Results Of Routine Laboratory Tests Research Articles

Evolution of serious and life-threatening COVID-19 pneumonia as the SARS-CoV-2 pandemic progressed: an observational study of mortality to 60 days after admission to a 15-hospital US health system

ObjectiveIn order to predict at hospital admission the prognosis of patients with serious and life-threatening COVID-19 pneumonia, we sought to understand the clinical characteristics of hospitalised patients at admission as...

Using Machine Learning for Non-Invasive Detection of Kidney Stones Based on Laboratory Test Results: A Case Study from a Saudi Arabian Hospital.

Kidney stone disease is a widespread urological disorder affecting millions globally. Timely diagnosis is crucial to avoid severe complications. Traditionally, renal stones are detected using computed tomography (CT), which, despite its effectiveness, is costly, resource-intensive, exposes patients to unnecessary radiation, and often results in delays due to radiology report wait times. This study presents a novel approach leveraging machine learning to detect renal stones early using routine laboratory test results. We utilized an extensive dataset comprising 2156 patient records from a Saudi Arabian hospital, featuring 15 attributes with challenges such as missing data and class imbalance. We evaluated various machine learning algorithms and imputation methods, including single and multiple imputations, as well as oversampling and undersampling techniques. Our results demonstrate that ensemble tree-based classifiers, specifically random forest (RF) and extra tree classifiers (ETree), outperform others with remarkable accuracy rates of 99%, recall rates of 98%, and F1 scores of 99% for RF, and 92% for ETree. This study underscores the potential of non-invasive, cost-effective laboratory tests for renal stone detection, promoting prompt and improved medical support.

Impact of COVID-19 on People Living With HIV: Data From Five Medical Monitoring Project Sites, 2020-2022.

The COVID-19 pandemic disrupted global economic and healthcare systems. People living with HIV (PLWH) represent a marginalized and stigmatized population who may have been particularly impacted. The purpose of this analysis was to describe the impact of the COVID-19 pandemic on PLWH in the United States. United States. We analyzed surveys of behavioral and clinical characteristics of PLWH residing in 5 states that participated in the Medical Monitoring Project between 2020 and 2022. We described the impact of COVID-19 illness, testing, and diagnoses; receipt of medical care; social service access; employment; and preventive measures by project site and demographic characteristics. Unweighted data from 1715 PLWH were analyzed. A high proportion of PLWH had medical care disrupted by the pandemic; 31% of PLWH missed medical appointments, 26% missed routine laboratory test results, and 7% missed antiretroviral therapy doses. In total, 30% of PLWH reported losing wages and 19% reported difficulty in accessing social services. Overall, 88% reported receiving at least 1 dose of COVID-19 vaccine, but vaccine uptake was low among younger, Black, and Hispanic or Latina/o/x PLWH. This descriptive analysis reinforces previous findings that show that COVID-19 negatively impacted PLWH and their ability to obtain medical care. Additional efforts will be critical to ameliorating the longer-term impacts of COVID-19 on the health of PLWH and supporting PLWH through future pandemics and healthcare system disruptions.

Post-COVID syndrome: status of carbohydrate metabolism in patients with hypertension and stable ischemic heart disease

Background. Post-COVID syndrome is associated with a wide range of chronic symptoms or conditions. Coronavirus di­sease 2019 (COVID-19) causes metabolic disorders such as hyperglycemia, insulin resistance. Insulin-like growth factor 1 (IGF-1) is associated with an impaired glucose tolerance and a higher risk of developing type 2 diabetes. Currently, the study of molecules regulating carbohydrate metabolism as potential biomarkers of post-COVID syndrome and targets for therapeutic influence is relevant. The purpose of the study was to assess the level of IGF-1 and its relationship with regulation of carbohydrate metabolism, as well as the dynamics of the evaluated indicators under the influence of drug treatment in patients with hypertension and stable coronary heart disease who suffered from COVID-19. Materials and methods. Fifty-five patients who had a mild and moderate COVID-19 were included in the study; their average age was 55.87 [40; 75] years. The results of routine laboratory tests were registered and analyzed. The content of insulin and IGF-1 was assessed by enzyme immunoassay. The HOMA-IR was calculated. Results. In patients with a mode­rate COVID-19 compared to those with a mild one, a significant decrease in the level of IGF-1 (р = 0.008) was found against the background of increased reference values of glucose, insulin, and HOMA-IR (р < 0.01). The influence of the degree of hypertension on the IGF-1 level was revealed (р = 0.004). An inverse relationship between IGF-1 and glucose level was noted (r = –0.28, p = 0.034). The use of meldonium contributed to a significant decrease in glucose and insulin content (p < 0.001), and an increase in IGF-1. Against the background of taking meldonium, the dynamics of the shift towards a decrease in the HOMA-IR was more significant. Conclusions. An inverse relationship was found between IGF-1 and blood glucose, regardless of the level of insulin and HOMA-IR, in patients with hypertension and stable ischemic heart disease in the post-COVID period. Meldonium has been shown to improve carbohydrate metabolism by normalizing insulin levels and redu­cing the phenomena of insulin resistance as a potential drug target. Meldonium improved the clinical course of ischemic heart disease and demonstrated good tolerability and safety.

The relationship between hepatocellular carcinoma and Resolvin D1

Aims: Liver cancer is the second most common cause of cancer death, and hepatocellular carcinoma (HCC) is the most common hepatic primary tumor. Hepatocellular carcinoma develops based on inflammation in the cirrhotic liver. The aim of our study was to determine the relationship between the decrease in Resolvin D1, the lipid mediator involved in the resolution, and hepatocarcinogenesis. Methods: Thirty patients with HCC, thirty patients with cirrhosis, and thirty healthy subjects followed in our clinic between March 2018 and June 2019 were included in the study. Routine laboratory test results of the patients were recorded from the institutional database. Resolvin D1 and other parameters of the study groups were compared. Results: The Resolvin D1 levels were found to be significantly different from each other group, with the ranking as follows: the HCC group (1.71 ± 1.46 ng/ml)< the cirrhotic group (3.63 ± 2.92 ng/ml)< the healthy control group (6.24 ± 3.18 ng/ml). Moreover, Resolvin D1 levels were negatively correlated with ?-fetoprotein (AFP) level and tumor stage. Conclusion: Reduced lipid mediators that aid in the resolution process cause an increase in pro-inflammatory cytokines involved in the development of hepatocellular carcinoma. A decrease in Resolvin D1 may serve as a biomarker contributing to the diagnosis of progression to HCC in cirrhotic patients by triggering chronic inflammation and hepatocarcinogenesis.

Dynamic Modelling for Cancer-Associated Venous Thromboembolism

Background: Cancer-associated venous thromboembolism (VTE) is an important source of morbidity and mortality. Current risk prediction models for thrombosis are handicapped by static applicability (i.e. one-time evaluation prior to the first cycle of chemotherapy). We hypothesized that event forecasting using time series modelling could account for important changes in clinical state, variations in treatment and aspects of cancer progression over time. Methods: The cohort consisted of adult patients with a solid tumor enrolled in the MSK-IMPACT study between 2014 and 2019. VTE was defined as any symptomatic or asymptomatic pulmonary embolus or lower extremity deep vein thrombosis. We derived time series mixed-effects logistic regression models in order to predict cancer-associated VTE events using baseline patient characteristics (cancer type, time from diagnosis and age) along with time-dependent covariates including total white blood cell count, hemoglobin, platelet count, albumin, creatinine and parenteral chemotherapy type. Time-periods of one month were used and models were fitted to predict the presence or absence of a VTE episode in the following period (time = t+1) based on predictor values in the current period (time = t). The laboratory test result ascribed to a given period could be either the last value in the interval or the mean of all interval-specific values (both approaches tested). Missing laboratory results were imputed using the previously known value when available. Differences in laboratory values between periods t-1 and t were included as predictors in the initial model along with indicators of missingness (see Figure for flow of information between time-period cells). We hypothesized that absence of laboratory testing for a time-period would be a marker of stable medical state and hence associated with a lower risk of VTE. A previously defined training set comprising 80% of the cohort was used and different modelling approaches were evaluated using 5-fold cross-validation. The final selected model was evaluated in a separate dedicated validation set including the remaining 20% of the cohort. Results: The original cohort included 35,391 patients, from which 26,075 individuals with no cancer-associated VTE episode before observation start and sufficient laboratory test information could be included in the analysis, comprising 276,579 months of observation and 1,463 VTE events. The initial logistic regression model including all available predictors was fitted on the training set, revealing associations between predictors and VTE risk largely consistent with data reported by other groups (see Table). After adjusting for multiple comparisons, significant predictors of cancer-associated VTE identified included albumin, cell count result missingness (decreased risk for missing value), treatment with a platinum analogue (increased risk), hemoglobin, white blood cell count, breast cancer (decreased risk) and high-grade glioma (increased risk). Based on model metrics and prior knowledge from published literature, the set of covariates selected for inclusion in the final model consisted of cell counts along with the corresponding missingness indicator, platelet count change over time, albumin and chemotherapy types. The c-index for the final model using 5-fold cross-validation in the training set was 0.77 (95% CI = 0.75-0.80), compared to 0.77 (95% CI = 0.74-0.79) in the validation set. The latter corresponded to a specificity of 0.83 (95% CI = 0.79-0.86) when fixing sensitivity at 50%. Conclusions: A simple mixed-effects logistic regression time series model demonstrated promising potential to estimate the risk of cancer-associated VTE on a dynamic basis using routine laboratory test results and chemotherapy prescription data widely available in most electronic health record systems. Validation of this dynamic VTE risk model has the potential to optimize outpatient thromboprophylaxis during periods of high thrombotic risk.

P228 Machine learning models utilizing simple laboratory tests may assess endoscopic activity in ulcerative colitis

Abstract Background Together with the implementation of innovative therapies for inflammatory bowel disease, mucosal healing (MH) became the novel therapeutic goal. Currently, a golden standard for evaluation of the colonic mucosa is an invasive and expensive colonoscopy. We propose here a machine learning (ML) based model as a noninvasive tool trained on biochemical patient data. Employed random forest technique used aggregation and randomization of predictors constituted of biochemical tests to accomplish a high degree of predictive accuracy reaching 95%. SHapley Additive exPlanations (SHAP) known from cooperative game theory were used to boost the interpretability of ML model based on additive feature importance across all possible predictors in the feature space. Methods The study group consisted of 252 ulcerative colitis patients hospitalized in theDepartment of Gastroenterology and Hepatology, Wroclaw Medical University. Routine laboratory test results and colonoscopy reports with the Mayo scores were collected. The data were processed with different ML classifiers. The most performant model was the random forest classifier. Then the SHAP model for the importance of consecutive clinical data was calculated. Results We found a set of particular biochemical predictors. Total protein, C-reactive protein, creatinine, and fecal calprotectin had the most significant impact on the prediction of endoscopic Mayo score, as shown in Figure 1. Conclusion Novel numerical models may predict the endoscopic activity of ulcerative colitis based on simple biochemical tests.

Prevalence and factors associated with drug therapy problems among hypertensive patients at hypertension clinic of Mbarara Regional Referral Hospital, Uganda: a |cross-sectional study.

Despite the use of safe and effective conventional drugs, drug therapy problems (DTPs) pose a threat to the successful management of hypertension. DTPs are of a great concern in health care because of their serious consequences such as poor quality of life, increased health care costs, morbidity and mortality. However, there is no published information regarding the prevalence of DTPs and associated factors among hypertensive patients in Uganda. The aim of the study was to determine the prevalence and factors associated with DTPs among hypertensive patients at the hypertension clinic of Mbarara Regional Referral Hospital (MRRH). A cross-sectional study was conducted at the hypertension clinic, MRRH, Uganda among 228 hypertensive patients. Data were collected from medical records using a data abstraction tool and patients were interviewed using a structured questionnaire. Data analysis was done using Statistical Package for Social Sciences (SPSS) version 22.0. Descriptive analysis was used to determine the prevalence of DTPs. Logistic regression was used to determine the association between the independent and dependent variables. Variables were considered statistically significant at p-value <0.05. A total of 178 DTPs were identified among 141 hypertensive patients. The prevalence of antihypertensive-related DTPs was 61.8% (95% confidence interval [CI]: 55.3-67.5) with an average of 1.26 ± 0.52 DTPs per patient. Out of 141 participants with DTPs, 109 (77.3%) had one DTP, 27 (19.1%) had 2 DTPs, and 5 (3.5%) had 3 DTPs. The most common types of antihypertensive-related DTPs were 'dosage too low' which accounted for 53 (29.8%), followed by 'adverse drug reactions' which accounted for 48 (27%). Uncontrolled blood pressure (BP; adjusted odds ratio [AOR]: 4.17; 95% CI: 2.33-7.45, p < 0.001) and routine laboratory test results (AOR: 1.87; 95% CI: 1.04-3.36, p = 0.036) were significantly associated with antihypertensive-related DTPs among hypertensive patients. Almost two-thirds of study participants had antihypertensive-related DTPs. The most common DTPs were 'dosage too low' and 'adverse drug reactions' which both accounted for almost a third of the total DTPs each. Uncontrolled BP and routine laboratory test results were significantly associated with antihypertensive-related DTPs among the study participants. Our study emphasizes the need for improved patient care by clinical pharmacists to identify and prevent DTPs among hypertensive patients.

COVID19PREDICTOR: KLİNİK VERİLERE VE RUTİN TESTLERE DAYALI OLARAK COVID-19 TEŞHİSİ İÇİN MAKİNE ÖĞRENİMİ MODELLERİ GELİŞTİRMEYE YARAYAN WEB TABANLI ARAYÜZ

Objective: The Covid-19 outbreak has become the primary health problem of many countries due to health related, social, economic and individual effects. In addition to the development of outbreak prediction models, the examination of risk factors of the disease and the development of models for diagnosis are of high importance. This study introduces the Covid19PredictoR interface, a workflow where machine learning approaches are used for diagnosing Covid-19 based on clinical data such as routine laboratory test results, risk factors, information on co-existing health conditions.&#x0D; Method: Covid19PredictoR interface is an open source web based interface on R/Shiny (https://biodatalab.shinyapps.io/Covid19PredictoR/). Logistic regression, C5.0, decision tree, random forest and XGBoost models can be developed within the framework. These models can also be used for predictive purposes. Descriptive statistics, data pre-processing and model tuning steps are additionally provided during model development.&#x0D; Results: Einsteindata4u dataset was analyzed with the Covid19PredictoR interface. With this example, the complete operation of the interface and the demonstration of all steps of the workflow have been shown. High performance machine learning models were developed for the dataset and the best models were used for prediction. Analysis and visualization of features (age, admission data and laboratory tests) were carried out for the case per model.&#x0D; Conclusion: The use of machine learning algorithms to evaluate Covid-19 disease in terms of related risk factors is rapidly increasing. The application of these algorithms on various platforms creates application difficulties, repeatability and reproducibility problems. The proposed pipeline, which has been transformed into a standard workflow with the interface, offers a user-friendly structure that healthcare professionals with various background can easily use and report.

An Unusual Cause of Esophagitis

An Unusual Cause of Esophagitis

Upper Gastrointestinal Bleeding: Do Emergency Endoscopic Evaluations Affect Clinical Outcomes?

Objective: We aimed to reveal the effect of time from admission to endoscopy on clinical outcomes such as mortality, rebleeding, and prolonged hospitalization among patients with acute upper gastrointestinal bleeding.Methods: Patients aged ≥18 years with acute upper gastrointestinal bleeding were enrolled in the study. Those who had variceal bleeding during endoscopy, those whose hospital stay was shorter than 24 hours, those who did not undergo endoscopy, and those who underwent endoscopy after 24 hours were excluded from the study. Clinical findings, routine laboratory test results, and imaging findings of the patients were retrospectively reviewed through the hospital’s records system.Results: A total of 252 patients were enrolled in the study. At admission, 30.2% (76) of patients were at clinically high risk of death or rebleeding, 71.8% had melena, and 51.2% had hematemesis. While 72 (28.6%) of the patients had high-risk endoscopic stigmata, 89 (35.3%) had low-risk endoscopic stigmata. The median hospital stay was 6 (1-91) days. In-hospital mortality occurred in 8 (3.2%) cases, rebleeding developed in 16 (6.3%) cases, endoscopic intervention was required in 103 (40.9%) cases, and prolonged hospital stay was required in 43 (17.1%) cases. High-risk endoscopic stigmata were identified in 63 (34.1%) cases in the urgent group and in 9 (13.4%) in the early group (p=0.001). Endoscopic intervention was required in 47.0% cases in the urgent group, while the incidence was 23.9% in the early group (p=0.001).Conclusion: While no significant difference was found between the urgent and early groups in terms of mortality and re-bleeding, the need for endoscopic intervention and the incidence of high-risk endoscopic stigmata were found to be significantly higher in the urgent group.

Heparin-induced thrombocytopenia post-cardiovascular interventional therapy: a case report

BackgroundHeparin-induced thrombocytopenia (HIT) is an antibody-mediated adverse drug reaction characterized by thrombocytopenia and thromboembolism. Herein, we present a case of HIT with subcutaneous hemorrhage after cardiovascular interventional therapy.Case presentationA 74-year-old man was admitted to the hospital for elective atrial fibrillation (AF) catheter ablation and left atrial appendage closure because of intermittent dizziness and palpitations. At presentation, the routine laboratory test results showed no abnormalities. He received subcutaneous enoxaparin for stroke prevention and unfractionated heparin for intraprocedural anticoagulation during coronary angiography and the AF procedure. On the second day after the AF procedure, the patient developed profound thrombocytopenia, moderate anemia, and mild subcutaneous hematoma. Blood tests and imaging examinations excluded acute hemolysis and other active bleeding. A 4Ts score of 5 and markedly positive platelet factor 4 IgG antibody established the diagnosis of HIT. Due to progressive subcutaneous hemorrhage in the thighs that could not be suppressed by pressure dressing, the patient received platelet transfusion and rivaroxaban for anticoagulation. The following days, the patient remained clinically stable from the hemorrhage, and his platelet count recovered. No thrombotic events occurred during hospitalization or follow-up.ConclusionThis case emphasizes the significance of suspecting HIT in patients with unexplained rapid thrombocytopenia after frequent heparin exposure. Decision-making regarding alternative anticoagulation and platelet transfusion in HIT with hemorrhage must be based on unique patient characteristics.

Inflammatory Markers Combined With Metalloproteinase-9, Neopterin, and S100B Concentrations May Indicate the Pathogenesis of Central Nervous System Diseases in Children.

The identification of central nervous system inflammation etiology leads to adjusted therapy. We analyzed the potential inflammatory and neuro-axonal damage markers in children. Our target was to correlate the findings with a disease's course or a sequalae risk and assess their clinical usefulness. The study included 96 children with symptoms of central nervous system inflammation who underwent diagnostics. The research group involved 24 children with autoimmune disorders and 31 with neuroinfection. The control group included patients with both etiologies excluded. We analyzed the results of routine laboratory tests together with chosen serum (neopterin, interleukin [IL]-1β, IL-6) and CSF (metalloproteinase [MMP]-9, S100B protein) markers. In the whole cohort, CSF MMP-9 correlated with CSF cytosis and serum IL-6 and CRP. In the undivided neuroinflammatory group, CSF S100B correlated with serum IL-6 and IgM concentrations. CSF cytosis was associated with CSF MMP-9 and serum neopterin levels. Among the infective patients, IL-6 was linked with increased CSF MMP-9. We conclude that astroglial protein S100B, neopterin, and cytokine concentrations may enable predicting long-term consequences, whereas CSF MMP-9 concentration may reflect the actual central nervous system injury regardless of etiology.

Automated prediction of low ferritin concentrations using a machine learning algorithm.

Computational algorithms for the interpretation of laboratory test results can support physicians and specialists in laboratory medicine. The aim of this study was to develop, implement and evaluate a machine learning algorithm that automatically assesses the risk of low body iron storage, reflected by low ferritin plasma levels, in anemic primary care patients using a minimal set of basic laboratory tests, namely complete blood count and C-reactive protein (CRP). Laboratory measurements of anemic primary care patients were used to develop and validate a machine learning algorithm. The performance of the algorithm was compared to twelve specialists in laboratory medicine from three large teaching hospitals, who predicted if patients with anemia have low ferritin levels based on laboratory test reports (complete blood count and CRP). In a second round of assessments the algorithm outcome was provided to the specialists in laboratory medicine as a decision support tool. Two separate algorithms to predict low ferritin concentrations were developed based on two different chemistry analyzers, with an area under the curve of the ROC of 0.92 (Siemens) and 0.90 (Roche). The specialists in laboratory medicine were less accurate in predicting low ferritin concentrations compared to the algorithms, even when knowing the output of the algorithms as support tool. Implementation of the algorithm in the laboratory system resulted in one new iron deficiency diagnosis on average per day. Low ferritin levels in anemic patients can be accurately predicted using a machine learning algorithm based on routine laboratory test results. Moreover, implementation of the algorithm in the laboratory system reduces the number of otherwise unrecognized iron deficiencies.

Characteristics and clinical outcomes of patients over 80 years of age with acute pancreatitis.

In this study, we aimed to compare differences in disease course, etiology of acute pancreatitis admission to the intensive care unit, in-hospital mortality, disease severity, and local and systemic complications between patients aged ≥ 80years (octogenarians) and patients aged between 65 and 79years (non-octogenarians), all of whom were being followed with the diagnosis of acute pancreatitis. Patients aged 65years and older with a diagnosis of acute biliary pancreatitis were included in the study. Clinical findings, routine laboratory test results, and imaging findings of all patients were reviewed retrospectively via the hospital's records system. Of a total of 402 enrolled patients, 238 (59.2%) were female. Mean age was 77.1 ± 7.37years. Pancreatitis after endoscopic retrograde cholangiopancreatography was detected more frequently among octogenarians than non-octogenarians (12.9% versus 5.3%, respectively; p = 0.007). Levels of blood urea nitrogen, creatinine, and total bilirubin were higher among octogenarians, while lymphocyte, calcium, and albumin levels were lower. The frequency of patients without local complications was higher among octogenarians than non-octogenarians (75.5% versus 63.6%, respectively; p = 0.013). Necrosis was less frequent in the octogenarian group than the non-octogenarian group (2.6% versus 8.9%, respectively; p = 0.012). Mild acute pancreatitis was higher in the octogenarian group, while moderate acute pancreatitis was higher in the non-octogenarian group (p = 0.028 and p = 0.012, respectively). The frequencies of prolonged hospitalization, intensive care unit admission, and in-hospital mortality were similar in the octogenarian and non-octogenarian groups. In terms of disease severity, mild acute pancreatitis was higher in the octogenarian group, while moderate acute pancreatitis was higher in the non-octogenarian group.

Factors Influencing Pneumococcal Vaccination-Induced Total IgG, IgG2, and IgA Levels in Vaccinated Splenectomized Patients

Splenectomy is closely associated with a lifetime risk of pneumococcal and other encapsulated bacterial infections. In this study, it was aimed to investigate the change of antibody levels after vaccination against Streptococcus pneumoniae according to age, gender, years after splenectomy and the possible effect of splenectomy on IgG avidity. In addition the education and awareness levels of the participants about post-splenectomy vaccination and infectious diseases were also analyzed. In the first of the three phases of this study, 32 individuals with splenectomy were enrolled. The awareness of the patients about the possible risks after splenectomy was investigated with a simple questionnaire. Routine laboratory test results were obtained and clinical examinations were performed. In the second stage, total Ig values of 29 splenectomy patients were determined. In the third phase, 14 splenectomy and 5 healthy volunteers were vaccinated according to the Vaccination Practices Advisory Committee (ACIP) guidelines. Pneumococcal-specific antibody levels and IgG avidity were detected by enzyme linked immunosorbent assay (ELISA). It was determined that 68.8% of the splenectomized patients were unaware of their vaccination status and 78.2% of them were unaware of the increased risk of infectious diseases in asplenic conditions. . According to the hospital information management system, all 31 (96.87%) patients, except one, were vaccinated with PPV23. As expected, vaccinated patients exhibited high levels of vaccine-specific antibody production with IgG, IgG2, and IgA antibody concentrations of 321 ± 76.68 mg/l, 73.07 ± 8.273 mg/l, and 117.8 ± 14.94 mg/l, respectively, but unvaccinated patients had very low antibody (IgG, IgG2 and IgA antibody concentrations were 11.5 mg/l, 1.3 mg/l and 1.2 mg/l, respectively) levels. Although there was no correlation between antibody titers and gender, age groups or presence of fever history, the decrease in total IgG, IgG2 and IgA titers were strongly correlated with the time since splenectomy. Antibody titers were found to be significantly lower in splenectomized patients vaccinated more than 10 years ago. Routine laboratory results were at normal levels except for low platelet count. On the other hand, both splenectomized and healthy control subjects displayed similar IgG avidity index values (%61.8 ve %64.4% inhibition in control and splenectomized subjects, respectively) after the vaccination schedule. It was shown that post-splenectomy vaccination with PPV23 induced high levels of pneumococcus-specific antibody production that can last for more than five years. It was determined that more efforts should be made to increase the level of knowledge about pneumococcal and other overwhelming post-splenectomy infections (OPSI) as the awareness of the patients about the risks of infection after splenectomy was poor. In particular, patients with splenectomy operation more than 10 years ago should be very careful about being asplenic as they were determined to have significantly lower level of vaccine-specific antibody production. Our study was also the first to show that splenectomy does not alter IgG avidity induced by pneumococcal vaccination.

Comorbid laboratory abnormalities in female pattern hair loss patients

Background: Female pattern hair loss (FPHL) is the main cause of alopecia in women and has a debilitating impact on the quality of life. However, certain comorbid conditions causing hair loss are often masked by FPHL and may remain undetected. Hence, it is imperative to identify them to administer appropriate treatment. The necessity of laboratory tests to evaluate these comorbidities in patients with FPHL has not been established. Objectives: This study aimed to identify the frequency of comorbid abnormalities detected by laboratory tests in patients with FPHL. Methods: Routine laboratory test results of patients with FPHL, who visit our hair clinic for the first time, were retrospectively collected and analyzed. These tests assessed the serum testosterone, thyroid-stimulating hormone, free thyroxin, zinc and ferritin levels, hemoglobin, as well as antinuclear antibody (ANA), and rapid plasma reagin test results. The correlation between age and hair loss severity and the frequency of comorbidities was evaluated using subgroup analysis. Results: Among the 356 patients with FPHL, coexisting zinc deficiency, iron depletion, and thyroid abnormalities were common and present in 41.7%, 39.7%, and 11.3% of them, respectively. The rate of concomitantly increased ANA titers was relatively low (4.4%), while that of syphilis and high serum testosterone levels was extremely low (0.3% and 0%, respectively). Younger patients (<50 years) with FPHL had a higher rate of coexisting iron deficiency than patients in the older age group. Conclusion: Testing for concomitant iron depletion, zinc deficiency, and thyroid disease is recommended in female patients with FPHL.

ST-elevation myocardial infarction complicated by cardiac arrest in a young patient with familiar dyslipidemia

Coronary heart disease in young patients always poses great challenges for every healthcare system with differences in clinical manifestations, etiology, epidemiology, angiographic characteristics and prognosis. The objective of this study was to describe a case of ST-elevation myocardial infarction complicated by cardiac arrest in a young patient with familial dyslipidemia. A 30-year-old male visited our hospital with typical angina. During the examination, he suffered a sudden loss of consciousness, the monitor showed ventricular fibrillation. After successful resuscitation of cardiac arrest, electrocardiography showed apparent ST-elevation from V2 to V6 leads consistent with the diagnosis of anterolateral infarction. Emergency coronary angiogram showed severe three-vessel lesions including complete occlusion of the LAD artery and 80 - 90% stenosis of the other two coronary branches. Our patient’s coronary arteries were revascularized using drug-eluting stents in LAD artery and subsequently RCA artery, stem cell therapy was applied during the interventional process. Routine laboratory test results showed dyslipidemia and his family records suggested familiar (hereditary) dyslipidemia which affected his mother and sister. 1-month follow-up echocardiography showed a drastic improvement of LVEF by roughly 15%. The combination of revascularization, stem cell therapy, and lipid-lowering therapy has shown a good therapeutic effect.

Immature granulocyte percentage for prediction of sepsis in severe burn patients: a machine leaning-based approach

BackgroundOf the existing sepsis markers, immature granulocytes (IG) most frequently reflect the presence of an infection. The importance of IG as an early predictor of sepsis and bacteremia is evaluated differently for each study. This study aimed to evaluate the effectiveness of the Sysmex XN series’ IG% as an independent prognostic indicator of sepsis using machine learning.MethodsA total of 2465 IG% results from 117 severe burn patients in the intensive care unit of one institution were retrospectively analyzed. We evaluated the IG% for sepsis using the receiver operating characteristic, logistic regression, and partial dependence plot analyses. Clinical characteristics and other laboratory markers associated with sepsis, including WBC, procalcitonin, and C-reactive protein, were compared with the IG% values.ResultsTwenty-six of the 117 patients were diagnosed with sepsis. The median IG% value was 2.6% (95% CI: 1.4–3.1). The area under the receiver operating characteristic curve was 0.77 (95% CI: 0.78–0.84) and the optimal cut-off value was 3%, with a sensitivity of 76.9% and specificity of 68.1%. The partial dependence plot of IG% on predicting sepsis showed that an IG% < 4% had low predictability, but increased thereafter. The interaction plot of IG% and C-reactive protein showed an increase in sepsis probability at an IG% of 6% and C-reactive protein of 160 mg/L.ConclusionsIG% is moderately useful for predicting sepsis. However, since it can be determined from routine laboratory test results and requires no additional intervention or cost, it could be particularly useful as an auxiliary marker.

Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests.

ObjectivesThe present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results.MethodsWe developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription–polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil.ResultsThe accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study’s data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%).ConclusionsML models presented in this study can be used as clinical decision support tools to contribute to physicians’ clinical judgment for COVID-19 diagnoses.