scholarly journals Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients

2021 ◽  
Author(s):  
Caidong Liu ◽  
Ziyu Wang ◽  
Wei Wu ◽  
Changgang Xiang ◽  
Lingxiang Wu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Early Stage ◽  
Case Series ◽  
Low Risk ◽  
Training Dataset ◽  
Support Vector ◽  
Machine Model ◽  
Testing Dataset ◽  
Probability Of Death

Abstract Objectives: To classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators.Design, Setting, and Patients: This is a case series of patients from a China healthcare system in Wuhan. In this retrospective cohort, 3563 patients confirmed COVID-19 pneumonia, including 548 patients in the training dataset, and 3015 patients in the testing dataset.Interventions: NoneMeasurements and Main Results:We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC greater than 0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission. Results showed that this model could stratify the patients in the testing dataset effectively (AUC=0.89). Moreover, laboratory indicators detected in the first week after admission were able to estimate the probability of death (AUC=0.95). Besides, we could diagnose COVID-19 and differentiated it from other kinds of viral pneumonia based on laboratory indicators (accuracy=0.97).Conclusions:Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19.

scholarly journals Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients

2021 ◽  
Author(s):  
Caidong Liu ◽  
Ziyu Wang ◽  
Wei Wu ◽  
Changgang Xiang ◽  
Lingxiang Wu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Early Stage ◽  
Case Series ◽  
Low Risk ◽  
Training Dataset ◽  
Support Vector ◽  
Machine Model ◽  
Testing Dataset ◽  
Probability Of Death

Abstract Aims: To classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators.Design, Setting, and Patients: This is a case series of patients from a China healthcare system in Wuhan. In this retrospective cohort, 3563 patients confirmed COVID-19 pneumonia, including 548 patients in the training dataset, and 3015 patients in the testing dataset.Interventions: NoneMeasurements and Main Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC greater than 0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission. Results showed that this model could stratify the patients in the testing dataset effectively (AUC=0.89). Moreover, laboratory indicators detected in the first week after admission were able to estimate the probability of death (AUC=0.95). Besides, we could diagnose COVID-19 and differentiated it from other kinds of viral pneumonia based on laboratory indicators (accuracy=0.97).Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19.

scholarly journals Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients

2020 ◽  
Author(s):  
Caidong Liu ◽  
Ziyu Wang ◽  
Wei Wu ◽  
Changgang Xiang ◽  
Lingxiang Wu ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Support Vector Machine Model ◽  
Early Stage ◽  
Laboratory Data ◽  
Training Dataset ◽  
Support Vector ◽  
Machine Model ◽  
Testing Dataset ◽  
Risk Stratification Model ◽  
Probability Of Death

Abstract The progression from mild to critical illness is the main reason leading to the death of COVID-19 patients. Rapid risk-stratification at admission is important for precise management of COVID-19. Here, we developed a practical admission stratification model to predict the severity during hospitalization of COVID-19 patients using laboratory data from 3563 patients, including 548 patients in the training dataset, and 3015 patients in the testing dataset. We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage (NEUT%), lymphocytes percentage (LYMPH%), creatinine (CREA), and blood urea nitrogen (BUN) with AUC greater than 0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission. Results showed that this model could stratify the patients in the testing dataset effectively (AUC=0.89). Moreover, laboratory indicators detected in the first week after admission were able to estimate the probability of death (AUC=0.95). Besides, we could diagnose COVID-19 and differentiated it from other kinds of viral pneumonia based on laboratory indicators (accuracy=0.97). Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19.

scholarly journals Laboratory Testing Implications of Risk-Stratification and Management of COVID-19 Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Caidong Liu ◽  
Ziyu Wang ◽  
Wei Wu ◽  
Changgang Xiang ◽  
Lingxiang Wu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
High Risk ◽  
Risk Stratification ◽  
Random Sampling ◽  
Early Stage ◽  
Viral Pneumonia ◽  
Low Risk ◽  
Training Dataset ◽  
Support Vector ◽  
Testing Dataset

Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators.Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared.Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators.Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.

scholarly journals A profile of cases of gestational trophoblastic neoplasia at a tertiary care centre in South India

2020 ◽  
Vol 9 (5) ◽  
pp. 1788
Author(s):  
Rakesh M. P. ◽  
Kalaichelvi K. ◽  
Govind Gangadharan ◽  
Vishnu Sreedath
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Tertiary Care ◽  
Single Agent ◽  
Case Series ◽  
Response To Therapy ◽  
Low Risk ◽  
Gestational Trophoblastic Neoplasia ◽  
First Line ◽  
Baseline Characteristics

Background: Gestational trophoblastic disease (GTD) comprises a spectrum of diseases ranging from molar pregnancy to malignant gestational trophoblastic neoplasia (GTN). GTN are highly chemo-sensitive tumours which are treated as per FIGO risk stratification. The rarity of the disease limits the evidence regarding the disease to case series and reports. The objective of this study was to study incidence, baseline characteristics of patients and clinical outcome of GTN patients treated at this centre.Methods: This is a retrospective descriptive study based on medical records of patients of GTD who were registered in department of medical oncology, from January 2015 to December 2018 (4 years). GTN was diagnosed based on serum beta HCG values. Their baseline characteristics, risk score, serum β HCG levels, and treatment regimens were investigated. The incidence of GTD and response to treatment were analysed.Results: Out of 211 GTD patients, 56 developed GTN. The incidence was 3.4 per 10000 deliveries. Low risk cases (n=38) were treated with methotrexate and actinomycin in first line while high risk cases received EMACO and EP followed by EMACO as the first line. A cure rate of 100% for low risk cases and 94.4% (n=17) for high risk cases were recorded. Resistance to MTX was 32.3% while EMACO was resistant in 46.6% as first line. Neutropenia and alopecia were the most common treatment related adverse events. Predictors of resistance to single agent in low risk GTN include higher pre-treatment βHCG values and higher risk scores.Conclusions: GTN exemplifies a rare, highly aggressive but curable malignancy. Serum βHCG is the most reliable diagnostic as well as prognostic marker in management of GTD. EMACO is the preferred regimen for high risk GTN. FIGO staging and risk stratification help in individualizing the treatment to ensure maximum response to therapy thus making GTN a curable malignancy.

scholarly journals CanAssist Breast Impacting Clinical Treatment Decisions in Early-Stage HR+ Breast Cancer Patients: Indian Scenario

2019 ◽  
Author(s):  
Satish Sankaran ◽  
Jyoti Bajpai Dikshit ◽  
Chandra Prakash SV ◽  
SE Mallikarjuna ◽  
SP Somashekhar ◽  
...  
Keyword(s):  
High Risk ◽  
Early Stage ◽  
Risk Groups ◽  
Treatment Decisions ◽  
Low Risk ◽  
Not Given ◽  
Breast Cancer Patients ◽  
Number Of Patients ◽  
Risk Of Cancer ◽  
The Impact

AbstractCanAssist Breast (CAB) has thus far been validated on a retrospective cohort of 1123 patients who are mostly Indians. Distant metastasis–free survival (DMFS) of more than 95% was observed with significant separation (P < 0.0001) between low-risk and high-risk groups. In this study, we demonstrate the usefulness of CAB in guiding physicians to assess risk of cancer recurrence and to make informed treatment decisions for patients. Of more than 500 patients who have undergone CAB test, detailed analysis of 455 patients who were treated based on CAB-based risk predictions by more than 140 doctors across India is presented here. Majority of patients tested had node negative, T2, and grade 2 disease. Age and luminal subtypes did not affect the performance of CAB. On comparison with Adjuvant! Online (AOL), CAB categorized twice the number of patients into low risk indicating potential of overtreatment by AOL-based risk categorization. We assessed the impact of CAB testing on treatment decisions for 254 patients and observed that 92% low-risk patients were not given chemotherapy. Overall, we observed that 88% patients were either given or not given chemotherapy based on whether they were stratified as high risk or low risk for distant recurrence respectively. Based on these results, we conclude that CAB has been accepted by physicians to make treatment planning and provides a cost-effective alternative to other similar multigene prognostic tests currently available.

Comparison of the Prognoses of FIGO Stage I to Stage II Adenosquamous Carcinoma and Adenocarcinoma of the Uterine Cervix Treated With Radical Hysterectomy

2012 ◽  
Vol 22 (8) ◽  
pp. 1389-1397 ◽  
Author(s):  
Seiji Mabuchi ◽  
Mika Okazawa ◽  
Yasuto Kinose ◽  
Koji Matsuo ◽  
Masateru Fujiwara ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cervical Cancer ◽  
High Risk ◽  
Uterine Cervix ◽  
Radical Hysterectomy ◽  
Adenosquamous Carcinoma ◽  
Early Stage ◽  
Univariate Analysis ◽  
Low Risk ◽  
Histological Subtype

ObjectivesTo evaluate the significance of adenosquamous carcinoma (ASC) compared with adenocarcinoma (AC) in the survival of surgically treated early-stage cervical cancer.MethodsWe retrospectively reviewed the medical records of 163 patients with International Federation of Gynecology and Obstetrics stage IA2 to stage IIB cervical cancer who had been treated with radical hysterectomy with or without adjuvant radiotherapy between January 1998 and December 2008. The patients were classified according to the following: (1) histological subtype (ASC group or AC group) and (2) pathological risk factors (low-risk or intermediate/high-risk group). Survival was evaluated using the Kaplan-Meier method and compared using the log-rank test. Multivariate analysis of progression-free survival (PFS) was performed using the Cox proportional hazards regression model to investigate the prognostic significance of histological subtype.ResultsClinicopathological characteristics were similar between the ASC and AC histology groups. Patients with the ASC histology displayed a PFS rate similar to that of the patients with the AC histology in both the low-risk and intermediate/high-risk groups. Neither the recurrence rate nor the pattern of recurrence differed between the ASC group and the AC group. Univariate analysis revealed that patients with pelvic lymph node metastasis and parametrial invasion achieved significantly shorter PFS than those without these risk factors.ConclusionsCharacteristics of the patients and the tumors as well as survival outcomes of ASC were comparable to adenocarcinoma of early-stage uterine cervix treated with radical hysterectomy. Our results in part support that the management of ASC could be the same as the one of AC of the uterine cervix.

Identification of Molecular Biomarkers and Pathways for Risk Stratification in Human Papilloma Virus-Associated Cancers

2021 ◽  
Author(s):  
Eun Jung Kwon ◽  
Hye Ran Lee ◽  
Ju Ho Lee ◽  
Mihyang Ha ◽  
Yun Hak Kim ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Expression Patterns ◽  
Risk Groups ◽  
Molecular Biomarkers ◽  
Low Risk ◽  
Receptor Interaction ◽  
Prognostic Impact ◽  
Tumor Microenvironments ◽  
Cell Immunity

Abstract Background: Human papillomavirus (HPV) is the major cause of cervical cancer (CC) etiology; its contribution to head and neck cancer (HNC) incidence is steadily increasing. As individual patients’ response to the treatment of HPV-associated cancer is variable, there is a pressing need for the identification of biomarkers for risk stratification that can help determine the intensity of treatment. Methods: We have previously reported a novel prognostic and predictive indicator (HPPI) scoring system in HPV-associated cancers regardless of the anatomical locations by analyzing the TCGA and GEO databases. In this study, we comprehensively investigated the association of group-specific expression patterns of common differentially expressed genes (DEGs) between high-risk and low-risk groups in HPV-associated CC and HNC, identifying a molecular biomarkers and pathways for the risk stratification. Results: Among the identified 174 DEGs, expression of the genes associated with extracellular matrix (ECM)-receptor interaction pathway (ITGA5, ITGB1, LAMB1, LAMC1) were increased in high-risk groups in both HPV-associated CC and HNC while expression of the genes associated with the T-cell immunity (CD3D, CD3E, CD8B, LCK, and ZAP70) were decreased vise versa. The individual genes showed statistically significant prognostic impact on HPV-associated cancers but not on HPV-negative cancers. The expression levels of identified genes were similar between HPV-negative and HPV-associated high-risk groups with distinct expression patterns only in HPV-associated low-risk groups. Each group of genes showed negative correlations, and distinct patterns of immune cell infiltration in tumor microenvironments. Conclusion: These results identify molecular biomarkers and pathways for risk stratification in HPV-associated cancers regardless of anatomical locations. The identified targets are selectively working in only HPV-associated cancers, but not in HPV-negative cancers indicating possibility of the selective targets governing HPV-infective tumor microenvironments.

scholarly journals Pre-hospital risk assessment in suspected non-ST-elevation acute coronary syndrome: A prospective observational study

2018 ◽  
Vol 9 (1_suppl) ◽  
pp. 5-12 ◽  
Author(s):  
Dominique N van Dongen ◽  
Rudolf T Tolsma ◽  
Marion J Fokkert ◽  
Erik A Badings ◽  
Aize van der Sluis ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Heart Score ◽  
Coronary Syndrome ◽  
Risk Patients ◽  
St Elevation

Background: Pre-hospital risk stratification of non-ST-elevation acute coronary syndrome (NSTE-ACS) by the complete HEART score has not yet been assessed. We investigated whether pre-hospital risk stratification of patients with suspected NSTE-ACS using the HEART score is accurate in predicting major adverse cardiac events (MACE). Methods: This is a prospective observational study, including 700 patients with suspected NSTE-ACS. Risk stratification was performed by ambulance paramedics, using the HEART score; low risk was defined as HEART score ⩽ 3. Primary endpoint was occurrence of MACE within 45 days after inclusion. Secondary endpoint was myocardial infarction or death. Results: A total of 172 patients (24.6%) were stratified as low risk and 528 patients (75.4%) as intermediate to high risk. Mean age was 53.9 years in the low risk group and 66.7 years in the intermediate to high risk group ( p<0.001), 50% were male in the low risk group versus 60% in the intermediate to high risk group ( p=0.026). MACE occurred in five patients in the low risk group (2.9%) and in 111 (21.0%) patients at intermediate or high risk ( p<0.001). There were no deaths in the low risk group and the occurrence of acute myocardial infarction in this group was 1.2%. In the high risk group six patients died (1.1%) and 76 patients had myocardial infarction (14.4%). Conclusions: In suspected NSTE-ACS, pre-hospital risk stratification by ambulance paramedics, including troponin measurement, is accurate in differentiating between low and intermediate to high risk. Future studies should investigate whether transportation of low risk patients to a hospital can be avoided, and whether high risk patients benefit from immediate transfer to a hospital with early coronary angiography possibilities.

scholarly journals Establishment of an Ultrasound Malignancy Risk Stratification Model for Thyroid Nodules Larger Than 4 cm

2021 ◽  
Vol 11 ◽  
Author(s):  
Xuehua Xi ◽  
Ying Wang ◽  
Luying Gao ◽  
Yuxin Jiang ◽  
Zhiyong Liang ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Low Risk ◽  
Validation Dataset ◽  
Intermediate Risk ◽  
Solid Mass ◽  
Incidence And Mortality ◽  
Risk Stratification Model

BackgroundThe incidence and mortality of thyroid cancer, including thyroid nodules &gt; 4 cm, have been increasing in recent years. The current evaluation methods are based mostly on studies of patients with thyroid nodules &lt; 4 cm. The aim of the current study was to establish a risk stratification model to predict risk of malignancy in thyroid nodules &gt; 4 cm.MethodsA total of 279 thyroid nodules &gt; 4 cm in 267 patients were retrospectively analyzed. Nodules were randomly assigned to a training dataset (n = 140) and a validation dataset (n = 139). Multivariable logistic regression analysis was applied to establish a nomogram. The risk stratification of thyroid nodules &gt; 4 cm was established according to the nomogram. The diagnostic performance of the model was evaluated and compared with the American College Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS), Kwak TI-RADS and 2015 ATA guidelines using the area under the receiver operating characteristic curve (AUC).ResultsThe analysis included 279 nodules (267 patients, 50.6 ± 13.2 years): 229 were benign and 50 were malignant. Multivariate regression revealed microcalcification, solid mass, ill-defined border and hypoechogenicity as independent risk factors. Based on the four factors, a risk stratified clinical model was developed for evaluating nodules &gt; 4 cm, which includes three categories: high risk (risk value = 0.8-0.9, with more than 3 factors), intermediate risk (risk value = 0.3-0.7, with 2 factors or microcalcification) and low risk (risk value = 0.1-0.2, with 1 factor except microcalcification). In the validation dataset, the malignancy rate of thyroid nodules &gt; 4 cm that were classified as high risk was 88.9%; as intermediate risk, 35.7%; and as low risk, 6.9%. The new model showed greater AUC than ACR TI-RADS (0.897 vs. 0.855, p = 0.040), but similar sensitivity (61.9% vs. 57.1%, p = 0.480) and specificity (91.5% vs. 93.2%, p = 0.680).ConclusionMicrocalcification, solid mass, ill-defined border and hypoechogenicity on ultrasound may be signs of malignancy in thyroid nodules &gt; 4 cm. A risk stratification model for nodules &gt; 4 cm may show better diagnostic performance than ACR TI-RADS, which may lead to better preoperative decision-making.

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