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Quo Vadis Anesthesiologist? The Value Proposition of Future Anesthesiologists Lies in Preserving or Restoring Presurgical Health after Surgical Insult

Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, PA 19104, USA
Leonard Davis Institute for Healthcare Economics, University of Pennsylvania, Philadelphia, PA 19104, USA
Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
J. Clin. Med. 2022, 11(4), 1135;
Received: 30 January 2022 / Accepted: 18 February 2022 / Published: 21 February 2022
(This article belongs to the Collection Delivery of Anesthesia: Pre-operative and Post-operative)

1. Introduction

This Special Issue of the Journal of Clinical Medicine is devoted to anesthesia and perioperative care. While it is the glazed through aspect of surgical care, it is considered one of the most spectacular achievements of modern medicine. The scope of JCM’s Special Issue intends to highlight the debate regarding the role of anesthesia and the anesthesiologist in perioperative care and how future anesthesiologists can add new value to patient care.

2. New Paradigm of Anesthesia Care

The ability to take away the surgical patient’s consciousness and pain while rendering him or her motionless in a state of temporary amnesia has enabled great progress in surgery, imaging, and diseases prevention (Figure 1) [1]. Increased safety in delivering anesthesia has also made undeniable progress over the last 40 years. Between 1940 and 2020, anesthesia-related mortality was reduced from 1:1000 to 1:1,000,000. The invention of pulse oximetry, capnography, ultrasound, development of short-acting and liver- or renal-independent medications (esmolol, remifentanil, etc.), and a novel pharmacological method to reverse anesthesia (sugammadex) are advances behind the more modern successes of anesthesia (Figure 1) [2,3,4,5,6]. Improved workflows, safety procedures (checklists, simulations), and maintaining a high level of professional competency have also had a pivotal role in improving outcomes [7,8,9]. The drop in mortality is so profound that studies investigating anesthesia mortality are virtually impossible, with most of the current data published in case series or audits of legal cases [10,11,12]. A decrease in intra-operative morbidity accompanies decreased mortality [12,13,14,15,16,17]. The next milestone for anesthesia in developed countries is debatable, but minimizing maladaptive allostatic sequela after peri-surgical stress seems impactful, achievable, and uniquely positioned to be pursued by next generation of anesthesiologists (Figure 1).
Surgical procedures and related events can result in profound disturbances in homeostasis [18]. The physiological mechanisms designed to prevent or minimize potential damage during normal life may be superfluous during surgery. Their activation results in a less favorable outcome unless suppressed by anesthesia. For example, pain sensation triggers sympathetic discharge and the fight or flight reaction. However, during surgery, sympathetic activity may result in cardiomyopathy and other complications. Anesthesia partially abolishes this effect yet at the cost of side effects [19]. Concomitantly, surgical trauma induces a profound adaptive response, affecting several layers of bodily functioning. Most of these responses are adaptive, but they may lead to undesirable outcomes in certain circumstances. For example, activating the immune system is critical to healing. It also causes collateral damage, resulting in organ failure and increased hypercoagulability [20,21]. Finally, the anesthetic used may have an adverse effect during peri-operative period, resulting in neurotoxicity and potential neurodegeneration [22].
It is becoming increasingly apparent that regaining preoperative homeostasis during the postsurgical period is impossible in some individuals [19,20]. The postsurgical processes are different from the primary pathological process that leads to surgery as they center around surgical insult and anesthesia. For example, immune system activation resembles sterile inflammation, not a septic process [23,24,25,26,27,28]. Surgery involves significant tissue destruction, blood loss, alteration in microbiota with the possible leak of the inflammatory pathogen-associated molecular pattern (PAMP), and the induction of catabolism [26,29,30,31,32,33,34,35,36]. Similar abnormalities are seen in other critical care illnesses, including traumatic brain injury, COVID-19, ARDS, stroke, and acute coronary syndrome. However, some important differences exist. The postsurgical state may extend beyond immediate recovery and the postoperative period, lasting months or representing a new allesotatic state [28,37,38,39,40]. Consequently, an individual cannot regain preoperative health or homeostasis. Alternatively, the newly acquired process remains in place long enough to contribute to increased morbidity after several years, even if the change eventually resolves [41]. Allostasis assumes the emergence of a new adaptive balance, which may be beneficial or detrimental to the long-term health of the individual (Figure 1) [37,38]. Epigenetic mechanisms, miRNA, acquired autoreactivity, metabolic shifts, and the persistent subcortical changes in the central nervous system responsible for homeostasis are underpinning mechanisms that lead to persistent post-surgical sequelae and allostasis [21,36,42,43,44,45,46,47,48,49,50]. Individuals with pre-existing comorbidities and disadvantageous socia-economical backgrounds will be more vulnerable to the emergence and persistence of unfavorable allostasis [51].
The recovery trajectory depends on the inherited features of the patient and the nature and magnitude of the stressor (Figure 2). Even a single incident of anesthesia, surgery, or other critical grade insults may reverberate for months and even years [27,32,33,39,42]. These parameters are modifiable during the post- and perioperative period, placing anesthesiologists in a unique position to foster restoration of presurgical health.

3. The Effect of Anesthesia on Long-Term Postoperative Outcomes

The idea that anesthetic management affects long-term outcomes is often debated but relatively few new studies of high quality have been published. The short duration of anesthesia and the predominance of surgery-driven insult link the latter to long-term unfavorable declines [52]. Initial enthusiasm was quickly dampened by conflicting data, showing the variable effects of various anesthesia techniques on long-term outcomes with the almost “classical” failure of experimental data to yield a consistent clinical practice adoption [53,54,55,56,57]. Few studies are reviewed below to demonstrate methodological difficulty in proving the point.
The application of regional anesthesia seems to be advantageous as compared to general anesthesia via several mechanisms. First, it reduces inflammation and sympathetic discharge while providing more effective oxygenation that in turn implies a lack of hypotension [58]. However, translating the hypothetical and bench-driven benefits to the clinical realm is not straightforward despite several areas of potential benefits [59]. Poor quality studies and insufficient understanding of the interaction between cancer and anesthesia may be at fault [60,61]. The highly debated question of regional anesthesia reducing metastasis recurrence long-term is difficult to analyze given the plethora of studies that fail to provide clear benefits of using regional anesthesia despite the importance of the question [52,53,54,55,56,57,58,59,60,61,62,63,64]. Remarkably, the depth of anesthesia correlates in some studies with the emergence of postoperative delirium [65,66,67]. The emergence of delirium carries a significant risk of long-term decline, but definitive studies are missing. One study suggests that a singular dose of clonidine changes mortality for several years to come, but metanalysis does not confirm these initial observations [68,69,70]. Nevertheless, the introduction of dexmedetomidine reinvigorates the field with several groups, suggesting long-term protective benefits [71,72]. Interestingly, some authors suggest that dampening the initial sympathetic response results in a more effective resolution of the inflammation with potential long-term benefits. Still, large studies failed to demonstrate the benefit of pharmacological sympathectomy on outcomes delayed by years. There is a need to appreciate the extraordinary methodological obstacles to be overcome while studying the long-term effect of anesthesia.
Despite the lack of clear advantages of different strategies to execute anesthesia plan on long-term outcomes, interest should not abate. The application of the anesthesia technique has to be tailored to the patient’s condition in more detail and toward a resolution, allowing for the personalization of therapy. For example, identifying a particularly vulnerable population may yield tangible long-term benefits [73,74]. What makes this task particularly difficult is complexity of interventions. For example, ketamine has several properties, including a variable effect on the cardiovascular system, sympathetic system activation, and immunomodulation. This nexus of interactions and potential effects results in the difficult application of an anesthesia compound to precisely defined clinical targets. Anesthetic agents also have adverse effects on top of the primary desired rendering patients unconscious [75,76]. However, the first step in understanding the delivery of clinical interventions is to provide a standardized was to document interventions.

4. Long-Term Effects Intervention

The lack of clear success in enhancing long-term outcomes in patients undergoing surgery and anesthesia should not sway anesthesiologists from pursuing the goal of restoring patient health to presurgical homeostasis. The anesthesiologist could provide enhanced care before, during, and after surgical insult to restore pre-anesthesia homeostasis in routine cases (Figure 3).

4.1. Preoperative Period

Currently, anesthesiologists focus on perioperative care, often limited to preoperative visits or assessments, the delivery of anesthesia, and immediate recovery. Sometimes, a preoperative clinic allows for more pre-emptive engagement with patients. Such a short engagement makes long-term continuity of care impossible despite providing potential opportunities to prepare the patient for surgery.
The critical task at hand is the optimization of the patient before surgery. This may require a postponement of the surgery, but there is a relative paucity of data to guide optimal timing [77,78]. Some advanced computational techniques (artificial intelligence) may help determine the optimal procedure timing, allowing more time for patient optimization [74]. The same AI algorithm could identify which interventions need to be deployed before surgery, providing the most advantageous edge for the patient in preparation for surgical stress [79].
Methods exist to prepare an individual for surgical stress more resiliently. First in order of importance should be the elimination of any additional and modifiable stressors. Drug abuse, obesity, obstructive sleep apnea, uncontrolled diabetes, or hypertension are potential addressable factors. Encouraging patients to address them will positively impact their outcomes. Adequate nutrition is critical for the individual’s recovery as post-surgical catabolism is inevitable [80,81]. Finally, physical activity is critical before surgery as aerobic exercise positively affects the heart and lungs, prevents weight gain, and is linked to the favorable outcome of anesthesia and surgery. Cognitive engagement also has a positive effect [80,82,83,84]. Engagement of the patients in the process is critical [80]. The barrier may be reimbursement structure.
The nature of the approach increases the individual’s resilience to surgical stress before surgery. Preconditioning is the phenomenon that triggers an adaptive response to significant stress by providing a low-grade trigger before an anticipated major stressor occurs [85,86,87,88]. As a result, this adaptive response is more rapidly deployed in case of stress. Alternative mechanisms dampen the immunological response [89]. A human trial seemed to demonstrate the benefits of this approach when volatile anesthetics were used, but only short-term effects were studied [90]. Many preconditioning techniques involve significant surgical manipulation, but less invasive methods were also suggested, including electroacupuncture or the injection of danger-associated molecular patterns (DAMP) protein [91,92].
Another approach is to proactively mediating the immune system response to mitigate immune system activation in peri-operative time. However, the failure of several cytokine-targeted therapies in several critical care conditions suggests that this approach must be quite selective. One alternative may be to reprogram the innate immune mechanism before the surgery to mitigate potential overactivation without globally suppressing it [93,94,95]. Manipulation with TLR pathways may be particularly beneficial in individuals undergoing procedures with a high risk of pathogen-associated molecular patterns (PAMP) release. Protectin is particularly effective in modulating granulocyte activation [96,97,98]. Finally, inducing M1 to M2 switch or modulating T cell population is another potential pre-emptive strategy [99,100]. Alternative means may involve manipulation with glucose metabolism and inflammation by increasing their resilience to surgical stress [101,102,103].

4.2. Operative Period

So far, the primary effect on long-term peri-operative mortality demonstrated that a minimization of the surgical insult by introducing laparoscopic or robotic surgery and shortening the duration of cases in general. The anesthesia management of interoperative care should entail carefully titratable anesthesia at a level tailored to the procedure, utilizing an objective measurement while preventing repetitive hypotension and hypoxemia [104,105,106,107]. However, a more precise definition of hypotension is needed. That definition should be contextualized and personalized to each patient based on observation during the preoperative period [104]. The data to decide which anesthetic is preferable are conflicting. Some studies have demonstrated the benefit of volatile anesthetic, while others are more supportive of intravenous anesthesia [108,109]. Tailoring anesthesia drugs to specific surgery needs is a potential strategy, yet adequate studies need to be conducted. Data on regional anesthesia are also inconclusive but suggest that utilizing regional techniques in elderly patients in pre-existing decline are potentially more beneficial long-term [59,60,63,64].

4.3. Postoperative Period

Current evidence strongly support that enhanced recovery after surgery (ERAS) philosophy accelerates patients return nominal activity. Inherently, several protocols in ERAS seem to minimize iatrogenic injury despite challenging pre-existing dogmas. Physiologically, ERAS protocol minimizes patient exposure to a challenging hospital environment, focusing on rapid discharge from the hospital. Post-discharge engagement in a rehabilitation plan is critical to minimize long-term decline [110]. Postoperative care has also included the reconciliation of medication to address pre-existing conditions. Two studies demonstrated the interesting effect of diminished frequency of statin intake, suggesting that perception of potential complications may lead to the withdrawal of beneficial medications.
Several studies suggested that surgery results in smoldering inflammation and altered metabolism. The removal of DAMP would be the logical strategy and is close to being deployed clinically [24,26,96]. However, the therapeutic means to address it are somewhat limited as the current diagnostic means to characterize immune system activation are still very constrained. There is a need for more advanced, multidimensional techniques to characterize the post-surgical landscape as it was already demonstrated in trauma [111]. In terms of intervention, several means are available for clinical testing based on already conducted bench research. Lipoxin, annexin, HIF-1, H2S, and resolvins are examples of endogenous mediators that could be employed to terminate smoldering inflammation [48,100,112,113,114]. Vagal nerve stimulation seems to be even more reachable, considering their significant utilization in other fields of medicine [115,116]. Finally, overactivated genes can be targeted with genome therapy [117,118]. All these measures should aim at the restoration of presurgical leukocyte status and metabolome. However, a precise understanding of the immune and metabolic systems must first be attained as the immune and metabolic systems are multidimensional and differentially activated [48,49,119].

5. Innovation in Service

The implementation of any strategy requires a qualitative change in healthcare delivery. Obamacare, corporatization, and COVID-19 are rapidly accelerating innovation in medicine [120,121]. This is remarkable achievement considering that medicine is a conservative field. Several innovative techniques will challenge current anesthesiology practice, while their adaptation should provide a much-needed qualitative jump to improve long-term outcomes.
AI (artificial intelligence)/CDSS (computer-decision support systems) are increasing in importance in healthcare delivery, resulting in greater investment [122,123,124]. Their current role is mostly focused on radiology and image analysis, but it is only a question of time before they enter the anesthesia world in the domains of monitoring, adverse event prediction, and drug delivery. They can augment the delivery of anesthesia by serving as virtual assistants [125]. Plus, they are pivotal in analyzing long-term continuous data as well as taking into account several variables at the same time. Consequently, a correlation between a truly significant clinical factor and long-term outcome can be uncovered [126]. Hopefully, they will be used to assess the risk of optimal timing for surgery [77]. They can predict what is a critical, pre-emptive intervention to avoid hypotension and hypoxia during surgery [105]. Finally, as a neuronal network, they can be utilized to simulate the effect of suggested therapies on multi-omics physiology, providing a more precise selection of intervention [126,127]. Understanding AI/CDSS intricacies and accepting their guidance will meet resistance. Yet, it is hard to imagine that AI/CDSS will stay away from medicine for long despite initial implementation setbacks [128].
Nanotechnology offers a unique forey into the new world of possibilities [129,130]. The most relevant and daring is the example of nanotechnology providing a new way of delivering and maintaining anesthesia to surgical patients [129]. In this Special Issue of JCM, an article is devoted to this prospect. Nanotechnology will be critical in creating a modern way to improve health, deliver medications, and create brain–mind interfaces.
Genomic technologies allow for precise gene manipulation [131]. This may be critical in subduing inflammation post-surgery by either inducing a protective mechanism or supporting a proinflammatory one [132]. The fidelity of CRISP delivers high-resolution gene editing, yet clinical consequences are difficult to predict with AI. mRNA-based gene delivery may be a way to affect the expression of proteins in a safer way.
Robotics has enjoyed an increased footprint in hospital and surgical theaters. Initially, they were used to augment pharmacy and laboratory services. With the introduction of the DaVinci system, a new era in healthcare was delivered, demonstrating that surgery may be less burdensome and taxing, thus resulting in more favorable outcomes [133,134,135,136]. Surgical robotic systems are revolutionary in surgery, nonetheless, and their introduction in anesthesia is relatively slow [137]. Pharmacological robots utilize a close loop, semiautonomous system to maintain certain parameters by manipulating the delivery of anesthesia under the supervision of an untrained professional. The first commercial system failed secondary to a built-in assumption, as well as engineering and regulatory constraints. However, with the progression of AI, it is only a matter of time before a more sophisticated system will emerge [138]. The pharmacological system provides a close loop medication administration to achieve a certain goal such as arterial pressure or train-of-four with obvious implications for providing hemodynamic stability during surgery. Finally, robots entered the world of physical manipulation by assisting with intubation, regional anesthesia, and intravenous catheter placement [139,140,141].
The brain–computer interface (BCI) is a system that allows for the interaction of electronic devices with the brain. Bi-Spectral monitors can be classified as BCI devices in terms of being non-invasive, capturing filed potential, and producing visual input [142]. Some of these interfaces are “simple devices” implanted in specific areas of the brain aimed at the restoration/augmentation of a function [143]. These devices are of relatively large scale, while direct neuronal-silicone nanoscale junctions are being developed to allow for long-term implantation without compromising the device by the host’s immune mechanisms [144]. Several applications are possible, including the delivery of drugs to discrete areas of the brain, as compensation for function loss, or for better control of prosthetics devices [144,145]. Furthermore, BCI may allow for new ways of inducing anesthesia or the collaboration between providers during anesthesia services by means of direct information exchange in the brain and the utilization of external brainlets.
Finally, a wide array of strategies to augment human brain functions is suggested to support future anesthesiologist. In fact, many of these innovations were attempted, including the implementation of supercomputing power, direct application of artificial intelligence, and cloud computing, resulting in direct cognitive enhancement of the brain. Most of these innovations are years away from fruition; however, they may dramatically shape the world of anesthesia in the next 20 to 30 years. One of the most vexing ideas is “transparent shadowing”, allowing humans to experience the fully immersive life of other humans with the nano neuronal-robotic-assisted interface. It allows incredible insight into patient experiences for more precise diagnoses [146].

6. The Market Value of the New Paradigm of an Anesthesiologist

It is incredibly difficult to predict the job market’s effect on the future skills required by anesthesiologists to stay competitive. However, the US healthcare market is clearly evolving toward providing more value while relentless innovation continues to offer better care to patients.
Anesthesiologists are highly trained professionals; their job is inexplicably paired with hospital work and the surgical theater, placing them in the center of patient care. However, they come under increased pressure from alternative anesthesia providers, the consolidation of the markets, and the corporatization of medicine in the US [147,148,149]. Propagating anesthesia-related skills outside the operating room or delivering anesthesia by non-residency trained physicians (interventional radiologist, gastroenterologist, certified registered nurse anesthetist, anesthesia assistant, Sedasys®) is commonplace and increasing in frequency [138]. Some of these trends are also present outside the US market, such that there is an increasing concern about the future of anesthesiologist-provided care in terms of value as compared to other providers or the future delivery of anesthesia. The current demand for anesthesia services outstrips the supply, yet increased competition may undermine the current paradigm on which current anesthesiologist’s value relies [148,150]. Consequently, future anesthesiologists should look into the specific value they can deliver in perioperative care.
Providing complex, expanded perioperative care aimed at the restoration of presurgical care utilizing several innovative technique should be the ultimate goal of future anesthesiologist. This unique value proposition considers in-depth education, experience, and the available skills for the physician anesthesiologist. This is consistent with the position on the future of anesthesiology expressed by professional leadership despite the meager participation of anesthesiologists in national and regional associations. Focusing on long-term health restoration puts anesthesiologists in a different niche as certified nurse anesthetists or other anesthesia providers.


This research received no external funding.


I would like to thank Justin Wain for his assistance with this manuscript.

Conflicts of Interest

The author declares no conflict of interest.


  1. Robinson, D.H.; Toledo, A.H. Historical Development of Modern Anesthesia. J. Investig. Surg. 2012, 25, 141–149. [Google Scholar] [CrossRef] [PubMed]
  2. Saunders, R.; Struys, M.M.R.F.; Pollock, R.F.; Mestek, M.; Lightdale, J.R. Patient safety during procedural sedation using capnography monitoring: A systematic review and meta-analysis. BMJ Open 2017, 7, e013402. [Google Scholar] [CrossRef] [PubMed]
  3. Pedersen, T.; Moller, A.M.; Pedersen, B.D. Pulse oximetry for perioperative monitoring: Systematic review of randomized, controlled trials. Anesth. Analg. 2003, 96, 426–431. [Google Scholar] [PubMed]
  4. Li, J.; Krishna, R.; Zhang, Y.; Lam, D.; Vadivelu, N. Ultrasound-Guided Neuraxial Anesthesia. Curr. Pain Headache Rep. 2020, 24, 59. [Google Scholar] [CrossRef]
  5. Bignami, E.; Maffezzoni, M.; Bellini, V. Lung Ultrasound in Thoracic Anesthesia: Which Uses? J. Cardiothorac. Vasc. Anesth. 2021, 35, 374–375. [Google Scholar] [CrossRef]
  6. Carron, M.; Linassi, F.; De Cassai, A. Role of sugammadex in accelerating postoperative discharge: An updated meta-analysis. J. Clin. Anesth. 2020, 65, 109895. [Google Scholar] [CrossRef]
  7. van Veen-Berkx, E.; van Dijk, M.V.; Cornelisse, D.C.; Kazemier, G.; Mokken, F.C. Scheduling Anesthesia Time Reduces Case Cancellations and Improves Operating Room Workflow in a University Hospital Setting. J. Am. Coll. Surg. 2016, 223, 343–351. [Google Scholar] [CrossRef]
  8. Behrens, V.; Dudaryk, R.; Nedeff, N.; Tobin, J.M.; Varon, A.J. The Ryder Cognitive Aid Checklist for Trauma Anesthesia. Anesth. Analg. 2016, 122, 1484–1487. [Google Scholar] [CrossRef]
  9. Jelacic, S.; Bowdle, A.; Nair, B.G.; Togashi, K.; Boorman, D.J.; Cain, K.C.; Lang, J.D.; Dellinger, E.P. Aviation-Style Computerized Surgical Safety Checklist Displayed on a Large Screen and Operated by the Anesthesia Provider Improves Checklist Performance. Anesth. Analg. 2020, 130, 382–390. [Google Scholar] [CrossRef]
  10. Arbous, M.S.; Grobbee, D.E.; van Kleef, J.W.; de Lange, J.J.; Spoormans, H.H.A.J.M.; Touw, P.; Werner, F.M.; Meursing, A.E.E. Mortality associated with anaesthesia: A qualitative analysis to identify risk factors. Anaesthesia 2001, 56, 1141–1153. [Google Scholar] [CrossRef]
  11. Arbous, M.S.; Grobbee, D.E.; van Kleef, J.W.; Meursing, A.E. Dutch case-control study of anaesthesia-related morbidity and mortality. Rationale and methods. Anaesthesia 1998, 53, 162–168. [Google Scholar] [CrossRef] [PubMed]
  12. Arbous, M.S.; Meursing, A.E.E.; Van Kleef, J.W.; De Lange, J.J.; Spoormans, H.H.A.J.M.; Touw, P.; Werner, F.M.; Grobbee, D.E. Impact of Anesthesia Management Characteristics on Severe Morbidity and Mortality. Anesthesiology 2005, 102, 257–268. [Google Scholar] [CrossRef] [PubMed]
  13. Wong, G.Y.; Warner, D.O.; Schroeder, D.R.; Offord, K.P.; Warner, M.A.; Maxson, P.M.; Whisnant, J.P. Risk of Surgery and Anesthesia for Ischemic Stroke. Anesthesiology 2000, 92, 425–432. [Google Scholar] [CrossRef] [PubMed]
  14. Ko, S.-B. Perioperative stroke: Pathophysiology and management. Korean J. Anesthesiol. 2018, 71, 3–11. [Google Scholar] [CrossRef] [PubMed][Green Version]
  15. Hendén, P.L.; Rentzos, A.; Karlsson, J.-E.; Rosengren, L.; Leiram, B.; Sundeman, H.; Dunker, D.; Schnabel, K.; Wikholm, G.; Hellström, M.; et al. General Anesthesia Versus Conscious Sedation for Endovascular Treatment of Acute Ischemic Stroke. Stroke 2017, 48, 1601–1607. [Google Scholar] [CrossRef][Green Version]
  16. Tarhan, S.; Moffitt, E.A.; Taylor, W.F.; Giuliani, E.R. Myocardial Infarction after General Anesthesia. Anesth. Analg. 1977, 56, 455–461. [Google Scholar] [CrossRef][Green Version]
  17. Harwin, B.; Formanek, B.; Spoonamore, M.; Robertson, D.; Buser, Z.; Wang, J.C. The incidence of myocardial infarction after lumbar spine surgery. Eur. Spine J. 2019, 28, 2070–2076. [Google Scholar] [CrossRef]
  18. Cannon, W. Homeostasis. 1 September 2014. Available online: (accessed on 15 January 2022).
  19. Eisenstein, T.K. The Role of Opioid Receptors in Immune System Function. Front. Immunol. 2019, 10, 2904. [Google Scholar] [CrossRef][Green Version]
  20. Osuka, A.; Ogura, H.; Ueyama, M.; Shimazu, T.; Lederer, J.A. Immune response to traumatic injury: Harmony and discordance of immune system homeostasis. Acute Med. Surg. 2014, 1, 63–69. [Google Scholar] [CrossRef]
  21. Laudanski, K.; Wyczechowska, D. Monocyte-related immunopathologies in trauma patients. Arch. Immunol. Ther. Exp. 2005, 53, 321–328. [Google Scholar]
  22. Bilotta, F.; Evered, L.A.; Gruenbaum, S.E. Neurotoxicity of anesthetic drugs: An update. Curr. Opin. Anaesthesiol. 2017, 30, 452–457. [Google Scholar] [CrossRef] [PubMed]
  23. Bortolotti, P.; Faure, E.; Kipnis, E. Inflammasomes in Tissue Damages and Immune Disorders after Trauma. Front. Immunol. 2018, 9, 1900. [Google Scholar] [CrossRef] [PubMed][Green Version]
  24. Rock, K.L.; Latz, E.; Ontiveros, F.; Kono, H. The Sterile Inflammatory Response. Annu. Rev. Immunol. 2010, 28, 321–342. [Google Scholar] [CrossRef] [PubMed][Green Version]
  25. Zindel, J.; Kubes, P. DAMPs, PAMPs, and LAMPs in Immunity and Sterile Inflammation. Annu. Rev. Pathol. Mech. Dis. 2020, 15, 493–518. [Google Scholar] [CrossRef][Green Version]
  26. Feldman, N.; Rotter-Maskowitz, A.; Okun, E. DAMPs as mediators of sterile inflammation in aging-related pathologies. Ageing Res. Rev. 2015, 24, 29–39. [Google Scholar] [CrossRef]
  27. Frye, C.C.; Bery, A.I.; Kreisel, D.; Kulkarni, H.S. Sterile inflammation in thoracic transplantation. Cell Mol. Life Sci. 2021, 78, 581–601. [Google Scholar] [CrossRef]
  28. Li, J.-Y.; Yao, Y.-M.; Tian, Y.-P. Ferroptosis: A Trigger of Proinflammatory State Progression to Immunogenicity in Necroinflammatory Disease. Front. Immunol. 2021, 12, 701163. [Google Scholar] [CrossRef]
  29. Aron-Wisnewsky, J.; Doré, J.; Clement, K. The importance of the gut microbiota after bariatric surgery. Nat. Rev. Gastroenterol. Hepatol. 2012, 9, 590–598. [Google Scholar] [CrossRef]
  30. Simsek, T.; Simsek, H.U.; Cantürk, N.Z. Response to trauma and metabolic changes: Posttraumatic metabolism. Turk. J. Surg. 2014, 30, 153–159. [Google Scholar] [CrossRef][Green Version]
  31. Relja, B.; Mörs, K.; Marzi, I. Danger signals in trauma. Eur. J. Trauma Emerg. Surg. 2018, 44, 301–316. [Google Scholar] [CrossRef][Green Version]
  32. Hauser, C.; Otterbein, L.E. Danger signals from mitochondrial DAMPS in trauma and post-injury sepsis. Eur. J. Trauma Emerg. Surg. 2018, 44, 317–324. [Google Scholar] [CrossRef] [PubMed]
  33. Yamanashi, T.; Nagao, T.; Wahba, N.E.; Marra, P.S.; Crutchley, K.J.; Meyer, A.A.; Andreasen, A.J.; Hellman, M.M.; Jellison, S.S.; Hughes, C.G.; et al. DNA methylation in the inflammatory genes after neurosurgery and diagnostic ability of post-operative delirium. Transl. Psychiatry 2021, 11, 627. [Google Scholar] [CrossRef] [PubMed]
  34. Relja, B.; Land, W.G. Damage-associated molecular patterns in trauma. Eur. J. Trauma Emerg. Surg. 2019, 46, 751–775. [Google Scholar] [CrossRef][Green Version]
  35. Wilmore, D.W. Metabolic Response to Severe Surgical Illness: Overview. World J. Surg. 2000, 24, 705–711. [Google Scholar] [CrossRef]
  36. McBride, M.A.; Owen, A.M.; Stothers, C.L.; Hernandez, A.; Luan, L.; Burelbach, K.R.; Patil, T.K.; Bohannon, J.K.; Sherwood, E.R.; Patil, N.K. The Metabolic Basis of Immune Dysfunction Following Sepsis and Trauma. Front. Immunol. 2020, 11, 1043. [Google Scholar] [CrossRef] [PubMed]
  37. Logan, J.G.; Barksdale, D.J. Allostasis and allostatic load: Expanding the discourse on stress and cardiovascular disease. J. Clin. Nurs. 2008, 17, 201–208. [Google Scholar] [CrossRef]
  38. Ramsay, D.S.; Woods, S.C. Clarifying the roles of homeostasis and allostasis in physiological regulation. Psychol. Rev. 2014, 121, 225–247. [Google Scholar] [CrossRef][Green Version]
  39. Laudanski, K. Persistence of alterations in lipoproteins and cholesterol during and after septic episode—Review of current evidence of long-term post septic lipid profile aberrations and their implication for allostasis. Int. J. Mol. Sci. 2021, 22, 10517. [Google Scholar] [CrossRef]
  40. Yoshimura, A.; Ito, M. Resolution of inflammation and repair after ischemic brain injury. Neuroimmunol. Neuroinflamm. 2020, 7, 264–276. [Google Scholar] [CrossRef]
  41. Zhang, T.; Yan, L.L.; Chen, H.-S.; Jin, H.-Y.; Wu, C. Association between allostatic load and mortality among Chinese older adults: The Chinese Longitudinal Health and Longevity Study. BMJ Open 2021, 11, e045369. [Google Scholar] [CrossRef]
  42. Placek, K.; Schultze, J.L.; Aschenbrenner, A.C. Epigenetic reprogramming of immune cells in injury, repair, and resolution. J. Clin. Investig. 2019, 129, 2994–3005. [Google Scholar] [CrossRef] [PubMed][Green Version]
  43. Roquilly, A.; Jacqueline, C.; Davieau, M.; Mollé, A.; Sadek, A.; Fourgeux, C.; Rooze, P.; Broquet, A.; Misme-Aucouturier, B.; Chaumette, T.; et al. Alveolar macrophages are epigenetically altered after inflammation, leading to long-term lung immunoparalysis. Nat. Immunol. 2020, 21, 636–648. [Google Scholar] [CrossRef] [PubMed]
  44. Anderson, P. Post-transcriptional regulons coordinate the initiation and resolution of inflammation. Nat. Rev. Immunol. 2010, 10, 24–35. [Google Scholar] [CrossRef]
  45. Ji, R.-R.; Xu, Z.-Z.; Strichartz, G.; Serhan, C.N. Emerging roles of resolvins in the resolution of inflammation and pain. Trends Neurosci. 2011, 34, 599–609. [Google Scholar] [CrossRef] [PubMed][Green Version]
  46. Roy, S.; Sen, C.K. miRNA in Wound Inflammation and Angiogenesis. Microcirculation 2012, 19, 224–232. [Google Scholar] [CrossRef][Green Version]
  47. Cox, A.; Coles, A.; Nortje, J.; Bradley, P.; Chatfield, D.; Thompson, S.; Menon, D. An investigation of auto-reactivity after head injury. J. Neuroimmunol. 2006, 174, 180–186. [Google Scholar] [CrossRef] [PubMed]
  48. Rahtes, A.; Li, L. Polarization of Low-Grade Inflammatory Monocytes through TRAM-Mediated Up-Regulation of Keap1 by Super-Low Dose Endotoxin. Front. Immunol. 2020, 11, 1478. [Google Scholar] [CrossRef]
  49. Pradhan, K.; Yi, Z.; Geng, S.; Li, L. Development of Exhausted Memory Monocytes and Underlying Mechanisms. Front. Immunol. 2021, 12, 778830. [Google Scholar] [CrossRef]
  50. Bandyopadhyay, G.; De, A.; Laudanski, K.; Li, F.; Lentz, C.; Bankey, P.; Miller-Graziano, C. Negative signaling contributes to T-cell anergy in trauma patients. Crit. Care Med. 2007, 35, 794–801. [Google Scholar] [CrossRef]
  51. Beckie, T.M. A Systematic Review of Allostatic Load, Health, and Health Disparities. Biol. Res. Nurs. 2012, 14, 311–346. [Google Scholar] [CrossRef]
  52. Sessler, D.I. Long-term Consequences of Anesthetic Management. Anesthesiology 2009, 111, 1–4. [Google Scholar] [CrossRef] [PubMed][Green Version]
  53. Mestas, J.; Hughes, C.C.W. Of mice and not men: Differences between mouse and human immunology. J. Immunol. 2004, 172, 2731–2738. [Google Scholar] [CrossRef] [PubMed][Green Version]
  54. Ma, M.B.; Bartal, I.; Goldfarb, Y.; Levi, B.; Avraham, R.; Raz, A.; Ben-Eliyahu, S. Perioperative Use of β-blockers and COX-2 Inhibitors May Improve Immune Competence and Reduce the Risk of Tumor Metastasis. Ann. Surg. Oncol. 2008, 15, 2042–2052. [Google Scholar] [CrossRef]
  55. Bar-Yosef, S.; Melamed, R.; Page, G.G.; Shakhar, G.; Shakhar, K.; Ben-Eliyahu, S. Attenuation of the Tumor-promoting Effect of Surgery by Spinal Blockade in Rats. Anesthesiology 2001, 94, 1066–1073. [Google Scholar] [CrossRef][Green Version]
  56. Afsharimani, B.; Cabot, P.J.; Parat, M.-O. Morphine Use in Cancer Surgery. Front. Pharmacol. 2011, 2, 46. [Google Scholar] [CrossRef][Green Version]
  57. Demarco, G.J.; Nunamaker, E.A. A Review of the Effects of Pain and Analgesia on Immune System Function and Inflammation: Relevance for Preclinical Studies. Comp. Med. 2019, 69, 520–534. [Google Scholar] [CrossRef]
  58. Kabon, B.; Fleischmann, E.; Treschan, T.; Taguchi, A.; Kapral, S.; Kurz, A. Thoracic Epidural Anesthesia Increases Tissue Oxygenation during Major Abdominal Surgery. Anesth. Analg. 2003, 97, 1812–1817. [Google Scholar] [CrossRef]
  59. Hopkins, P.M. Does regional anaesthesia improve outcome? Br. J. Anaesth. 2015, 115, ii26–ii33. [Google Scholar] [CrossRef][Green Version]
  60. Muncey, A.R.; Patel, S.Y.; Whelan, C.J.; Ackerman, R.S.; Gatenby, R.A. The Intersection of Regional Anesthesia and Cancer Progression: A Theoretical Framework. Cancer Control 2020, 27. [Google Scholar] [CrossRef]
  61. Sessler, D.I.; Riedel, B. Anesthesia and Cancer Recurrence: Context for Divergent Study Outcomes. Anesthesiology 2019, 130, 3–5. [Google Scholar] [CrossRef]
  62. Cakmakkaya, O.S.; Kolodzie, K.; Apfel, C.C.; Pace, N.L. Anaesthetic techniques for risk of malignant tumour recurrence. Cochrane Database Syst. Rev. 2014, 11, CD008877. [Google Scholar] [CrossRef] [PubMed]
  63. Jakobsson, J.G.; Johnson, M.Z. Perioperative regional anaesthesia and postoperative longer-term outcomes. F1000Research 2016, 5, 2501. [Google Scholar] [CrossRef] [PubMed][Green Version]
  64. Sessler, D.I.; Pei, L.; Huang, Y.; Fleischmann, E.; Marhofer, P.; Kurz, A.; Mayers, D.B.; Meyer-Treschan, T.A.; Grady, M.; Tan, E.Y.; et al. Recurrence of breast cancer after regional or general anaesthesia: A randomised controlled trial. Lancet 2019, 394, 1807–1815. [Google Scholar] [CrossRef]
  65. Sieber, F.E.; Zakriya, K.J.; Gottschalk, A.; Blute, M.-R.; Lee, H.B.; Rosenberg, P.B.; Mears, S.C. Sedation Depth During Spinal Anesthesia and the Development of Postoperative Delirium in Elderly Patients Undergoing Hip Fracture Repair. Mayo Clin. Proc. 2010, 85, 18–26. [Google Scholar] [CrossRef][Green Version]
  66. Sieber, F.E.; Neufeld, K.; Gottschalk, A.; Bigelow, G.E.; Oh, E.S.; Rosenberg, P.B.; Mears, S.C.; Stewart, K.J.; Ouanes, J.-P.P.; Jaberi, M.; et al. Effect of Depth of Sedation in Older Patients Undergoing Hip Fracture Repair on Postoperative Delirium: The STRIDE Randomized Clinical Trial. JAMA Surg. 2018, 153, 987–995. [Google Scholar] [CrossRef]
  67. Fritz, B.A.; King, C.R.; Mickle, A.M.; Wildes, T.S.; Budelier, T.P.; Oberhaus, J.; Park, D.; Maybrier, H.R.; Ben Abdallah, A.; Kronzer, A.; et al. Effect of electroencephalogram-guided anaesthesia administration on 1-yr mortality: Follow-up of a randomised clinical trial. Br. J. Anaesth. 2021, 127, 386–395. [Google Scholar] [CrossRef]
  68. Wallace, A.W.; Galindez, D.; Salahieh, A.; Layug, E.L.; Lazo, E.A.; Haratonik, K.A.; Boisvert, D.M.; Kardatzke, D. Effect of Clonidine on Cardiovascular Morbidity and Mortality after Noncardiac Surgery. Anesthesiology 2004, 101, 284–293. [Google Scholar] [CrossRef]
  69. Wallace, A.W. Clonidine and modification of perioperative outcome. Curr. Opin. Anaesthesiol. 2006, 19, 411–417. [Google Scholar] [CrossRef]
  70. Duncan, D.; Sankar, A.; Beattie, W.S.; Wijeysundera, D.N. Alpha-2 adrenergic agonists for the prevention of cardiac complications among adults undergoing surgery. Cochrane Database Syst. Rev. 2018, 2018, CD004126. [Google Scholar] [CrossRef]
  71. Li, H.; Liu, J.; Shi, H. Effect of Dexmedetomidine on Perioperative Hemodynamics and Myocardial Protection in Thoracoscopic-Assisted Thoracic Surgery. Med Sci. Monit. 2021, 27, e929949. [Google Scholar] [CrossRef]
  72. Elgebaly, A.S.; Fathy, S.M.; Sallam, A.A.; Elbarbary, Y. Cardioprotective effects of propofol-dexmedetomidine in open-heart surgery: A prospective double-blind study. Ann. Card. Anaesth. 2020, 23, 134–141. [Google Scholar] [CrossRef] [PubMed]
  73. Kojima, Y.; Narita, M. Postoperative outcome among elderly patients after general anesthesia. Acta Anaesthesiol. Scand. 2005, 50, 19–25. [Google Scholar] [CrossRef] [PubMed]
  74. Jørgensen, M.E.; Torp-Pedersen, C.; Gislason, G.H.; Jensen, P.F.; Berger, S.M.; Christiansen, C.B.; Overgaard, C.; Schmiegelow, M.D.; Andersson, C. Time Elapsed After Ischemic Stroke and Risk of Adverse Cardiovascular Events and Mortality Following Elective Noncardiac Surgery. JAMA 2014, 312, 269–277. [Google Scholar] [CrossRef] [PubMed][Green Version]
  75. Banerjee, P.; Rossi, M.G.; Anghelescu, D.L.; Liu, W.; Breazeale, A.M.; Reddick, W.E.; Glass, J.O.; Phillips, N.S.; Jacola, L.M.; Sabin, N.D.; et al. Association Between Anesthesia Exposure and Neurocognitive and Neuroimaging Outcomes in Long-term Survivors of Childhood Acute Lymphoblastic Leukemia. JAMA Oncol. 2019, 5, 1456–1463. [Google Scholar] [CrossRef]
  76. Williams, R.K.; Black, I.H.; Howard, D.B.; Adams, D.C.; Mathews, D.M.; Friend, A.F.; Meyers, H.W.B. Cognitive Outcome After Spinal Anesthesia and Surgery During Infancy. Anesth. Analg. 2014, 119, 651–660. [Google Scholar] [CrossRef]
  77. Sanders, R.D.; Jørgensen, M.E.; Mashour, G.A. Perioperative stroke: A question of timing? Br. J. Anaesth. 2015, 115, 11–13. [Google Scholar] [CrossRef][Green Version]
  78. Matthews, C.R.; Hartman, T.; Madison, M.; Villelli, N.W.; Namburi, N.; Colgate, C.L.; Faiza, Z.; Lee, L.S. Preoperative stroke before cardiac surgery does not increase risk of postoperative stroke. Sci. Rep. 2021, 11, 9025. [Google Scholar] [CrossRef]
  79. Ancelin, M.-L.; de Roquefeuil, G.; Scali, J.; Bonnel, F.; Adam, J.-F.; Cheminal, J.-C.; Cristol, J.-P.; Dupuy, A.-M.; Carrière, I.; Ritchie, K. Long-Term Post-Operative Cognitive Decline in the Elderly: The Effects of Anesthesia Type, Apolipoprotein E Genotype, and Clinical Antecedents. J. Alzheimer’s Dis. 2010, 22, S105–S113. [Google Scholar] [CrossRef][Green Version]
  80. Humeidan, M.L.; Reyes, J.-P.C.; Mavarez-Martinez, A.; Roeth, C.; Nguyen, C.M.; Sheridan, E.; Zuleta-Alarcon, A.; Otey, A.; Abdel-Rasoul, M.; Bergese, S.D. Effect of Cognitive Prehabilitation on the Incidence of Postoperative Delirium Among Older Adults Undergoing Major Noncardiac Surgery: The Neurobics Randomized Clinical Trial. JAMA Surg. 2021, 156, 148–156. [Google Scholar] [CrossRef]
  81. Mbagwu, C.; Sloan, M.; Neuwirth, A.L.; Charette, R.S.; Baldwin, K.D.; Kamath, A.F.; Mason, B.S.; Nelson, C.L. Preoperative Albumin, Transferrin, and Total Lymphocyte Count as Risk Markers for Postoperative Complications After Total Joint Arthroplasty: A Systematic Review. J. Am. Acad. Orthop. Surg. Glob. Res. Rev. 2020, 4, e19.00057. [Google Scholar] [CrossRef]
  82. Vlisides, P.E.; Das, A.R.; Thompson, A.M.; Kunkler, B.; Zierau, M.; Cantley, M.J.; McKinney, A.M.; Giordani, B. Home-based Cognitive Prehabilitation in Older Surgical Patients: A Feasibility Study. J. Neurosurg. Anesthesiol. 2019, 31, 212–217. [Google Scholar] [CrossRef] [PubMed]
  83. O’Gara, B.P.; Mueller, A.; Gasangwa, D.V.I.; Patxot, M.; Shaefi, S.; Khabbaz, K.; Banner-Goodspeed, V.; Pascal-Leone, A.; Marcantonio, E.R.; Subramaniam, B. Prevention of Early Postoperative Decline: A Randomized, Controlled Feasibility Trial of Perioperative Cognitive Training. Anesth. Analg. 2020, 130, 586–595. [Google Scholar] [CrossRef] [PubMed]
  84. Ishizawa, Y. Does Preoperative Cognitive Optimization Improve Postoperative Outcomes in the Elderly? J. Clin. Med. 2022, 11, 445. [Google Scholar] [CrossRef] [PubMed]
  85. Vaage, J.; Valen, G. Preconditioning and cardiac surgery. Ann. Thorac. Surg. 2003, 75, S709–S714. [Google Scholar] [CrossRef]
  86. Späth, M.R.; Koehler, F.C.; Hoyer-Allo, K.J.R.; Grundmann, F.; Burst, V.; Müller, R.-U. Preconditioning strategies to prevent acute kidney injury. F1000Research 2020, 9, 237. [Google Scholar] [CrossRef][Green Version]
  87. Petrowsky, H.; McCormack, L.; Trujillo, M.; Selzner, M.; Jochum, W.; Clavien, P.-A. A Prospective, Randomized, Controlled Trial Comparing Intermittent Portal Triad Clamping Versus Ischemic Preconditioning With Continuous Clamping for Major Liver Resection. Ann. Surg. 2006, 244, 921–930. [Google Scholar] [CrossRef]
  88. Andoh, T.; Chock, P.B.; Chiueh, C.C. Preconditioning-Mediated Neuroprotection: Role of nitric oxide, cGMP, and new protein expression. Ann. N. Y. Acad. Sci. 2002, 962, 1–7. [Google Scholar] [CrossRef]
  89. Casanova, J.; Garutti, I.; Simon, C.; Giraldez, A.; Martin, B.; Gonzalez, G.; Azcarate, L.; Garcia, C.; Vara, E. The Effects of Anesthetic Preconditioning with Sevoflurane in an Experimental Lung Autotransplant Model in Pigs. Anesth. Analg. 2011, 113, 742–748. [Google Scholar] [CrossRef]
  90. Beck-Schimmer, B.; Breitenstein, S.; Urech, S.; De Conno, E.; Wittlinger, M.; Puhan, M.; Jochum, W.; Spahn, D.R.; Graf, R.; Clavien, P.-A. A Randomized Controlled Trial on Pharmacological Preconditioning in Liver Surgery Using a Volatile Anesthetic. Ann. Surg. 2008, 248, 909–918. [Google Scholar] [CrossRef][Green Version]
  91. Liang, R.; Tang, Q.; Song, W.; Zhang, M.; Teng, L.; Kang, Y.; Zhu, L. Electroacupuncture Preconditioning Reduces Oxidative Stress in the Acute Phase of Cerebral Ischemia-Reperfusion in Rats by Regulating Iron Metabolism Pathways. Evid.-Based Complement. Altern. Med. 2021, 2021, 3056963. [Google Scholar] [CrossRef]
  92. Fei, L.; Jingyuan, X.; Fangte, L.; Huijun, D.; Liu, Y.; Ren, J.; Jinyuan, L.; Linghui, P. Preconditioning with rHMGB1 ameliorates lung ischemia–reperfusion injury by inhibiting alveolar macrophage pyroptosis via the Keap1/Nrf2/HO-1 signaling pathway. J. Transl. Med. 2020, 18, 301. [Google Scholar] [CrossRef] [PubMed]
  93. Fensterheim, B.A.; Young, J.D.; Luan, L.; Kleinbard, R.R.; Stothers, C.L.; Patil, N.K.; McAtee-Pereira, A.G.; Guo, Y.; Trenary, I.; Hernandez, A.; et al. The TLR4 Agonist Monophosphoryl Lipid A Drives Broad Resistance to Infection via Dynamic Reprogramming of Macrophage Metabolism. J. Immunol. 2018, 200, 3777–3789. [Google Scholar] [CrossRef] [PubMed]
  94. Watts, B.A.; George, T.; Sherwood, E.R.; Good, D.W. Monophosphoryl lipid A induces protection against LPS in medullary thick ascending limb through a TLR4-TRIF-PI3K signaling pathway. Am. J. Physiol. 2017, 313, F103–F115. [Google Scholar] [CrossRef] [PubMed][Green Version]
  95. Weighardt, H.; Feterowski, C.; Veit, M.; Rump, M.; Wagner, H.; Holzmann, B. Increased Resistance Against Acute Polymicrobial Sepsis in Mice Challenged with Immunostimulatory CpG Oligodeoxynucleotides Is Related to an Enhanced Innate Effector Cell Response. J. Immunol. 2000, 165, 4537–4543. [Google Scholar] [CrossRef][Green Version]
  96. Serhan, C.N.; Chiang, N.; Van Dyke, T.E. Resolving inflammation: Dual anti-inflammatory and pro-resolution lipid mediators. Nat. Rev. Immunol. 2008, 8, 349–361. [Google Scholar] [CrossRef][Green Version]
  97. Marcheselli, V.L.; Mukherjee, P.K.; Arita, M.; Hong, S.; Antony, R.; Sheets, K.; Winkler, J.W.; Petasis, N.A.; Serhan, C.N.; Bazan, N.G. Neuroprotectin D1/protectin D1 stereoselective and specific binding with human retinal pigment epithelial cells and neutrophils. Prostaglandins Leukot. Essent. Fat. Acids 2010, 82, 27–34. [Google Scholar] [CrossRef][Green Version]
  98. Wu, Z.; Zhang, L.; Zhao, X.; Li, Z.; Lu, H.; Bu, C.; Wang, R.; Wang, X.; Cai, T.; Wu, D. Protectin D1 protects against lipopolysaccharide-induced acute lung injury through inhibition of neutrophil infiltration and the formation of neutrophil extracellular traps in lung tissue. Exp. Ther. Med. 2021, 22, 1074. [Google Scholar] [CrossRef]
  99. Perez-Hernandez, J.; Chiurchiù, V.; Perruche, S.; You, S. Regulation of T-Cell Immune Responses by Pro-Resolving Lipid Mediators. Front. Immunol. 2021, 12, 768133. [Google Scholar] [CrossRef]
  100. Marcon, R.; Bento, A.F.; Dutra, R.; Bicca, M.A.; Leite, D.F.P.; Calixto, J.B. Maresin 1, a Proresolving Lipid Mediator Derived from Omega-3 Polyunsaturated Fatty Acids, Exerts Protective Actions in Murine Models of Colitis. J. Immunol. 2013, 191, 4288–4298. [Google Scholar] [CrossRef][Green Version]
  101. Mandwie, M.; Karunia, J.; Niaz, A.; Keay, K.A.; Musumeci, G.; Rennie, C.; McGrath, K.; Al-Badri, G.; Castorina, A. Metformin Treatment Attenuates Brain Inflammation and Rescues PACAP/VIP Neuropeptide Alterations in Mice Fed a High-Fat Diet. Int. J. Mol. Sci. 2021, 22, 13660. [Google Scholar] [CrossRef]
  102. Qin, Z.; Zhou, C.; Xiao, X.; Guo, C. Metformin attenuates sepsis-induced neuronal injury and cognitive impairment. BMC Neurosci. 2021, 22, 78. [Google Scholar] [CrossRef]
  103. Zhou, C.; Peng, B.; Qin, Z.; Zhu, W.; Guo, C. Metformin attenuates LPS-induced neuronal injury and cognitive impairments by blocking NF-κB pathway. BMC Neurosci. 2021, 22, 73. [Google Scholar] [CrossRef]
  104. Bijker, J.B.; van Klei, W.A.; Kappen, T.H.; van Wolfswinkel, L.; Moons, K.G.; Kalkman, C.J. Incidence of intraoperative hypotension as a function of the chosen definition: Literature definitions applied to a retrospective cohort using automated data collection. Anesthesiology 2007, 107, 213–220. [Google Scholar] [CrossRef][Green Version]
  105. Bijker, J.B.; van Klei, W.A.; Vergouwe, Y.; Eleveld, D.J.; van Wolfswinkel, L.; Moons, K.G.M.; Kalkman, C. Intraoperative Hypotension and 1-Year Mortality after Noncardiac Surgery. Anesthesiology 2009, 111, 1217–1226. [Google Scholar] [CrossRef] [PubMed][Green Version]
  106. Charlson, M.E.; MacKenzie, C.R.; Gold, J.P.; Ales, K.L.; Topkins, M.; Shires, G.T. Preoperative characteristics predicting intraoperative hypotension and hypertension among hypertensives and diabetics undergoing noncardiac surgery. Ann. Surg. 1990, 212, 66–81. [Google Scholar] [CrossRef] [PubMed]
  107. Maheshwari, A.; McCormick, P.; Sessler, D.; Reich, D.; You, J.; Mascha, E.; Castillo, J.; Levin, M.; Duncan, A. Prolonged concurrent hypotension and low bispectral index (‘double low’) are associated with mortality, serious complications, and prolonged hospitalization after cardiac surgery. Br. J. Anaesth. 2017, 119, 40–49. [Google Scholar] [CrossRef] [PubMed][Green Version]
  108. Casanova, J.; Simon, C.; Vara, E.; Sanchez, G.; Rancan, L.; Abubakra, S.; Calvo, A.; Gonzalez, F.J.; Garutti, I. Sevoflurane anesthetic preconditioning protects the lung endothelial glycocalyx from ischemia reperfusion injury in an experimental lung autotransplant model. J. Anesth. 2016, 30, 755–762. [Google Scholar] [CrossRef] [PubMed]
  109. Rancan, L.; Huerta, L.; Cusati, G.; Erquicia, I.; Isea, J.; Paredes, S.D.; García, C.; Garutti, I.; Simón, C.; Vara, E. Sevoflurane Prevents Liver Inflammatory Response Induced by Lung Ischemia-Reperfusion. Transplantation 2014, 98, 1151–1157. [Google Scholar] [CrossRef]
  110. Resnick, B.; Beaupre, L.; McGilton, K.S.; Galik, E.; Liu, W.; Neuman, M.D.; Gruber-Baldini, A.L.; Orwig, D.; Magaziner, J. Rehabilitation Interventions for Older Individuals with Cognitive Impairment Post-Hip Fracture: A Systematic Review. J. Am. Med. Dir. Assoc. 2015, 17, 200–205. [Google Scholar] [CrossRef][Green Version]
  111. Laudanski, K.; Miller-Graziano, C.; Xiao, W.; Mindrinos, M.N.; Richards, D.R.; De, A.; Moldawer, L.L.; Maier, R.V.; Bankey, P.; Baker, H.V.; et al. Cell-specific expression and pathway analyses reveal alterations in trauma-related human T cell and monocyte pathways. Proc. Natl. Acad. Sci. USA 2006, 103, 15564–15569. [Google Scholar] [CrossRef][Green Version]
  112. Chiang, N.; Serhan, C.N.; Dahlén, S.-E.; Drazen, J.M.; Hay, D.W.P.; Rovati, G.; Shimizu, T.; Yokomizo, T.; Brink, C. The Lipoxin Receptor ALX: Potent Ligand-Specific and Stereoselective Actions in Vivo. Pharmacol. Rev. 2006, 58, 463–487. [Google Scholar] [CrossRef] [PubMed]
  113. Sugimoto, M.A.; Vago, J.P.; Teixeira, M.M.; Sousa, L.P. Annexin A1 and the Resolution of Inflammation: Modulation of Neutrophil Recruitment, Apoptosis, and Clearance. J. Immunol. Res. 2016, 2016, 8239258. [Google Scholar] [CrossRef] [PubMed][Green Version]
  114. Headland, S.E.; Norling, L.V. The resolution of inflammation: Principles and challenges. Semin. Immunol. 2015, 27, 149–160. [Google Scholar] [CrossRef] [PubMed]
  115. Lopez, N.; Krzyzaniak, M.; Costantini, T.; De Maio, A.; Baird, A.; Eliceiri, B.; Coimbra, R. Vagal Nerve Stimulation Blocks Peritoneal Macrophage Inflammatory Responsiveness After Severe Burn Injury. Shock 2012, 38, 294–300. [Google Scholar] [CrossRef][Green Version]
  116. Andersson, U.; Tracey, K.J. Neural reflexes in inflammation and immunity. J. Exp. Med. 2012, 209, 1057–1068. [Google Scholar] [CrossRef]
  117. Laudanski, K.; Zawadka, M.; Polosak, J.; Modi, J.; DiMeglio, M.; Gutsche, J.; Szeto, W.Y.; Puzianowska-Kuznicka, M. Acquired immunological imbalance after surgery with cardiopulmonary bypass due to epigenetic over-activation of PU.1/M-CSF. J. Transl. Med. 2018, 16, 143. [Google Scholar] [CrossRef]
  118. DiMeglio, M.; Furey, W.; Hajj, J.; Lindekens, J.; Patel, S.; Acker, M.; Bavaria, J.; Szeto, W.Y.; Atluri, P.; Haber, M.; et al. Observational study of long-term persistent elevation of neurodegeneration markers after cardiac surgery. Sci. Rep. 2019, 9, 7177. [Google Scholar] [CrossRef] [PubMed]
  119. Kox, M.; Pompe, J.C.; Pickkers, P.; Hoedemaekers, C.W.; Van Vugt, A.B.; Van Der Hoeven, J.G. Increased vagal tone accounts for the observed immune paralysis in patients with traumatic brain injury. Neurology 2008, 70, 480–485. [Google Scholar] [CrossRef][Green Version]
  120. Heinonen, K.; Strandvik, T. Reframing service innovation: COVID-19as a catalyst for imposed service innovation. J. Serv. Manag. 2020, 32, 101–112. [Google Scholar] [CrossRef]
  121. COVID Innovations. 2021. Available online: (accessed on 18 December 2021).
  122. Gutierrez, G. Artificial Intelligence in the Intensive Care Unit. Crit. Care 2020, 24, 2–9. [Google Scholar] [CrossRef][Green Version]
  123. Matheny, M.E.; Whicher, D.; Israni, S.T. Artificial Intelligence in Health Care: A Report from the National Academy of Medicine. JAMA 2020, 323, 509. [Google Scholar] [CrossRef] [PubMed]
  124. Loftus, T.J.; Vlaar, A.P.; Hung, A.J.; Bihorac, A.; Dennis, B.M.; Juillard, C.; Hashimoto, D.A.; Kaafarani, H.M.; Tighe, P.J.; Kuo, P.C.; et al. Executive summary of the artificial intelligence in surgery series. Surgery, 2021, in press. [CrossRef] [PubMed]
  125. Holzinger, A.; Mohammadzadeh, N.; Park, S.J.; Lee, E.J.; Kim, S.I.; Kong, S.-H.; Jeong, C.W.; Kim, H.S. Clinical Desire for an Artificial Intelligence-Based Surgical Assistant System: Electronic Survey-Based Study. JMIR Med. Inform. 2020, 8, e17647. [Google Scholar] [CrossRef]
  126. Suri, J.S.; Puvvula, A.; Biswas, M.; Majhail, M.; Saba, L.; Faa, G.; Singh, I.M.; Oberleitner, R.; Turk, M.; Chadha, P.S.; et al. COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review. Comput. Biol. Med. 2020, 124, 103960. [Google Scholar] [CrossRef] [PubMed]
  127. Yang, X.; Wang, Y.; Byrne, R.; Schneider, G.; Yang, S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem. Rev. 2019, 119, 10520–10594. [Google Scholar] [CrossRef][Green Version]
  128. Yuan, K.-C.; Tsai, L.-W.; Lee, K.-H.; Cheng, Y.-W.; Hsu, S.-C.; Lo, Y.-S.; Chen, R.-J. The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Int. J. Med. Inform. 2020, 141, 104176. [Google Scholar] [CrossRef]
  129. Jaynes, T.L. “Compoundless Anaesthesia”, Controlled Administration, and Post-Operative Recovery Acceleration: Musings on Theoretical Nanomedicine Applications. J. Clin. Med. 2022, 11, 256. [Google Scholar] [CrossRef]
  130. Anjum, S.; Ishaque, S.; Fatima, H.; Farooq, W.; Hano, C.; Abbasi, B.; Anjum, I. Emerging Applications of Nanotechnology in Healthcare Systems: Grand Challenges and Perspectives. Pharmaceuticals 2021, 14, 707. [Google Scholar] [CrossRef]
  131. Hampson, G.; Towse, A.; Pearson, S.D.; Dreitlein, W.B.; Henshall, C. Gene therapy: Evidence, value and affordability in the US health care system. J. Comp. Eff. Res. 2018, 7, 15–28. [Google Scholar] [CrossRef][Green Version]
  132. Shah, S.A.; Garrett, R.A. CRISPR/Cas and Cmr modules, mobility and evolution of adaptive immune systems. Res. Microbiol. 2011, 162, 27–38. [Google Scholar] [CrossRef]
  133. Zhang, J.; Qi, X.; Yi, F.; Cao, R.; Gao, G.; Zhang, C. Comparison of Clinical Efficacy and Safety Between da Vinci Robotic and Laparoscopic Intersphincteric Resection for Low Rectal Cancer: A Meta-Analysis. Front. Surg. 2021, 8, 615. [Google Scholar] [CrossRef] [PubMed]
  134. Jin, T.; Liu, H.-D.; Yang, K.; Chen, Z.-H.; Zhang, Y.-X.; Hu, J.-K. Effectiveness and safety of robotic gastrectomy versus laparoscopic gastrectomy for gastric cancer: A meta-analysis of 12,401 gastric cancer patients. Updat. Surg. 2021, 74, 267–281. [Google Scholar] [CrossRef] [PubMed]
  135. Garas, G.; Cingolani, I.; Panzarasa, P.; Darzi, A.; Athanasiou, T. Network analysis of surgical innovation: Measuring value and the virality of diffusion in robotic surgery. PLoS ONE 2017, 12, e0183332. [Google Scholar] [CrossRef] [PubMed]
  136. Koh, M.H.; Yen, S.-C.; Leung, L.Y.; Gans, S.; Sullivan, K.; Adibnia, Y.; Pavel, M.; Hasson, C.J. Exploiting telerobotics for sensorimotor rehabilitation: A locomotor embodiment. J. Neuroeng. Rehabil. 2021, 18, 66. [Google Scholar] [CrossRef]
  137. Sadeghi, A.H.; el Mathari, S.; Abjigitova, D.; Maat, A.P.M.; Taverne, Y.J.J.; Bogers, A.J.C.; Mahtab, E.A. Current and Future Applications of Virtual, Augmented, and Mixed Reality in Cardiothoracic Surgery. Ann. Thorac. Surg. 2020, 113, 681–691. [Google Scholar] [CrossRef]
  138. Goudra, B.; Singh, P.M. Failure of Sedasys: Destiny or Poor Design? Anesth. Analg. 2017, 124, 686–688. [Google Scholar] [CrossRef]
  139. Xiao, X.; Poon, H.; Lim, C.M.; Meng, M.Q.-H.; Ren, H. Pilot Study of Trans-oral Robotic-Assisted Needle Direct Tracheostomy Puncture in Patients Requiring Prolonged Mechanical Ventilation. Front. Robot. AI 2020, 7, 575445. [Google Scholar] [CrossRef]
  140. Lederman, D. Endotracheal Intubation Confirmation Based on Video Image Classification Using a Parallel GMMs Framework: A Preliminary Evaluation. Ann. Biomed. Eng. 2010, 39, 508–516. [Google Scholar] [CrossRef]
  141. Hemmerling, T.M.; Wehbe, M.; Zaouter, C.; Taddei, R.; Morse, J. The Kepler Intubation System. Anesth. Analg. 2012, 114, 590–594. [Google Scholar] [CrossRef]
  142. Kissin, I. Depth of Anesthesia and Bispectral Index Monitoring. Anesth. Analg. 2000, 90, 1114–1117. [Google Scholar] [CrossRef]
  143. Li, C.; Wei, J.; Huang, X.; Duan, Q.; Zhang, T. Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke. J. Health Eng. 2021, 2021, 4710044. [Google Scholar] [CrossRef] [PubMed]
  144. Birbaumer, N. Breaking the silence: Brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 2006, 43, 517–532. [Google Scholar] [CrossRef] [PubMed]
  145. Parks, N. Brain chips and future of human evolution. In Proceedings of the Harvard Model Congress Boston 2022, Boston, MA, USA, 24–27 February 2022. [Google Scholar]
  146. Venkatesan, M.; Mohan, H.; Ryan, J.R.; Schürch, C.M.; Nolan, G.P.; Frakes, D.H.; Coskun, A.F. Virtual and augmented reality for biomedical applications. Cell Rep. Med. 2021, 2, 100348. [Google Scholar] [CrossRef] [PubMed]
  147. Hayes-O’Neil, T.; Dixon, K. Hospital Markets and the Effects of Consolidation. AAF—American Action Forum. 2022. Available online: (accessed on 5 February 2022).
  148. Stein, E.J.; Mesrobian, J.R.; Szokol, J.W.; Abouleish, A.E. The 2016 job market for graduating anesthesiology residents. ASA Monit. 2016, 81, 56–62. [Google Scholar]
  149. Erstad, B.L. Value-Based Medicine: Dollars and Sense. Crit. Care Med. 2016, 44, 375–380. [Google Scholar] [CrossRef] [PubMed]
  150. Rochaix, L. Information asymmetry and search in the market for physicians’ services. J. Health Econ. 1989, 8, 53–84. [Google Scholar] [CrossRef]
Figure 1. Anesthesia historical milestone.
Figure 1. Anesthesia historical milestone.
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Figure 2. Presurgical stress can trigger different trajectories among patients, including maladaptive allostasis balance leading to long-term consequences.
Figure 2. Presurgical stress can trigger different trajectories among patients, including maladaptive allostasis balance leading to long-term consequences.
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Figure 3. Strategies to improve peri-operative and long-term outcomes.
Figure 3. Strategies to improve peri-operative and long-term outcomes.
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Laudanski, K. Quo Vadis Anesthesiologist? The Value Proposition of Future Anesthesiologists Lies in Preserving or Restoring Presurgical Health after Surgical Insult. J. Clin. Med. 2022, 11, 1135.

AMA Style

Laudanski K. Quo Vadis Anesthesiologist? The Value Proposition of Future Anesthesiologists Lies in Preserving or Restoring Presurgical Health after Surgical Insult. Journal of Clinical Medicine. 2022; 11(4):1135.

Chicago/Turabian Style

Laudanski, Krzysztof. 2022. "Quo Vadis Anesthesiologist? The Value Proposition of Future Anesthesiologists Lies in Preserving or Restoring Presurgical Health after Surgical Insult" Journal of Clinical Medicine 11, no. 4: 1135.

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