Exploring the Clinical Usefulness of Undergraduate Medical Research: A Mixed-Methods Study.

A significant amount of published clinical research has no measurable impact on health and disease outcomes, and research in undergraduate medical education is viewed as especially susceptible. The aims of this mixed methods study were to (a) to use group concept mapping (GCM) to explore key features identified by hospital physicians, medical educators, and medical students as central to clinical usefulness in an undergraduate medical research context, and (b) review a sample of undergraduate medical research projects based on usefulness criteria described by Ioannidis (2016). In the GCM procedure, 54 respondents (39 students, 15 physicians) from an Irish medical school participated across each of three phases: brainstorming, sorting, and rating. Data was analysed using multidimensional scaling and hierarchical clustering. A retrospective analysis of 252 student projects was also completed using a rubric based on Ioannidis's (2016) six domains of "clinical usefulness": problem base, context placement and information gain, pragmatism, patient-centredness, feasibility, and transparency. Projects were scored for each domain by three assessors. Results were analysed and presented using descriptive analysis.GCM analysis revealed the following "clinically useful" research characteristics: optimal design and methodology, practicality, research skills development, translational impact, patient-centredness, and asking a clinical question. Following a rubric-based analysis of projects, the highest scoring categories (mean rating; range of 1-4) were feasibility (3.57), transparency (3.32), and problem base (3.05). The lowest scoring areas were context placement and information gain (2.73), pragmatism (2.68), and patient-centredness (212). We identified considerable conceptual overlap between stakeholder consensus views on "clinical usefulness" as applied to undergraduate research and Ioannidis's criteria. Patient-centredness was identified as a domain requiring greater emphasis during the design of undergraduate medical research. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-024-02035-7.

Predictors of seizure detection and EEG clinical impact in an italian tertiary emergency department.

BACKGROUND AND OBJECTIVES: Epileptic seizures pose challenges in emergency departments (ED), affecting up to 10% of admitted patients. This study aimed to assess emergency electroencephalogram (EmEEG) utilization, identifying factors predicting seizure detection and its influence on clinical decisions. METHODS: A retrospective review of 1135 EmEEGs on 1017 patients at a tertiary teaching hospital between June 2022 and June 2023 was conducted. Data included demographics, medical history, EmEEG indications, neuroimaging findings, and clinical outcomes. Statistical analyses utilized Fisher's exact tests and logistic regression models. RESULTS: EmEEG detected status epilepticus-related seizures in 5.40% of cases, seizures without status epilepticus in 3.05%, and status epilepticus without discrete seizures in 3.74%. Epileptiform abnormalities were noted in 22.12% of EmEEGs. EmEEG influenced initial diagnoses (21.24%), antiseizure medication changes (20.85%), and discharge decisions (39.04%). Predictors for seizures/status epilepticus included previous neurosurgery, seizures in the ED, and cognitive/behavioral impairment (p < 0.001). EmEEG significantly altered initial diagnoses based on witnessed seizures, involuntary movements, epileptiform abnormalities, and 1-2 Hz generalized periodic discharges (p < 0.001). Changes in antiseizure medications correlated with seizure occurrence, neuroimaging results, epileptiform abnormalities, and EEG background slowing (p < 0.001). Factors influencing discharge decisions included previous neurosurgery, consciousness impairment, acute neuroimaging pathology, EEG focal slowing, and EEG background slowing (p < 0.001). DISCUSSION: The study clarifies EmEEG's role in modifying initial diagnoses, treatment approaches, and discharge decisions. The study provides insights into the nuanced impact of EmEEG in different clinical scenarios, offering valuable guidance for clinicians in selecting patients for EmEEG, particularly in conditions of limited EEG availability.

Decision Curve Analysis for Personalized Treatment Choice between Multiple Options.

BACKGROUND: Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial. OBJECTIVES: Our main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA). METHODS: We describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as "treat none" or "treat all patients with a specific treatment" strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo. RESULTS: We illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the "treat patients according to the prediction model" strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness. CONCLUSIONS: This novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making. HIGHLIGHTS: Decision curve analysis is extended into a (network) meta-analysis framework.Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials.Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined.This extension of decision curve analysis can be applied to (network) meta-analysis-based prediction models to evaluate their use to aid treatment decision making.

Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review.

BACKGROUND: Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. METHODS: We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. RESULTS: Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. CONCLUSION: Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating.

Utility of Antibodies in the Diagnoses of Thyroid Diseases: A Review Article.

Thyroid problems are among the most widespread endocrine illnesses, affecting individuals in India and the global population. A thyroid function test is used to diagnose, screen, and monitor patients. Hyperthyroidism is a clinical condition due to excessive circulation of thyroid hormone; in contrast, hypothyroidism is due to a deficiency of thyroid hormone. Graves' disease (GD) is a form of hyperthyroidism due to thyroid-stimulating hormone receptor autoantibodies (TRAb), and anti-thyroid peroxidase antibodies (anti-TPO antibodies). The most common reason for hypothyroidism is Hashimoto's thyroiditis (HT), in which patients have thyroid receptor antibodies (TRAb), antibodies to thyroid peroxidase (TPO), and thyroglobulin antibodies. Many essential genes, including the thyroid-specific genes thyroglobulin (TSGT), TSH-receptor gene, human leukocyte antigen (HLA) genes, cytotoxic T lymphocyte-associated antigen (CTLA) genes, thyroglobulin gene, vitamin D receptor gene, and many immune-regulatory genes were associated with autoimmune thyroid diseases' (AITDs') etiology. This review paper aims to determine if antibodies are beneficial in detecting autoimmune thyroid disease or not. We have also discussed the etiology of autoimmune thyroid illness, serum antibodies in autoimmune thyroid disease, pathophysiology, and TSH receptor features.

External Validation of Finnish Diabetes Risk Score and Australian Diabetes Risk Assessment Tool Prediction Models to Identify People with Undiagnosed Type 2 Diabetes: A Cross-sectional Study in Iran.

Background: Noninvasive risk prediction models have been widely used in various settings to identify individuals with undiagnosed diabetes. Objectives: We aimed to evaluate the discrimination, calibration, and clinical usefulness of the Finnish Diabetes Risk Score (FINDRISC) and Australian Diabetes Risk Assessment (AUSDRISK) to screen undiagnosed diabetes in Kerman, Iran. Methods: We analyzed data from 2014 to 2018 in the second round of the Kerman Coronary Artery Disease Risk Factors Study (KERCADRS), Iran. Participants aged 35 - 65 with no history of confirmed diabetes were eligible. The area under the receiver operating characteristic curve (AUROC) and decision curve analysis were applied to evaluate the discrimination power and clinical usefulness of the models, respectively. The calibration was assessed by the Hosmer-Lemeshow test and the calibration plots. Results: Out of 3262 participants, 145 (4.44%) had undiagnosed diabetes. The estimated AUROCs were 0.67 and 0.62 for the AUSDRISK and FINDRISC models, respectively (P < 0.001). The chi-square test results for FINDRISC and AUSDRISC were 7.90 and 16.47 for the original model and 3.69 and 14.61 for the recalibrated model, respectively. Based on the decision curves, useful threshold ranges for the original models of FINDRIS and AUSDRISK were 4% to 10% and 3% to 13%, respectively. Useful thresholds for the recalibrated models of FINDRISC and AUSDRISK were 4% to 8% and 4% to 9%, respectively. Conclusions: The original AUSDRISK model performs better than FINDRISC in identifying patients with undiagnosed diabetes and could be used as a simple and noninvasive tool where access to laboratory facilities is costly or limited.

A new nomogram for the prediction of bone metastasis in patients with prostate cancer.

OBJECTIVE: This study aimed to establish a new prognostic nomogram for bone metastasis in patients with prostate cancer (PCa). METHODS: This study retrospectively analyzed clinical data from 332 patients diagnosed with PCa from 2014 to 2019, and patients were randomly divided into a training set (n = 184) and a validation set (n = 148). Multivariate logistic regression analysis was used to establish a prediction model based on the training set, and a nomogram was constructed for visual presentation. The calibration, discrimination and clinical usefulness of the model were evaluated using the validation set. RESULTS: Total prostate-specific antigen, clinical tumor stage, Gleason score, prostate volume, red cell distribution width and serum alkaline phosphatase were selected as predictors to develop a prediction model of bone metastasis. After evaluation, the model developed in our study exhibited good discrimination (area under the curve: 0.958; 95% confidence interval: 0.93-0.98), calibration (U = 0.01) and clinical usefulness. CONCLUSIONS: The new proposed model showed high accuracy for bone metastasis prediction in patients with PCa and good clinical usefulness.

Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis.

BACKGROUND: Prognosis for the occurrence of relapses in individuals with relapsing-remitting multiple sclerosis (RRMS), the most common subtype of multiple sclerosis (MS), could support individualized decisions and disease management and could be helpful for efficiently selecting patients for future randomized clinical trials. There are only three previously published prognostic models on this, all of them with important methodological shortcomings. OBJECTIVES: We aim to present the development, internal validation, and evaluation of the potential clinical benefit of a prognostic model for relapses for individuals with RRMS using real-world data. METHODS: We followed seven steps to develop and validate the prognostic model: (1) selection of prognostic factors via a review of the literature, (2) development of a generalized linear mixed-effects model in a Bayesian framework, (3) examination of sample size efficiency, (4) shrinkage of the coefficients, (5) dealing with missing data using multiple imputations, (6) internal validation of the model. Finally, we evaluated the potential clinical benefit of the developed prognostic model using decision curve analysis. For the development and the validation of our prognostic model, we followed the TRIPOD statement. RESULTS: We selected eight baseline prognostic factors: age, sex, prior MS treatment, months since last relapse, disease duration, number of prior relapses, expanded disability status scale (EDSS) score, and number of gadolinium-enhanced lesions. We also developed a web application that calculates an individual's probability of relapsing within the next 2 years. The optimism-corrected c-statistic is 0.65 and the optimism-corrected calibration slope is 0.92. For threshold probabilities between 15 and 30%, the "treat based on the prognostic model" strategy leads to the highest net benefit and hence is considered the most clinically useful strategy. CONCLUSIONS: The prognostic model we developed offers several advantages in comparison to previously published prognostic models on RRMS. Importantly, we assessed the potential clinical benefit to better quantify the clinical impact of the model. Our web application, once externally validated in the future, could be used by patients and doctors to calculate the individualized probability of relapsing within 2 years and to inform the management of their disease.

Dynamic prediction models improved the risk classification of type 2 diabetes compared with classical static models.

OBJECTIVE: Dynamic prediction models use the repeated measurements of predictors to estimate coefficients that link the longitudinal predictors to a static model (i.e. Cox regression). This study aims to develop and validate a dynamic prediction for incident type 2 diabetes (T2DM) as the outcome. STUDY DESIGN AND SETTING: Data from the Tehran lipid and glucose study was used to develop (n = 5291 individuals; phases 1 to 3) and validate (n = 3147 individuals; phases 3 to 6) the dynamic prediction model among individuals aged ≥ 20 years. We used repeated measurements of fasting plasma glucose (FPG) or waist circumference (WC) in the framework of the joint modeling (JM) of longitudinal and time-to-event analysis. RESULTS: Compared with the Cox which used just baseline data, JM showed the same discrimination, better calibration, and higher clinical usefulness (i.e. with a net benefit considering both true and false positive decisions); all were shown with repeated measurements of FPG/WC. Additionally, in our study, the dynamic models improve the risk reclassification (net reclassification index 33% for FPG and 24% for WC model). CONCLUSION: Dynamic prediction models, compared with the static one could yield significant improvements in the prediction of T2DM. The complexity of the dynamic models could be addressed by using decision support systems.

The usefulness of the Electronic Patient Visit Assessment (ePVA)© as a clinical support tool for real-time interventions in head and neck cancer.

BACKGROUND: Patients with head and neck cancer (HNC) experience painful, debilitating symptoms and functional limitations that can interrupt cancer treatment, and decrease their health-related quality of life (HRQoL). The Electronic Patient Visit Assessment (ePVA) for head and neck is a web-based mHealth patient-reported measure that asks questions about 21 categories of symptoms and functional limitations common to HNC. This article presents the development and usefulness of the ePVA as a clinical support tool for real-time interventions for patient-reported symptoms and functional limitations in HNC. METHODS: Between January 2018 and August 2019, 75 participants were enrolled in a clinical usefulness study of the ePVA. Upon signing informed consent, participants completed the ePVA and the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ) general (C30) questionnaire v3.0 (scores range from 0 to 100 with 100 representing best HRQoL). Clinical usefulness of the ePVA was defined as demonstration of reliability, convergent validity with HRQoL, and acceptability of the ePVA (i.e., >70% of eligible participants complete the ePVA at two or more visits and >70% of ePVA reports are read by providers). Formal focus group discussions with the interdisciplinary team that cared for patients with HNC guided the development of the ePVA as a clinical support tool. Qualitative and quantitative methods were used throughout the study. Descriptive statistics consisting of means and frequencies, Pearson correlation coefficient, and Student's t-tests were calculated using SAS 9.4 and STATA. RESULTS: The participants were primarily male (71%), White (76%), diagnosed with oropharyngeal or oral cavity cancers (53%), and undergoing treatment for HNC (69%). Data analyses supported the reliability (alpha =0.85), convergent validity with HRQoL scores, and acceptability of the ePVA. Participants with the highest number of symptoms and functional limitations reported significantly worse HRQoL (sum of symptoms: r=-0.50, P<0.0001; sum of function limitations: r=-0.56, P<0.0001). Ninety-two percent of participants (59 of 64) who had follow-up visits within the 6-month study period completed the ePVA at two or more visits and providers read 89% (169 of 189) of automated ePVA reports. The use of the ePVA as a clinical support tool for real-time interventions for symptoms and functional limitations reported by patients is described in a clinical exemplar. CONCLUSIONS: This research indicates that the ePVA may be a useful mHealth tool as a clinical support tool for real-time interventions for patient-reported symptoms and functional limitations in HNC. The study findings support future translational research to enhance the usefulness of the ePVA in real world settings for early interventions that decrease symptom burden and improve the QoL of patients with HNC.

Developing a prediction model for the self-evaluation of erectile dysfunction in an adult male population.

The purpose of this study was to develop an erectile dysfunction (ED) risk assessment tool suitable for the general population. Based on an ED network survey of the general adult male population in China from October to November 2019, a total of 612 cases with a mean age of 31 years (interquartile range: 28-37) with valid data were collected: 357 cases were assigned to the training set and 255 to the validation set. The ED risk prediction model was established by multifactor logistic regression analysis, and nomograms were constructed for visualisation. In the validation set, a receiver operating characteristic curve, calibration curve analysis and decision curve analysis were used to evaluate the discrimination, calibration and clinical usefulness of the ED risk prediction model. Based on multivariate logistic regression, education, smoking, chronic diseases, feelings about one's spouse, frequency of sexual intercourse, masturbation and self-reported sexual satisfaction were selected as predictors to develop the ED prediction model. The model had good discrimination, calibration and clinical applicability. The ED risk prediction model developed in this study can effectively predict ED risk in the general population.

Amyloid Hypothesis: The Emperor's New Clothes?

The lengthy debate on the validity of the amyloid hypothesis and the usefulness of amyloid imaging and anti-amyloid therapeutic interventions in dementia continues unabated, even though none of them have been able to convince the medical world of their correctness and clinical value. There are huge financial interests associated with promoting both, but in spite of the large sums of money in their support, no effective anti-amyloid treatments or diagnostic use of amyloid imaging have emerged. There are solid scientific reasons that explain these negative results, and it is time to move forward to other promising options for the benefit of the patients.

The Impact of Rapid Exome Sequencing on Medical Management of Critically Ill Children.

OBJECTIVES: To evaluate the clinical usefulness of rapid exome sequencing (rES) in critically ill children with likely genetic disease using a standardized process at a single institution. To provide evidence that rES with should become standard of care for this patient population. STUDY DESIGN: We implemented a process to provide clinical-grade rES to eligible children at a single institution. Eligibility included (a) recommendation of rES by a consulting geneticist, (b) monogenic disorder suspected, (c) rapid diagnosis predicted to affect inpatient management, (d) pretest counseling provided by an appropriate provider, and (e) unanimous approval by a committee of 4 geneticists. Trio exome sequencing was sent to a reference laboratory that provided verbal report within 7-10 days. Clinical outcomes related to rES were prospectively collected. Input from geneticists, genetic counselors, pathologists, neonatologists, and critical care pediatricians was collected to identify changes in management related to rES. RESULTS: There were 54 patients who were eligible for rES over a 34-month study period. Of these patients, 46 underwent rES, 24 of whom (52%) had at least 1 change in management related to rES. In 20 patients (43%), a molecular diagnosis was achieved, demonstrating that nondiagnostic exomes could change medical management in some cases. Overall, 84% of patients were under 1 month old at rES request and the mean turnaround time was 9 days. CONCLUSIONS: rES testing has a significant impact on the management of critically ill children with suspected monogenic disease and should be considered standard of care for tertiary institutions who can provide coordinated genetics expertise.

Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis.

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19. Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness. Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram. Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

Reliability and Clinical Usefulness of Current Classifications in Traumatic Thoracolumbar Fractures: A Systematic Review of the Literature.

BACKGROUND: A validated classification remains the key to an appropriate treatment algorithm of traumatic thoracolumbar fractures. Considering the development of many classifications, it is remarkable that consensus about treatment is still lacking. We conducted a systematic review to investigate which classification can be used best for treatment decision making in thoracolumbar fractures. METHODS: A comprehensive search was conducted using PubMed, Embase, CINAHL, and Cochrane using the following search terms: classification (mesh), spinal fractures (mesh), and corresponding synonyms. All hits were viewed by 2 independent researchers. Papers were included if analyzing the reliability (kappa values) and clinical usefulness (specificity or sensitivity of an algorithm) of currently most used classifications (Magerl/AO, thoracolumbar injury classification and severity score [TLICS] or thoracolumbar injury severity score, and the new AO spine). RESULTS: Twenty articles are included. The presented kappa values indicate moderate to substantial agreement for all 3 classifications. Regarding the clinical usefulness, > 90% agreement between actual treatment and classification recommendation is reported for most fractures. However, it appears that over 50% of the patients with a stable burst fracture (TLICS 2, AO-A3/A4) in daily practice are operated, so in these cases treatment decision is not primarily based on classification. CONCLUSION: AO, TLICS, and new AO spine classifications have acceptable accuracy (kappa > 0.4), but are limited in clinical usefulness since the treatment recommendation is not always implemented in clinical practice. Differences in treatment decision making arise from several causes, such as surgeon and patient preferences and prognostic factors that are not included in classifications yet. The recently validated thoracolumbar AO spine injury score seems promising for use in clinical practice, because of inclusion of patient-specific modifiers. Future research should prove its definite value in treatment decision making. LEVEL OF EVIDENCE: 2. CLINICAL RELEVANCE: Without the appropriate treatment, the impact of traumatic thoracolumbar fractures can be devastating. Therefore it is important to achieve consensus in the treatment of thoracolumbar fractures.

Semi-supervised learning to improve generalizability of risk prediction models.

The utility of a prediction model depends on its generalizability to patients drawn from different but related populations. We explored whether a semi-supervised learning model could improve the generalizability of colorectal cancer (CRC) risk prediction relative to supervised learning methods. Data on 113,141 patients diagnosed with nonmetastatic CRC from 2004 to 2012 were obtained from the Surveillance Epidemiology End Results registry for model development, and data on 1149 patients from the Second Affiliated Hospital, Zhejiang University School of Medicine, who were diagnosed between 2004 and 2011, were collected for generalizability testing. A clinical prediction model for CRC survival risk using a semi-supervised logistic regression method was developed and validated to investigate the model discrimination, calibration, generalizability, interpretability and clinical usefulness. Rigorous model performance comparisons with other supervised learning models were performed. The area under the curve of the logistic membership model revealed a large heterogeneity between the development cohort and validation cohort, which is typical of generalizability studies of prediction models. The discrimination was good for all models. Calibration was poor for supervised learning models, while the semi-supervised logistic regression model exhibited a good calibration on the validation cohort, which indicated good generalizability. Clinical usefulness analysis showed that semi-supervised logistic regression can lead to better clinical outcomes than supervised learning methods. These results increase our current understanding of the generalizability of different models and provide a reference for predictive model development for clinical decision-making.

External Validation and Clinical Usefulness of First Trimester Prediction Models for the Risk of Preeclampsia: A Prospective Cohort Study.

INTRODUCTION: This study assessed the external validity of all published first trimester prediction models for the risk of preeclampsia (PE) based on routinely collected maternal predictors. Moreover, the potential utility of the best-performing models in clinical practice was evaluated. MATERIAL AND METHODS: Ten prediction models were systematically selected from the literature. We performed a multicenter prospective cohort study in the Netherlands between July 1, 2013, and December 31, 2015. Eligible pregnant women completed a web-based questionnaire before 16 weeks' gestation. The outcome PE was established using postpartum questionnaires and medical records. Predictive performance of each model was assessed by means of discrimination (c-statistic) and a calibration plot. Clinical usefulness was evaluated by means of decision curve analysis and by calculating the potential impact at different risk thresholds. RESULTS: The validation cohort contained 2,614 women of whom 76 developed PE (2.9%). Five models showed moderate discriminative performance with c-statistics ranging from 0.73 to 0.77. Adequate calibration was obtained after refitting. The best models were clinically useful over a small range of predicted probabilities. DISCUSSION: Five of the ten included first trimester prediction models for PE showed moderate predictive performance. The best models may provide more benefit compared to risk selection as used in current guidelines.

Forty years of structural brain imaging in mental disorders: is it clinically useful or not?

Structural brain imaging was introduced into routine clinical practice more than 40 years ago with the hope that it would support the diagnosis and treatment of mental disorders. It is now widely used to exclude organic brain disease (eg, brain tumors, cardiovascular, and inflammatory processes) in mental disorders. However, questions have been raised about whether structural brain imaging is still needed today and whether it could also be clinically useful to apply new biostatistical methods, such as machine learning. Therefore, the current paper not only reviews structural findings in Alzheimer disease, depression, bipolar disorder, and schizophrenia but also discusses the role of structural imaging in supporting diagnostic, prognostic, and therapeutic processes in mental disorders. Thus, it attempts to answer the questions whether, after four decades of use, structural brain imaging is clinically useful in mental disorders or whether it will become so in the future.

Las imágenes estructurales del cerebro se introdujeron en la práctica clínica de rutina hace más de 40 años con la esperanza que respaldarían el diagnóstico y el tratamiento de los trastornos mentales. En la actualidad se emplean ampliamente para excluir enfermedades cerebrales orgánicas (como tumores cerebrales, alteraciones cardiovasculares y procesos inflamatorios) en los trastornos mentales. Sin embargo, hoy en día han surgido dudas de si aún se requieren imágenes cerebrales estructurales y también de si podría ser útil clínicamente la aplicación de nuevos métodos bioestadísticos, como el aprendizaje de máquinas. Por lo tanto, en este artículo no solo se revisan los hallazgos estructurales en la Enfermedad de Alzheimer, la depresión, el trastorno bipolar y la esquizofrenia, sino que también se analiza el papel de las imágenes estructurales en el apoyo a los procesos diagnóstico, pronóstico y terapéutico en los trastornos mentales. Por consiguiente, se intenta responder a las preguntas sobre si, después de cuatro décadas de empleo, las imágenes cerebrales estructurales son clínicamente útiles en los trastornos mentales o si lo serán en el futuro.

L'imagerie cérébrale structurelle a été introduite en pratique clinique de routine il y a plus de 40 ans avec l'espoir qu'elle aiderait au diagnostic et au traitement des troubles mentaux. Aujourd'hui elle est largement utilisée pour exclure une maladie cérébrale organique (par exemple, des tumeurs cérébrales, des processus cardiovasculaires et inflammatoires) dans les maladies mentales. Cependant, les questions suivantes se posent: l'imagerie cérébrale structurelle est-elle encore utile aujourd'hui? De nouvelles méthodes biostatistiques comme l'apprentissage automatique ne seraient-elles pas cliniquement utiles? Par conséquent, cet article ne s'attache pas seulement à analyser les résultats structuraux dans la maladie d'Alzheimer, la dépression, les troubles bipolaires et la schizophrénie mais discute aussi du rôle de l'imagerie structurelle dans l'aide au diagnostic, au pronostic et aux processus thérapeutiques dans les troubles mentaux. Il tente donc de répondre aux questions de l'utilité clinique de l'imagerie cérébrale structurelle dans les troubles mentaux après 40 ans d'utilisation et de son devenir.

Designing medical technology for resilience: integrating health economics and human factors approaches.

INTRODUCTION: The slow adoption of innovation into healthcare calls into question the manner of evidence generation for medical technology. This paper identifies potential reasons for this including a lack of attention to human factors, poor evaluation of economic benefits, lack of understanding of the existing healthcare system and a failure to recognise the need to generate resilient products. Areas covered: Recognising a cross-disciplinary need to enhance evidence generation early in a technology's life cycle, the present paper proposes a new approach that integrates human factors and health economic evaluation as part of a wider systems approach to the design of technology. This approach (Human and Economic Resilience Design for Medical Technology or HERD MedTech) supports early stages of product development and is based on the recent experiences of the National Institute for Health Research London Diagnostic Evidence Co-operative in the UK. Expert commentary: HERD MedTech i) proposes a shift from design for usability to design for resilience, ii) aspires to reduce the need for service adaptation to technological constraints iii) ensures value of innovation at the time of product development, and iv) aims to stimulate discussion around the integration of pre- and post-market methods of assessment of medical technology.