The Role of Cell Cycle Arrest Biomarkers for Predicting Acute Kidney Injury in Critically Ill COVID-19 Patients: A Multicenter, Observational Study.

OBJECTIVES: Patients with COVID-19-associated acute respiratory distress syndrome (ARDS) have a high risk for developing acute kidney injury (AKI) which is associated with an increased risk of death and persistent renal failure. Early prediction of AKI is crucial in order to implement preventive strategies. The purpose of this study was to investigate the predictive performance of tissue inhibitor of metalloproteinases 2 and insulin like growth factor binding protein 7 (TIMP-2) × (IGFBP7) in critically ill patients with COVID-19-associated ARDS. DESIGN: Multicenter, prospective, observational study. SETTING: Twelve centers across Europe and United Kingdom. PATIENTS: Patients with moderate or severe COVID-19-associated ARDS were included and serial measurements of (TIMP-2) × (IGFBP7) were performed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary endpoint was the development of moderate or severe AKI according to the Kidney Disease: Improving Global Outcomes definition. Three hundred patients were available for the primary analysis, and 39 met the primary endpoint. At enrollment, urinary (TIMP-2) × (IGFBP7) had high predictive value for the primary endpoint with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.84-0.93). (TIMP-2) × (IGFBP7) was significantly higher in endpoint-positive patients at enrollment and at 12 hours. CONCLUSIONS: Urinary (TIMP-2) × (IGFBP7) predicts the occurrence of AKI in critically ill patients with COVID-19-associated ARDS.

Hypotension Prediction Index guided Goal Directed therapy and the amount of Hypotension during Major Gynaecologic Oncologic Surgery: a Randomized Controlled clinical Trial.

Intraoperative hypotension (IOH) is associated with increased morbidity and mortality. Hypotension Prediction Index (HPI) is a machine learning derived algorithm that predicts IOH shortly before it occurs. We tested the hypothesis that the application of the HPI in combination with a pre-defined Goal Directed Therapy (GDT) hemodynamic protocol reduces IOH during major gynaecologic oncologic surgery. We enrolled women scheduled for major gynaecologic oncologic surgery under general anesthesia with invasive arterial pressure monitoring. Patients were randomized to a GDT protocol aimed at optimizing stroke volume index (SVI) or hemodynamic management based on HPI guidance in addition to GDT. The primary outcome was the amount of IOH, defined as the timeweighted average (TWA) mean arterial pressure (MAP) < 65 mmHg. Secondary outcome was the TWA-MAP < 65 mmHg during the first 20 min after induction of GA. After exclusion of 10 patients the final analysis included 60 patients (30 in each group). The median (25-75th IQR) TWA-MAP < 65 mmHg was 0.14 (0.04-0.66) mmHg in HPI group versus 0.77 (0.36-1.30) mmHg in Control group, P < 0.001. During the first 20 min after induction of GA, the median TWA-MAP < 65 mmHg was 0.53 (0.06-1.8) mmHg in the HPI group and 2.15 (0.65-4.2) mmHg in the Control group, P = 0.001. Compared to a GDT protocol aimed to SVI optimization, a machine learning-derived algorithm for prediction of IOH combined with a GDT hemodynamic protocol, reduced IOH and hypotension after induction of general anesthesia in patients undergoing major gynaecologic oncologic surgery.Trial registration number: NCT04547491. Date of registration: 10/09/2020.

Dynamic arterial elastance as a predictor of arterial pressure response to fluid administration: a validation study.

INTRODUCTION: Functional assessment of arterial load by dynamic arterial elastance (Eadyn), defined as the ratio between pulse pressure variation (PPV) and stroke volume variation (SVV), has recently been shown to predict the arterial pressure response to volume expansion (VE) in hypotensive, preload-dependent patients. However, because both SVV and PPV were obtained from pulse pressure analysis, a mathematical coupling factor could not be excluded. We therefore designed this study to confirm whether Eadyn, obtained from two independent signals, allows the prediction of arterial pressure response to VE in fluid-responsive patients. METHODS: We analyzed the response of arterial pressure to an intravenous infusion of 500 ml of normal saline in 53 mechanically ventilated patients with acute circulatory failure and preserved preload dependence. Eadyn was calculated as the simultaneous ratio between PPV (obtained from an arterial line) and SVV (obtained by esophageal Doppler imaging). A total of 80 fluid challenges were performed (median, 1.5 per patient; interquartile range, 1 to 2). Patients were classified according to the increase in mean arterial pressure (MAP) after fluid administration in pressure responders (≥ 10%) and non-responders. RESULTS: Thirty-three fluid challenges (41.2%) significantly increased MAP. At baseline, Eadyn was higher in pressure responders (1.04 ± 0.28 versus 0.60 ± 0.14; P < 0.0001). Preinfusion Eadyn was related to changes in MAP after fluid administration (R (2) = 0.60; P < 0.0001). At baseline, Eadyn predicted the arterial pressure increase to volume expansion (area under the receiver operating characteristic curve, 0.94; 95% confidence interval (CI): 0.86 to 0.98; P < 0.0001). A preinfusion Eadyn value ≥ 0.73 (gray zone: 0.72 to 0.88) discriminated pressure responder patients with a sensitivity of 90.9% (95% CI: 75.6 to 98.1%) and a specificity of 91.5% (95% CI: 79.6 to 97.6%). CONCLUSIONS: Functional assessment of arterial load by Eadyn, obtained from two independent signals, enabled the prediction of arterial pressure response to fluid administration in mechanically ventilated, preload-dependent patients with acute circulatory failure.