Various autoantibodies have been identified in sera from patients with systemic lupus erythematosus (SLE) and autoantibodies against neutrophil have been reported. It was suggested that antineutrophil autoantibodies might be involved in the pathogenesis of neutrocytopenia; however, the role of autoantibodies against neutrophil precursors and their specific target autoantigen(s) remained further characterized. The objective was to investigate the target antigens and clinical associations of autoantibodies against neutrophils and neutrophil precursors in patients with SLE. Sera were collected from 92 patients with SLE and renal biopsy proven lupus nephritis. Cell lysates of peripheral neutrophils (as mature neutrophils) from a normal blood donor and white blood cells from a patient with blast crisis of chronic granulocytic leukemia (CGL) (as neutrophil precursors) were used as antigens in Western blot analysis to detect autoantibodies in sera from patients with SLE. The clinical significance of antineutrophil autoantibodies that recognized different antigens were further analysed. Using normal peripheral neutrophils as antigens, two bands could be blotted: 64 kD (33/92, 35.9%) and 50 kD (13/92, 14.1%). The prevalence of anti-64 kD autoantibody in patients with positive rheumatic factor was significantly higher than that in patients without (54.5 versus 18.8%, P < 0.05). Using CGL white cells as antigen, five bands could be blotted: 60 kD (34/92, 37.0%), 50 kD (32/92, 34.8%), 29 kD (27/92, 29.3%), 42 kD (19/92, 20.7%) and 18 kD (16/92, 17.4%). The prevalence of anti-60 kD autoantibody was significantly higher in patients with neutrocytopenia than that in patients without neutrocytopenia (100 versus 48.3%, P < 0.01). The prevalence of anti-29 kD autoantibody was significantly higher in patients with alopecia than that in patients without alopecia (45.8 versus 20.8%, P < 0.05). Furthermore, the prevalences of anti-60 kD, anti-50 kD and anti-42 kD autoantibodies were significantly higher in patients with anti-Ro autoantibody than those in patients without; the prevalences of anti-29 kD and anti-18 kD autoantibodies were significantly higher in patients with anti-Sm autoantibody than those in patients without. We conclude that there are heterogeneous autoantibodies against both neutrophils and their precursors in sera from patients with SLE. Different autoantibodies may have different clinical significance.

SummaryThe mechanism of disease progression in Hashimoto's thyroiditis (HT) is still unclear. Thyroglobulin antibody (TgAb) is a diagnostic hallmark of HT. The aim of our study was to evaluate the avidity of TgAb in sera from HT patients with different thyroid functional status. Sera from 50 patients with newly diagnosed HT were collected and divided into three groups according to thyroid function: patients with hypothyroidism (H, n = 18), subclinical hypothyroidism (sH, n = 18) and euthyroidism (Eu, n = 14). Titres and avidity of TgAb were determined by enzyme-linked immunosorbent assays (ELISAs). Avidity constant (aK) was determined as the reciprocal value of the thyroglobulin molar concentration in the liquid phase resulting in 50% inhibition of TgAb binding to thyroglobulin in solid-phase ELISAs. The titres and aK of TgAb were performed using log-transformation, and expressed as lgT and lgaK, respectively. Mean lgT of TgAb in sera was 4·19 Ϯ 0·60 in H, 3·77 Ϯ 0·63 in sH, and 3·29 Ϯ 0·64 in Eu, respectively. The median avidity of TgAb was 2·30 ¥ 10 9 in H, 8·80 ¥ 10 8 in sH, 2·00 ¥ 10 7 in Eu, respectively. lgT and lgaK of TgAb were at significantly lower levels in Eu than in sH and H (P < 0·05). Correlation was found between lgT and lgaK (r = 0·594, P < 0·05). lgaK was also related to TSH (r=0·308, P < 0·05). Our study indicated that patients with high-avidity TgAb might be at high risk of developing subclinical, even to overt, hypothyroidism.

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Different blood pressure (BP) indices had varying associations with carotid intima-media thickness (cIMT) and plaques in clinical practice. However, insufficient evidence has focused on this issue, especially in Chinese population. Herein we examined associations of different BP indices with cIMT and plaques cross-sectionally in a community-based atherosclerosis cohort. We qualitatively measured cIMT and plaques, and also measured central systolic blood pressure and brachial systolic blood pressure (baSBP), from which pulse pressure (PP), and second PP (PP2) were calculated. Logistic multivariate regression was used to assess the associations with BP indices and carotid artery hypertrophy (increased cIMT) and the extent of atherosclerosis (presence of plaques). Each BP index was significantly and independently associated with increased cIMT and plaques except the association of baSBP with plaques. When every two BP indices were put into one model, brachial pressure indices were associated with increased cIMT independently of central pressures, whereas the association between central pressure indices and plaque presence were stronger than those of brachial pressures. In addition, SBP indices were associated with increased cIMT independently of PP indices, whereas PP indices were more strongly related to plaques. In conclusion, central and PP indices might be associated with plaques; however, brachial and SBP indices might be associated with increased cIMT. Nevertheless, whether these BP indices predict increased cIMT and plaque progression warrants further longitudinal and laboratory studies.

Introduction Clinical data repositories (CDR) including electronic health record (EHR) data have great potential for outcome prediction and risk modeling. However, most CDRs were only used for data displaying, and using data from CDR for outcome prediction often requires careful study design and sophisticated modeling techniques before a hypothesis can be tested. Purpose We built a prediction tool integrated with CDR based on pattern discovery aiming to bridge the above gap and demonstrated a case study on contrast related acute kidney injury (AKI) with the system. Methods A cardiovascular CDR integrated with multiple hospital informatics systems was established. For the case study on AKI, we included patients undergoing cardiac catheterization from January 13, 2015 to April 27, 2017, excluding those with dialysis, end-stage renal disease, renal transplant, and missing pre- or post-procedural creatinine. To handle missing data, a prior-history-note composer was designed to fill in structured data of 14 diseases related to cardiovascular problem. Crucial data such as ejective fraction was extracted from the structured reports. AKI was defined according to Acute Kidney Injury Network by increase of serum creatinine from most recent baseline to the post-procedure 7-day peak. To build predictive modeling, we selected 17 variables covered in existing AKI models. Pattern discovery was recently developed as an interpretable predictive model which works on incomplete noisy data. In this study, we developed a pattern discovery based visual analytics tool, and trained it on 70% data up to August 2016 with three interactive knowledge incorporation modes to develop 3 models: 1) pure data-driven, 2) domain knowledge, and 3) clinician-interactive. In last two modes, a physician using the visual analytics could change the variables and further refine the model, respectively. We tested and compared it with other models on the 30% consecutive patients dated afterwards, which is shown in Figure 1. Results Among 2,560 patients in the final dataset with 17 pre-procedure variables derived from CDR data, 169 (7.3%) had AKI. We measured 4 existing models, whose areas under curves (AUCs) of receiver operating characteristics curve for the test set were 0.70 (Mehran's), 0.72 (Chen's), 0.67 (Gao's) and 0.62 (AGEF), respectively. A pure data-driven machine learning method achieves AUC of 0.72 (Easy Ensemble). The AUCs of our 3 models are 0.77, 0.80, 0.82, respectively, with the last being top where physician knowledge is incorporated. Demo and demonstration Conclusions We developed a novel pattern-discovery-based outcome prediction tool integrated with CDR and purely using EHR data. On the case of predicting contrast related AKI, the tool showed user-friendliness by physicians, and demonstrated a competitive performance in comparison with the state-of-the-art models.