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1.
Rev Esp Quimioter ; 36(6): 612-620, 2023 Dec.
Article in Spanish | MEDLINE | ID: mdl-37743661

ABSTRACT

OBJECTIVE: To assess the frequency of bacterial coinfection upon ICU admission in SARS-CoV-2 pneumonia patients, its microbiology, and impact on prognosis.The secondary objective was to identify risk factors for coinfection on admission. METHODS: Retrospective study, including patients with SARS-CoV-2 pneumonia admitted to the ICU.We defined bacterial coinfection by respiratory symptoms, radiological data, positive and clinically significant microbiological results in samples obtained in the first 48 h of admission and/or a determination of procalcitonin ≥ 0.5 ng/mL in the first 48 h.We evaluated demographic variables, comorbidities, SARS-CoV-2 infection data, severity scores, treatments received, need for respiratory support and outcomes (ICU and hospital mortality). RESULTS: A total of 182 patients were analyzed, 62 (34.1%) with bacterial coinfection.The most frequent microbiology was S. pneumoniae and M. pneumoniae. 96.1% of the patients received antibiotic therapy on admission, 98.9% corticosteroids, 27.5% tocilizumab, and 7.7% remdesivir.85.7% required invasive mechanical ventilation.The SOFA score (OR: 1.315, 95% CI1.116-1.548) and the delay in ICU admission (OR: 0.899, 95% CI 0.831-0.972) were related to the risk of coinfection. Bacterial coinfection increases the risk of death in hospital (OR 2.283; 95% CI 1.011.5.151; p=0.047). CONCLUSIONS: Bacterial coinfection is common in COVID patients admitted to the ICU and increases the risk of death. It is not possible to identify with certainty, at the time of admission, which patients do not benefit from antibiotic treatment.


Subject(s)
Anti-Infective Agents , COVID-19 , Coinfection , Humans , COVID-19/complications , COVID-19/epidemiology , SARS-CoV-2 , Critical Illness , Coinfection/drug therapy , Coinfection/epidemiology , Retrospective Studies , Incidence , Intensive Care Units
2.
Med. intensiva (Madr., Ed. impr.) ; 46(9): 508-520, sept. 2022.
Article in Spanish | IBECS | ID: ibc-209956

ABSTRACT

Pseudomonas aeruginosa es el microorganismo que participa con mayor frecuencia en las principales infecciones adquiridas en la UCI, con especial importancia en la neumonía asociada a ventilación mecánica. Su importancia radica, además de en su elevada incidencia en el paciente crítico, en la gravedad de las infecciones que causa y en la dificultad de su tratamiento antimicrobiano, directamente relacionada con el elevado porcentaje de resistencias a los antibióticos considerados clásicamente de primera línea. Recientemente se han desarrollado nuevos antibióticos activos frente a Pseudomonas aeruginosa, incluso frente a cepas multirresistentes. La presente revisión analiza tanto las características diferenciales de las infecciones por Pseudomonas aeruginosa como las nuevas opciones terapéuticas, centrando el foco en la Pseudomonas aeruginosa multirresistente (AU)


Pseudomonas aeruginosa is the microorganism most frequently involved in the main ICU-acquired infections, with special importance in ventilator associated pneumonia. Its importance lies, in addition to its high incidence in critically ill patients, in the severity of the infections it causes and in the difficulty of its antimicrobial treatment, directly related to the high percentage of resistance to antibiotics classically considered first-line. New active antibiotics have recently been developed against Pseudomonas aeruginosa, even against multi-drug resistant strains. This review analyzes both the differential characteristics of Pseudomonas aeruginosa infections and the new therapeutic options, focusing on multi-drug resistant Pseudomonas aeruginosa (AU)


Subject(s)
Humans , Anti-Bacterial Agents/therapeutic use , Pseudomonas Infections/drug therapy , Pseudomonas aeruginosa , Severity of Illness Index , Drug Resistance, Multiple , Risk Factors , Prognosis
3.
Med Intensiva (Engl Ed) ; 46(9): 508-520, 2022 09.
Article in English | MEDLINE | ID: mdl-35840495

ABSTRACT

Pseudomonas aeruginosa is the microorganism most frequently involved in the main ICU-acquired infections, with special importance in ventilator associated pneumonia. Its importance lies, in addition to its high incidence in critically ill patients, in the severity of the infections it causes and in the difficulty of its antimicrobial treatment, directly related to the high percentage of resistance to antibiotics classically considered first-line. New active antibiotics have recently been developed against Pseudomonas aeruginosa, even against multi-drug resistant strains. This review analyzes both the differential characteristics of Pseudomonas aeruginosa infections and the new therapeutic options, focusing on multi-drug resistant Pseudomonas aeruginosa.


Subject(s)
Pneumonia, Ventilator-Associated , Pseudomonas Infections , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/therapeutic use , Drug Resistance, Multiple, Bacterial , Humans , Pneumonia, Ventilator-Associated/drug therapy , Pseudomonas Infections/drug therapy , Pseudomonas aeruginosa
4.
Med. intensiva (Madr., Ed. impr.) ; 46(3): 140-156, Mar. 2022. tab
Article in English | IBECS | ID: ibc-204235

ABSTRACT

Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is increasingly being adopted in developing innovative Clinical Decision Support Systems in many areas of medicine, showing a great potential for automatic prediction of diverse patient conditions, as well as assistance in clinical decision making. In this context, this work conducts a narrative review to provide an overview of how specific Machine Learning techniques can be used to improve sepsis management, discussing the main tasks addressed, the most popular methods and techniques, as well as the obtained results, in terms of both intelligent system accuracy and clinical outcomes improvement (AU)


La sepsis representa un problema de salud pública de primer orden y es una de las principales causas de muerte a nivel mundial. El retraso en el inicio del tratamiento, junto con la no adherencia a las guías de práctica clínica se asocian a una mayor mortalidad. El aprendizaje automático o machine learning están siendo empleados en el desarrollo de sistemas de apoyo a la decisión clínica, innovadores en muchas áreas de la medicina, mostrando un gran potencial para la predicción de diversas condiciones del paciente, así como en la asistencia durante el proceso de toma de decisiones médicas. En este sentido, este trabajo lleva a cabo una revisión narrativa para proporcionar una visión general de cómo las técnicas de machine learning pueden ser empleadas para mejorar el manejo de la sepsis, discutiendo las principales tareas que tratan de resolver, los métodos y las técnicas más empleados, así como los resultados obtenidos, tanto en términos de precisión de los sistemas inteligentes, como en la mejora de los resultados clínicos (AU)


Subject(s)
Humans , Clinical Decision-Making , Machine Learning , Sepsis/diagnosis , Sepsis/therapy
5.
Med Intensiva (Engl Ed) ; 46(3): 140-156, 2022 Mar.
Article in English | MEDLINE | ID: mdl-35221003

ABSTRACT

Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is increasingly being adopted in developing innovative Clinical Decision Support Systems in many areas of medicine, showing a great potential for automatic prediction of diverse patient conditions, as well as assistance in clinical decision making. In this context, this work conducts a narrative review to provide an overview of how specific Machine Learning techniques can be used to improve sepsis management, discussing the main tasks addressed, the most popular methods and techniques, as well as the obtained results, in terms of both intelligent system accuracy and clinical outcomes improvement.


Subject(s)
Machine Learning , Sepsis , Clinical Decision-Making , Humans , Sepsis/diagnosis , Sepsis/therapy
6.
Med Intensiva ; 46(2): 81-89, 2022 Feb.
Article in Spanish | MEDLINE | ID: mdl-34545260

ABSTRACT

The COVID-19 pandemic has led to the admission of a high number of patients to the ICU, generally due to severe respiratory failure. Since the appearance of the first cases of SARS-CoV-2 infection, at the end of 2019, in China, a huge number of treatment recommendations for this entity have been published, not always supported by sufficient scientific evidence or with methodological rigor necessary. Thanks to the efforts of different groups of researchers, we currently have the results of clinical trials, and other types of studies, of higher quality. We consider it necessary to create a document that includes recommendations that collect this evidence regarding the diagnosis and treatment of COVID-19, but also aspects that other guidelines have not considered and that we consider essential in the management of critical patients with COVID-19. For this, a drafting committee has been created, made up of members of the SEMICYUC Working Groups more directly related to different specific aspects of the management of these patients.

7.
Med Intensiva (Engl Ed) ; 46(2): 81-89, 2022 02.
Article in English | MEDLINE | ID: mdl-34903475

ABSTRACT

The COVID-19 pandemic has led to the admission of a high number of patients to the ICU, generally due to severe respiratory failure. Since the appearance of the first cases of SARS-CoV-2 infection, at the end of 2019, in China, a huge number of treatment recommendations for this entity have been published, not always supported by sufficient scientific evidence or with methodological rigor necessary. Thanks to the efforts of different groups of researchers, we currently have the results of clinical trials, and other types of studies, of higher quality. We consider it necessary to create a document that includes recommendations that collect this evidence regarding the diagnosis and treatment of COVID-19, but also aspects that other guidelines have not considered and that we consider essential in the management of critical patients with COVID-19. For this, a drafting committee has been created, made up of members of the SEMICYUC Working Groups more directly related to different specific aspects of the management of these patients.


Subject(s)
COVID-19 , Critical Illness/therapy , Humans , Intensive Care Units , Pandemics , SARS-CoV-2
8.
Med Intensiva (Engl Ed) ; 46 Suppl 1: 60-71, 2022 Apr.
Article in Spanish | MEDLINE | ID: mdl-38341261

ABSTRACT

Fluid and vasopressor resuscitation is, along with antimicrobial therapy and control of the focus of infection, a basic issue of the treatment of sepsis and septic shock. There is currently no accepted protocol that we can follow for the resuscitation of these patients and the Surviving Sepsis Campaign proposes controversial measures and without sufficient evidence support to establish firm recommendations. We propose a resuscitation strategy adapted to the situation of each patient: in the patient in whom community sepsis is suspected, we consider that the early administration of 30mL/kg of crystalloids is effective and safe; in the patient with nosocomial sepsis, we must carry out a more in-depth evaluation before initiating aggressive resuscitation. In patients who do not respond to initial resuscitation, it is necessary to increase monitoring level and, depending on the hemodynamic profile, administer more fluids, a second vasopressor or inotropes.

13.
Rev Esp Quimioter ; 34(1): 33-43, 2021 Feb.
Article in English | MEDLINE | ID: mdl-33317261

ABSTRACT

OBJECTIVE: To assess the impact of corticosteroids on inflammatory and respiratory parameters of patients with COVID-19 and acute respiratory distress syndrome (ARDS). METHODS: Longitudinal, retrospective, observational study conducted in an ICU of a second level hospital. Adult patients with COVID-19 were included. Baseline characteristics, data on SARS-CoV-2 infection, treatment received, evolution of respiratory and inflammatory parameters, and ICU and hospital stay and mortality were analyzed. RESULTS: A total of 27 patients were included, 63% men, median age: 68.4 (51.8, 72.2) years. All patients met ARDS criteria and received MV and corticosteroids. After corticosteroids treatment we observed a reduction in the O2 A-a gradient [day 0: 322 (249, 425); day 3: 169 (129.5, 239.5) p<0.001; day 5: 144 (127.5, 228.0) p<0.001; day 7: 192 (120, 261) p=0.002] and an increase in the pO2/FiO2 ratio on days 3 and 5, but not on day 7 [day 0: 129 (100, 168); day 3: 193 (140, 236) p=0.002; day 5: 183 (141, 255) p=0.004; day 7: 170 (116, 251) p=0.057]. CRP also decreased on days 3 and 5 and increased again on day 7 [day 0: 16 (8.6, 24); day 3: 3.4 (1.7, 10.2) p<0.001; day 5: 4.1 (1.4, 10.2) p<0.001; day 7: 13.5 (6.8, 17.3) p=0.063]. Persistence of moderate ARDS on day 7 was related to a greater risk of poor outcome (OR 6.417 [1.091-37.735], p=0.040). CONCLUSIONS: Corticosteroids appears to reduce the inflammation and temporarily improve the oxygenation in COVID-19 and ARDS patients. Persistence of ARDS after 7 days treatment is a predictor of poor outcome.


Subject(s)
COVID-19 Drug Treatment , Oxygen Consumption/drug effects , Respiratory Distress Syndrome/drug therapy , SARS-CoV-2 , Aged , COVID-19/metabolism , Female , Humans , Intensive Care Units , Longitudinal Studies , Male , Middle Aged , Oxygen Consumption/physiology , Respiration, Artificial , Respiratory Distress Syndrome/metabolism , Retrospective Studies , Secondary Care Centers , Spain , Time Factors , Treatment Outcome
16.
Article in English, Spanish | MEDLINE | ID: mdl-32482370

ABSTRACT

Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is increasingly being adopted in developing innovative Clinical Decision Support Systems in many areas of medicine, showing a great potential for automatic prediction of diverse patient conditions, as well as assistance in clinical decision making. In this context, this work conducts a narrative review to provide an overview of how specific Machine Learning techniques can be used to improve sepsis management, discussing the main tasks addressed, the most popular methods and techniques, as well as the obtained results, in terms of both intelligent system accuracy and clinical outcomes improvement.

17.
Med. intensiva (Madr., Ed. impr.) ; 43(supl.1): 7-12, mar. 2019. tab
Article in Spanish | IBECS | ID: ibc-188589

ABSTRACT

Los antisépticos son sustancias químicas que, aplicadas de forma tópica sobre la piel intacta, las mucosas o las heridas, reducen (o eliminan por completo) la población de microorganismos vivos en dichos tejidos. Tenemos a nuestra disposición diferentes tipos de antisépticos. Los más empleados en la práctica clínica habitual son los alcoholes, los compuestos yodados y la clorhexidina. A la hora de decantarnos por uno u otro, debemos tener en cuenta su espectro de actividad antimicrobiana, su latencia, su efecto residual, las posibles interferencias de la presencia de material orgánico en la actividad del antiséptico, sus efectos secundarios, su compatibilidad con otros antisépticos y su coste


Antiseptics are chemical substances that when applied topically onto intact skin, mucous membranes or wounds partially or completely reduces the population of living microorganisms in those tissues. Different types of antiseptics are available - those most commonly used in clinical practice being alcohols, iodinated compounds and chlorhexidine. When using an antiseptic, consideration is required of its spectrum of antimicrobial activity, latency, residual effects, possible interferences of the presence of organic material with the activity of the antiseptic, its side effects, compatibility with other antiseptics, and cost


Subject(s)
Humans , Anti-Infective Agents/classification , Anti-Infective Agents/standards , Critical Care , Intensive Care Units/standards , Anti-Infective Agents/adverse effects
18.
Med Intensiva (Engl Ed) ; 43 Suppl 1: 7-12, 2019 Mar.
Article in English, Spanish | MEDLINE | ID: mdl-30447857

ABSTRACT

Antiseptics are chemical substances that when applied topically onto intact skin, mucous membranes or wounds partially or completely reduces the population of living microorganisms in those tissues. Different types of antiseptics are available - those most commonly used in clinical practice being alcohols, iodinated compounds and chlorhexidine. When using an antiseptic, consideration is required of its spectrum of antimicrobial activity, latency, residual effects, possible interferences of the presence of organic material with the activity of the antiseptic, its side effects, compatibility with other antiseptics, and cost. This article is part of a supplement entitled "Antisepsis in the critical patient", which is sponsored by Becton Dickinson.


Subject(s)
Alcohols/pharmacology , Anti-Infective Agents, Local/pharmacology , Iodine Compounds/pharmacology , Alcohols/adverse effects , Anti-Infective Agents, Local/adverse effects , Anti-Infective Agents, Local/classification , Cations/adverse effects , Cations/pharmacology , Chlorhexidine/adverse effects , Chlorhexidine/pharmacology , Drug Interactions , Ethanol/adverse effects , Ethanol/pharmacology , Humans , Hydrogen Peroxide/adverse effects , Hydrogen Peroxide/therapeutic use , Intensive Care Units , Iodine/adverse effects , Iodine/pharmacology , Iodine Compounds/adverse effects , Iodophors/adverse effects , Iodophors/pharmacology , Mercury Compounds/pharmacology , Propranolol/adverse effects , Propranolol/pharmacology , Sulfadiazine/adverse effects , Sulfadiazine/pharmacology , Triclosan/adverse effects , Triclosan/pharmacology
19.
Vet Rec ; 174(25): 634, 2014 Jun 21.
Article in English | MEDLINE | ID: mdl-24836427

ABSTRACT

RB51 vaccination can minimise the diagnostic problems associated with S19 vaccination of adult cattle, but its use for bovine brucellosis (BB) control remains controversial. Here, the evolution of BB prevalence in five high prevalence areas in Spain subjected to different control measures is described: herd depopulation of infected herds (I-III) or mass vaccination with RB51 and S19-RB51 vaccination of replacement heifers (IV-V). Annual data from the eradication campaigns were analysed at the special incidence area (SIA) level and the time to obtain herd prevalence levels of <1 per cent ('controlled status') was obtained at the local veterinary unit (LVU) level and compared using Cox's proportional hazard model. A higher annual rate of decrease in herd prevalence was observed in the SIAs subjected to vaccination (46.9%, 95% CI 43.5% to 50.0%) compared with those managed using stamping out (14.9%, 95% CI 9.6% to 19.9%). No significant differences in the time to achieve controlled status were observed between the stamping-out and vaccination strategies used at the LVU level, with median times of 60 (stamping-out LVUs) and 63 (vaccination LVUs) months. These results suggest that RB51 mass vaccination, in combination with the S19-RB51 vaccination of replacement heifers and strict implementation of other eradication measures, may provide results at least comparable with those resulting from a herd depopulation based strategy.


Subject(s)
Brucella Vaccine/administration & dosage , Brucellosis, Bovine/prevention & control , Endemic Diseases/veterinary , Mass Vaccination/veterinary , Population Control , Animals , Brucellosis, Bovine/epidemiology , Cattle , Endemic Diseases/prevention & control , Prevalence , Spain/epidemiology
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