Functional and psychosocial impact of COVID-19 pandemic on rheumatic patients’ quality of life in Saudi Arabia

Quality of Life Research - The COVID-19 pandemic might add to the stressors experienced by people living with rheumatic diseases. This study aimed to examine rheumatic patients’ functional...     

Treatment of multiple adjacent RT 1 gingival recessions with the modified coronally advanced tunnel (MCAT) technique and a collagen matrix or palatal connective tissue graft: 9-year results of a split-mouth randomized clinical trial

Clinical Oral Investigations - To evaluate t he long-term outcomes following treatment of RT 1 multiple adjacent gingival recessions (MAGR) using the modified coronally advanced tunnel (MCAT) with...     

Bayesian consensus clustering for multivariate longitudinal data

In clinical and epidemiological studies, there is a growing interest in studying the heterogeneity among patients based on longitudinal characteristics to identify subtypes of the study population. Compared to clustering a single longitudinal marker, simultaneously clustering multiple longitudinal markers allow additional information to be incorporated into the clustering process, which reveals co-existing longitudinal patterns and generates deeper biological insight. In the current study, we propose a Bayesian consensus clustering (BCC) model for multivariate longitudinal data. Instead of arriving at a single overall clustering, the proposed model allows each marker to follow marker-specific local clustering and these local clusterings are aggregated to find a global (consensus) clustering. To estimate the posterior distribution of model parameters, a Gibbs sampling algorithm is proposed. We apply our proposed model to the primary biliary cirrhosis study to identify patient subtypes that may be associated with their prognosis. We also perform simulation studies to compare the clustering performance between the proposed model and existing models under several scenarios. The results demonstrate that the proposed BCC model serves as a useful tool for clustering multivariate longitudinal data.     

Effect of iron deficiency without anaemia on days alive and out of hospital in patients undergoing valvular heart surgery

Comprehensive evidence regarding the treatment of non-anaemic iron deficiency in patients undergoing valvular heart surgery is lacking. This study aimed to investigate the association between non-anaemic iron deficiency and postoperative outcomes in these patients. We retrospectively analysed 321 patients of which 180 (56%) had iron deficiency (defined as serum ferritin < 100 ng.ml^-1 or < 300 ng.ml^-1 with transferrin saturation < 20%). While the iron-deficient group had lower pre-operative haemoglobin levels than the non-iron deficient group (median (IQR [range]) 134 (127–141 [120–172]) g.l^-1, 143 (133–150 [120–179]) g.l^-1, p = 0.001), there was no between-group difference in allogeneic red blood cell transfusion. Median (IQR [range]) days alive and out of hospital at postoperative day 90 was 1 day shorter in the iron-deficient group (80 (77–82 [9–85]) days vs. 81 (79–83 [0–85]) days, p = 0.026). In multivariable analysis, only cardiopulmonary bypass duration (p = 0.032) and intra-operative allogeneic red blood cell transfusion (p = 0.011) were significantly associated with reduced days alive and out of hospital at postoperative day 90. Iron deficiency did not exert any adverse influence on secondary outcomes except length of hospital stay. Our findings indicate that non-anaemic iron deficiency alone is not associated with adverse effects in patients undergoing valvular heart surgery when it does not translate into an increased risk of allogeneic transfusion.