Advances in modern anesthesiology have significantly reduced the risk of anesthesia
compared to the last century, however, the level of perioperative hospital mortality of
planned operations at the moment is on average about 0.5% (ISOS group, 2016). Weiser et
al. (2016) estimated that more than 313 million adults worldwide are subject to surgery
each year. Thus, the number of deaths may result in several million each year worldwide.
However, the study of the mortality risk is associated with certain difficulties, because
over the past half century, this figure has decreased a hundred times and the study
requires studies that include a large number of participants.
Current research focuses on other outcome criteria - postoperative complications. Thus,
anesthetic risk often refers to the risk of postoperative complications. The frequency of
these complications varies in a wide range, ranging from 3 to 18 % (Gawande AA, 1999,
Kable AK, 2002, Malik OS, 2018). The differences in the data are explained by the lack of
clear definitions and differences in the design of studies, but the fact that the
development of postoperative complications increases the risk of death several times
(ISOS group, 2016) can be considered undoubted. However, despite the importance of this
issue, in modern literature there is no clear idea of what is considered a high risk and
which of the patients corresponds to this category.
Understanding whether a patient is at high risk is an essential task - it allows you to
obtain meaningful informed consent of the patient, as well as to understand whether to
apply strategies for the prevention of complications (targeted infusion therapy,
protective respiratory support, especially monitoring in the postoperative period, etc.).
Attempts at preoperative risk stratification have been made for many decades, some scales
estimate the initial physical status (ASA scale) (Young J, 2015) and predict mortality,
others estimate the risk of specific complications (Lee index, respiratory risk scale,
etc.) .
Scales including intraoperative and postoperative parameters such as the POSSUM series of
scales (Whiteley MS, 1996) are also being developed. The analysis shows that in routine
clinical practice, these scales are not used very often, due to their limitations:
subjectivity, technical complexity and often - low specificity and sensitivity.
Concomitant diseases are the strongest predictors of postoperative adverse events and
annual mortality. Monk et al. (2005) demonstrated that Charlson's comorbidity score of 3
or more significantly increased the risk of death. In addition, in most clinical studies,
the ASAclassification of physical status as a kind of comprehensive assessment of patient
comorbidity has repeatedly proved to be one of the strongest independent predictors of
postoperative morbidity and mortality, despite the fact that this assessment is based on
subjective perception (Watt J., 2018).
The main concomitant diseases that are independent predictors of perioperative
complications are diseases of the cardiovascular and respiratory systems (Van Diepen S,
2011). Increasing age, anemia, obesity, diabetes - these conditions also increase the
risk of an adverse outcome. Diseases of the Central nervous system and neuromuscular
diseases significantly disrupt the function of respiration, can change the level of the
Autonomous regulation of the cardiovascular system, lead to significant cognitive
disorders and nutritional deficiency, which also increases the risk of perioperative
complications (Hachenberg T, 2014).
On the other hand, large-scale observational studies conducted in recent years in a
number of countries have not identified comorbidities as independent predictors of
postoperative complications (Malik, 2018).
Thus, data on the risk effects of comorbidities are contradictory and may be influenced
by differences in the frequency and structure of these diseases in heterogeneous
populations, as well as in different treatment strategies for cardiovascular, respiratory
and other diseases. The identification of these risk factors is necessary to understand
the pathophysiology of complications and identify potential ways to reduce anesthetic
risk, such as the correction of concomitant disease.
The degree of risk of surgery, of course, depends not only on the presence of
comorbidities and their combinations, but also on the severity of surgical injury (Pearse
RM, 2012, ISOS group, 2017), as well as the level of exposure to drugs for anesthesia and
anesthetic techniques (Malik OS, 2018), therefore, the allocation of risk groups without
these factors is also not appropriate.
Objective: to assess the frequency and structure of comorbidities in patients undergoing
surgery on the abdominal organs and to stratify the risk of postoperative complications
by determining independent
Evaluated parameters in study:
- Age, gender; 2. Class of physical status by ASA; 3. The presence and type of
concomitant disease; 3.1 CHD; 3.2 CHF; 3.3 Heart rhythm disorders; 3.4 COPD; 3.5
Bronchial Asthma; 3.6 CKD; 3.7 CNS diseases; 3.7.1 Stroke; 3.7.2 Epilepsy; 3.7.3
Parkinson's Disease; 3.7.4 Alzheimer's Disease; 3.8 Neuromuscular diseases; 3.9
Diabetes; 3.10 Anemia; 4 Treatment received by the patient; 4.1 β-blockers; 4.2 ACE
Inhibitors; 4.3 Aldosterone antagonists; 4.4 Statins; 4.5 Anticoagulants; 4.6
Diuretics; 4.7 Bronchodilators; 4.8 Corticosteriods; 4.9 Insulin; 4.10
Anticonvulsants; 5. The type and severity of surgery ; 5.1 Open surgery on the
organs of the upper abdomen; 5.2 Coloproctological operations; 5.3 Gynecological
surgery; 5.4 Urological surgery; 5.5 Operations on vessels of the abdominal cavity;
5.6 Abdominal wall surgery; 5.7 Laparoscopic surgery; 6 Type of anesthesia; 6.1
Spinal; 6.2 Epidural; 6.3 Combined spinal-epidural; 6.4 Intravenous; 6.5 Combined;
6.6 General+epidural; 7. Integral scales; 7.1 The cognitive function of the Montreal
scale ; 7.2 Respiratory risk ; 7.3 Lee's Cardiovascular Risk Scale ; 7.4 NSQIP
Cardiac risk scale ; 7.5 Hepatic insufficiency according to MELD; 7.6 CKD Stage by
Level of GFR and Albuminuria; 7.7 COPD degree by GOLD.
Order of conduct
The data is registered in the Excel electronic database in a uniform format for all
centers (the form will be sent by the coordinator to all centers participating in
the study prior to the inclusion of patients).
All centers need to get approval by the local ethics committee before the start of
the study. The study protocol will be registered in Clinicaltrial.gov.
The study includes all patients operated on within one operational day at the
discretion of the center and meeting the inclusion criteria with registration in the
questionnaire of the day of the week.
All patients could sign informed consent to participate in the study prior to
inclusion in the study.
Before surgery, data on the patient and all studied factors specified in the study
protocol are entered into the database.
All patients included in the study are monitored before discharge from the hospital
with registration of the data specified in the protocol.
Every last day of the working week, all completed cases are sent as a separate Excel
file to the study coordinator by email to trembachnv@mail.ru 7. The originals of the
questionnaires are stored in the centers for the entire study time and for 3 years
after its completion.
The summary database is formed by the study coordinator and provided to the centers
after the end of the study.
Statistical analysis The sample size was calculated taking into account the fact that at
least 10 cases of postoperative complications per one factor included in the final
regression model are required. Given the wide range of complication rates in previous
studies (from 3% to 20%), we have chosen a lower bound for a more accurate assessment. To
include 20 potential risk factors in the regression model, 200 cases of postoperative
complications are required, which at a frequency of 3% is not less than 7000 people.
Taking into account the risk of data loss, and taking into account as many potential risk
factors as possible, the size of the required sample was increased to 12,000 people,
which will also assess the contribution of comorbidities to certain groups of
complications. For validation of predictive models will be recruited 4,000 additional.
The inclusion of the patient in the main and validation group will be carried out
randomly.
The character of distribution of studied parameters will be evaluated using the criterion
Kolmogorov-Smirnov. The continuous data will be presented as the median and interquartile
range for the nonparametric distribution and as the mean and standard deviation for the
parametric distribution. Categorical variables will be presented as the number of
patients and a percentage of the total number of patients.
For the initial assessment of the Association of the factor with postoperative
complications, a single-factor analysis using the χ2 criterion and the Mann-Whitney test
will be carried out. All variables with a reliable relationship identified in the
univariate analysis (p less than 0.05) will be included in logistic regression if there
is no collinearity between them (correlation coefficient less than 0.25). The logistic
regression model will be constructed using a step-by-step reverse inclusion procedure in
which the presence of a complication will be a dependent variable. Potential predictors
will be removed if this exception does not cause a significant change in the log
likelihood ratio. The criterion for excluding the factor will be set at the significance
level of 0.05. Adjusted odds ratios and 95% confidence intervals will also be calculated.
The resulting predictive model will be evaluated in the validation group using ROC
analysis and the Hosmer-Lemeshov test.