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Purpose: Numerous studies have modified the Trauma Injury and Severity Score (TRISS) to improve its predictive accuracy for specific trauma populations. The aim of this study was to develop and validate a simple and practical prediction model that accurately predicts mortality for all acute trauma admissions. Methods: This retrospective study used Dutch National Trauma Registry data recorded between 2015 and 2018. New models were developed based on nonlinear transformations of TRISS variables (age, systolic blood pressure (SBP), Glasgow Coma Score (GCS) and Injury Severity Score (ISS)), the New Injury Severity Score (NISS), the sex–age interaction, the best motor response (BMR) and the American Society of Anesthesiologists (ASA) physical status classification. The models were validated in 2018 data and for specific patient subgroups. The models’ performance was assessed based on discrimination (areas under the curve (AUCs)) and by calibration plots. Multiple imputation was applied to account for missing values. Results: The mortality rates in the development and validation datasets were 2.3% (5709/245363) and 2.5% (1959/77343), respectively. A model with sex, ASA class, and nonlinear transformations of age, SBP, the ISS and the BMR showed significantly better discrimination than the TRISS (AUC 0.915 vs. 0.861). This model was well calibrated and demonstrated good discrimination in different subsets of patients, including isolated hip fractures patients (AUC: 0.796), elderly (AUC: 0.835), less severely injured (ISS16) (AUC: 878), severely injured (ISS ≥ 16) (AUC: 0.889), traumatic brain injury (AUC: 0.910). Moreover, discrimination for patients admitted to the intensive care (AUC: s0.846), and for both non-major and major trauma center patients was excellent, with AUCs of 0.940 and 0.895, respectively. Conclusion: This study presents a simple and practical mortality prediction model that performed well for important subgroups of patients as well as for the heterogeneous population of all acute trauma admissions in the Netherlands. Because this model includes widely available predictors, it can also be used for international evaluations of trauma care within institutions and trauma systems.

Objective To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning. Background RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking. Methods Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy. Results The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively. Conclusion This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.

Purpose: Patients’ expectations during recovery after a trauma can affect the recovery. The aim of the present study was to identify different physical recovery trajectories based on Latent Markov Models (LMMs) and predict these recovery states based on individual patient characteristics. Methods: The data of a cohort of adult trauma patients until the age of 75 years with a length of hospital stay of 3 days and more were derived from the Brabant Injury Outcome Surveillance (BIOS) study. The EuroQol-5D 3-level version and the Health Utilities Index were used 1 week, and 1, 3, 6, 12, and 24 months after injury. Four prediction models, for mobility, pain, self-care, and daily activity, were developed using LMMs with ordinal latent states and patient characteristics as predictors for the latent states. Results: In total, 1107 patients were included. Four models with three ordinal latent states were developed, with different covariates in each model. The prediction of the (ordinal) latent states in the LMMs yielded pseudo-R2 values between 40 and 53% and between 21 and 41% (depending of the type R2 used) and classification errors between 24 and 40%. Most patients seem to recover fast as only about a quarter of the patients remain with severe problems after 1 month. Conclusion: The use of LMMs to model the development of physical function post-injury is a promising way to obtain a prediction of the physical recovery. The step-by-step prediction fits well with the outpatient follow-up and it can be used to inform the patients more tailor-made to manage the expectations.

Over the last decades, the Dutch trauma care have seen major improvements. To assess the performance of the Dutch trauma system, in 2007, the Dutch Nationwide Trauma Registry (DNTR) was established, which developed into rich source of information for quality assessment, quality improvement of the trauma system, and for research purposes. The DNTR is one of the most comprehensive trauma registries in the world as it includes 100% of all trauma patients admitted to the hospital through the emergency department. This inclusive trauma registry has shown its benefit over less inclusive systems; however, it comes with a high workload for high-quality data collection and thus more expenses. The comprehensive prospectively collected data in the DNTR allows multiple types of studies to be performed. Recent changes in legislation allow the DNTR to include the citizen service numbers, which enables new possibilities and eases patient follow-up. However, in order to maximally exploit the possibilities of the DNTR, further development is required, for example, regarding data quality improvement and routine incorporation of health-related quality of life questionnaires. This would improve the quality assessment and scientific output from the DNTR. Finally, the DNTR and all other (European) trauma registries should strive to ensure that the trauma registries are eligible for comparisons between countries and healthcare systems, with the goal to improve trauma patient care worldwide.

Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised techniques are employed to cluster patients based on longitudinal recovery profiles. Subsequently, these data-driven clusters were assessed on clinical validity by experts and used as targets in supervised machine learning models. We present a formalised analysis of the obtained clusters that incorporates evaluation of (i) statistical and machine learning metrics, (ii) clusters clinical validity with descriptive statistics and medical expertise. Clusters quality assessment revealed that clusters obtained through a Bayesian method (High Dimensional Supervised Classification and Clustering) and a Deep Gaussian Mixture model, in combination with oversampling and a Random Forest for supervised learning of the cluster assignments provided among the most clinically sensible partitioning of patients. Other methods that obtained higher classification accuracy suffered from cluster solutions with large majority classes or clinically less sensible classes. Models that used just physical or a mix of physical and psychological outcomes proved to be among the most sensible, suggesting that clustering on psychological outcomes alone yields recovery profiles that do not conform to known risk factors.

Background: high sensitive Troponin T (hsTnT) values between 14–50 ng/L represent a challenge in diagnosing acute coronary syndrome (ACS) at the Emergency Department (ED). The European Society for Cardiology (ESC) recommends a second hsTnT measurement 3 h later to distinguish between ACS and other causes depending on the Δ hsTnT. Our study aims to evaluate the predictive power this approach in a clinical setting by following patients presenting at the ED with hsTnT values 14–51 ng/L. Materials and methods: patients presenting with chest pain or dyspnea and a hsTnT value between 14 and 50 ng/L at the Erasmus MC ED in 2012–2013 were included and retrospectively monitored for 90 days after initial presentation for the occurrence of a cardiac revascularization. Patient records were reviewed according to the standing protocol, which depended on the Δ hsTnT. The “event-group” consists of patients receiving cardiac revascularization within 90 days after the ED visit, whereas the “no event-group” consisted of patients without revascularization. Results: a total of 889 patients patient records were reviewed. After excluding out-of-hospital-cardia-arrests (60), non-cardiological chest pain (373) and incomplete follow-up (100), 356 patients remained for final analysis. In 207 patients, a second hsTnT was actually performed (58%). From these 207 patients, 68 (33%) had a Δ hsTnT ≥7 ng/L. In these patients, 37 (54%) experienced an event within 90 days. In the 139 patients with a Δ hsTnT < 7 ng/L, 23 (17%) presented with an event within 90 days. Conclusion: our study demonstrated a sensitivity of 62%, a specificity of 79%, a positive predicted value (PPV) of 54% and a negative predictive value (NPV) of 83% for using a 3-h Δ hsTnT ≥7 ng/L cut-off, related to risk of an event in 90 days following ED presentation.

Background Minimally invasive surgery is complex and associated with substantial learning curves. Computer-aided anatomy recognition, such as artificial intelligence-based algorithms, may improve anatomical orientation, prevent tissue injury, and improve learning curves. The study objective was to provide a comprehensive overview of current literature on the accuracy of anatomy recognition algorithms in intrathoracic and -abdominal surgery. Methods This systematic review is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Pubmed, Embase, and IEEE Xplore were searched for original studies up until January 2022 on computer-aided anatomy recognition, without requiring intraoperative imaging or calibration equipment. Extracted features included surgical procedure, study population and design, algorithm type, pre-training methods, pre- and post-processing methods, data augmentation, anatomy annotation, training data, testing data, model validation strategy, goal of the algorithm, target anatomical structure, accuracy, and inference time. Results After full-text screening, 23 out of 7124 articles were included. Included studies showed a wide diversity, with six possible recognition tasks in 15 different surgical procedures, and 14 different accuracy measures used. Risk of bias in the included studies was high, especially regarding patient selection and annotation of the reference standard. Dice and intersection over union (IoU) scores of the algorithms ranged from 0.50 to 0.98 and from 74 to 98%, respectively, for various anatomy recognition tasks. High-accuracy algorithms were typically trained using larger datasets annotated by expert surgeons and focused on less-complex anatomy. Some of the high-accuracy algorithms were developed using pre-training and data augmentation. Conclusions The accuracy of included anatomy recognition algorithms varied substantially, ranging from moderate to good. Solid comparison between algorithms was complicated by the wide variety of applied methodology, target anatomical structures, and reported accuracy measures. Computer-aided intraoperative anatomy recognition is an upcoming research discipline, but still at its infancy. Larger datasets and methodological guidelines are required to improve accuracy and clinical applicability in future research. Trial registration: PROSPERO registration number: CRD42021264226

Background: The goal of trauma systems is to match patient care needs to the capabilities of the receiving centre. Severely injured patients have shown better outcomes if treated in a major trauma centre (MTC). We aimed to evaluate patient distribution in the Dutch trauma system. Furthermore, we sought to identify factors associated with the undertriage and transport of severely injured patients (Injury Severity Score (ISS) >15) to the MTC by emergency medical services (EMS).Methods: Data on all acute trauma admissions in the Netherlands (2015-2016) were extracted from the Dutch national trauma registry. An ambulance driving time model was applied to calculate MTC transport times and transport times of ISS >15 patients to the closest MTC and non-MTC. A multivariable logistic regression analysis was performed to identify factors associated with ISS >15 patients' EMS undertriage to an MTC.Results: Of the annual average of 78,123 acute trauma admissions, 4.9% had an ISS >15. The nonseverely injured patients were predominantly treated at non-MTCs (79.2%), and 65.4% of patients with an ISS >15 received primary MTC care. This rate varied across the eleven Dutch trauma networks (36.8%-88.4%) and was correlated with the transport times to an MTC (Pearson correlation -0.753, p=0.007). The trauma networks also differed in the rates of secondary transfers of ISS >15 patients to MTC hospitals (7.8% 59.3%) and definitive MTC care (43.6% - 93.2%). Factors associated with EMS undertriage of ISS >15 patients to the MTC were female sex, older age, severe thoracic and abdominal injury, and longer additional EMS transport times.Conclusions: Approximately one-third of all severely injured patients in the Netherlands are not initially treated at an MTC. Special attention needs to be directed to identifying patient groups with a high risk of undertriage. Furthermore, resources to overcome longer transport times to an MTC, including the availability of ambulance and helicopter services, may improve direct MTC care and result in a decrease in the variation of the undertriage of severely injured patients to MTCs among the Dutch trauma networks. Furthermore, attention needs to be directed to improving primary triage guidelines and instituting uniform interfacility transfer agreements. (C) 2021 Elsevier Ltd. All rights reserved.

Using patient outcomes to monitor medical centre performance has become an essential part of modern health care. However, classic league tables generally inflict stigmatization on centres rated as "poor performers", which has a negative effect on public trust and professional morale. In the present study, we aim to illustrate that funnel plots, including trends over time, can be used as a method to control the quality of data and to monitor and assure the quality of trauma care. Moreover, we aimed to present a set of regulations on how to interpret and act on underperformance or overperformance trends presented in funnel plots.A retrospective observational cohort study was performed using the Dutch National Trauma Registry (DNTR). Two separate datasets were created to assess the effects of healthy and multiple imputations to cope with missing values. Funnel plots displaying the performance of all trauma-receiving hospitals in 2020 were generated, and in-hospital mortality was used as the main indicator of centre performance. Indirect standardization was used to correct for differences in the types of cases. Comet plots were generated displaying the performance trends of two level-I trauma centres since 2017 and 2018.Funnel plots based on data using healthy imputation for missing values can highlight centres lacking good data quality. A comet plot illustrates the performance trend over multiple years, which is more indicative of a centre's performance compared to a single measurement. Trends analysis offers the opportunity to closely monitor an individual centres' performance and direct evaluation of initiated improvement strategies.This study describes the use of funnel and comet plots as a method to monitor and assure high-quality data and to evaluate trauma centre performance over multiple years. Moreover, this is the first study to provide a regulatory blueprint on how to interpret and act on the under- or overperformance of trauma centres. Further evaluations are needed to assess its functionality.Retrospective study, level III.

Background With the improved survival rates after trauma, the population of patients with disabilities increased. The knowledge about functional outcome and the relationship between health status and functional outcome is limited. The aim of the present prospective cohort study was to describe the functional outcome and health status over time, and the relationship between both. Methods Adult severely injured patients (ISS≥16) were included if hospitalised in Noord-Brabant within 48 h after injury between August 2015 and December 2016. The functional outcome (Glasgow Outcome Scale Extended - GOSE) and health status (EQ-5D) were measured at 1, 3, 6, 12 and 24 months after injury. Logistic and linear mixed models were used to examine functional outcome and health status over time. Measurements were divided into short- (1–3 months), mid- (6–12 months) and long-term (24 months). Results In total 239 severely injured patients were included. Functional outcome and health status improved over time. Prognostic factors during two years were a longer hospital length of stay, female gender and Glasgow Coma Scale. Besides age was a prognostic factor for health status and education level for functional outcome. A higher ASA classification was a long-term prognostic factor for a lower functional outcome and a lower health status. The patients with a good functional recovery showed a significant higher EQ-5D utility score and patients with a poor functional recovery reported significant more problems in the EQ-5 domains. Conclusion There is a good relationship between the functional outcome and the health status during two years after a severe injury. It appears reliable to use functional outcome in terms of physical impairments in daily clinic to determine patients at risk for both a lower functional outcome and a lower health status over time.

Detail morphological evaluation for ASD is essential to achieve successful transcatheter closure. Three-dimensional transesophageal echocardiography (3D-TEE) is emerging, but few studies have comprehensively verified the usefulness of 3D-TEE. We divided 329 patients who underwent transcatheter ASD closure at our university hospital into 157 in the Conventional group evaluated with 2-dimensional transesophageal echocardiography and balloon sizing (BS), and 172 in the 3D-TEE group evaluated with 3D-TEE additionally. We assessed usefulness of 3D-TEE and consider appropriate device selection procedure. Overall, the percentage with re-sizing of device tended to be lower in the 3D-TEE group than in the Conventional group (10.1% vs 6.0%, p = 0.187). Among preprocedural modalities, the device size was mainly decided based on the BS diameter. A logistic regression analysis demonstrated that large atrial septum aneurysms (ASA) were associated with a ≥ 2 mm discrepancy of the BS diameter from the preprocedural 3D-TEE diameter (p

Purpose While survival rates after a trauma are increasing a considerable part of the trauma population are still at risk for both short and long term disabilities. Little is known about prognostic factors over time after a severe trauma. The aim of the present prospective cohort study was to examine trauma and patient related prognostic factors for a lower health status over time after a severe trauma. Methods A multicentre prospective observational cohort study was conducted. Adult trauma patients with severe injuries (ISS ≥ 16) were included from August 2015 until November 2016 if admitted to one of the hospitals in Noord-Brabant (the Netherlands). Outcome measure was health status, measured by the EuroQol-5D (EQ-5D utility and EQ-Visual analogue scale) and the Health Utilities Index (HUI2 and HUI3) one week and one, three, six, and twelve months after injury. Patient and trauma characteristics were analysed as prognostic factors with linear mixed models. The effect of each prognostic factor over time was analysed by adding the interaction term between the prognostic factor and time point in a multivariable linear mixed model, adjusted for confounders. Additionally, the risk factors for problems in the EQ-5 dimensions of HS and cognition were analysed. Results In total 239 severely injured patients participated. Pre-injury health status, hospital length of stay, ISS and comorbidities were significant prognostic factors for a lower health status. A younger age and extremity injury were prognostic factors for a lower health status until one month after trauma and unemployment before trauma and comorbidities six until twelve months after trauma. In the EQ-5 dimensions 44.1% remained problems in mobility, 15.3% in self-care, 46.4% in activity, 53.3% in pain, 32.5% in anxiety and 35.7% in cognition. Conclusions Lower pre-injury health status, longer hospital length of stay, higher ISS, and comorbidities were significant prognostic factors for a lower health status during one year after a severe injury. A younger age and an extremity injury were short-term prognostic factors and unemployment before trauma and comorbidities were long-term prognostic factors. Even after twelve months patients in our population reported more problems in all EQ-5D dimensions when compared to the Dutch reference population.

Background and aim The relation between tumor characteristics and the pattern of lymph node (LN) metastases in gastric cancer is unclear, especially following neoadjuvant chemotherapy (NAC). Therefore, the aim of this study is to analyze the pattern of LN metastases of gastric cancer. Methods Individual LN stations were separately collected (no. 7–9, 11 and 12a) or clearly marked at the resection specimen (no. 1–6), and were analyzed for all patients included in the LOGICA-trial. The LOGICA-trial was a multicenter randomized trial comparing laparoscopic versus open gastrectomy in ten hospitals. Total and distal D2-gastrectomy were performed for resectable gastric cancer (cT1–4aN0–3bM0). The pattern of metastases per LN station was related to tumor location, cT-stage, Lauren classification and NAC-treatment. In addition, the distribution of LN metastases over the individual LN stations was assessed for four subgroups based on tumor location, cT-stage, Lauren classification and NAC-treatment, and several combinations of these characteristics. Results Between 2015–2018, 212 patients underwent D2-gastrectomy, of whom 158 (75%) received NAC. LN metastases were present in 121 patients (57%). Proximal tumors metastasized predominantly to proximal LN stations (no. 1, 2, 7 and 9; OR > 1, P 1, P > 0,05). However, distal tumors still metastasized to proximal LN stations, and vice versa. Each individual LN station (no. 1–9, 11 and 12a) showed metastases, regardless of the tumor location, cT-stage, histological subtype and NAC-treatment, including station 12a for cT1N0-tumors. Although LN metastases were present more frequently in cT3–4- versus cT1–2-stage (59% versus 51%; P = 0,259) and in diffuse versus intestinal tumors (66% versus 52%; P = 0,048), the pattern of LN metastases was similar for these subgroups. A sensitivity analysis was performed with only the NAC-treated patients to test the robustness of these results, which showed a similar pattern of LN metastases after NAC. Conclusion Although the pattern of LN metastases is related to tumor location in gastric cancer, metastatic spread occurred in all LN stations (no. 1–9, 11 and 12a), regardless of the tumor location, cT-stage (including cT1N0-tumors), histological subtype and NAC-treatment. Therefore, all LN stations (including 12a; D2-lymphadenectomy) should be routinely dissected during gastrectomy.

In Ireland, traditionally, most public Adult Mental Health Services (AMHSs) had a small cohort of service users with eating disorders (EDs) in their service. However, over the last 5 years, the National Clinical Programmes have been encouraging Mental Health Services to develop ED programmes in each catchment area. This has culminated in a model of care for EDs for children and adults. It appears that in relation to AMHSs, meaningful inclusion of families/significant other(s) in ED programmes is somewhat inconsistent. This paper will discuss the possible impact of excluding or minimising family/significant other(s)’ inclusion. It will also outline a suggested approach of including families/significant other(s) in a meaningful way in an out-patient ED programme.

Objective:To evaluate the impact of the COVID-19 pandemic on the outcome of major trauma patients in the Netherlands. Summary Background Data:Major trauma patients highly rely on immediate access to specialized services, including ICUs, shortages caused by the impact of the COVID-19 pandemic may influence their outcome. Methods:A multi-center observational cohort study, based on the Dutch National Trauma Registry was performed. Characteristics, resource usage, and outcome of major trauma patients (injury severity score ≥16) treated at all trauma-receiving hospitals during the first COVID-19 peak (March 23 through May 10) were compared with those treated from the same period in 2018 and 2019 (reference period).Results:During the peak period, 520 major trauma patients were admitted, versus 570 on average in the pre-COVID-19 years. Significantly fewer patients were admitted to ICU facilities during the peak than during the reference period (49.6% vs 55.8%; P=0.016). Patients with less severe traumatic brain injuries in particular were less often admitted to the ICU during the peak (40.5% vs 52.5%; P=0.005). Moreover, this subgroup showed an increased mortality compared to the reference period (13.5% vs 7.7%; P=0.044). These results were confirmed using multivariable logistic regression analyses. In addition, a significant increase in observed versus predicted mortality was recorded for patients who had a priori predicted mortality of 50% to 75% (P=0.012). Conclusions:The COVID-19 peak had an adverse effect on trauma care as major trauma patients were less often admitted to ICU and specifically those with minor through moderate brain injury had higher mortality rates.

The Berlin poly-trauma definition (BPD) has proven to be a valuable way of identifying patients with at least a 20% risk of mortality, by combining anatomical injury characteristics with the presence of physiological risk factors (PRFs). Severe isolated injuries (SII) are excluded from the BPD. This study describes the characteristics, resource use and outcomes of patients with SII according to their injured body region, and compares them with those included in the BPD.Data were extracted from the Dutch National Trauma Registry between 2015 and 2019. SII patients were defined as those with an injury with an Abbreviated Injury Scale (AIS) score ≥ 4 in one body region, with at most minor additional injuries (AIS ≤ 2). We performed an SII subgroup analysis per AIS region of injury. Multivariable linear and logistic regression models were used to calculate odds ratios (ORs) for SII subgroup patient outcomes, and resource needs.A total of 10.344 SII patients were included; 47.8% were ICU admitted, and the overall mortality was 19.5%. The adjusted risk of death was highest for external (2.5, CI 1.9-3.2) and for head SII (2.0, CI 1.7-2.2). Patients with SII to the abdomen (2.3, CI 1.9-2.8) and thorax (1.8, CI 1.6-2.0) had a significantly higher risk of ICU admission. The highest adjusted risk of disability was recorded for spine injuries (10.3, CI 8.3-12.8). The presence of ≥ 1 PRFs was associated with higher mortality rates compared to their poly-trauma counterparts, displaying rates of at least 15% for thoracic, 17% for spine, 22% for head and 49% for external SII.A severe isolated injury is a high-risk entity and should be recognized and treated as such. The addition of PRFs to the isolated anatomical injury criteria contributes to the identification of patients with SII at risk of worse outcomes.

ObjectiveTo explore experiences of recovery after physical trauma and identify long-term needs for posthospital care.Design, participants and settingA qualitative study was conducted consisting of seven online focus groups among working-age adults who sustained their injury between 9 months and 5 years ago. Trauma patients discharged from a level 1 trauma centre in the Netherlands were divided into three groups based on the type of their physical trauma (monotrauma, polytrauma and traumatic brain injury). Group interviews were transcribed verbatim, and thematic analysis was conducted.ResultsDespite differences in type and severity of their injuries, participants all struggled with the impact that trauma had on various aspects of their lives. They experienced recovery as an unpredictable and inconstant process aimed at resuming a meaningful life. Work was often perceived as an important part of recovery, though the value attributed to work could change over time. Participants struggled to bring the difficulties they encountered in their daily lives and at work to the attention of healthcare professionals (HCPs). While posthospital care needs varied between and across groups, all people stressed the need for flexible access to person-centred, multidisciplinary care and support after hospital discharge.ConclusionsThis study reveals that people with a broad variety of injury experience recovery as a process towards resuming a meaningful life and report the need to expand trauma care to include comprehensive support to live well long term. Person-centred care might be helpful to enable HCPs to take people’s individual long-term needs and life situations into account. Furthermore, providing timely access to coordinated, multidisciplinary care after discharge is advocated. Integrated care models that span a network of multidisciplinary support around the person may help align existing services and may facilitate easy and timely access to the most suitable support for injured people and their loved ones.

Background Trauma care faces challenges to innovating their services, such as with mobile health (mHealth) app, to improve the quality of care and patients’ health experience. Systematic needs inquiries and collaborations with professional and patient end users are highly recommended to develop and prepare future implementations of such innovations. Objective This study aimed to develop a trauma mHealth app for patient information and support in accordance with the Center for eHealth Research and Disease Management road map and describe experiences of unmet information and support needs among injured patients with trauma, barriers to and facilitators of the provision of information and support among trauma care professionals, and drivers of value of an mHealth app in patients with trauma and trauma care professionals. Methods Formative evaluations were conducted using quantitative and qualitative methods. Ten semistructured interviews with patients with trauma and a focus group with 4 trauma care professionals were conducted for contextual inquiry and value specification. User requirements and value drivers were applied in prototyping. Furthermore, a complementary quantitative discrete choice experiment (DCE) was conducted with 109 Dutch trauma surgeons, which enabled triangulation on value specification results. In the DCE, preferences were stated for hypothetical mHealth products with various attributes. Panel data from the DCE were analyzed using conditional and mixed logit models. Results Patients disclosed a need for more psychosocial support and easy access to more extensive information on their injury, its consequences, and future prospects. Health care professionals designated workload as an essential issue; a digital solution should not require additional time. The conditional logit model of DCE results suggested that access to patient app data through electronic medical record integration (odds ratio [OR] 3.3, 95% CI 2.55-4.34; P Conclusions In the context of trauma care, end users have many requirements for an mHealth solution that addresses psychosocial functioning; dependable information; and, possibly, a prediction of how a patient’s recovery trajectory is evolving. A structured development approach provided insights into value drivers and facilitated mHealth prototype enhancement. The findings imply that iterative development should move on from simple and easily implementable mHealth solutions to those that are suitable for broader innovations of care pathways that most—but plausibly not yet all—end users in trauma care will value. This study could inspire the trauma care community.

BACKGROUND Trauma care faces challenges to innovating their services, such as with mobile health (mHealth) app, to improve the quality of care and patients’ health experience. Systematic needs inquiries and collaborations with professional and patient end users are highly recommended to develop and prepare future implementations of such innovations. OBJECTIVE This study aimed to develop a trauma mHealth app for patient information and support in accordance with the Center for eHealth Research and Disease Management road map and describe experiences of unmet information and support needs among injured patients with trauma, barriers to and facilitators of the provision of information and support among trauma care professionals, and drivers of value of an mHealth app in patients with trauma and trauma care professionals. METHODS Formative evaluations were conducted using quantitative and qualitative methods. Ten semistructured interviews with patients with trauma and a focus group with 4 trauma care professionals were conducted for contextual inquiry and value specification. User requirements and value drivers were applied in prototyping. Furthermore, a complementary quantitative discrete choice experiment (DCE) was conducted with 109 Dutch trauma surgeons, which enabled triangulation on value specification results. In the DCE, preferences were stated for hypothetical mHealth products with various attributes. Panel data from the DCE were analyzed using conditional and mixed logit models. RESULTS Patients disclosed a need for more psychosocial support and easy access to more extensive information on their injury, its consequences, and future prospects. Health care professionals designated workload as an essential issue; a digital solution should not require additional time. The conditional logit model of DCE results suggested that access to patient app data through electronic medical record integration (odds ratio [OR] 3.3, 95% CI 2.55-4.34; PPPPPPP=.001). CONCLUSIONS In the context of trauma care, end users have many requirements for an mHealth solution that addresses psychosocial functioning; dependable information; and, possibly, a prediction of how a patient’s recovery trajectory is evolving. A structured development approach provided insights into value drivers and facilitated mHealth prototype enhancement. The findings imply that iterative development should move on from simple and easily implementable mHealth solutions to those that are suitable for broader innovations of care pathways that most—but plausibly not yet all—end users in trauma care will value. This study could inspire the trauma care community.

The Patient-Reported Outcomes Information System (PROMIS®) is more and more extensively being used in medical literature in patients with an orthopedic fracture. Yet, many articles studied heterogeneous groups with chronic orthopedic disorders in which fracture patients were included as well. At this moment, there is no systematic overview of the exact use of PROMIS measures in the orthopedic fracture population. Therefore this review aimed to provide an overview of the PROMIS health domains physical health, mental health and social health in patients suffering an orthopedic fracture.This systematic review was documented according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines. We searched Embase, Medline, Web of Science Core Collection, and Cochrane Central Register of controlled Trials, CINAHL and Google Scholar in December 2020 using a combination of MeSH terms and specific index terms related to orthopedic fractures and PROMIS questionnaires. Inclusion criteria were available full text articles that were describing the use of any PROMIS questionnaires in both the adult and pediatric extremity fracture population.We included 51 relevant articles of which most were observational studies (n = 47, 92.2%). A single fracture population was included in 47 studies of which 9 involved ankle fractures (9/51; 17.6%), followed by humeral fractures (8/51; 15.7%), tibia fractures (6/51; 11.8%) and radial -or ulnar fractures (5/51; 9.8%). PROMIS Physical Function (n = 32, 32/51 = 62.7%) and PROMIS Pain Interference (n = 21, 21/51 = 41.2%) were most frequently used questionnaires. PROMIS measures concerning social (n = 5/51 = 9.8%) and mental health (10/51 = 19.6%) were much less often used as outcome measures in the fracture population. A gradually increasing use of PROMIS questionnaires in the orthopedic fracture population was seen since 2017.Many different PROMIS measures on multiple domains are available and used in previous articles with orthopedic fracture patients. With physical function and pain interference as most popular PROMIS measures, it is important to emphasize that other health-domains such as mental and social health can also be essential to fracture patients.