Prediction of hospital acute myocardial infarction and heart failure 30-day mortality rates using publicly reported performance measures

Stability of hospital quality indicators over time: A multi-year observational study of German hospital data

BACKGROUND

Retrospective hospital quality indicators can only be useful if they are trustworthy signals of current or future quality. Despite extensive longitudinal quality indicator data and many hospital quality public reporting initiatives, research on quality indicator stability over time is scarce and skepticism about their usefulness widespread.

OBJECTIVE

Based on aggregated, widely available hospital-level quality indicators, this paper sought to determine whether quality indicators are stable over time. Implications for health policy were drawn and the limited methodological foundation for stability assessments of hospital-level quality indicators enhanced.

METHODS

Two longitudinal datasets (self-reported and routine data), including all hospitals in Germany and covering the period from 2004 to 2017, were analysed. A logistic regression using Generalized Estimating Equations, a time-dependent, graphic quintile representation of risk-adjusted rates and Spearman's rank correlation coefficient were used.

RESULTS

For a total of eight German quality indicators significant stability over time was demonstrated. The probability of remaining in the best quality cluster in the future across all hospitals reached from 46.9% (CI: 42.4-51.6%) for hip replacement reoperations to 80.4% (CI: 76.4-83.8%) for decubitus. Furthermore, graphical descriptive analysis showed that the difference in adverse event rates for the 20% top performing compared to the 20% worst performing hospitals in the two following years is on average between 30% for stroke and AMI and 79% for decubitus. Stability over time has been shown to vary strongly between indicators and treatment areas.

CONCLUSION

Quality indicators were found to have sufficient stability over time for public reporting. Potentially, increasing case volumes per hospital, centralisation of medical services and minimum-quantity regulations may lead to more stable and reliable quality of care indicators. Finally, more robust policy interventions such as outcome-based payment, should only be applied to outcome indicators with a higher level of stability over time. This should be subject to future research.

Composite measures of quality of health care: Evidence mapping of methodology and reporting

Background

Quality indicators are used to quantify the quality of care. A large number of quality indicators makes assessment of overall quality difficult, time consuming and impractical. There is consequently an increasing interest for composite measures based on a combination of multiple indicators.

Objective

To examine the use of different approaches to construct composite measures of quality of care and to assess the use of methodological considerations and justifications.

Methods

We conducted a literature search on PubMed and EMBASE databases (latest update 1 December 2020). For each publication, we extracted information on the weighting and aggregation methodology that had been used to construct composite indicator(s).

Results

A total of 2711 publications were identified of which 145 were included after a screening process. Opportunity scoring with equal weights was the most used approach (86/145, 59%) followed by all-or-none scoring (48/145, 33%). Other approaches regarding aggregation or weighting of individual indicators were used in 32 publications (22%). The rationale for selecting a specific type of composite measure was reported in 36 publications (25%), whereas 22 papers (15%) addressed limitations regarding the composite measure.

Conclusion

Opportunity scoring and all-or-none scoring are the most frequently used approaches when constructing composite measures of quality of care. The attention towards the rationale and limitations of the composite measures appears low.

Discussion

Considering the widespread use and the potential implications for decision-making of composite measures, a high level of transparency regarding the construction process of the composite and the functionality of the measures is crucial.

Predictors of 30-day mortality in patients admitted to ED for acute heart failure

OBJECTIVES

Acute heart failure (AHF) is a leading cause of admission in emergency departments (ED). It is associated with significant in-hospital mortality, suggesting that there is room for improvement of care. Our aims were to investigate clinical patterns, biological characteristics and determinants of 30-day mortality.

METHODS

We conducted a single site, retrospective review of adult patients (≥18years) admitted to ED for AHF over a 12-month period. Data collected included demographics, clinical, biological and outcomes data. Epidemiologic data were collected at baseline, and patients were followed up during a 30-day period.

RESULTS

There were a total of 322 patients. Mean age was 83.9±9.1years, and 47% of the patients were men. Among them, 59 patients (18.3%) died within 30days of admission to the ED. The following three characteristics were associated with increased mortality: age>85years (OR=1.5[95%CI:0.8-2.7], p=0.01), creatinine clearance <30mL/min (OR=2.6[95%CI:1.4-5], p<0.001) and Nt-proBNP >5000pg/mL (OR=2.2[95%CI:1.2-4], p<0.001). The best Nt-proBNP cut-off value to predict first-day mortality was 9000pg/mL (area under the curve (AUC) [95%CI] of 0.790 [0.634-0.935], p<0.001). For 7-day mortality, it was 7900pg/mL (0.698 [0.578-0.819], p<0.001) and for 30-day mortality, 5000pg/mL (0.667 [0.576-0.758], p<0.001).

CONCLUSIONS

Nt-proBNP level on admission, age and creatinine clearance, are predictive of 30-day mortality in adult patients admitted to ED for AHF.

The Financial Effect of Value-Based Purchasing and the Hospital Readmissions Reduction Program on Safety-Net Hospitals in 2014: A Cohort Study

BACKGROUND

Medicare's value-based purchasing (VBP) and the Hospital Readmissions Reduction Program (HRRP) could disproportionately affect safety-net hospitals.

OBJECTIVE

To determine whether safety-net hospitals incur larger financial penalties than other hospitals under VBP and HRRP.

DESIGN

Cross-sectional analysis.

SETTING

United States in 2014.

PARTICIPANTS

3022 acute care hospitals participating in VBP and the HRRP.

MEASUREMENTS

Safety-net hospitals were defined as being in the top quartile of the Medicare disproportionate share hospital (DSH) patient percentage and Medicare uncompensated care (UCC) payments per bed. The differences in penalties in both total dollars and dollars per bed between safety-net hospitals and other hospitals were estimated with the use of bivariate and graphical regression methods.

RESULTS

Safety-net hospitals in the top quartile of each measure were more likely to be penalized under VBP than other hospitals (62.9% vs. 51.0% under the DSH definition and 60.3% vs. 51.5% under the UCC per-bed definition). This was also the case under the HRRP (80.8% vs. 69.0% and 81.9% vs. 68.7%, respectively). Safety-net hospitals also had larger payment penalties ($115 900 vs. $66 600 and $150 100 vs. $54 900, respectively). On a per-bed basis, this translated to $436 versus $332 and $491 versus $314, respectively. Sensitivity analysis setting the cutoff at the top decile rather than the top quartile decile led to similar conclusions with somewhat larger differences between safety-net and other hospitals. The quadratic fit of the data indicated that the larger effect of these penalties is in the middle of the distribution of the DSH and UCC measures.

LIMITATION

Only 2 measures of safety-net status were included in the analyses.

CONCLUSION

Safety-net hospitals were disproportionately likely to be affected under VBP and the HRRP, but most incurred relatively small payment penalties in 2014.

PRIMARY FUNDING SOURCE

Patient-Centered Outcomes Research Institute.