scholarly journals Characteristics and Prognosis of Abdominal or Thoracic Aortic Aneurysm Patients Admitted to Intensive Care Units After Surgery Therapy: A Multicenter Retrospective Observational Study

2020 ◽  
Author(s):  
Qinchang Chen ◽  
Qingui Chen ◽  
Yanchen Ye ◽  
Ridong Wu ◽  
Shenming Wang ◽  
...  
Keyword(s):  
Intensive Care ◽  
Aortic Aneurysm ◽  
Roc Curve ◽  
Large Scale ◽  
Thoracic Aortic Aneurysm ◽  
Elective Surgery ◽  
Predictive Performance ◽  
Research Database ◽  
Icu Mortality ◽  
Factors Associated

Abstract Background: Subsequent intensive care unit (ICU) admissions postoperatively are not rare for patients with abdominal or thoracic aortic aneurysm (AAA or TAA), but a large-scale investigation on these patients is absent. The study aimed to investigate the characteristics and prognosis of AAA or TAA patients admitted to ICU postoperatively.Methods: Patients admitted to ICU postoperatively with a primary diagnosis of AAA or TAA were screened in the eICU Collaborative Research Database, which contained data from multiple ICUs throughout the continental United States in 2014 and 2015. Baseline characteristics and comorbidities and were investigated and factors associated with ICU mortality were explored using univariable logistic regression. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the prognosis predictive performance of the widely used severity scoring system APACHE IVa.Results: 974 patients including 677 AAA and 297 TAA patients admitted to ICU postoperatively were included finally. Compared with TAA, AAA patients had a significantly higher median age (72 versus 64 years). 10.19% AAA and 2.36% TAA patients suffered from rupture of aortic aneurysm, and 89.07% AAA and 84.51% TAA patients underwent elective surgery. Hypertension requiring treatment was the most common comorbidity (57.31% for AAA and 61.95% for TAA). TAA patients had significantly higher ICU mortality (9.43% versus 2.36%) than AAA. Several factors were found to be significantly associated with ICU mortality, including urgent surgery, rupture of aortic aneurysm, TAA, and a higher APACHE IVa score on ICU admission. APACHE IVa showed a good predictive performance for ICU mortality with an area under the ROC curve of 0.9176 (95% CI 0.8789-0.9390).Conclusions: Prognosis of aortic aneurysm patients admitted to ICU postoperatively is yet to improve, and factors associated with prognosis are mainly related to the condition itself. APACHE IVa can be used for prognosis prediction.

Abstract 15129: Outcomes Related to Age of Elective Surgery and Age of Dissection Within Genetically Triggered Aneurysm Conditions: The Gentac Experience

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Kathryn W Holmes ◽  
Scott A Lemaire ◽  
Richard B Devereux ◽  
William J Ravekes ◽  
Shaine A Morris ◽  
...  
Keyword(s):  
Aortic Aneurysm ◽  
Thoracic Aortic Aneurysm ◽  
Elective Surgery ◽  
Aortic Surgery ◽  
Reference Population ◽  
Ehlers Danlos Syndrome ◽  
Aortic Replacement ◽  
Fourth Decade ◽  
Ascending Aortic Replacement ◽  
Aortic Dissections

Introduction: The GenTAC Registry ( G enetically Triggered T horacic A ortic Aneurysms and Cardiovascular C onditions) followed patients with aortopathies over 8 years among 8 centers with the goal of evaluating cardiovascular outcomes. Methods: Enrollment initiated in 2007, and data were collected until 2015. We included diagnoses with >100 participants: Bicuspid aortic valve with aneurysm (BAV, n=879), Marfan syndrome (MFS, n=861), Familial thoracic aortic aneurysm or dissection (FTAAD, n=378), Other thoracic aortic aneurysm at < 50 years of age (Other<50, n=524), Turner syndrome (TS, n=298), Vascular Ehlers Danlos syndrome (VEDS, n=149), and Loeys-Dietz syndrome (LDS, n=121). We identified patients who underwent elective ascending aortic replacement, total unique dissections, and time to first dissection. With MFS as a reference population and adjusted for sex, endpoints were analyzed by a Firth penalized Cox-PH regression model to account for diagnosis groups with low event numbers. Results: LDS participants at a mean age of (24.5 ± 15.0y) were youngest at elective aortic surgery followed by MFS (32.3 ±12.3y), TS (37.6 ±13.6y), VEDS (35.0 ±SD 7.4y), Other<50 (40.3 ±SD 10.3y), FTAAD (42.9 ±14.2y), and BAV(49.4 ± 13.8 y). Dissections were reported in all diagnosis groups with a total of 472 unique dissections in 3210 patients (14%). Mean age at first dissection was in the third decade for LDS, TS, MFS, VEDS and in the fourth decade for BAV, FTAD, and Other<50. Adjusted hazard ratio for time to first dissection was higher in LDS, 1.77 (95%CI 1.14- 2.77), compared to MFS and other diagnosis groups (Figure 1). Conclusions: Reported aortic dissections were prominent in the GenTAC cohort. Despite elective surgery at a younger age, LDS patients had a higher hazard risk of dissection compared to other diagnosis groups.

scholarly journals Factors Associated with Acute Community-Acquired Pyelonephritis Caused by Extended-Spectrum β-Lactamase-Producing Escherichia coli

2021 ◽  
Vol 10 (21) ◽  
pp. 5192
Author(s):  
Mónica Romero Nieto ◽  
Sara Maestre Verdú ◽  
Vicente Gil ◽  
Carlos Pérez Barba ◽  
Jose Antonio Quesada Rico ◽  
...  
Keyword(s):  
Roc Curve ◽  
Goodness Of Fit ◽  
Logistic Models ◽  
Predictive Performance ◽  
Demographic Characteristics ◽  
E Coli ◽  
Factors Associated ◽  
Extended Spectrum ◽  
Male Sex ◽  
Tract Infections

This study aimed to identify the factors associated with the presence of extended-spectrum ß-lactamase-(ESBL) in patients with acute community-acquired pyelonephritis (APN) caused by Escherechia coli (E. coli), with a view of optimising empirical antibiotic therapy in this context. We performed a retrospective analysis of patients with community-acquired APN and confirmed E. coli infection, collecting data related to demographic characteristics, comorbidities, and treatment. The associations of these factors with the presence of ESBL were quantified by fitting multivariate logistic models. Goodness-of-fit and predictive performance were measured using the ROC curve. We included 367 patients of which 51 presented with ESBL, of whom 90.1% had uncomplicated APN, 56.1% were women aged ≤55 years, 33.5% had at least one mild comorbidity, and 12% had recently taken antibiotics. The prevalence of ESBL-producing E. coli was 13%. In the multivariate analysis, the factors independently associated with ESBL were male sex (OR 2.296; 95% CI 1.043–5.055), smoking (OR 4.846, 95% CI 2.376–9.882), hypertension (OR 3.342, 95% CI 1.423–7.852), urinary incontinence (OR 2.291, 95% CI 0.689–7.618) and recurrent urinary tract infections (OR 4.673, 95% CI 2.271–9.614). The area under the ROC curve was 0.802 (IC 95% 0.7307–0.8736), meaning our model can correctly classify an individual with ESBL-producing E. coli infection in 80.2% of cases.

Intensive Care Utilization during Hospital Admission for Delivery

2000 ◽  
Vol 92 (6) ◽  
pp. 1537-1544 ◽  
Author(s):  
Sumedha Panchal ◽  
Amelia M. Arria ◽  
Andrew P. Harris
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Hospital Admission ◽  
Hospital Setting ◽  
Mortality Rates ◽  
Cerebrovascular Event ◽  
Icu Mortality ◽  
Factors Associated ◽  
Icu Admission ◽  
Common Risk Factors

Background During childbirth, the maternal need for intensive care unit (ICU) services is not well-defined. This information could influence the decision whether to incorporate ICU services into the labor and delivery suite. Methods This study reports (1) ICU use and mortality rates in a statewide population of obstetric patients during their hospital admission for childbirth, and (2) the risk factors associated with ICU admission and mortality. A case-control design using patient records from a state-maintained anonymous database for the years 1984-1997 was used. Outcome variables included ICU use and mortality rates. Results Of the 822,591 hospital admissions for delivery of neonates during the study period, there were 1,023 ICU admissions (0.12%) and 34 ICU deaths (3.3%). Age, race, hospital type, volume of deliveries, and source of admission independently and in combination were associated with ICU admission (P &lt; 0.05). The most common risk factors associated with ICU admission included cesarean section, preeclampsia or eclampsia, and postpartum hemorrhage (P &lt; 0.001). Black race, high hospital volume of deliveries, and longer duration of ICU stay were associated with ICU mortality (P &lt; 0.05). The most common risk factors associated with ICU mortality included pulmonary complications, shock, cerebrovascular event, and drug dependence (P &lt; 0.05). Conclusions This study shows that ICU use and mortality rate during hospital admission for delivery of a neonate is low. These results may influence the location of perinatal ICU services in the hospital setting.

scholarly journals Prognostic Factors to Predict ICU Mortality in Patients with ARDS Who Received Early and Prolonged Prone Positioning Therapy

2021 ◽  
Author(s):  
Po-Hsin Lee ◽  
Pin-Kuei Fu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prognostic Factors ◽  
Mortality Risk ◽  
Risk Group ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Prone Positioning ◽  
Icu Mortality ◽  
Factors Associated

Abstract Background: Early and prolonged prone positioning (PP) could reduce the mortality in patients with moderate to severe ARDS, however, factors associated with mortality in the intensive care unit (ICU) remain unclear. The aim of this study is to identified factors associated with mortality and create the prognostic score in patients with ARDS who underwent early and prolonged PP. Methods: This retrospective study included patients with moderate to severe ARDS admitted to the intensive care unit (ICU) from January 2015 to June 2018 in a tertiary referral center in Taiwan and who received early and prolonged PP. Demographic data, disease severity score, comorbidities, and clinical outcomes were recorded. Univariate and multivariate regression models were used to estimate the odds ratio (OR) of ICU mortality. Receiver operating characteristic (ROC) curve analysis were performed to identify the cutoff value of parameters. Results: A total of 116 patients were enrolled. In the multivariate analysis, three factors were significantly associated with mortality: renal replacement therapy (RRT; OR: 3.38, 1.55–7.36), malignant comorbidity (OR: 7.42, 2.06–26.70), and noninfluenza-related ARDS (OR: 3.78, 1.07–13.29). Age, RRT, noninfluenza-related ARDS, malignant comorbidity, and APACHE II score were included in a composite prone score, which demonstrated an area under the curve of 0.816 for predicting mortality risk. The mortality risk in ICU was 27.1% in the low-risk group (prone score: 0–2) and 84.2% in the high-risk group (prone score: 3–5). Conclusions: For patients with moderate to severe ARDS even receiving early and prolonged PP in ICU, poor prognostic factors were age, RRT, malignant comorbidity, noninfluenza-related ARDS, and higher APACHE II score. High mortality should be informed to the family of patient if their prone score was more than 3 points.

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 Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jiuzhou Jiang ◽  
Hao Pan ◽  
Mobai Li ◽  
Bao Qian ◽  
Xianfeng Lin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Roc Curve ◽  
Large Scale ◽  
Learning Algorithm ◽  
Gradient Boosting ◽  
Research Database ◽  
Training Set ◽  
Survival Status ◽  
Extreme Gradient Boosting ◽  
Validation Set

AbstractOsteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve [AUC] = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings.

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 ICU-Mortality in Old and Very Old Patients Suffering From Sepsis and Septic Shock

2021 ◽  
Vol 8 ◽  
Author(s):  
Raphael Romano Bruno ◽  
Bernhard Wernly ◽  
Behrooz Mamandipoor ◽  
Richard Rezar ◽  
Stephan Binnebössel ◽  
...  
Keyword(s):  
Septic Shock ◽  
Intensive Care ◽  
Length Of Stay ◽  
Research Database ◽  
Icu Mortality ◽  
Icu Patients ◽  
Old Patients ◽  
Very Old ◽  
Sepsis And Septic Shock ◽  
Very Old Patients

Purpose: Old (&gt;64 years) and very old (&gt;79 years) intensive care patients with sepsis have a high mortality. In the very old, the value of critical care has been questioned. We aimed to compare the mortality, rates of organ support, and the length of stay in old vs. very old patients with sepsis and septic shock in intensive care.Methods: This analysis included 9,385 patients, from the multi-center eICU Collaborative Research Database, with sepsis; 6184 were old (aged 65–79 years), and 3,201 were very old patients (aged 80 years and older). A multi-level logistic regression analysis was used to fit three sequential regression models for the binary primary outcome of ICU mortality. A sensitivity analysis in septic shock patients (n = 1054) was also conducted.Results: In the very old patients, the median length of stay was shorter (50 ± 67 vs. 56 ± 72 h; p &lt; 0.001), and the rate of a prolonged ICU stay was lower (&gt;168 h; 9 vs. 12%; p &lt; 0.001) than the old patients. The mortality from sepsis was higher in very old patients (13 vs. 11%; p = 0.005), and after multi-variable adjustment being very old was associated with higher odds for ICU mortality (aOR 1.32, 95% CI 1.09–1.59; p = 0.004). In patients with septic shock, mortality was also higher in the very old patients (38 vs. 36%; aOR 1.50, 95% CI 1.10–2.06; p = 0.01).Conclusion: Very old ICU-patients suffer from a slightly higher ICU mortality compared with old ICU-patients. However, despite the statistically significant differences in mortality, the clinical relevance of such minor differences seems to be negligible.

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