PurposeThe aim of this study was to develop and internally validate a medication nonadherence risk nomogram in a Chinese population of patients with inflammatory rheumatic diseases.Patients and methodsWe developed a prediction model based on a training dataset of 244 IRD patients, and data were collected from March 2016 to May 2016. Adherence was evaluated using 19-item Compliance Questionnaire Rheumatology. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the medication nonadherence risk model. Multivariable logistic regression analysis was applied to build a predicting model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predicting model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was assessed using the bootstrapping validation.ResultsPredictors contained in the prediction nomogram included use of glucocorticoid (GC), use of nonsteroidal anti-inflammatory drugs, number of medicine-related questions, education level, and the distance to hospital. The model displayed good discrimination with a C-index of 0.857 (95% confidence interval: 0.807–0.907) and good calibration. High C-index value of 0.847 could still be reached in the interval validation. Decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 14%.ConclusionThis novel nonadherence nomogram incorporating the use of GC, the use of nonsteroidal anti-inflammatory drugs, the number of medicine-related questions, education level, and distance to hospital could be conveniently used to facilitate the individual medication nonadherence risk prediction in IRD patients.

Background To explore the inadequacies of health service and its impact on clinical outcomes of patients with systemic lupus erythematosus (SLE) in China. Methods A total of 210 SLE patients were randomly recruited between January 2017 and January 2018. Each patient received self-report questionnaires to assess medication adherence [Compliance Questionnaire for Rheumatology (CQR)], beliefs about medicines [Beliefs about Medicines Questionnaire (BMQ)] and satisfaction about medicine information [the Satisfaction with Information about Medicines Scale (SIMS)]. Associations between SLE disease activity index (SLEDAI-2 K) and observed factors were analyzed by multiple logistic regression. Results Based on CQR, only 28.10% patients were adherent. The score of BMQ was 2.85 ± 5.42, and merely 32.38% patients were satisfied with the information about their prescribed medicines. Disease activity was associated with SIMS, EuroQol five-dimensions [EQ5D], Systemic Lupus International Collaborating Clinics (SLICC), depression, use of NSAID ( P ≤ 0.05). Remission of disease was positively correlated with SIMS (OR = 0.16, 95% CI: [0.06, 0.40]), and BMQ (OR = 0.64, 95%CI: [0.43, 0.94]). Conclusion In this study, the scores of BMQ and SIMS were low, implying defects in the patient education of health service system, which led to disease flare in Chinese SLE patients. Trial registration ClinicalTrials.gov ID: NCT03024307 . Registered January 18, 2017. Electronic supplementary material The online version of this article (10.1186/s12913-019-4206-y) contains supplementary material, which is available to authorized users.

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ObjectiveTo confirm that metformin prevents flares in patients with SLE with low disease activity, we performed a post hoc analysis combining our previous two randomised trials.MethodsPost hoc analyses were performed on data from the open-labelled proof-of-concept trial (n=113, ChiCTR-TRC-12002419) and placebo-controlled ‘Met Lupus’ trial (n=140, NCT02741960) comparing the efficacy of metformin versus placebo/nil add-on to standard therapy in patients with SLE with low disease activity (SELENA-SLEDAI ≤4). The primary endpoint was defined by the SELENA-SLEDAI Flare Index at 12-month follow-up. A subgroup analysis was performed.ResultsOverall, 201 eligible patients were included, with 99 allocated to metformin group and 102 allocated to the placebo/nil group. By 12 months of follow-up, 21 patients (21.2%) flared in the metformin group, as compared with 36 (35.3%) in the placebo/nil group (p=0.027, risk ratio=0.68, 95% CI 0.46 to 0.96). Subgroup analysis showed that patients with negative anti-dsDNA antibody and normal complement at baseline, and a disease duration <5 years with concomitant use of hydroxychloroquine had a better response to metformin.ConclusionPost hoc pooled analyses suggested that metformin reduced subsequent disease flares in patients with SLE with low disease activity, especially for serologically quiescent patients.

ObjectiveTo evaluate the risk of major infections and the relationship between major infections and mortality in patients with newly diagnosed SLE.MethodsA newly diagnosed (<3 months) hospitalised Systemic Lupus Inception Cohort (hSLIC) in our centre during 1 January 2013 and 1 November 2020 was established. All patients were followed up for at least 1 year or until death. Patient baseline characteristics were collected. Major infection events were recorded during follow-up, which were defined as microbiological/clinical-based diagnosis treated with intravenous antimicrobials. The cohort was further divided into a training set and a testing set. Independent predictors of major infections were identified using multivariable logistic regression analysis. Kaplan-Meier survival analyses were conducted.ResultsAmong the 494 patients enrolled in the hSLIC cohort, there were 69 documented episodes of major infections during the first year of follow-up in 67 (14%) patients. The major infection events predominantly occurred within the first 4 months since enrolment (94%, 65/69) and were associated with all-cause mortality. After adjustments for glucocorticoid and immunosuppressant exposure, a prediction model based on SLE Disease Activity Index >10, peripheral lymphocyte count <0.8×109/L and serum creatinine >104 µmol/L was established to identify patients at low risk (3%–5%) or high risk (37%–39%) of major infections within the first 4 months.ConclusionsNewly onset active SLE is susceptible to major infections, which is probably due to underlying profound immune disturbance. Identifying high-risk patients using an appropriate prediction tool might lead to better tailored management and better outcome.

The recently described role of RNA methylation in regulating immune cell infiltration into tumors has attracted interest, given its potential impact on immunotherapy response. YTHDF1 is a versatile and powerful m6A reader, but the understanding of its impact on immune evasion is limited. Here, we reveal that tumor-intrinsic YTHDF1 drives immune evasion and immune checkpoint inhibitor (ICI) resistance. Additionally, YTHDF1 deficiency converts cold tumors into responsive hot tumors, which improves ICI efficacy. Mechanistically, YTHDF1 deficiency inhibits the translation of lysosomal genes and limits lysosomal proteolysis of the major histocompatibility complex class I (MHC-I) and antigens, ultimately restoring tumor immune surveillance. In addition, we design a system for exosome-mediated CRISPR/Cas9 delivery to target YTHDF1 in vivo, resulting in YTHDF1 depletion and antitumor activity. Our findings elucidate the role of tumor-intrinsic YTHDF1 in driving immune evasion and its underlying mechanism.

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