Development and Validation of a Machine Learning Algorithm Using Clinical Pages to Predict Imminent Clinical Deterioration.

BACKGROUND: Early detection of clinical deterioration among hospitalized patients is a clinical priority for patient safety and quality of care. Current automated approaches for identifying these patients perform poorly at identifying imminent events. OBJECTIVE: Develop a machine learning algorithm using pager messages sent between clinical team members to predict imminent clinical deterioration. DESIGN: We conducted a large observational study using long short-term memory machine learning models on the content and frequency of clinical pages. PARTICIPANTS: We included all hospitalizations between January 1, 2018 and December 31, 2020 at Vanderbilt University Medical Center that included at least one page message to physicians. Exclusion criteria included patients receiving palliative care, hospitalizations with a planned intensive care stay, and hospitalizations in the top 2% longest length of stay. MAIN MEASURES: Model classification performance to identify in-hospital cardiac arrest, transfer to intensive care, or Rapid Response activation in the next 3-, 6-, and 12-hours. We compared model performance against three common early warning scores: Modified Early Warning Score, National Early Warning Score, and the Epic Deterioration Index. KEY RESULTS: There were 87,783 patients (mean [SD] age 54.0 [18.8] years; 45,835 [52.2%] women) who experienced 136,778 hospitalizations. 6214 hospitalized patients experienced a deterioration event. The machine learning model accurately identified 62% of deterioration events within 3-hours prior to the event and 47% of events within 12-hours. Across each time horizon, the model surpassed performance of the best early warning score including area under the receiver operating characteristic curve at 6-hours (0.856 vs. 0.781), sensitivity at 6-hours (0.590 vs. 0.505), specificity at 6-hours (0.900 vs. 0.878), and F-score at 6-hours (0.291 vs. 0.220). CONCLUSIONS: Machine learning applied to the content and frequency of clinical pages improves prediction of imminent deterioration. Using clinical pages to monitor patient acuity supports improved detection of imminent deterioration without requiring changes to clinical workflow or nursing documentation.

The prehospital NEW score to assess septic shock in-hospital, 30-day and 90-day mortality.

BACKGROUND: The early identification of sepsis presenting a high risk of deterioration is a daily challenge to optimise patient pathway. This is all the most crucial in the prehospital setting to optimize triage and admission into the appropriate unit: emergency department (ED) or intensive care unit (ICU). We report the association between the prehospital National Early Warning Score 2 (NEWS-2) and in-hospital, 30 and 90-day mortality of SS patients cared for in the pre-hospital setting by a mobile ICU (MICU). METHODS: Septic shock (SS) patients cared for by a MICU between 2016, April 6th and 2021 December 31st were included in this retrospective cohort study. The NEWS-2 is based on 6 physiological variables (blood pressure, heart rate, respiratory rate, temperature, oxygen saturation prior oxygen supplementation, and level of consciousness) and ranges from 0 to 20. The Inverse Probability Treatment Weighting (IPTW) propensity method was applied to assess the association with in-hospital, 30 and 90-day mortality. A NEWS-2 ≥ 7 threshold was chosen for increased clinical deterioration risk definition and usefulness in clinical practice based on previous reports. RESULTS: Data from 530 SS patients requiring MICU intervention in the pre-hospital setting were analysed. The mean age was 69 ± 15 years and presumed origin of sepsis was pulmonary (43%), digestive (25%) or urinary (17%) infection. In-hospital mortality rate was 33%, 30 and 90-day mortality were respectively 31% and 35%. A prehospital NEWS-2 ≥ 7 is associated with an increase in-hospital, 30 and 90-day mortality with respective RRa = 2.34 [1.39-3.95], 2.08 [1.33-3.25] and 2.22 [1.38-3.59]. Calibration statistic values for in-hospital mortality, 30-day and 90-day mortality were 0.54; 0.55 and 0.53 respectively. CONCLUSION: A prehospital NEWS-2 ≥ 7 is associated with an increase in in-hospital, 30 and 90-day mortality of septic shock patients cared for by a MICU in the prehospital setting. Prospective studies are needed to confirm the usefulness of NEWS-2 to improve the prehospital triage and orientation to the adequate facility of sepsis.

Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal-external validation.

BACKGROUND: Binary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration. METHODS: We developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal-external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches. RESULTS: Our data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks. CONCLUSION: Time-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable.

Workload associated with manual assessment of vital signs as compared with continuous wireless monitoring.

BACKGROUND: Vital sign monitoring is considered an essential aspect of clinical care in hospitals. In general wards, this relies on intermittent manual assessments performed by clinical staff at intervals of up to 12 h. In recent years, continuous monitoring of vital signs has been introduced to the clinic, with improved patient outcomes being one of several potential benefits. The aim of this study was to determine the workload difference between continuous monitoring and manual monitoring of vital signs as part of the National Early Warning Score (NEWS). METHODS: Three wireless sensors continuously monitored blood pressure, heart rate, respiratory rate, and peripheral oxygen saturation in 20 patients admitted to the general hospital ward. The duration needed for equipment set-up and maintenance for continuous monitoring in a 24-h period was recorded and compared with the time spent on manual assessments and documentation of vital signs performed by clinical staff according to the NEWS. RESULTS: The time used for continuous monitoring was 6.0 (IQR 3.2; 7.2) min per patient per day vs. 14 (9.7; 32) min per patient per day for the NEWS. Median difference in duration for monitoring of vital signs was 9.9 (95% CI 5.6; 21) min per patient per day between NEWS and continuous monitoring (p < .001). Time used for continuous monitoring in isolated patients was 6.6 (4.6; 12) min per patient per day as compared with 22 (9.7; 94) min per patient per day for NEWS. CONCLUSION: The use of continuous monitoring was associated with a significant reduction in workload in terms of time for monitoring as compared with manual assessment of vital signs.

The combined use of the Charlson Comorbidity Index and National Early Warning Score 2 helps predict the prognosis of candidemia.

BACKGROUND: The National Early Warning Score 2 (NEWS2) standardizes assessment and response to acute illnesses using vital signs. Whether NEWS2 is useful in predicting the prognosis of candidemia remains to be determined. METHODS: Our study, conducted as a rigorous and retrospective analysis, examined patients with candidemia who were hospitalized between January 2014 and December 2023. We assessed candidemia severity using the Pitt Bacteremia Score (PBS) and NEWS2, while the Charlson Comorbidity Index (CCI) was used to assess underlying medical conditions. The endpoint was all-cause mortality within 30 days of candidemia onset, ensuring comprehensive evaluation of the patient's prognosis. RESULTS: Overall, 93 patients with candidemia were included. The 30-day all-cause mortality rate was 29.0 %. The area under the receiver operating characteristic curve (AUC) for CCI, PBS, and NEWS2 were 0.87 (95 % confidence interval [CI]: 0.80-0.95), 0.75 (95 % CI: 0.66-0.85), and 0.92 (95 % CI: 0.87-0.97), respectively, for predicting the 30-day mortality in patients with candidemia. The AUC values for CCI combined with PBS and NEWS2 were 0.89 (95 % CI: 0.83-0.96) and 0.96 (95 % CI: 0.93-1.00) for predicting the 30-day mortality in candidemia. Among the items that were significant in the univariate analysis, multivariate analysis showed that the combination of NEWS2 ≥ 10 and CCI ≥4 was the helpful prognostic factor for 30-day mortality. CONCLUSIONS: The combination of NEWS2 ≥ 10 and CCI ≥4 scores may be useful in predicting the risk of 30-day mortality in patients with candidemia.

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.

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.

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.

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.

Evaluation of a modified paediatric early warning score for children with congenital heart disease.

BACKGROUND: Paediatric early warning score systems are used for early detection of clinical deterioration of patients in paediatric wards. Several paediatric early warning scores have been developed, but most of them are not suitable for children with cyanotic CHD who are adapted to lower arterial oxygen saturation. AIM: The present study compared the original paediatric early warning system of the Royal College of Physicians of Ireland with a modification for children with cyanotic CHD. DESIGN: Retrospective single-centre study in a paediatric cardiology intermediate care unit at a German university hospital. RESULTS: The distribution of recorded values showed a significant shift towards higher score values in patients with cyanotic CHD (p < 0.001) using the original score, but not with the modification. An analysis of sensitivity and specificity for the factor "requirement of action" showed an area under the receiver operating characteristic for non-cyanotic patients of 0.908 (95% CI 0.862-0.954). For patients with cyanotic CHD, using the original score, the area under the receiver operating characteristic was reduced to 0.731 (95% CI 0.637-0.824, p = 0.001) compared to 0.862 (95% CI 0.809-0.915, p = 0.207), when the modified score was used. Using the critical threshold of scores ≥ 4 in patients with cyanotic CHD, sensitivity and specificity for the modified score was higher than for the original (sensitivity 78.8 versus 72.7%, specificity 78.2 versus 58.4%). CONCLUSION: The modified score is a uniform scoring system for identifying clinical deterioration, which can be used in children with and without cyanotic CHD.

An Evaluation of the National Early Warning Score 2 and the Laboratory Data Decision Tree Early Warning Score in Predicting Mortality in Geriatric Patients.

BACKGROUND: Due to the high rate of geriatric patient visits, scoring systems are needed to predict increasing mortality rates. OBJECTIVE: In this study, we aimed to investigate the in-hospital mortality prediction power of the National Early Warning Score 2 (NEWS2) and the Laboratory Data Decision Tree Early Warning Score (LDT-EWS), which consists of frequently performed laboratory parameters. METHODS: We retrospectively analyzed 651 geriatric patients who visited the emergency department (ED), were not discharged on the same day from ED, and were hospitalized. The patients were categorized according to their in-hospital mortality status. The NEWS2 and LDT-EWS values of these patients were calculated and compared on the basis of deceased and living patients. RESULTS: Median (interquartile range [IQR]) NEWS2 and LDT-EWS values of the 127 patients who died were found to be statistically significantly higher than those of the patients who survived (NEWS2: 5 [3-8] vs. 3 [1-5]; p < 0.001; LDT-EWS: 8 [7-10] vs. 6 [5-8]; p < 0.001). In the receiver operating characteristic curve analysis, the NEWS2, LDT-EWS, and NEWS2+LDT-EWS-formed by the sum of the two scoring systems-resulted in 0.717, 0.705, and 0.775 area under curve values, respectively. CONCLUSIONS: The NEWS2 and LDT-EWS were found to be valuable for predicting in-hospital mortality in geriatric patients. The power of the NEWS2 to predict in-hospital mortality increased when used with the LDT-EWS.

Early warning scores for sepsis identification and prediction of in-hospital mortality in adults with sepsis: A systematic review and meta-analysis.

AIM: The early warning scores (EWS), quick Sequential Organ Failure Assessment (qSOFA) and systemic inflammatory response syndrome (SIRS) criteria have been proposed as sepsis screening tools. This review aims to summarise and compare the performance of EWS with the qSOFA and SIRS criteria for predicting sepsis diagnosis and in-hospital mortality in patients with sepsis. DESIGN: A systematic review with meta-analysis. REVIEW METHODS: Seven databases were searched from January 1, 2016 until March 10, 2022. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Sensitivity, specificity, likelihood ratios and diagnostic odd ratios were pooled by using the bivariate random effects model. Overall performance was summarised by using the hierarchical summary receiver-operating characteristics curve. This paper adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. RESULTS: Ten studies involving 52,474 subjects were included in the review. For predicting sepsis diagnosis, the pooled sensitivity of EWS (65%, 95% CI: 55, 75) was similar to SIRS ≥2 (70%, 95% CI: 49, 85) and higher than qSOFA ≥2 (37%, 95% CI: 20, 59). The pooled specificity of EWS (77%, 95% CI: 64, 86) was higher than SIRS ≥2 (62%, 95% CI: 41, 80) but lower than qSOFA ≥2 (94%, 95% CI: 86, 98). Results were similar for the secondary outcome of in-hospital mortality. CONCLUSIONS: Although no one scoring system had both high sensitivity and specificity, the EWS had at least equivalent values in most measures of diagnostic accuracy compared with SIRS or qSOFA. IMPLICATIONS FOR THE PROFESSION: Healthcare systems in which EWS is already in place should consider whether there is any clinical benefit in adopting qSOFA or SIRS. NO PATIENT OR PUBLIC CONTRIBUTION: This systematic review did not directly involve patient or public contribution to the manuscript.

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.

Postoperative circadian patterns in wearable sensor measured heart rate: a prospective observational study.

PURPOSE: This study aimed to describe the 24-hour cycle of wearable sensor-obtained heart rate in patients with deterioration-free recovery and to compare it with patients experiencing postoperative deterioration. METHODS: A prospective observational trial was performed in patients following bariatric or major abdominal cancer surgery. A wireless accelerometer patch (Healthdot) continuously measured postoperative heart rate, both in the hospital and after discharge, for a period of 14 days. The circadian pattern, or diurnal rhythm, in the wearable sensor-obtained heart rate was described using peak, nadir and peak-nadir excursions. RESULTS: The study population consisted of 137 bariatric and 100 major abdominal cancer surgery patients. In the latter group, 39 experienced postoperative deterioration. Both surgery types showed disrupted diurnal rhythm on the first postoperative days. Thereafter, the bariatric group had significantly lower peak heart rates (days 4, 7-12, 14), lower nadir heart rates (days 3-14) and larger peak-nadir excursions (days 2, 4-14). In cancer surgery patients, significantly higher nadir (days 2-5) and peak heart rates (days 2-3) were observed prior to deterioration. CONCLUSIONS: The postoperative diurnal rhythm of heart rate is disturbed by different types of surgery. Both groups showed recovery of diurnal rhythm but in patients following cancer surgery, both peak and nadir heart rates were higher than in the bariatric surgery group. Especially nadir heart rate was identified as a potential prognostic marker for deterioration after cancer surgery.

Diagnostic agreement between emergency medical service and emergency department physicians, a prospective multicentre study.

INTRODUCTION: Early and adequate preliminary diagnosis reduce emergency department (ED) and hospital stay and may reduce mortality. Several studies demonstrated adequate preliminary diagnosis as stated by emergency medical services (EMS) ranging between 61 and 77%. Dutch EMS are highly trained, but performance of stating adequate preliminary diagnosis remains unknown. METHODS: This prospective observational study included 781 patients (> 18years), who arrived in the emergency department (ED) by ambulance in two academic hospitals. For each patient, the diagnosis as stated by EMS and the ED physician was obtained and compared. Diagnosis was categorized based on the International Classification of Diseases, 11th Revision. RESULTS: The overall diagnostic agreement was 79% [95%-CI: 76-82%]. Agreement was high for traumatic injuries (94%), neurological emergencies (90%), infectious diseases (84%), cardiovascular (78%), moderate for mental and drug related (71%), gastrointestinal (70%), and low for endocrine and metabolic (50%), and acute internal emergencies (41%). There is no correlation between 28-day mortality, the need for ICU admission or the need for hospital admission with an adequate preliminary diagnosis. CONCLUSION: In the Netherlands, the extent of agreement between EMS diagnosis and ED discharge diagnosis varies between categories. Accuracy is high in diseases with specific observations, e.g., neurological failure, detectable injuries, and electrocardiographic abnormalities. Further studies should use these findings to improve patient outcome.

Development and inter-rater reliability of a simple prehospital mobility score for use in emergency patients.

BACKGROUND: Mobility assessment enhances the ability of vital sign-based early warning scores to predict risk. Currently mobility is not routinely assessed in a standardized manner in Denmark during the ambulance transfer of unselected emergency patients. The aim of this study was to develop and test the inter-rater reliability of a simple prehospital mobility score for pre-hospital use in ambulances and to test its inter-rater reliability. METHOD: Following a pilot study, we developed a 4-level prehospital mobility score based of the question"How much help did the patient need to be mobilized to the ambulance trolley". Possible scores were no-, a little-, moderate-, and a lot of help. A cross-sectional study of inter-rater agreement among ambulance personnel was then carried out. Paramedics on ambulance runs in the North- and Central Denmark Region, as well as The Fareoe Islands, were included as a convenience sample between July 2020-May 2021. The simple prehospital mobility score was tested, both by the paramedics in the ambulance and by an additional observer. The study outcomes were inter-rater agreements by weighted kappa between the paramedics and between observers and paramedics. RESULTS: We included 251 mobility assessments where the patient mobility was scored. Paramedics agreed on the mobility score for 202 patients (80,5%). For 47 (18.7%), there was a deviation of one between scores, in two (< 1%) there was a deviation of two and none had a deviation of three (Table 1). Inter-rater agreement between paramedics in all three regions showed a kappa-coefficient of 0.84 (CI 95%: 0.79;0.88). Between observers and paramedics in North Denmark Region and Faroe Islands the kappa-coefficient was 0.82 (CI 95%: 0.77;0.86). CONCLUSION: We developed a simple prehospital mobility score, which was feasible in a prehospital setting and with a high inter-rater agreement between paramedics and observers.

Unveiling the performance of the prehospital Rapid Emergency Medicine Score (pREMS): How the predictive score impacts in-hospital outcomes in traumatic brain injury (TBI): A retrospective observational cohort study.

INTRODUCTION: This study aimed to evaluate the predictive accuracy of the prehospital rapid emergency medicine score (pREMS) for predicting the outcomes of hospitalized patients with traumatic brain injury (TBI) who died, were discharged, were admitted to the intensive care unit (ICU), or were admitted to the operating room (OR) within 72 h. METHODS: A retrospective cohort analysis was performed on a sample of 513 TBI patients admitted to the emergency department (ED) of Besat Hospital in 2023. Only patients of both sexes aged 18 years or older who were not pregnant and had adequate documentation of vital signs were included in the analysis. Patients who died during transport and patients who were transferred from other hospitals were excluded. The predictive power of the pREMS for each outcome was assessed by calculating the sensitivity and specificity curves and by analyzing the area under the receiver operating characteristic curve (AUROC). RESULTS: The mean pREMS scores for hospital discharge, death, ICU admission and OR admission were 11.97 ± 3.84, 6.32 ± 3.15, 8.24 ± 5.17 and 9.88 ± 2.02, respectively. pREMS accurately predicted hospital discharge and death (AOR = 1.62, P < 0.001) but was not a good predictor of ICU or OR admission (AOR = 1.085, P = 0.603). The AUROCs for the ability of the pREMS to predict outcomes in hospitalized TBI patients were 0.618 (optimal cutoff point = 7) for ICU admission and OR and 0.877 (optimal cutoff point = 9.5) for hospital discharge and death at 72 h. CONCLUSION: The results indicate that the pREMS, a new preclinical trauma score for traumatic brain injury, is a useful tool for prehospital risk stratification (RST) in TBI patients. The pREMS showed good discriminatory power for predicting in-hospital mortality within 72 h in patients with traumatic brain injury.

General ward nurses detection and response to clinical deterioration in three hospitals at the Kenyan coast: a convergent parallel mixed methods study.

BACKGROUND: In low and middle-income countries like Kenya, critical care facilities are limited, meaning acutely ill patients are managed in the general wards. Nurses in these wards are expected to detect and respond to patient deterioration to prevent cardiac arrest or death. This study examined nurses' vital signs documentation practices during clinical deterioration and explored factors influencing their ability to detect and respond to deterioration. METHODS: This convergent parallel mixed methods study was conducted in the general medical and surgical wards of three hospitals in Kenya's coastal region. Quantitative data on the extent to which the nurses monitored and documented the vital signs 24 h before a cardiac arrest (death) occurred was retrieved from patients' medical records. In-depth, semi-structured interviews were conducted with twenty-four purposefully drawn registered nurses working in the three hospitals' adult medical and surgical wards. RESULTS: This study reviewed 405 patient records and found most of the documentation of the vital signs was done in the nursing notes and not the vital signs observation chart. During the 24 h prior to death, respiratory rate was documented the least in only 1.2% of the records. Only a very small percentage of patients had any vital event documented for all six-time points, i.e. four hourly. Thematic analysis of the interview data identified five broad themes related to detecting and responding promptly to deterioration. These were insufficient monitoring of vital signs linked to limited availability of equipment and supplies, staffing conditions and workload, lack of training and guidelines, and communication and teamwork constraints among healthcare workers. CONCLUSION: The study showed that nurses did not consistently monitor and record vital signs in the general wards. They also worked in suboptimal ward environments that do not support their ability to promptly detect and respond to clinical deterioration. The findings illustrate the importance of implementation of standardised systems for patient assessment and alert mechanisms for deterioration response. Furthermore, creating a supportive work environment is imperative in empowering nurses to identify and respond to patient deterioration. Addressing these issues is not only beneficial for the nurses but, more importantly, for the well-being of the patients they serve.

Epidemiology and Severity of Medical Events for Mountain Bikers and Hikers Transported by Ambulance in Western Australia, 2015 to 2020.

INTRODUCTION: Outdoor activities offer physical and mental health benefits. However, incidents can occur requiring ambulance transport to hospital. This study aimed to describe the epidemiology and severity of traumatic and medical incidents for mountain bikers and hikers transported by ambulance within Western Australia. METHODS: This was a retrospective cohort study of ambulance-transported mountain bikers and hikers within Western Australia from 2015 to 2020. Data were extracted from ambulance electronic patient care records. Multivariable analyses were undertaken to identify variables associated with higher patient severity based on the National Early Warning Score 2 (NEWS2). RESULTS: A total of 610 patients required ambulance transport to hospital while mountain biking (n=329; 54%) or hiking (n = 281; 46%). Median age of mountain bikers and hikers was 38 (24-48) y and 49 (32-63) y, respectively. Paramedics reported a fracture in 92 (28%) mountain bikers and 78 (28%) hikers. The predominant injury locations for mountain bikers were upper limbs and for hikers, lower limbs. Cases were trauma related in 92% of mountain bikers and 55% of hikers. A significant association (P<0.001) between the etiology of the ambulance callout and patient severity was found. In trauma etiology cases, the frequency of medium-risk+ NEWS2 severity was 21.4%. In medical cases, the frequency of medium-risk+ severity was 40.8%. CONCLUSION: Both mountain bikers and hikers experienced incidents requiring ambulance transport to hospital. Incidents of a medical etiology had a higher clinical risk, as determined by the NEWS2 scores, regardless of activity being undertaken.

Comparison of Performance Characteristics in Early Warning Scoring Tools for Diagnosis of Intubation and Mortality Among COVID-19 Patients.

INTRODUCTION: Early warning scores serve as valuable tools for predicting adverse events in patients. This study aimed to compare the diagnostic performance of National Early Warning Score, Hamilton Early Warning Score, Standardized Early Warning Score, and Triage Early Warning Score in forecasting intubation and mortality among patients with coronavirus disease 2019. METHODS: This predictive correlation study included 370 patients admitted to the emergency department of 22 Bahman Hospital in Neyshabur, Iran, from December 2021 to March 2022. The aforementioned scores were assessed daily upon patient admission and throughout a 1-month hospitalization period, alongside intubation and mortality occurrences. Data analysis used SPSS 26 and MEDCALC 20.0.13 software. We adhered to the Standards for Reporting of Diagnostic Accuracy Studies guidelines to ensure the accurate reporting of our study. RESULTS: The patients' mean age was 65.03 ± 18.47 years, with 209 (56.5%) being male. Both Standardized Early Warning Score and Hamilton Early Warning Score demonstrated high diagnostic performance, with area under the curve values of 0.92 and 0.95, respectively. For Standardized Early Warning Score, the positive likelihood ratio was 10.81 for intubation and 17.90 for mortality, whereas for Hamilton Early Warning Score, the positive likelihood ratio was 7.88 for intubation and 10.40 for mortality. The negative likelihood ratio values were 0.23 and 0.17 for Standardized Early Warning Score and 0.21 and 0.18 for Hamilton Early Warning Score, respectively, for the 24-hour period preceding intubation events and mortality. DISCUSSION: Findings suggest that Standardized Early Warning Score, followed by Hamilton Early Warning Score, has superior diagnostic performance in predicting intubation and mortality in patients with coronavirus disease 2019 within 24 hours before these outcomes. Therefore, serial assessments of Hamilton Early Warning Score or Standardized Early Warning Score may be valuable tools for health care providers in identifying high-risk patients with coronavirus disease 2019 who require intubation or are at increased risk of mortality.