Dataset Information

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

ABSTRACT: 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. Lay Summary Sepsis is a syndrome caused by an infection resulting in organ dysfunction and high rates of death, is implicated in nearly half of all inpatient deaths, and is the costliest inpatient condition in the United States. Early recognition and treatment are critical to the management of septic patients. As a result, over time, researchers have developed numerous early warning scores that use clinical measurements such as vital signs and lab results to generate a value that is indicative of the severity of illness and is predictive of clinical deterioration. Increasingly, these scores have been used as screening tools for sepsis management. To understand the comparative performance of these early warning scores in the general ward setting, electronic health records data were used to calculate the scores. Of the compared scores, the National Early Warning Score (NEWS 2) outperformed the rest. However, partially due to the low prevalence of sepsis in the general ward, even NEWS 2 was prone to false positives, highlighting the potential for improvement using more advanced computational methods.

SUBMITTER: Yu S

PROVIDER: S-EPMC8607822 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:IntroductionProviding optimal critical care in developing countries is limited by lack of recognition of critical illness and lack of essential resources. The Modified Early Warning Score (MEWS), based on physiological parameters, is validated in adult medical and surgical patients as a predictor of mortality. The objective of this study performed in Uganda was to determine the prevalence of critical illness on the wards as defined by the MEWS, to evaluate the MEWS as a predictor of death, and to describe additional risk factors for mortality.MethodsWe conducted a prospective observational study at Mulago National Referral Teaching Hospital in Uganda. We included medical and surgical ward patients over 18 years old, excluding patients discharged the day of enrolment, obstetrical patients, and patients who self-discharged prior to study completion. Over a 72-hour study period, we collected demographic and vital signs, and calculated MEWS; at 7-days we measured outcomes. Patients discharged prior to 7 days were assumed to be alive at study completion. Descriptive and inferential statistical analyses were performed.ResultsOf 452 patients, the median age was 40.5 (IQR 29-54) years, 53.3% were male, 24.3% were HIV positive, and 45.1% had medical diagnoses. MEWS ranged from 0 to 9, with higher scores representing hemodynamic instability. The median MEWS was 2 [IQR 1-3] and the median length of hospital stay was 9 days [IQR 4-24]. In-hospital mortality at 7-days was 5.5%; 41.4% of patients were discharged and 53.1% remained on the ward. Mortality was independently associated with medical admission (OR: 7.17; 95% CI: 2.064-24.930; p = 0.002) and the MEWS ? 5 (OR: 5.82; 95% CI: 2.420-13.987; p<0.0001) in the multivariable analysis.ConclusionThere is a significant burden of critical illness at Mulago Hospital, Uganda. Implementation of the MEWS could provide a useful triage tool to identify patients at greatest risk of death. Future research should include refinement of MEWS for low-resource settings, and development of appropriate interventions for patients identified to be at high risk of death based on early warning scores.

Project description:ImportanceEarly warning scores (EWSs) are designed for in-hospital use but are widely used in the prehospital field, especially in select groups of patients potentially at high risk. To be useful for paramedics in daily prehospital clinical practice, evaluations are needed of the predictive value of EWSs based on first measured vital signs on scene in large cohorts covering unselected patients using ambulance services.ObjectiveTo validate EWSs' ability to predict mortality and intensive care unit (ICU) stay in an unselected cohort of adult patients who used ambulances.Design, setting, and participantsThis prognostic study conducted a validation based on a cohort of adult patients (aged ≥18 years) who used ambulances in the North Denmark Region from July 1, 2016, to December 31, 2020. EWSs (National Early Warning Score 2 [NEWS2], modified NEWS score without temperature [mNEWS], Quick Sepsis Related Organ Failure Assessment [qSOFA], Rapid Emergency Triage and Treatment System [RETTS], and Danish Emergency Process Triage [DEPT]) were calculated using first vital signs measured by ambulance personnel. Data were analyzed from September 2022 through May 2023.Main outcomes and measuresThe primary outcome was 30-day-mortality. Secondary outcomes were 1-day-mortality and ICU admission. Discrimination was assessed using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC).ResultsThere were 107 569 unique patients (52 650 females [48.9%]; median [IQR] age, 65 [45-77] years) from the entire cohort of 219 323 patients who used ambulance services, among whom 119 992 patients (54.7%) had called the Danish national emergency number. NEWS2, mNEWS, RETTS, and DEPT performed similarly concerning 30-day mortality (AUROC range, 0.67 [95% CI, 0.66-0.68] for DEPT to 0.68 [95% CI, 0.68-0.69] for mNEWS), while qSOFA had lower performance (AUROC, 0.59 [95% CI, 0.59-0.60]; P vs other scores < .001). All EWSs had low AUPRCs, ranging from 0.09 (95% CI, 0.09-0.09) for qSOFA to 0.14 (95% CI, 0.13-0.14) for mNEWS.. Concerning 1-day mortality and ICU admission NEWS2, mNEWS, RETTS, and DEPT performed similarly, with AUROCs ranging from 0.72 (95% CI, 0.71-0.73) for RETTS to 0.75 (95% CI, 0.74-0.76) for DEPT in 1-day mortality and 0.66 (95% CI, 0.65-0.67) for RETTS to 0.68 (95% CI, 0.67-0.69) for mNEWS in ICU admission, and all EWSs had low AUPRCs. These ranged from 0.02 (95% CI, 0.02-0.03) for qSOFA to 0.04 (95% CI, 0.04-0.04) for DEPT in 1-day mortality and 0.03 (95% CI, 0.03-0.03) for qSOFA to 0.05 (95% CI, 0.04-0.05) for DEPT in ICU admission.Conclusions and relevanceThis study found that EWSs in daily clinical use in emergency medical settings performed moderately in the prehospital field among unselected patients who used ambulances when assessed based on initial measurements of vital signs. These findings suggest the need of appropriate triage and early identification of patients at low and high risk with new and better EWSs also suitable for prehospital use.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets