Validation of the Turkish version of the Modified Early Obstetric Warning System (MEOWS) chart.

BACKGROUND: The Modified Early Obstetric Warning System (MEOWS) is a score-based or color-coded system that detects changes in physiological parameters and enables earlier diagnosis and care of worsening obstetric patients. The aim of this study is to evaluate the tool's performance and contribute to its use in Türkiye by translating MEOWS into Turkish. METHODS: This prospective and descriptive study, approved by the local ethics committee, included 350 obstetric in-patients who gave birth at Samsun Training and Research Hospital, Gynecology and Children's Hospital between April and August 2022. The study involved patients with a gestational week greater than 28 weeks and up to six weeks postpartum. RESULTS: The average age of the patients was 28.9±5.9 (18-40) years, with trigger values occurring in 34.6% (n=121) and morbidity occurring in 30.9% (n=108) of the cases. The most common trigger among the individual physiological indicators was high systolic blood pressure (28.3%). When the performance of MEOWS was evaluated, a statistically significant correlation was found between trigger and morbidity (Kappa=0.605; p<0.001). The sensitivity of MEOWS in estimating morbidity was 77.78% (95% confidence interval [CI]: 68.76-85.21%), specificity was 84.71% (95% CI: 79.55-89.00%), Positive Predictive Value (PPV) was 69.42% (95% CI: 62.40-75.64%), Negative Predictive Value (NPV) was 89.52% (95% CI: 85.67-92.43%), and accuracy was 82.57% (95% CI: 78.18-86.40%). CONCLUSION: MEOWS was found to be an effective screening tool for predicting morbidity in this study and performs well in Turkish with sufficient sensitivity, specificity, and accuracy. However, the inclusion of long-term results would provide a more comprehensive understanding of the effectiveness of MEOWS.

Comparison of Modified Early Warning Score (MEWS), Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA), and Acute Physiology and Chronic Health Evaluation II (APACHE II) for early prediction of septic shock in diabetic patients in Emergency Departments.

INTRODUCTION: Sepsis is a severe medical condition that can be life-threatening. If sepsis progresses to septic shock, the mortality rate increases to around 40%, much higher than the 10% mortality observed in sepsis. Diabetes increases infection and sepsis risk, making management complex. Various scores of screening tools, such as Modified Early Warning Score (MEWS), Simplified Acute Physiology Score (SAPS II), Sequential Organ Failure Assessment Score (SOFA), and Acute Physiology and Chronic Health Evaluation (APACHE II), are used to predict the severity or mortality rate of disease. Our study aimed to compare the effectiveness and optimal cutoff points of these scores. We focused on the early prediction of septic shock in patients with diabetes in the Emergency Department (ED). METHODS: We conducted a retrospective cohort study to collect data on patients with diabetes. We collected prediction factors and MEWS, SOFA, SAPS II and APACHE II scores to predict septic shock in these patients. We determined the optimal cutoff points for each score. Subsequently, we compared the identified scores with the gold standard for diagnosing septic shock by applying the Sepsis-3 criteria. RESULTS: Systolic blood pressure (SBP), peripheral oxygen saturation (SpO2), Glasgow Coma Scale (GCS), pH, and lactate concentrations were significant predictors of septic shock (p < 0.001). The SOFA score performed well in predicting septic shock in patients with diabetes. The area under the receiver operating characteristics (ROC) curve for the SOFA score was 0.866 for detection within 48 h and 0.840 for detection after 2 h of admission to the ED, with the optimal cutoff score of ≥ 6. CONCLUSION: SBP, SpO2, GCS, pH, and lactate concentrations are crucial for the early prediction of septic shock in patients with diabetes. The SOFA score is a superior predictor for the onset of septic shock in patients with diabetes compared with MEWS, SAPS II, and APACHE II scores. Specifically, a cutoff of ≥ 6 in the SOFA score demonstrates high accuracy in predicting shock within 48 h post-ED visit and as early as 2 h after ED admission.

Clinical evaluation of a machine learning-based early warning system for patient deterioration.

BACKGROUND: The implementation and clinical impact of machine learning-based early warning systems for patient deterioration in hospitals have not been well described. We sought to describe the implementation and evaluation of a multifaceted, real-time, machine learning-based early warning system for patient deterioration used in the general internal medicine (GIM) unit of an academic medical centre. METHODS: In this nonrandomized, controlled study, we evaluated the association between the implementation of a machine learning-based early warning system and clinical outcomes. We used propensity score-based overlap weighting to compare patients in the GIM unit during the intervention period (Nov. 1, 2020, to June 1, 2022) to those admitted during the pre-intervention period (Nov. 1, 2016, to June 1, 2020). In a difference-indifferences analysis, we compared patients in the GIM unit with those in the cardiology, respirology, and nephrology units who did not receive the intervention. We retrospectively calculated system predictions for each patient in the control cohorts, although alerts were sent to clinicians only during the intervention period for patients in GIM. The primary outcome was non-palliative in-hospital death. RESULTS: The study included 13 649 patient admissions in GIM and 8470 patient admissions in subspecialty units. Non-palliative deaths were significantly lower in the intervention period than the pre-intervention period among patients in GIM (1.6% v. 2.1%; adjusted relative risk [RR] 0.74, 95% confidence interval [CI] 0.55-1.00) but not in the subspecialty cohorts (1.9% v. 2.1%; adjusted RR 0.89, 95% CI 0.63-1.28). Among high-risk patients in GIM for whom the system triggered at least 1 alert, the proportion of non-palliative deaths was 7.1% in the intervention period, compared with 10.3% in the pre-intervention period (adjusted RR 0.69, 95% CI 0.46-1.02), with no meaningful difference in subspecialty cohorts (10.4% v. 10.6%; adjusted RR 0.98, 95% CI 0.60-1.59). In the difference-indifferences analysis, the adjusted relative risk reduction for non-palliative death in GIM was 0.79 (95% CI 0.50-1.24). INTERPRETATION: Implementing a machine learning-based early warning system in the GIM unit was associated with lower risk of non-palliative death than in the pre-intervention period. Machine learning-based early warning systems are promising technologies for improving clinical outcomes.

Comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set: a diagnostic study.

BACKGROUND: In the recent years, National Early Warning Score2 (NEWS2) is utilized to predict early on, the worsening of clinical status in patients. To this date the predictive accuracy of National Early Warning Score (NEWS2), Revised Trauma Score (RTS), and Trauma and injury severity score (TRISS) regarding the trauma patients' mortality rate have not been compared. Therefore, the objective of this study is comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set. METHODS: This cross-sectional retrospective diagnostic study performed on 6905 trauma patients, of which 4191 were found eligible, referred to the largest trauma center in southern Iran, Shiraz, during 2022-2023 based on their prehospital data set in order to compare the prognostic power of NEWS2, RTS, and TRISS in predicting in-hospital mortality rate. Patients are divided into deceased and survived groups. Demographic data, vital signs, and GCS were obtained from the patients and scoring systems were calculated and compared between the two groups. TRISS and ISS are calculated with in-hospital data set; others are based on prehospital data set. RESULTS: A total of 129 patients have deceased. Age, cause of injury, length of hospital stay, SBP, RR, HR, temperature, SpO2, and GCS were associated with mortality (p-value < 0.001). TRISS and RTS had the highest sensitivity and specificity respectively (77.52, CI 95% [69.3-84.4] and 93.99, CI 95% [93.2-94.7]). TRISS had the highest area under the ROC curve (0.934) followed by NEWS2 (0.879), GCS (0.815), RTS (0.812), and ISS (0.774). TRISS and NEWS were superior to RTS, GCS, and ISS (p-value < 0.0001). CONCLUSION: This novel study compares the accuracy of NEWS2, TRISS, and RTS scoring systems in predicting mortality rate based on prehospital data. The findings suggest that all the scoring systems can predict mortality, with TRISS being the most accurate of them, followed by NEWS2. Considering the time consumption and ease of use, NEWS2 seems to be accurate and quick in predicting mortality based on prehospital data set.

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care.

BACKGROUND: Successful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation. METHODS: Nurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment. RESULTS: Fifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management. CONCLUSIONS: Prediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.

Unobtrusive Monitoring of Clinical Deterioration in Smart Homes.

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings. This study aims to design a CD detection system for health monitoring at home (HM@H) that automatically identifies unstable vital signs and alarms the medical emergency team. We conduct a requirement analysis by interviewing experts. We use unified modeling language (UML) diagrams to define the behavioral and structural aspects of HM@H. We developed a prototype using a SQL-based database and Python to calculate the EWS in the front end. A team of five experts assessed the accuracy and validity of the designed system. The requirement analysis for four main users yielded 30 data elements and 10 functions. Three main components of HM@H are the graphical user interface (GUI), the application programming interface (API), and the server. Results show the possibility of using unobtrusive sensors to collect smart home residents' vital signs and calculate their EWS scores in real-time. However, further implementation with real data, for frail elderly and hospital-discharged patients is required.

NEWS2, S/F-ratio and ROX-index at emergency department for the prediction of adverse outcomes in COVID-19 patients: An external validation study.

BACKGROUND: In the context of the COVID-19 pandemic, the early and accurate identification of patients at risk of deterioration was crucial in overcrowded and resource-limited emergency departments. This study conducts an external validation for the evaluation of the performance of the National Early Warning Score 2 (NEWS2), the S/F ratio, and the ROX index at ED admission in a large cohort of COVID-19 patients from Colombia, South America, assessing the net clinical benefit with decision curve analysis. METHODS: A prospective cohort study was conducted on 6907 adult patients with confirmed COVID-19 admitted to a tertiary care ED in Colombia. The study evaluated the diagnostic performance of NEWS2, S/F ratio, and ROX index scores at ED admission using the area under the receiver operating characteristic curve (AUROC) for discrimination, calibration, and decision curve analysis for the prediction of intensive care unit admission, invasive mechanical ventilation, and in-hospital mortality. RESULTS: We included 6907 patients who presented to the ED with confirmed SARS-CoV-2 infection from March 2020 to November 2021. Mean age was 51 (35-65) years and 50.4% of patients were males. The rate of intensive care unit admission was 28%, and in-hospital death was 9.8%. All three scores have good discriminatory performance for the three outcomes based on the AUROC. S/F ratio showed miscalibration at low predicted probabilities and decision curve analysis indicated that the NEWS2 score provided a greater net benefit compared to other scores across at a 10% threshold to decide ED admission at a high-level of care facility. CONCLUSIONS: The NEWS2, S/F ratio, and ROX index at ED admission have good discriminatory performances in COVID-19 patients for the prediction of adverse outcomes, but the NEWS2 score has a higher net benefit underscoring its clinical utility in optimizing patient management and resource allocation in emergency settings.

Oncological pediatric early warning score: a dedicated tool to predict patient's clinical deterioration and need for pediatric intensive care treatment.

Pediatric oncohematological patients frequently require PICU admission during their clinical history. The O-PEWS is a specific score developed to predict the need for PICU admission of oncohematological children. This study aimed at i) describing the trend of the O-PEWS in a cohort of patients hospitalized in the Pediatric Oncohematology ward and transferred to the PICU of Padua University Hospital, measured at different time-points in the 24 hours before PICU admission and to evaluate its association with mortality and presence of organ failure; ii) investigating the association between the recorded O-PEWS, and PIM3, number of organ failure and the need for ventilation, dialysis and inotropes.This retrospective single-center study enrolled oncohematological children admitted to the PICU between 2017 and 2021. The O-PEWS, ranging between 0 and 15, was calculated on the available medical records and the TIPNet-Network database at 24 (T-24), 12 (T-12), 6 (T-6) and 0 (T0) hours before PICU admission.RESULTS: 101 PICU admissions, related to 80 children, were registered. During the 24 hours prior to PICU admission, the O-PEWS progressively increased in all the patients. At T-24 the median O-PEWS was 3 (IQR 1-5), increasing to a median value of 6 (IQR 4-8) at T0. The O-PEWS was positively associated with mortality, organ failure and the need for ventilation at all the analyzed time-points and with the need for dialysis at T-6.The O-PEWS appears as a useful tool for predicting early clinical deterioration in oncohematological patients and for anticipating the initiation of life-support treatments.

Pediatric Early Warning Score (PEWS) in predicting prognosis of critical pediatric trauma patients: a retrospective study.

BACKGROUND: This study aimed to compare the predictive value of Pediatric Early Warning Score (PEWS) to Pediatric Risk of Mortality-3 (PRISM-3), Pediatric Trauma Score (PTS), and Pediatric Glasgow Coma Score (pGCS) in determining clinical severity and mortality among critical pediatric trauma patients. METHOD: A total of 122 patients monitored due to trauma in the pediatric intensive care unit between 2020 and 2023 were included in the study. Physical examination findings, vital parameters, laboratory values, and all scoring calculations for patients during emergency room admissions and on the first day of intensive care follow-up were recorded. Comparisons were made between two groups identified as survivors and non-survivors. RESULTS: The study included 85 (69.7%) male and 37 (30.3%) female patients, with an average age of 75 ± 59 months for all patients. Forty-one patients (33.6%) required Invasive Mechanical Ventilation (IMV) and 11 patients (9%) required inotropic therapy. Logistic regression analysis revealed a significant association between mortality and PEWS (p < 0.001), PRISM-3 (p < 0.001), PTS (p < 0.001), and pGCS (p < 0.001). Receiver operating characteristics curve analysis demonstrated that the PEWS score (cutoff > 6.5, AUC = 0.953, 95% CI 0.912-0.994) was highly predictive of mortality, showing similar performance to the PRISM-3 score (cutoff > 21, AUC = 0.999, 95% CI 0.995-1). Additionally, the PEWS score was found to be highly predictive in forecasting the need for IMV and inotropic therapy. CONCLUSION: The Pediatric Early Warning Score serves as a robust determinant of mortality in critical pediatric trauma patients. Simultaneously, it demonstrates strong predictability in anticipating the need for IMV and inotropic therapy.

The accuracy of predicting hospital admission by emergency medical service and emergency department personnel compared to the prehospital MEWS: a prospective multicenter study.

INTRODUCTION: Overcrowding in the emergency department (ED) is a global problem. Early and accurate recognition of a patient's disposition could limit time spend at the ED and thus improve throughput and quality of care provided. This study aims to compare the accuracy among healthcare providers and the prehospital Modified Early Warning Score (MEWS) in predicting the requirement for hospital admission. METHODS: A prospective, observational, multi-centre study was performed including adult patients brought to the ED by ambulance. Involved Emergency Medical Service (EMS) personnel, ED nurses and physicians were asked to predict the need for hospital admission using a structured questionnaire. Primary endpoint was the comparison between the accuracy of healthcare providers and prehospital MEWS in predicting patients' need for hospital admission. RESULTS: In total 798 patients were included of whom 393 (49.2%) were admitted to the hospital. Sensitivity of predicting hospital admission varied from 80.0 to 91.9%, with physicians predicting hospital admission significantly more accurately than EMS and ED nurses (p < 0.001). Specificity ranged from 56.4 to 67.0%. All healthcare providers outperformed MEWS ≥ 3 score on predicting hospital admission (sensitivity 80.0-91.9% versus 44.0%; all p < 0.001). Predictions for ward admissions specifically were significantly more accurate than MEWS (specificity 94.7-95.9% versus 60.6%, all p < 0.001). CONCLUSIONS: Healthcare providers can accurately predict the need for hospital admission, and all providers outperformed the MEWS score.

Comparison of NEWS2 and PSI as mortality predictors in patients with community acquired pneumonia.

In the West, National Early Warning Score 2 (NEWS2) is commonly applied to predict the severity of illness using only bedside variables unlike the extensive Pneumonia Severity Index (PSI). The objective of this study was to compare these scores as mortality predictors in patients admitted with community acquired pneumonia (CAP). This cross-sectional study was conducted in Jinnah Postgraduate Medical Centre, Karachi, Pakistan, for six months in 2020 on 116 patients presenting with CAP. Cases of aspiration pneumonia, hospital acquired pneumonia, pulmonary tuberculosis, pulmonary embolism, and pulmonary oedema were excluded. In-hospital mortality was taken as the outcome of this study. The mean age of the participants was 46.9±20.5 years. The in-hospital mortalities were 45(38.8%). NEWS2 was 97.8% sensitive but only 15.5% specific in predicting the outcome, whereas PSI was less sensitive (68.9%) but more specific (50.7%), which showed that in comparison with PSI, NEWS2 is a more sensitive mortality predicting score among hospitalised CAP patients.

Performance of 21 Early Warning System scores in predicting in-hospital deterioration among undifferentiated admitted patients managed by ambulance services.

BACKGROUND: The optimal Early Warning System (EWS) scores for identifying patients at risk of clinical deterioration among those transported by ambulance services remain uncertain. This retrospective study compared the performance of 21 EWS scores to predict clinical deterioration using vital signs (VS) measured in the prehospital or emergency department (ED) setting. METHODS: Adult patients transported to a single ED by ambulances and subsequently admitted to the hospital between 1 January 2019 and 18 April 2019 were eligible for inclusion. The primary outcome was 30-day mortality; secondary outcomes included 3-day mortality, admission to intensive care or coronary care units, length of hospital stay and emergency call activations. The discriminative ability of the EWS scores was assessed using the area under the receiver operating characteristic curve (AUROC). Subanalyses compared the performance of EWS scores between surgical and medical patient types. RESULTS: Of 1414 patients, 995 (70.4%) (53.1% male, mean age 68.7±17.5 years) were included. In the ED setting, 30-day mortality was best predicted by VitalPAC EWS (AUROC 0.71, 95% CI (0.65 to 0.77)) and National Early Warning Score (0.709 (0.65 to 0.77)). All EWS scores calculated in the prehospital setting had AUROC <0.70. Rapid Emergency Medicine Score (0.83 (0.73 to 0.92)) and New Zealand EWS (0.88 (0.81 to 0.95)) best predicted 3-day mortality in the prehospital and ED settings, respectively. EWS scores calculated using either prehospital or ED VS were more effective in predicting 3-day mortality in surgical patients, whereas 30-day mortality was best predicted in medical patients. Among the EWS scores that achieved AUROC ≥0.70, no statistically significant differences were detected in their discriminatory abilities to identify patients at risk of clinical deterioration. CONCLUSIONS: EWS scores better predict 3-day as opposed to 30-day mortality and are more accurate when estimated using VS measured in the ED. The discriminatory performance of EWS scores in identifying patients at higher risk of clinical deterioration may vary by patient type.

Effect of Digital Early Warning Scores on Hospital Vital Sign Observation Protocol Adherence: Stepped-Wedge Evaluation.

BACKGROUND: Early warning scores (EWS) are routinely used in hospitals to assess a patient's risk of deterioration. EWS are traditionally recorded on paper observation charts but are increasingly recorded digitally. In either case, evidence for the clinical effectiveness of such scores is mixed, and previous studies have not considered whether EWS leads to changes in how deteriorating patients are managed. OBJECTIVE: This study aims to examine whether the introduction of a digital EWS system was associated with more frequent observation of patients with abnormal vital signs, a precursor to earlier clinical intervention. METHODS: We conducted a 2-armed stepped-wedge study from February 2015 to December 2016, over 4 hospitals in 1 UK hospital trust. In the control arm, vital signs were recorded using paper observation charts. In the intervention arm, a digital EWS system was used. The primary outcome measure was time to next observation (TTNO), defined as the time between a patient's first elevated EWS (EWS ≥3) and subsequent observations set. Secondary outcomes were time to death in the hospital, length of stay, and time to unplanned intensive care unit admission. Differences between the 2 arms were analyzed using a mixed-effects Cox model. The usability of the system was assessed using the system usability score survey. RESULTS: We included 12,802 admissions, 1084 in the paper (control) arm and 11,718 in the digital EWS (intervention) arm. The system usability score was 77.6, indicating good usability. The median TTNO in the control and intervention arms were 128 (IQR 73-218) minutes and 131 (IQR 73-223) minutes, respectively. The corresponding hazard ratio for TTNO was 0.99 (95% CI 0.91-1.07; P=.73). CONCLUSIONS: We demonstrated strong clinical engagement with the system. We found no difference in any of the predefined patient outcomes, suggesting that the introduction of a highly usable electronic system can be achieved without impacting clinical care. Our findings contrast with previous claims that digital EWS systems are associated with improvement in clinical outcomes. Future research should investigate how digital EWS systems can be integrated with new clinical pathways adjusting staff behaviors to improve patient outcomes.

Early Warning Scoring Systems: What is the Bottom Line?

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Early Warning Systems for Critical Illness Outside the Intensive Care Unit.

Early warning systems (EWSs) are designed and deployed to create a rapid assessment and response for patients with clinical deterioration outside the intensive care unit (ICU). These models incorporate patient-level data such as vital signs and laboratory values to detect or prevent adverse clinical events, such as vital signs and laboratories to allow detection and prevention of adverse clinical events such as cardiac arrest, intensive care transfer, or sepsis. The applicability, development, clinical utility, and general perception of EWS in clinical practice vary widely. Here, we review the field as it has grown from early vital sign-based scoring systems to contemporary multidimensional algorithms and predictive technologies for clinical decompensation outside the ICU.

Predicting the potentially exacerbation of severe viral pneumonia in hospital by MuLBSTA score joint CD4 + and CD8 +T cell counts: construction and verification of risk warning model.

PURPOSE: This study mainly focuses on the immune function and introduces CD4+, CD8+ T cells and their ratios based on the MuLBSTA score, a previous viral pneumonia mortality risk warning model, to construct an early warning model of severe viral pneumonia risk. METHODS: A retrospective single-center observational study was operated from January 2021 to December 2022 at the People's Hospital of Liangjiang New Area, Chongqing, China. A total of 138 patients who met the criteria for viral pneumonia in hospital were selected and their data, including demographic data, comorbidities, laboratory results, CT scans, immunologic and pathogenic tests, treatment regimens, and clinical outcomes, were collected and statistically analyzed. RESULTS: Forty-one patients (29.7%) developed severe or critical illness. A viral pneumonia severe risk warning model was successfully constructed, including eight parameters: age, bacterial coinfection, CD4+, CD4+/CD8+, multiple lung lobe infiltrations, smoking, hypertension, and hospital admission days. The risk score for severe illness in patients was set at 600 points. The model had good predictive performance (AUROC = 0.94397), better than the original MuLBSTA score (AUROC = 0.8241). CONCLUSION: A warning system constructed based on immune function has a good warning effect on the risk of severe conversion in patients with viral pneumonia.

A mixed-methods study to investigate feasibility and acceptability of an early warning score for preterm infants in neonatal units in Kenya: results of the NEWS-K study : Neonatal early warning scores in Kenya.

Preterm birth (< 37 weeks gestation) complications are the leading cause of neonatal mortality. Early-warning scores (EWS) are charts where vital signs (e.g., temperature, heart rate, respiratory rate) are recorded, triggering action. To evaluate whether a neonatal EWS improves clinical outcomes in low-middle income countries, a randomised trial is needed. Determining whether the use of a neonatal EWS is feasible and acceptable in newborn units, is a prerequisite to conducting a trial. We implemented a neonatal EWS in three newborn units in Kenya. Staff were asked to record infants' vital signs on the EWS during the study, triggering additional interventions as per existing local guidelines. No other aspects of care were altered. Feasibility criteria were pre-specified. We also interviewed health professionals (n = 28) and parents/family members (n = 42) to hear their opinions of the EWS. Data were collected on 465 preterm and/or low birthweight (< 2.5 kg) infants. In addition to qualitative study participants, 45 health professionals in participating hospitals also completed an online survey to share their views on the EWS. 94% of infants had the EWS completed at least once during their newborn unit admission. EWS completion was highest on the day of admission (93%). Completion rates were similar across shifts. 15% of vital signs triggered escalation to a more senior member of staff. Health professionals reported liking the EWS, though recognised the biggest barrier to implementation was poor staffing. Newborn unit infant to staff ratios varied between 10 and 53 staff per 1 infant, depending upon time of shift and staff type. A randomised trial of neonatal EWS in Kenya is possible and acceptable, though adaptations are required to the form before implementation.

The impact of mandated use early warning system tools on the development of nurses' higher-order thinking: A systematic review.

AIM: Ascertain the impact of mandated use of early warning systems (EWSs) on the development of registered nurses' higher-order thinking. DESIGN: A systematic literature review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklist (Page et al., 2021). DATA SOURCES: CINAHL, Medline, Embase, PyscInfo. REVIEW METHODS: Eligible articles were quality appraised using the MMAT tool. Data extraction was conducted independently by four reviewers. Three investigators thematically analysed the data. RESULTS: Our review found that EWSs can support or suppress the development of nurses' higher-order thinking. EWS supports the development of higher-order thinking in two ways; by confirming nurses' subjective clinical assessment of patients and/or by providing a rationale for the escalation of care. Of note, more experienced nurses expressed their view that junior nurses are inhibited from developing effective higher-order thinking due to reliance on the tool. CONCLUSION: EWSs facilitate early identification of clinical deterioration in hospitalised patients. The impact of EWSs on the development of nurses' higher-order thinking is under-explored. We found that EWSs can support and suppress nurses' higher-order thinking. EWS as a supportive factor reinforces the development of nurses' heuristics, the mental shortcuts experienced clinicians call on when interpreting their subjective clinical assessment of patients. Conversely, EWS as a suppressive factor inhibits the development of nurses' higher-order thinking and heuristics, restricting the development of muscle memory regarding similar presentations they may encounter in the future. Clinicians' ability to refine and expand on their catalogue of heuristics is important as it endorses the future provision of safe and effective care for patients who present with similar physiological signs and symptoms. IMPACT: This research impacts health services and education providers as EWS and nurses' development of higher-order thinking skills are essential aspects of delivering safe, quality care. NO PATIENT OR PUBLIC CONTRIBUTION: This is a systematic review, and therefore, comprises no contribution from patients or the public.