Clinical effectiveness and cost-effectiveness of emergency surgery for adult emergency hospital admissions with common acute gastrointestinal conditions: the ESORT study

Days at home after surgery as a perioperative outcome: scoping review and recommendations for use in health services research

BACKGROUND

Days at home after surgery is a promising new patient-centred outcome metric that measures time spent outside of healthcare institutions and mortality. The aim of this scoping review was to synthesize the use of days at home in perioperative research and evaluate how it has been termed, defined, and validated, with a view to inform future use.

METHODS

The search was run on MEDLINE, Embase, and Scopus on 30 March 2023 to capture all perioperative research where days at home or equivalent was measured. Days at home was defined as any outcome where time spent outside of hospitals and/or healthcare institutions was calculated.

RESULTS

A total of 78 articles were included. Days at home has been increasingly used, with most studies published in 2022 (35, 45%). Days at home has been applied in multiple study design types, with varying terminology applied. There is variability in how days at home has been defined, with variation in measures of healthcare utilization incorporated across studies. Poor reporting was noted, with 14 studies (18%) not defining how days at home was operationalized and 18 studies (23%) not reporting how death was handled. Construct and criterion validity were demonstrated across seven validation studies in different surgical populations.

CONCLUSION

Days at home after surgery is a robust, flexible, and validated outcome measure that is being increasingly used as a patient-centred metric after surgery. With growing use, there is also growing variability in terms used, definitions applied, and reporting standards. This review summarizes these findings to work towards coordinating and standardizing the use of days at home after surgery as a patient-centred policy and research tool.

An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions

BACKGROUND

Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop an approach that combines clinical judgment with ML to generate appropriate comparative effectiveness evidence for informing decision making.

METHODS

We motivate this approach in evaluating the effectiveness of nonemergency surgery (NES) strategies, such as antibiotic therapy, for people with acute appendicitis who have multiple long-term conditions (MLTCs) compared with emergency surgery (ES). Our 4-stage approach 1) draws on clinical judgment about which patient characteristics and morbidities modify the relative effectiveness of NES; 2) selects additional covariates from a high-dimensional covariate space (P > 500) by applying an ML approach, least absolute shrinkage and selection operator (LASSO), to large-scale administrative data (N = 24,312); 3) generates estimates of comparative effectiveness for relevant subgroups; and 4) presents evidence in a suitable form for decision making.

RESULTS

This approach provides useful evidence for clinically relevant subgroups. We found that overall NES strategies led to increases in the mean number of days alive and out-of-hospital compared with ES, but estimates differed across subgroups, ranging from 21.2 (95% confidence interval: 1.8 to 40.5) for patients with chronic heart failure and chronic kidney disease to -10.4 (-29.8 to 9.1) for patients with cancer and hypertension. Our interactive tool for visualizing ML output allows for findings to be customized according to the specific needs of the clinical decision maker.

CONCLUSIONS

This principled approach of combining clinical judgment with an ML approach can improve trust, relevance, and usefulness of the evidence generated for clinical decision making.

HIGHLIGHTS

Machine learning (ML) methods have many potential applications in medical decision making, but the lack of model interpretability and usability constitutes an important barrier for the wider adoption of ML evidence in practice.We develop a 4-stage approach for integrating clinical judgment into the way an ML approach is used to estimate and report comparative effectiveness.We illustrate the approach in undertaking an evaluation of nonemergency surgery (NES) strategies for acute appendicitis in patients with multiple long-term conditions and find that NES strategies lead to better outcomes compared with emergency surgery and that the effects differ across subgroups.We develop an interactive tool for visualizing the results of this study that allows findings to be customized according to the user's preferences.

Adapting Patient and Public Involvement processes in response to the Covid-19 pandemic

BACKGROUND

The COVID-19 pandemic brought rapid and major changes to research, and those wishing to carry out Patient and Public Involvement (PPI) activities faced challenges, such as restrictions on movement and contact, illness, bereavement and risks to potential participants. Some researchers moved PPI to online settings during this time but remote consultations raise, as well as address, a number of challenges. It is important to learn from PPI undertaken in this period as face-to-face consultation may no longer be the dominant method for PPI.

METHODS

UK stay-at-home measures announced in March 2020 necessitated immediate revisions to the intended face-to-face methods of PPI consultation for the ESORT Study, which evaluated emergency surgery for patients with common acute conditions. PPI plans and methods were modified to all components being online. We describe and reflect on: initial plans and adaptation; recruitment; training and preparation; implementation, contextualisation and interpretation. Through first-hand accounts we show how the PPI processes were developed, experienced and viewed by different partners in the process.

DISCUSSION AND CONCLUSIONS

While concerns have been expressed about the possible limiting effects of forgoing face-to-face contact with PPI partners, we found important benefits from the altered dynamic of the online PPI environment. There were increased opportunities for participation which might encourage the involvement of a broader demographic, and unexpected benefits in that the online platform seemed to have a 'democratising' effect on the meetings, to the benefit of the PPI processes and outcomes. Other studies may however find that their particular research context raises particular challenges for the use of online methods, especially in relation to representation and inclusion, as new barriers to participation may be raised. It is important that methodological challenges are addressed, and researchers provide detailed examples of novel methods for discussion and empirical study.

PATIENT AND PUBLIC CONTRIBUTION

We report a process which involved people with lived experience of emergency conditions and members of the public. A patient member was involved in the design and implementation, and two patients with lived experience contributed to the manuscript.