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Anesthesiologists should bring in their expertise during the early postoperative period to improve surgical outcome

Loer, Stephan A.

Current Opinion in Anesthesiology: December 2018 - Volume 31 - Issue 6 - p 713–715
doi: 10.1097/ACO.0000000000000663

Department of Anesthesiology, Amsterdam University Medical Centers, VU University Medical Center, Amsterdam, The Netherlands

Correspondence to Stephan A. Loer, MD, PhD, Department of Anesthesiology, Amsterdam University Medical Centers, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands. Tel: +31 20 4443058; e-mail:

Postoperative mortality and morbidity remain major challenges. In a European cohort study, Pearse et al.[1] observed that 4% patients did not survive to leave hospital. Much more patients encounter postoperative complications including pulmonary, cardiovascular, neurological complications, and renal failure; some experience chronic postsurgical pain. Most of these complications develop during the early postoperative period when patients have left the recovery room. It is common knowledge that postoperative complications should be detected and treated as early as possible. Ghaferi et al.[2] suggest that improving the care that patients receive once complications have occurred is crucial for reducing mortality. They conclude that strategies for ensuring the timely recognition and effective management of these complications are all-dominant. During their operation and stay in the recovery ward, qualified medical personnel monitor vital functions of patients continuously and recognize and treat deviations, such as intravascular hypovolemia, hypothermia, anemia, hypercapnia, hypoxia, or pain. In contrast, monitoring and qualified expertise is usually available quite infrequently as soon as patients have left the recovery ward. The question arises as to how we can improve prediction and detection of complications. Intriguing developments such as remote monitoring, electronic health records analytics, Big Data, smart algorithms to calculate early warning scores, artificial intelligence, and machine learning for clinical decision support open new possibilities that might enable us to identify postoperative patients at risk much earlier, which in turn might help us to make the best decisions. For instance, it would be highly beneficial if we could reliably detect sepsis developing in patients at a much earlier stage during the postoperative period.

In this context, this issue on Technology, Education and Safety of the journal Current Opinion in Anesthesiology addresses some of the challenging topics relevant for the early postoperative period as well as for pain therapy.

Boer et al.[3] review the state of remote monitoring of vital function on surgical wards. They suggest that remote technologies and automated notification systems may help to improve the appropriateness, reliability, and speed of diagnosis and therapeutic interventions in deteriorating surgical patients. However, they also conclude that the use of continuous remote monitoring of single parameters is only one factor in a complex situation. More advanced steps should include the automated calculation of early warning scores, automated notice of mild deterioration, and the prediction of deterioration. Thus, continuous collection of the appropriate data via remote monitoring represents an important first step that should be followed by smart algorithms and artificial intelligence to really improve postoperative care. We may hope that the combination of tools will help us to detect mild deviations reliably and improve our clinical decision-making.

With the introduction of electronic health records, anesthesiologists are dealing with a continuous stream of digital vital data that can also be sent to large national or international registries. Liem et al.[4] address the question of what anesthesiologists can learn from Big Data to improve perioperative outcomes. They describe registries specified in the field of anesthesiology and conclude that Big Data offer anesthesiologists a way to explore rare perioperative complications and outcomes and discover trends. They also emphasize that processes customized for individual patients can be improved when Big Data are combined with genomics, machine learning, and real-time decision support, for example, for administration of perioperative antibiotic prophylaxis, antiemetics, or intraoperative glucose management.

Can early warning scores help to identify postoperative patients at risk? De Grooth et al.[5] address this question in a review about the current state of early warning score research. They suggest that early warning scores may indeed facilitate protocoled escalation of care for postoperative patients at risk of adverse events but high nonevent rates can restrict their usefulness. They conclude that in the future, patient-tailored algorithms may significantly improve the detection horizon and balance between missed events and false alarms; however, currently there is no credible evidence that present early warning scores improve patient outcomes.

During the time of my training, the screen of an ultrasound machine often showed something resembling a snow flurry with mysterious blurred structures. Since then, the quality of available ultrasonography equipment has improved impressively. Currently, ultrasonography offers direct visualization of various soft tissues, including nerves, muscles, ligaments, vessels, interfacial planes, and bone surface [6]. The application of ultrasonography in pain medicine has emerged as a popular technique in the recent decade [6]. Pain intervention under ultrasound guidance is particularly valuable in peripheral and musculoskeletal procedures. Hoydonckx and Peng [6] summarize the advantages and limitations of ultrasound-guided pain interventions. They emphasize that the success of the blocks is operator-dependent and image-dependent and that the practitioners require enough experience to obtain a good image and direct the needle safely to the target structure. They also emphasize that extensive knowledge of sonoanatomy is a prerequisite. Modern anesthesiology resident training programs should include lectures about sonoanatomy to comply with these needs.

Why do some patients experience more postoperative pain than others following exposure to comparable stimuli? Why do some patients react completely differently to pain medication than others? In the recent years, an incredible amount of information on genetics of nociception and analgesia has become available and genetic variations seem to play an important part in a person's pain experience and response to perioperative analgesic therapy [7]. Packiasabapathy et al.[7] give us an overview of the genetics of nociception relevant to perioperative pain, chronic postoperative pain, and pharmacogenetics of analgesic drugs. They indicate that pharmacogenetics of perioperative analgesics may explain ineffective analgesia in some cases and excessive adverse events in others. They conclude that understanding genetic contributions to interindividual variability will help tailor perioperative pain management and optimize outcomes. They also address the role of epigenetic modification in the development of chronic postsurgical pain, which remains a major challenge.

Sometimes patients are afraid that anesthesia could impact their postoperative cognitive functions. Indeed, patients may develop postoperative delirium or longer lasting postoperative cognitive dysfunction. The exact incidence of these postoperative neurologic complications is unknown, but it is presumably much higher than what we expect due to many unreported or undetected cases. Borozdina et al.[8] review the recent clinical studies and report new insights into preoperative, intraoperative, and postoperative risk factors, prevention, diagnostic tools, and treatment options of postoperative cognitive dysfunction and delirium. They suggest that several perioperative risk factors such as sleep disturbance, vitamin D deficiency, preoperative cognitive impairment, and many other factors may influence the incidence of postoperative delirium and postoperative cognitive dysfunction. The administration of some drugs such as ketamine, certain opioids, and benzodiazepines may increase the incidence of cognitive dysfunction, whereas others may reduce the incidence. Treatment options include unspecific interventions such as infection control as well as specific pharmacological protocols. It is important to realize that postoperative delirium and cognitive dysfunction remain the most common neurologic complications after surgery with short-term and long-term consequences.

In conclusion, the early postoperative period is a crucial period for surgical patients as they are at risk of developing severe complications. Anesthesiologists are perfectly trained to control vital functions. They should bring in their enormous expertise during the postoperative period to improve surgical outcomes. Inspiring developments including remote monitoring, healthcare data processing, artificial intelligence, and smart algorithms will provide excellent tools for early detection and treatment of patients at risk. Anesthesia has to take on part of the responsibility beyond the recovery ward.

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Conflicts of interest

There are no conflicts of interest.

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1. Pearse RM, Moreno RP, Bauer P, et al. Mortality after surgery in Europe: a 7 day cohort study. Lancet 2012; 380:1059–1065.
2. Ghaferi AA, Birkmeyer JD, Dimick JB. Variation in hospital mortality associated with inpatient surgery. N Engl J Med 2009; 361:1368–1375.
3. Boer C, Touw HR, Loer SA. Postanesthesia care by remote monitoring of vital signs in surgical wards. Curr Opin Anesthesiol 2018; 31:716–722.
4. Liem VGB, Hoeks SE, van Lier F, de Graaff JC. What we can learn from Big Data about factors influencing perioperative outcome. Curr Opin Anesthesiol 2018; 31:723–731.
5. De Grooth H-J, Girbes AR, Loer SA. Early warning scores in the perioperative period: applications and clinical operating characteristics. Curr Opin Anesthesiol 2018; 31:732–738.
6. Hoydonckx Y, Peng P. Echo-guided invasive pain therapy: indications and limitations. Curr Opin Anesthesiol 2018; 31:739–748.
7. Packiasabapathy S, Horn N, Sadhasivam S. Genetics of perioperative pain management. Curr Opin Anesthesiol 2018; 37:749–755.
8. Borozdina A, Qeva E, Cinicola M, Bilotta F. Perioperative cognitive evaluation. Curr Opin Anesthesiol 2018; 31:756–761.
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