Cognitive Reserve and the Risk of Postoperative Cognitive Dysfunction

A Systematic Review and Meta-Analysis

Background: Post-operative cognitive dysfunction (POCD) occurs in 10 to 54% of older patients during the first few weeks after surgery, but little is known about risk factors predisposing to POCD.

Methods: Systematic literature review and meta-analysis of cognitive reserve indicators and POCD risk.

Results: Fifteen studies on 5104 patients were included. Follow-up periods spanned 1 day to 6 months. Educational level was the most commonly assessed cognitive reserve indicator, and a longer time spent in education was associated with a reduced risk of POCD (relative risk [RR] per year increment 0.90; 95% confidence interval: [0.87; 0.94]), i.e. each year increase in education was associated with a 10% reduced risk. Similar findings were made for some analyses on education as a categorical predictor (high school versus further/higher education, RR 1.71, [1.30; 2.25]; lower than high school versus further/higher education, RR 1.69, [1.17; 2.44]) though risk was equivalent for patients with high school education and those with lower than high school education (RR 1.02; [0.78; 1.32]).

Conclusion: Patients with a relatively higher level of education are at reduced risk of POCD. Risk stratification of surgical patients according to educational level may prove useful.

Post-operative cognitive dysfunction (POCD) occurs relatively frequently, in 10 to 54% of patients during the first few weeks after surgery (1). It is usually transient (2), but unlike for post-operative delirium (POD), clear diagnostic criteria are lacking for POCD (3, 4).

Despite its high prevalence, POCD is underresearched and well-established risk factors for POCD are few and far between (for a review, see [2]) so that at present the cognitive risk of a surgical patient is unpredictable. Recent research has shown that diabetes (5) and pre-existing cognitive impairment (6) may predispose patients to POCD. Compared with these types of clinical risk factors, however, research into the contribution of cognitive reserve to POCD has essentially been neglected.

Cognitive reserve is a theoretical construct that aims to explain links between factors such as a lower level of education, lower socioeconomic status (SES), or lower pre-morbid cognitive ability and an increased risk of cognitive impairment in older age (7–11). The account assumes that people differ in their ability to functionally ‘buffer’ neuropathological insult due to aging and disease according to their cognitive reserve capacity (12–14). Simply put, brain networks of high-reserve individuals are thought to be better able to cope with disruptions due to working more efficiently and more flexibly compared with low-reserve individuals. Neuropathological burden may further be compensated for through recruitment of novel brain networks (13).

POCD is known to negatively impact on subjective cognitive function and quality of life in affected patients (15, 16). Studies suggest that it also increases the risk of dementia and mortality (17–19). POCD is thus a cause for concern from a public health perspective that exceeds problems associated with cognitive deficits alone. With a lower cognitive reserve capacity as a predictor of age-related cognitive impairment, it appears reasonable to expect an association with POCD. A lower level of education is indeed frequently discussed as a contributing factor to POCD, though empirical evidence is rarely mentioned (2, 20–23). If such evidence was to be confirmed, measures of cognitive reserve could supplement cognitive risk prediction on the basis of clinical risk factors. Because low cognitive reserve could reasonably constitute the starting point of a causal chain leading up to POCD, the identification of cognitive reserve parameters as risk factors for POCD would further add to our understanding of the processes underlying the condition.

Here, we aim to integrate the current epidemiological evidence on cognitive reserve and the risk of POCD in view to providing guidance for clinical practice.

Methods

Systematic search strategy

An electronic search (eTable 1, eBox 1) was performed by one investigator (IF) in accordance with the MOOSE and PRISMA guidelines (24, 25).

Table

Overview of included studies

eBox 1

Systematic search strategy

eTable 1

Parameters assessed in the present review with corresponding search terms

Study selection

Studies were eligible for inclusion if they

• followed a prospective study design,
• included human adults undergoing surgery (age ≥18 years),
• had full texts published in English
• reported original data on associations of cognitive reserve indicators (eTable 1) with POCD in the form of odds ratios or relative risks (RR; both termed RR in the present analysis) or as descriptive data that allowed calculation of RR. Any operationalization of POCD qualified for inclusion provided it was based on performance-based neuropsychological assessment.

Data extraction

Fully adjusted RR statistics were extracted unless no adjustment was made. If more than one article reported on the same sample, the article with the most complete reporting was selected. Data were extracted on the longest follow-up period. For 2 studies with multivariate-adjusted data at 7 day follow-up but not at 3 months, the 7 day follow-up was selected (26, 27). For one study comparing three levels of cognitive change, ‘severe deterioration’ was used to represent POCD (28). Enquiries were made to corresponding authors for unreported information.

Data synthesis and analysis

Studies were analyzed separately for each cognitive reserve indicator. We used the standard I² index to identify statistical heterogeneity (29) and inverse variance fixed-effect models to calculate summary estimates of RR (95% CI) in meta-analyses across studies. Forest plots were generated to present pooled estimates. The main meta-analyses were repeated using random-effects models (eBox 2). Potential sources of heterogeneity were explored in subgroup and meta-regression analyses. Review Manager 5.3 and SAS Enterprise Guide 4.3 were used.

eBox 2

Results of main analyses in fixed effects models and random effects models

Results

The search retrieved 109 unique articles (eFigure 1); an independent search identified a further 28 articles. Overall, 64 full text articles were assessed. Of these, 40 did not meet our inclusion criteria and 9 articles (30–38) were excluded due to suspected duplicate reporting (19, 26, 27, 39). In total, 15 articles were included.

Figure

Forest plot

eFigure 1

Flow chart for search on cognitive reserve and postoperative cognitive reserve (POCD)

The included studies originated in Europe, USA, Australia, and Asia (eTable 2). Surgical procedures included cardiac surgery (n = 7) and non-cardiac surgery (n = 8) under general (n = 9) or a combination of general and regional anesthesia (n = 4) (where reported) (Table). A total of 5104 patients were analyzed. Sample characteristics varied substantially between studies. The proportion of males ranged from 26% to 79% and the mean sample age from 51 to 75 years (mean 63 ± 8 years). Patients were followed up for between 1 and 180 days after surgery (median: 25 days; interquartile range: 7 to 45 days). All studies except one (40) applied detailed batteries of neuropsychological tests, though the criteria used to define POCD were heterogeneous. POCD occurred in 8% to 67% of patients.

eTable 2

Detailed summary of included studies

Findings of included studies and meta-analysis

a) Years of education—Eight articles reported data on years of education. The mean years of education in these studies ranged from 8 years in 2 studies from Italy and China (28, 40) to 14 years in 2 US studies (17, e1) (mean 12 ± 3 years). When effects were pooled, each year increase in education was associated with a 0.90 risk of POCD (RR 0.90 per year increment; 95% CI: [0.87; 0.94]; p<0.001) (Figure), i.e. a 10% reduced risk of POCD. Statistical heterogeneity between studies was moderate (chi² [7] = 12.49; I² = 44%; p = 0.09) with no evidence of publication bias (eFigure 3). The finding was universal across study designs and sample characteristics (eTable 3, eFigure 2).

eFigure 2

Years of education and risk of POCD in subgroup analyses according

eFigure 3

Funnel plot for meta-analysis of years of education and POCD

eTable 3

Subgroup analyses of included studies on education (years) and POCD (total N = 8)

b) to d) Level of education as a categorical predictor—Five studies assessed education as a categorical variable. Of these, 3 studies ascertained whether patients had completed a lower level than high school, had completed high school and/or had completed further/higher education (26, 27, e2). For the purpose of the present analyses, “middle school education” in one Chinese study (e3) was equated with “high school education.” For one US study (19), “ >16 years of education” was equated with “further/higher education.”

b) High school education versus further/higher education—When effects were pooled across 4 studies (19, 27, e2, e3), high school level of education was associated with a 71% increased risk of POCD compared with a higher level of education (RR 1.71 [1.30, 2.25]; p<0001) (Figure; eFigure 4). No statistical heterogeneity was indicated (chi² [3] = 0.67; I² = 0%; p = 0.88).

eFigure 4

Funnel plot for meta-analysis of high school education versus further/higher education and POCD

c) High school versus lower than high school education—No statistically significant associations emerged on high school versus not having attained high school education when effects were pooled across 4 studies (26, 27, e2, e3) (RR 1.02 [0.78; 1.32]; p = 0.89) (Figure; eFigure 5). Statistical heterogeneity was substantial (chi² [3] = 15.19; I² = 80%; p = 0.002).

eFigure 5

Funnel plot for meta-analysis of high school education versus lower than high school education and POCD

d) Lower than high school education versus further/higher education—Four studies compared the POCD risk of patients with lower than high school education with that of those with further/higher education (26, 27, e2, e3). Two of these studies (26, 27) adjusted their analyses for a range of covariates. Across all four studies, having attained lower than high school education was associated with a 69% increased risk of POCD (RR 1.69 [1.17; 2.44]; p = 0.005) (Figure; eFigure 6). Statistical heterogeneity was low (chi² [3] = 3.08; I² = 3%; p = 0.38).

eFigure 6

Funnel plot for meta-analysis of lower than high school education versus further/higher education and POCD

Other indicators of cognitive reserve

One study (e2) showed a trend for an increased risk of POCD in illiterate patients; however, this was not statistically significant (RR 1.47 [0.46; 4.69]; p = 0.52). Another study (e4), that derived a composite measure of reserve capacity from occupation, vocabulary, education, ethnicity, geographical region of the country, and sex found a statistically non-significantly reduced risk of POCD in low-reserve patients (RR 0.71 [0.45; 1.12]; p = 0.14). In one study (e5), no association was found between National Adult Reading Test (NART) scores and POCD risk (RR per NART score increment: 1.01 [0.96; 1.07]; p = 0.68).

Discussion

Here, we set out to integrate reports on indicators of cognitive reserve and risk of POCD. Only few studies were identified. Education was the most commonly ascertained reserve indicator and overall having attained a higher level of education was associated with a reduced risk of POCD. Due to considerable heterogeneity between studies, we are unable to comment on a potential dose–response relationship.

Several studies controlled for baseline level of cognitive function (39, e1, e6, e7) which in the assessment of education as a predictor represents overadjustment. True effects may therefore be larger than reported here though the role of confounding factors is entirely unclear. Our finding by no means implies causation. Null findings for reserve indicators other than education (e2, e4, e5) may be due to limited statistical power and the low study number. As reserve indicators tend to correlate (e8–e11), lower pre-morbid ability and illiteracy, too, may be identified as contributing to POCD in the future. One study found a trend for a protective effect of lower cognitive reserve (e4). As cognitive reserve in that study was defined by a range of reserve indicators as well as demographics, this may suggest an influence by some factor other than education.

Our findings are supported by several studies that report associations of low education level with an increased risk of POCD in their abstracts but were excluded due to non-English language (e12–e15). A lower reserve capacity may also increase the risk of post-operative delirium (POD) (e16, e17) and is well-established as a risk factor for age-related cognitive impairment. For instance, lower compared to higher levels of education have been associated with a 59 to 88% increased risk of dementia (7, 8).

Candidate pathophysiological mechanisms underlying POCD include surgery-induced inflammation (e18) and, potentially, anesthesia-induced neurodegeneration (e19). In line with the cognitive reserve model (12–14, e20), patients with a higher cognitive reserve as indicated by a higher level of education may have a functional advantage: they may be able to better cope with such damage through adjusting existing or recruiting novel brain networks. Morphological advantages, such as a larger brain size, correlate with cognitive reserve (e21) and may also play a role (e22). Finally, associations may be mediated by clinical and lifestyle factors (e23). Low-reserve individuals tend to be exposed to higher levels of environmental hazards (e24) and detrimental lifestyle (e25) across their life span, yielding greater brain pathology in older age (e26). Low-reserve patients may then have presented for surgery with greater subclinical neuropathology, such as beta amyloid burden (e27), which was exacerbated by surgery to become expressed as cognitive deficits. This account is the most plausible explanation of the health consequences associated with POCD (17–19, e28) and could be evaluated through adjustment of analyses for lifestyle and clinical risk factors; however in the studies included here, adjustment was inconsistent.

Research into the epidemiology of POCD is in its infancy and firm knowledge of all of its risk factors is lacking. We have recently shown that the metabolic syndrome may be associated with POCD (5, e29). The present findings are in line with that type of evidence as a lower cognitive reserve predisposes to metabolic syndrome in later life (e30). Clearly, further studies in this area are needed. These should consider multimorbidity as well as lifespan developments and could—once all risk factors for POCD have been identified—feed into the development of a risk score and of preventive measures. A large proportion of older patients in hospitals is cognitively impaired (e31) so that putting a halt to POCD would be immensely beneficial to global health.

Limitations

A number of limitations must be considered. There was substantial overlap in studies included in meta-analyses b) to d), level of education as a categorical predictor, which each were based on a small number of studies. Thus, no firm conclusions should be drawn on the basis of those analyses. RR and OR estimates are also not strictly equivalent (e32) but were equated here. Further, a single investigator performed the search and non-English articles were excluded from our analysis. Included studies were heterogeneous with respect to sample characteristics and definitions of POCD, so that the generalizability of our findings is uncertain. With the inclusion of studies that applied no adjustment at all, confounding of our results by any other sociodemographic and/or clinical variables is plausible. Grouping studies according to the categories “lower than high school education”; “high school education”, and “further/higher education” may have been suboptimal for those that did not explicitly refer to these categories (19, e3), and included studies were set in a total of 13 countries. Thus, our results will have been affected by cross-cultural differences between school systems and may not necessarily transfer to German hospitals. Readers are also advised that the relative risk estimates presented here do not reflect absolute risks. Nonetheless, our findings illustrate a trend to suggest that—while considering other coexisting factors—
enquiry into patients’ educational background during pre-surgery interview may be a straightforward and non-invasive way to identify at-risk patients.

Conclusion

The importance of patients’ cognitive reserve capacity is becoming increasingly recognized (e33). Virtually all previous studies of POCD have assessed indicators of cognitive reserve and could re-analyze their data to determine the roles of the frontal cortex and associated cognitive function, potential lifestyle mediators, and clinical mediators. Attention should be paid to overadjustment for variables that are closely related to both predictor and outcome and render statistical analyses non-significant despite an underlying relationship. Here, we identified only two studies that used a vocabulary-based estimate of peak pre-morbid ability as a reserve indicator. Future investigations are well-advised to take advantage of such tests, which are easily administered and well-validated (e34) and are unaffected by surgery (e35) or, unlike education, by societal constraints.

Our results show that middle-aged to older surgical patients with a higher level of education are at reduced risk of POCD compared with less educated patients. Mechanisms, contributing clinical and environmental factors, and strategies to reduce POCD risk in low-education patients warrant detailed research, but for now, we recommend that anesthetists and surgeons consider routine ascertainment of patients’ level of education in geriatric surgery.

Funding
This study was supported by funding from the European Union, Seventh Framework Programme [FP7/2007–2013], under the grant agreement No. HEALTH-F2–2014–602461 BioCog (Biomarker Development for Postoperative Cognitive Impairment in the Elderly).

Conflict of interest statement

Prof Winterer is coordinator of the BioCog Consortium and chief executive of the company Pharmaimage Biomarker Solutions GmbH. The company is one of the partners of the BioCog Consortium.

The remaining authors declare that they have no conflicts of interest.

Manuscript received on 25 May 2016, revised version accepted on 22 September 2016.

Corresponding author
Insa Feinkohl, PhD
Max-Delbrück-Centrum für Molekulare Medizin
in der Helmholtz-Gemeinschaft (MDC)
Robert-Rössle-Str. 10, 13092 Berlin, Germany

Supplementary material
For eReferences please refer to:
www.aerzteblatt-international.de/ref0717

eFigures, eBoxes, eTables:
www.aerzteblatt-international.de/17m0110

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