Nikita V. Trembach

INTRODUCTION: Postoperative complications under diagnosis-related group (DRG) reimbursement increase hospital costs and prolong ward and intensive care unit (ICU) stay, which additional payments may not fully offset. OBJECTIVES: To quantify the impact of complications on hospital and ICU length of stay (LOS, ICU LOS), estimate related hospital costs, and model the economic effect of a risk prediction model. MATERIALS AND METHODS: Retrospective multicentre cohort study of the STOPRISK registry (n = 11,081 episodes). Complications were defined by 19 binary indicators (18 tabulated). Outcomes: LOS, ICU LOS and in-hospital mortality as median (Q1; Q3). Economic modelling was an exploratory scenario cost–consequence analysis with return on investment (ROI) from the hospital perspective; resource cost equivalents 6,040 RUB/ward day and 66,225 RUB/ICU day. RESULTS: Complications occurred in 508/11,081 (4.58 %), death in 70 (0.63 %) and ICU stay in 3,428 (30.9 %); 946 events were recorded (1.86 per affected patient). All 18 complications had higher median LOS and ICU LOS; the largest increases were wound dehiscence (+23 days), sepsis (+19) and pulmonary embolism (+18). Additional resource costs per event were 109,000–777,000 RUB; the total annual event-related burden was 328.2 million RUB. In the base-case A + B scenario (δ = 20 %) annual savings were 54.1 million RUB, ROI 10.8× and payback 1.1 months; across the δ = 10–30 % range, 29.8–73.0 million RUB, ROI 6.0–24.3× and 0.5–2.0 months. Complication rates were 3.14 % (< 65 years) and 8.06 % (≥ 65 years); failure-to-rescue ≈13.8 %. CONCLUSIONS: Postoperative complications generate substantial event-related resource burden through longer LOS and ICU LOS, with patients ≥ 65 years contributing a disproportionately high share of costs. Under the specified assumptions, risk stratification and clinical pathways may be economically justified from the hospital perspective; the benefit depends on actual use of the tool, case-mix, tariffs and ICU occupancy. The findings are exploratory and require prospective validation.

Background: Postoperative neurocognitive disorders (PONCD) may develop after surgery, negatively affecting recovery, functional independence, quality of life, and increasing the risk of dementia, while also affecting patient survival. Patients undergoing corneal transplantation are particularly vulnerable to pre-existing cognitive impairment associated with visual dysfunction and other perioperative risk factors for PONCD. Consequently, anesthesiologists should perform perioperative risk stratification in these patients. Objective: To develop a predictive model for PONCD by identifying risk factors contributing to their development. Materials and methods : The single-center prospective study included 190 patients undergoing elective penetrating keratoplasty. Recovery after anesthesia, quality of life, and the development of PONCD were assessed on postoperative days 7, 30, and 90. Patients with and without PONCD were compared regarding baseline characteristics, surgical variables, and anesthesia-related factors. Logistic regression analysis was performed to identify factors associated with PONCD, and a predictive model was developed based on multivariate analysis. The model was subsequently compared with similar predictive models. Results : On postoperative day 7, PONCD were observed in 37.85% of patients, on day 30 in 19.46%, and on day 90 in 12.63%. The predictive model of PONCD demonstrates the following performance: AUROC=0.873, mean squared error=0.0265, 95% CI 0.817–0.917, p < 0.001. Conclusions: Four independent factors were identified: patient age, Clinical Frailty Scale, initial cognitive impairment assessed using the MoCA-22 scale, and initial visual impairment. The developed predictive model demonstrated high accuracy (AUROC=0.873).

Objective . To substantiate the concept of perioperative infectious risk by analyzing current approaches to classifying complications, methods for predicting them, and the key factors influencing surgical outcomes. Materials and Methods. We conducted an analytical review based on expert judgement and a systematic search of publications from the preceding 10 years in PubMed and Lens.org. The analysis included data on the epidemiology of infectious complications, classification systems (CDC, Clavien-Dindo, Accordion), prognostic scores (ASA, SOFA, ACS-NSQIP, SORT), laboratory markers (CRP, IL-6, procalcitonin, neutrophil-tolymphocyte ratio), and machine learning models (PERISCOPE AI, Random Forest, SVM, ensemble methods). Results . The risk of infectious complications was shown to be determined by the cumulative effect of modifiable and nonmodifiable factors related to both the patient and the healthcare facility. Laboratory markers demonstrated high prognostic value: IL-6 levels above 432 pg/mL and CRP 150 mg/L on postoperative day 3 were statistically significantly associated with adverse outcomes. Advanced prognostic tools provided high accuracy: the AUROC for ACS-NSQIP reached 0.80, and for PERISCOPE AI, 0.82–0.91. The use of scoring systems and machine learning made it possible to identify high-risk patients before clinical symptoms appeared. Conclusion . Risk of perioperative infections is multifactorial and requires an integrated approach to risk stratification, incorporating clinical data, risk assessment scales, laboratory markers, and AI models. The use of structured prognostic tools enables early identification of complications, optimize prevention, and reduce mortality.

Perioperative respiratory risk represents a major interdisciplinary challenge, as postoperative pulmonary complications (PPCs) are associated with increased mortality, prolonged hospital stay, and higher healthcare costs. Heterogeneity in the definitions of complications complicates the interpretation of the literature; initiatives such as Standardised Endpoints in Perioperative Medicine (StEP) and the European Society of Anaesthesiology working group on European definitions have proposed harmonised definitions and severity grading systems, thereby improving comparability across studies and clinical applicability of results. This narrative review synthesises data on the epidemiology of PPCs, patient- and procedure-related risk factors, methods of risk stratification and prevention, and presents contemporary prognostic models ranging from clinical risk scores to machine learning algorithms, with particular emphasis on validation requirements and reporting standards. Practical implications include the need to apply StEP definitions with severity grading alongside discrete registry-based outcomes according to the NSQIP (National Surgical Quality Improvement Program) classification, to perform external validation of predictive models, and to implement bundled preventive strategies throughout all phases of perioperative care in high-risk populations. Integration of standardised outcomes, validated risk models, and multistage preventive interventions, including the use of machine learning algorithms, provides a foundation for reducing the incidence of PPCs and improving long-term outcomes.

INTRODUCTION: Older age is recognized as an independent risk factor for adverse perioperative outcomes; however, quantitative data on the impact of early old age (65–75 years) on 30-day postoperative complications in a multicenter Russian cohort are lacking. OBJECTIVE: To determine whether older age (65–75 years) is associated with the incidence, pattern, severity, and timing of 30-day postoperative complications and mortality compared with patients aged 18–64 years undergoing elective abdominal surgery. MATERIALS AND METHODS: A multicenter prospective cohort study was conducted (secondary analysis of the STOPRISK database, NCT03945968; STROBE). From 2019 to 2024, 12,000 patients from 38 centers across the Russian Federation were enrolled. After exclusions, the analytical sample comprised 10,915 patients: 18–64 years (n = 8,119) and 65–75 years (n = 2,796). The primary outcome was any 30-day postoperative complication (ESA/ESICM). Secondary outcomes were 30-day mortality, complication severity (Clavien-Dindo), and timing of occurrence. RESULTS: The 30-day complication rate in patients aged 65–75 years was more than twice that in the 18–64-year group (7.15 vs 3.09 %), and 30-day mortality was more than threefold higher (1.18 vs 0.33 %). The greatest relative risk increase was observed for postoperative delirium (RR 7.55), acute myocardial infarction (RR 5.08), arrhythmias (RR 4.26), and acute kidney injury (RR 3.87). The distribution of complication severity according to Clavien-Dindo did not differ between groups, most complications in both groups occurred within the first 5 days. After multivariable adjustment, older age remained an independent predictor of any complications, severe complications, and mortality. CONCLUSIONS: Older age (65–75 years) is an independent predictor of postoperative complications and mortality following elective abdominal surgery, increasing the risk of complications 1.7-fold and the risk of death 2.2-fold after confounder adjustment. The identified profile of high-risk specific complications supports the need for targeted perioperative management protocols in elderly patients.

Highlights This review introduces for the first time an integrative conceptual model of high perioperative cardiac risk, combining three key components: surgical risk, patient vulnerability, and dynamic monitoring, enabling a systematic approach to patient stratification prior to noncardiac surgery. It demonstrates a critical gap between the high predictive accuracy of modern machine learning models (AUROC > 0.90) and the lack of their clinical validation in real-world practice, particularly in the Russian population, highlighting the need for a national risk calculator. For the first time in Russian literature, the role of MINS (myocardial injury after noncardiac surgery) as a major driver of postoperative mortality (contributing 3.9% to overall mortality) is analyzed in detail, justifying the shift from reactive to preventive perioperative cardiac risk management based on routine monitoring of high-sensitivity troponins and natriuretic peptides. Abstract This review focuses on contemporary approaches to the assessment and management of perioperative cardiac risk in patients undergoing non-cardiac surgery. The topic's relevance stems from the high frequency of major adverse cardiovascular events (MACE), a leading cause of perioperative mortality. The work analyzes the paradigm shift from the question “is surgery feasible?” to the strategy “how to improve outcomes?” which includes pre-operative optimization (prehabilitation), active monitoring, and a multidisciplinary approach. Based on a literature search, the epidemiology and key risk factors are reviewed, with detailed classifications and definitions of complications (MACE, myocardial injury after noncardiac surgery (MINS), intraoperative critical incidents). Particular attention is paid to the role of biomarkers (NT-proBNP/BNP for prediction, high-sensitivity troponin for diagnosing MINS) and hemodynamic control in risk stratification and early detection of myocardial injury. A comparative analysis of prognostic tools is conducted: from traditional clinical scales (Lee index, NSQIP) to modern machine learning models demonstrating high accuracy. Problems with validation, clinical interpretability, and the integration of these tools into routine practice are noted. Contemporary recommendations for risk minimization, based on staged personalized assessment, patient optimization, and active postoperative monitoring, are summarized. The conclusion emphasizes the need to develop integrative, transparent, and clinically applicable algorithms that combine data from scales, biomarkers, and dynamic monitoring to transition from reactive to preventive management of perioperative cardiac risk.

Introduction. Anesthetic safety in children remains one of the key challenges of modern clinical practice. Despite advances in monitoring and procedural standardization, the risk of intraoperative critical incidents (CI) in children remains significantly higher than in adults. Simple non-invasive tools for preoperative risk assessment based on key physiological parameters require validation. Objective. To analyze breath-holding test (BHT) after deep inspiration as a predictor of intraoperative CI in children undergoing mechanical ventilation (MV). Material and methods. A prospective study enrolled 135 children aged 5—10 years scheduled for elective abdominal surgery under general anesthesia with MV. Preoperative triple BHT was administered, and mean value used for analysis. Recording intraoperative CIs was the primary objective of intraoperative monitoring. Predictive value of BHT for CIs was assessed using ROC curves. Results. Intraoperative CIs were identified in 20.7% of patients. Shorter BHT after deep inspiration was significantly associated with higher incidence of intraoperative CI. AUC was 0.847 for overall critical incidents, 0.903 for hemodynamic, and 0.853 for respiratory events (p<0.001). Optimal cutoff values for BHT were ≤28 seconds for overall and hemodynamic incidents and ≤27 seconds for respiratory incidents. Conclusion. BHT after deep inspiration is a simple, noninvasive, and informative method for preoperative risk stratification and demonstrates high predictive value for intraoperative CI in mechanically ventilated pediatric patients.

INTRODUCTION: The epidemiology of adverse outcomes after abdominal surgery remains one of the key problems of modern surgery and anesthesiology-resuscitation because of an aging population and an increasing prevalence of comorbidity. OBJECTIVE: To describe the epidemiology of postoperative complications in high risk patients undergoing elective abdominal surgery and to compare their outcomes with the overall STOPRISK cohort. MATERIALS AND METHODS: A prospective, multicenter observational cohort study was conducted in 38 Russian centers and included 11 478 adults undergoing elective surgery on abdominal and pelvic organs; among them, 1367 patients (11.9 %) met guideline based criteria for high surgical risk. Postoperative complications within 30 days were recorded according to EPCO definitions and graded using the Clavien—Dindo classification, with detailed assessment of frequency, structure, severity and timing. RESULTS: In the high risk subgroup, at least one complication occurred in 14.2 % of patients and mortality was 1.8 %, approximately threefold higher than in the overall cohort (4.5 and 0.62 %, respectively). The most frequent complications were postoperative ileus (4.8 %), postoperative bleeding (2.6 %), wound infection and pneumonia (2.4 % each), anastomotic leakage (2.1 %), acute kidney injury and postoperative delirium (1.9 % each). Overall, 68.5 % of events were severe complications (Clavien—Dindo ≥ III), usually requiring intensive care and/or invasive interventions. Two thirds of all complications occurred within the first 5 postoperative days, which also concentrated most severe and fatal events, including circulatory arrest, severe respiratory and renal failure. CONCLUSIONS: High risk surgical patients, while representing a small proportion of the elective abdominal population, account for a disproportionally large share of severe complications and deaths. These findings support the implementation of targeted protocols for intensive monitoring, early diagnosis and prevention during the early postoperative window, as well as refinement of national perioperative risk stratification tools and resource planning.

Objective. To determine prognostic value of laboratory tests and clinical scoring systems at admission to ICU in early stage of disease for predicting severity of acute pancreatitis. Material and methods. A single-center retrospective study included ICU patients with moderate-to-severe acute pancreatitis in 2022—2024. Patients were stratified into groups depending on disease severity. We evaluated APACHE II, BISAP, and SOFA scores, as well as laboratory biomarkers and composite indices. Analysis included ROC curve with AUC, multivariate logistic regression analysis and identification of predictors of severe disease. Results. Final analysis included data on 142 eligible patients divided into the moderate pancreatitis group (n=58) and severe pancreatitis group (n=84). In multivariate analysis, SOFA and BISAP scores, along with Red Cell Distribution Width — Coefficient of Variation (RDW-CV), were identified as independent risk factors of severe disease. Combination of SOFA and RDW-CV demonstrated the highest predictive value (AUC=0.844, sensitivity 71.4%, specificity 93.1%). Conclusion. SOFA score and RDW-CV may be useful for early identification of patients with high risk of severe acute pancreatitis and optimization of treatment.

BACKGROUND: Adequate postoperative pain control after bariatric laparoscopic resection is a key factor for rapid recovery and reduced risk of complications. Published data provide conflicting results regarding the comparative efficacy of the nonsteroidal anti-inflammatory drugs ibuprofen and ketorolac as components of multimodal analgesia in perioperative pain management. AIM: The work aimed to compare the efficacy and safety of perioperative analgesia with ibuprofen and ketorolac within a multimodal approach in patients undergoing elective bariatric surgery. METHODS: A prospective analysis included data from 90 patients divided into two groups: the ibuprofen group (n = 45) and the ketorolac group (n = 45). The primary outcome was pain intensity assessed using the visual analog scale at rest and during movement 0.5, 2, 4, 6, 12, and 24 hours after surgery. Secondary outcomes included time to first analgesic request, need for rescue analgesia, bowel function (time to first flatus), mobilization (time to ambulation), quality of recovery according to the QoR-15 scale, and incidence of adverse effects. Statistical analysis was performed using the t test or Mann–Whitney U test for continuous variables and Fisher exact test for categorical variables. RESULTS: The groups were comparable in age, body mass index, ASA physical status, and duration of surgery (p 0.05). At rest, ibuprofen demonstrated significant benefits after 4 hours (median visual analog scale [VAS] score, 32 mm vs 37 mm; p = 0.0004) and 12 hours (29 mm vs 36 mm; p = 0.020), and during movement after 4 hours (35 mm vs 44 mm; p = 0.0002) and 12 hours (33 mm vs 45 mm; p = 0.0097). The frequency of rescue analgesia was lower in the ibuprofen group (26.7٪ vs 46.7٪; p = 0.049), and the time to the first analgesic dose was longer (median, 124 min vs 72 min; p = 0.008). Bowel function recovery occurred earlier (15 h vs 23 h; p 0.0001), and the quality of recovery measured by the QoR-15 scale was higher after 48 and 72 hours (p = 0.0002; p 0.0001, respectively). The incidence of adverse effects was low and did not differ significantly between groups. CONCLUSION: The preventive use of ibuprofen as part of multimodal analgesia provides earlier and more pronounced pain reduction, decreases the need for rescue analgesia, and accelerates bowel function recovery and overall well-being without increasing the risk of complications compared with ketorolac.