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1.
Acta Anaesthesiol Scand ; 65(1): 68-75, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-32929715

RESUMO

BACKGROUND: Most data on intensive care unit (ICU) patients with COVID-19 originate in selected populations from stressed healthcare systems with shorter term follow-up. We present characteristics, interventions and longer term outcomes of the entire, unselected cohort of all ICU patients with COVID-19 in Denmark where the ICU capacity was not exceeded. METHODS: We identified all patients with SARS-CoV-2 admitted to any Danish ICU from 10 March to 19 May 2020 and registered demographics, chronic comorbidities, use of organ support, length of stay, and vital status from patient files. Risk factors for death were analyzed using adjusted Cox regression analysis. RESULTS: There were 323 ICU patients with confirmed COVID-19. Median age was 68 years, 74% were men, 50% had hypertension, 21% diabetes, and 20% chronic pulmonary disease; 29% had no chronic comorbidity. Invasive mechanical ventilation was used in 82%, vasopressors in 83%, renal replacement therapy in 26%, and extra corporeal membrane oxygenation in 8%. ICU stay was median 13 days (IQR 6-22) and hospital stay 19 days (11-30). Median follow-up was 79 days. At end of follow-up, 118 had died (37%), 15 (4%) were still in hospital hereof 4 in ICU as of 16 June 2020. Risk factors for mortality included male gender, age, chronic pulmonary disease, active cancer, and number of co-morbidities. CONCLUSIONS: In this nationwide, population-based cohort of ICU patients with COVID-19, longer term survival was high despite high age and substantial use of organ support. Male gender, age, and chronic co-morbidities, in particular chronic pulmonary disease, were associated with increased risk of death.


Assuntos
/terapia , Cuidados Críticos , Idoso , Estudos de Coortes , Comorbidade , Revisão Concomitante , Demografia , Dinamarca , Feminino , Mortalidade Hospitalar , Humanos , Tempo de Internação , Masculino , Pessoa de Meia-Idade , Fatores de Risco , Resultado do Tratamento , Sinais Vitais
2.
Emerg Med Clin North Am ; 39(1): 155-172, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33218655

RESUMO

The differential diagnosis for the comatose patient is includes structural abnormality, seizure, encephalitis, metabolic derangements, and toxicologic etiologies. Identifying and treating the underlying pathology in a timely manner is critical for the patient's outcome. We provide a structured approach to taking a history and performing a physical examination for this patient population. We discuss diagnostic testing and treatment methodologies for each of the common causes of coma. Our current understanding of the mechanisms of coma is insufficient to accurately predict the patient's clinical trajectory and more work needs to be done to investigate potential treatments for this often fatal disorder.


Assuntos
Coma/diagnóstico , Coma/etiologia , Coma/terapia , Diagnóstico Diferencial , Serviço Hospitalar de Emergência , Humanos , Exame Físico , Sinais Vitais
3.
Sci Rep ; 10(1): 21545, 2020 12 09.
Artigo em Inglês | MEDLINE | ID: mdl-33298991

RESUMO

Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66-1.92]), male sex (OR, 1.57 [95% CI 1.30-1.90]), higher BMI (OR, 1.03 [95% CI 1.102-1.05]), higher heart rate (OR, 1.01 [95% CI 1.00-1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03-1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93-0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20-1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.


Assuntos
Hospitalização , Aprendizado de Máquina , Modelos Biológicos , Pandemias , Sinais Vitais , Idoso , Idoso de 80 Anos ou mais , /fisiopatologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Cidade de Nova Iorque , Valor Preditivo dos Testes , Estudos Retrospectivos , Fatores de Risco
4.
Medicine (Baltimore) ; 99(50): e23446, 2020 Dec 11.
Artigo em Inglês | MEDLINE | ID: mdl-33327277

RESUMO

The first confirmed community transmission of coronavirus disease 2019 in Daegu Metropolitan City, South Korea, occurred on February 18, 2020. In the following 70-day period, approximately 6000 new cases occurred, severely impacting the medical service system. This study investigated the crisis-impact on the local emergency transport system.Emergency medical service activity reports were retrospectively reviewed to determine patient demographics and initial vital signs. Delay in reaching the patient, transporting the patient to the hospital, and returning to the fire station were assessed and categorized based on patients' initial vital signs. The study period was divided into 4 groups (1/1-2/18, 2/19-3/3, 3/4-3/31, and 4/1-04/30) and intergroup differences were analyzed.When compared to Period 1, the time-difference between the request to attend a scene and arrival at the scene was delayed in Periods 2, 3, and 4 by 4 minute 58 s, 3  minute 24 seconds, and 2 minute 20 seconds, respectively; that between arriving at the scene and at the hospital was delayed by 7  minute 43 seconds, 6 minutes 59 seconds, and 4 minutes 30 seconds, respectively; and that between arriving at the hospital and returning to the fire station was delayed by 29  minute 3 second, 25  minute 55 second, and 18  minute 44 second, respectively. In Period 2, for patients with symptoms of severe illness when compared to patients lacking such symptoms, the time-difference between the request to attend the scene and arrival at a hospital and between arrival at the hospital and returning to the fire station were 6 to 23 minute and 12 to 48 minute longer, respectively. Most of the delays impacted patients with a fever. In terms of condition, the statistical effect size for delay in transport time was from large to small: fever, hypoxia, abnormal respiratory rate, respiratory symptom, and hypotension.Outbreaks of infectious disease cause a paradoxical state in emergency medical transport systems, inducing delays in the transport of severely ill patients. Therefore, maintenance and improvement of the medical service system for both patients with infectious disease and those with other severe illnesses is required.


Assuntos
/terapia , Serviços Médicos de Emergência/estatística & dados numéricos , Fatores de Tempo , Transporte de Pacientes/estatística & dados numéricos , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , República da Coreia , Estudos Retrospectivos , Sinais Vitais
5.
PLoS One ; 15(11): e0241920, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33152007

RESUMO

BACKGROUND: Due to an aging population and the increasing proportion of patients with various comorbidities, the number of patients with acute ischemic heart disease (AIHD) who present to the emergency department (ED) with atypical chest pain is increasing. The aim of this study was to develop and validate a prediction model for AIHD in patients with atypical chest pain. METHODS AND RESULTS: A chest pain workup registry, ED administrative database, and clinical data warehouse database were analyzed and integrated by using nonidentifiable key factors to create a comprehensive clinical dataset in a single academic ED from 2014 to 2018. Demographic findings, vital signs, and routine laboratory test results were assessed for their ability to predict AIHD. An extreme gradient boosting (XGB) model was developed and evaluated, and its performance was compared to that of a single-variable model and logistic regression model. The area under the receiver operating characteristic curve (AUROC) was calculated to assess discrimination. A calibration plot and partial dependence plots were also used in the analyses. Overall, 4,978 patients were analyzed. Of the 3,833 patients in the training cohort, 453 (11.8%) had AIHD; of the 1,145 patients in the validation cohort, 166 (14.5%) had AIHD. XGB, troponin (single-variable), and logistic regression models showed similar discrimination power (AUROC [95% confidence interval]: XGB model, 0.75 [0.71-0.79]; troponin model, 0.73 [0.69-0.77]; logistic regression model, 0.73 [0.70-0.79]). Most patients were classified as non-AIHD; calibration was good in patients with a low predicted probability of AIHD in all prediction models. Unlike in the logistic regression model, a nonlinear relationship-like threshold and U-shaped relationship between variables and the probability of AIHD were revealed in the XGB model. CONCLUSION: We developed and validated an AIHD prediction model for patients with atypical chest pain by using an XGB model.


Assuntos
Dor no Peito/diagnóstico , Serviço Hospitalar de Emergência/estatística & dados numéricos , Infarto do Miocárdio/diagnóstico , Doença Aguda , Idoso , Algoritmos , Área Sob a Curva , Estudos de Coortes , Eletrocardiografia/métodos , Feminino , Frequência Cardíaca , Humanos , Modelos Logísticos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Isquemia Miocárdica/diagnóstico , Estudos Prospectivos , Curva ROC , República da Coreia/epidemiologia , Troponina , Sinais Vitais
6.
Complement Ther Clin Pract ; 41: 101243, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-33010649

RESUMO

OBJECTIVE: In this study, an investigation was made of the effect of the use of a stress ball, a method of distraction-attracting the attention elsewhere - on stress, vital signs andcomfort levels in hemodialysis patients. METHODS: This randomized, controlled experimental study, between July 2019 and September 2019 was carried out in a dialysis unit in the inner regions of Turkey. The study was conducted with 45 patients (23 experiments, 22 controls) who were receiving hemodialysis treatment. The experimental group were asked to squeeze a stress ball for approximately 10-15 min throughout eight successive dialysis sessions. The data were obtained with an Individual Description Form, the Distress Thermometer and the Hemodialysis Comfort Scale. RESULTS: At the end of the study, no significant difference was found in the vital signs and comfort levels of the experimental and control groups (p > 0.05). However, while the stress score of the experimental group decreased significantly, the stress score of the control groups increased (p < 0.05). CONCLUSION: This study shows that although the use of the stress ball did not affect vital signs and comfort in hemodialysis patients, it had a positive effect on stress.


Assuntos
Conforto do Paciente , Diálise Renal , Humanos , Turquia , Sinais Vitais
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 489-493, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018034

RESUMO

Respiratory rate (RR) is one of the vital signs which is commonly measured by contact-based methods, such as using a breathing belt. Recently, significant research has been conducted related to contactless RR monitoring - however, the majority of experiments are performed in situations when the subject is oriented towards the radar. In this research, we are interested in monitoring the breathing of subjects who can be anywhere in the room. A system of three impulse radio ultrawideband (IR-UWB) radars is used to cover the whole room. A Kinect camera that can track subjects' joints 3D coordinates was employed to localize the subjects. The results of RR monitoring using IR-UWB radars and Kinect camera show good performance in single/multiple subject(s) tracking and RR estimation.


Assuntos
Taxa Respiratória , Processamento de Sinais Assistido por Computador , Algoritmos , Humanos , Radar , Sinais Vitais
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5733-5736, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019276

RESUMO

Populations around the world are rapidly ageing. Age-friendly environments address the significance of continuous inhome vital sign monitoring. Impulse Radio Ultra-WideBand (IR-UWB) radar serves as a household healthcare assistance, providing non-contact vital sign monitoring without privacy issues and illumination limitation. However, the body movements bring difficulty in extracting heartbeat from radar signals, let alone obtaining complete information with body occlusions among multiple targets. This paper proposes a Multiple Moving Targets Heartbeat Estimation And Recovery (MMT-HEAR) approach to extract vital signs using IR-UWB radars. CLEAN and Joint Probability Data Association (JPDA) algorithms are firstly performed on each radar to estimate target-to-antenna distances of multiple targets. Considering signal obstruction and attenuation for targets occluded by others, the location-based distance optimization is proposed to refine these distances by combining information from all radars. Then the mapping from signal amplitudes to refined distances is introduced and combined with the Variational Nonlinear Chirp Mode Decomposition (VNCMD) to extract vital signs with body movements. To the best of our knowledge, this is the first attempt to monitor vital signs of multiple moving targets with radars. The averaging accuracy for two moving targets heartbeat monitoring during a 20-minutes observation is 85.93% with MMT-HEAR. Compared to two other conventional methods, the MMT-HEAR approach yields improvements of 16.11% and 10.16%, revealing the efficiency and robustness of this proposed approach.


Assuntos
Radar , Processamento de Sinais Assistido por Computador , Frequência Cardíaca , Compostos Organometálicos , Sinais Vitais
9.
Artigo em Inglês | MEDLINE | ID: mdl-33017933

RESUMO

Early prediction of sepsis is essential to give the patient timely treatment since each hour of delayed treatment has been associated with an increase in mortality. Current sepsis detection systems rely on empirical Clinical Decision Rules(CDR)s, which are based on vital signs that can be collected from the bedside. The main disadvantages of CDRs include questions of generalizability and performance variance when applied to the populations different from the groups used for derivation and often take years to develop and validate. This paper proposes a deep learning model using Bi-Directional Gated Recurrent Units(GRU), which uses a wide range of parameters that are associated with vitals, laboratory, and demographics of patients. The proposed model has an area under the receiver operating characteristic (AUROC) of 0.97, outperforming all the existing systems in the current literature. The model can handle the missing data, and irregular sampling intervals frequently present in medical records.Clinical relevance-The proposed model can be used to predict the onset of sepsis 6 hours ahead of time by the use of a machine learning algorithm. This proposed method outperforms the sepsis prediction machine learning models found in the current literature.


Assuntos
Sepse , Bases de Dados Genéticas , Diagnóstico Precoce , Humanos , Aprendizado de Máquina , Sepse/diagnóstico , Sinais Vitais
10.
Sci Rep ; 10(1): 18529, 2020 10 28.
Artigo em Inglês | MEDLINE | ID: mdl-33116150

RESUMO

A clinical study was designed to record a wide range of physiological values from patients undergoing haemodialysis treatment in the Renal Unit of the Churchill Hospital in Oxford. Video was recorded for a total of 84 dialysis sessions from 40 patients during the course of 1 year, comprising an overall video recording time of approximately 304.1 h. Reference values were provided by two devices in regular clinical use. The mean absolute error between the heart rate estimates from the camera and the average from two reference pulse oximeters (positioned at the finger and earlobe) was 2.8 beats/min for over 65% of the time the patient was stable. The mean absolute error between the respiratory rate estimates from the camera and the reference values (computed from the Electrocardiogram and a thoracic expansion sensor-chest belt) was 2.1 breaths/min for over 69% of the time for which the reference signals were valid. To increase the robustness of the algorithms, novel methods were devised for cancelling out aliased frequency components caused by the artificial light sources in the hospital, using auto-regressive modelling and pole cancellation. Maps of the spatial distribution of heart rate and respiratory rate information were developed from the coefficients of the auto-regressive models. Most of the periods for which the camera could not produce a reliable heart rate estimate lasted under 3 min, thus opening the possibility to monitor heart rate continuously in a clinical environment.


Assuntos
Monitorização Fisiológica/métodos , Sinais Vitais/fisiologia , Idoso , Algoritmos , Eletrocardiografia , Feminino , Frequência Cardíaca/fisiologia , Humanos , Masculino , Pessoa de Meia-Idade , Oximetria/métodos , Oxigênio/metabolismo , Diálise Renal , Taxa Respiratória/fisiologia , Processamento de Sinais Assistido por Computador , Gravação em Vídeo/métodos
11.
Acute Med ; 19(3): 138-144, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33020757

RESUMO

BACKGROUND: Accurate efficient prognostication in acute medical admissions remains challenging. METHODS: We constructed a Vital Sign based Risk Calculator using vital parameters and Major Disease Categories to predict 30-day in-hospital mortality using a multivariable fractional polynomial model. RESULTS: We evaluated 113,807 admissions in 58,126 patients. The Vital Sign based Risk Calculator predicted 30-day inhospital mortality to increase from 2 points - 3.6% (95%CI 3.4, 3.7) to 12 points - 14.8% (95%CI 14.0, 15.7). AUROC was 0.74 (95%CI 0.72, 0.74). The addition of illness severity and comorbidity data improved AUROC to 0.90 (95%CI 0.89, 0.90). CONCLUSION: The Vital Sign based Risk Calculator is limited by its simplicity; inclusion of illness severity and comorbidity data improve prediction.


Assuntos
Hospitalização , Sinais Vitais , Comorbidade , Mortalidade Hospitalar , Humanos , Prognóstico , Medição de Risco
12.
J Trauma Acute Care Surg ; 89(4): 834-841, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-33017137

RESUMO

INTRODUCTION: In the far forward combat environment, the use of whole blood is recommended for the treatment of hemorrhagic shock after injury. In 2016, US military special operations teams began receiving low titer group O whole blood (LTOWB) for use at the point of injury (POI). This is a case series of the initial 15 patients who received LTOWB on the battlefield. METHODS: Patients were identified in the Department of Defense Trauma Registry, and charts were abstracted for age, sex, nationality, mechanism of injury, injuries and physiologic criteria that triggered the transfusion, treatments at the POI, blood products received at the POI and the damage-control procedures done by the first surgical team, next level of care, initial interventions by the second surgical team, Injury Severity Score, and 30-day survival. Descriptive statistics were used to characterize the clinical data when appropriate. RESULTS: Of the 15 casualties, the mean age was 28, 50% were US military, and 63% were gunshot wounds. Thirteen patients survived to discharge, one died of wounds after arrival at the initial resuscitative surgical care, and two died prehospital. The mean Injury Severity Score was 21.31 (SD, 18.93). Eleven (68%) of the causalities received additional blood products during evacuation/role 2 and/or role 3. Vital signs were available for 10 patients from the prehospital setting and 9 patients upon arrival at the first surgical capable facility. The mean systolic blood pressure was 80.5 prehospital and 117 mm Hg (p = 0.0002) at the first surgical facility. The mean heart rate was 105 beats per minute prehospital and 87.4 beats per minute (p = 0.075) at the first surgical facility. The mean hospital stay was 24 days. CONCLUSION: The use of cold-stored LTOWB at POI is feasible during combat operations. Further data are needed to validate and inform best practice for POI transfusion. LEVEL OF EVIDENCE: Therapeutic study, level V.


Assuntos
Sistema ABO de Grupos Sanguíneos , Transfusão de Sangue/métodos , Choque Hemorrágico/terapia , Ferimentos por Arma de Fogo/complicações , Adulto , Transfusão de Componentes Sanguíneos/métodos , Serviços Médicos de Emergência , Feminino , Humanos , Escala de Gravidade do Ferimento , Masculino , Medicina Militar , Militares , Sistema de Registros , Ressuscitação/métodos , Choque Hemorrágico/diagnóstico , Estados Unidos , Sinais Vitais , Adulto Jovem
13.
Crit Care ; 24(1): 541, 2020 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-32873326

RESUMO

BACKGROUND: The effectiveness and indications of open-chest cardiopulmonary resuscitation (OCCPR) have been still debatable. Although current guidelines state that the presence of signs of life (SOL) is an indication for OCCPR, scientific evidence corroborating this recommendation has been scarce. This study aimed to compare the effectiveness of OCCPR to closed-chest cardiopulmonary resuscitation (CCCPR) in severe trauma patients with SOL upon arrival at the emergency department (ED). METHODS: A retrospective cohort study analyzing data from the Trauma Quality Improvement Program (TQIP) database, a nationwide trauma registry in the USA, between 2010 and 2016 was conducted. Severe trauma patients who had SOL upon arrival at the hospital and received cardiopulmonary resuscitation within the first 6 h of ED admission were identified. Survival to hospital discharge was evaluated using logistic regression analysis, instrumental variable analysis, and propensity score matching analysis adjusting for potential confounders. RESULTS: A total of 2682 patients (OCCPR 1032; CCCPR 1650) were evaluated; of those 157 patients (15.2%) in the OCCPR group and 193 patients (11.7%) in the CCCPR group survived. OCCPR was significantly associated with higher survival to hospital discharge in both the logistic regression analysis (adjusted odds ratio [95% confidence interval] = 1.99 [1.42-2.79], p <  0.001) and the instrumental variable analysis (adjusted odds ratio [95% confidence interval] = 1.16 [1.02-1.31], p = 0.021). In the propensity score matching analysis, 531 matched pairs were generated, and the OCCPR group still showed significantly higher survival at hospital discharge (89 patients [16.8%] in the OCCPR group vs 58 patients [10.9%] in the CCCPR group; odds ratio [95% confidence interval] = 1.66 [1.13-2.42], p = 0.009). CONCLUSIONS: Compared to CCCPR, OCCPR was associated with significantly higher survival at hospital discharge in severe trauma patients with SOL upon ED arrival. Further studies to confirm these results and to assess long-term neurologic outcomes are needed.


Assuntos
Reanimação Cardiopulmonar/métodos , Ferimentos e Lesões/terapia , Adulto , Serviço Hospitalar de Emergência , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Resultado do Tratamento , Sinais Vitais , Adulto Jovem
14.
Clin Transl Sci ; 13(6): 1034-1044, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32866314

RESUMO

The novel coronavirus disease 2019 (COVID-19) global pandemic has shifted how many patients receive outpatient care. Telehealth and remote monitoring have become more prevalent, and measurements taken in a patient's home using biometric monitoring technologies (BioMeTs) offer convenient opportunities to collect vital sign data. Healthcare providers may lack prior experience using BioMeTs in remote patient care, and, therefore, may be unfamiliar with the many versions of BioMeTs, novel data collection protocols, and context of the values collected. To make informed patient care decisions based on the biometric data collected remotely, it is important to understand the engineering solutions embedded in the products, data collection protocols, form factors (physical size and shape), data quality considerations, and availability of validation information. This article provides an overview of BioMeTs available for collecting vital signs (temperature, heart rate, blood pressure, oxygen saturation, and respiratory rate) and discusses the strengths and limitations of continuous monitoring. We provide considerations for remote data collection and sources of validation information to guide BioMeT use in the era of COVID-19 and beyond.


Assuntos
Biometria/métodos , Telemedicina/métodos , Sinais Vitais , Temperatura Corporal , Coleta de Dados , Humanos , Oxigênio/sangue , Respiração
15.
Sci Data ; 7(1): 291, 2020 09 08.
Artigo em Inglês | MEDLINE | ID: mdl-32901032

RESUMO

Using Radar it is possible to measure vital signs through clothing or a mattress from the distance. This allows for a very comfortable way of continuous monitoring in hospitals or home environments. The dataset presented in this article consists of 24 h of synchronised data from a radar and a reference device. The implemented continuous wave radar system is based on the Six-Port technology and operates at 24 GHz in the ISM band. The reference device simultaneously measures electrocardiogram, impedance cardiogram and non-invasive continuous blood pressure. 30 healthy subjects were measured by physicians according to a predefined protocol. The radar was focused on the chest while the subjects were lying on a tilt table wired to the reference monitoring device. In this manner five scenarios were conducted, the majority of them aimed to trigger hemodynamics and the autonomic nervous system of the subjects. Using the database, algorithms for respiratory or cardiovascular analysis can be developed and a better understanding of the characteristics of the radar-recorded vital signs can be gained.


Assuntos
Monitorização Ambulatorial/instrumentação , Radar , Sinais Vitais , Algoritmos , Sistema Nervoso Autônomo , Voluntários Saudáveis , Hemodinâmica , Humanos
16.
Resuscitation ; 156: 99-106, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32918984

RESUMO

BACKGROUND: The global pandemic of coronavirus disease 2019 (COVID-19) has placed a huge strain on UK hospitals. Early studies suggest that patients can deteriorate quickly after admission to hospital. The aim of this study was to model changes in vital signs for patients hospitalised with COVID-19. METHODS: This was a retrospective observational study of adult patients with COVID-19 admitted to one acute hospital trust in the UK (CV) and a cohort of patients admitted to the same hospital between 2013-2017 with viral pneumonia (VI). The primary outcome was the start of continuous positive airway pressure/non-invasive positive pressure ventilation, ICU admission or death in hospital. We used non-linear mixed-effects models to compare changes in vital sign observations prior to the primary outcome. Using observations and FiO2 measured at discharge in the VI cohort as the model of normality, we also combined individual vital signs into a single novelty score. RESULTS: There were 497 cases of COVID-19, of whom 373 had been discharged from hospital. 135 (36.2%) of patients experienced the primary outcome, of whom 99 died in hospital. In-hospital mortality was over 4-times higher in the CV than the VI cohort (26.5% vs 6%). For those patients who experienced the primary outcome, CV patients became increasingly hypoxaemic, with a median estimated FiO2 (0.75) higher than that of the VI cohort (estimated FiO2 of 0.35). Prior to the primary outcome, blood pressure remained within normal range, and there was only a small rise in heart rate. The novelty score showed that patients with COVID-19 deteriorated more rapidly that patients with viral pneumonia. CONCLUSIONS: Patients with COVID-19 who deteriorate in hospital experience rapidly-worsening respiratory failure, with low SpO2 and high FiO2, but only minor abnormalities in other vital signs. This has potential implications for the ability of early warning scores to identify deteriorating patients.


Assuntos
Betacoronavirus , Infecções por Coronavirus/diagnóstico , Pneumonia Viral/diagnóstico , Triagem/métodos , Sinais Vitais , Idoso , Idoso de 80 Anos ou mais , Infecções por Coronavirus/epidemiologia , Feminino , Mortalidade Hospitalar/tendências , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias , Pneumonia Viral/epidemiologia , Estudos Retrospectivos , Taxa de Sobrevida/tendências , Reino Unido/epidemiologia
17.
Sci Rep ; 10(1): 15974, 2020 09 29.
Artigo em Inglês | MEDLINE | ID: mdl-32994487

RESUMO

Many clinical studies have evaluated the effect of probiotics, but only a few have assessed their dose effects on gut microbiota and host. We conducted a randomized, double-blind, controlled intervention clinical trial to assess the safety (primary endpoint) of and gut microbiota response (secondary endpoint) to the daily ingestion for 4 weeks of two doses (1 or 3 bottles/day) of a fermented milk product (Test) in 96 healthy adults. The Test product is a multi-strain fermented milk product, combining yogurt strains and probiotic candidate strains Lactobacillus paracasei subsp. paracasei CNCM I-1518 and CNCM I-3689 and Lactobacillus rhamnosus CNCM I-3690. We assessed the safety of the Test product on the following parameters: adverse events, vital signs, hematological and metabolic profile, hepatic, kidney or thyroid function, inflammatory markers, bowel habits and digestive symptoms. We explored the longitudinal gut microbiota response to product consumption and dose, by 16S rRNA gene sequencing and functional contribution by shotgun metagenomics. Safety results did not show any significant difference between the Test and Control products whatever the parameters assessed, at the two doses ingested daily over a 4-week-period. Probiotic candidate strains were detected only during consumption period, and at a significantly higher level for the three strains in subjects who consumed 3 products bottles/day. The global structure of the gut microbiota as assessed by alpha and beta-diversity, was not altered by consumption of the product for four weeks. A zero-inflated beta regression model with random effects (ZIBR) identified a few bacterial genera with differential responses to test product consumption dose compared to control. Shotgun metagenomics analysis revealed a functional contribution to the gut microbiome of probiotic candidates.


Assuntos
Bactérias/classificação , Produtos Fermentados do Leite/microbiologia , Microbioma Gastrointestinal/efeitos dos fármacos , Probióticos/administração & dosagem , Adulto , Bactérias/genética , Bactérias/isolamento & purificação , DNA Bacteriano/genética , DNA Ribossômico/genética , Método Duplo-Cego , Feminino , Voluntários Saudáveis , Humanos , Lactobacillus/fisiologia , Lactobacillus rhamnosus/fisiologia , Masculino , Pessoa de Meia-Idade , Filogenia , Probióticos/farmacologia , RNA Ribossômico 16S/genética , Análise de Sequência de DNA , Sinais Vitais/efeitos dos fármacos , Adulto Jovem
18.
Fisioterapia (Madr., Ed. impr.) ; 42(4): 170-176, jul.-ago. 2020. tab
Artigo em Espanhol | IBECS | ID: ibc-193504

RESUMO

INTRODUCCIÓN: Las tecnologías para la rehabilitación son instrumentos, equipos, sistemas o dispositivos, que aportan a los procesos de recuperación de las capacidades humanas. La 4.ª revolución industrial ha hecho que se utilice la realidad virtual en procesos de rehabilitación, por lo cual es necesario conocer sus efectos fisiológicos en las personas. OBJETIVO: Determinar el efecto de la exposición a la RV sobre los signos vitales en 7 adultos mayores aparentemente sanos. MÉTODO: Se presenta un estudio epidemiológico descriptivo de una serie de 7 casos que permitió evaluar el comportamiento de los signos vitales. Los participantes fueron adultos mayores con edades entre 50 a 75 años, sin ningún tipo de patología osteomuscular y neuromuscular que impidan la ejecución de programa. Se contó con 4 tipos de ambientes virtuales programados progresivamente desde un ambiente de adaptación hasta el ambiente virtual de demandas reales. RESULTADOS: En la recolección de los signos vitales se evidenció un aumento significativo en FC, FR, TAM y SaO2 (P < 0,05), no se encontraron diferencias significativas de los signos vitales tomados previos a la exposición y 10 minutos posterior (P < 0,05). CONCLUSIÓN: Los cambios hemodinámicos antes de la exposición a RV no son permanentes en el tiempo. Los signos vitales 10 minutos posterior a la exposición regresan a los valores iniciales; lo cual permite aplicar la RV en personas mayores aparentemente sanas como estrategia terapéutica sin riesgo de presentar cambios fisiológicos concurrentes y nocivos, de acuerdo a la muestra de este estudio


INTRODUCTION: The technologies for rehabilitation are instruments, equipment, systems or devices, which contribute to the processes of recovery of human capabilities. The 4.th industrial revolution has brought about the use of virtual reality in rehabilitation processes, and therefore it is necessary to be aware of its physiological effects on people. OBJECTIVE: To determine the effect of VR exposure on vital signs in 7 apparently healthy older adults. METHOD: We present a descriptive epidemiological study of a series of 7 cases that allowed us to evaluate the behaviour of vital signs. The participants were older adults aged between 50 and 75 years, without any musculoskeletal or neuromuscular pathology to prevent them undertaking the programme. There were 4 types of virtual environments programmed progressively from an adaptation environment to the virtual environment of real demands. RESULTS: In the collection of vital signs, a significant increase in HR, FR, TAM and SaO2 (P < .05) was evidenced, no significant differences were found in the vital signs taken before and 10minutes after exposure (P < .05). CONCLUSION: Haemodynamic changes before exposure to RV are not permanent over time. Vital signs 10minutes after exposure return to initial values, which allows the application of RV in apparently healthy older people as a therapeutic strategy without risk of presenting concurrent and harmful physiological changes, according to the sample of this study


Assuntos
Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Realidade Virtual , Sinais Vitais/fisiologia , Terapia por Exercício , 24960 , Hemodinâmica , Técnicas de Exercício e de Movimento/instrumentação , Eletrocardiografia/métodos , Arritmias Cardíacas/diagnóstico por imagem
20.
PLoS One ; 15(7): e0235835, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32658901

RESUMO

BACKGROUND: Although machine learning-based prediction models for in-hospital cardiac arrest (IHCA) have been widely investigated, it is unknown whether a model based on vital signs alone (Vitals-Only model) can perform similarly to a model that considers both vital signs and laboratory results (Vitals+Labs model). METHODS: All adult patients hospitalized in a tertiary care hospital in Japan between October 2011 and October 2018 were included in this study. Random forest models with/without laboratory results (Vitals+Labs model and Vitals-Only model, respectively) were trained and tested using chronologically divided datasets. Both models use patient demographics and eight-hourly vital signs collected within the previous 48 hours. The primary and secondary outcomes were the occurrence of IHCA in the next 8 and 24 hours, respectively. The area under the receiver operating characteristic curve (AUC) was used as a comparative measure. Sensitivity analyses were performed under multiple statistical assumptions. RESULTS: Of 141,111 admitted patients (training data: 83,064, test data: 58,047), 338 had an IHCA (training data: 217, test data: 121) during the study period. The Vitals-Only model and Vitals+Labs model performed comparably when predicting IHCA within the next 8 hours (Vitals-Only model vs Vitals+Labs model, AUC = 0.862 [95% confidence interval (CI): 0.855-0.868] vs 0.872 [95% CI: 0.867-0.878]) and 24 hours (Vitals-Only model vs Vitals+Labs model, AUC = 0.830 [95% CI: 0.825-0.835] vs 0.837 [95% CI: 0.830-0.844]). Both models performed similarly well on medical, surgical, and ward patient data, but did not perform well for intensive care unit patients. CONCLUSIONS: In this single-center study, the machine learning model predicted IHCAs with good discrimination. The addition of laboratory values to vital signs did not significantly improve its overall performance.


Assuntos
Parada Cardíaca/diagnóstico , Aprendizado de Máquina , Idoso , Feminino , Hospitalização , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Estudos Retrospectivos , Sinais Vitais
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