other emphysema; emphysema (lung or pulmonary): NOS, centriacinar, centrilobular, obstructive, panacinar, panlobular, unilateral, vesicular; MacLeod's syndrome; Swyer‐James syndrome; unilateral hyperlucent lung |
| 493.20 | Chronic obstructive asthma; asthma with COPD, chronic asthmatic bronchitis, unspecified |
| 493.21 | Chronic obstructive asthma; asthma with COPD, chronic asthmatic bronchitis, with status asthmaticus |
| 493.22 | Chronic obstructive asthma; asthma with COPD, chronic asthmatic bronchitis, with (acute) exacerbation |
| 496 | Chronic: nonspecific lung disease, obstructive lung disease, obstructive pulmonary disease (COPD) NOS. (Note: This code is not to be used with any code from categories 491493.) |
| 518.81a | Other diseases of lung; acute respiratory failure; respiratory failure NOS |
| 518.82a | Other diseases of lung; acute respiratory failure; other pulmonary insufficiency, acute respiratory distress |
| 518.84a | Other diseases of lung; acute respiratory failure; acute and chronic respiratory failure |
| 799.1a | Other ill‐defined and unknown causes of morbidity and mortality; respiratory arrest, cardiorespiratory failure |
If a patient was discharged and readmitted to a second hospital on the same or the next day, we combined the 2 acute care admissions into a single episode of care and assigned the mortality outcome to the first admitting hospital. We excluded admissions for patients who were enrolled in Medicare Hospice in the 12 months prior to or on the first day of the index hospitalization. An index admission was any eligible admission assessed in the measure for the outcome. We also excluded admissions for patients who were discharged against medical advice, those for whom vital status at 30 days was unknown or recorded inconsistently, and patients with unreliable data (eg, age >115 years). For patients with multiple hospitalizations during a single year, we randomly selected 1 admission per patient to avoid survival bias. Finally, to assure adequate risk adjustment we limited the analysis to patients who had continuous enrollment in Medicare Fee‐for‐Service Parts A and B for the 12 months prior to their index admission so that we could identify comorbid conditions coded during all prior encounters.
Outcomes
The outcome of 30‐day mortality was defined as death from any cause within 30 days of the admission date for the index hospitalization. Mortality was assessed at 30 days to standardize the period of outcome ascertainment,[19] and because 30 days is a clinically meaningful time frame, during which differences in the quality of hospital care may be revealed.
Risk‐Adjustment Variables
We randomly selected half of all COPD admissions in 2008 that met the inclusion and exclusion criteria to create a model development sample. Candidate variables for inclusion in the risk‐standardized model were selected by a clinician team from diagnostic groups included in the Hierarchical Condition Category clinical classification system[20] and included age and comorbid conditions. Sleep apnea (International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] condition codes 327.20, 327.21, 327.23, 327.27, 327.29, 780.51, 780.53, and 780.57) and mechanical ventilation (ICD‐9‐CM procedure codes 93.90, 96.70, 96.71, and 96.72) were also included as candidate variables.
We defined a condition as present for a given patient if it was coded in the inpatient, outpatient, or physician claims data sources in the preceding 12 months, including the index admission. Because a subset of the condition category variables can represent a complication of care, we did not consider them to be risk factors if they appeared only as secondary diagnosis codes for the index admission and not in claims submitted during the prior year.
We selected final variables for inclusion in the risk‐standardized model based on clinical considerations and a modified approach to stepwise logistic regression. The final patient‐level risk‐adjustment model included 42 variables (Table 2).
| Variable | Development Sample (150,035 Admissions at 4537 Hospitals) | Validation Sample (149,646 Admissions at 4535 Hospitals) | ||||
|---|---|---|---|---|---|---|
| Frequency, % | OR | 95% CI | Frequency, % | OR | 95% CI | |
| ||||||
| Demographics | ||||||
| Age 65 years (continuous) | 1.03 | 1.03‐1.04 | 1.03 | 1.03‐1.04 | ||
| Cardiovascular/respiratory | ||||||
| Sleep apnea (ICD‐9‐CM: 327.20, 327.21, 327.23, 327.27, 327.29, 780.51, 780.53, 780.57)a | 9.57 | 0.87 | 0.81‐0.94 | 9.72 | 0.84 | 0.78‐0.90 |
| History of mechanical ventilation (ICD‐9‐CM: 93.90, 96.70, 96.71, 96.72)a | 6.00 | 1.19 | 1.11‐1.27 | 6.00 | 1.15 | 1.08‐1.24 |
| Respirator dependence/respiratory failure (CC 7778)a | 1.15 | 0.89 | 0.77‐1.02 | 1.20 | 0.78 | 0.68‐0.91 |
| Cardiorespiratory failure and shock (CC 79) | 26.35 | 1.60 | 1.53‐1.68 | 26.34 | 1.59 | 1.52‐1.66 |
| Congestive heart failure (CC 80) | 41.50 | 1.34 | 1.28‐1.39 | 41.39 | 1.31 | 1.25‐1.36 |
| Chronic atherosclerosis (CC 8384)a | 50.44 | 0.87 | 0.83‐0.90 | 50.12 | 0.91 | 0.87‐0.94 |
| Arrhythmias (CC 9293) | 37.15 | 1.17 | 1.12‐1.22 | 37.06 | 1.15 | 1.10‐1.20 |
| Vascular or circulatory disease (CC 104106) | 38.20 | 1.09 | 1.05‐1.14 | 38.09 | 1.02 | 0.98‐1.06 |
| Fibrosis of lung and other chronic lung disorder (CC 109) | 16.96 | 1.08 | 1.03‐1.13 | 17.08 | 1.11 | 1.06‐1.17 |
| Asthma (CC 110) | 17.05 | 0.67 | 0.63‐0.70 | 16.90 | 0.67 | 0.63‐0.70 |
| Pneumonia (CC 111113) | 49.46 | 1.29 | 1.24‐1.35 | 49.41 | 1.27 | 1.22‐1.33 |
| Pleural effusion/pneumothorax (CC 114) | 11.78 | 1.17 | 1.11‐1.23 | 11.54 | 1.18 | 1.12‐1.25 |
| Other lung disorders (CC 115) | 53.07 | 0.80 | 0.77‐0.83 | 53.17 | 0.83 | 0.80‐0.87 |
| Other comorbid conditions | ||||||
| Metastatic cancer and acute leukemia (CC 7) | 2.76 | 2.34 | 2.14‐2.56 | 2.79 | 2.15 | 1.97‐2.35 |
| Lung, upper digestive tract, and other severe cancers (CC 8)a | 5.98 | 1.80 | 1.68‐1.92 | 6.02 | 1.98 | 1.85‐2.11 |
| Lymphatic, head and neck, brain, and other major cancers; breast, prostate, colorectal and other cancers and tumors; other respiratory and heart neoplasms (CC 911) | 14.13 | 1.03 | 0.97‐1.08 | 14.19 | 1.01 | 0.95‐1.06 |
| Other digestive and urinary neoplasms (CC 12) | 6.91 | 0.91 | 0.84‐0.98 | 7.05 | 0.85 | 0.79‐0.92 |
| Diabetes and DM complications (CC 1520, 119120) | 38.31 | 0.91 | 0.87‐0.94 | 38.29 | 0.91 | 0.87‐0.94 |
| Protein‐calorie malnutrition (CC 21) | 7.40 | 2.18 | 2.07‐2.30 | 7.44 | 2.09 | 1.98‐2.20 |
| Disorders of fluid/electrolyte/acid‐base (CC 2223) | 32.05 | 1.13 | 1.08‐1.18 | 32.16 | 1.24 | 1.19‐1.30 |
| Other endocrine/metabolic/nutritional disorders (CC 24) | 67.99 | 0.75 | 0.72‐0.78 | 67.88 | 0.76 | 0.73‐0.79 |
| Other gastrointestinal disorders (CC 36) | 56.21 | 0.81 | 0.78‐0.84 | 56.18 | 0.78 | 0.75‐0.81 |
| Osteoarthritis of hip or knee (CC 40) | 9.32 | 0.74 | 0.69‐0.79 | 9.33 | 0.80 | 0.74‐0.85 |
| Other musculoskeletal and connective tissue disorders (CC 43) | 64.14 | 0.83 | 0.80‐0.86 | 64.20 | 0.83 | 0.80‐0.87 |
| Iron deficiency and other/unspecified anemias and blood disease (CC 47) | 40.80 | 1.08 | 1.04‐1.12 | 40.72 | 1.08 | 1.04‐1.13 |
| Dementia and senility (CC 4950) | 17.06 | 1.09 | 1.04‐1.14 | 16.97 | 1.09 | 1.04‐1.15 |
| Drug/alcohol abuse, without dependence (CC 53)a | 23.51 | 0.78 | 0.75‐0.82 | 23.38 | 0.76 | 0.72‐0.80 |
| Other psychiatric disorders (CC 60)a | 16.49 | 1.12 | 1.07‐1.18 | 16.43 | 1.12 | 1.06‐1.17 |
| Quadriplegia, paraplegia, functional disability (CC 6769, 100102, 177178) | 4.92 | 1.03 | 0.95‐1.12 | 4.92 | 1.08 | 0.99‐1.17 |
| Mononeuropathy, other neurological conditions/emnjuries (CC 76) | 11.35 | 0.85 | 0.80‐0.91 | 11.28 | 0.88 | 0.83‐0.93 |
| Hypertension and hypertensive disease (CC 9091) | 80.40 | 0.78 | 0.75‐0.82 | 80.35 | 0.79 | 0.75‐0.83 |
| Stroke (CC 9596)a | 6.77 | 1.00 | 0.93‐1.08 | 6.73 | 0.98 | 0.91‐1.05 |
| Retinal disorders, except detachment and vascular retinopathies (CC 121) | 10.79 | 0.87 | 0.82‐0.93 | 10.69 | 0.90 | 0.85‐0.96 |
| Other eye disorders (CC 124)a | 19.05 | 0.90 | 0.86‐0.95 | 19.13 | 0.98 | 0.85‐0.93 |
| Other ear, nose, throat, and mouth disorders (CC 127) | 35.21 | 0.83 | 0.80‐0.87 | 35.02 | 0.80 | 0.77‐0.83 |
| Renal failure (CC 131)a | 17.92 | 1.12 | 1.07‐1.18 | 18.16 | 1.13 | 1.08‐1.19 |
| Decubitus ulcer or chronic skin ulcer (CC 148149) | 7.42 | 1.27 | 1.19‐1.35 | 7.42 | 1.33 | 1.25‐1.42 |
| Other dermatological disorders (CC 153) | 28.46 | 0.90 | 0.87‐0.94 | 28.32 | 0.89 | 0.86‐0.93 |
| Trauma (CC 154156, 158161) | 9.04 | 1.09 | 1.03‐1.16 | 8.99 | 1.15 | 1.08‐1.22 |
| Vertebral fractures (CC 157) | 5.01 | 1.33 | 1.24‐1.44 | 4.97 | 1.29 | 1.20‐1.39 |
| Major complications of medical care and trauma (CC 164) | 5.47 | 0.81 | 0.75‐0.88 | 5.55 | 0.82 | 0.76‐0.89 |
Model Derivation
We used hierarchical logistic regression models to model the log‐odds of mortality as a function of patient‐level clinical characteristics and a random hospital‐level intercept. At the patient level, each model adjusts the log‐odds of mortality for age and the selected clinical covariates. The second level models the hospital‐specific intercepts as arising from a normal distribution. The hospital intercept represents the underlying risk of mortality, after accounting for patient risk. If there were no differences among hospitals, then after adjusting for patient risk, the hospital intercepts should be identical across all hospitals.
Estimation of Hospital Risk‐Standardized Mortality Rate
We calculated a risk‐standardized mortality rate, defined as the ratio of predicted to expected deaths (similar to observed‐to‐expected), multiplied by the national unadjusted mortality rate.[21] The expected number of deaths for each hospital was estimated by applying the estimated regression coefficients to the characteristics of each hospital's patients, adding the average of the hospital‐specific intercepts, transforming the data by using an inverse logit function, and summing the data from all patients in the hospital to obtain the count. The predicted number of deaths was calculated in the same way, substituting the hospital‐specific intercept for the average hospital‐specific intercept.
Model Performance, Validation, and Reliability Testing
We used the remaining admissions in 2008 as the model validation sample. We computed several summary statistics to assess the patient‐level model performance in both the development and validation samples,[22] including over‐fitting indices, predictive ability, area under the receiver operating characteristic (ROC) curve, distribution of residuals, and model 2. In addition, we assessed face validity through a survey of members of the technical expert panel. To assess reliability of the model across data years, we repeated the modeling process using qualifying COPD admissions in both 2007 and 2009. Finally, to assess generalizability we evaluated the model's performance in an all‐payer sample of data from patients admitted to California hospitals in 2006.
Analyses were conducted using SAS version 9.1.3 (SAS Institute Inc., Cary, NC). We estimated the hierarchical models using the GLIMMIX procedure in SAS.
The Human Investigation Committee at the Yale University School of Medicine/Yale New Haven Hospital approved an exemption (HIC#0903004927) for the authors to use CMS claims and enrollment data for research analyses and publication.
RESULTS
Model Derivation
After exclusions were applied, the development sample included 150,035 admissions in 2008 at 4537 US hospitals (Figure 1). Factors that were most strongly associated with the risk of mortality included metastatic cancer (odds ratio [OR] 2.34), protein calorie malnutrition (OR 2.18), nonmetastatic cancers of the lung and upper digestive tract, (OR 1.80) cardiorespiratory failure and shock (OR 1.60), and congestive heart failure (OR 1.34) (Table 2).
Model Performance, Validation, and Reliability
The model had a C statistic of 0.72, indicating good discrimination, and predicted mortality in the development sample ranged from 1.52% in the lowest decile to 23.74% in the highest. The model validation sample, using the remaining cases from 2008, included 149,646 admissions from 4535 hospitals. Variable frequencies and ORs were similar in both samples (Table 2). Model performance was also similar in the validation samples, with good model discrimination and fit (Table 3). Ten of 12 technical expert panel members responded to the survey, of whom 90% at least somewhat agreed with the statement, the COPD mortality measure provides an accurate reflection of quality. When the model was applied to patients age 18 years and older in the 2006 California Patient Discharge Data, overall discrimination was good (C statistic, 0.74), including in those age 18 to 64 years (C statistic, 0.75; 65 and above C statistic, 0.70).
| Development | Validation | Data Years | ||
|---|---|---|---|---|
| Indices | Sample, 2008 | Sample, 2008 | 2007 | 2009 |
| ||||
| Number of admissions | 150,035 | 149,646 | 259,911 | 279,377 |
| Number of hospitals | 4537 | 4535 | 4636 | 4571 |
| Mean risk‐standardized mortality rate, % (SD) | 8.62 (0.94) | 8.64 (1.07) | 8.97 (1.12) | 8.08 (1.09) |
| Calibration, 0, 1 | 0.034, 0.985 | 0.009, 1.004 | 0.095, 1.022 | 0.120, 0.981 |
| Discriminationpredictive ability, lowest decile %highest decile % | 1.5223.74 | 1.6023.78 | 1.5424.64 | 1.4222.36 |
| Discriminationarea under the ROC curve, C statistic | 0.720 | 0.723 | 0.728 | 0.722 |
| Residuals lack of fit, Pearson residual fall % | ||||
| 2 | 0 | 0 | 0 | 0 |
| 2, 0 | 91.14 | 91.4 | 91.08 | 91.93 |
| 0, 2 | 1.66 | 1.7 | 1.96 | 1.42 |
| 2+ | 6.93 | 6.91 | 6.96 | 6.65 |
| Model Wald 2 (number of covariates) | 6982.11 (42) | 7051.50 (42) | 13042.35 (42) | 12542.15 (42) |
| P value | <0.0001 | <0.0001 | <0.0001 | <0.0001 |
| Between‐hospital variance, (standard error) | 0.067 (0.008) | 0.078 (0.009) | 0.067 (0.006) | 0.072 (0.006) |
Reliability testing demonstrated consistent performance over several years. The frequency and ORs of the variables included in the model showed only minor changes over time. The area under the ROC curve (C statistic) was 0.73 for the model in the 2007 sample and 0.72 for the model using 2009 data (Table 3).
Hospital Risk‐Standardized Mortality Rates
The mean unadjusted hospital 30‐day mortality rate was 8.6% and ranged from 0% to 100% (Figure 2a). Risk‐standardized mortality rates varied across hospitals (Figure 2b). The mean risk‐standardized mortality rate was 8.6% and ranged from 5.9% to 13.5%. The odds of mortality at a hospital 1 standard deviation above average was 1.20 times that of a hospital 1 standard deviation below average.
DISCUSSION
We present a hospital‐level risk‐standardized mortality measure for patients admitted with COPD based on administrative claims data that are intended for public reporting and that have achieved endorsement by the National Quality Forum, a voluntary consensus standards‐setting organization. Across more than 4500 US hospitals, the mean 30‐day risk‐standardized mortality rate in 2008 was 8.6%, and we observed considerable variation across institutions, despite adjustment for case mix, suggesting that improvement by lower‐performing institutions may be an achievable goal.
Although improving the delivery of evidence‐based care processes and outcomes of patients with acute myocardial infarction, heart failure, and pneumonia has been the focus of national quality improvement efforts for more than a decade, COPD has largely been overlooked.[23] Within this context, this analysis represents the first attempt to systematically measure, at the hospital level, 30‐day all‐cause mortality for patients admitted to US hospitals for exacerbation of COPD. The model we have developed and validated is intended to be used to compare the performance of hospitals while controlling for differences in the pretreatment risk of mortality of patients and accounting for the clustering of patients within hospitals, and will facilitate surveillance of hospital‐level risk‐adjusted outcomes over time.
In contrast to process‐based measures of quality, such as the percentage of patients with pneumonia who receive appropriate antibiotic therapy, performance measures based on patient outcomes provide a more comprehensive view of care and are more consistent with patients' goals.[24] Additionally, it is well established that hospital performance on individual and composite process measures explains only a small amount of the observed variation in patient outcomes between institutions.[25] In this regard, outcome measures incorporate important, but difficult to measure aspects of care, such as diagnostic accuracy and timing, communication and teamwork, the recognition and response to complications, care coordination at the time of transfers between levels of care, and care settings. Nevertheless, when used for making inferences about the quality of hospital care, individual measures such as the risk‐standardized hospital mortality rate should be interpreted in the context of other performance measures, including readmission, patient experience, and costs of care.
A number of prior investigators have described the outcomes of care for patients hospitalized with exacerbations of COPD, including identifying risk factors for mortality. Patil et al. carried out an analysis of the 1996 Nationwide Inpatient Sample and described an overall in‐hospital mortality rate of 2.5% among patients with COPD, and reported that a multivariable model containing sociodemographic characteristics about the patient and comorbidities had an area under the ROC curve of 0.70.[3] In contrast, this hospital‐level measure includes patients with a principal diagnosis of respiratory failure and focuses on 30‐day rather than inpatient mortality, accounting for the nearly 3‐fold higher mortality rate we observed. In a more recent study that used clinical from a large multistate database, Tabak et al. developed a prediction model for inpatient mortality for patients with COPD that contained only 4 factors: age, blood urea nitrogen, mental status, and pulse, and achieved an area under the ROC curve of 0.72.[4] The simplicity of such a model and its reliance on clinical measurements makes it particularly well suited for bedside application by clinicians, but less valuable for large‐scale public reporting programs that rely on administrative data. In the only other study identified that focused on the assessment of hospital mortality rates, Agabiti et al. analyzed the outcomes of 12,756 patients hospitalized for exacerbations of COPD, using similar ICD‐9‐CM diagnostic criteria as in this study, at 21 hospitals in Rome, Italy.[26] They reported an average crude 30‐day mortality rate of 3.8% among a group of 5 benchmark hospitals and an average mortality of 7.5% (range, 5.2%17.2%) among the remaining institutions.
To put the variation we observed in mortality rates into a broader context, the relative difference in the risk‐standardized hospital mortality rates across the 10th to 90th percentiles of hospital performance was 25% for acute myocardial infarction and 39% for heart failure, whereas rates varied 30% for COPD, from 7.6% to 9.9%.[27] Model discrimination in COPD (C statistic, 0.72) was also similar to that reported for models used for public reporting of hospital mortality in acute myocardial infarction (C statistic, 0.71) and pneumonia (C statistic, 0.72).
This study has a number of important strengths. First, the model was developed from a large sample of recent Medicare claims, achieved good discrimination, and was validated in samples not limited to Medicare beneficiaries. Second, by including patients with a principal diagnosis of COPD, as well as those with a principal diagnosis of acute respiratory failure when accompanied by a secondary diagnosis of COPD with acute exacerbation, this model can be used to assess hospital performance across the full spectrum of disease severity. This broad set of ICD‐9‐CM codes used to define the cohort also ensures that efforts to measure hospital performance will be less influenced by differences in documentation and coding practices across hospitals relating to the diagnosis or sequencing of acute respiratory failure diagnoses. Moreover, the inclusion of patients with respiratory failure is important because these patients have the greatest risk of mortality, and are those in whom efforts to improve the quality and safety of care may have the greatest impact. Third, rather than relying solely on information documented during the index admission, we used ambulatory and inpatient claims from the full year prior to the index admission to identify comorbidities and to distinguish them from potential complications of care. Finally, we did not include factors such as hospital characteristics (eg, number of beds, teaching status) in the model. Although they might have improved overall predictive ability, the goal of the hospital mortality measure is to enable comparisons of mortality rates among hospitals while controlling for differences in patient characteristics. To the extent that factors such as size or teaching status might be independently associated with hospital outcomes, it would be inappropriate to adjust away their effects, because mortality risk should not be influenced by hospital characteristics other than through their effects on quality.
These results should be viewed in light of several limitations. First, we used ICD‐9‐CM codes derived from claims files to define the patient populations included in the measure rather than collecting clinical or physiologic information prospectively or through manual review of medical records, such as the forced expiratory volume in 1 second or whether the patient required long‐term oxygen therapy. Nevertheless, we included a broad set of potential diagnosis codes to capture the full spectrum of COPD exacerbations and to minimize differences in coding across hospitals. Second, because the risk‐adjustment included diagnoses coded in the year prior to the index admission, it is potentially subject to bias due to regional differences in medical care utilization that are not driven by underlying differences in patient illness.[28] Third, using administrative claims data, we observed some paradoxical associations in the model that are difficult to explain on clinical grounds, such as a protective effect of substance and alcohol abuse or prior episodes of respiratory failure. Fourth, although we excluded patients from the analysis who were enrolled in hospice prior to, or on the day of, the index admission, we did not exclude those who choose to withdraw support, transition to comfort measures only, or enrolled in hospice care during a hospitalization. We do not seek to penalize hospitals for being sensitive to the preferences of patients at the end of life. At the same time, it is equally important that the measure is capable of detecting the outcomes of suboptimal care that may in some instances lead a patient or their family to withdraw support or choose hospice. Finally, we did not have the opportunity to validate the model against a clinical registry of patients with COPD, because such data do not currently exist. Nevertheless, the use of claims as a surrogate for chart data for risk adjustment has been validated for several conditions, including acute myocardial infarction, heart failure, and pneumonia.[29, 30]
CONCLUSIONS
Risk‐standardized 30‐day mortality rates for Medicare beneficiaries with COPD vary across hospitals in the US. Calculating and reporting hospital outcomes using validated performance measures may catalyze quality improvement activities and lead to better outcomes. Additional research would be helpful to confirm that hospitals with lower mortality rates achieve care that meets the goals of patients and their families better than at hospitals with higher mortality rates.
Acknowledgment
The authors thank the following members of the technical expert panel: Darlene Bainbridge, RN, MS, NHA, CPHQ, CPHRM, President/CEO, Darlene D. Bainbridge & Associates, Inc.; Robert A. Balk, MD, Director of Pulmonary and Critical Care Medicine, Rush University Medical Center; Dale Bratzler, DO, MPH, President and CEO, Oklahoma Foundation for Medical Quality; Scott Cerreta, RRT, Director of Education, COPD Foundation; Gerard J. Criner, MD, Director of Temple Lung Center and Divisions of Pulmonary and Critical Care Medicine, Temple University; Guy D'Andrea, MBA, President, Discern Consulting; Jonathan Fine, MD, Director of Pulmonary Fellowship, Research and Medical Education, Norwalk Hospital; David Hopkins, MS, PhD, Senior Advisor, Pacific Business Group on Health; Fred Martin Jacobs, MD, JD, FACP, FCCP, FCLM, Executive Vice President and Director, Saint Barnabas Quality Institute; Natalie Napolitano, MPH, RRT‐NPS, Respiratory Therapist, Inova Fairfax Hospital; Russell Robbins, MD, MBA, Principal and Senior Clinical Consultant, Mercer. In addition, the authors acknowledge and thank Angela Merrill, Sandi Nelson, Marian Wrobel, and Eric Schone from Mathematica Policy Research, Inc., Sharon‐Lise T. Normand from Harvard Medical School, and Lein Han and Michael Rapp at The Centers for Medicare & Medicaid Services for their contributions to this work.
Disclosures
Peter K. Lindenauer, MD, MSc, is the guarantor of this article, taking responsibility for the integrity of the work as a whole, from inception to published article, and takes responsibility for the content of the manuscript, including the data and data analysis. All authors have made substantial contributions to the conception and design, or acquisition of data, or analysis and interpretation of data; have drafted the submitted article or revised it critically for important intellectual content; and have provided final approval of the version to be published. Preparation of this manuscript was completed under Contract Number: HHSM‐5002008‐0025I/HHSM‐500‐T0001, Modification No. 000007, Option Year 2 Measure Instrument Development and Support (MIDS). Sponsors did not contribute to the development of the research or manuscript. Dr. Au reports being an unpaid research consultant for Bosch Inc. He receives research funding from the NIH, Department of Veterans Affairs, AHRQ, and Gilead Sciences. The views of the this manuscript represent the authors and do not necessarily represent those of the Department of Veterans Affairs. Drs. Drye and Bernheim report receiving contract funding from CMS to develop and maintain quality measures.
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- Patient Protection and Affordable Care Act [H.R. 3590], Pub. L. No. 111–148, §2702, 124 Stat. 119, 318–319 (March 23, 2010). Available at: http://www.gpo.gov/fdsys/pkg/PLAW‐111publ148/html/PLAW‐111publ148.htm. Accessed July 15, 2012.
- National Quality Forum. NQF Endorses Additional Pulmonary Measure. 2013. Available at: http://www.qualityforum.org/News_And_Resources/Press_Releases/2013/NQF_Endorses_Additional_Pulmonary_Measure.aspx. Accessed January 11, 2013.
- National Quality Forum. National voluntary consensus standards for patient outcomes: a consensus report. Washington, DC: National Quality Forum; 2011.
- The Measures Management System. The Centers for Medicare and Medicaid Services. Available at: http://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/MMS/index.html?redirect=/MMS/. Accessed August 6, 2012.
- , , , et al. Standards for statistical models used for public reporting of health outcomes: an American Heart Association Scientific Statement from the Quality of Care and Outcomes Research Interdisciplinary Writing Group: cosponsored by the Council on Epidemiology and Prevention and the Stroke Council. Endorsed by the American College of Cardiology Foundation. Circulation. 2006;113(3):456–462.
- , , , et al. Comparison of hospital risk‐standardized mortality rates calculated by using in‐hospital and 30‐day models: an observational study with implications for hospital profiling. Ann Intern Med. 2012;156(1 pt 1):19–26.
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Chronic obstructive pulmonary disease (COPD) affects as many as 24 million individuals in the United States, is responsible for more than 700,000 annual hospital admissions, and is currently the nation's third leading cause of death, accounting for nearly $49.9 billion in medical spending in 2010.[1, 2] Reported in‐hospital mortality rates for patients hospitalized for exacerbations of COPD range from 2% to 5%.[3, 4, 5, 6, 7] Information about 30‐day mortality rates following hospitalization for COPD is more limited; however, international studies suggest that rates range from 3% to 9%,[8, 9] and 90‐day mortality rates exceed 15%.[10]
Despite this significant clinical and economic impact, there have been no large‐scale, sustained efforts to measure the quality or outcomes of hospital care for patients with COPD in the United States. What little is known about the treatment of patients with COPD suggests widespread opportunities to increase adherence to guideline‐recommended therapies, to reduce the use of ineffective treatments and tests, and to address variation in care across institutions.[5, 11, 12]
Public reporting of hospital performance is a key strategy for improving the quality and safety of hospital care, both in the United States and internationally.[13] Since 2007, the Centers for Medicare and Medicaid Services (CMS) has reported hospital mortality rates on the Hospital Compare Web site, and COPD is 1 of the conditions highlighted in the Affordable Care Act for future consideration.[14] Such initiatives rely on validated, risk‐adjusted performance measures for comparisons across institutions and to enable outcomes to be tracked over time. We present the development, validation, and results of a model intended for public reporting of risk‐standardized mortality rates for patients hospitalized with exacerbations of COPD that has been endorsed by the National Quality Forum.[15]
METHODS
Approach to Measure Development
We developed this measure in accordance with guidelines described by the National Quality Forum,[16] CMS' Measure Management System,[17] and the American Heart Association scientific statement, Standards for Statistical Models Used for Public Reporting of Health Outcomes.[18] Throughout the process we obtained expert clinical and stakeholder input through meetings with a clinical advisory group and a national technical expert panel (see Acknowledgments). Last, we presented the proposed measure specifications and a summary of the technical expert panel discussions online and made a widely distributed call for public comments. We took the comments into consideration during the final stages of measure development (available at
Data Sources
We used claims data from Medicare inpatient, outpatient, and carrier (physician) Standard Analytic Files from 2008 to develop and validate the model, and examined model reliability using data from 2007 and 2009. The Medicare enrollment database was used to determine Medicare Fee‐for‐Service enrollment and mortality.
Study Cohort
Admissions were considered eligible for inclusion if the patient was 65 years or older, was admitted to a nonfederal acute care hospital in the United States, and had a principal diagnosis of COPD or a principal diagnosis of acute respiratory failure or respiratory arrest when paired with a secondary diagnosis of COPD with exacerbation (Table 1).
| ICD‐9‐CM | Description |
|---|---|
| |
| 491.21 | Obstructive chronic bronchitis; with (acute) exacerbation; acute exacerbation of COPD, decompensated COPD, decompensated COPD with exacerbation |
| 491.22 | Obstructive chronic bronchitis; with acute bronchitis |
| 491.8 | Other chronic bronchitis; chronic: tracheitis, tracheobronchitis |
| 491.9 | Unspecified chronic bronchitis |
| 492.8 | Other emphysema; emphysema (lung or pulmonary): NOS, centriacinar, centrilobular, obstructive, panacinar, panlobular, unilateral, vesicular; MacLeod's syndrome; Swyer‐James syndrome; unilateral hyperlucent lung |
| 493.20 | Chronic obstructive asthma; asthma with COPD, chronic asthmatic bronchitis, unspecified |
| 493.21 | Chronic obstructive asthma; asthma with COPD, chronic asthmatic bronchitis, with status asthmaticus |
| 493.22 | Chronic obstructive asthma; asthma with COPD, chronic asthmatic bronchitis, with (acute) exacerbation |
| 496 | Chronic: nonspecific lung disease, obstructive lung disease, obstructive pulmonary disease (COPD) NOS. (Note: This code is not to be used with any code from categories 491493.) |
| 518.81a | Other diseases of lung; acute respiratory failure; respiratory failure NOS |
| 518.82a | Other diseases of lung; acute respiratory failure; other pulmonary insufficiency, acute respiratory distress |
| 518.84a | Other diseases of lung; acute respiratory failure; acute and chronic respiratory failure |
| 799.1a | Other ill‐defined and unknown causes of morbidity and mortality; respiratory arrest, cardiorespiratory failure |
If a patient was discharged and readmitted to a second hospital on the same or the next day, we combined the 2 acute care admissions into a single episode of care and assigned the mortality outcome to the first admitting hospital. We excluded admissions for patients who were enrolled in Medicare Hospice in the 12 months prior to or on the first day of the index hospitalization. An index admission was any eligible admission assessed in the measure for the outcome. We also excluded admissions for patients who were discharged against medical advice, those for whom vital status at 30 days was unknown or recorded inconsistently, and patients with unreliable data (eg, age >115 years). For patients with multiple hospitalizations during a single year, we randomly selected 1 admission per patient to avoid survival bias. Finally, to assure adequate risk adjustment we limited the analysis to patients who had continuous enrollment in Medicare Fee‐for‐Service Parts A and B for the 12 months prior to their index admission so that we could identify comorbid conditions coded during all prior encounters.
Outcomes
The outcome of 30‐day mortality was defined as death from any cause within 30 days of the admission date for the index hospitalization. Mortality was assessed at 30 days to standardize the period of outcome ascertainment,[19] and because 30 days is a clinically meaningful time frame, during which differences in the quality of hospital care may be revealed.
Risk‐Adjustment Variables
We randomly selected half of all COPD admissions in 2008 that met the inclusion and exclusion criteria to create a model development sample. Candidate variables for inclusion in the risk‐standardized model were selected by a clinician team from diagnostic groups included in the Hierarchical Condition Category clinical classification system[20] and included age and comorbid conditions. Sleep apnea (International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] condition codes 327.20, 327.21, 327.23, 327.27, 327.29, 780.51, 780.53, and 780.57) and mechanical ventilation (ICD‐9‐CM procedure codes 93.90, 96.70, 96.71, and 96.72) were also included as candidate variables.
We defined a condition as present for a given patient if it was coded in the inpatient, outpatient, or physician claims data sources in the preceding 12 months, including the index admission. Because a subset of the condition category variables can represent a complication of care, we did not consider them to be risk factors if they appeared only as secondary diagnosis codes for the index admission and not in claims submitted during the prior year.
We selected final variables for inclusion in the risk‐standardized model based on clinical considerations and a modified approach to stepwise logistic regression. The final patient‐level risk‐adjustment model included 42 variables (Table 2).
| Variable | Development Sample (150,035 Admissions at 4537 Hospitals) | Validation Sample (149,646 Admissions at 4535 Hospitals) | ||||
|---|---|---|---|---|---|---|
| Frequency, % | OR | 95% CI | Frequency, % | OR | 95% CI | |
| ||||||
| Demographics | ||||||
| Age 65 years (continuous) | 1.03 | 1.03‐1.04 | 1.03 | 1.03‐1.04 | ||
| Cardiovascular/respiratory | ||||||
| Sleep apnea (ICD‐9‐CM: 327.20, 327.21, 327.23, 327.27, 327.29, 780.51, 780.53, 780.57)a | 9.57 | 0.87 | 0.81‐0.94 | 9.72 | 0.84 | 0.78‐0.90 |
| History of mechanical ventilation (ICD‐9‐CM: 93.90, 96.70, 96.71, 96.72)a | 6.00 | 1.19 | 1.11‐1.27 | 6.00 | 1.15 | 1.08‐1.24 |
| Respirator dependence/respiratory failure (CC 7778)a | 1.15 | 0.89 | 0.77‐1.02 | 1.20 | 0.78 | 0.68‐0.91 |
| Cardiorespiratory failure and shock (CC 79) | 26.35 | 1.60 | 1.53‐1.68 | 26.34 | 1.59 | 1.52‐1.66 |
| Congestive heart failure (CC 80) | 41.50 | 1.34 | 1.28‐1.39 | 41.39 | 1.31 | 1.25‐1.36 |
| Chronic atherosclerosis (CC 8384)a | 50.44 | 0.87 | 0.83‐0.90 | 50.12 | 0.91 | 0.87‐0.94 |
| Arrhythmias (CC 9293) | 37.15 | 1.17 | 1.12‐1.22 | 37.06 | 1.15 | 1.10‐1.20 |
| Vascular or circulatory disease (CC 104106) | 38.20 | 1.09 | 1.05‐1.14 | 38.09 | 1.02 | 0.98‐1.06 |
| Fibrosis of lung and other chronic lung disorder (CC 109) | 16.96 | 1.08 | 1.03‐1.13 | 17.08 | 1.11 | 1.06‐1.17 |
| Asthma (CC 110) | 17.05 | 0.67 | 0.63‐0.70 | 16.90 | 0.67 | 0.63‐0.70 |
| Pneumonia (CC 111113) | 49.46 | 1.29 | 1.24‐1.35 | 49.41 | 1.27 | 1.22‐1.33 |
| Pleural effusion/pneumothorax (CC 114) | 11.78 | 1.17 | 1.11‐1.23 | 11.54 | 1.18 | 1.12‐1.25 |
| Other lung disorders (CC 115) | 53.07 | 0.80 | 0.77‐0.83 | 53.17 | 0.83 | 0.80‐0.87 |
| Other comorbid conditions | ||||||
| Metastatic cancer and acute leukemia (CC 7) | 2.76 | 2.34 | 2.14‐2.56 | 2.79 | 2.15 | 1.97‐2.35 |
| Lung, upper digestive tract, and other severe cancers (CC 8)a | 5.98 | 1.80 | 1.68‐1.92 | 6.02 | 1.98 | 1.85‐2.11 |
| Lymphatic, head and neck, brain, and other major cancers; breast, prostate, colorectal and other cancers and tumors; other respiratory and heart neoplasms (CC 911) | 14.13 | 1.03 | 0.97‐1.08 | 14.19 | 1.01 | 0.95‐1.06 |
| Other digestive and urinary neoplasms (CC 12) | 6.91 | 0.91 | 0.84‐0.98 | 7.05 | 0.85 | 0.79‐0.92 |
| Diabetes and DM complications (CC 1520, 119120) | 38.31 | 0.91 | 0.87‐0.94 | 38.29 | 0.91 | 0.87‐0.94 |
| Protein‐calorie malnutrition (CC 21) | 7.40 | 2.18 | 2.07‐2.30 | 7.44 | 2.09 | 1.98‐2.20 |
| Disorders of fluid/electrolyte/acid‐base (CC 2223) | 32.05 | 1.13 | 1.08‐1.18 | 32.16 | 1.24 | 1.19‐1.30 |
| Other endocrine/metabolic/nutritional disorders (CC 24) | 67.99 | 0.75 | 0.72‐0.78 | 67.88 | 0.76 | 0.73‐0.79 |
| Other gastrointestinal disorders (CC 36) | 56.21 | 0.81 | 0.78‐0.84 | 56.18 | 0.78 | 0.75‐0.81 |
| Osteoarthritis of hip or knee (CC 40) | 9.32 | 0.74 | 0.69‐0.79 | 9.33 | 0.80 | 0.74‐0.85 |
| Other musculoskeletal and connective tissue disorders (CC 43) | 64.14 | 0.83 | 0.80‐0.86 | 64.20 | 0.83 | 0.80‐0.87 |
| Iron deficiency and other/unspecified anemias and blood disease (CC 47) | 40.80 | 1.08 | 1.04‐1.12 | 40.72 | 1.08 | 1.04‐1.13 |
| Dementia and senility (CC 4950) | 17.06 | 1.09 | 1.04‐1.14 | 16.97 | 1.09 | 1.04‐1.15 |
| Drug/alcohol abuse, without dependence (CC 53)a | 23.51 | 0.78 | 0.75‐0.82 | 23.38 | 0.76 | 0.72‐0.80 |
| Other psychiatric disorders (CC 60)a | 16.49 | 1.12 | 1.07‐1.18 | 16.43 | 1.12 | 1.06‐1.17 |
| Quadriplegia, paraplegia, functional disability (CC 6769, 100102, 177178) | 4.92 | 1.03 | 0.95‐1.12 | 4.92 | 1.08 | 0.99‐1.17 |
| Mononeuropathy, other neurological conditions/emnjuries (CC 76) | 11.35 | 0.85 | 0.80‐0.91 | 11.28 | 0.88 | 0.83‐0.93 |
| Hypertension and hypertensive disease (CC 9091) | 80.40 | 0.78 | 0.75‐0.82 | 80.35 | 0.79 | 0.75‐0.83 |
| Stroke (CC 9596)a | 6.77 | 1.00 | 0.93‐1.08 | 6.73 | 0.98 | 0.91‐1.05 |
| Retinal disorders, except detachment and vascular retinopathies (CC 121) | 10.79 | 0.87 | 0.82‐0.93 | 10.69 | 0.90 | 0.85‐0.96 |
| Other eye disorders (CC 124)a | 19.05 | 0.90 | 0.86‐0.95 | 19.13 | 0.98 | 0.85‐0.93 |
| Other ear, nose, throat, and mouth disorders (CC 127) | 35.21 | 0.83 | 0.80‐0.87 | 35.02 | 0.80 | 0.77‐0.83 |
| Renal failure (CC 131)a | 17.92 | 1.12 | 1.07‐1.18 | 18.16 | 1.13 | 1.08‐1.19 |
| Decubitus ulcer or chronic skin ulcer (CC 148149) | 7.42 | 1.27 | 1.19‐1.35 | 7.42 | 1.33 | 1.25‐1.42 |
| Other dermatological disorders (CC 153) | 28.46 | 0.90 | 0.87‐0.94 | 28.32 | 0.89 | 0.86‐0.93 |
| Trauma (CC 154156, 158161) | 9.04 | 1.09 | 1.03‐1.16 | 8.99 | 1.15 | 1.08‐1.22 |
| Vertebral fractures (CC 157) | 5.01 | 1.33 | 1.24‐1.44 | 4.97 | 1.29 | 1.20‐1.39 |
| Major complications of medical care and trauma (CC 164) | 5.47 | 0.81 | 0.75‐0.88 | 5.55 | 0.82 | 0.76‐0.89 |
Model Derivation
We used hierarchical logistic regression models to model the log‐odds of mortality as a function of patient‐level clinical characteristics and a random hospital‐level intercept. At the patient level, each model adjusts the log‐odds of mortality for age and the selected clinical covariates. The second level models the hospital‐specific intercepts as arising from a normal distribution. The hospital intercept represents the underlying risk of mortality, after accounting for patient risk. If there were no differences among hospitals, then after adjusting for patient risk, the hospital intercepts should be identical across all hospitals.
Estimation of Hospital Risk‐Standardized Mortality Rate
We calculated a risk‐standardized mortality rate, defined as the ratio of predicted to expected deaths (similar to observed‐to‐expected), multiplied by the national unadjusted mortality rate.[21] The expected number of deaths for each hospital was estimated by applying the estimated regression coefficients to the characteristics of each hospital's patients, adding the average of the hospital‐specific intercepts, transforming the data by using an inverse logit function, and summing the data from all patients in the hospital to obtain the count. The predicted number of deaths was calculated in the same way, substituting the hospital‐specific intercept for the average hospital‐specific intercept.
Model Performance, Validation, and Reliability Testing
We used the remaining admissions in 2008 as the model validation sample. We computed several summary statistics to assess the patient‐level model performance in both the development and validation samples,[22] including over‐fitting indices, predictive ability, area under the receiver operating characteristic (ROC) curve, distribution of residuals, and model 2. In addition, we assessed face validity through a survey of members of the technical expert panel. To assess reliability of the model across data years, we repeated the modeling process using qualifying COPD admissions in both 2007 and 2009. Finally, to assess generalizability we evaluated the model's performance in an all‐payer sample of data from patients admitted to California hospitals in 2006.
Analyses were conducted using SAS version 9.1.3 (SAS Institute Inc., Cary, NC). We estimated the hierarchical models using the GLIMMIX procedure in SAS.
The Human Investigation Committee at the Yale University School of Medicine/Yale New Haven Hospital approved an exemption (HIC#0903004927) for the authors to use CMS claims and enrollment data for research analyses and publication.
RESULTS
Model Derivation
After exclusions were applied, the development sample included 150,035 admissions in 2008 at 4537 US hospitals (Figure 1). Factors that were most strongly associated with the risk of mortality included metastatic cancer (odds ratio [OR] 2.34), protein calorie malnutrition (OR 2.18), nonmetastatic cancers of the lung and upper digestive tract, (OR 1.80) cardiorespiratory failure and shock (OR 1.60), and congestive heart failure (OR 1.34) (Table 2).
Model Performance, Validation, and Reliability
The model had a C statistic of 0.72, indicating good discrimination, and predicted mortality in the development sample ranged from 1.52% in the lowest decile to 23.74% in the highest. The model validation sample, using the remaining cases from 2008, included 149,646 admissions from 4535 hospitals. Variable frequencies and ORs were similar in both samples (Table 2). Model performance was also similar in the validation samples, with good model discrimination and fit (Table 3). Ten of 12 technical expert panel members responded to the survey, of whom 90% at least somewhat agreed with the statement, the COPD mortality measure provides an accurate reflection of quality. When the model was applied to patients age 18 years and older in the 2006 California Patient Discharge Data, overall discrimination was good (C statistic, 0.74), including in those age 18 to 64 years (C statistic, 0.75; 65 and above C statistic, 0.70).
| Development | Validation | Data Years | ||
|---|---|---|---|---|
| Indices | Sample, 2008 | Sample, 2008 | 2007 | 2009 |
| ||||
| Number of admissions | 150,035 | 149,646 | 259,911 | 279,377 |
| Number of hospitals | 4537 | 4535 | 4636 | 4571 |
| Mean risk‐standardized mortality rate, % (SD) | 8.62 (0.94) | 8.64 (1.07) | 8.97 (1.12) | 8.08 (1.09) |
| Calibration, 0, 1 | 0.034, 0.985 | 0.009, 1.004 | 0.095, 1.022 | 0.120, 0.981 |
| Discriminationpredictive ability, lowest decile %highest decile % | 1.5223.74 | 1.6023.78 | 1.5424.64 | 1.4222.36 |
| Discriminationarea under the ROC curve, C statistic | 0.720 | 0.723 | 0.728 | 0.722 |
| Residuals lack of fit, Pearson residual fall % | ||||
| 2 | 0 | 0 | 0 | 0 |
| 2, 0 | 91.14 | 91.4 | 91.08 | 91.93 |
| 0, 2 | 1.66 | 1.7 | 1.96 | 1.42 |
| 2+ | 6.93 | 6.91 | 6.96 | 6.65 |
| Model Wald 2 (number of covariates) | 6982.11 (42) | 7051.50 (42) | 13042.35 (42) | 12542.15 (42) |
| P value | <0.0001 | <0.0001 | <0.0001 | <0.0001 |
| Between‐hospital variance, (standard error) | 0.067 (0.008) | 0.078 (0.009) | 0.067 (0.006) | 0.072 (0.006) |
Reliability testing demonstrated consistent performance over several years. The frequency and ORs of the variables included in the model showed only minor changes over time. The area under the ROC curve (C statistic) was 0.73 for the model in the 2007 sample and 0.72 for the model using 2009 data (Table 3).
Hospital Risk‐Standardized Mortality Rates
The mean unadjusted hospital 30‐day mortality rate was 8.6% and ranged from 0% to 100% (Figure 2a). Risk‐standardized mortality rates varied across hospitals (Figure 2b). The mean risk‐standardized mortality rate was 8.6% and ranged from 5.9% to 13.5%. The odds of mortality at a hospital 1 standard deviation above average was 1.20 times that of a hospital 1 standard deviation below average.
DISCUSSION
We present a hospital‐level risk‐standardized mortality measure for patients admitted with COPD based on administrative claims data that are intended for public reporting and that have achieved endorsement by the National Quality Forum, a voluntary consensus standards‐setting organization. Across more than 4500 US hospitals, the mean 30‐day risk‐standardized mortality rate in 2008 was 8.6%, and we observed considerable variation across institutions, despite adjustment for case mix, suggesting that improvement by lower‐performing institutions may be an achievable goal.
Although improving the delivery of evidence‐based care processes and outcomes of patients with acute myocardial infarction, heart failure, and pneumonia has been the focus of national quality improvement efforts for more than a decade, COPD has largely been overlooked.[23] Within this context, this analysis represents the first attempt to systematically measure, at the hospital level, 30‐day all‐cause mortality for patients admitted to US hospitals for exacerbation of COPD. The model we have developed and validated is intended to be used to compare the performance of hospitals while controlling for differences in the pretreatment risk of mortality of patients and accounting for the clustering of patients within hospitals, and will facilitate surveillance of hospital‐level risk‐adjusted outcomes over time.
In contrast to process‐based measures of quality, such as the percentage of patients with pneumonia who receive appropriate antibiotic therapy, performance measures based on patient outcomes provide a more comprehensive view of care and are more consistent with patients' goals.[24] Additionally, it is well established that hospital performance on individual and composite process measures explains only a small amount of the observed variation in patient outcomes between institutions.[25] In this regard, outcome measures incorporate important, but difficult to measure aspects of care, such as diagnostic accuracy and timing, communication and teamwork, the recognition and response to complications, care coordination at the time of transfers between levels of care, and care settings. Nevertheless, when used for making inferences about the quality of hospital care, individual measures such as the risk‐standardized hospital mortality rate should be interpreted in the context of other performance measures, including readmission, patient experience, and costs of care.
A number of prior investigators have described the outcomes of care for patients hospitalized with exacerbations of COPD, including identifying risk factors for mortality. Patil et al. carried out an analysis of the 1996 Nationwide Inpatient Sample and described an overall in‐hospital mortality rate of 2.5% among patients with COPD, and reported that a multivariable model containing sociodemographic characteristics about the patient and comorbidities had an area under the ROC curve of 0.70.[3] In contrast, this hospital‐level measure includes patients with a principal diagnosis of respiratory failure and focuses on 30‐day rather than inpatient mortality, accounting for the nearly 3‐fold higher mortality rate we observed. In a more recent study that used clinical from a large multistate database, Tabak et al. developed a prediction model for inpatient mortality for patients with COPD that contained only 4 factors: age, blood urea nitrogen, mental status, and pulse, and achieved an area under the ROC curve of 0.72.[4] The simplicity of such a model and its reliance on clinical measurements makes it particularly well suited for bedside application by clinicians, but less valuable for large‐scale public reporting programs that rely on administrative data. In the only other study identified that focused on the assessment of hospital mortality rates, Agabiti et al. analyzed the outcomes of 12,756 patients hospitalized for exacerbations of COPD, using similar ICD‐9‐CM diagnostic criteria as in this study, at 21 hospitals in Rome, Italy.[26] They reported an average crude 30‐day mortality rate of 3.8% among a group of 5 benchmark hospitals and an average mortality of 7.5% (range, 5.2%17.2%) among the remaining institutions.
To put the variation we observed in mortality rates into a broader context, the relative difference in the risk‐standardized hospital mortality rates across the 10th to 90th percentiles of hospital performance was 25% for acute myocardial infarction and 39% for heart failure, whereas rates varied 30% for COPD, from 7.6% to 9.9%.[27] Model discrimination in COPD (C statistic, 0.72) was also similar to that reported for models used for public reporting of hospital mortality in acute myocardial infarction (C statistic, 0.71) and pneumonia (C statistic, 0.72).
This study has a number of important strengths. First, the model was developed from a large sample of recent Medicare claims, achieved good discrimination, and was validated in samples not limited to Medicare beneficiaries. Second, by including patients with a principal diagnosis of COPD, as well as those with a principal diagnosis of acute respiratory failure when accompanied by a secondary diagnosis of COPD with acute exacerbation, this model can be used to assess hospital performance across the full spectrum of disease severity. This broad set of ICD‐9‐CM codes used to define the cohort also ensures that efforts to measure hospital performance will be less influenced by differences in documentation and coding practices across hospitals relating to the diagnosis or sequencing of acute respiratory failure diagnoses. Moreover, the inclusion of patients with respiratory failure is important because these patients have the greatest risk of mortality, and are those in whom efforts to improve the quality and safety of care may have the greatest impact. Third, rather than relying solely on information documented during the index admission, we used ambulatory and inpatient claims from the full year prior to the index admission to identify comorbidities and to distinguish them from potential complications of care. Finally, we did not include factors such as hospital characteristics (eg, number of beds, teaching status) in the model. Although they might have improved overall predictive ability, the goal of the hospital mortality measure is to enable comparisons of mortality rates among hospitals while controlling for differences in patient characteristics. To the extent that factors such as size or teaching status might be independently associated with hospital outcomes, it would be inappropriate to adjust away their effects, because mortality risk should not be influenced by hospital characteristics other than through their effects on quality.
These results should be viewed in light of several limitations. First, we used ICD‐9‐CM codes derived from claims files to define the patient populations included in the measure rather than collecting clinical or physiologic information prospectively or through manual review of medical records, such as the forced expiratory volume in 1 second or whether the patient required long‐term oxygen therapy. Nevertheless, we included a broad set of potential diagnosis codes to capture the full spectrum of COPD exacerbations and to minimize differences in coding across hospitals. Second, because the risk‐adjustment included diagnoses coded in the year prior to the index admission, it is potentially subject to bias due to regional differences in medical care utilization that are not driven by underlying differences in patient illness.[28] Third, using administrative claims data, we observed some paradoxical associations in the model that are difficult to explain on clinical grounds, such as a protective effect of substance and alcohol abuse or prior episodes of respiratory failure. Fourth, although we excluded patients from the analysis who were enrolled in hospice prior to, or on the day of, the index admission, we did not exclude those who choose to withdraw support, transition to comfort measures only, or enrolled in hospice care during a hospitalization. We do not seek to penalize hospitals for being sensitive to the preferences of patients at the end of life. At the same time, it is equally important that the measure is capable of detecting the outcomes of suboptimal care that may in some instances lead a patient or their family to withdraw support or choose hospice. Finally, we did not have the opportunity to validate the model against a clinical registry of patients with COPD, because such data do not currently exist. Nevertheless, the use of claims as a surrogate for chart data for risk adjustment has been validated for several conditions, including acute myocardial infarction, heart failure, and pneumonia.[29, 30]
CONCLUSIONS
Risk‐standardized 30‐day mortality rates for Medicare beneficiaries with COPD vary across hospitals in the US. Calculating and reporting hospital outcomes using validated performance measures may catalyze quality improvement activities and lead to better outcomes. Additional research would be helpful to confirm that hospitals with lower mortality rates achieve care that meets the goals of patients and their families better than at hospitals with higher mortality rates.
Acknowledgment
The authors thank the following members of the technical expert panel: Darlene Bainbridge, RN, MS, NHA, CPHQ, CPHRM, President/CEO, Darlene D. Bainbridge & Associates, Inc.; Robert A. Balk, MD, Director of Pulmonary and Critical Care Medicine, Rush University Medical Center; Dale Bratzler, DO, MPH, President and CEO, Oklahoma Foundation for Medical Quality; Scott Cerreta, RRT, Director of Education, COPD Foundation; Gerard J. Criner, MD, Director of Temple Lung Center and Divisions of Pulmonary and Critical Care Medicine, Temple University; Guy D'Andrea, MBA, President, Discern Consulting; Jonathan Fine, MD, Director of Pulmonary Fellowship, Research and Medical Education, Norwalk Hospital; David Hopkins, MS, PhD, Senior Advisor, Pacific Business Group on Health; Fred Martin Jacobs, MD, JD, FACP, FCCP, FCLM, Executive Vice President and Director, Saint Barnabas Quality Institute; Natalie Napolitano, MPH, RRT‐NPS, Respiratory Therapist, Inova Fairfax Hospital; Russell Robbins, MD, MBA, Principal and Senior Clinical Consultant, Mercer. In addition, the authors acknowledge and thank Angela Merrill, Sandi Nelson, Marian Wrobel, and Eric Schone from Mathematica Policy Research, Inc., Sharon‐Lise T. Normand from Harvard Medical School, and Lein Han and Michael Rapp at The Centers for Medicare & Medicaid Services for their contributions to this work.
Disclosures
Peter K. Lindenauer, MD, MSc, is the guarantor of this article, taking responsibility for the integrity of the work as a whole, from inception to published article, and takes responsibility for the content of the manuscript, including the data and data analysis. All authors have made substantial contributions to the conception and design, or acquisition of data, or analysis and interpretation of data; have drafted the submitted article or revised it critically for important intellectual content; and have provided final approval of the version to be published. Preparation of this manuscript was completed under Contract Number: HHSM‐5002008‐0025I/HHSM‐500‐T0001, Modification No. 000007, Option Year 2 Measure Instrument Development and Support (MIDS). Sponsors did not contribute to the development of the research or manuscript. Dr. Au reports being an unpaid research consultant for Bosch Inc. He receives research funding from the NIH, Department of Veterans Affairs, AHRQ, and Gilead Sciences. The views of the this manuscript represent the authors and do not necessarily represent those of the Department of Veterans Affairs. Drs. Drye and Bernheim report receiving contract funding from CMS to develop and maintain quality measures.
Chronic obstructive pulmonary disease (COPD) affects as many as 24 million individuals in the United States, is responsible for more than 700,000 annual hospital admissions, and is currently the nation's third leading cause of death, accounting for nearly $49.9 billion in medical spending in 2010.[1, 2] Reported in‐hospital mortality rates for patients hospitalized for exacerbations of COPD range from 2% to 5%.[3, 4, 5, 6, 7] Information about 30‐day mortality rates following hospitalization for COPD is more limited; however, international studies suggest that rates range from 3% to 9%,[8, 9] and 90‐day mortality rates exceed 15%.[10]
Despite this significant clinical and economic impact, there have been no large‐scale, sustained efforts to measure the quality or outcomes of hospital care for patients with COPD in the United States. What little is known about the treatment of patients with COPD suggests widespread opportunities to increase adherence to guideline‐recommended therapies, to reduce the use of ineffective treatments and tests, and to address variation in care across institutions.[5, 11, 12]
Public reporting of hospital performance is a key strategy for improving the quality and safety of hospital care, both in the United States and internationally.[13] Since 2007, the Centers for Medicare and Medicaid Services (CMS) has reported hospital mortality rates on the Hospital Compare Web site, and COPD is 1 of the conditions highlighted in the Affordable Care Act for future consideration.[14] Such initiatives rely on validated, risk‐adjusted performance measures for comparisons across institutions and to enable outcomes to be tracked over time. We present the development, validation, and results of a model intended for public reporting of risk‐standardized mortality rates for patients hospitalized with exacerbations of COPD that has been endorsed by the National Quality Forum.[15]
METHODS
Approach to Measure Development
We developed this measure in accordance with guidelines described by the National Quality Forum,[16] CMS' Measure Management System,[17] and the American Heart Association scientific statement, Standards for Statistical Models Used for Public Reporting of Health Outcomes.[18] Throughout the process we obtained expert clinical and stakeholder input through meetings with a clinical advisory group and a national technical expert panel (see Acknowledgments). Last, we presented the proposed measure specifications and a summary of the technical expert panel discussions online and made a widely distributed call for public comments. We took the comments into consideration during the final stages of measure development (available at
Data Sources
We used claims data from Medicare inpatient, outpatient, and carrier (physician) Standard Analytic Files from 2008 to develop and validate the model, and examined model reliability using data from 2007 and 2009. The Medicare enrollment database was used to determine Medicare Fee‐for‐Service enrollment and mortality.
Study Cohort
Admissions were considered eligible for inclusion if the patient was 65 years or older, was admitted to a nonfederal acute care hospital in the United States, and had a principal diagnosis of COPD or a principal diagnosis of acute respiratory failure or respiratory arrest when paired with a secondary diagnosis of COPD with exacerbation (Table 1).
| ICD‐9‐CM | Description |
|---|---|
| |
| 491.21 | Obstructive chronic bronchitis; with (acute) exacerbation; acute exacerbation of COPD, decompensated COPD, decompensated COPD with exacerbation |
| 491.22 | Obstructive chronic bronchitis; with acute bronchitis |
| 491.8 | Other chronic bronchitis; chronic: tracheitis, tracheobronchitis |
| 491.9 | Unspecified chronic bronchitis |
| 492.8 | Other emphysema; emphysema (lung or pulmonary): NOS, centriacinar, centrilobular, obstructive, panacinar, panlobular, unilateral, vesicular; MacLeod's syndrome; Swyer‐James syndrome; unilateral hyperlucent lung |
| 493.20 | Chronic obstructive asthma; asthma with COPD, chronic asthmatic bronchitis, unspecified |
| 493.21 | Chronic obstructive asthma; asthma with COPD, chronic asthmatic bronchitis, with status asthmaticus |
| 493.22 | Chronic obstructive asthma; asthma with COPD, chronic asthmatic bronchitis, with (acute) exacerbation |
| 496 | Chronic: nonspecific lung disease, obstructive lung disease, obstructive pulmonary disease (COPD) NOS. (Note: This code is not to be used with any code from categories 491493.) |
| 518.81a | Other diseases of lung; acute respiratory failure; respiratory failure NOS |
| 518.82a | Other diseases of lung; acute respiratory failure; other pulmonary insufficiency, acute respiratory distress |
| 518.84a | Other diseases of lung; acute respiratory failure; acute and chronic respiratory failure |
| 799.1a | Other ill‐defined and unknown causes of morbidity and mortality; respiratory arrest, cardiorespiratory failure |
If a patient was discharged and readmitted to a second hospital on the same or the next day, we combined the 2 acute care admissions into a single episode of care and assigned the mortality outcome to the first admitting hospital. We excluded admissions for patients who were enrolled in Medicare Hospice in the 12 months prior to or on the first day of the index hospitalization. An index admission was any eligible admission assessed in the measure for the outcome. We also excluded admissions for patients who were discharged against medical advice, those for whom vital status at 30 days was unknown or recorded inconsistently, and patients with unreliable data (eg, age >115 years). For patients with multiple hospitalizations during a single year, we randomly selected 1 admission per patient to avoid survival bias. Finally, to assure adequate risk adjustment we limited the analysis to patients who had continuous enrollment in Medicare Fee‐for‐Service Parts A and B for the 12 months prior to their index admission so that we could identify comorbid conditions coded during all prior encounters.
Outcomes
The outcome of 30‐day mortality was defined as death from any cause within 30 days of the admission date for the index hospitalization. Mortality was assessed at 30 days to standardize the period of outcome ascertainment,[19] and because 30 days is a clinically meaningful time frame, during which differences in the quality of hospital care may be revealed.
Risk‐Adjustment Variables
We randomly selected half of all COPD admissions in 2008 that met the inclusion and exclusion criteria to create a model development sample. Candidate variables for inclusion in the risk‐standardized model were selected by a clinician team from diagnostic groups included in the Hierarchical Condition Category clinical classification system[20] and included age and comorbid conditions. Sleep apnea (International Classification of Diseases, 9th Revision, Clinical Modification [ICD‐9‐CM] condition codes 327.20, 327.21, 327.23, 327.27, 327.29, 780.51, 780.53, and 780.57) and mechanical ventilation (ICD‐9‐CM procedure codes 93.90, 96.70, 96.71, and 96.72) were also included as candidate variables.
We defined a condition as present for a given patient if it was coded in the inpatient, outpatient, or physician claims data sources in the preceding 12 months, including the index admission. Because a subset of the condition category variables can represent a complication of care, we did not consider them to be risk factors if they appeared only as secondary diagnosis codes for the index admission and not in claims submitted during the prior year.
We selected final variables for inclusion in the risk‐standardized model based on clinical considerations and a modified approach to stepwise logistic regression. The final patient‐level risk‐adjustment model included 42 variables (Table 2).
| Variable | Development Sample (150,035 Admissions at 4537 Hospitals) | Validation Sample (149,646 Admissions at 4535 Hospitals) | ||||
|---|---|---|---|---|---|---|
| Frequency, % | OR | 95% CI | Frequency, % | OR | 95% CI | |
| ||||||
| Demographics | ||||||
| Age 65 years (continuous) | 1.03 | 1.03‐1.04 | 1.03 | 1.03‐1.04 | ||
| Cardiovascular/respiratory | ||||||
| Sleep apnea (ICD‐9‐CM: 327.20, 327.21, 327.23, 327.27, 327.29, 780.51, 780.53, 780.57)a | 9.57 | 0.87 | 0.81‐0.94 | 9.72 | 0.84 | 0.78‐0.90 |
| History of mechanical ventilation (ICD‐9‐CM: 93.90, 96.70, 96.71, 96.72)a | 6.00 | 1.19 | 1.11‐1.27 | 6.00 | 1.15 | 1.08‐1.24 |
| Respirator dependence/respiratory failure (CC 7778)a | 1.15 | 0.89 | 0.77‐1.02 | 1.20 | 0.78 | 0.68‐0.91 |
| Cardiorespiratory failure and shock (CC 79) | 26.35 | 1.60 | 1.53‐1.68 | 26.34 | 1.59 | 1.52‐1.66 |
| Congestive heart failure (CC 80) | 41.50 | 1.34 | 1.28‐1.39 | 41.39 | 1.31 | 1.25‐1.36 |
| Chronic atherosclerosis (CC 8384)a | 50.44 | 0.87 | 0.83‐0.90 | 50.12 | 0.91 | 0.87‐0.94 |
| Arrhythmias (CC 9293) | 37.15 | 1.17 | 1.12‐1.22 | 37.06 | 1.15 | 1.10‐1.20 |
| Vascular or circulatory disease (CC 104106) | 38.20 | 1.09 | 1.05‐1.14 | 38.09 | 1.02 | 0.98‐1.06 |
| Fibrosis of lung and other chronic lung disorder (CC 109) | 16.96 | 1.08 | 1.03‐1.13 | 17.08 | 1.11 | 1.06‐1.17 |
| Asthma (CC 110) | 17.05 | 0.67 | 0.63‐0.70 | 16.90 | 0.67 | 0.63‐0.70 |
| Pneumonia (CC 111113) | 49.46 | 1.29 | 1.24‐1.35 | 49.41 | 1.27 | 1.22‐1.33 |
| Pleural effusion/pneumothorax (CC 114) | 11.78 | 1.17 | 1.11‐1.23 | 11.54 | 1.18 | 1.12‐1.25 |
| Other lung disorders (CC 115) | 53.07 | 0.80 | 0.77‐0.83 | 53.17 | 0.83 | 0.80‐0.87 |
| Other comorbid conditions | ||||||
| Metastatic cancer and acute leukemia (CC 7) | 2.76 | 2.34 | 2.14‐2.56 | 2.79 | 2.15 | 1.97‐2.35 |
| Lung, upper digestive tract, and other severe cancers (CC 8)a | 5.98 | 1.80 | 1.68‐1.92 | 6.02 | 1.98 | 1.85‐2.11 |
| Lymphatic, head and neck, brain, and other major cancers; breast, prostate, colorectal and other cancers and tumors; other respiratory and heart neoplasms (CC 911) | 14.13 | 1.03 | 0.97‐1.08 | 14.19 | 1.01 | 0.95‐1.06 |
| Other digestive and urinary neoplasms (CC 12) | 6.91 | 0.91 | 0.84‐0.98 | 7.05 | 0.85 | 0.79‐0.92 |
| Diabetes and DM complications (CC 1520, 119120) | 38.31 | 0.91 | 0.87‐0.94 | 38.29 | 0.91 | 0.87‐0.94 |
| Protein‐calorie malnutrition (CC 21) | 7.40 | 2.18 | 2.07‐2.30 | 7.44 | 2.09 | 1.98‐2.20 |
| Disorders of fluid/electrolyte/acid‐base (CC 2223) | 32.05 | 1.13 | 1.08‐1.18 | 32.16 | 1.24 | 1.19‐1.30 |
| Other endocrine/metabolic/nutritional disorders (CC 24) | 67.99 | 0.75 | 0.72‐0.78 | 67.88 | 0.76 | 0.73‐0.79 |
| Other gastrointestinal disorders (CC 36) | 56.21 | 0.81 | 0.78‐0.84 | 56.18 | 0.78 | 0.75‐0.81 |
| Osteoarthritis of hip or knee (CC 40) | 9.32 | 0.74 | 0.69‐0.79 | 9.33 | 0.80 | 0.74‐0.85 |
| Other musculoskeletal and connective tissue disorders (CC 43) | 64.14 | 0.83 | 0.80‐0.86 | 64.20 | 0.83 | 0.80‐0.87 |
| Iron deficiency and other/unspecified anemias and blood disease (CC 47) | 40.80 | 1.08 | 1.04‐1.12 | 40.72 | 1.08 | 1.04‐1.13 |
| Dementia and senility (CC 4950) | 17.06 | 1.09 | 1.04‐1.14 | 16.97 | 1.09 | 1.04‐1.15 |
| Drug/alcohol abuse, without dependence (CC 53)a | 23.51 | 0.78 | 0.75‐0.82 | 23.38 | 0.76 | 0.72‐0.80 |
| Other psychiatric disorders (CC 60)a | 16.49 | 1.12 | 1.07‐1.18 | 16.43 | 1.12 | 1.06‐1.17 |
| Quadriplegia, paraplegia, functional disability (CC 6769, 100102, 177178) | 4.92 | 1.03 | 0.95‐1.12 | 4.92 | 1.08 | 0.99‐1.17 |
| Mononeuropathy, other neurological conditions/emnjuries (CC 76) | 11.35 | 0.85 | 0.80‐0.91 | 11.28 | 0.88 | 0.83‐0.93 |
| Hypertension and hypertensive disease (CC 9091) | 80.40 | 0.78 | 0.75‐0.82 | 80.35 | 0.79 | 0.75‐0.83 |
| Stroke (CC 9596)a | 6.77 | 1.00 | 0.93‐1.08 | 6.73 | 0.98 | 0.91‐1.05 |
| Retinal disorders, except detachment and vascular retinopathies (CC 121) | 10.79 | 0.87 | 0.82‐0.93 | 10.69 | 0.90 | 0.85‐0.96 |
| Other eye disorders (CC 124)a | 19.05 | 0.90 | 0.86‐0.95 | 19.13 | 0.98 | 0.85‐0.93 |
| Other ear, nose, throat, and mouth disorders (CC 127) | 35.21 | 0.83 | 0.80‐0.87 | 35.02 | 0.80 | 0.77‐0.83 |
| Renal failure (CC 131)a | 17.92 | 1.12 | 1.07‐1.18 | 18.16 | 1.13 | 1.08‐1.19 |
| Decubitus ulcer or chronic skin ulcer (CC 148149) | 7.42 | 1.27 | 1.19‐1.35 | 7.42 | 1.33 | 1.25‐1.42 |
| Other dermatological disorders (CC 153) | 28.46 | 0.90 | 0.87‐0.94 | 28.32 | 0.89 | 0.86‐0.93 |
| Trauma (CC 154156, 158161) | 9.04 | 1.09 | 1.03‐1.16 | 8.99 | 1.15 | 1.08‐1.22 |
| Vertebral fractures (CC 157) | 5.01 | 1.33 | 1.24‐1.44 | 4.97 | 1.29 | 1.20‐1.39 |
| Major complications of medical care and trauma (CC 164) | 5.47 | 0.81 | 0.75‐0.88 | 5.55 | 0.82 | 0.76‐0.89 |
Model Derivation
We used hierarchical logistic regression models to model the log‐odds of mortality as a function of patient‐level clinical characteristics and a random hospital‐level intercept. At the patient level, each model adjusts the log‐odds of mortality for age and the selected clinical covariates. The second level models the hospital‐specific intercepts as arising from a normal distribution. The hospital intercept represents the underlying risk of mortality, after accounting for patient risk. If there were no differences among hospitals, then after adjusting for patient risk, the hospital intercepts should be identical across all hospitals.
Estimation of Hospital Risk‐Standardized Mortality Rate
We calculated a risk‐standardized mortality rate, defined as the ratio of predicted to expected deaths (similar to observed‐to‐expected), multiplied by the national unadjusted mortality rate.[21] The expected number of deaths for each hospital was estimated by applying the estimated regression coefficients to the characteristics of each hospital's patients, adding the average of the hospital‐specific intercepts, transforming the data by using an inverse logit function, and summing the data from all patients in the hospital to obtain the count. The predicted number of deaths was calculated in the same way, substituting the hospital‐specific intercept for the average hospital‐specific intercept.
Model Performance, Validation, and Reliability Testing
We used the remaining admissions in 2008 as the model validation sample. We computed several summary statistics to assess the patient‐level model performance in both the development and validation samples,[22] including over‐fitting indices, predictive ability, area under the receiver operating characteristic (ROC) curve, distribution of residuals, and model 2. In addition, we assessed face validity through a survey of members of the technical expert panel. To assess reliability of the model across data years, we repeated the modeling process using qualifying COPD admissions in both 2007 and 2009. Finally, to assess generalizability we evaluated the model's performance in an all‐payer sample of data from patients admitted to California hospitals in 2006.
Analyses were conducted using SAS version 9.1.3 (SAS Institute Inc., Cary, NC). We estimated the hierarchical models using the GLIMMIX procedure in SAS.
The Human Investigation Committee at the Yale University School of Medicine/Yale New Haven Hospital approved an exemption (HIC#0903004927) for the authors to use CMS claims and enrollment data for research analyses and publication.
RESULTS
Model Derivation
After exclusions were applied, the development sample included 150,035 admissions in 2008 at 4537 US hospitals (Figure 1). Factors that were most strongly associated with the risk of mortality included metastatic cancer (odds ratio [OR] 2.34), protein calorie malnutrition (OR 2.18), nonmetastatic cancers of the lung and upper digestive tract, (OR 1.80) cardiorespiratory failure and shock (OR 1.60), and congestive heart failure (OR 1.34) (Table 2).
Model Performance, Validation, and Reliability
The model had a C statistic of 0.72, indicating good discrimination, and predicted mortality in the development sample ranged from 1.52% in the lowest decile to 23.74% in the highest. The model validation sample, using the remaining cases from 2008, included 149,646 admissions from 4535 hospitals. Variable frequencies and ORs were similar in both samples (Table 2). Model performance was also similar in the validation samples, with good model discrimination and fit (Table 3). Ten of 12 technical expert panel members responded to the survey, of whom 90% at least somewhat agreed with the statement, the COPD mortality measure provides an accurate reflection of quality. When the model was applied to patients age 18 years and older in the 2006 California Patient Discharge Data, overall discrimination was good (C statistic, 0.74), including in those age 18 to 64 years (C statistic, 0.75; 65 and above C statistic, 0.70).
| Development | Validation | Data Years | ||
|---|---|---|---|---|
| Indices | Sample, 2008 | Sample, 2008 | 2007 | 2009 |
| ||||
| Number of admissions | 150,035 | 149,646 | 259,911 | 279,377 |
| Number of hospitals | 4537 | 4535 | 4636 | 4571 |
| Mean risk‐standardized mortality rate, % (SD) | 8.62 (0.94) | 8.64 (1.07) | 8.97 (1.12) | 8.08 (1.09) |
| Calibration, 0, 1 | 0.034, 0.985 | 0.009, 1.004 | 0.095, 1.022 | 0.120, 0.981 |
| Discriminationpredictive ability, lowest decile %highest decile % | 1.5223.74 | 1.6023.78 | 1.5424.64 | 1.4222.36 |
| Discriminationarea under the ROC curve, C statistic | 0.720 | 0.723 | 0.728 | 0.722 |
| Residuals lack of fit, Pearson residual fall % | ||||
| 2 | 0 | 0 | 0 | 0 |
| 2, 0 | 91.14 | 91.4 | 91.08 | 91.93 |
| 0, 2 | 1.66 | 1.7 | 1.96 | 1.42 |
| 2+ | 6.93 | 6.91 | 6.96 | 6.65 |
| Model Wald 2 (number of covariates) | 6982.11 (42) | 7051.50 (42) | 13042.35 (42) | 12542.15 (42) |
| P value | <0.0001 | <0.0001 | <0.0001 | <0.0001 |
| Between‐hospital variance, (standard error) | 0.067 (0.008) | 0.078 (0.009) | 0.067 (0.006) | 0.072 (0.006) |
Reliability testing demonstrated consistent performance over several years. The frequency and ORs of the variables included in the model showed only minor changes over time. The area under the ROC curve (C statistic) was 0.73 for the model in the 2007 sample and 0.72 for the model using 2009 data (Table 3).
Hospital Risk‐Standardized Mortality Rates
The mean unadjusted hospital 30‐day mortality rate was 8.6% and ranged from 0% to 100% (Figure 2a). Risk‐standardized mortality rates varied across hospitals (Figure 2b). The mean risk‐standardized mortality rate was 8.6% and ranged from 5.9% to 13.5%. The odds of mortality at a hospital 1 standard deviation above average was 1.20 times that of a hospital 1 standard deviation below average.
DISCUSSION
We present a hospital‐level risk‐standardized mortality measure for patients admitted with COPD based on administrative claims data that are intended for public reporting and that have achieved endorsement by the National Quality Forum, a voluntary consensus standards‐setting organization. Across more than 4500 US hospitals, the mean 30‐day risk‐standardized mortality rate in 2008 was 8.6%, and we observed considerable variation across institutions, despite adjustment for case mix, suggesting that improvement by lower‐performing institutions may be an achievable goal.
Although improving the delivery of evidence‐based care processes and outcomes of patients with acute myocardial infarction, heart failure, and pneumonia has been the focus of national quality improvement efforts for more than a decade, COPD has largely been overlooked.[23] Within this context, this analysis represents the first attempt to systematically measure, at the hospital level, 30‐day all‐cause mortality for patients admitted to US hospitals for exacerbation of COPD. The model we have developed and validated is intended to be used to compare the performance of hospitals while controlling for differences in the pretreatment risk of mortality of patients and accounting for the clustering of patients within hospitals, and will facilitate surveillance of hospital‐level risk‐adjusted outcomes over time.
In contrast to process‐based measures of quality, such as the percentage of patients with pneumonia who receive appropriate antibiotic therapy, performance measures based on patient outcomes provide a more comprehensive view of care and are more consistent with patients' goals.[24] Additionally, it is well established that hospital performance on individual and composite process measures explains only a small amount of the observed variation in patient outcomes between institutions.[25] In this regard, outcome measures incorporate important, but difficult to measure aspects of care, such as diagnostic accuracy and timing, communication and teamwork, the recognition and response to complications, care coordination at the time of transfers between levels of care, and care settings. Nevertheless, when used for making inferences about the quality of hospital care, individual measures such as the risk‐standardized hospital mortality rate should be interpreted in the context of other performance measures, including readmission, patient experience, and costs of care.
A number of prior investigators have described the outcomes of care for patients hospitalized with exacerbations of COPD, including identifying risk factors for mortality. Patil et al. carried out an analysis of the 1996 Nationwide Inpatient Sample and described an overall in‐hospital mortality rate of 2.5% among patients with COPD, and reported that a multivariable model containing sociodemographic characteristics about the patient and comorbidities had an area under the ROC curve of 0.70.[3] In contrast, this hospital‐level measure includes patients with a principal diagnosis of respiratory failure and focuses on 30‐day rather than inpatient mortality, accounting for the nearly 3‐fold higher mortality rate we observed. In a more recent study that used clinical from a large multistate database, Tabak et al. developed a prediction model for inpatient mortality for patients with COPD that contained only 4 factors: age, blood urea nitrogen, mental status, and pulse, and achieved an area under the ROC curve of 0.72.[4] The simplicity of such a model and its reliance on clinical measurements makes it particularly well suited for bedside application by clinicians, but less valuable for large‐scale public reporting programs that rely on administrative data. In the only other study identified that focused on the assessment of hospital mortality rates, Agabiti et al. analyzed the outcomes of 12,756 patients hospitalized for exacerbations of COPD, using similar ICD‐9‐CM diagnostic criteria as in this study, at 21 hospitals in Rome, Italy.[26] They reported an average crude 30‐day mortality rate of 3.8% among a group of 5 benchmark hospitals and an average mortality of 7.5% (range, 5.2%17.2%) among the remaining institutions.
To put the variation we observed in mortality rates into a broader context, the relative difference in the risk‐standardized hospital mortality rates across the 10th to 90th percentiles of hospital performance was 25% for acute myocardial infarction and 39% for heart failure, whereas rates varied 30% for COPD, from 7.6% to 9.9%.[27] Model discrimination in COPD (C statistic, 0.72) was also similar to that reported for models used for public reporting of hospital mortality in acute myocardial infarction (C statistic, 0.71) and pneumonia (C statistic, 0.72).
This study has a number of important strengths. First, the model was developed from a large sample of recent Medicare claims, achieved good discrimination, and was validated in samples not limited to Medicare beneficiaries. Second, by including patients with a principal diagnosis of COPD, as well as those with a principal diagnosis of acute respiratory failure when accompanied by a secondary diagnosis of COPD with acute exacerbation, this model can be used to assess hospital performance across the full spectrum of disease severity. This broad set of ICD‐9‐CM codes used to define the cohort also ensures that efforts to measure hospital performance will be less influenced by differences in documentation and coding practices across hospitals relating to the diagnosis or sequencing of acute respiratory failure diagnoses. Moreover, the inclusion of patients with respiratory failure is important because these patients have the greatest risk of mortality, and are those in whom efforts to improve the quality and safety of care may have the greatest impact. Third, rather than relying solely on information documented during the index admission, we used ambulatory and inpatient claims from the full year prior to the index admission to identify comorbidities and to distinguish them from potential complications of care. Finally, we did not include factors such as hospital characteristics (eg, number of beds, teaching status) in the model. Although they might have improved overall predictive ability, the goal of the hospital mortality measure is to enable comparisons of mortality rates among hospitals while controlling for differences in patient characteristics. To the extent that factors such as size or teaching status might be independently associated with hospital outcomes, it would be inappropriate to adjust away their effects, because mortality risk should not be influenced by hospital characteristics other than through their effects on quality.
These results should be viewed in light of several limitations. First, we used ICD‐9‐CM codes derived from claims files to define the patient populations included in the measure rather than collecting clinical or physiologic information prospectively or through manual review of medical records, such as the forced expiratory volume in 1 second or whether the patient required long‐term oxygen therapy. Nevertheless, we included a broad set of potential diagnosis codes to capture the full spectrum of COPD exacerbations and to minimize differences in coding across hospitals. Second, because the risk‐adjustment included diagnoses coded in the year prior to the index admission, it is potentially subject to bias due to regional differences in medical care utilization that are not driven by underlying differences in patient illness.[28] Third, using administrative claims data, we observed some paradoxical associations in the model that are difficult to explain on clinical grounds, such as a protective effect of substance and alcohol abuse or prior episodes of respiratory failure. Fourth, although we excluded patients from the analysis who were enrolled in hospice prior to, or on the day of, the index admission, we did not exclude those who choose to withdraw support, transition to comfort measures only, or enrolled in hospice care during a hospitalization. We do not seek to penalize hospitals for being sensitive to the preferences of patients at the end of life. At the same time, it is equally important that the measure is capable of detecting the outcomes of suboptimal care that may in some instances lead a patient or their family to withdraw support or choose hospice. Finally, we did not have the opportunity to validate the model against a clinical registry of patients with COPD, because such data do not currently exist. Nevertheless, the use of claims as a surrogate for chart data for risk adjustment has been validated for several conditions, including acute myocardial infarction, heart failure, and pneumonia.[29, 30]
CONCLUSIONS
Risk‐standardized 30‐day mortality rates for Medicare beneficiaries with COPD vary across hospitals in the US. Calculating and reporting hospital outcomes using validated performance measures may catalyze quality improvement activities and lead to better outcomes. Additional research would be helpful to confirm that hospitals with lower mortality rates achieve care that meets the goals of patients and their families better than at hospitals with higher mortality rates.
Acknowledgment
The authors thank the following members of the technical expert panel: Darlene Bainbridge, RN, MS, NHA, CPHQ, CPHRM, President/CEO, Darlene D. Bainbridge & Associates, Inc.; Robert A. Balk, MD, Director of Pulmonary and Critical Care Medicine, Rush University Medical Center; Dale Bratzler, DO, MPH, President and CEO, Oklahoma Foundation for Medical Quality; Scott Cerreta, RRT, Director of Education, COPD Foundation; Gerard J. Criner, MD, Director of Temple Lung Center and Divisions of Pulmonary and Critical Care Medicine, Temple University; Guy D'Andrea, MBA, President, Discern Consulting; Jonathan Fine, MD, Director of Pulmonary Fellowship, Research and Medical Education, Norwalk Hospital; David Hopkins, MS, PhD, Senior Advisor, Pacific Business Group on Health; Fred Martin Jacobs, MD, JD, FACP, FCCP, FCLM, Executive Vice President and Director, Saint Barnabas Quality Institute; Natalie Napolitano, MPH, RRT‐NPS, Respiratory Therapist, Inova Fairfax Hospital; Russell Robbins, MD, MBA, Principal and Senior Clinical Consultant, Mercer. In addition, the authors acknowledge and thank Angela Merrill, Sandi Nelson, Marian Wrobel, and Eric Schone from Mathematica Policy Research, Inc., Sharon‐Lise T. Normand from Harvard Medical School, and Lein Han and Michael Rapp at The Centers for Medicare & Medicaid Services for their contributions to this work.
Disclosures
Peter K. Lindenauer, MD, MSc, is the guarantor of this article, taking responsibility for the integrity of the work as a whole, from inception to published article, and takes responsibility for the content of the manuscript, including the data and data analysis. All authors have made substantial contributions to the conception and design, or acquisition of data, or analysis and interpretation of data; have drafted the submitted article or revised it critically for important intellectual content; and have provided final approval of the version to be published. Preparation of this manuscript was completed under Contract Number: HHSM‐5002008‐0025I/HHSM‐500‐T0001, Modification No. 000007, Option Year 2 Measure Instrument Development and Support (MIDS). Sponsors did not contribute to the development of the research or manuscript. Dr. Au reports being an unpaid research consultant for Bosch Inc. He receives research funding from the NIH, Department of Veterans Affairs, AHRQ, and Gilead Sciences. The views of the this manuscript represent the authors and do not necessarily represent those of the Department of Veterans Affairs. Drs. Drye and Bernheim report receiving contract funding from CMS to develop and maintain quality measures.
- FASTSTATS—chronic lower respiratory disease. Available at: http://www.cdc.gov/nchs/fastats/copd.htm. Accessed September 18, 2010.
- National Heart, Lung and Blood Institute. Morbidity and mortality chartbook. Available at: http://www.nhlbi.nih.gov/resources/docs/cht‐book.htm. Accessed April 27, 2010.
- , , , . In‐hospital mortality following acute exacerbations of chronic obstructive pulmonary disease. Arch Intern Med. 2003;163(10):1180–1186.
- , , , , . Mortality and need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: development and validation of a simple risk score. Arch Intern Med. 2009;169(17):1595–1602.
- , , , , , . Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease. Ann Intern Med. 2006;144(12):894–903.
- , , , , . Use of beta blockers and the risk of death in hospitalised patients with acute exacerbations of COPD. Thorax. 2008;63(4):301–305.
- , , , , . HCUP facts and figures: statistics on hospital‐based care in the United States, 2007. 2009. Available at: http://www.hcup‐us.ahrq.gov/reports.jsp. Accessed August 6, 2012.
- , . Predictors of long‐term survival in elderly patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease. Respirology. 2008;13(6):851–855.
- , , , , , . The impact on risk‐factor analysis of different mortality outcomes in COPD patients. Eur Respir J 2008;32(3):629–636.
- , , , , , . Clinical audit indicators of outcome following admission to hospital with acute exacerbation of chronic obstructive pulmonary disease. Thorax. 2002;57(2):137–141.
- , , , et al. The quality of obstructive lung disease care for adults in the United States as measured by adherence to recommended processes. Chest. 2006;130(6):1844–1850.
- , , , , , . Management of acute exacerbations of chronic obstructive pulmonary disease in the elderly: physician practices in the community hospital setting. J Okla State Med Assoc. 2004;97(6):227–232.
- , , . Leadership by Example: Coordinating Government Roles in Improving Health Care Quality. Washington, DC: National Academies Press; 2002.
- Patient Protection and Affordable Care Act [H.R. 3590], Pub. L. No. 111–148, §2702, 124 Stat. 119, 318–319 (March 23, 2010). Available at: http://www.gpo.gov/fdsys/pkg/PLAW‐111publ148/html/PLAW‐111publ148.htm. Accessed July 15, 2012.
- National Quality Forum. NQF Endorses Additional Pulmonary Measure. 2013. Available at: http://www.qualityforum.org/News_And_Resources/Press_Releases/2013/NQF_Endorses_Additional_Pulmonary_Measure.aspx. Accessed January 11, 2013.
- National Quality Forum. National voluntary consensus standards for patient outcomes: a consensus report. Washington, DC: National Quality Forum; 2011.
- The Measures Management System. The Centers for Medicare and Medicaid Services. Available at: http://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/MMS/index.html?redirect=/MMS/. Accessed August 6, 2012.
- , , , et al. Standards for statistical models used for public reporting of health outcomes: an American Heart Association Scientific Statement from the Quality of Care and Outcomes Research Interdisciplinary Writing Group: cosponsored by the Council on Epidemiology and Prevention and the Stroke Council. Endorsed by the American College of Cardiology Foundation. Circulation. 2006;113(3):456–462.
- , , , et al. Comparison of hospital risk‐standardized mortality rates calculated by using in‐hospital and 30‐day models: an observational study with implications for hospital profiling. Ann Intern Med. 2012;156(1 pt 1):19–26.
- , , , et al. Diagnostic cost group hierarchical condition category models for Medicare risk adjustment. Report prepared for the Health Care Financing Administration. Health Economics Research, Inc.; 2000. Available at: http://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/Reports/downloads/pope_2000_2.pdf. Accessed November 7, 2009.
- , . Statistical and clinical aspects of hospital outcomes profiling. Stat Sci. 2007;22(2):206–226.
- , . Using full probability models to compute probabilities of actual interest to decision makers. Int J Technol Assess Health Care. 2001;17(1):17–26.
- , , . COPD performance measures: missing opportunities for improving care. Chest. 2010;137(5):1181–1189.
- , , , , . Measuring Performance For Treating Heart Attacks And Heart Failure: The Case For Outcomes Measurement. Health Aff. 2007;26(1):75–85.
- , , , et al. Hospital quality for acute myocardial infarction: correlation among process measures and relationship with short‐term mortality. JAMA. 2006;296(1):72–78.
- , , , et al. Profiling hospital performance to monitor the quality of care: the case of COPD. Eur Respir J. 2010;35(5):1031–1038.
- , , , et al. Patterns of hospital performance in acute myocardial infarction and heart failure 30‐day mortality and readmission. Circ Cardiovasc Qual Outcomes. 2009;2(5):407–413.
- , , , , . Geographic variation in diagnosis frequency and risk of death among Medicare beneficiaries. JAMA. 2011;305(11):1113–1118.
- , , , et al. An administrative claims model for profiling hospital 30‐day mortality rates for pneumonia patients. PLoS ONE. 2011;6(4):e17401.
- , , , et al. An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30‐Day Mortality Rates Among Patients With Heart Failure. Circulation. 2006;113(13):1693–1701.
- FASTSTATS—chronic lower respiratory disease. Available at: http://www.cdc.gov/nchs/fastats/copd.htm. Accessed September 18, 2010.
- National Heart, Lung and Blood Institute. Morbidity and mortality chartbook. Available at: http://www.nhlbi.nih.gov/resources/docs/cht‐book.htm. Accessed April 27, 2010.
- , , , . In‐hospital mortality following acute exacerbations of chronic obstructive pulmonary disease. Arch Intern Med. 2003;163(10):1180–1186.
- , , , , . Mortality and need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: development and validation of a simple risk score. Arch Intern Med. 2009;169(17):1595–1602.
- , , , , , . Quality of care for patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease. Ann Intern Med. 2006;144(12):894–903.
- , , , , . Use of beta blockers and the risk of death in hospitalised patients with acute exacerbations of COPD. Thorax. 2008;63(4):301–305.
- , , , , . HCUP facts and figures: statistics on hospital‐based care in the United States, 2007. 2009. Available at: http://www.hcup‐us.ahrq.gov/reports.jsp. Accessed August 6, 2012.
- , . Predictors of long‐term survival in elderly patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease. Respirology. 2008;13(6):851–855.
- , , , , , . The impact on risk‐factor analysis of different mortality outcomes in COPD patients. Eur Respir J 2008;32(3):629–636.
- , , , , , . Clinical audit indicators of outcome following admission to hospital with acute exacerbation of chronic obstructive pulmonary disease. Thorax. 2002;57(2):137–141.
- , , , et al. The quality of obstructive lung disease care for adults in the United States as measured by adherence to recommended processes. Chest. 2006;130(6):1844–1850.
- , , , , , . Management of acute exacerbations of chronic obstructive pulmonary disease in the elderly: physician practices in the community hospital setting. J Okla State Med Assoc. 2004;97(6):227–232.
- , , . Leadership by Example: Coordinating Government Roles in Improving Health Care Quality. Washington, DC: National Academies Press; 2002.
- Patient Protection and Affordable Care Act [H.R. 3590], Pub. L. No. 111–148, §2702, 124 Stat. 119, 318–319 (March 23, 2010). Available at: http://www.gpo.gov/fdsys/pkg/PLAW‐111publ148/html/PLAW‐111publ148.htm. Accessed July 15, 2012.
- National Quality Forum. NQF Endorses Additional Pulmonary Measure. 2013. Available at: http://www.qualityforum.org/News_And_Resources/Press_Releases/2013/NQF_Endorses_Additional_Pulmonary_Measure.aspx. Accessed January 11, 2013.
- National Quality Forum. National voluntary consensus standards for patient outcomes: a consensus report. Washington, DC: National Quality Forum; 2011.
- The Measures Management System. The Centers for Medicare and Medicaid Services. Available at: http://www.cms.gov/Medicare/Quality‐Initiatives‐Patient‐Assessment‐Instruments/MMS/index.html?redirect=/MMS/. Accessed August 6, 2012.
- , , , et al. Standards for statistical models used for public reporting of health outcomes: an American Heart Association Scientific Statement from the Quality of Care and Outcomes Research Interdisciplinary Writing Group: cosponsored by the Council on Epidemiology and Prevention and the Stroke Council. Endorsed by the American College of Cardiology Foundation. Circulation. 2006;113(3):456–462.
- , , , et al. Comparison of hospital risk‐standardized mortality rates calculated by using in‐hospital and 30‐day models: an observational study with implications for hospital profiling. Ann Intern Med. 2012;156(1 pt 1):19–26.
- , , , et al. Diagnostic cost group hierarchical condition category models for Medicare risk adjustment. Report prepared for the Health Care Financing Administration. Health Economics Research, Inc.; 2000. Available at: http://www.cms.gov/Research‐Statistics‐Data‐and‐Systems/Statistics‐Trends‐and‐Reports/Reports/downloads/pope_2000_2.pdf. Accessed November 7, 2009.
- , . Statistical and clinical aspects of hospital outcomes profiling. Stat Sci. 2007;22(2):206–226.
- , . Using full probability models to compute probabilities of actual interest to decision makers. Int J Technol Assess Health Care. 2001;17(1):17–26.
- , , . COPD performance measures: missing opportunities for improving care. Chest. 2010;137(5):1181–1189.
- , , , , . Measuring Performance For Treating Heart Attacks And Heart Failure: The Case For Outcomes Measurement. Health Aff. 2007;26(1):75–85.
- , , , et al. Hospital quality for acute myocardial infarction: correlation among process measures and relationship with short‐term mortality. JAMA. 2006;296(1):72–78.
- , , , et al. Profiling hospital performance to monitor the quality of care: the case of COPD. Eur Respir J. 2010;35(5):1031–1038.
- , , , et al. Patterns of hospital performance in acute myocardial infarction and heart failure 30‐day mortality and readmission. Circ Cardiovasc Qual Outcomes. 2009;2(5):407–413.
- , , , , . Geographic variation in diagnosis frequency and risk of death among Medicare beneficiaries. JAMA. 2011;305(11):1113–1118.
- , , , et al. An administrative claims model for profiling hospital 30‐day mortality rates for pneumonia patients. PLoS ONE. 2011;6(4):e17401.
- , , , et al. An Administrative Claims Model Suitable for Profiling Hospital Performance Based on 30‐Day Mortality Rates Among Patients With Heart Failure. Circulation. 2006;113(13):1693–1701.
Copyright © 2013 Society of Hospital Medicine
Guidelines issued on radiation-induced heart disease
Cancer patients undergoing radiation therapy need to have baseline studies of cardiac function and routine screening for heart disease, according to recommendations from the European Society of Cardiology and the American Society of Echocardiography published July 16 in the European Heart Journal–Cardiovascular Imaging.
The groups recommend baseline preradiation echocardiography along with a cardiac exam as well as screening for risk factors. An annual cardiac history and physical should be performed to check for new-onset heart problems.
Within 10 years of treatment, 10%-30% of patients who undergo radiation therapy develop radiation-induced heart diseases (RIHD), including chronic pericarditis, myocardial fibrosis, coronary artery disease, aortic calcification, and valve regurgitation or stenosis. The hope of screening is to catch early RIHD, but screening is not currently routine.
"We wrote the expert consensus to raise the alarm that the risks of radiation-induced heart disease should not be ignored. The prevalence ... is increasing because the rate of cancer survival has improved," said Dr. Patrizio Lancellotti, who is a professor of cardiology at the University Hospital of Liège, Belgium, and led the recommendations task force.
Radiotherapy is given in more targeted form and at lower doses than it once was, but "patients are still at increased risk of RIHD, particularly when the heart is in the radiation field. This applies to patients treated for lymphoma, breast cancer, and esophageal cancer. Patients who receive radiotherapy for neck cancer are also at risk because lesions can develop on the carotid artery and increase the risk of stroke," Dr. Lancellotti said in a statement.
Using targeted radiation and alternate radiation fields, with avoidance and shielding of the heart, remain "the most important interventions to prevent" cardiac complications, the authors noted.
The task force advises that high-risk patients without evidence of heart disease on history and physical should have screening echocardiography every 5 years and noninvasive stress testing every 5-10 years; low-risk patients should have screening echocardiography every 10 years. If heart disorders are detected, routine monitoring should include echocardiography, cardiac magnetic resonance imaging, or carotid ultrasound as appropriate.
High-risk patients include those who received radiotherapy at younger ages; those who have cardiovascular risk factors or preexisting heart disease; and those who receive high-dose radiation (greater than 30 Gy), concomitant chemotherapy, radiation without shielding, or anterior or left chest radiation (Eur. Heart J. Cardiovasc. Imaging 2013;14:721-40).
The recommendations are based on an extensive literature review and analysis by Dr. Lancellotti and other specialists.
The authors reported no financial conflicts or outside funding for their work.
Cancer patients undergoing radiation therapy need to have baseline studies of cardiac function and routine screening for heart disease, according to recommendations from the European Society of Cardiology and the American Society of Echocardiography published July 16 in the European Heart Journal–Cardiovascular Imaging.
The groups recommend baseline preradiation echocardiography along with a cardiac exam as well as screening for risk factors. An annual cardiac history and physical should be performed to check for new-onset heart problems.
Within 10 years of treatment, 10%-30% of patients who undergo radiation therapy develop radiation-induced heart diseases (RIHD), including chronic pericarditis, myocardial fibrosis, coronary artery disease, aortic calcification, and valve regurgitation or stenosis. The hope of screening is to catch early RIHD, but screening is not currently routine.
"We wrote the expert consensus to raise the alarm that the risks of radiation-induced heart disease should not be ignored. The prevalence ... is increasing because the rate of cancer survival has improved," said Dr. Patrizio Lancellotti, who is a professor of cardiology at the University Hospital of Liège, Belgium, and led the recommendations task force.
Radiotherapy is given in more targeted form and at lower doses than it once was, but "patients are still at increased risk of RIHD, particularly when the heart is in the radiation field. This applies to patients treated for lymphoma, breast cancer, and esophageal cancer. Patients who receive radiotherapy for neck cancer are also at risk because lesions can develop on the carotid artery and increase the risk of stroke," Dr. Lancellotti said in a statement.
Using targeted radiation and alternate radiation fields, with avoidance and shielding of the heart, remain "the most important interventions to prevent" cardiac complications, the authors noted.
The task force advises that high-risk patients without evidence of heart disease on history and physical should have screening echocardiography every 5 years and noninvasive stress testing every 5-10 years; low-risk patients should have screening echocardiography every 10 years. If heart disorders are detected, routine monitoring should include echocardiography, cardiac magnetic resonance imaging, or carotid ultrasound as appropriate.
High-risk patients include those who received radiotherapy at younger ages; those who have cardiovascular risk factors or preexisting heart disease; and those who receive high-dose radiation (greater than 30 Gy), concomitant chemotherapy, radiation without shielding, or anterior or left chest radiation (Eur. Heart J. Cardiovasc. Imaging 2013;14:721-40).
The recommendations are based on an extensive literature review and analysis by Dr. Lancellotti and other specialists.
The authors reported no financial conflicts or outside funding for their work.
Cancer patients undergoing radiation therapy need to have baseline studies of cardiac function and routine screening for heart disease, according to recommendations from the European Society of Cardiology and the American Society of Echocardiography published July 16 in the European Heart Journal–Cardiovascular Imaging.
The groups recommend baseline preradiation echocardiography along with a cardiac exam as well as screening for risk factors. An annual cardiac history and physical should be performed to check for new-onset heart problems.
Within 10 years of treatment, 10%-30% of patients who undergo radiation therapy develop radiation-induced heart diseases (RIHD), including chronic pericarditis, myocardial fibrosis, coronary artery disease, aortic calcification, and valve regurgitation or stenosis. The hope of screening is to catch early RIHD, but screening is not currently routine.
"We wrote the expert consensus to raise the alarm that the risks of radiation-induced heart disease should not be ignored. The prevalence ... is increasing because the rate of cancer survival has improved," said Dr. Patrizio Lancellotti, who is a professor of cardiology at the University Hospital of Liège, Belgium, and led the recommendations task force.
Radiotherapy is given in more targeted form and at lower doses than it once was, but "patients are still at increased risk of RIHD, particularly when the heart is in the radiation field. This applies to patients treated for lymphoma, breast cancer, and esophageal cancer. Patients who receive radiotherapy for neck cancer are also at risk because lesions can develop on the carotid artery and increase the risk of stroke," Dr. Lancellotti said in a statement.
Using targeted radiation and alternate radiation fields, with avoidance and shielding of the heart, remain "the most important interventions to prevent" cardiac complications, the authors noted.
The task force advises that high-risk patients without evidence of heart disease on history and physical should have screening echocardiography every 5 years and noninvasive stress testing every 5-10 years; low-risk patients should have screening echocardiography every 10 years. If heart disorders are detected, routine monitoring should include echocardiography, cardiac magnetic resonance imaging, or carotid ultrasound as appropriate.
High-risk patients include those who received radiotherapy at younger ages; those who have cardiovascular risk factors or preexisting heart disease; and those who receive high-dose radiation (greater than 30 Gy), concomitant chemotherapy, radiation without shielding, or anterior or left chest radiation (Eur. Heart J. Cardiovasc. Imaging 2013;14:721-40).
The recommendations are based on an extensive literature review and analysis by Dr. Lancellotti and other specialists.
The authors reported no financial conflicts or outside funding for their work.
FROM THE EUROPEAN HEART JOURNAL – CARDIOVASCULAR IMAGING
Family therapy in Romania and lessons for the West
In the United States, family psychiatrists continue to deal with the fallout from the 1950s and 1960s, when the early family therapists located mental illness within the family and then touted family therapy as the cure. Families felt blamed and shied away from "family therapy."
Yet, research shows that family treatment for many psychiatric and medical illnesses, whether it is family inclusion or psychoeducation, is very effective in reducing morbidity. Stigma and fear about family involvement have resulted in family treatment lagging behind other psychotherapies in its acceptance as a valid therapeutic intervention.
As a contrast, it is therefore interesting to look at Romania, a postcommunist country, where all psychotherapies were deemed "unnecessary" under communism. According to Dr. Ileana-Mihaela Botezat-Antonescu, "Psychotherapy and psychoanalysis were known as the studies of the soul during the communist regime and went underground. Secret psychotherapy meetings were held in Sibiu and Timisoara, but after the 1989 revolution, we had access to information from abroad," she said during a presentation this year at the World Psychiatric Association meeting in Bucharest, Romania.
"In 1990, freedom occurs, but nobody tells you what to do. You cannot count on anything. It alienated people seeking help. It was a process that took time," said Dr. Botezat-Antonescu, a psychiatrist and psychoanalyst who was trained in the mid-1990s by trainers from the Dutch Psychoanalytic Association and serves as chair of the National Center for Mental Health.
Psychotherapists in Romania must somehow address the traumatic environment that lasted a generation. Young people strive to gain their sense of identity and belonging and, at the same time, are challenged with reestablishing a connection between the generations. The sense of intergenerational trauma and loss extends back to grandparents who lost their farms, houses, and social position.
An understanding of the intergenerational transmission of trauma can inform psychotherapists across the globe in their care of young people. Family therapy is a type of psychotherapy that is well suited to address this intergenerational trauma.
Psychological trauma is passed down through the generations in subtle and unspoken ways. It is important for therapists to recognize when this is occurring and work with the whole family. Family therapy that specifically addresses the intergenerational transmission of trauma can help move a family from feelings of helplessness toward resilience.
Development of family therapy
Family therapy developed in Romania through training courses in Cluj, Târgu Mures, and Timisoara. As there were no Romanian trainers, these courses were taught by family therapists from countries such as Ireland, France, and Yugoslavia. Families and trainees spoke Romanian or Hungarian, and during live supervision, simultaneous translation occurred. All courses, readings, and assignments were in English. Family therapy developed in Romania through training courses in Cluj, Târgu Mures and Timisoara.
Trainees saw families in their own work contexts, for example, psychotherapy centers; psychiatry hospitals; and community centers, such as family planning clinics and domestic violence shelters. Currently, there are 16 family therapy professional organizations (Contemp. Fam. Ther. 2013;35:275-87), including:
• Systemic Family Therapy Association in Cluj
• Association of Family Therapy in Bucharest
• Romanian Association for Family and Systemic Therapy in Timisoara
• Association Crisdu Areopagus in Timisoara
• Pro Familia – Family Therapy Association in Miercurea-Ciuc
• Association for Couple and Family Psychotherapy in Iasi
• Association for Family Counselling and Therapy in Iasi
Dr. Zoltán Kónya and Dr. Ágnes Kónya run the family therapy center in Cluj and have written about the challenges of practicing family therapy in Romania (Context 2007;92:2-4 and Contemporary Family Therapy 2013;35:1). Since family therapy training courses developed at different times, in different places, with trainers invited from different countries, the sense of what constitutes family therapy varies across Romania.
The meaning of the word "systemic" has proved particularly contentious. For some, systemic is synonymous with the Milan approach – which is based on the notion that "families are self-regulating systems that function based on self-developed rules tested over time through a process of trial and error" (Case Conceptualization in Family Therapy, Boston: Pearson, 2013). However, others consider family therapy as more than a systemic approach. Some promote systemic thinking as an all-encompassing epistemological frame for consultation with individuals, families, and organizations, but others do not attach much importance to the term "systemic."
The challenge of organizing into one Romanian family therapy institution with one outlook is great. This challenge also replicates one of the major problems in our field – the idea that family therapy means different things to different people. In Romania, well-meaning outside attempts ended up in a fractured national family therapy identity.
The Kónyas, trained by Irish family therapists, identify additional challenges of introducing family therapy into a culture unfamiliar with the concept. "The fit between systemic therapy and Romanian culture has been a concern of ours since the beginning of our training," they wrote in 2003. "There had been no tradition of people seeing psychotherapists in times of distress. Also, the vast majority of health care professionals had not even heard about family therapy. Would families come to therapy?"
They found that not only did clients come to therapy, but they also were ready to work hard when they did.
"We admire our clients’ courage in facing a series of challenges involved in the therapy process: consulting an outsider for a family problem, participating in sessions as a family, being asked unusual questions, being videotaped and, sometimes, being observed by a team and/or supervisors from abroad," they said.
In their work with patients, the Kónyas write, they have encountered difficulties tied to the use of words and phrases used in systemic therapy.
"For example, the Batesonian phrase 'a difference that makes a difference' is very difficult, if not impossible to properly translate into Romanian – of course, this may only be a problem in a training context, not in therapy. In response to the Romanian or Hungarian translation of the question: ‘And how has this been a problem for you?’ clients almost invariably demonstrate a lack of comprehension: ‘Would you please say that again? I didn’t understand.’ The difficulty here is not that the question makes no sense, but that it is culturally unusual – and therefore potentially therapeutic," they write (Context 2007)
"Also, certain words that might sound neutral in the West sometimes trigger strong emotions in our country. For example, monitoring progress on a 1-10 scale might recall painful experiences connected with school, because in Romania, marks are from 1 to 10. Also, talking about ‘systems’ may trigger discomfort, as this is the word people used during communism to describe the oppressive dictatorial regime. People who challenged the dominant ideas used to be called ‘the enemies of the system.’ "
The communist ideal of "systemization" broke families. Women were encouraged to give birth in a pronatal policy that resulted in orphans and unwanted children. After the 1989 revolution, more than 300,000 Romanians were living in psychiatric institutions. Communist factories were closed, and displaced workers had no place. Raising a voice against the regime risked imprisonment as an enemy of the system. The word "system" is associated with oppression in Romania.
Are there lessons for us in the West? I think the answer is yes and that the Romanian experience highlights several imperatives that are useful for Western mental health professionals. Among those imperatives:
• Family psychiatry needs to agree on a definition of family therapy. Is it any approach that includes families? Do we need to be systemic to be considered family therapists? Does family support qualify as family therapy? Does family psychoeducation qualify as family therapy? Can we embrace these two levels, as well as a third, more-skilled level, a systemic family therapy? Can we accept a three-level definition of family treatment?
• Can we incorporate all the family therapy models into one approach that people will recognize as a generic approach to families? Can we use the common factors approach described by Douglas H. Sprenkle, Ph.D., Sean D. Davis, Ph.D., and Jay L. Lebow, Ph.D., in "Common Factors in Couple and Family Therapy" (New York: The Guilford Press, 2009)? For couples and family therapists, common factors over and above the well-recognized individual psychotherapy factors are conceptualizing the problems in relational terms, using therapy that aims to disrupt dysfunctional relational patterns, expanding treatment to include family members of the index patient, and fostering an expanded therapeutic alliance, according to Dr. Sprenkle, Dr. Davis, and Dr. Lebow.
• Can we develop a protocol that beginners can follow? Aaron Beck’s cognitive-behavioral therapy (CBT) provides a basic template that is easy for the novice therapist and the patient to use, yet brings a unique perspective to psychotherapy. CBT has gone on to develop in several diverse directions, but all CBT models have the same basic set of beliefs. Can a family approach or protocol be both simple AND allow for more sophisticated elaboration? Can we develop a set of basic steps that define family treatment?
• Should there be an approach to families that all disciplines can follow? Family therapy is practiced by physicians, nurses, social workers, and marriage and family therapists. Each discipline tends to work with different populations. Physicians tend to see families in which one person is the identified patient. Social workers tend to see families that have been referred for social services, families who are frequently struggling with such problems as housing, financial, and legal difficulties. Marriage and family therapists are often employed by community and hospital agencies as health care extenders and might work alongside other health care professionals. Would a single approach to families be useful?
In summary, if family therapy is to endure, it must be teachable, translatable, and relevant across disciplines and national boundaries. The systemic paradigm is an important perspective from which all practitioners can benefit. We must continue to disseminate evidence-based family treatments and teach family principles that can be incorporated on a daily basis by all mental health professionals.
Dr. Heru is with the department of psychiatry at the University of Colorado at Denver, Aurora. She is editor of the recently published book, "Working With Families in Medical Settings: A Multidisciplinary Guide for Psychiatrists and Other Health Professionals" (New York: Routledge, 2013).
In the United States, family psychiatrists continue to deal with the fallout from the 1950s and 1960s, when the early family therapists located mental illness within the family and then touted family therapy as the cure. Families felt blamed and shied away from "family therapy."
Yet, research shows that family treatment for many psychiatric and medical illnesses, whether it is family inclusion or psychoeducation, is very effective in reducing morbidity. Stigma and fear about family involvement have resulted in family treatment lagging behind other psychotherapies in its acceptance as a valid therapeutic intervention.
As a contrast, it is therefore interesting to look at Romania, a postcommunist country, where all psychotherapies were deemed "unnecessary" under communism. According to Dr. Ileana-Mihaela Botezat-Antonescu, "Psychotherapy and psychoanalysis were known as the studies of the soul during the communist regime and went underground. Secret psychotherapy meetings were held in Sibiu and Timisoara, but after the 1989 revolution, we had access to information from abroad," she said during a presentation this year at the World Psychiatric Association meeting in Bucharest, Romania.
"In 1990, freedom occurs, but nobody tells you what to do. You cannot count on anything. It alienated people seeking help. It was a process that took time," said Dr. Botezat-Antonescu, a psychiatrist and psychoanalyst who was trained in the mid-1990s by trainers from the Dutch Psychoanalytic Association and serves as chair of the National Center for Mental Health.
Psychotherapists in Romania must somehow address the traumatic environment that lasted a generation. Young people strive to gain their sense of identity and belonging and, at the same time, are challenged with reestablishing a connection between the generations. The sense of intergenerational trauma and loss extends back to grandparents who lost their farms, houses, and social position.
An understanding of the intergenerational transmission of trauma can inform psychotherapists across the globe in their care of young people. Family therapy is a type of psychotherapy that is well suited to address this intergenerational trauma.
Psychological trauma is passed down through the generations in subtle and unspoken ways. It is important for therapists to recognize when this is occurring and work with the whole family. Family therapy that specifically addresses the intergenerational transmission of trauma can help move a family from feelings of helplessness toward resilience.
Development of family therapy
Family therapy developed in Romania through training courses in Cluj, Târgu Mures, and Timisoara. As there were no Romanian trainers, these courses were taught by family therapists from countries such as Ireland, France, and Yugoslavia. Families and trainees spoke Romanian or Hungarian, and during live supervision, simultaneous translation occurred. All courses, readings, and assignments were in English. Family therapy developed in Romania through training courses in Cluj, Târgu Mures and Timisoara.
Trainees saw families in their own work contexts, for example, psychotherapy centers; psychiatry hospitals; and community centers, such as family planning clinics and domestic violence shelters. Currently, there are 16 family therapy professional organizations (Contemp. Fam. Ther. 2013;35:275-87), including:
• Systemic Family Therapy Association in Cluj
• Association of Family Therapy in Bucharest
• Romanian Association for Family and Systemic Therapy in Timisoara
• Association Crisdu Areopagus in Timisoara
• Pro Familia – Family Therapy Association in Miercurea-Ciuc
• Association for Couple and Family Psychotherapy in Iasi
• Association for Family Counselling and Therapy in Iasi
Dr. Zoltán Kónya and Dr. Ágnes Kónya run the family therapy center in Cluj and have written about the challenges of practicing family therapy in Romania (Context 2007;92:2-4 and Contemporary Family Therapy 2013;35:1). Since family therapy training courses developed at different times, in different places, with trainers invited from different countries, the sense of what constitutes family therapy varies across Romania.
The meaning of the word "systemic" has proved particularly contentious. For some, systemic is synonymous with the Milan approach – which is based on the notion that "families are self-regulating systems that function based on self-developed rules tested over time through a process of trial and error" (Case Conceptualization in Family Therapy, Boston: Pearson, 2013). However, others consider family therapy as more than a systemic approach. Some promote systemic thinking as an all-encompassing epistemological frame for consultation with individuals, families, and organizations, but others do not attach much importance to the term "systemic."
The challenge of organizing into one Romanian family therapy institution with one outlook is great. This challenge also replicates one of the major problems in our field – the idea that family therapy means different things to different people. In Romania, well-meaning outside attempts ended up in a fractured national family therapy identity.
The Kónyas, trained by Irish family therapists, identify additional challenges of introducing family therapy into a culture unfamiliar with the concept. "The fit between systemic therapy and Romanian culture has been a concern of ours since the beginning of our training," they wrote in 2003. "There had been no tradition of people seeing psychotherapists in times of distress. Also, the vast majority of health care professionals had not even heard about family therapy. Would families come to therapy?"
They found that not only did clients come to therapy, but they also were ready to work hard when they did.
"We admire our clients’ courage in facing a series of challenges involved in the therapy process: consulting an outsider for a family problem, participating in sessions as a family, being asked unusual questions, being videotaped and, sometimes, being observed by a team and/or supervisors from abroad," they said.
In their work with patients, the Kónyas write, they have encountered difficulties tied to the use of words and phrases used in systemic therapy.
"For example, the Batesonian phrase 'a difference that makes a difference' is very difficult, if not impossible to properly translate into Romanian – of course, this may only be a problem in a training context, not in therapy. In response to the Romanian or Hungarian translation of the question: ‘And how has this been a problem for you?’ clients almost invariably demonstrate a lack of comprehension: ‘Would you please say that again? I didn’t understand.’ The difficulty here is not that the question makes no sense, but that it is culturally unusual – and therefore potentially therapeutic," they write (Context 2007)
"Also, certain words that might sound neutral in the West sometimes trigger strong emotions in our country. For example, monitoring progress on a 1-10 scale might recall painful experiences connected with school, because in Romania, marks are from 1 to 10. Also, talking about ‘systems’ may trigger discomfort, as this is the word people used during communism to describe the oppressive dictatorial regime. People who challenged the dominant ideas used to be called ‘the enemies of the system.’ "
The communist ideal of "systemization" broke families. Women were encouraged to give birth in a pronatal policy that resulted in orphans and unwanted children. After the 1989 revolution, more than 300,000 Romanians were living in psychiatric institutions. Communist factories were closed, and displaced workers had no place. Raising a voice against the regime risked imprisonment as an enemy of the system. The word "system" is associated with oppression in Romania.
Are there lessons for us in the West? I think the answer is yes and that the Romanian experience highlights several imperatives that are useful for Western mental health professionals. Among those imperatives:
• Family psychiatry needs to agree on a definition of family therapy. Is it any approach that includes families? Do we need to be systemic to be considered family therapists? Does family support qualify as family therapy? Does family psychoeducation qualify as family therapy? Can we embrace these two levels, as well as a third, more-skilled level, a systemic family therapy? Can we accept a three-level definition of family treatment?
• Can we incorporate all the family therapy models into one approach that people will recognize as a generic approach to families? Can we use the common factors approach described by Douglas H. Sprenkle, Ph.D., Sean D. Davis, Ph.D., and Jay L. Lebow, Ph.D., in "Common Factors in Couple and Family Therapy" (New York: The Guilford Press, 2009)? For couples and family therapists, common factors over and above the well-recognized individual psychotherapy factors are conceptualizing the problems in relational terms, using therapy that aims to disrupt dysfunctional relational patterns, expanding treatment to include family members of the index patient, and fostering an expanded therapeutic alliance, according to Dr. Sprenkle, Dr. Davis, and Dr. Lebow.
• Can we develop a protocol that beginners can follow? Aaron Beck’s cognitive-behavioral therapy (CBT) provides a basic template that is easy for the novice therapist and the patient to use, yet brings a unique perspective to psychotherapy. CBT has gone on to develop in several diverse directions, but all CBT models have the same basic set of beliefs. Can a family approach or protocol be both simple AND allow for more sophisticated elaboration? Can we develop a set of basic steps that define family treatment?
• Should there be an approach to families that all disciplines can follow? Family therapy is practiced by physicians, nurses, social workers, and marriage and family therapists. Each discipline tends to work with different populations. Physicians tend to see families in which one person is the identified patient. Social workers tend to see families that have been referred for social services, families who are frequently struggling with such problems as housing, financial, and legal difficulties. Marriage and family therapists are often employed by community and hospital agencies as health care extenders and might work alongside other health care professionals. Would a single approach to families be useful?
In summary, if family therapy is to endure, it must be teachable, translatable, and relevant across disciplines and national boundaries. The systemic paradigm is an important perspective from which all practitioners can benefit. We must continue to disseminate evidence-based family treatments and teach family principles that can be incorporated on a daily basis by all mental health professionals.
Dr. Heru is with the department of psychiatry at the University of Colorado at Denver, Aurora. She is editor of the recently published book, "Working With Families in Medical Settings: A Multidisciplinary Guide for Psychiatrists and Other Health Professionals" (New York: Routledge, 2013).
In the United States, family psychiatrists continue to deal with the fallout from the 1950s and 1960s, when the early family therapists located mental illness within the family and then touted family therapy as the cure. Families felt blamed and shied away from "family therapy."
Yet, research shows that family treatment for many psychiatric and medical illnesses, whether it is family inclusion or psychoeducation, is very effective in reducing morbidity. Stigma and fear about family involvement have resulted in family treatment lagging behind other psychotherapies in its acceptance as a valid therapeutic intervention.
As a contrast, it is therefore interesting to look at Romania, a postcommunist country, where all psychotherapies were deemed "unnecessary" under communism. According to Dr. Ileana-Mihaela Botezat-Antonescu, "Psychotherapy and psychoanalysis were known as the studies of the soul during the communist regime and went underground. Secret psychotherapy meetings were held in Sibiu and Timisoara, but after the 1989 revolution, we had access to information from abroad," she said during a presentation this year at the World Psychiatric Association meeting in Bucharest, Romania.
"In 1990, freedom occurs, but nobody tells you what to do. You cannot count on anything. It alienated people seeking help. It was a process that took time," said Dr. Botezat-Antonescu, a psychiatrist and psychoanalyst who was trained in the mid-1990s by trainers from the Dutch Psychoanalytic Association and serves as chair of the National Center for Mental Health.
Psychotherapists in Romania must somehow address the traumatic environment that lasted a generation. Young people strive to gain their sense of identity and belonging and, at the same time, are challenged with reestablishing a connection between the generations. The sense of intergenerational trauma and loss extends back to grandparents who lost their farms, houses, and social position.
An understanding of the intergenerational transmission of trauma can inform psychotherapists across the globe in their care of young people. Family therapy is a type of psychotherapy that is well suited to address this intergenerational trauma.
Psychological trauma is passed down through the generations in subtle and unspoken ways. It is important for therapists to recognize when this is occurring and work with the whole family. Family therapy that specifically addresses the intergenerational transmission of trauma can help move a family from feelings of helplessness toward resilience.
Development of family therapy
Family therapy developed in Romania through training courses in Cluj, Târgu Mures, and Timisoara. As there were no Romanian trainers, these courses were taught by family therapists from countries such as Ireland, France, and Yugoslavia. Families and trainees spoke Romanian or Hungarian, and during live supervision, simultaneous translation occurred. All courses, readings, and assignments were in English. Family therapy developed in Romania through training courses in Cluj, Târgu Mures and Timisoara.
Trainees saw families in their own work contexts, for example, psychotherapy centers; psychiatry hospitals; and community centers, such as family planning clinics and domestic violence shelters. Currently, there are 16 family therapy professional organizations (Contemp. Fam. Ther. 2013;35:275-87), including:
• Systemic Family Therapy Association in Cluj
• Association of Family Therapy in Bucharest
• Romanian Association for Family and Systemic Therapy in Timisoara
• Association Crisdu Areopagus in Timisoara
• Pro Familia – Family Therapy Association in Miercurea-Ciuc
• Association for Couple and Family Psychotherapy in Iasi
• Association for Family Counselling and Therapy in Iasi
Dr. Zoltán Kónya and Dr. Ágnes Kónya run the family therapy center in Cluj and have written about the challenges of practicing family therapy in Romania (Context 2007;92:2-4 and Contemporary Family Therapy 2013;35:1). Since family therapy training courses developed at different times, in different places, with trainers invited from different countries, the sense of what constitutes family therapy varies across Romania.
The meaning of the word "systemic" has proved particularly contentious. For some, systemic is synonymous with the Milan approach – which is based on the notion that "families are self-regulating systems that function based on self-developed rules tested over time through a process of trial and error" (Case Conceptualization in Family Therapy, Boston: Pearson, 2013). However, others consider family therapy as more than a systemic approach. Some promote systemic thinking as an all-encompassing epistemological frame for consultation with individuals, families, and organizations, but others do not attach much importance to the term "systemic."
The challenge of organizing into one Romanian family therapy institution with one outlook is great. This challenge also replicates one of the major problems in our field – the idea that family therapy means different things to different people. In Romania, well-meaning outside attempts ended up in a fractured national family therapy identity.
The Kónyas, trained by Irish family therapists, identify additional challenges of introducing family therapy into a culture unfamiliar with the concept. "The fit between systemic therapy and Romanian culture has been a concern of ours since the beginning of our training," they wrote in 2003. "There had been no tradition of people seeing psychotherapists in times of distress. Also, the vast majority of health care professionals had not even heard about family therapy. Would families come to therapy?"
They found that not only did clients come to therapy, but they also were ready to work hard when they did.
"We admire our clients’ courage in facing a series of challenges involved in the therapy process: consulting an outsider for a family problem, participating in sessions as a family, being asked unusual questions, being videotaped and, sometimes, being observed by a team and/or supervisors from abroad," they said.
In their work with patients, the Kónyas write, they have encountered difficulties tied to the use of words and phrases used in systemic therapy.
"For example, the Batesonian phrase 'a difference that makes a difference' is very difficult, if not impossible to properly translate into Romanian – of course, this may only be a problem in a training context, not in therapy. In response to the Romanian or Hungarian translation of the question: ‘And how has this been a problem for you?’ clients almost invariably demonstrate a lack of comprehension: ‘Would you please say that again? I didn’t understand.’ The difficulty here is not that the question makes no sense, but that it is culturally unusual – and therefore potentially therapeutic," they write (Context 2007)
"Also, certain words that might sound neutral in the West sometimes trigger strong emotions in our country. For example, monitoring progress on a 1-10 scale might recall painful experiences connected with school, because in Romania, marks are from 1 to 10. Also, talking about ‘systems’ may trigger discomfort, as this is the word people used during communism to describe the oppressive dictatorial regime. People who challenged the dominant ideas used to be called ‘the enemies of the system.’ "
The communist ideal of "systemization" broke families. Women were encouraged to give birth in a pronatal policy that resulted in orphans and unwanted children. After the 1989 revolution, more than 300,000 Romanians were living in psychiatric institutions. Communist factories were closed, and displaced workers had no place. Raising a voice against the regime risked imprisonment as an enemy of the system. The word "system" is associated with oppression in Romania.
Are there lessons for us in the West? I think the answer is yes and that the Romanian experience highlights several imperatives that are useful for Western mental health professionals. Among those imperatives:
• Family psychiatry needs to agree on a definition of family therapy. Is it any approach that includes families? Do we need to be systemic to be considered family therapists? Does family support qualify as family therapy? Does family psychoeducation qualify as family therapy? Can we embrace these two levels, as well as a third, more-skilled level, a systemic family therapy? Can we accept a three-level definition of family treatment?
• Can we incorporate all the family therapy models into one approach that people will recognize as a generic approach to families? Can we use the common factors approach described by Douglas H. Sprenkle, Ph.D., Sean D. Davis, Ph.D., and Jay L. Lebow, Ph.D., in "Common Factors in Couple and Family Therapy" (New York: The Guilford Press, 2009)? For couples and family therapists, common factors over and above the well-recognized individual psychotherapy factors are conceptualizing the problems in relational terms, using therapy that aims to disrupt dysfunctional relational patterns, expanding treatment to include family members of the index patient, and fostering an expanded therapeutic alliance, according to Dr. Sprenkle, Dr. Davis, and Dr. Lebow.
• Can we develop a protocol that beginners can follow? Aaron Beck’s cognitive-behavioral therapy (CBT) provides a basic template that is easy for the novice therapist and the patient to use, yet brings a unique perspective to psychotherapy. CBT has gone on to develop in several diverse directions, but all CBT models have the same basic set of beliefs. Can a family approach or protocol be both simple AND allow for more sophisticated elaboration? Can we develop a set of basic steps that define family treatment?
• Should there be an approach to families that all disciplines can follow? Family therapy is practiced by physicians, nurses, social workers, and marriage and family therapists. Each discipline tends to work with different populations. Physicians tend to see families in which one person is the identified patient. Social workers tend to see families that have been referred for social services, families who are frequently struggling with such problems as housing, financial, and legal difficulties. Marriage and family therapists are often employed by community and hospital agencies as health care extenders and might work alongside other health care professionals. Would a single approach to families be useful?
In summary, if family therapy is to endure, it must be teachable, translatable, and relevant across disciplines and national boundaries. The systemic paradigm is an important perspective from which all practitioners can benefit. We must continue to disseminate evidence-based family treatments and teach family principles that can be incorporated on a daily basis by all mental health professionals.
Dr. Heru is with the department of psychiatry at the University of Colorado at Denver, Aurora. She is editor of the recently published book, "Working With Families in Medical Settings: A Multidisciplinary Guide for Psychiatrists and Other Health Professionals" (New York: Routledge, 2013).
BEST PRACTICES IN: Thermal Integrity of Shipping Containers Used by Private Cord Blood Banks
A supplement to Ob.Gyn. News. This supplement was sponsored by CORD:USE Cord Blood Bank, Inc.
• Introduction
• Materials and methods
• Results
• Discussion
Faculty/Faculty Disclosure
Robert N. Wolfson, MD, PhD, FACOG, FAIUM
Specialists in Women’s Health, LLC
Colorado Springs, Colorado
Dr Wolfson has nothing to disclose.
Copyright ©2013 by Frontline Medical Communications Inc.
A supplement to Ob.Gyn. News. This supplement was sponsored by CORD:USE Cord Blood Bank, Inc.
• Introduction
• Materials and methods
• Results
• Discussion
Faculty/Faculty Disclosure
Robert N. Wolfson, MD, PhD, FACOG, FAIUM
Specialists in Women’s Health, LLC
Colorado Springs, Colorado
Dr Wolfson has nothing to disclose.
Copyright ©2013 by Frontline Medical Communications Inc.
A supplement to Ob.Gyn. News. This supplement was sponsored by CORD:USE Cord Blood Bank, Inc.
• Introduction
• Materials and methods
• Results
• Discussion
Faculty/Faculty Disclosure
Robert N. Wolfson, MD, PhD, FACOG, FAIUM
Specialists in Women’s Health, LLC
Colorado Springs, Colorado
Dr Wolfson has nothing to disclose.
Copyright ©2013 by Frontline Medical Communications Inc.
From the Vascular Community
Please submit your short meeting reports, comings and goings, upcoming meetings, obituary announcements, etc., to From the Vascular Community in care of vascularspecialist@frontlinemedcom.com.
Meeting News
Reports
The South-Asian Association for Vascular Surgery (SAAVS) held their second annual meeting on May 30, 2013. Founded in 2011, the SAAVS is a member organization of the SVS with a mission to promote vascular health and disseminate the latest in vascular surgical techniques throughout South Asia. In just 2 years, the SAAVS has 100 registered members including 23 from overseas. During the meeting, Dr. Anil Hingorani began his tenure as President and Dr. Dipankar Mukherjee was voted President-Elect. Dr. Anahit Dua was presented an $800 prize for the outstanding resident research award. Dr. Krishna Jain and Dr. Bhagwan Satiani spoke on current issues facing vascular surgeons in the United States while Dr. Kumud Rai and Dr. Ramesh Tripathi spoke on the status of the field in India. The SAAVS is focusing its energy on establishing a "vascular update" with a 2-week didactic and practical course in South Asia. It is actively partnering with vascular societies in India to fulfill its mission. Medical students, trainees, and vascular surgeons from all backgrounds and geographic areas who are interested in advancing vascular care in South Asia are welcome to join. Visit http://saavsociety.org for more information.
Upcoming
The Canadian Society for Vascular Surgery will be holding its annual meeting September 13-14, 2013, at The Westin Edmonton, Edmonton, Alberta, Canada. The invited guest speaker is Dr. Ronald Lee Dalman II, who is the Dr. Walter C. Chidester Professor of Surgery, at Stanford University School of Medicine. Visit http://canadianvascular.ca for more details.
Obituaries
As we are beginning this new section, we are including obituaries from 2012.
Harold Clifton Urschel, Jr.
Dr. Urschel passed away on Nov. 12, 2012, at the age of 82. At the time of his death he was at the American Heart Association meeting in Los Angeles, where he was presenting material on his latest research interest: the use of stem cells for the treatment of heart failure. He was the past president of the Society of Thoracic Surgeons, the Southern Thoracic Surgical Association, the American College of Chest Physicians, and the Texas Surgical Association and a Distinguished fellow of the Society for Vascular Surgery. He has been a Governor of the American College of Surgeons, Chairman of the American Board of Thoracic Surgery, Chairman of the Residency Review Committee for Thoracic Surgery and also a member of every important national and international medical and surgical society.
Max R. Gaspar
Dr. Gaspar, an internationally reputed vascular surgeon, died Oct. 7, 2012. He was 97. Gaspar, formerly of Long Beach, had been chief of vascular surgery for 25 years at Los Angeles County-USC Medical Center, where he also served as attending surgeon for 50 years. He had a practice in Long Beach and had also performed surgeries at St. Mary Medical Center, Memorial Medical Center, and Community Medical. He attended the University of South Dakota Medical School but finished his training at USC in 1938, and earned his M.D. in 1940. During World War II, he served in the Navy as a doctor in the Pacific. Dr. Gaspar remained active in medicine and teaching. About 17 years ago, USC established the Max R. Gaspar Symposium, which addressed a specific topic of interest to physicians and surgeons who care for patients with vascular disease. He also authored numerous articles and contributed about 14 chapters to various texts. He was one of the early pioneers in our field.
Edwin Salzman
Dr. Salzman, a professor of surgery emeritus at Harvard Medical School, died Oct. 3, 2012, at Beth Israel Deaconess Medical Center, in a room not far from his old office. His surgical career was cut short by Parkinson’s disease in the mid-1970s. Turning full attention to the scientific research that had always been his parallel career, he helped pioneer using aspirin to prevent DVT and spent a dozen years working part-time as deputy editor of the New England Journal of Medicine. Along with the findings in the 1970s about aspirin, he made significant contributions to research involving heparin and other methods that prevent postoperative pulmonary embolism.
Geoffrey Hamilton White
Dr. White died peacefully in Australia on Jan. 26, 2012, at the age of 60. He was at UCLA from 1984 to 1989 as Assistant Professor of Surgery at the UCLA School of Medicine and Chief of Vascular Surgery at the VA Wadsworth Medical Center. He was later appointed head of the department at Royal Prince Alfred Hospital and Professor of Vascular Surgery at Macquarie University Hospital, both in Australia. He had a richly deserved international reputation for his many contributions to the development of the endovascular treatment abdominal aortic aneurysms. He also coined the term "endoleak," which is nowpart of the nomenclature.
Deceased Members
(Reported to the SVS as of April 19, 2013; presented in order of receiving):
• Johann Ehrenhaft, MD Iowa City, IA
• J. Harold Harrison, MD Bartow, GA
• George Kish, MD Henderson, NV
• Malcolm Thomas, MD Phoenix, AZ
• Norman Rosenberg, MD Lantana, FL
• Michael Seremetis, MD Washington, DC
• Andrew Michalski, MD St. Catherines, Ontario, Canada
• Dean Wasserman, MD Paramus, NJ
• Duncan W. Campbell, MD Tucson, AZ
• Edwin Salzman, MD Cambridge, MA
• John Vander Woude, MD Sioux Falls, SD
• William A. Holbrook, MD Chevy Chase, MD
• Lewis H. Bosher, MD Richmond, VA
• Joseph Graham, MD Joplin, MO
• William D. Byrne McLean, VA
• John Waldhausen, MD Lemoyne, PA
• David Wulkan, MD Boca Raton, FL
• Max Gaspar, MD Seal Beach, CA
• Hugh E. Stephenson, MD Columbia, MO
• Harold C. Urschel, Jr., MD Dallas, TX
• Geoffrey H. White, MD Sydney, Australia
• Henning Loeprecht, MD Augsburg, Germany
Please submit your short meeting reports, comings and goings, upcoming meetings, obituary announcements, etc., to From the Vascular Community in care of vascularspecialist@frontlinemedcom.com.
Meeting News
Reports
The South-Asian Association for Vascular Surgery (SAAVS) held their second annual meeting on May 30, 2013. Founded in 2011, the SAAVS is a member organization of the SVS with a mission to promote vascular health and disseminate the latest in vascular surgical techniques throughout South Asia. In just 2 years, the SAAVS has 100 registered members including 23 from overseas. During the meeting, Dr. Anil Hingorani began his tenure as President and Dr. Dipankar Mukherjee was voted President-Elect. Dr. Anahit Dua was presented an $800 prize for the outstanding resident research award. Dr. Krishna Jain and Dr. Bhagwan Satiani spoke on current issues facing vascular surgeons in the United States while Dr. Kumud Rai and Dr. Ramesh Tripathi spoke on the status of the field in India. The SAAVS is focusing its energy on establishing a "vascular update" with a 2-week didactic and practical course in South Asia. It is actively partnering with vascular societies in India to fulfill its mission. Medical students, trainees, and vascular surgeons from all backgrounds and geographic areas who are interested in advancing vascular care in South Asia are welcome to join. Visit http://saavsociety.org for more information.
Upcoming
The Canadian Society for Vascular Surgery will be holding its annual meeting September 13-14, 2013, at The Westin Edmonton, Edmonton, Alberta, Canada. The invited guest speaker is Dr. Ronald Lee Dalman II, who is the Dr. Walter C. Chidester Professor of Surgery, at Stanford University School of Medicine. Visit http://canadianvascular.ca for more details.
Obituaries
As we are beginning this new section, we are including obituaries from 2012.
Harold Clifton Urschel, Jr.
Dr. Urschel passed away on Nov. 12, 2012, at the age of 82. At the time of his death he was at the American Heart Association meeting in Los Angeles, where he was presenting material on his latest research interest: the use of stem cells for the treatment of heart failure. He was the past president of the Society of Thoracic Surgeons, the Southern Thoracic Surgical Association, the American College of Chest Physicians, and the Texas Surgical Association and a Distinguished fellow of the Society for Vascular Surgery. He has been a Governor of the American College of Surgeons, Chairman of the American Board of Thoracic Surgery, Chairman of the Residency Review Committee for Thoracic Surgery and also a member of every important national and international medical and surgical society.
Max R. Gaspar
Dr. Gaspar, an internationally reputed vascular surgeon, died Oct. 7, 2012. He was 97. Gaspar, formerly of Long Beach, had been chief of vascular surgery for 25 years at Los Angeles County-USC Medical Center, where he also served as attending surgeon for 50 years. He had a practice in Long Beach and had also performed surgeries at St. Mary Medical Center, Memorial Medical Center, and Community Medical. He attended the University of South Dakota Medical School but finished his training at USC in 1938, and earned his M.D. in 1940. During World War II, he served in the Navy as a doctor in the Pacific. Dr. Gaspar remained active in medicine and teaching. About 17 years ago, USC established the Max R. Gaspar Symposium, which addressed a specific topic of interest to physicians and surgeons who care for patients with vascular disease. He also authored numerous articles and contributed about 14 chapters to various texts. He was one of the early pioneers in our field.
Edwin Salzman
Dr. Salzman, a professor of surgery emeritus at Harvard Medical School, died Oct. 3, 2012, at Beth Israel Deaconess Medical Center, in a room not far from his old office. His surgical career was cut short by Parkinson’s disease in the mid-1970s. Turning full attention to the scientific research that had always been his parallel career, he helped pioneer using aspirin to prevent DVT and spent a dozen years working part-time as deputy editor of the New England Journal of Medicine. Along with the findings in the 1970s about aspirin, he made significant contributions to research involving heparin and other methods that prevent postoperative pulmonary embolism.
Geoffrey Hamilton White
Dr. White died peacefully in Australia on Jan. 26, 2012, at the age of 60. He was at UCLA from 1984 to 1989 as Assistant Professor of Surgery at the UCLA School of Medicine and Chief of Vascular Surgery at the VA Wadsworth Medical Center. He was later appointed head of the department at Royal Prince Alfred Hospital and Professor of Vascular Surgery at Macquarie University Hospital, both in Australia. He had a richly deserved international reputation for his many contributions to the development of the endovascular treatment abdominal aortic aneurysms. He also coined the term "endoleak," which is nowpart of the nomenclature.
Deceased Members
(Reported to the SVS as of April 19, 2013; presented in order of receiving):
• Johann Ehrenhaft, MD Iowa City, IA
• J. Harold Harrison, MD Bartow, GA
• George Kish, MD Henderson, NV
• Malcolm Thomas, MD Phoenix, AZ
• Norman Rosenberg, MD Lantana, FL
• Michael Seremetis, MD Washington, DC
• Andrew Michalski, MD St. Catherines, Ontario, Canada
• Dean Wasserman, MD Paramus, NJ
• Duncan W. Campbell, MD Tucson, AZ
• Edwin Salzman, MD Cambridge, MA
• John Vander Woude, MD Sioux Falls, SD
• William A. Holbrook, MD Chevy Chase, MD
• Lewis H. Bosher, MD Richmond, VA
• Joseph Graham, MD Joplin, MO
• William D. Byrne McLean, VA
• John Waldhausen, MD Lemoyne, PA
• David Wulkan, MD Boca Raton, FL
• Max Gaspar, MD Seal Beach, CA
• Hugh E. Stephenson, MD Columbia, MO
• Harold C. Urschel, Jr., MD Dallas, TX
• Geoffrey H. White, MD Sydney, Australia
• Henning Loeprecht, MD Augsburg, Germany
Please submit your short meeting reports, comings and goings, upcoming meetings, obituary announcements, etc., to From the Vascular Community in care of vascularspecialist@frontlinemedcom.com.
Meeting News
Reports
The South-Asian Association for Vascular Surgery (SAAVS) held their second annual meeting on May 30, 2013. Founded in 2011, the SAAVS is a member organization of the SVS with a mission to promote vascular health and disseminate the latest in vascular surgical techniques throughout South Asia. In just 2 years, the SAAVS has 100 registered members including 23 from overseas. During the meeting, Dr. Anil Hingorani began his tenure as President and Dr. Dipankar Mukherjee was voted President-Elect. Dr. Anahit Dua was presented an $800 prize for the outstanding resident research award. Dr. Krishna Jain and Dr. Bhagwan Satiani spoke on current issues facing vascular surgeons in the United States while Dr. Kumud Rai and Dr. Ramesh Tripathi spoke on the status of the field in India. The SAAVS is focusing its energy on establishing a "vascular update" with a 2-week didactic and practical course in South Asia. It is actively partnering with vascular societies in India to fulfill its mission. Medical students, trainees, and vascular surgeons from all backgrounds and geographic areas who are interested in advancing vascular care in South Asia are welcome to join. Visit http://saavsociety.org for more information.
Upcoming
The Canadian Society for Vascular Surgery will be holding its annual meeting September 13-14, 2013, at The Westin Edmonton, Edmonton, Alberta, Canada. The invited guest speaker is Dr. Ronald Lee Dalman II, who is the Dr. Walter C. Chidester Professor of Surgery, at Stanford University School of Medicine. Visit http://canadianvascular.ca for more details.
Obituaries
As we are beginning this new section, we are including obituaries from 2012.
Harold Clifton Urschel, Jr.
Dr. Urschel passed away on Nov. 12, 2012, at the age of 82. At the time of his death he was at the American Heart Association meeting in Los Angeles, where he was presenting material on his latest research interest: the use of stem cells for the treatment of heart failure. He was the past president of the Society of Thoracic Surgeons, the Southern Thoracic Surgical Association, the American College of Chest Physicians, and the Texas Surgical Association and a Distinguished fellow of the Society for Vascular Surgery. He has been a Governor of the American College of Surgeons, Chairman of the American Board of Thoracic Surgery, Chairman of the Residency Review Committee for Thoracic Surgery and also a member of every important national and international medical and surgical society.
Max R. Gaspar
Dr. Gaspar, an internationally reputed vascular surgeon, died Oct. 7, 2012. He was 97. Gaspar, formerly of Long Beach, had been chief of vascular surgery for 25 years at Los Angeles County-USC Medical Center, where he also served as attending surgeon for 50 years. He had a practice in Long Beach and had also performed surgeries at St. Mary Medical Center, Memorial Medical Center, and Community Medical. He attended the University of South Dakota Medical School but finished his training at USC in 1938, and earned his M.D. in 1940. During World War II, he served in the Navy as a doctor in the Pacific. Dr. Gaspar remained active in medicine and teaching. About 17 years ago, USC established the Max R. Gaspar Symposium, which addressed a specific topic of interest to physicians and surgeons who care for patients with vascular disease. He also authored numerous articles and contributed about 14 chapters to various texts. He was one of the early pioneers in our field.
Edwin Salzman
Dr. Salzman, a professor of surgery emeritus at Harvard Medical School, died Oct. 3, 2012, at Beth Israel Deaconess Medical Center, in a room not far from his old office. His surgical career was cut short by Parkinson’s disease in the mid-1970s. Turning full attention to the scientific research that had always been his parallel career, he helped pioneer using aspirin to prevent DVT and spent a dozen years working part-time as deputy editor of the New England Journal of Medicine. Along with the findings in the 1970s about aspirin, he made significant contributions to research involving heparin and other methods that prevent postoperative pulmonary embolism.
Geoffrey Hamilton White
Dr. White died peacefully in Australia on Jan. 26, 2012, at the age of 60. He was at UCLA from 1984 to 1989 as Assistant Professor of Surgery at the UCLA School of Medicine and Chief of Vascular Surgery at the VA Wadsworth Medical Center. He was later appointed head of the department at Royal Prince Alfred Hospital and Professor of Vascular Surgery at Macquarie University Hospital, both in Australia. He had a richly deserved international reputation for his many contributions to the development of the endovascular treatment abdominal aortic aneurysms. He also coined the term "endoleak," which is nowpart of the nomenclature.
Deceased Members
(Reported to the SVS as of April 19, 2013; presented in order of receiving):
• Johann Ehrenhaft, MD Iowa City, IA
• J. Harold Harrison, MD Bartow, GA
• George Kish, MD Henderson, NV
• Malcolm Thomas, MD Phoenix, AZ
• Norman Rosenberg, MD Lantana, FL
• Michael Seremetis, MD Washington, DC
• Andrew Michalski, MD St. Catherines, Ontario, Canada
• Dean Wasserman, MD Paramus, NJ
• Duncan W. Campbell, MD Tucson, AZ
• Edwin Salzman, MD Cambridge, MA
• John Vander Woude, MD Sioux Falls, SD
• William A. Holbrook, MD Chevy Chase, MD
• Lewis H. Bosher, MD Richmond, VA
• Joseph Graham, MD Joplin, MO
• William D. Byrne McLean, VA
• John Waldhausen, MD Lemoyne, PA
• David Wulkan, MD Boca Raton, FL
• Max Gaspar, MD Seal Beach, CA
• Hugh E. Stephenson, MD Columbia, MO
• Harold C. Urschel, Jr., MD Dallas, TX
• Geoffrey H. White, MD Sydney, Australia
• Henning Loeprecht, MD Augsburg, Germany
Stopping the ooze
Many of us will do anything or use any product available to stop oozing from suture needle holes. After all, waiting for bleeding to stop is usually not something most vascular surgeons enjoy. Most hemostatic agents are quite expensive and some don’t work very well at all.
Our group has found a cheap alternative that is freely available in every OR and, although not perfect, works well enough in most cases – the standard ultrasonic transmission Doppler gel that you use to listen to arteries in the operative field. We usually have these available in sterile packets (Fig. 1). We cut off one end and squeeze the contents as a large glob onto the patch or anastomosis (Fig. 2). Presumably the weight of the material is enough to stop the needle-hole bleeds.
Active bleeding will usually only occur between stitches and is evidence that another stitch would be prudent. Since the gel is clear, any bleeding is easily seen. We routinely also use protamine reversal for our carotid artery endarterectomies and bypasses and so we leave the jelly on until all the protamine has been given. By that time, the bleeding has almost always stopped.
The jelly can be sucked away (it makes a great sounding noise in the suction!) or just diluted out with saline. I do note on the package insert that Doppler gel is not for internal use, and it is not FDA approved for this indication, but I believe we all use it anyway?
Dr. Samson is a clinical associate professor of surgery (vascular), Florida State University Medical School and a member of Sarasota Vascular Specialists, Sarasota, Fl., and the Medical Editor of Vascular Specialist.
[Editor’s Note: Please submit your own helpful tips and tricks for inclusion in this column to [email protected].]
Many of us will do anything or use any product available to stop oozing from suture needle holes. After all, waiting for bleeding to stop is usually not something most vascular surgeons enjoy. Most hemostatic agents are quite expensive and some don’t work very well at all.
Our group has found a cheap alternative that is freely available in every OR and, although not perfect, works well enough in most cases – the standard ultrasonic transmission Doppler gel that you use to listen to arteries in the operative field. We usually have these available in sterile packets (Fig. 1). We cut off one end and squeeze the contents as a large glob onto the patch or anastomosis (Fig. 2). Presumably the weight of the material is enough to stop the needle-hole bleeds.
Active bleeding will usually only occur between stitches and is evidence that another stitch would be prudent. Since the gel is clear, any bleeding is easily seen. We routinely also use protamine reversal for our carotid artery endarterectomies and bypasses and so we leave the jelly on until all the protamine has been given. By that time, the bleeding has almost always stopped.
The jelly can be sucked away (it makes a great sounding noise in the suction!) or just diluted out with saline. I do note on the package insert that Doppler gel is not for internal use, and it is not FDA approved for this indication, but I believe we all use it anyway?
Dr. Samson is a clinical associate professor of surgery (vascular), Florida State University Medical School and a member of Sarasota Vascular Specialists, Sarasota, Fl., and the Medical Editor of Vascular Specialist.
[Editor’s Note: Please submit your own helpful tips and tricks for inclusion in this column to [email protected].]
Many of us will do anything or use any product available to stop oozing from suture needle holes. After all, waiting for bleeding to stop is usually not something most vascular surgeons enjoy. Most hemostatic agents are quite expensive and some don’t work very well at all.
Our group has found a cheap alternative that is freely available in every OR and, although not perfect, works well enough in most cases – the standard ultrasonic transmission Doppler gel that you use to listen to arteries in the operative field. We usually have these available in sterile packets (Fig. 1). We cut off one end and squeeze the contents as a large glob onto the patch or anastomosis (Fig. 2). Presumably the weight of the material is enough to stop the needle-hole bleeds.
Active bleeding will usually only occur between stitches and is evidence that another stitch would be prudent. Since the gel is clear, any bleeding is easily seen. We routinely also use protamine reversal for our carotid artery endarterectomies and bypasses and so we leave the jelly on until all the protamine has been given. By that time, the bleeding has almost always stopped.
The jelly can be sucked away (it makes a great sounding noise in the suction!) or just diluted out with saline. I do note on the package insert that Doppler gel is not for internal use, and it is not FDA approved for this indication, but I believe we all use it anyway?
Dr. Samson is a clinical associate professor of surgery (vascular), Florida State University Medical School and a member of Sarasota Vascular Specialists, Sarasota, Fl., and the Medical Editor of Vascular Specialist.
[Editor’s Note: Please submit your own helpful tips and tricks for inclusion in this column to [email protected].]
Premature baby is severely handicapped: $21M verdict
AT 31 2/7 WEEKS' GESTATION, a woman was admitted to the hospital for hypertension. A maternal-fetal medicine specialist determined that a vaginal delivery was reasonable as long as the mother and fetus remained clinically stable; a cesarean delivery would be required if the status changed. An ObGyn and nurse midwife took over the mother’s care. Before dinoprostone and oxytocin were administered the next morning, a second ObGyn conducted a vaginal exam and found the mother’s cervix to be 4-cm dilated. After noon, the fetal heart rate became nonreassuring, with late and prolonged variable decelerations. The baby was born shortly after 5:00 pm with the umbilical cord wrapped around his neck. He was pale, lifeless, and had Apgar scores of 4 and 7 at 1 and 5 minutes, respectively. He required initial positive pressure ventilation due to bradycardia and poor respiratory effort.
The boy has cerebral palsy; although not cognitively impaired, he is severely physically handicapped. He has had several operations because one leg is shorter than the other. He has 65% function of his arms, making it impossible for him to complete normal, daily tasks by himself.
PARENTS' CLAIM A cesarean delivery should have been performed 3 hours earlier.
DEFENDANT' DEFENSE Fetal heart-rate monitoring was reassuring during the last 40 minutes of labor. An Apgar score of 7 at 5 minutes is normal. Blood gases taken at birth were normal (7.3 pH). Ultrasonography of the baby’s head at age 3 days showed normal findings. Problems were not evident on the head ultrasound until the child was 2 weeks of age, showing that the injury occurred after birth and was due to prematurity. Defendants included both ObGyns, the midwife, and the hospital.
VERDICT A $21 million Maryland verdict was returned, including $1 million in noneconomic damages that was reduced to $650,000 under the state cap.
PHYSICIAN APOLOGIZED: DIDN'T READ BIOPSY REPORT BEFORE SURGERY
A 34-YEAR-OLD WOMAN with a family history of breast cancer found a lump in her left breast. After fine-needle aspiration, a general surgeon diagnosed cancer and performed a double mastectomy.
At the first postoperative visit, the surgeon told the patient that she did not have breast cancer, and that the fine-needle aspiration results were negative. The surgeon apologized for never looking at the biopsy report prior to surgery, and admitted that is she had seen the report, she would have cancelled surgery.
PATIENT'S CLAIM The surgeon was negligent in performing bilateral mastectomies without first reading biopsy results.
PHYSICIAN'S DEFENSE The case was settled before trial.
VERDICT Michigan case evaluation delivered an award of $542,000, which both parties accepted.
CYSTOSCOPY BLAMED FOR URETERAL OBSTRUCTION, POOR KIDNEY FUNCTION
WHEN A 59-YEAR-OLD WOMAN underwent gynecologic surgery that included a cystoscopy, her uterers were functioning normally. During the following month, the ObGyn performed several follow-up examinations. A year later, the patient's right ureter was completely obstructed. The obstruction was repaired, but the patient lost function in her right kidney. She must take a drug to improve kidney function for the rest of her life.
PATIENT'S CLAIM The obstruction was caused by ligation that occurred during cystoscopy. The ObGyn should have diagnosed the obstruction during the weeks following surgery.
PHYSICIAN'S DEFENSE The cystoscopy was properly performed. The patient had not reported any symptoms after the procedure that suggested the presence of an obstruction. The obstruction gradually developed and could not have been diagnosed earlier.
VERDICT A New York defense verdict was returned.
INFERIOR VENA CAVA DAMAGED DURING ROBOTIC HYSTERECTOMY
A HYSTERECTOMY AND SALPINGO-OOPHORECTOMY were performed on a 64-year-old woman using the da Vinci Surgical System. The gynecologist also removed a cancerous endometrial mass and dissected the periaortic lymph nodes. When the gynecologist used the robot to lift a lymph fat pad, the inferior vena cava was injured and the patient lost 3 L of blood. After converting the laparotomy, a vascular surgeon implanted an artificial graft to repair the inferior vena cava. The patient fully recovered.
PATIENT'S CLAIM The gynecologist did not perform robotic surgery properly, and the patient was not told of all of the risks associated with robotic surgery. Due to the uncertainty regarding the graft's effectiveness, the patient developed posttraumatic stress disorder.
PHYSICIAN'S DEFENSE The vascular injury was a known risk associated with the procedure. The vena cava was not lacerated or transected: perforator veins that joined the lymph fat pad were unintentionally pulled out. The injury was most likely due to the application of pressure, not laceration by the surgical instrument.
VERDICT A $300,000 New York settlement was reached.
READ: The robot is gaining ground in gynecologic surgery. Should you be using it? A roundtable discussion with Arnold P. Advincula, MD; Cheryl B. Iglesia, MD; Rosanne M. Kho, MD; Jamal Mourad, DO; Marie Fidela R. Paraiso, MD; and Jason D. Wright, MD (April 2013)
FETAL DISTRESS CAUSED BRAIN INJURY: $13.9M
DURING THE LAST 2 HOURS OF LABOR, the mother was febrile, the baby's heart rate rose to over 160 bpm, and fetal monitoring indicated fetal distress. Oxytocin was administered to hasten delivery, but the mother's uterus became hyperstimulated. After nearly 17 hours of labor, the child was born without respirations. A video of the vaginal birth shows that the child was blue and unresponsive. The baby was resuscitated, and was subsequently found to have cerebral palsy, epilepsy, and mental retardation. At the time of trial, the 10-year-old had the mental capacity of a 3-year-old.
PARENTS' CLAIM The child suffered brain injury due to hypoxic ischemic encephalopathy. A cesarean delivery should have been performed as soon as fetal distress was evident. The doctors and nurses misread the baseline heart rate, and did not react when the baby did not recover well from the mother's contractions. Brain imaging did not show damage caused by infection or meningitis.
PHYSICIAN'S DEFENSE The girl's condition was caused by an infection or meningitis.
VERDICT A confidential settlement was reached with the midwife before the trial. The ObGyn was dismissed because he was never alerted to any problem by the labor and delivery team. A $13.9 million Georgia verdict was returned against the hospital system.
UTERINE ARTERY INJURED DURING CESAREAN DELIVERY
AFTER A SCHEDULED CESAREAN delivery, the 29-year-old mother had low blood pressure and an altered state of consciousness When she returned to the OR several hours later, her ObGyn found a uterine artery hematoma and laceration. After the laceration was clamped and sutured, uterine atony was noted and an emergency hysterectomy was performed
PATIENT'S CLAIM The mother was no longer able to bear children. The ObGyn was negligent in lacerating the uterine artery, failing to recognize the laceration during cesarean surgery, failing to properly monitor the patient after surgery, and failing to repair the artery in a timely manner. The patient's low blood pressure and altered state of consciousness should have been an indication that she had severe blood loss. The hospital's nursing staff failed to properly check her vital signs after surgery, and failed to report the abnormalities in blood pressure and consciousness to the ObGyn.
DEFENDANTS' DEFENSE The ObGyn claimed that a uterine laceration is a known risk of cesarean delivery; it can occur in the absence of negligence. The hospital also denied negligence.
VERDICT A Texas defense verdict was returned.
These cases were selected by the editors of OBG Management from Medical Malpractice Verdicts, Settlements & Experts, with permission of the editor, Lewis Laska (www.versictslaska.com). The information available to the editors about the cases presented here is sometimes incomplete. Moreover, the cases may or may not have merit. Nevertheless, these cases represent the types of clinical situations that typically result in litigation and are meant to illustrate nationwide variation in jury verdicts and awards.
AT 31 2/7 WEEKS' GESTATION, a woman was admitted to the hospital for hypertension. A maternal-fetal medicine specialist determined that a vaginal delivery was reasonable as long as the mother and fetus remained clinically stable; a cesarean delivery would be required if the status changed. An ObGyn and nurse midwife took over the mother’s care. Before dinoprostone and oxytocin were administered the next morning, a second ObGyn conducted a vaginal exam and found the mother’s cervix to be 4-cm dilated. After noon, the fetal heart rate became nonreassuring, with late and prolonged variable decelerations. The baby was born shortly after 5:00 pm with the umbilical cord wrapped around his neck. He was pale, lifeless, and had Apgar scores of 4 and 7 at 1 and 5 minutes, respectively. He required initial positive pressure ventilation due to bradycardia and poor respiratory effort.
The boy has cerebral palsy; although not cognitively impaired, he is severely physically handicapped. He has had several operations because one leg is shorter than the other. He has 65% function of his arms, making it impossible for him to complete normal, daily tasks by himself.
PARENTS' CLAIM A cesarean delivery should have been performed 3 hours earlier.
DEFENDANT' DEFENSE Fetal heart-rate monitoring was reassuring during the last 40 minutes of labor. An Apgar score of 7 at 5 minutes is normal. Blood gases taken at birth were normal (7.3 pH). Ultrasonography of the baby’s head at age 3 days showed normal findings. Problems were not evident on the head ultrasound until the child was 2 weeks of age, showing that the injury occurred after birth and was due to prematurity. Defendants included both ObGyns, the midwife, and the hospital.
VERDICT A $21 million Maryland verdict was returned, including $1 million in noneconomic damages that was reduced to $650,000 under the state cap.
PHYSICIAN APOLOGIZED: DIDN'T READ BIOPSY REPORT BEFORE SURGERY
A 34-YEAR-OLD WOMAN with a family history of breast cancer found a lump in her left breast. After fine-needle aspiration, a general surgeon diagnosed cancer and performed a double mastectomy.
At the first postoperative visit, the surgeon told the patient that she did not have breast cancer, and that the fine-needle aspiration results were negative. The surgeon apologized for never looking at the biopsy report prior to surgery, and admitted that is she had seen the report, she would have cancelled surgery.
PATIENT'S CLAIM The surgeon was negligent in performing bilateral mastectomies without first reading biopsy results.
PHYSICIAN'S DEFENSE The case was settled before trial.
VERDICT Michigan case evaluation delivered an award of $542,000, which both parties accepted.
CYSTOSCOPY BLAMED FOR URETERAL OBSTRUCTION, POOR KIDNEY FUNCTION
WHEN A 59-YEAR-OLD WOMAN underwent gynecologic surgery that included a cystoscopy, her uterers were functioning normally. During the following month, the ObGyn performed several follow-up examinations. A year later, the patient's right ureter was completely obstructed. The obstruction was repaired, but the patient lost function in her right kidney. She must take a drug to improve kidney function for the rest of her life.
PATIENT'S CLAIM The obstruction was caused by ligation that occurred during cystoscopy. The ObGyn should have diagnosed the obstruction during the weeks following surgery.
PHYSICIAN'S DEFENSE The cystoscopy was properly performed. The patient had not reported any symptoms after the procedure that suggested the presence of an obstruction. The obstruction gradually developed and could not have been diagnosed earlier.
VERDICT A New York defense verdict was returned.
INFERIOR VENA CAVA DAMAGED DURING ROBOTIC HYSTERECTOMY
A HYSTERECTOMY AND SALPINGO-OOPHORECTOMY were performed on a 64-year-old woman using the da Vinci Surgical System. The gynecologist also removed a cancerous endometrial mass and dissected the periaortic lymph nodes. When the gynecologist used the robot to lift a lymph fat pad, the inferior vena cava was injured and the patient lost 3 L of blood. After converting the laparotomy, a vascular surgeon implanted an artificial graft to repair the inferior vena cava. The patient fully recovered.
PATIENT'S CLAIM The gynecologist did not perform robotic surgery properly, and the patient was not told of all of the risks associated with robotic surgery. Due to the uncertainty regarding the graft's effectiveness, the patient developed posttraumatic stress disorder.
PHYSICIAN'S DEFENSE The vascular injury was a known risk associated with the procedure. The vena cava was not lacerated or transected: perforator veins that joined the lymph fat pad were unintentionally pulled out. The injury was most likely due to the application of pressure, not laceration by the surgical instrument.
VERDICT A $300,000 New York settlement was reached.
READ: The robot is gaining ground in gynecologic surgery. Should you be using it? A roundtable discussion with Arnold P. Advincula, MD; Cheryl B. Iglesia, MD; Rosanne M. Kho, MD; Jamal Mourad, DO; Marie Fidela R. Paraiso, MD; and Jason D. Wright, MD (April 2013)
FETAL DISTRESS CAUSED BRAIN INJURY: $13.9M
DURING THE LAST 2 HOURS OF LABOR, the mother was febrile, the baby's heart rate rose to over 160 bpm, and fetal monitoring indicated fetal distress. Oxytocin was administered to hasten delivery, but the mother's uterus became hyperstimulated. After nearly 17 hours of labor, the child was born without respirations. A video of the vaginal birth shows that the child was blue and unresponsive. The baby was resuscitated, and was subsequently found to have cerebral palsy, epilepsy, and mental retardation. At the time of trial, the 10-year-old had the mental capacity of a 3-year-old.
PARENTS' CLAIM The child suffered brain injury due to hypoxic ischemic encephalopathy. A cesarean delivery should have been performed as soon as fetal distress was evident. The doctors and nurses misread the baseline heart rate, and did not react when the baby did not recover well from the mother's contractions. Brain imaging did not show damage caused by infection or meningitis.
PHYSICIAN'S DEFENSE The girl's condition was caused by an infection or meningitis.
VERDICT A confidential settlement was reached with the midwife before the trial. The ObGyn was dismissed because he was never alerted to any problem by the labor and delivery team. A $13.9 million Georgia verdict was returned against the hospital system.
UTERINE ARTERY INJURED DURING CESAREAN DELIVERY
AFTER A SCHEDULED CESAREAN delivery, the 29-year-old mother had low blood pressure and an altered state of consciousness When she returned to the OR several hours later, her ObGyn found a uterine artery hematoma and laceration. After the laceration was clamped and sutured, uterine atony was noted and an emergency hysterectomy was performed
PATIENT'S CLAIM The mother was no longer able to bear children. The ObGyn was negligent in lacerating the uterine artery, failing to recognize the laceration during cesarean surgery, failing to properly monitor the patient after surgery, and failing to repair the artery in a timely manner. The patient's low blood pressure and altered state of consciousness should have been an indication that she had severe blood loss. The hospital's nursing staff failed to properly check her vital signs after surgery, and failed to report the abnormalities in blood pressure and consciousness to the ObGyn.
DEFENDANTS' DEFENSE The ObGyn claimed that a uterine laceration is a known risk of cesarean delivery; it can occur in the absence of negligence. The hospital also denied negligence.
VERDICT A Texas defense verdict was returned.
These cases were selected by the editors of OBG Management from Medical Malpractice Verdicts, Settlements & Experts, with permission of the editor, Lewis Laska (www.versictslaska.com). The information available to the editors about the cases presented here is sometimes incomplete. Moreover, the cases may or may not have merit. Nevertheless, these cases represent the types of clinical situations that typically result in litigation and are meant to illustrate nationwide variation in jury verdicts and awards.
AT 31 2/7 WEEKS' GESTATION, a woman was admitted to the hospital for hypertension. A maternal-fetal medicine specialist determined that a vaginal delivery was reasonable as long as the mother and fetus remained clinically stable; a cesarean delivery would be required if the status changed. An ObGyn and nurse midwife took over the mother’s care. Before dinoprostone and oxytocin were administered the next morning, a second ObGyn conducted a vaginal exam and found the mother’s cervix to be 4-cm dilated. After noon, the fetal heart rate became nonreassuring, with late and prolonged variable decelerations. The baby was born shortly after 5:00 pm with the umbilical cord wrapped around his neck. He was pale, lifeless, and had Apgar scores of 4 and 7 at 1 and 5 minutes, respectively. He required initial positive pressure ventilation due to bradycardia and poor respiratory effort.
The boy has cerebral palsy; although not cognitively impaired, he is severely physically handicapped. He has had several operations because one leg is shorter than the other. He has 65% function of his arms, making it impossible for him to complete normal, daily tasks by himself.
PARENTS' CLAIM A cesarean delivery should have been performed 3 hours earlier.
DEFENDANT' DEFENSE Fetal heart-rate monitoring was reassuring during the last 40 minutes of labor. An Apgar score of 7 at 5 minutes is normal. Blood gases taken at birth were normal (7.3 pH). Ultrasonography of the baby’s head at age 3 days showed normal findings. Problems were not evident on the head ultrasound until the child was 2 weeks of age, showing that the injury occurred after birth and was due to prematurity. Defendants included both ObGyns, the midwife, and the hospital.
VERDICT A $21 million Maryland verdict was returned, including $1 million in noneconomic damages that was reduced to $650,000 under the state cap.
PHYSICIAN APOLOGIZED: DIDN'T READ BIOPSY REPORT BEFORE SURGERY
A 34-YEAR-OLD WOMAN with a family history of breast cancer found a lump in her left breast. After fine-needle aspiration, a general surgeon diagnosed cancer and performed a double mastectomy.
At the first postoperative visit, the surgeon told the patient that she did not have breast cancer, and that the fine-needle aspiration results were negative. The surgeon apologized for never looking at the biopsy report prior to surgery, and admitted that is she had seen the report, she would have cancelled surgery.
PATIENT'S CLAIM The surgeon was negligent in performing bilateral mastectomies without first reading biopsy results.
PHYSICIAN'S DEFENSE The case was settled before trial.
VERDICT Michigan case evaluation delivered an award of $542,000, which both parties accepted.
CYSTOSCOPY BLAMED FOR URETERAL OBSTRUCTION, POOR KIDNEY FUNCTION
WHEN A 59-YEAR-OLD WOMAN underwent gynecologic surgery that included a cystoscopy, her uterers were functioning normally. During the following month, the ObGyn performed several follow-up examinations. A year later, the patient's right ureter was completely obstructed. The obstruction was repaired, but the patient lost function in her right kidney. She must take a drug to improve kidney function for the rest of her life.
PATIENT'S CLAIM The obstruction was caused by ligation that occurred during cystoscopy. The ObGyn should have diagnosed the obstruction during the weeks following surgery.
PHYSICIAN'S DEFENSE The cystoscopy was properly performed. The patient had not reported any symptoms after the procedure that suggested the presence of an obstruction. The obstruction gradually developed and could not have been diagnosed earlier.
VERDICT A New York defense verdict was returned.
INFERIOR VENA CAVA DAMAGED DURING ROBOTIC HYSTERECTOMY
A HYSTERECTOMY AND SALPINGO-OOPHORECTOMY were performed on a 64-year-old woman using the da Vinci Surgical System. The gynecologist also removed a cancerous endometrial mass and dissected the periaortic lymph nodes. When the gynecologist used the robot to lift a lymph fat pad, the inferior vena cava was injured and the patient lost 3 L of blood. After converting the laparotomy, a vascular surgeon implanted an artificial graft to repair the inferior vena cava. The patient fully recovered.
PATIENT'S CLAIM The gynecologist did not perform robotic surgery properly, and the patient was not told of all of the risks associated with robotic surgery. Due to the uncertainty regarding the graft's effectiveness, the patient developed posttraumatic stress disorder.
PHYSICIAN'S DEFENSE The vascular injury was a known risk associated with the procedure. The vena cava was not lacerated or transected: perforator veins that joined the lymph fat pad were unintentionally pulled out. The injury was most likely due to the application of pressure, not laceration by the surgical instrument.
VERDICT A $300,000 New York settlement was reached.
READ: The robot is gaining ground in gynecologic surgery. Should you be using it? A roundtable discussion with Arnold P. Advincula, MD; Cheryl B. Iglesia, MD; Rosanne M. Kho, MD; Jamal Mourad, DO; Marie Fidela R. Paraiso, MD; and Jason D. Wright, MD (April 2013)
FETAL DISTRESS CAUSED BRAIN INJURY: $13.9M
DURING THE LAST 2 HOURS OF LABOR, the mother was febrile, the baby's heart rate rose to over 160 bpm, and fetal monitoring indicated fetal distress. Oxytocin was administered to hasten delivery, but the mother's uterus became hyperstimulated. After nearly 17 hours of labor, the child was born without respirations. A video of the vaginal birth shows that the child was blue and unresponsive. The baby was resuscitated, and was subsequently found to have cerebral palsy, epilepsy, and mental retardation. At the time of trial, the 10-year-old had the mental capacity of a 3-year-old.
PARENTS' CLAIM The child suffered brain injury due to hypoxic ischemic encephalopathy. A cesarean delivery should have been performed as soon as fetal distress was evident. The doctors and nurses misread the baseline heart rate, and did not react when the baby did not recover well from the mother's contractions. Brain imaging did not show damage caused by infection or meningitis.
PHYSICIAN'S DEFENSE The girl's condition was caused by an infection or meningitis.
VERDICT A confidential settlement was reached with the midwife before the trial. The ObGyn was dismissed because he was never alerted to any problem by the labor and delivery team. A $13.9 million Georgia verdict was returned against the hospital system.
UTERINE ARTERY INJURED DURING CESAREAN DELIVERY
AFTER A SCHEDULED CESAREAN delivery, the 29-year-old mother had low blood pressure and an altered state of consciousness When she returned to the OR several hours later, her ObGyn found a uterine artery hematoma and laceration. After the laceration was clamped and sutured, uterine atony was noted and an emergency hysterectomy was performed
PATIENT'S CLAIM The mother was no longer able to bear children. The ObGyn was negligent in lacerating the uterine artery, failing to recognize the laceration during cesarean surgery, failing to properly monitor the patient after surgery, and failing to repair the artery in a timely manner. The patient's low blood pressure and altered state of consciousness should have been an indication that she had severe blood loss. The hospital's nursing staff failed to properly check her vital signs after surgery, and failed to report the abnormalities in blood pressure and consciousness to the ObGyn.
DEFENDANTS' DEFENSE The ObGyn claimed that a uterine laceration is a known risk of cesarean delivery; it can occur in the absence of negligence. The hospital also denied negligence.
VERDICT A Texas defense verdict was returned.
These cases were selected by the editors of OBG Management from Medical Malpractice Verdicts, Settlements & Experts, with permission of the editor, Lewis Laska (www.versictslaska.com). The information available to the editors about the cases presented here is sometimes incomplete. Moreover, the cases may or may not have merit. Nevertheless, these cases represent the types of clinical situations that typically result in litigation and are meant to illustrate nationwide variation in jury verdicts and awards.
SVS Resident Research Prize given to AAA study
Dr. Nathan D. Airhart, Washington University School of Medicine, St. Louis, was the recipient of this year’s SVS Foundation Resident Research Prize Paper, which was presented at the Vascular Annual Meeting as part of the William J. von Liebig Forum, which features the best in resident research.
Dr. Airhart, his mentor, Dr. John A. Curci, and his colleagues studied the specific contribution of the vascular smooth muscle cells (SMCs) to the destruction of the elastic proteins that are uniquely absent in the walls of abdominal aortic aneurysms (AAAs). "Although the SMC is the dominant cell type in the aortic wall, our understanding of the role of these cells in aneurysms has been very limited," said Dr. Airhart.
To directly study the function of these cells, Dr. Airhart and his colleagues embarked on an ambitious project to isolate live SMCs from AAAs, normal abdominal aorta (NAA), and plaque from carotid endarterectomy (CEA) procedures. The group profiled the mRNA produced by these cultured cells by microarray and clearly demonstrated a unique pattern of expression of the AAA-SMC.
"The mRNA profiles confirmed that the AAA cells were likely interacting with the matrix differently than the other SMCs, but it did not necessarily tell us how they were influencing aneurysm development," said Dr. Airhart. To better understand the role of these cells, the investigators evaluated the ability of these cells to break down elastic fibers in culture.
Under standard culture conditions, AAA-SMCs were able to degrade three times more elastin than the NAA-SMCs. "Even more remarkable was the finding that co-culture with activated macrophages – a cell type always found in the wall of aneurysms – resulted in a further doubling of the elastic fiber damage by the AAA-SMCs. Co-culture of macrophages with NAA-SMCs had no effect on the elastin degraded," said Dr. Airhart.
Further experiments suggested that the enzymes principally responsible for the elastolytic activity of these cells are the matrix metalloproteinases (MMPs). Increases in the production and/or activation of MMP-2 and/or MMP-9 were prominently found in cultures of AAA-SMCs.
"These studies present the strongest evidence that AAA-SMCs exhibit a disease-specific gene expression pattern and can very potently damage the elastic fiber matrix in the aortic wall. The unique and remarkable synergy with activated inflammatory cells might help explain the characteristic elastin loss of aortic aneurysms. Future studies will allow us to understand and alter the cellular mechanisms which lead to increased production and activation of elastolytic MMPs by these cells," Dr. Curci concluded.
The prestigious Resident Research Prize is intended to motivate new physicians to pursue vascular research. The prize recipient is invited to present his or her research results at the Society for Vascular Surgery’s Vascular Annual Meeting and the prize includes a 1-year subscription to the Journal of Vascular Surgery.
Dr. Nathan D. Airhart, Washington University School of Medicine, St. Louis, was the recipient of this year’s SVS Foundation Resident Research Prize Paper, which was presented at the Vascular Annual Meeting as part of the William J. von Liebig Forum, which features the best in resident research.
Dr. Airhart, his mentor, Dr. John A. Curci, and his colleagues studied the specific contribution of the vascular smooth muscle cells (SMCs) to the destruction of the elastic proteins that are uniquely absent in the walls of abdominal aortic aneurysms (AAAs). "Although the SMC is the dominant cell type in the aortic wall, our understanding of the role of these cells in aneurysms has been very limited," said Dr. Airhart.
To directly study the function of these cells, Dr. Airhart and his colleagues embarked on an ambitious project to isolate live SMCs from AAAs, normal abdominal aorta (NAA), and plaque from carotid endarterectomy (CEA) procedures. The group profiled the mRNA produced by these cultured cells by microarray and clearly demonstrated a unique pattern of expression of the AAA-SMC.
"The mRNA profiles confirmed that the AAA cells were likely interacting with the matrix differently than the other SMCs, but it did not necessarily tell us how they were influencing aneurysm development," said Dr. Airhart. To better understand the role of these cells, the investigators evaluated the ability of these cells to break down elastic fibers in culture.
Under standard culture conditions, AAA-SMCs were able to degrade three times more elastin than the NAA-SMCs. "Even more remarkable was the finding that co-culture with activated macrophages – a cell type always found in the wall of aneurysms – resulted in a further doubling of the elastic fiber damage by the AAA-SMCs. Co-culture of macrophages with NAA-SMCs had no effect on the elastin degraded," said Dr. Airhart.
Further experiments suggested that the enzymes principally responsible for the elastolytic activity of these cells are the matrix metalloproteinases (MMPs). Increases in the production and/or activation of MMP-2 and/or MMP-9 were prominently found in cultures of AAA-SMCs.
"These studies present the strongest evidence that AAA-SMCs exhibit a disease-specific gene expression pattern and can very potently damage the elastic fiber matrix in the aortic wall. The unique and remarkable synergy with activated inflammatory cells might help explain the characteristic elastin loss of aortic aneurysms. Future studies will allow us to understand and alter the cellular mechanisms which lead to increased production and activation of elastolytic MMPs by these cells," Dr. Curci concluded.
The prestigious Resident Research Prize is intended to motivate new physicians to pursue vascular research. The prize recipient is invited to present his or her research results at the Society for Vascular Surgery’s Vascular Annual Meeting and the prize includes a 1-year subscription to the Journal of Vascular Surgery.
Dr. Nathan D. Airhart, Washington University School of Medicine, St. Louis, was the recipient of this year’s SVS Foundation Resident Research Prize Paper, which was presented at the Vascular Annual Meeting as part of the William J. von Liebig Forum, which features the best in resident research.
Dr. Airhart, his mentor, Dr. John A. Curci, and his colleagues studied the specific contribution of the vascular smooth muscle cells (SMCs) to the destruction of the elastic proteins that are uniquely absent in the walls of abdominal aortic aneurysms (AAAs). "Although the SMC is the dominant cell type in the aortic wall, our understanding of the role of these cells in aneurysms has been very limited," said Dr. Airhart.
To directly study the function of these cells, Dr. Airhart and his colleagues embarked on an ambitious project to isolate live SMCs from AAAs, normal abdominal aorta (NAA), and plaque from carotid endarterectomy (CEA) procedures. The group profiled the mRNA produced by these cultured cells by microarray and clearly demonstrated a unique pattern of expression of the AAA-SMC.
"The mRNA profiles confirmed that the AAA cells were likely interacting with the matrix differently than the other SMCs, but it did not necessarily tell us how they were influencing aneurysm development," said Dr. Airhart. To better understand the role of these cells, the investigators evaluated the ability of these cells to break down elastic fibers in culture.
Under standard culture conditions, AAA-SMCs were able to degrade three times more elastin than the NAA-SMCs. "Even more remarkable was the finding that co-culture with activated macrophages – a cell type always found in the wall of aneurysms – resulted in a further doubling of the elastic fiber damage by the AAA-SMCs. Co-culture of macrophages with NAA-SMCs had no effect on the elastin degraded," said Dr. Airhart.
Further experiments suggested that the enzymes principally responsible for the elastolytic activity of these cells are the matrix metalloproteinases (MMPs). Increases in the production and/or activation of MMP-2 and/or MMP-9 were prominently found in cultures of AAA-SMCs.
"These studies present the strongest evidence that AAA-SMCs exhibit a disease-specific gene expression pattern and can very potently damage the elastic fiber matrix in the aortic wall. The unique and remarkable synergy with activated inflammatory cells might help explain the characteristic elastin loss of aortic aneurysms. Future studies will allow us to understand and alter the cellular mechanisms which lead to increased production and activation of elastolytic MMPs by these cells," Dr. Curci concluded.
The prestigious Resident Research Prize is intended to motivate new physicians to pursue vascular research. The prize recipient is invited to present his or her research results at the Society for Vascular Surgery’s Vascular Annual Meeting and the prize includes a 1-year subscription to the Journal of Vascular Surgery.
Veith's Views: Second opinions are overrated
A middle aged man goes to his primary care physician for his annual check-up. Because of an abnormal physical finding or laboratory test, he is referred to a specialist, who, after additional tests, recommends an operation with considerable risks. Before agreeing to the procedure, the man decides to seek a "second opinion." This sequence of events occurs routinely as the second opinion is generally accepted as one of the sacred cows of American medical care.
Let’s examine this sacred cow to see if it is a good thing or an overrated practice that serves little useful purpose. First the potential advantages. If the second specialist agrees with the first opinion, it can be reassuring to the patient and his family, but it really is unnecessary.
On the other hand, if the original specialist is less than optimal or motivated by the financial rewards of performing his recommended procedure, the second opinion can possibly benefit the patient by saving him from an unnecessary, wrong or possibly harmful operation. However, why not solicit the opinion of the second, better specialist first.
Now the downside. If the second specialist disagrees with the first, the patient faces a dilemma. He has to pick between the two specialists. How does he do this? Does he follow the advice of the more articulate and likeable specialist? Does he pick the opinion he likes despite being a non-expert? Does he solicit a third opinion – a tie breaker? Taking a vote on a medical or scientific question does not ensure arriving at the correct answer – especially if the vote is 2:1 and especially if one of the specialists is self-appointed or a full-fledged phony. So disagreement between the first and second specialist does not ensure better care. It can lead to confusion and uncertainty. It may lead to the wrong course of action. Our second opinion process may therefore be unnecessary or misleading, and is in reality not worth much.
What should replace this flawed sacred cow? In principle it is simple, in practice not so simple. The first specialist referral should be to an exemplary medical practitioner, one whose knowledge, judgment, skill level, and motivation can be trusted. Finding such an individual is complex. Referral patterns can be flawed and based on proximity, personality or economic considerations.
Examining a "top doctors list" can also be misleading since inclusion in some of these listings can be based on flawed criteria or even payment of a fee. Similar considerations may apply to some listings of top hospitals. Moreover, not every specialist in top hospitals is expert in all aspects of his or her specialty.
The key to finding an initial exemplary specialist whose first opinion can be trusted is to have that specialist identified by another knowledgeable physician who represents the patient’s interests. Such a "physician-trustee" can be a primary care physician with whom the patient has a solid relationship.
Alternatively, it can be a physician who is a friend, relative or acquaintance. In either case the physician-trustee has to take the time and make the effort to identify specialists he knows in the field in which the patient needs care. The physician-trustee must then make the additional effort to use these contacts to identify a first-rate specialist in the field and to explore the qualities, reputation, and results of this specialist by speaking to those who have worked with him directly and know him first hand.
Making such an effort is not a casual business in today’s complex medical environment. Yet it is one for which there is no other substitute. I have done it for friends and family on a number of occasions – often for patients who live in other cities and countries. It may take a number of phone calls to individuals in my own and other specialties within my own and other institutions. It does, however, produce positive results and solve the problem.
Unfortunately many who require expert specialty care in the United States do not have access to a dependable primary care giver or a trusted physician friend or relative who can serve as a physician-trustee.
Moreover, many insurance plans discourage specialist referrals or will only cover the costs of their selected, less than optimal in-network specialists. Finally, in the U.S. health care system even under the Affordable Care Act, no financial compensation is provided for physician-trustee services and the time and effort involved.
This deficiency must be corrected since physician-trustees can provide a uniquely valuable service. They can eliminate unnecessary financially motivated procedures; they facilitate identification of genuinely superior care-givers; and they enable patients to obtain referral to a specialist whose first opinion can be counted on to be dependable and who will deliver exemplary care. They also obviate the need for flawed and unnecessary second opinions.
Dr. Veith is Professor of Surgery at New York University Medical Center and the Cleveland Clinic. He is an associate medical editor for Vascular Specialist.
The ideas and opinions expressed in Vascular Specialist do not necessarily reflect those of the Society or Publisher.
A middle aged man goes to his primary care physician for his annual check-up. Because of an abnormal physical finding or laboratory test, he is referred to a specialist, who, after additional tests, recommends an operation with considerable risks. Before agreeing to the procedure, the man decides to seek a "second opinion." This sequence of events occurs routinely as the second opinion is generally accepted as one of the sacred cows of American medical care.
Let’s examine this sacred cow to see if it is a good thing or an overrated practice that serves little useful purpose. First the potential advantages. If the second specialist agrees with the first opinion, it can be reassuring to the patient and his family, but it really is unnecessary.
On the other hand, if the original specialist is less than optimal or motivated by the financial rewards of performing his recommended procedure, the second opinion can possibly benefit the patient by saving him from an unnecessary, wrong or possibly harmful operation. However, why not solicit the opinion of the second, better specialist first.
Now the downside. If the second specialist disagrees with the first, the patient faces a dilemma. He has to pick between the two specialists. How does he do this? Does he follow the advice of the more articulate and likeable specialist? Does he pick the opinion he likes despite being a non-expert? Does he solicit a third opinion – a tie breaker? Taking a vote on a medical or scientific question does not ensure arriving at the correct answer – especially if the vote is 2:1 and especially if one of the specialists is self-appointed or a full-fledged phony. So disagreement between the first and second specialist does not ensure better care. It can lead to confusion and uncertainty. It may lead to the wrong course of action. Our second opinion process may therefore be unnecessary or misleading, and is in reality not worth much.
What should replace this flawed sacred cow? In principle it is simple, in practice not so simple. The first specialist referral should be to an exemplary medical practitioner, one whose knowledge, judgment, skill level, and motivation can be trusted. Finding such an individual is complex. Referral patterns can be flawed and based on proximity, personality or economic considerations.
Examining a "top doctors list" can also be misleading since inclusion in some of these listings can be based on flawed criteria or even payment of a fee. Similar considerations may apply to some listings of top hospitals. Moreover, not every specialist in top hospitals is expert in all aspects of his or her specialty.
The key to finding an initial exemplary specialist whose first opinion can be trusted is to have that specialist identified by another knowledgeable physician who represents the patient’s interests. Such a "physician-trustee" can be a primary care physician with whom the patient has a solid relationship.
Alternatively, it can be a physician who is a friend, relative or acquaintance. In either case the physician-trustee has to take the time and make the effort to identify specialists he knows in the field in which the patient needs care. The physician-trustee must then make the additional effort to use these contacts to identify a first-rate specialist in the field and to explore the qualities, reputation, and results of this specialist by speaking to those who have worked with him directly and know him first hand.
Making such an effort is not a casual business in today’s complex medical environment. Yet it is one for which there is no other substitute. I have done it for friends and family on a number of occasions – often for patients who live in other cities and countries. It may take a number of phone calls to individuals in my own and other specialties within my own and other institutions. It does, however, produce positive results and solve the problem.
Unfortunately many who require expert specialty care in the United States do not have access to a dependable primary care giver or a trusted physician friend or relative who can serve as a physician-trustee.
Moreover, many insurance plans discourage specialist referrals or will only cover the costs of their selected, less than optimal in-network specialists. Finally, in the U.S. health care system even under the Affordable Care Act, no financial compensation is provided for physician-trustee services and the time and effort involved.
This deficiency must be corrected since physician-trustees can provide a uniquely valuable service. They can eliminate unnecessary financially motivated procedures; they facilitate identification of genuinely superior care-givers; and they enable patients to obtain referral to a specialist whose first opinion can be counted on to be dependable and who will deliver exemplary care. They also obviate the need for flawed and unnecessary second opinions.
Dr. Veith is Professor of Surgery at New York University Medical Center and the Cleveland Clinic. He is an associate medical editor for Vascular Specialist.
The ideas and opinions expressed in Vascular Specialist do not necessarily reflect those of the Society or Publisher.
A middle aged man goes to his primary care physician for his annual check-up. Because of an abnormal physical finding or laboratory test, he is referred to a specialist, who, after additional tests, recommends an operation with considerable risks. Before agreeing to the procedure, the man decides to seek a "second opinion." This sequence of events occurs routinely as the second opinion is generally accepted as one of the sacred cows of American medical care.
Let’s examine this sacred cow to see if it is a good thing or an overrated practice that serves little useful purpose. First the potential advantages. If the second specialist agrees with the first opinion, it can be reassuring to the patient and his family, but it really is unnecessary.
On the other hand, if the original specialist is less than optimal or motivated by the financial rewards of performing his recommended procedure, the second opinion can possibly benefit the patient by saving him from an unnecessary, wrong or possibly harmful operation. However, why not solicit the opinion of the second, better specialist first.
Now the downside. If the second specialist disagrees with the first, the patient faces a dilemma. He has to pick between the two specialists. How does he do this? Does he follow the advice of the more articulate and likeable specialist? Does he pick the opinion he likes despite being a non-expert? Does he solicit a third opinion – a tie breaker? Taking a vote on a medical or scientific question does not ensure arriving at the correct answer – especially if the vote is 2:1 and especially if one of the specialists is self-appointed or a full-fledged phony. So disagreement between the first and second specialist does not ensure better care. It can lead to confusion and uncertainty. It may lead to the wrong course of action. Our second opinion process may therefore be unnecessary or misleading, and is in reality not worth much.
What should replace this flawed sacred cow? In principle it is simple, in practice not so simple. The first specialist referral should be to an exemplary medical practitioner, one whose knowledge, judgment, skill level, and motivation can be trusted. Finding such an individual is complex. Referral patterns can be flawed and based on proximity, personality or economic considerations.
Examining a "top doctors list" can also be misleading since inclusion in some of these listings can be based on flawed criteria or even payment of a fee. Similar considerations may apply to some listings of top hospitals. Moreover, not every specialist in top hospitals is expert in all aspects of his or her specialty.
The key to finding an initial exemplary specialist whose first opinion can be trusted is to have that specialist identified by another knowledgeable physician who represents the patient’s interests. Such a "physician-trustee" can be a primary care physician with whom the patient has a solid relationship.
Alternatively, it can be a physician who is a friend, relative or acquaintance. In either case the physician-trustee has to take the time and make the effort to identify specialists he knows in the field in which the patient needs care. The physician-trustee must then make the additional effort to use these contacts to identify a first-rate specialist in the field and to explore the qualities, reputation, and results of this specialist by speaking to those who have worked with him directly and know him first hand.
Making such an effort is not a casual business in today’s complex medical environment. Yet it is one for which there is no other substitute. I have done it for friends and family on a number of occasions – often for patients who live in other cities and countries. It may take a number of phone calls to individuals in my own and other specialties within my own and other institutions. It does, however, produce positive results and solve the problem.
Unfortunately many who require expert specialty care in the United States do not have access to a dependable primary care giver or a trusted physician friend or relative who can serve as a physician-trustee.
Moreover, many insurance plans discourage specialist referrals or will only cover the costs of their selected, less than optimal in-network specialists. Finally, in the U.S. health care system even under the Affordable Care Act, no financial compensation is provided for physician-trustee services and the time and effort involved.
This deficiency must be corrected since physician-trustees can provide a uniquely valuable service. They can eliminate unnecessary financially motivated procedures; they facilitate identification of genuinely superior care-givers; and they enable patients to obtain referral to a specialist whose first opinion can be counted on to be dependable and who will deliver exemplary care. They also obviate the need for flawed and unnecessary second opinions.
Dr. Veith is Professor of Surgery at New York University Medical Center and the Cleveland Clinic. He is an associate medical editor for Vascular Specialist.
The ideas and opinions expressed in Vascular Specialist do not necessarily reflect those of the Society or Publisher.