Hospital-, Anesthesiologist-, Surgeon-, and Patient-level Variation in Neuraxial Anesthesia Use for Lower Limb Revascularization Surgery: A Population-based Cross-sectional Study

Roberts, Derek J.; Mor, Rahul S.; Rosen, Michael N.; Talarico, Robert; Lalu, Manoj M.; Jerath, Angela; Wijeysundera, Duminda N.; McIsaac, Daniel I.

doi:10.1016/j.jvs.2022.07.052

Cited by 1 publication

(12 citation statements)

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“…The classification of features is shown in Figure 1 Race and ethnicity 18 (39) 18 [9][10][11][12][13][14][15]17,21,23,28,29,[35][36][37][38]45,49 Social Socioeconomic status 30 (65) 30 [9][10][11][12][13][14][15][16] Social risk factors 12 (26) 12 [10][11][12][13][14][15][16][18][19][20][21]45 Clinical characteristics Patient profile N/A Body mass index 7 (15) 7 20,23,34,[39][40][41]49 History Comorbidity 29 (63) 29 [9][10][11]13,[15]…”

Section: Feature Categoriesmentioning

confidence: 99%

“…Age and gender are the most utilized demographic features in all the studies, Table [17][18][19] drug or alcohol abuse 20 or smoking. 21 The SRF features are treated as binary or multi-level nominals.…”

Section: Sociodemographic Featuresmentioning

confidence: 99%

“…The standard indices include Australia's Socioeconomic Indexes for Areas (SEIFA), 22 and Index of Relative Socioeconomic Disadvantage (IRSD), 16 the UK's Index of Multiple Deprivation (IMD). [23][24][25][26][27] The ad hoc measures include income levels, 11,12,15,19,[28][29][30][31][32][33] education levels, 11,15,28,34 neighbourhood income or rural setting, 15,19,20,35 non-English speakers, 18 and insurance type. [12][13][14][15][16]21,[35][36][37][38]…”

Section: Sociodemographic Featuresmentioning

confidence: 99%

“…Seven (15%) studies reported BMI, with values represented as numeric BMI value 39,40 or as numeric weight value, 23 binary 34,41 or nominalised to weight levels. 20,23 The clinical history feature is reported in 22 (49%) articles (Table S2). Under history, comorbidity appears in 22 (56%) articles.…”

Section: Patient Profile Featuresmentioning

confidence: 99%

“…The first is listing all the comorbid diseases. 11,[19][20][21][22]34,36,[38][39][40][42][43][44][45] 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.…”

Section: Patient Profile 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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