Social determinants of health and sepsis: a case-control study.

PURPOSE: We aimed to identify whether social determinants of health (SDoH) are associated with the development of sepsis and assess the differences between individuals living within systematically disadvantaged neighbourhoods compared with those living outside these neighbourhoods. METHODS: We conducted a single-centre case-control study including 300 randomly selected adult patients (100 patients with sepsis and 200 patients without sepsis) admitted to the emergency department of a large academic tertiary care hospital in Hamilton, ON, Canada. We collected data on demographics and a limited set of SDoH variables, including neighbourhood household income, smoking history, social support, and history of alcohol disorder. We analyzed study data using multivariate logistic regression models. RESULTS: The study included 100 patients with sepsis with a median [interquartile range (IQR)] age of 75 [58-84] yr and 200 patients without sepsis with a median [IQR] age of 72 [60-83] yr. Factors significantly associated with sepsis included arrival by ambulance, absence of a family physician, higher Hamilton Early Warning Score, and a recorded history of dyslipidemia. Important SDoH variables, such as individual or household income and race, were not available in the medical chart. In patients with SDoH available in their medical records, no SDoH was significantly associated with sepsis. Nevertheless, compared with their proportion of the Hamilton population, the rate of sepsis cases and sepsis deaths was approximately two times higher among patients living in systematically disadvantaged neighbourhoods. CONCLUSIONS: This study revealed the lack of available SDoH data in electronic health records. Despite no association between the SDoH variables available and sepsis, we found a higher rate of sepsis cases and sepsis deaths among individuals living in systematically disadvantaged neighbourhoods. Including SDoH in electronic health records is crucial to study their effect on the risk of sepsis and to provide equitable care.

RéSUMé: OBJECTIF: Nous avons cherché à déterminer si les déterminants sociaux de la santé (DSS) étaient associés à l'apparition de sepsis et à évaluer les différences entre les personnes vivant dans des quartiers systématiquement défavorisés et celles vivant à l'extérieur de ces quartiers. MéTHODE: Nous avons mené une étude cas témoins monocentrique portant sur 300 patient·es adultes sélectionné·es au hasard (100 personnes atteintes de sepsis et 200 témoins sans sepsis) admis·es au service des urgences d'un grand hôpital universitaire de soins tertiaires à Hamilton, ON, Canada. Nous avons recueilli des données démographiques et un ensemble limité de variables de DSS, y compris le revenu des ménages du quartier, les antécédents de tabagisme, le soutien social et les antécédents de troubles liés à l'alcool. Nous avons analysé les données de l'étude à l'aide de modèles de régression logistique multivariés. RéSULTATS: L'étude a inclus 100 patient·es atteint·es de sepsis avec un âge médian [écart interquartile (ÉIQ)] de 75 [58-84] ans et 200 patient·es sans sepsis avec un âge médian [ÉIQ] de 72 [60-83] ans. Les facteurs significativement associés au sepsis comprenaient l'arrivée en ambulance, l'absence de médecin de famille, un score Hamilton Early Warning Score plus élevé et des antécédents enregistrés de dyslipidémie. D'importantes variables de DSS, telles que le revenu individuel et du ménage et la race, n'étaient pas disponibles dans le dossier médical. Chez les personnes dont les DSS étaient disponibles dans leur dossier médical, aucun DSS n'était significativement associé au sepsis. Néanmoins, comparativement à leur proportion dans la population de Hamilton, le taux de cas de sepsis et de décès dus au sepsis était environ deux fois plus élevé chez les personnes vivant dans des quartiers systématiquement défavorisés. CONCLUSION: Cette étude a révélé le manque de données disponibles sur les DSS dans les dossiers de santé électroniques. Bien qu'il n'y ait pas d'association entre les variables disponibles et le sepsis, nous avons constaté un taux plus élevé de cas de sepsis et de décès dus à la septicémie chez les personnes vivant dans des quartiers systématiquement défavorisés. L'inclusion des DSS dans les dossiers de santé électroniques est cruciale pour étudier leur effet sur le risque de sepsis et pour dispenser des soins équitables.

Comparing Continuous with Periodic Vital Sign Scoring for Clinical Deterioration Using a Patient Data Model.

To evaluate a minute-by-minute monitoring algorithm against a periodic early warning score (EWS) in detecting clinical deterioration and workload. Periodic EWSs suffer from large measurement intervals, causing late detection of deterioration. This might be prevented by continuous vital sign monitoring with a real-time algorithm such as the Visensia Safety Index (VSI). This prospective comparative data modeling cohort study (NCT04189653) compares continuous algorithmic alerts against periodic EWS in continuous monitored medical and surgical inpatients. We evaluated sensitivity, frequency, number of warnings needed to evaluate (NNE) and time of initial alert till escalation of care (EOC): Rapid Response Team activation, unplanned ICU admission, emergency surgery, or death. Also, the percentage of VSI alerting minutes was compared between patients with or without EOC. In 1529 admissions continuous VSI warned for 55% of EOC (95% CI: 45-64%) versus 51% (95% CI: 41-61%) by periodic EWS. NNE for VSI was 152 alerts per detected EOC (95% CI: 114-190) compared to 21 (95% CI: 17-28). It generated 0.99 warnings per day per patient compared to 0.13. Time from detection score till escalation was 8.3 hours (IQR: 2.6-24.8) with VSI versus 5.2 (IQR: 2.7-12.3) hours with EWS (P=0.074). The percentage of warning VSI minutes was higher in patients with EOC than in stable patients (2.36% vs 0.81%, P<0.001). Although sensitivity of detection was not significantly improved continuous vital sign monitoring shows potential for earlier alerts for deterioration compared to periodic EWS. A higher percentage of alerting minutes may indicate risk for deterioration.

Evaluation of Early Warning Scores on In-Hospital Mortality in COVID-19 Patients: A Tertiary Hospital Study from Taiwan.

Coronavirus disease 2019 (COVID-19) remains a global pandemic. Early warning scores (EWS) are used to identify potential clinical deterioration, and this study evaluated the ability of the Rapid Emergency Medicine score (REMS), National Early Warning Score (NEWS), and Modified EWS (MEWS) to predict in-hospital mortality in COVID-19 patients. This study retrospectively analyzed data from COVID-19 patients who presented to the emergency department and were hospitalized between 1 May and 31 July 2021. The area under curve (AUC) was calculated to compare predictive performance of the three EWS. Data from 306 COVID-19 patients (61 ± 15 years, 53% male) were included for analysis. REMS had the highest AUC for in-hospital mortality (AUC: 0.773, 95% CI: 0.69-0.85), followed by NEWS (AUC: 0.730, 95% CI: 0.64-0.82) and MEWS (AUC: 0.695, 95% CI: 0.60-0.79). The optimal cut-off value for REMS was 6.5 (sensitivity: 71.4%; specificity: 76.3%), with positive and negative predictive values of 27.9% and 95.4%, respectively. Computing REMS for COVID-19 patients who present to the emergency department can help identify those at risk of in-hospital mortality and facilitate early intervention, which can lead to better patient outcomes.

Medical Emergency Team: A Game Changer in the Wards.

How to cite this article: Chandrasegarane S, Amte RK. Medical Emergency Team: A Game Changer in the Wards. Indian J Crit Care Med 2023;27(1):77.

Screening for infectious maternal morbidity - knowledge, attitudes and perceptions among healthcare providers and managers in Malawi: a qualitative study.

BACKGROUND: Maternal morbidity and mortality related to infection is an international public health concern, but detection and assessment is often difficult as part of routine maternity care in many low- and middle-income countries due to lack of easily accessible diagnostics. Front-line healthcare providers are key for the early identification and management of the unwell woman who may have infection. We sought to investigate the knowledge, attitudes, and perceptions of the use of screening tools to detect infectious maternal morbidity during and after pregnancy as part of routine antenatal and postnatal care. Enabling factors, barriers, and potential management options for the use of early warning scores were explored. METHODS: Key informant interviews (n = 10) and two focus group discussions (n = 14) were conducted with healthcare providers and managers (total = 24) working in one large tertiary public hospital in Blantyre, Malawi. Transcribed interviews were coded by topic and then grouped into categories. Thematic framework analysis was undertaken to identify emerging themes. RESULTS: Most healthcare providers are aware of the importance of the early detection of infection and would seek to better identify women with infection if resources were available to do so. In current practice, an early warning score was used in the high dependency unit only. Routine screening was not in place in the antenatal or postnatal departments. Barriers to implementing routine screening included lack of trained staff and time, lack of thermometers, and difficulties with the interpretation of the early warning scores. A locally adapted early warning screening tool was considered an enabler to implementing routine screening for infectious morbidity. Local ownership and clinical leadership were considered essential for successful and sustainable implementation for clinical change. CONCLUSIONS: Although healthcare providers considered infection during and after pregnancy and childbirth a danger sign and significant morbidity, standardised screening for infectious maternal morbidity was not part of routine antenatal or postnatal care. The establishment of such a service requires the availability of free and easy to access rapid diagnostic testing, training in interpretation of results, as well as affordable targeted treatment. The implementation of early warning scores and processes developed in high-income countries need careful consideration and validation when applied to women accessing care in low resource settings.

Detecting Deteriorating Patients in the Hospital: Development and Validation of a Novel Scoring System.

Rationale: Late recognition of patient deterioration in hospital is associated with worse outcomes, including higher mortality. Despite the widespread introduction of early warning score (EWS) systems and electronic health records, deterioration still goes unrecognized. Objectives: To develop and externally validate a Hospital- wide Alerting via Electronic Noticeboard (HAVEN) system to identify hospitalized patients at risk of reversible deterioration. Methods: This was a retrospective cohort study of patients 16 years of age or above admitted to four UK hospitals. The primary outcome was cardiac arrest or unplanned admission to the ICU. We used patient data (vital signs, laboratory tests, comorbidities, and frailty) from one hospital to train a machine-learning model (gradient boosting trees). We internally and externally validated the model and compared its performance with existing scoring systems (including the National EWS, laboratory-based acute physiology score, and electronic cardiac arrest risk triage score). Measurements and Main Results: We developed the HAVEN model using 230,415 patient admissions to a single hospital. We validated HAVEN on 266,295 admissions to four hospitals. HAVEN showed substantially higher discrimination (c-statistic, 0.901 [95% confidence interval, 0.898-0.903]) for the primary outcome within 24 hours of each measurement than other published scoring systems (which range from 0.700 [0.696-0.704] to 0.863 [0.860-0.865]). With a precision of 10%, HAVEN was able to identify 42% of cardiac arrests or unplanned ICU admissions with a lead time of up to 48 hours in advance, compared with 22% by the next best system. Conclusions: The HAVEN machine-learning algorithm for early identification of in-hospital deterioration significantly outperforms other published scores such as the National EWS.

Quick Sequential Organ Failure Assessment Score and the Modified Early Warning Score for Predicting Clinical Deterioration in General Ward Patients Regardless of Suspected Infection.

BACKGROUND: The quick sequential organ failure assessment (qSOFA) score is suggested to use for screening patients with a high risk of clinical deterioration in the general wards, which could simply be regarded as a general early warning score. However, comparison of unselected admissions to highlight the benefits of introducing qSOFA in hospitals already using Modified Early Warning Score (MEWS) remains unclear. We sought to compare qSOFA with MEWS for predicting clinical deterioration in general ward patients regardless of suspected infection. METHODS: The predictive performance of qSOFA and MEWS for in-hospital cardiac arrest (IHCA) or unexpected intensive care unit (ICU) transfer was compared with the areas under the receiver operating characteristic curve (AUC) analysis using the databases of vital signs collected from consecutive hospitalized adult patients over 12 months in five participating hospitals in Korea. RESULTS: Of 173,057 hospitalized patients included for analysis, 668 (0.39%) experienced the composite outcome. The discrimination for the composite outcome for MEWS (AUC, 0.777; 95% confidence interval [CI], 0.770-0.781) was higher than that for qSOFA (AUC, 0.684; 95% CI, 0.676-0.686; P < 0.001). In addition, MEWS was better for prediction of IHCA (AUC, 0.792; 95% CI, 0.781-0.795 vs. AUC, 0.640; 95% CI, 0.625-0.645; P < 0.001) and unexpected ICU transfer (AUC, 0.767; 95% CI, 0.760-0.773 vs. AUC, 0.716; 95% CI, 0.707-0.718; P < 0.001) than qSOFA. Using the MEWS at a cutoff of ≥ 5 would correctly reclassify 3.7% of patients from qSOFA score ≥ 2. Most patients met MEWS ≥ 5 criteria 13 hours before the composite outcome compared with 11 hours for qSOFA score ≥ 2. CONCLUSION: MEWS is more accurate that qSOFA score for predicting IHCA or unexpected ICU transfer in patients outside the ICU. Our study suggests that qSOFA should not replace MEWS for identifying patients in the general wards at risk of poor outcome.

Implementing a Rapid Response System in a tertiary-care hospital. A cost-effectiveness study.

PURPOSE: The occurrence of adverse events (AE) in hospitalized patients substancially increases the risk of disability or death, having a major negative clinical and economic impact on public health. For early identification of patients at risk and to establish preventive measures, different healthcare systems have implemented rapid response systems (RRS). The aim of this study was to carry out a cost-effectiveness analysis of implementing a RRS in a tertiary-care hospital. METHODS: We included all the patients admitted to Hospital Clínic de Barcelona from 1 to 2016 to 31 December 2016. The cost-effectiveness analysis was summarized as the incremental cost-effectiveness ratio (incremental cost divided by the incremental effectiveness of the two alternatives, RRS versus non-RRS). The effectiveness of the RRS, defined as improvements in health outcomes (AE, cardiopulmonary arrest and mortality), was obtained from the literature and applied to the included patient cohort. A budget impact analysis on the implementation of the RRS from a hospital perspective was performed over a 5-year time horizon. RESULTS: 42,409 patients were included, and 448 (1.05%) had severe AE requiring ICU admission. The cost-effectiveness analysis showed an incremental cost (savings) of EUR - 1,471,101 of RRS versus the non-RRS. The budgetary impact showed a cost reduction of EUR 896,762.00 in the first year and EUR 1,588,579.00 from the second to the fifth year. CONCLUSIONS: The present analysis shows the RRS as a dominant, less costly and more effective structure compared to the non-RRS.

Association of transcutaneous CO2 with respiratory support: a prospective double blind observational study in children with bronchiolitis and reactive airway disease.

The use of clinical scoring to assess for severity of respiratory distress and respiratory failure is challenging due to subjectivity and interrater variability. Transcutaneous Capnography (TcpCO2) can be used as an objective tool to assess a patient's ventilatory status. This study was designed to assess for any correlation of continuous monitoring of TcpCO2 with the respiratory clinical scores and deterioration in children admitted for acute respiratory distress. A prospective observational study over one year on children aged 2 weeks to 5 years admitted with acute respiratory distress or failure secondary to Bronchiolitis and Reactive airway disease was performed. Continuous TcpCO2 monitoring for upto 48 h was recorded. Investigators, bedside physicians, respiratory therapists, and nurses were blinded from the transcutaneous trends at the time of data collection. Total of 813 TcpCO2 measurements at standard intervals of 30 min were obtained on 38 subjects. Subjects with abnormal TcpCO2 (> 45 mmHg) were younger (6.9 ± 5.2 vs. 23.05 ± 17.7 months,) and were more likely to be on higher oxygen flow rate (0.52 L/min/kg vs 0.46 lier/min/kg, p = 0.004) and higher FiO2 (38.4 vs 33.6, p < 0.001 using heated high flow nasal cannula. No difference was found in bronchiolitis score or PEW score in subjects with normal and abnormal TcpCO2. A small but statistically significant increase in TcpCO2 was observed at the escalation of care. Even though odds of escalation of care are higher with abnormal TcpCO2 (OR 1.92), this difference did not reach statistical significance. pCO2 can provide additive information for non-invasive clinical monitoring of children requiring varying respiratory support; however, it does not provide predictive value for escalation or de-escalation of care.

[The early use of sepsis scores to predict respiratory failure and mortality in non-ICU patients with COVID-19]. / Utilidad de las escalas de sepsis para predecir el fallo respiratorio y la muerte en pacientes con COVID-19 fuera de las Unidades de Cuidados Intensivos.

This observational retrospective study aimed to investigate the usefulness of Sequential Organ Failure Assessment (SOFA), Quick SOFA (qSOFA), National Early Warning Score (NEWS), and quick NEWS in predicting respiratory failure and death among patients with COVID-19 hospitalized outside of intensive care units (ICU). We included 237 adults hospitalized with COVID-19 who were followed-up on for one month or until death. Respiratory failure was defined as a PaO2/FiO2 ratio ≤ 200 mmHg or the need for mechanical ventilation. Respiratory failure occurred in 77 patients (32.5%), 29 patients (12%) were admitted to the ICU, and 49 patients (20.7%) died. Discrimination of respiratory failure was slightly higher in NEWS, followed by SOFA. Regarding mortality, SOFA was more accurate than the other scores. In conclusion, sepsis scores are useful for predicting respiratory failure and mortality in COVID-19 patients. A NEWS score ≥ 4 was found to be the best cutoff point for predicting respiratory failure.

Increased time from physiological derangement to critical care admission associates with mortality.

BACKGROUND: Rapid response systems aim to achieve a timely response to the deteriorating patient; however, the existing literature varies on whether timing of escalation directly affects patient outcomes. Prior studies have been limited to using 'decision to admit' to critical care, or arrival in the emergency department as 'time zero', rather than the onset of physiological deterioration. The aim of this study is to establish if duration of abnormal physiology prior to critical care admission ['Score to Door' (STD) time] impacts on patient outcomes. METHODS: A retrospective cross-sectional analysis of data from pooled electronic medical records from a multi-site academic hospital was performed. All unplanned adult admissions to critical care from the ward with persistent physiological derangement [defined as sustained high National Early Warning Score (NEWS) > / = 7 that did not decrease below 5] were eligible for inclusion. The primary outcome was critical care mortality. Secondary outcomes were length of critical care admission and hospital mortality. The impact of STD time was adjusted for patient factors (demographics, sickness severity, frailty, and co-morbidity) and logistic factors (timing of high NEWS, and out of hours status) utilising logistic and linear regression models. RESULTS: Six hundred and thirty-two patients were included over the 4-year study period, 16.3% died in critical care. STD time demonstrated a small but significant association with critical care mortality [adjusted odds ratio of 1.02 (95% CI 1.0-1.04, p = 0.01)]. It was also associated with hospital mortality (adjusted OR 1.02, 95% CI 1.0-1.04, p = 0.026), and critical care length of stay. Each hour from onset of physiological derangement increased critical care length of stay by 1.2%. STD time was influenced by the initial NEWS, but not by logistic factors such as out-of-hours status, or pre-existing patient factors such as co-morbidity or frailty. CONCLUSION: In a strictly defined population of high NEWS patients, the time from onset of sustained physiological derangement to critical care admission was associated with increased critical care and hospital mortality. If corroborated in further studies, this cohort definition could be utilised alongside the 'Score to Door' concept as a clinical indicator within rapid response systems.

NEWS2 to assess suspected COVID-19 in the community: a service evaluation of a primary care assessment centre.

BACKGROUND: Primary care has played a central role in the community response to the coronavirus disease-19 (COVID-19) pandemic. The use of the National Early Warning Score 2 (NEWS2) has been advocated as a tool to guide escalation decisions in the community. The performance of this tool applied in this context is unclear. AIM: To evaluate the process of escalation of care to the hospital within a primary care assessment centre (PCAC) designed to assess patients with suspected COVID-19 in the community. DESIGN AND SETTING: A retrospective service evaluation of all adult patients assessed between 30 March and 22 April 2020 within a COVID-19 primary care assessment centre within Sandwell West Birmingham CCG. METHOD: A database of patient demographics, healthcare interactions and physiological observations was constructed. NEWS2 and CRB65 scores were calculated retrospectively. The proportion of patients escalated was within risk groups defined by NHSE guidelines in place during the evaluation period was determined. RESULTS: A total of 150 patients were identified. Following assessment 13.3% (n = 20) patients were deemed to require escalation. The proportion of patients escalated with a NEWS2 greater than or equal to 3 was 46.9% (95% CI 30.8-63.6%). The proportion of patients escalated to secondary care using NHSE defined risk thresholds was 0% in the green group, 22% (n = 4) in the amber group, and 81.3% (n = 13) in the red group. CONCLUSION: Clinical decisions to escalate care to the hospital did not follow initial guidance written for the COVID-19 outbreak but were demonstrated to be safe.

In most cases, coronavirus disease-19 (COVID-19) is a mild illness that resolves on its own. Some patients develop severe disease requiring hospital treatment. Identifying which patients are likely to need hospital treatment is a challenge. Many GP practices have developed specific services designed to assess patients with suspected COVID-19 and establish whether hospital treatment is necessary. We evaluated a service providing this function in Birmingham. We examined the care pathway of 150 patients assessed within the service to established factors associated with the need for hospital assessment. We found a national decision tool designed to aid the process was a poor descriptor of what happened in practice.

Laboratory-derived early warning score for the prediction of in-hospital mortality, intensive care unit admission, medical emergency team activation and cardiac arrest in general medical wards.

BACKGROUND: General medical wards admit a varied cohort of patients from the emergency department, some of whom deteriorate during their hospital stay. Currently, we use vital signs based warning scores to predict patients at risk of imminent deterioration, but there is now a growing body of literature that commonly available laboratory results may also help to identify those at risk. AIM: To assess whether a laboratory-based admission score can predict in hospital mortality, intensive care unit (ICU) admission, medical emergency team (MET) activation or cardiac arrest in a cohort of Australian general medical patients admitted through the emergency department (ED). METHODS: We performed a retrospective observational study of all general medical admissions to hospital through the ED in 2015. Admission pathology was used to calculate a risk score. In-patient outcomes of death, ICU transfer, MET call activation or cardiac arrest were collected from hospital records. RESULTS: We studied 2942 admissions derived from 2521 patients, with a median age of 81 years. There were 143 in-patient deaths, 82 ICU admissions, 277 MET calls and 14 cardiac arrest calls. The laboratory-based admission score had an area under the receiver operating characteristic curve (AUC-ROC) of 0.76 (95% confidence interval (CI): 0.72-0.80) for inpatient death, an AUC-ROC of 0.79 (95% CI: 0.66-0.93) for inpatient cardiac arrest, an AUC-ROC of 0.64 (95% CI: 0.58-0.70) for ICU transfer and an AUC-ROC of 0.59 (95% CI: 0.55-0.62) for MET call activation. When patients aged over 75 were analysed separately, the AUC-ROC for prediction of in-patient death was 0.74 (95% CI: 0.70-0.78) and increased to 0.86 (95% CI: 0.73-0.98) for the prediction of in-patient cardiac arrest. CONCLUSION: A simple laboratory-derived score obtained at patient admission is a fair to good predictor of subsequent in-patient death or cardiac arrest in general medical patients and in the older patient cohort. Prospective interventional studies are required to ascertain the clinical utility of this admission score.

Introduction of a standardised maternity early warning system: indicative data from a before-and-after study at a large pilot site before national rollout in Aotearoa New Zealand.

Strong evidence now demonstrates that recognition and response systems using standardised early warning scores can help prevent harm associated with in-hospital clinical deterioration in non-pregnant adult patients. However, a standardised maternity-specific early warning system has not yet been agreed in the UK. In Aotearoa New Zealand, following the nationwide implementation of the standardised New Zealand Early Warning Score (NZEWS) for adult inpatients, a modified maternity-specific variation (NZMEWS) was piloted in a major tertiary hospital in Auckland, before national rollout. Following implementation in July 2018, we observed a significant and sustained reduction in severe maternal morbidity as measured by emergency response calls to women who were very unwell (emergency response team call), and a non-significant reduction in cardiorespiratory arrest team calls. Emergency response team calls to maternity wards fell from a median of 0.8 per 100 births at baseline (January 2017-May 2018) to 0.6 per 100 births monthly (from March 2019 to December 2020) (p < 0.0001). Cardiorespiratory arrest team calls to maternity wards fell from 0.14 per 100 births per quarter (quarter 1 2017-quarter 2 2018) to 0.09 calls per 100 births per quarter after NZMEWS was introduced (quarter 3 2018-quarter 4 2020) (p = 0.2593). These early results provide evidence that NZMEWS can detect and prevent deterioration of pregnant women, although there are multiple factors that may have contributed to the reduction in emergency response calls noted.

Effectiveness of the rapid emergency medicine score and the rapid acute physiology score in prognosticating mortality in patients presenting to the emergency department with COVID-19 symptoms.

OBJECTIVE: We investigated the effectiveness of the Rapid Emergency Medicine Score and the Rapid Acute Physiology Score in identifying critical patients among those presenting to the emergency department with COVID-19 symptoms. MATERIAL AND METHODS: This prospective, observational, cohort study included patients with COVID-19 symptoms presenting to the emergency department over a two-month period. Demographics, clinical characteristics, and the data of all-cause mortality within 30 days after admission were noted, and the Rapid Emergency Medicine Score and the Rapid Acute Physiology Score were calculated by the researchers. The receiver operating characteristic curve analysis was performed to determine the discriminative ability of the scores. RESULTS: A total of 555 patients with a mean of age of 49.4 ± 16.8 years were included in the study. The rate of 30-day mortality was 3.9% for the whole study cohort, 7.2% for the patients with a positive rt-PCR test result for SARS-CoV-2, and 1.2% for those with a negative rt-PCR test result for SARS-CoV-2. In the group of patients with COVID-19 symptoms, according to the best Youden's index, the cut-off value for the Rapid Emergency Medicine Score was determined as 3.5 (sensitivity: 81.82%, specificity: 73.08%), and the area under curve (AUC) value was 0.840 (95% confidence interval 0.768-0.913). In the same group, according to the best Youden's index, the cut-off value for the Rapid Acute Physiology Score was 2.5 (sensitivity: 90.9%, specificity: 97.38%), and the AUC value was 0.519 (95% confidence interval 0.393-0.646). CONCLUSION: REMS is able to predict patients with COVID-19-like symptoms without positive rt-PCR for SARS-CoV-2 that are at a high-risk of 30-day mortality. Prospective multicenter cohort studies are needed to provide best scoring system for triage in pandemic clinics.

Predicting Patient Deterioration: A Review of Tools in the Digital Hospital Setting.

BACKGROUND: Early warning tools identify patients at risk of deterioration in hospitals. Electronic medical records in hospitals offer real-time data and the opportunity to automate early warning tools and provide real-time, dynamic risk estimates. OBJECTIVE: This review describes published studies on the development, validation, and implementation of tools for predicting patient deterioration in general wards in hospitals. METHODS: An electronic database search of peer reviewed journal papers from 2008-2020 identified studies reporting the use of tools and algorithms for predicting patient deterioration, defined by unplanned transfer to the intensive care unit, cardiac arrest, or death. Studies conducted solely in intensive care units, emergency departments, or single diagnosis patient groups were excluded. RESULTS: A total of 46 publications were eligible for inclusion. These publications were heterogeneous in design, setting, and outcome measures. Most studies were retrospective studies using cohort data to develop, validate, or statistically evaluate prediction tools. The tools consisted of early warning, screening, or scoring systems based on physiologic data, as well as more complex algorithms developed to better represent real-time data, deal with complexities of longitudinal data, and warn of deterioration risk earlier. Only a few studies detailed the results of the implementation of deterioration warning tools. CONCLUSIONS: Despite relative progress in the development of algorithms to predict patient deterioration, the literature has not shown that the deployment or implementation of such algorithms is reproducibly associated with improvements in patient outcomes. Further work is needed to realize the potential of automated predictions and update dynamic risk estimates as part of an operational early warning system for inpatient deterioration.

Pre-hospital use of early warning scores to improve detection and outcomes of sepsis.

Sepsis is a life-threatening complication from infection. The early detection of sepsis pre-hospital is challenging. Early warning scores (EWS) are used in hospitals to identify deteriorating patients. The pre-hospital setting could be a beneficial extension to the use of EWSs. This review aimed to determine whether EWSs can identify patients with sepsis pre-hospital and predict patient outcomes. Bibliographic databases were searched for studies evaluating the pre-hospital use of EWSs. Studies were screened using eligibility criteria. Two studies examined the ability of pre-hospital EWSs to identify patients with critical illness, showing high sensitivity but low specificity. Four studies determined the prognostic effects of the National Early Warning Score (NEWS). The patients identified by NEWS to be high-risk were associated with worse outcomes. This systematic review demonstrated the successful use of EWSs in the pre-hospital setting, in identifying patients most at risk of deterioration and as a useful tool for decision-making.

If early warning systems are used, would it be possible to estimate early clinical deterioration risk and prevent readmission to intensive care?

BACKGROUND: Although the intensive care unit (ICU) admission criteria are specified clearly, it is difficult to make the decision of discharge from ICU. AIMS: The purpose of this study is to test whether or not early warning scores will allow us to estimate early clinical deterioration within 24 hours and predict readmission to intensive care. A total of 1330 patients were included in the retrospective study. PATIENTS AND METHODS: All the patients' age, gender, ICU hospitalization reasons and Acute Physiological and Chronic Health Evaluation (APACHE II) scores were recorded. National Early Warning Score (NEWS) and VitalpacTM early warning score (VIEWS) scores were calculated using the physiological and neurological examination records. Discharge NEWS and VIEWS values of the patients who were readmitted to intensive care 24 hours after discharge were compared with the patients who were not readmitted to intensive care. The statistical analysis was performed using the IBM SPSS version 21 package software. RESULTS: Age average of all the patients was 64.3 ± 20.8 years. The number of the patients who were readmitted to intensive care was 118 (8.87%). When examining the factors that affect early clinical deterioration, it was found that advanced age, high APACHE II scores, higher NEWS and VIEWS scores, lower DAP values and the patient's transfer from the ward were significantly predictive (P < 0.05). CONCLUSIONS: In this study, high NEWS and VIEWS are strong scoring systems that can be used in estimating early clinical deterioration risk and are easy-to-use and less time consuming.

Predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision.

Medical error is a leading cause of patient death in the United States. Among the different types of medical errors, harm to patients caused by doctors missing early signs of deterioration is especially challenging to address due to the heterogeneity of patients' physiological patterns. In this study, we implemented risk prediction models using the gradient boosted tree method to derive risk estimates for acute onset diseases in the near future. The prediction model uses physiological variables as input signals and the time of the administration of outcome-related interventions and discharge diagnoses as labels. We examine four categories of acute onset illness: acute heart failure (AHF), acute lung injury (ALI), acute kidney injury (AKI), and acute liver failure (ALF). To develop and test the model, we consider data from two sources: 23,578 admissions to the Intensive Care Unit (ICU) from the MIMIC-3 dataset (Beth-Israel Hospital) and 16,612 ICU admissions on hospitals affiliated with our institution (University of Washington Medical Center and Harborview Medical Center, the UW-CDR dataset). We systematically identify outcome-related interventions for each acute organ failure, then use them, along with discharge diagnoses, to label proxy events to train gradient boosted trees. The trained models achieve the highest F1 score with a value of 0.6018 when predicting the need for life-saving interventions for ALI within the next 24 h in the MIMIC-3 dataset while showing a median F1 score of 0.3850 from all acute organ failures in both datasets. The approach also achieves the highest F1 score of 0.6301 when classifying a patient's ALI status at the time of discharge from the MIMIC-3 dataset, with a median F1 score of 0.4307 in both datasets. This study shows the potential for using the time of outcome-related intervention administrations and discharge diagnoses as labels to train supervised machine learning models that predict the risk of acute onset illnesses.

Development and validation of early warning score system: A systematic literature review.

OBJECTIVES: This review aims to: 1) evaluate the quality of model reporting, 2) provide an overview of methodology for developing and validating Early Warning Score Systems (EWSs) for adult patients in acute care settings, and 3) highlight the strengths and limitations of the methodologies, as well as identify future directions for EWS derivation and validation studies. METHODOLOGY: A systematic search was conducted in PubMed, Cochrane Library, and CINAHL. Only peer reviewed articles and clinical guidelines regarding developing and validating EWSs for adult patients in acute care settings were included. 615 articles were extracted and reviewed by five of the authors. Selected studies were evaluated based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. The studies were analyzed according to their study design, predictor selection, outcome measurement, methodology of modeling, and validation strategy. RESULTS: A total of 29 articles were included in the final analysis. Twenty-six articles reported on the development and validation of a new EWS, while three reported on validation and model modification. Only eight studies met more than 75% of the items in the TRIPOD checklist. Three major techniques were utilized among the studies to inform their predictive algorithms: 1) clinical-consensus models (n = 6), 2) regression models (n = 15), and 3) tree models (n = 5). The number of predictors included in the EWSs varied from 3 to 72 with a median of seven. Twenty-eight models included vital signs, while 11 included lab data. Pulse oximetry, mental status, and other variables extracted from electronic health records (EHRs) were among other frequently used predictors. In-hospital mortality, unplanned transfer to the intensive care unit (ICU), and cardiac arrest were commonly used clinical outcomes. Twenty-eight studies conducted a form of model validation either within the study or against other widely-used EWSs. Only three studies validated their model using an external database separate from the derived database. CONCLUSION: This literature review demonstrates that the characteristics of the cohort, predictors, and outcome selection, as well as the metrics for model validation, vary greatly across EWS studies. There is no consensus on the optimal strategy for developing such algorithms since data-driven models with acceptable predictive accuracy are often site-specific. A standardized checklist for clinical prediction model reporting exists, but few studies have included reporting aligned with it in their publications. Data-driven models are subjected to biases in the use of EHR data, thus it is particularly important to provide detailed study protocols and acknowledge, leverage, or reduce potential biases of the data used for EWS development to improve transparency and generalizability.