Surgeons and anaesthetists predict 30-day outcomes after major lower limb amputation more accurately than most prediction tools:early results from the PERCEIVE (PrEdiction of Risk and Communication of outcomE following major lower limb amputation: a collaboratIVE) study

Background The accuracy with which healthcare professionals (HCPs) and risk prediction tools predict outcomes after major lower limb amputation (MLLA) is uncertain. The aim of this study was to evaluate the accuracy of predicting short-term (30 days after MLLA) mortality, morbidity, and revisional surgery. Methods The PERCEIVE (PrEdiction of Risk and Communication of outcomE following major lower limb amputation: a collaboratIVE) study was launched on 1 October 2020. It was an international multicentre study, including adults undergoing MLLA for complications of peripheral arterial disease and/or diabetes. Preoperative predictions of 30-day mortality, morbidity, and MLLA revision by surgeons and anaesthetists were recorded. Probabilities from relevant risk prediction tools were calculated. Evaluation of accuracy included measures of discrimination, calibration, and overall performance. Results Some 537 patients were included. HCPs had acceptable discrimination in predicting mortality (931 predictions; C-statistic 0.758) and MLLA revision (565 predictions; C-statistic 0.756), but were poor at predicting morbidity (980 predictions; C-statistic 0.616). They overpredicted the risk of all outcomes. All except three risk prediction tools had worse discrimination than HCPs for predicting mortality (C-statistics 0.789, 0.774, and 0.773); two of these significantly overestimated the risk compared with HCPs. SORT version 2 (the only tool incorporating HCP predictions) demonstrated better calibration and overall performance (Brier score 0.082) than HCPs. Tools predicting morbidity and MLLA revision had poor discrimination (C-statistics 0.520 and 0.679). Conclusion Clinicians predicted mortality and MLLA revision well, but predicted morbidity poorly. They overestimated the risk of mortality, morbidity, and MLLA revision. Most short-term risk prediction tools had poorer discrimination or calibration than HCPs. The best method of predicting mortality was a statistical tool that incorporated HCP estimation

Long-term risk prediction after major lower limb amputation: one-year results of the PERCEIVE study

BACKGROUND: Decision-making when considering major lower limb amputation is complex and requires individualized outcome estimation. It is unknown how accurate healthcare professionals or relevant outcome prediction tools are at predicting outcomes at 1-year after major lower limb amputation.METHODS: An international, multicentre prospective observational study evaluating healthcare professional accuracy in predicting outcomes 1 year after major lower limb amputation and evaluation of relevant outcome prediction tools identified in a systematic search of the literature was undertaken. Observed outcomes at 1 year were compared with: healthcare professionals' preoperative predictions of death (surgeons and anaesthetists), major lower limb amputation revision (surgeons) and ambulation (surgeons, specialist physiotherapists and vascular nurse practitioners); and probabilities calculated from relevant outcome prediction tools.RESULTS: A total of 537 patients and 2244 healthcare professional predictions of outcomes were included. Surgeons and anaesthetists had acceptable discrimination (C-statistic = 0.715), calibration and overall performance (Brier score = 0.200) when predicting 1-year death, but performed worse when predicting major lower limb amputation revision and ambulation (C-statistics = 0.627 and 0.662 respectively). Healthcare professionals overestimated the death and major lower limb amputation revision risks. Consultants outperformed trainees, especially when predicting ambulation. Allied healthcare professionals marginally outperformed surgeons in predicting ambulation. Two outcome prediction tools (C-statistics = 0.755 and 0.717, Brier scores = 0.158 and 0.178) outperformed healthcare professionals' discrimination, calibration and overall performance in predicting death. Two outcome prediction tools for ambulation (C-statistics = 0.688 and 0.667) marginally outperformed healthcare professionals.CONCLUSION: There is uncertainty in predicting 1-year outcomes following major lower limb amputation. Different professional groups performed comparably in this study. Two outcome prediction tools for death and two for ambulation outperformed healthcare professionals and may support shared decision-making.</p

Short-term risk prediction after major lower limb amputation: PERCEIVE study

Background The accuracy with which healthcare professionals (HCPs) and risk prediction tools predict outcomes after major lower limb amputation (MLLA) is uncertain. The aim of this study was to evaluate the accuracy of predicting short-term (30 days after MLLA) mortality, morbidity, and revisional surgery. Methods The PERCEIVE (PrEdiction of Risk and Communication of outcomE following major lower limb amputation: a collaboratIVE) study was launched on 1 October 2020. It was an international multicentre study, including adults undergoing MLLA for complications of peripheral arterial disease and/or diabetes. Preoperative predictions of 30-day mortality, morbidity, and MLLA revision by surgeons and anaesthetists were recorded. Probabilities from relevant risk prediction tools were calculated. Evaluation of accuracy included measures of discrimination, calibration, and overall performance. Results Some 537 patients were included. HCPs had acceptable discrimination in predicting mortality (931 predictions; C-statistic 0.758) and MLLA revision (565 predictions; C-statistic 0.756), but were poor at predicting morbidity (980 predictions; C-statistic 0.616). They overpredicted the risk of all outcomes. All except three risk prediction tools had worse discrimination than HCPs for predicting mortality (C-statistics 0.789, 0.774, and 0.773); two of these significantly overestimated the risk compared with HCPs. SORT version 2 (the only tool incorporating HCP predictions) demonstrated better calibration and overall performance (Brier score 0.082) than HCPs. Tools predicting morbidity and MLLA revision had poor discrimination (C-statistics 0.520 and 0.679). Conclusion Clinicians predicted mortality and MLLA revision well, but predicted morbidity poorly. They overestimated the risk of mortality, morbidity, and MLLA revision. Most short-term risk prediction tools had poorer discrimination or calibration than HCPs. The best method of predicting mortality was a statistical tool that incorporated HCP estimation

Short-term risk prediction after major lower limb amputation: PERCEIVE study

This multicentre cohort study of 537 patients evaluated the accuracy of preoperative predictions of outcomes by healthcare professionals and several relevant risk prediction tools. Surgeons and anaesthetists predicted 30-day outcomes after major lower limb amputation more accurately than most risk prediction tools. The best performing method of predicting mortality was a tool that incorporated healthcare professional estimation of risk.Background The accuracy with which healthcare professionals (HCPs) and risk prediction tools predict outcomes after major lower limb amputation (MLLA) is uncertain. The aim of this study was to evaluate the accuracy of predicting short-term (30 days after MLLA) mortality, morbidity, and revisional surgery. Methods The PERCEIVE (PrEdiction of Risk and Communication of outcomE following major lower limb amputation: a collaboratIVE) study was launched on 1 October 2020. It was an international multicentre study, including adults undergoing MLLA for complications of peripheral arterial disease and/or diabetes. Preoperative predictions of 30-day mortality, morbidity, and MLLA revision by surgeons and anaesthetists were recorded. Probabilities from relevant risk prediction tools were calculated. Evaluation of accuracy included measures of discrimination, calibration, and overall performance. Results Some 537 patients were included. HCPs had acceptable discrimination in predicting mortality (931 predictions; C-statistic 0.758) and MLLA revision (565 predictions; C-statistic 0.756), but were poor at predicting morbidity (980 predictions; C-statistic 0.616). They overpredicted the risk of all outcomes. All except three risk prediction tools had worse discrimination than HCPs for predicting mortality (C-statistics 0.789, 0.774, and 0.773); two of these significantly overestimated the risk compared with HCPs. SORT version 2 (the only tool incorporating HCP predictions) demonstrated better calibration and overall performance (Brier score 0.082) than HCPs. Tools predicting morbidity and MLLA revision had poor discrimination (C-statistics 0.520 and 0.679). Conclusion Clinicians predicted mortality and MLLA revision well, but predicted morbidity poorly. They overestimated the risk of mortality, morbidity, and MLLA revision. Most short-term risk prediction tools had poorer discrimination or calibration than HCPs. The best method of predicting mortality was a statistical tool that incorporated HCP estimation

Income deprivation and groin wound surgical site infection: cross-sectional analysis from the groin wound infection after vascular exposure multicenter cohort study

Background: Living in deprived areas is associated with poorer outcomes after certain vascular procedures and surgical site infection in other specialties. Our primary objective was to determine whether living in more income-deprived areas was associated with groin wound surgical site infection after arterial intervention. Secondary objectives were to determine whether living in more income-deprived areas was associated with mortality and clinical consequences of surgical site infection. Methods: Postal code data for patients from the United Kingdom who were included in the Groin Wound Infection after Vascular Exposure (GIVE) multicenter cohort study was used to determine income deprivation, based on index of multiple deprivation (IMD) data. Patients were divided into three IMD groups for descriptive analysis. Income deprivation score was integrated into the final multivariable model for predicting surgical site infection. Results: Only patients from England had sufficient postal code data, analysis included 772 groin incisions (624 patients from 22 centers). Surgical site infection occurred in 9.7% incisions (10.3% of patients). Surgical site infection was equivalent between income deprivation tertiles (tertile 1 = 9.5%; tertile 2 = 10.3%; tertile 3 = 8.6%; p = 0.828) as were the clinical consequences of surgical site infection and mortality. Income deprivation was not associated with surgical site infection in multivariable regression analysis (odds ratio [OR], 0.574; 95% confidence interval [CI], 0.038–8.747; p = 0.689). Median age at time of procedure was lower for patients living in more income-deprived areas (tertile 1 = 68 years; tertile 2 = 72 years; tertile 3 = 74 years; p < 0.001). Conclusions: We found no association between living in an income-deprived area and groin wound surgical site infection, clinical consequences of surgical site infection and mortality after arterial intervention. Patients living in more income-deprived areas presented for operative intervention at a younger age, with similar rates of comorbidities to patients living in less income-deprived areas. Groin wound surgical site infection (SSI) after arterial surgery is common [1], and research into reducing SSIs in vascular surgery is recognized as a priority by both clinicians and patient/caregiver representatives [2]. Despite the substantial potential morbidity and mortality of these SSIs [3,4], the available evidence relating to contributory factors is largely historic or reliant on retrospective data [5–7]. Further research on the epidemiology of SSI in this patient group is needed to allow better risk stratification, improve pre-operative discussions of risk with patients, and to guide targeted SSI prevention strategies that often include expensive prophylactic interventions [8]. However, little is currently known about the impact of socioeconomic characteristics on groin wound SSIs in this population. Socioeconomic deprivation is linked to health [9], and lifestyle-influenced cardiovascular diseases are more prevalent in more deprived areas [10]. Higher rates of unhealthy lifestyles (smoking, poor diet, and lack of physical exercise) in deprived areas are postulated to cause higher rates of cardiovascular risk increasing comorbidities, such as obesity and hyperlipidemia [10–12]. Several cardiovascular risk factors (e.g., smoking, body mass index, and diabetes mellitus), and peripheral arterial disease itself, are well recognized risk factors for SSI [13–16]. The association between socioeconomic deprivation and SSIs has previously been demonstrated in orthopedic surgery, cardiac surgery, and general surgery [17–19]. It is currently unknown whether living in an income-deprived area is associated with groin wound SSIs after arterial intervention. It was recently demonstrated in a large registry study in the United Kingdom, that outcomes following endovascular intervention for occlusive peripheral arterial disease were worse for patients living in deprived areas [20]. To the best of our knowledge, this aspect of outcomes after arterial intervention through a groin incision has not been investigated. Furthermore, studies demonstrating higher prevalence of cardiovascular disease risk factors in more deprived areas are now mostly historic and have not specifically investigated those presenting for arterial intervention through a groin incision for demographic differences in relation to deprivation [9–12]. Updated, prospective evidence is required to determine whether health inequalities persist for such patients today. Our primary objective was to determine whether residing in a more income-deprived area was associated with a higher risk of groin wound SSI after arterial intervention, by analyzing a subset of patients enrolled in the Groin wound Infection after Vascular Exposure (GIVE) multicenter cohort study [1,21]. Secondary objectives were to determine whether living in more income-deprived areas was associated with 30-day mortality and the clinical sequelae of SSI, and whether patients living in more income-deprived areas differed in terms of demographics and comorbidities compared with patients from less income-deprived areas