Early Warning Score Research Articles

821-P: Treatment and Outcomes of Nonemergent Hyperglycemia in the Emergency Department

Background: Patients with hyperglycemia often seek care in the emergency department (ED). Hyperglycemia is associated with longer hospital stays and increased risk of mortality. Methods: Using electronic health record data from patients ≥18 seen at an academic medical center ED (11/1/2015-12/31/2020), we described presentation of non-emergent hyperglycemia (blood glucose (BG) 180-600mg/dL) and examined associations between insulin receipt in ED and in-hospital mortality and hospitalization. We excluded patients with initial BG >600mg/dl, decompensated diabetes, stroke, myocardial infarction, pregnancy, or sepsis, and calculated National Early Warning Score to control for illness severity. We examined associations between insulin receipt and hospitalization and mortality in patients with BG 180-600mg/dL using logistic regression, and stratified analyses by diabetes diagnosis and BG value to assess for effect modification. Results: Of 177,176 eligible patients, 10.9% (19,378) had BG 180-600mg/dL (mean (SD) age=54.6 (15); 52.5% male). Of these patients, 6.8% (1,312) received insulin, 41.8% (8,097) were hospitalized, and 0.3% (278) died. Regression results appear in Figure 1. Conclusions: Hyperglycemia in the ED was common. Treatment with insulin, though uncommon, yielded lower odds of mortality but not hospitalization. Diabetes diagnosis and severity of hyperglycemia modified this association. Disclosure K.Murphy: None. M.E.Lacy: None. K.Heier: None. S.A.Claas: None. J.Keck: None. Funding National Institutes of Health (UL1TR001998)

Prediction of GutCheck NEC and Its Relation to Severity of Illness and Measures of Deterioration in Necrotizing Enterocolitis.

Necrotizing enterocolitis (NEC) risk has been shown to arise from multiple sources and risk awareness may be supported using bedside tools. The purpose of this research was to examine the extent to which GutCheck NEC was associated with scores for clinical deterioration, severity of illness, and clinical outcome, and further to examine how scores might improve NEC prediction. A retrospective, correlational case-control study with infant data from 3 affiliated neonatal intensive care units was conducted. Of 132 infants (44 cases, 88 controls), most were 28 weeks of gestation at birth and less (74%). Median age at NEC onset was 18 days (range: 6-34 days), with two-thirds diagnosed before 21 days. At 68 hours of life, higher GutCheck NEC scores were associated with NEC requiring surgery or resulting in death (relative risk ratio [RRR] = 1.06, P = .036), associations that persisted at 24 hours prior to diagnosis (RRR = 1.05, P = .046), and at the time of diagnosis (RRR = 1.05, P = .022) but showed no associations for medical NEC. GutCheck NEC scores were significantly correlated with pediatric early warning scores (PEWS) ( r > 0.30; P < .005) and SNAPPE-II scores ( r > 0.44, P < .0001). Increasing numbers of clinical signs and symptoms were positively associated with GutCheck NEC and PEWS at the time of diagnosis ( r = 0.19, P = .026; and r = 0.25, P = .005, respectively). GutCheck NEC provides structure to streamline assessment and communication about NEC risk. Yet, it is not intended to be diagnostic. Research is needed on how GutCheck NEC impacts timely recognition and treatment.

Predicting ICU admission and death in the Emergency Department: A comparison of six early warning scores

AimTo compare the ability of the most used Early Warning Scores (EWS) to identify adult patients at risk of poor outcomes in the emergency department (ED). MethodsSingle-center, retrospective observational study. We evaluated the digital records of consecutive ED admissions in patients ≥ 18 years from 2010 to 2019 and calculated NEWS, NEWS2, MEWS, RAPS, REMS, and SEWS based on parameters measured on ED arrival. We assessed the discrimination and calibration performance of each EWS in predicting death/ICU admission within 24 hours using ROC analysis and visual calibration. We also measured the relative weight of clinical and physiological derangements that identified patients missed by EWS risk stratification using neural network analysis. ResultsAmong 225,369 patients assessed in the ED during the study period, 1941 (0.9%) were admitted to ICU or died within 24 hours. NEWS was the most accurate predictor (area under the receiver operating characteristic [AUROC] curve 0.904 [95% CI 0.805–0.913]), followed by NEWS2 (AUROC 0.901). NEWS was also well calibrated. In patients judged at low risk (NEWS < 2), 359 events occurred (18.5% of the total). Neural network analysis revealed that age, systolic BP, and temperature had the highest relative weight for these NEWS-unpredicted events. ConclusionsNEWS is the most accurate EWS for predicting the risk of death/ICU admission within 24 h from ED arrival. The score also had a fair calibration with few events occurring in patients classified at low risk. Neural network analysis suggests the need for further improvements by focusing on the prompt diagnosis of sepsis and the development of practical tools for the measurement of the respiratory rate.

National Early Warning Score-2 for Identification of Patients with Intermediate-High-Risk Pulmonary Embolism.

Consensus statements have proposed the use of the National Early Warning Score 2 (NEWS2) to identify stable patients with acute pulmonary embolism (PE) and an intermediate-high risk of adverse outcomes. We aimed to externally validate NEWS2 and compare it to another predictive score (Bova). Using NEWS2 (cutoff ≥5 and ≥7) and the Bova score (cutoff >4), we classified patients as intermediate-high risk (vs. non-intermediate-high risk), and we compared the test characteristics of these risk classification tools for a complicated course within 30 days after PE diagnosis. We also assessed the validity of NEWS2 for predicting a complicated course by adding the results of echocardiography and troponin testing to the model. Of the 848 enrolled patients, the NEWS2 score ≥5 classified 471 (55.5%) and the Bova score classified 37 (4.4%) as intermediate-high risk. NEWS2 had a significantly lower specificity for a 30-day complicated course than Bova (45.4 vs. 96.3%, respectively; p < 0.001). Using the higher score threshold (≥7), the NEWS2 classified 99 (11.7%) as intermediate-high risk, and the specificity was 88.9% (difference with Bova, 7.4%; p < 0.001). The proportion of patients with intermediate-high risk PE was 2.4% for the combination of a positive troponin testing and echocardiographic right ventricle dysfunction and a positive NEWS2 (score ≥7), while the specificity was 97.8% (difference with Bova, 1.5%; p = 0.07). Bova outperforms NEWS2 for predicting a complicated course among stable patients with PE. Addition of troponin testing and echocardiography improved the specificity of NEWS2, although it was not superior to Bova. CLINICALTRIALS.GOV NUMBER: : NCT02238639.

Evidence for Use of Validated Sepsis Screening Tools in the Prehospital Population: A Scoping Review

Introduction Early detection and treatment of sepsis improves chances of survival; however, sepsis is often difficult to diagnose initially. This is especially true in the prehospital setting, where resources are scarce, yet time is of great significance. Early warning scores (EWS) based on vital signs were originally developed to guide medical practitioners in determining the degree of illness of a patient in the in-patient setting. These EWS were adapted for use in the prehospital setting to predict critical illness and sepsis. We performed a scoping review to evaluate the existing evidence for use of validated EWS to identify prehospital sepsis. Methods We performed a systematic search using the CINAHL, Embase, Ovid-MEDLINE, and PubMed databases on September 1, 2022. Articles that examined the use of EWS to identify prehospital sepsis were included and assessed. Results Twenty-three studies were included in this review: one validation study, two prospective studies, two systematic reviews, and 18 retrospective studies. Study characteristics, classification statistics, and primary conclusions of each article were extracted and tabulated. Classification statistics varied markedly for prehospital sepsis identification across all included EWS: sensitivities ranged from 0.02–1.00, specificities from 0.07–1.00, and PPV and NPV from 0.19–0.98 and 0.32–1.00, respectively. Conclusions All studies demonstrated inconsistency for the identification of prehospital sepsis. The variety of available EWS and study design heterogeneity suggest it is unlikely that new research can identify a single gold standard score. Based on our findings in this scoping review, we recommend future efforts focus on combining standardized prehospital care with clinical judgment to provide timely interventions for unstable patients where infection is considered a likely etiology, in addition to improving sepsis education for prehospital clinicians. At most, EWS can be used as an adjunct to these efforts, but they should not be relied on alone for prehospital sepsis identification.

Benefits of MR-proADM-guided decision-making in the emergency department: clinical and economic evaluation in Italy, Germany, Spain, and the UK

AimIn a randomized control trial mid-regional proadrenomedullin (MR-proADM)-guided decision-making has been proven to safely reduce hospital admissions based on an accurate assessment of disease severity in the Emergency Department (ED). This study aimed to assess the impact of a MR-proADM-Guided Triage (MR-GT) versus standard Hospital Triage (HT) on clinical and economic outcomes in ED patients with suspected infection in Spain, Italy, Germany, and the UK, using Patient-Level Data (PLD) from two observational studies.MethodsPLD was collected from patients enrolled at a Spanish hospital during two observational studies. Logistic regression was used to identify predictors of hospitalization. A patient-level simulation model was subsequently developed to evaluate the clinical and economic impact of MR-GT versus HT using results from the statistical analysis and country-specific cost data from the published literature. Probabilistic and deterministic sensitivity analyses were carried out.ResultsFour hundred seventy-three patients were enrolled in this study. MR-proADM had the strongest association with hospital admission, followed by age and National Early Warning Score (NEWS). In the simulation model, MR-GT was associated with an overall reduction in hospitalization relative to HT, equal to 22.6 percentage points (40.9 vs. 63.5%). In addition, the use of MR-GT would reduce the total hospital cost per patient presenting to the ED with suspected infection by roughly 30%, with a mean cost saving per patient of €626, €1,484, €1,154, and £1,113 in Spain, Italy, Germany, and the UK, respectively. The robustness of these findings was confirmed by sensitivity analyses.LimitationsThe statistical analyses were not performed on the same population simulated in the model. Clinical input parameters were assumed to be the same for all countries.ConclusionsMR-proADM showed to be the main predictor of hospitalization. An MR-proADM decision algorithm provides cost savings in Spain, Italy, Germany, and the UK.

Preventing unrecognized deterioration & improving outcomes of critically ill patients using the National Early Warning Score 2 in a high dependency unit in Bangladesh: A quality improvement project.

This Quality Improvement Project (QIP) aimed to assess the acceptability and utility of the National Early Warning Score 2 (NEWS2) in a Bangladeshi level-2 care setting. All nurses and physicians were trained on NEWS2 scores and a proper response before starting the QIP. Utilization of NEWS2 and patient outcome were documented and analyzed. Acceptability was acknowledged by increase in utilization, and utility by reduction in unrecognized deterioration of patients. The modified NEWS2 was well adopted and utilized by the nursing staff. There was a statistically significant reduction in unrecognized deterioration leading to cardiac arrest and the need for transfer to the Intensive Care Unit after implementation of NEWS2. With adequate training, motivation and appropriate modification, NEWS2 can become a well-accepted, widely adopted and realistic bedside monitoring tool in resource-limited settings like Bangladesh.

Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator

Technological advances seen in recent years have introduced the possibility of changing the way hospitalized patients are monitored by abolishing the traditional track-and-trigger systems and implementing continuous monitoring using wearable biosensors. However, this new monitoring paradigm raise demand for novel ways of analyzing the data streams in real time. The aim of this study was to design a stability index using kernel density estimation (KDE) fitted to observations of physiological stability incorporating the patients’ circadian rhythm. Continuous vital sign data was obtained from two observational studies with 491 postoperative patients and 200 patients with acute exacerbation of chronic obstructive pulmonary disease. We defined physiological stability as the last 24 h prior to discharge. We evaluated the model against periods of eight hours prior to events defined either as severe adverse events (SAE) or as a total score in the early warning score (EWS) protocol of ≥ 6, ≥ 8, or ≥ 10. The results found good discriminative properties between stable physiology and EWS-events (area under the receiver operating characteristics curve (AUROC): 0.772–0.993), but lower for the SAEs (AUROC: 0.594–0.611). The time of early warning for the EWS events were 2.8–5.5 h and 2.5 h for the SAEs. The results showed that for severe deviations in the vital signs, the circadian KDE model can alert multiple hours prior to deviations being noticed by the staff. Furthermore, the model shows good generalizability to another cohort and could be a simple way of continuously assessing patient deterioration in the general ward.

A review of deaths in surgery using the structured judgement case note review process

Introduction In December 2016, the Care Quality Commission published a review of how National Health Service trusts investigate patient deaths. Provider organisations and commissioners must work together to review and improve their local approach following the death of people receiving care from their services. Methods The Royal College of Physicians led the National Mortality Case Record Review programme based on the qualitative approach developed by Professor Hutchinson and colleagues at the Yorkshire and Humber Academic Health Science Network. The structured case note review (SCNR) is divided into six phases followed by an overall judgement of the quality of care and of the medical records. We have modified the SCNR to include a section for returns to theatre. The process also includes an assessment of whether any shortcomings in the quality of care contributed to death. Results This paper reports the results of the first 172 SCNRs undertaken in surgery at the Royal United Hospital in Bath. The vast majority (97%) of patients were admitted as emergencies. Overall care was graded as poor in four cases, with problems identified in three cases that were likely to have contributed to death. Two of the recurring themes for learning were failure to escalate a deterioration in the National Early Warning Score and delays in recognising deterioration due to acute surgical problems on medical wards. Conclusions Learning from mistakes is fundamental. The SCNR offers a process to identify both good and poor practice as well as bringing to light previously unrecognised care delivery problems.

Utility of the New Early Warning Score (NEWS) in combination with the neutrophil–lymphocyte ratio for the prediction of prognosis in older patients with pneumonia

ObjectivePredictors of prognosis are necessary for use in routine clinical practice for older patients with pneumonia, given the ageing of the population. Recently, the National Early Warning Score (NEWS), a...

Anzhen Risk Evaluation System for Acute Aortic Syndrome (AZSCORE-AAS): protocol for a multicentre prospective cohort study in northern China

IntroductionAcute aortic syndrome (AAS) is a group of acute and critical conditions, including acute aortic dissection (AAD), acute intramural haematoma and penetrating aortic ulcer. High mortality and morbidity rates result...

P47: Development of a VAD Specific Modified Early Warning Score

Background: The modified Early Warning – Sepsis Recognition Score (MEWS-SRS) is a widely utilized tool to identify patients who may be demonstrating early signs of systemic inflammatory response syndrome or sepsis. The components calculating MEWS-SRS include: temperature (T), heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), mental status and white blood count (WBC). A MEWS-SRS score greater than or equal to six, greater than or equal to a five if mental status change is excluded in scoring and/or a three in any single category prompts notification to a physician for assessment and evaluation of an infection. MEWS-SRS has subsequently been adapted to identify early signs of clinical compromise across disease entities, but the efficacy of this system in the ventricular assist device (VAD) specific population is unknown. Methods: We conducted a single-center retrospective analysis of all VAD patients who required in-patient admission between 1/1/15 – 12/31/20. Patients that had a diagnosis of sepsis on admission, were admitted for VAD implantation, or were younger than 18 years old were excluded.The MEWS-SRS score was calculated for all patients at time of admission and every 4 hours for 3 days. A VAD specific score (VAD-MEWS) was then calculated at the same interval for all patients, substituting the SBP with a Doppler return-to-flow (RTF). The two scores were then compared. Results: 92 patients met inclusion criteria. Mean age was 56.9 years, 27.2% were female and 41.3% were Non-White. Mean length of stay was 8.7 days. In-patient mortality was 5.5%. 10.9% of patients were noted to have a MEWS-SRS > 3, which increased to 19.6% when using the VAD-MEWS (p < 0.05, Fig 1). The mean change in score overall was 1.04. Among patients who did not survive the admission, the mean change from SRS-MEWS to the VAD-MEWS score was higher in comparison to those who survived (1.8 vs. 1.01, p=0.08). Conclusions: A VAD specific MEWS scoring system reclassifies a significant proportion of patients into a higher category, with a trend towards better identification of those who met in-patient mortality. Further enhancement of such nursing tools specific for VAD patients may lead to better patient outcomes.

NURSE-LED proactive rounding and automatic early-warning score systems to prevent resuscitation incidences among Adults in ward-based Hospitalised patients

ObjectivesIn this study, we investigated the impact of critical care outreach implemented to overcome the problem of rapid response system (RRS) activation. The aim was to evaluate the impact of nurse-led proactive rounding on the rate of adverse events in a hospital setting using an automatic early-warning score system, without a call-activated team. MethodsThis observational study was conducted at a university hospital in Japan. Beginning in September 2019, critical care outreach via nurse-led proactive rounding of the general ward was conducted, using an automatic early-warning score system. We retrospectively assessed the computerised records of all inpatient days (N = 497,284) of adult inpatients admitted to the hospital from September 2017 to 2020. We compared the adverse event occurrences before and after implementation of the critical care outreach program. The main outcome measures were: unexpected death in the general ward, code blue (an in-hospital resuscitation request code directed towards all staff via broadcast) for non-intensive care unit inpatients and unexpected intensive care unit admissions from the general ward. The secondary outcome was the proportion of patients who received respiratory rate measurement. ResultsThe incidence rate ratios of the occurrence of unexpected deaths (0.19, 95% confidence interval: 0.04–0.57) and code blue in the general ward (0.15, 95% confidence interval: 0.025–0.50) decreased. There was no change in unexpected intensive care unit admissions from the general ward (1.25, confidence interval: 0.84–1.82). The proportion of patients who received respiratory rate measurement increased (10.2% vs 16.2%). ConclusionOur results suggest that in RRSs, drastic control of the failure of the mechanism to activate a response team may produce positive outcomes. Proactive rounding that bypasses the mechanism to activate a response team component of RRSs may relieve ward nurses of activation failure responsibility and help them overcome the hierarchical hospital structure.

Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department.

The study aimed to compare the prognostic accuracy of six different severity-of-illness scoring systems for predicting in-hospital mortality among patients with confirmed SARS-COV2 who presented to the emergency department (ED). The scoring systems assessed were worthing physiological score (WPS), early warning score (EWS), rapid acute physiology score (RAPS), rapid emergency medicine score (REMS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA). A cohort study was conducted using data obtained from electronic medical records of 6,429 confirmed SARS-COV2 patients presenting to the ED. Logistic regression models were fitted on the original severity-of-illness scores to assess the models' performance using the Area Under the Curve for ROC (AUC-ROC) and Precision-Recall curves (AUC-PR), Brier Score (BS), and calibration plots were used to assess the models' performance. Bootstrap samples with multiple imputations were used for internal validation. The mean age of the patients was 64 years (IQR:50-76) and 57.5% were male. The WPS, REMS, and NEWS models had AUROC of 0.714, 0.705, and 0.701, respectively. The poorest performance was observed in the RAPS model, with an AUROC of 0.601. The BS for the NEWS, qSOFA, EWS, WPS, RAPS, and REMS was 0.18, 0.09, 0.03, 0.14, 0.15, and 0.11 respectively. Excellent calibration was obtained for the NEWS, while the other models had proper calibration. The WPS, REMS, and NEWS have a fair discriminatory performance and may assist in risk stratification for SARS-COV2 patients presenting to the ED. Generally, underlying diseases and most vital signs are positively associated with mortality and were different between the survivors and non-survivors. Rahmatinejad Z, Hoseini B, Reihani H, Hanna AA, Pourmand A, Tabatabaei SM, et al. Comparison of Six Scoring Systems for Predicting In-hospital Mortality among Patients with SARS-COV2 Presenting to the Emergency Department. Indian J Crit Care Med 2023;27(6):416-425.

Acil Serviste COVID-19 Hastalarının Prognozunu Tahmin Etmek İçin Erken Uyarı Puanlama Sistemlerinin Kullanımı

Aim: Increased emergency department (ED) admissions and the need for intensive care unit (ICU) brought with the pandemic has led to the need to make fast and accurate decisions. Early warning scores (EWS) may be useful in ED in this regard. This study was performed to evaluate the effectiveness of EWS in predicting mortality and need for ICU of patients with COVID-19.&#x0D; Materials and methods: This retrospective observational study was performed with subjects presented to the ED and were later admitted to a general ward or to the ICU because of COVID-19. Subjects aged ≥18 years with positive RT-PCR were included in the study. Subjects experienced a cardiac or respiratory arrest or intubated at the time of presentation to the ED and pregnant women were excluded from the study. MEWS, NEWS, NEWS-2, REMS, and qSOFA scores were calculated using patients’ data on first presentation. We examined the association of these scoring systems with mortality and need for ICU.&#x0D; Results: While 60(10%) of the 600 patients participating in the study were admitted to the ICU, 222(37%) patients died. The scoring systems’ negative predictive values for predicting ICU admission were 0.95, 0.98, 0.97, 0.96, and 0.96 respectively and for predicting mortality were 0.61, 0.67, 0.67, 0.66, and 0.61 respectively. All scorings assessed were significant predictors of the need for ICU and mortality in patients with COVID-19.&#x0D; Conclusions: All evaluated scoring systems were successful; however, NEWS and NEWS-2 had the highest predictive value both for the need for ICU and mortality.

The Use of the Initial National Early Warning Score 2 at the Emergency Department as a Predictive Tool of In-Hospital Mortality in Hemodialysis Patients.

Background The diagnosis of sepsis in the emergency department (ED) is difficult due to the ambiguous nature of its expression and its non-specific symptoms. Multiple scoring tools have been utilized to detect the severity and prognosis of sepsis. This study aimed to evaluate the use of the initial National Early Warning Score 2 (NEWS-2) at the ED as a predictive tool of in-hospital mortality in hemodialysis patients. Methodology We performed a retrospective, observational study to review the records of hemodialysis patients admitted to King Abdulaziz Medical City in Riyadh with suspected sepsis from the 1st of January to the 31st of December 2019 using a convenient sampling technique. Results The results showed that NEWS-2 had a higher sensitivity in predicting sepsis compared to the Quick Sequential Organ Failure Assessment (qSOFA)(16.28% vs. 11.54%). However, qSOFA had a higher specificity in predicting sepsis compared to the NEWS-2 scoring system (81.16% vs. 74.14%). It was found that the NEWS-2 scoring system was more sensitive in predicting mortality compared to qSOFA (26% vs. 20%). However, qSOFA was more specific in predicting mortality compared to NEWS-2 (88.50% vs. 82.98%). Conclusions Our findings demonstrated that the initial NEWS-2 is a subpar screening tool for sepsis and in-hospital mortality in hemodialysis patients. The use of qSOFA at the time of ED presentation was found to have a relatively higher specificity in predicting sepsis and mortality when compared to NEWS-2. To assess the application of the initial NEWS-2 in the ED setting, additional research should be conducted.

The effects of Alpha-2a and Beta-1a Interferons on the Coronavirus disease 2019 prognosis.

The pandemic coronavirus disease 2019 (COVID-19) has been associated with substantial mortality worldwide. Efforts have continued to find an effective treatment for COVID-19. In vitro activity of interferon (IFN) subtypes has been shown against the SARS-CoV and MERS-CoV. Furthermore, the superiority of IFN-β over IFN-α2b and IFN-α2a has been demonstrated in MERS treatment. Early studies showed a low plasma level of IFNs in the peripheral blood or lungs of patients with severe COVID-19. This study assessed the effects of IFN-alpha-2a and -beta-1a on the prognosis of patients with covid-19 infection. We conducted a triple-blind randomized clinical trial on adult patients with moderate to severe COVID-19 from April 2021 to June 2021. The patients were diagnosed based on clinical and laboratory findings and randomly assigned into four groups (A, B, C, and D) using the envelope allocation method. Patients in group A received IFN β-1a; group B received IFN β-1a placebo; group C received IFN α-2a, and group D was treated with IFN α-2a placebo. All patients concomitantly received the national protocol medications as well. A total of 95 eligible patients were randomly assigned into groups. National Early Warning Score 2 (NEWS2) index showed significant differences between groups only on the first day of admission (p-value = 0.001). CT scan scores on the first and tenth days slightly improved, although they were not statistically significant. Duration of hospitalization and hospital discharge did not significantly differ among all treated groups (Table 1). Mortality rates showed no significant statistical difference between the groups. However, viral clearance significantly accelerated in the patients receiving IFN β-1a or IFN α-2a (p < 0.05). It seems that IFN α-2a and IFN β-1a are ineffective in treating COVID-19 patients. Further randomized clinical trials with large sample sizes are needed to estimate the effects of IFN α-2a or IFN β-1a on the outcomes of COVID-19 disease.

Risk of in-hospital Deterioration for Children with Single Ventricle Physiology.

Children with single ventricle physiology (SV) are at high risk of in-hospital morbidity and mortality. Identifying children at risk for deterioration may allow for earlier escalation of care and subsequently decreased mortality.We conducted a retrospective chart review of all admissions to the pediatric cardiology non-ICU service from 2014 to 2018 for children < 18 years old. We defined clinical deterioration as unplanned transfer to the ICU or inpatient mortality. We selected children with SV by diagnosis codes and defined infants as children < 1 year old. We compared demographic, vital sign, and lab values between infants with and without a deterioration event. We evaluated vital sign and medical therapy changes before deterioration events.Among infants with SV (129 deterioration events over 225 admissions, overall 25% with hypoplastic left heart syndrome), those who deteriorated were younger (p = 0.001), had lower baseline oxygen saturation (p = 0.022), and higher baseline respiratory rate (p = 0.022), heart rate (p = 0.023), and hematocrit (p = 0.008). Median Duke Pediatric Early Warning Score increased prior to deterioration (p < 0.001). Deterioration was associated with administration of additional oxygen support (p = 0.012), a fluid bolus (p < 0.001), antibiotics (p < 0.001), vasopressor support (p = 0.009), and red blood cell transfusion (p < 0.001).Infants with SV are at high risk for deterioration. Integrating baseline and dynamic patient data from the electronic health record to identify the highest risk patients may allow for earlier detection and intervention to prevent clinical deterioration.

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning

BackgroundBoth in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear.MethodsThe Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram.ResultsAmong the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram.ConclusionsThe LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.

Prevention of Unnoticed Deterioration And Improvement In Outcome of Critically Ill Patients By Implementation of National Early Warning Score 2 In A High Dependency Unit In Bangladesh: A Quality Improvement Project

This Quality Improvement Project (QIP) was aimed to assess the acceptability and utility of the National Early Warning Score 2 (NEWS2) in a Bangladeshi level-2 care setting.All nurses and physicians were trained on NEWS2 scores and proper response before starting the QIP. Utilization of NEWS2 and patient outcome were documented and analyzed. Acceptability was recognized by increase in utilization, and utility by reduction in unnoticed deterioration of patients. With modified NEWS2 chart, it became well adopted and utilized by the nurses. Data from 3828 patients showed statistically significant reduction in unnoticed deterioration evident by lowering of sudden cardiac arrest and ICU transfer (0.35% vs 2.6% and 2.33% vs 6.6%, p= &lt;0.0005) after and before implementation of the NEWS2, respectively.With adequate training, motivation, and appropriate modification, the NEWS2 can become a well-accepted, widely adopted, and realistic bedside monitoring tool in resource limited settings like Bangladesh. Bangladesh J Medicine 2023; Vol. 34, No. 2(1) Supplement: 188