The Effect of Evolving Fluid Resuscitation on the Outcome of Severely Injured Patients : An 8-year Experience at a Tertiary Trauma Center

Background and Aims: Fluid resuscitation of severely injured patients has shifted over the last decade toward less crystalloids and more blood products. Helsinki University trauma center implemented the massive transfusion protocol in the end of 2009. The aim of the study was to review the changes in fluid resuscitation and its influence on outcome of severely injured patients with hemodynamic compromise treated at the single tertiary trauma center. Material and Methods: Data on severely injured patients (New Injury Severity Score>15) from Helsinki University Hospital trauma center's trauma registry was reviewed over 2006-2013. The isolated head-injury patients, patients without hemodynamic compromise on admission (systolic blood pressure>90 or base excess>-5.0), and those transferred in from another hospital were excluded. The primary outcome measure was 30-day in-hospital mortality. The study period was divided into three phases: 2006-2008 (pre-protocol, 146 patients), 2009-2010 (the implementation of massive transfusion protocol, 85 patients), and 2011-2013 (post massive transfusion protocol, 121 patients). Expected mortality was calculated using the Revised Injury Severity Classification score II. The Standardized Mortality Ratio, as well as the amounts of crystalloids, colloids, and blood products (red blood cells, fresh frozen plasma, platelets) administered prehospital and in the emergency room were compared. Results: Of the 354 patients that were included, Standardized Mortality Ratio values decreased (indicating better survival) during the study period from 0.97 (pre-protocol), 0.87 (the implementation of massive transfusion protocol), to 0.79 (post massive transfusion protocol). The amount of crystalloids used in the emergency room decreased from 3870mL (pre-protocol), 2390mL (the implementation of massive transfusion protocol), to 2340mL (post massive transfusion protocol). In these patients, the blood products' (red blood cells, fresh frozen plasma, and platelets together) relation to crystalloids increased from 0.36, 0.70, to 0.74, respectively, in three phases. Conclusion: During the study period, no other major changes in the protocols on treatment of severely injured patients were implemented. The overall awareness of damage control fluid resuscitation and introduction of massive transfusion protocol in a trauma center has a significant positive effect on the outcome of severely injured patients.Peer reviewe

Cross-validation of two prognostic trauma scores in severely injured patients

Introduction Trauma scoring systems are important tools for outcome prediction and severity adjustment that informs trauma quality assessment and research. Discrimination and precision of such systems is tested in validation studies. The German TraumaRegister DGU® (TR-DGU) and the Trauma Audit and Research Network (TARN) from the UK agreed on a cross-validation study to validate their prediction scores (RISC II and PS14, respectively). Methods Severe trauma patients with an Injury Severity Score (ISS) ≥ 9 documented in 2015 and 2016 were selected in both registries (primary admissions only). The predictive scores from each registry were applied to the selected data sets. Observed and predicted mortality were compared to assess precision; area under the receiver operating characteristic curve was used for discrimination. Hosmer–Lemeshow statistic was calculated for calibration. A subgroup analysis including patients treated in intensive care unit (ICU) was also carried out. Results From TR-DGU, 40,638 patients were included (mortality 11.7%). The RISC II predicted mortality was 11.2%, while PS14 predicted 16.9% mortality. From TARN, 64,622 patients were included (mortality 9.7%). PS14 predicted 10.6% mortality, while RISC II predicted 17.7%. Despite the identical cutoff of ISS ≥ 9, patient groups from both registries showed considerable difference in need for intensive care (88% versus 18%). Subgroup analysis of patients treated on ICU showed nearly identical values for observed and predicted mortality using RISC II. Discussion Each score performed well within its respective registry, but when applied to the other registry a decrease in performance was observed. Part of this loss of performance could be explained by different development data sets: the RISC II is mainly based on patients treated in an ICU, while the PS14 includes cases mainly cared for outside ICU with more moderate injury severity. This is according to the respective inclusion criteria of the two registries. Conclusion External validations of prediction models between registries are needed, but may show that prediction models are not fully transferable to other health-care settings

The sequential trauma score - a new instrument for the sequential mortality prediction in major trauma*

<p>Abstract</p> <p>Background</p> <p>There are several well established scores for the assessment of the prognosis of major trauma patients that all have in common that they can be calculated at the earliest during intensive care unit stay. We intended to develop a sequential trauma score (STS) that allows prognosis at several early stages based on the information that is available at a particular time.</p> <p>Study design</p> <p>In a retrospective, multicenter study using data derived from the Trauma Registry of the German Trauma Society (2002-2006), we identified the most relevant prognostic factors from the patients basic data (P), prehospital phase (A), early (B1), and late (B2) trauma room phase. Univariate and logistic regression models as well as score quality criteria and the explanatory power have been calculated.</p> <p>Results</p> <p>A total of 2,354 patients with complete data were identified. From the patients basic data (P), logistic regression showed that age was a significant predictor of survival (AUC_{model p}, area under the curve = 0.63). Logistic regression of the prehospital data (A) showed that blood pressure, pulse rate, Glasgow coma scale (GCS), and anisocoria were significant predictors (AUC_{model A}= 0.76; AUC_{model P + A}= 0.82). Logistic regression of the early trauma room phase (B1) showed that peripheral oxygen saturation, GCS, anisocoria, base excess, and thromboplastin time to be significant predictors of survival (AUC_{model B1}= 0.78; AUC_{model P +A + B1}= 0.85). Multivariate analysis of the late trauma room phase (B2) detected cardiac massage, abbreviated injury score (AIS) of the head ≥ 3, the maximum AIS, the need for transfusion or massive blood transfusion, to be the most important predictors (AUCmodel B2 = 0.84; AUCfinal model P + A + B1 + B2 = 0.90). The explanatory power - a tool for the assessment of the relative impact of each segment to mortality - is 25% for P, 7% for A, 17% for B1 and 51% for B2. A spreadsheet for the easy calculation of the sequential trauma score is available at: <url>http://www.sequential-trauma-score.com</url></p> <p>Conclusions</p> <p>This score is the first sequential, dynamic score to provide a prognosis for patients with blunt major trauma at several points in time. With every additional piece of information the precision increases. The medical team has a simple, useful tool to identify patients at high risk and to predict the prognosis of an individual patient with major trauma very early, quickly and precisely.</p

How to Validate Data Quality in a Trauma Registry? The Helsinki Trauma Registry Internal Audit

Background and Aims: Trauma registry data are used for analyzing and improving patient care, comparison of different units, and for research and administrative purposes. Data should therefore be reliable. The aim of this study was to audit the quality of the Helsinki Trauma Registry internally. We describe how to conduct a validation of a regional or national trauma registry and how to report the results in a readily comprehensible form. Materials and Methods: Trauma registry database of Helsinki Trauma Registry from year 2013 was re-evaluated. We assessed data quality in three different parts of the data input process: the process of including patients in the trauma registry (case completeness); the process of calculating Abbreviated Injury Scale (AIS) codes; and entering the patient variables in the trauma registry (data completeness, accuracy, and correctness). We calculated the case completeness results using raw agreement percentage and Cohen's kappa value. Percentage and descriptive methods were used for the remaining calculations. Results: In total, 862 patients were evaluated; 853 were rated the same in the audit process resulting in a raw agreement percentage of 99%. Nine cases were missing from the registry, yielding a case completeness of 97.1% for the Helsinki Trauma Registry. For AIS code data, we analyzed 107 patients with severe thorax injury with 941 AIS codes. Completeness of codes was 99.0% (932/941), accuracy was 90.0% (841/932), and correctness was 97.5% (909/932). The data completeness of patient variables was 93.4% (3899/4174). Data completeness was 100% for 16 of 32 categories. Data accuracy was 94.6% (3690/3899) and data correctness was 97.2% (3789/3899). Conclusion: The case completeness, data completeness, data accuracy, and data correctness of the Helsinki Trauma Registry are excellent. We recommend that these should be the variables included in a trauma registry validation process, and that the quality of trauma registry data should be systematically and regularly reviewed and reported.Peer reviewe

Determination of the absorption coefficient of chromophoric dissolved organic matter from underway spectrophotometry

Measurements of the absorption coefficient of chromophoric dissolved organic matter (ay) are needed to validate existing ocean-color algorithms. In the surface open ocean, these measurements are challenging because of low ay values. Yet, existing global datasets demonstrate that ay could contribute between 30% to 50% of the total absorption budget in the 400–450 nm spectral range, thus making accurate measurement of ay essential to constrain these uncertainties. In this study, we present a simple way of determining ay using a commercially-available in-situ spectrophotometer operated in underway mode. The obtained ay values were validated using independent collocated measurements. The method is simple to implement, can provide measurements with very high spatio-temporal resolution, and has an accuracy of about 0.0004 m−1 and a precision of about 0.0025 m−1 when compared to independent data (at 440 nm). The only limitation for using this method at sea is that it relies on the availability of relatively large volumes of ultrapure water. Despite this limitation, the method can deliver the ay data needed for validating and assessing uncertainties in ocean-colour algorithms

Duration and predictors of emergency surgical operations - basis for medical management of mass casualty incidents

<p>Abstract</p> <p>Background</p> <p>Hospitals have a critically important role in the management of mass causality incidents (MCI), yet there is little information to assist emergency planners. A significantly limiting factor of a hospital's capability to treat those affected is its surgical capacity. We therefore intended to provide data about the duration and predictors of life saving operations.</p> <p>Methods</p> <p>The data of 20,815 predominantly blunt trauma patients recorded in the Trauma Registry of the German-Trauma-Society was retrospectively analyzed to calculate the duration of life-saving operations as well as their predictors. Inclusion criteria were an ISS ≥ 16 and the performance of relevant ICPM-coded procedures within 6 h of admission.</p> <p>Results</p> <p>From 1,228 patients fulfilling the inclusion criteria 1,793 operations could be identified as life-saving operations. Acute injuries to the abdomen accounted for 54.1% followed by head injuries (26.3%), pelvic injuries (11.5%), thoracic injuries (5.0%) and major amputations (3.1%). The mean cut to suture time was 130 min (IQR 65-165 min). Logistic regression revealed 8 variables associated with an emergency operation: AIS of abdomen ≥ 3 (OR 4,00), ISS ≥ 35 (OR 2,94), hemoglobin level ≤ 8 mg/dL (OR 1,40), pulse rate on hospital admission < 40 or > 120/min (OR 1,39), blood pressure on hospital admission < 90 mmHg (OR 1,35), prehospital infusion volume ≥ 2000 ml (OR 1,34), GCS ≤ 8 (OR 1,32) and anisocoria (OR 1,28) on-scene.</p> <p>Conclusions</p> <p>The mean operation time of 130 min calculated for emergency life-saving surgical operations provides a realistic guideline for the prospective treatment capacity which can be estimated and projected into an actual incident admission capacity. Knowledge of predictive factors for life-saving emergency operations helps to identify those patients that need most urgent operative treatment in case of blunt MCI.</p

Effects of Alzheimer's disease and formalin fixation on the different mineralised-iron forms in the human brain

Iron accumulation in the brain is a phenomenon common to many neurodegenerative diseases, perhaps most notably Alzheimer's disease (AD). We present here magnetic analyses of post-mortem brain tissue of patients who had severe Alzheimer's disease, and compare the results with those from healthy controls. Isothermal remanent magnetization experiments were performed to assess the extent to which different magnetic carriers are affected by AD pathology and formalin fixation. While Alzheimer's brain material did not show higher levels of magnetite/maghemite nanoparticles than corresponding controls, the ferrihydrite mineral, known to be found within the core of ferritin proteins and hemosiderin aggregates, almost doubled in concentration in patients with Alzheimer's pathology, strengthening the conclusions of our previous studies. As part of this study, we also investigated the effects of sample preparation, by performing experiments on frozen tissue as well as tissue which had been fixed in formalin for a period of 5 months. Our results showed that the two different preparations did not critically affect the concentration of magnetic carriers in brain tissue, as observable by SQUID magnetometry.Functional Genomics of Muscle, Nerve and Brain Disorder