Prediction of Post-Weaning Fibrinogen Status during Cardiopulmonary Bypass: An Observational Study in 110 Patients.

BACKGROUND: After cardiac surgery with cardiopulmonary bypass (CPB), acquired coagulopathy often leads to post-CPB bleeding. Though multifactorial in origin, this coagulopathy is often aggravated by deficient fibrinogen levels. OBJECTIVE: To assess whether laboratory and thrombelastometric testing on CPB can predict plasma fibrinogen immediately after CPB weaning. PATIENTS / METHODS: This prospective study in 110 patients undergoing major cardiovascular surgery at risk of post-CPB bleeding compares fibrinogen level (Clauss method) and function (fibrin-specific thrombelastometry) in order to study the predictability of their course early after termination of CPB. Linear regression analysis and receiver operating characteristics were used to determine correlations and predictive accuracy. RESULTS: Quantitative estimation of post-CPB Clauss fibrinogen from on-CPB fibrinogen was feasible with small bias (+0.19 g/l), but with poor precision and a percentage of error >30%. A clinically useful alternative approach was developed by using on-CPB A10 to predict a Clauss fibrinogen range of interest instead of a discrete level. An on-CPB A10 ≤10 mm identified patients with a post-CPB Clauss fibrinogen of ≤1.5 g/l with a sensitivity of 0.99 and a positive predictive value of 0.60; it also identified those without a post-CPB Clauss fibrinogen <2.0 g/l with a specificity of 0.83. CONCLUSIONS: When measured on CPB prior to weaning, a FIBTEM A10 ≤10 mm is an early alert for post-CPB fibrinogen levels below or within the substitution range (1.5-2.0 g/l) recommended in case of post-CPB coagulopathic bleeding. This helps to minimize the delay to data-based hemostatic management after weaning from CPB.

Prédiction des complications après duodénopancréatectomie céphalique: validation d'un score de complications postopératoires = Prediction of complications after pancreaticoduodenectomy: validation of a postoperative complication score

Objectifs La chirurgie pancréatique reste associée à une morbidité postopératoire importante. Les efforts sont concentrés la plupart du temps sur la diminution de cette morbidité, mais la détection précoce de patients à risque de complications pourrait être une autre stratégie valable. Un score simple de prédiction des complications après duodénopancréatectomie céphalique a récemment été publié par Braga et al. La présente étude a pour but de valider ce score et de discuter de ses possibles implications cliniques. Méthodes De 2000 à 2015, 245 patients ont bénéficié d'une duodénopancréatectomie céphalique dans notre service. Les complications postopératoires ont été recensées selon la classification de Dindo et Clavien. Le score de Braga se base sur quatre paramètres : le score ASA (American Society of Anesthesiologists), la texture du pancréas, le diamètre du canal de Wirsung (canal pancréatique principal) et les pertes sanguines intra-opératoires. Un score de risque global de 0 à 15 peut être calculé pour chaque patient. La puissance de discrimination du score a été calculée en utilisant une courbe ROC (receiver operating characteristic). Résultats Des complications majeures sont apparues chez 31% des patients, alors que 17% des patients ont eu des complications majeures dans l'article de Braga. La texture du pancréas et les pertes sanguines étaient statistiquement significativement corrélées à une morbidité accrue. Les aires sous la courbe étaient respectivement de 0.95 et 0.99 pour les scores classés en quatre catégories de risques (de 0 à 3, 4 à 7, 8 à 11 et 12 à 15) et pour les scores individuels (de 0 à 15). Conclusions Le score de Braga permet donc une bonne discrimination entre les complications mineures et majeures. Notre étude de validation suggère que ce score peut être utilisé comme un outil pronostique de complications majeures après duodénopancréatectomie céphalique. Les implications cliniques, c'est-à-dire si les stratégies de prise en charge postopératoire doivent être adaptées en fonction du risque individuel du patient, restent cependant à élucider. -- Objectives Pancreatic surgery remains associated with important morbidity. Efforts are most commonly concentrated on decreasing postoperative morbidity, but early detection of patients at risk could be another valuable strategy. A simple prognostic score has recently been published. This study aimed to validate this score and discuss possible clinical implications. Methods From 2000 to 2012, 245 patients underwent pancreaticoduodenectomy. Complications were graded according to the Dindo-Clavien classification. The Braga score is based on American Society of Anesthesiologists score, pancreatic texture, Wirsung duct diameter, and blood loss. An overall risk score (from 0 to 15) can be calculated for each patient. Score discriminant power was calculated using a receiver operating characteristic curve. Results Major complications occurred in 31% of patients compared to 17% in Braga's data. Pancreatic texture and blood loss were independently statistically significant for increased morbidity. The areas under curve were 0.95 and 0.99 for 4-risk categories and for individual scores, respectively. Conclusions The Braga score discriminates well between minor and major complications. Our validation suggests that it can be used as prognostic tool for major complications after pancreaticoduodenectomy. The clinical implications, i.e., whether postoperative treatment strategies should be adapted according to the patient's individual risk, remain to be elucidated.

Improving the Prediction of Spontaneous and Post-thrombolytic Recanalization in Ischemic Stroke Patients.

BACKGROUND: Endovascular treatment for acute ischemic stroke patients was recently shown to improve recanalization rates and clinical outcome in a well-defined study population. Intravenous thrombolysis (IVT) alone is insufficiently effective to recanalize in certain patients or of little value in others. Accordingly, we aimed at identifying predictors of recanalization in patients treated with or without IVT. METHODS: In the observational Acute Stroke Registry and Analysis of Lausanne (ASTRAL) registry, we selected those stroke patients (1) with an arterial occlusion on computed tomography angiography (CTA) imaging, (2) who had an arterial patency assessment at 24 hours (CTA/magnetic resonance angiography/transcranial Doppler), and (3) who were treated with IVT or had no revascularization treatment. Based on 2 separate logistic regression analyses, predictors of spontaneous and post-thrombolytic recanalization were generated. RESULTS: Partial or complete recanalization was achieved in 121 of 210 (58%) thrombolyzed patients. Recanalization was associated with atrial fibrillation (odds ratio , 1.6; 95% confidence interval, 1.2-3.0) and absence of early ischemic changes on CT (1.1, 1.1-1.2) and inversely correlated with the presence of a significant extracranial (EC) stenosis or occlusion (.6, .3-.9). In nonthrombolyzed patients, partial or complete recanalization was significantly less frequent (37%, P < .01). The recanalization was independently associated with a history of hypercholesterolemia (2.6, 1.2-5.6) and the proximal site of the intracranial occlusion (2.5, 1.2-5.4), and inversely correlated with a decreased level of consciousness (.3, .1-.8), and EC (.3, .1-.6) and basilar artery pathology (.1, .0-.6). CONCLUSIONS: Various clinical findings, cardiovascular risk factors, and arterial pathology on acute CTA-based imaging are moderately associated with spontaneous and post-thrombolytic arterial recanalization at 24 hours. If confirmed in other studies, this information may influence patient selection toward the most appropriate revascularization strategy.

Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients.

PURPOSE: The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. METHODS: The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients' clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. RESULTS: The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679±0.068, Akaike's information criterion 566.7, P<0.001). CONCLUSION: A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.

Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.

Prediction of multiple infections after severe burn trauma: a prospective cohort study.

OBJECTIVE: To develop predictive models for early triage of burn patients based on hypersusceptibility to repeated infections. BACKGROUND: Infection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking. METHODS: Secondary analysis of 459 burn patients (≥16 years old) with 20% or more total body surface area burns recruited from 6 US burn centers. We compared blood transcriptomes with a 180-hour cutoff on the injury-to-transcriptome interval of 47 patients (≤1 infection episode) to those of 66 hypersusceptible patients [multiple (≥2) infection episodes (MIE)]. We used LASSO regression to select biomarkers and multivariate logistic regression to built models, accuracy of which were assessed by area under receiver operating characteristic curve (AUROC) and cross-validation. RESULTS: Three predictive models were developed using covariates of (1) clinical characteristics; (2) expression profiles of 14 genomic probes; (3) combining (1) and (2). The genomic and clinical models were highly predictive of MIE status [AUROCGenomic = 0.946 (95% CI: 0.906-0.986); AUROCClinical = 0.864 (CI: 0.794-0.933); AUROCGenomic/AUROCClinical P = 0.044]. Combined model has an increased AUROCCombined of 0.967 (CI: 0.940-0.993) compared with the individual models (AUROCCombined/AUROCClinical P = 0.0069). Hypersusceptible patients show early alterations in immune-related signaling pathways, epigenetic modulation, and chromatin remodeling. CONCLUSIONS: Early triage of burn patients more susceptible to infections can be made using clinical characteristics and/or genomic signatures. Genomic signature suggests new insights into the pathophysiology of hypersusceptibility to infection may lead to novel potential therapeutic or prophylactic targets.

Intracranial Aneurysms: Wall Motion Analysis for Prediction of Rupture.

Intracranial aneurysms are a common pathologic condition with a potential severe complication: rupture. Effective treatment options exist, neurosurgical clipping and endovascular techniques, but guidelines for treatment are unclear and focus mainly on patient age, aneurysm size, and localization. New criteria to define the risk of rupture are needed to refine these guidelines. One potential candidate is aneurysm wall motion, known to be associated with rupture but difficult to detect and quantify. We review what is known about the association between aneurysm wall motion and rupture, which structural changes may explain wall motion patterns, and available imaging techniques able to analyze wall motion.

Validation of and comparison between a semidistributed rainfall-runoff hydrological model (PREVAH) and a spatially distributed snow-evolution model (SnowModel) for snow cover prediction in mountain ecosystems

Snow cover is an important control in mountain environments and a shift of the snow-free period triggered by climate warming can strongly impact ecosystem dynamics. Changing snow patterns can have severe effects on alpine plant distribution and diversity. It thus becomes urgent to provide spatially explicit assessments of snow cover changes that can be incorporated into correlative or empirical species distribution models (SDMs). Here, we provide for the first time a with a lower overestimation comparison of two physically based snow distribution models (PREVAH and SnowModel) to produce snow cover maps (SCMs) at a fine spatial resolution in a mountain landscape in Austria. SCMs have been evaluated with SPOT-HRVIR images and predictions of snow water equivalent from the two models with ground measurements. Finally, SCMs of the two models have been compared under a climate warming scenario for the end of the century. The predictive performances of PREVAH and SnowModel were similar when validated with the SPOT images. However, the tendency to overestimate snow cover was slightly lower with SnowModel during the accumulation period, whereas it was lower with PREVAH during the melting period. The rate of true positives during the melting period was two times higher on average with SnowModel with a lower overestimation of snow water equivalent. Our results allow for recommending the use of SnowModel in SDMs because it better captures persisting snow patches at the end of the snow season, which is important when modelling the response of species to long-lasting snow cover and evaluating whether they might survive under climate change.

Prediction of Complications After Pancreaticoduodenectomy: Validation of a Postoperative Complication Score.

OBJECTIVES: Pancreatic surgery remains associated with important morbidity. Efforts are most commonly concentrated on decreasing postoperative morbidity, but early detection of patients at risk could be another valuable strategy. A simple prognostic score has recently been published. This study aimed to validate this score and discuss possible clinical implications. METHODS: From 2000 to 2012, 245 patients underwent a pancreaticoduodenectomy. Complications were graded according to the Dindo-Clavien Classification. The Braga score is based on American Society of Anesthesiologists score, pancreatic texture, Wirsung duct diameter, and blood loss. An overall risk score (0-15) can be calculated for each patient. Score discriminant power was calculated using a receiver operating characteristic curve. RESULTS: Major complications occurred in 31% of patients compared with 17% in Braga's data. Pancreatic texture and blood loss were independently statistically significant for increased morbidity. Areas under the curve were 0.95 and 0.99 for 4-risk categories and for individual scores, respectively. CONCLUSIONS: The Braga score discriminates well between minor and major complications. Our validation suggests that it can be used as a prognostic tool for major complications after pancreaticoduodenectomy. The clinical implications, that is, whether postoperative treatment strategies should be adapted according to the patient's individual risk, remain to be elucidated.

Association between parental history and genetic risk scores for coronary heart disease prediction: The population-based CoLaus study.

BACKGROUND AND AIMS: Parental history (PH) and genetic risk scores (GRSs) are separately associated with coronary heart disease (CHD), but evidence regarding their combined effects is lacking. We aimed to evaluate the joint associations and predictive ability of PH and GRSs for incident CHD. METHODS: Data for 4283 Caucasians were obtained from the population-based CoLaus Study, over median follow-up time of 5.6 years. CHD was defined as incident myocardial infarction, angina, percutaneous coronary revascularization or bypass grafting. Single nucleotide polymorphisms for CHD identified by genome-wide association studies were used to construct unweighted and weighted versions of three GRSs, comprising of 38, 53 and 153 SNPs respectively. RESULTS: PH was associated with higher values of all weighted GRSs. After adjustment for age, sex, smoking, diabetes, systolic blood pressure, low and high density lipoprotein cholesterol, PH was significantly associated with CHD [HR 2.61, 95% CI (1.47-4.66)] and further adjustment for GRSs did not change this estimate. Similarly, one standard deviation change of the weighted 153-SNPs GRS was significantly associated with CHD [HR 1.50, 95% CI (1.26-1.80)] and remained so, after further adjustment for PH. The weighted, 153-SNPs GRS, but not PH, modestly improved discrimination [(C-index improvement, 0.016), p = 0.048] and reclassification [(NRI improvement, 8.6%), p = 0.027] beyond cardiovascular risk factors. After including both the GRS and PH, model performance improved further [(C-index improvement, 0.022), p = 0.006]. CONCLUSION: After adjustment for cardiovascular risk factors, PH and a weighted, polygenic GRS were jointly associated with CHD and provided additive information for coronary events prediction.

Predicting recurrence after unprovoked venous thromboembolism: prospective validation of the updated Vienna Prediction Model.

The updated Vienna Prediction Model for estimating recurrence risk after an unprovoked venous thromboembolism (VTE) has been developed to identify individuals at low risk for VTE recurrence in whom anticoagulation (AC) therapy may be stopped after 3 months. We externally validated the accuracy of the model to predict recurrent VTE in a prospective multicenter cohort of 156 patients aged ≥65 years with acute symptomatic unprovoked VTE who had received 3 to 12 months of AC. Patients with a predicted 12-month risk within the lowest quartile based on the updated Vienna Prediction Model were classified as low risk. The risk of recurrent VTE did not differ between low- vs higher-risk patients at 12 months (13% vs 10%; P = .77) and 24 months (15% vs 17%; P = 1.0). The area under the receiver operating characteristic curve for predicting VTE recurrence was 0.39 (95% confidence interval [CI], 0.25-0.52) at 12 months and 0.43 (95% CI, 0.31-0.54) at 24 months. In conclusion, in elderly patients with unprovoked VTE who have stopped AC, the updated Vienna Prediction Model does not discriminate between patients who develop recurrent VTE and those who do not. This study was registered at www.clinicaltrials.gov as #NCT00973596.

Trends and characteristics of attendance at the emergency department of a Swiss university hospital: 2002-2012.

BACKGROUND: The numbers of people attending emergency departments (EDs) at hospitals are increasing. We aimed to analyse trends in ED attendance at a Swiss university hospital between 2002 and 2012, focussing on age-related differences and hospital admission criteria. METHODS: We used hospital administrative data for all patients aged ≥16 years who attended the ED (n = 298,306) at this university hospital between 1 January 2002, and 31 December 2012. We descriptively analysed the numbers of ED visits according to the admission year and stratified by age (≥65 vs <65 years). RESULTS: People attending the ED were on average 46.6 years old (standard deviation 20 years, maximum range 16‒99 years). The annual number of ED attendances grew by n = 6,639 (27.6%) from 24,080 in 2002 to 30,719 in 2012. In the subgroup of patients aged ≥65 the relative increase was 42.3%, which is significantly higher (Pearson's χ2 = 350.046, df = 10; p = 0.000) than the relative increase of 23.4% among patients <65 years. The subgroup of patients ≥65 years attended the ED more often because of diseases (n = 56,307; 85%) than accidents (n = 9,844; 14.9%). This subgroup (patients ≥65 years) was also more often admitted to hospital (Pearson's χ2 = 23,377.190; df = 1; p = 0.000) than patients <65 years. CONCLUSIONS: ED attendance of patients ≥65 years increased in absolute and relative terms. The study findings suggest that staff of this ED may want to assess the needs of patients ≥65 years and, if necessary, adjust the services (e.g., adapted triage scales, adapted geriatric screenings, and adapted hospital admission criteria).

A Meta-Analysis of Trabecular Bone Score in Fracture Risk Prediction and Its Relationship to FRAX.

Trabecular bone score (TBS) is a gray-level textural index of bone microarchitecture derived from lumbar spine dual-energy X-ray absorptiometry (DXA) images. TBS is a bone mineral density (BMD)-independent predictor of fracture risk. The objective of this meta-analysis was to determine whether TBS predicted fracture risk independently of FRAX probability and to examine their combined performance by adjusting the FRAX probability for TBS. We utilized individual-level data from 17,809 men and women in 14 prospective population-based cohorts. Baseline evaluation included TBS and the FRAX risk variables, and outcomes during follow-up (mean 6.7 years) comprised major osteoporotic fractures. The association between TBS, FRAX probabilities, and the risk of fracture was examined using an extension of the Poisson regression model in each cohort and for each sex and expressed as the gradient of risk (GR; hazard ratio per 1 SD change in risk variable in direction of increased risk). FRAX probabilities were adjusted for TBS using an adjustment factor derived from an independent cohort (the Manitoba Bone Density Cohort). Overall, the GR of TBS for major osteoporotic fracture was 1.44 (95% confidence interval [CI] 1.35-1.53) when adjusted for age and time since baseline and was similar in men and women (p > 0.10). When additionally adjusted for FRAX 10-year probability of major osteoporotic fracture, TBS remained a significant, independent predictor for fracture (GR = 1.32, 95% CI 1.24-1.41). The adjustment of FRAX probability for TBS resulted in a small increase in the GR (1.76, 95% CI 1.65-1.87 versus 1.70, 95% CI 1.60-1.81). A smaller change in GR for hip fracture was observed (FRAX hip fracture probability GR 2.25 vs. 2.22). TBS is a significant predictor of fracture risk independently of FRAX. The findings support the use of TBS as a potential adjustment for FRAX probability, though the impact of the adjustment remains to be determined in the context of clinical assessment guidelines. © 2015 American Society for Bone and Mineral Research.