Evidence review for preoperative risk stratification tools

National Guideline Centre (UK)

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Evidence review for preoperative risk stratification tools

Perioperative care in adults

Evidence review C

NICE Guideline, No. 180

National Guideline Centre (UK).

London: National Institute for Health and Care Excellence (NICE); 2020 Aug.

ISBN-13: 978-1-4731-3827-8

1. Preoperative risk stratification tools

1.1. Review question: Which validated preoperative risk stratification tools best identify increased risk of mortality and morbidity in adults who will be undergoing surgery?

1.2. Introduction

The conundrum facing all perioperative clinicians when evaluating patients for surgery remains how best to evaluate and quantify the risk of undergoing the anticipated procedure. There are a number of reasons why this is a key element of evaluation during the preoperative clinical encounter. Firstly, establishing objective understanding of the anticipated mortality and morbidity risk allows and directs discussions with other involved clinicians about the appropriateness of the planned surgery and whether it should proceed as planned, should be abbreviated, or whether alternative non-surgical options should be considered. Secondly, being able to quantify morbidity risk allows planning for post-operative destination, discussions about quality of life and recovery or convalescence and to give insight to the patient about the anticipated clinical course. Understanding these elements allows frank discussions about what the patients actually wish to achieve from the surgical encounter. Furthermore this opens the discussions amongst all parties for shared decision making about the best outcome decision that will meet the goals of the involved parties.

Thus it becomes incumbent on perioperative clinicians to find robust, reliable and accurate tools that will allows us to determine bespoke perioperative risk for each individual patient allowing these discussions and decisions to proceed smoothly. Current practice appears to be that many perioperative clinicians use risk stratification tools but not in a uniform or unified fashion. Different tools are used with different sensitivities and specificities and are not uniformly applied to all surgical populations. There does not exist a national recommendation or standard on which tools to use, how they should be applied, nor even that a risk stratification tool should be consistently used in the perioperative setting at all.

The committee agreed this was a fundamental aspect that required investigation of existing evidence around such tools with the intention to set a recommendation standard in this area of perioperative care.

1.3. PICO table

For full details see the review protocol in Appendix A:.

Table 1

PICO characteristics of review question.

1.4. Clinical evidence

1.4.1. Included studies

Sixty studies were included in the review;¹¹^, ¹⁴^, ¹⁷^, ¹⁸^, ²⁰^, ²²^, ²⁴^, ²⁵^, ²⁷^, ³²^, ³³^, ³⁷^, ³⁹^, ⁴³^, ⁴⁵^, ⁴⁷^, ⁴⁸^, ⁵¹^, ⁵⁴^, ⁵⁶^, ⁵⁷^, ⁵⁹^, ⁶¹^–⁶³^, ⁶⁵^, ⁶⁹^, ⁷⁰^, ⁷⁴^, ⁷⁸^, ⁸⁴^, ⁸⁸^, ⁹³^, ⁹⁴^, ¹⁰¹^, ¹⁰⁵^, ¹¹⁰^, ¹¹⁶^, ¹¹⁸^, ¹²⁰^, ¹³⁶^, ¹³⁸^, ¹⁴²^, ¹⁴⁹^, ¹⁵¹^, ¹⁵⁵^, ¹⁵⁷^, ¹⁶¹^, ¹⁶²^, ¹⁶⁵^, ¹⁶⁶^, ¹⁷⁰^, ¹⁷¹^, ¹⁷⁷^, ¹⁷⁹^, ¹⁸²^, ¹⁸³^, ¹⁸⁶^, ¹⁸⁷^, ¹⁸⁹ these are summarised in Table 2 below. Evidence from these studies is summarised in the clinical evidence summary below (Table 3).

See also the study selection flow chart in Appendix C: and study evidence tables in Appendix D:.

1.4.2. Excluded studies

See the excluded studies list in appendix J.

1.4.3. Summary of clinical studies included in the evidence review

Table 2

Summary of studies included in the evidence review.

See Appendix D:for full evidence tables.

1.4.4. Quality assessment of clinical studies included in the evidence review

1.1. Discrimination

Table 3

Clinical evidence profile.

1.2. Calibration

Risk tool	No of studies	n	Risk of bias	Inconsistency	Indirectness	Imprecision	Observed/Expected ratio (median, range)	Quality
Mortality
POSSUM	10	5252	Serious risk of bias	Serious inconsistency	No serious indirectness	not estimable	0.86 (0–1.73)	Very low
P-POSSUM	10	8029	Serious risk of bias	Serious inconsistency	No serious indirectness	not estimable	1.03 (0.56–15.87)	Very low
NSQIP	4	2634	Serious risk of bias	Serious inconsistency	No serious indirectness	not estimable	1.23 (0.64–1.28)	Very low
E-PASS	1	100	Serious risk of bias	No serious inconsistency	No serious indirectness	not estimable	1	Low
ASA	1	1186	Serious risk of bias	No serious inconsistency	No serious indirectness	not estimable	1.08	Low
SRS	1	949	Serious risk of bias	No serious inconsistency	No serious indirectness	not estimable	0.81	Low
Morbidity (composite outcome)
POSSUM	9	3356	Serious risk of bias	Serious inconsistency	No serious indirectness	not estimable	1 (0.8–1.44)	Very low
NSQIP	5	3510	Serious risk of bias	Serious inconsistency	No serious indirectness	not estimable	1.06 (0.76–1.84)	Very low

a): Risk of bias was assessed using the PROBAST checklist. Downgraded by 1 increment if the majority of the evidence was at high risk of bias, and downgraded by 2 increments if the majority of the evidence was at very high risk of bias
b): Inconsistency was assessed by visual inspection of a plotted summary where reported.
c): The judgement of precision was not possible in the absence of the confidence region of the O/E ratio, summary ratios were downgraded due to this limitation.

1.3. Economic evidence

1.3.1. Included studies

No health economic studies were included.

1.3.2. Excluded studies

No relevant health economic studies were excluded due to assessment of limited applicability or methodological limitations.

See also the health economic study selection flow chart in Appendix H:.

1.4. Evidence statements

1.4.1. Clinical evidence statements

Risk tools mortality concordance

Thirteen studies reported an accuracy of 47–95% with POSSUM predicting mortality, with a median c-statistic of 82% (n=10811, Very low quality evidence)

Eighteen studies reported an accuracy of 56–94% with P-POSSUM predicting mortality, with a median c-statistic of 81% (n=15579, Very low quality evidence)

Eight studies reported an accuracy of 62–97% with NSQIP predicting mortality, with a median c-statistic of 83% (n=241905, Low quality evidence)

Two studies reported an accuracy of 82–84% with E-PASS for predicting mortality, with a median c-statistic of 83% (n=5372, Low quality evidence)

Seven studies reported an accuracy of 59–93% with ASA for predicting mortality, with a median c-statistic of 77% (n=58056, Very low quality evidence)

Four studies reported an accuracy of 58–86% with Charlson Comorbidity Index for predicting mortality, with a median c-statistic of 77% (n=136995, Very low quality evidence)

One study reported an accuracy of 80% with SORT for predicting mortality (n=78, Very low quality evidence)

Five studies reported an accuracy of 66–93% with SRS for predicting mortality of, with a median c-statistic of 85% (n=8160, Very low quality evidence)

Risk tools morbidity concordance

Nine studies reported an accuracy of 56–84% with POSSUM for predicting morbidity, with a median c-statistic of 75% (n=2673, Very low quality evidence)

One study reported an accuracy of 61% with P-POSSUM for predicting morbidity (n=113, Low quality evidence)

Eight studies reported an accuracy of 55–88% with NSQIP for predicting morbidity, with a median c-statistic of 62.5% (n=4819, Very low quality evidence)

Three studies reported an accuracy of 59–68% with E-PASS for predicting morbidity, with a median c-statistic of 67% (n=1093, Very low quality evidence)

Ten studies reported an accuracy of 52–93% with ASA for predicting morbidity, with a median c-statistic of 69% (n=66792, Very low quality evidence)

Four studies reported an accuracy of 56–69% with Charlson Comorbidity Index for predicting morbidity, with a median c-statistic of 64% (n=103357, Very low quality evidence)

Risk tools mortality calibration

Ten studies reported a predictive accuracy of POSSUM for mortality with median O/E ratio of 0.86 (n=5252, Very low quality evidence)

Ten studies reported a predictive accuracy of P-POSSUM for mortality with median O/E ratio of 1.03 (n=8029, Very low quality evidence)

Four studies reported a predictive accuracy of NSQIP for mortality with median O/E ratio of 1.23 (n=2634, Very low quality evidence)

One study reported a predictive accuracy of E-PASS for mortality with median O/E ratio of 1 (n=100, Low quality evidence)

One study reported a predictive accuracy of ASA for mortality with median O/E ratio of 1.08 (n=1186, Low quality evidence)

One study reported a predictive accuracy of SRS for mortality with median O/E ratio of 0.81 (n=949, Low quality evidence)

Risk tools morbidity calibration

Nine studies reported a predictive accuracy of POSSUM for morbidity with median O/E ratio of 1 (n=3356, Very low quality evidence)

Five studies reported a predictive accuracy of NSQIP for morbidity with median O/E ratio of 1.06 (n=3510, Very low quality evidence)

1.4.2. Health economic evidence statements

No relevant economic evaluations were identified.

1.5. The committee’s discussion of the evidence

Please see recommendations 1.3.1 – 1.3.2 in the guideline.

1.5.1. Interpreting the evidence

1.5.1.1. The outcomes that matter most

The committee highlighted that a key goal of preoperative risk assessment is to identify and stratify those at increased risk of mortality and morbidity. As such, the main outcomes included in this evidence review was the predictive accuracy of risk tools, as measured by sensitivity, specificity, predictive values, c-statistic data, and predicted risk versus observed risk (calibration data). The risk prediction tools do not predict or report specific morbidities, rather morbidity rate as a composite outcome.

1.5.1.2. The quality of the evidence

The quality of evidence varied from low to very low. Studies were downgraded for risk of bias inconsistency and imprecision. Risk of bias was generally serious or very serious due to unclear methodology in terms of blinding of risk tool and outcome data. A large proportion of the available concordance data had no reported variance data (such as 95% CI). As such, many of the outcomes were downgraded for a subsequent risk of inconsistency and possible imprecision. Due to the method of reporting and analysis of the calibration data with observed/expected ratios, it was also not possible to ascertain variance data. These outcomes were subsequently downgraded due to the uncertainty around outcome precision.

1.5.1.3. Benefits and harms

The committee agreed that an accurate risk prediction tool can have benefits in directing discussions between clinicians about the appropriateness of the planned surgery and whether it should proceed as planned, should be abbreviated, or whether alternative non-surgical options should be considered. Additionally, the committee suggested that being able to quantify morbidity risk allows planning for post-operative destination, discussions about recovery or convalescence and the anticipated clinical course. Effective risk tools can subsequently have a benefit on patient experience and postoperative quality of life. One possible disadvantage (harm) of using risk tools is underestimating mortality or morbidity risk, which may lead to insufficient attention to preventable risks, insufficient monitoring or surgery being performed when alternative options may be more appropriate. Another potential harm is over-estimating operative risk, which can lead to unnecessary over-vigilance and possibly reluctance on the part of the patient (and maybe clinician) to commence surgery. Thus using accurate risk prediction was seen by the GC as vital to maximise benefits and minimise harms.

The committee discussed the results and utility of the risk tools reviewed and agreed that a concordance (c-statistic) of >80% represents a good level of predictive accuracy, with results of >90% demonstrating an excellent test. The committee added that a test yielding <70% accuracy would be considered poor. The committee also noted that calibration data showing a test observed/expected ratio of 0.9–1.1 would be considered a fair level of accuracy, adding that it would be better to overestimate the event rate than to underestimate morbidity or mortality.

The committee agreed that tools such as POSSUM, P-POSSUM, NSQIP, E-PASS and SRS showed a fair level of accuracy for mortality with median c-statistic of ~85%. The committee highlighted that there was notable inconsistency in the accuracy of tools in the prediction of mortality and morbidity, with most tools ranging from ~60% to ~90% accuracy for predicting mortality.

The committee noted that all tools were less accurate in predicting morbidity showing a predictive accuracy of ~60–70%, but agreed that this was expectedly lower than the accuracy in predicting mortality and could still be informative for a healthcare professional and patient scheduled to undergo surgery.

The committee agreed that the evidence on risk tool calibration showed significant inconsistency between studies, limiting the utility of these results. As such, the committee weighted the majority of their discussions on the benefits and harms of risk tools on risk tool concordance evidence.

The committee considered that the noted variation in results could be due to the heterogeneity in study populations, with included studies providing risk prediction for a range of varied types of surgery. This was a notable concern to the committee, and while they felt confident that risk tools can have a benefit in the preoperative setting in predicting morbidity and mortality, they were not able to determine which risk tool should be used.

1.5.2. Cost effectiveness and resource use

No economic evaluations were identified for this question.

All of the different risk tools are freely available, and therefore do not have a cost associated with using them. Although they require some time to complete, the committee stated it would usually take less than 5 minutes during a preoperative assessment. The different types of risk tools do require different information, for example, some require information on the adult’s haemoglobin levels, however, all of these tests are already carried out as part of preoperative assessment.

The committee highlighted that if a risk tool is not accurate at estimating mortality and morbidity, then the wrong people may be given targeted interventions before surgery (incorrectly identified as high risk), or the wrong people may not be receiving interventions they should have (incorrectly identified as low risk). These targeted interventions vary, but could require being referred to a Consultant Anaesthetist, Cardiologist or Care of the Elderly specialist, or being admitted to a specialist area after surgery. Therefore, the committee highlighted the importance of accurately identifying who is at risk, as these downstream interventions can have a high cost associated with them, or quality of life could be lost from people not receiving interventions they require.

A recommendation was made to use a validated risk tool as part of a preoperative assessment. The committee agreed that the most commonly used tools such as P-POSSUM, NSQIP, E-PASS and SORT showed similar level of accuracy in predicting mortality and therefore will not lead to differences in the downstream interventions that are implemented in relation to patient risk. As current practice already involves using a validated risk tool as part of a preoperative assessment, the recommendation will not have a substantial resource impact.

1.5.3. Other factors the committee took into account

The committee recognised that it may be more appropriate to use a surgery specific risk tool rather than a generic tool. In addition, the committee agreed that the tool could simply be recording the American Society of Anaesthesiologists status of the patient for lower risk, less complex surgery.

The committee noted that a validated risk stratification tool can also help to frame discussions about risk with the person having surgery. Planned surgery is recognised as a ‘teachable moment’ when patients are more receptive and motivated to undertake healthy lifestyle changes such as smoking cessation or increasing the exercise they undertake. Healthcare professionals involved in the perioperative pathway can be trained to use motivational behavioural change techniques to help support these interactions with patients.

The committee noted that a validated risk stratification tool can also help to frame discussions about risk with the person having surgery as well as the wider perioperative team on the impact of surgical management on overall outcome. They agreed that the risk of postoperative morbidity is an important concern for people when they are making decisions about surgery. The committee noted that the recommendation was applicable to people undergoing dental surgery.

The committee considered that the findings of risk tools could have an influence over allocation of resources, although this would not be solely based on the risk tool findings, but alongside clinical assessment and judgement.

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Appendices

Appendix A. Review protocols

Table 4. Review protocol: Preoperative risk stratification tools (PDF, 94K)

Table 5. Health economic review protocol

Appendix B. Literature search strategies

The literature searches for this review are detailed below and complied with the methodology outlined in Developing NICE guidelines: the manual 2014, updated 2018.¹¹⁵

For more detailed information, please see the Methodology Review.

B.1. Clinical search literature search strategy

Searches were constructed using a PICO framework where population (P) terms were combined with Intervention (I) and in some cases Comparison (C) terms. Outcomes (O) are rarely used in search strategies for interventions as these concepts may not be well described in title, abstract or indexes and therefore difficult to retrieve. Search filters were applied to the search where appropriate.

Table 6. Database date parameters and filters used

Medline (Ovid) search terms

Embase (Ovid) search terms

Cochrane Library (Wiley) search terms

B.2. Health Economics literature search strategy

Health economic evidence was identified by conducting a broad search relating to the perioperative care population in NHS Economic Evaluation Database (NHS EED – this ceased to be updated after March 2015) and the Health Technology Assessment database (HTA) with no date restrictions. NHS EED and HTA databases are hosted by the Centre for Research and Dissemination (CRD). Additional health economics searches were run on Medline and Embase.

Table 7. Database date parameters and filters used

Medline (Ovid) search terms

Embase (Ovid) search terms

NHS EED and HTA (CRD) search terms

Appendix I. Health economic evidence tables

None.

Appendix J. Excluded studies

J.1. Excluded clinical studies

Table 8. Studies excluded from the clinical review

J.2. Excluded health economic studies

Published health economic studies that met the inclusion criteria (relevant population, comparators, economic study design, published 2003 or later and not from non-OECD country or USA) but that were excluded following appraisal of applicability and methodological quality are listed below. See the health economic protocol for more details.

Table 9. Studies excluded from the health economic review

Final

Evidence reviews underpinning recommendations 1.3.1 and 1.3.2 in the NICE guideline

This evidence review was developed by the National Guideline Centre

Disclaimer: The recommendations in this guideline represent the view of NICE, arrived at after careful consideration of the evidence available. When exercising their judgement, professionals are expected to take this guideline fully into account, alongside the individual needs, preferences and values of their patients or service users. The recommendations in this guideline are not mandatory and the guideline does not override the responsibility of healthcare professionals to make decisions appropriate to the circumstances of the individual patient, in consultation with the patient and, where appropriate, their carer or guardian.

Local commissioners and providers have a responsibility to enable the guideline to be applied when individual health professionals and their patients or service users wish to use it. They should do so in the context of local and national priorities for funding and developing services, and in light of their duties to have due regard to the need to eliminate unlawful discrimination, to advance equality of opportunity and to reduce health inequalities. Nothing in this guideline should be interpreted in a way that would be inconsistent with compliance with those duties.

NICE guidelines cover health and care in England. Decisions on how they apply in other UK countries are made by ministers in the Welsh Government, Scottish Government, and Northern Ireland Executive. All NICE guidance is subject to regular review and may be updated or withdrawn.

Bookshelf ID: NBK561975PMID: 32931173

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National Guideline Centre (UK). Evidence review for preoperative risk stratification tools: Perioperative care in adults: Evidence review C. London: National Institute for Health and Care Excellence (NICE); 2020 Aug. (NICE Guideline, No. 180.)
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Evidence review for preoperative risk stratification tools

1. Preoperative risk stratification tools

1.1. Review question: Which validated preoperative risk stratification tools best identify increased risk of mortality and morbidity in adults who will be undergoing surgery?

1.2. Introduction

1.3. PICO table

Table 1

1.4. Clinical evidence

1.4.1. Included studies

1.4.2. Excluded studies

1.4.3. Summary of clinical studies included in the evidence review

Table 2

1.4.4. Quality assessment of clinical studies included in the evidence review

1.1. Discrimination

Table 3

1.2. Calibration

1.3. Economic evidence

1.3.1. Included studies

1.3.2. Excluded studies

1.4. Evidence statements

1.4.1. Clinical evidence statements

Risk tools mortality concordance

Risk tools morbidity concordance

Risk tools mortality calibration

Risk tools morbidity calibration

1.4.2. Health economic evidence statements

1.5. The committee’s discussion of the evidence

1.5.1. Interpreting the evidence

1.5.1.1. The outcomes that matter most

1.5.1.2. The quality of the evidence

1.5.1.3. Benefits and harms

1.5.2. Cost effectiveness and resource use

1.5.3. Other factors the committee took into account

References

Appendices

Appendix A. Review protocols

Appendix B. Literature search strategies

B.1. Clinical search literature search strategy

B.2. Health Economics literature search strategy

Appendix C. Clinical evidence selection

Appendix D. Clinical evidence tables

Appendix E. PROBAST checklist

Appendix F. C-statistic plots

Appendix G. Calibration plots

Appendix H. Health economic evidence selection

Appendix I. Health economic evidence tables

Appendix J. Excluded studies

J.1. Excluded clinical studies

J.2. Excluded health economic studies

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