scholarly journals Differential Diagnostic Utility of Clinical Laboratory Data in Patients with Severe and Non-severe COVID-19

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Ali Hosseinzadeh ◽  
Mohammad Hassan Emamian ◽  
Marzieh Rohani-Rasaf ◽  
Ahmad Khosravi ◽  
Fariba Zare ◽  
...  
Keyword(s):  
Disease Severity ◽  
Clinical Laboratory ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
International Normalized Ratio ◽  
Therapeutic Effects ◽  
Neutrophil Lymphocyte Ratio ◽  
Laboratory Variables ◽  
Age Range

Background: Coronavirus disease 2019 (COVID-19) is a coronavirus outbreak caused by severe acute respiratory syndrome coronavirus 2 infection. Objectives: This study aimed to investigate the relationship between laboratory variables and COVID-19 severity. Methods: A total of 731 confirmed cases were included in this study. Based on the clinical course of the disease, the patients were divided into non-severe (n = 599) and severe (n = 132) groups. The area under the curve was estimated for each of the significant predictive factors by the receiver operating characteristic curve. Youden’s index was used to determine the optimal cut-off points to predict the severity of COVID-19. Results: Out of 731 patients, 407 (55.56%) cases were male. The mean age value and age range of the patients were 58.37 and 1 - 98 years, respectively. The age (OR = 1.03, 95% CI: 1.02 - 1.05), international normalized ratio (INR) (OR = 2.09, 95% CI: 1.11 - 3.96), lactate dehydrogenase (LDH) (OR = 1.003, 95% CI: 1.001 - 1.1.003), and neutrophil/lymphocyte ratio (NLR) (OR = 1.08, 95% CI: 1.02 - 1.14) were associated with the severity of COVID-19 in the multivariate analyses. The areas under the curve of LDH, NLR, and INR for the diagnosis of disease severity were 0.76, 0.69, and 0.62, respectively. Conclusions: The results of this study revealed that LDH, NLR, and INR could help to discriminate between non-severe and severe COVID-19 cases. Therefore, clinicians can use these variables to improve therapeutic effects and reduce disease severity.

scholarly journals Clinical risk scores for the early prediction of severe outcomes in patients hospitalized for COVID-19

2021 ◽  
Author(s):  
Walter Ageno ◽  
◽  
Chiara Cogliati ◽  
Martina Perego ◽  
Domenico Girelli ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Predictive Value ◽  
Clinical Laboratory ◽  
Area Under The Curve ◽  
Orotracheal Intubation ◽  
Risk Scores ◽  
Algorithm Selection ◽  
Derivation Cohort ◽  
Neutrophil Lymphocyte Ratio ◽  
Predictive Risk

AbstractCoronavirus disease of 2019 (COVID-19) is associated with severe acute respiratory failure. Early identification of high-risk COVID-19 patients is crucial. We aimed to derive and validate a simple score for the prediction of severe outcomes. A retrospective cohort study of patients hospitalized for COVID-19 was carried out by the Italian Society of Internal Medicine. Epidemiological, clinical, laboratory, and treatment variables were collected at hospital admission at five hospitals. Three algorithm selection models were used to construct a predictive risk score: backward Selection, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. Severe outcome was defined as the composite of need for non-invasive ventilation, need for orotracheal intubation, or death. A total of 610 patients were included in the analysis, 313 had a severe outcome. The subset for the derivation analysis included 335 patients, the subset for the validation analysis 275 patients. The LASSO selection identified 6 variables (age, history of coronary heart disease, CRP, AST, D-dimer, and neutrophil/lymphocyte ratio) and resulted in the best performing score with an area under the curve of 0.79 in the derivation cohort and 0.80 in the validation cohort. Using a cut-off of 7 out of 13 points, sensitivity was 0.93, specificity 0.34, positive predictive value 0.59, and negative predictive value 0.82. The proposed score can identify patients at low risk for severe outcome who can be safely managed in a low-intensity setting after hospital admission for COVID-19.

scholarly journals Deep Learning Could Diagnose Diabetic Nephropathy with Renal Pathological Immunofluorescent Images

Diagnostics ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 466
Author(s):  
Shinji Kitamura ◽  
Kensaku Takahashi ◽  
Yizhen Sang ◽  
Kazuhiko Fukushima ◽  
Kenji Tsuji ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Deep Learning ◽  
Clinical Laboratory ◽  
Clinical Course ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
Medical Doctors ◽  
Diagnostic Ratio ◽  
Interpretable Model ◽  
Microscopy Images

Artificial Intelligence (AI) imaging diagnosis is developing, making enormous steps forward in medical fields. Regarding diabetic nephropathy (DN), medical doctors diagnose them with clinical course, clinical laboratory data and renal pathology, mainly evaluate with light microscopy images rather than immunofluorescent images because there are no characteristic findings in immunofluorescent images for DN diagnosis. Here, we examined the possibility of whether AI could diagnose DN from immunofluorescent images. We collected renal immunofluorescent images from 885 renal biopsy patients in our hospital, and we created a dataset that contains six types of immunofluorescent images of IgG, IgA, IgM, C3, C1q and Fibrinogen for each patient. Using the dataset, 39 programs worked without errors (Area under the curve (AUC): 0.93). Five programs diagnosed DN completely with immunofluorescent images (AUC: 1.00). By analyzing with Local interpretable model-agnostic explanations (Lime), the AI focused on the peripheral lesion of DN glomeruli. On the other hand, the nephrologist diagnostic ratio (AUC: 0.75833) was slightly inferior to AI diagnosis. These findings suggest that DN could be diagnosed only by immunofluorescent images by deep learning. AI could diagnose DN and identify classified unknown parts with the immunofluorescent images that nephrologists usually do not use for DN diagnosis.

scholarly journals Role of Blood Stasis Syndrome of Kampo Medicine in the Early Pathogenic Stage of Atherosclerosis: A Retrospective Cross-Sectional Study

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Akira Morita ◽  
Takao Namiki ◽  
Toshiya Nakaguchi ◽  
Kazunari Murai ◽  
Yuki Watanabe ◽  
...  
Keyword(s):  
Glucose Tolerance ◽  
Characteristic Curve ◽  
Multiple Logistic Regression Analysis ◽  
Univariate Analysis ◽  
Laboratory Data ◽  
Blood Stasis ◽  
Area Under The Curve ◽  
Kampo Medicine ◽  
Blood Stasis Syndrome ◽  
History Of

In Kampo medicine, blood stasis (BS) syndrome is strongly associated with microangiopathy and can lead to atherosclerosis. Vascular endothelial dysfunction (VED), evaluated through flow-mediated dilation (FMD), plays an important role in the early stages of atherosclerosis. However, the association of BS syndrome with VED, as determined using FMD, has not been reported. This study investigated the association between BS syndrome and VED using FMD. Forty-one patients with normal glucose tolerance or impaired glucose tolerance (IGT) and without macrovascular complications were evaluated using FMD from May 2017 to August 2017. Based on the BS score, the patients were divided into the non-BS (n = 19) and BS syndrome (n = 22) groups. Physical and background characteristics, physiological function test results, and laboratory data were compared. Univariate analysis revealed that FMD and a history of dyslipidemia/IGT were significantly different between the two groups ( p < 0.05 ). Multiple logistic regression analysis showed that BS syndrome was significantly associated with FMD (odds ratio: 6.26; p = 0.03 ) after adjusting for the history of dyslipidemia/IGT. The receiver operating characteristic curve showed that the area under the curve for BS syndrome (0.74; p < 0.001 ) and history of IGT ( p < 0.007 ) provided good diagnostic accuracy for FMD. The area under the curve for “BS syndrome + IGT” showed very good accuracy (0.80; p < 0.0001 ) and was higher than that for BS syndrome or IGT alone. In conclusion, the results of this study suggest that the BS score in Kampo medicine could be a useful tool for detecting the early pathogenic stages of atherosclerosis.

scholarly journals Novel predictor of the occurrence of DKA in T1DM patients without infection: A combination of neutrophil/lymphocyte ratio and white blood cells

2021 ◽  
Vol 16 (1) ◽  
pp. 1365-1376
Author(s):  
Yiping Cheng ◽  
Wenhao Yu ◽  
Yuping Zhou ◽  
Tao Zhang ◽  
Haiyan Chi ◽  
...  
Keyword(s):  
Blood Cells ◽  
Low Cost ◽  
Characteristic Curve ◽  
White Blood Cells ◽  
Area Under The Curve ◽  
Control Group ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio

Abstract The role of inflammation has been identified in the pathogenesis of diabetic ketoacidosis (DKA). The neutrophil/lymphocyte ratio (NLR) and white blood cells (WBC) can be used to predict a systemic inflammatory response. Changes in NLR and WBC levels have never been explored in type 1 diabetes mellitus (T1DM) patients with DKA and an uninfected state. This retrospective study included a total of 644 participants. NLR and WBC were measured in the control group (n = 316) and in T1DM patients with mild-DKA (n = 92), severe-DKA (n = 52), and non-DKA (n = 184) in an uninfected state. Then, we assessed the independent predictors of DKA occurrence in T1DM patients in an uninfected state. The diagnostic performance of variables was determined by receiver operating characteristic curve analysis. Serum NLR of T1DM patients is significantly higher than that of normal controls, and if DKA occurs, NLR increases further and increases with the severity of DKA. In addition to diastolic blood pressure, blood urea nitrogen, glycated hemoglobin (HbA1c), and WBC, NLR was also independently associated with DKA in T1DM patients with an uninfected state (OR = 1.386, 95% CI: 1.127–1.705, p = 0.002). Furthermore, the diagnosis analysis showed that except for NLR and WBC, the area under the curve (AUC) of indicators with a statistical difference in patients with and without DKA were 0.747 for DKA diagnosis, and after the addition of NLR and WBC, the AUC was 0.806. The increased NLR level represents a low-cost and highly accessible predictor for DKA in T1DM patients with an uninfected state. The addition of inflammation indicators can play a statistically significant role in the prediction model of the DKA occurrence.

scholarly journals C-Reactive Protein Level May Predict the Risk of COVID-19 Aggravation

2020 ◽  
Vol 7 (5) ◽  
Author(s):  
Guyi Wang ◽  
Chenfang Wu ◽  
Quan Zhang ◽  
Fang Wu ◽  
Bo Yu ◽  
...  
Keyword(s):  
Early Stage ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
Threshold Value ◽  
Clinical Findings ◽  
Optimal Threshold ◽  
C Reactive Protein ◽  
Related Factors ◽  
Reactive Protein

Abstract Background Clinical findings indicated that a fraction of coronavirus disease 2019 (COVID-19) patients diagnosed as mild early may progress to severe cases. However, it is difficult to distinguish these patients in the early stage. The present study aimed to describe the clinical characteristics of these patients, analyze related factors, and explore predictive markers of the disease aggravation. Methods Clinical and laboratory data of nonsevere adult COVID-19 patients in Changsha, China, were collected and analyzed on admission. A logistic regression model was adopted to analyze the association between the disease aggravation and related factors. The receiver operating characteristic curve (ROC) was utilized to analyze the prognostic ability of C-reactive protein (CRP). Results About 7.7% (16/209) of nonsevere adult COVID-19 patients progressed to severe cases after admission. Compared with nonsevere patients, the aggravated patients had much higher levels of CRP (median [range], 43.8 [12.3–101.9] mg/L vs 12.1 [0.1–91.4] mg/L; P = .000). A regression analysis showed that CRP was significantly associated with aggravation of nonsevere COVID-19 patients, with an area under the curve of 0.844 (95% confidence interval, 0.761–0.926) and an optimal threshold value of 26.9 mg/L. Conclusions CRP could be a valuable marker to anticipate the possibility of aggravation of nonsevere adult COVID-19 patients, with an optimal threshold value of 26.9 mg/L.

INFLAMMATORY MARKERS IN THE DIAGNOSIS OF COMPLICATED ACUTE APPENDICITIS

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
J Michi Campos ◽  
M Merayo Álvarez ◽  
L García González ◽  
B Carrasco Aguilera ◽  
J L Rodicio Miravalles ◽  
...  
Keyword(s):  
Inflammatory Markers ◽  
Clinical Laboratory ◽  
Complete Blood Count ◽  
Area Under The Curve ◽  
Tertiary Hospital ◽  
Complicated Appendicitis ◽  
Neutrophil Lymphocyte Ratio ◽  
Reactive Protein ◽  
Surgical Emergencies ◽  
Relationship Of

Abstract INTRODUCTION Appendicitis constitutes one of the most frequent surgical emergencies. New inflammatory markers such as the neutrophil-lymphocyte ratio (NLR) have recently emerged, which are added to others such as leukocyte count and C-reactive protein (CRP), and whose role in the diagnosis of appendicitis remains unclear. MATERIAL AND METHODS We conducted an observational, descriptive, longitudinal and retrospective study of 484 adults appendectomized between April 2017 and May 2019 in a tertiary hospital. Sociodemographic, clinical, laboratory, imaging and surgical variables were collected. RESULTS 32.2% of appendicitis were complicated. All patients had a complete blood count and basic biochemistry (98.5% with CRP). Complicated appendicitis had a mean of 14538 leukocytes, 12.4 CRP and 8.7 NLR, and uncomplicated appendicitis had 14667 leukocytes, 5 CRP and 10.7 NLR. When analyzing the relationship of inflammatory markers with the existence or not of complicated appendicitis, CRP yielded an area under the curve (AUC) of 73.1% (95% CI: 0.684-0.779, p &lt; 0.01), while leukocytes and NLR had 52.6% and 55.9%. The CRP cut-off point was determined to be 3.5 which presented a higher discriminative power to predict complicated appendicitis, with a sensitivity and specificity of 70.1% and 65.3% respectively. CONCLUSIONS Of the inflammatory markers studied, only CRP proved to be a valid parameter to help differentiate preoperatively those appendicitis uncomplicated from complicated appendicitis.

scholarly journals FACTORS ASSOCIATED WITH SEVERITY OF DISEASE IN PSORIATIC ARTHRITIS: A CROSS-SECTIONAL STUDY

2017 ◽  
Vol 26 (1) ◽  
pp. 24-29
Author(s):  
Tania Gudu ◽  
◽  
Alexandra Peltea ◽  
Andra Balanescu ◽  
Violeta Bojinca ◽  
...  
Keyword(s):  
Disease Severity ◽  
Clinical Laboratory ◽  
Cross Sectional Study ◽  
Severe Psoriasis ◽  
Sectional Study ◽  
Related Factors ◽  
Biologic Dmards ◽  
Cross Sectional ◽  
History Of ◽  
Laboratory Variables

Objectives. The objectives of this study were to evaluate the prevalence of disease severity in PsA and to assess the factors that might explain it. Methods. This was a cross-sectional study of unselected PsA patients. Severity was defined according to GRAPPA criteria of severity. Factors potentially associated with severity (demographical, clinical, laboratory variables, treatment related factors and comorbidities) were assessed by uni- and multivariate logistic regressions. Results. A total of 129 PsA patients were analysed: 77 (59.7%) women, mean±standard deviation age 53.5±11.8 years, and mean disease duration 7±7.4 years. Twenty-for patients (18.6%) had severe PsA. In the univariate regression, disease severity was associated with psoriasis duration, PsA duration, current moderate/severe skin disease, nail disease, history of corticotherapy, and total number of previous synthetic and biologic DMARDs. In the multivariate analysis, PsA severity was explained by the presence of current moderate/severe psoriasis – odds ratio 5.88 (95% confidence interval 1.39; 25.00) and history of corticosteroids – 4.65 (1.13; 18.87). Conclusion. PsA severity is best explained by the presence of moderate or severe psoriasis and past treatment with corticosteroids, but further longitudinal studies are needed to identify predictive factors.

scholarly journals Score for Predicting Active Cancer in Patients with Ischemic Stroke: A Retrospective Study

2020 ◽  
Author(s):  
Jiwei Jiang ◽  
Jirui Wang ◽  
Meihui Cao ◽  
Jinming Zhao ◽  
Xiuli Shang
Keyword(s):  
Ischemic Stroke ◽  
Retrospective Study ◽  
Clinical Laboratory ◽  
Characteristic Curve ◽  
Elevated Serum ◽  
Area Under The Curve ◽  
Clinical Score ◽  
History Of ◽  
D Dimer ◽  
Active Cancer

Abstract Background: We aimed to examine the differences between the clinical characteristics of patients with ischemic stroke and active cancer and those without cancer and develop a clinical score for predicting occult cancer in patients with ischemic stroke.Methods: This retrospective study enrolled consecutive adult patients with acute ischemic stroke, who were admitted to our department between December 2017 and January 2019. The demographic, clinical, laboratory, and neuroimaging characteristics of patients with ischemic stroke with active cancer and those without cancer were compared. Multivariate analysis was performed to identify independent factors associated with active cancer. Subsequently, a predictive cancer-risk score was developed using the area under the receiver operating characteristic curve.Results: Fifty-three (6.63%) of 799 patients with ischemic stroke had active cancer. The absence of a history of hyperlipidemia [odds ratio (OR)=0.17, 95% confidence interval (CI): 0.06–0.48, P<0.01], elevated serum fibrinogen (OR=1.72, 95% CI: 1.33–2.22, P<0.01) and D-dimer levels (OR=1.43, 95% CI: 1.24–1.64, P<0.01), and stroke of undetermined etiology (OR=22.87, 95% CI: 9.91–52.78, P<0.01) were independently associated with active cancer. Thus, a score based on the absence of hyperlipidemia and serum fibrinogen ≥4.00 g/L and D-dimer ≥2.00 μg/mL predicted active cancer with an area under the curve of 0.83 (95% CI: 0.77–0.89, P<0.01). The probability of active cancer was 59% at a supposed prevalence of 6.63%, if all three independent factors were present in a patient with ischemic stroke.Conclusions: We devised a score to predict active cancer in patients with ischemic stroke based on the absence of a history of hyperlipidemia and elevated serum D-dimer and fibrinogen that highlights the importance of hypercoagulability in these patients and may help determine early intervention and management.

scholarly journals Time Points for Gonadotropin-Releasing Hormone Stimulation Test Results in Korean Children

2021 ◽  
Vol 10 (2) ◽  
pp. 252
Author(s):  
Rihwa Choi ◽  
Aerin Kwon ◽  
Youngju Oh ◽  
Sang Gon Lee ◽  
Eun Hee Lee
Keyword(s):  
Characteristic Curve ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
Central Precocious Puberty ◽  
Laboratory Diagnosis ◽  
Gonadotropin Releasing Hormone ◽  
Test Results ◽  
Korean Children ◽  
Releasing Hormone ◽  
Time Points

The gold standard for the laboratory diagnosis of central precocious puberty is based on the measurement of luteinizing hormone (LH) after gonadotropin-releasing hormone (GnRH) stimulation. We sought to investigate the laboratory data for GnRH stimulation testing using samples collected from Korean children at different time points. Sampling times were at the basal time point (0) and 15, 30, 45, 60, 90, and 120 min after GnRH stimulation. Pubertal response was defined as occurring when the peak LH concentration was 5 IU/L or more and rose to at least 2 times the basal LH concentration after GnRH stimulation. During the study period, 19,990 test results from 1958 Korean children (1841 females aged 1.3–8.9 years and 117 males aged 7.3–9.9 years) were obtained. Among the 1958 children, 1232 (62.9%) showed pubertal responses. The receiver operating characteristic curve that demonstrated the greatest area under the curve (AUC) among all examined time points was 45 min after GnRH stimulation in males (AUC 0.982, 95% CI 0.938–0.998) and 60 min in females (AUC 0.975, 95% CI 0.967–0.981). The combination of 45 min and 60 min showed the greatest AUC (0.996, 95% confidence interval 0.991–0.998), with a sensitivity level of 99.1% and a specificity of 100% for all children. The results of this study provide a possibility for a reduction in sampling time points (45 min and 60 min) to identify the presence of a pubertal response after GnRH stimulation in Korean children.

scholarly journals Z-score is a possible predictor of the risk of coronary artery lesion development in patients with Kawasaki disease in Japan

2021 ◽  
Author(s):  
Takayuki Suzuki ◽  
Nobuyuki Kakimoto ◽  
Tomoya Tsuchihashi ◽  
Tomohiro Suenaga ◽  
Takashi Takeuchi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery ◽  
Kawasaki Disease ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Delayed Diagnosis ◽  
Area Under The Curve ◽  
Coronary Artery Lesion ◽  
Z Score ◽  
Male Sex

Abstract ABSTRACT Risk factors for coronary artery lesion (CAL) development in patients with Kawasaki disease (KD) include male sex, age <12 months, intravenous immunoglobulin (IVIG) resistance, and delayed diagnosis. We aimed to explore the relationship between CAL development and Z-score. We enrolled 281 patients with KD who were treated with our protocol. Echocardiography was performed in three phases: pre-treatment (P1), post-treatment (P2), and 4 weeks after onset (P3). The highest Z-score of the right, left main, left anterior descending, and left circumflex coronary arteries was expressed as Zmax at each phase. P3-Zmax ≥2.5 represented CAL development. Clinical parameters, such as laboratory data and Z-scores, were retrospectively compared between patients with and without CAL development. Sixty-seven patients (23.8%) showed a P1-Zmax ≥2.0, and CAL development occurred in 21 patients (7.5%). Independent risk factors associated with CAL development were P1-Zmax, a ΔZmax (P2-Zmax − P1-Zmax) ≥1, male sex, <12 months of age, and resistant to the first IVIG administration (adjusted odds ratio [95% confidence interval]: 1.98 [1.01–3.92], 4.04 [1.11-14.7], 6.62 [1.33–33.04], 4.71 [1.51–14.68], 5.26 [1.62–17.13], respectively). Using receiver operating characteristic curve analysis, a P1-Zmax ≥1.43 detected CAL development with an area under the curve of 0.64 (sensitivity = 81.0%; specificity = 48.1%). Conclusions : Our results suggest that P1-Zmax and a ΔZmax (P2-Zmax − P1-Zmax) ≥1 may predict CAL development.

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