Racial-Ethnic Disparities in Quality of Care Among Medicaid Beneficiaries With Schizophrenia.

OBJECTIVE: The authors sought to update and expand the evidence on the quality of health care and disparities in care among Medicaid beneficiaries with schizophrenia. METHODS: Adult beneficiaries of New York State Medicaid with schizophrenia receiving care during 2016-2019 were identified. Composite quality scores were derived from item response theory models by using evidence-based indicators of the quality of mental and general medical health care. Risk-adjusted racial-ethnic differences in quality were estimated and summarized as percentiles relative to White beneficiaries' mean quality scores. RESULTS: The study included 71,013 beneficiaries; 42.8% were Black, 22.9% Latinx, 27.4% White, and 6.9% other race-ethnicity. Overall, 68.8% had a mental health follow-up within 30 days of discharge, and 90.2% had no preventable hospitalizations for chronic obstructive pulmonary disease or asthma. Among beneficiaries receiving antipsychotic medications, medication adherence was adequate for 43.7%. Fourteen indicators for mental and general medical health care quality yielded three composites: two for mental health care (pharmacological and ambulatory) and one for acute mental and general medical health care. Mean quality of pharmacological mental health care for Black and Latinx beneficiaries was lower than for White beneficiaries (39th and 44th percentile, respectively). For Black beneficiaries, mean quality of ambulatory mental health care was also lower (46th percentile). In New York City, Black beneficiaries received lower-quality care in all domains. The only meaningful group difference in the quality of acute mental and general medical health care indicated higher-quality care for individuals with other race-ethnicity. CONCLUSIONS: Disparities in the quality of Medicaid-financed health care persist, particularly for Black beneficiaries. Regional differences merit further attention.

Targeted learning in observational studies with multi-valued treatments: An evaluation of antipsychotic drug treatment safety.

We investigate estimation of causal effects of multiple competing (multi-valued) treatments in the absence of randomization. Our work is motivated by an intention-to-treat study of the relative cardiometabolic risk of assignment to one of six commonly prescribed antipsychotic drugs in a cohort of nearly 39 000 adults with serious mental illnesses. Doubly-robust estimators, such as targeted minimum loss-based estimation (TMLE), require correct specification of either the treatment model or outcome model to ensure consistent estimation; however, common TMLE implementations estimate treatment probabilities using multiple binomial regressions rather than multinomial regression. We implement a TMLE estimator that uses multinomial treatment assignment and ensemble machine learning to estimate average treatment effects. Our multinomial implementation improves coverage, but does not necessarily reduce bias, relative to the binomial implementation in simulation experiments with varying treatment propensity overlap and event rates. Evaluating the causal effects of the antipsychotics on 3-year diabetes risk or death, we find a safety benefit of moving from a second-generation drug considered among the safest of the second-generation drugs to an infrequently prescribed first-generation drug known for having low cardiometabolic risk.

Healthcare Access for a Diverse Population with Schizophrenia Following the Onset of the COVID-19 Pandemic.

COVID-19 has had a disproportionate impact on the most disadvantaged members of society, including minorities and those with disabling chronic illnesses such as schizophrenia. We examined the pandemic's impacts among New York State's Medicaid beneficiaries with schizophrenia in the immediate post-pandemic surge period, with a focus on equity of access to critical healthcare. We compared changes in utilization of key behavioral health outpatient services and inpatient services for life-threatening conditions between the pre-pandemic and surge periods for White and non-White beneficiaries. We found racial and ethnic differences across all outcomes, with most differences stable over time. The exception was pneumonia admissions-while no differences existed in the pre-pandemic period, Black and Latinx beneficiaries were less likely than Whites to be hospitalized in the surge period despite minorities' heavier COVID-19 disease burden. The emergence of racial and ethnic differences in access to scarce life-preserving healthcare may hold lessons for future crises.

Antipsychotics and the risk of diabetes and death among adults with serious mental illnesses.

BACKGROUND: Individuals with schizophrenia exposed to second-generation antipsychotics (SGA) have an increased risk for diabetes, with aripiprazole purportedly a safer drug. Less is known about the drugs' mortality risk or whether serious mental illness (SMI) diagnosis or race/ethnicity modify these effects. METHODS: Authors created a retrospective cohort of non-elderly adults with SMI initiating monotherapy with an SGA (olanzapine, quetiapine, risperidone, and ziprasidone, aripiprazole) or haloperidol during 2008-2013. Three-year diabetes incidence or all-cause death risk differences were estimated between each drug and aripiprazole, the comparator, as well as effects within SMI diagnosis and race/ethnicity. Sensitivity analyses evaluated potential confounding by indication. RESULTS: 38 762 adults, 65% White and 55% with schizophrenia, initiated monotherapy, with haloperidol least (6%) and quetiapine most (26·5%) frequent. Three-year mortality was 5% and diabetes incidence 9.3%. Compared with aripiprazole, haloperidol and olanzapine reduced diabetes risk by 1.9 (95% CI 1.2-2.6) percentage points, or a 18.6 percentage point reduction relative to aripiprazole users' unadjusted risk (10.2%), with risperidone having a smaller advantage. Relative to aripiprazole users' unadjusted risk (3.4%), all antipsychotics increased mortality risk by 1.1-2.2 percentage points, representing 32.4-64.7 percentage point increases. Findings within diagnosis and race/ethnicity were generally consistent with overall findings. Only quetiapine's higher mortality risk held in sensitivity analyses. CONCLUSIONS: Haloperidol's, olanzapine's, and risperidone's lower diabetes risks relative to aripiprazole were not robust in sensitivity analyses but quetiapine's higher mortality risk proved robust. Findings expand the evidence on antipsychotics' risks, suggesting a need for caution in the use of quetiapine among individuals with SMI.

Statistical Approaches for Quantifying the Quality of Neurosurgical Care.

BACKGROUND: Quantifying quality of health care can provide valuable information to patients, providers, and policy makers. However, the observational nature of measuring quality complicates assessments. METHODS: We describe a conceptual model for defining quality and its implications about the data collected, how to make inferences about quality, and the assumptions required to provide statistically valid estimates. Twenty-one binary or polytomous quality measures collected from 101,051 adult Medicaid beneficiaries aged 18-64 years with schizophrenia from 5 U.S. states show methodology. A categorical principal components analysis establishes dimensionality of quality, and item response theory models characterize the relationship between each quality measure and a unidimensional quality construct. Latent regression models estimate racial/ethnic and geographic quality disparities. RESULTS: More than 90% of beneficiaries filled at least 1 antipsychotic prescription and 19% were hospitalized for schizophrenia during a 12-month observational period in our multistate cohort with approximately 2/3 nonwhite beneficiaries. Four quality constructs emerged: inpatient, emergency room, pharmacologic/ambulatory, and ambulatory only. Using a 2-parameter logistic model, pharmacologic/ambulatory care quality varied from -2.35 to 1.26 (higher = better quality). Black and Latinx beneficiaries had lower pharmacologic/ambulatory quality compared with whites. Race/ethnicity modified the association of state and pharmacologic/ambulatory care quality in latent regression modeling. Average quality ranged from -0.28 (95% confidence interval, -2.15 to 1.04) for blacks in New Jersey to 0.46 [95% confidence interval, -0.89 to 1.40] for whites in Michigan. CONCLUSIONS: By combining multiple quality measures using item response theory models, a composite measure can be estimated that has more statistical power to detect differences among subjects than the observed mean per subject.

Reevaluating Congenital Heart Surgery Center Performance Using Operative Mortality.

BACKGROUND: The Society of Thoracic Surgeons Congenital Heart Surgery Database (STS-CHSD) provides observed-to-expected (O/E) operative mortality ratios to more than 100 congenital heart centers in North America. We compared the current approach for estimating O/E ratios to approaches incorporating information on diagnosis as moderators of procedures, other unused risk factors, and additional variation in confidence interval construction to characterize center performance. METHODS: Bayesian additive regression trees (BART) and lasso models linked operative mortality to diagnosis-procedure categories, procedure-specific risk factors, and syndromes/abnormalities. Bootstrapping accounted for variation in the STS-CHSD (STS bootstrap) and lasso CIs. We compared O/E estimates, interquartile range of CI widths, and concordance of center performance categorizations (worse-than-, as-, or better-than-expected mortality) of the new approaches to the STS-CHSD. RESULTS: In 110 surgical centers including 98,822 surgical operative encounters, there were 2818 (2.85%) operative mortalities (center range, 0.37%-10%). Compared with the STS-CHSD, BART- and lasso-estimated O/E ratios varied more and had narrower confidence intervals (interquartile range of confidence interval: STS-CHSD = 1.11, STS bootstrap = 0.98; lasso = 0.80; BART = 0.96). Concordance of performance categorization with the STS-CHSD ranged from 84% (lasso) to 91% (STS Bootstrap); more than 70% of discordant centers improved categories. Discordant centers had smaller volumes, fewer operative mortalities, and treated more patients with congenital lung abnormalities. CONCLUSIONS: Relative to the STS-CHSD, up to 16% of hospitals changed performance categories, most improving performance. Given the significance of quality reports for congenital heart centers, inclusion of additional risk factors and unaddressed variation should be considered.

Mortality Prediction After Cardiac Surgery in Children: An STS Congenital Heart Surgery Database Analysis.

BACKGROUND: The Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database (CHSD) provides risk-adjusted operative mortality rates to approximately 120 North American congenital heart centers. Optimal case-mix adjustment methods for operative mortality risk prediction in this population remain unclear. METHODS: A panel created diagnosis-procedure combinations of encounters in the CHSD. Models for operative mortality using the new diagnosis-procedure categories, procedure-specific risk factors, and syndromes or abnormalities included in the CHSD were estimated using Bayesian additive regression trees and least absolute shrinkage and selector operator (lasso) models. Performance of the new models was compared with the current STS CHSD risk model. RESULTS: Of 98 825 operative encounters (69 063 training; 29 762 testing), 2818 (2.85%) STS-defined operative mortalities were observed. Differences in sensitivity, specificity, and true and false positive predicted values were negligible across models. Calibration for mortality predictions at the higher end of risk from the lasso and Bayesian additive regression trees models was better than predictions from the STS CHSD model, likely because of the new models' inclusion of diagnosis-palliative procedure variables affecting <1% of patients overall but accounting for 27% of mortalities. Model discrimination varied across models for high-risk procedures, hospital volume, and hospitals. CONCLUSIONS: Overall performance of the new models did not differ meaningfully from the STS CHSD risk model. Adding procedure-specific risk factors and allowing diagnosis to modify predicted risk for palliative operations may augment model performance for very high-risk surgical procedures. Given the importance of risk adjustment in estimating hospital quality, a comparative assessment of surgical program quality evaluations using the different models is warranted.

Significance and Factors Associated with Antipsychotic Polypharmacy Utilization Among Publicly Insured US Adults.

Antipsychotic polypharmacy (APP) lacks evidence of effectiveness in the care of schizophrenia or other disorders for which antipsychotic drugs are indicated, also exposing patients to more risks. Authors assessed APP prevalence and APP association with beneficiary race/ethnicity and payer among publicly-insured adults regardless of diagnosis. Retrospective repeated panel study of fee-for-service (FFS) Medicare, Medicaid, and dually-eligible white, black, and Latino adults residing in California, Georgia, Iowa, Mississippi, Oklahoma, South Dakota, or West Virginia, filling antipsychotic prescriptions between July 2008 and June 2013. Primary outcome was any monthly APP utilization. Across states and payers, 11% to 21% of 397,533 antipsychotic users and 12% to 19% of 9,396,741 person-months had some APP utilization. Less than 50% of person-months had a schizophrenia diagnosis and up to 19% had no diagnosed mental illness. Payer modified race/ethnicity effects on APP utilization only in CA; however, the odds of APP utilization remained lower for minorities than for whites. Elsewhere, the odds varied by race/ethnicity only in OK, with Latinos having lower odds than whites (odds ratio 0.76; 95% confidence interval 0.60-0.96). The odds of APP utilization varied by payer in several study states, with odds generally higher for Dual eligibles, although the differences were generally small; the odds also varied by year (lower at study end). APP was frequently utilized but mostly declined over time. APP utilization patterns varied across states, with no consistent association with race/ethnicity and small payer effects. Greater use of APP-reducing strategies are needed, particularly among non-schizophrenia populations.

All-Cause Mortality and Progression to End-Stage Kidney Disease Following Percutaneous Revascularization or Surgical Coronary Revascularization in Patients with CKD.

INTRODUCTION: Relative impacts of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) on mortality and end-stage kidney disease (ESKD) in chronic kidney disease (CKD) are uncertain. METHODS: Data from Massachusetts residents with CKD undergoing CABG or PCI from 2003 to 2012 were linked to the United States Renal Data System. Associations with death, ESKD, and combined death and ESKD were analyzed in propensity score-matched multivariable survival models. RESULTS: We identified 6805 CABG and 17,494 PCI patients. Among 3775 matched-pairs, multi-vessel disease was present in 97%, and stage 4 CKD was present in 11.9% of CABG and 12.2% of PCI patients. One-year mortality (CABG 7.7%, PCI 11.0%) was more frequent than ESKD (CABG 1.4%, PCI 1.7%). Overall survival was improved and ESKD risk decreased with CABG compared to PCI, but effects differed in the presence of left main disease and prior myocardial infarction (MI). Survival was worse following PCI than following CABG among patients with left main disease and without MI (hazard ratio = 3.7, 95% confidence interval = 1.3-10.5). ESKD risk was higher with PCI for individuals with left main disease and prior infarction (hazard ratio = 8.1, 95% confidence interval = 1.7-39.2). CONCLUSION: Risks following CABG and PCI were modified by left main disease and prior MI. In individuals with CKD, survival was greater after CABG than after PCI in patients with left main disease but without MI, whereas ESKD risk was lower with CABG in those with left main and MI. Absolute risks of ESKD were markedly lower than for mortality, suggesting prioritizing mortality over ESKD in clinical decision making.

Factors Associated With Off-Label Utilization of Second-Generation Antipsychotics Among Publicly Insured Adults.

OBJECTIVE: Off-label utilization of second-generation antipsychotic medications may expose patients to significant risks. The authors examined the prevalence, temporal trends, and factors associated with off-label utilization of second-generation antipsychotics among publicly insured adults. METHODS: A retrospective repeated panel was used to examine monthly off-label utilization of second-generation antipsychotics among fee-for-service Medicare, Medicaid, and dually eligible White, Black, and Latino adult beneficiaries filling prescriptions for second-generation antipsychotics in California, Georgia, Mississippi, and Oklahoma from July 2008 through June 2013. RESULTS: Among 301,367 users of second-generation antipsychotics, between 36.5% and 41.9% had utilization that was always off-label. Payer did not modify effects of race-ethnicity on off-label utilization. Compared with Whites, Blacks had lower monthly odds of off-label utilization in all four states, and Latinos had lower odds of utilization in California and Georgia. Payer was associated with off-label utilization in California, Mississippi, and Oklahoma. California Medicaid beneficiaries were 1.12 (95% confidence interval=1.10-1.13) times as likely as dually eligible beneficiaries to have off-label utilization. Off-label utilization increased relative to the baseline year in all states, but a downward trend followed in three states. CONCLUSIONS: Off-label utilization of second-generation antipsychotics was prevalent despite the drugs' cardiometabolic risks and little evidence of their effectiveness. The lower likelihood of off-label utilization among patients from racial-ethnic minority groups might stem from prescribers' efforts to minimize risks, given a higher baseline risk for these groups, or from disparities-associated factors. Variation among payers suggests that payer policies can affect off-label utilization.

Assessment of Second-Generation Diabetes Medication Initiation Among Medicare Enrollees From 2007 to 2015.

Importance: Little is known about how new and expensive drugs diffuse into practice affects health care costs. Objective: To describe the variation in second-generation diabetes drug use among Medicare enrollees between 2007 and 2015. Design, Setting, and Participants: This population-based, cross-sectional study included data from 100% of Medicare Parts A, B, and D enrollees who first received diabetes drug therapy from January 1, 2007, to December 31, 2015. Patients with type 1 diabetes were excluded. Data were analyzed beginning in the spring of 2018, and revisions were completed in 2019. Exposures: For each patient, the initial diabetes drug choice was determined; drugs were classified as first generation (ie, approved before 2000) or second generation (ie, approved after 2000, including dipeptidyl peptidase 4 [DPP-4] inhibitors, glucagon-like peptide-1 [GLP-1] receptor agonists, and sodium-glucose cotransporter-2 [SGLT-2] inhibitors). Main Outcomes and Measures: The primary outcome was the between-practice variation in use of second-generation diabetes drugs between 2007 and 2015. Practices with use rates of second-generation diabetes drugs more than 1 SD above the mean were considered high prescribing, while those with use rates more than 1 SD below the mean were considered low prescribing. Results: Among 1 182 233 patients who initiated diabetes drug therapy at 42 977 practices between 2007 and 2015, 1 104 718 (93.4%) were prescribed a first-generation drug (mean [SD] age, 75.4 [6.7] years; 627 134 [56.8%] women) and 77 515 (6.6%) were prescribed a second-generation drug (mean [SD] age, 76.5 [7.2] years; 44 697 [57.7%] women). By December 2015, 22 457 practices (52.2%) had used DPP-4 inhibitors once, compared with 3593 practices (8.4%) that had used a GLP-1 receptor agonist once. Furthermore, 17 452 practices (40.6%) were using DPP-4 inhibitors in 10% of eligible patients, while 1286 practices (3.0%) were using GLP-1 receptor agonists in 10% of eligible patients, and SGLT-2 inhibitors, available after March 2013, were used at least once by 1716 practices (4.0%) and used in 10% of eligible patients by 872 practices (2.0%) by December 2015. According to Poisson random-effect regression models, beneficiaries in high-prescribing practices were more than 3-fold more likely to receive DPP-4 inhibitors (relative risk, 3.55 [95% CI, 3.42-3.68]), 24-fold more likely to receive GLP-1 receptor agonists (relative risk, 24.06 [95% CI, 14.14-40.94]) and 60-fold more likely to receive SGLT-2 inhibitors (relative risk, 60.41 [95% CI, 15.99-228.22]) compared with beneficiaries in low-prescribing practices. Conclusions and Relevance: These findings suggest that there was substantial between-practice variation in the use of second-generation diabetes drugs between 2007 and 2015, with a concentration of use among a few prescribers and practices responsible for much of the early diffusion.

Do Patients at Sites With High RCT Enrollment Propensity Have Better Outcomes?

BACKGROUND: Concerns about randomized controlled trial (RCT) generalizability typically center on characteristics of RCT patient participants. Possibly there are RCT site characteristics that distinguish RCT outcomes from those that can be expected in non-RCT settings. OBJECTIVES: To examine whether site propensity toward RCT enrollment is associated with recovery outcomes for patients and whether the association differs between patients who participate in a RCT compared with those who remain in an observational (OBS) treatment environment. DATA: Study participants with acute bipolar depression from The Systematic Treatment Enhancing Program for Bipolar Disorder acute depression pharmacotherapy RCT (N=337) and OBS treatment arm (N=1581). METHODS: A longitudinal OBS study comparing the likelihood of recovery in the RCT to the OBS arm, allowing effect modification by site high RCT enrollment propensity (defined as >the median) and other predictors over a 6-month follow-up period. RESULTS: Non-RCT participants who received care in sites with high RCT enrollment propensity had a higher probability of recovering from their bipolar-depression episode compared with participants from low propensity sites [odds ratio (95% confidence interval)=2.13 (1.28-3.55)]. RCT enrollment propensity was not associated with recovery outcomes for RCT participants [1.03 (0.35-3.03)]. CONCLUSIONS: Sites with high propensity to enroll patients in RCTs appear to have unobserved characteristics, which play a significant role in outcomes for non-RCT patients. For RCT participants in low-enrollment sites, possibly RCT protocols, which proscribe care delivery and monitoring, attenuate this effect. These results have implications for future research to improve outcomes in nonresearch care settings.

Predicting Participation in Psychiatric Randomized Controlled Trials: Insights From the STEP-BD.

OBJECTIVE: Differences between patients who do and do not participate in randomized controlled trials (RCTs) could diminish the generalizability of results. This study examined whether RCT participants differ from non-RCT participants who are recruited from the same patient and provider population. METHODS: The Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) was an observational study in which participants also could enroll in an RCT during exacerbations of acute depression. The odds that a patient was enrolled in the STEP-BD acute depression RCTs (pharmacotherapy or psychotherapy) were estimated by fitting logistic regression models to STEP-BD participants with acute bipolar depression (total N=2,222; RCT, N=413; observational arm, N=1,809). Predictor variables included demographic characteristics, clinical information (including severity scales and comorbidities), and study site. The extent to which site determined RCT participation was estimated by using the area under the receiver operating characteristic curve (AUC). RESULTS: RCT participation was associated with having no insurance (odds ratio [OR]=1.58, 95% confidence interval [CI]=1.16-2.15), a Clinical Global Impression score indicating greater severity (severe versus mild: OR=1.52, CI=1.08-2.15), and site (predicted probability range 8%-31%). Site was the most significant predictor of RCT enrollment (model excluding site, AUC=.61, CI=.58-.64; full model, AUC=.70, CI=.67-.73). CONCLUSIONS: STEP-BD RCT participants differed from those in the observational arm in few clinical or demographic characteristics. Site was the strongest predictor of RCT participation. Future study is needed to understand site characteristics associated with RCT participation and whether these characteristics are associated with patient outcomes and to test these findings in usual-care settings.

Causes of short-term readmission after percutaneous coronary intervention.

BACKGROUND: Rehospitalization within 30 days after an admission for percutaneous coronary intervention (PCI) is common, costly, and a future target for Medicare penalties. Causes of readmission after PCI are largely unknown. METHODS AND RESULTS: To illuminate the causes of PCI readmissions, patients with PCI readmitted within 30 days of discharge between 2007 and 2011 at 2 hospitals were identified, and their medical records were reviewed. Of 9288 PCIs, 9081 (97.8%) were alive at the end of the index hospitalization. Of these, 893 patients (9.8%) were readmitted within 30 days of discharge and included in the analysis. Among readmitted patients, 341 patients (38.1%) were readmitted for evaluation of recurrent chest pain or other symptoms concerning for angina, whereas 59 patients (6.6%) were readmitted for staged PCI without new symptoms. Complications of PCI accounted for 60 readmissions (6.7%). For cases in which chest pain or other symptoms concerning for angina prompted the readmission, 21 patients (6.2%) met criteria for myocardial infarction, and repeat PCI was performed in 54 patients (15.8%). The majority of chest pain patients (288; 84.4%) underwent ≥1 diagnostic imaging test, most commonly coronary angiography, and only 9 (2.6%) underwent target lesion revascularization. CONCLUSIONS: After PCI, readmissions within 30 days were seldom related to PCI complications but often for recurrent chest pain. Readmissions with recurrent chest pain infrequently met criteria for myocardial infarction but were associated with high rates of diagnostic testing.

A prediction model to identify patients at high risk for 30-day readmission after percutaneous coronary intervention.

BACKGROUND: The Affordable Care Act creates financial incentives for hospitals to minimize readmissions shortly after discharge for several conditions, with percutaneous coronary intervention (PCI) to be a target in 2015. We aimed to develop and validate prediction models to assist clinicians and hospitals in identifying patients at highest risk for 30-day readmission after PCI. METHODS AND RESULTS: We identified all readmissions within 30 days of discharge after PCI in nonfederal hospitals in Massachusetts between October 1, 2005, and September 30, 2008. Within a two-thirds random sample (Developmental cohort), we developed 2 parsimonious multivariable models to predict all-cause 30-day readmission, the first incorporating only variables known before cardiac catheterization (pre-PCI model), and the second incorporating variables known at discharge (Discharge model). Models were validated within the remaining one-third sample (Validation cohort), and model discrimination and calibration were assessed. Of 36,060 PCI patients surviving to discharge, 3760 (10.4%) patients were readmitted within 30 days. Significant pre-PCI predictors of readmission included age, female sex, Medicare or State insurance, congestive heart failure, and chronic kidney disease. Post-PCI predictors of readmission included lack of ß-blocker prescription at discharge, post-PCI vascular or bleeding complications, and extended length of stay. Discrimination of the pre-PCI model (C-statistic=0.68) was modestly improved by the addition of post-PCI variables in the Discharge model (C-statistic=0.69; integrated discrimination improvement, 0.009; P<0.001). CONCLUSIONS: These prediction models can be used to identify patients at high risk for readmission after PCI and to target high-risk patients for interventions to prevent readmission.

Sources of hospital variation in short-term readmission rates after percutaneous coronary intervention.

BACKGROUND: Risk-standardized all-cause 30-day readmission rates (RSRRs) after percutaneous coronary intervention (PCI) have been endorsed as a national measure of hospital quality. Little is known about variation in the performance of hospitals on this measure, and whether high hospital rates of readmission after PCI are due to modifiable deficiencies in quality of care has not been assessed. METHODS AND RESULTS: We estimated 30-day, all-cause RSRRs for all nonfederal PCI-performing hospitals in Massachusetts, adjusted for clinical and angiographic variables, between 2005 and 2008. We assessed if differences in race, insurance type, and PCI and post-PCI characteristics, including procedural complications and discharge characteristics, could explain variation between hospitals using nested hierarchical logistic regression models. Of 36 060 patients undergoing PCI at 24 hospitals and surviving to discharge, 4469 (12.4%) were readmitted within 30 days of discharge. Hospital RSRRs ranged from 9.5% to 17.9%, with 8 of 24 hospitals being identified as outliers (4 lower than expected and 4 higher than expected). Differences in race, insurance, PCI, and post-PCI factors accounted for 10.4% of the between-hospital variance in RSRRs. CONCLUSIONS: We observed wide variation in hospital 30-day all-cause RSRRs after PCI, most of which could not be explained by identifiable differences in procedural and postprocedural factors. A better understanding of etiologies of hospital variation is necessary to determine whether this measure is an actionable assessment of hospital quality, and, if so, how hospitals might improve their performance.

Accurately predicting bipolar disorder mood outcomes: implications for the use of electronic databases.

BACKGROUND: Monitoring mental health treatment outcomes for populations requires an understanding as to which patient information is needed in electronic format and is feasible to obtain in routine care. OBJECTIVE: To examine whether bipolar disorder outcomes can be accurately predicted and how much clinical detail is needed to do so. RESEARCH DESIGN, DATA SOURCES, AND PARTICIPANTS: Longitudinal study of bipolar disorder patients treated during 2000 to 2004 in the 19-site Systematic Treatment Enhancement Program for Bipolar Disorder observational study arm (N=3168). Clinical data were obtained at baseline and quarterly for over 1 year. We fit a "gold standard" longitudinal random-effects regression model using a detailed clinical information and estimated the area under the receiver operating characteristic curve (AUC) to predict accuracy using a validation sample. The model was then modified to include patient characteristics feasible in routinely collected electronic data (eg, administrative data). We compared the AUCs for the "limited-detail" and gold standard models, testing for differences between the AUCs using the validation sample. MEASURE: Remission, defined as Montgomery-Asberg Depression Rating Scale score <5 and Young Mania Rating Scale score <4. RESULTS: The gold standard models had baseline AUC=0.80 (95% confidence interval=0.74 to 0.86) and 0.75(0.64 to 0.86) at 1-year follow-up. The predicted accuracies of the limited-detail model were lower at baseline [AUC=0.67(0.60 to 0.75)]; correlated test χ=14.25, P=0.002] and not statistically different from the gold standard model at 1 year [AUC=0.67(0.54-0.80); correlated test χ=2.88, P=0.090]. CONCLUSIONS: Future work is needed to develop clinically accurate and feasible models to predict bipolar disorder outcomes. Clinically detailed and limited models performed similarly for shorter-term prediction at 1-year; however, there is room for improvement in prediction accuracy.

Improvement in mortality risk prediction after percutaneous coronary intervention through the addition of a "compassionate use" variable to the National Cardiovascular Data Registry CathPCI dataset: a study from the Massachusetts Angioplasty Registry.

OBJECTIVES: This study investigated the impact of adding novel elements to models predicting in-hospital mortality after percutaneous coronary interventions (PCIs). BACKGROUND: Massachusetts mandated public reporting of hospital-specific PCI mortality in 2003. In 2006, a physician advisory group recommended adding to the prediction models 3 attributes not collected by the National Cardiovascular Data Registry instrument. These "compassionate use" (CU) features included coma on presentation, active hemodynamic support during PCI, and cardiopulmonary resuscitation at PCI initiation. METHODS: From October 2005 through September 2007, PCI was performed during 29,784 admissions in Massachusetts nonfederal hospitals. Of these, 5,588 involved patients with ST-segment elevation myocardial infarction or cardiogenic shock. Cases with CU criteria identified were adjudicated by trained physician reviewers. Regression models with and without the CU composite variable (presence of any of the 3 features) were compared using areas under the receiver-operator characteristic curves. RESULTS: Unadjusted mortality in this high-risk subset was 5.7%. Among these admissions, 96 (1.7%) had at least 1 CU feature, with 69.8% mortality. The adjusted odds ratio for in-hospital death for CU PCIs (vs. no CU criteria) was 27.3 (95% confidence interval: 14.5 to 47.6). Discrimination of the model improved after including CU, with areas under the receiver-operating characteristic curves increasing from 0.87 to 0.90 (p < 0.01), while goodness of fit was preserved. CONCLUSIONS: A small proportion of patients at extreme risk of post-PCI mortality can be identified using pre-procedural factors not routinely collected, but that heighten predictive accuracy. Such improvements in model performance may result in greater confidence in reporting of risk-adjusted PCI outcomes.

Impact of independent data adjudication on hospital-specific estimates of risk-adjusted mortality following percutaneous coronary interventions in massachusetts.

BACKGROUND: As part of state-mandated public reporting of outcomes after percutaneous coronary interventions (PCIs) in Massachusetts, procedural and clinical data were prospectively collected. Variables associated with higher mortality were audited to ensure accuracy of coding. We examined the impact of adjudication on identifying hospitals with possible deficiencies in the quality of PCI care. METHODS AND RESULTS: From October 2005 to September 2006, 15 721 admissions for PCI occurred in 21 hospitals. Of the 864 high-risk variables from 822 patients audited by committee, 201 were changed, with reassignment to lower acuities in 97 (30%) of the 321 shock cases, 24 (43%) of the 56 salvage cases, and 73 (15%) of the 478 emergent cases. Logistic regression models were used to predict patient-specific in-hospital mortality. Of 241 (1.5%) patients who died after PCI, 30 (12.4%) had a lower predicted mortality with adjudicated than with unadjudicated data. Model accuracy was excellent with either adjudicated or unadjudicated data. Hospital-specific risk-standardized mortality rates were estimated using both adjudicated and unadjudicated data through hierarchical logistic regression. Although adjudication reduced between-hospital variation by one third, risk-standardized mortality rates were similar using unadjudicated and adjudicated data. None of the hospitals were identified as statistical outliers. However, cross-validated posterior-predicted P values calculated with adjudicated data increased the number of borderline hospital outliers compared with unadjudicated data. CONCLUSIONS: Independent adjudication of site-reported high-risk features may increase the ability to identify hospitals with higher risk-adjusted mortality after PCI despite having little impact on the accuracy of risk prediction for the entire population.