Leukocyte cell population data from the blood cell analyzer as a predictive marker for severity of acute pancreatitis

Interleukin-6 and Leukocyte Cell Population Data in Newly Diagnosed Sepsis-A Prospective Study

Background and Objectives: Sepsis still represents a syndrome with a high mortality. A timely sepsis diagnosis and an early intervention are crucial for the disease outcomes. Sepsis-associated acute kidney injury (SA-AKI) is highly prevalent but often diagnosed late. We aimed to investigate whether serum interleukin-6 (IL-6) and leukocyte cell population data (CPD) could be adequate biomarkers for the prediction of survival and SA-AKI development. Materials and Methods: We conducted a prospective observational study in a medical intensive care unit of a tertiary hospital centre in Zagreb, Croatia from June 2020 to October 2023. Adult patients with newly diagnosed sepsis were included and classified as immunocompetent or immunocompromised. Blood samples were collected upon admission. Results: A total of 150 patients were included in the study. Ninety-six (64%) patients were immunocompetent and fifty-four (36%) were immunocompromised. The median SOFA score was 8 (6-11). SA-AKI was diagnosed in 108 (72%) patients. ICU and hospital mortality was 27.3% and 37.3%, with no significant difference between groups. Significantly higher serum IL-6 levels were noted in the immunocompromised group, while neutrophil granularity intensity was higher in the immunocompetent group. According to logistic regression analyses, elevated IL-6 levels predicted a lethal ICU outcome, while elevated IL-6 levels and neutrophil reactivity intensity were predictors of SA-AKI development. A cluster analysis revealed two patient groups with different IL-6 concentrations, and further studies indicated that the group with higher IL-6 values had significantly higher SA-AKI occurrence and increased lethal outcomes. Conclusions: An early serum IL-6 measurement regardless of the patients' immune status indicates disease severity. Its measurement in the early phase of disease presentation, potentially in the emergency department, might facilitate ICU admission. Further research is warranted in the field of leukocyte CDP application.

Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients

BACKGROUND

Bacteremia is a critical condition with high mortality that requires prompt detection to prevent progression to life-threatening sepsis. Traditional diagnostic approaches, such as blood cultures, are time-consuming. This limitation has encouraged the exploration of rapid prediction methodologies. Cellular Population Data (CPD), which provides detailed insights into white blood cell morphology and functionality, is a promising technique for the early detection of bacteremia.

METHODS

This study applied machine learning models to analyze laboratory data from hospitalized patients at risk of bacteremia from three hospitals. Using complete blood count (CBC), differential count (DC), and CPD, collected at various time intervals, we trained two sets of artificial intelligence models: one trained using data from patients in the Emergency Department (ED) and another specifically designed for and trained using data from a hospitalized cohort. We evaluated the performance of both models by applying them to the same hospitalized population and comparing their outcomes.

RESULTS

The study encompassed analysis of over 66,000 CBC samples. The model tailored for hospitalized patients exhibited superior performance in bacteremia prediction across all cohorts compared with the ED-model, achieving an area under the receiver operating characteristic curve (AUROC) of 0.772 in the validation cohort from China Medical University Hospital and 0.808 and 0.843 in two other hospital cohorts. Notably, nearly half of the top fifteen important features identified by shapely additive explanations values were CPD parameters, underscoring the pivotal role of CPD in predictive models for bacteremia.

CONCLUSIONS

Artificial intelligence models incorporating CPD data can accurately predict bacteremia in hospitalized patients. Models specifically trained on hospitalized patient data demonstrate enhanced performance over those based on ED data in predicting bacteremia occurrences. Future research must explore the clinical effects of these models, focusing on their potential to assist physicians in managing antibiotic use and patient health.

Assessment of point-of-care quantitative serum canine pancreatic lipase testing for diagnosing acute pancreatitis in dogs

Introduction

Current point-of-care testing for canine-specific pancreatic lipase (CPL) provides semi-quantitative measurements with binary results. Recently, a commercial point-of-care testing method (Vcheck CPL) that offers quantitative measurement of CPL has emerged. However, clinical studies on its value (or utility) are limited. Therefore, this study aimed to evaluate the clinical utility of this commercial point-of-care CPL in diagnosing dogs with suspected acute pancreatitis and to assess its correlation with a commercial semi-quantitative test and other clinicopathological variables.

Methods

A prospective observational study included 33 dogs with suspected acute pancreatitis and 20 clinically healthy dogs. Serum Vcheck CPL and SNAP ® cPL were tested, and clinical consensus scores were determined by 5 internists. Eleven dogs with suspected acute pancreatitis underwent follow-up testing during hospitalization. The intra-class correlation coefficient (ICC) was used for statistical analysis to assess the agreement between assays and the internists' consensus score.

Results

Dogs with suspected acute pancreatitis had significantly higher serum Vcheck CPL (median: 843 μg/L, range: 77-2001, p < 0.0001) than healthy control dogs (median: 94 μg/L, range: 49-294). By day 3 of hospitalization, serum Vcheck CPL had significantly decreased in dogs with suspected acute pancreatitis compared to day 1. The ICC score between the clinical consensus score, Vcheck CPL, and SNAP ® cPL was 0.75, indicating good agreement. Serum Vcheck CPL concentration was significantly correlated with serum concentrations of amylase, lipase, creatinine, ALP, and CRP.

Discussion

This study found good agreement between Vcheck CPL and SNAP ® cPL. This quantitative Vcheck CPL testing could serve as an adjunctive tool in diagnosing dogs with acute pancreatitis.

B Cells and Double-Negative B Cells (CD27-IgD-) Are Related to Acute Pancreatitis Severity

Acute pancreatitis (AP) is an increasingly frequent disease in which inflammation plays a crucial role. Fifty patients hospitalized with AP were included and peripheral blood samples were analyzed for B and T cell subpopulations at the time of hospitalization and 48 h after diagnosis. The Bedside Index of Severity in Acute Pancreatitis (BISAP) and length of hospital stay were also recorded. A healthy control (HC) group of 15 outpatients was included. AP patients showed higher neutrophil/lymphocyte (N/L) ratios and higher percentages of B cells than the HC group. The total B cell percentages were higher in patients with moderate/severe AP than in patients with mild AP. The percentages of B cells as well as the percentages of the CD27-IgD- B cell subset decreased from admission to 48 h after admission. The patients with higher BISAP scores showed lower percentages of peripheral lymphocytes but higher percentages of CD27-IgD- B cells. Higher BISAP scores, N/L ratios, and peripheral blood B cell levels emerged as predictors of hospital stay length in AP patients. Our findings underscore the importance of early markers for disease severity. Additionally, the N/L ratio along with the BISAP score and circulating B cell levels form a robust predictive model for hospital stay duration of AP patients.

High-density lipoprotein cholesterol level as an independent protective factor against aggravation of acute pancreatitis: a case-control study

Background and aims

At present, evidence on the association between high-density lipoprotein cholesterol (HDL-C) levels and aggravation of acute pancreatitis (AP) is limited. This study aimed to investigate the relationship between the lowest HDL-C level during intensive care units (ICU) stay and AP aggravation and to determine the optimum cutoff lowest HDL-C level.

Methods

Patients admitted to the ICU of the Shandong Provincial Hospital for AP from 2015 to 2021 were included. The lowest HDL-C level during ICU stay was set as the independent variable, and the progression or non-progression to severe AP (SAP) was set as the dependent variable. Univariate and multivariate analyses were performed to determine the relationship between the two variables, and receiver operating characteristic (ROC) curves were plotted to analyze the predictive ability of the lowest HDL-C level for progression to SAP.

Results

This study included 115 patients. The difference in the lowest HDL-C level between the SAP and moderately SAP groups was significant (P < 0.05). After adjusting for covariates, the lowest HDL-C level showed a negative correlation with the occurrence of SAP, with a relative risk of 0.897 (95% confidence interval: 0.827-0.973). The area under the ROC curve for prediction of AP aggravation by the lowest HDL-C level was 0.707, and the optimum cutoff lowest HDL-C level was 0.545 mmol/L.

Conclusion

No less than 0.545 mmol/L of the HDL-C level during ICU stay may be an independent protective factor for the aggravation of AP.

Clinical usefulness of scoring systems to predict severe acute pancreatitis: A systematic review and meta-analysis with pre and post-test probability assessment

BACKGROUND

Scoring systems for severe acute pancreatitis (SAP) prediction should be used in conjunction with pre-test probability to establish post-test probability of SAP, but data of this kind are lacking.

OBJECTIVE

To investigate the predictive value of commonly employed scoring systems and their usefulness in modifying the pre-test probability of SAP.

METHODS

Following PRISMA statement and MOOSE checklists after PROSPERO registration, PubMed was searched from inception until September 2022. Retrospective, prospective, cross-sectional studies or clinical trials on patients with acute pancreatitis defined as Revised Atlanta Criteria, reporting rate of SAP and using at least one score among Bedside Index for Severity in Acute Pancreatitis (BISAP), Acute Physiology and Chronic Health Examination (APACHE)-II, RANSON, and Systemic Inflammatory Response Syndrome (SIRS) with their sensitivity and specificity were included. Random effects model meta-analyses were performed. Pre-test probability and likelihood ratio (LR) were combined to estimate post-test probability on Fagan nomograms. Pooled severity rate was used as pre-test probability of SAP and pooled sensitivity and specificity to calculate LR and generate post-test probability. A priori hypotheses for heterogeneity were developed and sensitivity analyses planned.

RESULTS

43 studies yielding 14,116 acute pancreatitis patients were included: 42 with BISAP, 30 with APACHE-II, 27 with Ranson, 8 with SIRS. Pooled pre-test probability of SAP ranged 16.6%-25.3%. The post-test probability of SAP with positive/negative score was 47%/6% for BISAP, 43%/5% for APACHE-II, 48%/5% for Ranson, 40%/12% for SIRS. In 18 studies comparing BISAP, APACHE-II, and Ranson in 6740 patients with pooled pre-test probability of SAP of 18.7%, post-test probability when scores were positive was 48% for BISAP, 46% for APACHE-II, 50% for Ranson. When scores were negative, post-test probability dropped to 7% for BISAP, 6% for Ranson, 5% for APACHE-II. Quality, design, and country of origin of the studies did not explain the observed high heterogeneity.

CONCLUSIONS

The most commonly used scoring systems to predict SAP perform poorly and do not aid in decision-making.

Circulating Blood Lymphocytes and Acute Pancreatitis Severity: A Systematic Review

Acute pancreatitis is an acute inflammatory process of the pancreas with high prevalence and varying degrees of severity that can be potentially life-threatening. Much is still unknown about which mechanisms determine the course and severity of acute pancreatitis. The primary objective of this review is to identify the potential association between circulating lymphocytes and the severity of acute pancreatitis. A systematic search was performed in Medline, Web of Science, Cochrane Central Register of Controlled Trials and ClinicalTrails.gov. The authors independently did the selection process as well as data extraction that was recorded into a flow diagram following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Our initial search identified 27,783 studies which were narrowed down to 13 by applying strict inclusion and exclusion algorithms. The consistent findings across the studies indicated that peripheral blood lymphocytes are related to acute pancreatitis severity.

Hematology and Machine Learning

BACKGROUND

Substantial improvements in computational power and machine learning (ML) algorithm development have vastly increased the limits of what autonomous machines are capable of. Since its beginnings in the 19th century, laboratory hematology has absorbed waves of progress yielding improvements in both of accuracy and efficiency. The next wave of change in laboratory hematology will be the result of the ML revolution that has already touched many corners of healthcare and society at large.

CONTENT

This review will describe the manifestations of ML and artificial intelligence (AI) already utilized in the clinical hematology laboratory. This will be followed by a topical summary of the innovative and investigational applications of this technology in each of the major subdomains within laboratory hematology.

SUMMARY

Application of this technology to laboratory hematology will increase standardization and efficiency by reducing laboratory staff involvement in automatable activities. This will unleash time and resources for focus on more meaningful activities such as the complexities of patient care, research and development, and process improvement.

The Role of Innate Immune Cells in the Prediction of Early Renal Allograft Injury Following Kidney Transplantation

Background: Despite recent advances and refinements in perioperative management of kidney transplantation (KT), early renal graft injury (eRGI) remains a critical problem with serious impairment of graft function as well as short- and long-term outcome. Serial monitoring of peripheral blood innate immune cells might be a useful tool in predicting post-transplant eRGI and graft outcome after KT. Methods: In this prospective study, medical data of 50 consecutive patients undergoing KT at the University Hospital of Leipzig were analyzed starting at the day of KT until day 10 after the transplantation. The main outcome parameter was the occurrence of eRGI and other outcome parameters associated with graft function/outcome. eRGI was defined as graft-related complications and clinical signs of renal IRI (ischemia reperfusion injury), such as acute tubular necrosis (ATN), delayed graft function (DGF), initial nonfunction (INF) and graft rejection within 3 months following KT. Typical innate immune cells including neutrophils, natural killer (NK) cells, monocytes, basophils and dendritic cells (myeloid, plasmacytoid) were measured in all patients in peripheral blood at day 0, 1, 3, 7 and 10 after the transplantation. Receiver operating characteristics (ROC) curves were performed to assess their predictive value for eRGI. Cutoff levels were calculated with the Youden index. Significant diagnostic immunological cutoffs and other prognostic clinical factors were tested in a multivariate logistic regression model. Results: Of the 50 included patients, 23 patients developed eRGI. Mean levels of neutrophils and monocytes were significantly higher on most days in the eRGI group compared to the non-eRGI group after transplantation, whereas a significant decrease in NK cell count, basophil levels and DC counts could be found between baseline and postoperative course. ROC analysis indicated that monocytes levels on POD 7 (AUC: 0.91) and NK cell levels on POD 7 (AUC: 0.92) were highly predictive for eRGI after KT. Multivariable analysis identified recipient age (OR 1.53 (95% CI: 1.003−2.350), p = 0.040), recipient body mass index > 25 kg/m2 (OR 5.6 (95% CI: 1.36−23.9), p = 0.015), recipient cardiovascular disease (OR 8.17 (95% CI: 1.28−52.16), p = 0.026), donor age (OR 1.068 (95% CI: 1.011−1.128), p = 0.027), <0.010), deceased-donor transplantation (OR 2.18 (95% CI: 1.091−4.112), p = 0.027) and cold ischemia time (CIT) of the renal graft (OR 1.005 (95% CI: 1.001−1.01), p = 0.019) as clinically relevant prognostic factors associated with increased eRGI following KT. Further, neutrophils > 9.4 × 103/μL on POD 7 (OR 16.1 (95% CI: 1.31−195.6), p = 0.031), monocytes > 1150 cells/ul on POD 7 (OR 7.81 (95% CI: 1.97−63.18), p = 0.048), NK cells < 125 cells/μL on POD 3 (OR 6.97 (95% CI: 3.81−12.7), p < 0.01), basophils < 18.1 cells/μL on POD 10 (OR 3.45 (95% CI: 1.37−12.3), p = 0.02) and mDC < 4.7 cells/μL on POD 7 (OR 11.68 (95% CI: 1.85−73.4), p < 0.01) were revealed as independent biochemical predictive variables for eRGI after KT. Conclusions: We show that the combined measurement of immunological innate variables (NK cells and monocytes on POD 7) and specific clinical factors such as prolonged CIT, increased donor and recipient age and morbidity together with deceased-donor transplantation were significant and specific predictors of eRGI following KT. We suggest that intensified monitoring of these parameters might be a helpful clinical tool in identifying patients at a higher risk of postoperative complication after KT and may therefore help to detect and—by diligent clinical management—even prevent deteriorated outcome due to IRI and eRGI after KT.

Analysis of the Application Value of Echocardiography Combined with CK-MB, Alb, and CysC in the Prognosis Assessment of Patients with Chronic HF

In order to evaluate the diagnostic and prognostic value of echocardiography combined with serum creatine kinase-MB (CK-MB), albumin (Alb), and cystatin C (CysC) in patients with chronic heart failure (HF), 93 patients diagnosed with chronic HF in our hospital from March 2019 to January 2020 are retrospectively analyzed and included in the HF group. Another 100 healthy subjects who come to our hospital for general physical examination are selected as the control group. Echocardiography is used to detect the cardiac parameters of each group. The experimental results show that echocardiography parameters combined with CK-MB, Alb, and CysC have high application value in diagnosis and evaluation of patients with chronic HF, which can provide theoretical basis for improving the prognosis of patients with chronic HF through real-time monitoring of the above indicators.

Correlation of Different Serum Biomarkers with Prediction of Early Pancreatic Graft Dysfunction Following Simultaneous Pancreas and Kidney Transplantation

Background: Despite recent advances and refinements in perioperative management of simultaneous pancreas−kidney transplantation (SPKT) early pancreatic graft dysfunction (ePGD) remains a critical problem with serious impairment of early and long-term graft function and outcome. Hence, we evaluated a panel of classical blood serum markers for their value in predicting early graft dysfunction in patients undergoing SPKT. Methods: From a prospectively collected database medical data of 105 patients undergoing SPKT between 1998 and 2018 at our center were retrospectively analyzed. The primary study outcome was the detection of occurrence of early pancreatic graft dysfunction (ePGD), the secondary study outcome was early renal graft dysfunction (eRGD) as well as all other outcome parameters associated with the graft function. In this context, ePGD was defined as pancreas graft-related complications including graft pancreatitis, pancreatic abscess/peritonitis, delayed graft function, graft thrombosis, bleeding, rejection and the consecutive need for re-laparotomy due to graft-related complications within 3 months. With regard to analyzing ePGD, serum levels of white blood cell count (WBC), C-reactive protein (CRP), procalcitonin (PCT), pancreatic lipase as well as neutrophil−lymphocyte ratio (NLR) and platelet−lymphocyte ratio (PLR) were measured preoperatively and at postoperative days (POD) 1, 2, 3 and 5. Further, peak serum levels of CRP and lipase during the first 72 h were evaluated. Receiver operating characteristics (ROC) curves were performed to assess their predictive value for ePGD and eRGD. Cut-off levels were calculated with the Youden index. Significant diagnostic biochemical cut-offs as well as other prognostic clinical factors were tested in a multivariate logistic regression model. Results: Of the 105 patients included, 43 patients (41%) and 28 patients (27%) developed ePGD and eRGD following SPKT, respectively. The mean WBC, PCT, NLR, PLR, CRP and lipase levels were significantly higher on most PODs in the ePGD group compared to the non-ePGD group. ROC analysis indicated that peak lipase (AUC: 0.82) and peak CRP levels (AUC: 0.89) were highly predictive for ePGD after SPKT. The combination of both achieved the highest AUC (0.92; p < 0.01) in predicting ePGD. Concerning eRGD, predictive accuracy of all analyzed serological markers was moderate (all AUC < 0.8). Additionally, multivariable analysis identified previous dialysis/no preemptive transplantation (OR 2.4 (95% CI: 1.41−4.01), p = 0.021), donor age (OR 1.07 (95% CI: 1.03−1.14), p < 0.010), donor body mass index (OR 1.32 (95% CI: 1.01−1.072), p = 0.04), donors cerebrovascular cause of death (OR 7.8 (95% CI: 2.21−26.9), p < 0.010), donor length of ICU stay (OR 1.27 (95% CI: 1.08−1.49), p < 0.010), as well as CIT pancreas (OR 1.07 (95% CI: 1.03−1.14), p < 0.010) as clinical relevant prognostic predictors for ePGD. Further, a peak of lipase (OR 1.04 (95% CI: 1.02−1.07), p < 0.010), peak of CRP levels (OR 1.12 (95% CI: 1.02−1.23), p < 0.010), pancreatic serum lipase concentration on POD 2 > 150 IU/L (OR 2.9 (95% CI: 1.2−7.13), p = 0.021) and CRP levels of ≥ 180 ng/mL on POD 2 (OR 3.6 (95% CI: 1.54−8.34), p < 0.01) and CRP levels > 150 ng/mL on POD 3 (OR 4.5 (95% CI: 1.7−11.4), p < 0.01) were revealed as independent biochemical predictive variables for ePGD after transplantation. Conclusions: In the current study, the combination of peak lipase and CRP levels were highly effective in predicting early pancreatic graft dysfunction development following SPKT. In contrast, for early renal graft dysfunction the predictive value of this parameter was less sensitive. Intensified monitoring of these parameters may be helpful for identifying patients at a higher risk of pancreatic ischemia reperfusion injury and various IRI- associated postoperative complications leading to ePGD and thus deteriorated outcome.

Leukocyte cell population data from the blood cell analyzer as a predictive marker for severity of acute pancreatitis

BACKGROUND

The prediction for severe acute pancreatitis (SAP) is the key to give timely targeted treatment. Leukocyte cell population data (CPD) have been widely applied in early prediction and diagnosis of many diseases, but their predictive ability for SAP remains unexplored. We aim to testify whether CPD could be an indicator of AP severity in the early stage of the disease.

METHODS

The prospective observational study was conducted in the emergency department ward of a territory hospital in Shanghai. The enrolled AP patients should meet 2012 Atlanta guideline.

RESULTS

Totally, 103 AP patients and 62 healthy controls were enrolled and patients were classified into mild AP (n = 30), moderate SAP (n = 42), and SAP (n = 31). Forty-two CPD parameters were examined in first 3 days of admission. Four CPD parameters were highest in SAP on admission and were constantly different among 3 groups during first 3 days of hospital stay. Eighteen CPD parameters were found correlated with the occurrence of SAP. Stepwise multivariate logistic regression analysis identified a scoring system of 4 parameters (SD_LALS_NE, MN_LALS_LY, SD_LMALS_MO, and SD_AL2_MO) with a sensitivity of 96.8%, specificity of 65.3%, and AUC of 0.87 for diagnostic accuracy on early identification of SAP. AUC of this scoring system was comparable with MCTSI, SOFA, APACHE II, MMS, BISAP, or biomarkers as CRP, PCT, and WBC in prediction of SAP and ICU transfer or death.

CONCLUSIONS

Several leukocyte CPD parameters have been identified different among MAP, MSAP, and SAP. They might be ultimately incorporated into a predictive system marker for severity of AP.