Do dynamic changes in haematological and biochemical parameters predict mortality in critically ill COVID-19 patients?

BACKGROUND: Critically ill COVID-19 patients are usually subjected to clinical, laboratory, and radiological diagnostic procedures resulting in numerous findings. Utilizing these findings as indicators for disease progression or outcome prediction is particularly intriguing. OBJECTIVES: Exploring the significance of dynamic changes in haematological and biochemical parameters in predicting the mortality of critically ill COVID-19 patients. METHODS: The present study was a prospective and observational study involving mechanically ventilated 75 critically ill adult COVID-19 patients with hypoxemic respiratory failure. The collected data included baseline patient characteristics, treatment options, outcome, and laboratory findings at admission and 7 days after. The dynamics of the obtained findings were compared between survivors and non-survivors. RESULTS: The 28-day survival rate was 61.3%. In the group of non-survivors significant dynamic changes were found for C-reactive protein (p= 0.001), interleukin-6 (p< 0.001), lymphocyte (p= 0.003), neutrophil-lymphocyte ratio (p= 0.003), platelets (p< 0.001), haemoglobin (p< 0.001), iron (p= 0.012), and total iron-binding capacity (p< 0.001). Statistically significant changes over time were found for ferritin (p= 0.010), D-dimer (p< 0.001), hs-troponin T (p< 0.002), lactate dehydrogenase (p= 0.001), glucose (p= 0.023), unsaturated iron-binding capacity (p= 0.008), and vitamin D (p< 0.001). CONCLUSION: The dynamic changes in inflammatory, haematological and biochemical parameters can predict disease severity, and outcome.

Establishment and validation of a prognostic model based on common laboratory indicators for SARS-CoV-2 infection in Chinese population.

BACKGROUND: At the beginning of December 2022, the Chinese government made major adjustments to the epidemic prevention and control measures. The epidemic infection data and laboratory makers for infected patients based on this period may help with the management and prognostication of COVID-19 patients. METHODS: The COVID-19 patients hospitalized during December 2022 were enrolled. Logistic regression analysis was used to screen significant factors associated with mortality in patients with COVID-19. Candidate variables were screened by LASSO and stepwise logistic regression methods and were used to construct logistic regression as the prognostic model. The performance of the models was evaluated by discrimination, calibration, and net benefit. RESULTS: 888 patients were eligible, consisting of 715 survivors and 173 all-cause deaths. Factors significantly associated with mortality in COVID-19 patients were: lactate dehydrogenase (LDH), albumin (ALB), procalcitonin (PCT), age, smoking history, malignancy history, high density lipoprotein cholesterol (HDL-C), lactate, vaccine status and urea. 335 of the 888 eligible patients were defined as ICU cases. Seven predictors, including neutrophil to lymphocyte ratio, D-dimer, PCT, C-reactive protein, ALB, bicarbonate, and LDH, were finally selected to establish the prognostic model and generate a nomogram. The area under the curve of the receiver operating curve in the training and validation cohorts were respectively 0.842 and 0.853. In terms of calibration, predicted probabilities and observed proportions displayed high agreements. Decision curve analysis showed high clinical net benefit in the risk threshold of 0.10-0.85. A cutoff value of 81.220 was determined to predict the outcome of COVID-19 patients via this nomogram. CONCLUSIONS: The laboratory model established in this study showed high discrimination, calibration, and net benefit. It may be used for early identification of severe patients with COVID-19.

The effect of a nurse-led low carbohydrate regimen on anthropometric and laboratory parameters of patients with metabolic syndrome: a quasi-experimental study.

Introduction: Metabolic syndrome is a global health concern. It is a condition that includes a cluster of various risk factors for type 2 diabetes and cardiovascular disease. This quasi-experimental study investigates the effect of a nurse-led low-carbohydrate regimen on anthropometric and laboratory parameters in metabolic syndrome patients. Methods: The study used a quasi-experimental design conducted at the University of Mosul; 128 participants meeting the metabolic syndrome criteria were recruited and divided into the intervention and control groups. The intervention group received personalized counseling and support in implementing a low-carb regime, while the control group received standard advice. The study participants were assessed by anthropometry, and laboratory parameters were evaluated pre- and post-intervention. Statistical data analysis was conducted using IBM-SPSS 27, including chi-square, Fisher's exact test, t-tests, and the Mcnemar test, which were performed to compare the changes within and between groups. Results: The mean age of the participants in the intervention and control groups was 50.72 ± 6.43 years and 49.14 ± 6.89 years, respectively. Compared to the control group, the intervention group experienced a significant positive reduction in anthropometric measures and laboratory parameters, including weight, body mass index (BMI), waist circumference, lipid profiles, and HbA1c. Conclusion: A tangible effect of nurse-led interventions based on low-carbohydrate regimens in managing metabolic syndrome was empirically authenticated. Positive changes were observed in the intervention group regarding anthropometric measures and laboratory parameters. However, future research may require a larger sample size and a longer follow-up to confirm these effects and evaluate long-term metabolic impacts.

Severe COVID-19 multisystem inflammatory syndrome versus severe dengue in children from Indonesia: a cross-sectional study.

INTRODUCTION: Severe multisystem inflammatory syndrome in children (MIS-C) and severe dengue are challenging to identify during the COVID-19 pandemic in dengue-endemic areas. Fever, multiorgan involvement, and shock characterize both severe MIS-C and severe dengue. Distinguishing between the two diseases is beneficial in initiating proper management. METHODS: Medical records of children < 18 years old who were hospitalized at Hasan Sadikin General Hospital's PICU between December 2020 and July 2022 with severe MIS-C or severe dengue were recorded. Differences were assessed using comparative and descriptive analyses. RESULTS: Seventeen severe dengue patients and 4 severe MIS-C were included. The average age of severe MIS-C was 11.5 years (SD ± 2.9, 95% CI), and that of severe dengue patients was 6.2 years (SD ± 4.4, 95% CI) (p value = 0.034, 95%). Fever and abdominal pain were the most common symptoms in both groups (p = 0.471, 95% CI). Rash (p = 0.049) and nonpurulent conjunctivitis (p = 0.035) were two symptoms with significant differences. The highest platelet count (p-value = 0.006, 95% CI), AST (p-value = 0.026, 95% CI), and D-dimer level (p-value = 0.025, 95% CI) were significantly different between the two cohorts. Cardiac abnormalities were found in all (100%) severe MIS-C patients, but only one (5.9%) in severe dengue patients. CONCLUSION: Age, rash, nonpurulent conjunctivitis, platelet count, AST and D-dimer level may distinguish severe MIS-C from severe dengue fever.

Prediction equations for detecting COVID-19 infection using basic laboratory parameters.

Objectives: Coronavirus disease 2019 (COVID-19) emerged as a global pandemic during 2019 to 2022. The gold standard method of detecting this disease is reverse transcription-polymerase chain reaction (RT-PCR). However, RT-PCR has a number of shortcomings. Hence, the objective is to propose a cheap and effective method of detecting COVID-19 infection by using machine learning (ML) techniques, which encompasses five basic parameters as an alternative to the costly RT-PCR. Materials and Methods: Two machine learning-based predictive models, namely, Artificial Neural Network (ANN) and Multivariate Adaptive Regression Splines (MARS), are designed for predicting COVID-19 infection as a cheaper and simpler alternative to RT-PCR utilizing five basic parameters [i.e., age, total leucocyte count, red blood cell count, platelet count, C-reactive protein (CRP)]. Each of these parameters was studied, and correlation is drawn with COVID-19 diagnosis and progression. These laboratory parameters were evaluated in 171 patients who presented with symptoms suspicious of COVID-19 in a hospital at Kharagpur, India, from April to August 2022. Out of a total of 171 patients, 88 and 83 were found to be COVID-19-negative and COVID-19-positive, respectively. Results: The accuracies of the predicted class are found to be 97.06% and 91.18% for ANN and MARS, respectively. CRP is found to be the most significant input parameter. Finally, two predictive mathematical equations for each ML model are provided, which can be quite useful to detect the COVID-19 infection easily. Conclusion: It is expected that the present study will be useful to the medical practitioners for predicting the COVID-19 infection in patients based on only five very basic parameters.

Predictive parameters for early detection of clinically relevant abdominal trauma in multiple-injury or polytraumatised patients: a retrospective analysis.

Diagnosis of relevant organ injury after blunt abdominal injury (AI) in multiple-injury/polytraumatised patients is challenging. AI can be distinguished between injuries of parenchymatous organs (POI) of the upper abdomen (liver, spleen) and bowel and mesenteric injuries (BMI). Still, such injuries may be associated with delays in diagnosis and treatment. The present study aimed to verify laboratory parameters, imaging diagnostics, physical examination and related injuries to predict intraabdominal injuries. This retrospective, single-centre study includes data from multiple-injury/polytraumatised patients between 2005 and 2017. Two main groups were defined with relevant abdominal injury (AI+) and without abdominal injury (AI-). The AI+ group was divided into three subgroups: BMI+, BMI+/POI+, and POI+. Groups were compared in a univariate analysis for significant differences. Logistic regression analysis was used to determine predictors for AI+, BMI+ and POI+. 26.3% (271 of 1032) of the included patients had an abdominal injury. Subgroups were composed of 4.7% (49 of 1032) BMI+, 4.7% (48 of 1032) BMI+/POI+ and 16.8% (174 of 1032) POI+. Pathological abdominal signs had a sensitivity of 48.7% and a specificity of 92.4% for AI+. Transaminases were significantly higher in cases of AI+. Pathological computed tomography (CT) (free fluid, parenchymal damage, Bowel Injury Prediction Score (BIPS), CT Grade > 4) was summarised and had a sensitivity of 94.8%, a specificity of 98%, positive predictive value (PPV) of 94.5% and, negative predictive value (NPV) of 98.2% for AI+. The detected predictors for AI+ were pathological abdominal findings (odds ratio (OR) 3.93), pathological multi-slice computed tomography (MSCT) (OR 668.9), alanine (ALAT) ≥ 1.23 µmol/ls (OR 2.35) and associated long bone fractures (OR 3.82). Pathological abdominal signs, pathological MSCT and lactate (LAC) levels ≥ 1.94 mmol/l could be calculated as significant risk factors for BMI+. For POI+ pathological abdominal MSCT, ASAT ≥ 1.73 µmol/ls and concomitant thoracic injuries had significant relevance. The study presents reliable risk factors for abdominal injury and its sub-entities. The predictors can be explained by the anatomy of the trunk and existing studies. Elevated transaminases predicted abdominal injury (AI+) and, specifically, the POI+. The pathological MSCT was the most reliable predictive parameter. However, it was essential to include further relevant parameters.

Predictiveness of preoperative laboratory values for postoperative delirium.

Background: Postoperative delirium (POD) is a common postoperative complication, especially in patients over 60 years, with an incidence ranging from 15% to 50%. In most cases, POD manifests in the first 5 days after surgery. Multiple contributing risk factors for POD have been detected. Besides the predisposing factors such as higher age, cognitive impairment, high blood pressure, atrial fibrillation, and past stroke, pathophysiological mechanisms like neuroinflammation are also considered as contributing factors. Methods: In a subanalysis of the "PRe- Operative  Prediction of postoperative  DElirium by appropriate  SCreening" (PROPDESC) study, the preoperative laboratory values of sodium, potassium, total protein, hemoglobin concentration (Hgb), and white blood cells as well as the biomarkers creatinine, HbA1c, NT-pro-BNP, high sensitive Troponin T (hsTnT), and C-reactive protein (CRP) were assessed to investigate a possible relationship to the occurrence of POD. Results: After correction for age, physical status classification, surgery risk after Johns Hopkins, and operative discipline (cardiac surgery vs. noncardiac surgery), male patients with a Hgb <13 g/dL had significantly higher odds for POD (p = 0.025). Furthermore, patients with CRP ≥ 10 mg/L, HbA1c value ≥ 8.5% as well as patients with hypernatraemia (>145 mmol/L) presented significantly higher odds to develop POD (p = 0.011, p < 0.001, and p = 0.021, respectively). A raised (>14-52 ng/L) or high (>52 ng/L) hsTnT value was also associated with a significantly higher chance for POD compared to the patient group with hsTnT <14 ng/L (p < 0.001 and p = 0.016, respectively). Conclusions: Preoperative Hgb, CRP, HbA1c, sodium, and hsTnT could be used to complement and refine the preoperative screening for patients at risk for POD. Further studies should track these correlations to investigate the potential of targeted POD protection and enabling hospital staff to initiate POD-preventing measures in time.

Novel approach exploring the correlation between presepsin and routine laboratory parameters using explainable artificial intelligence.

Although presepsin, a crucial biomarker for the diagnosis and management of sepsis, has gained prominence in contemporary medical research, its relationship with routine laboratory parameters, including demographic data and hospital blood test data, remains underexplored. This study integrates machine learning with explainable artificial intelligence (XAI) to provide insights into the relationship between presepsin and these parameters. Advanced machine learning classifiers provide a multilateral view of data and play an important role in highlighting the interrelationships between presepsin and other parameters. XAI enhances analysis by ensuring transparency in the model's decisions, especially in selecting key parameters that significantly enhance classification accuracy. Utilizing XAI, this study successfully identified critical parameters that increased the predictive accuracy for sepsis patients, achieving a remarkable ROC AUC of 0.97 and an accuracy of 0.94. This breakthrough is possibly attributed to the comprehensive utilization of XAI in refining parameter selection, thus leading to these significant predictive metrics. The presence of missing data in datasets is another concern; this study addresses it by employing Extreme Gradient Boosting (XGBoost) to manage missing data, effectively mitigating potential biases while preserving both the accuracy and relevance of the results. The perspective of examining data from higher dimensions using machine learning transcends traditional observation and analysis. The findings of this study hold the potential to enhance patient diagnoses and treatment, underscoring the value of merging traditional research methods with advanced analytical tools.

Differentiating COVID-19 and influenza in children: hemogram parameters as diagnostic tools.

Introduction: It is not always possible to differentiate between influenza and COVID-19 based on symptoms alone. This is a topic of significant importance as it aims to determine whether there are specific hematological parameters that can be used to distinguish between influenza and COVID-19 in children. Methodology: Two hundred thirty-one children between the ages of 1 month and 18 years who presented to the children's outpatient clinic between June 2021 and June 2022 with similar symptoms and were tested with an influenza test and a COVID-19 PCR test were included in the study. Of the patients included in the study, 130 tested positive for COVID-19 and 101 positive for influenza. The patients were evaluated for hematological parameters. Results: Age, eosinophils and monocyte factors were shown to be statistically significantly effective in COVID-19. The risk of COVID-19 increased 1,484-fold with age, 10,708-fold with increasing eosinophil count, and 1,591-fold with increasing monocyte count. The performance of the monocyte count and eosinophil count was assessed by receiver operating characteristic curve (ROC) analysis. According to the performed ROC analysis, the area under the curve (AUC) value was observed to be 0.990 for monocytes. According to the cutoff point >1.50, the sensitivity value was determined as 98.4% and the specificity value as 97.0%. AUC significance for eosinophils was found to be 0.989. According to the cutoff point >0.02, the sensitivity value was determined as 99.2% and the specificity value as 93.1%. Conclusion: In the diagnosis of COVID-19, the eosinophil count and monocyte count are easily accessible, inexpensive, and important parameters in terms of differential diagnosis and can help in the differentiation of COVID-19 from influenza during seasonal outbreaks of the latter. Developing parameters for clinicians to use in diagnosing COVID-19 and influenza can facilitate their work in practice.

Safety analysis of laboratory parameters in paediatric patients with spinal muscular atrophy treated with nusinersen.

BACKGROUND: Spinal muscular atrophy (SMA) is a progressive neurodegenerative disorder that can be treated with intrathecal nusinersen, an antisense oligonucleotide. In addition to efficacy, safety is a determining factor in the success of any therapy. Here, we aim to assess the safety of nusinersen therapy in paediatric patients with SMA. METHODS: Laboratory data of paediatric patients with SMA who received nusinersen between October 2019 and May 2022 were retrospectively analysed. RESULTS: During the observation period, 46 infants and children aged 2.9 months to 13.6 years received a total of 213 nusinersen doses without safety concerns. Inflammatory markers were stable throughout the study. International normalized ratio was increased by 0.09 per injection. Urea levels were increased by 0.108 mmol/L, and cystatin C decreased by 0.029 mg/L per injection. There were no significant changes in platelet count, activated partial thrombin time, creatinine levels or liver enzyme levels during treatment. The cerebrospinal fluid (CSF) leukocyte count remained stable, and total protein increased by 24.038 mg/L per injection. CONCLUSION: Our data showed that nusinersen therapy is generally safe in children with SMA. Laboratory monitoring did not identify any persistent or significantly abnormal findings. CSF protein should be monitored to gain more insights.

Cq values as an indicator for COVID-19 outcomes: A study of the correlation between laboratory parameters.

OBJECTIVE: The ongoing outbreak of the respiratory disease coronavirus disease 2019 (COVID-19) is currently presenting a major global health threat. This pandemic is unprecedented in recent human history. The objective of this study was to examine the relationship between cycle quantitation (Cq) and laboratory parameters in COVID-19 patients, aiming to determine if Cq levels can provide valuable insights into the COVID-19 disease. METHODS: This study involved 234 participants who were divided into case and control groups. Real-time PCR tests were used to diagnose COVID-19 cases in the study participants. Blood tests, including complete blood count, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), lactate dehydrogenase (LDH), D-dimer, IgG, and IgM, were also conducted. Statistical analysis was performed using SPSS 22 software. RESULTS: The findings showed that COVID-19-positive cases had significantly higher levels of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), D-dimer, ESR, CRP, and LDH compared to normal cases. Additionally, the case group had significantly lower lymphocyte and platelet counts. There was a statistically significant positive correlation between Cq levels and lymphocyte count (r = .124, p = .014). Conversely, there was a statistically significant inverse correlation between Cq levels and NLR (r = -.208, p = .017). Furthermore, the evaluation of hematological, inflammatory, and biochemical indexes in COVID-19 patients using the receiver-operating characteristics curve demonstrated statistically appropriate sensitivity and specificity. CONCLUSION: Our outcomes indicated a significant association between Cq levels and PLR, NLR, D-dimer, CRP, and ESR in COVID-19 patients. Consequently, including the report of laboratory parameters alongside Cq values offers a promising prognosis.

Comprehensive analysis of the prognostic role of laboratory indices in advanced lung cancer patients.

OBJECTIVE: Lung cancer, the most common cause of cancer-related death, is diagnosed mostly in advanced stages, and 5-year survival is approximately 5.8%. It is critical to identify reliable prognostic factors to optimize treatment responses, guide therapeutic strategies and pave the way to new research. In this study, we aimed to investigate the strongest prognostic factors for advanced non-small cell lung cancer (NSCLC). METHODS: We retrospectively analyzed 278 patients with NSCLC. We evaluated the association between potential prognostic factors and overall survival (OS) times using Kaplan-Meier analysis and Cox regression analysis. RESULTS: The median OS in all patients was 15.3 months. In univariate analysis, gender, histologic type, performance status, immunotherapy, radiotherapy, hemoglobin level, serum albumin, sodium-globulin ratio (SGR), neutrophil-lymphocyte ratio (NLR), systemic immune inflammation index (SII), hemoglobin-albumin-lymphocyte-platelet score (HALP), and advanced lung cancer index (ALI) were associated with survival. Models were established for multivariate analyses. In the models, NLR, SGR, HALP, immunotherapy, radiotherapy, and Eastern Cooperative Oncology Group (ECOG) performance status showed independent prognostic features (p < 0.001, p = 0.003, p = 0.002, p < 0.001, p = 0.010, and p = 0.025, respectively). In addition, in the subgroup analysis, prognostic indexes (NLR, SGR, and HALP) were found to have a prognostic effect on survival in multiple subgroups. CONCLUSIONS: Pretreatment NLR, SGR, HALP, immunotherapy, radiotherapy, and ECOG performance status are independent prognostic factors for advanced NSCLC patients. These prognostic factors can be used in clinical practice as easily accessible, simple, and useful tools for clinicians.

Clinical Course and Factors Associated With Hospital Admission and Mortality among Sars-Cov 2 Patients within Nairobi Metropolitan Area.

This study aims to investigate the clinical course and factors associated with hospital admission and mortality among SARS-CoV-2 patients within the Nairobi Metropolitan Area. The study utilizes a multicenter retrospective cohort design, collecting clinical characteristics and laboratory parameters of hospitalized patients from March 2020 to May 2022. Data analysis includes percentages, frequencies, chi-square tests, Kaplan-Meier analysis, pairwise comparisons, and multivariate regression models. Ethical considerations are observed throughout the research process. The study findings highlight significant associations between comorbidities, such as hypertension, and increased mortality risk due to COVID-19. Symptoms including fever, cough, dyspnea, chest pain, sore throat, and loss of smell/taste are also identified as predictors of mortality. Abnormal laboratory parameters, such as oxygen saturation, procalcitonin, glucose levels, serum creatinine, and gamma-glutamyl transpeptidase, are associated with mortality. However, demographic factors and certain vital signs do not exhibit significant associations. Recommendations based on this study suggest increased monitoring and management of comorbidities, early identification and management of symptoms, regular monitoring of laboratory parameters, continued research and collaboration, and implementation of preventive measures. Overall, a multidisciplinary approach involving healthcare professionals, researchers, policymakers, and the public is crucial to improve COVID-19 outcomes and reduce mortality rates. Adaptation of strategies based on emerging evidence and resource allocation is essential for effective management of the pandemic.

Investigation of the relationship between coagulation parameters and mortality in COVID-19 infection.

This study, which included patients over the age of 18 who were diagnosed with coronavirus disease 2019 (COVID-19) in the emergency clinic, aims to determine the relationship between coagulation parameters and mortality. Epidemiologic data such as age, gender, medical history, vital parameters at emergency department admission, clinical findings, coagulation parameters such as d-dimer, prothrombin time (PT), active partial thromboplastin time (aPTT), international normalized ration (INR), fibrinogen, and platelet were evaluated. Patients with positive computerized tomography (CT) findings and positive polymerase chain reaction (PCR) together were included in the study. It was revealed that d-dimer, fibrinogen, INR, and PT values were higher in the elderly group. It was shown that there was a significant relationship between hospitalization days (ward or intensive care unit) and d-dimer levels. It was observed that d-dimer, fibrinogen elevation was significantly associated with prognosis by increasing mortality, and that platelet and aPTT values were also associated with prognosis and were lower in the mortality group. On the other hand, in receiver operating characteristic (ROC) analysis, the sensitivity and specificity data were 80.3%/80.0% for d-dimer, 70.5%/72.2% for fibrinogen, 58.2%/59.4% for aPTT, and 59.7%/59.2% for platelet, respectively. The overall classification success was 88.6% and mortality prediction success was 37.7% in the regression model of some coagulation parameters (d-dimer, fibrinogen, aPTT, and platelet) which were effective on prognosis. In conclusion, it was determined that d-dimer, fibrinogen, aPTT, and platelet parameters were directly associated with mortality and when these coagulation parameters were used together with the clinical, vital, and demographic data of the patients, the success of mortality prediction increased significantly.

Interplay of Demographic Influences, Clinical Manifestations, and Longitudinal Profile of Laboratory Parameters in the Progression of SARS-CoV-2 Infection: Insights from the Saudi Population.

Understanding the factors driving SARS-CoV-2 infection progression and severity is complex due to the dynamic nature of human physiology. Therefore, we aimed to explore the severity risk indicators of SARS-CoV-2 through demographic data, clinical manifestations, and the profile of laboratory parameters. The study included 175 patients either hospitalized at King Abdulaziz Medical City-Riyadh or placed in quarantine at designated hotels in Riyadh, Saudi Arabia, from June 2020 to April 2021. Hospitalized patients were followed up through the first week of admission. Demographic data, clinical presentations, and laboratory results were retrieved from electronic patient records. Our results revealed that older age (OR: 1.1, CI: [1.1-1.12]; p < 0.0001), male gender (OR: 2.26, CI: [1.0-5.1]; p = 0.047), and blood urea nitrogen level (OR: 2.56, CI: [1.07-6.12]; p = 0.034) were potential predictors of severity level. In conclusion, the study showed that apart from laboratory parameters, age and gender could potentially predict the severity of SARS-CoV-2 infection in the early stages. To our knowledge, this study is the first in Saudi Arabia to explore the longitudinal profile of laboratory parameters among risk factors, shedding light on SARS-CoV-2 infection progression parameters.

Comprehensive evaluation of immune dysregulation in secondary hemophagocytic lymphohistiocytosis.

Host immune dysfunction plays a crucial role in the onset, progression, and outcome of hemophagocytic lymphohistiocytosis (HLH). This study aimed to comprehensively evaluate the peripheral immune profiles in patients with newly diagnosed secondary hemophagocytic lymphohistiocytosis (sHLH), and explore their predictive value for patient prognosis. A total of 77 patients with sHLH were enrolled in this study, with 31 of them experiencing mortality. Flow cytometry was used to assess the percentages, absolute numbers, and phenotypes of lymphocyte subsets. Simultaneously, cytokine levels and routine laboratory indicators were also collected. In sHLH patients, lymphocyte subset absolute numbers were significantly impaired, accompanied by T cell hyperactivation, B cell hyperactivation, and increased plasmablast proliferation. Prognostic analysis revealed that lower CD8+ T cell percentages, elevated APTT, IL-6, IL-10 levels, and increased CD4+CD28null T cell proportions were associated with poor patient outcomes. The study demonstrates dysregulation in the counts and phenotypes of lymphocyte subsets in sHLH patients. Several key factors, including IL-6, IL-10, APTT, and various T cell percentages, have potential as prognostic markers and therapeutic targets in sHLH.

Identifying patients at risk for myasthenic crisis with hemogram and inflammation-related laboratory parameters - a pilot study.

Background: Myasthenia gravis (MG) is a rare autoimmune disease characterized by fatigable weakness of the voluntary muscles and can exacerbate to life-threatening myasthenic crisis (MC), requiring intensive care treatment. Routine laboratory parameters are a cost-effective and widely available method for estimating the clinical outcomes of several diseases, but so far, such parameters have not been established to detect disease progression in MG. Methods: We conducted a retrospective analysis of selected laboratory parameters related to inflammation and hemogram for MG patients with MC compared to MG patients without MC. To identify potential risk factors for MC, we applied time-varying Cox regression for time to MC and, as a sensitivity analysis, generalized estimating equations logistic regression for the occurrence of MC at the next patient visit. Results: 15 of the 58 examined MG patients suffered at least one MC. There was no notable difference in the occurrence of MC by antibody status or sex. Both regression models showed that higher counts of basophils (per 0.01 unit increase: HR = 1.32, 95% CI = 1.02-1.70), neutrophils (per 1 unit increase: HR = 1.40, 95% CI = 1.14-1.72), potentially leukocytes (per 1 unit increase: HR = 1.15, 95% CI = 0.99-1.34), and platelets (per 100 units increase: HR = 1.54, 95% CI = 0.99-2.38) may indicate increased risk for a myasthenic crisis. Conclusion: This pilot study provides proof of the concept that increased counts of basophils, neutrophils, leukocytes, and platelets may be associated with a higher risk of developing MC in patients with MG.

Investigation of peripheral inflammatory biomarkers in association with suicide risk in major depressive disorder.

Suicide is the most severe complication of major depressive disorder (MDD). Novel research assumes the role of immunological dysregulation in the background - several studies have reported alterations in the number of inflammatory cells related to both MDD and suicidality. There are currently no objective, routinely measured parameters to indicate suicidal vulnerability. However, altered inflammatory cell numbers and ratios have been proposed as potential biomarkers of suicide risk (SR). The present research aims to examine changes of these values related to increased SR in MDD as an assumed inflammatory state. We investigated laboratory parameters of psychiatric in-patients diagnosed with MDD (n = 101) retrospectively. Individuals with recent suicide attempt (SA) (n = 22) and with past SA (n = 19) represented the high SR group. MDD patients with no history of SA (n = 60) composed the intermediate SR group. We compared the number of neutrophil granulocytes, monocytes, lymphocytes, platelets, white blood cell count (WBC), neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte ratio (PLR), mean platelet volume (MPV), red blood cell distribution width (RDW) and erythrocyte sedimentation rate (ESR). Furthermore, we evaluated alterations of these parameters related to antidepressant (AD) and antipsychotic (AP) treatment, which have been proved to have anti-inflammatory effects. We found a significant increase in neutrophil granulocyte count, NLR, monocyte count, MLR, WBC and ESR in patients with recent SA compared to patients with no history of SA. Moreover, there was a significant elevation in monocyte count, MLR, ESR and RDW in patients with high SR compared to patients with intermediate SR. AD treatment resulted in a significant decrease in neutrophil granulocyte count and NLR, however, it did not affect monocyte count and MLR. Assuming immunological mechanisms in the background of MDD and suicidality, our findings support the role of NLR as a biomarker of acute SR, though its alterations may be masked by possible anti-inflammatory effects of AD treatment in the long term. However, MLR, a marker exhibiting changes which are not attenuated by pharmacotherapy, may be a possible indicator of both acute and long-term suicidal vulnerability.

Prognostic Factors and Construction of Nomogram Prediction Model of Lung Cancer Patients Using Clinical and Blood Laboratory Parameters.

Objective: This work aimed to explore the prognostic risk factors of lung cancer (LC) patients and establish a line chart prediction model. Methods: A total of 322 LC patients were taken as the study subjects. They were randomly divided into a training set (n = 202) and a validation set (n = 120). Basic information and laboratory indicators were collected, and the progression-free survival (PFS) and overall survival (OS) were followed up. Single-factor and cyclooxygenase (COX) multivariate analyses were performed on the training set to construct a Nomogram prediction model, which was validated with 120 patients in the validation set, and Harrell's consistency was analyzed. Results: Single-factor analysis revealed significant differences in PFS (P<0.05) between genders, body mass index (BMI), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), squamous cell carcinoma antigen (SCCA), treatment methods, treatment response evaluation, smoking status, presence of pericardial effusion, and programmed death ligand 1 (PD-L1) at 0 and 1-50%. Significant differences in OS (P<0.05) were observed for age, tumor location, treatment methods, White blood cells (WBC), uric acid (UA), CA125, pro-gastrin-releasing peptide (ProGRP), SCCA, cytokeratin fragment 21 (CYFRA21), and smoking status. COX analysis identified male gender, progressive disease (PD) as treatment response, and SCCA > 1.6 as risk factors for LC PFS. The consistency indices of the line chart models for predicting PFS and OS were 0.782 and 0.772, respectively. Conclusion: Male gender, treatment response of PD, and SCCA > 1.6 are independent risk factors affecting the survival of LC patients. The PFS line chart model demonstrates good concordance.

Impact of Upadacitinib on Laboratory Parameters and Related Adverse Events in Patients with RA: Integrated Data Up to 6.5 Years.

INTRODUCTION: Upadacitinib (UPA) is a Janus kinase inhibitor that has demonstrated efficacy in moderate-to-severe rheumatoid arthritis (RA) with an acceptable safety profile. We investigated laboratory parameter changes in UPA RA clinical trials. METHODS: Pooled data from six randomized trials in the SELECT phase 3 program were included. Key laboratory parameters and safety data were measured for UPA 15 and 30 mg once daily (QD), adalimumab (ADA) 40 mg every other week + methotrexate (MTX), and MTX monotherapy. Exposure-adjusted event rates (EAERs) of adverse events were calculated. RESULTS: A total of 3209 patients receiving UPA 15 mg QD (10 782.7 patient-years [PY]), 1204 patients receiving UPA 30 mg QD (3162.5 PY), 579 patients receiving ADA + MTX (1573.2 PY), and 314 patients receiving MTX monotherapy (865.1 PY) were included, representing up to 6.5 years of total exposure. Decreases in mean levels of hemoglobin, neutrophils, and lymphocytes, and increases in mean levels of liver enzymes and creatinine phosphokinase were observed with UPA, with grade 3 or 4 changes observed in some patients. Mean low- and high-density lipoprotein cholesterol ratios remained stable for patients receiving UPA 15 mg QD. EAERs of anemia and neutropenia occurred at generally consistent rates between UPA and active comparators (3.1-4.3 and 1.7-5.0 events [E]/100 PY across treatment groups, respectively). Rates of hepatic disorder were higher with MTX monotherapy, UPA 15 mg and UPA 30 mg (10.8, 9.7, and 11.0 E/100 PY, respectively) versus ADA + MTX (6.4 E/100 PY). Rates of lymphopenia were highest with MTX monotherapy (3.2 E/100 PY). Treatment discontinuations due to laboratory-related events were rare, occurring in 1.1% and 2.2% of patients treated with UPA 15 and 30 mg QD, respectively. CONCLUSIONS: The results of this integrated long-term analysis of laboratory parameters continue to support an acceptable safety profile of UPA 15 mg QD for moderate-to-severe RA.