Dataset Information

SuPAR Cut-Offs for Risk Stratification in Patients With Symptoms of COVID-19.

ABSTRACT:

Objectives

Elevated soluble urokinase Plasminogen Activator Receptor (suPAR) is a biomarker associated with adverse outcomes. We aimed to investigate the associations between plasma suPAR levels (testing the cut-offs ⩽4, 4-6, and ⩾6 ng/mL) with risk of 14-day mortality, and with the risk of mechanical ventilation in patients that tested positive for SARS-CoV-2.

Methods

Observational cohort study of patients presenting with symptoms of COVID-19 at Department of Emergency Medicine, Amager and Hvidovre Hospital, Denmark from March 19th, 2020 to April 3rd, 2020. Plasma suPAR was measured using suPARnostic technologies. Patients were followed for development of mechanical ventilation and mortality for 14 days. Validation of our findings were carried out in a similar sized COVID-19 patient cohort from Mikkeli Central Hospital, Finland.

Results

Among 386 patients with symptoms of COVID-19, the median (interquartile range) age was 64 years (46-77), 57% were women, median suPAR was 4.0 ng/mL (2.7-5.9). In total, 35 patients (9.1%) died during the 14 days follow-up. Patients with suPAR ⩽4 ng/mL (N = 196; 50.8%) had a low risk of mortality (N = 2; 1.0%; negative predictive value of 99.0%, specificity 55.3%, sensitivity 95.2%, positive predictive value 17.4%). Among patients with suPAR ⩾6 ng/mL (N = 92; 23.8%), 16 died (17.4%). About 99 patients (25.6%) tested positive for SARS CoV-2 and of those 12 (12.1%) developed need for mechanical ventilation. None of the SARS-CoV-2 positive patients with suPAR ⩽4 ng/mL (N = 28; 38.8%) needed mechanical ventilation or died. The Mikkeli Central Hospital validation cohort confirmed our findings concerning suPAR cut-offs for risk of development of mechanical ventilation and mortality.

Conclusions

Patients with symptoms of COVID-19 and suPAR ⩽4 or ⩾6 ng/mL had low or high risk, respectively, concerning the need for mechanical ventilation or mortality. We suggest cut-offs for identification of risk groups in patients presenting to the ED with symptoms of or confirmed COVID-19.

SUBMITTER: Altintas I

PROVIDER: S-EPMC8371731 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesWe investigated specific lactate thresholds for adverse outcomes in patients presenting to emergency departments (EDs) with suspected sepsis identified based on the performance of a sepsis screening algorithm.Design and settingA standardised sepsis bundle was implemented across public hospitals in New South Wales, Australia, as a quality improvement initiative. A register of all adult ED presentations (≥18 years) meeting predefined criteria for sepsis was created, using a combination of data linkage and direct reporting from 97 participating sites.ParticipantsA total of 12 349 adult ED presentations with 8310 (67.3%) having serum lactate analysis on arrival. Analysis of outcomes was based on dataset for 12 349 subjects obtained through multiple imputation for missing data.InterventionsA sepsis management bundle including early antibiotic prescribing, fluid therapy and referral to intensive care unit (ICU) services was implemented.Outcome measuresA primary composite adverse event (AE) outcome of inhospital mortality (IHM) and/or prolonged ICU stay ≥72 hours (ICU 72 hours) was used for this study.ResultsThere was statistically significant increase both in the ORs of AE and IHM with each integer increase in serum lactate values. After adjusting for the presence of hypotension, the estimated ORs for the combined AE outcome were 2.71 (95% CI 2.05 to 3.57), 2.65 (95% CI 2.29 to 3.08), 3.10 (95% CI 2.71 to 3.53) and 3.89 (95% CI 3.36 to 4.50) for serum lactate levels at or above 1, 2, 3 and 4 mmol/L, respectively. The corresponding ORs for IHM were 2.93 (95% CI 2.08 to 4.13), 2.77 (95% CI 2.34 to 3.29), 3.26 (95% CI 2.80 to 3.80) and 4.01 (95% CI 3.40 to 4.73), respectively (all P<0.0001). More than 10% of patients with suspected sepsis and with serum lactate ≥2 mmol/L experienced a prolonged ICU stay or died in hospital.ConclusionsED sepsis screening algorithms intended to identify patient adverse outcomes should incorporate a serum lactate cut-off of ≥2 mmol/L as a threshold for the initiation of specific interventions and increased monitoring.

Project description:BackgroundStudies have reported an increase in the prevalence of depression during the COVID-19 pandemic. The accuracy of screening tools may change with the prevalence and distribution of a disease in a population or sample: the "Spectrum Effect".MethodsFirst, we selected commonly used screening tools and developed search strategies for the inclusion of original studies during the pandemic. Second, we searched PsycINFO, EMBASE, and MEDLINE from March 2020 to September 2022 to obtain original studies that investigated the accuracy of depression screening tools during the pandemic. We then searched these databases to identify meta-analyses summarizing the accuracy of these tools conducted before the pandemic and compared the optimal cut-offs for depression screening tools during the pandemic with those before.ResultFour original studies evaluating the optimal cut-offs for four screening tools (Beck Depression Inventory [BDI-II], Hospital Anxiety and Depression Scale-Depression [HADS-D], Patient Health Questionnaire-9 [PHQ-9], and Geriatric Depression Scale-4 [GDS-4]) were published during the pandemic. Four meta-analyses summarizing these tools before the pandemic. We found that the optimal cut-off of BDI-II was 14 during the pandemic (23.8% depression prevalence, screening patients with Type 2 diabetes) and 14.5 before the pandemic (17.6% depression prevalence, screening psychiatric, primary care, and healthy populations); HADS-D was 10 during the pandemic (23.8% depression prevalence, screening patients with type 2 diabetes) and 7 before the pandemic (15.0% depression prevalence, screening medically ill patients); PHQ-9 was 11 during the pandemic (14.5% depression prevalence, screening university students) and 8 before the pandemic (10.9% depression prevalence, screening the unrestricted population), and GDS-4 was 1.8 during the pandemic (29.0% depression prevalence, screening adults seen in a memory clinic setting) and 3 before the pandemic (18.5% depression prevalence, screening older adults).ConclusionThe optimal cut-off for different screening tools may be sensitive to changes in study populations and reference standards. And potential spectrum effects that should be considered in post-COVID time which aiming to improve diagnostic accuracy.

Project description:Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators. Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared. Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators. Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.