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INTRODUCTION: Various Machine Learning (ML) models have been used to predict sepsis-associated mortality. We conducted a systematic review to evaluate the methodologies employed in studies to predict mortality among patients with sepsis. METHODS: Following a pre-established protocol registered at the International Prospective Register of Systematic Reviews, we performed a comprehensive search of databases from inception to February 2024. We included peer-reviewed articles reporting predicting mortality in critically ill adult patients with sepsis. RESULTS: Among the 1822 articles, 31 were included, involving 1,477,200 adult patients with sepsis. Nineteen studies had a high risk of bias. Among the diverse ML models, Logistic regression and eXtreme Gradient Boosting were the most frequently used, in 22 and 16 studies, respectively. Nine studies performed internal and external validation. Compared with conventional scoring systems such as SOFA, the ML models showed slightly higher performance in predicting mortality (AUROC ranges: 0.62-0.90 vs. 0.47-0.86). CONCLUSIONS: ML models demonstrate a modest improvement in predicting sepsis-associated mortality. The certainty of these findings remains low due to the high risk of bias and significant heterogeneity. Studies should include comprehensive methodological details on calibration and hyperparameter selection, adopt a standardized definition of sepsis, and conduct multicenter prospective designs along with external validations.
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Continuous kidney replacement therapy (CKRT) is commonly used to manage critically ill patients with severe acute kidney injury. While recent trials focused on the correct dosing and timing of CKRT, our understanding regarding the optimum dose of net ultrafiltration is limited to retrospective data. The Restrictive versus Liberal Rate of Extracorporeal Volume Removal Evaluation in Acute Kidney Injury (RELIEVE-AKI) trial has been conducted to assess the feasibility of a prospective randomized trial in determining the optimum net ultrafiltration rate. This paper outlines the relevant challenges and solutions in implementing this complex ICU-based trial. Several difficulties were encountered, starting with clinical issues related to conducting a trial on patients with rapidly changing hemodynamics, low patient recruitment rates, increased nursing workload, and the enormous volume of data generated by patients undergoing prolonged CKRT. Following several brainstorming sessions, several points were highlighted to be considered, including the need to streamline the intervention, add more flexibility in the trial protocols, ensure comprehensive a priori planning, particularly regarding nursing roles and their compensation, and enhance data management systems. These insights are critical for guiding future ICU-based dynamically titrated intervention trials, leading to more efficient trial management, improved data quality, and enhanced patient safety.
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INTRODUCTION: The association between magnesium level and progression to acute kidney disease (AKD) in acute kidney injury (AKI) patients was not well studied. With AKI transition to AKD, the burden of the disease on mortality, morbidity, and healthcare costs increases. Serum magnesium disturbances are linked with a decline in renal function and increased risk of death in CKD and hemodialysis patients. This study aims to assess the significance of magnesium derangements as a risk factor for the progression of AKI to AKD in critically ill patients. METHODS: This study was conducted among patients with AKI admitted to the intensive care units at Mayo Clinic from 2007 to 2017. Serum magnesium at AKI onset was categorized into five groups of <1.7, 1.7-1.9, 1.9-2.1, 2.1-2.3, and ≥2.3 mg/dL, with 1.9-2.1 mg/dL as the reference group. AKD was defined as AKI that persisted >7 days following the AKI onset. Logistic regression was used to evaluate the association between magnesium and AKD. RESULTS: Among 20,198 critically ill patients with AKI, the mean age was 66 ± 16 years, and 57% were male. The mean serum magnesium at AKI onset was 1.9 ± 0.4 mg/dL. The overall incidence of AKD was 31.4%. The association between serum magnesium and AKD followed a U-shaped pattern. In multivariable analysis, serum magnesium levels were associated with increased risk of AKD with the odds ratio of 1.17 (95% CI: 1.07-1.29), 1.13 (95% CI: 1.01-1.26), and 1.65 (95% CI: 1.48-1.84) when magnesium levels were <1.7, 2.1-2.3, and ≥2.3 mg/dL, respectively. CONCLUSION: Among patients with AKI, magnesium level derangement was an independent risk for AKD in critically ill AKI patients. Monitoring serum magnesium and proper correction in critically ill patients with AKI should be considered an AKD preventive intervention in future trials.
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India has been a part of 5 major wars since its independence in 1947 and hosts over 212,413 refugees from Sri Lanka, Tibet and Bangladesh. So, a wide spectrum of trauma survivors, both civilian and military, live in this country and require mental healthcare. We discuss the psychological impact of armed conflict and how the country and culture tint it uniquely. We not only explore the current scene but the resources available and what can be done to make such vulnerable parts of the Indian population feel safer.
The spectrum of victims of armed conflict in India and the impact on their mental health.The current deficient governmental and non-governmental psychosocial support available.Recommendations for optimum management of these trauma victims through programmes and individual therapy.
