The impact of the Trauma Triage App on pre-hospital trauma triage: design and protocol of the stepped-wedge, cluster-randomized TESLA trial

Abstract Background Field triage of trauma patients is crucial to get the right patient to the right hospital within a particular time frame. Minimization of undertriage, overtriage, and interhospital transfer rates could substantially reduce mortality rates, life-long disabilities, and costs. Identification of patients in need of specialized trauma care is predominantly based on the judgment of Emergency Medical Services professionals and a pre-hospital triage protocol. The Trauma Triage App is a smartphone application that includes a prediction model to aid Emergency Medical Services professionals in the identification of patients in need of specialized trauma care. The aim of this trial is to assess the impact of this new digital approach to field triage on the primary endpoint undertriage. Methods The Trauma triage using Supervised Learning Algorithms (TESLA) trial is a stepped-wedge cluster-randomized controlled trial with eight clusters defined as Emergency Medical Services regions. These clusters are an integral part of five inclusive trauma regions. Injured patients, evaluated on-scene by an Emergency Medical Services professional, suspected of moderate to severe injuries, will be assessed for eligibility. This unidirectional crossover trial will start with a baseline period in which the default pre-hospital triage protocol is used, after which all clusters gradually implement the Trauma Triage App as an add-on to the existing triage protocol. The primary endpoint is undertriage on patient and cluster level and is defined as the transportation of a severely injured patient (Injury Severity Score ≥ 16) to a lower-level trauma center. Secondary endpoints include overtriage, hospital resource use, and a cost-utility analysis. Discussion The TESLA trial will assess the impact of the Trauma Triage App in clinical practice. This novel approach to field triage will give new and previously undiscovered insights into several isolated components of the diagnostic strategy to get the right trauma patient to the right hospital. The stepped-wedge design allows for within and between cluster comparisons. Trial registration Netherlands Trial Register, NTR7243. Registered 30 May 2018, https://www.trialregister.nl/trial/7038

The ARID1B spectrum in 143 patients: from nonsyndromic intellectual disability to Coffin–Siris syndrome

Purpose: Pathogenic variants in ARID1B are one of the most frequent causes of intellectual disability (ID) as determined by large-scale exome sequencing studies. Most studies published thus far describe clinically diagnosed Coffin–Siris patients (ARID1B-CSS) and it is unclear whether these data are representative for patients identified through sequencing of unbiased ID cohorts (ARID1B-ID). We therefore sought to determine genotypic and phenotypic differences between ARID1B-ID and ARID1B-CSS. In parallel, we investigated the effect of different methods of phenotype reporting. Methods: Clinicians entered clinical data in an extensive web-based survey. Results: 79 ARID1B-CSS and 64 ARID1B-ID patients were included. CSS-associated dysmorphic features, such as thick eyebrows, long eyelashes, thick alae nasi, long and/or broad philtrum, small nails and small or absent fifth distal phalanx and hypertrichosis, were observed significantly more often (p < 0.001) in ARID1B-CSS patients. No other significant differences were identified. Conclusion: There are only minor differences between ARID1B-ID and ARID1B-CSS patients. ARID1B-related disorders seem to consist of a spectrum, and patients should be managed similarly. We demonstrated that data collection methods without an explicit option to report the absence of a feature (such as most Human Phenotype Ontology-based methods) tended to underestimate gene-related features

Compliance to prehospital trauma triage protocols worldwide: A systematic review

Background: Emergency medical services (EMS) providers must determine the injury severity on-scene, using a prehospital trauma triage protocol, and decide on the most appropriate hospital destination for the patient. Many severely injured patients are not transported to higher-level trauma centres. An accurate triage protocol is the base of prehospital trauma triage; however, ultimately the quality is dependent on the destination decision by the EMS provider. The aim of this systematic review is to describe compliance to triage protocols and evaluate compliance to the different categories of triage protocols. Methods: An extensive search of MEDLINE/Pubmed, Embase, CINAHL and Cochrane library was performed to identify all studies, published before May 2018, describing compliance to triage protocols in a trauma system. The search terms were a combination of synonyms for ‘compliance,’ ‘trauma,’ and ‘triage’. Results: After selection, 11 articles were included. The studies showed a variety in compliance rates, ranging from 21% to 93% for triage protocols, and 41% to 94% for the different categories. The compliance rate was highest for the criterion: penetrating injury. The category of the protocol with the lowest compliance rate was: vital signs. Compliance rates were lower for elderly patients, compared to adults under the age of 55. The methodological quality of most studies was poor. One study with good methodological quality showed that the triage protocol identified only a minority of severely injured patients, but many of whom were transported to higher-level trauma centres. Conclusions: The compliance rate ranged from 21% to 94%. Prehospital trauma triage effectiveness could be increased with an accurate triage protocol and improved compliance rates. EMS provider judgment could lower the undertriage rate, especially for severely injured patients meeting none of the criteria. Future research should focus on the improvement of triage protocols and the compliance rate

Effectiveness of prehospital trauma triage systems in selecting severely injured patients: Is comparative analysis possible?

Introduction: In an optimal trauma system, prehospital trauma triage ensures transport of the right patient to the right hospital. Incorrect triage results in undertriage and overtriage. The aim of this systematic review is to evaluate and compare prehospital trauma triage system quality worldwide and determine effectiveness in terms of undertriage and overtriage for trauma patients. Methods: A systematic search of Pubmed/MEDLINE, Embase, and Cochrane Library databases was performed, using “trauma” “trauma center,” or “trauma system” combined with “triage” “undertriage,” or “overtriage” as search terms. All studies describing ground transport and actual destination hospital of patients with and without severe injuries, using prehospital triage, published before November 2017, were eligible for inclusion. To assess the quality of these studies, a critical appraisal tool was developed. Results: A total of 33 articles were included. The percentage of undertriage ranged from 1% to 68%; overtriage from 5% to 99%. Older age and increased geographical distance were associated with undertriage. Mortality was lower for severely injured patients transferred to a higher-level trauma center. The majority of the included studies were of poor methodological quality. The studies of good quality showed poor performance of the triage protocol, but additional value of EMS provider judgment in the identification of severely injured patients. Conclusion: In most of the evaluated trauma systems, a substantial part of the severely injured patients is not transported to the appropriate level trauma center. Future research should come up with new innovative ways to improve the quality of prehospital triage in trauma patients

Development and Validation of a Prediction Model for Prehospital Triage of Trauma Patients

Importance: Prehospital trauma triage protocols are used worldwide to get the right patient to the right hospital and thereby improve the chance of survival and avert lifelong disabilities. The American College of Surgeons Committee on Trauma set target levels for undertriage rates of less than 5%. None of the existing triage protocols has been able to achieve this target in isolation. Objective: To develop and validate a new prehospital trauma triage protocol to improve current triage rates. Design, Setting, and Participants: In this multicenter cohort study, all patients with trauma who were 16 years and older and transported to a trauma center in 2 different regions of the Netherlands were included in the analysis. Data were collected from January 1, 2012, through June 30, 2014, in the Central Netherlands region for the design data cohort and from January 1 through December 31, 2015, in the Brabant region for the validation cohort. Data were analyzed from May 3, 2017, through July 19, 2018. Main Outcomes and Measures: A new prediction model was developed in the Central Netherlands region based on prehospital predictors associated with severe injury. Severe injury was defined as an Injury Severity Score greater than 15. A full-model strategy with penalized maximum likelihood estimation was used to construct a model with 8 predictors that were chosen based on clinical reasoning. Accuracy of the developed prediction model was assessed in terms of discrimination and calibration. The model was externally validated in the Brabant region. Results: Using data from 4950 patients with trauma from the Central Netherlands region for the design data set (58.3% male; mean [SD] age, 47 [21] years) and 6859 patients for the validation Brabant region (52.2% male; mean [SD] age, 51 [22] years), the following 8 significant predictors were selected for the prediction model: age; systolic blood pressure; Glasgow Coma Scale score; mechanism criteria; penetrating injury to the head, thorax, or abdomen; signs and/or symptoms of head or neck injury; expected injury in the Abbreviated Injury Scale thorax region; and expected injury in 2 or more Abbreviated Injury Scale regions. The prediction model showed a C statistic of 0.823 (95% CI, 0.813-0.832) and good calibration. The cutoff point with a minimum specificity of 50.0% (95% CI, 49.3%-50.7%) led to a sensitivity of 88.8% (95% CI, 87.5%-90.0%). External validation showed a C statistic of 0.831 (95% CI, 0.814-0.848) and adequate calibration. Conclusions and Relevance: The new prehospital trauma triage prediction model may lower undertriage rates to approximately 10% with an overtriage rate of 50%. The next step should be to implement this prediction model with the use of a mobile app for emergency medical services professionals

The ARID1B spectrum in 143 patients: from nonsyndromic intellectual disability to Coffin-Siris syndrome

Purpose: Pathogenic variants in ARID1B are one of the most frequent causes of intellectual disability (ID) as determined by large-scale exome sequencing studies. Most studies published thus far describe clinically diagnosed Coffin-Siris patients (ARID1BCSS) and it is unclear whether these data are representative for patients identified through sequencing of unbiased ID cohorts (ARID1B-ID). We therefore sought to determine genotypic and phenotypic differences between ARID1B-ID and ARID1B-CSS. In parallel, we investigated the effect of different methods of phenotype reporting. Methods: Clinicians entered clinical data in an extensive webbased survey. Results: 79 ARID1B-CSS and 64 ARID1B-ID patients were included. CSS-associated dysmorphic features, such as thick eyebrows, long eyelashes, thick alae nasi, long and/or broad philtrum, small nails and small or absent fifth distal phalanx and hypertrichosis, were observed significantly more often (p <0.001) in ARID1B-CSS patients. No other significant differences were identified. Conclusion: There are only minor differences between ARID1BID and ARID1B-CSS patients. ARID1B-related disorders seem to consist of a spectrum, and patients should be managed similarly. We demonstrated that data collection methods without an explicit option to report the absence of a feature (such as most Human Phenotype Ontology-based methods) tended to underestimate gene-related features

Correction

PubMedScopu

The ARID1B spectrum in 143 patients: from nonsyndromic intellectual disability to Coffin–Siris syndrome

Purpose: Pathogenic variants in ARID1B are one of the most frequent causes of intellectual disability (ID) as determined by large-scale exome sequencing studies. Most studies published thus far describe clinically diagnosed Coffin–Siris patients (ARID1B-CSS) and it is unclear whether these data are representative for patients identified through sequencing of unbiased ID cohorts (ARID1B-ID). We therefore sought to determine genotypic and phenotypic differences between ARID1B-ID and ARID1B-CSS. In parallel, we investigated the effect of different methods of phenotype reporting. Methods: Clinicians entered clinical data in an extensive web-based survey. Results: 79 ARID1B-CSS and 64 ARID1B-ID patients were included. CSS-associated dysmorphic features, such as thick eyebrows, long eyelashes, thick alae nasi, long and/or broad philtrum, small nails and small or absent fifth distal phalanx and hypertrichosis, were observed significantly more often (p < 0.001) in ARID1B-CSS patients. No other significant differences were identified. Conclusion: There are only minor differences between ARID1B-ID and ARID1B-CSS patients. ARID1B-related disorders seem to consist of a spectrum, and patients should be managed similarly. We demonstrated that data collection methods without an explicit option to report the absence of a feature (such as most Human Phenotype Ontology-based methods) tended to underestimate gene-related features