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Mortality Predictors and Associated Factors in Patients in the Intensive Care Unit: A Cross-Sectional Study.

ABSTRACT: Background:Mortality in the intensive care unit (ICU) has been associated to an array of risk factors. Identification of risk factors potentially contribute to predict and reduce mortality rates in the ICU. The objectives of the study were to determine the prevalence and the factors associated with the mortality and to analyze the survival. Method:A cross-sectional study conducted in two clinical and surgical ICU in the state of Sergipe, northeastern Brazil. We enrolled 316 patients with at least 48?h of hospitalization, minimum age of 18 years old, sedated or weaned, with RASS???-3, between July 2017 and April 2018. We categorized data in (1) age and gender, (2) clinical condition, and (3) prevalence of delirium. Data from enrolled patients were collected from enrollment until death or ICU discharge. Patients' outcomes were categorized in (1) death and (2) nondeath (discharge). Results:Twenty-one percent of participants died. Age (53?±?17 years vs. 45?±?18 years, p < 0.01), electrolyte disturbance (30.3% vs 18.1%, p=0.029), glycemic index (33.3% vs 18.2%, p=0.008), tube feeding (83.3% vs 67.1%, p=0.01), mechanical ventilation (50% vs 35.7%, p=0.035), sedation with fentanyl (24.2 vs 13.6, p=0.035), use of insulin (33.8% vs 21.7%, p=0.042), and higher Charlson score (2.61 vs 2.17, p=0.041) were significantly associated with death on the adjusted model. However, the regression model indicated that patients admitted from the emergency (HR?=?0.40, p=0.006) and glycemic index alterations (HR?=?1.68, p=0.047) were associated with mortality. There was no statistically significant difference (p=0.540) in survival between patients with and without delirium, based on the survival analysis and length of hospitalization. Conclusion:The prevalence of death was 21%, and age, electrolyte disturbance, glycemic index, tube feeding, mechanical ventilation, sedation with fentanyl, use of insulin, and higher Charlson score were associated with mortality.

SUBMITTER: Soares Pinheiro FGM

PROVIDER: S-EPMC7416226 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Mortality Predictors and Associated Factors in Patients in the Intensive Care Unit: A Cross-Sectional Study.

Soares Pinheiro Fernanda G de M FGM Santana Santos Eduesley E Barreto Íkaro Daniel de C ÍDC Weiss Carleara C Vaez Andreia C AC Oliveira Jussiely C JC Melo Matheus S MS Silva Francilene A FA

Critical care research and practice 20200801

<h4>Background</h4>Mortality in the intensive care unit (ICU) has been associated to an array of risk factors. Identification of risk factors potentially contribute to predict and reduce mortality rates in the ICU. The objectives of the study were to determine the prevalence and the factors associated with the mortality and to analyze the survival.<h4>Method</h4>A cross-sectional study conducted in two clinical and surgical ICU in the state of Sergipe, northeastern Brazil. We enrolled 316 patien ...[more]

PMID: 32802502

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Project description:BackgroundThe transmissibility and associated morbidity and mortality of severe acute respiratory syndrome-related coronavirus (SARS-Cov-2), have overwhelmed worldwide healthcare systems, resulting in an urgent need to understand this virus and its associated effects. The aim of our study was to identify patient symptoms, clinical characteristics, laboratory, and radiology findings that are associated with serious morbidity and mortality in COVID-19 patients.MethodsA cross sectional study was conducted in Jaber Al Ahmad Hospital, the designated COVID-19 center in Kuwait between August 1st, 2020 and January 31st, 2021. The main outcomes measured in this study were to identify variables associated with intensive care unit (ICU) admission, as proxy for serious morbidity, and in hospital mortality.ResultsTwo hundred and seventy-six patients were included in the study. Thirty-six (13%) patients were admitted to intensive care unit (ICU) and 33 (12%) patients expired. On multivariate analysis we found having elevated fibrinogen [OR 1.39, 95% CI 1.08-1.64, P = 0.04], low estimated glomerular filtration rate (eGFR) [OR 0.89, 95% CI 0.81-0.95, P = 0.02], and having bilateral patchy lung shadowing [OR 6.68, 95% CI 1.85-15.28, P < 0.01] to be significantly associated with increase odds of ICU admission. Elevated CRP [OR 1.25, 95% CI 1.10-1.98, P < 0.01], low eGFR [OR 0.95, 95% CI 0.90-0.99, P = 0.05] and having ischemic heart disease [OR 7.03, 95% CI 1.60-46.42, P = 0.04] were independently associated with increased odds of mortality.ConclusionCertain inflammatory and coagulopathy markers, and having certain lung radiological features, in addition to having medical comorbidities, specifically, ischemic heart disease and renal impairment are key predictors for serious morbidity and mortality in patients infected with COVID-19. These should be incorporated into medical institutes risk assessment tools used by physicians and policy makers to instigate, prioritize, and reprioritize care in patients with COVID-19 and instigate preventative strategy to reduce the impact of future outbreak.

Project description:BackgroundOpioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem.ObjectiveThe objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD.MethodsWe performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR).ResultsThe strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95% CI 0.44-0.96; >64 years: aOR 0.16, 95% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82% of all SBDH information was identified by the NLP model, in contrast to only 0.18% identified by the ICD codes.ConclusionsThis is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.

Project description:Background & aimsDespite advances in critical care medicine, the mortality rate is high among critically ill patients with cirrhosis. We aimed to identify factors that predict early (7 d) mortality among patients with cirrhosis admitted to the intensive care unit (ICU) and to develop a risk-stratification model.MethodsWe collected data from patients with cirrhosis admitted to the ICU at Indiana University (IU-ICU) from December 1, 2006, through December 31, 2009 (n = 185), or at the University of Pennsylvania (Penn-ICU) from May 1, 2005, through December 31, 2010 (n = 206). Factors associated with mortality within 7 days of admission (7-d mortality) were determined by logistic regression analyses. A model was constructed based on the predictive parameters available on the first day of ICU admission in the IU-ICU cohort and then validated in the Penn-ICU cohort.ResultsMedian Model for End-stage Liver Disease (MELD) scores at ICU admission were 25 in the IU-ICU cohort (interquartile range, 23-34) and 32 in the Penn-ICU cohort (interquartile range, 26-41); corresponding 7-day mortalities were 28.3% and 53.6%, respectively. MELD score (odds ratio, 1.13; 95% confidence interval [CI], 1.07-1.2) and mechanical ventilation (odds ratio, 5.7; 95% CI, 2.3-14.1) were associated independently with 7-day mortality in the IU-ICU. A model based on these 2 variables separated IU-ICU patients into low-, medium-, and high-risk groups; these groups had 7-day mortalities of 9%, 27%, and 74%, respectively (concordance index, 0.80; 95% CI, 0.72-0.87; P < 10(-8)). The model was applied to the Penn-ICU cohort; the low-, medium-, and high-risk groups had 7-day mortalities of 33%, 56%, and 71%, respectively (concordance index, 0.67; 95% CI, 0.59-0.74; P < 10(-4)).ConclusionsA model based on MELD score and mechanical ventilation on day 1 can stratify risk of early mortality in patients with cirrhosis admitted to the ICU. More studies are needed to validate this model and to enhance its clinical utility.

Project description:Background Hyperkalemia has been associated with increased mortality in patients with myocardial infarction, but few data exist regarding hyperkalemia in cardiac intensive care unit ( CICU ) patients. We hypothesize that hyperkalemia is associated with increased mortality in unselected CICU patients. Methods and Results We retrospectively reviewed a historical cohort of 9681 CICU patients admitted from January 2007 to December 2015. Hyperkalemia was defined as admission potassium ≥5.0 mEq/L and hypokalemia as admission potassium <3.5 mEq/L. Multivariate logistic regression was used to determine predictors of in-hospital mortality. Postdischarge survival was assessed using Kaplan-Meier analysis and Cox proportional hazards models. The mean age of included patients was 67±15 years, with 36% females, and in-hospital mortality was 9%. Hyperkalemia occurred in 1187 (12.3%) and hypokalemia occurred in 719 (7.4%) patients. Both patients with hyperkalemia (unadjusted odds ratio, 2.85; 95% CI, 2.40-3.39; P<0.001) and patients with hypokalemia (unadjusted odds ratio, 2.31; 95% CI, 1.85-2.88; P<0.001) were at increased risk of unadjusted in-hospital mortality. After adjustment for illness severity and renal function, only patients with hyperkalemia demonstrated increased risk of in-hospital death (adjusted odds ratio, 1.44; 95% CI, 1.11-1.87; P=0.006). Among hospital survivors, only patients with hyperkalemia had lower postdischarge survival by Kaplan-Meier analysis ( P<0.001). After adjustment for illness severity and renal function, hospital survivors with admission hyperkalemia remained at increased risk for postdischarge mortality (adjusted hazard ratio, 1.20; 95% CI, 1.08-1.34; P<0.001). Conclusions Hyperkalemia on CICU admission is associated with higher in-hospital and postdischarge mortality, independent of renal function and illness severity. These findings emphasize the importance of potassium abnormalities as a risk predictor in patients admitted to the CICU .

Dataset Information

Mortality Predictors and Associated Factors in Patients in the Intensive Care Unit: A Cross-Sectional Study.

Publications

Mortality Predictors and Associated Factors in Patients in the Intensive Care Unit: A Cross-Sectional Study.

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