Variation of red blood cell parameters in Behcet's disease: association with disease severity and vascular involvement

Assessment of the CLOT (children's likelihood of thrombosis) real-time risk prediction model of hospital-associated venous thromboembolism in children with congenital heart disease

BACKGROUND

Children with congenital heart disease (CHD) are at high risk for hospital-associated venous thromboembolism (HA-VTE). The children's likelihood of thrombosis (CLOT) trial validated a real-time predictive model for HA-VTE using data extracted from the EHR for pediatric inpatients. We tested the hypothesis that addition of CHD specific data would improve model prediction in the CHD population.

METHODS

Model performance in CHD patients from 2010 to 2022, was assessed using 3 iterations of the CLOT model: 1) the original CLOT model, 2) the original model refit using only data from the CHD cohort, and 3) the model updated with the addition of cardiopulmonary bypass time, STAT Mortality Category, height, and weight as covariates. The discrimination of the three models was quantified and compared using AUROC.

RESULTS

Our CHD cohort included 1457 patient encounters (median 2.0 IQR [0.5-5.2] years-old). HA-VTE was present in 5% of our CHD cohort versus 1% in the general pediatric population. Several features from the original model were associated with thrombosis in the CHD cohort including younger age, thrombosis history, infectious disease consultation, and EHR coding of a central venous line. Lower height and weight were associated with thrombosis. HA-VTE rate was 12% (18/149) amongst those with STAT Category 4-5 operation versus 4% (49/1256) with STAT Category 1-3 operation (P < .001). Longer cardiopulmonary bypass time (124 [92-205] vs. 94 [65-136] minutes, P < .001) was associated with thrombosis. The AUROC for the original (0.80 95% CI [0.75-0.85]), refit (0.85 [0.81-0.89]), and updated (0.86 [0.81-0.90]) models demonstrated excellent discriminatory ability within the CHD cohort.

CONCLUSION

The automated approach with EHR data extraction makes the applicability of such models appealing for ease of clinical use. The addition of cardiac specific features improved model discrimination; however, this benefit was marginal compared to refitting the original model to the CHD cohort. This suggests strong predictive generalized models, such as CLOT, can be optimized for cohort subsets without additional data extraction, thus reducing cost of model development and deployment.

Development and validation of diagnostic and activity-assessing models for relapsing polychondritis based on laboratory parameters

Background

Relapsing polychondritis (RP) as a rare autoimmune disease is characterized by recurrent inflammation of the organs containing cartilage. Currently, no biomarkers have been integrated into clinical practice. This study aimed to construct and evaluate models based on laboratory parameters to aid in RP diagnosis, assess activity assessment, and explore associations with the pathological process.

Methods

RP patients and healthy controls (HCs) were recruited at the Peking Union Medical College Hospital from July 2017 to July 2023. Clinical data including Relapsing Polychondritis Disease Activity Index (RPDAI) score and laboratory tests were collected. Differences in laboratory data between RP patients and HCs and active and inactive patients were analyzed.

Results

The discovery cohort (cohort 1) consisted of 78 RP patients and 94 HCs. A model based on monocyte counts and neutrophil to lymphocyte ratio (NLR) could effectively distinguish RP patients from HCs with an AUC of 0.845. Active RP patients exhibited increased erythrocyte sedimentation rate, complement 3, platelet to lymphocyte ratio (PLR), NLR, and C-reactive protein to albumin ratio (CAR) compared with stable patients, which were also positively correlated with RPDAI. Notably, CAR emerged as an independent risk factor of disease activity (OR = 4.422) and could identify active patients with an AUC of 0.758. To confirm the reliability and stability of the aforementioned models, a replication cohort (cohort 2) was enrolled, including 79 RP patients and 94 HCs. The monocyte-combined NLR and CAR showed a sensitivity of 0.886 and 0.577 and a specificity of 0.830 and 0.833 in RP diagnosis and activity prediction, respectively. Furthermore, lower natural killer cell levels in RP patients and higher B-cell levels in active patients may contribute to elucidating the pathological mechanisms of disease occurrence and exacerbation.

Conclusions

The utilization of laboratory parameters provides cost-effective and valuable markers that can assist in RP diagnosis, identify disease activity, and elucidate pathogenic mechanisms.

Autoimmune Heart Disease: A Comprehensive Summary for Forensic Practice

Autoimmune heart disease is a non-random condition characterised by immune system-mediated aggression against cardiac tissue. Cardiac changes often exhibit nonspecific features and, if unrecognised, can result in fatal outcomes even among seemingly healthy young individuals. In the absence of reliable medical history, the primary challenge lies in differentiating between the various cardiopathies. Numerous immunohistochemical and genetic studies have endeavoured to characterise distinct types of cardiopathies, facilitating their differentiation during autopsy examinations. However, the presence of a standardised protocol that forensic pathologists can employ to guide their investigations would be beneficial. Hence, this summary aims to present the spectrum of autoimmune cardiopathies, including emerging insights such as SARS-CoV-2-induced cardiopathies, and proposes the utilisation of practical tools, such as blood markers, to aid forensic pathologists in their routine practice.

Importance of hematological markers in familial Mediterranean fever in terms of disease severity and amyloidosis

There are limited follow-up parameters for familial Mediterranean fever (FMF) related to disease severity and amyloidosis. Some hematological markers are emerging to assess inflammation. In this study, we hypothesized that some hematological parameters could be used to determine disease severity and amyloidosis in FMF. We included 274 adult FMF patients, and evaluated the relationship between neutrophil lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR) and platelet-lymphocyte ratio (PLR), platelet counts and leukocyte counts, mean erythrocyte haemoglobin concentration (MCHC) and mean erythrocyte haemoglobin (MCH) with disease severity and amyloidosis. First, we classified patients according to disease severity and presence of amyloidosis. We then compared the parameters within the groups. In addition, we determined predictive cut-off values with ROC analysis. Finally, we correlated the change in ISSF scores with the change in hematological parameters of 52 patients with follow-up hematological indices after six months. The patients with severe-moderate group had higher CRP levels (p < 0.001), white blood cell (p = 0.002) and neutrophil counts (p = 0.004) and, conversely, lower MCHC levels (p = 0.001) than patients with mild disease severity. FMF patients with amyloidosis had higher neutrophil (p = 0.04) and monocyte count (p = 0.02), increased NLR (p = 0.01) and lower MLR (p = 0.02) levels than those without. In addition, MCHC levels were also lower in the severe-moderate group in the follow-up analyses after sixth months (p = 0.03). MCHC, neutrophil and monocyte counts, NLR, MLR may be associated with poor prognosis in FMF patients. These parameters can be used in conjunction with acute phase reactant and clinical features to assess disease status.

Finding the Needle in the Haystack: Serological and Urinary Biomarkers in Behçet's Disease: A Systematic Review

Urinary and serological markers play an essential role in the diagnostic process of autoimmune diseases. However, to date, specific and reliable biomarkers for diagnosing Behçet's disease (BD) are still lacking, negatively affecting the management of these patients. To analyze the currently available literature on serological and urinary BD biomarkers investigated in the last 25 years, we performed a systematic literature review using the Population, Intervention, Comparison, and Outcomes (PICO) strategy. One hundred eleven studies met the eligibility criteria (6301 BD patients, 5163 controls). Most of them were retrospective, while five (5%) were prospective. One hundred ten studies (99%) investigated serological biomarkers and only two (2%) focused on urinary biomarkers. One hundred three studies (93%) explored the diagnostic potential of the biomolecules, whereas sixty-two (56%) tested their effect on disease activity monitoring. Most articles reported an increase in inflammatory markers and pro-oxidant molecules, with a decrease in antioxidants. Promising results have been shown by the omics sciences, offering a more holistic approach. Despite the vast number of investigated markers, existing evidence indicates a persistent gap in BD diagnostic/prognostic indices. While new steps have been taken in the direction of pathogenesis and disease monitoring, international efforts for the search of a diagnostic marker for BD are still needed.

A Real-time Risk-Prediction Model for Pediatric Venous Thromboembolic Events

BACKGROUND

Hospital-associated venous thromboembolism (HA-VTE) is an increasing cause of morbidity in pediatric populations, yet identification of high-risk patients remains challenging. General pediatric models have been derived from case-control studies, but few have been validated. We developed and validated a predictive model for pediatric HA-VTE using a large, retrospective cohort.

METHODS

The derivation cohort included 111 352 admissions to Monroe Carell Jr. Children's Hospital at Vanderbilt. Potential variables were identified a priori, and corresponding data were extracted. Logistic regression was used to estimate the association of potential risk factors with development of HA-VTE. Variable inclusion in the model was based on univariate analysis, availability in routine medical records, and clinician expertise. The model was validated by using a separate cohort with 44 138 admissions.

RESULTS

A total of 815 encounters were identified with HA-VTE in the derivation cohort. Variables strongly associated with HA-VTE include history of thrombosis (odds ratio [OR] 8.7; 95% confidence interval [CI] 6.6-11.3; P < .01), presence of a central line (OR 4.9; 95% CI 4.0-5.8; P < .01), and patients with cardiology conditions (OR 4.0; 95% CI 3.3-4.8; P < .01). Eleven variables were included, which yielded excellent discriminatory ability in both the derivation cohort (concordance statistic = 0.908) and the validation cohort (concordance statistic = 0.904).

CONCLUSIONS

We created and validated a risk-prediction model that identifies pediatric patients at risk for HA-VTE development. We anticipate early identification of high-risk patients will increase prophylactic interventions and decrease the incidence of pediatric HA-VTE.