Background Adequate postoperative analgesia, especially after major abdominal surgery is important for recovery, early mobility, and patient satisfaction. We aimed to study the effects of cryotherapy via an ice pack in the immediate postoperative period, for patients undergoing major abdominal operations. Methods This prospective study was conducted at our tertiary care referral center in a low-middle-income country setting. The preoperative patient characteristics, intra-operative variables, and postoperative outcomes were compared between two sets of patients. Cryotherapy was delivered via frozen gel packs for 24 h immediately following laparotomy. Pain relief was assessed with visual analog pain scores (VAS). Comparisons between groups were measured by Chi-square test, Fischer's exact test, or Mann-Whitney U test as appropriate. Results Sixty-eight patients were included in the study: 33 in the cryotherapy group and 35 in the non-cryotherapy group. Mean postoperative pain scores (VAS) were significantly lower in the cryotherapy group versus the control group (3.97 ± 0.6 vs. 4.9 ± 0.7 on postoperative day (POD) 1; p \ 0.001, and 3 ± 0.5 vs. 09 ± 0.8 on POD2; p\ 0.001). The median narcotic use in morphine equivalents was lesser in the cryotherapy group from POD 1-3 (66 (IQR-16) vs. 89 (IQR-17); p = 0.001). No significant difference was seen in the NSAID use between the groups. The cryotherapy group was also found to have a lesser incidence of surgical site infection (p = 0.03) and better lung function based on incentive spirometry (p = 0.01) and demonstrated earlier functional recovery based on their ability to perform the sit-to-stand test (p = 0.001). Conclusion Ice packs are a simple, cost-effective adjuvant to standard postoperative pain management which reduce pain and narcotic use and promote early rehabilitation.

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Background The variations in outcome and frequent occurrence of kidney allograft failure continue to pose important clinical and research challenges despite recent advances in kidney transplantation. The aim of this systematic review was to examine the current application of machine learning models in kidney transplantation and perform a meta-analysis of these models in the prediction of graft survival. Methods This review was registered with the PROSPERO database (CRD42021247469) and all peer-reviewed original articles that reported machine learning model-based prediction of graft survival were included. Quality assessment was performed by the criteria defined by Qiao and risk-of-bias assessment was performed using the PROBAST tool. The diagnostic performance of the meta-analysis was assessed by a meta-analysis of the area under the receiver operating characteristic curve and a hierarchical summary receiver operating characteristic plot. Results A total of 31 studies met the inclusion criteria for the review and 27 studies were included in the meta-analysis. Twenty-nine different machine learning models were used to predict graft survival in the included studies. Nine studies compared the predictive performance of machine learning models with traditional regression methods. Five studies had a high risk of bias and three studies had an unclear risk of bias. The area under the hierarchical summary receiver operating characteristic curve was 0.82 and the summary sensitivity and specificity of machine learning-based models were 0.81 (95 per cent c.i. 0.76 to 0.86) and 0.81 (95 per cent c.i. 0.74 to 0.86) respectively for the overall model. The diagnostic odds ratio for the overall model was 18.24 (95 per cent c.i. 11.00 to 30.16) and 29.27 (95 per cent c.i. 13.22 to 44.46) based on the sensitivity analyses. Conclusion Prediction models using machine learning methods may improve the prediction of outcomes after kidney transplantation by the integration of the vast amounts of non-linear data.

Background: We aimed to assess the predictive value of the absolute and relative intact parathormone (iPTH) decline levels as reliable markers of postoperative hypocalcemia. Materials & methods: iPTH levels were measured 4 h after surgery and the following morning after surgery (postoperative day 1). iPTH, absolute iPTH decline (ΔPTH) and relative iPTH decline (ΔPTH%) were calculated and correlated with symptomatic hypocalcemia. Results: Of the 95 patients, 20% of patients (n = 19) developed symptomatic hypocalcemia. The ΔPTH (U = 206; p < 0.001) and ΔPTH% (U = 127; p < 0.001) were significantly higher in patients with symptomatic hypocalcemia. A ΔPTH% of 20% (sensitivity of 84%; specificity of 91%); and an absolute iPTH decline of 3.75 pg/ml (sensitivity of 74%; specificity of 87%) were highly predictive of symptomatic hypocalcemia. Conclusion: Postoperative ΔPTH and ΔPTH% have the potential to be predictors of symptomatic hypocalcemia following thyroidectomy and could facilitate a safe early discharge.

Introduction Kidney transplantation (KT) is currently the renal replacement therapy of choice for most patients with end-stage kidney disease. Despite many advancements, the variations in outcome and frequent occurrence of graft failure continue to pose important clinical and research challenges. The aim of this study was to carry out a systematic review of the current application of Machine Learning (ML) models in KT and perform a meta-analysis of these models in the prediction of graft outcomes. Methods This review was registered with the PROSPERO database (CRD42021247469) and all peer reviewed and preprint original articles that reported the sensitivity and specificity of AI-based models were included in the meta-analysis. Data were analysed using MetaDTA,,an interactive online software for meta-analysis of diagnostic studies. The diagnostic performance of the meta-analysis was assessed by a summary receiver operating characteristics (sROC) plot. Results 38 studies met the inclusion criteria for the review and 12 studies met the inclusion criteria for the meta-analysis. The most common models used were artificial neural networks, decision trees and Bayesian belief networks. Seven studies compared the predictive performance of ML models with traditional regression methods. The summary sensitivity and specificity of ML-based models were 0.84 (95% CI, 0.72–0.91) and 0.68 (95% CI, 0.57–0.77), respectively. The area under the SROC for all the available evidence was 0.83. The Diagnostic Odds Ratio of ML models was 11.19 (95% CI 6.66–18.75). Conclusion Our study shows that ML models can accurately predict outcomes following KT by the integration of the vast amounts of available clinical data. Take-home message This study confirms the superior ability of ML Models in handling complex relationships between large datasets, features and outcomes, which has definitely led to improved precision and accuracy of outcomes.