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Perioperative optimisation in low- and middle-income countries (LMICs): A systematic review and meta-analysis of enhanced recovery after surgery (ERAS)

Aya M Riad1, Aisling Barry1, Stephen R Knight2, Carlie J Arbaugh3, Parvez D Haque4, Thomas G Weiser3, Ewen M Harrison2

1 Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
2 Centre for Medical Informatics, Usher Institute Edinburgh, UK
3 Department of Surgery, Stanford Health Care and Stanford University, Stanford, California, USA
4 Department of General Surgery, Christian Medical College and Hospital, Punjab, India
† Joint senior authorship.

DOI: 10.7189/jogh.13.04114


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Surgery is a physiologically demanding event [1,2], and modifying aspects of the endocrine stress response and postoperative catabolic processes to optimise care in the perioperative period is increasingly being recognised as a means of improving outcomes [3]. This began with the concept of fast track surgery [4], followed by enhanced recovery after surgery (ERAS) [5] which described pre-, intra-, and post-operative factors to improve patient outcomes after surgery. There has recently been increased interest in prehabilitation [6], multimodal exercise, and nutritional and psychological interventions [7,8] aiming to better prepare patients for surgery. Prehabilitation, preoperative nutritional care, and preoperative optimisation (including optimising comorbid medical conditions and alcohol and smoking cessation) are now incorporated in the 2018 ERAS Recommendations for elective colorectal surgery [9].

There is little doubts about the benefits of perioperative optimisation protocols [10], yet their feasibility and benefits in low- and middle-income countries (LMICs) remain unexplored [11]. The Lancet Commission on Global Surgery [12] highlighted the great inequity in surgical care provision in LMICs, with nearly five billion people still lacking access to safe and affordable surgical care [13]. Large multicentre studies such as GlobalSurg 3 [14] and the African Surgical Outcomes Study (ASOS) [15] have shown postoperative mortality to be significantly worse in LMICs, with a proportion of these deaths attributed to a reduced “capacity to rescue” patients from complications in the postoperative period [16]. Thus, an intervention such as ERAS, which has been cost-effective in high-income settings [17] and is focused on increasing the physiological reserve of patients and reducing postoperative complications, theoretically could be significantly beneficial in LMICs.

There is a need for increased uptake of validated perioperative optimisation protocols in LMICs [18,19], further shown by the anticipated release of LMIC-specific ERAS guidelines [9]. However, considerations for how they may be adapted and implemented are lacking. We aimed to determine the feasibility of perioperative optimisation for surgery in LMICs and its impact on postoperative length of hospital stay (LOS).


Search strategy

We searched MEDLINE, PubMed, Global Health (CABI), WHO Global Index Medicus, Index Medicus (WPRIM for the Western Pacific and IMSAR for the South Asian region) and Latin American and Caribbean Health Sciences Literature (LILACS) for randomised controlled trials (RCTs) and prospective or retrospective cohort studies conducted in lower middle- or low-income countries that compared perioperative interventions, comprising either aspects of the ERAS pathway or prehabilitation to routine care. We defined LMIC status according to World Bank Analytical Classification based on gross national income (GNI) per capita at the time the search was undertaken (23 December 2021) [20]. We used Cochrane LMIC filters [21] in non-LMIC databases (MEDLINE and PubMed). Additionally, we hand-searched references and citations of included studies and relevant reviews or editorials for additional eligible studies.

We excluded reviews, editorials, case reports, surveys, protocols, studies only reporting quality of life (QOL) outcomes without using validated patient-reported outcome measures (PROMs), and studies conducted in high- or upper-middle-income countries. We also excluded studies conducted on paediatric or non-surgical patients; for reference, we defined surgical patients as those undergoing a procedure requiring general or neuraxial anaesthesia and thus excluded patients only undergoing endoscopy. There were no limits placed on date or language. We registered this systematic review on the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42021279053) and performed it according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [22] (Appendix 1 in the Online Supplementary Document).

Study selection and data extraction

We managed the study selection process via Covidence (Covidence, Melbourne, Australia). Two reviewers (AMR and AB) independently screened all abstracts, while a third reviewer (SRK) who did not have access to the initial reviewers’ decisions resolved any discrepancies. LMIC status of all studies undergoing full-text review were checked against historical World Bank classifications. Two independent reviewers (AMR and AB) undertook full text review and risk of bias assessment.

Two reviewers (AMR and AB) piloted a data extraction template and made appropriate changes, after which they independently extracted data from each study using a standardised extraction form within the Covidence platform. We collected study details, intervention and ERAS elements applied, outcomes, and qualitative aspects of perceived benefits and adaptations required in a lower-income setting for each study (Appendix 2 in the Online Supplementary Document). The reviewers first attempted to resolve discrepancies through discussion; if unsuccessful, a third member of the study team (EMH) resolved the dispute.

Quality assessment

We assessed quality and risk of bias using the Newcastle-Ottawa Scale for non-randomised and the Cochrane Risk of Bias tool for randomised studies [23,24]. Two reviewers (AMR and AB) independently assessed the quality of all studies with conflicts resolved by a third reviewer (EMH). We determined a priori that no studies would be excluded on the basis of low quality or surgical specialty and that we would not limit inclusion to randomised control trials due to the small numbers of available studies and similarities in ERAS protocol across specialties. However, we undertook a sensitivity analysis, including only general surgical patients to quantify the impact of this decision. The 2018 ERAS protocol for elective colorectal surgery [7] was used to compare elements implemented in all studies.

Statistical analysis

Using the meta package in R, version 3.6.1. (R Core Team, Auckland, New Zealand) [22], we completed a meta-analysis of LOS, as this was the outcome reported most consistently amongst studies and the main outcome used in ERAS studies [10]. As we anticipated high levels of between-study heterogeneity (studies unlikely to be measuring the same underlying effect), we used a random-effects model with standardised mean differences. We used the restricted maximum likelihood estimator method [25] to calculate τ2 and Knapp-Hartung adjustments [26] for the pooled effect confidence interval. We also calculated a predication interval indicating the effect size future studies were likely to find. We considered a P-value of <0.05 to be statistically significant.


We screened 1205 studies with 70 undergoing full text review (Figure 1); six were eligible for inclusion [2732], with all except Gopakumar et al [30] reporting sufficient data to be included in quantitative analysis. The intervention in all studies was ERAS and the comparator was routine practice in the study hospitals. All studies included general surgical patients, except for Elayat et al. [32], which included neurosurgical patients. Two studies [29,31] exclusively included stoma reversal procedures. Only two studies [28,31] were RCTs. Three studies were conducted in India, which, alongside Egypt (where one study was based) was classified as a lower-middle-income country. Two studies were conducted in low income countries (Nepal and Pakistan) (Table 1 and Appendix 3 in the Online Supplementary Document).

Figure 1. PRISMA flowchart.

Table 1. Study characteristics

ICU – intensive care unit, ASA – American Society of Anaesthesiologists, BMI – body-mass index

Patient population

The inclusion and exclusion criteria used by studies are summarized in Table 1. Most studies included cancer patients; two (Nanavati et al [27] and Pirzada et al [31]) noted the main reason for surgery being inflammatory bowel disease (IBD). Patients with uncontrolled comorbid conditions (e.g. diabetes or cardiac disease) were excluded from all studies apart from Shetiwy et al. [28] and Gopakumar et al. [30] . Additionally, Kurmi [29] required patients to have a body mass index (BMI) <30, to live within two hours of the hospital, and to have access to a phone, transport, and a responsible next of kin staying in the hospital with them for at least 24 hours (Table 1).


The intervention in all included studies was an ERAS protocol. There are currently no official ERAS guidelines in neurosurgery. Principles of an ERAS protocol for elective craniotomies were first suggested by Hagan et al. [33] and later adapted by study groups for use in trials [34], as was the case in Elayat et al. [32] (Table 2). All interventions took place exclusively in hospital settings, with the intervention duration being at most 48 hours preoperatively to discharge. Details of who was responsible for administering the intervention were unclear, as most studies focused on the surgeon and surgical trainees, as well as family members staying with patients. Only Elayat et al. [32] reported ERAS training for nursing and surgical trainees. Most studies did not report on compliance with the intervention; only Nanavati et al. [27] reported the number of patients who underwent each aspect of the protocol and Elayat et al. [32] described the use of an ERAS checklist for each patient to monitor compliance without reporting results.

Table 2. ERAS Protocol followed by studies

ERAS – enhanced recovery after surgery

*Based on 2018 ERAS protocol for elective colorectal surgery [9]. Interventions not applicable to craniotomies or stoma reversal procedures are indicated.


The primary outcome in most studies was LOS, defined as the number of days postoperatively until discharge. Most studies reported mortality as a secondary outcome, with low rates across all studies (Table 3). Kurmi et al. [29] were only ones to use a standardised definition of complications (Clavien-Dindo), preventing meaningful grouping of this outcome in meta-analysis. Length of follow-up was 30 days for all studies, except for Gopakumar et al. [30], for whom it was three months, and Pirzada et al. [31], who did not report a follow-up length of time. Elayat et al. [32] (the only neurosurgical study) reported the highest mortality rate; two patients died in the ERAS group and three in the control group within 30 days of their operation. Three patients had a pulmonary embolism (PE) in the control group in Shetiwy et al. [28], with two resulting in death. Other adverse events included two anastomotic leaks (one in each group) in Nanavati et al. [27] and one anastomotic leak in the control group for Kurmi et al. [29].

Table 3. Study demographics and outcomes

The pooled analysis for LOS (Figure 2) showed reductions in the ERAS group with a standardised mean difference of -2.18 (95% confidence interval (CI) = -4.13, -.0.05; P < 0.01) days lower than the control group. However, the prediction interval was large (95% CI = -7.85, 3.48) and I2 was 94%, indicating substantial between-study heterogeneity [35]. A sensitivity analysis [36] found no outliers and there was no marked asymmetry in the funnel plot analysis (Figure S1 in the Online Supplementary Document), although Kurmi et al. [29] had a high effect size and high standard error. A sensitivity analysis excluding Elayat et al. [32] and including only general surgical patients showed similar results, with a standardised mean difference of -2.69 (95% CI = -4.86, -0.52; P < 0.01) and I2 of 84% (Figure S2 in the Online Supplementary Document). Due to high variability in reporting between studies and low study numbers, we did not conduct a meta-regression or pooled analysis of other outcomes.

Figure 2. Forest plot of pooled length of stay analysis (measured in days).

A qualitative summary of the benefits and barriers to implementation of ERAS is summarised in Table S1 in the Online Supplementary Document.


Our systematic review and meta-analysis showed that ERAS protocols reduced the length of hospital stay when implemented in LMICs. We observed high levels of between-study heterogeneity and variable uptake of ERAS components. For example, prehabilitation, preoperative optimisation, and management of anaemia were not reported by any studies.

The reduction in observed LOS is consistent with findings of a meta-analysis conducted in high-income settings [37], which found a reduction in LOS after colorectal surgery of 2.55 days, as compared to 2.18 in this analysis. Kurmi et al. [29] observed a larger LOS reduction (standardised mean error (SME) = -4.90; 95% CI = -6.40, -3.39) than other studies and was likely a significant contributor to heterogeneity. There are several potential explanations for this. For example, the study design had intervention patients cared for in a different surgical unit by a different surgeon and team than control patients. Furthermore, there was a lack of randomisation in most studies. Reduction in length of stay is considered unequivocally advantageous in high income settings, where there is a reliance on primary care and easy access to hospitals for identification and treatment of early postoperative complications. In LMICs, where healthcare infrastructure and distance from home is variable, there may need to be a more nuanced appreciation of the consequences of patients returning home soon after their surgery and how this can be safely facilitated.

Qualitatively, ERAS was perceived as improving outcomes and being economically beneficial for service provision by reducing LOS, yet most authors observed resistance to the introduction of a new protocol and a need for further education. While the application of the ERAS protocol was variable, prehabilitation and preoperative optimisation were consistently not undertaken across all studies. This finding was reiterated in a systematic review of Cochrane reviews, which found any pre-admission or pre-operative interventions to be exclusively studied in high-income countries [38]. Factors such as increased distance of the hospital from home may contribute to difficulty in patients presenting separately for prehabilitation prior to admission for surgery [12]. More fundamentally, this reflects differences in healthcare systems across income levels.

ERAS relies on the underlying healthcare system: it requires a multidisciplinary team and substantial nursing care, which may be challenging in LMICs with a high patient-to-nurse ratio [17]. It is underpinned by a continuous audit process requiring infrastructure that is commonly absent in LMICs and associated with high expenditure [39]. There are different stakeholders involved in lower-income settings. For instance, Elayat et al. [32] describe the role of relatives in postoperative patient feeding and mobilisation which would normally be undertaken by nurses or other support staff in high-income countries.

Resistance to change has also been consistently described as a barrier. Despite high-quality evidence, inertia is still present. This was a pattern observed in the introduction of the WHO Surgical Safety Checklist [40]. Applying individual ERAS protocol elements in the absence of stakeholder buy-in misses the opportunity for an enhanced perioperative care environment delivered by an integrated multidisciplinary team [41]. The utilisation of implementation science strategies [42] is essential in improving the uptake of ERAS protocols through a better understanding of the real-world challenges faced in LMIC settings. Importantly, this will only lead to limited benefits in the absence of strategies to strengthen surgical care systems [43].

There are several limitations to this analysis. The studies included have a relatively high risk of bias and substantial between-study heterogeneity, so the results of the pooled analysis should be interpreted with caution. Heterogeneity was likely further increased by grouping studies evaluating different surgical specialities; ideally, our analysis would have focused on similar procedures and patient populations, but was limited by numbers (although our sensitivity analysis excluding neurosurgical patients showed similar results). While LOS is widely used as an endpoint in ERAS literature, is an imperfect outcome measure and could be influenced by confounding variables beyond the intervention. Furthermore, most studies were not RCTs and all were conducted in single centres with small sample sizes, limiting the ability to address confounding and undertake meaningful meta-regression analyses. Studies failed to provide missing data at the level of each ERAS component or details on the number of patients receiving each component, which made it difficult to ascertain which components influence outcomes. Extensive exclusion criteria employed by most studies means the sample in this analysis is likely different to the target population in LMICs, introducing an added layer of uncertainty around the real-world effectiveness of the intervention. Additionally, LMICs are not a homogenous population; there are significant between and within countries which limit generalisability.

All studies in this analysis excluded either elderly patients or those with low functional status or uncontrolled comorbid conditions (Table 1). There are no recommendations to exclude patients from ERAS protocols on these criteria, and ERAS protocols have been shown to be as effective in elderly patients with comorbidities as in younger patients despite potential differences in the number of ERAS components adhered to [44]. Nanavati et al. [27] and Pirzada et al. [31] both reported particularly low mean ages (23.87 (standard deviation (SD) = 4.56) vs. 33.50 (SD = 12.36)) for the ERAS group, which are not fully explained by the exclusion criteria. This may reflect the surgical population in LMICs being inherently different, which is consistent with literature describing surgical patients in lower-income settings as often being younger and presenting with different disease aetiologies [19].

These limitations have likely contributed to the exclusion of LMIC studies from most meta-analyses conducted on the effectiveness of ERAS protocols and other perioperative interventions, leading to a lack of high-level evidence on the effectiveness of these interventions in LMIC settings. Future studies should focus on multicentre data entry into centralised databases which enable compliance with individual elements of the protocol to be determined and multivariable analysis to be undertaken. There is also a need for qualitative analysis of barriers to implementation to improve the understanding of the perioperative environment in lower-income settings, which could help in formulating future LMIC-specific guidelines.


Despite high levels of between-study heterogeneity, we found that implementation of ERAS protocols is feasible in LMICs, with implications for reduced length of hospital stay as a meaningful outcome. High-quality data on perioperative practices in LMICs and supplementary qualitative analysis are needed to further understand barriers to perioperative optimisation in LMIC healthcare settings.

Additional material

Online Supplementary Document


We are grateful to Ruth Jenkins, Academic Support Librarian at the University of Edinburgh, UK for her assistance in developing the search strategies.

[1] Financial disclosure Funding: No funding was received for this research.

[2] Authorship contributions: AMR – generating study question, systematic review registration, search strategy, abstract screening, study selection, data extraction, quality assessment, statistical analysis, manuscript writing, manuscript editing. AB – abstract screening, study selection, data extraction, quality assessment, manuscript writing, manuscript editing. SRK – generating study question, search strategy, abstract screening, study selection, data extraction, quality assessment, statistical analysis, manuscript writing, manuscript editing. CJA – generating study question, search strategy, manuscript writing, manuscript editing. DPH – generating study question, manuscript writing, manuscript editing. TGW – generating study question, systematic review registration, search strategy, abstract screening, study selection, data extraction, quality assessment, statistical analysis, manuscript writing, manuscript editing. EWH – generating study question, systematic review registration, search strategy, abstract screening, study selection, data extraction, quality assessment, statistical analysis, manuscript writing, manuscript editing.

[3] Conflicts of interest Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests.



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Correspondence to:
Professor Ewen M Harrison, Professor of Surgery and Data Science
NIHR Global Health Unit on Global Surgery, Centre for Medical Informatics, Usher Institute, University of Edinburgh
Edinburgh, EH16 4UX
United Kingdom
[email protected]