Laboratory Blood Test Results Research Articles

МНОЖЕСТВЕННАЯ МИЕЛОМА: ОСОБЕННОСТИ ТЕЧЕНИЯ И ОТВЕТА НА ТЕРАПИЮ У ПАЦИЕНТОВ РАЗНЫХ ВОЗРАСТНЫХ ГРУПП

Background: Multiple myeloma (MM) remains an incurable disease with the development of relapses, sometimes with an uncontrolled course leading to death regardless of the age of the patient. The use of new schemes and methods of treatment has not only significantly improved outcomes in younger MM patients but also improved the quality of life and survival in people over 65 years of age. Objective: to determine the clinical features of the course of the disease and response to therapy in patients of different age groups with newly diagnosed multiple myeloma. Material and Methods: An analysis of the data of 139 patients with newly diagnosed MM was carried out. The median follow-up was 25 months, with a maximum of 58 months. Patients were divided into study groups depending on age at the time of diagnosis: the first group - age up to 65 years (n=65); the second group - age from 65 years and older (n=74). The material of the study was clinical and anamnestic data, the results of laboratory blood tests: biochemical analysis and immunochemical analysis. All the patients underwent the whole body CT scan, diffusely weighted whole body MRI and bone marrow aspiration biopsy with immunophenotypic study. As an induction therapy, according to international recommendations, courses of chemotherapy were used using various regimens containing bortezomib, depending on age and comorbidity. Results. The MM variant with IgA secretion occurred 1.76 times more often in the first group (p=0.128). In the first group hypercalcemia was somewhat more common (2.4 times, p=0.099) and an excess of LDH levels (2.9 times, p=0.072). In contrast, the presence of infectious complications (by 1.2 times, p=0.392), excess of the level of β2-microglobulin > 3 mg/l (by 1.6 times, p=0.086) and kidney damage (by 1.45 times, p=0.037) were more common in the second group, and in the latter case the differences were statistically significant. Disease stage (p=0.0001), excess of β2 microglobulin level (p=0.004), excess of LDH level (p=0.03), presence of anemic syndrome (p=0.003), hypercalcemia (p=0.02), presence of infectious complications (p=0.01) at the time of diagnosis were predictors of worse survival in the general group of MM patients. Conclusion. The active use of new therapeutic strategies and regimens improves treatment outcomes not only in patients under 65 years of age, but also in older patients. New studies are needed to identify prognostic factors in individuals of different age groups in order to start personalized therapy and improve treatment outcomes.

Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters

BackgroundBacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters.MethodsData for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model.ResultsAfter feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715–0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632–0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579–0.746) and 0.615 (95%CI = 0.526–0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible.ConclusionThe present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.

Biochemical, Hematological, and Immunological Biomarkers as Predictors for Intensive Care Unit Admission in Patients with COVID-19.

To identify the biochemical, hematological, and immune biomarkers in COVID-19 patients on admission that are predictive of eventual admission to the intensive care unit (ICU). This retrospective cohort study was conducted on all confirmed COVID-19 cases hospitalized at Royal Hospital, Oman from 24 February to 30 July 2020. The demographic, clinical, and laboratory data were collected from the hospital information system. Patients were divided into two groups: non-ICU admitted group and ICU admitted group. Out of 445 patients, 276 (62.0%) were male and 169 (38.0%) were female; 259 (58.2%) patients were admitted to COVID-19 general wards whereas 186 (41.8%) were admitted to ICU. Admission to ICU was more likely when patient had the following comorbidities: diabetes (OR = 1.8; 95% CI: 1.3-2.7), liver diseases (OR = 2.1; 95% CI: 1.1-4.3), and respiratory diseases (OR = 2.0; 95% CI: 1.1-3.7). Between ICU and non-ICU patients, there were significant differences in on-admission laboratory blood/serum parameters: total white blood cells (WBCs) count, lymphocytes count, C-reactive protein (CRP), ferritin, corrected calcium, interleukin 6 (IL-6), D-dimer, alanine transaminase (ALT), lactate dehydrogenase (LDH), albumin, and troponin. The current study identified the presence of the comorbidities (i.e., diabetes, liver diseases, and respiratory diseases) and on-admission laboratory blood and serum test results (i.e., WBC, lymphocytes, CRP, ferritin, corrected calcium, IL-6, D-dimer, ALT, LDH, albumin, and troponin) that are associated with ICU admission.

Evaluation of Severe Acute Respiratory Syndrome Coronavirus-2 RNA in tear specimens of hospitalised patients with confirmed novel Coronavirus disease 2019

Aim: We aimed to detect the presence of Severe Acute Respiratory Syndrome Coronavirus-2 RNA in the tears of patients with confirmed novel Coronavirus disease 2019. Materials and Methods: This prospective study was performed at Health Science University Kartal Dr. Lutfi Kırdar City Hospital between 13-21 May 2020. Nasopharyngeal and tear samples of 15 patients with novel Coronavirus disease 2019 was performed for real-time polymerase chain reaction assay. Ocular and systemic signs and symptoms, chest computed tomographic scans, and results of laboratory blood tests as well as the drugs used for the treatment were noted and analyzed. Results: Of the 15 patients nine (60%) were male and six (40%) were female. Mean age of the patients was 53.86±20.20 (21-89) years. Among them nine patients (60%) were positive for novel coronavirus disease 2019 on real-time polymerase chain reaction from nasopharyngeal specimens. None of the patients was positive for real-time polymerase chain reaction for Severe Acute Respiratory Syndrome Coronavirus-2 in tear specimens. Only two of 15 patients had bilateral conjunctivitis. These two patients had positive results for Severe Acute Respiratory Syndrome Coronavirus-2 on real-time polymerase chain reaction from nasopharyngeal swabs. Conclusion: In this study Severe Acute Respiratory Syndrome Coronavirus-2 RNA could not be detected in the tear samples of the patients with confirmed novel Coronavirus disease 2019 with or without ocular symptoms and signs.

Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on Information Theory and Historical Conditional Probability: Observational Study.

BackgroundRedundancy in laboratory blood tests is common in intensive care units (ICUs), affecting patients’ health and increasing health care expenses. Medical communities have made recommendations to order laboratory tests more judiciously. Wise selection can rely on modern data-driven approaches that have been shown to help identify low-yield laboratory blood tests in ICUs. However, although conditional entropy and conditional probability distribution have shown the potential to measure the uncertainty of yielding an abnormal test, no previous studies have adapted these techniques to include them in machine learning models for predicting abnormal laboratory test results.ObjectiveThis study aimed to address the limitations of previous reports by adapting conditional entropy and conditional probability to extract features for predicting abnormal laboratory blood test results.MethodsWe used an ICU data set collected across Alberta, Canada, which included 55,689 ICU admissions from 48,672 patients. We investigated the features of conditional entropy and conditional probability by comparing the performances of 2 machine learning approaches for predicting normal and abnormal results for 18 blood laboratory tests. Approach 1 used patients’ vitals, age, sex, and admission diagnosis as features. Approach 2 used the same features plus the new conditional entropy–based and conditional probability–based features. Both approaches used 4 different machine learning models (fuzzy model, logistic regression, random forest, and gradient boosting trees) and 10 metrics (sensitivity, specificity, accuracy, precision, negative predictive value [NPV], F1 score, area under the curve [AUC], precision-recall AUC, mean G, and index balanced accuracy) to assess the performance of the approaches.ResultsApproach 1 achieved an average AUC of 0.86 for all 18 laboratory tests across the 4 models (sensitivity 78%, specificity 84%, precision 82%, NPV 75%, F1 score 79%, and mean G 81%), whereas approach 2 achieved an average AUC of 0.89 (sensitivity 84%, specificity 84%, precision 83%, NPV 81%, F1 score 83%, and mean G 84%). We found that the inclusion of the new features resulted in significant differences for most of the metrics in favor of approach 2. Sensitivity significantly improved for 8 and 15 laboratory tests across the different classifiers (minimum P<.001 and maximum P=.04). Mean G and index balanced accuracy, which are balanced performance metrics, also improved significantly across the classifiers for 6 to 10 and 6 to 11 laboratory tests. The most relevant feature was the pretest probability feature, which is the probability that a test result was normal when a certain number of consecutive prior tests was already normal.ConclusionsThe findings suggest that conditional entropy–based features and pretest probability improve the capacity to discriminate between normal and abnormal laboratory test results. Detecting the next laboratory test result is an intermediate step toward developing guidelines for reducing overtesting in the ICU.

Genomic landscape and peripheral blood biomarkers of advanced triple-negative breast cancer treated with immune checkpoint blockade: An exploratory analysis of the TQB2450-Ib-07 trial.

1080 Background: Immune checkpoint inhibitor (ICI) has emerged as a novel therapeutic option for advanced triple-negative breast cancer (aTNBC). However, no robust biomarker indicative of clinical outcomes has been identified. Herein, we portraited the genomic landscape and explored the biomarkers for patients with aTNBC receiving ICI-based therapy. Methods: This is a prospective, multicenter, phase 1b clinical trial (NCT03855358) to assess the efficacy and safety profiles of TQB2450, a humanized monoclonal PD-L1 antibody, plus anlotinib in pretreated TNBC. Eligible patients undergo liquid biopsy at baseline and the timepoint of disease progression. NGS-based assay was performed based on circulating tumor DNA (ctDNA) in the bloodstream. Meanwhile, results of laboratory blood tests were dynamically collected and blood markers, including neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), and platelet to lymphocyte ratio (PLR), were successively evaluated. The primary endpoints were progression-free survival (PFS) and clinical efficacy assigned via guidelines for response criteria for use in trials testing immunotherapies (iRECIST). Results: Between May 29, 2019, and December 31, 2020, 34 patients with aTNBC were enrolled. Gene alternations primarily comprised mutation, amplification, or deletion of TP53, MLL3, DNMT3A, PI3KCA, EP300, PTEN, LRP1B, MDM2, and NCOR1. The median maximum somatic allele frequency (MSAF) was 9.97% significantly indicative of PFS, which was 3.58 months for the MSAF-high group and 13.34 months for the MSAF-low group (P = 3e-04), respectively. Else, a strong association was also signified between MSAF and tumor shrinkage (CR/PR vs. SD/PD, P = 0.012). For blood tumor mutation burden (bTMB), the median was 6.72 muts/Mb, which the bTMB-low group was suggestive of a better PFS (11.09 months vs. 5.52 months, P = 0.007), yet no obvious association existing in terms of clinical response. Dynamic analysis revealed that a decline in MSAF was significantly associated with a better PFS (7.10 months vs. 2.74 months, P = 0.018), while no correlations were detected between bTMB and PFS. Based on NLR week 2/0 of 0.95, PFS was significantly worse in the NLR-low group (11.0 months vs. 3.5 months, P = 0.006) and likely distinguished the clinical response (CR/PR vs. SD vs. PD, P = 0.049; non-PD vs. PD, P = 0.022). Moreover, NLR week 2/0 could notably foretell the clinical response for patients with aTNBC with the AUC of 0.82 (0.61-1.00). No comparable utilities were identified regarding LMR and PLR. Conclusions: For aTNBC treated with ICI, MSAF tended to be a robust indicator for both PFS and clinical response. NLR week 2/0 presented a favorable profile indicative of PFS as well as a strong predictor for clinical responsiveness of patients with aTNBC receiving immune checkpoint blockade. Clinical trial information: NCT03855358.

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.

Серозные менингиты у детей: расширение диагностических возможностей

Обследованы 42 ребенка в возрасте 12 мес. — 10 лет, госпитализированных с предварительным диагнозом «менингит». Анализировали симптомы заболевания, результаты лабораторных исследований крови, ликвора, активность лактатдегидрогеназы (ЛДГ) ликвора. Тяжесть больных с серозными менингитами оценена по шкале тяжести AMSS. Была установлена статистически достоверно высшая активность лактатдегидрогеназы ликвора у пациентов с серозными менингитами. Активность ЛДГ зависела от уровня цитоза ликвора, концентрации в ликворе белка, общего количества лимфоцитов в периферической крови, что указывает на возможность использования исследования активности ЛДГ ликвора в качестве дополнительного показателя в диагностике серозных менингитов у детей.

Development of neural network models for prediction of the outcome of COVID-19 hospitalized patients based on initial laboratory findings, demographics, and comorbidities.

During the process of the treatment of COVID-19 hospitalized patients, physicians still face a lot of unknowns and problems. Despite the application of the treatment protocol, it is still unknown why the medical status of a certain number of patients worsens and ends with death. Many factors were analyzed for the prediction of the clinical outcome of the patients using different methods. The aim of this paper was to develop a prediction model based on initial laboratory blood test results, accompanying comorbidities, and demographics to help physicians to better understand the medical state of patients with respect to possible clinical outcomes using neural networks, hypothesis testing, and confidence intervals. The research had retrospective-prospective, descriptive, and analytical character. As inputs for this research, 12 components of laboratory blood test results, six accompanying comorbidities, and demographics (age and gender) data were collected from hospital information system in Sarajevo for each patient from a sample of 634 hospitalized patients. Clinical outcome of the hospitalized patients, survival or death, was recorded 30 days after admission to the hospital. The prediction model was designed using a neural network. In addition, formal hypothesis tests were performed to investigate whether there were significant differences in laboratory blood test results and age between patients who died and those who survived, including the construction of 95% confidence intervals. In this paper, 11 neural networks were developed with different threshold values to determine the optimal neural network with the highest prediction performance. The performances of the neural networks were evaluated by accuracy, precision, sensitivity, and specificity. Optimal neural network model evaluation metrics are: accuracy = 87.78%, precision = 96.37%, sensitivity = 90.07%, and specificity = 62.16%. Significantly higher values (P < 0.05) of blood laboratory result components and age were detected in patients who died. Optimal neural network model, results of hypothesis tests, and confidence intervals could help to predict, analyze, and better understand the medical state of COVID-19 hospitalized patients and thus reduce the mortality rate.

Management strategies for patients with subclinical hypothyroidism: a protocol for an umbrella review

BackgroundSubclinical hypothyroidism is a thyroid disorder diagnosed from the laboratory blood test results of otherwise asymptomatic patients. It has been associated with poor cardiovascular outcomes, mortality and progression to overt thyroid hormone deficiency. Current guidelines on the management of subclinical hypothyroidism differ because of conflicting evidence on long-term treatment benefits. Even though there are several existing systematic reviews on its clinical outcomes, no definitive conclusion has been reached yet. As such, a new synthesis could help provide more insight and consensus on this topic. To this purpose, this umbrella review will evaluate and synthesise current evidence on the long-term clinical outcomes of the different management strategies for subclinical hypothyroidism.MethodsThis is a protocol for an umbrella review on the management strategies for subclinical hypothyroidism. We will conduct literature searches in multiple electronic databases (from inception onwards), namely MEDLINE, EMBASE, Scopus, Web of Science, Cochrane Database of Systematic Reviews, JBI Evidence Synthesis, Epistemonikos database, PDQ Evidence and the PROSPERO register. There will be no restriction on the date or language of publication. Additional material will be identified through grey literature searches and citation chaining. Review inclusion criteria will be patients with subclinical hypothyroidism, receiving treatment or monitoring, no restrictions on the comparators used and with cardiovascular events, frailty fractures, quality of life and all-cause mortality as primary outcomes of interest. Two reviewers will independently screen all citations, full-text articles and abstract data on a pre-piloted form in duplicate. Methodological quality (or bias) of included studies will be appraised using AMSTAR-2. Any conflicts that arise will be resolved through discussion or involving a third reviewer. A narrative synthesis will be provided with information presented in the main text and tables to summarise and explain the characteristics and findings of the included reviews. Even so, it is not expected that a meta-analysis will be performed due to review variability. Study limitations and methodological quality assessments will also be reported to provide context for the overall summary of evidence.DiscussionThis review will provide a comprehensive summary of the effects of the pharmacological and non-pharmacological management of subclinical hypothyroidism on specific long-term clinical outcomes. It is anticipated that the findings of this umbrella review will aid in the development of consensus-based clinical recommendations for subclinical hypothyroidism, as well as highlight areas for future research. Review findings will be disseminated primarily through peer-reviewed publications.Systematic review registrationPROSPERO CRD42021235172

CLINICAL ANALYSIS OF BLOOD TRACES OF RATS AFTER SUBCUTANEOUS IMPLANTATION OF Nd-Fe-B MAGNETS WITH PROTECTIVE PVD ZrN COATINGS

It is possible to increase the effectiveness of treatment of various anomalies of occlusion with the help of orthodontic appliances, including permanent magnets as components. But a significant disadvantage of magnetic materials is low corrosion resistance, which can be prevented by applying to the magnets various PVD protective coatings leading to passivation of the surface of the magnetic material. The effect of Nd-Fe-B magnets with and without protective ZrN coating after subcutaneous implantation in rats on the results of laboratory blood tests has been evaluated.

Development and validation of a nomogram for predicting the probability of nontraumatic osteonecrosis of the femoral head in Chinese population

Although corticosteroids and alcohol are two major risk factors for nontraumatic osteonecrosis of the femoral head (NONFH), the effects of other factors have rarely been studied, thereby making early diagnosis and treatment of NONFH difficult. This study aimed to develop and validate a nomogram to NONFH, but patients with alcohol- and steroid-related NONFH are not at all taken into account in this study. A training cohort of 790 patients (n = 434, NONFH; n = 356, femoral neck fractures [non-NONFH]) diagnosed in our hospital from January 2011 to December 2016 was used for model development. A least absolute shrinkage and selection operator (lasso) regression model was used for date dimension reduction and optimal predictor selection. A predictive model was developed from univariate and multivariate logistic regression analyses. Performance characterisation of the resulting nomogram included calibration, discriminatory ability, and clinical usefulness. After internal validation, the nomogram was further evaluated in a separate cohort of 300 consecutive patients included between January 2017 and December 2018. The simple prediction nomogram included five predictors from univariate and multivariate analyses, including gender, total cholesterol levels, triglyceride levels, white blood cell count, and platelet count. Internal validation showed that the model had good discrimination [area under the receiver operating characteristic curve (AUC) = 0.80] and calibration. Good discrimination (AUC = 0.81) and calibration were preserved in the validation cohort. Decision curve analysis showed that the predictive nomogram was clinically useful. The simple diagnostic nomogram, which combines demographic data and laboratory blood test results, was able to quantify the probability of NONFH in cases of early screening and diagnosis.

Assessment and monitoring of liver graft viability and initial function using interstitial microdialysis

Assessing the viability and monitoring the function of liver graft in the early postoperative period are critical clinical tasks. One possible solution is to determine the changes in concentration of blood glucose, its metabolites and glycerol in the graft using interstitial microdialysis. Objective: to study the dynamics of interstitial glucose, lactate, pyruvate and glycerol in the early post-liver transplant period – depending on the initial graft function (IGF) – and to compare with the results of standard laboratory blood tests. Materials and methods. Four selected clinical observations of deceased donor liver transplantation are presented. Two of the observations showed normal IGF, one observation – early allograft dysfunction (EAD), complicated by hepatic artery thrombosis (HAT), while one observation demonstrated primary non-function (PNF). Collection of microdialysis samples began after arterial reperfusion of the liver graft and continued continuously for 7 days or until death. Standard blood biochemistry and coagulation tests were performed at least once a day. Results. With normal IGF and a smooth postoperative period, interstitial concentrations of glucose, lactate, pyruvate and glycerol remained stable throughout the observation period, ranging from 5 to 20 mmol/L, 1.1 to 7.5 mmol/L, 90 to 380 μmol/L, and 10–100 μmol/L, respectively. EAD was associated with initially higher levels of glucose, lactate, and pyruvate. With HAT development, there was a rapid (within 2–4 hours) five-fold increase in interstitial concentration of lactate with simultaneous decrease in glucose and pyruvate levels to 0.1 mmol/L and 11 μmol/L, respectively. In the case of PNF, there was an initially high concentration of interstitial lactate – 16.4 mmol/L, which increased further to 35.5 mmol/L. Glucose concentration was close to 0. Changes in interstitial glucose, its metabolites and glycerol concentrations chronologically preceded the corresponding changes in peripheral blood composition by 3–5 hours. Conclusion. Microdialysis measurement of interstitial glucose, lactate, pyruvate and glycerol concentrations facilitates real-time monitoring of liver graft viability and function. The high sensitivity of the method could help in accelerating diagnosis of vascular complications (HAT in particular), as well as graft dysfunction with other causes. Therefore, the method is feasible in clinical practice.

Pre-operative hemoglobin level and use of sedative-hypnotics are independent risk factors for post-operative delirium following total knee arthroplasty

BackgroundDelirium is a well-known complication following surgery, especially with the increasing age of patients undergoing surgery. The increasing demands resulting from a prolonged healthy life expectancy has resulted in more arthroplasties despite their age and existing comorbidities. The purpose of this study is to explore the various risk factors that may contribute to delirium in unilateral and bilateral total knee arthroplasties in the elderly population.Methods170 patients who underwent unilateral or bilateral total knee arthroplasties were analyzed retrospectively for delirium. Age, sex, comorbidities, use of sedative-hypnotics, peri-operative blood loss, pre- and post-operative laboratory blood test results were investigated and analyzed.ResultsThe incidence of post-operative delirium was 6.5% (11 out of 170 patients) with a mean age of 79.5 (± 6.9) years, compared to 73.0 (± 9.0) years in the non-delirium group. Higher age, use of sedative-hypnotics, low pre-operative Hb and Ht, low post-operative Hb, Ht and BUN were observed in the delirium group. Multivariate logistic regression analysis identified that the use of sedative-hypnotics and pre-operative Hb level were independent risk factors for post-operative delirium after TKA. The odds ratios for the use of sedative-hypnotics and pre-operative Hb level were 4.6 and 0.53, respectively. Receiver operating characteristic curve analysis showed that pre-operative Hb of less than 11.1 g/dL was a predictor for the development of delirium, with a sensitivity of 54.6% and a specificity of 91.6%.ConclusionPatients with a pre-operative Hb level of < 11.1 g/dL or those using sedative-hypnotics are associated with post-operative delirium. Peri-operative management and preventative measures are therefore needed to reduce the risks of post-operative delirium in such patients.

Prognostic factors of Rapid symptoms progression in patients with newly diagnosed parkinson’s disease

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients' condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. In this work, an analysis of baseline PD characteristics is performed using machine learning techniques, to identify prognostic factors for early rapid progression of PD symptoms. Using open data from the Parkinson's Progression Markers Initiative (PPMI) study, an extensive set of baseline patient evaluation outcomes is examined to isolate determinants of rapid progression within the first two and four years of PD. The rate of symptoms progression is estimated by tracking the change of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score over the corresponding follow-up period. Patients are ranked according to their progression rates and those who expressed the highest rates of MDS-UPDRS total score increase per year of follow-up period are assigned into the rapid progression class, using 5- and 10-quantiles partition. Classification performance against the rapid progression class was evaluated in a per quantile partition analysis scheme and in quantile-independent approach, respectively. The results shown a more accurate patient discrimination with quantile partitioning, however, a much more compact subset of baseline factors is extracted in the latter, making a more suitable for actual interventions in practice. Classification accuracy improved in all cases when using the longer 4-year follow-up period to estimate PD progression, suggesting that a prolonged patient evaluation can provide better outcomes in identifying rapid progression phenotype. Non-motor symptoms are found to be the main determinants of rapid symptoms progression in both follow-up periods, with autonomic dysfunction, mood impairment, anxiety, REM sleep behavior disorders, cognitive decline and memory impairment being alarming signs at baseline evaluation, along with rigidity symptoms, certain laboratory blood test results and genetic mutations.

Conjunctivitis in patients with atopic dermatitis treated with dupilumab is associated with higher baseline serum levels of immunoglobulin E and thymus and activation-regulated chemokine but not clinical severity in a real-world setting

To the Editor: Conjunctivitis has been observed as an adverse event in patients with atopic dermatitis (AD) receiving dupilumab. Its incidence rate ranges from 8.6% to 21.44% in clinical trials,1Akinlade B. Guttman-Yassky E. de Bruin-Weller M. et al.Conjunctivitis in dupilumab clinical trials.Br J Dermatol. 2019; 181: 459-473Crossref PubMed Scopus (178) Google Scholar whereas real-world data have shown an even higher prevalence.2Uchida H. Kamata M. Mizukawa I. et al.Real-world effectiveness and safety of dupilumab for the treatment of atopic dermatitis in Japanese patients: a single-centre retrospective study.Br J Dermatol. 2019; 181: 1083-1085Crossref PubMed Scopus (25) Google Scholar We analyzed our data to explore practical predictors of later development of conjunctivitis in patients with AD initiating dupilumab in a real-world setting. The study included adult patients with AD who had been treated with dupilumab in our hospital for more than 3 months as of October 1, 2019. Other inclusion criteria were the same as those described in our previous report.2Uchida H. Kamata M. Mizukawa I. et al.Real-world effectiveness and safety of dupilumab for the treatment of atopic dermatitis in Japanese patients: a single-centre retrospective study.Br J Dermatol. 2019; 181: 1083-1085Crossref PubMed Scopus (25) Google Scholar Baseline clinical severity, patents’ history of conjunctivitis, and results from laboratory blood tests were compared between patients who developed conjunctivitis and those who did not. Data on 57 Japanese adult patients (49 men, 8 women) with AD were analyzed. The mean ± standard deviation age at starting dupilumab was 38.1 ± 11.8 years. Conjunctivitis was observed in 22.8% (13 of 57) of patients after starting dupilumab. The mean time to develop conjunctivitis after beginning dupilumab was 5.3 ± 3.9 weeks. Conjunctivitis developed at a higher frequency after starting dupilumab in patients who had a history of conjunctivitis at baseline than in those who did not. Patients in whom conjunctivitis occurred after starting dupilumab had significantly higher serum levels of thymus and activation-regulated chemokine (TARC) and immunoglobulin E (IgE) at baseline than those who did not (Fig 1). The cutoff values at baseline were 3342 pg/mL (sensitivity, 69.23%; specificity, 79.55%) for TARC and 11,200 IU/mL (sensitivity, 76.92%; specificity, 69.77%) for IgE, with moderate accuracy. No significant differences were observed between them in clinical severity, including Investigator's Global Assessment, affected body surface area, the Eczema Area and Severity Index, and results of other laboratory blood tests. Clinical trials have demonstrated that baseline disease-related factors, including AD severity, prior conjunctivitis history, and certain biomarkers (TARC, IgE, eosinophils), are associated with an increased incidence of conjunctivitis.1Akinlade B. Guttman-Yassky E. de Bruin-Weller M. et al.Conjunctivitis in dupilumab clinical trials.Br J Dermatol. 2019; 181: 459-473Crossref PubMed Scopus (178) Google Scholar It is reasonable, because levels of certain biomarkers, such as TARC, IgE, and eosinophils, increase with AD severity.3Beck L.A. Thaci D. Hamilton J.D. et al.Dupilumab treatment in adults with moderate-to-severe atopic dermatitis.N Engl J Med. 2014; 371: 130-139Crossref PubMed Scopus (879) Google Scholar, 4Hamilton J.D. Suarez-Farinas M. Dhingra N. et al.Dupilumab improves the molecular signature in skin of patients with moderate-to-severe atopic dermatitis.J Allergy Clin Immunol. 2014; 134: 1293-1300Abstract Full Text Full Text PDF PubMed Scopus (297) Google Scholar, 5Guttman-Yassky E. Bissonnette R. Ungar B. et al.Dupilumab progressively improves systemic and cutaneous abnormalities in patients with atopic dermatitis.J Allergy Clin Immunol. 2019; 143: 155-172Abstract Full Text Full Text PDF PubMed Scopus (244) Google Scholar However, our real-world data revealed that only baseline serum TARC and IgE levels were significantly higher in patients who developed conjunctivitis and that a history of conjunctivitis and eosinophils showed its tendency, whereas baseline clinical severity did not demonstrate any association with incidence of conjunctivitis. Even in data from clinical trials, the difference in the incidence of conjunctivitis between patients with AD with a baseline score of 3 on the 5-point Investigator's Global Assessment scale and those with a baseline of 4 was quite small in CHRONOS (NCT02260986) (0.06 vs 0.09 per 100 patient-years) and in CAFÉ (NCT02755649) (0.22 vs 0.23).1Akinlade B. Guttman-Yassky E. de Bruin-Weller M. et al.Conjunctivitis in dupilumab clinical trials.Br J Dermatol. 2019; 181: 459-473Crossref PubMed Scopus (178) Google Scholar Distinguishing the subtle differences in disease severity of AD among patients with moderate to severe AD (not including mild AD) is difficult for physicians. Our study underscores that among those parameters, serum levels of TARC and IgE could reflect subtle differences in predisposition for conjunctivitis more accurately than the disease severity of AD evaluated by physicians, especially among patients with moderate to severe AD, indicating that these objective parameters are useful as practical predictors of later development of conjunctivitis.

Fibrodysplasia ossificans progressiva (stone man syndrome): a case report

BackgroundFibrodysplasia ossificans progressiva is an ultrarare autosomal dominant disorder and disabling syndrome characterized by postnatal progressive heterotopic ossification of the connective tissue and congenital malformation of the big toes. Fibrodysplasia ossificans progressiva has worldwide prevalence of about 1 in 2 million births. Nearly 90% of patients with fibrodysplasia ossificans progressiva are misdiagnosed and mismanaged and thus undergo unnecessarily interventions. So far, the number of reported existing cases worldwide is about 700. Clinical examination, radiological evaluation, and genetic analysis for mutation of the ACVR1 gene are considered confirmatory tools for early diagnosis of the disease. Association of fibrodysplasia ossificans progressiva with heterotopic ossification is well documented; however, postsurgical exaggerated response has never been reported previously, to the best of our knowledge.Case presentationWe report a case of a 10-year-old Pakistani boy brought by his parents to our institution. He had clinical and radiological features of fibrodysplasia ossificans progressive and presented with multiple painful lumps on his back due to hard masses and stiffness of his shoulders, neck, and left hip. He underwent surgical excision of left hip ossification followed by an exaggerated response in ossification with early disability. Radiological examination revealed widespread heterotopic ossification. All of his laboratory blood test results were normal.ConclusionFibrodysplasia ossificans progressiva is a very rare and disabling disorder that, if misdiagnosed, can lead to unnecessary surgical intervention and disastrous results of early disability. We need to spread knowledge to physicians and patients’ family members about the disease, as well as its features for early diagnosis and how to prevent flare-up of the disease to promote better quality of life in these patients.

Food allergies in weaned piglets in experiment and spontaneous occurrence

Food allergies in weaned piglets in experiment and spontaneous occurrence

An application of machine learning to haematological diagnosis

Quick and accurate medical diagnoses are crucial for the successful treatment of diseases. Using machine learning algorithms and based on laboratory blood test results, we have built two models to predict a haematologic disease. One predictive model used all the available blood test parameters and the other used only a reduced set that is usually measured upon patient admittance. Both models produced good results, obtaining prediction accuracies of 0.88 and 0.86 when considering the list of five most likely diseases and 0.59 and 0.57 when considering only the most likely disease. The models did not differ significantly, which indicates that a reduced set of parameters can represent a relevant “fingerprint” of a disease. This knowledge expands the model’s utility for use by general practitioners and indicates that blood test results contain more information than physicians generally recognize. A clinical test showed that the accuracy of our predictive models was on par with that of haematology specialists. Our study is the first to show that a machine learning predictive model based on blood tests alone can be successfully applied to predict haematologic diseases. This result and could open up unprecedented possibilities for medical diagnosis.

Zawał serca z uniesieniem odcinka ST u młodej kobiety z trombofilią złożoną

A 31-year-old woman was admitted to the clinic due to severe chest pain. Based on the clinical status and additional tests results myocardial infarction with ST elevation was diagnosed. Urgent coronary angiography and occluded left anterior descending artery recanalization with drug eluting stent implantation were successfully performed and resulted in clinical improvement. Anamnesis revealed two prominent risk factors for atherosclerosis: hypertension and hypercholesterolemia. Laboratory blood test results revealed the presence of complex thrombophilia consisted of: hyperhomocysteinemia, MTHFR gene mutation, protein S deficiency and factor VIII excess. Due to the high risk of thromboembolic complications, rivaroxaban was added to the dual antiplatelet therapy. This case report demonstrates the importance of the determination of additional risk factors in secondary prevention of cardiovascular events in young patients.