Comparison of Mechanical Insufflation-Exsufflation and Hypertonic Saline and Hyaluronic Acid With Conventional Open Catheter Suctioning in Intubated Patients.

BACKGROUND: Open respiratory secretion suctioning with a catheter causes pain and tracheobronchial mucosal injury in intubated patients. The goal of mechanical insufflation-exsufflation (MI-E) is to move secretions proximally and noninvasively by generating a high peak expiratory air flow. Nebulized hypertonic saline with hyaluronic acid (HS-HA) may facilitate suctioning by hydration. We assessed the safety and tolerance of a single session of airway clearance with MI-E and HS-HA in critically ill intubated patients. METHODS: Adults with a cuffed artificial airway were randomized to (1) open suctioning, (2) open suctioning after HS-HA, (3) MI-E, or (4) MI-E with HS-HA. Adverse events, pain and sedation/agitation scores, and respiratory and hemodynamic variables were collected before, during, and 5-min and 60-min post intervention. RESULTS: One-hundred twenty subjects were enrolled and completed the study. Median (interquartile range [IQR]) Acute Physiology and Chronic Health Evaluation II (APACHE II) score was 22 (16-28); median (IQR) age was 69.0 (57.0-75.7) y, and 90 (75%) were male. Baseline respiratory and hemodynamic variables were comparable. Adverse events occurred in 30 subjects (25%), with no between-group differences. Behavioral pain equivalents and Richmond Agitation-Sedation Scale were higher during suctioning in groups 1 (P < .001) and 2 (P < .001). Independent predictive variables for higher pain and agitation/sedation scores were study groups 1 and 2 and simultaneous analgosedation, respectively. Noradrenaline infusion rates were lower at 60 min in groups 2 and 4. PaO2 /FIO2 had decreased at 5 min after open suctioning in group 1 and increased at 60 min in group 3. CONCLUSIONS: We observed no difference in adverse events. MI-E avoids pain and agitation.

Decompressive craniectomy in traumatic brain injury: The intensivist's point of view.

OBJETIVE: To perform a score with early clinical and radiological findings after a TBI that identifies the patients who in their subsequent evolution are going to undergo DC. METHOD: Observational study of a retrospective cohort of patients who, after a TBI, enter the Neurocritical Section of the Intensive Care Unit of our hospital for a period of 5 years (2014-2018). Detection of clinical and radiological criteria and generation of all possible models with significant, clinically relevant and easy to detect early variables. Selection of the one with the lowest Bayesian Information Criterion and Akaike Information Criterion values for the creation of the score. Calibration and internal validation of the score using the Hosmer-Lemeshow and a bootstrapping analysis with 1000 re-samples respectively. RESULTS: 37 DC were performed in 153 patients who were admitted after a TBI. The resulting final model included Cerebral Midline Deviation, GCS and Ventricular Collapse with an Area under ROC Curve: 0.84 (95% IC 0.78-0.91) and Hosmer-Lemeshow p=0.71. The developed score detected well those patients who were going to need an early DC (first 24h) after a TBI (2.5±0.5) but not those who would need it in a later stage of their disease (1.7±0.8). However, it seems to advice us about the patients who, although not requiring an early DC are likely to need it later in their evolution (DC after 24h vs. do not require DC, 1.7±0.8 vs. 1±0.7; p=0.002). CONCLUSION: We have developed a prognostic score using early clinical-radiological criteria that, in our environment, detects with good sensitivity and specificity those patients who, after a TBI, will require a DC.

Decompressive craniectomy in traumatic brain injury: the intensivist's point of view. / Craniectomías descompresivas en el traumatismo craneoencefálico: la visión del intensivista.

OBJETIVE: To perform a score with early clinical and radiological findings after a TBI that identifies the patients who in their subsequent evolution are going to undergo DC. METHOD: Observational study of a retrospective cohort of patients who, after a TBI, enter the Neurocritical Section of the Intensive Care Unit of our hospital for a period of 5 years (2014-2018). Detection of clinical and radiological criteria and generation of all possible models with significant, clinically relevant and easy to detect early variables. Selection of the one with the lowest Bayesian Information Criterion and Akaike Information Criterion values for the creation of the score. Calibration and internal validation of the score using the Hosmer-Lemeshow and a bootstrapping analysis with 1,000 re-samples respectively. RESULTS: 37 DC were performed in 153 patients who were admitted after a TBI. The resulting final model included Cerebral Midline Deviation, GCS and Ventricular Collapse with an Area under ROC Curve: 0.84 (95% IC 0.78-0.91) and Hosmer-Lemeshow p=0.71. The developed score detected well those patients who were going to need an early DC (first 24hours) after a TBI (2.5±0.5) but not those who would need it in a later stage of their disease (1.7±0.8). However, it seems to advice us about the patients who, although not requiring an early DC are likely to need it later in their evolution (DC after 24hours vs do not require DC, 1.7±0.8 vs 1±0.7; p=0.002). CONCLUSION: We have developed a prognostic score using early clinical-radiological criteria that, in our environment, detects with good sensitivity and specificity those patients who, after a TBI, will require a DC.

Big data and machine learning in critical care: Opportunities for collaborative research.

The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready clinical data that can be used to improve patient outcomes and the allocation of resources, as well as suggest topics for randomized clinical trials. Clinicians, however, usually lack the necessary training for the analysis of large databases. In addition, there are issues referred to patient privacy and consent, and data quality. Multidisciplinary collaboration among clinicians, data engineers, machine-learning experts, statisticians, epidemiologists and other information scientists may overcome these problems. A multidisciplinary event (Critical Care Datathon) was held in Madrid (Spain) from 1 to 3 December 2017. Under the auspices of the Spanish Critical Care Society (SEMICYUC), the event was organized by the Massachusetts Institute of Technology (MIT) Critical Data Group (Cambridge, MA, USA), the Innovation Unit and Critical Care Department of San Carlos Clinic Hospital, and the Life Supporting Technologies group of Madrid Polytechnic University. After presentations referred to big data in the critical care environment, clinicians, data scientists and other health data science enthusiasts and lawyers worked in collaboration using an anonymized database (MIMIC III). Eight groups were formed to answer different clinical research questions elaborated prior to the meeting. The event produced analyses for the questions posed and outlined several future clinical research opportunities. Foundations were laid to enable future use of ICU databases in Spain, and a timeline was established for future meetings, as an example of how big data analysis tools have tremendous potential in our field.

Infección por Inermicapsifer Madagarcariensis. (Davaine, 1870; Baer, 1956) / From Inermicapsifer Madagarcariensis (Davaine, 1870, Baer, 1956)

Se reportó un caso de parasitismo por Inermicapsifer Madagascariensis, en un joven de 24 años de edad de la raza blanca. Este cestodo es más común en los primeros años de vida, pero como se observa hay que tenerlo en cuenta en otras edades.  En Cuba, Pedro Kourí en 1938 describió con el nombre de Railletina cubensis, un cestodo que ahora se denomina Inermicapsifer Madagascariensis. En aquella época solo había sido reportado en hiraooides y roedores Africanos, se desconocía entonces su presencia en humanos, además el escolex de ese primer ejemplar no fue examinado a profundidad por temor a destruirlo en su preparación, por eso este autor consideró pertenecía al género Railletina, sin embargo, en 1939, cuando examinaba más ejemplares, se da cuenta que el escolex no presentaba ganchos y los renombra como Inermicapsifer cubensis.l En la actualidad ha quedado resuelto de cierta manera, lo concerniente a la historia del descubrimiento y clasificación de dicho helminto, pues Baer dio a conocer la descripción del mismo, después de un viaje que realizó a Inglaterra, renombrando el espécimen descrito por Grenet y Davaine, lo acepta como la misma especie reportada por Kouri y aplica la "Ley de la Prioridad" de acuerdo con el código Internacional de Nomenclatura Zoológica, por lo que el nombre de Inermicapsifer Cubensis y todos los anteriores pasan a sinonimia y es cambiado por el de Inermicapsifer Madagascariensis (Davaine 1870; Baes 1952).[AU]