Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients.

ABSTRACT: Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

SUBMITTER: Yanamala N

PROVIDER: S-EPMC8178379 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

COVID-19 versus seasonal influenza 2019-2020: USA.

Project description:Severe mitigation efforts in the USA to reduce the incidence of COVID-19 infections have led to a massive rise in unemployment, social disruption, and appear to be leading to a severe economic depression. In contrast, no such interventions were regarded as necessary to manage seasonal influenza in 2019-2020. Another mitigation approach is proposed for COVID-19 that would allow society to function and yet should still be effective.

| S-EPMC7276496 | biostudies-literature

A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children.

Project description:ObjectivesClinical deterioration in hospitalized children is associated with increased risk of mortality and morbidity. A prediction model capable of accurate and early identification of pediatric patients at risk of deterioration can facilitate timely assessment and intervention, potentially improving survival and long-term outcomes. The objective of this study was to develop a model utilizing vital signs from electronic health record data for predicting clinical deterioration in pediatric ward patients.DesignObservational cohort study.SettingAn urban, tertiary-care medical center.PatientsPatients less than 18 years admitted to the general ward during years 2009-2018.InterventionsNone.Measurements and main resultsThe primary outcome of clinical deterioration was defined as a direct ward-to-ICU transfer. A discrete-time logistic regression model utilizing six vital signs along with patient characteristics was developed to predict ICU transfers several hours in advance. Among 31,899 pediatric admissions, 1,375 (3.7%) experienced the outcome. Data were split into independent derivation (yr 2009-2014) and prospective validation (yr 2015-2018) cohorts. In the prospective validation cohort, the vital sign model significantly outperformed a modified version of the Bedside Pediatric Early Warning System score in predicting ICU transfers 12 hours prior to the event (C-statistic 0.78 vs 0.72; p < 0.01).ConclusionsWe developed a model utilizing six commonly used vital signs to predict risk of deterioration in hospitalized children. Our model demonstrated greater accuracy in predicting ICU transfers than the modified Bedside Pediatric Early Warning System. Our model may promote opportunities for timelier intervention and risk mitigation, thereby decreasing preventable death and improving long-term health.

| S-EPMC7483876 | biostudies-literature

Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.

Project description:OBJECTIVES:We validate a machine learning-based sepsis-prediction algorithm (InSight) for the detection and prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specific data using transfer learning and generalisability to new settings. DESIGN:A machine-learning algorithm with gradient tree boosting. Features for prediction were created from combinations of six vital sign measurements and their changes over time. SETTING:A mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, California, USA) as the primary source, an intensive care unit dataset from the Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) as a transfer-learning source and four additional institutions' datasets to evaluate generalisability. PARTICIPANTS:684?443 total encounters, with 90?353 encounters from June 2011 to March 2016 at UCSF. INTERVENTIONS:None. PRIMARY AND SECONDARY OUTCOME MEASURES:Area under the receiver operating characteristic (AUROC) curve for detection and prediction of sepsis, severe sepsis and septic shock. RESULTS:For detection of sepsis and severe sepsis, InSight achieves an AUROC curve of 0.92 (95% CI 0.90 to 0.93) and 0.87 (95% CI 0.86 to 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 to 0.98) and severe sepsis with an AUROC of 0.85 (95% CI 0.79 to 0.91). CONCLUSIONS:InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customised to novel hospital data using a small fraction of site data and retains strong discrimination across all institutions.

| S-EPMC5829820 | biostudies-other

Risk of thrombotic complications in influenza versus COVID-19 hospitalized patients.

Project description:BackgroundWhereas accumulating studies on patients with coronavirus disease 2019 (COVID-19) report high incidences of thrombotic complications, large studies on clinically relevant thrombosis in patients with other respiratory tract infections are lacking. How this high risk in COVID-19 patients compares to those observed in hospitalized patients with other viral pneumonias such as influenza is unknown.ObjectivesTo assess the incidence of venous and arterial thrombotic complications in hospitalized patients with influenza as opposed to that observed in hospitalized patients with COVID-19.MethodsThis was a retrospective cohort study; we used data from Statistics Netherlands (study period: 2018) on thrombotic complications in hospitalized patients with influenza. In parallel, we assessed the cumulative incidence of thrombotic complications-adjusted for competing risk of death-in patients with COVID-19 in three Dutch hospitals (February 24 to April 26, 2020).ResultsOf the 13 217 hospitalized patients with influenza, 437 (3.3%) were diagnosed with thrombotic complications, versus 66 (11%) of the 579 hospitalized patients with COVID-19. The 30-day cumulative incidence of any thrombotic complication in influenza was 11% (95% confidence interval [CI], 9.4-12) versus 25% (95% CI, 18-32) in COVID-19. For venous thrombotic (VTC) complications and arterial thrombotic complications alone, these numbers were, respectively, 3.6% (95% CI, 2.7-4.6) and 7.5% (95% CI, 6.3-8.8) in influenza versus 23% (95% CI, 16-29) and 4.4% (95% CI, 1.9-8.8) in COVID-19.ConclusionsThe incidence of thrombotic complications in hospitalized patients with influenza was lower than in hospitalized patients with COVID-19. This difference was mainly driven by a high risk of VTC complications in the patients with COVID-19 admitted to the Intensive Care Unit. Remarkably, patients with influenza were more often diagnosed with arterial thrombotic complications.

| S-EPMC8014477 | biostudies-literature

Vital sign predictors of severe influenza among children in an emergent care setting.

Project description:BackgroundDecisions regarding the evaluation of children with influenza infection rely on the likelihood of severe disease. The role of early vital signs as predictors of severe influenza infection in children is not well known. Our objectives were to determine the value of vital signs in predicting hospitalization/recurrent emergency department (ED) visits due to influenza infection in children.MethodsWe conducted a prospective study of children aged 6 months to 8 years of age with influenza like illness evaluated at an ED/UC from 2016-2018. All children underwent influenza testing by PCR. We collected heart rate, respiratory rate and temperature, and converted heart rate (HR) and respiratory rate (RR) to z-scores by age. HR z scores were further adjusted for temperature. Our primary outcome was hospitalization/recurrent ED visits within 72 hours. Vital sign predictors with p< 0.2 and other clinical covariates were entered into a multivariable logistic regression model to determine odds ratios (OR) and 95% CI; model performance was assessed using the Brier score and discriminative ability with the C statistic.ResultsAmong 1478 children, 411 (27.8%) were positive for influenza, of which 42 (10.2%) were hospitalized or had a recurrent ED visit. In multivariable analyses, adjusting for age, high-risk medical condition and school/daycare attendance, higher adjusted respiratory rate (OR 2.09, 95%CI 1.21-3.61, p = 0.0085) was a significant predictor of influenza hospitalization/recurrent ED visits.ConclusionsHigher respiratory rate adjusted for age was the most useful vital sign predictor of severity among young children with PCR-confirmed influenza.

| S-EPMC9374253 | biostudies-literature

Seasonal influenza A virus in feces of hospitalized adults.

Project description:In a cohort of hospitalized adults with seasonal influenza A in Hong Kong, viral RNA was frequently (47%) detected in stool specimens. Viable virus was rarely isolated. Viral RNA positivity had little correlation with gastrointestinal symptoms and outcomes. In vitro studies suggested low potential for seasonal influenza viruses to cause direct intestinal infections.

| S-EPMC3310558 | biostudies-literature

Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit.

Project description:BackgroundEven brief hypotension is associated with increased morbidity and mortality. We developed a machine learning model to predict the initial hypotension event among intensive care unit (ICU) patients and designed an alert system for bedside implementation.Materials and methodsFrom the Medical Information Mart for Intensive Care III (MIMIC-3) dataset, minute-by-minute vital signs were extracted. A hypotension event was defined as at least five measurements within a 10-min period of systolic blood pressure ≤ 90 mmHg and mean arterial pressure ≤ 60 mmHg. Using time series data from 30-min overlapping time windows, a random forest (RF) classifier was used to predict risk of hypotension every minute. Chronologically, the first half of extracted data was used to train the model, and the second half was used to validate the trained model. The model's performance was measured with area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Hypotension alerts were generated using risk score time series, a stacked RF model. A lockout time were applied for real-life implementation.ResultsWe identified 1307 subjects (1580 ICU stays) as the hypotension group and 1619 subjects (2279 ICU stays) as the non-hypotension group. The RF model showed AUROC of 0.93 and 0.88 at 15 and 60 min, respectively, before hypotension, and AUPRC of 0.77 at 60 min before. Risk score trajectories revealed 80% and > 60% of hypotension predicted at 15 and 60 min before the hypotension, respectively. The stacked model with 15-min lockout produced on average 0.79 alerts/subject/hour (sensitivity 92.4%).ConclusionClinically significant hypotension events in the ICU can be predicted at least 1 h before the initial hypotension episode. With a highly sensitive and reliable practical alert system, a vast majority of future hypotension could be captured, suggesting potential real-life utility.

| S-EPMC7687996 | biostudies-literature

Risk of Death in Patients Hospitalized for COVID-19 vs Seasonal Influenza in Fall-Winter 2022-2023.

Project description: Not available

| S-EPMC10080400 | biostudies-literature

Biosensor vital sign detects multiple sclerosis progression.

Project description:ObjectiveTo determine whether a small, wearable multisensor device can discriminate between progressive versus relapsing multiple sclerosis (MS) and capture limb progression over a short interval, using finger and foot tap data.MethodsPatients with MS were followed prospectively during routine clinic visits approximately every 6 months. At each visit, participants performed finger and foot taps wearing the MYO-band, which includes accelerometer, gyroscope, and surface electromyogram sensors. Metrics of within-patient limb progression were created by combining the change in signal waveform features over time. The resulting upper (UE) and lower (LE) extremity metrics' discrimination of progressive versus relapsing MS were evaluated with calculation of AUROC. Comparisons with Expanded Disability Status Scale (EDSS) scores were made with Pearson correlation.ResultsParticipants included 53 relapsing and 15 progressive MS (72% female, baseline mean age 48 years, median disease duration 11 years, median EDSS 2.5, median 10 months follow-up). The final summary metrics differentiated relapsing from secondary progressive MS with AUROC UE 0.93 and LE 0.96. The metrics were associated with baseline EDSS (UE P = 0.0003, LE P = 0.0007). While most had no change in EDSS during the short follow-up, several had evidence of progression by the multisensor metrics.InterpretationWithin a short follow-up interval, this novel multisensor algorithm distinguished progressive from relapsing MS and captured changes in limb function. Inexpensive, noninvasive and easy to use, this novel outcome is readily adaptable to clinical practice and trials as a MS vital sign. This approach also holds promise to monitor limb dysfunction in other neurological diseases.

| S-EPMC7818086 | biostudies-literature

Complications among adults hospitalized with influenza: a comparison of seasonal influenza and the 2009 H1N1 pandemic.

Project description:BackgroundPersons with influenza can develop complications that result in hospitalization and death. These are most commonly respiratory related, but cardiovascular or neurologic complications or exacerbations of underlying chronic medical conditions may also occur. Patterns of complications observed during pandemics may differ from typical influenza seasons, and characterizing variations in influenza-related complications can provide a better understanding of the impact of pandemics and guide appropriate clinical management and planning for the future.MethodsUsing a population-based surveillance system, we compared clinical complications using International Classification of Diseases, Ninth Revision (ICD-9) discharge diagnosis codes in adults hospitalized with seasonal influenza (n = 5270) or 2009 pandemic influenza A(H1N1) (H1N1pdm09; n = 4962).ResultsAdults hospitalized with H1N1pdm09 were younger (median age, 47 years) than those with seasonal influenza (median age, 68 years; P < .01), and differed in the frequency of certain underlying medical conditions. Whereas there was similar risk for many influenza-associated complications, after controlling for age and type of underlying medical condition, adults hospitalized with H1N1pdm09 were more likely to have lower respiratory tract complications, shock/sepsis, and organ failure than those with seasonal influenza. They were also more likely to be admitted to the intensive care unit, require mechanical ventilation, or die. Young adults, in particular, had 2-4 times the risk of severe outcomes from H1N1pdm09 than persons of the same ages with seasonal influenza.ConclusionsAlthough H1N1pdm09 was thought of as a relatively mild pandemic, these data highlight the impact of the 2009 pandemic on the risk of severe influenza, especially among younger adults, and the impact this virus may continue to have.

| S-EPMC7314251 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data