Short-term risk prediction after major lower limb amputation: PERCEIVE study
Author:
Gwilym Brenig L1, Pallmann Philip2, Waldron Cherry-Ann2, Thomas-Jones Emma2, Milosevic Sarah2, Brookes-Howell Lucy2, Harris Debbie2, Massey Ian3, Burton Jo3, Stewart Phillippa3, Samuel Katie4, Jones Sian5, Cox David5, Clothier Annie1, Edwards Adrian6, Twine Christopher P7, Bosanquet David C1, Ambler G, Benson R, Birmpili P, Blair R, Bosanquet D C, Dattani N, Dovell G, Forsythe R, Gwilym B L, Hitchman L, Machin M, Nandhra S, Onida S, Preece R, Saratzis A, Shalhoub J, Singh A, Forget P, Gannon M, Celnik A, Duguid M, Campbell A, Duncan K, Renwick B, Moore J, Maresch M, Tolba M, Kamal D, Kabis M, Hatem M, Juszczak M, Dattani N, Travers H, Shalan A, Elsabbagh M, Rocha-Neves J, Pereira-Neves A, Teixeira J, Lyons O, Lim E, Hamdulay K, Makar R, Zaki S, Francis C T, Azer A, Ghatwary-Tantawy T, Elsayed K, Mittapalli D, Melvin R, Barakat H, Taylor J, Veal S, Hamid H K S, Baili E, Kastrisios G, Maltezos C, Maltezos K, Anastasiadou C, Pachi A, Skotsimara A, Saratzis A, Vijaynagar B, Lau S, Velineni R, Bright E, Montague-Johnstone E, Stewart K, King W, Karkos C, Mitka M, Papadimitriou C, Smith G, Chan E, Shalhoub J, Machin M, Agbeko A E, Amoako J, Vijay A, Roditis K, Papaioannou V, Antoniou A, Tsiantoula P, Bessias N, Papas T, Dovell G, Goodchild F, Nandhra S, Rammell J, Dawkins C, Lapolla P, Sapienza P, Brachini G, Mingoli A, Hussey K, Meldrum A, Dearie L, Nair M, Duncan A, Webb B, Klimach S, Hardy T, Guest F, Hopkins L, Contractor U, Clothier A, McBride O, Hallatt M, Forsythe R, Pang D, Tan L E, Altaf N, Wong J, Thurston B, Ash O, Popplewell M, Grewal A, Jones S, Wardle B, Twine C, Ambler G, Condie N, Lam K, Heigberg-Gibbons F, Saha P, Hayes T, Patel S, Black S, Musajee M, Choudhry A, Hammond E, Costanza M, Shaw P, Feghali A, Chawla A, Surowiec S, Encalada R Zerna, Benson R, Cadwallader C, Clayton P, Van Herzeele I, Geenens M, Vermeir L, Moreels N, Geers S, Jawien A, Arentewicz T, Kontopodis N, Lioudaki S, Tavlas E, Nyktari V, Oberhuber A, Ibrahim A, Neu J, Nierhoff T, Moulakakis K, Kakkos S, Nikolakopoulos K, Papadoulas S, D'Oria Mario, Lepidi S, Kent F, Lowry D, Ooi S, Enemosah I, Patterson B, Williams S, Elrefaey G H, Gaba K A, Williams G F, Rodriguez D U, Khashram M, Gormley S, Hart O, Suthers E, French S,
Affiliation:
1. South East Wales Vascular Network, Aneurin Bevan University Health Board, Royal Gwent Hospital , Newport , UK 2. Centre for Trials Research, Cardiff University , Cardiff , UK 3. Artificial Limb and Appliance Centre, Rookwood Hospital, Cardiff and Vale University Health Board , Cardiff , UK 4. Department of Anaesthesia, North Bristol NHS Trust , Bristol , UK 5. c/o INVOLVE Health and Care Research Wales , Cardiff , UK 6. Division of Population Medicine, Cardiff University , Cardiff , UK 7. Bristol, Bath and Weston Vascular Network, North Bristol NHS Trust, Southmead Hospital , Bristol , UK
Abstract
Abstract
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.
Funder
Research for Patient and Public Benefit (RfPPB) programme, Health and Care Research Wales
Publisher
Oxford University Press (OUP)
Cited by
8 articles.
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