Missed opportunities in hospital quality measurement during the COVID-19 pandemic: a retrospective investigation of US hospitals' CMS Star Ratings and 30-day mortality during the early pandemic.

OBJECTIVES: In the USA and UK, pandemic-era outcome data have been excluded from hospital rankings and pay-for-performance programmes. We assessed the relationship between US hospitals' pre-pandemic Centers for Medicare and Medicaid Services (CMS) Overall Hospital Star ratings and early pandemic 30-day mortality among both patients with COVID and non-COVID to understand whether pre-existing structures, processes and outcomes related to quality enabled greater pandemic resiliency. DESIGN AND DATA SOURCE: A retrospective, claim-based data study using the 100% Inpatient Standard Analytic File and Medicare Beneficiary Summary File including all US Medicare Fee-for-Service inpatient encounters from 1 April 2020 to 30 November 2020 linked with the CMS Hospital Star Ratings using six-digit CMS provider IDs. OUTCOME MEASURE: The outcome was risk-adjusted 30-day mortality. We used multivariate logistic regression adjusting for age, sex, Elixhauser mortality index, US Census Region, month, hospital-specific January 2020 CMS Star rating (1-5 stars), COVID diagnosis (U07.1) and COVID diagnosis×CMS Star Rating interaction. RESULTS: We included 4 473 390 Medicare encounters from 2533 hospitals, with 92 896 (28.2%) mortalities among COVID-19 encounters and 387 029 (9.3%) mortalities among non-COVID encounters. There was significantly greater odds of mortality as CMS Star Ratings decreased, with 18% (95% CI 15% to 22%; p<0.0001), 33% (95% CI 30% to 37%; p<0.0001), 38% (95% CI 34% to 42%; p<0.0001) and 60% (95% CI 55% to 66%; p<0.0001), greater odds of COVID mortality comparing 4-star, 3-star, 2-star and 1-star hospitals (respectively) to 5-star hospitals. Among non-COVID encounters, there were 17% (95% CI 16% to 19%; p<0.0001), 24% (95% CI 23% to 26%; p<0.0001), 32% (95% CI 30% to 33%; p<0.0001) and 40% (95% CI 38% to 42%; p<0.0001) greater odds of mortality at 4-star, 3-star, 2-star and 1-star hospitals (respectively) as compared with 5-star hospitals. CONCLUSION: Our results support a need to further understand how quality outcomes were maintained during the pandemic. Valuable insights can be gained by including the reporting of risk-adjusted pandemic era hospital quality outcomes for high and low performing hospitals.

Hospital quality reporting in the pandemic era: to what extent did hospitals' COVID-19 census burdens impact 30-day mortality among non-COVID Medicare beneficiaries?

OBJECTIVES: Highly visible hospital quality reporting stakeholders in the USA such as the US News & World Report (USNWR) and the Centers for Medicare & Medicaid Services (CMS) play an important health systems role via their transparent public reporting of hospital outcomes and performance. However, during the pandemic, many such quality measurement stakeholders and pay-for-performance programmes in the USA and Europe have eschewed the traditional risk adjustment paradigm, instead choosing to pre-emptively exclude months or years of pandemic era performance data due largely to hospitals' perceived COVID-19 burdens. These data exclusions may lead patients to draw misleading conclusions about where to seek care, while also masking genuine improvements or deteriorations in hospital quality that may have occurred during the pandemic. Here, we assessed to what extent hospitals' COVID-19 burdens (proportion of hospitalised patients with COVID-19) were associated with their non-COVID 30-day mortality rates from March through November 2020 to inform whether inclusion of pandemic-era data may still be appropriate. DESIGN: This was a retrospective cohort study using the 100% CMS Inpatient Standard Analytic File and Master Beneficiary Summary File to include all US Medicare inpatient encounters with admission dates from 1 April 2020 through 30 November 2020, excluding COVID-19 encounters. Using linear regression, we modelled the association between hospitals' COVID-19 proportions and observed/expected (O/E) ratios, testing whether the relationship was non-linear. We calculated alternative hospital O/E ratios after selective pandemic data exclusions mirroring the USNWR data exclusion methodology. SETTING AND PARTICIPANTS: We analysed 4 182 226 consecutive Medicare inpatient encounters from across 2601 US hospitals. RESULTS: The association between hospital COVID-19 proportion and non-COVID O/E 30-day mortality was statistically significant (p<0.0001), but weakly correlated (r2=0.06). The median (IQR) pairwise relative difference in hospital O/E ratios comparing the alternative analysis with the original analysis was +3.7% (-2.5%, +6.7%), with 1908/2571 (74.2%) of hospitals having relative differences within ±10%. CONCLUSIONS: For non-COVID patient outcomes such as mortality, evidence-based inclusion of pandemic-era data is methodologically plausible and must be explored rather than exclusion of months or years of relevant patient outcomes data.

Association of Neutralizing Antispike Monoclonal Antibody Treatment With Coronavirus Disease 2019 Hospitalization and Assessment of the Monoclonal Antibody Screening Score.

Objective: To test the hypothesis that the Monoclonal Antibody Screening Score performs consistently better in identifying the need for monoclonal antibody infusion throughout each "wave" of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant predominance during the coronavirus disease 2019 (COVID-19) pandemic and that the infusion of contemporary monoclonal antibody treatments is associated with a lower risk of hospitalization. Patients and Methods: In this retrospective cohort study, we evaluated the efficacy of monoclonal antibody treatment compared with that of no monoclonal antibody treatment in symptomatic adults who tested positive for SARS-CoV-2 regardless of their risk factors for disease progression or vaccination status during different periods of SARS-CoV-2 variant predominance. The primary outcome was hospitalization within 28 days after COVID-19 diagnosis. The study was conducted on patients with a diagnosis of COVID-19 from November 19, 2020, through May 12, 2022. Results: Of the included 118,936 eligible patients, hospitalization within 28 days of COVID-19 diagnosis occurred in 2.52% (456/18,090) of patients who received monoclonal antibody treatment and 6.98% (7,037/100,846) of patients who did not. Treatment with monoclonal antibody therapies was associated with a lower risk of hospitalization when using stratified data analytics, propensity scoring, and regression and machine learning models with and without adjustments for putative confounding variables, such as advanced age and coexisting medical conditions (eg, relative risk, 0.15; 95% CI, 0.14-0.17). Conclusion: Among patients with mild to moderate COVID-19, including those who have been vaccinated, monoclonal antibody treatment was associated with a lower risk of hospital admission during each wave of the COVID-19 pandemic.

A Multisite Assessment of Inpatient Safety Event Rates During the Coronavirus Disease 2019 Pandemic.

To date, there has been a notable lack of peer-reviewed or publicly available data documenting rates of hospital quality outcomes and patient safety events during the coronavirus disease 2019 pandemic era. The dearth of evidence is perhaps related to the US health care system triaging resources toward patient care and away from reporting and research and also reflects that data used in publicly reported hospital quality rankings and ratings typically lag 2-5 years. At our institution, a learning health system assessment is underway to evaluate how patient safety was affected by the pandemic. Here we share and discuss early findings, noting the limitations of self-reported safety event reporting, and suggest the need for further widespread investigations at other US hospitals. During the 2-year study period from January 1, 2020, through December 31, 2021 across 3 large US academic medical centers at our institution, we documented an overall rate of 25.8 safety events per 1000 inpatient days. The rate of events meeting "harm" criteria was 12.4 per 1000 inpatient days, the rate of nonharm events was 11.1 per 1000 inpatient days, and the fall rate was 2.3 per 1000 inpatient days. This descriptive exploratory analysis suggests that patient safety event rates at our institution did not increase over the course of the pandemic. However, increasing health care worker absences were nonlinearly and strongly associated with patient safety event rates, which raises questions regarding the mechanisms by which patient safety event rates may be affected by staff absences during pandemic peaks.

State and Government Administrative Databases: Medicare, National Inpatient Sample (NIS), and State Inpatient Databases (SID) Programs.

The availability of large state and federally run administrative health-care databases provides potentially comprehensive population-wide information that can dramatically impact both medical and health-policy decision-making. Specific opportunities and important limitations exist with all administrative databases based on what information is collected and how reliably specific data elements are reported. Access to patient identifiable-level information can be critical for certain long-term outcome studies but can be difficult (although not impossible) due to patient privacy protections, while more easily available de-identified information can provide important insights that may be more than sufficient for some short-term operative or in-hospital outcome questions. The first section of this paper by Sarah K. Meier and Benjamin D. Pollock discusses Medicare and the different data files available to health-care researchers. They describe what is and is not generally available from even the most granular Medicare Standard Analytic Files, and provide an analysis of the strengths and weaknesses of Medicare administrative data as well as the resulting best and inappropriate uses of these data. In the second section, the Nationwide Inpatient Sample and complementary State Inpatient Database programs are reviewed by Steven M. Kurtz and Edmund Lau, with insights into the origins of these programs, the data elements that are recorded relating to the operative procedure and hospital stay, and examples of the types of studies that optimally utilize these data sources. They also detail the limitations of these databases and identify studies that they are not well-suited for, especially those involving linkage or longitudinal studies over time. Both sections provide useful guidance on the best uses and pitfalls related to these important large representative national administrative data sources.

A Learning Health System Approach to Hospital Quality Performance Benchmarking: The Composite Hospital Quality Index.

US hospital quality rankings and ratings use disparate methodologies and are weakly correlated. This causes confusion for patients and hospital quality staff. At the authors' institution, a Composite Hospital Quality Index (CHQI) was developed to combine hospital quality ratings. This approach is described and a calculator is shared here for other health systems to explore their performance. Among the US News and World Report Top 50 Hospitals, hospital-specific numeric summary scores were aggregated from the 2021 Centers for Medicare and Medicaid Services (CMS) Hospital Overall Star Rating, the Spring 2021 Leapfrog Safety Grade, and the April 2021 Hospital Consumer Assessment of Healthcare Providers and Systems Star Rating. The CHQI is the hospital-specific sum of the national percentile-rankings across these 3 ratings. In this example, mean (SD) percentiles were as follows: CMS Stars 74 (19), Hospital Consumer Assessment of Healthcare Providers and Systems 63 (19), Leapfrog 65 (24), with mean (SD) CHQI of 202 (49). The CHQI is used at the authors' institution to identify improvement opportunities and ensure that high-quality care is delivered across the health system.

Association of Do-Not-Resuscitate Patient Case Mix With Publicly Reported Risk-Standardized Hospital Mortality and Readmission Rates.

Importance: The Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings. Objective: To identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty. Design, Setting, and Participants: This cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis. Exposures: Present-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions. Main Outcomes and Measures: Hospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no). Results: Included in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001). Conclusions and Relevance: This cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.

Postoperative atrial fibrillation: Sex-specific characteristics and effect on survival.

BACKGROUND: We sought to fill important gaps in the existing evidence regarding new-onset atrial fibrillation (AF) after coronary artery bypass graft surgery (CABG) by comparing the incidence, characteristics, and effect on long-term survival between men and women. METHODS: Nine thousand two hundred three consecutive patients without preoperative AF underwent isolated CABG from 2002 to 2010 at 3 US academic medical centers and 1 high-volume specialty cardiac hospital. Detailed data on CABG AF events detected via continuous in-hospital electrocardiogram/telemetry monitoring were supplemented with Society of Thoracic Surgeons data, and survival data, censored at October 31, 2011, using a copy of the Social Security Death Master File archived before state-owned data were removed (November 1, 2011). RESULTS: Propensity-adjusted (Society of Thoracic Surgeons-recognized risk factors) incidence of post-CABG AF was 31.5% overall, 32.8% in men, and 27.4% in women. Over the 9-year study period, women had a significantly lower risk of post-CABG AF (absolute difference, -5.3% [95% confidence interval (CI), -10.5% to -0.6%]), and significantly shorter first (-2.9 hours; 95% CI, -5.8 to 0.0), and longest (-4.3 hours; 95% CI, -8.3 to -0.3) AF duration. Post-CABG AF was associated with significantly increased risk of long-term mortality (overall hazard ratio [HR], 1.56; 95% CI, 1.45-1.67; men HR, 1.57; 95% CI, 1.49-1.65; women HR, 1.54; 95% CI, 1.14-2.07). CONCLUSIONS: In our study, women had lower adjusted risk of post-CABG AF and experienced shorter episodes. The adjusted risk of long-term mortality was 56% greater among patients who developed post-CABG AF compared with those who did not. The effect of post-CABG AF on long-term survival did not differ between the sexes.

Cardiovascular Risk and the American Dream: Life Course Observations From the BHS (Bogalusa Heart Study).

BACKGROUND: Economic literature shows that a child's future earnings are predictably influenced by parental income, providing an index of "socioeconomic mobility," or the ability of a person to move towards a higher socioeconomic status from childhood to adulthood. We adapted this economic paradigm to examine cardiovascular risk mobility (CRM), or whether there is life course mobility in relative cardiovascular risk. METHODS AND RESULTS: Participants from the BHS (Bogalusa Heart Study) with 1 childhood and 1 adult visit from 1973 to 2016 (n=7624) were considered. We defined population-level CRM as the rank-rank slope (ß) from the regression of adult cardiovascular disease (CVD) risk percentile ranking onto childhood CVD risk percentile ranking (ß=0 represents complete mobility; ß=1 represents no mobility). After defining and measuring relative CRM, we assessed its correlation with absolute cardiovascular health using the American Heart Association's Ideal Cardiovascular Health metrics. Overall, there was substantial mobility, with black participants having marginally better CRM than whites (ßblack=0.10 [95% confidence interval, 0.05-0.15]; ßwhite=0.18 [95% confidence interval, 0.14-0.22]; P=0.01). Having high relative CVD risk at an earlier age significantly reduced CRM (ßage×slope=-0.02; 95% confidence interval, -0.03 to -0.01; P<0.001). Relative CRM was strongly correlated with life course changes in Ideal Cardiovascular Health sum (r=0.62; 95% confidence interval, 0.60-0.65). CONCLUSIONS: Results from this novel application of an economic mobility index to cardiovascular epidemiology indicated substantial CRM, supporting the paradigm that life course CVD risk is highly modifiable. High CRM implies that the children with the best relative CVD profiles may only maintain a slim advantage over their peers into adulthood.

Categorizing body mass index biases assessment of the association with post-coronary artery bypass graft mortality.

OBJECTIVES: The high prevalence of obesity makes accurately estimating the impact of anthropometric measures on cardiac surgery outcomes critical. The Society of Thoracic Surgeons coronary artery bypass graft (CABG) surgery risk model includes body surface area (as a continuous variable, using spline functions), but most studies apply various categorizations of body mass index (BMI)-contributing to the contradictory published findings. We assessed the association between BMI (modelled as a continuous variable without assumptions of linearity) and CABG operative mortality and examined the impact of applying previous studies' BMI modelling strategies. METHODS: We identified 25 studies investigating the BMI-operative mortality association: 22 categorized BMI, 2 as a linear continuous variable,1 used spline functions. Our cohort of 12 715 consecutive patients underwent isolated CABG at 32 cardiac surgery programmes in North Texas from 1 January 2008-31 December 2012. BMI was modelled using restricted cubic spline functions in a propensity-adjusted model (controlling for Society of Thoracic Surgeons risk factors) estimating operative mortality. The analysis was repeated using each categorization identified and modelling BMI as a linear continuous variable. RESULTS: BMI (modelled with a restricted cubic spline) was significantly associated with operative mortality (P < 0.0001). Risk was lowest for BMI near 30 kg/m2 and highest below 20 kg/m2 and above 40 kg/m2. No categorization, nor the linear continuous model, fully captured this association. CONCLUSIONS: BMI is strongly associated with CABG operative mortality. Categorizing BMI (or assuming a linear relationship) heavily biases estimates of its association with post-CABG mortality. In general, smoothing techniques should be used for all continuous risk factors to avoid bias.