scholarly journals A new approach to scoring systems to improve identification of acute medical admissions that will require critical care

2011 ◽  
Vol 56 (4) ◽  
pp. 195-202 ◽  
Author(s):  
H A Carmichael ◽  
E Robertson ◽  
J Austin ◽  
D Mccruden ◽  
C M Messow ◽  
...  
Keyword(s):  
Early Warning ◽  
Characteristic Curve ◽  
Medical Patient ◽  
Scoring Systems ◽  
Receiver Operator Characteristic Curve ◽  
Early Warning Score ◽  
Peripheral Oxygen Saturation ◽  
Logistic Regression Models ◽  
Prospective Application ◽  
Gas Measurements

Removal of the intensive care unit (ICU) at the Vale of Leven Hospital mandated the identification and transfer out of those acute medical admissions with a high risk of requiring ICU. The aim of the study was to develop triaging tools that identified such patients and compare them with other scoring systems. The methodology included a retrospective analysis of physiological and arterial gas measurements from 1976 acute medical admissions produced PREEMPT-1 (PRE-critical Emergency Medical Patient Triage). A simpler one for ambulance use (PREAMBLE-1 [PRE-Admission Medical Blue-Light Emergency]) was produced by the addition of peripheral oxygen saturation to a modification of MEWS (Modified Early Warning Score). Prospective application of these tools produced a larger database of 4447 acute admissions from which logistic regression models produced PREEMPT-2 and PREAMBLE-2, which were then compared with the original systems and seven other early warning scoring systems. Results showed that in patients with arterial gases, the area under the receiver operator characteristic curve was significantly higher in PREEMPT-2 (89·1%) and PREAMBLE-2 (84.4%) than all other scoring systems. Similarly, in all patients, it was higher in PREAMBLE-2 (92.4%) than PREAMBLE-1 (88.1%) and the other scoring systems. In conclusion, risk of requiring ICU can be more accurately predicted using PREEMPT-2 and PREAMBLE-2, as described here, than by other early warning scoring systems developed over recent years.

scholarly journals Haemoconcentration as a predictor of severity in acute pancreatitis

2016 ◽  
Vol 4 (1) ◽  
pp. 3-7
Author(s):  
Tanka Prasad Bohara ◽  
Dimindra Karki ◽  
Anuj Parajuli ◽  
Shail Rupakheti ◽  
Mukund Raj Joshi
Keyword(s):  
Acute Pancreatitis ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Cost Effective ◽  
Receiver Operator Characteristic Curve ◽  
First Episode ◽  
P Value ◽  
Receiver Operator Characteristic ◽  
Chi Square

Background: Acute pancreatitis is usually a mild and self-limiting disease. About 25 % of patients have severe episode with mortality up to 30%. Early identification of these patients has potential advantages of aggressive treatment at intensive care unit or transfer to higher centre. Several scoring systems are available to predict severity of acute pancreatitis but are cumbersome, take 24 to 48 hours and are dependent on tests that are not universally available. Haematocrit has been used as a predictor of severity of acute pancreatitis but some have doubted its role.Objectives: To study the significance of haematocrit in prediction of severity of acute pancreatitis.Methods: Patients admitted with first episode of acute pancreatitis from February 2014 to July 2014 were included. Haematocrit at admission and 24 hours of admission were compared with severity of acute pancreatitis. Mean, analysis of variance, chi square, pearson correlation and receiver operator characteristic curve were used for statistical analysis.Results: Thirty one patients were included in the study with 16 (51.61%) male and 15 (48.4%) female. Haematocrit at 24 hours of admission was higher in severe acute pancreatitis (P value 0.003). Both haematocrit at admission and at 24 hours had positive correlation with severity of acute pancreatitis (r: 0.387; P value 0.031 and r: 0.584; P value 0.001) respectively.Area under receiver operator characteristic curve for haematocrit at admission and 24 hours were 0.713 (P value 0.175, 95% CI 0.536 - 0.889) and 0.917 (P value 0.008, 95% CI 0.813 – 1.00) respectively.Conclusion: Haematocrit is a simple, cost effective and widely available test and can predict severity of acute pancreatitis.Journal of Kathmandu Medical College, Vol. 4(1) 2015, 3-7

scholarly journals Development and validation of an early warning tool for sepsis and decompensation in children during emergency department triage

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Louis Ehwerhemuepha ◽  
Theodore Heyming ◽  
Rachel Marano ◽  
Mary Jane Piroutek ◽  
Antonio C. Arrieta ◽  
...  
Keyword(s):  
Emergency Department ◽  
Severe Sepsis ◽  
Early Warning ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Warning System ◽  
Receiver Operator Characteristic Curve ◽  
Warning Signs ◽  
Gradient Boosting ◽  
Stochastic Gradient Boosting

AbstractThis study was designed to develop and validate an early warning system for sepsis based on a predictive model of critical decompensation. Data from the electronic medical records for 537,837 visits to a pediatric Emergency Department (ED) from March 2013 to December 2019 were collected. A multiclass stochastic gradient boosting model was built to identify early warning signs associated with death, severe sepsis, non-severe sepsis, and bacteremia. Model features included triage vital signs, previous diagnoses, medications, and healthcare utilizations within 6 months of the index ED visit. There were 483 patients who had severe sepsis and/or died, 1102 had non-severe sepsis, 1103 had positive bacteremia tests, and the remaining had none of the events. The most important predictors were age, heart rate, length of stay of previous hospitalizations, temperature, systolic blood pressure, and prior sepsis. The one-versus-all area under the receiver operator characteristic curve (AUROC) were 0.979 (0.967, 0.991), 0.990 (0.985, 0.995), 0.976 (0.972, 0.981), and 0.968 (0.962, 0.974) for death, severe sepsis, non-severe sepsis, and bacteremia without sepsis respectively. The multi-class macro average AUROC and area under the precision recall curve were 0.977 and 0.316 respectively. The study findings were used to develop an automated early warning decision tool for sepsis. Implementation of this model in pediatric EDs will allow sepsis-related critical decompensation to be predicted accurately after a few seconds of triage.

scholarly journals National Early Warning Score 2 (NEWS2) better predicts critical Coronavirus Disease 2019 (COVID-19) illness than COVID-GRAM, a multi-centre study

Infection ◽  
2021 ◽  
Author(s):  
Giuseppe Vittorio De Socio ◽  
Anna Gidari ◽  
Francesco Sicari ◽  
Michele Palumbo ◽  
Daniela Francisci
Keyword(s):  
Early Warning ◽  
Operating Characteristic ◽  
Primary Outcome ◽  
Characteristic Curve ◽  
Early Warning Score ◽  
Multi Centre Study ◽  
Umbria Region ◽  
Chinese Cohort ◽  
National Early Warning Score ◽  
Specific Score

Abstract Purpose Clinical scores to rapidly assess the severity illness of Coronavirus Disease 2019 (COVID-19) could be considered of help for clinicians. Recently, a specific score (named COVID-GRAM) for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection, based on a nationwide Chinese cohort, has been proposed. We routinely applied the National Early Warning Score 2 (NEWS2) to predict critical COVID-19. Aim of this study is to compare NEWS2 and COVID-GRAM score. Methods We retrospectively analysed data of 121 COVID-19 patients admitted in two Clinics of Infectious Diseases in the Umbria region, Italy. The primary outcome was critical COVID-19 illness defined as admission to the intensive care unit, invasive ventilation, or death. Accuracy of the scores was evaluated with the area under the receiver-operating characteristic curve (AUROC). Differences between scores were confirmed used Hanley–McNeil test. Results The NEWS2 AUROC curve measured 0.87 (standard error, SE 0.03; 95% CI 0.80–0.93; p < 0.0001). The COVID-GRAM score AUROC curve measured 0.77 (SE 0.04; 95% CI 0.68–0.85; p < 0.0001). Hanley–McNeil test showed that NEWS2 better predicted severe COVID-19 (Z = 2.03). Conclusions The NEWS2 showed superior accuracy to COVID-GRAM score for prediction of critical COVID-19 illness.

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.

scholarly journals The Role of Modified Early Warning Score (MEWS) in the Prognosis of Acute Pancreatitis

2021 ◽  
Vol 36 (3) ◽  
pp. e272-e272
Author(s):  
Amena Khan ◽  
Digvijoy Sarma ◽  
Chiranth Gowda ◽  
Gabriel Rodrigues
Keyword(s):  
Acute Pancreatitis ◽  
Early Warning ◽  
Prognostic Score ◽  
Scoring Systems ◽  
Early Warning Score ◽  
Predictive Values ◽  
True Value ◽  
Resource Limited ◽  
Primary Health Care Centers ◽  
Sensitivity Specificity

Objectives: Modified Early Warning Score (MEWS) is a reliable, safe, instant, and inexpensive score for prognosticating patients with acute pancreatitis (AP) due to its ability to reflect ongoing changes of the systemic inflammatory response syndrome associated with AP. Our study sought to determine an optimal MEWS value in predicting severity in AP and determine its accuracy in doing so. Methods: Patients diagnosed with AP and admitted to a single institution were analyzed to determine the value of MEWS in identifying severe AP (SAP). The highest MEWS (hMEWS) score for the day and the mean of all the scores of a given day (mMEWS) were determined for each day. Sensitivity, specificity, negative predictive value (NPV), and positive predictive values (PPV) were calculated for the optimal MEWS values obtained. Results: Two hundred patients were included in the study. The data suggested that an hMEWS value > 2 on day one is most accurate in predicting SAP, with a specificity of 90.8% and PPV of 83.3%. An mMEWS of > 1.2 on day two was the most accurate in predicting SAP, with a sensitivity of 81.2%, specificity of 76.6%, PPV of 69.8%, and NPV of 85.9%. These were found to be more accurate than previous studies. Conclusions: MEWS provides a novel, easy, instant, repeatable, and reliable prognostic score that is comparable, if not superior, to existing scoring systems. However, its true value may lie in its use in resource-limited settings such as primary health care centers.

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 Diagnostic and Prognostic Utility Compared Among Different Sepsis Scoring Systems in Adult Patients With Sepsis in Thailand: A Prospective Cohort Study

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Prat Pairattanakorn ◽  
Nasikarn Angkasekwinai ◽  
Rujipas Sirijatuphat ◽  
Walaiporn Wangchinda ◽  
Lalita Tancharoen ◽  
...  
Keyword(s):  
Cohort Study ◽  
Systemic Inflammatory Response Syndrome ◽  
Inflammatory Response ◽  
Early Warning ◽  
Scoring Systems ◽  
Mortality Prediction ◽  
Early Warning Score ◽  
Failure Assessment ◽  
Prognostic Utility ◽  
Sepsis Detection

Abstract Background The diagnostic and prognostic utility of various sepsis scores varied among different cohorts and settings. Methods A prospective cohort study in adult patients with sepsis at Siriraj Hospital (Bangkok, Thailand) was conducted during January to July 2019. The performance of sepsis assessments, including systemic inflammatory response syndrome (SIRS) score, sequential organ failure assessment (SOFA) score, quick sepsis-related organ failure assessment (qSOFA) score, modified early warning score (MEWS), and national early warning score (NEWS), for sepsis detection and mortality prediction were compared with agreement between 2 infectious disease (ID) specialists to determine their sepsis and septic shock status as the reference standard. Results Among the 470 subjects included in this study, 206 patients (43.8%) were determined by 2 ID specialists to have sepsis. Systemic inflammatory response syndrome ≥2, qSOFA ≥2, and NEWS ≥5 yielded the highest sensitivity (93.2%), specificity (81.3%), and accuracy (72.6%), respectively, for detecting sepsis. The SIRS ≥2 had the highest sensitivity (97.8%), whereas qSOFA ≥2 had the highest specificity (61%) and accuracy (69.7%) for predicting mortality among sepsis patients. Receiver operating characteristic (ROC) curve showed MEWS to have the highest discriminatory power for sepsis detection (area under the ROC curve [AUROC], 0.79; 95% confidence interval [CI], 0.74–0.83), whereas SOFA had the highest discriminatory power for predicting hospital mortality (AUROC, 0.76; 95% CI, 0.69–0.79). Conclusions The NEWS ≥5 and qSOFA ≥2 were the most accurate scoring systems for sepsis detection and mortality prediction, respectively. Each scoring system is useful for different specific purposes relative to early detection and mortality prediction in sepsis patients.

scholarly journals Comparison of the National Early Warning Score (NEWS) and the Modified Early Warning Score (MEWS) for predicting admission and in-hospital mortality in elderly patients in the pre-hospital setting and in the emergency department

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6947 ◽  
Author(s):  
Toshiya Mitsunaga ◽  
Izumu Hasegawa ◽  
Masahiko Uzura ◽  
Kenji Okuno ◽  
Kei Otani ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Hospital Mortality ◽  
Early Warning ◽  
Pressure Pulse ◽  
Vital Signs ◽  
Early Warning Score ◽  
Single Centre ◽  
Peripheral Oxygen Saturation ◽  
Significant Difference ◽  
National Early Warning Score

The aim of this study is to evaluate the usefulness of the pre-hospital National Early Warning Score (pNEWS) and the pre-hospital Modified Early Warning Score (pMEWS) for predicting admission and in-hospital mortality in elderly patients presenting to the emergency department (ED). We also compare the value of the pNEWS with that of the ED NEWS (eNEWS) and ED MEWS (eMEWS) for predicting admission and in-hospital mortality. This retrospective, single-centre observational study was carried out in the ED of Jikei University Kashiwa Hospital, in Chiba, Japan, from 1st April 2017 to 31st March 2018. All patients aged 65 years or older were included in this study. The pNEWS/eNEWS were derived from seven common physiological vital signs: respiratory rate, peripheral oxygen saturation, the presence of inhaled oxygen parameters, body temperature, systolic blood pressure, pulse rate and Alert, responds to Voice, responds to Pain, Unresponsive (AVPU) score, whereas the pMEWS/eMEWS were derived from six common physiological vital signs: respiratory rate, peripheral oxygen saturation, body temperature, systolic blood pressure, pulse rate and AVPU score. Discrimination was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC). The median pNEWS, pMEWS, eNEWS and eMEWS were significantly higher at admission than at discharge (p < 0.001). The median pNEWS, pMEWS, eNEWS and eMEWS of non-survivors were significantly higher than those of the survivors (p < 0.001). The AUC for predicting admission was 0.559 for the pNEWS and 0.547 for the pMEWS. There was no significant difference between the AUCs of the pNEWS and the pMEWS for predicting admission (p = 0.102). The AUCs for predicting in-hospital mortality were 0.678 for the pNEWS and 0.652 for the pMEWS. There was no significant difference between the AUCs of the pNEWS and the pMEWS for predicting in-hospital mortality (p = 0.081). The AUC for predicting admission was 0.628 for the eNEWS and 0.591 for the eMEWS. The AUC of the eNEWS was significantly greater than that of the eMEWS for predicting admission (p < 0.001). The AUC for predicting in-hospital mortality was 0.789 for the eNEWS and 0.720 for the eMEWS. The AUC of the eNEWS was significantly greater than that of the eMEWS for predicting in-hospital mortality (p < 0.001). For admission and in-hospital mortality, the AUC of the eNEWS was significantly greater than that of the pNEWS (p < 0.001, p < 0.001), and the AUC of the eMEWS was significantly greater than that of the pMEWS (p < 0.01, p < 0.05). Our single-centre study has demonstrated the low utility of the pNEWS and the pMEWS as predictors of admission and in-hospital mortality in elderly patients, whereas the eNEWS and the eMEWS predicted admission and in-hospital mortality more accurately. Evidence from multicentre studies is needed before introducing pre-hospital versions of risk-scoring systems.

scholarly journals Corneal Segmentation Analysis Increases Glaucoma Diagnostic Ability of Optic Nerve Head Examination, Heidelberg Retina Tomograph’s Moorfield’s Regression Analysis, and Glaucoma Probability Score

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
F. Saenz-Frances ◽  
L. Jañez ◽  
C. Berrozpe-Villabona ◽  
L. Borrego-Sanz ◽  
L. Morales-Fernández ◽  
...  
Keyword(s):  
Optic Nerve ◽  
Optic Nerve Head ◽  
Open Angle Glaucoma ◽  
Characteristic Curve ◽  
Corneal Thickness ◽  
Cross Sectional Study ◽  
Receiver Operator Characteristic Curve ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Segmentation Analysis

Purpose. To study whether a corneal thickness segmentation model, consisting in a central circular zone of 1 mm radius centered at the corneal apex (zone I) and five concentric rings of 1 mm width (moving outwards: zones II to VI), could boost the diagnostic accuracy of Heidelberg Retina Tomograph’s (HRT’s) MRA and GPS.Material and Methods. Cross-sectional study. 121 healthy volunteers and 125 patients with primary open-angle glaucoma. Six binary multivariate logistic regression models were constructed (MOD-A1, MOD-A2, MOD-B1, MOD-B2, MOD-C1, and MOD-C2). The dependent variable was the presence of glaucoma. In MOD-A1, the predictor was the result (presence of glaucoma) of the analysis of the stereophotography of the optic nerve head (ONH). In MOD-B1 and MOD-C1, the predictor was the result of the MRA and GPS, respectively. In MOD-B2 and MOD-C2, the predictors were the same along with corneal variables: central, overall, and zones I to VI thicknesses. This scheme was reproduced for model MOD-A2 (stereophotography along with corneal variables). Models were compared using the area under the receiver operator characteristic curve (AUC).Results. MOD-A1-AUC: 0.771; MOD-A2-AUC: 0.88; MOD-B1-AUC: 0.736; MOD-B2-AUC: 0.845; MOD-C1-AUC: 0.712; MOD-C2-AUC: 0.838.Conclusion. Corneal thickness variables enhance ONH assessment and HRT’s MRA and GPS diagnostic capacity.

scholarly journals A Multicenter Validation Study of the Deep Learning-based Early Warning Score for Predicting in-hospital Cardiac Arrest in Patients Admitted to General Wards

2020 ◽  
Author(s):  
Yeon Joo Lee ◽  
Kyung-Jae Cho ◽  
Oyeon Kwon ◽  
Hyunho Park ◽  
Yeha Lee ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Deep Learning ◽  
Early Warning ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Early Warning Score ◽  
Patients At Risk ◽  
Key Aspects ◽  
General Wards ◽  
Hospital Cardiac Arrest

Abstract Background: The recently developed deep learning (DL)-based early warning score (DEWS) has shown a potential in predicting deteriorating patients. We aimed to validate DEWS in multiple centers and compare the prediction, alarming and timeliness performance with those of the modified early warning score (MEWS) to identify patients at risk for in-hospital cardiac arrest (IHCA).Methods: This retrospective cohort study included adult patients admitted to the general wards of five hospitals during a 12-month period. We validated DEWS internally at two hospitals and externally at the other three hospitals. The occurrence of IHCA within 24 hours of vital sign observation was the outcome of interest. We used the area under the receiver operating characteristic curve (AUROC) as the main performance metric.Results: The study population consisted of 173,368 patients (224 IHCAs). The predictive performance of DEWS was superior to that of MEWS in both the internal (AUROC: 0.860 vs. 0.754, respectively) and external (AUROC: 0.905 vs. 0.785, respectively) validation cohorts. At the same specificity, DEWS had a higher sensitivity than MEWS, and at the same sensitivity, DEWS had a lower mean alarm count than MEWS, with nearly half of the alarm rate in MEWS. Additionally, DEWS was able to predict more IHCA patients in the 24 to 0.5 hours before the outcome.Conclusion: Our study showed that DEWS was superior to MEWS in the three key aspects (IHCA predictive, alarming, and timeliness performance). This study demonstrates the potential of DEWS as an effective, efficient screening tool in rapid response systems (RRSs) to identify high-risk patients.

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