ICU Admission Control: An Empirical Study of Capacity Allocation and Its Implication on Patient Outcomes

2012 ◽  
Author(s):  
Song-Hee Kim ◽  
Carri Chan ◽  
Marcelo Olivares ◽  
Gabriel J. Escobar
Keyword(s):  
Empirical Study ◽  
Admission Control ◽  
Patient Outcomes ◽  
Capacity Allocation ◽  
Icu Admission

ICU Admission Control: An Empirical Study of Capacity Allocation and Its Implication for Patient Outcomes

2015 ◽  
Vol 61 (1) ◽  
pp. 19-38 ◽  
Author(s):  
Song-Hee Kim ◽  
Carri W. Chan ◽  
Marcelo Olivares ◽  
Gabriel Escobar
Keyword(s):  
Empirical Study ◽  
Admission Control ◽  
Patient Outcomes ◽  
Capacity Allocation ◽  
Icu Admission

scholarly journals Patient Outcomes Associated With Phenobarbital Use With or Without Benzodiazepines for Alcohol Withdrawal Syndrome: A Systematic Review

2017 ◽  
Vol 52 (9) ◽  
pp. 607-616 ◽  
Author(s):  
Drayton A. Hammond ◽  
Jordan M. Rowe ◽  
Adrian Wong ◽  
Tessa L. Wiley ◽  
Kristen C. Lee ◽  
...  
Keyword(s):  
Systematic Review ◽  
Patient Outcomes ◽  
Alcohol Withdrawal ◽  
Withdrawal Syndrome ◽  
Observational Studies ◽  
Alcohol Withdrawal Syndrome ◽  
Hospital Length Of Stay ◽  
Cochrane Library ◽  
Controlled Trials ◽  
Icu Admission

Purpose: Benzodiazepines are the drug of choice for alcohol withdrawal syndrome (AWS); however, phenobarbital is an alternative agent used with or without concomitant benzodiazepine therapy. In this systematic review, we evaluate patient outcomes with phenobarbital for AWS. Methods: Medline, Cochrane Library, and Scopus were searched from 1950 through February 2017 for controlled trials and observational studies using [“phenobarbital” or “barbiturate”] and [“alcohol withdrawal” or “delirium tremens.”] Risk of bias was assessed using tools recommended by National Heart, Lung, and Blood Institute. Results: From 294 nonduplicative articles, 4 controlled trials and 5 observational studies (n = 720) for AWS of any severity were included. Studies were of good quality (n = 2), fair (n = 4), and poor (n = 3). In 6 studies describing phenobarbital without concomitant benzodiazepine therapy, phenobarbital decreased AWS symptoms ( P < .00001) and displayed similar rates of treatment failure versus comparator therapies (38% vs 29%). A study with 2 cohorts showed similar rates of intensive care unit (ICU) admission (phenobarbital: 16% and 9% vs benzodiazepine: 14%) and hospital length of stay (phenobarbital: 5.85 and 5.30 days vs benzodiazepine: 6.64 days). In 4 studies describing phenobarbital with concomitant benzodiazepine therapy, phenobarbital groups had similar ICU admission rates (8% vs 25%), decreased mechanical ventilation (21.9% vs 47.3%), decreased benzodiazepine requirements by 50% to 90%, and similar ICU and hospital lengths of stay and AWS symptom resolution versus comparator groups. Adverse effects with phenobarbital, including dizziness and drowsiness, rarely occurred. Conclusion: Phenobarbital, with or without concomitant benzodiazepines, may provide similar or improved outcomes when compared with alternative therapies, including benzodiazepines alone.

scholarly journals Extreme Dysbiosis of the Microbiome in Critical Illness

mSphere ◽  
2016 ◽  
Vol 1 (4) ◽  
Author(s):  
Daniel McDonald ◽  
Gail Ackermann ◽  
Ludmila Khailova ◽  
Christine Baird ◽  
Daren Heyland ◽  
...  
Keyword(s):  
Critical Illness ◽  
Organ Failure ◽  
Patient Outcomes ◽  
Pathogenic Bacteria ◽  
Hospital Acquired Infections ◽  
Icu Admission ◽  
Health Promoting ◽  
Hospital Acquired ◽  
Improve Patient ◽  
Normal Health

ABSTRACT Critical illness may be associated with the loss of normal, “health promoting” bacteria, allowing overgrowth of disease-promoting pathogenic bacteria (dysbiosis), which, in turn, makes patients susceptible to hospital-acquired infections, sepsis, and organ failure. This has significant world health implications, because sepsis is becoming a leading cause of death worldwide, and hospital-acquired infections contribute to significant illness and increased costs. Thus, a trial that monitors the ICU patient microbiome to confirm and characterize this hypothesis is urgently needed. Our study analyzed the microbiomes of 115 critically ill subjects and demonstrated rapid dysbiosis from unexpected environmental sources after ICU admission. These data may provide the first steps toward defining targeted therapies that correct potentially “illness-promoting” dysbiosis with probiotics or with targeted, multimicrobe synthetic “stool pills” that restore a healthy microbiome in the ICU setting to improve patient outcomes. Critical illness is hypothesized to associate with loss of “health-promoting” commensal microbes and overgrowth of pathogenic bacteria (dysbiosis). This dysbiosis is believed to increase susceptibility to nosocomial infections, sepsis, and organ failure. A trial with prospective monitoring of the intensive care unit (ICU) patient microbiome using culture-independent techniques to confirm and characterize this dysbiosis is thus urgently needed. Characterizing ICU patient microbiome changes may provide first steps toward the development of diagnostic and therapeutic interventions using microbiome signatures. To characterize the ICU patient microbiome, we collected fecal, oral, and skin samples from 115 mixed ICU patients across four centers in the United States and Canada. Samples were collected at two time points: within 48 h of ICU admission, and at ICU discharge or on ICU day 10. Sample collection and processing were performed according to Earth Microbiome Project protocols. We applied SourceTracker to assess the source composition of ICU patient samples by using Qiita, including samples from the American Gut Project (AGP), mammalian corpse decomposition samples, childhood (Global Gut study), and house surfaces. Our results demonstrate that critical illness leads to significant and rapid dysbiosis. Many taxons significantly depleted from ICU patients versus AGP healthy controls are key “health-promoting” organisms, and overgrowth of known pathogens was frequent. Source compositions of ICU patient samples are largely uncharacteristic of the expected community type. Between time points and within a patient, the source composition changed dramatically. Our initial results show great promise for microbiome signatures as diagnostic markers and guides to therapeutic interventions in the ICU to repopulate the normal, “health-promoting” microbiome and thereby improve patient outcomes. IMPORTANCE Critical illness may be associated with the loss of normal, “health promoting” bacteria, allowing overgrowth of disease-promoting pathogenic bacteria (dysbiosis), which, in turn, makes patients susceptible to hospital-acquired infections, sepsis, and organ failure. This has significant world health implications, because sepsis is becoming a leading cause of death worldwide, and hospital-acquired infections contribute to significant illness and increased costs. Thus, a trial that monitors the ICU patient microbiome to confirm and characterize this hypothesis is urgently needed. Our study analyzed the microbiomes of 115 critically ill subjects and demonstrated rapid dysbiosis from unexpected environmental sources after ICU admission. These data may provide the first steps toward defining targeted therapies that correct potentially “illness-promoting” dysbiosis with probiotics or with targeted, multimicrobe synthetic “stool pills” that restore a healthy microbiome in the ICU setting to improve patient outcomes. Podcast: A podcast concerning this article is available.

Association Among ICU Congestion, ICU Admission Decision, and Patient Outcomes*

2016 ◽  
Vol 44 (10) ◽  
pp. 1814-1821 ◽  
Author(s):  
Song-Hee Kim ◽  
Carri W. Chan ◽  
Marcelo Olivares ◽  
Gabriel J. Escobar
Keyword(s):  
Patient Outcomes ◽  
Icu Admission ◽  
Admission Decision

scholarly journals Validation of home oxygen saturations as a marker of clinical deterioration in patients with suspected COVID-19

2020 ◽  
Author(s):  
Matthew Inada-Kim ◽  
Francis P. Chmiel ◽  
Michael J. Boniface ◽  
Helen Pocock ◽  
John J. M. Black ◽  
...  
Keyword(s):  
Oxygen Saturation ◽  
Patient Outcomes ◽  
Predictive Analytics ◽  
Vital Signs ◽  
Predictive Ability ◽  
Clinical Deterioration ◽  
Short Term ◽  
Icu Admission ◽  
South Central ◽  
At Home

ABSTRACTBackgroundThe early identification of deterioration in suspected COVID-19 patients managed at home enables a more timely clinical intervention, which is likely to translate into improved outcomes. We undertook an analysis of COVID-19 patients conveyed by ambulance to hospital to investigate how oxygen saturation and measurements of other vital signs correlate to patient outcomes, to ascertain if clinical deterioration can be predicted with simple community physiological monitoring.MethodsA retrospective analysis of routinely collected clinical data relating to patients conveyed to hospital by ambulance was undertaken. We used descriptive statistics and predictive analytics to investigate how vital signs, measured at home by ambulance staff from the South Central Ambulance Service, correlate to patient outcomes. Information on patient comorbidities was obtained by linking the recorded vital sign measurements to the patient’s electronic health record at the Hampshire Hospitals NHS Foundation Trust. ROC analysis was performed using cross-validation to evaluate, in a retrospective fashion, the efficacy of different variables in predicting patient outcomes.ResultsWe identified 1,080 adults with a COVID-19 diagnosis who were conveyed by ambulance to either Basingstoke & North Hampshire Hospital or the Royal Hampshire County Hospital (Winchester) between March 1st and July 31st and whose diagnosis was clinically confirmed at hospital discharge. Vital signs measured by ambulance staff at first point of contact in the community correlated with patient short-term mortality or ICU admission. Oxygen saturations were the most predictive of mortality or ICU admission (AUROC 0.772 (95 % CI: 0.712-0.833)), followed by the NEWS2 score (AUROC 0.715 (95 % CI: 0.670-0.760), patient age (AUROC 0.690 (95 % CI: 0.642-0.737)), and respiration rate (AUROC 0.662 (95 % CI: 0.599-0.729)). Combining age with the NEWS2 score (AUROC 0.771 (95 % CI: 0.718-0.824)) or the measured oxygen saturation (AUROC 0.820 (95 % CI: 0.785-0.854)) increased the predictive ability but did not reach significance.ConclusionsInitial oxygen saturation measurements (on air) for confirmed COVID-19 patients conveyed by ambulance correlated with short-term (30-day) patient mortality or ICU admission, AUROC: 0.772 (95% CI: 0.712-0.833). We found that even small deflections in oxygen saturations of 1-2% below 96% confer an increased mortality risk in those with confirmed COVID at their initial community assessments.

scholarly journals A chest radiography-based artificial intelligence deep-learning model to predict severe Covid-19 patient outcomes: the CAPE (Covid-19 AI Predictive Engine) Model

2020 ◽  
Author(s):  
Charlene Liew ◽  
Jessica Quah ◽  
Han Leong Goh ◽  
Narayan Venkataraman
Keyword(s):  
Deep Learning ◽  
Patient Outcomes ◽  
Chest Radiography ◽  
Scoring Systems ◽  
Learning Model ◽  
Chest Radiographs ◽  
Icu Admission ◽  
Risk Scoring ◽  
Deep Learning Model ◽  
Severe Patient

AbstractBackgroundChest radiography may be used together with deep-learning models to prognosticate COVID-19 patient outcomesPurposeT o evaluate the performance of a deep-learning model for the prediction of severe patient outcomes from COVID-19 pneumonia on chest radiographs.MethodsA deep-learning model (CAPE: Covid-19 AI Predictive Engine) was trained on 2337 CXR images including 2103 used only for validation while training. The prospective test set consisted of CXR images (n=70) obtained from RT-PCR confirmed COVID-19 pneumonia patients between 1 January and 30 April 2020 in a single center. The radiographs were analyzed by the AI model. Model performance was obtained by receiver operating characteristic curve analysis.ResultsIn the prospective test set, the mean age of the patients was 46 (+/-16.2) years (84.2% male). The deep-learning model accurately predicted outcomes of ICU admission/mortality from COVID-19 pneumonia with an AUC of 0.79 (95% CI 0.79-0.96). Compared to traditional risk scoring systems for pneumonia based upon laboratory and clinical parameters, the model matched the EWS and MulBTSA risk scoring systems and outperformed CURB-65.ConclusionsA deep-learning model was able to predict severe patient outcomes (ICU admission and mortality) from COVID-19 on chest radiographs.Key ResultsA deep-learning model was able to predict severe patient outcomes (ICU admission and mortality) from COVID-19 from chest radiographs with an AUC of 0.79, which is comparable to traditional risk scoring systems for pneumonia.Summary StatementThis is a chest radiography-based AI model to prognosticate the risk of severe COVID-19 pneumonia outcomes.

scholarly journals Impact of Hospitalist Care Model on Patient Outcomes in Acute Medical Unit

2020 ◽  
Author(s):  
Hyun Jeong Kim ◽  
Jinhyun Kim ◽  
Jung Hun Ohn ◽  
Nak-Hyun Kim
Keyword(s):  
Hospital Mortality ◽  
Patient Outcomes ◽  
Waiting Time ◽  
Admission Rate ◽  
Care Hospital ◽  
Care Model ◽  
Medical Unit ◽  
Icu Admission ◽  
Medical Needs ◽  
Acute Medical Unit

Abstract BACKGROUND: The present study aimed to assess a newly introduced, hospitalist-run, acute medical unit (AMU) model in Korea. The AMU in our institution started in October 2015. Four hospitalists managed patients with acute medical needs that were admitted through the emergency department (ED). STUDY DESIGN: We conducted a retrospective cohort study of all medical inpatients admitted through the ED from June 1, 2016 to May 31, 2017, at a tertiary care hospital. We evaluated 6391 patients whether the hospitalist care in the AMU improved patient outcomes compared to standard non-hospitalist care. METHODS: We created multivariate analysis models to compare the clinical outcomes of patients cared for by hospitalists with the outcomes of patients cared for by non-hospitalists. RESULTS: In the adjusted models, compared to the non-hospitalist group, the AMU hospitalist group had a lower in-hospital mortality (OR: 0.46, P <0.001), a lower intensive care unit (ICU) admission rate (OR: 0.39, P <0 .001), a shorter hospital length of stay (coefficient: -1.349, SE: 0.217; P <0.001), and a shorter ED waiting time (coefficient: -3.021, SE: 0.256; P <0.001). There were no significant differences in the 10-day or 30-day re-admission rates (P = 0.493, P = 0.201; respectively). CONCLUSIONS: The AMU hospitalist care model was associated with reductions in in-hospital mortality, ICU admission rate, length of hospital stay, and ED waiting time. These findings suggested that this AMU hospitalist care model might be adaptable to other healthcare systems to improve care for patients with acute medical needs.

scholarly journals Abdominal Computed Tomography Imaging Findings in Hospitalized COVID-19 Patients: A Year-Long Experience and Associations Revealed by Explainable Artificial Intelligence

2021 ◽  
Vol 7 (12) ◽  
pp. 258
Author(s):  
Alice Scarabelli ◽  
Massimo Zilocchi ◽  
Elena Casiraghi ◽  
Pierangelo Fasani ◽  
Guido Giovanni Plensich ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pulmonary Embolism ◽  
Statistical Analysis ◽  
Patient Outcomes ◽  
Strong Relationship ◽  
Active Bleeding ◽  
Free Fluid ◽  
Icu Admission ◽  
Explainable Artificial Intelligence ◽  
Radiological Stage

The aim of this retrospective study is to assess any association between abdominal CT findings and the radiological stage of COVID-19 pneumonia, pulmonary embolism and patient outcomes. We included 158 adult hospitalized COVID-19 patients between 1 March 2020 and 1 March 2021 who underwent 206 abdominal CTs. Two radiologists reviewed all CT images. Pathological findings were classified as acute or not. A subset of patients with inflammatory pathology in ACE2 organs (bowel, biliary tract, pancreas, urinary system) was identified. The radiological stage of COVID pneumonia, pulmonary embolism, overall days of hospitalization, ICU admission and outcome were registered. Univariate statistical analysis coupled with explainable artificial intelligence (AI) techniques were used to discover associations between variables. The most frequent acute findings were bowel abnormalities (n = 58), abdominal fluid (n = 42), hematomas (n = 28) and acute urologic conditions (n = 8). According to univariate statistical analysis, pneumonia stage > 2 was significantly associated with increased frequency of hematomas, active bleeding and fluid-filled colon. The presence of at least one hepatobiliary finding was associated with all the COVID-19 stages > 0. Free abdominal fluid, acute pathologies in ACE2 organs and fluid-filled colon were associated with ICU admission; free fluid also presented poor patient outcomes. Hematomas and active bleeding with at least a progressive stage of COVID pneumonia. The explainable AI techniques find no strong relationship between variables.

Validity of the Taiwan Triage and Acuity Scale in mainland China: a retrospective observational study

2021 ◽  
pp. emermed-2019-208732
Author(s):  
Qing-Qing Chen ◽  
Sherry Yueh-Hsia Chiu ◽  
Lai-Yin Tsai ◽  
Rong-Fang Hu
Keyword(s):  
Observational Study ◽  
Hospital Admission ◽  
Patient Outcomes ◽  
Mainland China ◽  
Tertiary Hospital ◽  
Retrospective Observational Study ◽  
Receiver Operating Curve ◽  
Resource Utilisation ◽  
Icu Admission ◽  
Patient Disposition

ObjectivesThe Taiwan Triage and Acuity Scale (TTAS), developed for use in EDs, has been shown to be an excellent tool for triaging patients with high predictive performance, with an area under the receiver operating curve (AUROC) of 0.75. TTAS has been widely used in hospitals in Taiwan since 2010, but its utility has not been studied outside of Taiwan. Thus, the aim of this study was to evaluate the validity of using the TTAS in the ED of a tertiary hospital in mainland China to predict patient outcomes.MethodsA retrospective observational study was performed on patients 14 years of age or older attending the ED of a tertiary hospital in mainland China between 1 January 2016 and 31 March 2016. The validity of the TTAS in predicting hospital admission, intensive care unit (ICU) admission, death, ED length of stay (LOS) and ED resource utilisation was evaluated by determining the correlation of these outcomes with the TTAS, AUROC and test characteristics.ResultsA total of 7843 patients were included in this study. There were significant differences between the TTAS categories in disposition, ED LOS and ED resource utilisation (p<0.0001). The TTAS was significantly correlated with patient disposition at discharge, hospital admission, ICU admission and death in the ED (Kendall rank correlations were 0.254, –0.254, −0.079 and −0.071, respectively; p=0.001). The AUROCs for the prediction of hospital admissions, ICU admissions and deaths in the ED were 0.749 (95% CI 0.732 to 0.765), 0.869 (95% CI 0.797 to 0.942) and 0.998 (95% CI 0.995 to 1.000), respectively. Our results demonstrated better performance using the TTAS for predictions of ICU admission and death.ConclusionsThe TTAS had good validity in predicting patient outcomes and ED resource utilisation in a tertiary hospital in mainland China. Compared with the performance of the TTAS in Taiwan, our results suggest that the TTAS can usefully be applied outside of Taiwan.

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