Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data

STUDY QUESTION

The objective was to construct a model for predicting the probability of recurrent implantation failure (RIF) after assisted reproductive technology (ART) treatment based on the clinical characteristics and routine laboratory test data of infertile patients. A model was developed to predict RIF. The model showed high calibration in external validation, helped to identify risk factors for RIF, and improved the efficacy of ART therapy.

WHAT IS KNOWN ALREADY

Research on the influencing factors of RIF has focused mainly on embryonic factors, endometrial receptivity, and immune factors. However, there are many kinds of examinations regarding these aspects, and comprehensive screening is difficult because of the limited time and economic conditions. Therefore, we should try our best to analyse the results of routine infertility screenings to make general predictions regarding the occurrence of RIF.

STUDY DESIGN, SIZE, DURATION

A retrospective study was conducted with 5212 patients at the Reproductive Center of the First Affiliated Hospital of USTC from January 2018 to June 2022.

PARTICIPANTS/MATERIALS, SETTING, METHODS

This study included 462 patients in the RIF group and 4750 patients in the control group. The patients' basic characteristics, clinical treatment data, and laboratory test indices were compared. Logistic regression was used to analyse RIF-related risk factors, and the prediction model was evaluated by receiver operating characteristic (ROC) curves and the corresponding areas under the curve (AUCs). Further analysis of the influencing factors of live births in the first cycle of subsequent assisted reproduction treatment in RIF patients was performed, including the live birth subgroup (n = 116) and the no live birth subgroup (n = 200).

MAIN RESULTS AND THE ROLE OF CHANCE

(1) An increased duration of infertility (1.978; 95% CI, 1.264-3.097), uterine cavity abnormalities (2.267; 95% CI, 1.185-4.336), low AMH levels (0.504; 95% CI, 0.275-0.922), insulin resistance (3.548; 95% CI, 1.931-6.519), antinuclear antibody (ANA)-positive status (3.249; 95% CI, 1.20-8.797) and anti-β2-glycoprotein I antibody (A-β2-GPI Ab)-positive status (5.515; 95% CI, 1.481-20.536) were associated with an increased risk of RIF. The area under the curve of the logistic regression model was 0.900 (95% CI, 0.870-0.929) for the training cohort and 0.895 (95% CI, 0.865-0.925) for the testing cohort. (2) Advanced age (1.069; 95% CI, 1.015-1.126) was a risk factor associated with no live births after the first cycle of subsequent assisted reproduction treatment in patients with RIF. Blastocyst transfer (0.365; 95% CI = 0.181-0.736) increased the probability of live birth in subsequent cycles in patients with RIF. The area under the curve of the logistic regression model was 0.673 (95% CI, 0.597-0.748).

LIMITATIONS, REASONS FOR CAUTION

This was a single-centre regression study, for which the results need to be evaluated and verified by prospective large-scale randomized controlled studies. The small sample size for the analysis of factors influencing pregnancy outcomes in subsequent assisted reproduction cycles for RIF patients resulted in the inclusion of fewer covariates, and future studies with larger samples and the inclusion of more factors are needed for assessment and validation.

WIDER IMPLICATIONS OF THE FINDINGS

Prediction of embryo implantation prior to transfer will facilitate the clinical management of patients and disease prediction and further improve ART treatment outcomes.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the General Project of the National Natural Science Foundation of China (Nos. 82,201,792, 82,301,871, 81,971,446, and 82,374,212) and the Natural Science Foundation of Anhui Province (No. 2208085MH206). There are no conflicts of interest to declare.

TRIAL REGISTRATION NUMBER

This study was registered with the Chinese Clinical Trial Register (Clinical Trial Number: ChiCTR1800018298 ).

A CAR-T response prediction model for r/r B-NHL patients based on a T cell subset nomogram

BACKGROUND

Chimeric antigen receptor (CAR) T cells for refractory or relapsed (r/r) B cell no-Hodgkin lymphoma (NHL) patients have shown promising clinical effectiveness. However, the factors impacting the clinical response of CAR-T therapy have not been fully elucidated. We here investigate the independent influencing factors of the efficacy of CD19 CAR-T cell infusion in the treatment of r/r B-NHL and to establish an early prediction model.

METHODS

A total of 43 r/r B-NHL patients were enrolled in this retrospective study. The patients' general data were recorded, and the primary endpoint is the patients' treatment response. The independent factors of complete remission (CR) and partial remission (PR) were investigated by univariate and binary logistic regression analysis, and the prediction model of the probability of CR was constructed according to the determined independent factors. Receiver operating characteristic (ROC) and calibration plot were used to assess the discrimination and calibration of the established model. Furthermore, we collected 15 participators to validate the model.

RESULTS

Univariate analysis and binary logistic regression analysis of 43 patients showed that the ratio of central memory T cell (Tcm) and naïve T cell (Tn) in cytotoxic T cells (Tc) was an independent risk factor for response to CD19 CAR-T cell therapy in r/r B-NHL. On this basis, the area under the curve (AUC) of Tcm in the Tc and Tn in the Tc nomogram model was 0.914 (95%CI 0.832-0.996), the sensitivity was 83%, and the specificity was 74.2%, which had excellent predictive value. We did not found the difference of the progression-free survival (PFS).

CONCLUSIONS

The ratio of Tcm and Tn in Tc was found to be able to predict the treatment response of CD19 CAR-T cells in r/r B-NHL. We have established a nomogram model for the assessment of the CD19 CAR-T therapy response presented high specificity and sensitivity.

Relationships between Secreted Aspartyl Proteinase 2 and General Control Nonderepressible 4 gene in the Candida albicans resistant to itraconazole under planktonic and biofilm conditions

This study aimed to explore the roles of SAP2 and GCN4 in itraconazole (ITR) resistance of C. albicans under different conditions, and their correlations. A total of 20 clinical strains of C. albicans, including 10 ITR resistant strains and 10 sensitive strains, were used. Then, SAP2 sequencing and GCN4 sequencing were performed, and the biofilm formation ability of different C. albicans strains was determined. Finally, real-time quantitative PCR was used to measure the expression of SAP2 and GCN4 in C. albicans under planktonic and biofilm conditions, as well as their correlation was also analyzed. No missense mutations and three synonymous mutation sites, including T276A, G543A, and A675C, were found in SAP2 sequencing. GCN4 sequencing showed one missense mutation site (A106T (T36S)) and six synonymous mutation sites (A147C, C426T, T513C, T576A, G624A and C732T). The biofilm formation ability of drug-resistant C. albicans strains was significantly higher than that of sensitive strains (P < 0.05). Additionally, SAP2 and GCN4 were up-regulated in the ITR-resistant strains, and were both significantly higher in C. albicans under biofilm condition. The mRNA expression levels of SAP2 and GCN4 had significantly positive correlation. The higher expression levels of SAP2 and GCN4 were observed in the ITR-resistant strains of C. albicans under planktonic and biofilm conditions, as well as there was a positive correlation between SAP2 and GCN4 mRNA expression.