Patient characteristics, clinical course and factors associated to ICU mortality in critically ill patients infected with SARS-CoV-2 in Spain: A prospective, cohort, multicentre study

Abstract Background The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

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Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2,022 critically ill patients with COVID-19 in Spain

10.21203/rs.3.rs-125422/v2 ◽

2021 ◽

Author(s):

Alejandro Rodríguez ◽

Manuel Ruiz Botella ◽

Ignacio Matín-Loeches ◽

María Jiménez Herrera ◽

Jordi Solé-Violan ◽

...

Keyword(s):

Risk Factors ◽

Intensive Care ◽

Critically Ill ◽

Mortality Risk ◽

Critically Ill Patients ◽

Clustering Analysis ◽

Older Age ◽

Icu Mortality ◽

Factors Associated ◽

Clinical Phenotypes

Abstract Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results: The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A(mild) phenotype (537;26.7%) included older age (<65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623,30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C(severe) phenotype was the most common (857;42.5%) and was characterized by the interplay of older age (>65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.Conclusion: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

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Factors associated with physicians’ predictions of six-month mortality in critically ill patients

Journal of the Intensive Care Society ◽

10.1177/1751143719859761 ◽

2019 ◽

Vol 21 (3) ◽

pp. 202-209

Author(s):

Bruno L Ferreyro ◽

Michael O Harhay ◽

Michael E Detsky

Keyword(s):

Critically Ill ◽

Critically Ill Patients ◽

Patient Characteristics ◽

Key Factors ◽

Patient Factors ◽

Factors Associated ◽

Patient Level ◽

Lower Confidence ◽

Apache Iii ◽

Accuracy Of Prediction

Background Physician's estimates of a patient's prognosis are an important component in shared decision-making. However, the variables influencing physician's judgments are not well understood. We aimed to determine which physician and patient factors are associated with physicians' predictions of critically ill patients' six-month mortality and the accuracy and confidence of these predictions. Methods Prospective cohort study evaluating physicians' predictions of six-month mortality. Using univariate and multivariable generalized estimating equations, we assessed the association between baseline physician and patient characteristics with predictions of six-month death, as well as accuracy and confidence of these predictions. Results Our cohort was comprised 300 patients and 47 physicians. Physicians were asked to predict if patients would be alive or dead at six months and to report their confidence in these predictions. Physicians predicted that 99 (33%) patients would die. The key factors associated with both the direction and accuracy of prediction were older age of the patient, the presence of malignancy, being in a medical ICU, and higher APACHE III scores. The factors associated with lower confidence included older physician age, being in a medical ICU and higher APACHE III score. Conclusions Patient level factors are associated with predictions of mortality at six months. The accuracy and confidence of the predictions are associated with both physician and patients' factors. The influence of these factors should be considered when physicians reflect on how they make predictions for critically ill patients.

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Deploying Machine Learning to Validate Clinical Phenotypes and Factors Associated with Mortality risk in 2,022 Critically Ill Patients with COVID-19 in Spain

10.21203/rs.3.rs-125422/v1 ◽

2020 ◽

Author(s):

Alejandro Rodríguez ◽

Manuel Ruiz Botella ◽

Ignacio Matín-Loeches ◽

María Jiménez Herrera ◽

Jordi Solé-Violan ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Intensive Care ◽

Critically Ill ◽

Mortality Risk ◽

Critically Ill Patients ◽

Older Age ◽

Icu Mortality ◽

Factors Associated ◽

Clinical Phenotypes

Abstract Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. The objective was to analyze patient’s factors associated with mortality risk and utilize a Machine Learning(ML) to derive clinical COVID-19 phenotypes. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. An unsupervised clustering analysis was applied to determine presence of phenotypes. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results: The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the C(severe) phenotype was the most common (857;42.5%) and was characterized by the interplay of older age (>65 years), high severity of illness and a higher likelihood of development shock. The A(mild) phenotype (537;26.7%) included older age (>65 years), fewer abnormal laboratory values and less development of complications and B (moderate) phenotype (623,30.8%) had similar characteristics of A phenotype but were more likely to present shock. Crude ICU mortality was 45.4%, 25.0% and 20.3% for the C, B and A phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.Conclusion: The presented ML model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice. Funding: None

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Clinical course after identification of new-onset atrial fibrillation in critically ill patients: a multicenter prospective cohort study

10.21203/rs.2.19880/v1 ◽

2020 ◽

Author(s):

Takuo Yoshida ◽

Shigehiko Uchino ◽

Yusuke Sasabuchi

Keyword(s):

Atrial Fibrillation ◽

Cohort Study ◽

Adverse Events ◽

Hospital Mortality ◽

Critically Ill ◽

Critically Ill Patients ◽

Prospective Cohort ◽

Clinical Course ◽

Onset Atrial Fibrillation ◽

New Onset

Abstract BackgroundNew-onset atrial fibrillation (AF) in critically ill patients is reportedly associated with poor outcomes. However, epidemiological data in intensive care units (ICUs) after new-onset AF identification are lacking. This study aimed to describe the clinical course after the identification of new-onset atrial fibrillation.Methods This prospective cohort study of 32 ICUs in Japan during 2017-2018 enrolled adult patients with new-onset AF. We collected data on patient comorbidities, physiological information before and at the AF onset, interventions, transition of cardiac rhythms, adverse events, and in-hospital death and stroke.Results The incidence of new-onset AF in the ICU was 2.9% (423 patients). At the AF onset, the mean atrial pressure decreased, and the heart rate increased. Sinus rhythm returned spontaneously in 84 patients (20%), and 328 patients (78%) were treated with pharmacological interventions (rate-control drugs, 67%; rhythm-control drugs, 34%). In total, 173 (40%) patients were treated with anticoagulants. Adverse events were more frequent in nonsurvivors than in survivors (bleeding: 14% vs 5%; p = 0.002, arrythmia other than AF: 6% vs 2%; p = 0.048). There were 92 (22%) and 15 patients (4%) patients who continued to have AF at 48 hours and 168 hours after onset, respectively. The hospital mortality rate of those patients were 32% and 60%, respectively. The overall hospital mortality was 26%, and the incidence of in-hospital stroke was 4.5%.Conclusions Although the proportion of patients continued to have AF within 168 hours decreased with various treatments, these patients were at a high risk of death. Moreover, adverse events occurred more frequently in nonsurvivors than in survivors. Further research to assess the management of new-onset AF in critically ill patients is strongly warranted.

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Factors associated with renal Doppler resistive index in critically ill patients: a prospective cohort study

Annals of Intensive Care ◽

10.1186/s13613-019-0500-4 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 6

Author(s):

Raphael A. G. Oliveira ◽

Pedro V. Mendes ◽

Marcelo Park ◽

Leandro U. Taniguchi

Keyword(s):

Cohort Study ◽

Critically Ill ◽

Prospective Cohort Study ◽

Critically Ill Patients ◽

Prospective Cohort ◽

Resistive Index ◽

Factors Associated ◽

Renal Doppler

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Treatment of a Critically Ill COVID-19 Patient with the Seraph 100 Microbind Affinity Filter

TH Open ◽

10.1055/s-0041-1727121 ◽

2021 ◽

Vol 05 (02) ◽

pp. e134-e138

Author(s):

Anke Pape ◽

Jan T. Kielstein ◽

Tillman Krüger ◽

Thomas Fühner ◽

Reinhard Brunkhorst

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Critically Ill ◽

Critically Ill Patients ◽

Randomized Trials ◽

Clinical Course ◽

Pharmacological Therapy ◽

The European Union ◽

Positive Effects ◽

Special Patient

AbstractThe coronavirus disease 2019 (COVID-19) pandemic has a serious impact on health and economics worldwide. Even though the majority of patients present with moderate and mild symptoms, yet a considerable portion of patients need to be treated in the intensive care unit. Aside from dexamethasone, there is no established pharmacological therapy. Moreover, some of the currently tested drugs are contraindicated for special patient populations like remdesivir for patients with severely impaired renal function. On this background, several extracorporeal treatments are currently explored concerning their potential to improve the clinical course and outcome of critically ill patients with COVID-19. Here, we report the use of the Seraph 100 Microbind Affinity filter, which is licensed in the European Union for the removal of pathogens. Authorization for emergency use in patients with COVID-19 admitted to the intensive care unit with confirmed or imminent respiratory failure was granted by the U.S. Food and Drug Administration on April 17, 2020.A 53-year-old Caucasian male with a severe COVID-19 infection was treated with a Seraph Microbind Affinity filter hemoperfusion after clinical deterioration and commencement of mechanical ventilation. The 70-minute treatment at a blood flow of 200 mL/minute was well tolerated, and the patient was hemodynamically stable. The hemoperfusion reduced D-dimers dramatically.This case report suggests that the use of Seraph 100 Microbind Affinity filter hemoperfusion might have positive effects on the clinical course of critically ill patients with COVID-19. However, future prospective collection of data ideally in randomized trials will have to confirm whether the use of Seraph 100 Microbind Affinity filter hemoperfusion is an option of the treatment for COVID-19.

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Frailty status among older critically ill patients with severe acute kidney injury

Critical Care ◽

10.1186/s13054-021-03510-y ◽

2021 ◽

Vol 25 (1) ◽

Author(s):

William Beaubien-Souligny ◽

Alan Yang ◽

Gerald Lebovic ◽

Ron Wald ◽

Sean M. Bagshaw

Keyword(s):

Acute Kidney Injury ◽

Critically Ill ◽

Critically Ill Patients ◽

Multivariable Analysis ◽

Kidney Injury ◽

Multivariable Logistic Regression Analysis ◽

Clinical Frailty Scale ◽

Factors Associated ◽

Frailty Status ◽

Physiological Variables

Abstract Background Frailty status among critically ill patients with acute kidney injury (AKI) is not well described despite its importance for prognostication and informed decision-making on life-sustaining therapies. In this study, we aim to describe the epidemiology of frailty in a cohort of older critically ill patients with severe AKI, the outcomes of patients with pre-existing frailty before AKI and the factors associated with a worsening frailty status among survivors. Methods This was a secondary analysis of a prospective multicentre observational study that enrolled older (age > 65 years) critically ill patients with AKI. The clinical frailty scale (CFS) score was captured at baseline, at 6 months and at 12 months among survivors. Frailty was defined as a CFS score of ≥ 5. Demographic, clinical and physiological variables associated with frailty as baseline were described. Multivariable Cox proportional hazard models were constructed to describe the association between frailty and 90-day mortality. Demographic and clinical factors associated with worsening frailty status at 6 months and 12 months were described using multivariable logistic regression analysis and multistate models. Results Among the 462 patients in our cohort, median (IQR) baseline CFS score was 4 (3–5), with 141 (31%) patients considered frail. Pre-existing frailty was associated with greater hazard of 90-day mortality (59% (n = 83) for frail vs. 31% (n = 100) for non-frail; adjusted hazards ratio [HR] 1.49; 95% CI 1.11–2.01, p = 0.008). At 6 months, 68 patients (28% of survivors) were frail. Of these, 57% (n = 39) were not classified as frail at baseline. Between 6 and 12 months of follow-up, 9 (4% of survivors) patients transitioned from a frail to a not frail status while 10 (4% of survivors) patients became frail and 11 (5% of survivors) patients died. In multivariable analysis, age was independently associated with worsening CFS score from baseline to 6 months (adjusted odds ratio [OR] 1.08; 95% CI 1.03–1.13, p = 0.003). Conclusions Pre-existing frailty is an independent risk factor for mortality among older critically ill patients with severe AKI. A substantial proportion of survivors experience declining function and worsened frailty status within one year.

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