Receiver operator characteristic curve for continuous veno-venous hemofiltration (CVVH) duration

A receiver operating characteristic curve was used to assess the best cutoff for time to hemofiltration-filter clotting to predict a positive PF4 test. The arrow shows that a 6-hour duration of CVVH session is the most accurate cutoff (sensitivity 71%, specificity 85%, and area under the curve 0.83).<p><b>Copyright information:</b></p><p>Taken from "Anti-PF4/heparin antibodies associated with repeated hemofiltration-filter clotting: a retrospective study"</p><p>http://ccforum.com/content/12/3/R84</p><p>Critical Care 2008;12(3):R84-R84.</p><p>Published online 25 Jun 2008</p><p>PMCID:PMC2481483.</p><p></p

Duration and efficiency of continuous veno-venous hemofiltration (CVVH) sessions

Boxes represent medians, interquartile ranges, and 10th and 90th percentiles of mean duration of CVVH sessions (upper panel) and urea reduction ratios ([(urea before – urea after)/urea before] × 100, lower panel) observed in PF4(n = 7) and PF4(n = 21) patients when using unfractionated heparin (50 and 132 sessions for PF4and PF4patients, respectively) and in PF4patients when using danaparoid sodium for anticoagulation (17 sessions). *< 0.05 compared with PF4(using a Mann-Whitney test); = 0.027 compared with PF4(using a Wilcoxon rank test). There was no difference between PF4and danaparoid CVVH durations (= 0.17) or urea reduction (= 0.27). PF4, anti-PF4/heparin antibody-negative; PF4, anti-PF4/heparin antibody-positive.<p><b>Copyright information:</b></p><p>Taken from "Anti-PF4/heparin antibodies associated with repeated hemofiltration-filter clotting: a retrospective study"</p><p>http://ccforum.com/content/12/3/R84</p><p>Critical Care 2008;12(3):R84-R84.</p><p>Published online 25 Jun 2008</p><p>PMCID:PMC2481483.</p><p></p

Is the EuroSCORE II reliable to estimate operative mortality among octogenarians?

<div><p>Objectives</p><p>Concerns have been raised about the predictive performance (PP) of the EuroSCORE I (ES I) to estimate operative mortality (OM) of patients aged ≥80. The EuroSCORE II (ES II) has been described to have better PP of OM but external validations are scarce. Furthermore, the PP of ES II has not been investigated among the octogenarians. The goal of the study was to compare the PP of ES II and ES I among the overall population and patients ≥ 80.</p><p>Methods</p><p>The ES I and ES II were computed for 7161 consecutive patients who underwent major cardiac surgery in a 7-year period. Discrimination was assessed by using the c- index and calibration with the Hosmer-Lemeshow (HL) and calibration plot by comparing predicted and observed mortality.</p><p>Results</p><p>From the global cohort of 7161 patients, 832 (12%) were ≥80. The mean values of ES I and ES II were 7.4±9.4 and 5.2±9.1 respectively for the whole cohort, 6.3±8.6 and 4.7±8.5 for the patients <80, 15.1±11.8 and 8.5±11.0 for the patients ≥80. The mortality was 9.38% (≥80) versus 5.18% (<80). The discriminatory power was good for the two algorithms among the whole population and the <80 but less satisfying among the ≥80 (AUC 0.64 [0.58–0.71] for ES I and 0.67 [0.60–0.73] for the ES II without significant differences (p = 0.35) between the two scores. For the octogenarians, the ES II had a fair calibration until 10%-predicted values and over-predicted beyond.</p><p>Conclusions</p><p>The ES II has a better PP than the ES I among patients <80. Its discrimination and calibration are less satisfying in patients ≥80, showing an overestimation in the elderly at very high-surgical risk. Nevertheless, it shows an acceptable calibration until 10%- predicted mortality.</p></div

Calibration of ES I and ES II for in-hospital mortality among the overall population and according to age.

<p>Calibration of ES I and ES II for in-hospital mortality among the overall population and according to age.</p

Discrimination for in-hospital mortality among the overall population and according to age.

<p>Discrimination for in-hospital mortality among the overall population and according to age.</p

Characteristics for the whole population and for both age groups.

<p>Characteristics for the whole population and for both age groups.</p

Observed and predicted mortality of the study population.

<p>Observed and predicted mortality of the study population.</p

Calibration plots for the two models, for the entire population (Fig 2A) and the two studied groups (Fig 2B: ≥80 and Fig 2C: <80).

<p>Calibration plots for the two models, for the entire population (Fig 2A) and the two studied groups (Fig 2B: ≥80 and Fig 2C: <80).</p

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

<div><p>Background</p><p>The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models.</p><p>Methods and finding</p><p>We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold.</p><p>Conclusions</p><p>According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.</p></div

Table_1_Circulating microbiome analysis in patients with perioperative anaphylaxis.docx

BackgroundPerioperative anaphylaxis is a rare and acute systemic manifestation of drug-induced hypersensitivity reactions that occurs following anesthesia induction; the two main classes of drugs responsible for these reactions being neuromuscular blocking agents (NMBA) and antibiotics. The sensitization mechanisms to the drugs are not precisely known, and few risk factors have been described. A growing body of evidence underlines a link between occurrence of allergy and microbiota composition. However, no data exist on microbiota in perioperative anaphylaxis. The aim of this study was to compare circulating microbiota richness and composition between perioperative anaphylaxis patients and matched controls.MethodsCirculating 16s rDNA was quantified and sequenced in serum samples from 20 individuals with fully characterized IgE-mediated NMBA-related anaphylaxis and 20 controls matched on sex, age, NMBA received, type of surgery and infectious status. Microbiota composition was analyzed with a published bioinformatic pipeline and links with patients clinical and biological data investigated.ResultsAnalysis of microbiota diversity showed that anaphylaxis patients seem to have a richer circulating microbiota than controls, but no major differences of composition could be detected with global diversity indexes. Pairwise comparison showed a difference in relative abundance between patients and controls for Saprospiraceae, Enterobacteriaceae, Veillonellaceae, Escherichia-Shigella, Pseudarcicella, Rhodoferax, and Lewinella. Some taxa were associated with concentrations of mast cell tryptase and specific IgE.ConclusionWe did not find a global difference in terms of microbiota composition between anaphylaxis patient and controls. However, several taxa were associated with anaphylaxis patients and with their biological data. These findings must be further confirmed in different settings to broaden our understanding of drug anaphylaxis pathophysiology and identify predisposition markers.</p