Introduction: Intraoperative Hypotension (IOH) poses a substantial risk during surgical procedures. The integration of Artificial Intelligence (AI) in predicting IOH holds promise for enhancing detection capabilities, providing an opportunity to improve patient outcomes. This systematic review and meta analysis explores the intersection of AI and IOH prediction, addressing the crucial need for effective monitoring in surgical settings. Method: A search of Pubmed, Scopus, Web of Science, and Embase was conducted. Screening involved two-phase assessments by independent reviewers, ensuring adherence to predefined PICOS criteria. Included studies focused on AI models predicting IOH in any type of surgery. Due to the high number of studies evaluating the hypotension prediction index (HPI), we conducted two sets of meta-analyses: one involving the HPI studies and one including non-HPI studies. In the HPI studies the following outcomes were analyzed: cumulative duration of IOH per patient, time weighted average of mean arterial pressure < 65 (TWA-MAP < 65), area under the threshold of mean arterial pressure (AUT-MAP), and area under the receiver operating characteristics curve (AUROC). In the non-HPI studies, we examined the pooled AUROC of all AI models other than HPI. Results: 43 studies were included in this review. Studies showed significant reduction in IOH duration, TWA-MAP < 65 mmHg, and AUT-MAP < 65 mmHg in groups where HPI was used. AUROC for HPI algorithms demonstrated strong predictive performance (AUROC = 0.89, 95CI). Non-HPI models had a pooled AUROC of 0.79 (95CI: 0.74, 0.83). Conclusion: HPI demonstrated excellent ability to predict hypotensive episodes and hence reduce the duration of hypotension. Other AI models, particularly those based on deep learning methods, also indicated a great ability to predict IOH, while their capacity to reduce IOH-related indices such as duration remains unclear.

Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools. Artificial intelligence (AI) holds promise in enhancing outcome predictions for congenital cardiac surgery. This study aims to systematically review the utilization of AI in predicting post-operative outcomes in this population. Following PRISMA guidelines, a comprehensive search of Pubmed, Scopus, and Web of Science databases was conducted. Two independent reviewers screened articles based on predefined criteria. Included studies focused on AI models predicting various post-operative outcomes in congenital heart surgery. The review included 35 articles, primarily published within the last four years, indicating growing interest in AI applications. Models predominantly targeted mortality and survival (n = 16), prolonged length of hospital or ICU stay (n = 7), postoperative complications (n = 6), prolonged mechanical ventilatory support time (n = 4), with additional focus on specific outcomes such as peri-ventricular leucomalacia (n = 2) and malnutrition (n = 1). Performance metrics, such as area under the curve (AUC), ranged from 0.52 to 0.997. Notably, these AI models consistently outperformed traditional risk stratification categories. For instance, in assessing the risk of morbidity and mortality, the AI models demonstrated superior performance compared to conventional methods. AI-driven prediction models show significant promise in improving outcome predictions for congenital heart surgery. They surpass traditional risk prediction tools not only in immediate postoperative risks but also in long-term outcomes such as 1-year survival and malnutrition. Further studies with robust external validation are necessary to assess the practical applicability of these models in clinical settings. The protocol of this review was prospectively registered on PROSPERO (CRD42024550942).

Neuropathic pain is a debilitating complication following spinal cord injury (SCI). Currently, effective treatments for SCI-induced neuropathic pain are highly lacking. This clinical trial aimed to investigate the efficacy of combined intrathecal injection of Schwann cells (SCs) and bone marrow–derived mesenchymal stem cells (BMSCs) in improving SCI-induced neuropathic pain. This study was a parallel-group, randomized, open-label, active-controlled phase II trial with two arms, including treatment and control groups. Patients with complete SCI-induced neuropathic pain in the treatment group received a single combined intrathecal injection of BMSCs and SCs. Study outcome measures were International SCI Pain Basic Data Set (ISCIPBDS) and World Health Organization (WHO) Quality of Life Assessment Instrument (WHOQOL-BREF). A total of 37 (55.2%) and 30 (44.8%) patients in the treatment and control groups were followed up for 6 months, respectively. Significant reductions in mean scores of interference items in the treatment group, including daily activities ( P < 0.001), mood ( P < 0.001), and sleep ( P < 0.001), were found at 6 months after the injection compared with the control one. Similarly, pain frequency ( P = 0.002), mean ( P = 0.001), and worst ( P = 0.001) numeric rating scale (NRS) pain intensity scores showed significant reductions in the treatment group after 6 months compared with the control one. Based on multiple regression analysis controlled for potential confounders, significant associations between changes in all outcome measures over the study period and the treatment group were found. This clinical trial indicated the efficacy of combined cell therapy in improving the neuropathic pain and quality of life in complete SCI patients. Future investigations should evaluate the effects of combination of this strategy with other existing therapies for SCI-induced neuropathic pain. This clinical trial was also registered prospectively at the Iranian Registry of Clinical Trials (IRCT20200502047277N8).

Introduction: Machine learning (ML) models have been increasingly applied to predict post-heart transplantation (HT) mortality, aiming to improve decision-making and optimize outcomes. This systematic review and meta-analysis evaluates the performance of ML algorithms in predicting mortality and explores factors contributing to model accuracy.Method: A systematic search of PubMed, Scopus, Web of Science, and Embase identified relevant studies, with 17 studies included in the review and 12 in the meta-analysis. The algorithms assessed included random forests, CatBoost, neural networks, and others. Model performance was evaluated using pooled area under the curve (AUC) values, with subgroup analyses for algorithm type, validation methods, and prediction timeframes. The risk of bias was assessed using the QUADAS-2 tool.Results: The pooled AUC of all ML algorithms was 0.65 (95% CI: 0.64, 0.67), with no significant difference between machine learning and deep learning models (p = 0.67). Among the algorithms, CatBoost demonstrated the highest accuracy (AUC 0.80, 95% CI: 0.74, 0.86), while K-nearest neighbor had the lowest accuracy (AUC 0.53, 95% CI: 0.50, 0.55). A meta-regression indicated improved model performance with longer post-transplant periods (p = 0.008). When pooling only the best-performing models, the AUC improved to 0.73 (95% CI: 0.68, 0.78). The risk of bias was high in eight studies, with the flow and timing domains most commonly contributing to bias.Conclusion: ML models demonstrate moderate accuracy in predicting post-HT mortality, with CatBoost achieving the best performance. While ML shows potential for improving predictive precision, significant heterogeneity and biases highlight the need for standardized methods and further external validations to enhance clinical applicability.Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024509630, CRD42024509630