A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression

Wilder, Astrid Van; Cox, Bianca; Ridder, Dirk De; Tambeur, Wim; Boer, Guy Vanden; Brouwers, Jonas; Claessens, Fien; Bruyneel, Luk; Vanhaecht, Kris

doi:10.1016/j.health.2022.100123

Cited by 2 publications

(4 citation statements)

References 38 publications

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“…In addition, MORs for APR-DRGs with significant between-hospital variation were often higher than odds ratios for hospital characteristics, indicating between-hospital variation exceeds variation explained by the included hospitals characteristics. This confirms previous research on hospital-wide and urological Belgian hospital care 17,33 . Further investigation is required on other hospital context factors that might be contributing to this variation, including leadership characteristics, quality education or quality culture 34–36 .…”

Section: Discussionsupporting

confidence: 86%

“…This confirms previous research on hospital-wide and urological Belgian hospital care. 17,33 Further investigation is required on other hospital context factors that might be contributing to this variation, including leadership characteristics, quality education or quality culture. [34][35][36] In addition, staffing levels of physicians and nurses might play a part in outcome disparities.…”

Section: Variation Persists Despite Adjustments For Patient and Hospi...mentioning

confidence: 99%

“…We studied variability in in-hospital mortality, unplanned 30-day readmissions, and prolonged length of hospital stay (pLOS), measures that are available from routinely collected data and are strongly correlated with healthcare quality and spending 14–16 . Studies examining all 3 outcomes together are rare, 17–19 especially within cardiology. Yet, considering combined outcomes can help uncover potential competing risks between them 7,20–22 .…”

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confidence: 99%

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Call for Action to Target Interhospital Variation in Cardiovascular Mortality, Readmissions, and Length-of-Stay

Van Wilder,

Bruyneel,

Cox

et al. 2024

Medical Care

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Background: Excessive interhospital variation threatens healthcare quality. Data on variation in patient outcomes across the whole cardiovascular spectrum are lacking. We aimed to examine interhospital variability for 28 cardiovascular All Patient Refined-Diagnosis-related Groups (APR-DRGs). Methods: We studied 103,299 cardiovascular admissions in 99 (98%) Belgian acute-care hospitals between 2012 and 2018. Using generalized linear mixed models, we estimated hospital-specific and APR-DRG-specific risk-standardized rates for in-hospital mortality, 30-day readmissions, and length-of-stay above the APR-DRG-specific 90th percentile. Interhospital variation was assessed based on estimated variance components and time trends between the 2012–2014 and 2016–2018 periods were examined. Results: There was strong evidence of interhospital variation, with statistically significant variation across the 3 outcomes for 5 APR-DRGs after accounting for patient and hospital factors: percutaneous cardiovascular procedures with acute myocardial infarction, heart failure, hypertension, angina pectoris, and arrhythmia. Medical diagnoses, with in particular hypertension, heart failure, angina pectoris, and cardiac arrest, showed strongest variability, with hypertension displaying the largest median odds ratio for mortality (2.51). Overall, hospitals performing at the upper-quartile level should achieve improvements to the median level, and an annual 633 deaths, 322 readmissions, and 1578 extended hospital stays could potentially be avoided. Conclusions: Analysis of interhospital variation highlights important outcome differences that are not explained by known patient or hospital characteristics. Targeting variation is therefore a promising strategy to improve cardiovascular care. Considering their treatment in multidisciplinary teams, policy makers, and managers should prioritize heart failure, hypertension, cardiac arrest, and angina pectoris improvements by targeting guideline implementation outside the cardiology department.

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Section: Discussionsupporting

confidence: 86%

Section: Variation Persists Despite Adjustments For Patient and Hospi...mentioning

confidence: 99%

mentioning

confidence: 99%

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Call for Action to Target Interhospital Variation in Cardiovascular Mortality, Readmissions, and Length-of-Stay

Van Wilder,

Bruyneel,

Cox

et al. 2024

Medical Care

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“…However, overall yearly fluctuations in mortality were accounted for in both methods, and previous research indicated that in general, good-performing hospitals continue to outperform other hospitals in Belgium. 27…”

Section: Discussionmentioning

confidence: 99%

Convergent Validity of 2 Widely Used Methodologies for Calculating the Hospital Standardized Mortality Ratio in Flanders, Belgium

Cox

Wilder

Ridder³

et al. 2023

J Patient Saf

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Objectives To assess their construct validity, we compared results from 2 models used for estimating hospital standardized mortality ratios (HSMRs) in Belgium. The method of the Flemish Hospital Network (FHN) is based on a logistic regression for each of the 64 All Patient Refined Diagnosis-Related Groups that explain 80% of mortality and uses the Elixhauser score to correct for comorbidities. (H)SMRs published on the 3M-Benchmark-Portal are calculated by a simpler indirect standardization for All Patient Refined Diagnosis-Related Groups and risk of mortality (ROM) at discharge. Methods We used administrative data from all eligible hospital admissions in 22 Flemish hospitals between 2016 and 2019 (FHN, n = 682,935; 3M, n = 2,122,305). We evaluated model discrimination and accuracy and assessed agreement in estimated HSMRs between methods. Results The Spearman correlation between HSMRs generated by the FHN model and the standard 3M model was 0.79. Although 2 of 22 hospitals showed opposite classification results, that is, an HSMR significantly <1 according to the FHN method but significantly >1 according to the 3M model, classification agreement between methods was significant (agreement for 59.1% of hospitals, κ = 0.45). The 3M model (c statistic = 0.96, adjusted Brier score = 26%) outperformed the FHN model (0.87, 17%). However, using ROM at admission instead of at discharge in the 3M model significantly reduced model performance (c statistic = 0.94, adjusted Brier score = 21%), but yielded similar HSMR estimates and eliminated part of the discrepancy with FHN results. Conclusions Results of both models agreed relatively well, supporting convergent validity. Whereas the FHN method only adjusts for disease severity at admission, the ROM indicator of the 3M model includes diagnoses not present on admission. Although diagnosis codes generated by complications during hospitalization have the tendency to increase the predictive performance of a model, these should not be included in risk adjustment procedures.

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A comprehensive analysis of temporal trends of between-hospital variation in mortality, readmission and length of stay using logistic regression

Cited by 2 publications

References 38 publications

Call for Action to Target Interhospital Variation in Cardiovascular Mortality, Readmissions, and Length-of-Stay

Call for Action to Target Interhospital Variation in Cardiovascular Mortality, Readmissions, and Length-of-Stay

Convergent Validity of 2 Widely Used Methodologies for Calculating the Hospital Standardized Mortality Ratio in Flanders, Belgium

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