scholarly journals Epidemiology, species distribution, antifungal susceptibility and mortality risk factors of candidemia among critically ill patients: a retrospective study from 2011 to 2017 in a teaching hospital in China

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Zengli Xiao ◽  
Qi Wang ◽  
Fengxue Zhu ◽  
Youzhong An
Keyword(s):  
Risk Factors ◽  
Retrospective Study ◽  
Critically Ill ◽  
Teaching Hospital ◽  
Species Distribution ◽  
Mortality Risk ◽  
Critically Ill Patients ◽  
Antifungal Susceptibility

scholarly journals The incidence, risk factors and prognosis of acute kidney injury in severe and critically ill patients with COVID-19 in mainland China: a retrospective study

2020 ◽  
Author(s):  
Ling Sang ◽  
Sibei Chen ◽  
Xia Zheng ◽  
Weijie Guan ◽  
Zhihui Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Retrospective Study ◽  
Clinical Outcomes ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Right Heart Failure ◽  
Right Heart ◽  
Icu Admission ◽  
Incidence Risk

Abstract Background: Since the clinical correlates, prognosis and determinants of AKI in patients with Covid-19 remain largely unclear, we perform a retrospective study to evaluate the incidence, risk factors and prognosis of AKI in severe and critically ill patients with Covid-19.Methods: We reviewed medical records of all adult patients (>18 years) with laboratory-confirmed Covid-19 who were admitted to the intensive care unit (ICU) between January 23rd 2020 and April 6th 2020 at Wuhan JinYinTan Hospital and The First Affiliated Hospital of Guangzhou Medical University. The clinical data, including patient demographics, clinical symptoms and signs, laboratory findings, treatment [including respiratory supports, use of medications and continuous renal replacement therapy (CRRT)] and clinical outcomes, were extracted from the electronic records, and we access the incidence of AKI and the use of CRRT, risk factors for AKI, the outcomes of renal diseases, and the impact of AKI on the clinical outcomes.Results: Among 210 subjects, 131 were males (62.4%). The median age was 64 years (IQR: 56-71). Of 92 (43.8%) patients who developed AKI during hospitalization, 13 (14.1%), 15 (16.3%) and 64 (69.6%) patients were classified as stage 1, 2 and 3, respectively. 54 cases (58.7%) received CRRT. Age, sepsis, Nephrotoxic drug, IMV and elevated baseline Scr were associated with AKI occurrence. The renal recover during hospitalization among 16 AKI patients (17.4%), who had a significantly shorter time from admission to AKI diagnosis, lower incidence of right heart failure and higher P/F ratio. Of 210 patients, 93 patients deceased within 28 days of ICU admission. AKI stage 3, critical disease, greater age and minimum P/F <150mmHg independently associated with it.Conclusions: Among patients with Covid-19, the incidence of AKI was high. age , sepsis, nephrotoxic drug, IMV and baseline Scr were strongly associated with the development of AKI. Time from admission to AKI diagnosis, right heart failure and P/F ratio were independently associated with the potential of renal recovery. Finally, AKI KIDGO stage 3 independently predicted the risk of death within 28 days of ICU admission.

Survival in Critically Ill Patients with Acute Kidney Injury Treated with Early Hemodiafiltration

2012 ◽  
Vol 35 (12) ◽  
pp. 1039-1046 ◽  
Author(s):  
Nicolas Boussekey ◽  
Benoit Capron ◽  
Pierre-Yves Delannoy ◽  
Patrick Devos ◽  
Serge Alfandari ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Critically Ill ◽  
Mortality Risk ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Icu Patients ◽  
Impact On Survival ◽  
Baseline Characteristics ◽  
The Moment

Purpose Early renal replacement therapy (RRT) initiation should theoretically influence many physiological disorders related to acute kidney injury (AKI). Currently, there is no consensus about RRT timing in intensive care unit (ICU) patients. Methods We performed a retrospective analysis of all critically ill patients who received RRT in our ICU during a 3 year-period. Our goal was to identify mortality risk factors and if RRT initiation timing had an impact on survival. RRT timing was calculated from the moment the patient was classified as having acute kidney injury in the RIFLE classification. Results A hundred and ten patients received RRT. We identified four independent mortality risk factors: need for mechanical ventilation (OR = 12.82 (1.305 - 125.868, p = 0.0286); RRT initiation timing >16 h (OR = 5.66 (1.954 - 16.351), p = 0.0014); urine output on admission <500 ml/day (OR = 4.52 (1.666 - 12.251), p = 0.003); and SAPS II on admission >70 (OR = 3.45 (1.216 - 9.815), p = 0.02). The RRT initiation <16 h and RRT initiation >16 h groups presented the same baseline characteristics, except for more severe gravity scores and kidney failure in the early RRT group. Conclusions Early RRT in ICU patients with acute kidney injury or failure was associated with increased survival.

scholarly journals Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Alejandro Rodríguez ◽  
◽  
Manuel Ruiz-Botella ◽  
Ignacio Martín-Loeches ◽  
María Jimenez Herrera ◽  
...  
Keyword(s):  
Risk Factors ◽  
Critically Ill ◽  
Mortality Risk ◽  
Critically Ill Patients ◽  
Clustering Analysis ◽  
Unsupervised Clustering ◽  
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 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.

scholarly journals 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

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.

scholarly journals Clinical characteristics and risk factors for severe-critically ill COVID-19 adult patients in Jiangsu, China: a multiple-centered, retrospective study

2020 ◽  
Author(s):  
Jiangnan Zhao ◽  
Meiying Zhu ◽  
Xin Su ◽  
Mao Huang ◽  
Yi Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Retrospective Study ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Clinical Characteristics ◽  
Final Analysis ◽  
Adult Patients ◽  
Potential Risk Factors ◽  
Critically Illness ◽  
D Dimer

Abstract Background A number of reports have documented the clinical characteristics of patients with severe coronavirus disease 2019 (COVID-19) in Wuhan. Clinical features of severe-critically ill COVID-19 patients in Jiangsu, outside Wuhan, remains unknown. Methods This multi-centered retrospective study collected the information of 631 laboratory-confirmed COVID-19 patients hospitalized at 28 authorized hospitals in Jiangsu province between January 23, 2019 and March 13, 2020. Epidemiological and demographic information, clinical and radiological characteristics, laboratory results and treatment of these patients were analyzed. Results A total of 583 adult patients with laboratory-confirmed COVID-19 were enrolled for final analysis, including 84 severe-critically ill patients and 499 mild-moderate patients. Median age of the severe-critically ill patients was 57.0 years [interquartile range (IQR), 49.0-65.8], and 50 (59.5%) were males. Multisystemic laboratory abnormalities were observed on admission in severe-critically ill patients. The severe-critically ill patients showed more noticeable radiologic abnormalities and more coexisting health issues as compared to mild-moderate patients. Most of the severe-critically ill COVID-19 patients become deteriorated in two weeks after diagnosis. Age [odds ratio (OR) 1.08, 95% confidence interval (CI) (1.03-1.14)], D-dimer (OR 3.21, 95% CI 1.39-7.40), and lymphocytes (OR 0.28, 95% CI 0.04-0.88) were independently associated with the progression of severe-critically illness. Conclusions Older age, higher D-dimer levels and less lymphocyte counts on admission are potential risk factors for COVID-19 patients to develop into severe and critically illness. The results would help clinicians to identify high-risk patients in advance.

scholarly journals Bedside sonography by medicine residents in critically Ill patients: A retrospective study from a teaching hospital in India

2019 ◽  
Vol 8 (7) ◽  
pp. 2517
Author(s):  
Arvind Kumar ◽  
Manjit Mahendran ◽  
Kartik Gupta ◽  
Manasvini Bhatt ◽  
MaroofA Khan ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Critically Ill ◽  
Teaching Hospital ◽  
Critically Ill Patients

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

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

scholarly journals Epidemiology, clinical characteristics, and outcome of candidemia in critically ill patients in Germany: a single-center retrospective 10-year analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Maria Schroeder ◽  
Theresa Weber ◽  
Timme Denker ◽  
Sarah Winterland ◽  
Dominic Wichmann ◽  
...  
Keyword(s):  
Risk Factors ◽  
Critically Ill ◽  
Species Distribution ◽  
Critically Ill Patients ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Significant Risk ◽  
Antifungal Treatment ◽  
University Hospital ◽  
Single Center

Abstract Background Despite advances in the management of bloodstream infections (BSI) caused by Candida spp., the mortality still remains high in critically ill patients. The worldwide epidemiology of yeast-related BSI is subject to changing species distribution and resistance patterns, challenging antifungal treatment strategies. The aim of this single-center study was to identify predictors of mortality after 28 and 180 days in a cohort of mixed surgical and medical critically ill patients with candidemia. Methods Patients, who had been treated for laboratory-confirmed BSI caused by Candida spp. in one of 12 intensive care units (ICU) at a University hospital between 2008 and 2017, were retrospectively identified. We retrieved data including clinical characteristics, Candida species distribution, and antifungal management from electronic health records to identify risk factors for mortality at 28 and 180 days using a Cox regression model. Results A total of 391 patients had blood cultures positive for Candida spp. (incidence 4.8/1000 ICU admissions). The mortality rate after 28 days was 47% (n = 185) and increased to 60% (n = 234) after 180 days. Age (HR 1.02 [95% CI 1.01–1.03]), a history of liver cirrhosis (HR 1.54 [95% CI 1.07–2.20]), septic shock (HR 2.41 [95% CI 1.73–3.37]), the Sepsis-related Organ Failure Assessment score (HR 1.12 [95% CI 1.07–1.17]), Candida score (HR 1.25 [95% CI 1.11–1.40]), and the length of ICU stay at culture positivity (HR 1.01 [95% CI 1.00–1.01]) were significant risk factors for death at 180 days. Patients, who had abdominal surgery (HR 0.66 [95% CI 0.48–0.91]) and patients, who received adequate (HR 0.36 [95% CI 0.24–0.52]) or non-adequate (HR 0.31 [95% CI 0.16–0.62]) antifungal treatment, had a reduced mortality risk compared to medical admission and no antifungal treatment, respectively. Conclusions The mortality of critically ill patients with Candida BSI is high and is mainly determined by disease severity, multiorgan dysfunction, and antifungal management rather than species distribution and susceptibility. Our results underline the importance of timely treatment of candidemia. However, controversies remain on the optimal definition of adequate antifungal management.

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