TRENDS IN CANADIAN CORONARY INTENSIVE CARE UNIT CARDIAC AND NON-CARDIAC ADMISSION DIAGNOSIS AND MORTALITY: AN ANALYSIS OF NATIONAL POPULATION HEALTH DATA

2019 ◽  
Vol 35 (10) ◽  
pp. S28-S29
Author(s):  
S. Woolridge ◽  
S. Van Diepen ◽  
W. Alemayehu ◽  
P. Kaul ◽  
C. Fordyce ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Population Health ◽  
Health Data ◽  
Admission Diagnosis ◽  
National Population

Five-Year Outcomes After Implementing a Pain, Agitation, and Delirium Protocol in a Mixed Intensive Care Unit

2021 ◽  
pp. 088506662110634
Author(s):  
Jeffrey T. Fish ◽  
Jared T. Baxa ◽  
Ryan R. Draheim ◽  
Matthew J. Willenborg ◽  
Jared C. Mills ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Academic Medical Center ◽  
Admission Diagnosis ◽  
Surgical Intensive Care Unit ◽  
Standardized Mortality Ratio ◽  
Surgical Intensive Care ◽  
Mechanically Ventilated ◽  
Mortality Ratio ◽  
Apache Iv

Objective: Assess for continued improvements in patient outcomes after updating our institutional sedation and analgesia protocol to include recommendations from the 2013 Society of Critical Care Medicine (SCCM) Pain, Agitation, and Delirium (PAD) guidelines. Methods: Retrospective before-and-after study in a mixed medical/surgical intensive care unit (ICU) at an academic medical center. Mechanically ventilated adults admitted from September 1, 2011 through August 31, 2012 (pre-implementation) and October 1, 2012 through September 30, 2017 (post-implementation) were included. Measurements included number of mechanically ventilated patients, APACHE IV scores, age, type of patient (medical or surgical), admission diagnosis, ICU length of stay (LOS), hospital LOS, ventilator days, number of self-extubations, ICU mortality, ICU standardized mortality ratio, hospital mortality, hospital standardized mortality ratio, medication data including as needed (PRN) analgesic and sedative use, and analgesic and sedative infusions, and institutional savings. Results: Ventilator days (Pre-PAD = 4.0 vs. Year 5 post = 3.2, P < .0001), ICU LOS (Pre-PAD = 4.8 days vs. Year 5 post = 4.1 days, P = .0004) and hospital LOS (Pre-PAD = 14 days vs. Year 5 post = 12 days, P < .0001) decreased after protocol implementation. Hospital standardized mortality ratio (Pre-PAD = 0.69 vs. Year 5 post = 0.66) remained constant; while, APACHE IV scores (Pre-PAD = 77 vs. Year 5 post = 89, P < .0001) and number of intubated patients (Pre-PAD = 1146 vs. Year 5 post = 1468) increased over the study period. Using the decreased ICU and hospital LOS estimates, it is projected the institution saved $4.3 million over the 5 years since implementation. Conclusions: Implementation of an updated PAD protocol in a mixed medical/surgical ICU was associated with a significant decrease in ventilator time, ICU LOS, and hospital LOS without a change in the standardized mortality ratio over a five-year period. These favorable outcomes are associated with a significant cost savings for the institution.

scholarly journals Clinical Characteristics and Mortality of Obstetric Patients Admitted to a Tertiary Intensive Care Unit in the Brazilian Northeast

2018 ◽  
Vol 7 (1) ◽  
pp. 29-33
Author(s):  
Orivaldo Alves Barbosa ◽  
Rafhaela Monteiro de Lima ◽  
Renata Caetano Aguiar ◽  
Mariana Gabriella Correia Viana
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Cesarean Section ◽  
Clinical Characteristics ◽  
Admission Diagnosis ◽  
Postpartum Women ◽  
Hospital Data ◽  
The Subject ◽  
Brazilian Northeast ◽  
Type Of Delivery

Objectives: This study aimed to analyze the determinants of hospitalization and death in an intensive care unit (ICU) between pregnant and postpartum women. Materials and Methods: This is a quantitative and retrospective documentary research performed at Dr. César Cals Hospital. Data was collected through the analysis of charts obtained from puerperal or pregnant women admitted to the ICU. Results: Regarding the type of delivery, 41 (73.2%) women had a cesarean section (CS) and, considering the outcome, the majority of the patients (51%) were discharged from the hospi-tal. Comparing the admission diagnosis and the outcome, it was revealed that many patients presented more than one diagnosis at the admission, the highest rate of which was the hyper-tensive disorders of pregnancy (HDP) and complications comprising of 23 (38.3%) patients out of which 4 of them died. Sepsis was the second cause of hospitalization including 17 (28.3%) patients. In addition, 11 (18.3%) patients had hemorrhaged. Moreover, there were 2 death reports related to fulminant hepatitis. Conclusions: It is expected that the results of the present study contribute to the extension of the professionals’ knowledge on the subject, collaborating to prevent hospitalization and death of these patients.

scholarly journals Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anil K. Palepu ◽  
Aditya Murali ◽  
Jenna L. Ballard ◽  
Robert Li ◽  
Samiksha Ramesh ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Traumatic Brain Injury ◽  
Intensive Care ◽  
Brain Injury ◽  
Neurological Function ◽  
Series Data ◽  
Admission Diagnosis ◽  
Electronic Health Record Data ◽  
Prediction Of Mortality ◽  
Icu Discharge

AbstractTraumatic brain injury (TBI) is a leading neurological cause of death and disability across the world. Early characterization of TBI severity could provide a window for therapeutic intervention and contribute to improved outcome. We hypothesized that granular electronic health record data available in the first 24 h following admission to the intensive care unit (ICU) can be used to differentiate outcomes at discharge. Working from two ICU datasets we focused on patients with a primary admission diagnosis of TBI whose length of stay in ICU was ≥ 24 h (N = 1689 and 127). Features derived from clinical, laboratory, medication, and physiological time series data in the first 24 h after ICU admission were used to train elastic-net regularized Generalized Linear Models for the prediction of mortality and neurological function at ICU discharge. Model discrimination, determined by area under the receiver operating characteristic curve (AUC) analysis, was 0.903 and 0.874 for mortality and neurological function, respectively. Model performance was successfully validated in an external dataset (AUC 0.958 and 0.878 for mortality and neurological function, respectively). These results demonstrate that computational analysis of data routinely collected in the first 24 h after admission accurately and reliably predict discharge outcomes in ICU stratum TBI patients.

scholarly journals Prediction of intensive care unit mortality based on missing events

2021 ◽  
Author(s):  
Tatsuma Shoji ◽  
Hiroshi Yonekura ◽  
Yoshiharu Sato ◽  
yohei Kawashiki
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Missing Values ◽  
Integrated Model ◽  
Mortality Prediction ◽  
Sepsis Group ◽  
Admission Diagnosis ◽  
Complementary Subset ◽  
Imputation Approach

Abstract BackgroundThe increasing availability of electronic health records has made it possible to construct and implement models for predicting intensive care unit (ICU) mortality using machine learning. However, the algorithms used are not clearly described, and the performance of the model remains low owing to several missing values, which is unavoidable in big databases.MethodsWe developed an algorithm for subgrouping patients based on missing event patterns using the Philips eICU Research Institute (eRI) database as an example. The eRI database contains data associated with 200,859 ICU admissions from many hospitals (>400) and is freely available. We then constructed a model for each subgroup using random forest classifiers and integrated the models. Finally, we compared the performance of the integrated model with the Acute Physiology and Chronic Health Evaluation (APACHE) scoring system, one of the best known predictors of patient mortality, and the imputation approach-based model.ResultsSubgrouping and patient mortality prediction were separately performed on two groups: the sepsis group (the ICU admission diagnosis of which is sepsis) and the non-sepsis group (a complementary subset of the sepsis group). The subgrouping algorithm identified a unique, clinically interpretable missing event patterns and divided the sepsis and non-sepsis groups into five and seven subgroups, respectively. The integrated model, which comprises five models for the sepsis group or seven models for the non-sepsis group, greatly outperformed the APACHE IV or IVa, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% confidence interval 0.89–0.92) compared with 0.79 (0.76–0.81) for the APACHE system in the sepsis group and an AUROC of 0.90 (0.89–0.91) compared with 0.86 (0.85–0.87) in the non-sepsis group. Moreover, our model outperformed the imputation approach-based model, which had an AUROC of 0.85 (0.83–0.87) and 0.87 (0.86–0.88) in the sepsis and non-sepsis groups, respectively.ConclusionsWe developed a method to predict patient mortality based on missing event patterns. Our method more accurately predicts patient mortality than others. Our results indicate that subgrouping, based on missing event patterns, instead of imputation is essential and effective for machine learning against patient heterogeneity.Trial registrationNot applicable.

scholarly journals Impact of Ventilator-Associated Pneumonia on Resource Utilization and Patient Outcome

2004 ◽  
Vol 25 (12) ◽  
pp. 1090-1096 ◽  
Author(s):  
Stéphane Hugonnet ◽  
Philippe Eggimann ◽  
François Borst ◽  
Patrice Maricot ◽  
Jean-Claude Chevrolet ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Resource Utilization ◽  
Interquartile Range ◽  
University Teaching ◽  
Attributable Mortality ◽  
Admission Diagnosis ◽  
Ventilator Associated Pneumonia

AbstractObjective:To assess the effect of ventilator-associated pneumonia on resource utilization, morbidity, and mortality.Design:Retrospective matched cohort study based on prospectively collected data.Setting:Medical intensive care unit of a university teaching hospital.Patients:Case-patients were all patients receiving mechanical ventilation for 48 hours or more who experienced an episode of ventilator-associated pneumonia. Control-patients were matched for number of discharge diagnoses, duration of mechanical support before the onset of pneumonia among case-patients, age, admission diagnosis, gender, and study period.Results:One hundred six cases of ventilator-associated pneumonia were identified in 452 patients receiving mechanical ventilation. The matching procedure selected 97 pairs. Length of stay in the intensive care unit and duration of mechanical ventilation were greater among case-patients by a mean of 7.2 days (P< .001) and 5.1 days (P< .001), respectively. Median costs were $24,727 (interquartile range, $18,348 to $39,703) among case-patients and $17,438 (interquartile range, $12,261 to $24,226) among control-patients (P< .001). The attributable mortality rate was 7.3% (P = .26). The attributable extra hospital stay was 10 days with an extra cost of $15,986 per episode of pneumonia.Conclusion:Ventilator-associated pneumonia negatively affects patient outcome and represents a significant burden on intensive care unit and hospital resources.

scholarly journals Patterns of Admission and Clinical Outcomes Among Patients Admitted to Medical Intensive Care Unit of a Teaching and Referral Hospital, Northwest Ethiopia

2021 ◽  
Vol 10 ◽  
pp. 216495612198925
Author(s):  
Hailemariam Getachew Tesema ◽  
Girmay Fitiwi Lema ◽  
Nebiyu Mesfin ◽  
Demeke Yilkal Fentie ◽  
Nurhussien Rezik Arefayne
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Clinical Outcomes ◽  
Mental Status ◽  
Medical Intensive Care Unit ◽  
Care Delivery ◽  
Admission Diagnosis ◽  
P Value ◽  
Medical Intensive Care ◽  
Overall Mortality

Background The intensive care unit (ICU) is a health care delivery service for patients who are in critical condition with potentially recoverable diseases. Patients can benefit from more detailed observation, monitoring and advanced treatment than other wards or department. The care is advancing but in resource-limited settings, it is lagging far behind and mortality is still higher due to various reasons. Therefore, we aimed to determine the admission patterns, clinical outcomes and associated factors among patients admitted medical intensive care unit (MICU). Methods A retrospective cross-sectional study was conducted based on a record review of logbook and charts of patients admitted from September, 2015 to April, 2019. Data were entered and analysed using SPSS version 20. Both bivariate and multivariate logistic regression analyses were used and a P-value < 0.05 was considered statistically significant. Results A total of 738 patients were admitted to medical intensive care unit (MICU) during September, 2015 - April, 2019. Five hundred and four patients (68%) of all intensive care unit (ICU) admissions had complete data. Out of the 504 patients, 268 (53.2%) patients were females. Cardiovascular disease 182(36.1%) was the commonest categorical admission diagnosis. The overall mortality rate was 38.7%. In the multivariate analysis, mortality was associated with need for mechanical ventilation (AOR = 5.87, 95% CI: 3.24 - 10.65) and abnormal mental status at admission (AOR = 2.8, 95% CI: 1.83-4.29). Patients who had stay less than four days in MICU were 5 times more likely to die than those who has stay longer time (AOR= 5.58, 95% CI: 3.58- 8.69). Conclusions The overall mortality was considerably high and cardiovascular diseases were the most common cause of admission in MICU. Need for mechanical ventilator, length of intensive care unit stay and mental status at admission were strongly associated with clinical outcome of patients admitted to medical intensive care unit.

scholarly journals In-hospital emergencies versus out-of-hospital emergencies admitted to ICU: is the outcome different? An observational study

2021 ◽  
Author(s):  
Emi Cauchois ◽  
Jérémy Bourenne ◽  
Audrey Le Saux ◽  
Fouad Bouzana ◽  
Antoine Tilmont ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Hospital Admissions ◽  
Rapid Response ◽  
University Hospital ◽  
Admission Diagnosis ◽  
Medical Systems ◽  
Rapid Response Systems ◽  
Response Systems

Abstract Background: Rapid Response Systems (RRS) are now commonly implemented throughout hospital health systems to manage in-hospital emergencies (IHE). There is limited data on characteristics and outcomes of such patients admitted to an intensive care unit (ICU). The goal was to determine whether the hospital mortality of ICU patients was different depending on their admission pathway: in-hospital via rapid response teams (RRT), or out-of-hospital emergencies (OHE) via prehospital emergency medical systems. Results: Out of 422 ICU admissions (Timone University Hospital ICU), 241 patients were retrospectively (2019-2020) included: 74 IHE versus 167 OHE. In-hospital mortality rates did not differ between both cohorts (n = 31(42%) vs. 63(39%) respectively, NS). IHE patients were older and had more comorbidities (immunosuppression and ongoing malignancy). OHE patients had more severe organ failures at presentation with more frequent mechanical ventilation support. Independent global hospital mortality risk factors were ongoing malignancy (OR = 10.4 [2.7-40], p < 0.001), SAPS II (OR = 1.05 [1.03-1.08], p < 0.0001) and SOFA scores (OR = 1.14 [1.01-1.3], p < 0.05), hemorrhagic stroke as admission diagnosis (OR = 8.4 [2.7-26], p < 0.001), and arterial lactate on arrival (OR = 1.11 [1.03-1.2], p < 0.01). Conclusion: This study provides a thorough and comprehensive analysis of characteristics and outcomes of ICU admissions following a mature rapid response activation system, compared to the “conventional” out-of-hospital admission pathway. Despite the more vulnerable background of IHE patients, hospital mortality does not differ, supporting the use of early RRS to identify deteriorating ward patients. Take-home message: Hospital mortality does not differ between in-hospital emergencies admitted to intensive care unit and conventional out-of-hospital admissions, supporting the use of early rapid response systems and the importance of early intensive care unit admission.

scholarly journals Prediction of intensive care unit mortality based on missing events

2021 ◽  
Author(s):  
Tatsuma Shoji ◽  
Hiroshi Yonekura ◽  
Sato Yoshiharu ◽  
Yohei Kawasaki
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Missing Values ◽  
Integrated Model ◽  
Mortality Prediction ◽  
Sepsis Group ◽  
Admission Diagnosis ◽  
Complementary Subset ◽  
Imputation Approach

AbstractBackgroundThe increasing availability of electronic health records has made it possible to construct and implement models for predicting intensive care unit (ICU) mortality using machine learning. However, the algorithms used are not clearly described, and the performance of the model remains low owing to several missing values, which is unavoidable in big databases.MethodsWe developed an algorithm for subgrouping patients based on missing event patterns using the Philips eICU Research Institute (eRI) database as an example. The eRI database contains data associated with 200,859 ICU admissions from many hospitals (>400) and is freely available. We then constructed a model for each subgroup using random forest classifiers and integrated the models. Finally, we compared the performance of the integrated model with the Acute Physiology and Chronic Health Evaluation (APACHE) scoring system, one of the best known predictors of patient mortality, and the imputation approach-based model.ResultsSubgrouping and patient mortality prediction were separately performed on two groups: the sepsis group (the ICU admission diagnosis of which is sepsis) and the non-sepsis group (a complementary subset of the sepsis group). The subgrouping algorithm identified a unique, clinically interpretable missing event patterns and divided the sepsis and non-sepsis groups into five and seven subgroups, respectively. The integrated model, which comprises five models for the sepsis group or seven models for the non-sepsis group, greatly outperformed the APACHE IV or IVa, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% confidence interval 0.89–0.92) compared with 0.79 (0.76–0.81) for the APACHE system in the sepsis group and an AUROC of 0.90 (0.89–0.91) compared with 0.86 (0.85–0.87) in the non-sepsis group. Moreover, our model outperformed the imputation approach-based model, which had an AUROC of 0.85 (0.83–0.87) and 0.87 (0.86–0.88) in the sepsis and non-sepsis groups, respectively.ConclusionsWe developed a method to predict patient mortality based on missing event patterns. Our method more accurately predicts patient mortality than others. Our results indicate that subgrouping, based on missing event patterns, instead of imputation is essential and effective for machine learning against patient heterogeneity.Trial registrationNot applicable.

Sign in / Sign up

Export Citation Format

Share Document