Prehabilitation before general surgery: Worth the effort?

Prehabilitation, or interventions before surgery aimed at improving preoperative health and postoperative outcomes, has various forms. Although it may confer benefit to patients undergoing general surgery, this is not certain. Furthermore, although it may yield a net monetary gain, it is also likely to require substantial monetary and non-monetary investment. The impact of prehabilitation is highly variable and dependent on multiple factors. Physical function and pulmonary outcomes are likely to be improved by most forms of prehabilitation involving physical and multimodal exercise programmes. However, other surgical outcomes have demonstrated mixed results from prehabilitation. Within this issue, the measures used for evaluating baseline patient biopsychosocial health are important, and collecting sufficient data to accurately inform patient-centred prehabilitation programmes is only possible through thorough clinical and laboratory investigation and synthesised metrics such as cardiopulmonary exercise testing. Although a multimodal approach to prehabilitation is the current gold standard, societal factors may affect engagement with programmes that require a significant in-person activity. However, this is weighed against the substantial financial and non-financial investment that accompanies many programmes. The overall effectiveness and optimal mode of intervention across the discipline of general surgery remains unclear, and further research is needed to prove prehabilitation's full worth.

Surgery's Rosetta Stone: Natural language processing to predict discharge and readmission after general surgery.

BACKGROUND: This study aimed to examine the accuracy with which multiple natural language processing artificial intelligence models could predict discharge and readmissions after general surgery. METHODS: Natural language processing models were derived and validated to predict discharge within the next 48 hours and 7 days and readmission within 30 days (based on daily ward round notes and discharge summaries, respectively) for general surgery inpatients at 2 South Australian hospitals. Natural language processing models included logistic regression, artificial neural networks, and Bidirectional Encoder Representations from Transformers. RESULTS: For discharge prediction analyses, 14,690 admissions were included. For readmission prediction analyses, 12,457 patients were included. For prediction of discharge within 48 hours, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.86 and 0.86 for Bidirectional Encoder Representations from Transformers, 0.82 and 0.81 for logistic regression, and 0.82 and 0.81 for artificial neural networks. For prediction of discharge within 7 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.82 and 0.81 for Bidirectional Encoder Representations from Transformers, 0.75 and 0.72 for logistic regression, and 0.68 and 0.67 for artificial neural networks. For readmission prediction within 30 days, derivation and validation data set area under the receiver operator characteristic curves were, respectively: 0.55 and 0.59 for Bidirectional Encoder Representations from Transformers and 0.77 and 0.62 for logistic regression. CONCLUSION: Modern natural language processing models, particularly Bidirectional Encoder Representations from Transformers, can effectively and accurately identify general surgery patients who will be discharged in the next 48 hours. However, these approaches are less capable of identifying general surgery patients who will be discharged within the next 7 days or who will experience readmission within 30 days of discharge.

Prehabilitation for general surgery: a systematic review of randomized controlled trials.

BACKGROUND: Prehabilitation seeks to optimize patient health before surgery to improve outcomes. Randomized controlled trials (RCTs) have been conducted on prehabilitation, however an updated synthesis of this evidence is required across General Surgery to inform potential Supplementary discipline-level protocols. Accordingly, this systematic review of RCTs aimed to evaluate the use of prehabilitation interventions across the discipline of General Surgery. METHODS: This study was registered with PROSPERO (CRD42023403289), and adhered to PRISMA 2020 and SWiM guidelines. PubMed/MEDLINE and Ovid Embase were searched to 4 March 2023 for RCTs evaluating prehabilitation interventions within the discipline of General Surgery. After data extraction, risk of bias was assessed using the Cochrane RoB 2 tool. Quantitative and qualitative data were synthesized and analysed. However, meta-analysis was precluded due to heterogeneity across included studies. RESULTS: From 929 records, 36 RCTs of mostly low risk of bias were included. 17 (47.2%) were from Europe, and 14 (38.9%) North America. 30 (83.3%) investigated cancer populations. 31 (86.1%) investigated physical interventions, finding no significant difference in 16 (51.6%) and significant improvement in 14 (45.2%). Nine (25%) investigated psychological interventions: six (66.7%) found significant improvement, three (33.3%) found no significant difference. Five (13.9%) investigated nutritional interventions, finding no significant difference in three (60%), and significant improvement in two (40%). CONCLUSIONS: Prehabilitation interventions showed mixed levels of effectiveness, and there is insufficient RCT evidence to suggest system-level delivery across General Surgery within standardized protocols. However, given potential benefits and non-inferiority to standard care, they should be considered on a case-by-case basis.

Sociocultural and Demographic Factors Predict Readmissions for General Surgery Patients.

INTRODUCTION: Readmission is a poor outcome for both patients and healthcare systems. The association of certain sociocultural and demographic characteristics with likelihood of readmission is uncertain in general surgical patients. METHOD: A multi-centre retrospective cohort study of consecutive unique individuals who survived to discharge during general surgical admissions was conducted. Sociocultural and demographic variables were evaluated alongside clinical parameters (considered both as raw values and their proportion of change in the 1-2 days prior to admission) for their association with 7 and 30 days readmission using logistic regression. RESULTS: There were 12,701 individuals included, with 304 (2.4%) individuals readmitted within 7 days, and 921 (7.3%) readmitted within 30 days. When incorporating absolute values of clinical parameters in the model, age was the only variable significantly associated with 7-day readmission, and primary language and presence of religion were the only variables significantly associated with 30-day readmission. When incorporating change in clinical parameters between the 1-2 days prior to discharge, primary language and religion were predictive of 30-day readmission. When controlling for changes in clinical parameters, only higher comorbidity burden (represented by higher Charlson comorbidity index score) was associated with increased likelihood of 30-day readmission. CONCLUSIONS: Sociocultural and demographic patient factors such as primary language, presence of religion, age, and comorbidity burden predict the likelihood of 7 and 30-day hospital readmission after general surgery. These findings support early implementation a postoperative care model that integrates all biopsychosocial domains across multiple disciplines of healthcare.