INTENSIVE CARE UNTT (ICU) ADMISSION CRITERIA FOR PATIENTS WITH DIABETIC KETOACIDOSIS

Objective: To determine predictors of prolonged length of stay (LOS), 30-day readmission, and 30-day mortality in a multihospital health system. Methods: We performed a retrospective review of 531 adults admitted with diabetic ketoacidosis (DKA) to a multihospital health system between November 2015 and December 2016. Demographic and clinical data were collected. Linear regression was used to calculate odds ratios (ORs) for predictors and their association with prolonged LOS (3.2 days), 30-day readmission, and 30-day mortality. Results: Significant predictors for prolonged LOS included: intensive care unit (ICU) admission (OR, 2.12; 95% confidence interval [CI], 1.38 to 3.27), disease duration (nonlinear) (OR, 1.28; 95% CI, 1.10 to 1.49), non-white race (OR, 1.73; 95% CI, 1.15 to 2.60), age at admission (OR, 1.03; 95% CI, 1.01 to 1.04), and Elixhauser index (EI) (OR, 1.21; 95% CI, 1.13 to 1.29). Shorter time to consult after admission (median [Q1, Q3] of 11.3 [3.9, 20.7] vs. 14.8 [7.4, 37.3] hours, P<.001) was associated with a shorter LOS. Significant 30-day readmission predictors included: Medicare insurance (OR, 2.35; 95% CI, 1.13 to 4.86) and EI (OR, 1.31; 95% CI, 1.21 to 1.41). Endocrine consultation was associated with reduced 30-day readmission (OR, 0.51; 95% CI, 0.28 to 0.92). A predictive model for mortality was not generated because of low event rates. Conclusion: EI, non-white race, disease duration, age, Medicare, and ICU admission were associated with adverse outcomes. Endocrinology consultation was associated with lower 30-day readmission, and earlier consultation resulted in a shorter LOS. Abbreviations: CI = confidence interval; DKA = diabetic ketoacidosis; EI = Elixhauser index; HbA1c = hemoglobin A1c; ICD = International Classification of Diseases; ICU = intensive care unit; LOS = length of stay; OR = odds ratio; Q = quartile

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Designing and Evaluating the Establishment of Admission Criteria for Patients in an Intensive Care Unit: A Descriptive Study in Iran

Evidence Based Health Policy Management and Economics ◽

10.18502/jebhpme.v5i2.6554 ◽

2021 ◽

Author(s):

Fariba Hosseinpour ◽

Mahyar Sedighi ◽

Fariba Hashemi ◽

Sima Rafiei

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Length Of Stay ◽

Statistical Tests ◽

P Value ◽

Admission Criteria ◽

Total Cost ◽

Icu Admission ◽

Study Results ◽

Significant Difference

Background: A few studies have reviewed and revised ICU admission criteria based on specific circumstances and local conditions. The aim was to develop ICU admission criteria and compare the cost, mortality, and length of stay among identified admission priorities. Methods: This was a cross-sectional study conducted in an intensive care unit of a training hospital in Qazvin, Iran. The study was conducted among 127 patients admitted to ICU from July to September 2019. The data collection tool was a self-designed checklist, which included items regarding patients' clinical data and their billing, type of diagnosis, level of consciousness at the time of hospitalization based on GCS scale or Glasgow Coma Scale, length of stay, and patient status at the time of discharge. Descriptive statistical tests were used to describe study variables, and in order to determine the relationship between study variables, ANOVA and Chi-square test were used. Results: A set of criteria were designed to prioritize patient admissions in ICU. Based on the defined criteria, patients were categorized into four groups based on patient's stability, hemodynamic, and respiration. Study findings revealed that a significant percentage of patients were admitted to the ward while in the second and third priorities of hospitalization (26.8 % and 32.3 %, respectively). There was a statistically significant difference in the four groups in terms of patients' age, total cost, and insurance share of the total cost (P-value < 0.05). Conclusion: Study results emphasize the necessity to classify patients based on defined criteria to efficiently use available resources.

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Admission criteria in critically ill COVID-19 patients: a physiology-based approach.

10.1101/2021.05.30.21257382 ◽

2021 ◽

Author(s):

Samuele Ceruti ◽

Andrea Glotta ◽

Maira Biggiogero ◽

PierAndrea Maida ◽

Martino Marzano ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Survival Rate ◽

Mortality Rate ◽

Primary Outcome ◽

Secondary Outcome ◽

Admission Criteria ◽

Do Not Resuscitate ◽

Icu Admission ◽

Careful Management

Introduction: The COVID-19 pandemic required a careful management of intensive care unit (ICU) admissions, to reduce ICU overload while facing resources' limitations. We implemented standardized, physiology-based, ICU admission criteria and analyzed the mortality rate of patients refused from the ICU. Materials and Methods: COVID-19 patients proposed for ICU admission were consecutively analyzed; Do-not-resuscitate patients were excluded. Patients presenting a SpO2 lower than 85% and/or dyspnea and/or mental confusion resulted eligible for ICU admission; patients not presenting these criteria remained in the ward. Primary outcome was both groups' survival rate. Secondary outcome was a sub analysis correlating SpO2 cutoff with ICU admission. Results: From March 2020 to January 2021, 1623 patients were admitted to our Center; 208 DNR patients were excluded; 97 patients underwent intensivist evaluation. The ICU-admitted group mortality rate resulted 15.9% at 28 days and 27% at 40 days; the ICU-refused group mortality rate resulted 0% at both intervals (p < 0.001). With a SpO2 cut-off of 92%, the hypoxia rate distribution did not correlate with ICU admission (p = 0.26); with a SpO2 cut-off of 85%, a correlation was found (p = 0.009). A similar correlation was also found with dyspnea (p =0.0002). Conclusion: In COVID-19 patients, standardized ICU admission criteria appeared to reduce safely ICU overload. In the absence of dyspnea and/or confusion, a SpO2 cutoff up to 85% for ICU admission was not burdened by negative outcomes. In a pandemic context, the SpO2 cutoff of 92%, as a threshold for ICU admission, needs critical re-evaluation.

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In-Hospital Outcomes in Patients Admitted to the Intensive Care Unit after a Return Visit to the Emergency Department

Healthcare ◽

10.3390/healthcare9040431 ◽

2021 ◽

Vol 9 (4) ◽

pp. 431

Author(s):

Chun-Fu Lin ◽

Yi-Syun Huang ◽

Ming-Ta Tsai ◽

Kuan-Han Wu ◽

Chien-Fu Lin ◽

...

Keyword(s):

Intensive Care Unit ◽

Emergency Department ◽

Regression Analysis ◽

Intensive Care ◽

Medical Cost ◽

Total Medical Cost ◽

Hospital Outcomes ◽

Return Visit ◽

Icu Admission ◽

Ed Visits

Background: Intensive care unit (ICU) admission following a short-term emergency department (ED) revisit has been considered a particularly undesirable outcome among return-visit patients, although their in-hospital prognosis has not been discussed. We aimed to compare clinical outcomes between adult patients admitted to the ICU after unscheduled ED revisits and those admitted during index ED visits. Method: This retrospective study was conducted at two tertiary medical centers in Taiwan from 1 January 2016 to 31 December 2017. All adult non-trauma patients admitted to the ICU directly via the ED during the study period were included and divided into two comparison groups: patients admitted to the ICU during index ED visits and those admitted to the ICU during return ED visits. The outcomes of interest included in-hospital mortality, mechanical ventilation (MV) support, profound shock, hospital length of stay (HLOS), and total medical cost. Results: Altogether, 12,075 patients with a mean (standard deviation) age of 64.6 (15.7) years were included. Among these, 5.3% were admitted to the ICU following a return ED visit within 14 days and 3.1% were admitted following a return ED visit within 7 days. After adjusting for confounding factors for multivariate regression analysis, ICU admission following an ED revisit within 14 days was not associated with an increased mortality rate (adjusted odds ratio (aOR): 1.08, 95% confidence interval (CI): 0.89 to 1.32), MV support (aOR: 1.06, 95% CI: 0.89 to 1.26), profound shock (aOR: 0.99, 95% CI: 0.84 to 1.18), prolonged HLOS (difference: 0.04 days, 95% CI: −1.02 to 1.09), and increased total medical cost (difference: USD 361, 95% CI: −303 to 1025). Similar results were observed after the regression analysis in patients that had a 7-day return visit. Conclusion: ICU admission following a return ED visit was not associated with major in-hospital outcomes including mortality, MV support, shock, increased HLOS, or medical cost. Although ICU admissions following ED revisits are considered serious adverse events, they may not indicate poor prognosis in ED practice.

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Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

Journal of Clinical Medicine ◽

10.3390/jcm10050992 ◽

2021 ◽

Vol 10 (5) ◽

pp. 992

Author(s):

Martina Barchitta ◽

Andrea Maugeri ◽

Giuliana Favara ◽

Paolo Marco Riela ◽

Giovanni Gallo ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care ◽

Intensive Care Units ◽

Learning Algorithm ◽

Area Under The Curve ◽

Support Vector ◽

Icu Admission ◽

Risk Of Death ◽

Saps Ii ◽

Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

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Healthcare trajectories before and after critical illness: population-based insight on diverse patients clusters

Annals of Intensive Care ◽

10.1186/s13613-019-0599-3 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Youenn Jouan ◽

Leslie Grammatico-Guillon ◽

Noémie Teixera ◽

Claire Hassen-Khodja ◽

Christophe Gaborit ◽

...

Keyword(s):

Septic Shock ◽

Mechanical Ventilation ◽

Intensive Care ◽

Critical Illness ◽

Length Of Stay ◽

Population Based ◽

Hospital Length Of Stay ◽

Healthcare Use ◽

Icu Admission ◽

Icu Discharge

Abstract Background The post intensive care syndrome (PICS) gathers various disabilities, associated with a substantial healthcare use. However, patients’ comorbidities and active medical conditions prior to intensive care unit (ICU) admission may partly drive healthcare use after ICU discharge. To better understand retative contribution of critical illness and PICS—compared to pre-existing comorbidities—as potential determinant of post-critical illness healthcare use, we conducted a population-based evaluation of patients’ healthcare use trajectories. Results Using discharge databases in a 2.5-million-people region in France, we retrieved, over 3 years, all adult patients admitted in ICU for septic shock or acute respiratory distress syndrome (ARDS), intubated at least 5 days and discharged alive from hospital: 882 patients were included. Median duration of mechanical ventilation was 11 days (interquartile ranges [IQR] 8;20), mean SAPS2 was 49, and median hospital length of stay was 42 days (IQR 29;64). Healthcare use (days spent in healthcare facilities) was analyzed 2 years before and 2 years after ICU admission. Prior to ICU admission, we observed, at the scale of the whole study population, a progressive increase in healthcare use. Healthcare trajectories were then explored at individual level, and patients were assembled according to their individual pre-ICU healthcare use trajectory by clusterization with the K-Means method. Interestingly, this revealed diverse trajectories, identifying patients with elevated and increasing healthcare use (n = 126), and two main groups with low (n = 476) or no (n = 251) pre-ICU healthcare use. In ICU, however, SAPS2, duration of mechanical ventilation and length of stay were not different across the groups. Analysis of post-ICU healthcare trajectories for each group revealed that patients with low or no pre-ICU healthcare (which represented 83% of the population) switched to a persistent and elevated healthcare use during the 2 years post-ICU. Conclusion For 83% of ARDS/septic shock survivors, critical illness appears to have a pivotal role in healthcare trajectories, with a switch from a low and stable healthcare use prior to ICU to a sustained higher healthcare recourse 2 years after ICU discharge. This underpins the hypothesis of long-term critical illness and PICS-related quantifiable consequences in healthcare use, measurable at a population level.

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Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

Scientific Reports ◽

10.1038/s41598-020-80474-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bongjin Lee ◽

Kyunghoon Kim ◽

Hyejin Hwang ◽

You Sun Kim ◽

Eun Hee Chung ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care Unit ◽

Intensive Care ◽

Validation Cohort ◽

External Validation ◽

Learning Model ◽

Derivation Cohort ◽

Icu Admission ◽

Early Stages ◽

Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

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