Exploration of complement split products in plasma and urine as biomarkers of kidney graft rejection.

INTRODUCTION: The complement system, consisting of more than thirty different soluble and cell-bound proteins, exerts essential functions both in the innate and adaptive immune systems and is believed to be an important contributor to allograft injury in kidney transplantation. The anaphylatoxins C3a and C5a are powerful chemoattractants, recruiting immune effector cells toward the site of complement activation and enhance T-cell response, while C3dg binding to CR2 on B-cells, enhances B-cell immunity at several stages of the B-cell differentiation. Complement split products in plasma and urine could reflect ongoing inflammation and tissue injury. We, therefore, investigated if complement split products increase in plasma and urine in kidney transplant recipients with rejection. METHOD: In this case-control feasibility study, complement factors C3a, C3dg, C4a, and C5a were measured in plasma and C3dg and sC5b-9 associated C9 neoantigen in urine in 15 kidney transplant recipients with rejection (cases) and 15 kidney transplant recipients without (controls). The groups were matched on the type of transplantation and the time from transplantation to sampling. The complement split products were compared (i) between cases and controls and (ii) within the rejection group over time, comparing the measurements at rejection with measurements where the kidney transplant recipients were clinically stable. Possible moderators were explored, and results adjusted accordingly. P values < 0.05 were considered significant. Plasma C3dg was analyzed by immune-electrophoresis, plasma C3a, plasma C4a, and plasma C5a by flow cytometry, and urine C3dg and urine C9neo by ELISA. RESULTS: In plasma, there were no significant differences between the rejection and the control group. However, steroids and pretransplant C3dg levels significantly influenced C3dg. Within the rejection group, plasma C3a and C3dg were significantly higher at the time of rejection compared to the stable phase (p < 0.01). In urine, C3dg/creatinine and C9 neoantigen/creatinine ratios were not different between the rejection and the control group. Urine C3dg/creatinine and urine C9 neoantigen/creatinine ratios correlated to urine albumin and significantly increased after the transplantation (p < 0.001). CONCLUSION: This study shows increased plasma C3a and C3dg in kidney transplant recipients, primarily with T cell mediated rejection. This finding suggests that consecutive measurements of C3a and C3dg in plasma could be applicable to monitor alloreactivity in kidney transplant recipients. Urine complement split products are unsuitable as rejection biomarkers since the permeability of the glomerular filtration barrier strongly influences them. Prospective longitudinal studies on plasma C3a and C3dg dynamics will be needed to validate present findings.

Only one-third of referrals for fatty liver disease are on time: real-world study reveals opportunities to avoid unnecessary and delayed referrals.

BACKGROUND AND AIMS: Fatty liver disease is a global health concern, but in the absence of specific guidelines, current referral patterns differ according to the preferences of the general practitioners. Outpatient Gastroenterology clinics spend futile resources on liver-healthy patients while diagnosing decompensated patients delayed. We aimed to describe referral patterns to a regional outpatient Gastroenterology clinic. METHODS: We reviewed 9684 referrals from primary care for suspected liver disease in the years 2016-2017, during two years. Data were extracted from the patients' hospital records to assess the clinical workup and patient outcomes until a mean of 43 months after the time of referral. Referrals were categorized as unnecessary (no signs of liver disease), timely (significant fibrosis/compensated cirrhosis), or delayed (decompensated cirrhosis). RESULTS: We included 375 patient referrals from primary care. The main reason for referral was elevated transaminases. More than half (54%) of patients had no signs of liver disease, being unnecessarily referred for evaluation, while 17% had decompensated liver disease and were thus referred too late. CONCLUSIONS: Only one-third of patients referred on suspicion of liver disease were referred on time, either before presenting with decompensated liver cirrhosis or with some evidence of significant liver disease, e.g., liver fibrosis. There is a huge unmet need for clinical referral pathways in primary care. Strengths and Limitations of this StudyA strength of this study is the complete mapping of all potential referrals to the outpatient clinic in the two-year period. Instead of retrieving the historic data by ICD-10 diagnosis codes, and reflecting only those patients where the GP clearly suspects liver disease, we have a strong reliance on our methods. We screened all potentially relevant referrals, e.g., referrals due to weight loss or fatigue, which may reflect symptoms of cirrhosis. Thereby we are confident that we have not missed any patients that originally were referred with unspecific symptoms, but after evaluation are diagnosed with liver disease.Another strength of our study is the long follow-up period, which allows us to fully evaluate the course for the individual patient, and the potential later coming diagnoses.Finally, it is a strength of the study that we were not exclusive to one liver disease etiology, both ALD and NAFLD etiology were included in the study.A limitation of this study is the use of historic data, and the fact that it is a single-center study, showing only the referral patterns in one outpatient Gastroenterology clinic.

Deep learning detects and visualizes bleeding events in electronic health records.

BACKGROUND: Bleeding is associated with a significantly increased morbidity and mortality. Bleeding events are often described in the unstructured text of electronic health records, which makes them difficult to identify by manual inspection. OBJECTIVES: To develop a deep learning model that detects and visualizes bleeding events in electronic health records. PATIENTS/METHODS: Three hundred electronic health records with International Classification of Diseases, Tenth Revision diagnosis codes for bleeding or leukemia were extracted. Each sentence in the electronic health record was annotated as positive or negative for bleeding. The annotated sentences were used to develop a deep learning model that detects bleeding at sentence and note level. RESULTS: On a balanced test set of 1178   sentences, the best-performing deep learning model achieved a sensitivity of 0.90, specificity of 0.90, and negative predictive value of 0.90. On a test set consisting of 700 notes, of which 49 were positive for bleeding, the model achieved a note-level sensitivity of 1.00, specificity of 0.52, and negative predictive value of 1.00. By using a sentence-level model on a note level, the model can explain its predictions by visualizing the exact sentence in a note that contains information regarding bleeding. Moreover, we found that the model performed consistently well across different types of bleedings. CONCLUSIONS: A deep learning model can be used to detect and visualize bleeding events in the free text of electronic health records. The deep learning model can thus facilitate systematic assessment of bleeding risk, and thereby optimize patient care and safety.

Platelet parameters and leukocyte morphology is altered in COVID-19 patients compared to non-COVID-19 patients with similar symptomatology.

In this nested case-control study, we evaluated haematological and morphological parameters of hospitalised patients with real-time polymerase chain reaction verified COVID-19 infection compared to patients with similar symptomatology but without COVID-19 infection. Seventy-four COVID-19 positive and 228 COVID-19 negative patients were evaluated with routine haematological parameters. Severe disease was defined as death and/or need of intensive care treatment. Twenty-seven COVID-19 positive and 18 COVID-19 negative patients were furthermore included for morphological evaluation using smear examination. Significant differences were found for platelet indices and white blood cell parameters. Thus, platelet count and plateletcrit was lower in COVID-19 patients, whilst mean platelet volume, platelet distribution width, and platelet large cell ratio was significantly higher than in non-COVID-19 patients. Leukocyte, neutrophil, immature granulocyte, lymphocyte, monocyte, eosinophil, and basophil count was lower in COVID-19 patients. No significant differences were found for red blood cell count, haemoglobin, haematocrit or mean corpuscular haemoglobin for COVID-19 versus non-COVID-19 patients. COVID-19 patients with a severe disease course had higher levels of immature granulocytes, but lower lymphocyte and platelet counts compared to patients with non-severe COVID-19. In terms of morphology, 14.8% of COVID-19 patients had a normal smear examination, compared to 83.3% of non-COVID-19 patients. Hypogranulated neutrophils were more frequent in COVID-19 patients (p < .001), but non-COVID-19 patients had higher levels of reactive lymphocytes, compared to COVID-19 patients. In conclusion, several haematological morphological abnormalities are more frequent in patients with COVID-19 disease, and several findings indicate that platelets play a fundamental role in the pathophysiology of the disease.

Laboratory Tests and Outcome for Patients with Coronavirus Disease 2019: A Systematic Review and Meta-Analysis.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 causes coronavirus disease 2019 (COVID-19) and poses substantial challenges for healthcare systems. With a vastly expanding number of publications on COVID-19, clinicians need evidence synthesis to produce guidance for handling patients with COVID-19. In this systematic review and meta-analysis, we examine which routine laboratory tests are associated with severe COVID-19 disease. CONTENT: PubMed (Medline), Scopus, and Web of Science were searched until March 22, 2020, for studies on COVID-19. Eligible studies were original articles reporting on laboratory tests and outcome of patients with COVID-19. Data were synthesized, and we conducted random-effects meta-analysis, and determined mean difference (MD) and standard mean difference at the biomarker level for disease severity. Risk of bias and applicability concerns were evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. SUMMARY: 45 studies were included, of which 21 publications were used for the meta-analysis. Studies were heterogeneous but had low risk of bias and applicability concern in terms of patient selection and reference standard. Severe disease was associated with higher white blood cell count (MD, 1.28 ×109/L), neutrophil count (MD, 1.49 ×109/L), C-reactive protein (MD, 49.2 mg/L), lactate dehydrogenase (MD, 196 U/L), D-dimer (standardized MD, 0.58), and aspartate aminotransferase (MD, 8.5 U/L); all p < 0.001. Furthermore, low lymphocyte count (MD -0.32 × 109/L), platelet count (MD -22.4 × 109/L), and hemoglobin (MD, -4.1 g/L); all p < 0.001 were also associated with severe disease. In conclusion, several routine laboratory tests are associated with disease severity in COVID-19.

Application of adult reference intervals in children.

The aim of this study was to evaluate to which extend adult reference intervals (RIs) could be applied in children. A local paediatric population (aged 1 to < 20 years), based on first draw samples from general practitioners (GPs), was established. Children with samples taken at a hospital or > 3 samples from GPs were excluded. Analytes evaluated included haematological, liver and pancreatic function, kidney function, electrolytes, and metabolism parameters. Applicability of adult RIs in children aged 1-17 years was evaluated using individuals aged 18-19 years as reference groups for the adult RIs. The local population consisted of 31,024 children with 282,721 analyses in total. For each analyte, 17 age strata and two gender strata were established. Partitioning was not warranted in 51% of the male strata and in 69% of the female strata. Adult RIs could be applied in 42% for children aged 1-< 10 years, 57% for children aged 10-< 15 years, and 85% for children aged 15-<18 years.Conclusion: for certain analytes, there is no need to partition between adult and paediatric RIs, but a need for age- and gender-specific RIs remains for several clinical laboratory tests.What is Known:• Establishing paediatric reference intervals (RIs) is time consuming, costly, and not feasible for many laboratories. Transference of RIs established elsewhere often leads to misclassification of paediatric laboratory results.• Adult RIs are often more easily established and validated.What is New:• Adult RIs can be applied to children as young as 2 years for some analytes. Conversely, for some analytes, adult RIs cannot be applied in children aged 1-17 years.• Laboratory data can be applied in evaluating the need for partitioning in reference intervals.

EGFR and EGFR ligands in serum in healthy women; reference intervals and age dependency.

Background The epidermal growth factor receptor (EGFR) system is involved in cancer pathogenesis and serves as an important target for multiple cancer treatments. EGFR and its ligands epidermal growth factor (EGF), heparin-binding epidermal growth factor (HB-EGF), betacellulin (BTC), amphiregulin (AREG) and transforming growth factor α (TGF-α) have potential applications as prognostic or predictive serological biomarkers in cancer. The aim was to establish EGFR and EGFR ligand reference intervals in healthy women. Methods EGFR and EGFR ligands were measured in serum from 419 healthy women aged 26-78 years. The need for age partitioned reference intervals was evaluated using Lahti's method. EGFR and EGF were analyzed using ELISA assays, whereas HB-EGF, BTC, AREG and TGF-α were analyzed using the highly sensitive automated single molecule array (Simoa) enabling detection below the lower reference limit for all six biomarkers. Results Reference intervals for EGFR and the EGFR ligands were determined as the 2.5th and 97.5th percentiles. All six biomarkers were detectable in all serum samples. For EGFR, EGF, HB-EGF and TGF-α, reference intervals were established for women <55 years and for women >55 years, whilst common reference intervals were established for AREG and BTC including women aged 26-78 years. Conclusions Age specific reference intervals were determined for EGFR, EGF, HB-EGF, BTC, AREG and TGF-α.

Paediatric reference intervals are heterogeneous and differ considerably in the classification of healthy paediatric blood samples.

The aim was to elude differences in published paediatric reference intervals (RIs) and the implementations hereof in terms of classification of samples. Predicaments associated with transferring RIs published elsewhere are addressed. A local paediatric (aged 0 days to < 18 years) population of platelet count, haemoglobin level and white blood cell count, based on first draw samples from general practitioners was established. PubMed was used to identify studies with transferable RIs. The classification of local samples by the individual RIs was evaluated. Transference was done in accordance with the Clinical and Laboratory Standards Institute EP28-A3C guideline. Validation of transference was done using a quality demand based on biological variance. Twelve studies with a combined 28 RIs were transferred onto the local population, which was derived from 20,597 children. Studies varied considerably in methodology and results. In terms of classification, up to 63% of the samples would change classification from normal to diseased, depending on which RI was applied. When validating the transferred RIs, one RI was implementable in the local population. Conclusion: Published paediatric RIs are heterogeneous, making assessment of transferability problematic and resulting in marked differences in classification of paediatric samples, thereby potentially affecting diagnosis and treatment of children. What is Known: • Reference intervals (RIs) are fundamental for the interpretation of paediatric samples and thus correct diagnosis and treatment of the individual child. • Guidelines for the establishment of adult RIs exist, but there are no specific recommendations for establishing paediatric RIs, which is problematic, and laboratories often implement RIs published elsewhere as a consequence. What is New: • Paediatric RIs published in peer-reviewed scientific journals differ considerably in methodology applied for the establishment of the RI. • The RIs show marked divergence in the classification of local samples from healthy children.

The primary haemostasis is more preserved in thrombocytopenic patients with liver cirrhosis than cancer.

: In thrombocytopenia, differences in haemostatic capacity may explain discrepancies in bleeding risk between patients with cancer and patients with liver cirrhosis. The objective was to compare the haemostatic capacity in different thrombocytopenic patient populations. We evaluated platelet aggregation using impedance aggregometry (Multiplate Analyzer), von Willebrand factor antigen (VWF:Ag), VWF:ristocetin-cofactor activity (VWF:RCo), activated partial thromboplastin time (aPTT), coagulation factor VIII, fibrinogen, and thrombin generation in adult hospitalized patients with platelet count less than 80 × 10/l. Patients either had liver cirrhosis (n = 28), or cancer (n = 169; n = 49 had haematological cancer) with no difference among patients with liver cirrhosis and cancer. Median platelet count was 48 × 10/l [interquartile range (IQR) 32-63 × 10/l]. Median platelet aggregation was higher in patients with cirrhosis than cancer, 416 AU × min (IQR 257-676) versus 145 AU × min (IQR 50-326) for collagen-induced platelet aggregation, P < 0.001. There was no difference in activated partial thromboplastin time (aPTT), coagulation factor VIII, or thrombin generation between the patient groups. Fibrinogen activity was higher in patients with cancer compared with patients with cirrhosis [12.5 µmol/L (IQR 9.9-16.5) versus 7.2 µmol/l (IQR 5.6-10.2)], P < 0.003. Patients with liver cirrhosis had a more preserved primary haemostasis compared with patients with cancer.

Prediction of bleeding and prophylactic platelet transfusions in cancer patients with thrombocytopenia.

Studies on markers for bleeding risk among thrombocytopenic cancer patients are lacking. This prospective observational cohort study investigated whether platelet parameters and a standardised bleeding questionnaire predicted bleeding or prophylactic platelet transfusions in patients with cancer and thrombocytopenia. Admitted adult patients with cancer and platelet count <80 × 10(9)/L were enrolled, but excluded if they experienced surgery or trauma within 7 days or platelet transfusion within 14 days. Patients were interviewed, blood samples collected and, subsequently, spontaneous bleeding and prophylactic platelet transfusion within 30 days were registered. Of 197 patients enrolled, 56 (28%) experienced bleeding. In multivariate analyses, predictors of bleeding were infection (adjusted odds ratio (OR) = 2.65 and 95% confidence interval (95% CI) 1.04-6.74); treatment with platelet inhibitors, heparin or warfarin OR = 2.34, 95% CI 1.23-4.48; urea nitrogen OR = 1.15, 95% CI 1.07-1.25; creatinine OR = 1.01, 95% CI 1.01-1.01; and haemoglobin OR = 0.62, 95% CI 0.41-0.93. Specific information regarding previous gastrointestinal bleeding OR = 3.33, 95% CI 1.19-9.34 and haematuria OR = 3.00, 95% CI 1.20-7.52 predicted bleeding whereas the standardised bleeding questionnaire did not. Prophylactic platelet transfusions were administered to 97 patients. Predictors of prophylactic platelet transfusions were: platelet count OR = 0.96, 95% CI 0.94-0.97; fibrinogen OR = 0.88, 95% CI 0.83-0.95; mean platelet volume OR = 0.69, 95% CI 0.49-0.97; platelet aggregometry with OR = 2.48, 95% CI 1.09-5.64 for collagen-induced platelet aggregation within the lowest quartile; and albumin OR = 1.07, 95% CI 1.01-1.15. In conclusion, except for immature platelet fraction (IPF), platelet parameters predicted prophylactic platelet transfusion but not bleeding. Bleeding risk factors were previous haematuria or gastrointestinal bleeding, infection, antiplatelet or anticoagulant treatment, high urea nitrogen, low haemoglobin or high creatinine.