scholarly journals Extubation Failure in Critically Ill COVID-19 Patients: Risk Factors and Impact on In-Hospital Mortality

2021 ◽  
pp. 088506662110202
Author(s):  
Filip Ionescu ◽  
Markie S. Zimmer ◽  
Ioana Petrescu ◽  
Edward Castillo ◽  
Paul Bozyk ◽  
...  
Keyword(s):  
Critically Ill ◽  
Extubation Failure ◽  
Adverse Outcomes ◽  
High Peep ◽  
Clinical Factors ◽  
Risk Models ◽  
Determining Factors ◽  
Unit Increase ◽  
Prognostic Implications ◽  
Competing Risk Models

Purpose: We sought to identify clinical factors that predict extubation failure (reintubation) and its prognostic implications in critically ill COVID-19 patients. Materials and Methods: Retrospective, multi-center cohort study of hospitalized COVID-19 patients. Multivariate competing risk models were employed to explore the rate of reintubation and its determining factors. Results: Two hundred eighty-one extubated patients were included (mean age, 61.0 years [±13.9]; 54.8% male). Reintubation occurred in 93 (33.1%). In multivariate analysis accounting for death, reintubation risk increased with age (hazard ratio [HR] 1.04 per 1-year increase, 95% confidence interval [CI] 1.02 -1.06), vasopressors (HR 1.84, 95% CI 1.04-3.60), renal replacement (HR 2.01, 95% CI 1.22-3.29), maximum PEEP (HR 1.07 per 1-unit increase, 95% CI 1.02 -1.12), paralytics (HR 1.48, 95% CI 1.08-2.25) and requiring more than nasal cannula immediately post-extubation (HR 2.19, 95% CI 1.37-3.50). Reintubation was associated with higher mortality (36.6% vs 2.1%; P < 0.0001) and risk of inpatient death after adjusting for multiple factors (HR 23.2, 95% CI 6.45-83.33). Prone ventilation, corticosteroids, anticoagulation, remdesivir and tocilizumab did not impact the risk of reintubation or death. Conclusions: Up to 1 in 3 critically ill COVID-19 patients required reintubation. Older age, paralytics, high PEEP, need for greater respiratory support following extubation and non-pulmonary organ failure predicted reintubation. Extubation failure strongly predicted adverse outcomes.

Estimation of Distribution Function in Bivariate Competing Risk Models

2006 ◽  
Vol 48 (3) ◽  
pp. 399-410 ◽  
Author(s):  
P. G. Sankaran ◽  
J. F. Lawless ◽  
B. Abraham ◽  
Ansa Alphonsa Antony
Keyword(s):  
Distribution Function ◽  
Competing Risk ◽  
Risk Models ◽  
Estimation Of Distribution ◽  
Competing Risk Models

Epidemiology and outcomes of obese critically ill patients in Australia and New Zealand

2020 ◽  
Vol 22 (1) ◽  
pp. 35-44
Author(s):  
Paul Secombe ◽  
◽  
Richard Woodman ◽  
Sean Chan ◽  
David Pilcher ◽  
...  
Keyword(s):  
New Zealand ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Adult Population ◽  
Diagnostic Category ◽  
Class Ii ◽  
Adverse Outcomes ◽  
Class I ◽  
Class I Obesity

OBJECTIVE: The apparent survival benefit of being overweight or obese in critically ill patients (the obesity paradox) remains controversial. Our aim is to report on the epidemiology and outcomes of obesity within a large heterogenous critically ill adult population. DESIGN: Retrospective observational cohort study. SETTING: Intensive care units (ICUs) in Australia and New Zealand. PARTICIPANTS: Critically ill patients who had both height and weight recorded between 2010 and 2018. OUTCOME MEASURES: Hospital mortality in each of five body mass index (BMI) strata. Subgroups analysed included diagnostic category, gender, age, ventilation status and length of stay. RESULTS: Data were available for 381 855 patients, 68% of whom were overweight or obese. Increasing level of obesity was associated with lower unadjusted hospital mortality: underweight (11.9%), normal weight (7.7%), overweight (6.4%), class I obesity (5.4%), and class II obesity (5.3%). After adjustment, mortality was lowest for patients with class I obesity (adjusted odds ratio, 0.78; 95% CI, 0.74– 0.82). Adverse outcomes with class II obesity were only seen in patients with cardiovascular and cardiac surgery ICU admission diagnoses, where mortality risk rose with progressively higher BMIs. CONCLUSION: We describe the epidemiology of obesity within a critically ill Australian and New Zealand population and confirm that some level of obesity is associated with lower mortality, both overall and across a range of diagnostic categories and important subgroups. Further research should focus on potential confounders such as nutritional status and the appropriateness of BMI in isolation as an anthropometric measure in critically ill patients.

The risk and prognosis of secondary primary malignancy in lung cancer: a population-based study

2021 ◽  
Author(s):  
Jia Hong ◽  
Rongrong Wei ◽  
Chuang Nie ◽  
Anastasiia Leonteva ◽  
Xu Han ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Population Based ◽  
Second Primary ◽  
Second Primary Malignancy ◽  
Risk Models ◽  
Primary Malignancy ◽  
Population Based Study ◽  
Competing Risk Models

Aim: To assess and predict risk and prognosis of lung cancer (LC) patients with second primary malignancy (SPM). Methods: LC patients diagnosed from 1992 to 2016 were obtained through the Surveillance, Epidemiology, and End Results database. Standardized incidence ratios were calculated to evaluate SPM risk. Cox regression and competing risk models were applied to assess the factors associated with overall survival, SPM development and LC-specific survival. Nomograms were built to predict SPM probability and overall survival. Results & conclusion: LC patients remain at higher risk of SPM even though the incidence declines. Patients with SPM have a better prognosis than patients without SPM. The consistency indexes for nomograms of SPM probability and overall survival are 0.605 (95% CI: 0.598–0.611) and 0.644 (95% CI: 0.638–0.650), respectively.

Urinary biomarkers predict progression and adverse outcomes ofacute kidney injury in critical illness

2021 ◽  
Author(s):  
Stephen Duff ◽  
Ruairi Irwin ◽  
Jean Maxime Cote ◽  
Lynn Redahan ◽  
Blaithin A McMahon ◽  
...  
Keyword(s):  
Critically Ill ◽  
Cystatin C ◽  
Critically Ill Patients ◽  
Kidney Injury ◽  
Adverse Outcomes ◽  
Urinary Biomarkers ◽  
High Morbidity ◽  
Diagnostic Analysis ◽  
Prognostic Analysis ◽  
Stage 1

Abstract Background Acute Kidney Injury (AKI) is common in hospitalized patients and is associated with high morbidity and mortality. The Dublin Acute Biomarker Group Evaluation (DAMAGE) Study is a prospective cohort study of critically ill patients (n = 717). We hypothesised that novel urinary biomarkers would predict progression of AKI and associated outcomes. Methods The primary (diagnostic) analysis assessed the ability of biomarkers levels at the time of early Stage 1 or2 AKI to predict progression to higher AKI Stage, RRT or Death within 7 days of ICU admission. In the secondary (prognostic) analysis, we investigated the association between biomarker levels and RRT or Death within 30 days. Results In total, 186 patients had an AKI within 7 days of admission. In the primary (diagnostic) analysis, eight of the 14 biomarkers were independently associated with progression. The best predictors were Cystatin C (aOR 5.2; 95% CI, 1.3-23.6), IL-18 (aOR 5.1; 95% CI, 1.8-15.7), Albumin (aOR 4.9; 95% CI, 1.5-18.3) and NGAL (aOR 4.6; 95% CI, 1.4-17.9). ROC and Net Reclassification Index analyses similarly demonstrated improved prediction by these biomarkers. In the secondary (prognostic) analysis of Stage 1-3 AKI cases, IL-18, NGAL, Albumin, and MCP-1 were also independently associated with RRT or Death within 30 days. Conclusions Among 14 novel urinary biomarkers assessed, Cystatin C, IL-18, Albumin and NGAL were the best predictors of Stage 1-2 AKI progression. These biomarkers, after further validation, may have utility to inform diagnostic and prognostic assessment and guide management of AKI in critically ill patients.

scholarly journals Association Between Patient Survival and Clinician Variability in Treatment Rates for Aortic Valve Stenosis

2021 ◽  
Author(s):  
J. Matthew Brennan ◽  
Angela Lowenstern ◽  
Paige Sheridan ◽  
Isabel J. Boero ◽  
Vinod H. Thourani ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Language Processing ◽  
Severe Aortic Stenosis ◽  
The United States ◽  
Risk Models ◽  
Transcatheter Aortic Valve ◽  
Competing Risk Models ◽  
The Impact ◽  
Potential Challenge

Background Patients with symptomatic severe aortic stenosis (ssAS) have a high mortality risk and compromised quality of life. Surgical/transcatheter aortic valve replacement (AVR) is a Class I recommendation, but it is unclear if this recommendation is uniformly applied. We determined the impact of managing cardiologists on the likelihood of ssAS treatment. Methods and Results Using natural language processing of Optum electronic health records, we identified 26 438 patients with newly diagnosed ssAS (2011–2016). Multilevel, multivariable Fine‐Gray competing risk models clustered by cardiologists were used to determine the impact of cardiologists on the likelihood of 1‐year AVR treatment. Within 1 year of diagnosis, 35.6% of patients with ssAS received an AVR; however, rates varied widely among managing cardiologists (0%, lowest quartile; 100%, highest quartile [median, 29.6%; 25th–75th percentiles, 13.3%–47.0%]). The odds of receiving AVR varied >2‐fold depending on the cardiologist (median odds ratio for AVR, 2.25; 95% CI, 2.14–2.36). Compared with patients with ssAS of cardiologists with the highest treatment rates, those treated by cardiologists with the lowest AVR rates experienced significantly higher 1‐year mortality (lowest quartile, adjusted hazard ratio, 1.22, 95% CI, 1.13–1.33). Conclusions Overall AVR rates for ssAS were low, highlighting a potential challenge for ssAS management in the United States. Cardiologist AVR use varied substantially; patients treated by cardiologists with lower AVR rates had higher mortality rates than those treated by cardiologists with higher AVR rates.

scholarly journals Comparison of Sarcopenia with Modified Frailty Index as a Predictor of Adverse Outcomes in Critically Ill Elderly Patients

2021 ◽  
pp. 200-206
Author(s):  
Shrirang Bhurchandi ◽  
Sachin Agrawal ◽  
Sunil Kumar ◽  
Sourya Acharya
Keyword(s):  
Elderly Patients ◽  
Critically Ill ◽  
Health Care Cost ◽  
Frailty Index ◽  
The Elderly ◽  
Time Frame ◽  
Adverse Outcomes ◽  
Cross Sectional ◽  
Parametric Data ◽  
Modified Frailty Index

Background: Ageing is a global fact affecting both developed and developing countries.It brings out various catabolic changes in body resulting in frailty(i.e. the person is not able to with stand minor stresses of the environment, due to reduced reserves in psychologicalreserve of several organ system).Thus causing a great burden of disease, dependence & health care cost. Sarcopenia is the leading component for frailty in the elderly population, but very few studies have been done in India for correlating frailty with sarcopenia. Aim: To compare sarcopenia with modified frailty index (MFI) as a predictor of adverse outcomes in critically ill elderly patients. Methodology: Cross-sectional study will be performed on all the critically ill geriatric subjects/patients coming to all the ICU's of AVBRH, Sawangi (M), Wardha who will satisfy various inclusion and exclusion criteria for selection and all standard parametric & non-parametric data will be assessed by using standard descriptive & inferential statistics. Expected Results: In our study, we are anticipating that the Modified frailty index to be a better predictor of adverse outcomes in terms of mortality as compared to sarcopenia in the critically ill elderly patients. Also, we are anticipating that sarcopenia to be the most important contributor of frailty in critically ill elderly patients and the prevalence of frailty will be high in critically ill elderly patients. Limitation: Due to limited time frame & resources we will not be able to follow up the patients.

scholarly journals Evaluation of risk factors in patients with breast cancer in stages III and IV: comparison of Cox and Fine-Grey competing risk regression models

2018 ◽  
Vol 5 (02) ◽  
pp. 2022-2033
Author(s):  
Monireh Dehghani Arani ◽  
Alireza Abadi ◽  
Aarvin Yavari ◽  
Yousef Bashiri ◽  
Liley Mahmudi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Cox Model ◽  
Parametric Model ◽  
Hazard Ratio ◽  
Competing Risk ◽  
Age Group ◽  
Risk Models ◽  
Cancer Data ◽  
Competing Risk Models

Introduction: The aim of this study is to fit Fine-Grey competing risk model and compare its results with stratified Cox model and to examine its application in breast cancer data. Methods: The study was conducted on 15830 women diagnosed with breast cancer in British Columbia, Canada. They were divided into four groups according to patients' stage of disease then for patients with stage III and IV breast cancer was fitted Cox's model and Fine-Grey competing risk flexible models to each group. Results: The data show that Out of 1888 patients, 578 lied in the age group of below 50 years old, while 1310 were above 50 years of age. The results obtained from fitting stratified Cox regression model indicate that the variables of age and surgery are significant. The patients in the age group of below 50 years old have 70% less hazard in comparison with people older than 50 years of age (HR=0.83). Further, the patients receiving surgery have 38% less hazard in comparison with the patients not receiving surgery (HR=0.62). Then we fit Fine-Grey competing risk models. the variable of chemotherapy is significant in both parametric and semi-parametric competing risk models, and its hazard ratio is HR=1.15 and HR=1.14 in the two models, respectively. On the other hand, the variable of age has not become significant in any of the models, and its hazard ratio is HR=0.92 and HR=0.93, respectively. The variable of surgery in the competing risk parametric model is significant with an HR of 0.67. In Cox model, the variable of surgery is also significant with HR=0.62. Moreover, the variable of age in the competing risk parametric model has not become significant (HR=0.92), and in contrast the variable of age in the Cox model is significant (HR=0.83). Conclusion: The results of this study show that Considering the comparison of the two models, it is observed that regardless of the properties of competing risk data, estimations of hazard ratio and the extent of significance resulting from Cox models are different from those of competing risk models.   

Abstract 13910: Importance of Non-invasive Estimates of Left Ventricular Filling Pressures in Critically Ill Patients

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Abdelrahman M Ahmed ◽  
Brandon Wiley ◽  
Jacob C Jentzer ◽  
Nandan S Anavekar ◽  
Allan S Jaffe
Keyword(s):  
Diastolic Dysfunction ◽  
Diastolic Function ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Troponin T ◽  
Systolic Dysfunction ◽  
Left Ventricular ◽  
Adverse Outcomes ◽  
Icu Mortality ◽  
Lv Diastolic Function

Introduction: The presence of cardiac dysfunction predicts adverse outcomes in the intensive care unit (ICU). We explored the relationship of cardiac injury and left ventricular (LV) systolic and diastolic dysfunction (LVDD) to outcomes in critically ill patients. Methods: This is a retrospective analysis of adult medical ICU admissions from May, 2018 through October 2019. Patients with elevated high-sensitivity troponin T (hs-cTnT) and an echocardiogram performed within 72 hours of admission were included. Patients were classified as having normal LV diastolic function, isolated LVDD, concomitant LV diastolic and systolic dysfunction (LVDDSD) or indeterminate LV diastolic function based on American Society of Echocardiography 2016 guidelines. LV systolic dysfunction was defined as an ejection fraction (EF) < 50%. Results: Overall, 222 patients were included. LVDD was seen in 123 patients (55.4%). Thirty patients (13.5%) were classified with indeterminate diastolic function and 56 normal diastolic function (25.2%). Of those with LVDD , 59.3% had LVDDSD while isolated LVDD was seen in 40.7%.Patients with LVDDSD had a higher median hs-cTnT at baseline compared to patients with isolated LVDD [102ng/L IQR (50-257) vs. 77 ng/L (33.5-166); p=0.047]. Medial e’ velocity and tricuspid valve systolic regurgitant velocity were often associated with LV systolic dysfunction (p=0.0172 and 0.0013, respectively). LVDDSD was associated with a longer length of stay than patients with isolated LVDD [2.9 (1.6-4.0) vs.1.8 (1.1-3.3); p-value 0.03].Twenty-nine patients died during their ICU stay (13%). Patients with LVDDSD had 9.6-fold higher odds of dying in the ICU than patients with isolated LVDD (p=0.0048). Reduced medial e’ velocity (OR 0.63, CI 0.4-1.0, p=0.0285) and increased E/e’ (OR 1.08, CI 1.01-1.15, p=0.0192) were associated with ICU mortality. The association between LVEF<50% and ICU mortality was less pronounced (OR 0.95, CI 0.01-0.98; p=0.0023). Conclusions: Concomitant LV systolic and diastolic dysfunction and measures of increased cardiac filling pressures are strong predictors of mortality.

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