scholarly journals Laboratory findings associated with severe illness and mortality among hospitalized individuals with coronavirus disease 2019 in Eastern Massachusetts

2020 ◽  
Author(s):  
Victor M. Castro ◽  
Thomas H. McCoy ◽  
Roy H. Perlis
Keyword(s):  
Mechanical Ventilation ◽  
Severe Acute Respiratory Syndrome ◽  
Risk Stratification ◽  
Mortality Risk ◽  
Prediction Models ◽  
Severe Illness ◽  
Laboratory Findings ◽  
Laboratory Values ◽  
Inpatient Settings ◽  
Eastern Massachusetts

AbstractImportanceThe coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented stress on health systems across the world, and reliable estimates of risk for adverse hospital outcomes are needed.ObjectiveTo quantify admission laboratory and comorbidity features associated with critical illness and death and mortality risk across 6 Eastern Massachusetts hospitals.DesignRetrospective cohort study using hospital course, prior diagnoses, and laboratory values through June 5, 2020.SettingEmergency department and inpatient settings from 2 academic medical centers and 4 community hospitals.ParticipantsAll individuals with hospital admission and positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing across these 6 hospitals.Main Outcome or Measuresevere illness defined by ICU admission, mechanical ventilation, or death.ResultsAmong 2,511 hospitalized individuals who tested positive for SARS-CoV-2, 215 (8.6%) were eventually admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. L1-regression models developed in 3 of these hospitals yielded area under ROC curve (AUC) of 0.823 for severe illness and 0.847 for mortality in the 3 held-out hospitals. In total, 78% of deaths occurred in the highest-risk mortality quintile.Conclusions and RelevanceSpecific admission laboratory studies in concert with sociodemographic features and prior diagnosis facilitate risk stratification among individuals hospitalized for COVID-19.Funding1R56MH115187-01Trial RegistrationNoneKey PointsQuestionHow well can sociodemographic features, laboratory values, and comorbiditeis of individuals hospitalized with coronavirus disease 2019 (COVID-19) in Eastern Massachusetts through June 5, 2020 predict severe illness course?FindingsAmong 2,511 hospitalized individuals who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and were admitted to one of six hospitals, 215 (8.6%) were eventually admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. In a risk prediction model, 78% of deaths occurred in the top mortality-risk quintile.MeaningSimple prediction models may assist in risk stratification among hospitalized COVID-19 patients.

scholarly journals Predictive Value of MR-proADM in the Risk Stratification of COVID-19 Patients Assessed at the Triage of the Emergency Department

2021 ◽  
Author(s):  
Marilena Minieri ◽  
Vito N. Di Lecce ◽  
Maria Stella Lia ◽  
Massimo Maurici ◽  
Francesca Leonardis ◽  
...  
Keyword(s):  
Emergency Department ◽  
Mechanical Ventilation ◽  
Risk Stratification ◽  
Hospital Mortality ◽  
Mortality Risk ◽  
Predictive Value ◽  
Invasive Mechanical Ventilation ◽  
Discriminative Performance ◽  
Cox Regression Analysis ◽  
Laboratory Biomarkers

Abstract Background In the last two pandemic years, the Emergency Departments (ED) have been overrun with COVID-19 suspicious patients, creating a pressing need to optimize resources through risk stratification for those patients. For this reason, the assessment of prognostic tools and biomarkers have been necessary. Some dataon the role played by laboratory biomarkers in the early risk stratification of COVID-19 patients have been recently published. The aim of this study is to assess the potential role of the new biomarker mid-regional proadrenomedullim (MR-proADM) in stratifying the in-hospital mortality risk of COVID-19 patients at the triage in order to help the emergency physician in the decision-making process. A further goal of the present study is to evaluate whether MR-proADM together with other biochemical markers could play a key role in assessing the correct care level of these patients by predicting who could need intensive care and ventilation. Methods Data from 321 consecutive patients admitted to the triage of the emergency department with a COVID-19 infection were analyzed. Epidemiological, demographic, clinical, laboratory, and outcome data were assessed. C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), d-dimer and MR-proADM blood levels were also evaluated. Results All the biomarkers evaluated showed significant increased values at admission in the emergency department in non-survivorsvs survivors as well in ventilated as compared to non-ventilated patients. Moreover, all the biomarkers analyzed showed animportant role in predicting mortality, need of invasive mechanical ventilation (IMV) and non-invasive mechanical ventilation (NIMV) in patients admitted at the emergency department with COVID-19 infection as analyzed by the univariate Cox regression analysis. Pooling together both clinical and laboratory variables in a multivariate analysis, all biomarkers, except for PCT, seem to play a significant role in the mortality risk stratification at admission in the emergency department. Similarly, an increase of MR-proADM level at ED admission resulted independently associated with a threefold times higher risk of IMV. LDH showed a smaller but still significant power. CRP only showed a significant predictive value for the need of NIMV. In patients COVID-19 positive, MR-proADM assessed at the admission in the triage showed a good discriminative performance both for in-hospital mortality (AUC 0,85) and for prediction of IMV (AUC 0,81), whereas it was less effective for NIMV prediction (AUC 0,71). ROC curves and AUC resulted significantly greater for MR-proADM as compared to other laboratory biomarkers for the primary endpoint, i.e. in-hospital mortality, with the exception of CRP. Conclusion This study shows that MR-proADM seems to be particularly effective for early predicting mortality and the need of ventilation in COVID-19 patients admitted to the emergency department.

scholarly journals 699 Sleep Health Traits and COVID-19: Mortality Risk from the UK Biobank

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A273-A273
Author(s):  
Xi Zheng ◽  
Ma Cherrysse Ulsa ◽  
Peng Li ◽  
Lei Gao ◽  
Kun Hu
Keyword(s):  
Risk Factors ◽  
Sleep Apnea ◽  
Sleep Duration ◽  
Sex Education ◽  
Mortality Risk ◽  
Self Report ◽  
Severe Illness ◽  
Uk Biobank ◽  
Sleep Health ◽  
The Uk

Abstract Introduction While there is emerging evidence for acute sleep disruption in the aftermath of coronavirus disease 2019 (COVID-19), it is unknown whether sleep traits contribute to mortality risk. In this study, we tested whether earlier-life sleep duration, chronotype, insomnia, napping or sleep apnea were associated with increased 30-day COVID-19 mortality. Methods We included 34,711 participants from the UK Biobank, who presented for COVID-19 testing between March and October 2020 (mean age at diagnosis: 69.4±8.3; range 50.2–84.6). Self-reported sleep duration (less than 6h/6-9h/more than 9h), chronotype (“morning”/”intermediate”/”evening”), daytime dozing (often/rarely), insomnia (often/rarely), napping (often/rarely) and presence of sleep apnea (ICD-10 or self-report) were obtained between 2006 and 2010. Multivariate logistic regression models were used to adjust for age, sex, education, socioeconomic status, and relevant risk factors (BMI, hypertension, diabetes, respiratory diseases, smoking, and alcohol). Results The mean time between sleep measures and COVID-19 testing was 11.6±0.9 years. Overall, 5,066 (14.6%) were positive. In those who were positive, 355 (7.0%) died within 30 days (median = 8) after diagnosis. Long sleepers (>9h vs. 6-9h) [20/103 (19.4%) vs. 300/4,573 (6.6%); OR 2.09, 95% 1.19–3.64, p=0.009), often daytime dozers (OR 1.68, 95% 1.04–2.72, p=0.03), and nappers (OR 1.52, 95% 1.04–2.23, p=0.03) were at greater odds of mortality. Prior diagnosis of sleep apnea also saw a two-fold increased odds (OR 2.07, 95% CI: 1.25–3.44 p=0.005). No associations were seen for short sleepers, chronotype or insomnia with COVID-19 mortality. Conclusion Data across all current waves of infection show that prior sleep traits/disturbances, in particular long sleep duration, daytime dozing, napping and sleep apnea, are associated with increased 30-day mortality after COVID-19, independent of health-related risk factors. While sleep health traits may reflect unmeasured poor health, further work is warranted to examine the exact underlying mechanisms, and to test whether sleep health optimization offers resilience to severe illness from COVID-19. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.

Risk Stratification in Acute Pulmonary Embolism: The Latest Algorithms

2021 ◽  
Vol 42 (02) ◽  
pp. 183-198
Author(s):  
Georgios A. Triantafyllou ◽  
Oisin O'Corragain ◽  
Belinda Rivera-Lebron ◽  
Parth Rali
Keyword(s):  
Pulmonary Embolism ◽  
Risk Stratification ◽  
Prediction Models ◽  
Bleeding Risk ◽  
Hemodynamic Instability ◽  
Reperfusion Therapy ◽  
Mortality Prediction ◽  
Risk Scores ◽  
Short Term Mortality ◽  
Urgent Patient

AbstractPulmonary embolism (PE) is a common clinical entity, which most clinicians will encounter. Appropriate risk stratification of patients is key to identify those who may benefit from reperfusion therapy. The first step in risk assessment should be the identification of hemodynamic instability and, if present, urgent patient consideration for systemic thrombolytics. In the absence of shock, there is a plethora of imaging studies, biochemical markers, and clinical scores that can be used to further assess the patients' short-term mortality risk. Integrated prediction models incorporate more information toward an individualized and precise mortality prediction. Additionally, bleeding risk scores should be utilized prior to initiation of anticoagulation and/or reperfusion therapy administration. Here, we review the latest algorithms for a comprehensive risk stratification of the patient with acute PE.

scholarly journals Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis

2020 ◽  
Vol 58 (7) ◽  
pp. 1021-1028 ◽  
Author(s):  
Brandon Michael Henry ◽  
Maria Helena Santos de Oliveira ◽  
Stefanie Benoit ◽  
Mario Plebani ◽  
Giuseppe Lippi
Keyword(s):  
Serum Ferritin ◽  
Weighted Mean Difference ◽  
Meta Analysis ◽  
Severe Disease ◽  
Severe Illness ◽  
China National Knowledge Infrastructure ◽  
Discriminative Ability ◽  
Laboratory Findings ◽  
Liver And Kidney ◽  
Wbc Count

AbstractBackgroundAs coronavirus disease 2019 (COVID-19) pandemic rages on, there is urgent need for identification of clinical and laboratory predictors for progression towards severe and fatal forms of this illness. In this study we aimed to evaluate the discriminative ability of hematologic, biochemical and immunologic biomarkers in patients with and without the severe or fatal forms of COVID-19.MethodsAn electronic search in Medline (PubMed interface), Scopus, Web of Science and China National Knowledge Infrastructure (CNKI) was performed, to identify studies reporting on laboratory abnormalities in patients with COVID-19. Studies were divided into two separate cohorts for analysis: severity (severe vs. non-severe and mortality, i.e. non-survivors vs. survivors). Data was pooled into a meta-analysis to estimate weighted mean difference (WMD) with 95% confidence interval (95% CI) for each laboratory parameter.ResultsA total number of 21 studies was included, totaling 3377 patients and 33 laboratory parameters. While 18 studies (n = 2984) compared laboratory findings between patients with severe and non-severe COVID-19, the other three (n = 393) compared survivors and non-survivors of the disease and were thus analyzed separately. Patients with severe and fatal disease had significantly increased white blood cell (WBC) count, and decreased lymphocyte and platelet counts compared to non-severe disease and survivors. Biomarkers of inflammation, cardiac and muscle injury, liver and kidney function and coagulation measures were also significantly elevated in patients with both severe and fatal COVID-19. Interleukins 6 (IL-6) and 10 (IL-10) and serum ferritin were strong discriminators for severe disease.ConclusionsSeveral biomarkers which may potentially aid in risk stratification models for predicting severe and fatal COVID-19 were identified. In hospitalized patients with respiratory distress, we recommend clinicians closely monitor WBC count, lymphocyte count, platelet count, IL-6 and serum ferritin as markers for potential progression to critical illness.

scholarly journals Risk assessment in precapillary pulmonary hypertension: a comparative analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Thomas Sonnweber ◽  
Eva-Maria Schneider ◽  
Manfred Nairz ◽  
Igor Theurl ◽  
Günter Weiss ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Pulmonary Hypertension ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Assessment Tool ◽  
Risk Models ◽  
Non Invasive

Abstract Background Risk stratification is essential to assess mortality risk and guide treatment in patients with precapillary pulmonary hypertension (PH). We herein compared the accuracy of different currently used PH risk stratification tools and evaluated the significance of particular risk parameters. Methods We conducted a retrospective longitudinal observational cohort study evaluating seven different risk assessment approaches according to the current PH guidelines. A comprehensive assessment including multi-parametric risk stratification was performed at baseline and 4 yearly follow-up time-points. Multi-step Cox hazard analysis was used to analyse and refine risk prediction. Results Various available risk models effectively predicted mortality in patients with precapillary pulmonary hypertension. Right-heart catheter parameters were not essential for risk prediction. Contrary, non-invasive follow-up re-evaluations significantly improved the accuracy of risk estimations. A lack of accuracy of various risk models was found in the intermediate- and high-risk classes. For these patients, an additional evaluation step including assessment of age and right atrium area improved risk prediction significantly. Discussion Currently used abbreviated versions of the ESC/ERS risk assessment tool, as well as the REVEAL 2.0 and REVEAL Lite 2 based risk stratification, lack accuracy to predict mortality in intermediate- and high-risk precapillary pulmonary hypertension patients. An expanded non-invasive evaluation improves mortality risk prediction in these individuals.

scholarly journals Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients

2021 ◽  
Author(s):  
Victor Alfonso Rodriguez ◽  
Shreyas Bhave ◽  
Ruijun Chen ◽  
Chao Pang ◽  
George Hripcsak ◽  
...  
Keyword(s):  
At Risk ◽  
Mechanical Ventilation ◽  
Renal Replacement Therapy ◽  
Receiver Operating Characteristic ◽  
Replacement Therapy ◽  
Operating Characteristic ◽  
Prediction Models ◽  
External Validation ◽  
Electronic Health Record Data ◽  
Receiver Operating

Abstract Objective Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. Materials and Methods For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output. Results The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. Discussion Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. Conclusions We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

scholarly journals 375. COVID 19 infection in a Massachusetts community hospital

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S257-S258
Author(s):  
Raul Davaro ◽  
alwyn rapose
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Essential Hypertension ◽  
Severe Acute Respiratory Syndrome ◽  
High Mortality ◽  
Case Series ◽  
World Health ◽  
Minimal Risk ◽  
The Impact

Abstract Background The ongoing pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections has led to 105690 cases and 7647 deaths in Massachusetts as of June 16. Methods The study was conducted at Saint Vincent Hospital, an academic health medical center in Worcester, Massachusetts. The institutional review board approved this case series as minimal-risk research using data collected for routine clinical practice and waived the requirement for informed consent. All consecutive patients who were sufficiently medically ill to require hospital admission with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by positive result on polymerase chain reaction testing of a nasopharyngeal sample were included. Results A total of 109 consecutive patients with COVID 19 were admitted between March 15 and May 31. Sixty one percent were men, the mean age of the cohort was 67. Forty one patients (37%) were transferred from nursing homes. Twenty seven patients died (24%) and the majority of the dead patients were men (62%). Fifty one patients (46%) required admission to the medical intensive care unit and 34 necessitated mechanical ventilation, twenty two patients on mechanical ventilation died (63%). The most common co-morbidities were essential hypertension (65%), obesity (60%), diabetes (33%), chronic kidney disease (22%), morbid obesity (11%), congestive heart failure (16%) and COPD (14%). Five patients required hemodialysis. Fifty five patients received hydroxychloroquine, 24 received tocilizumab, 20 received convalescent plasma and 16 received remdesivir. COVID 19 appeared in China in late 2019 and was declared a pandemic by the World Health Organization on March 11, 2020. Our study showed a high mortality in patients requiring mechanical ventilation (43%) as opposed to those who did not (5.7%). Hypertension, diabetes and obesity were highly prevalent in this aging population. Our cohort was too small to explore the impact of treatment with remdesivir, tocilizumab or convalescent plasma. Conclusion In this cohort obesity, diabetes and essential hypertension are risk factors associated with high mortality. Patients admitted to the intensive care unit who need mechanical ventilation have a mortality approaching 50 %. Disclosures All Authors: No reported disclosures

scholarly journals Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e044500
Author(s):  
Yauhen Statsenko ◽  
Fatmah Al Zahmi ◽  
Tetiana Habuza ◽  
Klaus Neidl-Van Gorkom ◽  
Nazar Zaki
Keyword(s):  
Lymphocyte Count ◽  
Prediction Models ◽  
Model Performance ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Supervised Machine Learning ◽  
Study Cohort ◽  
Threshold Values ◽  
Laboratory Findings ◽  
Study Results

BackgroundDespite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use.ObjectivesTo identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU).MethodsThe study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening.ResultsWith the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×109/L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL.ConclusionThe performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.

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