Background: The optimal indication, dose, and timing of corticosteroids in sepsis is controversial. Here, we used reinforcement learning to derive the optimal steroid policy in septic patients based on data on 3051 ICU admissions from the AmsterdamUMCdb intensive care database. Methods: We identified septic patients according to the 2016 consensus definition. An actor-critic RL algorithm using ICU mortality as a reward signal was developed to determine the optimal treatment policy from time-series data on 277 clinical parameters. We performed off-policy evaluation and testing in independent subsets to assess the algorithm’s performance. Results: Agreement between the RL agent’s policy and the actual documented treatment reached 59%. Our RL agent’s treatment policy was more restrictive compared to the actual clinician behavior: our algorithm suggested withholding corticosteroids in 62% of the patient states, versus 52% according to the physicians’ policy. The 95% lower bound of the expected reward was higher for the RL agent than clinicians’ historical decisions. ICU mortality after concordant action in the testing dataset was lower both when corticosteroids had been withheld and when corticosteroids had been prescribed by the virtual agent. The most relevant variables were vital parameters and laboratory values, such as blood pressure, heart rate, leucocyte count, and glycemia. Conclusions: Individualized use of corticosteroids in sepsis may result in a mortality benefit, but optimal treatment policy may be more restrictive than the routine clinical practice. Whilst external validation is needed, our study motivates a ‘precision-medicine’ approach to future prospective controlled trials and practice.

Background: Inadvertent intraoperative hypothermia is a common complication that affects patient comfort and morbidity. As the development of hypothermia is a complex phenomenon, predicting it using machine learning (ML) algorithms may be superior to logistic regression. Methods: We performed a single-center retrospective study and assembled a feature set comprised of 71 variables. The primary outcome was hypothermia burden, defined as the area under the intraoperative temperature curve below 37 °C over time. We built seven prediction models (logistic regression, extreme gradient boosting (XGBoost), random forest (RF), multi-layer perceptron neural network (MLP), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and Gaussian naïve Bayes (GNB)) to predict whether patients would not develop hypothermia or would develop mild, moderate, or severe hypothermia. For each model, we assessed discrimination (F1 score, area under the receiver operating curve, precision, recall) and calibration (calibration-in-the-large, calibration intercept, calibration slope). Results: We included data from 87,116 anesthesia cases. Predicting the hypothermia burden group using logistic regression yielded a weighted F1 score of 0.397. Ranked from highest to lowest weighted F1 score, the ML algorithms performed as follows: XGBoost (0.44), RF (0.418), LDA (0.406), LDA (0.4), KNN (0.362), and GNB (0.32). Conclusions: ML is suitable for predicting intraoperative hypothermia and could be applied in clinical practice.

Tranexamic acid is an antifibrinolytic drug that is commonly administered for obstetric haemorrhage. Conventional viscoelastic tests are not sensitive to tranexamic acid, but the novel ClotPro® TPA-test can measure tranexamic acid-induced inhibition of fibrinolysis. We aimed to evaluate the TPA-test in pregnant and non-pregnant women. We performed an in vitro study of whole blood samples spiked with tranexamic acid from pregnant women in the first, second, and third trimester (n=20 per group) and from non-pregnant women (n=20). We performed ClotPro TPA-tests of whole blood sample and ClotPro EX-tests, FIB-tests, and TPA-tests. Clot lysis was inhibited in a concentration-dependent manner up to a tranexamic acid concentration of 6.25 mg L^-1. At tranexamic acid concentrations of 12.5 mg L^-1 and above, clot lysis was completely inhibited. The concentration-effect relationship of tranexamic acid did not differ in a clinically important manner in blood from pregnant women across all three trimesters or from non-pregnant controls. A median maximum lysis cut-off value of at9 least 16% (25-75th percentiles 15-18), a median clot lysis time of 3600 s (25-75th percentiles 3600-3600), or both was associated with a tranexamic acid concentration of least 12.5 mg L^-1. The ClotPro® TPA-test is sensitive in detecting inhibition of fibrinolysis by tranexamic acid in whole blood samples of pregnant and non-pregnant women. The concentration-effect relationship of tranexamic acid to inhibit fibrinolysis in whole blood did not differ for women in the first, second, and third trimester or for non-pregnant women.

Objectives In critically ill patients requiring mechanical ventilation for at least 21 days, 1-year mortality can be estimated using the ProVent score, calculated from four variables (age, platelet count, vasopressor use and renal replacement therapy). We aimed to externally validate discrimination and calibration of the ProVent score and, if necessary, to update its underlying regression model. Design Retrospective, observational, single-centre study. Setting 11 intensive care units at one tertiary academic hospital. Patients 780 critically ill adult patients receiving invasive mechanical ventilation for at least 21 days. Primary outcome measure 1-year mortality after intensive care unit discharge. Results 380 patients (49%) had died after 1 year. One-year mortality for ProVent scores from 0 to 5 were: 15%, 27%, 57%, 66%, 72% and 76%. Area under the receiver operating characteristic curve of the ProVent probability model was 0.76 (95% CI 0.72 to 0.79), calibration intercept was −0.43 (95% CI −0.59 to −0.27) and calibration slope was 0.76 (95% CI 0.62 to 0.89). Model recalibration and extension by inclusion of three additional predictors (total bilirubin concentration, enteral nutrition and surgical status) improved model discrimination and calibration. Decision curve analysis demonstrated that the original ProVent model had negative net benefit, which was avoided with the extended ProVent model. Conclusions The ProVent probability model had adequate discrimination but was miscalibrated in our patient cohort and, as such, could potentially be harmful. Use of the extended ProVent score developed by us could possibly alleviate this concern.

Background: Current guidelines recommend the monitoring of anti-factor Xa (anti-Xa) levels to avoid an accumulation of low-molecular-weight heparins in patients with acute kidney injury, but there is no evidence on how to proceed with such monitoring during continuous renal replacement therapy. Against this background, we investigated the potential accumulation of enoxaparin administered subcutaneously for venous thromboembolism prophylaxis in critically ill patients during continuous renal replacement therapy covered by regional citrate anticoagulation. Methods: Anti-Xa levels were measured at baseline (≤12 h before renal replacement therapy) and on three consecutive days (A to C) when enoxaparin had reached trough levels. Supplementary testing included modified assays of rotational thromboelastometry known to be highly sensitive for low-molecular-weight heparins. Results: The 16 men and 13 women included were adults comparable in age, body mass index, thromboembolism risk assessment, and clinical severity of the disease. Throughout the four examinations, the median trough levels of anti-Xa remained below the detection limit of the test (<0.1 IU mL⁻¹), with interquartile ranges of <0.1 to 0.14 IU mL⁻¹ at baseline and <0.1 to 0.16 IU mL⁻¹ on days A/B/C. All rotational thromboelastometry parameters of clot initiation and clot formation dynamics did not significantly change from baseline to day C. Conclusions: Neither anti-Xa levels nor modified assays of rotational thromboelastometry revealed any accumulation of enoxaparin administered for thromboprophylaxis during continuous renal replacement therapy covered by regional citrate anticoagulation. Although generally recommended in patients with acute kidney injury, monitoring of anti-Xa levels should be questioned in this defined setting.

Background: Deranged antepartum laboratory parameters may be risk factors for postpartum hemorrhage (PPH). However, whether this is also valid in women who give birth prematurely is currently unknown. Methods: We performed a retrospective single-center study to assess the role of antepartum hemoglobin, platelet count, fibrinogen, activated partial thromboplastin time, and prothrombin time as risk factors for PPH following caesarean section. We defined PPH as documented blood loss of at least 1 L and/or transfusion of red blood cell concentrates. We stratified the included patients according to gestational age: extremely preterm (gestational age < 28 weeks), very preterm (gestational age between 28 and 32 weeks), late and moderate preterm (gestational age between 32 and 37 weeks), and term (gestational age ≥ 37 weeks). Results: We included 1734 patients, 112 (6%) of whom had PPH. In total, 19 patients (10%) were in the extremely preterm group, 13 patients (10%) were in the very preterm group, 44 patients (9%) were in the late and moderate preterm group, and 36 patients (4%) were in the term group. Hemoglobin predicted PPH in all gestational age groups. Platelet count was associated with PPH in term, but not in preterm patients. Fibrinogen was associated with PPH in late prematurity but not in term patients and not in patients with early or extreme prematurity. Conclusions: Antepartum hemoglobin was the only factor predicting PPH in preterm and term caesarean sections. Platelet count and fibrinogen concentration were associated with PPH in term and late prematurity, respectively, but not in earlier stages of prematurity.