Last updated on July 2020

The Role of Concomitant Diseases in Postoperative Complications Risk Stratification.

Brief description of study

Study is conducted to assess the prevalence and structure of comorbidity among patients undergoing abdominal surgery and produce the stratification of the risk of postoperative complications by identifying independent predictors for its development.

Detailed Study Description

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:

  1. 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 -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

  1. 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).
  2. All centers need to get approval by the local ethics committee before the start of the study. The study protocol will be registered in
  3. 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.
  4. All patients could sign informed consent to participate in the study prior to inclusion in the study.
  5. Before surgery, data on the patient and all studied factors specified in the study protocol are entered into the database.
  6. All patients included in the study are monitored before discharge from the hospital with registration of the data specified in the protocol.
  7. Every last day of the working week, all completed cases are sent as a separate Excel file to the study coordinator by email to 7. The originals of the questionnaires are stored in the centers for the entire study time and for 3 years after its completion.
  8. 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 2 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.

Clinical Study Identifier: NCT03945968

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Regional clinical hospital 2

Krasnodar, Russian Federation
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Kuban State Medical University

Krasnodar, Russian Federation
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Rostov State Medical University

Rostov-on-Don, Russian Federation
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Regional clinical hospital 2

Vladivostok, Russian Federation
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Emergency hospital

Volgograd, Russian Federation
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South-Ural State Medical University

Chelyabinsk, Russian Federation
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Privolzhsky district medical center

Nizhny Novgorod, Russian Federation
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Recruitment Status: Open

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