The role of shuntography in diagnosis of mechanic complications after implantation of ventriculoperitoneal shunts in patients with idiopathic normal pressure hydrocephalus: a retrospective clinical evaluation.

BACKGROUND: Mechanical obstruction of ventriculoperitoneal shunt (VPS) during the first year after shunt implantation is a common complication and is widely described in the literature. In this paper, we evaluated the suitability of the shuntography for the diagnosis of mechanical complications of the VPS in patients with idiopathic normal pressure hydrocephalus (iNPH). METHODS: We retrospectively identified 49 patients with pathologic shuntography over of a period of 20 years in our hospital. The percentage of procedure-associated complications was determined. RESULTS: Ninety-eight percent (n = 48) of the patients who underwent shuntography showed clinical and radiographic signs of underdrainage prior to examination. Shuntography revealed mechanical complications of the VP shunt in 37% (n = 18) as a cause of clinical deterioration and following revision operation. During shuntography, mechanical obstruction was discovered in 78% (n = 14) and disconnection of shunt components in 22% (n = 4). In the obstruction group, in 50% (n = 7) the closure was detected in the ventricular catheter, in 29% (n = 4) in the distal catheter of the VPS, and in 21% (n = 3) in both sides of the VPS. In the case of an inconspicuous shuntography (63%, n = 31), the patients received symptomatic therapy (32%, n = 10) or re-adjustment of the valve setting (68%, n = 21). Fifty-seven percent of the patients who underwent surgical treatment improved clinically by at least one point according to the Kiefer score. CONCLUSION: Shuntography can produce valuable clinical information uncovering mechanic complications after implantation VPS in patients with idiopathic normal-pressure hydrocephalus. Patients with mechanical complications of their VPS needed revision surgery and showed clinical benefit after treatment.

Pain rates in general population for the period 1991-2015 and 10-years prediction: results from a multi-continent age-period-cohort analysis.

BACKGROUND: Pain is a common symptom, often associated with neurological and musculoskeletal conditions, and experienced especially by females and by older people. The aims of this study are to evaluate the temporal variations of pain rates among general populations for the period 1991-2015 and to project 10-year pain rates. METHODS: We used the harmonized dataset of ATHLOS project, which included 660,028 valid observations in the period 1990-2015 and we applied Bayesian age-period-cohort modeling to perform projections up to 2025. The harmonized Pain variable covers the content "self-reported pain experienced at the time of the interview", with a dichotomous (yes or no) modality. RESULTS: Pain rates were higher among females, older subjects, in recent periods, and among observations referred to cohorts of subjects born between the 20s and the 60s. The 10-year projections indicate a noteworthy increase in pain rates in both genders and particularly among subjects aged 66 or over, for whom a 10-20% increase in pain rate is foreseen; among females only, a 10-15% increase in pain rates is foreseen for those aged 36-50. CONCLUSIONS: Projected increase in pain rates will require specific interventions by health and welfare systems, as pain is responsible for limited quality of subjective well-being, reduced employment rates and hampered work performance. Worksite and lifestyle interventions will therefore be needed to limit the impact of projected higher pain rates.

Predictors of pain in general ageing populations: results from a multi-country analysis based on ATHLOS harmonized database.

BACKGROUND: Pain is a common symptom, often associated with neurological and musculoskeletal conditions, and experienced especially by females and by older people, and with increasing trends in general populations. Different risk factors for pain have been identified, but generally from studies with limited samples and a limited number of candidate predictors. The aim of this study is to evaluate the predictors of pain from a large set of variables and respondents. METHODS: We used part of the harmonized dataset of ATHLOS project, selecting studies and waves with a longitudinal course, and in which pain was absent at baseline and with no missing at follow-up. Predictors were selected based on missing distribution and univariable association with pain, and were selected from the following domains: Socio-demographic and economic characteristics, Lifestyle and health behaviours, Health status and functional limitations, Diseases, Physical measures, Cognition, personality and other psychological measures, and Social environment. Hierarchical logistic regression models were then applied to identify significant predictors. RESULTS: A total of 13,545 subjects were included of whom 5348 (39.5%) developed pain between baseline and the average 5.2 years' follow-up. Baseline risk factors for pain were female gender (OR 1.34), engaging in vigorous exercise (OR 2.51), being obese (OR 1.36) and suffering from the loss of a close person (OR 1.88) whereas follow-up risk factors were low energy levels/fatigue (1.93), difficulties with walking (1.69), self-rated health referred as poor (OR 2.20) or average to moderate (OR 1.57) and presence of sleep problems (1.80). CONCLUSIONS: Our results showed that 39.5% of respondents developed pain over a five-year follow-up period, that there are proximal and distal risk factors for pain, and that part of them are directly modifiable. Actions aimed at improving sleep, reducing weight among obese people and treating fatigue would positively impact on pain onset, and avoiding vigorous exercise should be advised to people aged 60 or over, in particular if female or obese.

Sociodemographic Indicators of Health Status Using a Machine Learning Approach and Data from the English Longitudinal Study of Aging (ELSA).

BACKGROUND Studies on the effects of sociodemographic factors on health in aging now include the use of statistical models and machine learning. The aim of this study was to evaluate the determinants of health in aging using machine learning methods and to compare the accuracy with traditional methods. MATERIAL AND METHODS The health status of 6,209 adults, age <65 years (n=1,585), 65-79 years (n=3,267), and >80 years (n=1,357) were measured using an established health metric (0-100) that incorporated physical function and activities of daily living (ADL). Data from the English Longitudinal Study of Ageing (ELSA) included socio-economic and sociodemographic characteristics and history of falls. Health-trend and personal-fitted variables were generated as predictors of health metrics using three machine learning methods, random forest (RF), deep learning (DL) and the linear model (LM), with calculation of the percentage increase in mean square error (%IncMSE) as a measure of the importance of a given predictive variable, when the variable was removed from the model. RESULTS Health-trend, physical activity, and personal-fitted variables were the main predictors of health, with the%incMSE of 85.76%, 63.40%, and 46.71%, respectively. Age, employment status, alcohol consumption, and household income had the%incMSE of 20.40%, 20.10%, 16.94%, and 13.61%, respectively. Performance of the RF method was similar to the traditional LM (p=0.7), but RF significantly outperformed DL (p=0.006). CONCLUSIONS Machine learning methods can be used to evaluate multidimensional longitudinal health data and may provide accurate results with fewer requirements when compared with traditional statistical modeling.

Red blood cells treated with the amustaline (S-303) pathogen reduction system: a transfusion study in cardiac surgery.

BACKGROUND: Nucleic acid-targeted pathogen inactivation technology using amustaline (S-303) and glutathione (GSH) was developed to reduce the risk of transfusion-transmitted infectious disease and transfusion-associated graft-versus-host disease with red blood cell (RBC) transfusion. STUDY DESIGN AND METHODS: A randomized, double-blind, controlled study was performed to assess the in vitro characteristics of amustaline-treated RBCs (test) compared with conventional (control) RBCs and to evaluate safety and efficacy of transfusion during and after cardiac surgery. The primary device efficacy endpoint was the postproduction hemoglobin (Hb) content of RBCs. Exploratory clinical outcomes included renal and hepatic failure, the 6-minute walk test (a surrogate for cardiopulmonary function), adverse events (AEs), and the immune response to amustaline-treated RBCs. RESULTS: A total of 774 RBC unis were produced. Mean treatment difference in Hb content was -2.27 g/unit (95% confidence interval, -2.61 to -1.92 g/unit), within the prespecified equivalence margins (±5 g/unit) to declare noninferiority. Amustaline-treated RBCs met European guidelines for Hb content, hematocrit, and hemolysis. Fifty-one (25 test and 26 control) patients received study RBCs. There were no significant differences in RBC usage or other clinical outcomes. Observed AEs were within the spectrum expected for patients of similar age undergoing cardiovascular surgery requiring RBCs transfusion. No patients exhibited an immune response specific to amustaline-treated RBCs. CONCLUSION: Amustaline-treated RBCs demonstrated equivalence to control RBCs for Hb content, have appropriate characteristics for transfusion, and were well tolerated when transfused in support of acute anemia. Renal impairment was characterized as a potential efficacy endpoint for pivotal studies of RBC transfusion in cardiac surgery.

Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk.

BACKGROUND: The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML methodologies on CVD prediction, especially compared to established risk tool, the HellenicSCORE. METHODS: Data from the ATTICA prospective study (n = 2020 adults), enrolled during 2001-02 and followed-up in 2011-12 were used. Three different machine-learning classifiers (k-NN, random forest, and decision tree) were trained and evaluated against 10-year CVD incidence, in comparison with the HellenicSCORE tool (a calibration of the ESC SCORE). Training datasets, consisting from 16 variables to only 5 variables, were chosen, with or without bootstrapping, in an attempt to achieve the best overall performance for the machine learning classifiers. RESULTS: Depending on the classifier and the training dataset the outcome varied in efficiency but was comparable between the two methodological approaches. In particular, the HellenicSCORE showed accuracy 85%, specificity 20%, sensitivity 97%, positive predictive value 87%, and negative predictive value 58%, whereas for the machine learning methodologies, accuracy ranged from 65 to 84%, specificity from 46 to 56%, sensitivity from 67 to 89%, positive predictive value from 89 to 91%, and negative predictive value from 24 to 45%; random forest gave the best results, while the k-NN gave the poorest results. CONCLUSIONS: The alternative approach of machine learning classification produced results comparable to that of risk prediction scores and, thus, it can be used as a method of CVD prediction, taking into consideration the advantages that machine learning methodologies may offer.

Influence of gender on postoperative outcome after intra-aortic balloon counter-pulsation and cardiac surgery.

INTRODUCTION: Female gender is an established risk factor for worse outcomes after cardiac surgery, and women are more likely to experience postoperative complications. Our aim was to analyze the influence of gender on outcome and postoperative complications after the use of intra-aortic balloon counter-pulsation (IABP) in cardiac surgery patients. METHODS: Fifty-seven consecutive female patients (mean age: 73 ± 9 years) requiring an IABP at our department from January 2007 to January 2010 were retrospectively analyzed and compared with 182 male patients receiving IABP support within the same period. The collected data included patient demographics, preoperative state, operative details, postoperative pharmacological treatment, IABP-associated complications, and inhospital mortality. Preoperative mortality risk was calculated by logistic EuroSCORE. RESULTS: There were no differences regarding the type of operation, preoperative renal or hepatic failure, though the prevalence of peripheral artery occlusive disease was higher in men. Furthermore, female patients receiving an IABP were significantly older (73 ± 9 vs. 67 ± 10 years), had a higher ejection fraction (EF) (45% ± 24% vs. 36% ± 14%), and had a higher EuroSCORE (25% ± 20% vs. 19% ± 17%; p < 0.05). Postoperative catecholamine support was significantly higher in the female patients. Women had a prolonged length of stay (LOS) at the ICU (10.64 ± 9.7 vs. 7.6 ± 7.6 days), higher incidence of renal replacement therapy, and a higher mortality (19 [19.4%] vs. 35 [33.9%]; p < 0.05) after the use of IABP. CONCLUSION: Women have a worse outcome after the use of IABP, including LOS at the ICU, postoperative renal failure, and inhospital mortality, despite higher EF, when compared with men.

Associated risk of recombinant activated factor VIIa application.

BACKGROUND: The recombinant human coagulation FVIIa was approved for the treatment of bleeding in hemophilia patients. The reports of a good hemostatic effect were followed by studies and applications without a regulatory extension of the therapeutic indication (off-label use). The aim of this retrospective study is the evaluation of thromboembolic adverse events and side effects in a large cohort of patients with FVIIa therapy. METHODS: In the period from January 2009 to March 2011, a total of 143/2453 (5.8%) cardiac surgical patients (69% male; age 67 ± 11 years; 39% thoracic aorta) were treated with different doses (mean, 6.1 mg; range, 1 to 27.2 mg) of factor VIIa. The administration of FVIIa was seen as a last therapeutic option and administered at the end of the treatment algorithm for severe bleeding. RESULTS: Due to an acute bleeding situation in 143 patients, 7.9 ± 5.8 units of packed red blood cells, 9.5 ± 6.1 units of fresh frozen plasma, 1740 ± 1860 IU PPSB (Prothrombin-Proconvertin-Stuart Factor-Antihemophilic Factor B), 5.6 ± 4 g fibrinogen, and 7.9 ± 7.6 units of platelets were administered. A re-thoracotomy was necessary, despite maximal procoagulant therapy, in 55% of patients. The in-hospital mortality was 36% (51/2453 = 2%). Thrombotic complications occurred with a frequency of 16% (mesenteric infarction, n = 9; stroke/transient ischemic attack, n = 3; myocardial infarction, n = 3; other, n = 8). CONCLUSION: The proof of direct causality of the events in relation to the administration of FVIIa is difficult because the temporal and therapeutic relationships with concomitant vasoconstrictive and procoagulant therapies were not obvious. However, there remains a suspicion that a higher rate of mesenteric infarctions may be provoked by the administration of FVIIa.

Pharmacokinetics of intraluminally administered serum papaverine for spasm prophylaxis of the internal mammary artery.

BACKGROUND: Papaverine (Paveron N™ Linden Arzneimittel Vertrieb GmbH, Germany) is a widely used agent for preventing spasm in mammary artery preparations. The question addressed in this study is whether the intraluminal administration of papaverine can result in detectable absorption of the drug into the systemic arterial circulation. METHODS: In 15 patients (age 65 ± 6 years; body mass index 28.9 ± 3.7), an internal mammary artery (IMA) was prepared during coronary artery bypass grafting (CABG). A maximum of 3 mL of a 1 mg/1 mL diluted papaverine solution was injected intravascularly (intraluminally) for spasm prophylaxis. The IMA was closed proximally and distally with bulldog clamps. Blood samples were taken immediately after administration (T1), after 20 minutes (T2), and at the end of the operation (T3). Samples were measured in a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system consisting of a binary pump from Agilent (Waldbronn, Germany) coupled to a high-throughput screening (HTS) PAL injection system (CTC, Zwingen, Switzerland) and a tandem mass spectrometer (API 4000, AB Sciex, Darmstadt, Germany). Papaverine was analyzed in positive mode using an electrospray ion source. Quantitation was performed using Analyst 1.5 software (AB Sciex, Darmstadt, Germany). RESULTS: The newly developed LC-MS/MS method was successfully established for the detection of papaverine in plasma samples. The highest plasma papaverine levels were determined at time point T1 (mean 54.7 ± 39 ng/mL, range 16.6-179 ng/mL). The concentration was already halved 20 minutes after administration (T2) (mean 23.3 ± 2 ng/mL, range 4.6-118 ng/mL). Because of the short half-life and the hemodilution in the extracorporeal circulation, at the end of the operation papaverine (T3) had already fallen to just above the limit of detection (mean 4.1 ± 3.9 ng/mL, range 1.3-16.9 ng/mL). At time point T1, a significant negative correlation was determined between plasma levels and systemic diastolic, but not systolic, blood pressure. CONCLUSION: Papaverine was successfully determined systemically in plasma by LC-MS/MS after intraluminal administration in the IMA. Systemic circulatory effects are dependent on the detected quantity. Group size and the absence of a control group are considerable limitations.

Cervical spine fracture detection in computed tomography using convolutional neural networks.

Objective.In the context of primary in-hospital trauma management timely reading of computed tomography (CT) images is critical. However, assessment of the spine is time consuming, fractures can be very subtle, and the potential for under-diagnosis or delayed diagnosis is relevant. Artificial intelligence is increasingly employed to assist radiologists with the detection of spinal fractures and prioritization of cases. Currently, algorithms focusing on the cervical spine are commercially available. A common approach is the vertebra-wise classification. Instead of a classification task, we formulate fracture detection as a segmentation task aiming to find and display all individual fracture locations presented in the image.Approach.Based on 195 CT examinations, 454 cervical spine fractures were identified and annotated by radiologists at a tertiary trauma center. We trained for the detection a U-Net via four-fold-cross validation to segment spine fractures and the spine via a multi-task loss. We further compared advantages of two image reformation approaches-straightened curved planar reformatted (CPR) around the spine and spinal canal aligned volumes of interest (VOI)-to achieve a unified vertebral alignment in comparison to processing the Cartesian data directly.Main results.Of the three data versions (Cartesian, reformatted, VOI) the VOI approach showed the best detection rate and a reduced computation time. The proposed algorithm was able to detect 87.2% of cervical spine fractures at an average number of false positives of 3.5 per case. Evaluation of the method on a public spine dataset resulted in 0.9 false positive detections per cervical spine case.Significance.The display of individual fracture locations as provided with high sensitivity by the proposed voxel classification based fracture detection has the potential to support the trauma CT reading workflow by reducing missed findings.

Obstacles and Solutions Driving the Development of a National Teleradiology Network.

BACKGROUND: Teleradiology has the potential to link medical experts and specialties despite geographical separation. In a project report about hospital-based teleradiology, the significance of technical and human factors during the implementation and growth of a teleradiology network are explored. EVALUATION: The article identifies major obstacles during the implementation and growth of the teleradiology network of the Berlin Trauma Hospital (BG Unfallkrankenhaus Berlin) between 2004 and 2020 in semi-structured interviews with senior staff members. Quantitative analysis of examination numbers, patient numbers, and profits relates the efforts of the staff members to the monetary benefits and success of the network. Identification of qualitative and quantitative factors for success: Soft and hard facilitators and solutions driving the development of the national teleradiology network are identified. Obstacles were often solved by technical innovations, but the time span between required personal efforts, endurance, and flexibility of local and external team members. The article describes innovations driven by teleradiology and hints at the impact of teleradiology on modern medical care by relating the expansion of the teleradiology network to patient transfers and profits. CONCLUSION: In addition to technical improvements, interpersonal collaborations were key to the success of the teleradiology network of the Berlin Trauma Hospital and remained a unique feature and selling point of this teleradiology network.

Gait Analysis Platform for Measuring Surgery Recovery.

Gait analysis has evolved significantly during last years due to the great development of the Medical Internet of Things (MIoT) platforms that allow an easy integration of sensors (inertial, magnetic and pressure in our case) to the complex analytics required to compute, not only relevant parameters, but also meaningful indexes. In this paper, we extend a previous development based on a fully wireless pair of insoles by implementing an updated version with more reliable and user-friendly devices, smartphone app and web front-end and back-end. We also extend previous work focused on fall analysis (with the corresponding fall risk index or FRI) with the proposal of a new surgery recovery index (SRI) to account for the individual speed recovery speed that can be measured either at clinical facilities or at home in a telemedicine environment or while doing daily life activities. This new index can be personalized for different types of surgeries that affect gait such as hip, knee, etc. This paper presents the case of hip recovery and is built on top of the clinical standard SPPB test and allows obtaining quantitative parameters directly from the sensors.

Development of a common scale for measuring healthy ageing across the world: results from the ATHLOS consortium.

BACKGROUND: Research efforts to measure the concept of healthy ageing have been diverse and limited to specific populations. This diversity limits the potential to compare healthy ageing across countries and/or populations. In this study, we developed a novel measurement scale of healthy ageing using worldwide cohorts. METHODS: In the Ageing Trajectories of Health-Longitudinal Opportunities and Synergies (ATHLOS) project, data from 16 international cohorts were harmonized. Using ATHLOS data, an item response theory (IRT) model was used to develop a scale with 41 items related to health and functioning. Measurement heterogeneity due to intra-dataset specificities was detected, applying differential item functioning via a logistic regression framework. The model accounted for specificities in model parameters by introducing cohort-specific parameters that rescaled scores to the main scale, using an equating procedure. Final scores were estimated for all individuals and converted to T-scores with a mean of 50 and a standard deviation of 10. RESULTS: A common scale was created for 343 915 individuals above 18 years of age from 16 studies. The scale showed solid evidence of concurrent validity regarding various sociodemographic, life and health factors, and convergent validity with healthy life expectancy (r = 0.81) and gross domestic product (r = 0.58). Survival curves showed that the scale could also be predictive of mortality. CONCLUSIONS: The ATHLOS scale, due to its reliability and global representativeness, has the potential to contribute to worldwide research on healthy ageing.

Improved survival of patients with metastatic breast cancer in routine care: results of a retrospective study in a community-based oncology group practice 1995-2005.

BACKGROUND: Little is known about the treatment reality in metastatic breast cancer (MBC) outside clinical trials. We undertook this analysis to evaluate the actual treatment reality for unselected patients with MBC in routine care. PATIENTS AND METHODS: All patients with MBC, who were treated in our community-based group practice between 1995 and 2005, were analyzed retrospectively concerning prognostic factors, treatment, and survival. RESULTS: 403 consecutive patients were evaluated with a median age of 60 years (range 32-93). Aromatase inhibitor therapy was used in 87% of all patients. 83% received chemotherapy with the median number of lines being 3 (1-15). An anthracycline was given to 49%, a taxane was used in 55%, vinorelbine in 42%, capecitabine in 36%, gemcitabine in 28%, and a platinum compound in 9%. 94% of patients with bone metastasis received a bisphosphonate, and 63% of HER-2/neu-positive patients were treated with trastuzumab. Median survival since the start of palliative therapy was 30 months. Statistical analysis revealed as major prognostic factors hormone receptor status and prevalence of only bone metastasis. CONCLUSIONS: Treatment reality of MBC in routine care reveals a prolonged median survival of 30 months which is probably due to the sequential use of the most effective treatment modalities.

Determinants of Health Trajectories in England and the United States: An Approach to Identify Different Patterns of Healthy Aging.

Background: Aging is a multidimensional process with a remarkable interindividual variability. This study is focused on identifying groups of population with similar aging patterns, and to define the health trajectories of these groups. Sociodemographic and health determinants of these trajectories are also identified. Methods: Data from the English Longitudinal Study of Aging (ELSA) and the Health and Retirement Study (HRS) were used. A set of self-reported health items and measured tests were used to generate a latent health metric by means of a Bayesian multilevel IRT model, assessing the ability of the metric to predict mortality. Then, a Growth Mixture Model (GMM) was conducted in each study to identify latent classes and assess health trajectories. Kaplan-Meier survival curves were obtained for each class and a multinomial logistic regression was used to identify determinants of these trajectories. Results: The health score generated showed an adequate ability to predict mortality over 10 years in ELSA (AUC = 0.74; 95% CI: 0.72, 0.75) and HRS (AUC = 0.74; 95% CI: 0.73, 0.75). By means of GMM, four latent classes were identified in ELSA and five in HRS. Chronic conditions, no qualification and low level of household wealth were associated to the classes which showed a higher mortality in both studies. Conclusion: The method based on the creation of a common metric of health and the use of GMM to identify similar patterns of aging, allows for the comparison of trajectories of health across longitudinal surveys. Multimorbidity, educational level, and household wealth could be considered as determinants associated to these trajectories.

Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the ATHLOS project.

A most challenging task for scientists that are involved in the study of ageing is the development of a measure to quantify health status across populations and over time. In the present study, a Bayesian multilevel Item Response Theory approach is used to create a health score that can be compared across different waves in a longitudinal study, using anchor items and items that vary across waves. The same approach can be applied to compare health scores across different longitudinal studies, using items that vary across studies. Data from the English Longitudinal Study of Ageing (ELSA) are employed. Mixed-effects multilevel regression and Machine Learning methods were used to identify relationships between socio-demographics and the health score created. The metric of health was created for 17,886 subjects (54.6% of women) participating in at least one of the first six ELSA waves and correlated well with already known conditions that affect health. Future efforts will implement this approach in a harmonised data set comprising several longitudinal studies of ageing. This will enable valid comparisons between clinical and community dwelling populations and help to generate norms that could be useful in day-to-day clinical practice.

Real-Time Constant Monitoring of Fall Risk Index by Means of Fully-Wireless Insoles.

Constant monitoring of gait in real life conditions is considered the best way to assess Fall Risk Index (FRI) since most falls happen out of the ideal conditions in which clinicians are currently analyzing the patient's behavior. This paper presents the WIISEL platform and results obtained through the use of the first full-wireless insole devices that can measure almost all gait related data directly on the feet (not in the upper part of the body as most existing wearable solutions). The platform consists of a complete tool-chain: insoles, smartphone & app, server & analysis tool, FRI estimation and user access. Results are obtained by combining parameters in a personalized way to build individual fall risk index assessed by experts with the help of data analytics. New FRI has been compared with standards that validate the quality of its prediction in a statistically significant way. That qualitatively relevant information is being provided to the platform users, being either end-users/patients, relatives or caregivers and the related clinicians to ideally assess about their long term evolution.

Relationship between factor XIII activity, fibrinogen, haemostasis screening tests and postoperative bleeding in cardiopulmonary bypass surgery.

We investigated the relationship between factor XIII, fibrinogen, blood coagulation screening tests and postoperative bleeding in 98 patients undergoing cardiopulmonary bypass (CPB) surgery. All patients received aprotinin. Blood samples were collected preoperatively (T1),after termination of CPB (T2),12 h (T3) and 24 h (T4) after surgery to determine FXIII activity, fibrinogen, platelet count, prothrombin time (PT), activated partial thromboplastin time (APTT) and D-dimers (DD). Laboratory results were correlated with the chest tube drainage 24 h after surgery and compared between patients with 24-hour chest tube drain volumes in the lower (Group 1) with those in the upper tertile (Group 3). Median FXIII and fibrinogen levels dropped by 33.9% and 34.2%, respectively, during CPB. No association between FXIII activity and the extent of postoperative bleeding was found. However, chest tube bleeding was significantly correlated with preoperative and postoperative fibrinogen. This was confirmed by comparing Groups 1 and 3. Group 3 patients had significantly lower fibrinogen levels than Group 1 at T1 - T4, although most fibrinogen values were within or above the reference range (medians, g/l: 3.5 vs. 4.0, p = 0.043 at T1; 2.3 vs. 2.7, p = 0.015 at T2; 2.9 vs. 3.3, p = 0.008 at T3; 4.2 vs. 5.2, p = 0.002 at T4). There was also a significant relationship of platelet count, PT and APTT, as measured after CPB (T2), with postoperative chest tube drainage. In conclusion, plasma FXIII activity does not influence postoperative bleeding in patients undergoing CPB surgery. There is however an inverse association between preoperative or postoperative plasma fibrinogen levels and postoperative bleeding. These findings indicate a modulation of postoperative bleeding by fibrinogen levels.

Early postoperative serum cystatin C predicts severe acute kidney injury following cardiac surgery: a post-hoc analysis of a randomized controlled trial.

OBJECTIVE: Acute kidney injury (AKI) after cardiac surgery procedures is associated with poor patient outcomes. Cystatin C as a marker for renal failure has been shown to be of prognostic value; however, a wide range of its predictive accuracy has been reported. The aim of the study was to evaluate whether the measurement of pre- and postoperative serum cystatin C improves the prediction of AKI. METHODS: In a single-centre, prospective study of 70 patients (74 ± 9 ys; range 47-85 ys; 77% male), cystatin C was measured six times: (T1=preoperative, T2=start cardiopulmonary bypass (CPB), T3=20 min after CPB, T4=end of operation; T5=24 h postoperatively; T6=7d postoperatively). Predictive property, in terms of the need for renal replacement therapy (RRT), was analysed by receiver operating characteristics (ROC) statistics and described by the area under the curve (AUC). RESULTS: With respect to RRT (n=8), serum cystatin C was significantly higher at the end of the operation (T4), 24 h postoperatively at T5 and at T6. The AUCs for preoperative T1 and intraoperative T2/3 cystatin C were <0.7 (95% CI, 0.47-0.85). The earliest significant predictive AUCs were found at the end of the operation (T4: p=0.03 95% CI 0.58-0.88 AUC 0.73) and 24 h postoperatively (T5: p=0.003 95% CI 0.74-0.96 AUC 0.85). CONCLUSIONS: Early postoperative serum cystatin C increase appears to be a moderate biomarker in the prediction of AKI, whereas a preoperative and intraoperative cystatin C increase has only a limited diagnostic and predictive value.