Mistakes to Avoid for Accurate and Transparent Reporting of Survival Analysis in Imaging Research

An exploratory review on the empirical evaluation of the quality of reporting and analyzing labor duration

INTRODUCTION

This exploratory review aimed to provide empirical evidence on the definitions of labor, the statistical approaches and measures reported in randomized controlled trials (RCTs) and observational studies measuring the duration of labor.

METHODS

A systematic electronic literature search was conducted using different databases. An extraction form was designed and used to extract relevant data. English, French, and German studies published between 1999 and 2019 have been included. Only RCTs and observational studies analyzing labor duration (or a phase of labor duration) as a primary outcome have been included.

RESULTS

Ninety-two RCTs and 126 observational studies were eligible. No definition of the onset of labor was provided in 21.7% (n = 20) of the RCTs and 23.8% (n = 30) of the observational studies. Mean was the most frequently applied measure of labor duration in the RCTs (89.1%, n = 82), and median in the observational studies (54.8%, n = 69). Most RCTs (83%, n = 76) and observational studies (70.6%, n = 89) analyzed labor duration using a bivariate method, with the t-test being the most frequently applied (45.7% and 27%, respectively). Only 10.8% (n = 10) of the RCTs and 52.4% (n = 66) of the observational studies conducted a multivariable regression: 3 (30%; out of 10) RCTs and 37 (56%; out of 66) observational studies used a time-to-event adapted model.

CONCLUSION

This survey reports a lack of agreement with respect to how the onset of labor and phases of labor duration are presented. Concerning the statistical approaches, few studies used survival analysis, which is the appropriate statistical framework to analyze time-to-event data.

Long-term outcomes of neoadjuvant immunotherapy plus chemotherapy in patients with early-stage triple-negative breast cancer: an extracted individual patient data and trial-level meta-analysis

BACKGROUND

Neoadjuvant immunotherapy (nIO) has emerged as a treatment option for stage II-III triple-negative breast cancer (TNBC). While randomised clinical trials (RCTs) demonstrated pathological complete response rate benefit to nIO added to chemotherapy, additional data on long-term outcomes is warranted. We performed this analysis to evaluate long-term efficacy outcomes of nIO in TNBC.

METHODS

We searched databases for RCTs evaluating nIO in early-stage TNBC. A meta-analysis of extracted individual patient data (EIPD) was performed to evaluate EFS and OS, with data from reported Kaplan-Meier plots. Additionally, we conducted a trial-level meta-analysis using fixed and random effects models.

RESULTS

The literature search resulted in four included RCTs with available EFS or OS (KEYNOTE-522, IMpassion031, I-SPY2 and GeparNuevo). EIPD showed that the addition of nIO to chemotherapy provides statistically significant benefits in EFS (HR 0.62, 0.50-0.76; p < 0.001) and OS (HR 0.62, 0.46-0.82, p < 0.001). Number needed to treat to avoid one EFS or OS event in 4 years was 9 and 14, respectively. Trial-level meta-analysis yielded similar results (EFS: HR 0.64, 0.51-0.79; OS: 0.57, 0.37-0.89).

CONCLUSIONS

Results show that nIO combined with chemotherapy can provide significant EFS and OS benefits, supporting its use as standard treatment for early-stage TNBC.

Left Atrial Strain Derived From Cardiac Magnetic Resonance Imaging Can Predict Outcomes of Patients With Acute Myocarditis

OBJECTIVE

There is increasing recognition that left atrial (LA) strain can be a prognostic marker of various cardiac diseases. However, its prognostic value in acute myocarditis remains unclear. Therefore, this study aimed to evaluate whether cardiovascular magnetic resonance (CMR)-derived parameters of LA strain can predict outcomes in patients with acute myocarditis.

MATERIALS AND METHODS

We retrospectively analyzed the data of 47 consecutive patients (44.2 ± 18.3 years; 29 males) with acute myocarditis who underwent CMR in 13.5 ± 9.7 days (range, 0-31 days) of symptom onset. Various parameters, including feature-tracked CMR-derived LA strain, were measured using CMR. The composite endpoints included cardiac death, heart transplantation, implantable cardioverter-defibrillator or pacemaker implantation, rehospitalization following a cardiac event, atrial fibrillation, or embolic stroke. The Cox regression analysis was performed to identify associations between the variables derived from CMR and the composite endpoints.

RESULTS

After a median follow-up of 37 months, 20 of the 47 (42.6%) patients experienced the composite events. In the multivariable Cox regression analysis, LA reservoir and conduit strains were independent predictors of the composite endpoints, with an adjusted hazard ratio per 1% increase of 0.90 (95% confidence interval [CI], 0.84-0.96; P = 0.002) and 0.91 (95% CI, 0.84-0.98; P = 0.013), respectively.

CONCLUSION

LA reservoir and conduit strains derived from CMR are independent predictors of adverse clinical outcomes in patients with acute myocarditis.

CT-Based Leiden Score Outperforms Confirm Score in Predicting Major Adverse Cardiovascular Events for Diabetic Patients with Suspected Coronary Artery Disease

OBJECTIVE

Evidence supports the efficacy of coronary computed tomography angiography (CCTA)-based risk scores in cardiovascular risk stratification of patients with suspected coronary artery disease (CAD). We aimed to compare two CCTA-based risk score algorithms, Leiden and Confirm scores, in patients with diabetes mellitus (DM) and suspected CAD.

MATERIALS AND METHODS

This single-center prospective cohort study consecutively included 1241 DM patients (54.1% male, 60.2 ± 10.4 years) referred for CCTA for suspected CAD in 2015-2017. Leiden and Confirm scores were calculated and stratified as < 5 (reference), 5-20, and > 20 for Leiden and < 14.3 (reference), 14.3-19.5, and > 19.5 for Confirm. Major adverse cardiovascular events (MACE) were defined as the composite outcomes of cardiovascular death, nonfatal myocardial infarction (MI), stroke, and unstable angina requiring hospitalization. The Cox model and Kaplan-Meier method were used to evaluate the effect size of the risk scores on MACE. The area under the curve (AUC) at the median follow-up time was also compared between score algorithms.

RESULTS

During a median follow-up of 31 months (interquartile range, 27.6-37.3 months), 131 of MACE were recorded, including 17 cardiovascular deaths, 28 nonfatal MIs, 64 unstable anginas requiring hospitalization, and 22 strokes. An incremental incidence of MACE was observed in both Leiden and Confirm scores, with an increase in the scores (log-rank p < 0.001). In the multivariable analysis, compared with Leiden score < 5, the hazard ratios for Leiden scores of 5-20 and > 20 were 2.37 (95% confidence interval [CI]: 1.53-3.69; p < 0.001) and 4.39 (95% CI: 2.40-8.01; p < 0.001), respectively, while the Confirm score did not demonstrate a statistically significant association with the risk of MACE. The Leiden score showed a greater AUC of 0.840 compared to 0.777 for the Confirm score (p < 0.001).

CONCLUSION

CCTA-based risk score algorithms could be used as reliable cardiovascular risk predictors in patients with DM and suspected CAD, among which the Leiden score outperformed the Confirm score in predicting MACE.