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OBJECTIVES: In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. STUDY DESIGN AND SETTING: We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. RESULTS: We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. CONCLUSION: The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence. ispartof: JOURNAL OF CLINICAL EPIDEMIOLOGY vol:157 pages:120-133 ispartof: location:United States status: published

Aims Multinomial logistic regression models allow one to predict the risk of a categorical outcome with > 2 categories. When developing such a model, researchers should ensure the number of participants ([Formula: see text]) is appropriate relative to the number of events ([Formula: see text]) and the number of predictor parameters ([Formula: see text]) for each category k. We propose three criteria to determine the minimum n required in light of existing criteria developed for binary outcomes. Proposed criteria The first criterion aims to minimise the model overfitting. The second aims to minimise the difference between the observed and adjusted [Formula: see text] Nagelkerke. The third criterion aims to ensure the overall risk is estimated precisely. For criterion (i), we show the sample size must be based on the anticipated Cox-snell [Formula: see text] of distinct ‘one-to-one’ logistic regression models corresponding to the sub-models of the multinomial logistic regression, rather than on the overall Cox-snell [Formula: see text] of the multinomial logistic regression. Evaluation of criteria We tested the performance of the proposed criteria (i) through a simulation study and found that it resulted in the desired level of overfitting. Criterion (ii) and (iii) were natural extensions from previously proposed criteria for binary outcomes and did not require evaluation through simulation. Summary We illustrated how to implement the sample size criteria through a worked example considering the development of a multinomial risk prediction model for tumour type when presented with an ovarian mass. Code is provided for the simulation and worked example. We will embed our proposed criteria within the pmsampsize R library and Stata modules.

Kaplan-Meier curves stratified by endoxifen levels using a quartile split.

Purpose: To evaluate the utility of preoperative diagnostic models for ovarian cancer based on ultrasound and/or biomarkers for referring patients to specialized oncology care. The investigated models were RMI, ROMA, and 3 models from the International Ovarian Tumor Analysis (IOTA) group [LR2, ADNEX, and the Simple Rules risk score (SRRisk)].Experimental Design: A secondary analysis of prospectively collected data from 2 cross-sectional cohort studies was performed to externally validate diagnostic models. A total of 2,763 patients (2,403 in dataset 1 and 360 in dataset 2) from 18 centers (11 oncology centers and 7 nononcology hospitals) in 6 countries participated. Excised tissue was histologically classified as benign or malignant. The clinical utility of the preoperative diagnostic models was assessed with net benefit (NB) at a range of risk thresholds (5%–50% risk of malignancy) to refer patients to specialized oncology care. We visualized results with decision curves and generated bootstrap confidence intervals.Results: The prevalence of malignancy was 41% in dataset 1 and 40% in dataset 2. For thresholds up to 10% to 15%, RMI and ROMA had a lower NB than referring all patients. SRRisks and ADNEX demonstrated the highest NB. At a threshold of 20%, the NBs of ADNEX, SRrisks, and RMI were 0.348, 0.350, and 0.270, respectively. Results by menopausal status and type of center (oncology vs. nononcology) were similar.Conclusions: All tested IOTA methods, especially ADNEX and SRRisks, are clinically more useful than RMI and ROMA to select patients with adnexal masses for specialized oncology care. Clin Cancer Res; 23(17); 5082–90. ©2017 AACR.

Supplementary Table 1. Validated prediction models; Supplementary Table 2. Data contributed to dataset 1 per center; Supplementary Table 3. Net Benefit of the LR2 model by IOTA (LR2), the Risk of Malignancy Index (RMI at cut-off 200), the IOTA Simple Rules Risk scoring system (SRRisks), and the IOTA ADNEX model in all patients; Supplementary Table 4. Difference between the Net Benefit (NB) of a model and that of the best default strategy; Supplementary Table 5. Difference between the Net Benefit of a model and that of the best default strategy; Supplementary Table 6. Difference between the Net Benefit of each model and the Net Benefit of the best model (NB best model - NB model) in premenopausal patients; Supplementary Table 7. Net Benefit of the International Ovarian Tumor Analysis (IOTA) logistic regression model 2 (LR2), the Risk of Malignancy Index (RMI, cut-off 200), and the Risk of Ovarian Malignancy Algorithm (ROMA) (n=360, 40% malignant tumors); Supplementary Table 8. Difference between the Net Benefit (NB) of a model and that of the best default strategy (NB model - NB best default) and between the NB of the best model and that of each of the other models; Supplementary Figure 1. Decision curves representing the Net Benefit of the Risk of Malignancy Index (RMI) with cut-offs 25, 100, 200, 250 and 450, the International Ovarian Tumor Analysis (IOTA) logistic regression model 2 (LR2), and the IOTA ADNEX model (ADNEX) and the ADNEX model without CA125 measurements.

Purpose: To externally validate and compare the performance of previously published diagnostic models developed to predict malignancy in adnexal masses.Experimental Design: We externally validated the diagnostic performance of 11 models developed by the International Ovarian Tumor Analysis (IOTA) group and 12 other (non-IOTA) models on 997 prospectively collected patients. The non-IOTA models included the original risk of malignancy index (RMI), three modified versions of the RMI, six logistic regression models, and two artificial neural networks. The ability of the models to discriminate between benign and malignant adnexal masses was expressed as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and likelihood ratios (LR+, LR−).Results: Seven hundred and forty-two (74%) benign and 255 (26%) malignant masses were included. The IOTA models did better than the non-IOTA models (AUCs between 0.941 and 0.956 vs. 0.839 and 0.928). The difference in AUC between the best IOTA and the best non-IOTA model was 0.028 [95% confidence interval (CI), 0.011–0.044]. The AUC of the RMI was 0.911 (difference with the best IOTA model, 0.044; 95% CI, 0.024–0.064). The superior performance of the IOTA models was most pronounced in premenopausal patients but was also observed in postmenopausal patients. IOTA models were better able to detect stage I ovarian cancer.Conclusion: External validation shows that the IOTA models outperform other models, including the current reference test RMI, for discriminating between benign and malignant adnexal masses. Clin Cancer Res; 18(3); 815–25. ©2011 AACR.

List of participating centres and number of patients included.

Background: The CA-125 tumor marker has limitations when used to distinguish between benign and malignant ovarian masses. We therefore establish likelihood curves of six subgroups of ovarian pathology based on CA-125 and menopausal status.Methods: This cross-sectional study conducted by the International Ovarian Tumor Analysis group involved 3,511 patients presenting with a persistent adnexal mass that underwent surgical intervention. CA-125 distributions for six tumor subgroups (endometriomas and abscesses, other benign tumors, borderline tumors, stage I invasive cancers, stage II–IV invasive cancers, and metastatic tumors) were estimated using kernel density estimation with stratification for menopausal status. Likelihood curves for the tumor subgroups were derived from the distributions.Results: Endometriomas and abscesses were the only benign pathologies with median CA-125 levels above 20 U/mL (43 and 45, respectively). Borderline and invasive stage I tumors had relatively low median CA-125 levels (29 and 81 U/mL, respectively). The CA-125 distributions of stage II–IV invasive cancers and benign tumors other than endometriomas or abscesses were well separated; the distributions of the other subgroups overlapped substantially. This held for premenopausal and postmenopausal patients. Likelihood curves and reference tables comprehensibly show how subgroup likelihoods change with CA-125 and menopausal status.Conclusions and Impact: Our results confirm the limited clinical value of CA-125 for preoperative discrimination between benign and malignant ovarian pathology. We have shown that CA-125 may be used in a different way. By using likelihood reference tables, we believe clinicians will be better able to interpret preoperative serum CA-125 results in patients with adnexal masses. Cancer Epidemiol Biomarkers Prev; 20(11); 2420–8. ©2011 AACR.

Supplementary Tables 1-5, Figure 1 from A Novel Approach to Predict the Likelihood of Specific Ovarian Tumor Pathology Based on Serum CA-125: A Multicenter Observational Study

Objectives: The aim of this study was to develop a model that can discriminate between different etiologies of abnormal uterine bleeding. Design: The International Endometrial Tumor Analysis 1 study is a multicenter observational diagnostic study in 18 bleeding clinics in 9 countries. Consecutive women with abnormal vaginal bleeding presenting for ultrasound examination (n = 2,417) were recruited. The histology was obtained from endometrial sampling, D&C, hysteroscopic resection, hysterectomy, or ultrasound follow-up for >1 year. Methods: A model was developed using multinomial regression based on age, body mass index, and ultrasound predictors to distinguish between: (1) endometrial atrophy, (2) endometrial polyp or intracavitary myoma, (3) endometrial malignancy or atypical hyperplasia, (4) proliferative/secretory changes, endometritis, or hyperplasia without atypia and validated using leave-center-out cross-validation and bootstrapping. The main outcomes are the model’s ability to discriminate between the four outcomes and the calibration of risk estimates. Results: The median age in 2,417 women was 50 (interquartile range 43–57). 414 (17%) women had endometrial atrophy; 996 (41%) had a polyp or myoma; 155 (6%) had an endometrial malignancy or atypical hyperplasia; and 852 (35%) had proliferative/secretory changes, endometritis, or hyperplasia without atypia. The model distinguished well between malignant and benign histology (c-statistic 0.88 95% CI: 0.85–0.91) and between all benign histologies. The probabilities for each of the four outcomes were over- or underestimated depending on the centers. Limitations: Not all patients had a diagnosis based on histology. The model over- or underestimated the risk for certain outcomes in some centers, indicating local recalibration is advisable. Conclusions: The proposed model reliably distinguishes between four histological outcomes. This is the first model to discriminate between several outcomes and is the only model applicable when menopausal status is uncertain. The model could be useful for patient management and counseling, and aid in the interpretation of ultrasound findings. Future research is needed to externally validate and locally recalibrate the model.

The increasing availability of large combined datasets (or big data), such as those from electronic health records and from individual participant data meta-analyses, provides new opportunities and challenges for researchers developing and validating (including updating) prediction models. These datasets typically include individuals from multiple clusters (such as multiple centres, geographical locations, or different studies). Accounting for clustering is important to avoid misleading conclusions and enables researchers to explore heterogeneity in prediction model performance across multiple centres, regions, or countries, to better tailor or match them to these different clusters, and thus to develop prediction models that are more generalisable. However, this requires prediction model researchers to adopt more specific design, analysis, and reporting methods than standard prediction model studies that do not have any inherent substantial clustering. Therefore, prediction model studies based on clustered data need to be reported differently so that readers can appraise the study methods and findings, further increasing the use and implementation of such prediction models developed or validated from clustered datasets. ispartof: BMJ-BRITISH MEDICAL JOURNAL vol:380 ispartof: location:England status: published

The TRIPOD-Cluster (transparent reporting of multivariable prediction models developed or validated using clustered data) statement comprises a 19 item checklist, which aims to improve the reporting of studies developing or validating a prediction model in clustered data, such as individual participant data meta-analyses (clustering by study) and electronic health records (clustering by practice or hospital). This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD-Cluster statement is explained in detail and accompanied by published examples of good reporting. The document also serves as a reference of factors to consider when designing, conducting, and analysing prediction model development or validation studies in clustered data. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, authors are recommended to include a completed checklist in their submission. ispartof: BMJ-BRITISH MEDICAL JOURNAL vol:380 ispartof: location:England status: published

Objectives: To systematically review the risk of bias and applicability of published prediction models for risk of central line-associated bloodstream infection (CLA-BSI) in hospitalized patients. Introduction: CLA-BSIs are the most common source of hospital-acquired infections (HAIs), and are always associated with higher morbidity, longer length of stay and increased financial burdens. As a priority target for prevention, tools that were developed to predict the risk of CLA-BSI for individuals may help improve the infection control in hospitals. In this systematic review, we evaluated the current risk prediction models for CLA-BSI and discussed the practical problems for implementing the models. Inclusion criteria: All inpatients (no age limit) with at least a central line in place during their hospitalization anytime. Methods: Four key databases including PubMed (MEDLINE), Embase (Embase.com), Web of Science Core Collection (SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC) and Scopus were used to conduct the search. The search was conducted on the 10th of June 2022. We included studies that describe the development or validation of a prediction model for CLABSI and have at least two predictor variables to build multivariable predictive models. Articles that do not report original research (i.e. reviews), or that are not full papers (i.e. letters, notes, and conference abstracts), or qualitative studies were excluded. References were imported and deduplicated using EndNote (Clarivate Analytics) and Rayyan (Qatar Computing Research Institute). Titles and abstracts were initially screened for exclusion by at least two authors. As interreviewer agreement was considered reliable, the remaining title-abstract screening was done by one author and irrelevant articles were excluded. Then, full text of the potentially relevant articles were screened independently by two authors. Discrepancies were resolved through discussion with a third author. We also used forward and backward snowballing from the final articles that will be included within this review.

Objectives: To systematically review the risk of bias and applicability of published prediction models for risk of central line-associated bloodstream infection (CLA-BSI) in hospitalized patients. Introduction: CLA-BSIs are the most common source of hospital-acquired infections (HAIs), and are always associated with higher morbidity, longer length of stay and increased financial burdens. As a priority target for prevention, tools that were developed to predict the risk of CLA-BSI for individuals may help improve the infection control in hospitals. In this systematic review, we evaluated the current risk prediction models for CLA-BSI and discussed the practical problems for implementing the models. Inclusion criteria: All inpatients (no age limit) with at least a central line in place during their hospitalization anytime. Methods: Four key databases including PubMed (MEDLINE), Embase (Embase.com), Web of Science Core Collection (SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC) and Scopus were used to conduct the search. The search was conducted on the 10th of June 2022. We included studies that describe the development or validation of a prediction model for CLABSI and have at least two predictor variables to build multivariable predictive models. Articles that do not report original research (i.e. reviews), or that are not full papers (i.e. letters, notes, and conference abstracts), or qualitative studies were excluded. References were imported and deduplicated using EndNote (Clarivate Analytics) and Rayyan (Qatar Computing Research Institute). Titles and abstracts were initially screened for exclusion by at least two authors. As interreviewer agreement was considered reliable, the remaining title-abstract screening was done by one author and irrelevant articles were excluded. Then, full text of the potentially relevant articles were screened independently by two authors. Discrepancies were resolved through discussion with a third author. We also used forward and backward snowballing from the final articles that will be included within this review.

Objectives: To systematically review the risk of bias and applicability of published prediction models for risk of central line-associated bloodstream infection (CLA-BSI) in hospitalized patients. Introduction: CLA-BSIs are the most common source of hospital-acquired infections (HAIs), and are always associated with higher morbidity, longer length of stay and increased financial burdens. As a priority target for prevention, tools that were developed to predict the risk of CLA-BSI for individuals may help improve the infection control in hospitals. In this systematic review, we evaluated the current risk prediction models for CLA-BSI and discussed the practical problems for implementing the models. Inclusion criteria: All inpatients (no age limit) with at least a central line in place during their hospitalization anytime. Methods: Four key databases including PubMed (MEDLINE), Embase (Embase.com), Web of Science Core Collection (SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC) and Scopus were used to conduct the search. The search was conducted on the 10th of June 2022. We included studies that describe the development or validation of a prediction model for CLABSI and have at least two predictor variables to build multivariable predictive models. Articles that do not report original research (i.e. reviews), or that are not full papers (i.e. letters, notes, and conference abstracts), or qualitative studies were excluded. References were imported and deduplicated using EndNote (Clarivate Analytics) and Rayyan (Qatar Computing Research Institute). Titles and abstracts were initially screened for exclusion by at least two authors. As interreviewer agreement was considered reliable, the remaining title-abstract screening was done by one author and irrelevant articles were excluded. Then, full text of the potentially relevant articles were screened independently by two authors. Discrepancies were resolved through discussion with a third author. We also used forward and backward snowballing from the final articles that will be included within this review.

Objectives: To systematically review the risk of bias and applicability of published prediction models for risk of central line-associated bloodstream infection (CLA-BSI) in hospitalized patients. Introduction: CLA-BSIs are the most common source of hospital-acquired infections (HAIs), and are always associated with higher morbidity, longer length of stay and increased financial burdens. As a priority target for prevention, tools that were developed to predict the risk of CLA-BSI for individuals may help improve the infection control in hospitals. In this systematic review, we evaluated the current risk prediction models for CLA-BSI and discussed the practical problems for implementing the models. Inclusion criteria: All inpatients (no age limit) with at least a central line in place during their hospitalization anytime. Methods: Four key databases including PubMed (MEDLINE), Embase (Embase.com), Web of Science Core Collection (SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC) and Scopus were used to conduct the search. The search was conducted on the 10th of June 2022. We included studies that describe the development or validation of a prediction model for CLABSI and have at least two predictor variables to build multivariable predictive models. Articles that do not report original research (i.e. reviews), or that are not full papers (i.e. letters, notes, and conference abstracts), or qualitative studies were excluded. References were imported and deduplicated using EndNote (Clarivate Analytics) and Rayyan (Qatar Computing Research Institute). Titles and abstracts were initially screened for exclusion by at least two authors. As interreviewer agreement was considered reliable, the remaining title-abstract screening was done by one author and irrelevant articles were excluded. Then, full text of the potentially relevant articles were screened independently by two authors. Discrepancies were resolved through discussion with a third author. We also used forward and backward snowballing from the final articles that will be included within this review.

ImportanceCorrect diagnosis of ovarian cancer results in better prognosis. Adnexal lesions can be stratified into the Ovarian-Adnexal Reporting and Data System (O-RADS) risk of malignancy categories with either the O-RADS lexicon, proposed by the American College of Radiology, or the International Ovarian Tumor Analysis (IOTA) 2-step strategy.ObjectiveTo investigate the diagnostic performance of the O-RADS lexicon and the IOTA 2-step strategy.Design, Setting, and ParticipantsRetrospective external diagnostic validation study based on interim data of IOTA5, a prospective international multicenter cohort study, in 36 oncology referral centers or other types of centers. A total of 8519 consecutive adult patients presenting with an adnexal mass between January 1, 2012, and March 1, 2015, and treated either with surgery or conservatively were included in this diagnostic study. Twenty-five patients were excluded for withdrawal of consent, 2777 were excluded from 19 centers that did not meet predefined data quality criteria, and 812 were excluded because they were already in follow-up at recruitment. The analysis included 4905 patients with a newly detected adnexal mass in 17 centers that met predefined data quality criteria. Data were analyzed from January 31 to March 1, 2022.ExposuresStratification into O-RADS categories (malignancy risk Main Outcomes and MeasuresObserved prevalence of malignancy in each O-RADS risk category, as well as sensitivity and specificity. The reference standard was the status of the tumor at inclusion, determined by histology or clinical and ultrasonographic follow-up for 1 year. Multiple imputation was used for uncertain outcomes owing to inconclusive follow-up information.ResultsMedian age of the 4905 patients was 48 years (IQR, 36-62 years). Data on race and ethnicity were not collected. A total of 3441 tumors (70%) were benign, 978 (20%) were malignant, and 486 (10%) had uncertain classification. Using the O-RADS lexicon resulted in 1.1% (24 of 2196) observed prevalence of malignancy in O-RADS 2, 4% (34 of 857) in O-RADS 3, 27% (246 of 904) in O-RADS 4, and 78% (732 of 939) in O-RADS 5; the corresponding results for the IOTA 2-step strategy were 0.9% (18 of 1984), 4% (58 of 1304), 30% (206 of 690), and 82% (756 of 927). At the 10% risk threshold (O-RADS 4-5), the O-RADS lexicon had 92% sensitivity (95% CI, 87%-96%) and 80% specificity (95% CI, 74%-85%), and the IOTA 2-step strategy had 91% sensitivity (95% CI, 84%-95%) and 85% specificity (95% CI, 80%-88%).Conclusions and RelevanceThe findings of this external diagnostic validation study suggest that both the O-RADS lexicon and the IOTA 2-step strategy can be used to stratify patients into risk groups. However, the observed malignancy rate in O-RADS 2 was not clearly below 1%.

ispartof: LANCET DIGITAL HEALTH vol:4 issue:12 pages:E853-E855 ispartof: location:England status: published

OBJECTIVE: To assess improvement in the completeness of reporting COVID-19 prediction models after the peer review process. STUDY DESIGN AND SETTING: Studies included in a living systematic review of COVID-19 prediction models, with both pre-print and peer-reviewed published versions available, were assessed. The primary outcome was the change in percentage adherence to the TRIPOD reporting guidelines between pre-print and published manuscripts. RESULTS: 19 studies were identified including seven (37%) model development studies, two external validations of existing models (11%), and 10 (53%) papers reporting on both development and external validation of the same model. Median percentage adherence amongst pre-print versions was 33% (min-max: 10 to 68%). The percentage adherence of TRIPOD components increased from pre-print to publication in 11/19 studies (58%), with adherence unchanged in the remaining eight studies. The median change in adherence was just 3 percentage points (pp, min-max: 0-14pp) across all studies. No association was observed between the change in percentage adherence and pre-print score, journal impact factor, or time between journal submission and acceptance. CONCLUSIONS: Pre-print reporting quality of COVID-19 prediction modelling studies is poor and did not improve much after peer review, suggesting peer review had a trivial effect on the completeness of reporting during the pandemic.

ispartof: LANCET DIGITAL HEALTH vol:4 issue:9 pages:E630-E630 ispartof: location:England status: published

Medical multi-category diagnostic problems may involve discrete biomarkers. Many traditional accuracy measures are based on the assumption that all biomarkers follow continuous distributions and co...

Observational studies have shown that fetoscopic endoluminal tracheal occlusion (FETO) has been associated with increased survival among infants with severe pulmonary hypoplasia due to isolated congenital diaphragmatic hernia on the left side, but data from randomized trials are lacking.In this open-label trial conducted at centers with experience in FETO and other types of prenatal surgery, we randomly assigned, in a 1:1 ratio, women carrying singleton fetuses with severe isolated congenital diaphragmatic hernia on the left side to FETO at 27 to 29 weeks of gestation or expectant care. Both treatments were followed by standardized postnatal care. The primary outcome was infant survival to discharge from the neonatal intensive care unit. We used a group-sequential design with five prespecified interim analyses for superiority, with a maximum sample size of 116 women.The trial was stopped early for efficacy after the third interim analysis. In an intention-to-treat analysis that included 80 women, 40% of infants (16 of 40) in the FETO group survived to discharge, as compared with 15% (6 of 40) in the expectant care group (relative risk, 2.67; 95% confidence interval [CI], 1.22 to 6.11; two-sided P = 0.009). Survival to 6 months of age was identical to the survival to discharge (relative risk, 2.67; 95% CI, 1.22 to 6.11). The incidence of preterm, prelabor rupture of membranes was higher among women in the FETO group than among those in the expectant care group (47% vs. 11%; relative risk, 4.51; 95% CI, 1.83 to 11.9), as was the incidence of preterm birth (75% vs. 29%; relative risk, 2.59; 95% CI, 1.59 to 4.52). One neonatal death occurred after emergency delivery for placental laceration from fetoscopic balloon removal, and one neonatal death occurred because of failed balloon removal. In an analysis that included 11 additional participants with data that were available after the trial was stopped, survival to discharge was 36% among infants in the FETO group and 14% among those in the expectant care group (relative risk, 2.65; 95% CI, 1.21 to 6.09).In fetuses with isolated severe congenital diaphragmatic hernia on the left side, FETO performed at 27 to 29 weeks of gestation resulted in a significant benefit over expectant care with respect to survival to discharge, and this benefit was sustained to 6 months of age. FETO increased the risks of preterm, prelabor rupture of membranes and preterm birth. (Funded by the European Commission and others; TOTAL ClinicalTrials.gov number, NCT01240057.).

The medical field has seen a rapid increase in the development of artificial intelligence (AI)-based prediction models. With the introduction of such AI-based prediction model tools and software in cardiovascular patient care, the cardiovascular researcher and healthcare professional are challenged to understand the opportunities as well as the limitations of the AI-based predictions. In this article, we present 12 critical questions for cardiovascular health professionals to ask when confronted with an AI-based prediction model. We aim to support medical professionals to distinguish the AI-based prediction models that can add value to patient care from the AI that does not. ispartof: EUROPEAN HEART JOURNAL vol:43 issue:31 pages:2921-2930 ispartof: location:England status: published

In 2019 we published a pair of articles in Statistics in Medicine that describe how to calculate the minimum sample size for developing a multivariable prediction model with a continuous outcome, or with a binary or time-to-event outcome. As for any sample size calculation, the approach requires the user to specify anticipated values for key parameters. In particular, for a prediction model with a binary outcome, the outcome proportion and a conservative estimate for the overall fit of the developed model as measured by the Cox-Snell R2 (proportion of variance explained) must be specified. This proposal raises the question of how to identify a plausible value for R2 in advance of model development. Our articles suggest researchers should identify R2 from closely related models already published in their field. In this letter, we present details on how to derive R2 using the reported C statistic (AUROC) for such existing prediction models with a binary outcome. The C statistic is commonly reported, and so our approach allows researchers to obtain R2 for subsequent sample size calculations for new models. Stata and R code is provided, and a small simulation study.