An Accumulated Deficits Model Predicts Perioperative and Long-Term Adverse Events After Carotid Endarterectomy

Sridharan, Natalie; Chaer, Rabih A.; Wu, Bryan; Eslami, Mohammad H.; Makaroun, Michel S.; Avgerinos, Efthymios D.

doi:10.1016/j.avsg.2017.03.144

Cited by 4 publications

(13 citation statements)

References 23 publications

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“…Our sensitivity analysis, evaluating the relationship between frailty and absolute length of hospitalization without accounting for type of surgery, also revealed a prediction error greater than 2 days for both indices. Many investigators report significant associations between measures of frailty and postoperative complications, 1,5,6,8,16,19,20,29,[36][37][38][39][40] but only some report C statistics, 6,29,30,33,36,37,39 which quantify clinical importance. Reported C statistics range from 0.53 to 0.68, with most being between 0.60 and 0.65, 6,29,30,39 which are similar to our findings.…”

Section: Discussionmentioning

confidence: 99%

Deficit Accumulation and Phenotype Assessments of Frailty Both Poorly Predict Duration of Hospitalization and Serious Complications after Noncardiac Surgery

et al. 2020

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Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Frailty is associated with adverse postoperative outcomes, but it remains unclear which measure of frailty is best. This study compared two approaches: the Modified Frailty Index, which is a deficit accumulation model (number of accumulated deficits), and the Hopkins Frailty Score, which is a phenotype model (consisting of shrinking, weakness, exhaustion, slowness, and low physical activity). The primary aim was to compare the ability of each frailty score to predict prolonged hospitalization. Secondarily, the ability of each score to predict 30-day readmission and/or postoperative complications was compared. Methods This study prospectively enrolled adults presenting for preanesthesia evaluation before elective noncardiac surgery. The Hopkins Frailty Score and Modified Frailty Index were both determined. The ability of each frailty score to predict the primary outcome (prolonged hospitalization) was compared using a ratio of root-mean-square prediction errors from linear regression models. The ability of each score to predict the secondary outcome (readmission and complications) was compared using ratio of root-mean-square prediction errors from logistic regression models. Results The study included 1,042 patients. The frailty rates were 23% (Modified Frailty Index of 4 or higher) and 18% (Hopkins Frailty Score of 3 or higher). In total, 12.9% patients were readmitted or had postoperative complications. The error of the Modified Frailty Index and Hopkins Frailty Score in predicting the primary outcome was 2.5 (95% CI, 2.2, 2.9) and 2.6 (95% CI, 2.2, 3.0) days, respectively, and their ratio was 1.0 (95% CI, 1.0, 1.0), indicating similarly poor prediction. Similarly, the error of respective frailty scores in predicting the probability of secondary outcome was high, specifically 0.3 (95% CI, 0.3, 0.4) and 0.3 (95% CI, 0.3, 0.4), and their ratio was 1.00 (95% CI, 1.0, 1.0). Conclusions The Modified Frailty Index and Hopkins Frailty Score were similarly poor predictors of perioperative risk. Further studies, with different frailty screening tools, are needed to identify the best method to measure perioperative frailty.

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Section: Discussionmentioning

confidence: 99%

Deficit Accumulation and Phenotype Assessments of Frailty Both Poorly Predict Duration of Hospitalization and Serious Complications after Noncardiac Surgery

et al. 2020

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“…Although useful at a population health level, the HFRS is not designed to be used as a clinical decision making toolpatient assessments should always be individualised. Even frailty tools developed with specific specialised conditions in mind do not exhibit sufficiently robust predictive characteristics to direct individual patient decision-making [2][3][4][5][6][7][8][9][10][11]. However, the knowledge of the risk of frailty should sensitise the clinician to think about holistic assessment and prognosis when helping patients decide the right approach to their care.…”

Section: Discussionmentioning

confidence: 99%

“…The main outcomes recorded in SUS relate to service metrics in the year following the index event (admissions, length of stay, readmission), mortality and some treatment specific complications (which had been prioritised by the CRG leads). The method for differentiating an admission from a readmission has been taken from the NHS Digital definition [4]. For 30 day readmissions these were defined as emergency admissions to any hospital in England occurring within 30 days of the last, previous discharge from hospital after admission excluding obstetrics related admissions.…”

Section: Methodsmentioning

confidence: 99%

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The Hospital Frailty Risk Score—outcomes in specialised services

Imam¹,

Konstant-Hambling²,

Fluck³

et al. 2020

Age and Ageing

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Background Frailty is increasingly used to risk stratify older people, but across specialised services there is no standardised approach. The aim of this study was to assess if the Hospital Frailty Risk Score (HFRS) could describe outcomes for older people within English specialised services. Design A retrospective cohort study was performed using the Secondary Uses Service (SUS) electronic database for people aged 75 or older admitted between April 2017 and March 2018. Methods Based on HFRS, the populations were risk stratified into mild, moderate and severe frailty risk. The relationships with length of stay, readmission rate, mortality and some selected condition specific treatment complications were quantified using descriptive statistics. Results Very few individuals (<2%) could not be risk stratified for frailty risk. Frailty was differentially distributed across the specialties; around one-third had mild frailty; another third had moderate frailty and one-quarter severe frailty. Increasing frailty risk was associated with increased length of stay for the index admission, more days in hospital in the year following intervention and increased risk of dying in hospital. Severe frailty was a powerful discriminator of the risk of death; between 25 and 40% of those with severe frailty risk died at 30 months across all specialties. Conclusions This study demonstrates the first application of the HFRS to a national dataset to describe service outcomes and mortality for older people undergoing a range of specialised interventions. This information could be used to identify those that might benefit from holistic assessment, aid prognostication, commissioning and service planning.

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“…After screening of 788 unique reports and assessing the full-texts of 59 for eligibility, we included 15 studies reporting 17 prediction models (Figure 1 and Table V in the Data Supplement). 24–38 Two (12%) models were developed in populations of symptomatic patients, 33,34 5 (29%) models in populations of asymptomatic patients, 35–37 and 9 (53%) in populations of both symptomatic and asymptomatic patients (Table 1). Symptomatic status was included as predictor in these nine prediction models 24–32 of which one used type of qualifying event as predictor.…”

Section: Resultsmentioning

confidence: 99%

External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy

Poorthuis

Herings

Dansey

et al. 2022

Stroke

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Background and Purpose: The net benefit of carotid endarterectomy (CEA) is determined partly by the risk of procedural stroke or death. Current guidelines recommend CEA if 30-day risks are <6% for symptomatic stenosis and <3% for asymptomatic stenosis. We aimed to identify prediction models for procedural stroke or death after CEA and to externally validate these models in a large registry of patients from the United States. Methods: We conducted a systematic search in MEDLINE and EMBASE for prediction models of procedural outcomes after CEA. We validated these models with data from patients who underwent CEA in the American College of Surgeons National Surgical Quality Improvement Program (2011–2017). We assessed discrimination using C statistics and calibration graphically. We determined the number of patients with predicted risks that exceeded recommended thresholds of procedural risks to perform CEA. Results: After screening 788 reports, 15 studies describing 17 prediction models were included. Nine were developed in populations including both asymptomatic and symptomatic patients, 2 in symptomatic and 5 in asymptomatic populations. In the external validation cohort of 26 293 patients who underwent CEA, 702 (2.7%) developed a stroke or died within 30-days. C statistics varied between 0.52 and 0.64 using all patients, between 0.51 and 0.59 using symptomatic patients, and between 0.49 to 0.58 using asymptomatic patients. The Ontario Carotid Endarterectomy Registry model that included symptomatic status, diabetes, heart failure, and contralateral occlusion as predictors, had C statistic of 0.64 and the best concordance between predicted and observed risks. This model identified 4.5% of symptomatic and 2.1% of asymptomatic patients with procedural risks that exceeded recommended thresholds. Conclusions: Of the 17 externally validated prediction models, the Ontario Carotid Endarterectomy Registry risk model had most reliable predictions of procedural stroke or death after CEA and can inform patients about procedural hazards and help focus CEA toward patients who would benefit most from it.

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An Accumulated Deficits Model Predicts Perioperative and Long-Term Adverse Events After Carotid Endarterectomy

Cited by 4 publications

References 23 publications

Deficit Accumulation and Phenotype Assessments of Frailty Both Poorly Predict Duration of Hospitalization and Serious Complications after Noncardiac Surgery

Deficit Accumulation and Phenotype Assessments of Frailty Both Poorly Predict Duration of Hospitalization and Serious Complications after Noncardiac Surgery

The Hospital Frailty Risk Score—outcomes in specialised services

External Validation of Risk Prediction Models to Improve Selection of Patients for Carotid Endarterectomy

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