A newly developed, easy-to-use prehospital drug-derived score compared with three conventional scores: A prospective multicenter study.

INTRODUCTION: The use of medications by emergency medical services (EMS) is increasing. Conventional scores are time-consuming and therefore difficult to use in an emergency setting. For early decision-making, an easy-to-use score based on the medications administered by the EMS may have prognostic value. The primary objective of this study was to develop the prehospital drug-derived score (PDDS) for 2-day mortality. METHODS: A prospective, multicenter, ambulance-based cohort study was conducted in adults with undifferentiated acute diseases treated by EMS and transferred to the emergency department. Demographic data, prehospital diagnosis data, prehospital medication and variables for the calculation of the National Early Warning Score 2 (NEWS2), Rapid Emergency Medicine Score (REMS), and Rapid Acute Physiology Score (RAPS) were collected. The PDDS was developed and validated, establishing three levels of risk of 2-day mortality. The predictive capability of each score was determined by the area under the curve of the receiver operating characteristic curve (AUROC) and compared using the Delong's test (p-value). RESULTS: A total of 6401 patients were included. The PDDS included age and the use of norepinephrine, analgesics, neuromuscular blocking agents, diuretics, antihypertensive agents, tranexamic acid, and bicarbonate. The AUROC of PDDS was .86 (95% CI: .816-.903) versus NEWS2 .866 (95% CI: .822-.911), p = .828; versus REMS .885 (95% CI: .845-.924), p = .311; versus RAPS .886 (95% CI: .846-.926), p = .335, respectively. CONCLUSION: The newly developed easy-to-use prehospital drug-derived PDDS score has an excellent predictive value of early mortality. The PDDS score was comparable to the conventional risk scores and therefore might serve as an alternative score in the prehospital emergency setting.

Utility of non-invasive monitoring of exhaled carbon dioxide and perfusion index in adult patients in the emergency department.

BACKGROUND: Several noninvasive solutions are available for the assessment of patients at risk of deterioration. Capnography, in the form of end-tidal exhaled CO2 (ETCO2) and perfusion index (PI), could provide relevant information about patient prognosis. The aim of the present project was to determine the association of ETCO2 and PI with mortality of patients admitted to the emergency department (ED). METHODS: Multicenter, prospective, cohort study of adult patients with acute disease who needed continuous monitoring in the ED. The study included two tertiary hospitals in Spain between October 2022 and June 2023. The primary outcome of the study was in-hospital mortality (all-cause). Demographics, vital signs, ETCO2 and PI were collected. RESULTS: A total of 687 patients were included in the study. The in-hospital mortality rate was 6.8%. The median age was 79 years (IQR: 69-86), and 63.3% were males. The median ETCO2 value was 30 mmHg (26-35) in survivors and 23 mmHg (16-30) in nonsurvivors (p = 0.001). For the PI, the medians were 4.7% (2.8-8.1) for survivors and 2.5% (0.98-4-4) for nonsurvivors (p < 0.001). The model that presented the best AUC was age (odds ratio (OR): 1.02 (1.00-1.05)), the respiratory rate (OR: 1.06 (1.02-1.11)), and the PI (OR: 0.83 (0.75-0.91)), with a result of 0.840 (95% CI: 0.795-0.886); the model with the respiratory rate (OR: 1.05 (1.01-1.10)), the PI (OR: 0.84 (0.76-0.93)), and the ETCO2 (no statistically significant OR), with an AUC of 0.838 (95% CI: 0.787-0.889). CONCLUSIONS: The present study showed that the PI and respiratory rate are independently associated with in-hospital mortality. Both the PI and ETCO2 are predictive parameters with improved prognostic performance compared with that of standard vital signs.

A Missense Variant in TP53 Could Be a Genetic Biomarker Associated with Bone Tissue Alterations.

Metabolic bone diseases cover a broad spectrum of disorders that share alterations in bone metabolism that lead to a defective skeleton, which is associated with increasing morbidity, disability, and mortality. There is a close connection between the etiology of metabolic bone diseases and genetic factors, with TP53 being one of the genes associated therewith. The single nucleotide polymorphism (SNP) Arg72Pro of TP53 is a genetic factor associated with several pathologies, including cancer, stroke, and osteoporosis. Here, we aim to analyze the influence of the TP53 Arg72Pro SNP on bone mass in humanized Tp53 Arg72Pro knock-in mice. This work reports on the influence of the TP53 Arg72Pro polymorphism in bone microarchitecture, OPG expression, and apoptosis bone status. The results show that the proline variant of the TP53 Arg72Pro polymorphism (Pro72-p53) is associated with deteriorated bone tissue, lower OPG/RANK ratio, and lower apoptosis in bone tissue. In conclusion, the TP53 Arg72Pro polymorphism modulates bone microarchitecture and may be a genetic biomarker that can be used to identify individuals with an increased risk of suffering metabolic bone alterations.

Prehospital Respiratory Early Warning Score for airway management in-ambulance: A score comparison.

BACKGROUND: Prehospital Respiratory Early Warning Scores to estimate the requirement for advanced respiratory support is needed. To develop a prehospital Respiratory Early Warning Score to estimate the requirement for advanced respiratory support. METHODS: Multicentre, prospective, emergency medical services (EMS)-delivered, longitudinal cohort derivationvalidation study carried out in 59 ambulances and five hospitals across five Spanish provinces. Adults with acute diseases evaluated, supported and discharged to the Emergency Department with high priority were eligible. The primary outcome was the need for invasive or non-invasive respiratory support (NIRS or IRS) in the prehospital scope at the first contact with the patient. The measures included the following: epidemiological endpoints, prehospital vital signs (respiratory rate, pulse oximetry saturation, fraction of inspired oxygen, systolic and diastolic mean blood pressure, heart rate, tympanic temperature and consciousness level by the GCS). RESULTS: Between 26 Oct 2018 and 26 Oct 2021, we enrolled 5793 cases. For NIRS prediction, the final model of the logistic regression included respiratory rate and pulse oximetry saturation/fraction of inspired oxygen ratio. For the IRS case, the motor response from the Glasgow Coma Scale was also included. The REWS showed an AUC of 0.938 (95% CI: 0.918-0.958), a calibration-in-large of 0.026 and a higher net benefit as compared with the other scores. CONCLUSIONS: Our results showed that REWS is a remarkably aid for the decision-making process in the management of advanced respiratory support in prehospital care. Including this score in the prehospital scenario could improve patients' care and optimise the resources' management.

Modified Rapid Emergency Medicine Score-Lactate (mREMS-L) performance to screen non-anticipated 30-day-related-mortality in emergency department.

BACKGROUND: The aim of this study was to compare the ability to predict 30-day in-hospital mortality of lactate versus the modified Rapid Emergency Medicine Score (mREMS) versus the arithmetic sum of the mREMS plus the numerical value of lactate (mREMS-L). METHODS: A prospective, multicentric, emergency department delivery, pragmatic study was conducted. To determine the predictive capacity of the scales, lactate was measured and the mREMS and mREMS-L were calculated in adult patients (aged>18 years) transferred with high priority by ambulance to the emergency department in five hospitals of Castilla y Leon between 1 January 2020 and 31 December 2021. The area under the receiver operating characteristic (ROC) curve of each of the scales was calculated in terms of mortality for 30 days. RESULTS: A total of 5371 participants were included, and the in-hospital mortality rate at 30 days was of 11.4% (615 cases). The best cut-off point determined in the mREMS was 7.0 points (sensitivity of 67% and specificity of 84%), and for lactate, the cut-off point was 1.4 mmol/L (sensitivity of 88% and specificity of 67%). Finally, the combined mREMS-L showed a cut-off point of 7.9 (sensitivity of 83% and a specificity of 83%). The area under the ROC curve of the mREMS, lactate and mREMS-L for 30-day mortality was 0.851, 0.853, and 0.903, respectively (p < 0.001 in all cases). CONCLUSIONS: The new score generated, mREMS-L, obtained better statistical results than its components (mREMS and lactate) separately.

Prehospital seizures: Short-term outcomes and risk stratification based in point-of-care testing.

BACKGROUND: Information for treatment or hospital derivation of prehospital seizures is limited, impairing patient condition and hindering patients risk assessment by the emergency medical services (EMS). This study aimed to determine the associated factors to clinical impairment, and secondarily, to determine risk factors associated to cumulative in-hospital mortality at 2, 7 and 30 days, in patients presenting prehospital seizures. METHODS: Prospective, multicentre, EMS-delivery study involving adult subjects with prehospital seizures, including five advanced life support units, 27 basic life support units and four emergency departments in Spain. All bedside variables: including demographic, standard vital signs, prehospital laboratory tests and presence of intoxication or traumatic brain injury (TBI), were analysed to construct a risk model using binary logistic regression and internal validation methods. RESULTS: A total of 517 patients were considered. Clinical impairment was present in 14.9%, and cumulative in-hospital mortality at 2, 7 and 30-days was 3.4%, 4.6% and 7.7%, respectively. The model for the clinical impairment indicated that respiratory rate, partial pressure of carbon dioxide, blood urea nitrogen, associated TBI or stroke were risk factors; higher Glasgow Coma Scale (GCS) scores mean a lower risk of impairment. Age, potassium, glucose, prehospital use of mechanical ventilation and concomitant stroke were risk factors associated to mortality; and oxygen saturation, a high score in GCS and haemoglobin were protective factors. CONCLUSION: Our study shows that prehospital variables could reflect the clinical impairment and mortality of patients suffering from seizures. The incorporation of such variables in the prehospital decision-making process could improve patient outcomes.

Tracking the National Early Warning Score 2 from Prehospital Care to the Emergency Department: A Prospective, Ambulance-Based, Observational Study.

Aim of the study: To assess the prognostic ability of the National Early Warning Score 2 (NEWS2) at three time points of care -at the emergency scene (NEWS2-1), just before starting the transfer by ambulance to the hospital (NEWS2- 2), and at the hospital triage box (NEWS2-3)- to estimate in-hospital mortality after two days since the index event.Methods: Prospective, multicenter, ambulance-based, cohort ongoing study in adults (>18 years) consecutively attended by advanced life support (ALS) and evacuated with high-priority to the emergency departments (ED) between October 2018 and May 2021. Vital sign measures were used to calculate the NEWS2 score at each time point, then this score was entered in a logistic regression model as the single predictor. Two outcomes were considered: first, all-cause mortality of the patients within 2 days of presentation to EMS, and second, unplanned ICU admission. The calibration and scores comparison was performed by representing the predicted vs the observed risk curves according to NEWS score value.Results: 4943 patients were enrolled. Median age was 69 years (interquartile range 53- 81). The NEWS2-3 presented the better performance for all-cause two-day in-hospital mortality with an AUC of 0.941 (95% CI: 0.917-0.964), showing statistical differences with both the NEWS2-1 (0.872 (95% CI: 0.833-0.911); p < 0.003) and with the NEWS2- 2 (0.895 (95% CI: 0.866-0.925; p < 0.05). The calibration and scores comparison results showed that the NEWS2-3 was the best predictive score followed by the NEWS2-2 and the NEWS2-1, respectively.Conclusions: The NEWS2 has an excellent predictive performance. The score showed a very consistent response over time with the difference between "at the emergency scene" and "pre-evacuation" presenting the sharpest change with decreased threshold values, thus displaying a drop in the risk of acute clinical impairment.

Development of a prehospital lactic acidosis score for early-mortality. A prospective, multicenter, ambulance-based, cohort study.

BACKGROUND: Lactic acidosis is a clinical status related to clinical worsening. Actually, higher levels of lactate is a well-established trigger of emergency situations. The aim of this work is to build-up a prehospital early warning score to predict 2-day mortality and intensive care unit (ICU) admission, constructed with other components of the lactic acidosis besides the lactate. METHODS: Prospective, multicenter, observational, derivation-validation cohort study of adults evacuated by ambulance and admitted to emergency department with acute diseases, between January 1st, 2020 and December 31st, 2021. Including six advanced life support, thirty-eight basic life support units, referring to four hospitals (Spain). The primary and secondary outcome of the study were 2-day all-cause mortality and ICU-admission. The prehospital lactic acidosis (PLA) score was derived from the analysis of prehospital blood parameters associated with the outcome using a logistic regression. The calibration, clinical utility, and discrimination of PLA were determined and compared to the performance of each component of the score alone. RESULTS: A total of 3334 patients were enrolled. The final PLA score included: lactate, pCO2, and pH. For 2-day mortality, the PLA showed an AUC of 0.941 (95%CI: 0.914-0.967), a better performance in calibration, and a higher net benefit as compared to the other score components alone. For the ICU admission, the PLA only showed a better performance for AUC: 0.75 (95%CI: 0.706-0.794). CONCLUSIONS: Our results showed that PLA predicts 2-day mortality better than other lactic acidosis components alone. Including PLA score in prehospital setting could improve emergency services decision-making.

Comorbidity-adjusted NEWS predicts mortality in suspected patients with COVID-19 from nursing homes: Multicentre retrospective cohort study.

AIMS: To assess the prognostic accuracy of comorbidity-adjusted National Early Warning Score in suspected Coronavirus disease 2019 patients transferred from nursing homes by the Emergency Department. DESIGN: Multicentre retrospective cohort study. METHODS: Patients transferred by high-priority ambulances from nursing homes to Emergency Departments with suspected severe acute respiratory syndrome coronavirus 2 infection, from March 12 to July 31 2020, were considered. Included variables were: clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and supplemental oxygen use), the presence of comorbidities and confirmatory analytical diagnosis of severe acute respiratory syndrome coronavirus 2 infection. The primary outcome was a 2-day mortality rate. The discriminatory capability of the National Early Warning Score was assessed by the area under the receiver operating characteristic curve in two different cohorts, the validation and the revalidation, which were randomly selected from the main cohort. RESULTS: A total of 337 nursing homes, 10 advanced life support units, 51 basic life support units and 8 hospitals in Spain entailing 1,324 patients (median age 87 years) was involved in this study. Two-day mortality was 11.5% (152 cases), with a positivity rate of severe acute respiratory syndrome coronavirus 2 of 51.2%, 77.7% of hospitalization from whom 1% was of intensive care unit admission. The National Early Warning Score results for the revalidation cohort presented an AUC of 0.771, and of 0.885, 0.778 and 0.730 for the low-, medium- and high-level groups of comorbidities. CONCLUSION: The comorbidity-adjusted National Early Warning Score provides a good short-term prognostic criterion, information that can help in the decision-making process to guide the best strategy for each older adult, under the current pandemic. IMPACT: What problem did the study address? Under the current coronavirus disease 2019 pandemic, targeting older adults at high risk of deterioration in nursing homes remains challenging. What were the main findings? Comorbidity-adjusted National Early Warning Score helps to forecast the risk of clinical deterioration more accurately. Where and on whom will the research have impact? A high NEWS, with a low level of comorbidity is associated with optimal predictive performance, making these older adults likely to benefit from continued follow up and potentially hospital referral under the current coronavirus disease 2019 pandemic.

Use of Mobile Applications to Increase Therapeutic Adherence in Adults: A Systematic Review.

Failure to comply with therapeutic treatments implies negative repercussions for the patient's quality of life, their social environment, and health system. The use of information and communication technologies, especially mobile applications, has favored the increase in global therapeutic adherence figures. The objective of this study is to characterize the use of mobile applications as a strategy to increase therapeutic adherence in adults. A systematic literature review in Web of Science and Scopus was performed following the Preferred Information elements for Systematic Reviews and Meta-analysis. Information such as: the year of publication, the study population, the medical conditions of the participants, the main characteristics or functionalities of the mobile applications, and the methods or tools used to measure treatment adherence were extracted from each included article. The risk of bias was assessed. Twelve randomized controlled trials (RCTs), published in English from 1996 to May 2021, were included. Chronic diseases have been mostly addressed through interventions with mobile applications. The most reported functions of mobile applications were reminders, educational modules, two-way communication, and games. Tools such as: "Morisky Medication Adherence Scale of eight items"; "Medication adherence questionnaire"; "Self-reported adherence"; among others, were used to evaluate and report the treatment adherence. In conclusion, including treatment interventions using mobile applications in clinical practice has proven to be beneficial to improve therapeutic adherence. However, it is necessary to develop high-quality clinical trials (size and duration) to generalize results and justify their use in conventional health services.

Novel Prehospital Phenotypes and Outcomes in Adult-Patients with Acute Disease.

An early identification of prehospital phenotypes may allow health care workers to speed up and improve patients' treatment. To determine emergency phenotypes by exclusively using prehospital clinical data, a multicenter, prospective, and observational ambulance-based study was conducted with a cohort of 3,853 adult patients treated consecutively and transferred with high priority from the scene to the hospital emergency department. Cluster analysis determined three clusters with highly different outcome scores and pathological characteristics. The first cluster presented a 30-day mortality after the index event of 45.9%. The second cluster presented a mortality of 26.3%, while mortality of the third cluster was 5.1%. This study supports the detection of three phenotypes with different risk stages and with different clinical, therapeutic, and prognostic considerations. This evidence could allow adapting treatment to each phenotype thereby helping in the decision-making process.

Lactate improves the predictive ability of the National Early Warning Score 2 in the emergency department.

AIMS: The aim of this study was to compare the ability to predict 2-, 7-, 14-, and 30-day in-hospital mortality of lactate vs the National Early Warning Score 2 (NEWS2) vs the arithmetic sum of the NEWS2 plus the numerical value of lactate (NEWS2-L). METHODS: This was a prospective, multicentric, emergency department delivery, pragmatic cohort study. To determine the predictive capacity of lactate, we calculated the NEWS2 and NEWS2-L in adult patients (aged >18 years) transferred with high priority by ambulance to the emergency department in five hospitals of Castilla y Leon (Spain) between November 1, 2019, and September 30, 2020. The area under the receiver operating characteristic curve of each of the scales was calculated in terms of mortality for every time frame (2, 7, 14, and 30 days). We determined the cut-off point of each scale that offered highest sensitivity and specificity using the Youden index. RESULTS: A total of 1716 participants were included, and the in-hospital mortality rates at 2, 7, 14, and 30 days were of 7.8% (134 cases), 11.6% (200 cases), 14.2% (243 cases), and 17.2% (295 cases), respectively. The best cut-off point determined in the NEWS2 was 6.5 points (sensitivity of 97% and specificity of 59%), and for lactate, the cut-off point was 3.3 mmol/L (sensitivity of 79% and specificity of 72%). Finally, the combined NEWS2-L showed a cut-off point of 11.7 (sensitivity of 86% and a specificity of 85%). The area under the receiver operating characteristic curve of the NEWS2, lactate, and NEWS2-L in the validation cohort for 2-day mortality was 0.889, 0.856, and 0.923, respectively (p<0.001 in all cases). CONCLUSIONS: The new score generated, NEWS2-L, obtained better statistical results than its components (NEWS2 and lactate) separately.

Prehospital troponin as a predictor of early clinical deterioration.

BACKGROUND AND OBJECTIVES: Elevated troponin T (cTnT) values are associated with comorbidities and early mortality, in both cardiovascular and noncardiovascular diseases. The objective of this study is to evaluate the prognostic accuracy of the sole utilization of prehospital point-of-care cardiac troponin T to identify the risk of early in-hospital deterioration, including mortality within 28 days. METHODS: We conducted a prospective, multicentric, controlled, ambulance-based, observational study in adults with acute diseases transferred with high priority by ambulance to emergency departments, between 1 January and 30 September 2020. Patients with hospital diagnosis of acute coronary syndrome were excluded. The discriminative power of the predictive cTnT was assessed through a discrimination model trained using a derivation cohort and evaluated by the area under the curve of the receiver operating characteristic on a validation cohort. RESULTS: A total of 848 patients were included in our study. The median age was 68 years (25th-75th percentiles: 50-81 years), and 385 (45.4%) were women. The mortality rate within 28 days was 12.4% (156 cases). The predictive ability of cTnT to predict mortality presented an area under the curve of 0.903 (95% CI: 0.85-0.954; P < .001). Risk stratification was performed, resulting in three categories with the following optimal cTnT cut-off points: high risk greater than or equal to 100, intermediate risk 40-100 and low risk less than 40 ng/L. In the high-risk group, the mortality rate was 61.7%, and on the contrary, the low-risk group presented a mortality of 2.3%. CONCLUSIONS: The implementation of a routine determination of cTnT on the ambulance in patients transferred with high priority to the emergency department can help to stratify the risk of these patients and to detect unknown early clinical deterioration.

The Value of Prehospital Early Warning Scores to Predict in - Hospital Clinical Deterioration: A Multicenter, Observational Base-Ambulance Study.

OBJECTIVES: Early warning scores are clinical tools capable of identifying prehospital patients with high risk of deterioration. We sought here to contrast the validity of seven early warning scores in the prehospital setting and specifically, to evaluate the predictive value of each score to determine early deterioration-risk during the hospital stay, including mortality at one, two, three and seven- days since the index event. Methods: A prospective multicenter observational based-ambulance study of patients treated by six advanced life support emergency services and transferred to five Spanish hospitals between October 1, 2018 and December 31, 2019. We collected demographic, clinical, and laboratory variables. Seven risk score were constructed based on the analysis of prehospital variables associated with death within one, two, three and seven days since the index event. The area under the receiver operating characteristics was used to determine the discriminant validity of each early warning score. Results: A total of 3,273 participants with acute diseases were accurately linked. The median age was 69 years (IQR, 54-81 years), 1,348 (41.1%) were females. The overall mortality rate for patients in the study cohort ranged from 3.5% for first-day mortality (114 cases), to 7% for seven-day mortality (228 cases). The scores with the best performances for one-day mortality were Vitalpac Early Warning Score with an area under the receiver operating characteristic (AUROC) of 0.873 (95% CI: 0.81-0.9), for two-day mortality, Triage Early Warning Score with an AUROC of 0.868 (95% CI: 0.83-0.9), for three and seven-days mortality the Modified Rapid Emergency Medicine Score with an AUROC of 0.857 (0.82-0.89) and 0.833 (95% CI: 0.8-0.86). In general, there were no significant differences between the scores analyzed. Conclusions: All the analyzed scores have a good predictive capacity for early mortality, and no statistically significant differences between them were found. The National Early Warning Score 2, at the clinical level, has certain advantages. Early warning scores are clinical tools that can help in the complex decision-making processes during critical moments, so their use should be generalized in all emergency medical services.

Time for a prehospital-modified sequential organ failure assessment score: An ambulance-Based cohort study.

BACKGROUND: To adapt the Sequential Organ Failure Assessment (SOFA) score to fit the prehospital care needs; to do that, the SOFA was modified by replacing platelets and bilirubin, by lactate, and tested this modified SOFA (mSOFA) score in its prognostic capacity to assess the mortality-risk at 2 days since the first Emergency Medical Service (EMS) contact. METHODS: Prospective, multicentric, EMS-delivery, ambulance-based, pragmatic cohort study of adults with acute diseases, referred to two tertiary care hospitals (Spain), between January 1st and December 31st, 2020. The discriminative power of the predictive variable was assessed through a prediction model trained using the derivation cohort and evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) on the validation cohort. RESULTS: A total of 1114 participants comprised two separated cohorts recruited from 15 ambulance stations. The 2-day mortality rate (from any cause) was 5.9% (66 cases). The predictive validity of the mSOFA score was assessed by the calculation of the AUC of ROC in the validation cohort, resulting in an AUC of 0.946 (95% CI, 0.913-0.978, p < .001), with a positive likelihood ratio was 23.3 (95% CI, 0.32-46.2). CONCLUSIONS: Scoring systems are now a reality in prehospital care, and the mSOFA score assesses multiorgan dysfunction in a simple and agile manner either bedside or en route. Patients with acute disease and an mSOFA score greater than 6 points transferred with high priority by EMS represent a high early mortality group. TRIAL REGISTRATION: ISRCTN48326533, Registered Octuber 312,019, Prospectively registered (doi:https://doi.org/10.1186/ISRCTN48326533).

Can capillary lactate improve early warning scores in emergency department? An observational, prospective, multicentre study.

AIMS: To determine the prognostic usefulness of the National Early Warning Score-2 (NEWS2) and quick Sepsis-related Organ Failure Assessment (qSOFA) scores, in isolation and combined with capillary lactate (CL), using the new NEWS2-L and qSOFA-L scores to predict the 30-day mortality risk. METHODS: Prospective, multicentre and observational study in patients across four EDs. We collected sets of vital signs and CL and subsequently calculated NEWS2, qSOFA, NEWS2-L and qSOFA-L scores when patients arrived at the ED. The main outcome measure was all-cause mortality 30 days from the index event. RESULTS: A total of 941 patients were included. Thirty-six patients (3.8%) died within 30 days of the index event. A high CL level has not been linked to a higher mortality. The NEWS2 presented AUROC of 0.72 (95% CI: 0.62-0.81), qSOFA of 0.66 (95% CI: 0.56-0.77) (P < .001 in both cases) and CL 0.55 (95% CI: 0.42-0.65; P = .229) to predict 30-day mortality. The addition of CL to the scores analysed does not improve the results of the scores used in isolation. CONCLUSION: NEWS2 and qSOFA scores are a very useful tool for assessing the status of patients who come to the ED in general for all types of patients in triage categories II and III and for detecting the 30-day mortality risk. CL determined systematically in the ED does not seem to provide information on the prognosis of the patients.

Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.

Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.

Machine Learning in Medical Emergencies: a Systematic Review and Analysis.

Despite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

Mobile Triage Applications: A Systematic Review in Literature and Play Store.

The main objective of this paper is to review and analysis of the state of the art regarding triage applications (apps) for health emergencies. This research is based on a systematic review of the literature in scientific databases from 2010 to early 2021, following a prism methodology. In addition, a Google Play Store search of the triage apps found in the literature was performed for further evaluation. A total of 26 relevant papers were obtained for this study, of which 13 apps were identified. After searching for each of these apps in the Google Play Store platform, only 2 of them were obtained, and these were subsequently evaluated together with another app obtained from the link provided in the corresponding paper. In the analysis carried out, it was detected that from 2019 onwards there has been an increase in research interest in this area, since the papers obtained from this year onwards represent 38.5% of the relevant papers. This increase may be caused by the need for early selection of the most serious patients in such difficult times for the health service. According to the review carried out, an increase in mobile app research focused on Emergency Triage and a decrease in app studies for triage catastrophe have been identified. In this study it was also observed that despite the existence of many researches in this sense, only 3 apps contained in them are accessible. "TRIAGIST" does not allow the entry of an unidentified user, "Major Trauma Triage Tool" presents negative comments from users who have used it and "ESITriage" lacks updates to improve its performance.

A predictive model for serious adverse events in adults with acute poisoning in prehospital and hospital care.

OBJECTIVE: The objective of this study was to design a risk model with variables determined before hospital arrival to predict the risk of serious adverse events in patients with acute poisoning. METHODS: A preliminary prospective, multicentre cohort study of adults with prehospital diagnosis of acute intoxication was conducted. The study was carried out in the Public Health System of the Community of Castilla-Leon (Spain), including seven advanced life support units and five hospitals, between April 1, 2018, and June 30, 2019. People aged >18 years with a main prehospital diagnosis of acute poisoning admitted to a referral hospital on advanced life support were included. The main outcome measure was prehospital and hospital serious adverse events in patients with acute poisoning. RESULTS: We included 221 patients, with a median age of 47 years (interquartile range: 33-61). The most frequent cause of poisoning was psychopharmaceuticals (111 cases, 49.8%): 38 (17.2%) patients had a serious adverse event, with a hospital mortality of 4.1% (nine cases) in the 30 days after the index event. The final model included age ≥65 years (odds ratio [OR]: 9.59, 95% confidence interval [CI]: 3.48-26.45; p < 0.001), oxygen saturation/fraction of inspired oxygen index ≤300 (OR: 15.03, 95% CI: 5.74-39.33; p < 0.001), and point-of-care lactate ≥4 mmol/L (OR: 7.68, 95% CI: 2.88-20.45; p < 0.001). The poisoning Early Warning Score was constructed from these three variables, and 1 point was assigned to each variable. The area under the curve of the score was 0.896 (95% CI: 0.82-0.96; p < 0.001). CONCLUSIONS: The poisoning Early Warning Score may help in decision-making and promote early identification of high-risk patients with acute poisoning in the prehospital context.