Aashray K. Gupta

Background: Ischaemic optic neuropathy (ION) is the most common cause of perioperative visual loss, with the highest incidence reported after cardiac surgery involving cardiopulmonary bypass (CPB). Despite devastating complications, the overall incidence and risk factors for patients with ION in the setting of cardiac surgery remain contentious. We performed a systematic review and meta‐analysis to evaluate the pooled incidence and risk factors for ION after cardiac surgery. Methods: This review conformed to PRISMA guidelines. PubMed, MEDLINE, Web of Science and Cochrane were searched up to 24 March 2024 for multivariable‐adjusted and propensity‐matched observational studies assessing the incidence of ION exclusively in patients undergoing cardiac surgery. Odds ratios (ORs) with 95% confidence intervals (CIs) for each risk factor were pooled from the selected studies using a random‐effects model. Results: Eleven studies totalling 6 471 577 patients were included. The pooled incidence of ION was approximately 1.50 per 10 000 cardiac surgeries. Significant risk factors for developing ION included coronary artery disease (OR 1.81, 95% CI 1.17–2.79), peripheral vascular disease (OR 1.67, 95% CI 1.10–2.54) and anaemia (OR 1.06, 95% CI 1.00–1.13). Notably, the incidence varied widely between studies, reflecting differences in diagnostic criteria and reporting standards. Newcastle–Ottawa risk of bias assessment found the studies to be of overall moderate methodological quality, with a high risk of bias. Conclusion: This study demonstrates a relatively low overall pooled incidence of ION following cardiac surgery, with identifiable risk factors such as pre‐existing coronary artery disease, peripheral vascular disease and perioperative anaemia.

Background: Coronary artery bypass grafting (CABG) is associated with significant morbidity and mortality. Traditional risk scores, such as the Society of Thoracic Surgery (STS) and EuroSCORE II, have limitations in predicting outcomes, particularly in high-risk patients. Machine learning (ML) models may address these issues by detecting nuanced data patterns not captured by conventional methods. This systematic review and meta-analysis compared the efficacy of ML models with traditional risk scores in predicting outcomes after CABG. Methods: A comprehensive literature search of records up to August 14 ^th 2025, was conducted using PubMed, Embase, Web of Science, and the Cochrane Library. Studies included used ML algorithms and traditional risk scores to predict all-cause mortality (in-hospital, 30-day, or longer term as reported by each study) following CABG. Data extraction and quality assessment were independently performed by two reviewers. Meta-analyses were conducted using a linear mixed-effects model, with C-statistics as the primary measure of discrimination. Results: Twenty-six studies, comprising 565 063 participants, met the inclusion criteria. The pooled C-statistic for ML models was 0.82 (95% CI 0.79-0.85), significantly higher than the 0.73 (95% CI 0.71-0.76) for traditional risk scores ( P < 0.0001). The top-performing ML model achieved a C-statistic of 0.98 (CI 0.95-1.00). Calibration was reported inconsistently across studies and was synthesised narratively rather than quantitatively. Where reported, ML calibration was generally adequate but a robust head-to-head comparison with traditional risk scores was not possible. Subgroup analyses revealed consistent superior performance of ML models across various algorithms and covariate sets. Conclusions: In this meta-analysis of predominantly internally-validated models, ML approaches showed higher pooled discrimination than traditional risk scores for mortality after CABG. However, given the small number of pooled studies, very high between-study heterogeneity (I ² = 98%), the predominance of high risk-of-bias studies, and the scarcity of external validation, these findings should be interpreted as supporting the promise of ML rather than establishing proof of clinical superiority. Confirmatory prospective, externally validated studies are required before ML can be recommended for routine pre-operative risk stratification.

Coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) are the two dominant revascularisation strategies for obstructive coronary artery disease, yet their relative roles continue to shift because they address coronary pathophysiology differently with ever-evolving techniques. PCI has advanced through iterative improvements, including balloon angioplasty, bare-metal stents, and drug-eluting stents, with contemporary outcomes increasingly driven by procedural optimisation using intracoronary imaging and physiology-guided lesion selection rather than device category alone. CABG has progressed through perioperative management, improvements in operative safety, and, critically, conduit durability. Recognition of progressive saphenous vein graft failure has underpinned a conduit-optimisation era in which the left internal mammary artery to left anterior descending artery remains the gold standard. Further, broader arterial grafting (including radial artery use, multiple arterial grafting, and selected total-arterial strategies) has been increasingly applied, albeit with deliverability and competing-risk constraints highlighted in randomised evidence. This perspective review reframes the CABG versus PCI comparison not as a binary contest, but as a context-dependent assessment in which the relative value of each strategy depends on the specific technologies, techniques, and conduits available at the time of comparison. We summarise comparative effectiveness where evidence is most consistent and where it remains sensitive to anatomy, comorbidity, and endpoint definitions. In diabetes with multivessel disease, trial data favour CABG for long-term survival and clinical outcomes despite higher stroke risk. In left main disease, outcomes depend on lesion pattern and overall complexity, with trial-era stent technology and composite endpoint definitions influencing conclusions. In ischaemic left ventricular dysfunction, a long-term survival benefit is established for CABG added to medical therapy, while multi-vessel PCI has not demonstrated comparable prognostic modification in contemporary data. We then examine hybrid coronary revascularisation as territory-specific allocation, highlighting its physiological rationale, program dependence, and limited, adequately powered randomised evidence. Finally, we outline how artificial intelligence (AI) and robotics may accelerate a precision revascularisation paradigm by standardising lesion assessment, supporting procedural planning, improving procedural reproducibility, and enabling more patient-specific selection among PCI, contemporary CABG with optimised conduits, and hybrid pathways.

Introduction: Sleep disturbance is a common issue affecting nearly 78% of cardiothoracic postoperative patients. There are no systematic reviews of randomised controlled trials (RCTs) that evaluate the impact of sleep interventions within this population. This study aims to evaluate the sleeping patterns in post‐operative cardiac surgery patients and identify if sleep interventions improve sleep quality. The primary outcomes were subjective and objective measures of sleep using the Pittsburgh Sleep Quality Index (PSQI) in addition to the duration of intensive care unit (ICU) and hospital admission. Methods: Searches on PubMed, Medline, Embase, Scopus and Cochrane were undertaken from database inception to 24 February 2023. Only RCTs that recorded a sleep intervention in addition to standardised sleep scoring measures in adult cardiac surgical patients were included. Results: A total of 26 studies were included ( n = 2323). Total PSQI score had a statistically significant standardised mean difference (SMD) between intervention and control groups for improved scores as indicated by the SMD of −0.73 (95% CI; −0.93; −0.52, p = 0.04). The duration of ICU admission had a SMD of −0.23 (95% CI; −0.50 to 0.03, p = 0.19) and was not statistically significant. Similarly, the duration of hospital admission had a SMD of −1.10 (95% CI; −2.56 to 0.37, p = < 0.01) and was not statistically significant. Conclusion: Whilst sleep interventions did not have a statistically significant effect on the duration of ICU or hospital admission, they did show a significant improvement in sleep quality as reported through the PSQI total score.

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