Association between prescription opioid use and unplanned intensive care unit admission and mortality in the adult population of the Netherlands: a registry study

Background: Opioid overdoses are increasing in the Netherlands, and there may be other harms associated with prescription opioid use. We investigated the relationship between prescription opioid use and unplanned ICU admission and death. Methods: This is an analysis of linked government registries of the adult Dutch population (age >= 18 years) alive on January 1, 2018. The co-primary outcomes were ICU admission and death up to 1 year. Crude event rates and eventspecific adjusted hazard rates (aHRs) with 95% confidence intervals (CIs) were calculated using multivariable analysis for people with and without exposure to an opioid prescription. Results: We included 13 813 173 individuals, of whom 32 831 were admitted to the ICU and 152 259 died during the 1 year follow-up. Rates of ICU admission and death amongst people who reimbursed an opioid prescription were 5.87 and 62.2 per 1000 person-years, and rates of ICU admission and death in those without a prescription were 2.03 and 6.34, respectively. Exposed individuals had a higher rate of both ICU admission (aHR 2.53; 95% CI: 2.45e2.60) and death (aHR 7.11; 95% CI: 7.02e7.19) compared with unexposed individuals. Both outcomes were more frequent amongst prescription opioid users across a range of subgroups. Conclusions: The rate of ICU admission and death was higher amongst prescription opioid users than non-users in the full cohort and in subgroups. These findings represent an important public health concern.Clinical epidemiolog

Microcirculatory alterations in critically ill COVID-19 patients analyzed using artificial intelligence

Background: The sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model's performance to differentiate critically ill COVID-19 patients from healthy volunteers. Methods: Four international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers (n = 59/n = 40) were used for neuronal network training and internal validation, alongside quantification of functional microcirculatory hemodynamic variables. Independent verification of the models was performed in a second cohort (n = 25/n = 33). Results: Six thousand ninety-two image sequences in 157 individuals were included. Bootstrapped internal validation yielded AUROC(CI) for detection of COVID-19 status of 0.75 (0.69-0.79), 0.74 (0.69-0.79) and 0.84 (0.80-0.89) for the algorithm-based, deep learning-based and combined models. Individual model performance in external validation was 0.73 (0.71-0.76) and 0.61 (0.58-0.63). Combined neuronal network and algorithm-based identification yielded the highest externally validated AUROC of 0.75 (0.73-0.78) (P < 0.0001 versus internal validation and individual models). Conclusions: We successfully trained a deep learning-based model to differentiate critically ill COVID-19 patients from heathy volunteers in sublingual HVM image sequences. Internally validated, deep learning was superior to the algorithmic approach. However, combining the deep learning method with an algorithm-based approach to quantify the functional state of the microcirculation markedly increased the sensitivity and specificity as compared to either approach alone, and enabled successful external validation of the identification of the presence of microcirculatory alterations associated with COVID-19 status. Keywords: Artificial intelligence; COVID-19; Deep learning; Microcirculation; Neuronal network

Long-Term Outcome of Patients With a Hematologic Malignancy and Multiple Organ Failure Admitted at the Intensive Care

Objectives: Historically, patients with a hematologic malignancy have one of the highest mortality rates among cancer patients admitted to the ICU. Therefore, physicians are often reluctant to admit these patients to the ICU. The aim of our study was to examine the survival of patients who have a hematologic malignancy and multiple organ failure admitted to the ICU. Design: This retrospective cohort study, part of the HEMA-ICU study group, was designed to study the survival of patients with a hematologic malignancy and organ failure after admission to the ICU. Patients were followed for at least 1 year. Setting: Five university hospitals in the Netherlands. Patients: One-thousand ninety-seven patients with a hematologic malignancy who were admitted at the ICU. Interventions: None. Measurements and Main Results: Primary outcome was 1-year survival. Organ failure was categorized as acute kidney injury, respiratory failure, hepatic failure, and hemodynamic failure; multiple organ failure was defined as failure of two or more organs. The World Health Organization performance score measured 3 months after discharge from the ICU was used as a measure of functional outcome. The 1-year survival rate among these patients was 38%. Multiple organ failure was inversely associated with long-term survival, and an absence of respiratory failure was the strongest predictor of 1-year survival. The survival rate among patients with 2, 3, and 4 failing organs was 27%, 22%, and 8%, respectively. Among all surviving patients for which World Health Organization scores were available, 39% had a World Health Organization performance score of 0-1 3 months after ICU discharge. Functional outcome was not associated with the number of failing organs. Conclusions: Our results suggest that multiple organ failure should not be used as a criterion for excluding a patient with a hematologic malignancy from admission to the ICU.</p

Development and Validation of a Prediction Model for 1-Year Mortality in Patients With a Hematologic Malignancy Admitted to the ICU

OBJECTIVES: To develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU. DESIGN: A retrospective cohort study. SETTING: Five university hospitals in the Netherlands between 2002 and 2015. PATIENTS: A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h.INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventilation, lowest platelet count, acute kidney injury, maximum heart rate, and type of malignancy. A bootstrap procedure reduced overfitting and improved the model's generalizability. This involved estimating the optimism in the initial model and shrinking the regression coefficients accordingly in the final model. We assessed performance using internal-external cross-validation by center and compared it with the Acute Physiology and Chronic Health Evaluation II model. Additionally, we evaluated clinical usefulness through decision curve analysis. The overall 1-year mortality rate observed in the study was 62% (95% CI, 59-65). Our 13-variable prediction model demonstrated acceptable calibration and discrimination at internal-external validation across centers (C-statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model (C-statistic 0.61; 95% CI, 0.57-0.65). Decision curve analysis indicated overall net benefit within a clinically relevant threshold probability range of 60-100% predicted 1-year mortality. CONCLUSIONS: Our newly developed 13-variable prediction model predicts 1-year mortality in hematologic malignancy patients admitted to the ICU more accurately than the Acute Physiology and Chronic Health Evaluation II model. This model may aid in shared decision-making regarding the continuation of ICU care and end-of-life considerations.</p

Leucocyte and platelet activation in cardiac surgery patients with and without lung injury: a prospective cohort study

OBJECTIONS: Development of acute lung injury after cardiac surgery is associated with an unfavourable outcome. Acute respiratory distress syndrome in general is, besides cytokine and interleukin activation, associated with activation of platelets, monocytes and neutrophils. In relation to pulmonary outcome after cardiac surgery, leucocyte and platelet activation is described in animal studies only. Therefore, we explored the perioperative time course of platelet and leucocyte activation in cardiac surgery and related these findings to acute lung injury assessed via PaO2/FiO(2) (P/F) ratio measurements. METHODS: A prospective cohort study was performed, including 80 cardiac surgery patients. At five time points, blood samples were directly assessed by flow cytometry. For time course analyses in low (200) P/F ratio groups, repeated measurement techniques with linear mixed models were used. RESULTS: Already before the start of the operation, platelet activatability (P = 0.003 for thrombin receptor-activator peptide and P = 0.017 for adenosine diphosphate) was higher, and the expression of neutrophil activation markers was lower (CD18/CD11; P = 0.001, CD62L; P = 0.013) in the low P/F group. After correction for these baseline differences, the peri- and postoperative thrombin receptor-activator peptide-induced thrombocyte activatability was decreased in the low P/F ratio group (P = 0.008), and a changed pattern of neutrophil activation markers was observed. CONCLUSIONS: Prior to surgery, an upregulated inflammatory state with higher platelet activatability and indications for higher neutrophil turnover were demonstrated in cardiac surgery patients who developed lung injury. It is difficult to distinguish whether these factors are mediators or are also aetiologically related to the development of lung injury after cardiac surgery. Further research is warranted.Thoracic Surger

Predicting 30-day mortality in intensive care unit patients with ischaemic stroke or intracerebral haemorrhage

BACKGROUND Stroke patients admitted to an intensive care unit (ICU) follow a particular survival pattern with a high short-term mortality, but if they survive the first 30 days, a relatively favourable subsequent survival is observed. OBJECTIVES The development and validation of two prognostic models predicting 30-day mortality for ICU patients with ischaemic stroke and for ICU patients with intracerebral haemorrhage (ICH), analysed separately, based on parameters readily available within 24 h after ICU admission, and with comparison with the existing Acute Physiology and Chronic Health Evaluation IV (APACHE-IV) model. DESIGN Observational cohort study. SETTING All 85 ICUs participating in the Dutch National Intensive Care Evaluation database. PATIENTS All adult patients with ischaemic stroke or ICH admitted to these ICUs between 2010 and 2019. MAIN OUTCOME MEASURES Models were developed using logistic regressions and compared with the existing APACHE-IV model. Predictive performance was assessed using ROC curves, calibration plots and Brier scores. RESULTS We enrolled 14 303 patients with stroke admitted to ICU: 8422 with ischaemic stroke and 5881 with ICH. Thirty-day mortality was 27% in patients with ischaemic stroke and 41% in patients with ICH. Important factors predicting 30-day mortality in both ischaemic stroke and ICH were age, lowest Glasgow Coma Scale (GCS) score in the first 24 h, acute physiological disturbance (measured using the Acute Physiology Score) and the application of mechanical ventilation. Both prognostic models showed high discrimination with an AUC 0.85 [95% confidence interval (CI), 0.84 to 0.87] for patients with ischaemic stroke and 0.85 (0.83 to 0.86) in ICH. Calibration plots and Brier scores indicated an overall good fit and good predictive performance. The APACHE-IV model predicting 30-day mortality showed similar performance with an AUC of 0.86 (95% CI, 0.85 to 0.87) in ischaemic stroke and 0.87 (0.86 to 0.89) in ICH. CONCLUSION We developed and validated two prognostic models for patients with ischaemic stroke and ICH separately with a high discrimination and good calibration to predict 30-day mortality within 24 h after ICU admission

Transfusion of fresh frozen plasma in non-bleeding ICU patients -TOPIC TRIAL: study protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Fresh frozen plasma (FFP) is an effective therapy to correct for a deficiency of multiple coagulation factors during bleeding. In past years, use of FFP has increased, in particular in patients on the Intensive Care Unit (ICU), and has expanded to include prophylactic use in patients with a coagulopathy prior to undergoing an invasive procedure. Retrospective studies suggest that prophylactic use of FFP does not prevent bleeding, but carries the risk of transfusion-related morbidity. However, up to 50% of FFP is administered to non-bleeding ICU patients. With the aim to investigate whether prophylactic FFP transfusions to critically ill patients can be safely omitted, a multi-center randomized clinical trial is conducted in ICU patients with a coagulopathy undergoing an invasive procedure.</p> <p>Methods</p> <p>A non-inferiority, prospective, multicenter randomized open-label, blinded end point evaluation (PROBE) trial. In the intervention group, a prophylactic transfusion of FFP prior to an invasive procedure is omitted compared to transfusion of a fixed dose of 12 ml/kg in the control group. Primary outcome measure is relevant bleeding. Secondary outcome measures are minor bleeding, correction of International Normalized Ratio, onset of acute lung injury, length of ventilation days and length of Intensive Care Unit stay.</p> <p>Discussion</p> <p>The Transfusion of Fresh Frozen Plasma in non-bleeding ICU patients (TOPIC) trial is the first multi-center randomized controlled trial powered to investigate whether it is safe to withhold FFP transfusion to coagulopathic critically ill patients undergoing an invasive procedure.</p> <p>Trial Registration</p> <p>Trial registration: Dutch Trial Register NTR2262 and ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01143909">NCT01143909</a></p

Long-Term Outcome of Patients With a Hematologic Malignancy and Multiple Organ Failure Admitted at the Intensive Care

Objectives: Historically, patients with a hematologic malignancy have one of the highest mortality rates among cancer patients admitted to the ICU. Therefore, physicians are often reluctant to admit these patients to the ICU. The aim of our study was to examine the survival of patients who have a hematologic malignancy and multiple organ failure admitted to the ICU. Design: This retrospective cohort study, part of the HEMA-ICU study group, was designed to study the survival of patients with a hematologic malignancy and organ failure after admission to the ICU. Patients were followed for at least 1 year. Setting: Five university hospitals in the Netherlands. Patients: One-thousand ninety-seven patients with a hematologic malignancy who were admitted at the ICU. Interventions: None. Measurements and Main Results: Primary outcome was 1-year survival. Organ failure was categorized as acute kidney injury, respiratory failure, hepatic failure, and hemodynamic failure; multiple organ failure was defined as failure of two or more organs. The World Health Organization performance score measured 3 months after discharge from the ICU was used as a measure of functional outcome. The 1-year survival rate among these patients was 38%. Multiple organ failure was inversely associated with long-term survival, and an absence of respiratory failure was the strongest predictor of 1-year survival. The survival rate among patients with 2, 3, and 4 failing organs was 27%, 22%, and 8%, respectively. Among all surviving patients for which World Health Organization scores were available, 39% had a World Health Organization performance score of 0–1 3 months after ICU discharge. Functional outcome was not associated with the number of failing organs. Conclusions: Our results suggest that multiple organ failure should not be used as a criterion for excluding a patient with a hematologic malignancy from admission to the ICU

Development and Validation of a Prediction Model for 1-Year Mortality in Patients With a Hematologic Malignancy Admitted to the ICU

OBJECTIVES: To develop and validate a prediction model for 1-year mortality in patients with a hematologic malignancy acutely admitted to the ICU. DESIGN: A retrospective cohort study. SETTING: Five university hospitals in the Netherlands between 2002 and 2015. PATIENTS: A total of 1097 consecutive patients with a hematologic malignancy were acutely admitted to the ICU for at least 24 h. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We created a 13-variable model from 22 potential predictors. Key predictors included active disease, age, previous hematopoietic stem cell transplantation, mechanical ventilation, lowest platelet count, acute kidney injury, maximum heart rate, and type of malignancy. A bootstrap procedure reduced overfitting and improved the model's generalizability. This involved estimating the optimism in the initial model and shrinking the regression coefficients accordingly in the final model. We assessed performance using internal-external cross-validation by center and compared it with the Acute Physiology and Chronic Health Evaluation II model. Additionally, we evaluated clinical usefulness through decision curve analysis. The overall 1-year mortality rate observed in the study was 62% (95% CI, 59-65). Our 13-variable prediction model demonstrated acceptable calibration and discrimination at internal-external validation across centers ( C-statistic 0.70; 95% CI, 0.63-0.77), outperforming the Acute Physiology and Chronic Health Evaluation II model ( C-statistic 0.61; 95% CI, 0.57-0.65). Decision curve analysis indicated overall net benefit within a clinically relevant threshold probability range of 60-100% predicted 1-year mortality. CONCLUSIONS: Our newly developed 13-variable prediction model predicts 1-year mortality in hematologic malignancy patients admitted to the ICU more accurately than the Acute Physiology and Chronic Health Evaluation II model. This model may aid in shared decision-making regarding the continuation of ICU care and end-of-life considerations