scholarly journals Corneal injuries: incidence and risk factors in the Intensive Care Unit

2011 ◽  
Vol 19 (5) ◽  
pp. 1088-1095 ◽  
Author(s):  
Andreza Werli-Alvarenga ◽  
Flávia Falci Ercole ◽  
Fernando Antônio Botoni ◽  
José Aloísio Dias Massote Mourão Oliveira ◽  
Tânia Couto Machado Chianca
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Corneal Ulcer ◽  
Ventilatory Support ◽  
Corneal Injury ◽  
Corneal Ulcers ◽  
Duration Of Hospitalization

Patients hospitalized in the Intensive Care Unit (ICU) may present risk for corneal injury due to sedation or coma. This study aimed to estimate the incidence of corneal injuries; to identify the risk factors and to propose a risk prediction model for the development of corneal injury, in adult patients, in an intensive care unit of a public hospital. This is a one year, prospective cohort study with 254 patients. The data were analyzed using descriptive statistics, univariate and logistic regression. Of the 254 patients, 59.4% had corneal injuries and the mean time to onset was 8.9 days. The independent variables that predispose to risk for punctate type corneal injury were: duration of hospitalization, other ventilatory support device, presence of edema and blinking less than five times a minute. The Glasgow Coma Scale and exposure of the ocular globe were the variables related to corneal ulcer type corneal injury. The injury frequencies were punctate type (55.1%) and corneal ulcers (11.8%). Risk prediction models for the development of punctate and corneal ulcer type corneal injury were established.

Abstract 15875: Prediction of Intensive Care Unit Admission Among Patients Hospitalized for COVID-19: The Intermountain Coronavirus ICU Risk Model (CORONA-ICU)

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Heidi T May ◽  
Joseph B Muhlestein ◽  
Benjamin D Horne ◽  
Kirk U Knowlton ◽  
Tami L Bair ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Model ◽  
Beta Blockers ◽  
Risk Scores ◽  
Channel Blockers ◽  
Icu Patients

Background: Treatment for COVID-19 has created surges in hospitalizations, intensive care unit (ICU) admissions, and the need for advanced medical therapy and equipment, including ventilators. Identifying patients early on who are at risk for more intensive hospital resource use and poor outcomes could result in shorter hospital stays, lower costs, and improved outcomes. Therefore, we created clinical risk scores (CORONA-ICU and -ICU+) to predict ICU admission among patients hospitalized for COVID-19. Methods: Intermountain Healthcare patients who tested positive for SARS-CoV-2 and were hospitalized between March 4, 2020 and June 8, 2020 were studied. Derivation of CORONA-ICU risk score models used weightings of commonly collected risk factors and medicines. The primary outcome was admission to the ICU during hospitalization, and secondary outcomes included death and ventilator use. Results: A total of 451 patients were hospitalized for a SARS-CoV-2 positive infection, and 191 (42.4%) required admission to the ICU. Patients admitted to the ICU were older (58.2 vs. 53.6 years), more often male (61.3% vs. 48.5%), and had higher rates of hyperlipidemia, hypertension, diabetes, and peripheral arterial disease. ICU patients more often took ACE inhibitors, beta-blockers, calcium channel blockers, diuretics, and statins. Table 1 shows variables that were evaluated and included in the CORONA-ICU risk prediction models. Models adding medications (CORONA-ICU+) improved risk-prediction. Though not created to predict death and ventilator use, these models did so with high accuracy (Table 2). Conclusion: The CORONA-ICU and -ICU+ models, composed of commonly collected risk factors without or with medications, were shown to be highly predictive of ICU admissions, death, and ventilator use. These models can be efficiently derived and effectively identify high-risk patients who require more careful observation and increased use of advanced medical therapies.

Skin Integrity in Critically Ill and Injured Children

2007 ◽  
Vol 16 (6) ◽  
pp. 568-574 ◽  
Author(s):  
Christine A. Schindler ◽  
Theresa A. Mikhailov ◽  
Kay Fischer ◽  
Gloria Lukasiewicz ◽  
Evelyn M. Kuhn ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Ventilatory Support ◽  
Pediatric Intensive Care ◽  
Cost Of Care ◽  
Respiratory Illnesses ◽  
Skin Breakdown ◽  
Potential Risk Factors

Background Skin breakdown increases the cost of care, may lead to increased morbidity, and has negative psychosocial implications because of secondary scarring or alopecia. The scope of this problem has not been widely studied in critically ill and injured children. Objectives To determine the incidence of skin breakdown in critically ill and injured children and to compare the characteristics of patients who experience skin breakdown with those of patients who do not. Methods Admission and follow-up data for a 15-week period were collected retrospectively on children admitted to a large pediatric intensive care unit. The incidence of skin breakdown was calculated. The risk for skin breakdown associated with potential risk factors (relative risk) and 95% confidence intervals were determined. Results The sample consisted of 401 distinct stays in the intensive care unit for 373 patients. During the 401 stays, skin breakdown occurred in 34 (8.5%), redness in 25 (6.2%), and breakdown and redness in 13 (3.2%); the overall incidence was 18%. Patients who had skin breakdown or redness were younger, had longer stays, and were more likely to have respiratory illnesses and require mechanical ventilatory support than those who did not. Patients who had skin breakdown or redness had a higher risk of mortality than those who did not. Conclusions Risk factors for skin breakdown were similar to those previously reported. Compared with children of other ages, children 2 years or younger are at higher risk for skin breakdown.

Epidemiologic, Clinical, and Economic Evaluation of an Outbreak of Clonal Multidrug-ResistantAcinetobacter baumanniiInfection in a Surgical Intensive Care Unit

2007 ◽  
Vol 28 (11) ◽  
pp. 1247-1254 ◽  
Author(s):  
Lisa S. Young ◽  
Allison L. Sabel ◽  
Connie S. Price
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Infection Control ◽  
Significant Risk ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Duration Of Hospitalization ◽  
Tract Infections ◽  
Pulsatile Lavage

Objectives.To determine risk factors for acquisition of multidrug-resistant (MDR)Acinetobacter baumanniiinfection during an outbreak, to describe the clinical manifestations of infection, and to ascertain the cost of infection.Design.Case-control study.Setting.Surgical intensive care unit in a 400-bed urban teaching hospital and level 1 trauma center.Patients.Case patients received a diagnosis of infection due toA. baumanniiisolates with a unique pattern of drug resistance (ie, susceptible to imipenem, variably susceptible to aminoglycosides, and resistant to all other antibiotics) between December 1, 2004, and August 31, 2005. Case patients were matched 1 : 1 with concurrently hospitalized control patients. Isolates' genetic relatedness was established by pulsed-field gel electrophoresis.Results.Sixty-seven patients met the inclusion criteria. Case and control patients were similar with respect to age, duration of hospitalization, and Charlson comorbidity score. MDRA. baumanniiinfections included ventilator-associated pneumonia (in 56.7% of patients), bacteremia (in 25.4%), postoperative wound infections (in 25.4%), central venous catheter-associated infections (in 20.9%), and urinary tract infections (in 10.4%). Conditional multiple logistic regression was used to determine statistically significant risk factors on the basis of results from the bivariate analyses. The duration of hospitalization and healthcare charges were modeled by multiple linear regression. Significant risk factors included higher Acute Physiology and Chronic Health Evaluation II score (odds ratio [OR], 1.1 per point increase;P= .06), duration of intubation (OR, 1.4 per day intubated;P<.01), exposure to bronchoscopy (OR, 22.7;P= .03), presence of chronic pulmonary disease (OR, 77.7;P= .02), receipt of fluconazole (OR, 73.3;P<.01), and receipt of levofloxacin (OR, 11.5;P= .02). Case patients had a mean of $60,913 in attributable excess patient charges and a mean of 13 excess hospital days.Interventions.Infection control measures included the following: limitations on the performance of pulsatile lavage wound debridement, the removal of items with upholstered surfaces, and the implementation of contact isolation for patients with suspected MDRA. baumanniiinfection.Conclusions.This large outbreak of infection due to clonal MDRA. baumanniicaused significant morbidity and expense. Aerosolization of MDRA. baumanniiduring pulsatile lavage debridement of infected wounds and during the management of respiratory secretions from colonized and infected patients may promote widespread environmental contamination. Multifaceted infection control interventions were associated with a decrease in the number of MDRA. baumanniiisolates recovered from patients.

scholarly journals Development and Evaluation of Models to Predict Readmission or Death After Discharge from Intensive Care 

2021 ◽  
Author(s):  
Jamie M Boyd ◽  
Matthew T James ◽  
Danny J Zuege ◽  
Henry Thomas Stelfox
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Local Data ◽  
Predictive Values ◽  
Risk Prediction Models ◽  
Icu Discharge

Abstract Background Patients being discharged from the intensive care unit (ICU) have variable risks of subsequent readmission or death; however, there is limited understanding of how to predict individual patient risk. We sought to derive risk prediction models for ICU readmission or death after ICU discharge to guide clinician decision-making. Methods Systematic review and meta-analysis to identify risk factors. Development and validation of risk prediction models using two retrospective cohorts of patients discharged alive from medical-surgical ICUs (n = 3 ICUs, n = 11,291 patients; n = 14 ICUs, n = 11,400 patients). Models were developed using literature and data-derived weighted coefficients. Results Sixteen variables identified from the systematic review were used to develop four risk prediction models. In the validation cohort there were 795 (7%) patients who were re-admitted to ICU and 703 (7%) patients who died after ICU discharge. The area under the curve (AUROC) for ICU readmission for the literature (0.615 [95%CI: 0.593, 0.637]) and data (0.652 [95%CI: 0.631, 0.674]) weighted models showed poor discrimination. The AUROC for death after ICU discharge for the literature (0.708 [95%CI: 0.687, 0.728]) and local data weighted (0.752 [95%CI: 0.733, 0.770]) models showed good discrimination. The negative predictive values for ICU readmission and death after ICU discharge ranged from 94%-98%. Conclusions Identifying risk factors and weighting coefficients using systematic review and meta-analysis to develop prediction models is feasible and can identify patients at low risk of ICU readmission or death after ICU discharge.

scholarly journals Rare cutaneous manifestation of COVID-19

2021 ◽  
Vol 14 (1) ◽  
pp. e240863
Author(s):  
Lisa Killion ◽  
Paula Evelyn Beatty ◽  
Asad Salim
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Neuropathic Pain ◽  
Intensive Care ◽  
Peripheral Neuropathy ◽  
Disease Burden ◽  
Ventilatory Support ◽  
Cutaneous Manifestations ◽  
History Of ◽  
Inflammatory Syndrome

The COVID-19 pandemic has resulted in an incomparable disease burden worldwide. One of the main contributors stems from the multisystem inflammatory syndrome associated with SARS-CoV-2 infection. The numbers of those affected continue to rise with the increasing number of confirmed COVID-19 cases. However, we are yet to fully comprehend the risk factors, disease progression and prognosis for individuals affected. We describe a case of a previously healthy 17-year-old boy who tested positive for the SARS-CoV-2 virus. He presented with a 5-day history of mild influenza-like symptoms, however, quickly required ventilatory support in the intensive care unit. Two months postdischarge, he developed an isolated petechial rash on his palms and soles. His cutaneous presentation was in association with a mixed sensorimotor peripheral neuropathy, debilitating neuropathic pain and intermittent respiratory distress. We postulate that cutaneous manifestations post-COVID-19 could be indicatory of the newly identified multisystem inflammatory syndrome.

scholarly journals Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

10.2196/23128 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23128
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

Incidence and Risk Factors of Early Onset Neonatal AKI

2019 ◽  
Vol 2019 (2) ◽  
pp. 1-1
Author(s):  
Jennifer R. Charlton ◽  
Louis Boohaker ◽  
David Askenazi ◽  
Patrick D. Brophy ◽  
Carl D’Angio ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Confidence Interval ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Admission Diagnosis ◽  
Clinical Trial Registry ◽  
Risk Of Death ◽  
Duration Of Hospitalization

Background and objectivesNeonatal AKI is associated with poor short- and long-term outcomes. The objective of this study was to describe the risk factors and outcomes of neonatal AKI in the first postnatal week.Design, setting, participants, & measurementsThe international retrospective observational cohort study, Assessment of Worldwide AKI Epidemiology in Neonates (AWAKEN), included neonates admitted to a neonatal intensive care unit who received at least 48 hours of intravenous fluids. Early AKI was defined by an increase in serum creatinine >0.3 mg/dl or urine output <1 ml/kg per hour on postnatal days 2–7, the neonatal modification of Kidney Disease: Improving Global Outcomes criteria. We assessed risk factors for AKI and associations of AKI with death and duration of hospitalization.ResultsTwenty-one percent (449 of 2110) experienced early AKI. Early AKI was associated with higher risk of death (adjusted odds ratio, 2.8; 95% confidence interval, 1.7 to 4.7) and longer duration of hospitalization (parameter estimate: 7.3 days 95% confidence interval, 4.7 to 10.0), adjusting for neonatal and maternal factors along with medication exposures. Factors associated with a higher risk of AKI included: outborn delivery; resuscitation with epinephrine; admission diagnosis of hyperbilirubinemia, inborn errors of metabolism, or surgical need; frequent kidney function surveillance; and admission to a children’s hospital. Those factors that were associated with a lower risk included multiple gestations, cesarean section, and exposures to antimicrobials, methylxanthines, diuretics, and vasopressors. Risk factors varied by gestational age strata.ConclusionsAKI in the first postnatal week is common and associated with death and longer duration of hospitalization. The AWAKEN study demonstrates a number of specific risk factors that should serve as “red flags” for clinicians at the initiation of the neonatal intensive care unit course.Clinical Trial registry name and registration number:Assessment of Worldwide AKI Epidemiology in Neonates (AWAKEN), NCT02443389

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