Faculty Opinions recommendation of Association between arterial catheter use and hospital mortality in intensive care units.

2014 ◽  
Author(s):  
Jay Koyner
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Arterial Catheter

scholarly journals Association Between Arterial Catheter Use and Hospital Mortality in Intensive Care Units

2014 ◽  
Vol 174 (11) ◽  
pp. 1746 ◽  
Author(s):  
Hayley B. Gershengorn ◽  
Hannah Wunsch ◽  
Damon C. Scales ◽  
Ryan Zarychanski ◽  
Gordon Rubenfeld ◽  
...  
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Arterial Catheter

Score for Neonatal Acute Physiology: Validation in Three Kaiser Permanente Neonatal Intensive Care Units

PEDIATRICS ◽  
1995 ◽  
Vol 96 (5) ◽  
pp. 918-922 ◽  
Author(s):  
Gabriel J. Escobar ◽  
Allen Fischer ◽  
De Kun Li ◽  
Robert Kremers ◽  
Mary Anne Armstrong
Keyword(s):  
Intensive Care ◽  
Birth Weight ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Neonatal Intensive Care ◽  
Severity Of Illness ◽  
Predictive Ability ◽  
Neonatal Intensive Care Units ◽  
Total Hospital ◽  
Total Hospital Stay

Background. Measurement of the severity of illness is a research area of growing importance in neonatal intensive care. Most severity of illness scales have been developed in tertiary care settings. Their applicability in community neonatal intensive care units has not been tested. Objectives. Our goal was to assess the operational characteristics of the score for neonatal acute physiology (SNAP): the relationship to birth weight, the length of total hospital stay, and in-hospital mortality. Methods. We assigned SNAP scores prospectively to all inborn admissions at three community neonatal intensive care units during an 11-month period. Data on other neonatal predictors (eg, birth weight and the presence of congenital heart disease) were also collected. We measured in-hospital mortality, the experience of interhospital transport to a higher level of care, and total hospital stay. Results. We found that the SNAP's relationship to birth weight was similar to previous reports. The SNAP's perinatal extension is a reliable predictor of newborn in-hospital mortality, with an area under the receiver operator characteristic curve of 0.95. The SNAP is also a good predictor of total hospital length of stay, whether by itself (by which it can explain 31% of the total stay) or in combination with other variables. Its predictive ability is better among infants of low birth weight (<2500 g) than among those of normal birth weight (≥2500 g). The SNAP's predictive power was most limited among infants admitted to rule out sepsis. The predictive ability of a model containing birth weight, the SNAP, and transport status was not improved by the inclusion of two major diagnostic categories, the presence of congenital heart disease or complex illness. Conclusion. Although it has definite limitations among infants who weigh 2500 g or more, the SNAP is a potent tool for outcomes research. Modification of some of its parameters could result in a multifunctional scale suitable for use with all birth weights.

Effects of Organizational Characteristics on Outcomes and Resource Use in Patients With Cancer Admitted to Intensive Care Units

2016 ◽  
Vol 34 (27) ◽  
pp. 3315-3324 ◽  
Author(s):  
Marcio Soares ◽  
Fernando A. Bozza ◽  
Luciano C.P. Azevedo ◽  
Ulysses V.A. Silva ◽  
Thiago D. Corrêa ◽  
...  
Keyword(s):  
Intensive Care ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Resource Use ◽  
Organizational Characteristics ◽  
General Hospitals ◽  
Cancer Centers ◽  
Processes Of Care ◽  
Patients With Cancer ◽  
Efficient Resource

Purpose To investigate the impact of organizational characteristics and processes of care on hospital mortality and resource use in patients with cancer admitted to intensive care units (ICUs). Patients and Methods We performed a retrospective cohort study of 9,946 patients with cancer (solid, n = 8,956; hematologic, n = 990) admitted to 70 ICUs (51 located in general hospitals and 19 in cancer centers) during 2013. We retrieved patients’ clinical and outcome data from an electronic ICU quality registry. We surveyed ICUs regarding structure, organization, staffing patterns, and processes of care. We used mixed multivariable logistic regression analysis to identify characteristics associated with hospital mortality and efficient resource use in the ICU. Results Median number of patients with cancer per center was 110 (interquartile range, 58 to 154), corresponding to 17.9% of all ICU admissions. ICU and hospital mortality rates were 15.9% and 25.4%, respectively. After adjusting for relevant patient characteristics, presence of clinical pharmacists in the ICU (odds ratio [OR], 0.67; 95% CI, 0.49 to 0.90), number of protocols (OR, 0.92; 95% CI, 0.87 to 0.98), and daily meetings between oncologists and intensivists for care planning (OR, 0.69; 95% CI, 0.52 to 0.91) were associated with lower mortality. Implementation of protocols (OR, 1.52; 95% CI, 1.11 to 2.07) and meetings between oncologists and intensivists (OR, 4.70; 95% CI, 1.15 to 19.22) were also independently associated with more efficient resource use. Neither admission to ICUs in cancer centers compared with general hospitals nor annual case volume had an impact on mortality or resource use. Conclusion Organizational aspects, namely the implementation of protocols and presence of clinical pharmacists in the ICU, and close collaboration between oncologists and ICU teams are targets to improve mortality and resource use in critically ill patients with cancer.

scholarly journals Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units

2021 ◽  
Vol 11 ◽  
Author(s):  
Ximing Nie ◽  
Yuan Cai ◽  
Jingyi Liu ◽  
Xiran Liu ◽  
Jiahui Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Random Forest ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Cerebral Hemorrhage ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Mortality Prediction ◽  
Machine Learning Algorithms

Objectives: This study aims to investigate whether the machine learning algorithms could provide an optimal early mortality prediction method compared with other scoring systems for patients with cerebral hemorrhage in intensive care units in clinical practice.Methods: Between 2008 and 2012, from Intensive Care III (MIMIC-III) database, all cerebral hemorrhage patients monitored with the MetaVision system and admitted to intensive care units were enrolled in this study. The calibration, discrimination, and risk classification of predicted hospital mortality based on machine learning algorithms were assessed. The primary outcome was hospital mortality. Model performance was assessed with accuracy and receiver operating characteristic curve analysis.Results: Of 760 cerebral hemorrhage patients enrolled from MIMIC database [mean age, 68.2 years (SD, ±15.5)], 383 (50.4%) patients died in hospital, and 377 (49.6%) patients survived. The area under the receiver operating characteristic curve (AUC) of six machine learning algorithms was 0.600 (nearest neighbors), 0.617 (decision tree), 0.655 (neural net), 0.671(AdaBoost), 0.819 (random forest), and 0.725 (gcForest). The AUC was 0.423 for Acute Physiology and Chronic Health Evaluation II score. The random forest had the highest specificity and accuracy, as well as the greatest AUC, showing the best ability to predict in-hospital mortality.Conclusions: Compared with conventional scoring system and the other five machine learning algorithms in this study, random forest algorithm had better performance in predicting in-hospital mortality for cerebral hemorrhage patients in intensive care units, and thus further research should be conducted on random forest algorithm.

scholarly journals HIV treatment non-adherence is associated with ICU mortality in HIV-positive critically ill patients

2020 ◽  
pp. 175114371989897 ◽  
Author(s):  
Nelson BF Neto ◽  
Luiz G Marin ◽  
Bruna G de Souza ◽  
Ana LD Moro ◽  
Wagner L Nedel
Keyword(s):  
Intensive Care Unit ◽  
Human Immunodeficiency Virus ◽  
Intensive Care ◽  
Antiretroviral Therapy ◽  
Intensive Care Unit Admission ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Therapy Adherence ◽  
Antiretroviral Therapy Adherence ◽  
Immunodeficiency Virus

Introduction Combined antiretroviral therapy has led to significant decreases in morbidity and mortality in acquired immunodeficiency syndrome patients. Survival among these patients admitted to intensive care units has also improved in the last years. However, the prognostic predictors of human immunodeficiency vírus patients in intensive care units have not been adequately studied. The main objective of this study was to evaluate if non-adherence to antiretroviral therapy is a predictor of hospital mortality. Methods A unicentric, retrospective, cohort study composed of patients admitted to a 59-bed mixed intensive care unit including all patients with human immunodeficiency vírus infection. Patients were excluded if exclusive palliative care was established before completing 48 h of intensive care unit admission. Clinical and treatment data were obtained, including demographic records, underlying diseases, Simplified Acute Physiology III score at the time of intensive care unit admission, CD4 lymphocyte count, antiretroviral therapy adherence, admission diagnosis, human immunodeficiency vírus-related diseases, sepsis and use of mechanical ventilation and hemodialysis. The outcome analyzed was hospital mortality. Results Overall, 167 patients were included in the study, and intensive care unit mortality was 34.7%. Multivariate analysis indicated that antiretroviral therapy adherence and the Simplified Acute Physiology 3 score were independently related to hospital mortality. antiretroviral therapy adherence was a protective factor (OR 0.2; 95% CI 0.05–0.71; P = 0.01), and Simplified Acute Physiology 3 (OR 1.04; 95% CI 1.01–1.08; P < 0.01) was associated with increased hospital mortality. Conclusion Non-adherence to antiretroviral therapy is associated with hospital mortality in this population. Highly active antiretroviral therapy non-adherence may be associated with other comorbidities that may be associated with a worst prognosis in this scenario.

scholarly journals Infections and risk-adjusted length of stay and hospital mortality in Polish Neonatology Intensive Care Units

2015 ◽  
Vol 35 ◽  
pp. 87-92 ◽  
Author(s):  
A. Różańska ◽  
J. Wójkowska-Mach ◽  
P. Adamski ◽  
M. Borszewska-Kornacka ◽  
E. Gulczyńska ◽  
...  
Keyword(s):  
Intensive Care ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Intensive Care Units

scholarly journals Characteristics and outcomes of patients admitted to adult intensive care units in Hong Kong: a population retrospective cohort study from 2008 to 2018

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Lowell Ling ◽  
Chun Ming Ho ◽  
Pauline Yeung Ng ◽  
King Chung Kenny Chan ◽  
Hoi Ping Shum ◽  
...  
Keyword(s):  
Hong Kong ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Intensive Care Units ◽  
Temporal Trends ◽  
Public Hospitals ◽  
Surgical Intensive Care ◽  
Icu Patients ◽  
Capita Health Expenditure ◽  
Apache Iv

Abstract Background Globally, mortality rates of patients admitted to the intensive care unit (ICU) have decreased over the last two decades. However, evaluations of the temporal trends in the characteristics and outcomes of ICU patients in Asia are limited. The objective of this study was to describe the characteristics and risk adjusted outcomes of all patients admitted to publicly funded ICUs in Hong Kong over a 11-year period. The secondary objective was to validate the predictive performance of Acute Physiology And Chronic Health Evaluation (APACHE) IV for ICU patients in Hong Kong. Methods This was an 11-year population-based retrospective study of all patients admitted to adult general (mixed medical-surgical) intensive care units in Hong Kong public hospitals. ICU patients were identified from a population electronic health record database. Prospectively collected APACHE IV data and clinical outcomes were analysed. Results From 1 April 2008 to 31 March 2019, there were a total of 133,858 adult ICU admissions in Hong Kong public hospitals. During this time, annual ICU admissions increased from 11,267 to 14,068, whilst hospital mortality decreased from 19.7 to 14.3%. The APACHE IV standard mortality ratio (SMR) decreased from 0.81 to 0.65 during the same period. Linear regression demonstrated that APACHE IV SMR changed by − 0.15 (95% CI − 0.18 to − 0.11) per year (Pearson’s R = − 0.951, p < 0.001). Observed median ICU length of stay was shorter than that predicted by APACHE IV (1.98 vs. 4.77, p < 0.001). C-statistic for APACHE IV to predict hospital mortality was 0.889 (95% CI 0.887 to 0.891) whilst calibration was limited (Hosmer–Lemeshow test p < 0.001). Conclusions Despite relatively modest per capita health expenditure, and a small number of ICU beds per population, Hong Kong consistently provides a high-quality and efficient ICU service. Number of adult ICU admissions has increased, whilst adjusted mortality has decreased over the last decade. Although APACHE IV had good discrimination for hospital mortality, it overestimated hospital mortality of critically ill patients in Hong Kong.

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