Development of a minimum clinical dataset for preoperative comprehensive geriatric assessment using a modified Delphi technique

Abstract

Objective

To construct a standardised, consensus‐guided minimum clinical dataset (MCDS) for preoperative comprehensive geriatric assessment and optimisation (CGA) in Australia and Aotearoa New Zealand.

Methods

We conducted a review of the international perioperative literature to identify CGA domains and tools for potential inclusion in the MCDS. We invited members of the Australian and New Zealand Society for Geriatric Medicine to participate in a Delphi study to obtain consensus on MCDS tools. Participants were asked to rate proposed tools using Likert scales (when >2 tools) or make a binary choice between two proposed tools. Consensus was considered to be achieved when there was at least 75% concordance between the two rounds amongst the participants, and at least one variable attaining over 50% of participants' votes. Domains that did not achieve consensus in Round 1 were carried over to Round 2.

Results

There were 73 participants in Round 1 of the Delphi study and 47 participants in Round 2. Consensus was achieved on tool/s recommended for every MCDS domain: Clinical Frailty Scale (frailty); sMMSE, RUDAS, MoCA (cognition); 4AT (delirium); timed‐up‐and‐go (physical function); GDS‐15 (mood); Barthel Index (functional status); and MUST (malnutrition).

Conclusions

We recommend clinicians delivering preoperative CGA consider the use of the MCDS we have constructed when assessing older people contemplating surgery, as part of a multicomponent and multidisciplinary approach to optimising perioperative outcomes.

Practice Impact.

This study presents the development of a minimum clinical dataset for preoperative CGA that includes seven CGA‐specific domains and tools for multidomain assessment. The MCDS can supplement standard preoperative care and be used to support multidisciplinary implementation of preoperative CGA through standardisation of clinical practice, service development and research.

1. INTRODUCTION

The implementation of comprehensive geriatric assessment and optimisation (CGA) in older surgical populations has been demonstrated to improve patient outcomes and be cost‐effective. ¹ , ² , ³ , ⁴ , ⁵ , ⁶ , ⁷ , ⁸ Accordingly, international guidelines recommend CGA in the provision of perioperative care of older people. ⁹ , ¹⁰ , ¹¹ CGA is defined as a multi‐dimensional diagnostic and therapeutic process that is focused on determining a frail older person's medical, functional, mental, and social capabilities and limitations with the goal of ensuring that problems are identified, quantified, and managed appropriately. ¹² Perioperative CGA is frequently geriatrician‐led and includes the assessment and management of domains including the severity and stability of a patient's medical conditions, their medications, cognition, functional status and social supports. ¹³ These domains are considered in the context of the planned surgical procedure and the patient's values, preferences and goals.

CGA‐based perioperative models of care are increasingly being implemented internationally, to help address the needs of older people undergoing surgery. ¹⁴ , ¹⁵ In the Australian context, the CGA‐based ‘orthogeriatric’ model of care is well‐established for older people with hip fracture and constitutes one of the seven quality statements in the Australian Commission on Safety and Quality in Healthcare (ACSQHC) Hip Fracture Clinical Care Standard. ¹⁶ , ¹⁷ However, outside the hip fracture setting, an implementation gap exists, with evidence‐based proactive CGA yet to be established as routine perioperative practice in Australia and Aotearoa New Zealand. ¹⁸

Recognition of this implementation gap has led to a growing momentum to establish routine proactive perioperative services for older people. In response, the Australian and New Zealand Society for Geriatric Medicine (ANZSGM) formed its Special Interest Group (SIG) for the perioperative care of older people, in 2021. The SIG identified the implementation gap in perioperative care of older people was partly explained by the burgeoning number of tools available to assess CGA domains and a lack of clarity regarding the choice and standardised use of these tools in the perioperative setting. ¹⁹ , ²⁰ In the United Kingdom, clinical proformas that standardise CGA domains and tools have been utilised to support the establishment of proactive perioperative CGA services and reduce variation in practice. ²¹ The ANZSGM Perioperative Care SIG sought to adopt this approach through consensus development of a preoperative CGA proforma for the Australian and Aotearoa New Zealand context. Delphi methodology has been previously used in the Australasian setting to generate consensus‐based guidelines relevant to the care of older people. ²²

The aim of this modified Delphi study was to generate the first Minimum Clinical Dataset (MCDS) for preoperative CGA in Australia and Aotearoa New Zealand. The intent is to guide developing clinical services in this area and to standardise the dataset for collaborative research purposes within Australasia.

2. METHODS

2.1. Selection of CGA domains: Literature review

We conducted an extensive literature review to identify preoperative CGA domains, and tools used to assess these domains. Published studies of CGA in the perioperative setting, and local and international perioperative guidelines and registries (e.g., hip fracture registries and National Emergency Laparotomy Audit) were reviewed by an expert group of geriatricians to identify preoperative CGA domains and tools. These expert geriatricians were purposively recruited through the authors' professional networks and the ANZSGM Perioperative Care SIG. All have extensive experience in the provision of perioperative clinical care for older adults within Australasia, and many have postgraduate research experience in perioperative care. We collated the identified domains (15 total) and tools and presented them to the wider ANZSGM Perioperative Care SIG. The members of the SIG reached agreement on seven preoperative CGA domains for inclusion in the MCDS, and identified a list of two to eight potential tools for each domain (Table 1).

TABLE 1.

Selection of CGA domains and tools based on literature review and expert consensus.

Consensus list of CGA domains	Tools to assess each domain
Frailty	Rockwood FI, Edmonton FS, reported EFS, Fried phenotype, CFS
Cognition	AMT, mini‐ACE, ACE III, sMMSE, Mini‐Cog, RUDAS, MoCA, KICA
Delirium	4AT, CAM
Physical function	TUG, 30‐s sit‐stand chair test, SPPB
Activities of daily living	Barthel Index, Katz ADLs
Mood	GDS‐15, HADS
Malnutrition	MUST, MNA

Abbreviations: ACE III, Addenbrooke's Cognitive Examination III; ADLs, activities of daily living; AMT, Abbreviated Mental Test; CAM, Confusion Assessment Method; CFS, Clinical Frailty Scale; CGA, comprehensive geriatric assessment and optimisation; EFS, Edmonton Frail Scale; FI, frailty index; FS, frailty scale; GDS‐15, Geriatric Depression Scale‐15; HADS, Hospital Anxiety and Depression Scale; KICA, Kimberley Indigenous Cognitive Assessment; mini‐ACE, mini‐Addenbrooke's Cognitive Examination; MNA, Mini Nutritional Assessment; MoCA, Montreal Cognitive Assessment; MUST, Malnutrition Universal Screening Tool; RUDAS, Rowland Universal Dementia Assessment Scale; SIG, Special Interest Group; sMMSE, standardised Mini‐Mental Status Examination; SPPB, Short Physical Performance Battery; TUG, timed‐up‐and‐go.

2.2. Selection of CGA tools: Modified Delphi study

Between May 2022 and October 2022, a binational two‐round modified Delphi study was performed to achieve a consensus on the tools used to assess the seven preoperative CGA domains. The survey was developed and pilot‐tested within the SIG for content, usability and technical functionality and then distributed via email to its target population, all members of the ANZSGM (Appendix S1). Participants were invited to voluntarily complete the survey, and they were provided information about the estimated length of time of the survey, who the investigators were and the purpose of the study. Only recipients of the invitation email could complete the survey and duplicate entries were avoided, utilising REDCap software. No incentives were provided to participants. The survey comprised an initial section for demographic data followed by questions about potential tools that could be used to assess each preoperative CGA domain (Appendix S1). Questions in the survey were not randomised and adaptive questioning was not utilised. Participants were presented with one question per page and the survey was distributed over 14 pages. Participants were able to review and change their answers prior to submission, and we did not utilise a completeness check. Given the convenience sampling technique we utilised, we did not calculate response rates.

The Delphi study was constructed to achieve consensus on a single preferred tool for each CGA domain, except cognition for which we aimed to achieve consensus on three preferred tools (reflective of the need to tailor cognitive screening tools, specifically, to individual patient characteristics). Consensus was considered to be achieved when there was at least 75% concordance between the two rounds amongst the participants, and at least one variable attaining over 50% of participants' votes. For domains with >2 proposed tools, participants were asked to rate their likelihood of using each tool with a 4‐point Likert scale (very low, low, high and very high). If there were only two proposed tools, participants were asked to make a choice between the two tools. In Round 1, we asked participants whether they had suggestions for tools we had not included. If we did not achieve consensus for a tool in Round 1, that domain was carried over to Round 2. In Round 2, participants were provided with summaries of their individual and the group's first‐round scores and asked to again rate their likelihood of using proposed tools. Three email reminders were sent in each round. Where incomplete surveys were submitted, completed domains were incorporated into the analysis and incomplete domains were excluded.

2.3. Data analysis

Consensus was established using descriptive statistics (modes and proportions).

2.4. Ethics approval

Ethics approval was granted by the Concord Repatriation General Hospital Human Research Ethics Committee (HREC reference number: 2022/ETH00874).

3. RESULTS

Seventy‐three people participated in Round 1 and 47 in Round 2. The majority (83% Round 1; 86% Round 2) of participants were consultant geriatricians, with a median of 6 years of practice at a consultant level. Following two rounds, consensus was achieved on tool/s recommended for all seven MCDS domains (Table 2).

TABLE 2.

Round 1 and Round 2 preferred CGA tools.

Domain	Preferred tool (s)	Likert Scale Measure of Central Tendency				‘Likely’ or ‘Very Likely’ to use (%)
		Round 1		Round 2		Round 1	Round 2
		Median	Mode	Median	Mode
Frailty	CFS	4	4	4	4	82	96
Cognition	RUDAS	3	3	3	3	63	72
	MoCA	3	4	3	3	63	68
	sMMSE	3	3	3	3	55	55
Delirium	4AT	NA	NA	NA	NA	86 preferred	NA
Physical function	TUG	3	3	3	3	83	96
Activities of daily living	Barthel Index	NA	NA	NA	NA	75 preferred	NA
Mood	GDS‐15	3	3	3	3	84	85
Malnutrition	MUST	2	2	2	2	46	40

Note: 2 = ‘unlikely’, 3 = ‘likely’, 4 = ‘very likely’ to use.

Abbreviations: CFS, Clinical Frailty Scale; GDS‐15, Geriatric Depression Scale – 15; MoCA, Montreal Cognitive Assessment; MUST, Malnutrition Universal Screening Tool; NA, not assessed; RUDAS, Rowland Universal Dementia Assessment Scale; sMMSE, standardised Mini‐Mental Status Examination; TUG, timed‐up‐and‐go.

3.1. Frailty

In Round 1, 82% of participants identified they were ‘likely’ or ‘very likely’ to use the Clinical Frailty Scale (CFS). This increased to 96% in Round 2.

3.2. Cognition

The top three preferred cognitive screening tools in Round 1 were the standardised Mini‐Mental Status Examination (sMMSE), Rowland Universal Dementia Assessment Scale (RUDAS) and Montreal Cognitive Assessment (MoCA) where participants were ‘likely’ or ‘very likely’ to use tools in 53%, 63% and 63% of cases, respectively. Round 2 saw stronger consensus for the RUDAS (72%) and MoCA (68%).

3.3. Delirium

An 86% consensus for the 4AT was reached in Round 1.

3.4. Physical function

In Round 1, 83% of participants identified they were ‘likely’ or ‘very likely’ to use the timed‐up‐and‐go (TUG) test. This increased to 96% in Round 2.

3.5. Activities of daily living

A 75% consensus for the Barthel Index was reached in Round 1.

3.6. Mood

In Round 1, 84% of participants selected ‘likely’ or ‘very likely’ to use the Geriatric Depression Scale‐15 (GDS‐15). This increased to 85% in Round 2.

3.7. Malnutrition

All three tools for malnutrition had a median use of ‘unlikely’ amongst participants; however, the tool with the highest proportion of ‘likely’ and ‘very likely’ to use was the Malnutrition Universal Screening Tool (MUST), rated at 46% in Round 1 and 40% in Round 2.

4. DISCUSSION

In this study, we have developed a MCDS that can be and is being used as a clinical proforma to support preoperative CGA. This MCDS represents the preferred assessment tools for select preoperative CGA domains in the Australian and Aotearoa New Zealand setting. Binational expert consensus and engagement have informed the creation of the dataset. This reflects the growing recognition of the need for specialised perioperative pathways for older people, and a wish to work collaboratively with other geriatricians to provide standardised evidence‐based care.

Our MCDS has several potential uses, including clinical practice, service development and research. ²³ The MCDS may facilitate multidisciplinary clinical practice, with tools that can be administered by both geriatricians and non‐geriatrician clinicians for preoperative assessment of the older person in emergency or planned‐care surgical settings. The international evidence base that informed our study means the MCDS is comprised of care processes associated with high‐quality perioperative care. ¹ , ⁹ Delivery of perioperative care processes has been supported by CGA proformas in other contexts, ²¹ , ²⁴ and our MCDS can be similarly utilised as a proforma for the incorporation of evidence‐based care processes in emerging perioperative services. The MCDS can also support established perioperative services in reducing variation of practice. Standardisation of practice using the MCDS may facilitate common data collection across sites to enable research and quality improvement opportunities.

While the MCDS that we have produced may support clinical practice, it is important to recognise that this dataset, alone, does not constitute preoperative CGA. Our expert SIG reached a consensus decision to narrow the 15 CGA domains identified in our literature review to seven and create a MCDS that was deliberately targeted in its scope. This means that some components of preoperative CGA, such as optimisation of comorbidities and medications, are not included in the dataset. ¹³ Instead, we have developed a MCDS that includes key domains and tools that differentiate preoperative CGA from ‘routine’ preoperative assessment. This is important, because as more older people contemplate and undergo surgery and we seek to close the implementation gap in the care they receive, we must increasingly consider flexible ways to deliver person‐centred, multidomain and multidisciplinary care that is not geriatrician‐dependent. ²⁵ The ability for non‐geriatrician clinicians to utilise a standardised, consensus‐guided MCDS to supplement standard preoperative care may go part of the way to resolving the implementation gap in perioperative care of older people. It is also important to recognise that preoperative CGA must go beyond solely assessment, and actively develop and institute a management plan that is tailored to the older person, if it is to deliver improved patient outcomes. ¹³ , ²⁶ Future research should thus consider if the addition of our MCDS to standard preoperative care can facilitate a person‐centred management plan that improves perioperative outcomes.

This study has several strengths. A diverse group of doctors undertook the survey, across two countries and a range of clinical contexts. Despite this diversity, a single tool was identified for each of the CGA domains. This likely reflects the familiarity and entrenchment of these tools in current clinical practice across Australasia, ²⁷ and this familiarity may support implementation and uptake of the MCDS. Another strength of this study is the extensive review of the perioperative literature that informed our survey. In addition, we provided the opportunity for participants to suggest any tools we may have overlooked, increasing the likelihood all relevant tools were included.

This study and our MCDS have some limitations. Our study canvassed expert opinion within geriatric medicine, and this opinion may differ to preoperative considerations of other clinicians within the perioperative team. We suggest the MCDS may be utilised by non‐geriatrician clinicians; however, this remains to be tested. This is an area for future research. Objective and standardised preoperative risk assessment should form part of routine care of the older surgical patient ⁹ , ²⁸ , ²⁹ ; however, the targeted scope of our MCDS means it does not include many commonly utilised surgical risk assessment tools. We thus recommend clinicians utilising the MCDS consider which risk assessment tools are relevant to their patient population, local health service risk management and escalation pathways, and relevant surgical pathology (e.g., the NELA risk prediction calculator for patients undergoing emergency laparotomy ³⁰ ).

5. CONCLUSIONS

In conclusion, this is the first published MCDS from Australia and Aotearoa New Zealand that can support standardised use of preoperative CGA domains and tools in the perioperative care of older people. In the advent of rapidly expanding perioperative services for older people, this dataset can support clinical practice, service development and research. The MCDS has been developed by geriatricians in Australia and Aotearoa New Zealand using evidence‐based CGA domains and tools and its use as a supplement to standard care, and by non‐geriatrician members of the perioperative team, can now be tested. Future directions also include dissemination and education to support implementation of the MCDS, and subsequent evaluation of its use and clinical effectiveness.

CONFLICT OF INTEREST STATEMENT

Dr Janani Thillainadesan is an associate editor of the Australasian Journal on Ageing. The other authors declare no conflict of interest.

Supporting information

Appendix S1.

Lodge M, Aitken R, Chong YH, Thillainadesan J. Development of a minimum clinical dataset for preoperative comprehensive geriatric assessment using a modified Delphi technique. Australas J Ageing. 2024;43:733‐739. doi: 10.1111/ajag.13334

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.