A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm.

The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline's ability to be retrained and used for other datasets collected using different measurements and with different distributions.

Deep Learning for Classifying Physical Activities from Accelerometer Data.

Physical inactivity increases the risk of many adverse health conditions, including the world's major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers' methods to monitor a patient's actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient's physical activities precisely for better treatment.

Strategic hospital partnerships: improved access to care and increased epilepsy surgical volume.

OBJECTIVE Surgical treatment of patients with medically refractory focal epilepsy is underutilized. Patients may lack access to surgically proficient centers. The University of California, Irvine (UCI) entered strategic partnerships with 2 epilepsy centers with limited surgical capabilities. A formal memorandum of understanding (MOU) was created to provide epilepsy surgery to patients from these centers. METHODS The authors analyzed UCI surgical and financial data associated with patients undergoing epilepsy surgery between September 2012 and June 2016, before and after institution of the MOU. Variables collected included the length of stay, patient age, seizure semiology, use of invasive monitoring, and site of surgery as well as the monthly number of single-surgery cases, complex cases (i.e., staged surgeries), and overall number of surgery cases. RESULTS Over the 46 months of the study, a total of 104 patients underwent a total of 200 operations; 71 operations were performed in 39 patients during the pre-MOU period (28 months) and 129 operations were performed in 200 patients during the post-MOU period (18 months). There was a significant difference in the use of invasive monitoring, the site of surgery, the final therapy, and the type of insurance. The number of single-surgery cases, complex-surgery cases, and the overall number of cases increased significantly. CONCLUSIONS Partnerships with outside epilepsy centers are a means to increase access to surgical care. These partnerships are likely reproducible, can be mutually beneficial to all centers involved, and ultimately improve patient access to care.

Impact of the implementation of a Sepsis Code hospital protocol in antibiotic prescription and clinical outcomes in an intensive care unit. / Impacto de la implantación de un Código Sepsis intrahospitalario en la prescripción de antibióticos y los resultados clínicos en una unidad de cuidados intensivos.

INTRODUCTION: A study was performed to analyze the impact of an in-hospital Sepsis Code (SC) program on use of antibiotic and clinical outcomes. DESIGN: Quasi-experimental observational retrospective study. SETTING: Polyvalent 11 beds ICU belonging to a tertiary Universitary hospital. PATIENTS: Patients admitted consecutively to the ICU with diagnosis of severe sepsis or septic shock. INTERVENTIONS: A post intervention group (POST-SC) (September 2012-August 2013) was compared with a historical group (PRE-SC) used as control (January-December 2010). VARIABLES: Antibiotic treatment, therapeutic antibiotic strategy, mortality and length of stay. Antibiotic consumption was expressed as defined daily doses (DDD)/ 100 stays. RESULTS: 42 patients with SS/SS in POST-SC group and 50 patients in PRE-SC group were consecutively recluted and further analyzed. Total antibiotic consumption (DDD) was similar in both groups. Rate of de-escalation therapy was significantly higher in POST-SC group (75% vs 30,8%, p<0,005) while prescription of restricted antibiotics was significantly lower (74% vs 52%, p=0,031). Finally POST-SC patients showed a significantly decrease in hospital and 28 days mortality rates [23% vs 44%, (p=0,035) and 31% vs 56% (p=0,014) respectively] as well as a reduction in ICU length of stay compared to PRE-SC cohort (5 days vs 10,5 days, p=0,05). CONCLUSION: The implementation of a Sepsis Code-hospital protocol is associated to an improvement in the management of antibiotic therapy with a significant increase in de-escalation therapy and lesser utilization of restricted use antibiotics, as well as a significant reduction in mortality, and a tendency towards shorter ICU length stay.

Distance Metric Based Oversampling Method for Bioinformatics and Performance Evaluation.

An imbalanced classification means that a dataset has an unequal class distribution among its population. For any given dataset, regardless of any balancing issue, the predictions made by most classification methods are highly accurate for the majority class but significantly less accurate for the minority class. To overcome this problem, this study took several imbalanced datasets from the famed UCI datasets and designed and implemented an efficient algorithm which couples Top-N Reverse k-Nearest Neighbor (TRkNN) with the Synthetic Minority Oversampling TEchnique (SMOTE). The proposed algorithm was investigated by applying it to classification methods such as logistic regression (LR), C4.5, Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN). This research also adopted different distance metrics to classify the same UCI datasets. The empirical results illustrate that the Euclidean and Manhattan distances are not only more accurate, but also show greater computational efficiency when compared to the Chebyshev and Cosine distances. Therefore, the proposed algorithm based on TRkNN and SMOTE can be widely used to handle imbalanced datasets. Our recommendations on choosing suitable distance metrics can also serve as a reference for future studies.

Predictive scoring systems in multiorgan failure: A cohort study.

OBJECTIVE: An evaluation is made of the hospital mortality predicting capacity of the main predictive scoring systems. DESIGN: A 2-year retrospective cohort study was carried out. SETTING: A third level ICU with surgical and medical patients. PATIENTS: All patients with multiorgan failure during the first day in the ICU. MAIN VARIABLES: APACHE II and IV, SAPS II and III, MPM II and hospital mortality. RESULTS: A total of 568 patients were included. Mortality rate: 39.8% (226 patients). Discrimination (area under the ROC curve; 95% CI): APACHE IV (0.805; 0.751-0.858), SAPS II (0.755; 0.697-0.814), MPM II (0.748; 0.688-0.809), SAPS III (0.737; 0.675-0.799) and APACHE II (0.699; 0.633-0.765). MPM II showed the best calibration, followed by SAPS III. APACHE II, SAPS II and APACHE IV showed very poor calibration. Standard mortality ratio (95% CI): APACHE IV 1.9 (1.78-2.02); APACHE II 1.1 (1.07-1.13); SAPS III 1.1 (1.06-1.14); SAPS II 1.03 (1.01-1.05); MPM 0.9 (0.86-0.94). CONCLUSIONS: APACHE IV showed the best discrimination, with poor calibration. MPM II showed good discrimination and the best calibration. SAPS II, in turn, showed the second best discrimination, with poor calibration. The APACHE II calibration and discrimination values currently disadvise its use. SAPS III showed good calibration with modest discrimination. Future studies at regional or national level and in certain critically ill populations are needed.

Innovation and new trends in critical trauma disease.

The management of critical trauma disease (CTD) has always trends the trends in military war experiences. These conflicts have historically revolutionized clinical concepts, clinical practice guidelines and medical devices, and have marked future lines of research and aspects of training and learning in severe trauma care. Moreover, in the civil setting, the development of intensive care, technological advances and the testing of our healthcare systems in the management of multiple victims, hasve also led to a need for innovation in our trauma care systems.

[Pain and fear in the ICU]. / Dolor y miedo en la UCI.

Pain and fear are still the most common memories that refer patients after ICU admission. Recently an important politician named the UCI as the branch of the hell. It is necessary to carry out profound changes in terms of direct relationships with patients and their relatives, as well as changes in environmental design and work and visit organization, to banish the vision that our society about the UCI. In a step which advocates for early mobilization of critical patients is necessary to improve analgesia and sedation strategies. The ICU is the best place for administering and monitoring analgesic drugs. The correct analgesia should not be a pending matter of the intensivist but a mandatory course.

Clostridium difficile in the ICU: study of the incidence, recurrence, clinical characteristics and complications in a university hospital.

OBJECTIVE: Although several studies have established the association between antibiotics and Clostridium difficile infection (CDI), there is a lack of epidemiological studies on the incidence of CDI in European Intensive Care Units outside the context of infection outbreaks. The present study describes the incidence, patient characteristics, complications, and recurrence rates of CDI in a Spanish ICU. DESIGN: A retrospective study was carried out. SETTING: A clinical-surgical ICU with 34 beds, a tertiary referral hospital with 1400 beds. PATIENTS: All patients over 18 years of age admitted to the ICU from January 2010 to December 2011 with diarrhea for more than 48 h. INTERVENTIONS: None. STUDY VARIABLES: Underlying diseases, risk factors, fever, leukocyte count, complications, recurrence of infection. RESULTS: A total of 1936 adult patients were admitted. Seven patients acquired CDI (0.36%), representing an infection rate of 3.1 per 10,000 bed-days and a cumulative incidence rate of 3.6 in two years. The mean age was 61 years. Six patients showed some degree of immunosuppression. The mean APACHE II score at ICU admission was 17 (IQR 13-24). Severe sepsis was reported in 5 cases of CDI, three of which presented shock and multiorgan dysfunction. Four patients presented recurrence of CDI during hospitalization. ICU admission was prolonged for a mean of 24 days (SD 17.8) after diagnosis. CONCLUSIONS: Less than 1% of the patients admitted to a clinical-surgical ICU in a large teaching institution in Spain developed CDI. However, a high risk of recurrence/complications was associated with prolonged ICU stay.

Epidemiological study of Clostridium difficile infection in critical patients admitted to the Intensive Care Unit.

UNLABELLED: Data on the epidemiology of infections caused by Clostridium difficile (CDI) in critically ill patients are scarce and center on studies with a limited time framework and/or epidemic outbreaks. OBJECTIVE: To describe the characteristics and risk factors of critically ill patients admitted to the ICU with CDI, as well as the treatments used for the control of such infections. MATERIAL AND METHODS: A retrospective study was made of patients included in the ENVIN-ICU registry with CDI in 2012. Patients were followed up to 72 h after discharge from the ICU. A case report form was used to record the following data: demographic variables, risk factors related to CDI, treatment and outcome. Infections were classified as community-acquired, nosocomial out-ICU and nosocomial in-ICU, according to the day on which Clostridium difficile isolates were obtained. Infection rates as episodes per 10,000 days of ICU stay are presented. The global in-ICU and hospital mortality rates were calculated. RESULTS: Sixty-eight episodes of CDI in 33 out of a total of 173 ICUs participating in the registry were recorded (19.1%) (2.1 episodes per 10,000 days of ICU stay). Forty-five patients were men (66.2%), with a mean (SD) age of 63.4 (16.4) years, a mean APACHE II score on ICU admission of 19.9 (7.4), and an underlying medical condition in 44 (64.7%). Sixty-two patients (91.2%) presented more than 3 liquid depositions/day, 40 (58.8%) in association with severe sepsis or septic shock. Community-acquired infection occurred in 13 patients (19.1%), nosocomial out-ICU infection in 13 (19.1%), and in-ICU infection in 42 (61.8%). Risk factors included age>64 years in 39 cases (57.4%), previous hospital admission (3 months) in 32 (45.6%), use of antimicrobials (previous 7 days) in 57 (83.8%), enteral nutrition in 23 (33.8%), and the use of H2 inhibitors in 39 (57.4%). Initial combined treatment was administered to 18 patients (26.5%). Metronidazole was used in 60 (88.2%) and vancomycin in 31 (45.6%). The in-ICU mortality rate was 25.0% (n=17), with a hospital mortality 27.9% (n=19). CONCLUSIONS: The rate of ICD in ICU patients is low, the infection affects severely ill patients, and is associated with high mortality. The presence of CDI is a marker of poor prognosis.

Description of Intensive Care and Intermediate Care resources managed by Anaesthesiology Departments in Spain and their adaptation capacity during the COVID-19 pandemic.

INTRODUCTION: It is essential to understand the strategic importance of intensive care resources in the sustainable organisation of healthcare systems. Our objective has been to identify the intensive and intermediate care beds managed by Anaesthesiology and Resuscitation Services (A-ICU and A-IMCU) in Spain, their human and technical resources, and the changes made to these resources during the COVID-19 pandemic. MATERIAL AND METHODS: Prospective observational study performed between December 2020 and July 2021 to register the number and characteristics of A-ICU and A-IMCU beds in hospitals listed in the catalogue published by the Spanish Ministry of Health. RESULTS: Data were obtained from 313 hospitals (98% of all hospitals with more than 500 beds, 70% of all hospitals with more than 100 beds). One hundred and forty seven of these hospitals had an A-ICU with a total of 1702 beds. This capacity increased to 2107 (124%) during the COVID-19 pandemic. Three hundred and eight hospitals had an A-IMCU with a total of 3470 beds, 52.9% (2089) of which provided long-term care. The hospitals had 1900 ventilators, at a ratio of 1.07 respirators per A-ICU; 1559 anaesthesiologists dedicated more than 40% of their working time to intensive care. The nurse-to-bed ratio in A-ICUs was 2.8. DISCUSSION: A large proportion of fully-equipped ICU and IMCU beds in Spanish hospitals are managed by the anaesthesiology service. A-ICU and A-IMCUs have shown an extraordinary capacity to adapt their resources to meet the increased demand for intensive care during the COVID-19 pandemic.

Cognitive function in Mexican older adults 6-months after recovering from SARS-CoV-2 infection.

BACKGROUND: SARS-CoV-2 infection has been associated with multiple short- and long-term complications including depression, and cognitive impairment (CI). However, older adults with CI after COVID-19 have not been fully documented. OBJECTIVE: To evaluate cognitive function in Mexican adults post-recovery from SARS-CoV-2 infection. METHODS: In this prospective observational cohort study, we assess cognitive function (CF) by the Montreal Cognitive Assessment (MOCA) test with a cut-off less than 26 points, and functional status via telemedicine. Eligible patients with a history of moderate-severe COVID-19 aged ≥60 years, cognitively healthy (evaluated by Everyday Cognition Scale) and required admission to an intensive care unit (ICU) were included. Patients with history of dementia, stroke, and delirium during the cognitive evaluation were excluded. The association between CI and COVID-19 was assessed with a Cox regression model. RESULTS: From the 634 patients admitted to the ICU, 415 survived, afterward 308 were excluded and 107 were analyzed. Mean age was 70 years, 58% were female, and 53% had severe COVID. The mean MoCA score was 21±5 points, CI was present in 61 patients (57%). Infection severity (RR 1.87; 95% CI: 1.11-3.15, p<0.05), lower education (RR 0.92; 95% CI: 0.87-0.97, p<0.01), and activity daily living disability (RR 1.87; 95% CI: 1.07-3.26, p<0.05) were the main factors associated with CI (unadjusted model by age and sex). The delayed recall, orientation, and language (83.2, 77.6 and 72.9% respectively) domains were the most affected in patients with CI. CONCLUSIONS: Fifty-seven percent of patients analyzed developed CI six months post-ICU discharge due to SARS-CoV-2, and COVID severity was the main factor associated to its outcome.

The effect of different definitions of hepatic injury on incidence and mortality rates in the ICU patient population with secondary hepatic injury.

OBJECTIVE: The aim was to investigate how different hepatic injury (HI) definitions used in the same study population change incidence and mortality rates and which would best diagnose secondary HI. DESIGN: Single-centre retrospective observational cohort study. SETTING: Tertiary hospital ICU, ANKARA, Turkey. PATIENTS: Four hundred seventy-eight adult patients were included in the study. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Three definitions of HI were compared. Taking the SOFA hepatic criteria (SOFA: Total bilirubin (TBL) > 1.2 mg/dl) as the gold standard, sensitivity, specificity, positive and negative predictive values, and accuracy of the modified 2017 definition by the American College of Gastroenterology (ACG) and the 2019 European Association for the Study of the Liver (EASL) were calculated. RESULTS: Incidence rates ranged from 10% to 45% according to the definition (p < 0.005), while mortality rates ranged from 38% to 57%. When the SOFA1.2 (TBL > 1.2 definition was taken as the gold standard, the diagnostic value of the ACG definition was high, and HI was found to be an independent risk factor that increased mortality four times. CONCLUSIONS: According to this study's results, the incidence and mortality rates of secondary HI vary greatly depending on the definition used. A definition that includes minimal increases in ALT, AST, and TBL predicts mortality with reasonable incidence rates.

HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals.

Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES detection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and prevention of further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on those extracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeley datasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain.

Practical approach to inhaled sedation in the critically ill patient. Sedation, analgesia and Delirium Working Group (GTSAD) of the Spanish Society of Intensive and Critical Care Medicine and Coronary Units (SEMICYUC).

The use of sedatives in Intensive Care Units (ICU) is essential for relieving anxiety and stress in mechanically ventilated patients, and it is related to clinical outcomes, duration of mechanical ventilation, and length of stay in the ICU. Inhaled sedatives offer benefits such as faster awakening and extubation, decreased total opioid and neuromuscular blocking agents (NMB) doses, as well as bronchodilator, anticonvulsant, and cardiopulmonary and neurological protective effects. Inhaled sedation is administered using a specific vaporizer. Isoflurane is the recommended agent due to its efficacy and safety profile. Inhaled sedation is recommended for moderate and deep sedation, prolonged sedation, difficult sedation, patients with acute respiratory distress syndrome (ARDS), status asthmaticus, and super-refractory status epilepticus. By offering these significant advantages, the use of inhaled sedatives allows for a personalized and controlled approach to optimize sedation in the ICU.

Evaluation of the last 2 decades in the characteristics of presentation, management and prognosis of serious spontaneous intracerebral hemorrhage in a third level hospital.

OBJECTIVE: To analyze the change in the characteristics of presentation, evolution and treatment in the ICU, as well as the functional evolution at 12 months of spontaneous intracranial hemorrhages (ICHs) treated in an ICU reference center. PATIENT AND METHODS: Descriptive, retrospective study in a Neurocritical Reference Hospital. All admissions of patients with HICE during three periods are studied: 1999-2001 (I), 2015-2016 (II) and 2020-2021 (III). Evolution in the three periods of demographic variables, baseline characteristics of the patients, clinical variables and characteristics of bleeding, evolutionary data in the ICU are studied. At one year we assessed the GOS scale (Glasgow Outcome Score) according to whether they had a poor (GOS 1-3) or good (GOS 4-5) prognosis. RESULTS: 300 admitted patients, distributed in periods: I: 28.7%, II: 36.3% and III: 35%. 56.7% were males aged 66 (55.5-74) years; ICH score 2 (1-3). The ICU stay was 5 (2-14) days with a mortality of 36.8%. GOS 1-3 a year in 67.3% and GOS 4-5 in 32.7%. Comparing the three periods, we observed a higher prevalence in women, and the presence of cardiovascular factors; no changes in etiology; in relation to the location, it increases cerebellar hemorrhage and in the brainstem. Although the severity was greater, the stay in the ICU, the use of invasive mechanical ventilation and tracheostomy were lower. Open surgery has decreased its use by 50%. Mortality continues to be high, stagnating in the ICU at 35% and entails a high degree of disability one year after assessment. CONCLUSIONS: Severe ICH is a complex pathology that has changed some characteristics in the last two decades, with more severe patients, with more cardiovascular history and a greater predominance of brainstem and cerebellar hemorrhage. Despite the increase in severity, better parameters during the ICU stay, with open surgery used 50% less. Mortality remains stagnant at 35% with high disability per year.

Effectiveness of a specific follow up program for the management of the mental components of post-intensive care syndrome and chronic pain after COVID-19: results from the PAIN-COVID randomized clinical trial.

BACKGROUND: Critical COVID-19 survivors are at risk of developing Post-intensive Care Syndrome (PICS) and Chronic ICU-Related Pain (CIRP). We determined whether a specific care program improves the quality of life (QoL) of patients at risk of developing PICS and CIRP after COVID-19. METHODS: The PAIN-COVID trial was a parallel-group, single-centre, single-blinded, randomized controlled trial. The intervention consisted of a follow up program, patient education on PICS and pain, and a psychological intervention based on Rehm's self-control model in patients with abnormal depression scores (≥8) in the Hospital Anxiety and Depression Scale (HADS) at the baseline visit. QoL was evaluated with the 5-level EQ 5D (EQ 5D 5 L), mood disorders with the HADS, post-traumatic stress disorder (PTSD) with the PCL-5 checklist, and pain with the Brief Pain Inventory short form, the Douleur Neuropathique 4 questionnaire, and the Pain Catastrophizing Scale. The primary outcome was to determine if the program was superior to standard-of-care on the EQ visual analogue scale (VAS) at 6 months after the baseline visit. The secondary outcomes were EQ VAS at 3 months, and EQ index, CIRP incidence and characteristics, and anxiety, depression, and PTSD at 3 and 6 months after baseline visits. CONCLUSIONS: This program was not superior to standard care in improving QoL in critical COVID-19 survivors as measured by the EQ VAS. However, our data can help establish better strategies for the study and management of PICS and CIRP in this population. TRIAL REGISTRATION: # NCT04394169, registered on 5/19/2020.

BGOA-TVG: Binary Grasshopper Optimization Algorithm with Time-Varying Gaussian Transfer Functions for Feature Selection.

Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions have the characteristics of a fast convergence speed and a strong global search capability to convert a continuous search space to a binary one. The BGOA-TVG is tested and compared to S-shaped and V-shaped binary grasshopper optimization algorithms and five state-of-the-art swarm intelligence algorithms for feature selection. The experimental results show that the BGOA-TVG has better performance in UCI, DEAP, and EPILEPSY datasets for feature selection.

Incidence of confusional syndrome (delirium) in a Latin American university hospital.

BACKGROUND: Little is known about the incidence of delirium and its subtypes in patients admitted to different departments of university hospitals in Latin America. OBJECTIVE: To determine the incidence of delirium and the frequency of its subtypes, as well as its associated factors, in patients admitted to different departments of a university hospital in Bogotá, Colombia. METHODS: A cohort of patients over 18 years of age admitted to the internal medicine (IM), geriatrics (GU), general surgery (GSU), orthopaedics (OU) and intensive care unit (ICU) services of a university hospital was followed up between January and June 2018. To detect the presence of delirium, we used the CAM (Confusion Assessment Method) and the CAM-ICU if the patient had decreased communication skills. The delirium subtype was characterised using the RASS (Richmond Agitation and Sedation Scale). Patients were assessed on their admission date and then every two days until discharged from the hospital. Those in whom delirium was identified were referred for specialised intra-institutional interdisciplinary management. RESULTS: A total of 531 patients admitted during the period were assessed. The overall incidence of delirium was 12% (95% CI, 0.3-14.8). They represented 31.8% of patients in the GU, 15.6% in the ICU, 8.7% in IM, 5.1% in the OU, and 3.9% in the GSU. The most frequent clinical display was the mixed subtype, at 60.9%, followed by the normoactive subtype (34.4%) and the hypoactive subtype (4.7%). The factors most associated with delirium were age (adjusted RR = 1.07; 95% CI, 1.05-1.09), the presence of four or more comorbidities (adjusted RR = 2.04; 95% CI, 1.31-3.20), and being a patient in the ICU (adjusted RR = 2.02; 95% CI, 1.22-3.35). CONCLUSIONS: The incidence of delirium is heterogeneous in the different departments of the university hospital. The highest incidence occurred in patients that were admitted to the GU. The mixed subtype was the most frequent one, and the main associated factors were age, the presence of four or more comorbidities, and being an ICU patient.

Limitation of life support treatments in Spanish Intensive Care Units: Analysis of the ETHICUS II study.

OBJECTIVE: The aim of this study is to describe the results of Spanish ICUs in ETHICUS II study. DESIGN: Planned substudy of patients from ETHICUS II study. SETTING: 12 Spanish ICU. PATIENTS OR PARTICIPANTS: Patients admitted to Spanish ICU who died or in whom a limitation of life-sustaining treatment (LLST) was decided during a recruitment period of 6 months. INTERVENTIONS: Follow-up of patients was performed until discharge from the ICU and 2 months after the decision of LLST or death. MAIN VARIABLES OF INTEREST: Demographic characteristics, clinical profile, type of decision of LLST, time and form in which it was adopted. Patients were classified into 4 categories according to the ETHICUS II study protocol: withholding or withdrawing life-sustaining therapy, active shortening of the dying process, failed cardiopulmonary resuscitation and patients with brain death. RESULTS: A total of 795 patients were analyzed; 129 patients died after CPR, 129 developed brain death. LLST was decided in 537 patients, 485 died in the ICU, 90.3%. The mean age was 66.19 years ± 14.36, 63.8% of male patients. In 221 (41%) it was decided to withdraw life-sustaining treatments and in 316 (59%) withholding life-sustaining treatments. Nineteen patients (2.38%) had advance living directives. CONCLUSIONS: The predominant clinical profile when LTSV was established was male patients over 65 years with mostly cardiovascular comorbidity. We observed that survival was higher in LLST decisions involving withholding of treatments compared to those in which withdrawal was decided. Spain has played a leading role in both patient and ICU recruitment participating in this worldwide multicenter study.