Development of a new score for early mortality prediction in trauma ICU patients: RETRASCORE

Background

Severity scores are commonly used for outcome adjustment and benchmarking of trauma care provided. No specific models performed only with critically ill patients are available. Our objective was to develop a new score for early mortality prediction in trauma ICU patients.

Methods

This is a retrospective study using the Spanish Trauma ICU registry (RETRAUCI) 2015–2019. Patients were divided and analysed into the derivation (2015–2017) and validation sets (2018–2019). We used as candidate variables to be associated with mortality those available in RETRAUCI that could be collected in the first 24 h after ICU admission. Using logistic regression methodology, a simple score (RETRASCORE) was created with points assigned to each selected variable. The performance of the model was carried out according to global measures, discrimination and calibration.

Results

The analysis included 9465 patients: derivation set 5976 and validation set 3489. Thirty-day mortality was 12.2%. The predicted probability of 30-day mortality was determined by the following equation: 1/(1 + exp (− y)), where y = 0.598 (Age 50–65) + 1.239 (Age 66–75) + 2.198 (Age > 75) + 0.349 (PRECOAG) + 0.336 (Pre-hospital intubation) + 0.662 (High-risk mechanism) + 0.950 (unilateral mydriasis) + 3.217 (bilateral mydriasis) + 0.841 (Glasgow ≤ 8) + 0.495 (MAIS-Head) − 0.271 (MAIS-Thorax) + 1.148 (Haemodynamic failure) + 0.708 (Respiratory failure) + 0.567 (Coagulopathy) + 0.580 (Mechanical ventilation) + 0.452 (Massive haemorrhage) − 5.432. The AUROC was 0.913 (0.903–0.923) in the derivation set and 0.929 (0.918–0.940) in the validation set.

Conclusions

The newly developed RETRASCORE is an early, easy-to-calculate and specific score to predict in-hospital mortality in trauma ICU patients. Although it has achieved adequate internal validation, it must be externally validated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03845-6.

Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

Background

Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with respect to the affected body areas. Our objective is to create different predictive models of the mortality of critically traumatic patients using machine learning techniques.

Methods

We used 9625 records from the RETRAUCI database (National Trauma Registry of 52 Spanish ICUs in the period of 2015–2019). Hospital mortality was 12.6%. Data on demographic variables, affected anatomical areas and physiological repercussions were used. The Weka Platform was used, along with a ten-fold cross-validation for the construction of nine supervised algorithms: logistic regression binary (LR), neural network (NN), sequential minimal optimization (SMO), classification rules (JRip), classification trees (CT), Bayesian networks (BN), adaptive boosting (ADABOOST), bootstrap aggregating (BAGGING) and random forest (RFOREST). The performance of the models was evaluated by accuracy, specificity, precision, recall, F-measure, and AUC.

Results

In all algorithms, the most important factors are those associated with traumatic brain injury (TBI) and organic failures. The LR finds thorax and limb injuries as independent protective factors of mortality. The CT generates 24 decision rules and uses those related to TBI as the first variables (range 2.0–81.6%). The JRip detects the eight rules with the highest risk of mortality (65.0–94.1%). The NN model uses a hidden layer of ten nodes, which requires 200 weights for its interpretation. The BN find the relationships between the different factors that identify different patient profiles. Models with the ensemble methodology (ADABOOST, BAGGING and RandomForest) do not have greater performance. All models obtain high values ​​in accuracy, specificity, and AUC, but obtain lower values ​​in recall. The greatest precision is achieved by the SMO model, and the BN obtains the best recall, F-measure, and AUC.

Conclusion

Machine learning techniques are useful for creating mortality classification models in critically traumatic patients. With clinical interpretation, the algorithms establish different patient profiles according to the relationship between the variables used, determine groups of patients with different evolutions, and alert clinicians to the presence of rules that indicate the greatest severity.

A prospective pilot study using metabolomics discloses specific fatty acid, catecholamine and tryptophan metabolic pathways as possible predictors for a negative outcome after severe trauma

Background

We wanted to define metabolomic patterns in plasma to predict a negative outcome in severe trauma patients.

Methods

A prospective pilot study was designed to evaluate plasma metabolomic patterns, established by liquid chromatography coupled to mass spectrometry, in patients allocated to an intensive care unit (in the University Hospital Arnau de Vilanova, Lleida, Spain) in the first hours after a severe trauma (n = 48). Univariate and multivariate statistics were employed to establish potential predictors of mortality.

Results

Plasma of patients non surviving to trauma (n = 5) exhibited a discriminating metabolomic pattern, involving basically metabolites belonging to fatty acid and catecholamine synthesis as well as tryptophan degradation pathways. Thus, concentration of several metabolites exhibited an area under the receiver operating curve (ROC) higher than 0.84, including 3-indolelactic acid, hydroxyisovaleric acid, phenylethanolamine, cortisol, epinephrine and myristic acid. Multivariate binary regression logistic revealed that patients with higher myristic acid concentrations had a non-survival odds ratio of 2.1 (CI 95% 1.1–3.9).

Conclusions

Specific fatty acids, catecholamine synthesis and tryptophan degradation pathways could be implicated in a negative outcome after trauma. The metabolomic study of severe trauma patients could be helpful for biomarker proposal.

Electronic supplementary material

The online version of this article (10.1186/s13049-019-0631-5) contains supplementary material, which is available to authorized users.

Location-dependent effects of trauma on oxidative stress in humans

Though circulating antioxidant capacity in plasma is homeostatically regulated, it is not known whether acute stressors (i.e. trauma) affecting different anatomical locations could have quantitatively different impacts. For this reason, we evaluated the relationship between the anatomical location of trauma and plasma total antioxidant capacity (TAC) in a prospective study, where the anatomical locations of trauma in polytraumatic patients (n = 66) were categorized as primary affecting the brain -traumatic brain injury (TBI)-, thorax, abdomen and pelvis or extremities. We measured the following: plasma TAC by 2 independent methods, the contribution of selected antioxidant molecules (uric acid, bilirubin and albumin) to these values and changes after 1 week of progression. Surprisingly, TBI lowered TAC (919 ± 335 μM Trolox equivalents (TE)) in comparison with other groups (thoracic trauma 1187 ± 270 μM TE; extremities 1025 ± 276 μM TE; p = 0.004). The latter 2 presented higher hypoxia (PaO₂/FiO₂ 272 ± 87 mmHg) and hemodynamic instability (inotrope use required in 54.5%) as well. Temporal changes in TAC are also dependent on anatomical location, as thoracic and extremity trauma patients’ TAC values decreased (1187 ± 270 to 1045 ± 263 μM TE; 1025 ± 276 to 918 ± 331 μM TE) after 1 week (p < 0.01), while in TBI these values increased (919 ± 335 to 961 ± 465 μM TE). Our results show that the response of plasma antioxidant capacity in trauma patients is strongly dependent on time after trauma and location, with TBI failing to induce such a response.

Characterization of the Epigenetic Changes During Human Gonadal Primordial Germ Cells Reprogramming

Epigenetic reprogramming is a central process during mammalian germline development. Genome-wide DNA demethylation in primordial germ cells (PGCs) is a prerequisite for the erasure of epigenetic memory, preventing the transmission of epimutations to the next generation. Apart from DNA demethylation, germline reprogramming has been shown to entail reprogramming of histone marks and chromatin remodelling. Contrary to other animal models, there is limited information about the epigenetic dynamics during early germ cell development in humans. Here, we provide further characterization of the epigenetic configuration of the early human gonadal PGCs. We show that early gonadal human PGCs are DNA hypomethylated and their chromatin is characterized by low H3K9me2 and high H3K27me3 marks. Similarly to previous observations in mice, human gonadal PGCs undergo dynamic chromatin changes concomitant with the erasure of genomic imprints. Interestingly, and contrary to mouse early germ cells, expression of BLIMP1/PRDM1 persists in through all gestational stages in human gonadal PGCs and is associated with nuclear lysine-specific demethylase-1. Our work provides important additional information regarding the chromatin changes associated with human PGCs development between 6 and 13 weeks of gestation in male and female gonads. Stem Cells 2016;34:2418-2428.

Classification of dermatological disorders in critical care patients: a prospective observational study

PURPOSE

The objective of this study was to identify dermatological disorders detected in the intensive care unit (ICU), to analyze their specific characteristics, and to define a useful classification for intensive care physicians.

MATERIALS AND METHODS

This was a prospective, observational study over a 3-year period (2006-2009) in a mixed ICU. This included all patients presenting with dermatological disorders that were detected at the time of ICU admission or developed along the ICU stay. We recorded the specific characteristics of the disorders and its evolution and treatment, which enabled us to classify the different observed conditions. As general variables, we analyzed demographic factors, the principal diagnosis, ICU procedures, the severity score (Acute Physiology and Chronic Health Evaluation II), length of stay, and mortality.

RESULTS

One hundred thirty-three patients showed at least one dermatological disorder (9.3%) and were classified into (1) preexisting dermatological disorders, (2) life-threatening dermatologic disorders, (3) systemic dermatological disorders, (4) infectious dermatological disorders, (5) reactive dermatological disorders, and (6) others.

CONCLUSIONS

Dermatological disorders are a frequent problem in the ICU, and their recognition is key to set up an appropriate care plan. We propose a classification and description of the different types of dermatological disorders that are most commonly found in ICUs.

Complete meiosis from human induced pluripotent stem cells

Gamete failure-derived infertility affects millions of people worldwide; for many patients, gamete donation by unrelated donors is the only available treatment. Embryonic stem cells (ESCs) can differentiate in vitro into germ-like cells, but they are genetically unrelated to the patient. Using an in vitro protocol that aims at recapitulating development, we have achieved, for the first time, complete differentiation of human induced pluripotent stem cells (hiPSCs) to postmeiotic cells. Unlike previous reports using human ESCs, postmeiotic cells arose without the over-expression of germline related transcription factors. Moreover, we consistently obtained haploid cells from hiPSCs of different origin (keratinocytes and cord blood), produced with a different number of transcription factors, and of both genetic sexes, suggesting the independence of our approach from the epigenetic memory of the reprogrammed somatic cells. Our work brings us closer to the production of personalized human gametes in vitro.

[Classification of mental disorders in the Intensive Care Unit]

PURPOSE

To determine the incidence and characteristics of mental disorders (MD) in the Intensive Care Unit (ICU), and to define a classification system adapted to the ICU environment.

DESIGN

A retrospective, descriptive analysis.

SETTING

Intensive Care Unit, Arnau de Vilanova Hospital in Lérida (Spain).

PATIENTS

All patients with MD admitted during 5-year period (January, 1 2004 to December 31, 2008).

MAIN VARIABLES

General variables included clinical-demographic data, diagnostic variables, procedures, severity score, length of stay and mortality. Specific variables included psychiatric history, screening for substance abuse, psychiatric assessment, monitoring and transfer to a psychiatric center. Classification of the MD was as follows: 1) acute substance intoxication (SI); 2) suicide attempts (SA); and 3) MD associated with the main diagnosis (AMD).

RESULTS

A total of 146 patients had MD (7.8%); they were predominantly male (74%) and were younger than the general ICU population (43.9 vs. 55.3 years, p<0.001). The ICU stays of the patients with MDs were shorter (4 days vs. 7 days, p<0.001), and there was less hospital mortality (17.1 vs. 25%, p<0.05). They also showed a higher incidence of pneumonia (19.9 vs. 13.8%, p<0.05), but no differences in the level of severity were observed. The SI group (24.7%) contained the highest number of young people; the SA group (36.3%) showed a predominance of women; and the AMD (39%) group had the longest stays and the highest mortality. Psychiatric consultation was carried out mainly in the SA group (62.3%).

CONCLUSIONS

MD is a relatively common problem in the ICU. Collaboration with the Psychiatry Department seldom occurs, but must be encouraged to develop fully integrated management of critical patients with MD.

Role of insulin, insulin-like growth factors, and muscle regulatory factors in the compensatory growth of the trout (Oncorhynchus mykiss)

To examine the various mechanisms involved in compensatory growth in Oncorhynchus mykiss, an experimental protocol involving 1, 2 or 4 weeks of fasting followed by a single ad libitum re-feeding period of 4 weeks was designed for alevins. Morphological parameters including body weight, specific growth rates (SGR), and coefficient factor decreased significantly during fasting. Re-feeding accelerated growth and restored final body weight in groups previously fasted. Plasma insulin and glucose decreased in fasting, while normal levels were restored in all re-fed groups. The expression profile of insulin-like growth factors (IGFs) in liver and of the main muscle growth regulators in white muscle was examined using real-time quantitative RT-PCR. Fasting decreased the expression of IGF-I mRNA in both tissues, while re-feeding restored expression to control values. In contrast, IGF-II expression was not affected by any treatment in either tissue. Insulin- and IGF-I-binding assays in partial semi-purifications (of soluble proteins) in white skeletal muscle showed that insulin binding was not affected by either fasting or re-feeding, whereas fasting up-regulated IGF-I binding. The expression of IGFRIb mRNA in white skeletal muscle also increased with fasting, while IGFRIa increased with re-feeding, indicating that the two receptor isoforms are differentially regulated. The mRNA expression of myogenic regulator factors and fibroblast growth factors (FGFs) was not affected throughout the experiment, except for myogenin, which first decreased and then showed a rebound effect after 4 weeks of fasting. Myostatin mRNA expression did not change during fasting, although re-feeding caused a significant decrease. In conclusion, re-feeding of previously fasted trout induced compensatory growth. The differential regulation in muscle expression of IGF-I, IGF-I receptors, and myostatin indicates their contribution to this compensatory mechanism.