21 research outputs found

    Associations of autozygosity with a broad range of human phenotypes

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    In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F-ROH) for >1.4 million individuals, we show that F-ROH is significantly associated (p <0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F-ROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F-ROH are confirmed within full-sibling pairs, where the variation in F-ROH is independent of all environmental confounding.Peer reviewe

    Data on sex differences in one-year outcomes of out-of-hospital cardiac arrest patients without ST-segment elevation

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    Sex differences in out-of-hospital cardiac arrest (OHCA) patients are increasingly recognized. Although it has been found that post-resuscitated women are less likely to have significant coronary artery disease (CAD) than men, data on follow-up in these patients are limited. Data for this data in brief article was obtained as a part of the randomized controlled Coronary Angiography after Cardiac Arrest without ST-segment elevation (COACT) trial. The data supplements the manuscript “Sex differences in out-of-hospital cardiac arrest patients without ST-segment elevation: A COACT trial substudy” were it was found that women were less likely to have significant CAD including chronic total occlusions, and had worse survival when CAD was present. The dataset presented in this paper describes sex differences on interventions, implantable-cardioverter defibrillator (ICD) shocks and hospitalizations due to heart failure during one-year follow-up in patients successfully resuscitated after OHCA. Data was derived through a telephone interview at one year with the patient or general practitioner. Patients in this randomized dataset reflects a homogenous study population, which can be valuable to further build on research regarding long-term sex differences and to further improve cardiac care

    Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

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    BACKGROUND: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. METHODS: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. RESULTS: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. DISCUSSION: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research

    Attitudes of Dutch intensive care unit clinicians towards oxygen therapy

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    Background: Over the last decade, there has been an increasing awareness for the potential harm of the administration of too much oxygen. We aimed to describe self-reported attitudes towards oxygen therapy by clinicians from a large representative sample of intensive care units (ICUs) in the Netherlands.Methods: In April 2019, 36 ICUs in the Netherlands were approached and asked to send out a questionnaire (59 questions) to their nursing and medical staff (ICU clinicians) eliciting self-reported behaviour and attitudes towards oxygen therapy in general and in specific ICU case scenarios.Results: In total, 1361 ICU clinicians (71% nurses, 24% physicians) from 28 ICUs returned the questionnaire. Of responding ICU clinicians, 64% considered oxygen-induced lung injury to be a major concern. The majority of respondents considered a partial pressure of oxygen (PaO2) of 6-10 kPa (45-75 mmHg) and an arterial saturation (SaO(2)) of 85-90% as acceptable for 15 minutes, and a PaO2 7-To kPa (53-75 mmHg) and SaO(2) 90-95% as acceptable for 24-48 hours in an acute respiratory distress syndrome (ARDS) patient. In most case scenarios, respondents reported not to change the fraction of inspired oxygen (FiO(2)) if SaO(2) was 90-95% or PaO2 was 12 kPa (90 mmHg).Conclusion: A representative sample of ICU clinicians from the Netherlands were concerned about oxygen-induced lung injury, and reported that they preferred PaO2 and SaO(2) targets in the lower physiological range and would adjust ventilation settings accordingly.Perioperative Medicine: Efficacy, Safety and Outcom

    Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality

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    BACKGROUND: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction. METHODS: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. RESULTS: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/-24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71-0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64-0.71], 0.61 [CI 0.58-0.66], 0.67 [CI 0.63-0.70], 0.70 [CI 0.67-0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). CONCLUSIONS: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far

    Attitudes of Dutch intensive care unit clinicians towards oxygen therapy

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    Background: Over the last decade, there has been an increasing awareness for the potential harm of the administration of too much oxygen. We aimed to describe self-reported attitudes towards oxygen therapy by clinicians from a large representative sample of intensive care units (ICUs) in the Netherlands.Methods: In April 2019, 36 ICUs in the Netherlands were approached and asked to send out a questionnaire (59 questions) to their nursing and medical staff (ICU clinicians) eliciting self-reported behaviour and attitudes towards oxygen therapy in general and in specific ICU case scenarios.Results: In total, 1361 ICU clinicians (71% nurses, 24% physicians) from 28 ICUs returned the questionnaire. Of responding ICU clinicians, 64% considered oxygen-induced lung injury to be a major concern. The majority of respondents considered a partial pressure of oxygen (PaO2) of 6-10 kPa (45-75 mmHg) and an arterial saturation (SaO(2)) of 85-90% as acceptable for 15 minutes, and a PaO2 7-To kPa (53-75 mmHg) and SaO(2) 90-95% as acceptable for 24-48 hours in an acute respiratory distress syndrome (ARDS) patient. In most case scenarios, respondents reported not to change the fraction of inspired oxygen (FiO(2)) if SaO(2) was 90-95% or PaO2 was 12 kPa (90 mmHg).Conclusion: A representative sample of ICU clinicians from the Netherlands were concerned about oxygen-induced lung injury, and reported that they preferred PaO2 and SaO(2) targets in the lower physiological range and would adjust ventilation settings accordingly.Perioperative Medicine: Efficacy, Safety and Outcom

    Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse

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    BACKGROUND: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. METHODS: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. RESULTS: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH(2)O. CONCLUSION: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes

    Sex differences in patients with out-of-hospital cardiac arrest without ST-segment elevation: A COACT trial substudy

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    Item does not contain fulltextBACKGROUND: Whether sex is associated with outcomes of out-of-hospital cardiac arrest (OHCA) is unclear. OBJECTIVES: This study examined sex differences in survival in patients with OHCA without ST-segment elevation myocardial infarction (STEMI). METHODS: Using data from the randomized controlled Coronary Angiography after Cardiac Arrest (COACT) trial, the primary point of interest was sex differences in OHCA-related one-year survival. Secondary points of interest included the benefit of immediate coronary angiography compared to delayed angiography until after neurologic recovery, angiographic and clinical outcomes. RESULTS: In total, 522 patients (79.1% men) were included. Overall one-year survival was 59.6% in women and 63.4% in men (HR 1.18; 95% CI: 0.76-1.81;p = 0.47). No cardiovascular risk factors were found that modified survival. Women less often had significant coronary artery disease (CAD) (37.0% vs. 71.3%;p < 0.001), but when present, they had a worse prognosis than women without CAD (HR 3.06; 95% CI 1.31-7.19;p = 0.01). This was not the case for men (HR 1.05; 95% CI 0.67-1.65;p = 0.83). In both sexes, immediate coronary angiography did not improve one-year survival compared to delayed angiography (women, odds ratio (OR) 0.87; 95% CI 0.58-1.30;p = 0.49; vs. men, OR 0.97; 95% CI 0.45-2.09;p = 0.93). CONCLUSION: In OHCA patients without STEMI, we found no sex differences in overall one-year survival. Women less often had significant CAD, but when CAD was present they had worse survival than women without CAD. This was not the case for men. Both sexes did not benefit from a strategy of immediate coronary angiography as compared to delayed strategy with respect to one-year survival. CLINICAL TRIAL REGISTRATION NUMBER: Netherlands trial register (NTR) 4973

    Ischaemic electrocardiogram patterns and its association with survival in out-of-hospital cardiac arrest patients without ST-segment elevation myocardial infarction: a COACT trials' post-hoc subgroup analysis

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    AIMS: ST-depression and T-wave inversion are frequently present on the post-resuscitation electrocardiogram (ECG). However, the prognostic value of ischaemic ECG patterns is unknown. METHODS AND RESULTS: In this post-hoc subgroup analysis of the Coronary Angiography after Cardiac arrest (COACT) trial, the first in-hospital post-resuscitation ECG in out-of-hospital cardiac arrest patients with a shockable rhythm was analysed for ischaemic ECG patterns. Ischaemia was defined as ST-depression of ≥0.1 mV, T-wave inversion in ≥2 contiguous leads, or both. The primary endpoint was 90-day survival. Secondary endpoints were rate of acute unstable lesions, levels of serum troponin-T, and left ventricular function. Of the 510 out-of-hospital cardiac arrest patients, 340 (66.7%) patients had ischaemic ECG patterns. Patients with ischaemic ECG patterns had a worse 90-day survival compared with those without [hazard ratio 1.51; 95% confidence interval (CI) 1.08-2.12; P = 0.02]. A higher sum of ST-depression was associated with lower survival (log-rank = 0.01). The rate of acute unstable lesions (14.5 vs. 15.8%; odds ratio 0.90; 95% CI 0.51-1.59) did not differ between the groups. In patients with ischaemic ECG patterns, maximum levels of serum troponin-T (μg/L) were higher [0.595 (interquartile range 0.243-1.430) vs. 0.359 (0.159-0.845); ratio of geometric means 1.58; 1.13-2.20] and left ventricular function (%) was worse (44.7 ± 12.5 vs. 49.9 ± 13.3; mean difference -5.13; 95% CI -8.84 to -1.42). Adjusted for age and time to return of spontaneous circulation, ischaemic ECG patterns were no longer associated with survival. CONCLUSION: Post-arrest ischaemic ECG patterns were associated with worse 90-day survival. A higher sum of ST-depression was associated with lower survival. Adjusted for age and time to return of spontaneous circulation, ischaemic ECG patterns were no longer associated with survival
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