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Background: Mechanical power (MP) serves as a crucial predictive indicator for ventilator-induced lung injury and plays a pivotal role in tailoring the management of mechanical ventilation. However, its application across different diseases and stages remains nuanced., Methods: Using AmsterdamUMCdb, we conducted a retrospective study to analyze the causal relationship between MP and outcomes of invasive mechanical ventilation, specifically SpO 2 /FiO 2 ratio (P/F) and ventilator-free days at day 28 (VFD28). We employed causal inferential analysis with backdoor linear regression and double machine learning, guided by directed acyclic graphs, to estimate the average treatment effect (ATE) in the whole population and conditional average treatment effect (CATE) in the individual cohort. Additionally, to enhance interpretability and identify MP thresholds, we conducted a simulation analysis., Results: In the study, we included 11,110 unique admissions into analysis, of which 58.3% (6391) were surgical admissions. We revealed a negative and significant causal effect of median MP on VFD28, with estimated ATEs of -0.135 (95% confidence interval [CI]: -0.15 to -0.121). The similar effect was not observed in Maximal MP and minimal MP. The effect of MP was more pronounced in the medical subgroup, with a CATE of -0.173 (95% CI: -0.197 to -0.143) determined through backdoor linear regression. Patients with cardio, respiratory, and infection diagnoses, who required long-term intubation, sustained higher impact on CATEs across various admission diagnoses. Our simulations showed that there is no single MP threshold that can be applied to all patients, as the optimal threshold varies depending on the patient's condition., Conclusion: Our study underscores the importance of tailoring MP adjustments on an individualized basis in ventilator management. This approach opens up new avenues for personalized treatment strategies and provides fresh insights into the real-time impact of MP in diverse clinical scenarios. It highlights the significance of median MP while acknowledging the absence of universally applicable thresholds., Competing Interests: Declarations. Ethics approval and consent to participate: This study, involving de-identified human data retrieved from the Amsterdam UMC database. Consent to participate and adherence to the ethics guidelines were as per the guidelines set by AmsterdamUMCdb. More information is available at https://amsterdammedicaldatascience.nl/amsterdamumcdb/ . Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests. Author JV is supported by National Heart, Lung, and Blood Institute of the National Institutes of Health (grant number T32HL155020)., (© 2025. The Author(s).)

Background. Acute kidney injury (AKI) is a frequent complication of critically ill patients. The impact of different risk factors associated with this entity in the ICU setting is unknown. Objectives. The purpose of this research was to assess the risk factors associated with the development of AKI in critically ill patients by meta-analyses of observational studies. Data Extraction. Two reviewers independently and in duplicate used a standardized form to collect data from published reports. Authors were contacted for missing data. The Newcastle-Ottawa scale assessed study quality. Data Synthesis. Data from 31 diverse studies that enrolled 504,535 critically ill individuals from a wide variety of ICUs were included. Separate random-effects meta-analyses demonstrated a significantly increased risk of AKI with older age, diabetes, hypertension, higher baseline creatinine, heart failure, sepsis/systemic inflammatory response syndrome, use of nephrotoxic drugs, higher severity of disease scores, use of vasopressors/inotropes, high risk surgery, emergency surgery, use of intra-aortic balloon pump, and longer time in cardiopulmonary bypass pump. Conclusion. The best available evidence suggests an association of AKI with 13 different risk factors in subjects admitted to the ICU. Predictive models for identification of high risk individuals for developing AKI in all types of ICU are required.