Estimating postoperative mortality in colorectal surgery- a systematic review of risk prediction models

Dosis, Alexios; Helliwell, Jack; Syversen, Aron; Tiernan, Jim; Zhang, Zhiqiang; Jayne, David

doi:10.1007/s00384-023-04455-0

Cited by 1 publication

(1 citation statement)

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“…The ASA system (American Society of Anaethesiology), the APACHE II (Acute Physiology and Chronic Health Evaluation) and the POSSUM (Physiological and Operative Severity Score forenUmeration of Mortality and morbidity) scores have been shown to have good predictive value [ 23 , 24 , 25 ]. A review of common preoperative tools has shown that they have comparable predictive performance to machine learning (ML) techniques [ 26 ]. However, these tools are not consistently employed in practice.…”

Section: Introductionmentioning

confidence: 99%

Wearable Sensors as a Preoperative Assessment Tool: A Review

Syversen,

Dosis,

Jayne

et al. 2024

Sensors

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Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.

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Section: Introductionmentioning

confidence: 99%