Waiting for ICU admission may increase the risk of death—A plea for better resource organization

ObjectiveThe aim of this paper is to describe evolution, epidemiology and clinical outcomes of COVID-19 in subjects tested at or admitted to hospitals in North West London.DesignObservational cohort study.SettingLondon North West Healthcare NHS Trust (LNWH).ParticipantsPatients tested and/or admitted for COVID-19 at LNWH during March and April 2020Main outcome measuresDescriptive and analytical epidemiology of demographic and clinical outcomes (intensive care unit (ICU) admission, mechanical ventilation and mortality) of those who tested positive for COVID-19.ResultsThe outbreak began in the first week of March 2020 and reached a peak by the end of March and first week of April. In the study period, 6183 tests were performed in on 4981 people. Of the 2086 laboratory confirmed COVID-19 cases, 1901 were admitted to hospital. Older age group, men and those of black or Asian minority ethnic (BAME) group were predominantly affected (p<0.05). These groups also had more severe infection resulting in ICU admission and need for mechanical ventilation (p<0.05). However, in a multivariate analysis, only increasing age was independently associated with increased risk of death (p<0.05). Mortality rate was 26.9% in hospitalised patients.ConclusionThe findings confirm that men, BAME and older population were most commonly and severely affected groups. Only older age was independently associated with mortality.

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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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Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

Scientific Reports ◽

10.1038/s41598-021-81844-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Espen Jimenez-Solem ◽

Tonny S. Petersen ◽

Casper Hansen ◽

Christian Hansen ◽

Christina Lioma ◽

...

Keyword(s):

Prediction Models ◽

Severe Disease ◽

External Validation ◽

Area Under The Curve ◽

Predictive Performance ◽

Mortality Prediction ◽

Icu Admission ◽

Risk Of Death ◽

Online Risk ◽

The United Kingdom

AbstractPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

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910. A Comprehensive, One Year, Hospital-Wide Snapshot of All Serious Infectious Complications in People Who Inject Drugs

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1098 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S489-S490

Author(s):

John T Henderson ◽

Evelyn Villacorta Cari ◽

Nicole Leedy ◽

Alice Thornton ◽

Donna R Burgess ◽

...

Keyword(s):

Multivariable Analysis ◽

Vertebral Osteomyelitis ◽

The United States ◽

Infectious Complications ◽

Data Set ◽

Cumulative Impact ◽

Mortality And Morbidity ◽

Icu Admission ◽

Risk Of Death ◽

The Impact

Abstract Background There has been a dramatic rise in IV drug use (IVDU) and its associated mortality and morbidity, however, the scope of this effect has not been described. Kentucky is at the epicenter of this epidemic and is an ideal place to better understand the health complications of IVDU in order to improve outcomes. Methods All adult in-patient admissions to University of Kentucky hospitals in 2018 with an Infectious Diseases (ID) consult and an ICD 9/10 code associated with IVDU underwent thorough retrospective chart review. Demographic, descriptive, and outcome data were collected and analyzed by standard statistical analysis. Results 390 patients (467 visits) met study criteria. The top illicit substances used were methamphetamine (37.2%), heroin (38.2%), and cocaine (10.3%). While only 4.1% of tested patients were HIV+, 74.2% were HCV antibody positive. Endocarditis (41.1%), vertebral osteomyelitis (20.8%), bacteremia without endocarditis (14.1%), abscess (12.4%), and septic arthritis (10.4%) were the most common infectious complications. The in-patient death rate was 3.0%, and 32.2% of patients were readmitted within the study period. The average length of stay was 26 days. In multivariable analysis, infectious endocarditis was associated with a statistically significant increase in risk of death, ICU admission, and hospital readmission. Although not statistically significant, trends toward mortality and ICU admission were identified for patients with prior endocarditis and methadone was correlated with decreased risk of readmission and ICU stay. FIGURE 1: Reported Substances Used FIGURE 2: Comorbidities FIGURE 3: Types of Severe Infectious Complications Conclusion We report on a novel, comprehensive perspective on the serious infectious complications of IVDU in an attempt to measure its cumulative impact in an unbiased way. This preliminary analysis of a much larger dataset (2008-2019) reveals some sobering statistics about the impact of IVDU in the United States. While it confirms the well accepted mortality and morbidity associated with infective endocarditis and bacteremia, there is a significant unrecognized impact of other infectious etiologies. Additional analysis of this data set will be aimed at identifying key predictive factors in poor outcomes in hopes of mitigating them. Disclosures All Authors: No reported disclosures

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Factors associated with COVID-19 related hospitalisation, critical care admission and mortality using linked primary and secondary care data

10.1101/2021.01.19.20241844 ◽

2021 ◽

Author(s):

Lisa Cummins ◽

Irene Ebyarimpa ◽

Nathan Cheetham ◽

Victoria Tzortziou Brown ◽

Katie Brennan ◽

...

Keyword(s):

Risk Factors ◽

Learning Disabilities ◽

Secondary Care ◽

Multivariate Logistic Regression Analysis ◽

Additional Patient ◽

Factors Associated ◽

Icu Admission ◽

Risk Of Death ◽

North East ◽

Increased Risk

AbstractBackgroundTo identify risk factors associated with increased risk of hospitalisation, intensive care unit (ICU) admission and mortality in inner North East London (NEL) during the first UK COVID-19 wave.MethodsMultivariate logistic regression analysis on linked primary and secondary care data from people aged 16 or older with confirmed COVID-19 infection between 01/02/2020-30/06/2020 determined odds ratios (OR), 95% confidence intervals (CI) and p-values for the association between demographic, deprivation and clinical factors with COVID-19 hospitalisation, ICU admission and mortality.ResultsOver the study period 1,781 people were diagnosed with COVID-19, of whom 1,195 (67%) were hospitalised, 152 (9%) admitted to ICU and 400 (23%) died. Results confirm previously identified risk factors: being male, or of Black or Asian ethnicity, or aged over 50. Obesity, type 2 diabetes and chronic kidney disease (CKD) increased the risk of hospitalisation. Obesity increased the risk of being admitted to ICU. Underlying CKD, stroke and dementia in-creased the risk of death. Having learning disabilities was strongly associated with increased risk of death (OR=4.75, 95%CI=(1.91,11.84), p=0.001). Having three or four co-morbidities increased the risk of hospitalisation (OR=2.34,95%CI=(1.55,3.54),p<0.001;OR=2.40, 95%CI=(1.55,3.73), p<0.001 respectively) and death (OR=2.61, 95%CI=(1.59,4.28), p<0.001;OR=4.07, 95% CI= (2.48,6.69), p<0.001 respectively).ConclusionsWe confirm that age, sex, ethnicity, obesity, CKD and diabetes are important determinants of risk of COVID-19 hospitalisation or death. For the first time, we also identify people with learning disabilities and multi-morbidity as additional patient cohorts that need to be actively protected during COVID-19 waves.

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Association of Proteinuria and Hematuria with Acute Kidney Injury and Mortality in Hospitalized Patients with COVID-19

Kidney & Blood Pressure Research ◽

10.1159/000511946 ◽

2020 ◽

Vol 45 (6) ◽

pp. 1018-1032

Author(s):

Imran Chaudhri ◽

Richard Moffitt ◽

Erin Taub ◽

Raji R. Annadi ◽

Minh Hoai ◽

...

Keyword(s):

Acute Kidney Injury ◽

Invasive Mechanical Ventilation ◽

Multivariable Analysis ◽

Kidney Injury ◽

Hospitalized Patients ◽

Hospital Death ◽

Study Cohort ◽

Icu Admission ◽

Risk Of Death ◽

Increased Risk

Introduction: Acute kidney injury (AKI) is strongly associated with poor outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19), but data on the association of proteinuria and hematuria are limited to non-US populations. In addition, admission and in-hospital measures for kidney abnormalities have not been studied separately. Methods: This retrospective cohort study aimed to analyze these associations in 321 patients sequentially admitted between March 7, 2020 and April 1, 2020 at Stony Brook University Medical Center, New York. We investigated the association of proteinuria, hematuria, and AKI with outcomes of inflammation, intensive care unit (ICU) admission, invasive mechanical ventilation (IMV), and in-hospital death. We used ANOVA, t test, χ2 test, and Fisher’s exact test for bivariate analyses and logistic regression for multivariable analysis. Results: Three hundred patients met the inclusion criteria for the study cohort. Multivariable analysis demonstrated that admission proteinuria was significantly associated with risk of in-hospital AKI (OR 4.71, 95% CI 1.28–17.38), while admission hematuria was associated with ICU admission (OR 4.56, 95% CI 1.12–18.64), IMV (OR 8.79, 95% CI 2.08–37.00), and death (OR 18.03, 95% CI 2.84–114.57). During hospitalization, de novo proteinuria was significantly associated with increased risk of death (OR 8.94, 95% CI 1.19–114.4, p = 0.04). In-hospital AKI increased (OR 27.14, 95% CI 4.44–240.17) while recovery from in-hospital AKI decreased the risk of death (OR 0.001, 95% CI 0.001–0.06). Conclusion: Proteinuria and hematuria both at the time of admission and during hospitalization are associated with adverse clinical outcomes in hospitalized patients with COVID-19.

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Emergency tracheal intubation during off-hours is not associated with increased mortality in hospitalized patients: a retrospective cohort study

BMC Anesthesiology ◽

10.1186/s12871-020-01188-3 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Jun-Le Liu ◽

Jian-Wen Jin ◽

Zhong-Meng Lai ◽

Jie-Bo Wang ◽

Jian-Sheng Su ◽

...

Keyword(s):

Cox Regression ◽

Vital Signs ◽

Hospitalized Patients ◽

University Teaching ◽

Factors Affecting ◽

Cox Regression Analysis ◽

Icu Admission ◽

Risk Of Death ◽

Surgical Ward ◽

Significant Factors

Abstract Background The prognosis of hospitalized patients after emergent endotracheal intubation (ETI) remains poor. Our aim was to evaluate the 30-d hospitalization mortality of subjects undergoing ETI during daytime or off-hours and to analyze the possible risk factors affecting mortality. Methods A single-center retrospective study was performed at a university teaching facility from January 2015 to December 2018. All adult inpatients who received ETI in the general ward were included. Information on patient demographics, vital signs, ICU (Intensive care unit) admission, intubation time (daytime or off-hours), the department in which ETI was performed (surgical ward or medical ward), intubation reasons, and 30-d hospitalization mortality after ETI were obtained from a database. Results Over a four-year period, 558 subjects were analyzed. There were more male than female in both groups (115 [70.1%] vs 275 [69.8%]; P = 0.939). A total of 394 (70.6%) patients received ETI during off-hours. The patients who received ETI during the daytime were older than those who received ETI during off-hours (64.95 ± 17.54 vs 61.55 ± 17.49; P = 0.037). The BMI of patients who received ETI during the daytime was also higher than that of patients who received ETI during off-hours (23.08 ± 3.38 vs 21.97 ± 3.25; P < 0.001). The 30-d mortality after ETI was 66.8% (373), which included 68.0% (268) during off-hours and 64.0% (105) during the daytime (P = 0.361). Multivariate Cox regression analysis found that the significant factors for the risk of death within 30 days included ICU admission (HR 0.312, 0.176–0.554) and the department in which ETI was performed (HR 0.401, 0.247–0.653). Conclusions The 30-d hospitalization mortality after ETI was 66.8%, and off-hours presentation was not significantly associated with mortality. ICU admission and ETI performed in the surgical ward were significant factors for decreasing the risk of death within 30 days. Trial registration This trial was retrospectively registered with the registration number of ChiCTR2000038549.

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Assessment of the HScore as a predictor of disease outcome in patients with COVID-19

BMC Pulmonary Medicine ◽

10.1186/s12890-021-01706-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Mohammadreza Bordbar ◽

Anahita Sanaei Dashti ◽

Ali Amanati ◽

Eslam Shorafa ◽

Yasaman Mansoori ◽

...

Keyword(s):

Disease Severity ◽

Hospital Length ◽

Hospital Length Of Stay ◽

Multivariable Logistic Regression Analysis ◽

Hospitalized Children ◽

Icu Admission ◽

Risk Of Death ◽

Low Levels ◽

Medical Wards ◽

Secondary Hemophagocytic Lymphohistiocytosis

AbstractSevere coronavirus disease 2019 (COVID-19) accompanies hypercytokinemia, similar to secondary hemophagocytic lymphohistiocytosis (sHLH). We aimed to find if HScore could predict disease severity in COVID-19. HScore was calculated in hospitalized children and adult patients with a proven diagnosis of COVID-19. The need for intensive care unit (ICU), hospital length of stay (LOS), and in-hospital mortality were recorded. The median HScore was 43.0 (IQR 0.0–63.0), which was higher in those who needed ICU care (59.7, 95% CI 46.4–72.7) compared to those admitted to non-ICU medical wards (38.8, 95% CI 32.2–45.4; P = 0.003). It was also significantly higher in patients who died of COVID-19 (105.1, 95% CI 53.7–156.5) than individuals who survived (41.5, 95% CI 35.8–47.1; P = 0.005). Multivariable logistic regression analysis revealed that higher HScore was associated with a higher risk of ICU admission (adjusted OR = 4.93, 95% CI 1.5–16.17, P = 0.008). The risk of death increased by 20% for every ten units increase in HScore (adjusted OR 1.02, 95% CI 1.00–1.04, P = 0.009). Time to discharge was statistically longer in high HScore levels than low levels (HR = 0.41, 95% CI 0.24–0.69). HScore is much lower in patients with severe COVID-19 than sHLH. Higher HScore is associated with more ICU admission, more extended hospitalization, and a higher mortality rate. A modified HScore with a new cut-off seems more practical in predicting disease severity in patients with severe COVID-19.

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Differences in the clinical characteristics and outcomes of COVID-19 patients in the epicenter and peripheral areas of the pandemic from China: a retrospective, large-sample, comparative analysis

10.21203/rs.3.rs-52333/v2 ◽

2020 ◽

Author(s):

Gang Wang ◽

Feng Ming Luo ◽

Dan Liu ◽

Jia Shen Liu ◽

Ye Wang ◽

...

Keyword(s):

Clinical Characteristics ◽

Lower Airway ◽

Limited Information ◽

Illness Onset ◽

Icu Admission ◽

Risk Of Death ◽

Airway Symptoms ◽

Peripheral Area ◽

Prolonged Duration ◽

Peripheral Areas

Abstract Background: There is limited information on difference of epidemiology, clinical characteristics and outcomes of the initial outbreak of the coronavirus disease (COVID-19) in Wuhan (the epicenter) and Sichuan (the peripheral area) in the early phase of the COVID-19 pandemic. This study was conducted to investigate the differences in the epidemiological and clinical characteristics of patients with COVID-19 between the epicenter and peripheral areas of pandemic and thereby generate information that would be potentially helpful in formulating clinical practice recommendations to tackle the COVID-19 pandemic.Methods: The Sichuan & Wuhan Collaboration Research Group for COVID-19 established two retrospective cohorts that separately reflect the epicenter and peripheral area during the early pandemic. The epidemiology, clinical characteristics and outcomes of patients in the two groups were compared. Multivariate regression analyses were used to estimate the adjusted odds ratios (aOR) with regard to the outcomes.Results: The Wuhan (epicenter) cohort included 710 randomly selected patients, and the peripheral (Sichuan) cohort included 474 consecutive patients. A higher proportion of patients from the periphery had upper airway symptoms, whereas a lower proportion of patients in the epicenter had lower airway symptoms and comorbidities. Patients in the epicenter had a higher risk of death (aOR=7.64), intensive care unit (ICU) admission (aOR=1.66), delayed time from illness onset to hospital and ICU admission (aOR=6.29 and aOR=8.03, respectively), and prolonged duration of viral shedding (aOR=1.64). Conclusions: The worse outcomes in the epicenter could be explained by the prolonged time from illness onset to hospital and ICU admission. This could potentially have been associated with elevated systemic inflammation secondary to organ dysfunction and prolonged duration of virus shedding independent of age and comorbidities. Thus, early supportive care could achieve better clinical outcomes.

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DEVELOPING AND VALIDATING COVID-19 ADVERSE OUTCOME RISK PREDICTION MODELS FROM A BI-NATIONAL EUROPEAN COHORT OF 5594 PATIENTS

10.1101/2020.10.06.20207209 ◽

2020 ◽

Author(s):

Espen Jimenez-Solem ◽

Tonny S Petersen ◽

Casper Hansen ◽

Christian Hansen ◽

Christina Lioma ◽

...

Keyword(s):

Hospital Admission ◽

Organ Dysfunction ◽

Prediction Models ◽

Severe Disease ◽

External Validation ◽

Manuscript Preparation ◽

Design Data ◽

Prognostic Features ◽

Icu Admission ◽

Risk Of Death

ABSTRACTBackgroundPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that Machine Learning (ML) models could be used to predict risks at different stages of management (at diagnosis, hospital admission and ICU admission) and thereby provide insights into drivers and prognostic markers of disease progression and death.MethodsFrom a cohort of approx. 2.6 million citizens in the two regions of Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. A cohort of SARS- CoV-2 positive cases from the United Kingdom Biobank was used for external validation.FindingsThe ML models predicted the risk of death (Receiver Operation Characteristics – Area Under the Curve, ROC-AUC) of 0.904 at diagnosis, 0.818, at hospital admission and 0.723 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. We identified some common risk factors, including age, body mass index (BMI) and hypertension as driving factors, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission.InterpretationML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. Prognostic features included age, BMI and hypertension, although markers of shock and organ dysfunction became more important in more severe cases.We provide access to an online risk calculator based on these findings.FundingThe study was funded by grants from the Novo Nordisk Foundation to MS (#NNF20SA0062879 and #NNF19OC0055183) and MN (#NNF20SA0062879). The foundation took no part in project design, data handling and manuscript preparation.

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