Routine Laboratory Biomarkers Research Articles

Development and validation of a prognostic nomogram model incorporating routine laboratory biomarkers for preoperative patients with endometrial cancer

BackgroundSome biomarkers collected from routine laboratory tests have shown important value in cancer prognosis. The study aimed to evaluate the prognostic significance of routine laboratory biomarkers in patients with endometrial cancer (EC) and to develop credible prognostic nomogram models for clinical application.MethodsA total of 727 patients were randomly divided into a training set and a validation set. Cox proportional hazards models were used to evaluate each biomarker’s prognostic value, and independent prognostic factors were used to generate overall survival (OS) and progression-free survival (PFS) nomgrams. The efficacy of the nomograms were evaluated by Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration curves, X-tile analysis and Kaplan‒Meier curves.ResultsTen significant biomarkers in multivariate Cox analysis were integrated to develop OS and PFS nomograms. The C-indices of the OS- nomogram in the training and validation sets were 0.885 (95% confidence interval (CI), 0.810–0.960) and 0.850 (95% CI, 0.761–0.939), respectively; those of the PFS- nomogram in the training and validation sets were 0.903 (95% CI, 0.866–0.940) and 0.825 (95% CI, 0.711–0.939), respectively. ROC, DCA and calibration curves showed better clinical application value for the nomograms incorporating routine laboratory biomarkers. X-tile analysis and Kaplan‒Meier curves showed that the nomograms were stable and credible in evaluating patients at different risks.ConclusionsNomogram models incorporating routine laboratory biomarkers, including NLR, MLR, fibrinogen, albumin and AB blood type, were demonstrated to be simple, reliable and favourable in predicting the outcomes of patients with EC.

Low circulating levels of miR-17 and miR-126-3p are associated with increased mortality risk in geriatric hospitalized patients affected by cardiovascular multimorbidity.

MultiMorbidity (MM), defined as the co-occurrence of two or more chronic conditions, is associated with poorer health outcomes, such as recurrent hospital readmission and mortality. As a group of conditions, cardiovascular disease (CVD) exemplifies several challenges of MM, and the identification of prognostic minimally invasive biomarkers to stratify mortality risk in patients affected by cardiovascular MM is a huge challenge. Circulating miRNAs associated to inflammaging and endothelial dysfunction, such as miR-17, miR-21-5p, and miR-126-3p, are expected to have prognostic relevance. We analyzed a composite profile of circulating biomarkers, including miR-17, miR-21-5p, and miR-126-3p, and routine laboratory biomarkers in a sample of 246 hospitalized geriatric patients selected for cardiovascular MM from the Report-AGE INRCA database and BioGER INRCA biobank, to evaluate the association with all-cause mortality during 31days and 12 and 24months follow-up. Circulating levels of miR-17, miR-126-3p, and some blood parameters, including neutrophil to lymphocyte ratio (NLR) and eGFR, were significantly associated with mortality in these patients. Overall, our results suggest that in a cohort of geriatric hospitalized patients affected by cardiovascular MM, lower circulating miR-17 and miR-126-3p levels could contribute to identify patients at higher risk of short- and medium-term mortality.

A-231 Prediction of Severe COVID-19 Based on Routine Biomarker Assessment

Abstract Background Disease courses in COVID-19 patients vary widely. Prior studies did not focus on initial values of various laboratory biomarkers, instead used data throughout the disease course. Prediction of disease severity based on parameters at the initial diagnosis would aid appropriate management. Objective of our study was to develop predictive models of COVID-19 severity based on demographic, clinical, and laboratory data collected at initial patient contact after diagnosis of COVID-19. Methods We evaluated de-identified demographic, clinical, and routine biomarkers data from 14 147 patients with COVID-19 diagnosed by polymerase chain reaction SARS-CoV-2 testing at Montefiore Health System from March 2020 to September 2021. We generated models predicting severe disease (death or more than 90 hospital days) vs mild disease (alive and fewer than 2 hospital days), starting with 58 parameters, by backward stepwise logistic regression. Results Of the 14 147 patients, 18% had severe, 24% had mild outcomes and the rest were moderate (cases who were alive but in the hospital for 2–89 days). We identified 4 proficient models in predicting patient outcomes: Inclusive model A, with the largest number (37) of parameters; Receiver Operating Characteristics (ROC) Model B, including 9 parameters with the highest Area under Curve (0.85); the Specific Model C, including 10 parameters with the highest specificity (81%) and Positive Predictive Value and the Sensitive Model D including 9 parameters with the highest sensitivity (91%) and Negative Predictive Value (Tables 1 and 2). The parameters that remained in all models were age, albumin, diastolic blood pressure, ferritin, lactic dehydrogenase, socioeconomic strength, procalcitonin, b-type natriuretic peptide, C reactive protein, d dimer and platelet count. Conclusions These findings suggest that certain clinical parameters and routine laboratory biomarkers within the specific and sensitive models could benefit healthcare providers, in their assessment of disease severity and clinical management of COVID-19 infection.

Investigating Biomarkers for COVID-19 Morbidity and Mortality.

This retrospective study aims to disclose further early parameters of COVID-19 morbidity and mortality. Three hundred and eighty-two COVID-19 patients, recruited between March and April 2020, were divided into three groups according to their outcome: (1) hospital ward group (patients who entered the hospital wards and survived); (2) intensive care unit (ICU) group (patients who attended the ICU and survived); (3) the deceased group (patients admitted to ICU with a fatal outcome). We investigated routine laboratory parameters such as albumin, glycemia, hemoglobin amylase, lipase, AST, ALT, GGT, LDH, CK, MGB, TnT-hs, IL-6, ferritin, CRP, PCT, WBC, RBC, PLT, PT, INR, APTT, FBG, and D-dimer. Blood withdrawal was carried out at the beginning of the hospitalization period. ANOVA and ROC data evidenced that the concomitant presence of alterations in albumin, lipase, AST, ALT, LDH, MGB, CK, IL-6, ferritin in women, CRP and D-dimer is an early sign of fatal outcomes. The present study confirms and extends the validity of routine laboratory biomarkers (i.e., lipase, AST, ALT, LDH, CK, IL-6, ferritin in women, CRP and D-dimer) as indicators of COVID-19 morbidity and mortality. Furthermore, the investigation suggests that both gross changes in albumin and MGB, markers of liver and heart damage, may early disclose COVID-19 fatal outcomes.

Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers

Early evaluation of patients who require special care and who have high death-expectancy in COVID-19, and the effective determination of relevant biomarkers on large sample-groups are important to reduce mortality. This study aimed to reveal the routine blood-value predictors of COVID-19 mortality and to determine the lethal-risk levels of these predictors during the disease process. The dataset of the study consists of 38 routine blood-values of 2597 patients who died (n = 233) and those who recovered (n = 2364) from COVID-19 in August–December, 2021. In this study, the histogram-based gradient-boosting (HGB) model was the most successful machine-learning classifier in detecting living and deceased COVID-19 patients (with squared F1 metrics F12 = 1). The most efficient binary combinations with procalcitonin were obtained with D-dimer, ESR, D-Bil and ferritin. The HGB model operated with these feature pairs correctly detected almost all of the patients who survived and those who died (precision > 0.98, recall > 0.98, F12 > 0.98). Furthermore, in the HGB model operated with a single feature, the most efficient features were procalcitonin (F12 = 0.96) and ferritin (F12 = 0.91). In addition, according to the two-threshold approach, ferritin values between 376.2 μg/L and 396.0 μg/L (F12 = 0.91) and procalcitonin values between 0.2 μg/L and 5.2 μg/L (F12 = 0.95) were found to be fatal risk levels for COVID-19. Considering all the results, we suggest that many features combined with these features, especially procalcitonin and ferritin, operated with the HGB model, can be used to achieve very successful results in the classification of those who live, and those who die from COVID-19. Moreover, we strongly recommend that clinicians consider the critical levels we have found for procalcitonin and ferritin properties, to reduce the lethality of the COVID-19 disease.

Dynamic inflammatory response among routine laboratory biomarkers and their predictive ability for mortality in patients with severe COVID-19.

The severity of coronavirus disease 2019 (COVID-19) is related to several factors, including age, sex, and comorbidities (obesity, type 2 diabetes, and hypertension). However, systemic inflammation plays a fundamental role in COVID-19 pathophysiology. Several studies have described this association employing specific biomarkers that are not routinely used in clinical practice. On the other hand, very few reports in the literature focused on the analysis of the routine laboratory biomarkers to predict the outcome of severe COVID-19 patients. We aimed to analyze the dynamic inflammatory response using routine laboratory biomarkers to predict in-hospital mortality in Mexican patients with severe COVID-19. This is a cohort study including patients with severe COVID-19. Demographic characteristics were retrieved from medical charts and biochemical parameters were measured at hospital admission and subsequently on days 3, 5, 7, 10, 14, and 21 during the hospital stay; measurements were stopped when patients were discharged from the hospital (alive or death). A total of 250 patients were included in the study, 40.8% of patients died. The analyzed routine laboratory parameters, such as serum levels of neutrophil-to-lymphocyte ratio, C-reactive protein, and D-dimer remained elevated in hospitalized patients who did not survive, whereas eosinophil and platelets were maintained at lower levels. In the multivariate analysis, leukocytes, and neutrophils were the best biomarkers for predicting mortality risk and were independent of age, gender, or comorbidities. Our results support the use of routine laboratory biomarkers as predictors of mortality in Mexican hospitalized patients with severe COVID-19.

A Review of Routine Laboratory Biomarkers for the Detection of Severe COVID-19 Disease.

As the COVID-19 pandemic continues, there is an urgent need to identify clinical and laboratory predictors of disease severity and prognosis. Once the coronavirus enters the cell, it triggers additional events via different signaling pathways. Cellular and molecular deregulation evoked by coronavirus infection can manifest as changes in laboratory findings. Understanding the relationship between laboratory biomarkers and COVID-19 outcomes would help in developing a risk-stratified approach to the treatment of patients with this disease. The purpose of this review is to investigate the role of hematological (white blood cell (WBC), lymphocyte, and neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet, and red blood cell (RBC) count), inflammatory (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and lactate dehydrogenase (LDH)), and biochemical (Albumin, aspartate aminotransferase (AST) and alanine aminotransferase (ALT), blood urea nitrogen (BUN), creatinine, D-dimer, total Cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL)) biomarkers in the pathogenesis of COVID-19 disease and how their levels vary according to disease severity.

Routine laboratory biomarkers used to predict Gram-positive or Gram-negative bacteria involved in bloodstream infections

This study evaluated routine laboratory biomarkers (RLB) to predict the infectious bacterial group, Gram-positive (GP) or Gram-negative (GN) associated with bloodstream infection (BSI) before the result of blood culture (BC). A total of 13,574 BC of 6787 patients (217 BSI-GP and 238 BSI-GN) and 68 different RLB from these were analyzed. The logistic regression model was built considering BSI-GP or BSI-GN as response variable and RLB as covariates. After four filters applied total of 320 patients and 16 RLB remained in the Complete-Model-CM, and 4 RLB in the Reduced-Model-RM (RLB p > 0.05 excluded). In the RM, only platelets, creatinine, mean corpuscular hemoglobin and erythrocytes were used. The reproductivity of both models were applied to a test bank of 2019. The new model presented values to predict BSI-GN of the area under the curve (AUC) of 0.72 and 0.69 for CM and RM, respectively; with sensitivity of 0.62 and 0.61 (CM and RM) and specificity of 0.67 for both. These data confirm the discriminatory capacity of the new models for BSI-GN (p = 0.64). AUC of 0.69 using only 4 RLB, associated with the patient's clinical data could be useful for better targeted antimicrobial therapy in BSI.

Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort.

The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11–30.54, and Cluster C 14.29 CI: 6.66–34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64–3.01, and Cluster-C 1.71 CI: 1.08–2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.

Using High-Resolution Differential Cell Counts (HRDCCs) in Bovine Milk and Blood to Monitor the Immune Status over the Entire Lactation Period

Simple SummarySince leukocytes enter the mammary gland as part of the immune defense, they can be analyzed in raw milk samples and used for diagnostics. In our study, we differentiated and measured several subtypes of immune cells in blood and milk samples of eight dairy cows over their whole lactation to monitor their immune status. In addition to these so-called high-resolution differential cell counts (HRDCCs), we determined routine laboratory biomarkers. We found considerable differences between the documented cell count progressions in the blood and milk. Some cell populations remained mostly stable, while others showed a certain dynamic during the course of lactation. Moreover, two types of lymphocytes were also noticeable due to changes in the case of inflammation. These findings suggest that HRDCCs can be a promising tool for more efficient milk diagnostics, ensuring milk quality, and supporting cattle health and wellbeing.Differential cell counts in milk offer a deeper insight into the immunology of the mammary gland and might even provide information about the systemic health status of a dairy cow. Consequently, their potential as a diagnostic method to identify biomarkers has been a subject of research for quite some time. The objective of our study was to closely monitor the immune status of eight healthy dairy cows throughout their whole lactation. For this, high-resolution differential cell counts in milk and blood were determined by means of flow cytometry, which included 10 subpopulations of the 3 main populations of immune cells and their viability. Milk and blood samples were taken twice a week in the first 100 days after calving and once a week during the remaining lactation period: in total, 55 (52–57) blood and 55 (52–57) milk samples per animal. In addition, six well-established routine laboratory biomarkers, i.e., haptoglobin, calcium, and different metabolic parameters (non-esterified fatty acids, β-hydroxybutyric acid, bilirubin, and glutamate dehydrogenase), were analyzed in all blood samples. Furthermore, a standard differential blood cell count was performed on all blood samples. We found substantial differences between cell count progressions in the blood and milk. The distribution of cell populations in the blood remained mostly stable throughout the lactation, albeit at different individual levels. Several cell populations in the milk showed a noticeable dynamic over time, which caused a separation of different lactation stages in clustering analyses. Gamma delta T cells and CD4+ T cells in the milk stood out as they showed characteristic fluctuations during the course of lactation as well as minor changes in the case of inflammation. The determination of a differential cell count has the potential to be a sensitive diagnostic and prognostic tool in bovine milk. Further studies need to show to what extent the method is suitable for routine diagnostics and how to deal with animal-specific differences.

Can Routine Laboratory Biomarkers Predict Bloodstream Infections by Gram-negative Bacteria?

Can Routine Laboratory Biomarkers Predict Bloodstream Infections by Gram-negative Bacteria?

Assessment of Reticulocyte and Erythrocyte Parameters From Automated Blood Counts in Vaso-Occlusive Crisis on Sickle Cell Disease.

Sickle cell disease is a complex genetic disease involving cell adhesion between red blood cells, white blood cells, platelets and endothelial cells, inducing painful vaso-occlusive crisis (VOC). We assessed reticulocyte and erythrocyte parameters in a cohort of confirmed SCD patients, and investigated whether a combination of these routine laboratory biomarkers of haemolysis could be used to predict VOC development. Reticulocyte and erythrocyte parameters were evaluated using the Sysmex XN-9000 analyser. A total of 98 patients with SCD were included, 72 in steady state and 26 in VOC. Among the 72 patients in steady state, 22 developed a VOC in the following year (median: 3 months [2–6]). The following parameters were increased in SCD patients with VOC development compared to SCD patients without VOC development in the following year: reticulocyte count (94.6 109/L [67.8–128] vs. 48.4 109/L [24.9–87.5]), immature reticulocyte count (259 109/L [181–334] vs. 152 109/L [129–208]) reticulocyte/immature reticulocyte fraction (IRF) ratio (6.63 109/(L*%) [4.67–9.56] vs. 4.94 109/(L*%) [3.96–6.61]), and medium fluorescence reticulocytes (MFR) (19.9% [17.4–20.7] vs. 17.1% [15.95–19.75]). The association of a reticulocyte count of >189.4 109/L and an MFR of >19.75% showed a sensitivity of 81.8% and a specificity of 88% to predict VOC development in the following year. Based on our findings, a combination of routine laboratory biomarkers, as reticulocyte count, immature reticulocyte count and fluorescent reticulocyte fraction at steady state, could be used to predict VOC development in SCD.

Routine laboratory parameters, including complete blood count, predict COVID-19 in-hospital mortality in geriatric patients

To reduce the mortality of COVID-19 older patients, clear criteria to predict in-hospital mortality are urgently needed. Here, we aimed to evaluate the performance of selected routine laboratory biomarkers in improving the prediction of in-hospital mortality in 641 consecutive COVID-19 geriatric patients (mean age 86.6 ± 6.8) who were hospitalized at the INRCA hospital (Ancona, Italy). Thirty-four percent of the enrolled patients were deceased during the in-hospital stay. The percentage of severely frail patients, assessed with the Clinical Frailty Scale, was significantly increased in deceased patients compared to the survived ones. The age-adjusted Charlson comorbidity index (CCI) score was not significantly associated with an increased risk of death. Among the routine parameters, neutrophilia, eosinopenia, lymphopenia, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein, procalcitonin, IL-6, and NT-proBNP showed the highest predictive values. The fully adjusted Cox regressions models confirmed that high neutrophil %, NLR, derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), and low lymphocyte count, eosinophil %, and lymphocyte-to-monocyte ratio (LMR) were the best predictors of in-hospital mortality, independently from age, gender, and other potential confounders. Overall, our results strongly support the use of routine parameters, including complete blood count, in geriatric patients to predict COVID-19 in-hospital mortality, independent from baseline comorbidities and frailty.

PDL-1 TESTING AND IMMUNOTHERAPY SELECTION—EARLY LABORATORY EXPERIENCE AND ITS POTENTIAL ROLE IN HEAD & NECK CANCER MANAGEMENT

<h3>Background and Objective</h3> Anti-programmed cell death protein 1 (PDL-1) therapy has been relatively recently approved in a defined context by NICE in adults for the management of recurrent and metastatic head and neck squamous cell carcinomas (HNSCCs). In this context, companion diagnostic PDL-1 testing, previously established at our center for lung and bladder tumors, was undertaken as per NICE recommendations and clinical requests in a few head and neck cases. <h3>Methods</h3> Histopathology reports incorporating PDL-1 test results were identified (5-year period January 2014 to January 2020; including HNSCCs)) and analyzed for tumor type, clone, and test result. <h3>Results</h3> PDL-1 testing was undertaken in 199 cases, including 3 with HNSCC, with low focal positive staining in all 3 cases (1%, 1%, and 15%). <h3>Conclusions</h3> Immunotherapy treatment in HNSCC has demonstrated a small but statistically significant improved overall survival in some studies. The strength of correlation between PDL1 expression and response to immune-oncology agents in HNSCC has, however, been established to a lesser degree in recent studies as opposed to some other tumor types, such as in lung carcinoma. This study aims to record data as per NICE recommendations and share them with a view to highlighting the case for improved routine laboratory biomarkers in HNSCC.

Utility of Routine Laboratory Biomarkers to Detect COVID-19: A Systematic Review and Meta-Analysis

No routine laboratory biomarkers perform well enough in diagnosing COVID-19 in isolation for them to be used as a standalone diagnostic test or to help clinicians prioritize patients for treatment. Instead, other diagnostic tests are needed. The aim of this work was to statistically summarise routine laboratory biomarker measurements in COVID-19-positive and -negative patients to inform future work. A systematic literature review and meta-analysis were performed. The search included names of commonly used, routine laboratory tests in the UK NHS, and focused on research papers reporting laboratory results of patients diagnosed with COVID-19. A random effects meta-analysis of the standardized mean difference between COVID-19-positive and -negative groups was conducted for each biomarker. When comparing reported laboratory biomarker results, we identified decreased white blood cell, neutrophil, lymphocyte, eosinophil, and platelet counts; while lactate dehydrogenase, aspartate aminotransferase, and alanine aminotransferase were elevated in COVID-19-positive compared to COVID-19-negative patients. Differences were identified across a number of routine laboratory biomarkers between COVID-19-positive and -negative patients. Further research is required to identify whether routine laboratory biomarkers can be used in the development of a clinical scoring system to aid with triage of patients.

Evaluation of the Role of Routine Laboratory Biomarkers in COVID-19 Patients: Perspective from a Tertiary Care Hospital in India.

To evaluate the role of routine laboratory biomarkers like C Reactive Protein (CRP), Lactate Dehydrogenase (LDH), Interleukin 6 (IL6), Ferritin, Creatinine, Procalcitonin (PCT), Aspartate aminotransferase (AST), Alanine aminotransferase (ALT), Serum Albumin, Total Bilirubin (T Bil), High Sensitive Troponin I (hs troponin I), N Terminal-pro B-type Natriuretic Peptide (NT proBNP), Blood Urea Nitrogen (BUN) and Blood Gases in COVID 19 patients who are admitted with SARS CoV-2 positive test results by real-time reverse transcriptase polymerase chain reaction (rRT PCR) in Kokilaben Dhirubhai Ambani Hospital & Medical Research Institute, Mumbai, India. 100 individuals detected with COVID-19 belonging to the age group 12–83 years (median age 62 years) within the period of 1st March 2020 to 10th July 2020 were studied. The case group consisted of 72 males and 28 females. 40 healthy adults without any history or clinical evidence suggestive of COVID-19 and without any comorbidities, like diabetes, hypertension chronic lung disease, cardiac disease, cancer, and immune-compromised individuals were considered as a control group for the study. Routine laboratory findings of these 100 patients were used to evaluate the abnormalities found in COVID-19 patients. Statistical analysis was carried out on the data after determining whether the data had a normal/log-normal distribution and their significance was determined by calculating the p-value. The percentage of patients showing a decrease or increase from the normal value was calculated. Trend analysis was carried out for the 100 patients considered in the case group. Among them, 6 patients were used as representatives to show the trend in these biomarkers during the course of hospital stay. These 5 severe cases consisted of 2 adult males, 2 adult females, and 1 adolescent girl. This selection is to demonstrate the representation of COVID-19 infection in adult males and females and pediatric multisystem inflammatory syndrome associated with COVID-19 in the younger age group. One mild case (adult male) was also selected in the case study. We found a significant increase in mean values of AST, ALT, Total Billirubin, Creatinine, CRP, PCT, LDH, IL6, Ferritin, Lactate, hsTroponin I, NT Pro BNP and decrease in mean values of Albumin, SO2, and PO2 in COVID 19 cases than control. We applied Receiver Operating Curve (ROC) curve to discriminate case population more precisely than the control population. Therefore, Routine laboratory biomarkers appear to play a significant role in COVID-19 patients.

PD-L1 testing and immunotherapy selection - early laboratory experience and its potential role in head and neck cancer management.

Anti-programmed cell death protein-1 (PD-1) therapy has been relatively recently approved in a defined context by NICE in adults in the management of recurrent and metastatic head and neck squamous cell carcinomas (HNSCC). In this context, companion diagnostic programmed cell death ligand-1 (PD-L1) testing, previously established at our center for lung and bladder tumors, was undertaken in a few head and neck cancer cases. The scope of this study was to audit the relevant PD-L1 data and integrate the findings in our current clinical practice, with a view to promote improved routine laboratory biomarkers in HNSCC. Histopathology reports documenting tumor type, PD-L1 result and type of clone/assay were included in this study. Over a 5-year period, PD-L1 testing was undertaken in 199 cancer cases, including 3 with head and neck squamous carcinoma with low focal positive staining. Immunotherapy treatment in HNSCC demonstrates a discreet but still significant improvement in the overall survival of PD-L1 positive subjects.

Association Between Implementation Of Novel Therapies And Improved Survival In Patients Starting Hemodialysis: The Swedish Renal Registry 2006-2015.

BackgroundThe recent years have witnessed significant therapeutic advances for patients on haemodialysis (HD). We evaluated temporal changes in treatments practices and survival rates among incident HD patients.MethodsThis was an observational study of patients initiating HD in Sweden in 2006–15. Trends of HD-related practices, medications and routine laboratory biomarkers were evaluated. The incidence of death and major cardiovascular events (MACEs) across calendar years were compared against the age- and sex-matched general population. Via Cox regression, we explored whether adjustment for implementation of therapeutic advances modified observed survival and MACE risks.ResultsAmong 6612 patients, age and sex were similar, but the burden of comorbidities increased over time. The proportion of patients receiving treatment by haemodiafiltration, ≥3 sessions/week, lower ultrafiltration rate and working fistulas increased progressively, as did use of non-calcium phosphate binders, cinacalcet and vitamin D3. The standardized 1-year mortality decreased from 13.2% in 2006–07 to 11.1% in 2014–15. The risk of death decreased by 6% [hazard ratio (HR) = 0.94, 95% confidence interval (CI) 0.90–0.99] every 2 years, and the risk of MACE by 4% (HR = 0.96, 95% CI 0.92–1.00). Adjustment for changes in treatment characteristics abrogated these associations (HR = 1.00, 95% CI 0.92–1.09 for death and 1.00, 0.94–1.06 for MACE). Compared with the general population, the risk of death declined from 6 times higher in 2006–07 [standardized incidence rate ratio (sIRR) = 6.0, 95% CI 5.3–6.9] to 5.6 higher in 2014–15 (sIRR = 5.57, 95% CI 4.8–6.4).ConclusionsGradual implementation of therapeutic advances over the last decade was associated with a parallel reduction in short-term risk of death and MACE among HD patients.

Predicting Survival After Bone Marrow Transplant Using Time Series of Routine Laboratory Biomarkers

Bone marrow transplant (BMT) is a curative therapy for patients with hematologic malignancies. However, there is still a high rate of relapse and mortality after BMT. It would be tremendously valuable if we can identify older adults at high-risk for mortality using readily available information. A number of biomarkers are routinely collected during follow-up for clinical care, but this information is seldom used in prediction models. We examined the data from 1011 patients who had BMT at Johns Hopkins between 2013 and 2019. There were 364 death over a median follow-up of 431 days. We considered 4 biomarkers: albumin, hemoglobin, lymphocytes count, and platelets. Biomarker data from one week pre-BMT to 8 weeks post-BMT was used for prediction using a random survival forest model. The model performed quite well and had a 5-fold cross-validated c-index of 0.733 (95%CI: 0.724-0.739). Routine laboratory biomarkers can help identify poorly resilient older BMT patients.

Differentiating Acute Food Protein–Induced Enterocolitis Syndrome From Its Mimics: A Comparison of Clinical Features and Routine Laboratory Biomarkers

Food protein-induced enterocolitis syndrome (FPIES) is frequently misdiagnosed and subject to diagnostic delay. Profuse vomiting, the cardinal feature of acute FPIES, may occur in more common pediatric disorders such as gastroenteritis and sepsis. We sought to determine differentiating features at acute presentation between FPIES, gastroenteritis, and sepsis in young children presenting to an emergency department (ED) with profuse vomiting. We conducted a retrospective case-control study of children aged 6 months to 4 years with a diagnosis of acute FPIES who had presented to ED and compared the clinical features, vital signs, and routine laboratory studies of this cohort to similarly aged children presenting to ED with vomiting diagnosed with bacterial/viral gastroenteritis or bacterial sepsis. A total of 181 acute FPIES ED presentations were compared with 55 gastroenteritis and 36 bacterial sepsis ED presentations. Children with FPIES were more likely to present with lethargy, floppiness, and pallor. Compared with children with FPIES, children with sepsis were likely to present with fever, tachycardia, tachypnea, and diarrhea, whereas those with gastroenteritis were likely to present with fever, diarrhea, and blood in stools. Normal C-reactive protein (CRP), leucocytosis, lymphocytosis, thrombocytosis, low MPV, and an elevated albumin/globulin ratio were more commonly seen in FPIES than in sepsis or gastroenteritis. No other clinical or laboratory markers examined reliably distinguished between the 3 disease groups. In the young vomiting child, lethargy, floppiness, pallor without fever, and normal CRP should alert clinicians to a possible diagnosis of FPIES. In contrast, a highly elevated CRP is not a feature of FPIES, and in such cases an alternative diagnosis must be considered.