Search Results (237)

Background: Systemic inflammation plays a central role in determining postoperative outcomes in patients undergoing isolated coronary artery bypass grafting with cardiopulmonary bypass. Traditional inflammatory indices such as the neutrophil-to-lymphocyte ratio and the platelet-to-lymphocyte ratio have prognostic value; however, their dynamic behavior during cardiopulmonary bypass remains insufficiently characterized. More comprehensive indices, including the systemic immune-inflammation index and the systemic inflammatory response index, may help characterize early intraoperative inflammatory activity; however, their prognostic relevance should be regarded as exploratory and requires prospective validation. Methods: This retrospective nested case–control study included 245 patients who underwent isolated coronary artery bypass grafting, and intraoperative inflammatory indices during cardiopulmonary bypass were evaluated. Because of the nested case–control design, mortality cases were intentionally overrepresented to improve statistical power; therefore, the observed mortality rate does not reflect the true institutional mortality rate. Inflammatory indices (NLR, PLR, SII, and SIRI) were calculated at induction, at the 5th, 45th, and 90th minutes during cardiopulmonary bypass, and in the early postoperative period. Associations between these indices and in-hospital mortality were evaluated using univariate and multivariable logistic regression analyses. Predictive performance was assessed using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Results: The final enriched analytical sample consisted of 51 mortality cases and 194 randomly sampled surviving controls. During cardiopulmonary bypass, inflammatory indices, particularly at the 5th minute, were significantly higher in patients who experienced mortality (p < 0.001 for all major indices). SII demonstrated the strongest predictive performance at the 5th minute (AUC = 0.790), followed by SIRI (AUC = 0.765), PLR (AUC = 0.687), and NLR (AUC = 0.681). In multivariable analysis, SII and SIRI measured at the 5th minute remained independent predictors of mortality. The addition of 5th-minute SII to the limited study-specific clinical model, which included age, ejection fraction, and preoperative creatinine, improved exploratory discrimination for in-hospital mortality (with AUC increasing from 0.698 to 0.797). Conclusions: Early intraoperative assessment of inflammatory indices during cardiopulmonary bypass may provide additional prognostic information in patients undergoing coronary artery bypass grafting. Composite indices, particularly SII and SIRI, showed stronger exploratory discrimination than traditional inflammatory markers in this enriched analytical sample. However, these findings should be considered hypothesis-generating and require prospective external validation before use in perioperative risk stratification or clinical decision-making can be recommended. Full article

Background/Objectives: Surgical site infection (SSI) remains a clinically important complication after spinal surgery. This study developed and assessed machine learning approaches for predicting postoperative SSI using routinely collected preoperative clinical variables, with emphasis on calibration and clinical applicability. Methods: In this retrospective single-center study, four prediction models were developed in patients undergoing spinal surgery: logistic regression, random forest, gradient boosting, and XGBoost. Model training used five-fold stratified cross-validation, and performance was evaluated using a hold-out internal test set. Performance was assessed using the area under the receiver operating characteristic curve (AUC), area under the precision–recall curve (AUPRC), sensitivity, precision, F1 score, Brier score, and calibration slope. SHAP analysis was performed to evaluate model interpretability. Results: The incidence of SSI was 16.6%. In cross-validation, discrimination performance was broadly comparable across models, with logistic regression showing the highest observed AUC (0.814) and AUPRC (0.484). In the hold-out test set, the same model showed the highest AUC (AUC 0.806, 95% CI 0.757–0.852) and the highest sensitivity (0.758). Calibration performance varied across models. SHAP analysis identified C-reactive protein, hemoglobin, albumin, and white blood cell count as the most influential predictors. Perioperative variables provided only modest incremental predictive value. Conclusions: Machine learning models showed acceptable performance for predicting SSI after spinal surgery. Logistic regression demonstrated performance comparable to that of the evaluated machine learning models, suggesting that conventional statistical approaches may remain clinically useful in structured datasets. Preoperative clinical and laboratory variables were the major contributors to prediction, supporting their use for routine preoperative risk stratification. Full article

Cardiac surgery remains a high-risk, resource-intensive domain in which perioperative complications significantly influence clinical outcomes, institutional performance, and healthcare expenditure. Despite advances in technique and protocol standardization, contemporary perioperative management largely relies on static risk stratification and reactive quality assessment. This narrative review synthesizes the current evidence on artificial intelligence (AI) and machine learning for perioperative risk prediction in cardiac surgery, spanning acute kidney injury, mortality, prolonged mechanical ventilation, postoperative atrial fibrillation, and intensive care unit deterioration, and critically appraises the methodological limitations, validation gaps, and fairness concerns that constrain clinical translation. Across these applications, predictive models have demonstrated incremental discrimination over conventional risk scores, yet remain predominantly endpoint-specific, single-institution, and disconnected from prospective clinical implementation. Building on this evidence, we propose Preventive Cardiovascular Intelligence (PCInt) as one possible organizing framework that integrates predictive analytics, dynamic risk trajectory modeling, and structured quality improvement methodologies, and we outline how such a framework might be operationalized across the surgical lifecycle. PCInt is presented as a conceptual proposal requiring prospective validation rather than as a validated system. We conclude by discussing implementation barriers, regulatory and ethical considerations, and priorities for future research toward anticipatory, value-based perioperative cardiovascular care. Full article

Background/Objectives: Postoperative pulmonary complications (PPCs) are a major source of morbidity following cardiac surgery, particularly in older adults. While malnutrition is linked to adverse outcomes, the optimal screening tool for identifying patients at risk of PPCs remains uncertain. This study compared the predictive performance of the Geriatric Nutritional Risk Index (GNRI), Mini Nutritional Assessment–Short Form (MNA-SF), Prognostic Nutritional Index (PNI), and Nutrition Alert Form (NAF) for PPCs in older adults undergoing elective cardiac surgery. Methods: This prospective cohort study enrolled 217 patients aged ≥ 60 years at a tertiary university hospital. Preoperative nutritional status was assessed using the GNRI, MNA-SF, PNI, and NAF. The primary outcome was PPC development during hospitalization. Predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis, and multivariable logistic regression identified independent predictors. Results: PPCs occurred in 86 patients (39.6%). Patients who developed PPCs had significantly higher NAF scores than those who did not (median [IQR]: 7.5 [3–12] vs. 5 [2–8], p < 0.001), whereas GNRI, MNA-SF, and PNI scores did not differ significantly. NAF demonstrated the highest predictive performance (AUC: 0.643, 95% CI: 0.567–0.719), followed by PNI, MNA-SF, and GNRI. However, after adjusting for clinical covariates, none of the nutritional assessment tools remained independently associated with PPCs. Conclusions: Among the four tools evaluated, NAF showed the highest predictive performance among the evaluated nutritional assessment tools; however, its discriminative ability was modest, and none of the nutritional assessment tools remained independently associated with PPCs after multivariable adjustment. Nutritional assessment should complement, rather than replace, established clinical risk factors in perioperative risk stratification. Full article

Perioperative myocardial ischemia and myocardial injury after noncardiac surgery (MINS) remain prevalent contributors to postoperative morbidity and mortality. Recent advances, including high-sensitivity biomarkers and updated 2024 American Heart Association/American College of Cardiology (AHA/ACC) guidelines, have modified the clinical approach to preoperative ischemic evaluation. This review intends to synthesize contemporary evidence and provide a framework for perioperative ischemic workup. A narrative review of the current literature and major society guidelines was conducted, focusing on perioperative risk stratification, functional capacity assessment, biomarker utilization, noninvasive and invasive diagnostic modalities, and perioperative medical optimization strategies. Contemporary perioperative evaluation favors a stepwise, risk-based approach that uses clinical risk indices, functional capacity, and selective diagnostic testing. Biomarkers such as natriuretic peptides and cardiac troponins enhance risk prediction and enable the detection of MINS, which is strongly associated with increased mortality. Evidence does not support routine preoperative stress testing or prophylactic coronary revascularization in stable patients. Guideline-directed medical therapy, including sustained statin use and attentive management of antiplatelet and beta-blocker therapy, remains central to risk mitigation. Modern perioperative ischemic workup prioritizes individualized, evidence-based evaluation over routine testing. Integration of biomarkers, structured risk assessment, and multidisciplinary management may improve outcomes, though additional research is needed to define optimal strategies for detecting and treating MINS. Full article

Cardiac arrhythmias complicate 20–50% of surgical procedures and contribute substantially to perioperative morbidity, mortality, and healthcare costs, with postoperative atrial fibrillation (POAF) being the most frequent form. Their genesis reflects the convergence of surgical stress, anaesthetic agents, autonomic imbalance, systemic inflammation, and electrolyte disturbances, explaining the limited efficacy of single-mechanism interventions. This narrative review synthesises contemporary evidence on pathophysiology, risk stratification, prevention, acute management, and emerging technologies, emphasising individualised, patient-tailored approaches. MEDLINE, Embase, and Cochrane CENTRAL were searched (January 2010–January 2026), prioritising randomised trials, meta-analyses, and guidelines. Contemporary risk stratification integrates clinical scores, biomarkers, and electrocardiographic parameters; machine-learning models show moderate discrimination (pooled AUC 0.84) and may enable more personalised prediction pending external validation. Evidence-based prophylaxis—beta-blockade, magnesium, selective amiodarone, and emerging anti-inflammatory strategies such as colchicine—reduces POAF in high-risk populations, while acute management is guided by haemodynamic status and individual risk. Anticoagulation follows CHA₂DS₂-VASc stratification, although optimal timing and duration remain undefined. Wearable monitoring, AI-based detection, and atrial-selective agents show clinical promise. Systematic, personalised integration of risk assessment, prophylaxis, monitoring, and management offers the clearest path to reducing arrhythmia-associated morbidity. Full article

Enhanced Recovery After Surgery (ERAS) pathways have improved perioperative outcomes by standardizing evidence-based interventions across the surgical continuum. However, substantial variability in postoperative recovery persists, even within well-implemented ERAS programs. This heterogeneity reflects differences in clinical risk, functional reserve, biological response to surgical stress, treatment responsiveness, and contextual factors that are not fully captured by uniform protocols. Precision medicine provides a potential framework for refining ERAS by integrating patient-specific data into perioperative risk assessment, intervention selection, patient monitoring, and recovery planning. Nevertheless, most precision medicine tools remain insufficiently validated for routine ERAS implementation, and their clinical utility is limited by heterogeneous evidence, data integration challenges, costs, workflow complexity, and equity concerns. Future progress will require prospective validation, pragmatic implementation studies, interoperable data systems, and evaluation of patient-centered outcomes. This narrative review examines the emerging role of precision medicine tools in perioperative practice and proposes an idealized conceptual model of “precision ERAS” in which standardized evidence-based care is preserved as the foundation, while selected interventions are adapted according to individual risk, biological phenotype, and recovery trajectory. Full article

Background/Objectives: Irritable bowel syndrome (IBS) is among the most prevalent disorders of gut–brain interaction, yet its implications for spine surgery remain poorly characterized. This narrative review examines how IBS influences symptom presentation and postoperative outcomes in spine surgery patients. Methods: We synthesized the neurobiologic, epidemiologic, and perioperative literature linking IBS with musculoskeletal pain, spine-related symptomatology, and surgical outcomes, drawing on spine-specific data where available and on related surgical and chronic-pain populations where it was not. Results: IBS is characterized by central sensitization, impaired descending inhibition, increased temporal summation, autonomic dysregulation, and a high prevalence of psychiatric comorbidity, which manifest as widespread hyperalgesia and symptom amplification that overlap with pain mechanisms common in spine surgery patients. Epidemiologic studies indicate that patients with IBS undergo musculoskeletal and spinal procedures at disproportionately high rates, reflecting both symptom burden and diagnostic uncertainty from viscerosomatic overlap. These same factors have been associated with greater postoperative pain, elevated opioid requirements, slower functional recovery, and reduced satisfaction after spine surgery, although direct IBS-specific spine data remain limited. IBS may also confound preoperative assessment by mimicking radicular, discogenic, or sacroiliac pain. Conclusions: IBS represents an under-recognized potential modifier of symptom localization, perioperative pain trajectories, and functional recovery in spine surgery. Greater awareness of IBS-related nociplastic and psychosocial mechanisms may improve preoperative evaluation, risk stratification, perioperative management, and the design of future outcome studies. Full article

Background and Objectives: Malnutrition is highly prevalent among older adults with hip fracture and is associated with poorer surgical outcomes, yet its prognostic relevance is often under-recognized in routine orthopaedic practice. The Controlling Nutritional Status (CONUT) score is an objective laboratory-based screening tool; however, evidence regarding its value for predicting long-term mortality after hip fracture remains limited. This study aimed to evaluate whether nutritional status assessed by the CONUT score independently predicts mortality in older patients with hip fracture. Materials and Methods: This retrospective observational cohort study included consecutive patients aged ≥70 years admitted for hip fracture to a tertiary hospital between 2014 and 2021. Nutritional status was assessed at admission using the CONUT score and categorized as no, mild, moderate, or severe nutritional risk. Demographic characteristics, comorbidity burden, perioperative variables, postoperative morbidity, and mortality up to five years were recorded. Survival was evaluated using Kaplan–Meier methods, and independent predictors of mortality were identified using multivariable Cox proportional hazards models adjusted for clinically relevant confounders. Results: A total of 2798 patients were included (mean age [SD] 84.3 [6.3] years; 26.4% male), of whom 79.2% presented some degree of nutritional risk at admission. Mortality increased overall with worsening nutritional status (p < 0.001). After comprehensive multivariable adjustment, higher CONUT scores remained independently associated with mortality, with each one-point increase associated with an approximately 22% higher risk of long-term death. Poorer nutritional status was also associated with higher postoperative complication rates, greater transfusion requirements, and longer hospital stay. Conclusions: Nutritional status assessed using the CONUT score is an independent predictor of short-, mid-, and long-term mortality in older patients undergoing surgery for hip fracture. Incorporation of objective nutritional screening into orthogeriatric pathways may improve perioperative risk stratification and support targeted multidisciplinary management. Full article

Background/Objectives: Frailty is increasingly recognized as a major determinant of postoperative outcomes in older patients undergoing oncologic surgery. The Primary Care Frailty Index (PC-FI), a deficit accumulation-based instrument derived from routinely available clinical information, has recently been proposed as a practical frailty assessment tool. However, evidence supporting its application in gastrointestinal surgical oncology remains limited. Methods: We conducted a retrospective cohort study including patients aged ≥65 years who underwent elective major colorectal or gastric cancer surgery between January 2022 and October 2025 at a tertiary Italian hospital. Patients with a PC-FI > 0.07 were included and categorized as having mild (0.07–0.13) or moderate-to-severe frailty (≥0.14). Postoperative outcomes included the Comprehensive Complication Index (CCI), Clavien–Dindo classification, and length of hospital stay. Receiver operating characteristic (ROC) analysis was performed to compare the discriminative performance of PC-FI, ASA classification, and the ACS Surgical Risk Calculator. Results: Ninety-two patients met the inclusion criteria. Patients with moderate-to-severe frailty were significantly older and had higher ASA class, Charlson Comorbidity Index, and ACS morbidity estimates than mildly frail patients. They also experienced a greater postoperative complication burden (mean CCI 25.96 vs. 16.40, p = 0.02) and longer hospital stay (9.89 vs. 7.43 days, p = 0.01). ROC analysis demonstrated modest discriminative performance for PC-FI (AUC 0.63), comparable to ASA classification (AUC 0.68) and the ACS morbidity score (AUC 0.70), without statistically significant differences among the three instruments. Conclusions: Higher PC-FI scores were associated with increased postoperative morbidity and prolonged recovery following major gastrointestinal oncologic surgery. Although its discriminative performance was modest and does not support its use as a stand-alone risk prediction tool, the PC-FI may represent a simple first-line frailty screening instrument to identify older patients who could benefit from comprehensive multidisciplinary perioperative assessment. Full article

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are increasingly prescribed for diabetes and weight loss, with many breast reconstruction candidates being prescribed these medications. However, perioperative risks remain unclear. This study evaluated the association between GLP-1RA use and postoperative complications in implant-based and autologous tissue breast reconstruction. Methods: A retrospective analysis of TriNetX identified patients undergoing implant-based or autologous tissue breast reconstruction. Preoperative GLP-1RA users were compared to matched controls. Patients were propensity score matched (1:1 and 1:3) for demographics and comorbidities including body-mass index and timing of reconstruction (delayed vs. immediate). Ninety-day outcomes were assessed using logistic regression. Results: Between 2014 and 2024, 57,987 patients were identified, of which 823 were GLP-1RA users. Of those users, 326 patients undergoing implant-based reconstruction and 51 patients undergoing autologous reconstruction were matched to controls. In implant-based cohorts, GLP-1RA use was associated with increased odds of implant failure (1:1 OR 1.70, 95% CI 1.18–2.45, p = 0.0046), wound healing complications (1:1 OR 1.90, p = 0.027), and higher readmission/ED utilization (1:1 OR 1.80, 95% CI 1.04–3.21, p = 0.040). No significant differences were observed for hematoma, seroma, or thromboembolism. In autologous reconstruction, GLP-1RA use was not associated with increased risks. Conclusions: GLP-1RA use is linked to higher rates of implant failure, wound healing complications, and readmission in implant-based breast reconstruction only. These findings highlight the need for risk stratification and counseling of GLP-1RA users undergoing implant-based procedures and for further research investigating the implications of perioperative use of these agents in plastic surgery. Full article

Surgical intervention in Crohn’s disease remains a significant contributor to patient morbidity, with postoperative complication rates reported between 20% and 50%. These complications include a broad spectrum of adverse outcomes, such as surgical site infections, intra-abdominal abscesses, and anastomotic leakage, all of which can substantially impact recovery, healthcare costs, and long-term prognosis. Although several clinical and perioperative risk factors have been identified, accurate prediction of postoperative outcomes remains challenging, highlighting the need for improved risk stratification strategies. In recent years, the evolution of biological therapies has transformed the management of Crohn’s disease, raising important questions regarding their influence on surgical outcomes and postoperative healing. Consequently, a more nuanced understanding of the interplay between medical and surgical approaches is required to optimize patient care. This systematic review aims to evaluate established and emerging predictive biomarkers associated with postoperative complications in Crohn’s disease surgery. Particular emphasis is placed on inflammatory markers, nutritional parameters, and novel molecular signatures. Furthermore, the review explores the growing role of multiomics approaches—including genomics, proteomics, and metabolomics—as well as the integration of machine learning models to enhance predictive accuracy. By synthesizing current evidence, this study underscores the potential of combining biomarkers with advanced analytical tools to support personalized risk assessment and guide clinical decision-making in Crohn’s disease surgery. Full article

Background/Objectives: Placenta accreta spectrum (PAS) is an increasingly prevalent and potentially life-threatening complication in women with placenta previa. Despite widespread clinical use, the inter-modality agreement between prenatal ultrasound and MRI and their comparative value for predicting perioperative hemorrhage remain poorly characterized, particularly in low-to-moderate risk populations where placenta accreta predominates. We aimed to compare inter-modality agreement between standardized ultrasound and MRI impressions and to evaluate each modality’s predictive value for perioperative hemorrhage. Methods: This prospective cohort study enrolled 47 women with placenta previa who underwent both standardized ultrasound and MRI prospectively between 28 + 0 and 32 + 6 weeks of gestation, with perioperative outcomes collected at the time of cesarean delivery. Both modalities were classified using a three-tier impression system (None/Suspected/Likely) based on standardized structural, vascular, and invasive marker composites. The primary outcome was inter-modality agreement (linearly weighted Cohen’s κ); secondary outcomes were the association of each modality’s impression with postpartum hemorrhage (PPH; estimated blood loss ≥ 1000 mL) and estimated blood loss (EBL). Results: PAS was confirmed in 18 of 47 women (38.3%), predominantly placenta accreta (83.3%). Inter-modality agreement was fair (weighted κ = 0.263), structural concordance was moderate (κ = 0.539), while vascular agreement was near-absent (κ = 0.085). Ultrasound impression demonstrated a dose-dependent association with PPH rates (38.5%, 52.9%, and 82.4% across None, Suspected, and Likely tiers; p = 0.048) and EBL (800, 1000, and 1800 mL; p = 0.003), with logistic regression confirming a 2.70-fold increase in PPH odds per tier (p = 0.018; AUC 0.657). MRI impression was not associated with PPH (p = 1.000), EBL (p = 0.743), or PAS status (p = 0.741; AUC 0.543). Serum AFP was significantly elevated in women with PPH (p = 0.005). Conclusions: In this accreta-predominant, low-to-moderate risk cohort, ultrasound—but not MRI—demonstrated a significant dose-dependent association with perioperative hemorrhage. These findings should not be interpreted as evidence of general MRI inadequacy but rather as reflecting the specific imaging context in which MRI’s strengths in deep invasion characterization are less clinically determinative. These results support ultrasound as the primary tool for hemorrhage risk stratification in this population. Full article

Background: Thymic epithelial tumors (TETs) are rare mediastinal malignancies whose prognosis is largely determined by histology. Current predictive models rely on clinical variables and subjective imaging interpretation, with unsatisfied performance. Non-invasive pre-treatment risk stratification could guide surgical planning and perioperative management in patients with TETs. The role of fluorine-18 (¹⁸F) fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (CT) in identifying aggressive disease is increasingly recognized. In this bicentric study, we aimed to evaluate a machine learning-based radiomics model using PET and CT images to differentiate between low-risk and high-risk TETs. Methods: Seventy-five patients who underwent PET/CT to evaluate the suspected anterior mediastinal mass and histopathologically diagnosed with TETs were included. On PET/CT images, the tumor was manually segmented by two experienced clinicians. First-order, shape, and texture features were extracted using the PyRadiomics library, resulting in 200 radiomics features (186 intensity/texture features and 14 shape features). In addition, rPET (i.e., tumor SUVmax/Liver SUVmax) parameter was included, yielding a grand total of 201 features. The feature set was reduced to 20 variables using ANOVA, with both selection and model evaluation performed via stratified 5-fold cross-validation. Results: The proposed approach achieved an average balanced accuracy of 0.58 ± 0.07 and an average AUC of 0.71 ± 0.04. Average sensitivity and specificity were 0.48 and 0.68, respectively. The model obtained an average Gmean of 0.57, indicating balanced and stable classification performance. Conclusions: Our ML models trained on PET/CT radiomic features showed moderate discriminatory performance for TET risk stratification. Full article

Dual antiplatelet therapy (DAPT) with aspirin and a P2Y12 inhibitor remains the cornerstone of antithrombotic management after myocardial revascularization. However, the traditional “one-size-fits-all” approach to DAPT duration and intensity fails to account for marked interindividual variability in drug response—driven by genetic polymorphisms, notably CYP2C19 variants like CYP2C19*2, which reach a frequency of up to 75% in specific groups like the Melanesian population—comorbidities such as diabetes and chronic kidney disease, and dynamic clinical factors including age and concomitant medications. We examine the current landscape of precision medicine tools for individualizing DAPT, including platelet function testing, point-of-care genotyping, validated clinical risk scores, and emerging artificial intelligence (AI)–based predictive models. Evidence from landmark trials is synthesized to evaluate escalation, de-escalation, and duration-tailoring strategies within the ischemic–bleeding trade-off framework. Special populations requiring individualized approaches are reviewed, including patients with atrial fibrillation, the elderly, and those requiring urgent noncardiac surgery with perioperative bridging. Future directions, including multi-omics integration, novel antiplatelet agents, and AI-driven clinical decision support systems, are also explored. As a narrative review, conclusions should be interpreted as reflective of current evidence synthesis rather than systematic-review-grade evidence, given the absence of formal risk-of-bias scoring or meta-analytic pooling. Personalized DAPT guided by complementary genetic and phenotypic testing, integrated with dynamic risk stratification, offers a paradigm shift from empiric therapy toward precision-guided antithrombotic management with the potential to simultaneously reduce ischemic and bleeding complications. Full article