Early Detection Of Patients Research Articles

The effectiveness of screening to identify patients with osteoporosis/high risk of fractures in primary health care

Background. The organization of screening for osteoporosis is an actual problem due to the need for early detection of patients with high risk of fractures and initiation of their treatment.Aim. The aim of our study was to study the effectiveness of screening for osteoporosis (high risk of fractures) in postmenopausal men and women 40 years and older.Materials and methods. Screening was performed by outpatient doctors for all people aged 40 years and older who applied for various reasons. The doctor asked standard questions included in the FRAX questionnaire.Results. In 2017– 2020, 11,013 people were screened by outpatient doctors — 31.7% of the total attached population aged 40 years and older. According to the screening results, 21.9% people were identified with a high risk of fractures. The average 10-year probability of major osteoporotic fractures in these patients (19.0±7.9%) was statistically significantly higher compared to the rest of the screened (7.6±3.3%), p=0.0001. 60% (1,450 people) of the group of individuals with an identified high risk of fractures were patients with FRAX in the area of therapeutic intervention. Among them were patients with indications of a fracture and without a history of fractures. In the remaining 40%, the high risk was determined only on the basis of the fact of a previously suffered fracture (their FRAX index was below the intervention threshold). In general, using only the FRAX calculator, 40 men and 1410 women were identified as having a high risk of fractures, which accounted for 1.3% of the male and 17.7% of the female screened population, respectively. With the simultaneous use of FRAX and anamnestic data on a previous fracture, 13.3% of men and 25.3% of women in the screening population had a high risk of fractures.Conclusion. Screening in primary health care has allowed us to identify 21.9% of the screened population of men and women aged 40 years and older (postmenopausal women) as having a high risk of fractures. Screening with simultaneous calculation of the 10-year risk of fractures according to FRAX and clarification of the presence of a history of fractures was the most effective.

Shedding light into the black box of out-of-hospital respiratory distress-A retrospective cohort analysis of discharge diagnoses, prehospital diagnostic accuracy, and predictors of mortality.

BackgroundAlthough respiratory distress is one of the most common complaints of patients requiring emergency medical services (EMS), there is a lack of evidence on important aspects.ObjectivesOur study aims to determine the accuracy of EMS physician diagnostics in the out-of-hospital setting, identify examination findings that correlate with diagnoses, investigate hospital mortality, and identify mortality-associated predictors.MethodsThis retrospective observational study examined EMS encounters between December 2015 and May 2016 in the city of Aachen, Germany, in which an EMS physician was present at the scene. Adult patients were included if the EMS physician initially detected dyspnea, low oxygen saturation, or pathological auscultation findings at the scene (n = 719). The analyses were performed by linking out-of-hospital data to hospital records and using binary logistic regressions.ResultsThe overall diagnostic accuracy was 69.9% (485/694). The highest diagnostic accuracies were observed in asthma (15/15; 100%), hypertensive crisis (28/33; 84.4%), and COPD exacerbation (114/138; 82.6%), lowest accuracies were observed in pneumonia (70/142; 49.3%), pulmonary embolism (8/18; 44.4%), and urinary tract infection (14/35; 40%). The overall hospital mortality rate was 13.8% (99/719). The highest hospital mortality rates were seen in pneumonia (44/142; 31%) and urinary tract infection (7/35; 20%). Identified risk factors for hospital mortality were metabolic acidosis in the initial blood gas analysis (odds ratio (OR) 11.84), the diagnosis of pneumonia (OR 3.22) reduced vigilance (OR 2.58), low oxygen saturation (OR 2.23), and increasing age (OR 1.03 by 1 year increase).ConclusionsOur data highlight the diagnostic uncertainties and high mortality in out-of-hospital emergency patients presenting with respiratory distress. Pneumonia was the most common and most frequently misdiagnosed cause and showed highest hospital mortality. The identified predictors could contribute to an early detection of patients at risk.

Deep learning predicts immune checkpoint inhibitor-related pneumonitis from pretreatment computed tomography images.

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer, including both non-small cell lung cancer and small cell lung cancer. Despite the promising results of immunotherapies, ICI-related pneumonitis (ICIP) is a potentially fatal adverse event. Therefore, early detection of patients at risk for developing ICIP before the initiation of immunotherapy is critical for alleviating future complications with early interventions and improving treatment outcomes. In this study, we present the first reported work that explores the potential of deep learning to predict patients who are at risk for developing ICIP. To this end, we collected the pretreatment baseline CT images and clinical information of 24 patients who developed ICIP after immunotherapy and 24 control patients who did not. A multimodal deep learning model was constructed based on 3D CT images and clinical data. To enhance performance, we employed two-stage transfer learning by pre-training the model sequentially on a large natural image dataset and a large CT image dataset, as well as transfer learning. Extensive experiments were conducted to verify the effectiveness of the key components used in our method. Using five-fold cross-validation, our method accurately distinguished ICIP patients from non-ICIP patients, with area under the receiver operating characteristic curve of 0.918 and accuracy of 0.920. This study demonstrates the promising potential of deep learning to identify patients at risk for developing ICIP. The proposed deep learning model enables efficient risk stratification, close monitoring, and prompt management of ICIP, ultimately leading to better treatment outcomes.

An Exosome-based Transcriptomic Signature for Noninvasive, Early Detection of Patients With Pancreatic Ductal Adenocarcinoma: A Multicenter Cohort Study

Background & AimsPancreatic ductal adenocarcinoma (PDAC) incidence is rising worldwide, and most patients present with an unresectable disease at initial diagnosis. Measurement of carbohydrate antigen 19-9 (CA19-9) levels lacks adequate sensitivity and specificity for early detection; hence, there is an unmet need to develop alternate molecular diagnostic biomarkers for PDAC. Emerging evidence suggests that tumor-derived exosomal cargo, particularly micro RNAs (miRNAs), offer an attractive platform for the development of cancer-specific biomarkers. Herein, genomewide profiling in blood specimens was performed to develop an exosome-based transcriptomic signature for noninvasive and early detection of PDAC. MethodsSmall RNA sequencing was undertaken in a cohort of 44 patients with an early-stage PDAC and 57 nondisease controls. Using machine-learning algorithms, a panel of cell-free (cf) and exosomal (exo) miRNAs were prioritized that discriminated patients with PDAC from control subjects. Subsequently, the performance of the biomarkers was trained and validated in independent cohorts (n = 191) using quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays. ResultsThe sequencing analysis initially identified a panel of 30 overexpressed miRNAs in PDAC. Subsequently using qRT-PCR assays, the panel was reduced to 13 markers (5 cf- and 8 exo-miRNAs), which successfully identified patients with all stages of PDAC (area under the curve [AUC] = 0.98 training cohort; AUC = 0.93 validation cohort); but more importantly, was equally robust for the identification of early-stage PDAC (stages I and II; AUC = 0.93). Furthermore, this transcriptomic signature successfully identified CA19–9 negative cases (<37 U/mL; AUC = 0.96), when analyzed in combination with CA19–9 levels, significantly improved the overall diagnostic accuracy (AUC = 0.99 vs AUC = 0.86 for CA19–9 alone). ConclusionsIn this study, an exosome-based liquid biopsy signature for the noninvasive and robust detection of patients with PDAC was developed.

Prediction of type 2 diabetes mellitus onset using logistic regression-based scorecards.

Type 2 diabetes (T2D) accounts for ~90% of all cases of diabetes, resulting in an estimated 6.7 million deaths in 2021, according to the International Diabetes Federation. Early detection of patients with high risk of developing T2D can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources or access to sophisticated computational resources, there is a need for accurate yet accessible prediction models. In this study, we analyzed data from 44,709 nondiabetic UK Biobank participants aged 40-69, predicting the risk of T2D onset within a selected time frame (mean of 7.3 years with an SD of 2.3 years). We started with 798 features that we identified as potential predictors for T2D onset. We first analyzed the data using gradient boosting decision trees, survival analysis, and logistic regression methods. We devised one nonlaboratory model accessible to the general population and one more precise yet simple model that utilizes laboratory tests. We simplified both models to an accessible scorecard form, tested the models on normoglycemic and prediabetes subcohorts, and compared the results to the results of the general cohort. We established the nonlaboratory model using the following covariates: sex, age, weight, height, waist size, hip circumference, waist-to-hip ratio, and body mass index. For the laboratory model, we used age and sex together with four common blood tests: high-density lipoprotein (HDL), gamma-glutamyl transferase, glycated hemoglobin, and triglycerides. As an external validation dataset, we used the electronic medical record database of Clalit Health Services. The nonlaboratory scorecard model achieved an area under the receiver operating curve (auROC) of 0.81 (95% confidence interval [CI] 0.77-0.84) and an odds ratio (OR) between the upper and fifth prevalence deciles of 17.2 (95% CI 5-66). Using this model, we classified three risk groups, a group with 1% (0.8-1%), 5% (3-6%), and the third group with a 9% (7-12%) risk of developing T2D. We further analyzed the contribution of the laboratory-based model and devised a blood test model based on age, sex, and the four common blood tests noted above. In this scorecard model, we included age, sex, glycated hemoglobin (HbA1c%), gamma glutamyl-transferase, triglycerides, and HDL cholesterol. Using this model, we achieved an auROC of 0.87 (95% CI 0.85-0.90) and a deciles' OR of ×48 (95% CI 12-109). Using this model, we classified the cohort into four risk groups with the following risks: 0.5% (0.4-7%); 3% (2-4%); 10% (8-12%); and a high-risk group of 23% (10-37%) of developing T2D. When applying the blood tests model using the external validation cohort (Clalit), we achieved an auROC of 0.75 (95% CI 0.74-0.75). We analyzed several additional comprehensive models, which included genotyping data and other environmental factors. We found that these models did not provide cost-efficient benefits over the four blood test model. The commonly used German Diabetes Risk Score (GDRS) and Finnish Diabetes Risk Score (FINDRISC) models, trained using our data, achieved an auROC of 0.73 (0.69-0.76) and 0.66 (0.62-0.70), respectively, inferior to the results achieved by the four blood test model and by the anthropometry models. The four blood test and anthropometric models outperformed the commonly used nonlaboratory models, the FINDRISC and the GDRS. We suggest that our models be used as tools for decision-makers to assess populations at elevated T2D risk and thus improve medical strategies. These models might also provide a personal catalyst for changing lifestyle, diet, or medication modifications to lower the risk of T2D onset. The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

Clinical Value of Combined Detection of UA and MMP-9 in Evaluating Bleeding Transformation and Prognosis After Thrombolysis in Acute Cerebral Infarction.

This paper presents an in-depth study and analysis of the assessment of hemorrhagic transformation and prognostic outcome after thrombolysis in acute cerebral infarction using a combined test and evaluates its clinical value. The ischemic tissue hemodynamic changes were compared and analyzed by the combined application of magnetic resonance conventional examination. Single-factor and multi-factor Logistic regression analysis was applied to the model group samples to determine the independent influencing factors of hemorrhage and to construct a risk prediction model. The Hosmer-Lemeshow chi-square test was used to test the fit of the model, and the area under the ROC curve was used to test the discriminatory ability of the model. The area under the ROC curve was used to test the discriminatory ability of the model. The main purpose of this study was to investigate the clinical diagnostic value of the combined D-D and Hcy and test for the early detection of patients with acute cerebral infarction disease. There was no significant correlation between single PWI-ASPECTS and clinical prognostic MRS score, which may be related to the site and volume of initial diffusion restriction; the percentage of the mismatched area between DWI-PWI and clinical prognostic mRS score was significantly correlated, which helps clinicians to assess the therapeutic effect of non-thrombolytic therapy and provide an important basis for clinical selection of appropriate interventions in the subacute phase of stroke. The sensitivity of D-D, Hcy, and cTnI in the acute cerebral infarction group was 59.4%, 79.6%, and 49.5%, and the specificity was 73.5%, 70.5%, and 91.1%, respectively, with the area under the curve of 0.606, 0.729, and 0.521. The sensitivity, specificity, and area under the curve of the combined assay were higher than those of the single assay. The detection level of high-risk group was the highest, followed by the low-risk group. Pearson correlation analysis suggests that there is a significant correlation between serum UA and MM-9 level and grace score.

Abstract 3389: An exosomal miRNA-based liquid biopsy signature for the noninvasive early detection of pancreatic ductal adenocarcinoma

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is projected to become the second leading cause of cancer-related deaths in the United States by 2030. Most of the PDAC patients are diagnosed with advanced disease, and less than 20% of patients are resectable at the time of diagnosis. The current imaging tools and blood markers (e.g. CA 19-9), are inadequate for early disease detection due to their poor specificity and sensitivity. This highlights the need to develop robust, noninvasive biomarkers for early detection of PDAC. While a large body of literature supports the use of cell-free miRNAs (cf-miRNAs) as potential diagnostic biomarkers in cancer, their tumor specificity is often debatable. Given the emerging evidence that exosomal cargo is a more robust representation of individual tumor types, in this study we sought to explore the diagnostic potential of cf-miRNAs along with exosomal miRNAs (exo-miRNAs), to establish a non-invasive miRNA signature for the early detection of PDAC. Methods: As part of the NCI’s Pancreatic Cancer Detection Consortium (PCDC) funded project, in this study, small RNA-sequencing was performed in exosome and cell-free (cf) samples from a cohort of 57 PDAC cases and 57 non-disease controls. Using rigorous bioinformatic and biostatistical approaches, we prioritized a panel of cf- and exo-miRNAs and evaluated its diagnostic performance in the sequencing-based discovery and validation cohorts. Subsequently, the performance of the discovered miRNA panel was validated using qRT-PCR assays in an independent clinical validation cohort of PDAC patients and non-disease controls (n=48/each group). The results were examined by ROC curve analysis to determine the diagnostic power of the biomarker panel individually and in combination for their ability to discriminate PDAC from controls. Results: The genomewide transcriptomic analyses led to the identification of a panel of 13 cf-miRNA and 17 exo-miRNA candidates. Sequencing validation in an independent cohort revealed that a combined panel of cf and exo-miRNAs exhibited an area under curve (AUC) of 0.89. Subsequent risk score analysis demonstrated that our biomarker signature was also robust in the identification of PDAC patients with early-stage cancers (stage I &amp; II) vs. controls (p &amp;lt;0.001). Moreover, when we combined the miRNA biomarker panel with CA19-9 values, the diagnostic performance was significantly superior when compared to the biomarker panel alone. Finally, the validation efforts in clinical cohorts by qRT-PCR revealed that the combined miRNA panel yielded an impressive accuracy with an AUC of 0.91, and a sensitivity of 0.88 and specificity of 0.87. Conclusions: In conclusion, we report a novel, exosome-based miRNA signature for the early detection of patients with PDAC; which could potentially improve early-detection efforts for this fatal malignancy. Citation Format: Kota Nakamura, Souvick Roy, Zhongxu Zhu, Eunsung Jun, Haiyong Han, Ruben M. Munoz, Satoshi Nishiwada, Geeta Sharma, Derek Cridebring, Frederic Zenhausern, Seungchan Kim, Denise Roe, Sourat Darabi, In Woong Han, Douglas Evans, Suguru Yamada, Michael Demure, Scott A. Celinski, Erkut Borazanci, Susan Tsai, John Bolton, Yasuhiro Kodera, Joon Oh Park, Song Cheol Kim, Xin Wang, Daniel Von Hoff, Ajay Goel. An exosomal miRNA-based liquid biopsy signature for the noninvasive early detection of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3389.

PREDICTING DIABETES MELLITUS USING CATBOOST CLASSIFIER AND SHAPLEY ADDITIVE EXPLANATION (SHAP) APPROACH

Diabetes mellitus as a metabolic disease characterized by hyperglycemia can be dangerous if it cannot be handled properly. Early detection of existing symptoms can reduce the impact of delays in treatment. This study aims to carry out early-detection patients with diabetes mellitus using a machine learning approach through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score). By using Shapley Additive Explanation (SHAP) which enables prioritization of feature that determine compound classification, this study shows that the CatBoost classifier has 14 features that significantly can be used for classification with feature ‘d1_glucose_max’ or the highest glucose concentration of the patient in their serum or plasma during the first 24 hours of their unit stay has the highest impact to classify diabetes mellitus patients, then followed by age and glucose APACHE. The selected features are then classified and get the validation AUC score of 86.86%.

Panoramic Indices: An Aid to Early Detection of Patients with Low Bone Mineral Density.

Objective An early identification of patients who are at an obvious increased risk of osteoporosis and subsequent high risk of pathological bone fractures is important to prevent morbidity and its subsequent impact on the quality of life of the affected patients. Dental professionals have a chance of identifying such cases in their very initial stages through routinely prescribed dental radiographs. The aim of this study was to assess the influence of gender and age on different parameters of alveolar bone loss using orthopantomograph (OPG) as an aid to identify patients with low bone mineral density (BMD). Materials and Methods This study included eighty subjects in whom after taking OPGs, mandibular cortical index (MCI), mandibular cortical width (MCW), and panoramic mandibular index were assessed, while the results obtained were subjected to statistical analysis. One-way analysis of variance followed by Tukey's test was used to compare the means, while p -value less than 0.05 was considered statistically significant. Results The findings of this study revealed a significant association between MCI and age for females, with C2 and C3 categories being more common with advancing age. Also, a significant difference could be seen in relation to gender ( p -value = 0.0315) for MCW with a concomitant decrease in the values of MCW in females over 60 years of age. Conclusion Panoramic radiographic measurements could provide valuable information and help in screening patients with low BMD.

Current Trends in the Diagnosis, Screening and Treatment of Spinal Muscular Atrophy

Spinal muscular atrophy is one of the most severe hereditary neuromuscular diseases and one of the main causes of infant mortality caused by hereditary diseases. Being a monogenic disease, SMA is characterized by a wide range of phenotypes, which are based on the influence of genetic modifiers of the disease. These modifiers determine the development of a more severe or milder form of the disease and can act as potential targets of disease therapy. To date, there are three certified drugs for the treatment of SMA, the action of two of them is directed at the transcript of the main modifier of the disease the SMN2 gene. With the advent of effective therapy, the issue of screening newborns for the purpose of early detection of patients and the beginning of treatment of SMA at the presymptomatic phase to achieve maximum effectiveness of drugs becomes relevant. In addition to neonatal screening, population screening plays an important role, which may result in a decrease in the frequency of births of children with SMA.

Handgrip strength is associated with length of stay and functional outcomes at 1-year follow up in fragility hip fracture patients

Background/Purpose Fragility hip fracture (FHF) is the most prevalent fragility fracture, causing deterioration in ambulatory ability and disability. Early detection of patients who are at risk of prolonged hospitalization and functional decline after FHF is crucial as this facilitates post-operative management and healthcare resources allocation. Handgrip strength (HGS) is an easily-administered and cost-effective tool to assess whole body strength. The objectives of this study were to examine the relationship between pre-operative HGS, length of stay (LOS) and functional outcomes at 1-year follow up in patients with FHF in Hong Kong. Methods Patients aged 65 years old or above who were admitted to a local acute hospital from April 2017 to March 2020 due to FHF that were treated operatively, and subsequently attended the multidisciplinary fragility fracture clinic at 1-year follow up were evaluated. HGS measured at pre-operative physiotherapy initial assessment, post-fracture total LOS, Modified Functional Ambulation Classification (MFAC) and Elderly Mobility Scale (EMS) at 1-year follow up were retrieved for analysis. Correlation between HGS and LOS was analyzed using Pearson's correlation coefficient. Correlations between HGS and MFAC, and HGS and EMS were analyzed using Spearman's rank correlation coefficient. Results One hundred and nineteen patients (mean age = 80.8 ± 7.3 years) (33 males, 86 females) were evaluated. Statistical significant negative correlation between HGS and LOS was found ( r = -0.245, p = 0.007), implying that the lower the HGS, the longer the period of hospitalization. HGS also showed statistical significant positive correlation with MFAC ( r = 0.358, p &lt; 0.001) and EMS ( r = 0.507, p &lt; 0.001), suggesting that the greater the HGS, the better the functional outcomes after 1 year. In short, pre-operative HGS measured by physiotherapists was shown to be associated with LOS and functional outcomes at 1-year follow up. Conclusion HGS is significantly associated with patient's LOS, MFAC and EMS after FHF. HGS is an easily-administered and cost-effective tool to facilitate frontline practitioners to determine and stratify patients’ rehabilitation potential and subsequently establish customized rehabilitation and discharge plan, such as intensive training, caregiver education and potential prediction of discharge destination. Key Messages HGS is significantly associated with patient's LOS, MFAC and EMS after FHF. HGS is an easily-administered and cost-effective tool to facilitate frontline practitioners to determine and stratify patients’ rehabilitation potential and subsequently establish customized rehabilitation and discharge plan.

Acute intermittent porphyria: prevalence of pathogenic HMBS variants in China, and epidemiological survey in Hebei Province, China.

BackgroundAcute intermittent porphyria (AIP) is a rare inherited disorder with extremely low prevalence. Early detection of patients with potential pathogenic hydroxymethylbilane synthase (HMBS) variants is crucial for clinical prognosis. This study was designed to investigate the prevalence of pathogenic HMBS variants in Chinese population.MethodsThe China Metabolic Analysis Project (ChinaMAP) database was employed to predict the prevalence of pathogenic HMBS variants in the Chinese population according to the variant interpretation guidelines of The American College of Medical Genetics and Genomics (ACMG). And the prevalence of pathogenic HMBS variants in Mixed American (AMR), African/African American (AFR), and Non-Finnish European (NFE) populations were estimated based on the Genome Aggregation Database (gnomAD) genome V3.0 database according to the guidelines of ACMG. An epidemiological investigation of AIP was conducted in Hebei Province, China through collecting the annual newly-diagnosed AIP cases of inpatients in 32 comprehensive grade III A hospitals from January 2011 to December 2020 in Hebei, China.ResultsA total of 5 pathogenic/likely pathogenic (P/LP) HMBS variants were identified and the prevalence of pathogenic HMBS variants was predicted to be 1/1,765. Furthermore, based on the gnomAD genome V3.0 database, the estimated prevalence of pathogenic HMBS variants was 1/1,367, 1/1,403, and 1/621 in AMR, AFR, and NFE populations, respectively. The distribution of these variants varied among different racial populations. Moreover, AIP patients were predominantly hospitalized in comprehensive grade III A tertiary hospitals in Hebei province over a 10-year period. A total of 39 patients were newly-diagnosed with AIP, and a majority of them were female (n=36). The annual incidence between 2011 and 2017 {0.03 [95% confidence interval (CI): 0.01 to 0.11] to 0.05 (95% CI: 0.07 to 0.14) per million population} was generally lower than [0.07 (95% CI: 0.03 to 0.17) to 0.08 (95% CI: 0.03 to 0.18) per million] in 2018 and thereafter.ConclusionsChina has made great strides in the management of AIP. More nationwide epidemiological surveys and study of the prevalence rate of AIP patients in China are urgently required.

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China.

PurposeTo identify more objectively predictive factors of severe outcome among patients hospitalized for coronavirus disease 2019 (COVID-19).Patients and MethodsA retrospective cohort of 479 hospitalized patients diagnosed with COVID-19 in Hunan Province was selected. The prognostic effects of factors such as age and laboratory indicators were analyzed using the Kaplan–Meier method and Cox proportional hazards model. A prognostic nomogram model was established to predict the progression of patients with COVID-19.ResultsA total of 524 patients in Hunan province with COVID-19 from December 2019 to October 2020 were retrospectively recruited. Among them, 479 eligible patients were randomly assigned into the training cohort (n = 383) and validation cohort (n = 96), at a ratio of 8:2. Sixty-eight (17.8%) and 15 (15.6%) patients developed severe COVID-19 after admission in the training cohort and validation cohort, respectively. The differences in baseline characteristics were not statistically significant between the two cohorts with regard to age, sex, and comorbidities (P > 0.05). Multivariable analyses included age, C-reactive protein, fibrinogen, lactic dehydrogenase, neutrophil-to-lymphocyte ratio, urea, albumin-to-globulin ratio, and eosinophil count as predictive factors for patients with progression to severe COVID-19. A nomogram was constructed with sufficient discriminatory power (C index = 0.81), and proper consistency between the prediction and observation, with an area under the ROC curve of 0.81 and 0.86 in the training and validation cohort, respectively.ConclusionWe proposed a simple nomogram for early detection of patients with non-severe COVID-19 but at high risk of progression to severe COVID-19, which could help optimize clinical care and personalized decision-making therapies.

Impact of the COVID-19 pandemic on the work of urological departments

BACKGROUND: The pandemic of a new coronavirus infection has had a significant impact on the work of the entire medical service, including urological departments.&#x0D; AIM: to assess the impact of the COVID-19 pandemic on the work of urological departments in private and public medical institutions.&#x0D; MATERIALS AND METHODS: We assessed the impact of the restrictions imposed by the pandemic on the work of the urology department of a private clinic (MC Avicenna, Novosibirsk) and the urogenital department of the Novosibirsk Research Institute of Tuberculosis for 2019 and 2020. We assessed the change in the range of surgical interventions, as well as the structure visits to the urologist.&#x0D; RESULTS: The urogenital department of the Novosibirsk Research Institute of Tuberculosis in March 2020 was redesigned into an observational one. The Avicenna Medical Center also established the procedure for anti-epidemic measures, which made it possible not only not to reduce, but in a number of positions to increase the volume of surgical care provided.&#x0D; CONCLUSION: Thus, thanks to anti-epidemic measures: separation of flows, introduction of mandatory testing of patients, early detection of patients and carriers among employees, mask regimen, opening of an observational department, single rooms, lack of contact between patients, short stays, it was possible to prevent an outbreak of infection among patients and staff. Both in the municipal and private urological clinics, well-organized work did not allow the negative impact of the pandemic to manifest itself.

ЗНАЧЕНИЕ ВРОЖДЕННЫХ ДЕФЕКТОВ ИММУНИТЕТА В ИЗУЧЕНИИ ЭПИДЕМИОЛОГИИ ИНФЕКЦИОННЫХ И НЕИНФЕКЦИОННЫХ ЗАБОЛЕВАНИЙ ЧЕЛОВЕКА

Primary immunodeficiencies (PID) are conditions caused by genetic defects in the immune system. Their main manifestation is an infectious syndrome (recurrent severe infections caused by common pathogens, with gross defects in immunity). However, as the PID study progressed, descriptions of patients with hypersensitivity to rare and opportunistic infections, mycobacterioses, including BCG infection, with severe/atypical course of common viral infections, including COVID-19, emerged. Due to the inability of the immune system of patients to control the oncogenic potential of various viruses, with defects in some immune mechanisms, these patients develop various tumors (Kaposi's sarcoma, EBV-associated smooth muscle tumor) or an increase in the frequency of, for example, EBV-associated lymphomas. The creation of the Russian register of PIDs makes it possible to predict the development of certain rare/opportunistic infectious and infectious diseases in the Russian Federation. The most important is the prevention of the development of rare/opportunistic infections by early detection of patients with PID and their pathogenetic treatment, including immunomodulatory, replacement therapy with human normal immunoglobulin preparations, as well as radical therapy – hematopoietic stem cell transplantation. In addition, an important epidemiological aspect of the study of PID is that patients are often unable to eliminate infectious pathogens for a long time and serve as a reservoir of various infections (vaccinal and wild strains of poliomyelitis, COVID-19).

Predicting Hematoma Expansion after Spontaneous Intracranial Hemorrhage Through a Radiomics Based Model.

Intracranial hemorrhage (ICH) is characterized as bleeding into the brain tissue, intracranial space, and ventricles and is the second most disabling form of stroke. Hematoma expansion (HE) following ICH has been correlated with significant neurological decline and death. For early detection of patients at risk, deep learning prediction models were developed to predict whether hematoma due to ICH will expand. This study aimed to explore the feasibility of HE prediction using a radiomic approach to help clinicians better stratify HE patients and tailor intensive therapies timely and effectively. Two hundred ICH patients with known hematoma evolution, were enrolled in this study. An open-source python package was utilized for the extraction of radiomic features from both non-contrast computed tomography (NCCT) and magnetic resonance imaging (MRI) scans through characterization algorithms. A total of 99 radiomic features were extracted and different features were selected for network inputs for the NCCT and MR models. Seven supervised classifiers: Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, K-Nearest Neighbor and Multilayer Perceptron were used to build the models. A training:testing split of 80:20 and 20 iterations of Monte Carlo cross validation were performed to prevent overfitting and assess the variability of the networks, respectively. The models were fed training datasets from which they learned to classify the data based on pre-determined radiomic categories. The highest sensitivity among the NCCT classifier models was seen with the support vector machine (SVM) and logistic regression (LR) of 72 ± 0.3% and 73 ± 0.5%, respectively. The MRI classifier models had the highest sensitivity of 68 ± 0.5% and 72 ± 0.5% for the SVM and LR models, respectively. This study indicates that the NCCT radiomics model is a better predictor of HE and that SVM and LR classifiers are better predictors of HE due to their more cautious approach indicated by a higher sensitivity metric.

Preoperative Hyponatremia Indicates Complicated Acute Appendicitis

Introduction Acute appendicitis is the most common surgical emergency. Early detection of patients with complicated appendicitis leads to prompt surgical management and better outcome. This study investigated the relationship between the severity of acute appendicitis and the presence of preoperative hyponatremia. Materials and Methods We retrospectively reviewed the medical files of adult patients operated on for acute appendicitis over a 6-year period. Hyponatremia was defined as serum sodium level of ≤135 mEq/L. Patients were classified into complicated appendicitis and noncomplicated appendicitis according to operative findings and/or histopathology reports. Results A total of 129 patients were identified and included in this study. Complicated appendicitis was found more frequently in female patients and older patients. Hyponatremia was found significantly more frequently in patients with complicated appendicitis (p < 0.001) and also in patients with perforation than without perforation (p=0.047). Conclusions The present study demonstrated that preoperative hyponatremia is associated with complicated appendicitis. Serum sodium levels, a routine, low-cost laboratory test, could act as an accessory marker aiding surgeons in earlier identification of gangrenous or perforated acute appendicitis.

Comparison of intuitive assessment and palliative care screening tool in the early identification of patients needing palliative care

The intuitive assessment of palliative care (PC) needs and Palliative Care Screening Tool (PCST) are the assessment tools used in the early detection of patients requiring PC. However, the comparison of their prognostic accuracies has not been extensively studied. This cohort study aimed to compare the validity of intuitive assessment and PCST in terms of recognizing patients nearing end-of-life (EOL) and those appropriate for PC. All adult patients admitted to Taipei City Hospital from 2016 through 2019 were included in this prospective study. We used both the intuitive assessment of PC and PCST to predict patients’ 6-month mortality and identified those appropriate for PC. The c-statistic value was calculated to indicate the predictive accuracies of the intuition and PCST. Of 111,483 patients, 4.5% needed PC by the healthcare workers’ intuitive assessment, and 6.7% had a PCST score ≥ 4. After controlling for other covariates, a positive response ‘yes’ to intuitive assessment of PC needs [adjusted odds ratio (AOR) = 9.89; 95% confidence interval (CI) 914–10.71] and a PCST score ≥ 4 (AOR = 6.59; 95%CI 6.17–7.00) were the independent predictors of 6-month mortality. Kappa statistics showed moderate concordance between intuitive assessment and PCST in predicting patients' 6-month mortality (k = 0.49). The c-statistic values of the PCST at recognizing patients’ 6-month mortality was significantly higher than intuition (0.723 vs. 0.679; p < 0.001). As early identification of patients in need of PC could improve the quality of EOL care, our results suggest that it is imperative to screen patients’ palliative needs by using a highly accurate screening tool of PCST.

Radiation diagnostics of the state of ventilation and pulmonary circulation in patients with COPD

COPD is one of the most common human diseases. WHO experts predict an increase in economic damage from COPD by 2020 and claim that they will rank first among respiratory diseases and third among all causes of death. In Ukraine, about 3 million people, or at least 7% of the population, suffer from COPD. The purpose of the study is to analyze the available literature sources to establish the current state of the problem of radiological diagnosis of COPD, to identify problematic issues. Based on the analysis of literature data, it can be concluded that for a comprehensive examination of patients with COPD or suspected COPD, and assessment of external respiration - clinical examination and spirometry, especially in the early stages of the disease - is not enough. It is believed that in the initial stages of COPD, when spirometry and clinical data do not reveal abnormalities, radiological diagnosis is more sensitive than functional tests. Among the modern methods of radiological diagnosis of lung diseases - the method of CT today is the most sensitive and specific method of detecting pathological changes in the lung parenchyma and respiratory tract, it is available and widely used in everyday practice. Also a promising area is the use of functional CT (inspiratory-expiratory CT) - which should improve the assessment of respiratory function, including early detection of patients with COPD, which will promote the in time start of specific treatment, reduce episodes of exacerbations during the disease, assess the dynamics of the pathological process and the effectiveness of treatment, as well as improving the prognosis of work and life expectancy of patients. However, given the lack of unifying works on the study of this method, further studies of the capabilities of computed tomography in the diagnosis of signs of dysfunction of external respiration in patients with COPD are required. First of all, further research is required on the distribution of air trap zones, especially in patients with emphysema, it is desirable that these future studies are not based only on the principle of visual assessment in the form of exclusion / confirmation of air trap zones.

The mood disorder spectrum vs. schizophrenia decision tree: EDIPHAS research into the childhood and adolescence of 205 patients

BackgroundThe early detection of patients at risk of developing schizophrenia and bipolar disorder, and more broadly mood spectrum disorder, is a public health concern. The phenotypical overlap between the prodromes in these disorders calls for a simultaneous investigation into both illness trajectories.MethodThis is an epidemiological, retrospective, multicentre, descriptive study conducted in the Grand-Est region of France in order to describe and compare early symptoms in 205 patients: 123 of which were diagnosed with schizophrenia and 82 with bipolar disorder or mood spectrum disorder. Data corresponding to the pre-morbid and prodromal phases, including a timeline of their onset, were studied in child and adolescent psychiatric records via a data grid based on the literature review conducted from birth to 17 years of age.ResultsTwo distinct trajectories were highlighted. Patients with schizophrenia tended to present more difficulties at each developmental stage, with the emergence of negative and positive behavioural symptoms during adolescence. Patients with mood spectrum disorder, however, were more likely to exhibit anxiety and then mood-related symptoms. Overall, our results corroborate current literature findings and are consistent with the neurodevelopmental process. We succeeded in extracting a decision tree with good predictability based on variables relating to one diagnosis: 77.6% of patients received a well-indexed diagnosis. An atypical profile was observed in future mood spectrum disorder patients as some exhibited numerous positive symptoms alongside more conventional mood-related symptoms.ConclusionThe combination of all these data could help promote the early identification of high-risk patients thereby facilitating early prevention and appropriate intervention in order to improve outcomes.