Development and validation of early warning score systems for COVID-19 patients.
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ABSTRACT:
SUBMITTER: Youssef A
PROVIDER: S-EPMC8239612 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCOVID-19 has a high risk of mortality, especially in patients with comorbid diseases such as cardiac disease, type 2 diabetes mellitus, chronic kidney disease, and hypertension. The National Early Warning Score (NEWS) is a tool that helps in identifying changes in patient conditions that require intensive treatment.ObjectiveAnalyzing NEWS-2 to identify the risk of death in COVID-19 patients.MethodsThis research was conducted from June to July 2020 by using quota sampling. The number of participants in this study was 112 participants (case group = 56 participants and control group = 56 participants). Participants were assessed for NEWS-2 and evaluated for their treatment outcomes. The analysis used in this study was the Chi-squared test and logistic regression with p < 0.05.Results45 participants died of having NEWS-2 score >5, and as many as 50 participants showed an improvement in their condition by having NEWS-2 score 5 (OR = 34.091; p < 0.001). The accuracy of NEWS-2's assessment of mortality of COVID-19 patients had a sensitivity of 80.4% and a specificity of 89.3%. There were several comorbid diseases that had a significant relationship on mortality of COVID-19 patients such as cardiac disease (β = 5.907; 1.107-31.527 95% CI; p = 0.038), T2DM (β = 3.143; 1.269-7.783 95% CI; p = 0.013), CKD (β = 3.851; 1.195-12.416 95% CI; p = 0.024), and hypertension (β = 2.820; 1.075-7.399 95% CI; p = 0.035).ConclusionThe NEWS-2 can be used to identify the risk of death of COVID-19 patients.
| S-EPMC8902861 | biostudies-literature
Project description:We sought to validate prognostic scores in coronavirus disease 2019 including National Early Warning Score, Modified Early Warning Score, and age-based modifications, and define their performance characteristics.DesignWe analyzed prospectively collected data from the Adaptive COVID-19 Treatment Trial. National Early Warning Score was collected daily during the trial, Modified Early Warning Score was calculated, and age applied to both scores. We assessed prognostic value for the end points of recovery, mechanical ventilation, and death for score at enrollment, average, and slope of score over the first 48 hours.SettingA multisite international inpatient trial.PatientsA total of 1,062 adult nonpregnant inpatients with severe coronavirus disease 2019 pneumonia.InterventionsAdaptive COVID-19 Treatment Trial 1 randomized participants to receive remdesivir or placebo. The prognostic value of predictive scores was evaluated in both groups separately to assess for differential performance in the setting of remdesivir treatment.Measurements and main resultsFor mortality, baseline National Early Warning Score and Modified Early Warning Score were weakly to moderately prognostic (c-index, 0.60-0.68), and improved with addition of age (c-index, 0.66-0.74). For recovery, baseline National Early Warning Score and Modified Early Warning Score demonstrated somewhat better prognostic ability (c-index, 0.65-0.69); however, National Early Warning Score+age and Modified Early Warning Score+age further improved performance (c-index, 0.68-0.71). For deterioration, baseline National Early Warning Score and Modified Early Warning Score were weakly to moderately prognostic (c-index, 0.59-0.69) and improved with addition of age (c-index, 0.63-0.70). All prognostic performance improvements due to addition of age were significant (p < 0.05).ConclusionsIn the Adaptive COVID-19 Treatment Trial 1 cohort, National Early Warning Score and Modified Early Warning Score demonstrated moderate prognostic performance in patients with severe coronavirus disease 2019, with improvement in predictive ability for National Early Warning Score+age and Modified Early Warning Score+age. Area under receiver operating curve for National Early Warning Score and Modified Early Warning Score improved in patients receiving remdesivir versus placebo early in the pandemic for recovery and mortality. Although these scores are simple and readily obtainable in myriad settings, in our data set, they were insufficiently predictive to completely replace clinical judgment in coronavirus disease 2019 and may serve best as an adjunct to triage, disposition, and resourcing decisions.
| S-EPMC8280088 | biostudies-literature
Project description:BackgroundTimely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients.MethodsWe retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 'wave' in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values.ResultsWe included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80-0.84]) compared to the National early warning score (0.72 [0.69-0.74]) and the Modified early warning score (0.67 [0.65-0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [- 0.09 to 0.14], slope = 0.79 [0.73-0.86]).ConclusionsThis study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed.
| S-EPMC9482891 | biostudies-literature
Project description:BackgroundWhile several parameters have emerged as predictors of prognosis of COVID-19, a simple clinical score at baseline might help early risk stratification. We determined the ability of National Early Warning Score 2 (NEWS2) to predict poor outcomes among adults with COVID-19.MethodsA prospective study was conducted on 399 hospitalised adults with confirmed SARS-CoV-2 infection between August and December 2020. Baseline NEWS2 score was determined. Primary outcome was poor outcomes defined as need for mechanical ventilation or death within 28 days. The sensitivity, specificity and Area under the curve were determined for NEWS2 scores of 5 and 6.ResultsMean age of patients was 55.5 ± 14.8 years and 275 of 399 (68.9%) were male. Overall mortality was 3.8% and 7.5% had poor outcomes. Median (interquartile range) NEWS2 score at admission was 2 (0-6). Sensitivity and specificity of NEWS 2 of 5 or more in predicting poor outcomes was 93.3% (95% CI: 76.5-98.8) and 70.7% (95% CI: 65.7-75.3) respectively [area under curve 0.88 (95% CI: 0.847-0.927)]. Age, baseline pulse rate, baseline oxygen saturation, need for supplemental oxygen and ARDS on chest X ray were independently associated with poor outcomes.ConclusionsNEWS2 score of 5 or more at admission predicts poor outcomes in patients with COVID-19 with good sensitivity and can easily be applied for risk stratification at baseline. Further studies are needed in the Indian setting to validate this simple score and recommend widespread use.
| S-EPMC8673675 | biostudies-literature
Project description:ObjectiveInvestigate the predictive value of NEWS2, NEWS-C, and COVID-19 Severity Index for predicting intensive care unit (ICU) transfer in the next 24 h.DesignRetrospective multicenter study.SettingTwo third-level hospitals in Argentina.PatientsAll adult patients with confirmed COVID-19, admitted on general wards, excluding patients with non-intubated orders.InterventionsPatients were divided between those who were admitted to ICU and non-admitted. We calculated the three scores for each day of hospitalization.VariablesWe evaluate the calibration and discrimination of the three scores for the outcome ICU admission within 24, 48 h, and at hospital admission.ResultsWe evaluate 13,768 days of hospitalizations on general medical wards of 1318 patients. Among these, 126 (9.5%) were transferred to ICU. The AUROC of NEWS2 was 0.73 (95%CI 0.68-0.78) 24 h before ICU admission, and 0.52 (95%CI 0.47-0.57) at hospital admission. The AUROC of NEWS-C was 0.73 (95%CI 0.68-0.78) and 0.52 (95%CI 0.47-0.57) respectively, and the AUROC of COVID-19 Severity Index was 0.80 (95%CI 0.77-0.84) and 0.61 (95%CI 0.58-0.66) respectively. COVID-19 Severity Index presented better calibration than NEWS2 and NEWS-C.ConclusionCOVID-19 Severity index has better calibration and discrimination than NEWS2 and NEWS-C to predict ICU transfer during hospitalization.
| S-EPMC8629741 | biostudies-literature
Project description:ObjectiveInvestigate the predictive value of NEWS2, NEWS-C, and COVID-19 Severity Index for predicting intensive care unit (ICU) transfer in the next 24h.DesignRetrospective multicenter study.SettingTwo third-level hospitals in Argentina.PatientsAll adult patients with confirmed COVID-19, admitted on general wards, excluding patients with non-intubated orders.InterventionsPatients were divided between those who were admitted to ICU and non-admitted. We calculated the three scores for each day of hospitalization.VariablesWe evaluate the calibration and discrimination of the three scores for the outcome ICU admission within 24, 48h, and at hospital admission.ResultsWe evaluate 13,768 days of hospitalizations on general medical wards of 1318 patients. Among these, 126 (9.5%) were transferred to ICU. The AUROC of NEWS2 was 0.73 (95%CI 0.68-0.78) 24h before ICU admission, and 0.52 (95%CI 0.47-0.57) at hospital admission. The AUROC of NEWS-C was 0.73 (95%CI 0.68-0.78) and 0.52 (95%CI 0.47-0.57) respectively, and the AUROC of COVID-19 Severity Index was 0.80 (95%CI 0.77-0.84) and 0.61 (95%CI 0.58-0.66) respectively. COVID-19 Severity Index presented better calibration than NEWS2 and NEWS-C.ConclusionCOVID-19 Severity index has better calibration and discrimination than NEWS2 and NEWS-C to predict ICU transfer during hospitalization.
| S-EPMC9579896 | biostudies-literature
Project description:ObjectivesIdentifying patients with a possible SARS-CoV-2 infection in the emergency department (ED) is challenging. Symptoms differ, incidence rates vary and test capacity may be limited. As PCR-testing all ED patients is neither feasible nor effective in most centres, a rapid, objective, low-cost early warning score to triage ED patients for a possible infection is developed.DesignCase-control study.SettingSecondary and tertiary hospitals in the Netherlands.ParticipantsThe study included patients presenting to the ED with venous blood sampling from July 2019 to July 2020 (n=10 417, 279 SARS-CoV-2-positive). The temporal validation cohort covered the period from July 2020 to October 2021 (n=14 080, 1093 SARS-CoV-2-positive). The external validation cohort consisted of patients presenting to the ED of three hospitals in the Netherlands (n=12 061, 652 SARS-CoV-2-positive).Primary outcome measuresThe primary outcome was one or more positive SARS-CoV-2 PCR test results within 1 day prior to or 1 week after ED presentation.ResultsThe resulting 'CoLab-score' consists of 10 routine laboratory measurements and age. The score showed good discriminative ability (AUC: 0.930, 95% CI 0.909 to 0.945). The lowest CoLab-score had high sensitivity for COVID-19 (0.984, 95% CI 0.970 to 0.991; specificity: 0.411, 95% CI 0.285 to 0.520). Conversely, the highest score had high specificity (0.978, 95% CI 0.973 to 0.983; sensitivity: 0.608, 95% CI 0.522 to 0.685). The results were confirmed in temporal and external validation.ConclusionsThe CoLab-score is based on routine laboratory measurements and is available within 1 hour after presentation. Depending on the prevalence, COVID-19 may be safely ruled out in over one-third of ED presentations. Highly suspect cases can be identified regardless of presenting symptoms. The CoLab-score is continuous, in contrast to the binary outcome of lateral flow testing, and can guide PCR testing and triage ED patients.
| S-EPMC9352566 | biostudies-literature
Project description:PurposeWhile more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization.MethodsWe developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16).ResultsThe LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface.ConclusionWe present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.
| S-EPMC8287547 | biostudies-literature
Project description:BackgroundEarly warning scores (EWS) have been developed as clinical prognostication tools to identify acutely deteriorating patients. In the past few years, there has been a proliferation of studies that describe the development and validation of novel machine learning-based EWS. Systematic reviews of published studies which focus on evaluating performance of both well-established and novel EWS have shown conflicting conclusions. A possible reason is the heterogeneity in validation methods applied. In this review, we aim to examine the methodologies and metrics used in studies which perform EWS validation.MethodsA systematic review of all eligible studies from the MEDLINE database and other sources, was performed. Studies were eligible if they performed validation on at least one EWS and reported associations between EWS scores and inpatient mortality, intensive care unit (ICU) transfers, or cardiac arrest (CA) of adults. Two reviewers independently did a full-text review and performed data abstraction by using standardized data-worksheet based on the TRIPOD (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) checklist. Meta-analysis was not performed due to heterogeneity.ResultsThe key differences in validation methodologies identified were (1) validation dataset used, (2) outcomes of interest, (3) case definition, time of EWS use and aggregation methods, and (4) handling of missing values. In terms of case definition, among the 48 eligible studies, 34 used the patient episode case definition while 12 used the observation set case definition, and 2 did the validation using both case definitions. Of those that used the patient episode case definition, 18 studies validated the EWS at a single point of time, mostly using the first recorded observation. The review also found more than 10 different performance metrics reported among the studies.ConclusionsMethodologies and performance metrics used in studies performing validation on EWS were heterogeneous hence making it difficult to interpret and compare EWS performance. Standardizing EWS validation methodology and reporting can potentially address this issue.
| S-EPMC7301346 | biostudies-literature
Project description:ObjectivesA simple evaluation tool for patients with novel coronavirus disease 2019 (COVID-19) could assist the physicians to triage COVID-19 patients effectively and rapidly. This study aimed to evaluate the predictive value of 5 early warning scores based on the admission data of critical COVID-19 patients.MethodsOverall, medical records of 319 COVID-19 patients were included in the study. Demographic and clinical characteristics on admission were used for calculating the Standardized Early Warning Score (SEWS), National Early Warning Score (NEWS), National Early Warning Score2 (NEWS2), Hamilton Early Warning Score (HEWS), and Modified Early Warning Score (MEWS). Data on the outcomes (survival or death) were collected for each case and extracted for overall and subgroup analysis. Receiver operating characteristic curve analyses were performed.ResultsThe area under the receiver operating characteristic curve for the SEWS, NEWS, NEWS2, HEWS, and MEWS in predicting mortality were 0.841 (95% CI: 0.765-0.916), 0.809 (95% CI: 0.727-0.891), 0.809 (95% CI: 0.727-0.891), 0.821 (95% CI: 0.748-0.895), and 0.670 (95% CI: 0.573-0.767), respectively.ConclusionsSEWS, NEWS, NEWS2, and HEWS demonstrated moderate discriminatory power and, therefore, offer potential utility as prognostic tools for screening severely ill COVID-19 patients. However, MEWS is not a good prognostic predictor for COVID-19.
| S-EPMC7596567 | biostudies-literature