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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.

The generalizability of predictive algorithms is of key relevance to application in clinical practice. We provide an overview of three types of generalizability, based on existing literature: temporal, geographical, and domain generalizability. These generalizability types are linked to their associated goals, methodology, and stakeholders. ispartof: NPJ DIGITAL MEDICINE vol:6 issue:1 ispartof: location:England status: published

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.

Results of ordinal regression models of the questionnaire items on endoxifen level after multiple imputation.

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.

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.

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.

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.

Bland-Altman plot of endoxifen (in µg/l) at 3 months and at 6 months.

Flowchart of patients who were included/excluded from the study.

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

Quality of Life Questionnaire

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.

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.).

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.

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.

Background: Two randomized controlled trials compared the neonatal and infant outcomes after fetoscopic endoluminal tracheal occlusion with expectant prenatal management in fetuses with severe and moderate isolated congenital diaphragmatic hernia, respectively. Fetoscopic endoluminal tracheal occlusion was carried out at 27 +0 to 29 +6 weeks’ gestation (referred to as “early”) for severe and at 30 +0 to 31 +6 weeks (“late”) for moderate hypoplasia. The reported absolute increase in the survival to discharge was 13% (95% confidence interval, −1 to 28; P=.059) and 25% (95% confidence interval, 6–46; P=.0091) for moderate and severe hypoplasia. Objective: Data from the 2 trials were pooled to study the heterogeneity of the treatment effect by observed over expected lung-to-head ratio and explore the effect of gestational age at balloon insertion. Study Design: Individual participant data from the 2 trials were reanalyzed. Women were assessed between 2008 and 2020 at 14 experienced fetoscopic endoluminal tracheal occlusion centers and were randomized in a 1:1 ratio to either expectant management or fetoscopic endoluminal tracheal occlusion. All received standardized postnatal management. The combined data involved 287 patients (196 with moderate hypoplasia and 91 with severe hypoplasia). The primary endpoint was survival to discharge from the neonatal intensive care unit. The secondary endpoints were survival to 6 months of age, survival to 6 months without oxygen supplementation, and gestational age at live birth. Penalized regression was used with the following covariates: intervention (fetoscopic endoluminal tracheal occlusion vs expectant), early balloon insertion (yes vs no), observed over expected lung-to-head ratio, liver herniation (yes vs no), and trial (severe vs moderate). The interaction between intervention and the observed over expected lung-to-head ratio was evaluated to study treatment effect heterogeneity. Results: For survival to discharge, the adjusted odds ratio of fetoscopic endoluminal tracheal occlusion was 1.78 (95% confidence interval, 1.05–3.01; P=.031). The additional effect of early balloon insertion was highly uncertain (adjusted odds ratio, 1.53; 95% confidence interval, 0.60–3.91; P=.370). When combining these 2 effects, the adjusted odds ratio of fetoscopic endoluminal tracheal occlusion with early balloon insertion was 2.73 (95% confidence interval, 1.15–6.49). The results for survival to 6 months and survival to 6 months without oxygen dependence were comparable. The gestational age at delivery was on average 1.7 weeks earlier (95% confidence interval, 1.1–2.3) following fetoscopic endoluminal tracheal occlusion with late insertion and 3.2 weeks earlier (95% confidence interval, 2.3–4.1) following fetoscopic endoluminal tracheal occlusion with early insertion compared with expectant management. There was no evidence that the effect of fetoscopic endoluminal tracheal occlusion depended on the observed over expected lung-to-head ratio for any of the endpoints. Conclusion: This analysis suggests that fetoscopic endoluminal tracheal occlusion increases survival for both moderate and severe lung hypoplasia. The difference between the results for the Tracheal Occlusion To Accelerate Lung growth trials, when considered apart, may be because of the difference in the time point of balloon insertion. However, the effect of the time point of balloon insertion could not be robustly assessed because of a small sample size and the confounding effect of disease severity. Fetoscopic endoluminal tracheal occlusion with early balloon insertion in particular strongly increases the risk for preterm delivery.

OBJECTIVE: To investigate the association between personal history, anthropometric features and lifestyle characteristics and endometrial malignancy in women with abnormal vaginal bleeding. METHODS: Prospective observational cohort assessed by descriptive and multivariable logistic regression analyses. Three features-age, body mass index (BMI; calculated as weight in kilograms divided by the square of height in meters), and nulliparity-were defined a priori for baseline risk assessment of endometrial malignancy. The following variables were tested for added value: intrauterine contraceptive device, bleeding pattern, age at menopause, coexisting diabetes/hypertension, physical exercise, fat distribution, bra size, waist circumference, smoking/drinking habits, family history, use of hormonal/anticoagulant therapy, and sonographic endometrial thickness. We calculated adjusted odds ratio, optimism-corrected area under the receiver operating characteristic curve (AUC), R2 , and Akaike's information criterion. RESULTS: Of 2417 women, 155 (6%) had endometrial malignancy or endometrial intraepithelial neoplasia. In women with endometrial cancer median age was 67 years (interquartile range [IQR] 56-75 years), median parity was 2 (IQR 0-10), and median BMI was 28 (IQR 25-32). Age, BMI, and parity produced an AUC of 0.82. Other variables marginally affected the AUC, adding endometrial thickness substantially increased the AUC in postmenopausal women. CONCLUSION: Age, parity, and BMI help in the assessment of endometrial cancer risk in women with abnormal uterine bleeding. Other patient information adds little, whereas sonographic endometrial thickness substantially improves assessment. ispartof: INTERNATIONAL JOURNAL OF GYNECOLOGY & OBSTETRICS vol:159 issue:1 pages:103-110 ispartof: location:United States status: published

Background Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. Methods We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. Results Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. Conclusions The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

Background Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain. Methods We conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published between 01/01/2019 and 05/09/2019. The primary outcome was risk of bias, judged using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We described risk of bias overall and for each domain, by development and validation analyses separately. Results We included 62 publications (48 development-only; 14 development with validation). 152 models were developed across all publications and 37 models were validated. 84% (95% CI: 77 to 89) of developed models and 51% (95% CI: 35 to 67) of validated models were at overall high risk of bias. Bias introduced in the analysis was the largest contributor to the overall risk of bias judgement for model development and validation. 123 (81%, 95% CI: 73.8 to 86.4) developed models and 19 (51%, 95% CI: 35.1 to 67.3) validated models were at high risk of bias due to their analysis, mostly due to shortcomings in the analysis including insufficient sample size and split-sample internal validation. Conclusions The quality of machine learning based prognostic models in the oncology domain is poor and most models have a high risk of bias, contraindicating their use in clinical practice. Adherence to better standards is urgently needed, with a focus on sample size estimation and analysis methods, to improve the quality of these models.

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 R

ObjectiveTo review and critically appraise published and preprint reports of models that aim to predict either (i) presence of existing COVID-19 infection, (ii) future complications in individuals already diagnosed with COVID-19, or (iii) models to identify individuals at high risk for COVID-19 in the general population.DesignRapid systematic review and critical appraisal of prediction models for diagnosis or prognosis of COVID-19 infection.Data sourcesPubMed, EMBASE via Ovid, Arxiv, medRxiv and bioRxiv until 24thMarch 2020.Study selectionStudies that developed or validated a multivariable COVID-19 related prediction model. Two authors independently screened titles, abstracts and full text.Data extractionData from included studies were extracted independently by at least two authors based on the CHARMS checklist, and risk of bias was assessed using PROBAST. Data were extracted on various domains including the participants, predictors, outcomes, data analysis, and prediction model performance.Results2696 titles were screened. Of these, 27 studies describing 31 prediction models were included for data extraction and critical appraisal. We identified three models to predict hospital admission from pneumonia and other events (as a proxy for covid-19 pneumonia) in the general population; 18 diagnostic models to detect COVID-19 infection in symptomatic individuals (13 of which were machine learning utilising computed tomography (CT) results); and ten prognostic models for predicting mortality risk, progression to a severe state, or length of hospital stay. Only one of these studies used data on COVID-19 cases outside of China. Most reported predictors of presence of COVID-19 in suspected patients included age, body temperature, and signs and symptoms. Most reported predictors of severe prognosis in infected patients included age, sex, features derived from CT, C-reactive protein, lactic dehydrogenase, and lymphocyte count.Estimated C-index estimates for the prediction models ranged from 0.73 to 0.81 in those for the general population (reported for all 3 general population models), from 0.81 to > 0.99 in those for diagnosis (reported for 13 of the 18 diagnostic models), and from 0.85 to 0.98 in those for prognosis (reported for 6 of the 10 prognostic models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and poor statistical analysis, including high risk of model overfitting. Reporting quality varied substantially between studies. A description of the study population and intended use of the models was absent in almost all reports, and calibration of predictions was rarely assessed.ConclusionCOVID-19 related prediction models are quickly entering the academic literature, to support medical decision making at a time where this is urgently needed. Our review indicates proposed models are poorly reported and at high risk of bias. Thus, their reported performance is likely optimistic and using them to support medical decision making is not advised. We call for immediate sharing of the individual participant data from COVID-19 studies to support collaborative efforts in building more rigorously developed prediction models and validating (evaluating) existing models. The aforementioned predictors identified in multiple included studies could be considered as candidate predictors for new models. We also stress the need to follow methodological guidance when developing and validating prediction models, as unreliable predictions may cause more harm than benefit when used to guide clinical decisions. Finally, studies should adhere to the TRIPOD statement to facilitate validating, appraising, advocating and clinically using the reported models.Systematic review registration protocolosf.io/ehc47/,registration: osf.io/wy245Summary boxesWhat is already known on this topic-The sharp recent increase in COVID-19 infections has put a strain on healthcare systems worldwide, necessitating efficient early detection, diagnosis of patients suspected of the infection and prognostication of COVID-19 confirmed cases.-Viral nucleic acid testing and chest CT are standard methods for diagnosing COVID-19, but are time-consuming.-Earlier reports suggest that the elderly, patients with comorbidity (COPD, cardiovascular disease, hypertension), and patients presenting with dyspnoea are vulnerable to more severe morbidity and mortality after COVID-19 infection.What this study adds-We identified three models to predict hospital admission from pneumonia and other events (as a proxy for COVID-19 pneumonia) in the general population.-We identified 18 diagnostic models for COVID-19 detection in symptomatic patients.-13 of these were machine learning models based on CT images.-We identified ten prognostic models for COVID-19 infected patients, of which six aimed to predict mortality risk in confirmed or suspected COVID-19 patients, two aimed to predict progression to a severe or critical state, and two aimed to predict a hospital stay of more than 10 days from admission.-Included studies were poorly reported compromising their subsequent appraisal, and recommendation for use in daily practice. All studies were appraised at high risk of bias, raising concern that the models may be flawed and perform poorly when applied in practice, such that their predictions may be unreliable.

ObjectivesTo investigate prognostic factors for anxiety, depression and post-traumatic stress (PTS) symptoms 1 month after early pregnancy loss (EPL).DesignA prospective cohort study. Consecutive women were recruited, and demographic and clinical data were collected. Surveys containing the Hospital Anxiety and Depression Scale (HADS) and Post-traumatic Stress Diagnostic Scale (PDS) were emailed 1 month after a loss. Univariable logistic regression was performed to link factors with caseness of anxiety, depression or PTS according to screening measures.SettingEarly pregnancy units of three central London hospitals.Participants737/1116 eligible women with an EPL were recruited. 492 responded to HADS and 487 to PDS.Primary and secondary outcome measuresPrimary outcome is the area under the curve (AUC) to predict any psychological morbidity (defined as moderate/severe anxiety or depression, or meeting screening criteria for PTS) for each variable. Further outcomes are explained variation (R2) and p value for any morbidity, and AUC, explained variation, and p value for each morbidity separately.ResultsWomen who had a past diagnosis of a psychiatric condition were more likely to meet criteria for anxiety, depression or PTS (75% for current diagnosis vs 55% for past vs 30% for no diagnosis; AUC 0.61; R28.4%; p24.3%; p2≤0.9%).ConclusionsWomen with a history of mental health problems, or those with previous losses, may be at higher risk of psychological illness 1 month after pregnancy loss. However, prognostic ability was poor overall. All women should be considered at risk.

CONTEXT: Urologists regularly develop clinical risk prediction models to support clinical decisions. In contrast to traditional performance measures, decision curve analysis (DCA) can assess the utility of models for decision making. DCA plots net benefit (NB) at a range of clinically reasonable risk thresholds. OBJECTIVE: To provide recommendations on interpreting and reporting DCA when evaluating prediction models. EVIDENCE ACQUISITION: We informally reviewed the urological literature to determine investigators' understanding of DCA. To illustrate, we use data from 3616 patients to develop risk models for high-grade prostate cancer (n=313, 9%) to decide who should undergo a biopsy. The baseline model includes prostate-specific antigen and digital rectal examination; the extended model adds two predictors based on transrectal ultrasound (TRUS). EVIDENCE SYNTHESIS: We explain risk thresholds, NB, default strategies (treat all, treat no one), and test tradeoff. To use DCA, first determine whether a model is superior to all other strategies across the range of reasonable risk thresholds. If so, that model appears to improve decisions irrespective of threshold. Second, consider if there are important extra costs to using the model. If so, obtain the test tradeoff to check whether the increase in NB versus the best other strategy is worth the additional cost. In our case study, addition of TRUS improved NB by 0.0114, equivalent to 1.1 more detected high-grade prostate cancers per 100 patients. Hence, adding TRUS would be worthwhile if we accept subjecting 88 patients to TRUS to find one additional high-grade prostate cancer or, alternatively, subjecting 10 patients to TRUS to avoid one unnecessary biopsy. CONCLUSIONS: The proposed guidelines can help researchers understand DCA and improve application and reporting. PATIENT SUMMARY: Decision curve analysis can identify risk models that can help us make better clinical decisions. We illustrate appropriate reporting and interpretation of decision curve analysis. ispartof: EUROPEAN UROLOGY vol:74 issue:6 pages:796-804 ispartof: location:Switzerland status: published

© 2018 Elsevier B.V. Background: The extent of coronary artery disease (CAD) is relevant for the evaluation and the choice of treatment of patients and consists of the severity of stenoses and their distribution within the coronary tree. Diagnosis is not easy and severe CAD should not be missed. For low-risk patients one wants to avoid the invasive angiography. We aim to propose a diagnostic prediction model of CAD respecting the degree of disease severity. Methods: We included 4888 patients from the Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. An ordinal regression model was applied to estimate the probabilities of five incrementally disease categories: no CAD, non-obstructive stenosis, and one-, two- and three-vessel disease. We included 11 predictors in the model: age, sex, chest pain, diabetes, hypertension, dyslipidaemia, smoking, HDL and LDL cholesterol, fibrinogen, and C-reactive protein. Bootstrapping was used to validate model performance (discrimination and calibration). Results: Age, sex, and three laboratory measures had a large predictive effect. The model poorly separated most adjacent disease categories, but performed well for categories far apart, with little optimism. The overall discrimination added up to a c statistic of 0.71 (95% CI 0.69 to 0.73). The model enables the estimation of individual patient probabilities of disease severity categories. Conclusions: The proposed ordinal diagnostic risk model, employing routinely obtainable variables, allows distinguishing the extent of CAD and can especially discriminate between non-obstructive stenosis and multi-vessel disease in our CARDIIGAN patients. This can help to decide on treatment strategy and thereby reduce the number of unnecessary angiographies. ispartof: INTERNATIONAL JOURNAL OF CARDIOLOGY vol:267 pages:8-12 ispartof: location:Netherlands status: published

BACKGROUND: Awareness is growing that cancer can be treated during pregnancy, but the effect of this change on maternal and neonatal outcomes is unknown. The International Network on Cancer, Infertility and Pregnancy (INCIP) registers the incidence and maternal, obstetric, oncological, and neonatal outcomes of cancer occurring during pregnancy. We aimed to describe the oncological management and obstetric and neonatal outcomes of patients registered in INCIP and treated in the past 20 years, and assess associations between cancer type or treatment modality and obstetric and neonatal outcomes. METHODS: This descriptive cohort study included pregnant patients with cancer registered from all 37 centres (from 16 countries) participating in the INCIP registry. Oncological, obstetric, and neonatal outcome data of consecutive patients diagnosed with primary invasive cancer during pregnancy between Jan 1, 1996, and Nov 1, 2016, were retrospectively and prospectively collected. We analysed changes over time in categorical patient characteristics, outcomes, and treatment methods with log-binomial regression. We used multiple logistic regression to analyse preterm, prelabour rupture of membranes (PPROM) or preterm contractions, small for gestational age, and admission to the neonatal intensive care unit (NICU). The INCIP registry study is registered with ClinicalTrials.gov, number NCT00330447, and is ongoing. FINDINGS: 1170 patients were included in the analysis and 779 (67%) received treatment during pregnancy. Breast cancer was the most common malignant disease (462 [39%]). Every 5 years, the likelihood of receiving treatment during pregnancy increased (relative risk [RR] 1·10, 95% CI 1·05-1·15), mainly related to an increase of chemotherapeutic treatment (1·31, 1·20-1·43). Overall, 955 (88%) of 1089 singleton pregnancies ended in a livebirth, of which 430 (48%) of 887 pregnancies ended preterm. Each 5 years, we observed more livebirths (RR 1·04, 95% CI 1·01-1·06) and fewer iatrogenic preterm deliveries (0·91, 0·84-0·98). Our data suggest a relationship between platinum-based chemotherapy and small for gestational age (odds ratio [OR] 3·12, 95% CI 1·45-6·70), and between taxane chemotherapy and NICU admission (OR 2·37, 95% CI 1·31-4·28). NICU admission seemed to depend on cancer type, with gastrointestinal cancers having highest risk (OR 7·13, 95% CI 2·86-17·7) and thyroid cancers having lowest risk (0·14, 0·02-0·90) when compared with breast cancer. Unexpectedly, the data suggested that abdominal or cervical surgery was associated with a reduced likelihood of NICU admission (OR 0·30, 95% CI 0·17-0·55). Other associations between treatment or cancer type and outcomes were less clear. INTERPRETATION: Over the years, the proportion of patients with cancer during pregnancy who received antenatal treatment increased, especially treatment with chemotherapy. Our data indicate that babies exposed to antenatal chemotherapy might be more likely to develop complications, specifically small for gestational age and NICU admission, than babies not exposed. We therefore recommend involving hospitals with obstetric high-care units in the management of these patients. FUNDING: Research Foundation-Flanders, European Research Council, Charles University, Ministry of Health of the Czech Republic. ispartof: The Lancet Oncology vol:19 issue:3 pages:337-346 ispartof: location:England status: published

Polytomous discrimination index is a novel and important diagnostic accuracy measure for multi-category classification. After reconstructing its probabilistic definition, we propose a nonparametric approach to the estimation of polytomous discrimination index based on an empirical sample of biomarker values. In this paper, we provide the finite-sample and asymptotic properties of the proposed estimators and such analytic results may facilitate the statistical inference. Simulation studies are performed to examine the performance of the nonparametric estimators. Two real data examples are analysed to illustrate our methodology.

ispartof: JOURNAL OF ULTRASOUND IN MEDICINE vol:40 issue:8 pages:1731-1732 ispartof: location:England status: published

The role of P-values for null hypothesis testing is under debate. We aim to explore the impact of the significance threshold on estimates for the strengths of associations ("effects") and the implications for different types of epidemiological research. We consider situations with normal distribution of a true effect, while varying the effect size. We confirm the occurrence of "testimation bias": estimating effect size only if the test was statistically significant leads to exaggerated results. The absolute bias is largest for true effects around 0.7 times the size of the standard error: +220% bias if effects are selected after testing with P < .05, and +335% if tested with P < .005. Less bias was found for testing with P < .20 (+130%) and larger true effect sizes. We conclude that a lower P-value threshold for declaring statistical significance implies more exaggeration in an estimated effect. This implies that if a low threshold is used, effect size estimation should not be attempted, for example in the context of selecting promising discoveries that need further validation. Confirmatory studies, such as randomized controlled trials, might stick to the 0.05 threshold if adequately powered, while prediction modelling studies should use an even higher threshold, such as 0.2, to avoid strongly biased effect estimates.

Background In childhood acute lymphoblastic leukemia (ALL), radiotherapy for CNS prophylaxis is not used in frontline therapy anymore. Standard treatment for ALL nowadays consists of polychemotherapy. Therefore, assessment of potential chemotherapy-induced cognitive side effects becomes important. Although neurotoxicity was demonstrated in cross-sectional studies, longitudinal studies remain scarce. Procedure We evaluated intellectual development of 94 pediatric ALL patients between 1990 and 1997, diagnosed before the age of 12 years, treated according to the European Organisation for Research and Treatment of Cancer Children's Leukemia Group 58881 protocol. Three assessments of the Wechsler Intelligence Scale for Children Revised were performed since diagnosis, according to age. Using repeated measures regression analysis, we investigated the effect of gender (low versus increased) risk group, parents' education, age at diagnosis, intelligence quotient (IQ) subscale (verbal (VIQ) versus performance (PIQ) intelligence), and test session. Results PIQ scores were lower than VIQ at baseline (−5.3 points on average, p = 0.0032), yet PIQ increased more strongly (PIQ: +3.9 points per test session; VIQ: +0.8, p = 0.0079), so this baseline difference disappeared (p = 0.0079). There were no clear effects of gender (girls: +0.6 points; p = 0.78) or risk group (low risk: +1.5 points; p = 0.49), but IQ scores were higher when one parent had followed higher education (+9.5 points, p

Study Question Do sonographic characteristics of ovarian endometriomas vary with age in premenopausal women? Summary Answer With increasing age, multilocular cysts and cysts with papillations and other solid components become more common whereas ground glass echogenicity of cyst fluid becomes less common. What is Known Already Expectant or medical management of women with endometriomas is now accepted. Therefore, the accuracy of non-invasive diagnosis of these cysts is pivotal. A clinically relevant question is whether the sonographic characteristics of ovarian endometriomas are the same irrespective of the age of the woman. Study Design, Size, Duration This is a secondary analysis of cross-sectional data in the International Ovarian Tumor Analysis (IOTA) database. The database contains clinical and ultrasound information collected pre-operatively between 1999 and 2012 from 5914 patients with adnexal masses in 24 ultrasound centres in 10 countries. Participants/Materials, Setting, Methods There were 1005 histologically confirmed endometriomas in adult premenopausal patients found in the database and these were used in our analysis. The following ultrasound variables (defined using IOTA terminology) were used to describe the ultrasound appearance of the endometriomas: tender mass at ultrasound, largest diameter of lesion, tumour type (unilocular, unilocular-solid, multilocular, multilocular-solid, solid), echogenicity of cyst content, presence of papillations, number of papillations, height (mm) of largest papillation, presence and proportion of solid tissue and number of cyst locules, as well as vascularity in papillations and colour content of the tumour scan (colour score) on colour or power Doppler ultrasounds. Results are reported as median difference or odds ratio (OR) per 10 years increase in age. Main Results and the Role of Chance Maximal lesion diameter did not vary substantially with age (+1.3 mm difference per 10 years increase in age, 95% confidence interval (CI)-1.4 to 4.0). Tender mass at scan was less common in the older the woman (OR 0.75, 95% CI 0.63-0.89), as were unilocular cysts relative to multilocular cysts (OR 0.70, 95% CI 0.57-0.85) and to lesions with solid components (OR 0.61, 95% CI 0.48-0.77), and ground glass echogenicity relative to homogeneous low-level echogenicity (OR 0.74, 95% CI 0.58-0.94) and other types of echogenicity of cyst contents (OR 0.64, 95% CI 0.50-0.81). Papillations were more common the older the woman (OR 1.65, 95% CI 1.24-2.21), but their height and vascularization showed no clear relation to age. LIMITATIONS, REASONS FOR CAUTION It is a limitation that we have little clinical information on the women included, e.g. previous surgery or medical treatment for endometriosis. It is important to emphasize that we do not know the age of the endometrioma itself and that our study is not longitudinal and so does not describe changes in endometriomas over time. The differences in the ultrasound appearance of endometriomas between women of different ages might be explained by previous surgery or medical treatment and might not be an effect of age per se. Wider Implications of the Findings Awareness of physicians that the ultrasound appearance of endometriomas differs between women of different ages may facilitate a correct diagnosis of endometrioma. STUDY FUNDING/COMPETING INTEREST(S) This study was supported in part by the Regione Autonoma della Sardegna (project code CPR-24750). B.V.C., A.C. and D.T. are supported by the Fund for Scientific Research Flanders, Belgium (FWO). The authors declare that there is no conflict of interest.

PURPOSE: Controversy remains around acceptable surgical delay of acute hip fractures with current guidelines ranging from 24 to 48 h. Increasing healthcare costs force us to consider the economic burden as well. We aimed to evaluate the adjusted effect of surgical delay for hip fracture surgery on early mortality, healthcare costs and readmission rate. We hypothesized that shorter delays resulted in lower early mortality and costs. METHODS: In this retrospective cohort study 2573 consecutive patients aged ≥50 years were included, who underwent surgery for acute hip fractures between 2009 and 2017. Main endpoints were thirty- and ninety-day mortality, total cost, and readmission rate. Multivariable regression included sex, age and ASA score as covariates. RESULTS: Thirty-day mortality was 5% (n = 133), ninety-day mortality 12% (n = 304). Average total cost was €11960, dominated by hospitalization (59%) and honoraria (23%). Per 24 h delay, the adjusted odds ratio was 1.07 (95% CI 0.98-1.18) for thirty-day mortality, 1.12 (95% CI 1.04-1.19) for ninety-day mortality, and 0.99 (95% CI = 0.88-1.12) for readmission. Per 24 h delay, costs increased with 7% (95% CI 6-8%). For mortality, delay was a weaker predictor than sex, age, and ASA score. For costs, delay was the strongest predictor. We did not find clear cut-points for surgical delay after which mortality or costs increased abruptly. CONCLUSIONS: Despite only modest associations with mortality, we observed a steady increase in healthcare costs when delaying surgery. Hence, a more pragmatic approach with surgery as soon as medically and organizationally possible seems justifiable over rigorous implementation of the current guidelines. ispartof: INJURY-INTERNATIONAL JOURNAL OF THE CARE OF THE INJURED vol:49 issue:6 pages:1169-1175 ispartof: location:Netherlands status: published

Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand. publisher: Elsevier articletitle: Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators journaltitle: Journal of Biomedical Informatics articlelink: http://dx.doi.org/10.1016/j.jbi.2015.05.001 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved. ispartof: Journal of Biomedical Informatics vol:56 pages:87-93 ispartof: location:United States status: published

Key content Introduction to preoperative classification of ovarian and tubal neoplasms. A discussion of existing guidelines by professional societies. Updated evidence and new strategies for preoperative diagnosis of ovarian cancer. Evidence-based recommendations for the diagnosis and characterisation of adnexal masses. Areas of future interest in the field of adnexal tumour diagnosis. Learning objectives To understand the importance of accurate preoperative discrimination of adnexal tumours to ensure optimal patient management. To critically appraise current guidelines by professional societies. To discuss findings from a recent systematic review and meta-analysis, and two diagnostic accuracy studies comparing different types of prediction models for ovarian cancer diagnosis. To understand the advantages and disadvantages of different International Ovarian Tumour Analysis (IOTA) prediction models. Ethical issues Consequences of misclassification of ovarian and tubal masses. Inappropriate use of IOTA prediction models.

Objective How adverse outcomes and complaints are managed may significantly impact on physician well-being and practice. We aimed to investigate how depression, anxiety and defensive medical practice are associated with doctors actual and perceived support, behaviour of colleagues and process issues regarding how complaints investigations are carried out. Design A survey study. Respondents were classified into three groups: no complaint, recent/current complaint (within 6 months) or past complaint. Each group completed specific surveys. Setting British Medical Association (BMA) members were invited to complete an online survey. Participants 95 636 members of the BMA were asked to participate. 7926 (8.3%) completed the survey, of whom 1780 (22.5%) had no complaint, 3889 (49.1%) had a past complaint and 2257 (28.5%) had a recent/current complaint. We excluded those with no complaints leaving 6144 in the final sample. Primary outcomes measures We measured anxiety and depression using the Generalised Anxiety Disorder Scale 7 and Physical Health Questionnaire 9. Defensive practice was assessed using a new measure for avoidance and hedging. Results Most felt supported by colleagues (61%), only 31% felt supported by management. Not following process (56%), protracted timescales (78%), vexatious complaints (49%), feeling bullied (39%) or victimised for whistleblowing (20%), and using complaints to undermine (31%) were reported. Perceived support by management (relative risk (RR) depression: 0.77, 95% CI 0.71 to 0.83; RR anxiety: 0.80, 95% CI 0.74 to 0.87), speaking to colleagues (RR depression: 0.64, 95% CI 0.48 to 0.84 and RR anxiety: 0.69, 95% CI 0.51 to 0.94, respectively), fair/accurate documentation (RR depression: 0.80, 95% CI 0.75 to 0.86; RR anxiety: 0.81, 95% CI 0.75 to 0.87), and being informed about rights (RR depression 0.96 (0.89 to 1.03) and anxiety 0.94 (0.87 to 1.02), correlated positively with well-being and reduced defensive practice. Doctors worried most about professional humiliation following a complaint investigation (80%). Conclusion Poor process, prolonged timescales and vexatious use of complaints systems are associated with decreased psychological welfare and increased defensive practice. In contrast, perceived support from colleagues and management is associated with a reduction in these effects.