Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis.

OBJECTIVE: Stratifying the risk of death in SSc-related interstitial lung disease (SSc-ILD) is a challenging issue. The extent of lung fibrosis on high-resolution CT (HRCT) is often assessed by a visual semiquantitative method that lacks reliability. We aimed to assess the potential prognostic value of a deep-learning-based algorithm enabling automated quantification of ILD on HRCT in patients with SSc. METHODS: We correlated the extent of ILD with the occurrence of death during follow-up, and evaluated the additional value of ILD extent in predicting death based on a prognostic model including well-known risk factors in SSc. RESULTS: We included 318 patients with SSc, among whom 196 had ILD; the median follow-up was 94 months (interquartile range 73-111). The mortality rate was 1.6% at 2 years and 26.3% at 10 years. For each 1% increase in the baseline ILD extent (up to 30% of the lung), the risk of death at 10 years was increased by 4% (hazard ratio 1.04, 95% CI 1.01, 1.07, P = 0.004). We constructed a risk prediction model that showed good discrimination for 10-year mortality (c index 0.789). Adding the automated quantification of ILD significantly improved the model for 10-year survival prediction (P = 0.007). Its discrimination was only marginally improved, but it improved prediction of 2-year mortality (difference in time-dependent area under the curve 0.043, 95% CI 0.002, 0.084, P = 0.040). CONCLUSION: The deep-learning-based, computer-aided quantification of ILD extent on HRCT provides an effective tool for risk stratification in SSc. It might help identify patients at short-term risk of death.

O-RADS MRI Classification of Indeterminate Adnexal Lesions: Time-Intensity Curve Analysis Is Better Than Visual Assessment.

Background The MRI Ovarian-Adnexal Reporting and Data System (O-RADS) enables risk stratification of sonographically indeterminate adnexal lesions, partly based on time-intensity curve (TIC) analysis, which may not be universally available. Purpose To compare the diagnostic accuracy of visual assessment with that of TIC assessment of dynamic contrast-enhanced MRI scans to categorize adnexal lesions as benign or malignant and to evaluate the influence on the O-RADS MRI score. Materials and Methods The European Adnex MR Study Group, or EURAD, database, a prospective multicenter study of women undergoing MRI for indeterminate adnexal lesions between March 2013 and March 2018, was queried retrospectively. Women undergoing surgery for an adnexal lesion with solid tissue were included. Solid tissue enhancement relative to outer myometrium was assessed visually and with TIC. Contrast material washout was recorded. Lesions were categorized according to the O-RADS MRI score with visual and TIC assessment. Per-lesion diagnostic accuracy was calculated. Results A total of 320 lesions (207 malignant, 113 benign) in 244 women (mean age, 55.3 years ± 15.8 [standard deviation]) were analyzed. Sensitivity for malignancy was 96% (198 of 207) and 76% (157 of 207) for TIC and visual assessment, respectively. TIC was more accurate than visual assessment (86% [95% CI: 81, 90] vs 78% [95% CI: 73, 82]; P < .001) for benign lesions, predominantly because of higher specificity (95% [95% CI: 92, 98] vs 76% [95% CI: 68, 81]). A total of 21% (38 of 177) of invasive lesions were rated as low risk visually. Contrast material washout and high-risk enhancement (defined as earlier enhancement than in the myometrium) were highly specific for malignancy for both TIC (97% [95% CI: 91, 99] and 94% [95% CI: 90, 97], respectively) and visual assessment (97% [95% CI: 92, 99] and 93% [95% CI: 88, 97], respectively). O-RADS MRI score was more accurate with TIC than with visual assessment (area under the receiver operating characteristic curve, 0.87 [95% CI: 0.83, 0.90] vs 0.73 [95% CI: 0.68, 0.78]; P < .001). Conclusion Time-intensity curve analysis was more accurate than visual assessment for achieving optimal diagnostic accuracy with the Ovarian-Adnexal Reporting and Data System MRI score. Clinical trial registration no. NCT01738789 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Vargas and Woo in this issue. An earlier incorrect version appeared online. This article was corrected on March 7, 2022.

Assessment of Treatment Effects and Long-term Benefits in Immune Checkpoint Inhibitor Trials Using the Flexible Parametric Cure Model: A Systematic Review.

Importance: Compared with standard cytotoxic therapies, randomized immune checkpoint inhibitor (ICI) phase 3 trials reveal delayed benefits in terms of patient survival and/or long-term response. Such outcomes generally violate the assumption of proportional hazards, and the classical Cox proportional hazards regression model is therefore unsuitable for these types of analyses. Objective: To evaluate the ability of the flexible parametric cure model (FPCM) to estimate treatment effects and long-term responder fractions (LRFs) independently of prespecified time points. Evidence Review: This systematic review used reconstructed individual patient data from ICI advanced or metastatic melanoma and lung cancer phase 3 trials extracted from the literature. Trials published between January 1, 2010, and October 1, 2019, with long-term follow-up periods (maximum follow-up, ≥36 months in first line and ≥30 months otherwise) were selected to identify LRFs. Individual patient data for progression-free survival were reconstructed from the published randomized ICI phase 3 trial results. The FPCM was applied to estimate treatment effects on the overall population and on the following components of the population: LRF and progression-free survival in non-long-term responders. Results obtained were compared with treatment effects estimated using the Cox proportional hazards regression model. Findings: In this systematic review, among the 23 comparisons studied using the FPCM, a statistically significant association between the time-to-event component and experimental treatment was observed in the main analyses and confirmed in the sensitivity analyses of 18 comparisons. Results were discordant for 4 comparisons that were not significant by the Cox proportional hazards regression model. The LRFs varied from 1.5% to 12.7% for the control arms and from 4.6% to 38.8% for the experimental arms. Differences in LRFs varied from 2% to 29% and were significantly increased in the experimental compared with the control arms, except for 4 comparisons. Conclusions and Relevance: This systematic review of reconstructed individual patient data found that the FPCM was a complementary approach that provided a comprehensive and pertinent evaluation of benefit and risk by assessing whether ICI treatment was associated with an increased probability of patients being long-term responders or with an improved progression-free survival in patients who were not long-term responders.

Addressing the issue of bias in observational studies: Using instrumental variables and a quasi-randomization trial in an ESME research project.

PURPOSE: Observational studies using routinely collected data are faced with a number of potential shortcomings that can bias their results. Many methods rely on controlling for measured and unmeasured confounders. In this work, we investigate the use of instrumental variables (IV) and quasi-trial analysis to control for unmeasured confounders in the context of a study based on the retrospective Epidemiological Strategy and Medical Economics (ESME) database, which compared overall survival (OS) with paclitaxel plus bevacizumab or paclitaxel alone as first-line treatment in patients with HER2-negative metastatic breast cancer (MBC). PATIENTS AND METHODS: Causal interpretations and estimates can be made from observation data using IV and quasi-trial analysis. Quasi-trial analysis has the same conceptual basis as IV, however, instead of using IV in the analysis, a "superficial" or "pseudo" randomized trial is used in a Cox model. For instance, in a multicenter trial, instead of using the treatment variable, quasi-trial analysis can consider the treatment preference in each center, which can be informative, and then comparisons of results between centers or clinicians can be informative. RESULTS: In the original analysis, the OS adjusted for major factors was significantly longer with paclitaxel and bevacizumab than with paclitaxel alone. Using the center-treatment preference as an instrument yielded to concordant results. For the quasi-trial analysis, a Cox model was used, adjusted on all factors initially used. The results consolidate those obtained with a conventional multivariate Cox model. CONCLUSION: Unmeasured confounding is a major concern in observational studies, and IV or quasi-trial analysis can be helpful to complement analysis of studies of this nature.

A Markov chain approach for ranking treatments in network meta-analysis.

When interpreting the relative effects from a network meta-analysis (NMA), researchers are usually aware of the potential limitations that may render the results for some comparisons less useful or meaningless. In the presence of sufficient and appropriate data, some of these limitations (eg, risk of bias, small-study effects, publication bias) can be taken into account in the statistical analysis. Very often, though, the necessary data for applying these methods are missing and data limitations cannot be formally integrated into ranking. In addition, there are other important characteristics of the treatment comparisons that cannot be addressed within a statistical model but only through qualitative judgments; for example, the relevance of data to the research question, the plausibility of the assumptions, and so on. Here, we propose a new measure for treatment ranking called the Probability of Selecting a Treatment to Recommend (POST-R). We suggest that the order of treatments should represent the process of considering treatments for selection in clinical practice and we assign to each treatment a probability of being selected. This process can be considered as a Markov chain model that allows the end-users of NMA to select the most appropriate treatments based not only on the NMA results but also to information external to the NMA. In this way, we obtain rankings that can inform decision-making more efficiently as they represent not only the relative effects but also their potential limitations. We illustrate our approach using a NMA comparing treatments for chronic plaque psoriasis and we provide the Stata commands.

A novel approach for identifying and addressing case-mix heterogeneity in individual participant data meta-analysis.

Case-mix heterogeneity across studies complicates meta-analyses. As a result of this, treatments that are equally effective on patient subgroups may appear to have different effectiveness on patient populations with different case mix. It is therefore important that meta-analyses be explicit for what patient population they describe the treatment effect. To achieve this, we develop a new approach for meta-analysis of randomized clinical trials, which use individual patient data (IPD) from all trials to infer the treatment effect for the patient population in a given trial, based on direct standardization using either outcome regression (OCR) or inverse probability weighting (IPW). Accompanying random-effect meta-analysis models are developed. The new approach enables disentangling heterogeneity due to case mix from that due to beyond case-mix reasons.

Identifying treatment responders using counterfactual modeling and potential outcomes.

Individualizing treatment according to patients' characteristics is central for personalized or precision medicine. There has been considerable recent research in developing statistical methods to determine optimal personalized treatment strategies by modeling the outcome of patients according to relevant covariates under each of the alternative treatments, and then relying on so-called predicted individual treatment effects. In this paper, we use potential outcomes and principal stratification frameworks and develop a multinomial model for left and right-censored data to estimate the probability that a patient is a responder given a set of baseline covariates. The model can apply to RCT or observational study data. This method is based on the monotonicity assumption, which implies that no patients would respond to the control treatment but not to the experimental one. We conduct a simulation study to evaluate the properties of the proposed estimation method. Results showed that the predictions of the probability of being a responder were well calibrated even if we observed variability and a small bias when many parameters were estimated. We finally applied the method to a cohort study on the selection of patients for additional radiotherapy after resection of a soft-tissue sarcoma.

A mapping of 115,000 randomized trials revealed a mismatch between research effort and health needs in non-high-income regions.

BACKGROUND: Concerns exist as to whether the allocation of resources in clinical research is aligned with public health needs. We evaluated the alignment between the effort of clinical research through the conduct of randomized controlled trials (RCTs) and health needs measured as the burden of diseases for all regions and a broad range of diseases. METHODS: We grouped countries into seven regions and diseases into 27 groups. We mapped all RCTs initiated between 2006 and 2015 that were registered at the WHO International Clinical Trials Registry Platform to regions and diseases. The burden of diseases in 2005 was mapped as disability-adjusted life years (DALYs), based on the 2010 Global Burden of Diseases study. Within regions, we defined a research gap when the proportion of RCTs concerning a disease in the region was less than half the relative burden of the disease. RESULTS: We mapped 117,180 RCTs planning to enroll 42.6 million patients and 2,220 million DALYs. In high- versus non-high-income countries, 130.9 versus 6.9 RCTs per million DALYs were conducted. We did not identify any research gap in high-income countries. We identified research gaps for all other regions. In particular, for Sub-Saharan Africa, we identified research gaps for common infectious diseases (CID) and neonatal disorders (ND): 5.8% (95% uncertainty interval 4.7-6.9) and 2.0% (0.9-4.5) of RCTs in Sub-Saharan Africa concerned CID and ND, although these diseases represented 22.9% and 11.6% of the burden in the region, respectively. For South Asia, we identified research gaps for the same two groups of diseases. CONCLUSIONS: In non-high-income regions, the conduct of RCTs was misaligned with the distribution of major causes of burden, in particular infectious diseases and neonatal disorders in Sub-Saharan Africa and South Asia.

Inverse probability weighting to control confounding in an illness-death model for interval-censored data.

Multistate models with interval-censored data, such as the illness-death model, are still not used to any considerable extent in medical research regardless of the significant literature demonstrating their advantages compared to usual survival models. Possible explanations are their uncommon availability in classical statistical software or, when they are available, by the limitations related to multivariable modelling to take confounding into consideration. In this paper, we propose a strategy based on propensity scores that allows population causal effects to be estimated: the inverse probability weighting in the illness semi-Markov model with interval-censored data. Using simulated data, we validated the performances of the proposed approach. We also illustrated the usefulness of the method by an application aiming to evaluate the relationship between the inadequate size of an aortic bioprosthesis and its degeneration or/and patient death. We have updated the R package multistate to facilitate the future use of this method.

Point of data saturation was assessed using resampling methods in a survey with open-ended questions.

OBJECTIVE: To describe methods to determine sample sizes in surveys using open-ended questions and to assess how resampling methods can be used to determine data saturation in these surveys. STUDY DESIGN AND SETTING: We searched the literature for surveys with open-ended questions and assessed the methods used to determine sample size in 100 studies selected at random. Then, we used Monte Carlo simulations on data from a previous study on the burden of treatment to assess the probability of identifying new themes as a function of the number of patients recruited. RESULTS: In the literature, 85% of researchers used a convenience sample, with a median size of 167 participants (interquartile range [IQR] = 69-406). In our simulation study, the probability of identifying at least one new theme for the next included subject was 32%, 24%, and 12% after the inclusion of 30, 50, and 100 subjects, respectively. The inclusion of 150 participants at random resulted in the identification of 92% themes (IQR = 91-93%) identified in the original study. CONCLUSION: In our study, data saturation was most certainly reached for samples >150 participants. Our method may be used to determine when to continue the study to find new themes or stop because of futility.

Drug-Induced- or Rheumatic- Valvular Heart Disease in Patients Exposed to Benfluorex?

There is a risk of misdiagnosis between benfluorex-induced VHD and acute rheumatic fever (ARF)-related VHD due to common characteristics of both etiologies. We aimed at estimating the probability for a patient exposed to benfluorex presenting with VHD to have, at the same time, a history of ARF-related VHD. Such epidemiological approach could help at reducing the risk of misdiagnosis. We used INSEE data and related literature as well as various modeling hypotheses to drive and test a formula for calculating the probability of a patient presenting with VHD and a history of benfluorex intake to have a prior history of ARF-related VHD. Different scenarios were estimated by a Markov model on the life course of people born in France between 1940 and 1960. Sensitivity analyses were performed under these scenarios. According to the different scenarios and gender, the probability that a patient born between 1940 and 1960 presenting with VHD and a history of benfluorex intake would have had a prior history of ARF-related VHD varied from 0.2% to 2.7%. The probabilities by the year of birth were as follows: 0.8%-2.7% for a patient born in 1940, < 0.5% in all scenarios for patients born after 1955, and < 0.2% in all scenarios for patients, born in 1960. Our results indicate that the burden of ARF-related VHD is low in the patient population exposed to benfluorex. The probability of ARF related VHD should not be over-estimated in the diagnostic procedure of VHD.

Performance of principal scores to estimate the marginal compliers causal effect of an intervention.

We examine the properties of principal scores methods to estimate the causal marginal odds ratio of an intervention for compliers in the context of a randomized controlled trial with non-compliers. The two-stage estimation approach has been proposed for a linear model by Jo and Stuart (Statistics in Medicine 2009; 28:2857-2875) under a principal ignorability (PI) assumption. Using a Monte Carlo simulation study, we compared the performance of several strategies to build and use principal score models and the robustness of the method to violations of underlying assumptions, in particular PI. Results showed that the principal score approach yielded unbiased estimates of the causal marginal log odds ratio under PI but that the method was sensitive to violations of PI, which occurs in particular when confounders are omitted from the analysis. For principal score analysis, probability weighting performed slightly better than full matching or 1:1 matching. Concerning the variables to be included in principal score models, the lowest mean squared error was generally obtained when using the true confounders. Using variables associated with the outcome only but not compliance however yielded very similar performance.

Differential Globalization of Industry- and Non-Industry-Sponsored Clinical Trials.

BACKGROUND: Mapping the international landscape of clinical trials may inform global health research governance, but no large-scale data are available. Industry or non-industry sponsorship may have a major influence in this mapping. We aimed to map the global landscape of industry- and non-industry-sponsored clinical trials and its evolution over time. METHODS: We analyzed clinical trials initiated between 2006 and 2013 and registered in the WHO International Clinical Trials Registry Platform (ICTRP). We mapped single-country and international trials by World Bank's income groups and by sponsorship (industry- vs. non- industry), including its evolution over time from 2006 to 2012. We identified clusters of countries that collaborated significantly more than expected in industry- and non-industry-sponsored international trials. RESULTS: 119,679 clinical trials conducted in 177 countries were analysed. The median number of trials per million inhabitants in high-income countries was 100 times that in low-income countries (116.0 vs. 1.1). Industry sponsors were involved in three times more trials per million inhabitants than non-industry sponsors in high-income countries (75.0 vs. 24.5) and in ten times fewer trials in low- income countries (0.08 vs. 1.08). Among industry- and non-industry-sponsored trials, 30.3% and 3.2% were international, respectively. In the industry-sponsored network of collaboration, Eastern European and South American countries collaborated more than expected; in the non-industry-sponsored network, collaboration among Scandinavian countries was overrepresented. Industry-sponsored international trials became more inter-continental with time between 2006 and 2012 (from 54.8% to 67.3%) as compared with non-industry-sponsored trials (from 42.4% to 37.2%). CONCLUSIONS: Based on trials registered in the WHO ICTRP we documented a substantial gap between the globalization of industry- and non-industry-sponsored clinical research. Only 3% of academic trials but 30% of industry trials are international. The latter appeared to be conducted in preferentially selected countries.

Assessment of the efficacy and safety of pre-emptive anti-cytomegalovirus (CMV) therapy in HIV-infected patients with CMV viraemia.

A number of studies have demonstrated that cytomegalovirus (CMV) viraemia is a strong predictor for CMV end-organ disease (EOD) and death in HIV-infected patients. We assess the efficacy and safety of pre-emptive anti-CMV therapy (PACT) for preventing these events. We performed a retrospective study of all HIV-infected patients seen in our institution who had detectable CMV viraemia in 2007. Seventy-one patients with advanced HIV disease (median CD4 cell count = 61 cells/mm(3)) were studied. Sixteen patients received PACT (mainly valganciclovir). Patients who received PACT had lower CD4 cell counts and higher blood CMV DNA levels. The cumulative incidence of CMV EOD and death at one year was 44% and 21% in patients with and without PACT, respectively (p = 0.013). Both PACT and high blood CMV DNA levels were significantly associated with CMV EOD and death in unadjusted analysis. In adjusted analyses, only blood CMV DNA levels remained significantly associated with the risk of CMV EOD and death, whereas PACT was associated with a non-significant trend towards reduced CMV EOD or death (hazard ratio: 0.25, p = 0.13). Five patients with PACT experienced severe drug-related adverse events. In conclusion, the use of PACT in HIV-infected patients with CMV viraemia could improve outcome but is associated with significant toxicity.

Ovarian reserve in breast cancer: assessment with anti-Müllerian hormone.

Anti-Müllerian hormone (AMH) levels fall during chemotherapy. Treatment-induced amenorrhoea is a reversible phenomenon, but few data are available on long-term AMH changes in breast cancer. The aim of the study was to describe serum AMH levels before, during and in the long term after chemotherapy, and to show a potential AMH recovery. Between May 2010 and June 2011, we selected 134 women aged 18-43 years at the time of breast cancer diagnosis who received chemotherapy between 2005 and 2011, and had not undergone an oophorectomy or had previous cytotoxic treatment. The AMH levels were assessed before, during and 4 months to 5.5 years after the end of chemotherapy. During chemotherapy, AMH was undetectable in 69% of women. After chemotherapy, a significant increase in AMH was found, with an average magnitude of +1.2% per month (95% credibility interval: 0.7 to 1.6). Older age and 12 months of amenorrhoea were found to be associated with a lower AMH recovery rate, whereas baseline AMH and number of chemotherapy cycles were not. The process of AMH changes during and after chemotherapy is dynamic, and shows recovery after ovarian injury. Caution should be exercised in interpreting individual AMH assessment in this context.