Unwarranted Between-hospital Variation in Mortality, Readmission, and Length of Stay of Urological Admissions: An Important Trigger for Prioritising Quality Targets

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

doi:10.1016/j.euf.2021.11.001

Cited by 5 publications

(10 citation statements)

References 24 publications

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“…The healthcare resources feature can be binary 19 or multiclass variable 15,20,44 and is also utilized to measure resource utilization 19 . As expected, disease‐related medical events dominate the history feature, 9,10,13,14,21,23,31,36,38,39,44,45,47 representing 57% of the reported 22 clinical history features.…”

Section: Resultsmentioning

confidence: 91%

“…The second method uses the Elixhauser Comorbidity Index 17,36 or Charlson Comorbidity Index 9,13,16,25,29,35 . The third method counts the number of comorbid conditions 10,17,23,32,46,47 . The fourth representation is a mix of the above methods 15,25,39,48 …”

Section: Resultsmentioning

confidence: 99%

“…9,13,16,25,29,35 The third method counts the number of comorbid conditions. 10,17,23,32,46,47 The fourth representation is a mix of the above methods. 15,25,39,48 Smoking history is represented as binary, 21,34,41 or multiclass.…”

Section: Patient Profile Featuresmentioning

confidence: 99%

“…The provider-related subcategory includes six features. The type of provider (e.g., private, public, academic, community), 10,16,20,21,24,26,36,38,47,52 location (e.g., rural, urban), 10,16,21,29,36,38,52 provider size, 36 case handling volume (such as low, medium, high), 10,20,21,24,38,45,47 capacity or resource availability (such as onsite diagnostic facilities or radiotherapy equipment), 10,20,21,24,38,45,47 and practice pattern (e.g., opioid prescription pattern 45 or IVF pattern 51 ).…”

Section: Local Context Featuresmentioning

confidence: 99%

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Identification and classification of principal features for analyzing unwarranted clinical variation

McOwiti,

Tao,

Tao

2023

Evaluation Clinical Practice

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Rationale, Aims, and ObjectiveUnwarranted clinical variation (UCV) is an undesirable aspect of a healthcare system, but analyzing for UCV can be difficult and time‐consuming. No analytic feature guidelines currently exist to aid researchers. We performed a systematic review of UCV literature to identify and classify the features researchers have identified as necessary for the analysis of UCV.MethodsThe literature search followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. We looked for articles with the terms ‘medical practice variation’ and ‘unwarranted clinical variation’ from four databases: Medline, Web of Science, EMBASE and CINAHL. The search was performed on 24 March 2023. The articles selected were original research articles in the English language reporting on UCV analysis in adult populations. Most of the studies were retrospective cohort analyses. We excluded studies reporting geographic variation based on the Atlas of Variation or small‐area analysis methods. We used ASReview Lab software to assist in identifying articles for abstract review. We also conducted subsequent reference searches of the primary articles to retrieve additional articles.ResultsThe search yielded 499 articles, and we reviewed 46. We identified 28 principal analytic features utilized to analyze for unwarranted variation, categorised under patient‐related or local healthcare context factors. Within the patient‐related factors, we identified three subcategories: patient sociodemographics, clinical characteristics, and preferences, and classified 17 features into seven subcategories. In the local context category, 11 features are classified under two subcategories. Examples are provided on the usage of each feature for analysis.ConclusionTwenty‐eight analytic features have been identified, and a categorisation has been established showing the relationships between features. Identifying and classifying features provides guidelines for known confounders during analysis and reduces the steps required when performing UCV analysis; there is no longer a need for a UCV researcher to engage in time‐consuming feature engineering activities.

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Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Patient Profile Featuresmentioning

confidence: 99%

Section: Local Context Featuresmentioning

confidence: 99%

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Identification and classification of principal features for analyzing unwarranted clinical variation

McOwiti,

Tao,

Tao

2023

Evaluation Clinical Practice

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“…Em se tratando dos procedimentos urológicos, um estudo conduzido na França, durante três anos, com 419.787 pacientes, encontrou uma frequência de reinternação de 18,4% (20) . Outro estudo conduzido na Bélgica, com dados de 98% dos hospitais do país, encontrou grandes heterogeneidade nas taxas de mortalidade, readmissão e o tempo de internação entre as instituições avaliadas, indicando que diferenças relacionadas à estrutura e processo de trabalho interferiram nos resultados pós cirurgias urológicas (21) . Muitos fatores podem interferir na ocorrência das IRAS nestas especialidades, incluindo as ações diretas e indiretas ao paciente.…”

Section: Discussionunclassified

Unplanned readmissions after surgery in a hospital in northern Brazil: prospective cohort

Pereira

Deus

Araújo

et al. 2022

R. Enferm. Cent. O. Min.

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Objective: to analyze unplanned readmissions after surgical procedures and their risk factors in a large hospital in Northern Brazil. Method: prospective cohort, with data collected from medical records and at the bedside. Descriptive, bivariate and multiple analysis were performed using Poisson regression in Stata® v.16.0. Results: of the total of 486 patients, 1.47% were readmitted. The incidence of readmissions was 68 per 1,000 procedures (95%CI: 47.10; 93.85). In the adjusted analysis, the risk factors were not being white (RR: 2.06; 95%CI 1.13; 3.75), using implants in surgery (RR: 2.00; 95%CI: 1.05; 3.81) and procedures of the urology/renal specialties (RR: 3.17; 95%CI: 1.59-6.31) and gynecology (RR: 2.18; 95%CI: 1.06-4.49). Conclusion: the incidence of readmissions in this region is higher than in other regions. Demographic characteristics and type of surgical procedure were risk factors for the outcome.

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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

Self Cite

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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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Unwarranted Between-hospital Variation in Mortality, Readmission, and Length of Stay of Urological Admissions: An Important Trigger for Prioritising Quality Targets

Cited by 5 publications

References 24 publications

Identification and classification of principal features for analyzing unwarranted clinical variation

Identification and classification of principal features for analyzing unwarranted clinical variation

Unplanned readmissions after surgery in a hospital in northern Brazil: prospective cohort

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

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