Advertisement

The importance of postoperative quality of recovery: influences, assessment, and clinical and prognostic implications

  • Andrea Bowyer
  • Colin RoyseEmail author
Review Article/Brief Review

Abstract

Quality of recovery is a complex construct whose definition is influenced heavily by the opinions and biases of the individual patient, clinician, or institution. As a result, recovery assessment tools differ in their fundamental definitions of recovery, breadth, and assessment time frame. Accurate assessment of recovery is essential as suboptimal recovery has both economic and prognostic implications. Quality of care is often substituted as a surrogate at the institutional level for quality of recovery, but it is ideologically distinct from patients’ perceived quality of care, recovery, and satisfaction. Recovery tools also differ in their assessment of recovery as a continuous vs dichotomous variable and in their focus at the group vs individual level. Ideally, recovery measures should assess outcomes in a simple dichotomous fashion and maintain relevancy by assessing in multiple domains at various time points. Assessment of recovery in a dichotomous fashion also has both clinical and research applications. It allows identification of suboptimal recovery at both individual and group levels, respectively, and when performed in real time, it allows the opportunity for timely targeted intervention specific to individual patients as well as for resource rationalization.

Keywords

Performance Indicator Poor Recovery Postanesthesia Care Unit Recovery Trajectory Cognitive Recovery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

L’importance de la qualité de récupération postopératoire: influences, évaluation et implications cliniques et pronostiques

Résumé

La qualité de récupération est un construit complexe dont la définition est fortement influencée par les opinions et les biais du patient, du clinicien, voire de l’établissement. Dès lors, les outils d’évaluation de la récupération diffèrent de par la définition-même de la récupération, leur envergure, et le cadre temporel de l’évaluation. Une évaluation précise de la récupération est essentielle; en effet, une récupération sous-optimale a des implications tant économiques que pronostiques. Au niveau institutionnel, on utilise souvent la qualité des soins comme substitut de la qualité de récupération mais, d’un point de vue idéologique, ce concept se distingue de la qualité des soins, de la récupération et de la satisfaction telles que perçues par les patients. Les outils de récupération diffèrent également dans leur évaluation de la récupération en tant que variable continue ou dichotomique et dans leur emphase sur le groupe ou l’individu. Dans l’idéal, les mesures de la récupération devraient évaluer les pronostics de manière dichotomique simple et maintenir leur pertinence en évaluant plusieurs domaines à différents moments dans le temps. L’évaluation dichotomique de la récupération a également des applications cliniques et en recherche. Elle permet d’identifier une récupération sous-optimale tant au niveau du groupe que de l’individu. D’autre part, lorsque ce type d’évaluation est réalisé en temps réel, elle nous donne la possibilité d’intervenir d’une manière ciblée et opportune spécifique à chaque patient tout en rationnalisant les ressources.

The abstract nature of both “quality” and “recovery” does not lend either to a universal definition; hence, their definitions and scope are heavily influenced by the perspective of the user. This often results in a lack of connection between the perception of perioperative recovery viewed by patient, clinician, and institution and the use of performance indicators and quality of care as surrogate markers for recovery.

Institution-focused recovery

At the institutional level, quality of recovery is often used interchangeably with quality of care; however, it is a discrete entity. Quality of care is defined as evidence-based treatment that maximizes the likelihood of desired health outcomes.1 It is often the preferred institutional standard for assessment of patient experience as it is inherently measurable using performance indicators that in turn link perceived quality of care to policy formation and healthcare funding.2 Nevertheless, this ignores the complex nature of recovery and erroneously assumes a direct causal relationship between both adherence to performance indicators and provision of high-quality healthcare and ultimate patient recovery.

The utility of an indicator is limited by the extent to which it can detect a true difference in quality, whether it is for quality of patient care or recovery. Performance indicators are direct measures of service provision but not necessarily patient care or recovery.3 They reflect high quality of care only by inference that it is correlated with adherence to selected indicators (e.g., absence of complications or a length of stay below the average equates to optimal quality of care).4 Although differences in performance indicators may represent true differences in quality of care, these differences do not necessarily equate to differences in recovery, and they are also influenced by variations in patient case mix, data measurement, or chance.2

Performance indicators relating to anesthesia primarily assess patient safety and effectiveness in the recovery room and immediate postoperative period,5 and they address occurrences of adverse symptomatology (e.g., postoperative nausea and vomiting, severe pain, hypothermia, respiratory distress) and increased resource utilization (e.g., unplanned admission to the intensive care unit [ICU], delayed stay in the postanesthesia care unit, epidural assessment.5 Nevertheless, although these indicators may influence patient status, they do not fully define either quality of care or recovery. Complications, as with other performance measures, are important in their own right, but they are contextual and form just one component of a multidimensional recovery construct. For example, they are unable to help differentiate between postoperative pain being either crucial or a transient tolerable inconvenience to a patient undergoing palliative vs curative surgery, respectively. Furthermore, quality of recovery can be independent of the quality of care received. For instance, patients can recover to previous function despite the provision of suboptimal care, while patients can receive excellent high-quality care yet experience suboptimal recovery.

There are fundamental issues regarding the use of performance indicators for assessment of both quality of care and recovery, specifically, they are unidimensional, lack context, and rarely improve patient status when used in isolation.6,7 Thus, they are only surrogate measures of quality of care, which in turn is a surrogate marker of quality of recovery. Furthermore, neither performance indicators nor quality of care address the contextual nature of the direct and indirect influences on ultimate patient recovery. It is important to emphasize that outcome performance indicators can assess unidimensional recovery (e.g., ambulation, independent activities of daily living [ADLs]) and can have direct implications in their own right on both the patient and the institution, impacting on patient experience and discharge readiness, respectively. Nevertheless, they cannot facilitate assessment of contextual multidimensional recovery, are often underpowered to help in the detection of rare clinical events,2 and lack sensitivity and specificity.8

Patient-focused recovery

At the institutional level, the focus on adherence to performance indicators as a measure of quality of care and a surrogate for recovery differs sharply from that of patients. Patients define recovery as a return to previous “normality” in their various daily roles, and the quality of their recovery is defined by the level of “normality” they attained and the process they experienced to reach their goal.9-11 Although patient-defined recovery often has an emphasis on the traditional parameters of return of physiological and physical function, its scope is often broadened to include nociceptive, emotive, social, satisfaction, and cognitive domains.9,12 Furthermore, recovery is not just the absence of complications or negative symptoms but the return to a resemblance of their previous life.3 Nevertheless, there is often a disconnect between patient perceived quality of recovery and traditional indicators, with the former being heavily influenced by individual patient personality traits, knowledge regarding normal recovery trajectory, preparedness, coping strategies, and a global sense of security.9

Patient satisfaction

Patient satisfaction, while important in its own right, must not be used as a surrogate for quality of recovery. Five dimensions have been identified as impacting on patient satisfaction: provision of information, physical comfort/discomfort, emotional support, involvement in care, and privacy.13 When compared with traditional quality indicators, satisfaction has been shown to correlate with large hospitals, high surgical volumes, and a low mortality index (P < 0.001) but not with other process indicators or patient outcomes (length of stay, complications (P = 0.491), and readmission (P = 0.056).9,10 It is heavily influenced by culture and institution and has a stronger correlation with patient expectations, subsequent patient experience, strong therapeutic relationships,14,15 work activity, and procedural outcome rather than with professionalism or anesthetic outcome.16,17 The discriminate utility of satisfaction as an indirect measure of quality of recovery is also limited by its lack of a uniform definition or assessment tool, rapid early recovery,18,19 and an inconsistent relationship with traditional recovery markers (nociceptive complications). Thus, a disconnect remains between an institution’s unidimensional performance indicators and patients’ perceived multidimensional recovery.

Implications of poor recovery

The importance of quality of recovery assessment lies in the fact that recovery itself is often incomplete and correlates with long-term morbidity and mortality.20 Delayed physical recovery is present in up to 60% and 50% of patients at three and six months, respectively.21,22 Cognitive recovery can be delayed at postoperative day 3 in 14% of the general population23 and can persist, especially in the elderly population.24 Cognitive and non-cognitive recovery are also intertwined, with failure of recovery in one being predictive of suboptimal recovery in the other.25,26 Incomplete early recovery is also predictive of long-term adverse outcomes. Long-term functional recovery is reduced in patients with longer initial hospital stay or early readmission.27 Similarly, significant acute postoperative pain is associated with poorer long-term nociceptive recovery.28-31 Cognitive dysfunction occurring at any point in the postoperative period is associated with increased short- and long-term sequelae, with postoperative cognitive dysfunction at discharge and three months being associated with increased mortality at three and 12 months, respectively.20 This does not prove causality between early cognitive dysfunction and long-term sequelae but is consistent with the increased mortality associated with rapid cognitive decline in the non-surgical community.32,33 Thus, the direct clinical utility of accurate early assessment of multidimensional recovery is the ability to identify those patients who are at risk of increased morbidity and mortality and may benefit from intervention. Nevertheless, early intervention to modulate quality of recovery is a new concept, and therefore, there are few data to validate this concept.

Measurement of recovery

Inherent in the assessment of recovery is the need for clear definitions and quantification. As recovery is defined by the stakeholder, current clinical recovery assessment tools differ in their definition of recovery, their breadth and timing of assessment, and overall validation, all of which reflect the original focus for developing the tool.34 Traditional recovery assessment tools were institutional and focused on the provider, limiting assessment to addressing restitution of physiological parameters in the immediate postoperative period.35 Historically, factors with a direct impact on discharge readiness and institutional cost were assessed—nausea and vomiting, severe pain,36-39 psychological distress,37,38,40-42 and basic physiological function.43-45 With the advent of patient-centred care, recovery has developed into a multidimensional construct, with recovery assessment tools addressing physical (nociceptive),36-39 psychological (emotive, satisfaction),37,38,40-42 functional (ADLs),36-42,46 and more recently, cognitive domains. The latter has become more pertinent in the advent of an aging population and increasing awareness of the interplay between the perioperative inflammatory state, anesthetic agents, and neurodegenerative processes.47-51 Assessment of cognitive recovery has thus progressed from simple assessments of orientation and comprehension38,41,46 to more formal neuropsychological-based assessment.26,37,52,53 More recent clinical recovery assessment tools, such as the Postoperative Quality of Recovery Scale (PostopQRS),37 assess cognitive recovery by applying formal neuropsychological tests in truncated form and assessing patient recovery in relation to individual baseline function both in real time and over multiple time points.

The timing of recovery assessment differs amongst tools, which directly affects clinical utility, as a tool is validated only for the time period (and patient group) for which it was originally designed. Ambulatory anesthesia requires patient recovery to be quantified in the short term, while subsequent recovery tools focus on quantifying factors that influence patient discharge on the first postoperative day38,46 or one week following surgery.36,40,42 While initial reporting of surgical recovery was comprehensive, it was principally focused on the provider and limited to in-hospital and 30-day performance indicators.22 Enhanced Recovery After Surgery (ERAS) pathways now extend recovery to include three phases (up to discharge from the postanesthesia care unit, hospital discharge, and restitution of normal function), but they are still primarily focused on performance indicators in the immediate postoperative period. In comparison, the development of recovery assessment tools that address early (over days),38,46 intermediate (over weeks),39,41 and late (over months)37,54 recovery allow for a broader and contextual assessment of persistent adverse symptoms, functional impairment, and cognitive decline beyond the traditional postoperative period.

Recovery involves the restitution of physiological processes, comprising an acute deterioration (surgery), followed by a time-dependent improvement in function, resolution of adverse symptomatology, and a return to baseline.10,12,22 Thus, an important advantage of a recovery assessment tool is its ability to aid in the assessment of recovery at multiple clinically relevant time points. By using validated repeat measures,36,37,41 it can model the trajectory of recovery over time, from discharge (early recovery), to resumption of basic social activities (intermediate recovery), to resolution of full cognitive function (late recovery).

It is essential for all stakeholders, namely, institutions, clinicians, and patients, to broaden the assessment time frame for recovery, as it enables clinical management to be judged in the context of both short- and long-term outcomes. For example, implementation of ERAS pathways has been associated with favourable short-term benefits, including a reduction in direct costs (anesthetic, nursing, laboratory medicine), hospital length of stay, readmission rates, and complications.55-58 Nevertheless, it is yet to be definitively determined whether this is an overall reduction or a reduction at the expense of cost transfer to the community through increased indirect costs due to delayed complications, losses in productivity (i.e., delayed return to employment), and utilization of community resources.21,22

Dichotomizing recovery

A fundamental difference in recovery tools is their assessment of recovery as a dichotomous vs a continuous variable at an individual vs a group level. Traditionally, recovery assessment was the domain of research and hence involved assessment of a continuous variable at the group level with differences in recovery between groups being quantified by the corresponding difference in mean change scores. Nevertheless, while continuous composite scores allow an assessment of the difference in the magnitude of recovery between groups, this approach neither identifies the specific domains in which recovery is suboptimal nor differentiates between statistical vs clinical significance.

In comparison, dichotomization of recovery facilitates identification of poor recovery at the group, individual, and domain levels. Individuals are scored to “recovered” or “not recovered” according to whether their postoperative performance meets or exceeds a predetermined value, the latter preferably their own preoperative performance. At the group level, recovery is assessed by a comparison of the incidence of recovery between groups. Dichotomized recovery can thus be assessed overall (recovery in all domains assessed) or “drilled down” to the domain (i.e., functional, cognitive, nociceptive), sub-domain, and raw data level. While the magnitude of recovery (or failure thereof) is not immediately evident with dichotomization, this can be mitigated by collecting data in its raw continuous form (where relevant) for assessment during the “drill down”. Thus, the inherent value in dichotomization of recovery is the potential to allow assessment of both individual and group recovery, both overall and at multiple levels.

The importance of patients functioning as their own preoperative control

It is integral to the assessment of patients’ recovery for them to function as their own preoperative controls. Recovery implies comparison of postoperative function with a control population, threshold value, or, ideally, individual patient preoperative function. Use of threshold values restricts the clinical utility and internal validity59 of an assessment tool or study results to the time period for which that threshold is valid (e.g., a patient with systolic blood pressure [SBP] 145 mmHg is “hypertensive” as long as the definition of hypertension is SBP >120 mmHg). Conversely, dichotomizing recovery by defining it as “a return to a patient’s own baseline or better” mitigates this restriction by assessing individual patients in relation to their preoperative function for that unique perioperative experience, independent of future events or patient ability. The use of population standard threshold values can thus be reserved for emergent perioperative experiences when baseline patient data cannot be collected. Having a patient function as their own immediate preoperative control for each perioperative experience also minimizes subjective bias resulting from response shift,60 an assumed equal difference of ordinal scales, and perioperative subjective impairment.38,54,61,62

Essential components for the future of quality of recovery

Real-time recovery (RTR)

The future of recovery assessment encompasses that which is multidimensional, patient focused, and occurring at multiple clinically relevant postoperative time points and in real time. Real-time recovery (or concurrent recovery monitoring), the synchronous collection, analysis, and reporting of data, is beneficial in any multifaceted time-dependent system as it minimizes the time delay in implementing a corrective intervention in response to any error or deviation from the expected norm.63 Real-time recovery can itself be classified as a clinical intervention, as it allows identification of those patients with poor recovery at the time the lapse occurs, thus minimizing the delay between identification and implementation of treatment. It is complimentary to, but distinct from, clinical care pathways, with the latter referring to an overarching document delineating proposed patient care based on best practice, ultimate care received, the indication for variances between the two, and ultimate patient outcome.64 Thus, the clinical utility of RTR is dependent on two factors, specifically, identification of poor recovery at the individual patient level and an effective corrective treatment to help the patient return to the expected recovery trajectory. The clinical utility of RTR is in its ability to optimize treatment and resource utilization. Real-time identification of patients with suboptimal recovery facilitates the possibility of rationalizing finite resources and targeting treatment to those patients who would most benefit in a time frame that maximizes clinical impact. Conversely, patients with acceptable recovery can be fast tracked and thus avoid exposure to unnecessary interventions and use of finite resources.

Domain-specific assessment

Optimal patient care is dependent on timely and targeted intervention that requires identifying the presence of suboptimal recovery as well as the domain(s) where it occurs. Recovery assessment tools that define recovery as a multidimensional dichotomized variable allow identification of suboptimal performance (failure to recover) as well as the particular domain AND the individual patients. Furthermore, recovery domains are interrelated, and it is only by this subsequent “drilling down” and assessing other recovery domains that appropriate clinical intervention targeted to the cause of suboptimal recovery can be instituted. For example, failure of overall recovery may be due to a myriad of factors, but it is only by drilling down and identifying persistent pain and its cause (e.g., anastomotic bowel leak or bile duct injury) that timely targeted treatment can be instituted to restore the recovery trajectory.65,66

Clinical vs research recovery assessment

Real-time recovery also highlights the difference between assessments of recovery in the research vs clinical setting. Research is inherently retrospective, thus inducing a necessary time delay between identification of poor recovery and timely intervention for the original study population. Comparison of mean change scores between groups indicates whether there is a statistical difference between groups, but it does not identify changes in individuals unless further analysis is performed. In comparison, a tool that performs RTR has both clinical and research applications. The ability to identify domain-specific recovery failure and implement targeted therapies to improve recovery is a new area for perioperative medicine. It requires a tool for early identification of recovery failure as well as for assessment of the outcomes following the interventions. As an example, the online PostopQRS has the facility to assess individual recovery in real time through automated scoring of the recovery data (www.PostopQRS.com), and the scale can be used to assess recovery after the interventions. Future directions in real-time recovery could involve automation that alerts clinicians, or even patients, that recovery has failed and specifies the recovery domain where the failure occurred.

Using recovery assessment to change patient outcome

There is a fundamental need for recovery to be assessed in real time because current risk stratification tools can identify only those patients at high risk of poor postoperative outcomes but not those patients in whom these events actually occur. Postoperative complication rates do parallel preoperative risk stratification, with cardiorespiratory complications increasing from 11-41% of patients in the first and fifth quintiles, respectively.67 Resource utilization and its associated costs also parallel preoperative risk, with base ICU rates increasing from 6-25% in patients in the first and fifth quintiles, respectively, correlating with a base increase of $5,909 per patient.67 When poor recovery and complications occur, mean total in-hospital costs increase twofold.68 Nevertheless, complications occur in all perioperative risk groups, with the majority (by number) occurring in lower-risk patients and being heavily influenced by factors other than perioperative risk, namely, patient age, comorbidities, and individual institutions.69 In addition, highest postoperative mortality is not limited only to those with high preoperative risk, it also includes those requiring an unanticipated transfer from a postoperative ward to ICU admission.70-72 This highlights that recovery is a multifactorial concept that preoperative risk stratification cannot predict in its entirety at the individual patient level.73,74 Thus, it is essential that there is real-time assessment of patient recovery in order to detect those patients who might benefit from a timely intervention.

Conclusion

Quality of recovery is an abstract construct whose definition, scope, and timing of assessment are heavily influenced by the user. At the institutional level, quality of recovery is often substituted by quality of care, which is measured using performance indicators but lacks the contextual milieu that is central to that of patient-perceived recovery. Quality of recovery has prognostic and economic implications and is best measured as a dichotomized multidimensional variable at multiple clinically relevant time points and, importantly, in real time, thus enabling both clinical and research applications.

Notes

Conflicts of interest

None declared.

Disclosures

Professor Colin Royse is the Chair of the PostopQRS scientific committee. Dr. Andrea Bowyer has no competing interests. There was no funding sought for this paper.

References

  1. 1.
    Rikkers LF, Hoyt DB, Flum DR, Malangoni MA. Quality: the key to surgery’s future. Ann Surg 2014; 260: 567-73.PubMedCrossRefGoogle Scholar
  2. 2.
    Mant J. Process versus outcome indicators in the assessment of quality of health care. Int J Qual Health Care 2001; 13: 475-80.PubMedCrossRefGoogle Scholar
  3. 3.
    McLellan AT, Chalk M, Bartlett J. Outcomes, performance, and quality: what’s the difference? J Subst Abuse Treat 2007; 32: 331-40.PubMedCrossRefGoogle Scholar
  4. 4.
    Haller G, Stoelwinder J, Myles PS, McNeil J. Quality and safety indicators in anesthesia: a systematic review. Anesthesiology 2009; 110: 1158-75.PubMedCrossRefGoogle Scholar
  5. 5.
    Australian Council on Healthcare Standards (ACHS). Australasian Clinical Indicator Report 2004-2011: 13th edition. Sydney, NSW: ACHS; 2012. Available from URL: http://www.achs.org.au/media/50245/achs_clinical_indicators_report_web.pdf (accessed August 2015).
  6. 6.
    Bahtsevani C, Uden G, Willman A. Outcomes of evidence-based clinical practice guidelines: a systematic review. Int J Technol Assess Health Care 2004; 20: 427-33.PubMedCrossRefGoogle Scholar
  7. 7.
    Grimshaw JM, Russell IT. Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet 1993; 342: 1317-22.PubMedCrossRefGoogle Scholar
  8. 8.
    Veloski J, Boex JR, Grasberger MJ, Evans A, Wolfson DB. Systematic review of the literature on assessment, feedback and physicians’ clinical performance: BEME Guide No. 7. Med Teach 2006; 28: 117-28.PubMedCrossRefGoogle Scholar
  9. 9.
    Berg K, Arestedt K, Kjellgren K. Postoperative recovery from the perspective of day surgery patients: a phenomenographic study. Int J Nurs Sud 2013; 50: 1630-8.CrossRefGoogle Scholar
  10. 10.
    Kennedy GD, Tevis SE, Kent KC. Is there a relationship between patient satisfaction and favorable outcomes? Ann Surg 2014; 260: 592-8.PubMedCrossRefPubMedCentralGoogle Scholar
  11. 11.
    Greenblatt DY, Weber SM, O’Connor ES, LoConte NK, Liou JI, Smith MA. Readmission after colectomy for cancer predicts one-year mortality. Ann Surg 2010; 251: 659-69.PubMedCrossRefPubMedCentralGoogle Scholar
  12. 12.
    Elliott MN, Swartz R, Adams J, Spritzer KL, Hays RD. Case-mix adjustment of the National CAHPS benchmarking data 1.0: a violation of model assumptions? Health Serv Res 2001; 36: 555-73.PubMedPubMedCentralGoogle Scholar
  13. 13.
    Chanthong P, Abrishami A, Wong J, Herrera F, Chung F. Systematic review of questionnaires measuring patient satisfaction in ambulatory anesthesia. Anesthesiology 2009; 110: 1061-7.PubMedCrossRefGoogle Scholar
  14. 14.
    Heidegger T, Saal D, Nubling M. Patient satisfaction with anaesthesia - Part 1: satisfaction as part of outcome - and what satisfies patients. Anaesthesia 2013; 68: 1165-72.PubMedCrossRefGoogle Scholar
  15. 15.
    Thompson AG, Sunol R. Expectations as determinants of patient satisfaction: concepts, theory and evidence. Int J Qual Health Care 1995; 7: 127-41.PubMedGoogle Scholar
  16. 16.
    Le May S, Hardy JF, Taillefer MC, Dupuis G. Patient satisfaction with anesthesia services. Can J Anesth 2001; 48: 153-61.PubMedCrossRefGoogle Scholar
  17. 17.
    Caljouw MA, van Beuzekom M, Boer F. Patient’s satisfaction with perioperative care: development, validation, and application of a questionnaire. Br J Anaesth 2008; 100: 637-44.PubMedCrossRefGoogle Scholar
  18. 18.
    Austin PC, Brunner LJ. Type I error inflation in the presence of a ceiling effect. Am Stat 2003; 57: 97-104.CrossRefGoogle Scholar
  19. 19.
    Maurice-Szamburski A, Bruder N, Loundou A, Capdevila X, Auquier P. Development and validation of a perioperative satisfaction questionnaire in regional anesthesia. Anesthesiology 2013; 118: 78-87.PubMedCrossRefGoogle Scholar
  20. 20.
    Monk TG, Weldon BC, Garvan CW, et al. Predictors of cognitive dysfunction after major noncardiac surgery. Anesthesiology 2008; 108: 18-30.PubMedCrossRefGoogle Scholar
  21. 21.
    Neville A, Lee L, Antonescu I, et al. Systematic review of outcomes used to evaluate enhanced recovery after surgery. Br J Surg 2014; 101: 159-70.PubMedCrossRefGoogle Scholar
  22. 22.
    Feldman LS, Lee L, Fiore J Jr. What outcomes are important in the assessment of Enhanced Recovery After Surgery (ERAS) pathways? Can J Anesth 2015; 62: 120-30.PubMedCrossRefGoogle Scholar
  23. 23.
    Royse CF, Newman S, Williams Z, Wilkinson DJ. A human volunteer study to identify variability in performance in the cognitive domain of the postoperative quality of recovery scale. Anesthesiology 2013; 119: 576-81.PubMedCrossRefGoogle Scholar
  24. 24.
    Price CC, Garvan CW, Monk TG. Type and severity of cognitive decline in older adults after noncardiac surgery. Anesthesiology 2008; 108: 8-17.PubMedCrossRefPubMedCentralGoogle Scholar
  25. 25.
    Stygall J, Newman SP, Fitzgerald G, et al. Cognitive change 5 years after coronary artery bypass surgery. Health Psychol 2003; 22: 579-86.PubMedCrossRefGoogle Scholar
  26. 26.
    Newman MF, Kirchner JL, Phillips-Bute B, et al. Longitudinal assessment of neurocognitive function after coronary-artery bypass surgery. N Engl J Med 2001; 344: 395-402.PubMedCrossRefGoogle Scholar
  27. 27.
    Magaziner J, Simonsick EM, Kashner TM, Hebel JR, Kenzora JE. Predictors of functional recovery one year following hospital discharge for hip fracture: a prospective study. J Gerontol 1990; 45: M101-7.PubMedCrossRefGoogle Scholar
  28. 28.
    Katz J, Jackson M, Kavanagh BP, Sandler AN. Acute pain after thoracic surgery predicts long-term post-thoracotomy pain. Clin J Pain 1996; 12: 50-5.PubMedCrossRefGoogle Scholar
  29. 29.
    Callesen T, Bech K, Kehlet H. Prospective study of chronic pain after groin hernia repair. Br J Surg 1999; 86: 1528-31.PubMedCrossRefGoogle Scholar
  30. 30.
    Tasmuth T, Estlanderb AM, Kalso E. Effect of present pain and mood on the memory of past postoperative pain in women treated surgically for breast cancer. Pain 1996; 68: 343-7.PubMedCrossRefGoogle Scholar
  31. 31.
    Kehlet H, Jensen TS, Woolf CJ. Persistent postsurgical pain: risk factors and prevention. Lancet 2006; 367: 1618-25.PubMedCrossRefGoogle Scholar
  32. 32.
    Schupf N, Tang MX, Albert SM, et al. Decline in cognitive and functional skills increases mortality risk in nondemented elderly. Neurology 2005; 65: 1218-26.PubMedCrossRefGoogle Scholar
  33. 33.
    Bosworth HB, Schaie KW, Willis SL. Cognitive and sociodemographic risk factors for mortality in the Seattle Longitudinal Study. J Gerontol B Psychol Sci Soc Sci 1999; 54: P273-82.PubMedCrossRefGoogle Scholar
  34. 34.
    Bowyer A, Jakobsson J, Ljungqvist O, Royse C. A review of the scope and measurement of postoperative quality of recovery. Anaesthesia 2014; 69: 1266-78.PubMedCrossRefGoogle Scholar
  35. 35.
    Aldrete JA, Kroulik D. A postanesthetic recovery score. Anesth Analg 1970; 49: 924-34.PubMedGoogle Scholar
  36. 36.
    Wong J, Tong D, De Silva Y, Abrishami A, Chung F. Development of the functional recovery index for ambulatory surgery and anesthesia. Anesthesiology 2009; 110: 596-602.PubMedCrossRefGoogle Scholar
  37. 37.
    Royse CF, Newman S, Chung F, et al. Development and feasibility of a scale to assess postoperative recovery: the post-operative quality recovery scale. Anesthesiology 2010; 113: 892-905.PubMedCrossRefGoogle Scholar
  38. 38.
    Myles PS, Weitkamp B, Jones K, Melick J, Hensen S. Validity and reliability of a postoperative quality of recovery score: the QoR-40. Br J Anaesth 2000; 84: 11-5.PubMedCrossRefGoogle Scholar
  39. 39.
    Talamini MA, Stanfield CL, Chang DC, Wu AW. The Surgical Recovery Index. Surg Endosc 2004; 18: 596-600.PubMedCrossRefGoogle Scholar
  40. 40.
    Swan BA, Maislin G, Traber KB. Symptom distress and functional status changes during the first seven days after ambulatory surgery. Anesth Analg 1998; 86: 739-45.PubMedGoogle Scholar
  41. 41.
    Paddison JS, Sammour T, Kahokehr A, Zargar-Shoshtari K, Hill AG. Development and validation of the Surgical Recovery Scale (SRS). J Surg Res 2011; 167: e85-91.PubMedCrossRefGoogle Scholar
  42. 42.
    Oakes CL, Ellington KJ, Oakes KJ, Olson RL, Neill KM, Vacchiano CA. Assessment of postanesthesia short-term quality of life: a pilot study. AANA J 2002; 70: 267-73.PubMedGoogle Scholar
  43. 43.
    Wu CL, Rowlingson AJ, Partin AW, et al. Correlation of postoperative pain to quality of recovery in the immediate postoperative period. Reg Anesth Pain Med 2005; 30: 516-22.PubMedCrossRefGoogle Scholar
  44. 44.
    White PF, Sacan O, Tufanogullari B, Eng M, Nuangchamnong N, Ogunnaike B. Effect of short-term postoperative celecoxib administration on patient outcome after outpatient laparoscopic surgery. Can J Anesth 2007; 54: 342-8.PubMedCrossRefGoogle Scholar
  45. 45.
    Sun T, Sacan O, White PF, Coleman J, Rohrich RJ, Kenkel JM. Perioperative versus postoperative celecoxib on patient outcomes after major plastic surgery procedures. Anesth Analg 2008; 106: 950-8.PubMedCrossRefGoogle Scholar
  46. 46.
    Hogue SL, Reese PR, Colopy M, et al. Assessing a tool to measure patient functional ability after outpatient surgery. Anesth Analg 2000; 91: 97-106.PubMedGoogle Scholar
  47. 47.
    Holmes C, Cunningham C, Zotova E, et al. Systemic inflammation and disease progression in Alzheimer disease. Neurology 2009; 73: 768-74.PubMedCrossRefPubMedCentralGoogle Scholar
  48. 48.
    Cunningham C, Wilcockson DC, Campion S, Lunnon K, Perry VH. Central and systemic endotoxin challenges exacerbate the local inflammatory response and increase neuronal death during chronic neurodegeneration. J Neurosci 2005; 25: 9275-84.PubMedCrossRefGoogle Scholar
  49. 49.
    Perry VH, Cunningham C, Holmes C. Systemic infections and inflammation affect chronic neurodegeneration. Nat Rev Immunol 2007; 7: 161-7.PubMedCrossRefGoogle Scholar
  50. 50.
    Xie Z, Dong Y, Maeda U, al. The common inhalation anesthetic isoflurane induces apoptosis and increases amyloid beta protein levels. Anesthesiology 2006; 104: 988-94.Google Scholar
  51. 51.
    Dong Y, Zhang G, Zhang B, et al. The common inhalational anesthetic sevoflurane induces apoptosis and increases beta-amyloid protein levels. Arch Neurol 2009; 66: 620-31.PubMedCrossRefPubMedCentralGoogle Scholar
  52. 52.
    Murkin JM, Stump DA, Blumenthal JA, McKhann G. Defining dysfunction: group means versus incidence analysis—a statement of consensus. Ann Thorac Surg 1997; 64: 904-5.PubMedCrossRefGoogle Scholar
  53. 53.
    Murkin JM, Newman SP, Stump DA, Blumenthal JA. Statement of consensus on assessment of neurobehavioral outcomes after cardiac surgery. Ann Thorac Surg 1995; 59: 1289-95.PubMedCrossRefGoogle Scholar
  54. 54.
    Myles PS, Reeves MD, Anderson H, Weeks AM. Measurement of quality of recovery in 5672 patients after anaesthesia and surgery. Anaesth Intensive Care 2000; 28: 276-80.PubMedGoogle Scholar
  55. 55.
    Greco M, Capretti G, Beretta L, Gemma M, Pecorelli N, Braga M. Enhanced recovery program in colorectal surgery: a meta-analysis of randomized controlled trials. World J Surg 2014; 38: 1531-41.PubMedCrossRefGoogle Scholar
  56. 56.
    Lee L, Li C, Landry T, et al. A systematic review of economic evaluations of enhanced recovery pathways for colorectal surgery. Ann Surg 2014; 259: 670-6.PubMedCrossRefGoogle Scholar
  57. 57.
    Schilling PL, Dimick JB, Birkmeyer JD. Prioritizing quality improvement in general surgery. J Am Coll Surg 2008; 207: 698-704.PubMedCrossRefGoogle Scholar
  58. 58.
    Varadhan KK, Neal KR, Dejong CH, Fearon KC, Ljungqvist O, Lobo DN. The enhanced recovery after surgery (ERAS) pathway for patients undergoing major elective open colorectal surgery: a meta-analysis of randomized controlled trials. Clin Nutr 2010; 29: 434-40.PubMedCrossRefGoogle Scholar
  59. 59.
    Streiner DL. Breaking up is hard to do: the heartbreak of dichotomizing continuous data. Can J Psychiatry 2002; 47: 262-6.PubMedGoogle Scholar
  60. 60.
    Schwartz CE, Andresen EM, Nosek MA. Krahn GL; RRTC Expert Panel on Health Status Measurement. Response shift theory: important implications for measuring quality of life in people with disability. Arch Phys Med Rehabil 2007; 88: 529-36.PubMedCrossRefGoogle Scholar
  61. 61.
    Myles PS, Williams DL, Hendrata M, Anderson H, Weeks AM. Patient satisfaction after anaesthesia and surgery: results of a prospective survey of 10,811 patients. Br J Anaesth 2000; 84: 6-10.PubMedCrossRefGoogle Scholar
  62. 62.
    Newman S, Klinger L, Venn G, Smith P, Harrison M, Treasure T. Subjective reports of cognition in relation to assessed cognitive performance following coronary artery bypass surgery. J Psychosom Res 1989; 33: 227-33.PubMedCrossRefGoogle Scholar
  63. 63.
    Roebuck K. LTE Advanced: High-impact Technology - What You Need to Know: Definitions. Emereo Publishing; 2012.Google Scholar
  64. 64.
    Michell V. Handbook of Research on Patient Safety and Quality Care through Health Informatics: IGI Global; 2013.Google Scholar
  65. 65.
    Rauws EA, Gouma DJ. Endoscopic and surgical management of bile duct injury after laparoscopic cholecystectomy. Best Pract Res Clin Gastroenterol 2004; 18: 829-46.PubMedCrossRefGoogle Scholar
  66. 66.
    Chambers WM, Mortensen NJ. Postoperative leakage and abscess formation after colorectal surgery. Best Pract Res Clin Gastroenterol 2004; 18: 865-80.PubMedCrossRefGoogle Scholar
  67. 67.
    Fleisher LA, Linde-Zwirble WT. Incidence, outcome, and attributable resource use associated with pulmonary and cardiac complications after major small and large bowel procedures. Perioper Med (Lond) 2014; 3: 7.CrossRefGoogle Scholar
  68. 68.
    Flynn DN, Speck RM, Mahmoud NN, David G, Fleisher LA. The impact of complications following open colectomy on hospital finances: a retrospective cohort study. Perioper Med (Lond) 2014; 3: 1.CrossRefGoogle Scholar
  69. 69.
    van Klei WA, Bryson GL, Yang H, Forster AJ. Effect of beta-blocker prescription on the incidence of postoperative myocardial infarction after hip and knee arthroplasty. Anesthesiology 2009; 111: 717-24.PubMedCrossRefGoogle Scholar
  70. 70.
    Kehlet H, Mythen M. Why is the surgical high-risk patient still at risk? Br J Anaesth 2011; 106: 289-91.PubMedCrossRefGoogle Scholar
  71. 71.
    Jhanji S, Thomas B, Ely A, Watson D, Hinds CJ, Pearse RM. Mortality and utilisation of critical care resources amongst high-risk surgical patients in a large NHS trust. Anaesthesia 2008; 63: 695-700.PubMedCrossRefGoogle Scholar
  72. 72.
    Goldhill DR. Preventing surgical deaths: critical care and intensive care outreach services in the postoperative period. Br J Anaesth 2005; 95: 88-94.PubMedCrossRefGoogle Scholar
  73. 73.
    Reilly CS. Can we accurately assess an individual’s perioperative risk? Br J Anaesth 2008; 101: 747-9.PubMedCrossRefGoogle Scholar
  74. 74.
    Kehlet H. Multimodal approach to control postoperative pathophysiology and rehabilitation. Br J Anaesth 1997; 78: 606-17.PubMedCrossRefGoogle Scholar

Copyright information

© Canadian Anesthesiologists' Society 2015

Authors and Affiliations

  1. 1.Department of Anaesthesia and Pain ManagementThe Royal Melbourne Hospital, RMHParkvilleAustralia
  2. 2.Department of SurgeryThe University of MelbourneParkvilleAustralia

Personalised recommendations