RESUMEN
BACKGROUND: There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients. METHODS: This was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model. RESULTS: The included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-ß-D-glucan test (G test). CONCLUSION: We successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.
Asunto(s)
Infecciones Comunitarias Adquiridas , Enfermedades del Tejido Conjuntivo , Unidades de Cuidados Intensivos , Aprendizaje Automático , Admisión del Paciente , Neumonía , Humanos , Infecciones Comunitarias Adquiridas/diagnóstico , Infecciones Comunitarias Adquiridas/epidemiología , Masculino , Enfermedades del Tejido Conjuntivo/diagnóstico , Enfermedades del Tejido Conjuntivo/epidemiología , Femenino , Persona de Mediana Edad , Estudios Retrospectivos , Unidades de Cuidados Intensivos/tendencias , Anciano , Admisión del Paciente/tendencias , Neumonía/diagnóstico , Neumonía/epidemiología , Valor Predictivo de las Pruebas , China/epidemiología , AdultoRESUMEN
Healthcare systems are large contributors to global emissions, and intensive care units (ICUs) are a complex and resource-intensive component of these systems. Recent global movements in sustainability initiatives, led mostly by Europe and Oceania, have tried to mitigate ICUs' notable environmental impact with varying success. However, there exists a significant gap in the U.S. knowledge and published literature related to sustainability in the ICU. After a narrative review of the literature and related industry standards, we share our experience with a Green ICU initiative at a large hospital system in Texas. Our process has led to a 3-step pathway to inform similar initiatives for sustainable (green) critical care. This pathway involves (1) establishing a baseline by quantifying the status quo carbon footprint of the affected ICU as well as the cumulative footprint of all the ICUs in the healthcare system; (2) forming alliances and partnerships to target each major source of these pollutants and implement specific intervention programs that reduce the ICU-related greenhouse gas emissions and solid waste; and (3) finally to implement a systemwide Green ICU which requires the creation of multiple parallel pathways that marshal the resources at the grass-roots level to engage the ICU staff and institutionalize a mindset that recognizes and respects the impact of ICU functions on our environment. It is expected that such a systems-based multi-stakeholder approach would pave the way for improved sustainability in critical care.
Asunto(s)
Unidades de Cuidados Intensivos , Humanos , Unidades de Cuidados Intensivos/organización & administración , Unidades de Cuidados Intensivos/tendencias , Cuidados Críticos/métodos , Cuidados Críticos/tendencias , Desarrollo Sostenible/tendencias , Huella de Carbono , Hospitales/tendencias , Hospitales/normas , TexasRESUMEN
BACKGROUND: The correlation between the triglyceride-glucose index (TyG) and the prognosis of ischemic stroke has been well established. This study aims to assess the influence of the TyG index on the clinical outcomes of critically ill individuals suffering from intracerebral hemorrhage (ICH). METHODS: Patients diagnosed with ICH were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Various statistical methods, including restricted cubic spline (RCS) regression, multivariable logistic regression, subgroup analysis, and sensitivity analysis, were employed to examine the relationship between the TyG index and the primary outcomes of ICH. RESULTS: A total of 791 patients from MIMIC-IV and 1,113 ones from eICU-CRD were analyzed. In MIMIC-IV, the in-hospital and ICU mortality rates were 14% and 10%, respectively, while in eICU-CRD, they were 16% and 8%. Results of the RCS regression revealed a consistent linear relationship between the TyG index and the risk of in-hospital and ICU mortality across the entire study population of both databases. Logistic regression analysis revealed a significant positive association between the TyG index and the likelihood of in-hospital and ICU death among ICH patients in both databases. Subgroup and sensitivity analysis further revealed an interaction between patients' age and the TyG index in relation to in-hospital and ICU mortality among ICH patients. Notably, for patients over 60 years old, the association between the TyG index and the risk of in-hospital and ICU mortality was more pronounced compared to the overall study population in both MIMIC-IV and eICU-CRD databases, suggesting a synergistic effect between old age (over 60 years) and the TyG index on the in-hospital and ICU mortality of patients with ICH. CONCLUSIONS: This study established a positive correlation between the TyG index and the risk of in-hospital and ICU mortality in patients over 60 years who diagnosed with ICH, suggesting that the TyG index holds promise as an indicator for risk stratification in this patient population.
Asunto(s)
Glucemia , Hemorragia Cerebral , Enfermedad Crítica , Mortalidad Hospitalaria , Triglicéridos , Humanos , Masculino , Femenino , Anciano , Enfermedad Crítica/mortalidad , Mortalidad Hospitalaria/tendencias , Hemorragia Cerebral/sangre , Hemorragia Cerebral/mortalidad , Hemorragia Cerebral/diagnóstico , Estudios Retrospectivos , Persona de Mediana Edad , Estudios de Casos y Controles , Triglicéridos/sangre , Glucemia/análisis , Glucemia/metabolismo , Unidades de Cuidados Intensivos/tendencias , Anciano de 80 o más Años , Pronóstico , Valor Predictivo de las PruebasRESUMEN
BACKGROUND: Antibiotic-associated diarrhea (AAD) can prolong hospitalization, increase medical costs, and even lead to higher mortality rates. Therefore, it is essential to predict the incidence of AAD in elderly intensive care unit(ICU) patients. The objective of this study was to create a prediction model that is both interpretable and generalizable for predicting the incidence of AAD in elderly ICU patients. METHODS: We retrospectively analyzed data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH) in China. We utilized the machine learning model Extreme Gradient Boosting (XGBoost) and Shapley's additive interpretation method to predict the incidence of AAD in elderly ICU patients in an interpretable manner. RESULTS: A total of 848 adult ICU patients were eligible for this study. The XGBoost model predicted the incidence of AAD with an area under the receiver operating characteristic curve (ROC) of 0.917, sensitivity of 0.889, specificity of 0.806, accuracy of 0.870, and an F1 score of 0.780. The XGBoost model outperformed the other models, including logistic regression, support vector machine (AUC = 0.809), K-nearest neighbor algorithm (AUC = 0.872), and plain Bayes (AUC = 0.774). CONCLUSIONS: While the XGBoost model may not excel in absolute performance, it demonstrates superior predictive capabilities compared to other models in forecasting the incidence of AAD in elderly ICU patients categorized based on their characteristics.
Asunto(s)
Antibacterianos , Diarrea , Unidades de Cuidados Intensivos , Aprendizaje Automático , Humanos , Diarrea/epidemiología , Diarrea/inducido químicamente , Diarrea/diagnóstico , Anciano , Masculino , Femenino , Estudios Retrospectivos , Incidencia , Unidades de Cuidados Intensivos/tendencias , Antibacterianos/efectos adversos , China/epidemiología , Anciano de 80 o más Años , Persona de Mediana EdadRESUMEN
PURPOSE: Invasive mechanical ventilation (IMV) is widely used in patients with cardiogenic shock following acute myocardial infarction (AMICS), but evidence to guide practice remains sparse. We sought to evaluate trends in the rate of IMV utilization, applied settings, and short term-outcome of a contemporary cohort of AMICS patients treated with IMV according to out-of-hospital cardiac arrest (OHCA) at admission. METHODS: Consecutive AMICS patients receiving IMV in an intensive care unit (ICU) at two tertiary centres between 2010 and 2017. Data were analysed in relation to OHCA. RESULTS: A total of 1274 mechanically ventilated AMICS patients were identified, 682 (54%) with OHCA. Frequency of IMV increased during the study period, primarily due to higher occurrence of OHCA admissions. Among 566 patients with complete ventilator data, positive-end-expiratory pressure, inspired oxygen fraction, and minute ventilation during the initial 24 h in ICU were monitored. No differences were observed between 30-day survivors and non-survivors with OHCA. In non-OHCA, these ventilator requirements were significantly higher among 30-day non-survivors (P for all<0.05), accompanied by a lower PaO2/FiO2 ratio (median 143 vs. 230, P < 0.001) and higher arterial lactate levels (median 3.5 vs. 1.5 mmol/L, P < 0.001) than survivors. Physiologically normal PaO2 and pCO2 levels were achieved in all patients irrespective of 30-day survival and OHCA status. CONCLUSION: In the present contemporary cohort of AMICS patients, physiologically normal blood gas values were achieved both in OHCA and non-OHCA in the early phase of admission. However, increased demand of ventilatory support was associated with poorer survival only in non-OHCA patients.
Asunto(s)
Infarto del Miocardio , Respiración Artificial , Choque Cardiogénico , Humanos , Choque Cardiogénico/terapia , Choque Cardiogénico/mortalidad , Choque Cardiogénico/etiología , Masculino , Femenino , Respiración Artificial/métodos , Respiración Artificial/tendencias , Anciano , Persona de Mediana Edad , Infarto del Miocardio/complicaciones , Infarto del Miocardio/terapia , Estudios de Cohortes , Dinamarca/epidemiología , Paro Cardíaco Extrahospitalario/terapia , Paro Cardíaco Extrahospitalario/mortalidad , Paro Cardíaco Extrahospitalario/complicaciones , Unidades de Cuidados Intensivos/tendencias , Estudios Retrospectivos , Anciano de 80 o más AñosRESUMEN
BACKGROUND: To study temporal trends of intensive care unit (ICU) admission in obstetric population after the introduction of obstetric high-dependency unit (HDU). METHODS: This is a retrospective study of consecutive obstetric patients admitted to the ICU/HDU in a provincial referral center in China from January 2014 to December 2019. The collected information included maternal demographic characteristics, indications for ICU and HDU admission, the length of ICU stay, the total length of in-hospital stay and APACHE II score. Chi-square and ANOVA tests were used to determine statistical significance. The temporal changes were assessed with chi-square test for linear trend. RESULTS: A total of 40,412 women delivered and 447 (1.11%) women were admitted to ICU in this 6-year period. The rate of ICU admission peaked at 1.59% in 2016 and then dropped to 0.67% in 2019 with the introduction of obstetric HDU. The average APACHE II score increased significantly from 6.8 to 12.3 (P < 0.001) and the average length of ICU stay increased from 1.7 to 7.1 days (P < 0.001). The main indications for maternal ICU admissions were hypertensive disorders in pregnancy (39.8%), cardiac diseases (24.8%), and other medical disorders (21.5%); while the most common reasons for referring to HDU were hypertensive disorders of pregnancy (46.5%) and obstetric hemorrhage (43.0%). The establishment of HDU led to 20% reduction in ICU admission, which was mainly related to obstetric indications. CONCLUSIONS: The introduction of HDU helps to reduce ICU utilization in obstetric population.
Asunto(s)
Cuidados Críticos/organización & administración , Unidades Hospitalarias/organización & administración , Unidades de Cuidados Intensivos/tendencias , Admisión del Paciente/tendencias , Complicaciones del Embarazo/terapia , APACHE , Adulto , China , Femenino , Humanos , Tiempo de Internación/tendencias , Embarazo , Complicaciones del Embarazo/epidemiología , Estudios RetrospectivosRESUMEN
Unselected data of nationwide studies of hospitalized patients with COVID-19 are still sparse, but these data are of outstanding interest to avoid exceeding hospital capacities and overloading national healthcare systems. Thus, we sought to analyze seasonal/regional trends, predictors of in-hospital case-fatality, and mechanical ventilation (MV) in patients with COVID-19 in Germany. We used the German nationwide inpatient samples to analyze all hospitalized patients with a confirmed COVID-19 diagnosis in Germany between 1 January and 31 December in 2020. We analyzed data of 176,137 hospitalizations of patients with confirmed COVID-19-infection. Among those, 31,607 (17.9%) died, whereby in-hospital case-fatality grew exponentially with age. Overall, age ≥ 70 years (OR 5.91, 95%CI 5.70-6.13, p < 0.001), pneumonia (OR 4.58, 95%CI 4.42-4.74, p < 0.001) and acute respiratory distress syndrome (OR 8.51, 95%CI 8.12-8.92, p < 0.001) were strong predictors of in-hospital death. Most COVID-19 patients were treated in hospitals in urban areas (n = 92,971) associated with the lowest case-fatality (17.5%), as compared to hospitals in suburban (18.3%) or rural areas (18.8%). MV demand was highest in November/December 2020 (32.3%, 20.3%) in patients between the 6th and 8th age decade. In the first age decade, 78 of 1861 children (4.2%) with COVID-19-infection were treated with MV, and five of them died (0.3%). The results of our study indicate seasonal and regional variations concerning the number of COVID-19 patients, necessity of MV, and case fatality in Germany. These findings may help to ensure the flexible allocation of intensive care (human) resources, which is essential for managing enormous societal challenges worldwide to avoid overloaded regional healthcare systems.
Asunto(s)
COVID-19/epidemiología , COVID-19/mortalidad , Mortalidad Hospitalaria/tendencias , Hospitalización/estadística & datos numéricos , Pacientes Internos/estadística & datos numéricos , Anciano , Anciano de 80 o más Años , Femenino , Alemania/epidemiología , Hospitalización/tendencias , Humanos , Unidades de Cuidados Intensivos/estadística & datos numéricos , Unidades de Cuidados Intensivos/tendencias , Masculino , Persona de Mediana Edad , Respiración Artificial/estadística & datos numéricos , Respiración Artificial/tendencias , Factores de Riesgo , SARS-CoV-2/patogenicidadRESUMEN
PURPOSE: This study aimed to evaluate the attributable mortality of new-onset acute kidney injury (AKI). METHODS: The data in the present study were derived from a multi-center, prospective cohort study in China that was performed at 18 Chinese ICUs. A propensity-matched analysis was performed between matched patients with and without AKI selected from all eligible patients to estimate the attributable mortality of new-onset AKI. RESULTS: A total of 2872 critically ill adult patients were eligible. The incidence of new-onset AKI was 29.1% (n = 837). After propensity score matching, 788 patients with AKI were matched 1:1 with 788 controls (patients without AKI). Thirty-day mortality was significantly higher among the patients with AKI than among their matched controls (25.5% versus 17.4%, p < 0.001). Subgroup analysis in terms of AKI classification showed that there was no significant difference (p = 0.509) in 30-day mortality between patients with stage 1 AKI and their matched controls. The attributable mortality values of stage 2 and stage 3 AKI were 12.4% [95% confidence interval (CI) 2.6-21.8%, p = 0.013] and 16.1% (95% CI 8.2-23.8%, p < 0.001), respectively. The attributable mortality of persistent AKI was 15.7% (95% CI 8.8-22.4%, p = 0.001), while no observable difference in 30-day mortality was identified between transient AKI patients and their matched non-AKI controls (p = 0.229). CONCLUSION: The absolute excess 30-day mortality that is statistically attributable to new-onset AKI is substantial (8.1%) among general ICU patients. However, neither stage 1 AKI nor transient AKI increases 30-day mortality.
Asunto(s)
Lesión Renal Aguda/mortalidad , Enfermedad Crítica , Lesión Renal Aguda/epidemiología , Lesión Renal Aguda/etiología , Adulto , Estudios de Casos y Controles , China/epidemiología , Estudios de Cohortes , Humanos , Incidencia , Unidades de Cuidados Intensivos/tendencias , Estudios Prospectivos , Estudios Retrospectivos , Factores de RiesgoRESUMEN
Delirium is a common condition affecting hospital inpatients, including those having surgery and on the intensive care unit. Delirium is also common in patients with COVID-19 in hospital settings, and the occurrence is higher than expected for similar infections. The short-term outcomes of those with COVID-19 delirium are similar to that of classical delirium and include increased length of stay and increased mortality. Management of delirium in COVID-19 in the context of a global pandemic is limited by the severity of the syndrome and compounded by the environmental constraints. Practical management includes effective screening, early identification and appropriate treatment aimed at minimising complications and timely escalation decisions. The pandemic has played out on the national stage and the effect of delirium on patients, relatives and healthcare workers remains unknown but evidence from the previous SARS outbreak suggests there may be long-lasting psychological damage.
Asunto(s)
COVID-19/epidemiología , COVID-19/psicología , Delirio/epidemiología , Delirio/psicología , Personal de Salud/psicología , Encéfalo/metabolismo , COVID-19/metabolismo , COVID-19/terapia , Delirio/metabolismo , Delirio/terapia , Humanos , Mediadores de Inflamación/metabolismo , Unidades de Cuidados Intensivos/tendenciasRESUMEN
BACKGROUND: Erythrocyte transfusions are independently associated with acute kidney injury. Kidney injury may be consequent to the progressive hematologic changes that develop during storage. This study therefore tested the hypothesis that prolonged erythrocyte storage increases posttransfusion acute kidney injury. METHODS: The Informing Fresh versus Old Red Cell Management (INFORM) trial randomized 31,497 patients to receive either the freshest or oldest available matching erythrocyte units and showed comparable mortality with both. This a priori substudy compared the incidence of posttransfusion acute kidney injury in the randomized groups. Acute kidney injury was defined by the creatinine component of the Kidney Disease: Improving Global Outcomes criteria. RESULTS: The 14,461 patients included in this substudy received 40,077 erythrocyte units. For patients who received more than one unit, the mean age of the blood units was used as the exposure. The median of the mean age of blood units transfused per patient was 11 days [interquartile range, 8, 15] in the freshest available blood group and 23 days [interquartile range, 17, 30] in the oldest available blood group. In the primary analysis, posttransfusion acute kidney injury was observed in 688 of 4,777 (14.4%) patients given the freshest available blood and 1,487 of 9,684 (15.4%) patients given the oldest available blood, with an estimated relative risk (95% CI) of 0.94 (0.86 to 1.02; P = 0.132). The secondary analysis treated blood age as a continuous variable (defined as duration of storage in days), with an estimated relative risk (95% CI) of 1.00 (0.96 to 1.04; P = 0.978) for a 10-day increase in the mean age of erythrocyte units. CONCLUSIONS: In a population of patients without severely impaired baseline renal function receiving fewer than 10 erythrocyte units, duration of blood storage had no effect on the incidence of posttransfusion acute kidney injury.
Asunto(s)
Lesión Renal Aguda/diagnóstico , Lesión Renal Aguda/epidemiología , Conservación de la Sangre/tendencias , Transfusión de Eritrocitos/tendencias , Eritrocitos/fisiología , Anciano , Anciano de 80 o más Años , Conservación de la Sangre/efectos adversos , Transfusión de Eritrocitos/efectos adversos , Femenino , Humanos , Unidades de Cuidados Intensivos/tendencias , Masculino , Persona de Mediana Edad , Factores de RiesgoRESUMEN
OBJECTIVES: To determine the association between time period of hospitalization and hospital mortality among critically ill adults with coronavirus disease 2019. DESIGN: Observational cohort study from March 6, 2020, to January 31, 2021. SETTING: ICUs at four hospitals within an academic health center network in Atlanta, GA. PATIENTS: Adults greater than or equal to 18 years with coronavirus disease 2019 admitted to an ICU during the study period (i.e., Surge 1: March to April, Lull 1: May to June, Surge 2: July to August, Lull 2: September to November, Surge 3: December to January). MEASUREMENTS AND MAIN RESULTS: Among 1,686 patients with coronavirus disease 2019 admitted to an ICU during the study period, all-cause hospital mortality was 29.7%. Mortality differed significantly over time: 28.7% in Surge 1, 21.3% in Lull 1, 25.2% in Surge 2, 30.2% in Lull 2, 34.7% in Surge 3 (p = 0.007). Mortality was significantly associated with 1) preexisting risk factors (older age, race, ethnicity, lower body mass index, higher Elixhauser Comorbidity Index, admission from a nursing home); 2) clinical status at ICU admission (higher Sequential Organ Failure Assessment score, higher d-dimer, higher C-reactive protein); and 3) ICU interventions (receipt of mechanical ventilation, vasopressors, renal replacement therapy, inhaled vasodilators). After adjusting for baseline and clinical variables, there was a significantly increased risk of mortality associated with admission during Lull 2 (relative risk, 1.37 [95% CI = 1.03-1.81]) and Surge 3 (relative risk, 1.35 [95% CI = 1.04-1.77]) as compared to Surge 1. CONCLUSIONS: Despite increased experience and evidence-based treatments, the risk of death for patients admitted to the ICU with coronavirus disease 2019 was highest during the fall and winter of 2020. Reasons for this increased mortality are not clear.
Asunto(s)
COVID-19/mortalidad , Mortalidad Hospitalaria/tendencias , Hospitalización/tendencias , Unidades de Cuidados Intensivos/tendencias , SARS-CoV-2 , Centros Médicos Académicos , Anciano , Estudios de Cohortes , Enfermedad Crítica , Femenino , Humanos , Masculino , Persona de Mediana Edad , Factores de TiempoRESUMEN
Introdução: A mobilização precoce impacta diretamente no aumento da sobrevida em pacientes críticos, diminui a chance de complicações pulmonares, reduz o tempo de desmame de ventilação mecânica e impulsiona o processo de recuperação. A justificativa deste estudo reside na ausência de um protocolo de mobilização precoce (PMP) na unidade de terapia intensiva (UTI) em que atuam os autores. Objetivo: Desenvolver um PMP para uma UTI adulto, a partir de uma revisão sobre protocolos disponíveis na literatura. Metodologia: Foi realizada uma revisão da literatura dos últimos 10 anos, utilizando os descritores: mobilização precoce e unidade de terapia intensiva, nas bases de dados Medline/PubMed, Lilacs e Scielo. Resultados: Foram identificados 302 artigos, dos quais foram incluídos cinco ensaios clínicos que aplicaram protocolos de mobilização diversos em relação aos exercícios incluídos, dosimetria das intervenções, tempos de aplicação e características sociodemográficas e clínicas dos pacientes incluídos. Foi verificada homogeneidade nos critérios de progressão das intervenções dos protocolos, sendo o nível de consciência e a força muscular periférica, os critérios mais utilizados. Conclusão: A partir desta revisão, foi desenvolvido um PMP para uma UTI adulta, baseado em níveis de progressão das intervenções, considerando características clínicas como nível de sedação, necessidade de suporte ventilatório invasivo, nível cognitivo e funcionalidade.
Introduction: Early mobilization has a direct impact on the increased survival in critically ill patients, reduces the chance of pulmonary complications, reduces the time to weaning from mechanical ventilation, and boosts the recovery process. This study is necessary since there is an absence of an early mobilization protocol (EMP) in the intensive care unit (ICU) where the authors work. Objective: To develop an EMP for an adult ICU, based on a review of protocols available in the literature. Methodology: A literature review of the last 10 years was performed, using the descriptors: early mobilization and intensive care unit on the Medline/PubMed, Lilacs, and Scielo databases. Results: From a total of 302 articles identified, five clinical trials were included in the analysis. These five trials applied different mobilization protocols regarding the included exercises, intervention dosimetry, application times, and sociodemographic and clinical characteristics of the included pa-tients. Homogeneity was verified in the criteria for the progression of the interventions in the protocols. Also, the level of consciousness and peripheral muscle strength were the most used criteria. Conclusion: From this review, an EMP was developed for an adult ICU based on levels of progression of interventions, based on clinical characteristics such as level of sedation, need for invasive ventilatory support, cognitive level, and functionality.
Asunto(s)
Unidades de Cuidados Intensivos/tendencias , Análisis de SupervivenciaRESUMEN
BACKGROUND: Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis. METHODS: We performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit. RESULTS: We performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79-1.21] and SRU was 1.15 [IQR: 0.95-1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18-1.88] vs. 1.7 [IQR: 1.36-2.00]) and nursing workload (168 hours [IQR: 168-291] vs 396 hours [IQR: 336-672]) but higher nurses per bed ratio (2.02 [1.16-2.48] vs. 1.71 [1.43-2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the "most efficient" quadrant. CONCLUSION: Data envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency.
Asunto(s)
Benchmarking/métodos , Eficiencia Organizacional/tendencias , Unidades de Cuidados Intensivos/tendencias , Brasil , Análisis de Datos , Hospitalización , Humanos , Enfermeras y Enfermeros , Médicos , Estudios Retrospectivos , Rendimiento Laboral/tendencias , Recursos Humanos , Carga de TrabajoRESUMEN
The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC.
Asunto(s)
COVID-19/epidemiología , Capacidad de Camas en Hospitales/estadística & datos numéricos , Hospitalización/tendencias , Unidades de Cuidados Intensivos/tendencias , Pandemias/estadística & datos numéricos , Atención a la Salud , Predicción , Humanos , North Carolina/epidemiologíaRESUMEN
BACKGROUND: COVID-19 is primarily a respiratory disease; however, there is also evidence that it causes endothelial damage in the microvasculature of several organs. The aim of the present study is to characterize in vivo the microvascular reactivity in peripheral skeletal muscle of severe COVID-19 patients. METHODS: This is a prospective observational study carried out in Spain, Mexico and Brazil. Healthy subjects and severe COVID-19 patients admitted to the intermediate respiratory (IRCU) and intensive care units (ICU) due to hypoxemia were studied. Local tissue/blood oxygen saturation (StO2) and local hemoglobin concentration (THC) were non-invasively measured on the forearm by near-infrared spectroscopy (NIRS). A vascular occlusion test (VOT), a three-minute induced ischemia, was performed in order to obtain dynamic StO2 parameters: deoxygenation rate (DeO2), reoxygenation rate (ReO2), and hyperemic response (HAUC). In COVID-19 patients, the severity of ARDS was evaluated by the ratio between peripheral arterial oxygen saturation (SpO2) and the fraction of inspired oxygen (FiO2) (SF ratio). RESULTS: Healthy controls (32) and COVID-19 patients (73) were studied. Baseline StO2 and THC did not differ between the two groups. Dynamic VOT-derived parameters were significantly impaired in COVID-19 patients showing lower metabolic rate (DeO2) and diminished endothelial reactivity. At enrollment, most COVID-19 patients were receiving invasive mechanical ventilation (MV) (53%) or high-flow nasal cannula support (32%). Patients on MV were also receiving sedative agents (100%) and vasopressors (29%). Baseline StO2 and DeO2 negatively correlated with SF ratio, while ReO2 showed a positive correlation with SF ratio. There were significant differences in baseline StO2 and ReO2 among the different ARDS groups according to SF ratio, but not among different respiratory support therapies. CONCLUSION: Patients with severe COVID-19 show systemic microcirculatory alterations suggestive of endothelial dysfunction, and these alterations are associated with the severity of ARDS. Further evaluation is needed to determine whether these observations have prognostic implications. These results represent interim findings of the ongoing HEMOCOVID-19 trial. Trial registration ClinicalTrials.gov NCT04689477 . Retrospectively registered 30 December 2020.
Asunto(s)
COVID-19/fisiopatología , Unidades de Cuidados Intensivos/tendencias , Microvasos/fisiopatología , Unidades de Cuidados Respiratorios/tendencias , Síndrome de Dificultad Respiratoria/fisiopatología , Índice de Severidad de la Enfermedad , Adulto , Anciano , Brasil/epidemiología , COVID-19/diagnóstico , COVID-19/epidemiología , Femenino , Humanos , Masculino , México/epidemiología , Microcirculación/fisiología , Persona de Mediana Edad , Músculo Esquelético/irrigación sanguínea , Músculo Esquelético/fisiopatología , Estudios Prospectivos , Síndrome de Dificultad Respiratoria/diagnóstico , Síndrome de Dificultad Respiratoria/epidemiología , España/epidemiologíaRESUMEN
BACKGROUND: Extracorporeal membrane oxygenation (ECMO) was frequently used to treat patients with severe coronavirus disease-2019 (COVID-19)-associated acute respiratory distress (ARDS) during the initial outbreak. Care of COVID-19 patients evolved markedly during the second part of 2020. Our objective was to compare the characteristics and outcomes of patients who received ECMO for severe COVID-19 ARDS before or after July 1, 2020. METHODS: We included consecutive adults diagnosed with COVID-19 in Paris-Sorbonne University Hospital Network ICUs, who received ECMO for severe ARDS until January 28, 2021. Characteristics and survival probabilities over time were estimated during the first and second waves. Pre-ECMO risk factors predicting 90-day mortality were assessed using multivariate Cox regression. RESULTS: Characteristics of the 88 and 71 patients admitted, respectively, before and after July 1, 2020, were comparable except for older age, more frequent use of dexamethasone (18% vs. 82%), high-flow nasal oxygenation (19% vs. 82%) and/or non-invasive ventilation (7% vs. 37%) after July 1. Respective estimated probabilities (95% confidence intervals) of 90-day mortality were 36% (27-47%) and 48% (37-60%) during the first and the second periods. After adjusting for confounders, probability of 90-day mortality was significantly higher for patients treated after July 1 (HR 2.27, 95% CI 1.02-5.07). ECMO-related complications did not differ between study periods. CONCLUSIONS: 90-day mortality of ECMO-supported COVID-19-ARDS patients increased significantly after July 1, 2020, and was no longer comparable to that of non-COVID ECMO-treated patients. Failure of prolonged non-invasive oxygenation strategies before intubation and increased lung damage may partly explain this outcome.
Asunto(s)
COVID-19/mortalidad , Oxigenación por Membrana Extracorpórea/mortalidad , Oxigenación por Membrana Extracorpórea/tendencias , Hospitalización/tendencias , Síndrome de Dificultad Respiratoria/mortalidad , Índice de Severidad de la Enfermedad , Adulto , COVID-19/terapia , Estudios de Cohortes , Femenino , Estudios de Seguimiento , Humanos , Unidades de Cuidados Intensivos/tendencias , Masculino , Persona de Mediana Edad , Mortalidad/tendencias , Paris/epidemiología , Síndrome de Dificultad Respiratoria/terapia , Resultado del TratamientoRESUMEN
The positivity rate of testing is currently used both as a benchmark of testing adequacy and for assessing the evolution of the COVID-19 pandemic. However, since the former is a prerequisite for the latter, its interpretation is often conflicting. We propose as a benchmark for COVID-19 testing effectiveness a new metric, termed 'Severity Detection Rate' (SDR), that represents the daily needs for new Intensive Care Unit (ICU) admissions, per 100 cases detected (t - i) days ago, per 10,000 tests performed (t - i) days ago. Based on the announced COVID-19 monitoring data in Greece from May 2020 until August 2021, we show that beyond a certain threshold of daily tests, SDR reaches a plateau of very low variability that begins to reflect testing adequacy. Due to the stabilization of SDR, it was possible to predict with great accuracy the daily needs for new ICU admissions, 12 days ahead of each testing data point, over a period of 10 months, with Pearson r = 0.98 (p = 10-197), RMSE = 7.16. We strongly believe that this metric will help guide the timely decisions of both scientists and government officials to tackle pandemic spread and prevent ICU overload by setting effective testing requirements for accurate pandemic monitoring. We propose further study of this novel metric with data from more countries to confirm the validity of the current findings.
Asunto(s)
Benchmarking/métodos , COVID-19/epidemiología , Admisión del Paciente/tendencias , COVID-19/inmunología , COVID-19/metabolismo , Prueba de COVID-19/métodos , Prueba de COVID-19/tendencias , Grecia/epidemiología , Humanos , Unidades de Cuidados Intensivos/tendencias , Modelos Teóricos , Pandemias/prevención & control , SARS-CoV-2/inmunología , SARS-CoV-2/patogenicidadRESUMEN
The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, , 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.
Asunto(s)
COVID-19/epidemiología , Predicción/métodos , Unidades de Cuidados Intensivos/tendencias , Área Bajo la Curva , Biología Computacional/métodos , Cuidados Críticos/estadística & datos numéricos , Cuidados Críticos/tendencias , Dinamarca/epidemiología , Hospitalización/tendencias , Hospitales/tendencias , Humanos , Aprendizaje Automático , Pandemias , Curva ROC , Respiración Artificial/estadística & datos numéricos , Respiración Artificial/tendencias , Estudios Retrospectivos , Medición de Riesgo/métodos , Factores de Riesgo , SARS-CoV-2/patogenicidad , Ventiladores Mecánicos/tendenciasRESUMEN
BACKGROUND: Data of critically ill COVID-19 patients are being evaluated worldwide, not only to understand the various aspects of the disease and to refine treatment strategies but also to improve clinical decision-making. For clinical decision-making in particular, prognostic factors of a lethal course of the disease would be highly relevant. METHODS: In this retrospective cohort study, we analyzed the first 59 adult critically ill Covid-19 patients treated in one of the intensive care units of the University Medical Center Regensburg, Germany. Using uni- and multivariable regression models, we extracted a set of parameters that allowed for prognosing in-hospital mortality. RESULTS: Within the cohort, 19 patients died (mortality 32.2%). Blood pH value, mean arterial pressure, base excess, troponin, and procalcitonin were identified as highly significant prognostic factors of in-hospital mortality. However, no significant differences were found for other parameters expected to be relevant prognostic factors, like low arterial partial pressure of oxygen or high lactate levels. In the multivariable logistic regression analysis, the pH value and the mean arterial pressure turned out to be the most influential prognostic factors for a lethal course.