scholarly journals Comparison of early warning scores for sepsis early identification and prediction in the general ward setting

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Sean C Yu ◽  
Nirmala Shivakumar ◽  
Kevin Betthauser ◽  
Aditi Gupta ◽  
Albert M Lai ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Early Warning ◽  
Predictive Value ◽  
Early Identification ◽  
Academic Medical Center ◽  
Scoring Systems ◽  
General Ward ◽  
Early Warning Score ◽  
Patient Acuity ◽  
Early Warning Scores

Abstract The objective of this study was to directly compare the ability of commonly used early warning scores (EWS) for early identification and prediction of sepsis in the general ward setting. For general ward patients at a large, academic medical center between early-2012 and mid-2018, common EWS and patient acuity scoring systems were calculated from electronic health records (EHR) data for patients that both met and did not meet Sepsis-3 criteria. For identification of sepsis at index time, National Early Warning Score 2 (NEWS 2) had the highest performance (area under the receiver operating characteristic curve: 0.803 [95% confidence interval [CI]: 0.795–0.811], area under the precision recall curves: 0.130 [95% CI: 0.121–0.140]) followed NEWS, Modified Early Warning Score, and quick Sequential Organ Failure Assessment (qSOFA). Using validated thresholds, NEWS 2 also had the highest recall (0.758 [95% CI: 0.736–0.778]) but qSOFA had the highest specificity (0.950 [95% CI: 0.948–0.952]), positive predictive value (0.184 [95% CI: 0.169–0.198]), and F1 score (0.236 [95% CI: 0.220–0.253]). While NEWS 2 outperformed all other compared EWS and patient acuity scores, due to the low prevalence of sepsis, all scoring systems were prone to false positives (low positive predictive value without drastic sacrifices in sensitivity), thus leaving room for more computationally advanced approaches.

scholarly journals 362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S251-S251
Author(s):  
Joanna S Cavalier ◽  
Benjamin Goldstein ◽  
Cara L O’Brien ◽  
Armando Bedoya
Keyword(s):  
Positive Predictive Value ◽  
Early Warning ◽  
Predictive Value ◽  
Risk Model ◽  
Medical Center ◽  
Academic Medical Center ◽  
Significant Proportion ◽  
Case Series ◽  
Early Warning Score ◽  
Severe Illness

Abstract Background The novel coronavirus disease (COVID-19) results in severe illness in a significant proportion of patients, necessitating a way to discern which patients will become critically ill and which will not. In one large case series, 5.0% of patients required an intensive care unit (ICU) and 1.4% died. Several models have been developed to assess decompensating patients. However, research examining their applicability to COVID-19 patients is limited. An accurate predictive model for patients at risk of decompensation is critical for health systems to optimally triage emergencies, care for patients, and allocate resources. Methods An early warning score (EWS) algorithm created within a large academic medical center, with methodology previously described, was applied to COVID-19 patients admitted to this institution. 122 COVID-19 patients were included. A decompensation event was defined as inpatient mortality or an unanticipated transfer to an ICU from an intermediate medical ward. The EWS was calculated at 12-hour and 24-hour intervals. Results Of 122 patients admitted with COVID-19, 28 had a decompensation event, yielding an event rate of 23.0%. 8 patients died, 13 transferred to the ICU, and 6 both transferred to the ICU and died. Decompensation within 12 and 24 hours were predicted with areas under the curve (AUC) of 0.850 and 0.817, respectively. Using a three-tiered risk model, use of the customized EWS score for patients identified as high risk of decompensation had a positive predictive value of 44.4% and 11.1% and specificity of 99.3% and 99.6% and 12- and 24-hour intervals. Amongst medium-risk patients, the score had a specificity of 85.0% and 85.4%, respectively. Conclusion This EWS allows for prediction of decompensation, defined as transfer to an ICU or death, in COVID-19 patients with excellent specificity and a high positive predictive value. Clinically, implementation of this score can help to identify patients before they decompensate in order to triage at time of presentation and allocate step-down beds, ICU beds, and treatments such as remdesivir. Disclosures All Authors: No reported disclosures

scholarly journals Predictive Value of 5 Early Warning Scores for Critical COVID-19 Patients

2020 ◽  
pp. 1-8
Author(s):  
Hai Hu ◽  
Ni Yao ◽  
Yanru Qiu
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Early Warning ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Early Warning Score ◽  
Early Warning Scores ◽  
Operating Characteristic Curve ◽  
Receiver Operating

ABSTRACT Objectives: A 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. Methods: Overall, 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. Results: The 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. Conclusions: SEWS, 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.

scholarly journals Predictive Value of an Age-Based Modification of the National Early Warning System in Hospitalized Patients with COVID-19

2021 ◽  
Author(s):  
Ryan C Maves ◽  
Stephanie A Richard ◽  
David A Lindholm ◽  
Nusrat Epsi ◽  
Derek T Larson ◽  
...  
Keyword(s):  
Negative Predictive Value ◽  
Early Warning ◽  
Predictive Value ◽  
Early Recognition ◽  
Invasive Mechanical Ventilation ◽  
Scoring Systems ◽  
Early Warning Score ◽  
Low Flow ◽  
Invasive Ventilation ◽  
Military Health

Abstract Background Early recognition of high-risk patients with COVID-19 may improve outcomes. Although many predictive scoring systems exist, their complexity may limit utility in COVID-19. We assessed the prognostic performance of the National Early Warning Score (NEWS) and an age-based modification (NEWS+age) among hospitalized COVID-19 patients enrolled in a prospective, multicenter U.S. Military Health System (MHS) observational cohort study. Methods Hospitalized adults with confirmed COVID-19 not requiring invasive mechanical ventilation at admission and a baseline NEWS were included. We analyzed each scoring system’s ability to predict key clinical outcomes, including progression to invasive ventilation or death, stratified by baseline severity (low (0-3), medium (4-6) and high (≥7)). Results Among 184 included participants, those with low baseline NEWS had significantly shorter hospitalizations (p<0.01) and lower maximum illness severity (p<0.001). Most (80.2%) of low NEWS versus 15.8% of high NEWS participants required no or at most low flow oxygen supplementation. Low NEWS (≤3) had a negative predictive value of 97.2% for progression to invasive ventilation or death; a high NEWS (≥7) had high specificity (93.1%) but low positive predictive value (42.1%) for such progression. NEWS+age performed similarly to NEWS at predicting invasive ventilation or death (NEWS+age: AUROC 0.69; 95% CI 0.65-0.73; NEWS: AUROC 0.70; 0.66-0.75). Conclusions NEWS and NEWS+age showed similar test characteristics in an MHS COVID-19 cohort. Notably, low baseline scores had excellent negative predictive value. Given their easy applicability, these scoring systems may be useful in resource-limited settings to identify COVID-19 patients who are unlikely to progress to critical illness.

scholarly journals Score for Emergency Risk Prediction (SERP): An Interpretable Machine Learning AutoScore–Derived Triage Tool for Predicting Mortality after Emergency Admissions

2021 ◽  
Author(s):  
Feng Xie ◽  
Marcus Eng Hock Ong ◽  
Johannes Nathaniel Min Hui Liew ◽  
Kenneth Boon Kiat Tan ◽  
Andrew Fu Wah Ho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Positive Predictive Value ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Early Warning ◽  
Predictive Value ◽  
Early Warning Score ◽  
Secondary Outcomes ◽  
Clinical Tools

AbstractImportanceTriage in the emergency department (ED) for admission and appropriate level of hospital care is a complex clinical judgment based on the tacit understanding of the patient’s likely acute course, availability of medical resources, and local practices. While a scoring tool could be valuable in triage, currently available tools have demonstrated limitations.ObjectiveTo develop a tool based on a parsimonious list of predictors available early at ED triage, to provide a simple, early, and accurate estimate of short-term mortality risk, the Score for Emergency Risk Prediction (SERP), and evaluate its predictive accuracy relative to published tools.Design, Setting, and ParticipantsWe performed a single-site, retrospective study for all emergency department (ED) patients between January 2009 and December 2016 admitted in a tertiary hospital in Singapore. SERP was derived using the machine learning framework for developing predictive models, AutoScore, based on six variables easily available early in the ED care process. Using internal validation, the SERP was compared to the current triage system, Patient Acuity Category Scale (PACS), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), Cardiac Arrest Risk Triage (CART), and Charlson Comorbidity Index (CCI) in predicting both primary and secondary outcomes in the study.Main Outcomes and MeasuresThe primary outcome of interest was 30-day mortality. Secondary outcomes include 2-day mortality, inpatient mortality, 30-day post-discharge mortality, and 1-year mortality. The SERP’s predictive power was measured using the area under the curve (AUC) in the receiver operating characteristic (ROC) analysis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated under the optimal threshold, defined as the point nearest to the upper-left corner of the ROC curve.ResultsWe included 224,666 ED episodes in the model training cohort, 56,167 episodes in the validation cohort, and 42,676 episodes in the testing cohort. 18,797 (5.8%) of them died in 30 days after their ED visits. Evaluated on the testing set, SERP outperformed several benchmark scores in predicting 30-day mortality and other mortality-related outcomes. Under cut-off score of 27, SERP achieved a sensitivity of 72.6% (95% confidence interval [CI]: 70.7-74.3%), a specificity of 77.8% (95% CI: 77.5-78.2), a positive predictive value of 15.8% (15.4-16.2%) and a negative predictive value of 98% (97.9-98.1%).ConclusionsSERP showed better prediction performance than existing triage scores while maintaining easy implementation and ease of ascertainment at the ED. It has the potential to be widely applied and validated in different circumstances and healthcare settings.Key pointsQuestionHow does a tool for predicting hospital outcomes based on a machine learning-based automatic clinical score generator, AutoScore, perform in a cohort of individuals admitted to hospital from the emergency department (ED) compared to other published clinical tools?FindingsThe new tool, the Score for Emergency Risk Prediction (SERP), is parsimonious and point-based. SERP was more accurate in identifying patients who died during short or long-term care, compared with other point-based clinical tools.MeaningSERP, a tool based on AutoScore is promising for triaging patients admitted from the ED according to mortality risk.

scholarly journals Investigating the discriminative value of Early Warning Scores in patients with respiratory disease using a retrospective cohort analysis of admissions to Nottingham University Hospitals Trust over a 2-year period

BMJ Open ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. e020269 ◽  
Author(s):  
Sarah Forster ◽  
Gemma Housley ◽  
Tricia M McKeever ◽  
Dominick E Shaw
Keyword(s):  
Respiratory Disease ◽  
Early Warning ◽  
Retrospective Cohort ◽  
Cohort Analysis ◽  
Scoring Systems ◽  
University Hospitals ◽  
Retrospective Cohort Analysis ◽  
Early Warning Scores ◽  
The Impact ◽  
Higher Sensitivity

ObjectiveEarly Warning Scores (EWSs) are used to monitor patients for signs of imminent deterioration. Although used in respiratory disease, EWSs have not been well studied in this population, despite the underlying cardiopulmonary pathophysiology often present. We examined the performance of two scoring systems in patients with respiratory disease.DesignRetrospective cohort analysis of vital signs observations of all patients admitted to a respiratory unit over a 2-year period. Scores were linked to outcome data to establish the performance of the National EWS (NEWS) compared results to a locally adapted EWS.SettingNottingham University Hospitals National Health Service Trust respiratory wards. Data were collected from an integrated electronic observation and task allocation system employing a local EWS, also generating mandatory referrals to clinical staff at set scoring thresholds.Outcome measuresProjected workload, and sensitivity and specificity of the scores in predicting mortality based on outcome within 24 hours of a score being recorded.Results8812 individual patient episodes occurred during the study period. Overall, mortality was 5.9%. Applying NEWS retrospectively (vs local EWS) generated an eightfold increase in mandatory escalations, but had higher sensitivity in predicting mortality at the protocol cut points.ConclusionsThis study highlights issues surrounding use of scoring systems in patients with respiratory disease. NEWS demonstrated higher sensitivity for predicting death within 24 hours, offset by reduced specificity. The consequent workload generated may compromise the ability of the clinical team to respond to patients needing immediate input. The locally adapted EWS has higher specificity but lower sensitivity. Statistical evaluation suggests this may lead to missed opportunities for intervention, however, this does not account for clinical concern independent of the scores, nor ability to respond to alerts based on workload. Further research into the role of warning scores and the impact of chronic pathophysiology is urgently needed.

scholarly journals Early identification of severe community-acquired pneumonia: a retrospective observational study

2019 ◽  
Vol 6 (1) ◽  
pp. e000438 ◽  
Author(s):  
Frances S Grudzinska ◽  
Kerrie Aldridge ◽  
Sian Hughes ◽  
Peter Nightingale ◽  
Dhruv Parekh ◽  
...  
Keyword(s):  
Early Warning ◽  
Assessment Tool ◽  
Characteristic Curve ◽  
Community Acquired Pneumonia ◽  
Adverse Outcomes ◽  
Assessment Tools ◽  
Early Warning Score ◽  
News Medium ◽  
Increased Risk ◽  
Early Warning Scores

BackgroundCommunity-acquired pneumonia (CAP) is a leading cause of sepsis worldwide. Prompt identification of those at high risk of adverse outcomes improves survival by enabling early escalation of care. There are multiple severity assessment tools recommended for risk stratification; however, there is no consensus as to which tool should be used for those with CAP. We sought to assess whether pneumonia-specific, generic sepsis or early warning scores were most accurate at predicting adverse outcomes.MethodsWe performed a retrospective analysis of all cases of CAP admitted to a large, adult tertiary hospital in the UK between October 2014 and January 2016. All cases of CAP were eligible for inclusion and were reviewed by a senior respiratory physician to confirm the diagnosis. The association between the CURB65, Lac-CURB-65, quick Sequential (Sepsis-related) Organ Failure Assessment tool (qSOFA) score and National Early Warning Score (NEWS) at the time of admission and outcome measures including intensive care admission, length of hospital stay, in-hospital, 30-day, 90-day and 365-day all-cause mortality was assessed.Results1545 cases were included with 30-day mortality of 19%. Increasing score was significantly associated with increased risk of poor outcomes for all four tools. Overall accuracy assessed by receiver operating characteristic curve analysis was significantly greater for the CURB65 and Lac-CURB-65 scores than qSOFA. At admission, a CURB65 ≥2, Lac-CURB-65 ≥moderate, qSOFA ≥2 and NEWS ≥medium identified 85.0%, 96.4%, 40.3% and 79.0% of those who died within 30 days, respectively. A Lac-CURB-65 ≥moderate had the highest negative predictive value: 95.6%.ConclusionAll four scoring systems can stratify according to increasing risk in CAP; however, when a confident diagnosis of pneumonia can be made, these data support the use of pneumonia-specific tools rather than generic sepsis or early warning scores.

Early Warning Score Communication Bundle: A Pilot Study

2018 ◽  
Vol 27 (3) ◽  
pp. 238-242
Author(s):  
Cheryl Gagne ◽  
Susan Fetzer
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Early Warning ◽  
Rapid Response Team ◽  
Rapid Response ◽  
Early Warning Score ◽  
Early Warning Scores ◽  
Response Team ◽  
Intensive Care Unit Nurses ◽  
The Impact

Background Unplanned admissions of patients to intensive care units from medical-surgical units often result from failure to recognize clinical deterioration. The early warning score is a clinical decision support tool for nurse surveillance but must be communicated to nurses and implemented appropriately. A communication process including collaboration with experienced intensive care unit nurses may reduce unplanned transfers. Objective To determine the impact of an early warning score communication bundle on medical-surgical transfers to the intensive care unit, rapid response team calls, and morbidity of patients upon intensive care unit transfer. Methods After an early warning score was electronically embedded into medical records, a communication bundle including notification of and telephone collaboration between medical-surgical and intensive care unit nurses was implemented. Data were collected 3 months before and 21 months after implementation. Results Rapid response team calls increased nonsignificantly during the study period (from 6.47 to 8.29 per 1000 patient-days). Rapid response team calls for patients with early warning scores greater than 4 declined (from 2.04 to 1.77 per 1000 patient-days). Intensive care unit admissions of patients after rapid response team calls significantly declined (P = .03), as did admissions of patients with early warning scores greater than 4 (P = .01), suggesting that earlier intervention for patient deterioration occurred. Documented reassessment response time declined significantly to 28 minutes (P = .002). Conclusion Electronic surveillance and collaboration with experienced intensive care unit nurses may improve care, control costs, and save lives. Critical care nurses have a role in coaching and guiding less experienced nurses.

scholarly journals Predicting PICU patient survival prognosis: Pediatric Logistic Organ Dysfunction vs Pediatric Index of Mortality scores

2012 ◽  
Vol 52 (3) ◽  
pp. 165 ◽  
Author(s):  
Edwina Winiarti ◽  
Muhammad Sholeh Kosim ◽  
Mohammad Supriatna
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Pediatric Intensive Care ◽  
Scoring Systems ◽  
Survival Prognosis ◽  
Logistic Organ Dysfunction ◽  
Risk Of Mortality ◽  
Sensitivity Specificity ◽  
Pediatric Index Of Mortality

Background Determining prognosis of patients using scoringsystems have been done in many pediatric intensive care units(PICU). The scoring systems frequently used are pediatric logisticorgan dy sfunction (PELOD), pediatric index of mortality (PIM)and pediatric risk of mortality (PRISM).Objective To compare the performance of PELOD and PIM scoresin predicting the prognosis of survival vs death in PICU patients.Methods A prognostic test in this prospective, cohort study wasconducted in the PICU of the Kariadi General Hospital, Semarang.PELOD and PIM calculations were performed using formulae frompreviously published articles. Statistical analyses included receiveroperating curve (ROC) characteristics to describe discriminationcapacity, sensitivity, specificity, positive predictive value, negativepredictive value and accuracy.Results Thirty-three patients fulfilling the inclusion criteria wereenrolled in the study. PELOD score for area under the ROCwas 0.87 (95% CI 0.73 to 1.0; P=0.003), while that for PIMwas 0.65 (95% CI 0.39 to 0.90; P=0.2). PELOD scores showedsensitivity 85.7% (95% CI 59.8 to 100), specificity 84.6% (95%CI 70.7 to 98.5), positive predictive value 60.0% (95% CI 29.6to 90.4) negative predictive value 95.6% (95% CI 87.3 to 100)and accuracy 84.8%. PIM scores showed sensitivity 85.7% (95%CI 59.8 to 100), specificity 50.0% (95% CI 30.8 to 69.2), positivepredictive value 31.6% (95% CI 10,7 to 52.5), negative predictivevalue 92.9% (95% CI 79.4 to 100) and accuracy 57.6%.Conclusion PELOD scoring had better specificity, positive predictivevalue, negative predictive value, accuracy and discrimination capacitythan PIM scoring for predicting the survival prognosis of patients inthe PICU. [Paediatr Indones. 2012;52:165-9].

Sign in / Sign up

Export Citation Format

Share Document