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The purpose of this study was to investigate associations between postoperative delirium (POD) and unplanned perioperative hypothermia (UPH) among adults undergoing noncardiac surgery.
Design
A retrospective, exploratory design was used.
Methods
A retrospective, exploratory study was conducted using electronic medical record data abstracted from a purposive convenience sample of adult patients undergoing noncardiac surgery from January 2014 to June 2017.
Findings
The analyzed data set included 22,548 surgeries, of which 9% experienced POD. Logistic regression indicated that American Society of Anesthesiologists (ASA) class was the strongest predictor of POD (χ2 = 1,207.11, df = 4, inclusive of all ASA class terms). A significant relationship between UPH and POD (χ2 = 54.94, df = 4, inclusive of all UPH terms) and a complex relationship among UPH, patient age, ASA class, and POD were also found.
Conclusions
Results support a relationship between UPH and POD. Notably, there is also a complex relationship in the noncardiac surgery population among UPH, age, ASA class, and POD. Preliminary understanding of this relationship is based on the pathophysiological response to surgical stress. Further research is indicated.
Postoperative delirium (POD) is a rapidly occurring syndrome presenting as an acute change in consciousness characterized by fluctuating episodes of inattention, disorganized thinking, and altered level of consciousness. Although typically developing on postoperative days 1 to 3,
Clinical Practice Guidelines for the prevention and management of pain, agitation/sedation, delirium, immobility, and sleep disruption in adult patients in the ICU.
research suggests that early POD seen on discharge from the postanesthesia care unit may lead to worsened outcomes and predict further delirium postoperatively.
The incidence of POD in patients undergoing noncardiac surgery ranges from 15% to 54%, with up to 80% of delirium seen in the intensive care surgical patient.
In postoperative surgical patients older than 65 years, delirium incidence may be as high as 74% depending on the type of surgery, comorbidities, and delirium assessment method.
Regardless of cause, POD and other forms of hospital-acquired delirium have been associated with increased health care costs of $4 to $16 billion annually.
Although there are mixed findings regarding the relationship of hypothermia to the development of POD, there is little to no supporting causal evidence in the literature.
The etiology of delirium remains largely unclear; however, there are numerous causal theories being discussed and tested in the literature, to include direct brain insults and abnormal stress responses.
Direct brain insults are indiscriminate and include basic energy deprivation to the brain, such as hypothermia, metabolic abnormalities including electrolyte imbalances, and drug effects and toxicities. Neurochemical changes and imbalances affect neurotransmission systems in the brain.
The underlying pathophysiology of POD is also poorly understood. Supporting etiology is thought to emerge from the interplay of numerous preoperative, intraoperative, and postoperative factors. Although some of these factors are modifiable, many are not. Pre-existing dementia, history of baseline hypertension, alcoholism, and admission severity of illness are the four primary, nonmodifiable preoperative risk factors that have been reported by multivariate analysis. Age has been supported as a risk factor in non–intensive care unit (ICU) patients; however, the evidence supporting the association of age to POD is conflicting.
Other preoperative factors that have been associated with POD include smoking, history of psychological disorders, trauma and emergency surgery, and history of other comorbidities such as diabetes, stroke, and renal disease.
The effect of the timing and dose of dexmedetomidine on postoperative delirium in elderly patients after laparoscopic major non-cardiac surgery: A double blind randomized controlled study.
The effect of the timing and dose of dexmedetomidine on postoperative delirium in elderly patients after laparoscopic major non-cardiac surgery: A double blind randomized controlled study.
; however, on the basis of a recent meta-analysis, further research is needed to validate the efficacy of this pharmacologic approach to delirium prevention.
Nonmodifiable factors include red blood cell transfusion of greater than 1 L, hypoalbuminemia, hematocrit less than 30%, hypoxemia, hypocarbia or hypercarbia, increased serum creatinine, increased total bilirubin, and electrolyte disorders.
Anesthesia agents used during surgery impair normal regulation of temperature mechanisms. Although it remains unknown exactly how anesthetics actually affect thermoregulatory control, reductions in thermal response are well documented.
All surgical patients receiving neuraxial and general anesthesia are at risk for unplanned perioperative hypothermia (UPH). Considered the most common perioperative complication, UPH refers to the nontherapeutic and uncontrolled core temperature drop less than the normal range of 36°C (96.8°F) during the course of a surgical experience.
Perioperative normothermia to reduce the incidence of surgical-wound infection and shorten hospitalization. Study of Wound Infection and Temperature Group.
Conceptualization of the association of UPH with the development of POD in noncardiac surgical patients is based on the pathophysiological response to surgical stress. The theoretically distinct effects of the stress response pathway, which normally is adaptive, suggest that POD represents a reaction to acute stress, mediated by extremely high glucocorticoid levels, oxidative stress response, and the action of cytokines on the blood-brain barrier, which then leads to impairment and death of brain neurons.
Surgery is a known stressor and stimulates an acute phase response, which leads to activation of proinflammatory activation cytokines and ultimately inflammation. Patients that experience POD demonstrate an impaired stress regulating system with significantly higher stress levels (eg, cortisol, C-reactive protein, and oxidative stress) compared with preoperative baseline levels and with nondelirious patients.
Surgical stress and perioperative temperature fluctuations are probable occurrences during a patient's surgical experience.
Purpose
Given the possible relationship of POD and UPH, an exploration of the relationships between each was warranted. Therefore, this retrospective, exploratory study aimed to explore the associations between UPH and the incidence of POD in patients undergoing noncardiac surgery.
We specifically aimed to:
1.
Explore the associations between UPH and the incidence of POD.
2.
Characterize risk factors and/or protective factors for POD in the adult surgical patient undergoing noncardiac surgery.
Methodology and Procedures
Design
A retrospective, exploratory research design using practice-based research methodologies was used.
Setting and Sample
A purposive convenience sample of medical records of all adult patients (aged ≥18 years) undergoing any noncardiac surgery over a 3.5-year period (January 2014 to June 2017) was included. Records of pediatric patients and cardiovascular surgery procedures were excluded. The study was conducted at a large, regional-referral community hospital at which approximately 15,000 noncardiac surgeries are completed annually. On the basis of a previous study conducted at the study institution, we anticipated approximately 20% of the study population to develop POD.
With an estimated 52,500 surgical records under review and the number of continuous and categorical predictors anticipated, statistical power was a nonissue for the logistic regression conducted.
Ethical Approval
The research methodology of the study used a retrospective, electronic medical record abstraction approach that involved minimal, if any, social, legal, financial, physical, psychological, or emotional risk to the patients. The hospital and the university institutional review boards completed ethics reviews and approved the study. All data were deidentified and there was no direct interaction with patients. Abstracted data were stored in a secure area and data entered on a secure server, password protected database. This study did not involve patient care; therefore, the study did not affect the standard of care.
Data Collection Procedures
On institutional review board approval, data from all medical records meeting inclusion and exclusion criteria were collected via electronic medical record abstraction. The primary outcome measure of interest was POD development in patients having major surgery. The independent variable of interest was UPH; however, other control variables included patient demographics and intrinsic and extrinsic POD risk factors as previously discussed. Data were electronically abstracted from the chart using a data abstraction program specifically designed for this study by the health institution's Clinical and Business Analytics Department.
The outcome variable of interest was POD. Early POD was defined as POD developing within the first 24 hours postoperatively. Delayed POD was defined as delirium occurring any time after that first 24 hours of postoperative period to discharge from the hospital. POD was operationalized with the Confusion Assessment Method for the ICU (CAM-ICU) score, and in the non-ICU through the use of the Nursing Delirium Score (NuDESC). The CAM-ICU yields a positive or negative score for delirium. It is supported by current evidence-based guidelines as having strong reliability and validity for both ventilated and nonventilated ICU patients.
The NuDESC identifies delirium as a score of 2 or greater. NuDESC has been shown to be a valid and reliable instrument with 97% sensitivity and 92% specificity in the non-ICU setting.
UPH was the independent variable of interest and was defined as a core body temperature of less than 36°C (96.8°F) at any time during the perioperative period of patient care. Hypothermia was operationalized by gathering temperature data immediately before surgery, specifically during the surgery itself, and immediately postoperatively in the postanesthesia care unit. The route, timing of temperature monitoring, and the use of warming devices provided further data for analysis. A known limitation of temperature monitoring is the inconsistency of method and route of temperature measurement during the perioperative care pathway. The American Society of Perianesthesia Nurses and the Association of periOperative Registered Nurses guidelines promote the same method of temperature measurement be used throughout the perioperative period when clinically feasible.
further states that temperature monitoring at near-core sites is approximately equivalent to core temperatures, although should not be deemed reliable in extreme value readings.
Data Analysis
To investigate the relationship between unplanned hypothermia and POD, we extracted electronic data from 47,295 adult patients (aged 18 years and older) undergoing noncardiac inpatient surgical procedures from January 2014 to June 2017. We further refined the population to exclude the following: 10,510 surgery patients receiving an anesthesia type other than general, spinal, or epidural; and 5,650 surgeries with procedures of pain management-pump, C-section, ophthalmology, organ harvest, and endoscopy. We additionally excluded 86 patients with an American Society of Anesthesiologists (ASA) class of V or VI. These selection criteria resulted in a data set with 31,049 surgeries (Figure 1).
Figure 1Study flow diagram. This figure is available in color online at www.jopan.org.
After removing 8,501 surgeries with incomplete data, we conducted a logistic regression predicting probability of POD from UPH and other known and suspected associated variables. Specifically, the model included patient age, body mass index (BMI), ASA class, duration of measured hypothermic temperatures in minutes, duration of anesthesia in minutes, anesthesia type (general vs regional), and the three, two-way interactions among patient age, minutes hypothermic, and ASA class. The patient age and BMI terms were specified as restricted cubic splines with three knots to allow for nonlinearities. All statistical analyses were performed using R (version 3.4).
Results
The final number of surgeries included was 22,548. Types of surgical procedures included noncardiac surgeries, with 91.4% of patients under general anesthesia and 61% of patients classified as ASA III and IV. Mean age was 62.23. A total of 9.4% (n = 2,125) of patients were identified to have POD. Other demographic details are provided in Tables 1 and 2.
Table 1Descriptive Statistics for the Analyzed Data Set (n = 22,548)
POD = No
POD = Yes
Total Numbers
20,423
2,125
μ (σ)
μ (σ)
Body mass Index
30.55 (7.99)
27.77 (7.58)
Minutes hypothermic
43.00 (54.68)
36.77 (59.51)
Anesthesia minutes
169.67 (78.37)
186.02 (109.46)
Patient age
62.90 (15.10)
51.80 (19.43)
n (%)
n (%)
Gender = male
9,032 (44.2)
1,038 (48.8)
Anesthesia type = general
18,561 (90.9)
2,059 (96.9)
ASA class
I
583 (2.9)
10 (0.5)
II
7,904 (38.7)
208 (9.8)
III
10,244 (50.2)
1,142 (53.7)
IV
1,692 (8.3)
765 (36.0)
ASA, American Society of Anesthesiologists; POD, postoperative delirium.
Results from the logistic regression predicting probability of POD from UPH and known and suspected associated variables are detailed in Table 3. The analysis indicates that a patient's ASA class is by far the strongest predictor of POD (χ2 = 1,269.19, df = 12). Of particular interest, we found a significant relationship between minutes hypothermic and POD (χ2 = 58.97, df = 9) and a complex relationship among minutes hypothermic, patient age, ASA class, and POD (Figure 2). Specifically, the significant interaction between patient age and minutes hypothermic (χ2 = 7.51, df = 2) suggests that experiencing hypothermia is protective for older surgery patients, but detrimental for younger surgery patients (Figure 3). Building on this complex relationship, the results also indicate a significant interaction between ASA class and age (χ2 = 66.05, df = 6), with higher ASA class increasing the likelihood of POD for all but the oldest patients. Finally, the significant interaction between ASA class and minutes hypothermic (χ2 = 11.54, df = 3) suggests the impact of hypothermia duration on POD increases as ASA class increases. Taken together, these results find hypothermia to be generally protective for older surgery patients, but risky for younger surgery patients, with the effect of hypothermia rapidly increasing as younger patients' ASA class increases as shown in Figure 3.
Table 3Results From the Logistic Regression Predicting Probability of Postoperative Delirium
Figure 2Complex relationship among minutes hypothermic, patient age, ASA class, and POD. ASA classes I-IV are depicted in quadrants. ASA, American Society of Anesthesiologists; POD, postoperative delirium. This figure is available in color online at www.jopan.org.
Figure 3Interaction between minutes hypothermic and ASA class for surgical patients at age 20 and 80 years. ASA, American Society of Anesthesiologists. This figure is available in color online at www.jopan.org.
The mean age, BMI, and ASA mix was consistent with the patient demographic mix of the study institution. Mean BMI was on the border of the overweight-obesity categories,
which was also consistent with regional demographics. The overall incidence of POD in this study (9%) was lower than the nationally reported incidence of 72% to 74%
; however, this national incidence rate was reported for surgical patients older than 65 years, which is higher than the mean age of the study population. In addition, delirium assessment documentation was not recorded in many of the younger patients in this study, excluding them from further analysis.
Although POD has been well studied in the older population,
this is the first study to specifically look at the relationship of UPH to the development of POD in a heterogeneous surgical population. ASA class was the strongest predictor of development of POD in this study; however, the particularly interesting interaction was the complex relationship of hypothermia, age, ASA class, and POD, suggesting that hypothermia may be protective for older surgery patients, but detrimental to the young, particularly at ASA class IV. Given that there is a higher incidence of POD in the elderly,
we were surprised to find that UPH was not a contributing factor unless they were the sicker of the sick. The probability of younger patients developing POD, however, was numerically greater than the probability in elderly patients at ASA class IV across all minutes hypothermic (Figure 3).
The authors hypothesize that these findings may be related to the surgical stress response and its inflammatory impact on the brain. The Society of Critical Care Medicine
Clinical Practice Guidelines for the prevention and management of pain, agitation/sedation, delirium, immobility, and sleep disruption in adult patients in the ICU.
concur that there is strong evidence supporting the association of emergency surgery and trauma, both of which initiate a systemic inflammatory stress response,
Surgical stress initiates a wide and varied immunologic response depending on patient age, prior health history, and type and duration of surgery and anesthesia.
Surgery is known as a major stressor and elicits an acute phase response leading to proinflammatory activation and inflammation. The theoretically distinct effects of the stress response pathway, which normally is adaptive, suggest that POD represents a reaction to acute stress, mediated by extremely high glucocorticoid levels, which then leads to impairment and death of brain neurons.
Patients that experience POD demonstrate an impaired stress regulating system with significantly higher stress hormone levels (eg cortisol and C-reactive protein) compared with preoperative baseline levels and with nondelirious patients.
We hypothesize that this response may be muted in the elderly, whereas exacerbated in the young. Instead of a self-limiting, restorative inflammatory response to tissue injury, surgical stress response in the young can be overwhelming and lead to an overexpressed inflammatory response, whereas in the older person, the stress response can surpass the body's ability to respond.
Limitations
The study was conducted at a large regional-referral center in the Southeast, thus limiting generalizability of findings to other populations and socioeconomic groups. Ethnicity was not captured as a demographic variable; however, the tertiary care center in which the study was conducted serves rural southern Appalachia, in which more than 90% of the population is white.
Data were abstracted from an electronic medical record, reflecting point-of-care documentation by the bedside care provider. As such, there was a greater chance of missing data. In addition, timing and documentation of the CAM-ICU and NuDESC were likely inconsistent between providers. Although all nursing staff were educated regarding assessment for the presence of POD, assessment of inter-rater reliability was not possible. Finally, participant temperature data came from different devices, different routes, and there was inconsistent monitoring between providers. Although the current analysis found an interesting relationship among patient age, ASA class, and minutes hypothermic, our complete case analysis excluded a larger proportion of younger patients compared with older patients because of missing POD documentation.
Implications
POD is a nurse-sensitive outcome currently recognized as a public health epidemic. Despite continued efforts to reduce the incidence of POD in the acute care setting, the syndrome continues to pose a significant health problem. Preoperative risk assessment is a critical step in the prevention of this devastating and costly complication. Unfortunately, there is a lack of consensus regarding risk factors. This retrospective, exploratory study sought to definitively identify the incidence of POD and any association with UPH in the noncardiac surgery population.
A clearer identification of risk factors for the development of POD will enable the perianesthesia nurse and interdisciplinary surgical team to work proactively in implementing tailored preventative interventions to reduce the occurrence of POD in the surgical population. Such interventions should empower the perianesthesia nurse to contribute to reduced costs and significantly improved patient outcomes.
Conclusions
This study builds the science of perianesthesia nursing by identifying the relationships among POD, perioperative temperature, and other risk factors related to this phenomenon. Further study is needed to explore the physiology associated with the impact of UPH on POD across the lifespan. Additional exploration of the risks and benefits of adverse effects of UPH as it relates to POD should be considered.
Acknowledgments
The views expressed in this publication represent those of the authors and do not necessarily represent the official views of HCA Healthcare or any of its affiliated entities.
References
American Association of Critical Care Nurses
AACN Practice Alert: Delirium Assessment and Management.
Clinical Practice Guidelines for the prevention and management of pain, agitation/sedation, delirium, immobility, and sleep disruption in adult patients in the ICU.
The effect of the timing and dose of dexmedetomidine on postoperative delirium in elderly patients after laparoscopic major non-cardiac surgery: A double blind randomized controlled study.
Perioperative normothermia to reduce the incidence of surgical-wound infection and shorten hospitalization. Study of Wound Infection and Temperature Group.
Vallire Hooper, who is co-editor of Journal of PeriAnesthesia Nursing, was not involved in the editorial review or decision to publish this article. The entire process from submission, referee assignment, and editorial decisions was handled by Jan Odom-Forren, the other co-editor of this journal.
Funding: This study was partially funded by a grant from the American Society of PeriAnesthesia Nurses.
Conflict of interest: Vallire Hooper serves as the coeditor of the Journal of PeriAnesthesia Nursing. All other authors have no conflicts of interest to disclose.
Dr Hooper was formerly affiliated with Research Institute, Mission Health, Asheville, NC. Dr Johnson was formerly affiliated with HCA National Group Data Science Team, Asheville, NC.
With a great interest we read the recent article by Wagner et al1 assessing the relationship of postoperative delirium (POD) and unplanned perioperative hypothermia (UPH) in adult patients undergoing noncardiac surgery. They showed a significant relationship between UPH and POD. Furthermore, there are complex relationships among UPH, age, ASA physical status class and POD. Given that POD is significantly associated with increased lengths of ICU and hospital stay, costs, morbidity and mortality after noncardiac surgery,2 their findings have potential implications.
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