Objective methods for preoperative assessment of functional capacity

Multimodal Prehabilitation for Patients with Crohn's Disease Scheduled for Major Surgery: A Narrative Review

Approximately 15-50% of patients with Crohn's disease (CD) will require surgery within ten years following the diagnosis. The management of modifiable risk factors before surgery is essential to reduce postoperative complications and to promote a better postoperative recovery. Preoperative malnutrition reduced functional capacity, sarcopenia, immunosuppressive medications, anemia, and psychological distress are frequently present in CD patients. Multimodal prehabilitation consists of nutritional, functional, medical, and psychological interventions implemented before surgery, aiming at optimizing preoperative status and improve postoperative recovery. Currently, studies evaluating the effect of multimodal prehabilitation on postoperative outcomes specifically in CD are lacking. Some studies have investigated the effect of a single prehabilitation intervention, of which nutritional optimization is the most investigated. The aim of this narrative review is to present the physiologic rationale supporting multimodal surgical prehabilitation in CD patients waiting for surgery, and to describe its main components to facilitate their adoption in the preoperative standard of care.

Prehabilitation: Who can benefit?

Prehabilitation is an intervention that occurs between cancer diagnosis and the start of an acute treatment. It involves physical, nutritional, and psychological assessments to establish a baseline functional level and provide targeted interventions to improve a person's health and prevent future impairments. Prehabilitation has been applied to surgical oncology and has shown positive results at improving functional capacity, reducing hospital stay, decreasing complications, and enhancing health-related quality of life. The importance of collaboration between various healthcare professionals and the implementation of multimodal interventions, including exercise training, nutrition optimization, and emotional support is discussed in this manuscript. The need for screening and assessment of conditions such as sarcopenia, frailty, or low functional status in order to identify patients who would benefit the most from prehabilitation is vital and should be a part of all prehabilitation programs. Exercise and nutrition play complementary roles in prehabilitation, enhancing anabolism and performance. However, in the presence of malnutrition and sarcopenia, exercise-related energy expenditure without sufficient protein intake can lead to muscle wasting and further deterioration of functional capacity, thus special emphasis on nutrition and protein intake should be made in these cases. Finally, the challenges and the need for a paradigm shift in perioperative care are discussed to effectively implement personalized prehabilitation programs.

Wearable Sensors as a Preoperative Assessment Tool: A Review

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.

Digital health coaching to improve patient preparedness for elective lower limb arthroplasty: a quality improvement project

Major surgery carries high risks with comorbidities, frailty and health risk behaviours meaning patients are often unprepared for the physiological insult. Since 2018, the Prepwell programme at South Tees Hospitals NHS Foundation Trust has supported patients to improve their preoperative health and fitness. In April 2020, the face-to-face service was suspended due to the pandemic, leading to the team implementing a three-tiered remote digital support pathway, including digital health coaching via a mobile phone application.

METHODS

Patients scheduled for elective lower limb arthroplasty were offered 8 weeks of digital health coaching preoperatively. Following consent, participants were assigned a personal health coach to set individual behaviour change goals supported by online resources, alongside a digitally delivered exercise programme. Participants completed self-assessment questionnaires at Entry to, and Exit from, the programme, with outcome data collected 21 days postoperatively. The primary outcome was the change in Patient Activation Measure (PAM).

RESULTS

Fifty-seven of 189 patients (30.2%) consented to referral for digital health coaching. Forty participants completed the 8-week programme. Median PAM increased from 58.1 to 67.8 (p=0.002). Thirty-five per cent of participants were in a non-activated PAM level at Entry, reducing to 15% at Exit with no participants in PAM level 1 at completion. Seventy-one percent of non-activated participants improved their PAM by one level or more, compared with 45% for the whole cohort. Median LOS was 2 days, 1 day less than the Trust's arthroplasty patient population during the study period (unadjusted comparison).

CONCLUSIONS

Digital health coaching was successfully implemented for patients awaiting elective lower limb arthroplasty. We observed significant improvements in participants' PAM scores after the programme, with the largest increase in participants with lower activation scores at Entry. Further study is needed to confirm the effects of digital health coaching in this and other perioperative groups.