Journal of clinical monitoring and computing
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J Clin Monit Comput · Apr 2024
Randomized Controlled Trial Multicenter StudyCan perioperative pCO2 gaps predict complications in patients undergoing major elective abdominal surgery randomized to goal-directed therapy or standard care? A secondary analysis.
The difference between venous and arterial carbon dioxide pressure (pCO2 gap), has been used as a diagnostic and prognostic tool. We aimed to assess whether perioperative pCO2 gaps can predict postoperative complications. This was a secondary analysis of a multicenter RCT comparing goal-directed therapy (GDT) to standard care in which 464 patients undergoing high-risk elective abdominal surgery were included. Arterial and central venous blood samples were simultaneously obtained at four time points: after induction, at the end of surgery, at PACU/ICU admission, and PACU/ICU discharge. ⋯ A weak correlation between ScvO2 and pCO2 gaps was found for all timepoints (ρ was between - 0.40 and - 0.29 for all timepoints, p < 0.001). The pCO2 gap did not differ between GDT and standard care at any of the selected time points. In our study, pCO2 gap was a poor predictor of major postoperative complications at all selected time points. Our research does not support the use of pCO2 gap as a prognostic tool after high-risk abdominal surgery. pCO2 gaps were comparable between GDT and standard care. Clinical trial registration Netherlands Trial Registry NTR3380.
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J Clin Monit Comput · Aug 2023
Multicenter StudyAgreement between pulse oximetry and arterial oxygen saturation measurement in critical care patients during COVID-19: a cross-sectional study.
Some publications suggest that pulse oximetry measurement (SpO2) might overestimate arterial oxygen saturation (SaO2) measurement in COVID-19 patients. This study aims to evaluate the agreement between SpO2 and SaO2 among COVID-19 and non-COVID-19 patients. We conducted a multicenter, prospective study including consecutive intensive care patients from October 15, 2020, to March 4, 2021, and compared for each measurement the difference between SpO2 and SaO2, also called the systematic bias. ⋯ In our population, agreement between SpO2 and SaO2 is acceptable. During the COVID-19 pandemic, SaO2 remains an efficient monitoring tool to characterise the level of hypoxemia and follow therapeutic interventions. As is already known about general intensive care unit patients, the greater hypoxemia, the weaker the correlation between SpO2 and SaO2.
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J Clin Monit Comput · Feb 2023
Multicenter Study Observational StudyIntra-abdominal hypertension in cardiac surgery patients: a multicenter observational sub-study of the Accuryn registry.
Intra-abdominal hypertension (IAH) is frequently present in the critically ill and is associated with increased morbidity and mortality. Conventionally, intermittent 'spot-check' manual measurements of bladder pressure in those perceived as high risk are used as surrogates for intra-abdominal pressure (IAP). True patterns of IAH remain unknown. ⋯ For maximum consecutive duration of IAH, 84% (115/137) of patients spent at least 12 h in grade I, 62% (85/137) in grade II, 18% (25/137) in grade III, and 2% (3/137) in grade IV IAH. During the first 48 h after cardiac surgery, IAH is common and persistent. Improved and automated monitoring of IAP will increase the detection of IAH-which normally would remain undetected using traditional intermittent monitoring methods.
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J Clin Monit Comput · Feb 2023
Multicenter StudyDevelopment and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults.
Acute kidney injury (AKI) recovery prediction remains challenging. The purpose of the present study is to develop and validate prediction models for AKI recovery at hospital discharge in critically ill patients with ICU-acquired AKI stage 3 (AKI-3). ⋯ Models to predict AKI recovery upon hospital discharge in critically ill patients with AKI-3 showed poor performance in the general ICU population, similar to the biomarker NGAL. In cardiac surgery patients, discrimination was acceptable, and better than NGAL. These findings demonstrate the difficulty of predicting non-reversible AKI early.
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J Clin Monit Comput · Oct 2022
Multicenter StudyPredicting hypoglycemia in critically Ill patients using machine learning and electronic health records.
Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. ⋯ The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.