Protocol paper for SMART OPS: Shared decision-making Multidisciplinary Approach – a Randomised controlled Trial in the Older adult Population considering Surgery

Abstract

Introduction

The Australian population presenting with surgical pathology is becoming older, frailer and more comorbid. Shared decision-making is rapidly becoming the gold standard of care for patients considering high-risk surgery to ensure that appropriate, value-based healthcare decisions are made. Positive benefits around patient perception of decision-making in the immediacy of the decision are described in the literature. However, short-term and long-term holistic patient-centred outcomes and cost implications for the health service require further examination to better understand the full impact of shared decision-making in this population.

Methods

We propose a novel multidisciplinary shared decision-making model of care in the perioperative period for patients considering high-risk surgery in the fields of general, vascular and head and neck surgery. We assess it in a two arm prospective randomised controlled trial. Patients are randomised to either ‘standard’ perioperative care, or to a multidisciplinary (surgeon, anaesthetist and end-of-life care nurse practitioner or social worker) shared decision-making consultation. The primary outcome is decisional conflict prior to any surgical procedure occurring. Secondary outcomes include the patient’s treatment choice, how decisional conflict changes longitudinally over the subsequent year, patient-centred outcomes including life impact and quality of life metrics, as well as morbidity and mortality. Additionally, we will report on healthcare resource use including subsequent admissions or representations to a healthcare facility up to 1 year.

Ethics and dissemination

This study has been approved by the Hunter New England Human Research Ethics Committee (2019/ETH13349). Study findings will be presented at local and national conferences and within scientific research journals.

Trial registration number

ACTRN12619001543178.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• This is one of the very few randomised controlled trials looking at the impact of perioperative shared decision-making models of care on patient outcomes.

• This is the only randomised controlled study reporting on longer term (up to 1 year) outcomes of a shared decision-making intervention for patients considering surgery.

• The chosen outcomes allow for benchmarking of this study with both existing shared decision-making literature as well as more broadly with the perioperative evidence base.

• This is a single-centre study at a tertiary centre.

• Patients are not blinded to the intervention, introducing potential bias to the study results.

Introduction

Background and rationale

The context is clear, undisputed and repeatedly stated: the Australian population is ageing.¹ Increased age is associated with increased frailty, comorbidity and dependence. These factors are in turn associated with an increased occurrence and severity of morbidity and mortality, especially when a patient undergoes a surgical procedure.^{2 3} Within this context, it is increasingly argued that patient consent for undergoing surgical procedures, should engage ‘shared decision-making’ (SDM) style models of care.^{4 5}

SDM appeared in healthcare literature in 1982,⁶ but lacks a singular unifying definition.

The Australian Commission on Safety and Quality in Healthcare describes SDM as involving ‘collaboration between a consumer and their healthcare provider…bringing together the consumer’s values, goals and preferences with the best available evidence about benefits, risks and uncertainties of treatment, in order to reach the most appropriate healthcare decisions for that person’.⁴ The National Institute for Health and Care Excellence emphasises ensuring ‘the person understands the risks, benefits and possible consequences of different options through discussion and information sharing’.⁷

Multiple SDM models exist, from decision-making tools to communication frameworks.⁸ The year 2022 saw a large increase in published trials (predominantly based out of North America, Canada and the Netherlands) but the heterogeneity of interventions and outcomes reported remains large. Observational studies are predominantly in the fields of orthopaedics, plastics surgery (eg, breast reconstruction) and general surgery.^9–16 Outcomes reported are predominantly on the practice and perception of SDM: In the surgical sphere, SDM is associated with higher patient satisfaction, but clinician and patient perceptions of if it has occurred do not always align; patients with lower socioeconomic status and depression often have worse perceptions of their SDM process. Randomised controlled trials are few and heterogeneous. They include studies in orthopaedics,¹⁷ urogynaecology,¹⁸ liver cancer¹⁹ and breast cancer,²⁰ with only one study looking specifically at high-risk individuals across a range of specialties.²¹ Reported outcomes include constructs around decisional conflict/satisfaction/regret, disease knowledge and perceived participation in SDM, most commonly only at the time of decision-making. At the time of writing, only one paper extends follow-up to 6 months post-treatment.¹⁷ The only intervention study to look at outcomes up to 12 months, is Paraskeva et al’s 2022 sequential trial looking at patients considering breast reconstruction.²² Notably, the reduction of decision regret for patients exposed to the SDM intervention in this study, was not sustained at 12 months. Overall, existing evidence tells us that patients want SDM.²³ Studies (including systematic reviews and Niburski et al’s 2020 meta-analysis) iterate the benefits of SDM in the context of surgery, including significant improvements in immediate patient decisional conflict, decision satisfaction, decision anxiety and health knowledge with SDM.17 19 24–29 The benefits persist when considering disadvantaged patient groups such as those with low levels of health literacy.³⁰

When considering SDM interventions, pertinent to this study are two particular features:

1. The difference between SDM and ‘informed decision-making’, as the emphasis on the patient as an ‘expert in themselves’, their priorities, preferences and values, as central to the process.

2. The emphasis on discussion of all treatment options and their potential consequences. For patients with surgical pathology, this includes the option of no surgical intervention. Note while some literature suggests that patients participating in SDM make more conservative choices,³¹ the rate of opting for a non-surgical option was not significant in Niburski et al’s meta-analysis.²⁷

Regarding (1) the presence of a healthcare professional in the perioperative appointment specifically designated to ensure the patient’s priorities and preferences are explicitly explored, is not described in the SDM literature. Regarding (2) while an anaesthetist usually leads the perioperative appointment with a patient, focus is traditionally on the medical component of a patient’s history and the perceived impact of this when combined with a specific surgery and anaesthetic. The anaesthetist’s exposure to non-operative treatment journeys is often not part of training or knowledge base. The use of multidisciplinary teams (MDTs) for provision of different perspectives in patient consultations is not novel. The evidence for MDT models of care being effective for complex patient care is well established for areas such as oncology and geriatrics.³² They are increasingly described for the complex patient considering surgery,³³ but vary greatly both in their focus or aims (optimisation vs decision-making), and make-up (including whether or not the patient is physically present). However, the presence of a surgeon at the perioperative appointment to reiterate all options and their consequences in context of a patient hearing about perioperative risk, is not described.

What else is missing from the SDM evidence base? Leung argues that consistency and agreement (across the perioperative community) are needed with more standardisation required around what outcomes are measured and reported.²⁵ Efforts to achieve consistency and standardisation in reported patient-centred perioperative outcomes such as health-related quality of life and life impact³⁴ have yet to cross over into the examination of SDM. Furthermore, the impact on the patient beyond the narrow focus of decision-making perception at a singular snapshot in time of decision-making, remains unexamined in the high-risk individual considering surgery. Finally, there are no robust data on the use of a SDM model (either short or medium term) within an MDT (surgeons, anaesthetists, social workers/nurses and patients) in the context of high-risk surgery.

In the current era of increasing demands on fewer healthcare resources, a thorough evidence base for proposed SDM models of care would give much needed validity to the investment of time and money into its continuing implementation.

Objectives

The main objective of this study is to evaluate the impact of a novel SDM MDT perioperative assessment model for patients considering high-risk surgery. The primary aim assesses if the proposed intervention improves decisional conflict at 24–72 hours post-decision. Secondary aims are exploratory and assess:

1. If decisional conflict subsequently varies with time (post undergoing a surgical procedure with or without associated morbidity, or post opting for a non-surgical course) up to 1 year.

2. The impact of the SDM MDT perioperative appointment on patient-centred outcomes including what treatment plan a patient opts for.

3. If the SDM MDT has any impact on patient morbidity or mortality.

4. The economic impact of this proposed model of care on the health service.

Given the persistence of the rhetoric among clinicians and processes that SDM is already practised and embedded, this study compares this intervention to standard preoperative care (detailed below). We hypothesise that patients randomised to the SDM MDT will report reduced decisional conflict in the immediate aftermath of a decision as compared with those in the ‘standard care’ pathway. However, it is unknown if this effect will be sustained through undergoing surgery, recovery, rehabilitation and any associated morbidity, or indeed through managing the impacts of no definitive surgical intervention for a surgical pathology.

Methods and analysis

Trial design

SMART OPS is a two-arm, randomised controlled trial. It examines the impact of a perioperative SDM MDT model of care when considering outcomes of decisional conflict, treatment decision, patient-centred outcomes (such as life impact and quality of life measures), morbidity and mortality, and resource utilisation and economic impact, both in the short term and at several time points up to 1 year. The comparator is the standard perioperative model of care.

Study setting

This is a single site study based at a tertiary level 1 trauma centre in New South Wales, Australia. The 796 bed hospital is a primary referral centre encompassing both metropolitan and rural and remote communities. Just under 9000 elective surgeries occur per year, and this is matched approximately 1:1 by emergency procedures.

Population to be studied and eligibility criteria

The population to be studied is older patients being considered for high-risk major general, vascular, or head and neck surgery under participating surgeons, either elective or emergency.

For the purposes of this study, age, major surgery and high-risk are defined by:

1. Aged >65 or >45 if Aboriginal or Torres Strait Islander.

2. Major surgery as per classification by the Royal Australasian College of Surgeons (RACS).³⁵

3. A Surgical Outcome Risk Tool (SORTv1) score^{36 37} correlating to a predicted 30-day mortality of >3%, or >4% for patients being considered for laparoscopic cholecystectomy.

The younger age cut-off for inclusion of patients identifying as Aboriginal or Torres Strait Islander aims to reflect the younger age of onset of chronic disease, associated earlier onset of physiological ageing, and substantially reduced life expectancy experienced by Aboriginal and Torres Strait Islander patients in Australia when compared with their non-indigenous counterparts (estimated at 12 years shorter for males and 10 years shorter for females).³⁸

Prior to defining SORT parameters for this study, the SORT score³⁶ was locally validated for our population. While considered ‘major’ surgery by both the developers of SORT³⁷ and RACS,³⁵ patients undergoing laparoscopic cholecystectomies in our local institution with equivalent SORT scores to patients undergoing alternative ‘major’ surgical procedures demonstrated lower levels of morbidity and mortality. The threshold for inclusion as ‘high risk’ was, therefore, altered for this patient group.

Recruitment and consent

Eligible participants are identified at any of the hospital’s three entry points for surgery:

1. For planned elective surgery at a later date, at submission of a ‘request for admission’ (RFA). Standard practice is for these patients to proceed to a preadmission clinic (PAC) appointment.

2. For consideration of elective surgery for high-risk outpatients with surgical pathology, at submission of a request for outpatient preoperative anaesthetic consultation. This route is used for patients deemed by the treating team as high risk and requiring anaesthetic consideration prior to booking surgery.

3. For emergency surgery, at submission of a request for inpatient preoperative anaesthetic consultation for consideration and/or preparation for emergency inpatient surgery.

Information from electronic records and the RFA is used to screen patients using the SORT calculator.³⁷ Screening logs are kept in accordance with Consolidated Standards of Reporting Trials (CONSORT) guidance³⁹ with reasons for non-randomisation recorded. Eligible participants are sent the participant pack including an ‘opt out’ consent form. A patient or patient carer can withdraw at any stage of the intervention. The patient’s admitting consultant surgeon and general practitioner is also contacted at this time to be notified of their patient’s eligibility for this study. For patients unable to consent for themselves, participation packs and opportunity to ‘opt out’ is shared with the patient and the person(s) responsible for their medical consent.

Randomisation and allocation

Enrolled patients are randomised to either the intervention group (SDM MDT) or to the control group (see figure 1). The control group is either PAC, or preoperative consultation (inpatient or outpatient), depending on the entry point outlined. Randomisation is via randomised computerisation generated in the Research Electronic Data Capture system (REDCap)^{40 41} at a 1:1 ratio, in blocks of 4. Neither the research nurses nor patients are blinded to allocated group, but all surveys used are scripted, and outcomes where possible, collected by blinded research assistants.

Figure 1.

Participant recruitment timeline. MDT, multidisciplinary team; PAC, preadmission clinic; RFA, request for admission; SDM, shared decision-making.

Patient and public involvement statement

A former patient has been invited to serve as collaborator. They were identified through giving feedback regarding complex surgery at the study institution. Their input informed the design of the study, and selection of relevant outcomes to collect.

Intervention and control group

The intervention is a SDM MDT meeting that replaces the ‘usual’ perioperative appointment with the patient. The SDM MDT meeting includes a consultant anaesthetist, consultant surgeon, and social worker or end-of-life care (EOLC) nurse practitioner, all trained in SDM communication, along with the patient and any support persons. Training involves face-to-face and teleconference immersive sessions with communications experts at Deakin University focusing on SDM frameworks and communication techniques. This includes broad role delineation.

The role of the social worker or EOLC nurse practitioner is to ensure that the consultation remains patient-centred with explicit exploration of: what their priorities and preferences are, what outcomes of care they would hope for, what would be considered ‘reasonable’ and what would be considered prohibitive. The introduction of a surgeon in the intervention arm perioperative assessment consultation is also new. They are responsible for ensuring that the patient has a reasonable understanding from their surgical consultation, of what all their options are, and the implications in context of their own health and priorities. Their presence is also to signal that this appointment can be a decision-making encounter rather than simply just a step on the road to inevitable surgery. It includes expertise regarding any uncertainty that exists in what is considered ‘best practice’ management, and the balancing view of what ‘doing nothing’ means for those patients for whom the risk of anaesthesia is being described as potentially prohibitively high. The anaesthetist is responsible for ensuring that the traditional roles of the perioperative appointment are fulfilled, but within the context of the values, priorities and preferences elicited, and all the management options available to this particular patient. For patients randomised to this arm, history taking occurs with the consultant anaesthetist by telephone prior to the SDM MDT meeting. All relevant specialists (including the general practitioner) involved in the patient’s care are contacted prior to the meeting for collateral history, investigative results, as well as perspectives on individual patient risks. The members for each MDT have designated time prior to each patient meeting to share and discuss any information gleaned from these conversations with each other.

The SDM MDT meeting uses a SDM communication framework and visual (informal) decision aids where deemed appropriate. Multiple frameworks exist but it is consistently acknowledged that SDM is not a linear process with steps often being fluid and flexible, and much overlap exists between the components of the various frameworks described.⁴² The elements considered essential to this intervention, based on the framework from Braddock et al,⁴³ are outlined in box 1.

Box 1. Components of shared decision-making communications framework.

1. Exploration of patient values and preferences.

2. Assessing patient understanding of pathology and options.

3. Discussion of the clinical issue and nature of the decision.

4. Discussion of all options, broadly considered as:

   1. The proposed surgery.

   2. Any alternative interventional option.

   3. No/ minimal intervention.

5. Discussing benefits, risks or worst case, best case, most likely case scenario for each option, with emphasis on components specifically relevant to values of importance already identified by that patient.

6. Discussion of uncertainties associated with the decision.

7. Discussion of the patient’s role in the decision-making.

The control is usual perioperative care. Usual perioperative care at this institution is anaesthetic led PAC or consultation (inpatient or outpatient), with standard use of health and risk assessment tools (detailed below), and consultation with relevant specialists (including general practitioners) as appropriate. It includes assessment of a patient’s medical and functional status, identification (and management if time allows), of any optimisable parameters, and discussion with patients and their support persons of their anaesthetic options for the proposed surgery and associated risks. For patients referred for an outpatient preoperative ‘consultation’ prior to booking for surgery (due to a perception of higher risk), the appointment is with a perioperative interested anaesthetic consultant who discusses the patient’s case with the perioperative interest group prior to preceding with advice to the patient and their surgical team. Typically, anaesthetic consent in ‘usual care’ consultations is ‘informed consent’ style discussion.

Data collection

Baseline data, routinely collected for all high-risk patients attending a preoperative appointment, include demographic information and baseline health and risk assessment measures such as Rockwood’s Clinical Frailty Scale,^{44 45} the Duke Activity Status Index⁴⁶ and the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) calculator scores.⁴⁷ In addition, the Charlson Comorbidity Index is collected for all patients as a measure of aggregate burden of comorbidity for all enrolled patients.⁴⁸

The primary outcome is patient decisional conflict, as measured by the Decisional Conflict Scale (DCS).^{49 50} Decisional conflict describes uncertainty around a course of action, particularly likely to occur when a person is confronted with a decision involving risk or uncertainty, or where value trade-offs are required to occur in order to select a course of action.⁴⁹ The DCS is a five domain scale examining information (about options, benefits, risks and side effects), value clarification, support (in making a decision), uncertainty and effective decision-making, culminating in an assessment of the quality of a person’s decision-making. A final subscale measures perceived effective decision-making.⁵⁰ The scores are standardised to range from 0 (no decisional conflict) to 100 points (extreme decisional conflict). Scores of 25 or lower are associated with follow-through with decisions, whereas scores that exceed 38 are associated with delay in decision-making.⁵¹ The DCS is reliable, discriminates between those who make or delay decisions, is sensitive to change, and discriminates between different decision supporting interventions.⁴⁹ Of the multiple measures that exist to measure if SDM occurred during a consultation, the DCS has been independently identified by the UK Department of Health’s National SDM programme as one of the most widely tested measures of SDM. As a multidimensional patient rating assessment of decision process and outcome, it acts as a means to evaluate the impact of a decision support intervention.⁵² For the primary outcome, the DCS is administered 24–72 hours after treatment trajectory decision-making, notably prior to any surgery taking place.

Secondary outcome measures (see table 1) are measured at 1, 3, 6 and 12 months. They include ongoing DCS scores, treatment trajectory chosen, patient-centred measures (health-related quality of life measured by the 5-level European Quality of Life Score (EQ-5D-5L) questionnaire⁵³ and life impact metric ‘days alive and out of hospital’⁵⁴) as well as a modified Rankin Scale as a measure of global disability,⁵⁵ and any complications suffered (using Clavien-Dindo classification⁵⁶). Indicators for economic evaluation include the ICEpop CAPability measure for Older people (ICECAP-O) score⁵⁷ and healthcare resource use including intensive care and hospital length of stays, and representations to healthcare facilities.

Table 1.

Schedule of measures during the study

Construct	Mapped to aim	Measure used	At baseline	24–72 hours	1 month	3 months	6 months	12 months
Decisional conflict	1.	DCS49		✓	✓	✓	✓	✓
Treatment trajectory taken	2.							✓
Quality of life	2.	EQ-5D-5L53	✓		✓	✓	✓	✓
Life Impact	2.	DAOH54			✓	✓	✓	✓
Functional status	2.	modified Rankin Scale55	✓		✓
Well-being (for the older adult)	2. and 4.	ICECAP-O57	✓		✓	✓	✓	✓
Patient impact and resource use	2. and 4.	Day of surgery cancellation			✓
Patient impact and resource use	2. and 4.	Intensive Care length of stay			✓	✓	✓	✓
Morbidity	3.	Clavien-Dindo classification56			✓	✓	✓	✓
Mortality	3.	Time to event	✓		✓	✓	✓	✓
Resource use	4.	Hospital length of stay			✓	✓	✓	✓
Patient healthcare service costs	4.	Patient encounters						✓
SDM MDT implementation and intervention costs, prospectively collected	4.	Time, staffing and resource use for intervention						✓

DAOH, days alive and out of hospital; DCS, Decisional Conflict Scale; EQ-5D-5L, 5-level European Quality of Life Score; ICECAP-O, ICEpop CAPability measure for Older people; MDT, multidisciplinary team; SDM, shared decision-making.

Safety reporting

All potential serious adverse events (SAEs) will be reported immediately to the principal investigator who will report to the approving Human Research Ethics Committee (HREC) within 72 hours. Adverse events will also be reported annually to the HREC and be reported and discussed in any publication that may result from this research.

Sample size

Previous studies have reported mean DCS scores of 30 with SD of 15. Sun has described that for every unit increase in the DCS, people were 5 times more likely to express decisional regret, 23 times more likely to delay their decision and 59 times more likely to change their mind.⁵⁸ Gattellari and Ward found that for every unit increase in DCS, patients were 19% more likely to blame their doctor for poor outcomes.⁵⁹ The sample size for this study is based on detecting a difference in outcome scales that is half of a standard deviation (Cohen’s d=0.5), or 7.5 units, consistent with a moderate effect size. This comes to 64 per arm, that is, 128 total, in order to yield 80% power at a 5% significance level for the primary outcome. Secondary outcomes are not powered for and are exploratory only.

Data management

Study data will be collected and managed using REDCap tools hosted at Hunter Medical Research Institute (HMRI).^{40 41} REDCap is a secure, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages and (4) procedures for data integration and interoperability with external sources. Data will be analysed and reported in a deidentified and grouped format by the Clinical Research Design and Statistics team at HMRI using data sourced from the REDCap database.

Statistical analysis

Results will be analysed using the intention to treat principle. Baseline characteristics of participants will be presented as means and SD for continuous measures and counts and percentages for categorical measures. The primary outcome measure, the DCS, will be analysed using a linear mixed model. This model will include fixed effects for time, treatment allocation and their interaction as well as terms for the stratification variables. The model will also include a random intercept for participant to account for the repeated measures on the same patient. The primary effect of interest from this model is the estimated difference between treatment groups 24–72 hours after decision of treatment trajectory is made, and its corresponding 95% CI. DCS scores at subsequent follow-up time points (1, 3, 6 and 12 months), which are secondary outcomes, are estimated from the same model as the primary outcome.

With respect to other secondary measures, ordinal outcomes will be modelled using a mixed effects ordinal regression with the same fixed and random effects specified as the primary analysis; count outcomes will be modelled using negative binomial regression, again with the same fixed and random effects specified earlier. Patient mortality will be examined using the Kaplan-Meier method, and a comparison between treatment allocations will be performed using Cox proportional hazard regression.

Economic analysis

A within trial cost–utility analyses (CUA) will be conducted from the perspective of Hunter New England Health (HNEH), the key decision-maker. Resource utilisation will account for the cost of implementing and delivering the intervention, as well as patient utilisation of HNEH services. Data for the volume of resource use will be prospectively collected through project management systems, health administration data and the established primary data collection framework. Standard Australian values will be used for unit costs. Quality adjusted life years will be calculated using the EQ-5D-5L health-related quality of life outcome measures, and appropriate preference-based utility weights. Missing data will be managed using multiple imputation.

Results will be expressed via an assessment of the incremental costs and outcomes of the intervention against usual care. The analysis will report the incremental cost-effectiveness ratio (ICER). Uncertainty in the estimated ICER will be estimated using bootstrapping techniques and will be graphically represented on a cost-effectiveness plane. Cost-effectiveness acceptability curves will be derived to illustrate the probability that the intervention is cost-effective in comparison with usual care, given commonly accepted thresholds for public investment. The analysis will be presented alongside additional economic considerations, such as differences in patient residence within aged care institutions and any impacts on primary or allied health service utilisation. The sensitivity of EQ-5D-5L to the patient journey and the intervention will be contrasted with the equivalent ICECAP-O measures to provide additional context for the results.

Ethics and dissemination

This study has been approved by the Hunter New England HREC (2019/ETH13349). ‘Opt out’ consent has been requested and granted by the Hunter New England HREC (2019/ETH13349) due to the potential for introducing recruitment bias into the study with an ‘opt in’ process, limiting generalisability. This study will be conducted according to the Note for Guidance on Good Clinical Practice (CPMP/ICH/135/95) in compliance with applicable laws and regulations. The study will be performed in accordance with the NHMRC Statement on Ethical Conduct in Research Involving Humans (Commonwealth of Australia 2007) and the principles laid down by the World Medical Association in 2008. The investigators shall comply with the protocol, except when a protocol deviation is required to eliminate immediate hazard to a participant.

Findings from this study will be disseminated locally to hospital teams, nationally via conference and presentation forums, as well as through publication in scientific journals. Deidentified and summarised results will be made available to outside parties on a case-by-case basis on request to the PI.

Discussion

Having reached a point of incredible technical expertise and safety for patients undergoing surgical procedures, future years will see clinicians assessing older, frailer and more comorbid patients with surgical pathology. Increasingly we need evidence-based approaches to ensure we are delivering value based healthcare that aligns with outcomes that are important to patients. This study aims to fill an evidence gap to examine a yet untested model of SDM in the form of an MDT including a surgeon, anaesthetist, and specialist nurse or social worker. Studying a range of surgical specialties providing high-risk surgical options to high-risk patients increases our knowledge around the role of a SDM intervention in these populations, as yet, not robustly examined. Reporting on the chosen outcomes will align the SDM evidence base with the perioperative evidence base by extending the standard examination to include patient-centred outcomes. Importantly, this study will test whether any impacts are sustained, and if there are any unintended harm or costs, specifically in the longer term, for these high-risk individuals.

Ethics statements

Patient consent for publication

Not applicable.