BACKGROUND: Pneumoconiosis mostly combines pulmonary and cardiovascular diseases, among which pulmonary heart disease (PHD) is of major concern due to its significant impact on the survival of pneumoconiosis patients. White cell count (WCC), red cell distribution width (RDW) and platelet parameters are thought to affect inflammatory responses and may be predictors of various cardiovascular diseases. However, very few studies have focused on PHD.
OBJECTIVE: To examine the relationship between baseline complete blood count parameters (WCC, RDW, platelet parameters) and the risk of incident PHD in pneumoconiosis patients.
METHODS: A retrospective cohort study.
METHODS: This was a single-centre, retrospective cohort study that used data from an Occupational Disease Hospital, Chengdu, Sichuan.
METHODS: A total of 946 pneumoconiosis patients from January 2012 to November 2021 were included in the study. Female patients and patients who had PHD, coronary heart disease, hypertensive heart disease, cardiomyopathy, heart failure, oncological disease, multiple organ dysfunction, AIDS at baseline and follow-up time of less than 6 months were also excluded.
METHODS: We identified PHD according to the patient\'s discharge diagnosis. We constructed Cox proportional hazard regression models to assess the HR of incident PHD in pneumoconiosis, as well as 95% CIs.
RESULTS: In the multiple Cox proportional hazard regression analysis, platelet count (PLT) and plateletcrit (PCT) above the median at baseline were associated with an increased risk of PHD in pneumoconiosis with adjusted HR of 1.52 (95% CI 1.09 to 2.12) and 1.42 (95% CI 1.02 to 1.99), respectively.
CONCLUSIONS: Higher baseline PLT and PCT are associated with a higher risk of PHD in pneumoconiosis.

OBJECTIVE: In response to Health Canada\'s March 2020 directive, patients on clozapine for over 12 months were allowed to extend hematological testing intervals from 4 to 8 weeks during the COVID-19 pandemic. We hypothesized that this change would not affect the timely detection of hematological abnormalities in patients with severe mental illness.
METHODS: A chart review was conducted of patients at the Royal Ottawa who were prescribed clozapine from March 2019 to March 2021. We analyzed clinical and hematological data from electronic health records and Clozaril Support and Assistance Network database to compare occurrences of hematological abnormalities [leukopenia (white blood cell count <3.5 × 109/L) and agranulocytosis (absolute neutrophil count <0.5 × 109/L)] from March 17, 2020 to March 16, 2021, between standard and extended monitoring protocols using binomial logistic and zero-inflated negative binomial regressions.
RESULTS: Of 621 patients, 196 were on extended blood monitoring, and 425 followed standard blood monitoring. Clozapine dose did not differ between groups (standard: 370 ± 201 mg; extended: 352 ± 172 mg; P = .14, ds = 0.10). Clozapine treatment duration up to March 2021 was 12.6 ± 8.3 years, with the extended group (10 ± 7.9 years) having a significantly (P < .01, ds = 0.50) shorter duration than the standard (14 ± 8.2 years). Extended monitoring did not significantly impact likelihood of detecting hematological abnormalities (OR = 0.83, 95% CI [0.58,1.41], P = .55) after controlling for age, sex, total bloodwork, and other psychotropics associated with neutrophil counts (ie, valproate, olanzapine). No patient on the extended regimen developed agranulocytosis.
CONCLUSIONS: Reducing blood monitoring frequency in patients on clozapine for more than 12 months did not compromise detection of hematological abnormalities.

UNASSIGNED: Blood counts and biochemical markers are among the most common tests performed in hospitals and most readily accepted by patients, and are widely regarded as reliable biomarkers in the literature. The aim of this study was to assess the causal relationship between blood counts, biochemical indicators and pulmonary arterial hypertension (PAH).
UNASSIGNED: A two-sample Mendelian randomization (MR) analysis was performed to assess the causal relationship between blood counts and biochemical indicators with PAH. The genome-wide association study (GWAS) for blood counts and biochemical indicators were obtained from the UK Biobank (UKBB), while the GWAS for PAH were sourced from the FinnGen Biobank. Inverse variance weighting (IVW) was used as the primary analysis method, supplemented by three sensitivity analyses to assess the robustness of the results. And we conducted an observational study using data from National Health and Nutrition Examination Survey (NHANES) 2003-2018 to verify the relationship.
UNASSIGNED: The MR analysis primarily using the IVW method revealed genetic variants of platelet count (OR=2.51, 95% CI 1.56-4.22, P<0.001), platelet crit(OR=1.87, 95% CI1.17-7.65, P=0.022), direct bilirubin (DBIL)(OR=1.71, 95%CI 1.18-2.47,P=0.004), insulin-like growth factor (IGF-1)(OR=0.51, 95% CI 0.27-0.96, P=0.038), Lipoprotein A (Lp(a))(OR=0.66, 95% CI 0.45-0.98, P=0.037) and total bilirubin (TBIL)(OR=0.51, 95% CI 0.27-0.96, P=0.038) were significantly associated with PAH. In NHANES, multivariate logistic regression analyses revealed a significant positive correlation between platelet count and volume and the risk of PAH, and a significant negative correlation between total bilirubin and PAH.
UNASSIGNED: Our study reveals a causal relationship between blood counts, biochemical indicators and pulmonary arterial hypertension. These findings offer novel insights into the etiology and pathological mechanisms of PAH, and emphasizes the important value of these markers as potential targets for the prevention and treatment of PAH.

OBJECTIVE: Placental abruption (PA) is associated with adverse maternal and neonatal outcomes and has an etiological mechanism that is not yet fully understood. The prediction of PA, which has been the subject of numerous studies, remains a challenge. In particular, there is evidence that PA can be considered a chronic process. So, this study aimed to show inflammatory biomarkers based on complete blood count parameters may be used to predict PA.
METHODS: A sample of 110 cases (pregnant women with PA) and 110 controls (healthy pregnant women with spontaneous labor) were required the study. The present case-control study included a total of 220 pregnant women. Inflammatory makers were used to evaluate the PA prediction RESULTS: Increases in body mass index, mean corpuscular volume and paletelet lymphocyte ratio are considered protective factors, while increases in neutrophil, the systemic inflammatory response index, neutrophil lymphocyte ratio and the pan-immune inflammation score are considered risk factors. Each 1 unit increase in neutrophil count increases the risk of a PA diagnosis by 1.81 times.
CONCLUSIONS: Recent studies indicate a strong heterogeneity of clinical courses leading to PA in premature and term births. In the present study, our results showed that, inflammation is associated with PA.

Differential white blood cell counts are frequently used in diagnosis, patient stratification, and treatment selection to optimize therapy responses. Referral laboratories are often used but challenged with use of different hematology platforms, variable blood shipping times and storage conditions, and the different sensitivities of specific cell types. To extend the scientific literature and knowledge on the temporal commutability of blood samples between hematology analyzers, we performed a comparative ex-vivo study using four of the most utilized commercial platforms, focusing on the assessment of eosinophils given its importance in asthma management. Whole blood from healthy volunteers with and without atopy (n = 6+6) and participants with eosinophilic asthma (n = 6) were stored under different conditions (at 4, 20, 30, and 37°C, with or without agitation) and analyzed at different time points (3, 6, 24, 48 and 72h post-sampling) in parallel on the Abbott CELL-DYN Sapphire, Beckman Coulter DxH900, Siemens ADVIA 2120i and Sysmex XN-1000V. In the same blood samples, eosinophil-derived neurotoxin (EDN), eosinophil activation and death markers were analyzed. All platforms gave comparable measurements of cell differentials on fresh blood within the same day of sampling. However, by 24 hours, significant temporal and temperature-dependent differences were observed, most markedly for eosinophils. None of the platforms performed perfectly across all temperatures tested during the 72 hours, showing that handling conditions should be optimized depending on the cell type of interest and the hematology analyzer. Neither disease status (healthy vs. asthma) nor agitation of the sample affected the cell quantification result or EDN release. The eosinophil activation markers measured by flow cytometry increased with time, were influenced by temperature, and were higher in those with asthma versus healthy participants. In conclusion, hematology analyzer, time window from sampling until analysis, and temperature conditions must be considered when analyzing blood cell differentials, particularly for eosinophils, via central labs to obtain counts comparable to the values obtained in freshly sampled blood.

BACKGROUND: Specimens with incorrect patient information are both a critical safety error and difficult to identify. Estimates of sample mislabelling rely on subjective identification of mislabelling, with the possibility that not all mislabelled samples are being caught.
METHODS: We determined the blood type of two or more complete blood count specimens with the same patient label and assessed for discrepancies. We additionally determined the rate of identified sample mislabelling for the study period.
RESULTS: We found a rate of 3.17 per 1000 discrepancies over the study period. These discrepancies most likely represent occult, or unidentified, mislabelled samples. In contrast, the rate of identified sample mislabelling was 1.15 per 1000.
CONCLUSIONS: This study suggests that specimens identified as, or known to be, mislabelled represent only a fraction of those mislabelled. These findings are currently being confirmed in our laboratory and are likely generalisable to other institutions.

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40-70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5-6.5 years post-CBC. Our ridge regression model, incorporating neutrophil-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64-0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

UNASSIGNED: Myasthenia gravis (MG) is a chronic autoimmune disease caused by neuromuscular junction (NMJ) dysfunction. Our current understanding of MG\'s inflammatory component remains poor. The systemic inflammatory response index (SIRI) presents a promising yet unexplored biomarker for assessing MG severity. This study aimed to investigate the potential relationship between SIRI and MG disease severity.
UNASSIGNED: We conducted a retrospective analysis of clinical data from 171 MG patients admitted between January 2016 and June 2021. Patients with incomplete data, other autoimmune diseases, or comorbidities were excluded. Disease severity was evaluated using the Myasthenia Gravis Foundation of America (MGFA) classification and Myasthenia Gravis Activities of Daily Living (MG-ADL) on admission. The association between SIRI and disease severity was assessed through logistic regression analysis, along with receiver operating characteristic (ROC) curve and decision curve analysis (DCA) comparisons with established inflammation indicators.
UNASSIGNED: After exclusion, 143 patients were analyzed in our study. SIRI levels significantly differed between patients with higher and lower disease severity (p < 0.001). Univariate logistic regression showed that SIRI had a significant effect on high disease severity (OR = 1.376, 95% CI 1.138-1.664, p = 0.001). This association remained significant even after adjusting for age, sex, disease duration, history of MG medication and thymoma (OR = 1.308, 95% CI 1.072-1.597, p = 0.008). Additionally, a positive correlation between SIRI and MG-ADL was observed (r = 0.232, p = 0.008). Significant interactions were observed between SIRI and immunosuppressor (p interaction = 0.001) and intravenous immunoglobulin (p interaction = 0.005). DCA demonstrated the superior net clinical benefit of SIRI compared to other markers when the threshold probability was around 0.2.
UNASSIGNED: Our findings indicate a strong independent association between SIRI and disease severity in MG, suggesting SIRI\'s potential as a valuable biomarker for MG with superior clinical benefit to currently utilized markers.

OBJECTIVE: To assess concordance between umbilical cord blood (UCB) and neonatal blood (NB) laboratory test results at birth.
METHODS: This retrospective study considered very preterm neonates (<32 weeks\' gestational age) admitted to a tertiary neonatal intensive care unit from 2012 to 2023. Inclusion criteria required neonates with a complete blood count measured in both UCB and NB drawn within 2 hours after birth. Median hemoglobin (Hb) and hematocrit (Hct) concentrations were compared between UCB (venous samples) and NB (venous, arterial, or capillary samples).
RESULTS: A total of 432 neonates with paired UCB and NB values were included in the study. Hb concentration in UCB was 14.7 g/dL (IQR 13.5-16.1 g/dL) compared with 14.8 g/dL (IQR 12.6-19.3 g/dL) in venous NB samples, 13.9 g/dL (IQR 12.9-15.3 g/dL) in arterial NB and 18.7 g/dL (IQR 16.6-20.8 g/dL) in capillary NB. The regression equation showed a correction factor of 1.08 for converting Hb values from UCB to venous NB. Median Hct concentration in UCB was 0.45 L/L (IQR: 0.41-0.49 L/L) compared with 0.48 L/L (IQR 0.43-0.54 L/L) in venous NB, 0.42 L/L (IQR 0.38-0.45 L/L) in arterial NB and 0.57 L/L, (IQR 0.51-0.63 L/L) in capillary NB.
CONCLUSIONS: Hb and Hct concentrations measured in UCB are similar to those measured in venous blood in very preterm infants and are valid alternatives for NB tests at birth. Hb and Hct concentrations in arterial and capillary NB are respectively lower and higher compared with UCB measurements.

Early recognition of bloodstream infection (BSI) in infants can be difficult, as symptoms may be non-specific, and culture can take up to 48 h. As a result, many infants receive unneeded antibiotic treatment while awaiting the culture results. In this study, we aimed to develop a model that can reliably identify infants who do not have positive blood cultures (and, by extension, BSI) based on the full blood count (FBC) and C-reactive protein (CRP) values. Several models (i.e. multivariable logistic regression, linear discriminant analysis, K nearest neighbors, support vector machine, random forest model and decision tree) were trained using FBC and CRP values of 2693 infants aged 7 to 60 days with suspected BSI between 2005 and 2022 in a tertiary paediatric hospital in Dublin, Ireland. All models tested showed similar sensitivities (range 47% - 62%) and specificities (range 85%-95%). A trained decision tree and random forest model were applied to the full dataset and to a dataset containing infants with suspected BSI in 2023 and showed good segregation of a low-risk and high-risk group. Negative predictive values for these two models were high for the full dataset (> 99%) and for the 2023 dataset (> 97%), while positive predictive values were low in both dataset (4%-20%).   Conclusion: We identified several models that can predict positive blood cultures in infants with suspected BSI aged 7 to 60 days. Application of these models could prevent administration of antimicrobial treatment and burdensome diagnostics in infants who do not need them. What is Known: • Bloodstream infection (BSI) in infants cause non-specific symptoms and may be difficult to diagnose. • Results of blood cultures can take up to 48 hours. What is New: • Machine learning models can contribute to clinical decision making on BSI in infants while blood culture results are not yet known.