Prospective validation of a risk model for first cycle neutropenic complications in patients receiving cancer chemotherapy

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 8561-8561 ◽  
Author(s):  
G. H. Lyman ◽  
N. M. Kuderer ◽  
J. Crawford ◽  
D. A. Wolff ◽  
E. Culakova ◽  
...  
Keyword(s):  
High Risk ◽  
Test Performance ◽  
Risk Model ◽  
Selection Process ◽  
Low Risk ◽  
Validation Dataset ◽  
Cancer Type ◽  
General Applicability ◽  
C Statistic ◽  
Increased Risk

8561 Background: A nationwide, prospective cohort study was undertaken to develop and validate a risk model for neutropenic complications (NC) in cancer patients receiving chemotherapy. Methods: 3,596 patients initiating a new chemotherapy regimen with solid tumors or lymphoma were registered at 115 randomly selected sites. Data on at least 1 cycle of chemotherapy were available on 3,468. A logistic regression model for cycle 1 NC was derived and then validated using a split sample random selection process. Results: The risk of cycle 1 NC ranged from 5.5%-30.2%, averaging 18.5% across tumor types. No significant differences in distribution of NC or predictive factors were observed between the derivation dataset (n=2,592) or the validation dataset (n=876). Major independent baseline clinical risk factors for cycle 1 NC in the derivation model include: prior chemotherapy (P=.044), number of myelosuppressive agents (P<.0001), anthracycline-based regimens (P<.0001), planned delivery >85% of standard (P<.0001), cancer type (P<.0001), concurrent antibiotics (P=.023) or phenothiazines (P=.006), abnormal alkaline phosphatase (P=.002), elevated bilirubin (P=.031), low platelets (P=.004), elevated glucose (P=.023) and reduced glomerular filtration rate (P=.013). Reduced risk of cycle 1 NC was associated with primary prophylaxis with a myeloid growth factor (P<.0001). Model R2 was 0.273 and c-statistic 0.80 [95% CI: 0.78–0.82; P<.0001]. At the median predicted risk of cycle 1 NC of 11%, model test performance consisted of: sensitivity 84%; specificity 57% and diagnostic odds ratio (DOR) 7.2 while cycle 1 NC risk was 31% and 6% among high risk and low risk half, respectively. The model performed well in the smaller validation dataset with a model R2 of 0.354 and c-statistic of 0.84 [95% CI: 0.81–0.87, P<.0001]. Test performance of the model in the validation sample included: sensitivity 90%; specificity 62%; DOR 14.1 and risks of 35% and 4% in high risk and low risk patients, respectively. Conclusions: Validation in a randomly selected patient sample suggests that this model has general applicability in identifying patients at increased risk for NC. Further validation in other independent cancer patient populations receiving chemotherapy is planned. [Table: see text]

A validated risk model for early neutropenic events in older cancer patients receiving systemic chemotherapy

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 9036-9036 ◽  
Author(s):  
M. Shayne ◽  
E. Culakova ◽  
D. C. Dale ◽  
M. S. Poniewierski ◽  
D. A. Wolff ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Predictive Value ◽  
Risk Model ◽  
Low Risk ◽  
Severe Neutropenia ◽  
Validation Dataset ◽  
Cancer Type ◽  
C Statistic ◽  
Risk Patients ◽  
Older Cancer Patients

9036 Background: A prospective, nationwide study was undertaken to develop and validate a risk model for early neutropenic events (NE) in older cancer patients undergoing chemotherapy. Methods: 1,386 patients =65 years of age with lung, breast, colorectal, ovarian cancer or lymphoma were prospectively registered at 117 randomly selected sites. Data on up to 4 cycles were collected upon initiation of chemotherapy. A logistic regression model for cycle 1 NE consisting of febrile neutropenia (FN; fever/infection and absolute neutrophil count nadir <1x109/L) or severe neutropenia (SN; neutrophils <.5x109/L) was derived on 1,378 patients with available data. Validation was performed using a split sample random selection process. Results: No significant differences in distribution of NE or predictive factors were observed between derivation dataset (n=922) and validation dataset (n=464). Major independent baseline clinical risk factors for cycle 1 NE in the derivation model (DM) included: anthracycline based regimens (p<.001), non-chemotherapy immune-modulatory agents (p=.003), elevated bilirubin (p=.016), reduced glomerular filtration rate (p<.001), cancer type (p=.02), planned relative dose intensity =85% (p=.027), and regimens containing cyclophosphamide (p<.001), etoposide (p=.002) or ifosfamide (p=.032). Reduced risk of cycle 1 NE was associated with myeloid growth factor (MGF) prophylaxis (p<.001). DM R2 was 0.478 and c-statistic 0.88 [95% CI 0.86–0.91; p<.001]. At median predicted risk of cycle 1 NE of 7%, model test performance (MTP) showed: sensitivity 90%; specificity 59%; and predictive value positive and negative of 32% and 97%, respectively. Cycle 1–4 FN risk in the DM was 16.6% and 3.3% among high and low risk patients, respectively. The validation model (VM) R2 was 0.508 and c-statistic 0.89 [95% CI: 0.86–0.93; p<.001]. MTP in the VM demonstrated: sensitivity 90%; specificity 65%; predictive value positive and negative of 36% and 97%, respectively. Cycle 1–4 FN risk in the VM was 16.8% and 1.6% in high and low risk patients, respectively. Conclusions: This validated risk model demonstrated good discrimination between older cancer patients at decreased risk for NE, and those at increased risk who may benefit from targeted prophylaxis with MGF. No significant financial relationships to disclose.

Prospective Validation of a Predictive Model for Early Anemia in Patients Receiving Cancer Chemotherapy.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 460-460
Author(s):  
Gary H. Lyman ◽  
David C. Dale ◽  
Nicole M. Kuderer ◽  
Debra A. Wolff ◽  
Eva Culakova ◽  
...  
Keyword(s):  
Predictive Model ◽  
Odds Ratio ◽  
Risk Model ◽  
Diagnostic Odds Ratio ◽  
Performance Characteristics ◽  
Model Fit ◽  
Lower Sensitivity ◽  
Cancer Type ◽  
C Statistic ◽  
Increased Risk

Abstract Anemia represents the most common hematological toxicity in cancer patients receiving chemotherapy and is associated with considerable morbidity and cost. ASH/ASCO guidelines call for intervention at a hemoglobin (hgb)&lt;10 g/dL. A meta-analysis has demonstrated the clinical value of early (hgb≥10 g/dL) versus late (hgb&lt;10 g/dL) intervention with an erythroid stimulating protein (ESP). An anemia predictive model may help guide intervention sufficiently early in the course of chemotherapy when it can be most effective. A prospective, nationwide study was undertaken to develop and validate risk models for hematologic toxicities of chemotherapy. The analysis presented here is based on 3,640 patients with cancer of the breast, lung, colon and ovary or malignant lymphoma receiving a new regimen prospectively registered at 117 randomly selected U.S. practices. A logistic regression model for hgb&lt;10 g/dL was developed and validated using a 2:1 random selection split sample methodology. Predictive performance characteristics were estimated [±95% CL]. Nadir hgb over 4 cycles of chemotherapy was &lt;8 g/dL in 113 (3%), 8–10 in 959 (26%), 10–12 in 1,847 (51%), and ≥12 in 721 (20%). No significant differences were observed between the two populations. Independent risk factors for nadir hgb&lt;10 g/dL (ORs) were: female gender (1.66); ECOG &gt;1 (1.70); CHF (1.54); history of vascular disease (2.66); ulcer disease (2.58); COPD (1.29); connective tissue disease (1.84); advanced cancer stage (1.19); cancer type and chemotherapy based on anthracyclines (2.15), carboplatin (2.40), gemcitabine (2.48), cyclophosphamide (1.60), etoposide (2.84), topotecan (4.21), or trastuzumab (1.43), planned cycle length &gt;1 week (2.0), while normal baseline hemoglobin, platelet count and GFR were associated with a reduced risk. Model fit was good (P&lt;.001), R2 = 0.35 and c-statistic = 0.81 [.79–.83, P&lt;.0001]. Mean and median predicted risk for hgb&lt;10 g/dL were 0.29 and 0.22, respectively. An increasing risk cutpoint was associated with lower sensitivity and higher specificity. In the highest risk half, quarter and quintile of patients, hgb&lt;10 g/dL was experienced by 47% [45–50], 64% [60–68], and 70% [65–74], respectively. Model performance characteristics at the median risk included: sensitivity: 82% [78–84]; specificity: 64% [62–66]; and diagnostic odds ratio: 7.80 [6.28–9.68]. Most covariates significant in the derivation model remained significant in the validation population. Model fit was good [P&lt;.001] with an R2=.40 and a c-statistic of 0.83 [.81–.86; P&lt;.001]. In the highest risk half, quarter and quintile of patients, hgb&lt;10 g/dL was experienced by 50% [46–54], 67% [62–71], and 70% [65–75], respectively. Test performance of the validation model at the median risk included: sensitivity of 83% [79–86], specificity of 62% [59–66], and a diagnostic odds ratio of 7.90 [5.84–10.69]. Based on good performance characteristics, this validated prediction model identified chemotherapy patients at increased risk for developing clinically significant anemia who may be candidates for early targeted intervention with an ESP. A conditional risk model for subsequent risk of hgb&lt;10 g/dL which includes changes during cycle 1 of chemotherapy has also been developed and will be presented.

Final Risk Prediction Model for Neutropenic Complications in Patients Receiving Cancer Chemotherapy

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1312-1312 ◽  
Author(s):  
Gary H. Lyman ◽  
Jeffrey Crawford ◽  
Nicole M. Kuderer ◽  
Debra Wolff ◽  
Eva Culakova ◽  
...  
Keyword(s):  
High Risk ◽  
Cancer Chemotherapy ◽  
Test Performance ◽  
Risk Model ◽  
Cumulative Risk ◽  
Risk Groups ◽  
Low Risk ◽  
Performance Characteristics ◽  
Risk Patients ◽  
Predicted Risk

Abstract Introduction: Neutropenic complications including severe neutropenia (SN) and febrile neutropenia (FN) represent major dose-limiting toxicities of cancer chemotherapy. A prospective study was undertaken to develop and validate a predictive model for neutropenic events in patients receiving cancer chemotherapy. The final risk model based on mature data is presented. Methods: Between 2002 and 2006, 4458 consenting patients starting a new chemotherapy regimen at 115 randomly selected community oncology practices throughout the United States were enrolled including 3760 with cancers of breast, lung, colorectum, ovary and malignant lymphoma receiving at least one cycle of treatment. Using a 2:1 random split sample methodlogy, a risk model for first-cycle SN or FN was derived and validated based on multivariate logistic regression analysis incorporating pretreatment variable information. The cumulative risk of events over the initial 120 days of treatment was estimated by the method of Kaplan and Meier. High and low risk groups were defined on the basis of the median predicted risk and model test performance characteristics were estimated. Results: Following adjustment for cancer type, important predictive factors included: older age, prior chemotherapy, abnormal hepatic or renal function, low pretreatment white blood count, immunosuppressive medications and planned relative dose intensity &gt;85% as well as use of several specific chemotherapeutic agents including anthracyclines, taxanes, alkylating agents, topoisomerase inhibitors, gemcitabine or vinorelbine. Lower risk of neutropenic complications were associated with primary prophylaxis with a colony-stimulating factor (CSF). Individual risk estimates based on the model ranged from 0–89% with mean and median of 19.2% and 10.1%, respectively. The model was associated with an R2 of 0.34 and demonstrated excellent discrimination with a c-statistic of 0.833 [95% CI: 0.813–0.852, P&lt;.001]. The model predicted risk of cycle 1 SN or FN in high and low risk groups was of 34% and 4%, respectively. The cumulative risk of FN over the initial 120 days was 20% in high risk patients and 5% in low risk patients. Model performance included sensitivity and specificity of 90% and 59%, respectively, with a model diagnostic odds ratio of 12.8 [95% CI: 9.3, 17.7]. Application of the model to the validation data set was associated with similar excellent discrimination and test performance characteristics. CSF prophylaxis applied to high risk patients was associated with significantly lower risk of FN over repeated cycles of chemotherapy [HR = 0.51; 95% CI: 0.35 – 0.75; P &lt;.0001]. Nearly two-thirds of patients classified as high risk but who did not receive primary CSF prophylaxis went on to receive secondary use during subsequent cycles. Discussion: Based on excellent test performance characteristics, the risk model identified patients with a cumulative incidence of FN of at least 20% who are candidates for targeted prophylaxis with a CSF. Further validation of this model in actual clinical practice is currently underway.

External validation of two predictive scores of chemotherapy toxicities among older patients with solid cancer, from ELCAPA prospective cohort.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 12050-12050
Author(s):  
Maxime Frelaut ◽  
Philippe Caillet ◽  
Stephane Culine ◽  
Elena Paillaud ◽  
Christophe Tournigand ◽  
...  
Keyword(s):  
High Risk ◽  
Prospective Cohort ◽  
Older Patients ◽  
Risk Groups ◽  
Low Risk ◽  
Solid Cancer ◽  
Cancer Type ◽  
Severe Toxicity ◽  
Increased Risk ◽  
Grade 3

12050 Background: Severe chemotherapy toxicities are frequent among older patients, and may have a major impact on mortality, comorbidities, and quality of life. Two scores were developed to predict severe toxicities: Chemotherapy Risk Assessment Scale for High-age patients (CRASH) score, and Cancer and Aging Research Group Study (CARG) score. The main objective of the present study was to evaluate the predictive value of both scores on an external cohort. Secondary objective was to identify individual predictive factors of severe chemotherapy toxicities. Methods: The Elderly Cancer Patients (ELCAPA) survey consists in a prospective cohort including patients aged 70 years or older referred for a geriatric assessment (GA) before anticancer treatment, such as chemotherapy for solid cancer. CARG and CRASH score were retrospectively collected. Main endpoint was grade 3/4/5 toxicities for CARG-score, hematologic grade 4/5 and non-hematologic grade 3/4/5 toxicities for CRASH-score. Calibration and discrimination (Area Under ROC Curve, AUC) were evaluated. Results: From July 2010 to March 2017, 248 patients were included. Among them, 150 (61%) experienced severe toxicity as defined in CARG study, and 126 (51%) as defined in CRASH study. There was no increased risk of toxicity in intermediate and high risk groups of CARG-score compared to low risk group (OR = 0.3, IC95% [0.1 – 1.4], p= 0.1; and OR = 0.4, IC95%[0.1 – 1.7], p= 0.2 respectively, AUC-ROC = 0.55). Similarly, there was no more risk of severe toxicities in intermediate low, intermediate high, and high risk groups compared to low risk groups of CRASH combined score (respectively OR = 1, IC95% [0.3 – 3.6], p= 0.99; OR = 1, IC95% [0.3 – 3.4], p= 0.9; OR = 1.5, IC95% [0.3 – 8.1], p= 0.67; AUC-ROC = 0.52). A multivariate predictive model including cancer type, performance status (PS 0 vs. PS 1-2), number of severe comorbidities (Cumulative Illness Rating Scale for Geriatrics, CIRS-G, ≥1 grade 3 or 4 comorbidity), body mass index (BMI > 25 kg/m² protective vs. normal BMI), and Chemotox score (1 vs. 0) had an AUC of 0.78. Conclusions: Neither CARG nor CRASH score was predictive of severe chemotherapy toxicities in the ELCAPA cohort. There is a need to identify new predictors of chemotherapy toxicity in older patients with solid cancers.

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

scholarly journals Multidimensional dynamic healthcare personnel (HCP)-centric model from a low-income and middle-income country to support and protect COVID-19 warriors: a large prospective cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e043837
Author(s):  
Usha Dutta ◽  
Anurag Sachan ◽  
Madhumita Premkumar ◽  
Tulika Gupta ◽  
Swapnajeet Sahoo ◽  
...  
Keyword(s):  
High Risk ◽  
Low Income ◽  
Tertiary Care ◽  
Low Risk ◽  
Transmission Risk ◽  
Healthcare Personnel ◽  
Risk Zone ◽  
Increased Risk ◽  
Medium Risk ◽  
Risk Zones

ObjectivesHealthcare personnel (HCP) are at an increased risk of acquiring COVID-19 infection especially in resource-restricted healthcare settings, and return to homes unfit for self-isolation, making them apprehensive about COVID-19 duty and transmission risk to their families. We aimed at implementing a novel multidimensional HCP-centric evidence-based, dynamic policy with the objectives to reduce risk of HCP infection, ensure welfare and safety of the HCP and to improve willingness to accept and return to duty.SettingOur tertiary care university hospital, with 12 600 HCP, was divided into high-risk, medium-risk and low-risk zones. In the high-risk and medium-risk zones, we organised training, logistic support, postduty HCP welfare and collected feedback, and sent them home after they tested negative for COVID-19. We supervised use of appropriate personal protective equipment (PPE) and kept communication paperless.ParticipantsWe recruited willing low-risk HCP, aged <50 years, with no comorbidities to work in COVID-19 zones. Social distancing, hand hygiene and universal masking were advocated in the low-risk zone.ResultsBetween 31 March and 20 July 2020, we clinically screened 5553 outpatients, of whom 3012 (54.2%) were COVID-19 suspects managed in the medium-risk zone. Among them, 346 (11.4%) tested COVID-19 positive (57.2% male) and were managed in the high-risk zone with 19 (5.4%) deaths. One (0.08%) of the 1224 HCP in high-risk zone, 6 (0.62%) of 960 HCP in medium-risk zone and 23 (0.18%) of the 12 600 HCP in the low-risk zone tested positive at the end of shift. All the 30 COVID-19-positive HCP have since recovered. This HCP-centric policy resulted in low transmission rates (<1%), ensured satisfaction with training (92%), PPE (90.8%), medical and psychosocial support (79%) and improved acceptance of COVID-19 duty with 54.7% volunteering for re-deployment.ConclusionA multidimensional HCP-centric policy was effective in ensuring safety, satisfaction and welfare of HCP in a resource-poor setting and resulted in a willing workforce to fight the pandemic.

scholarly journals Polygenic risk scores predict diabetic complications and their response to therapy

2019 ◽  
Author(s):  
J. Tremblay ◽  
M. Haloui ◽  
F. Harvey ◽  
R. Tahir ◽  
F.-C. Marois-Blanchet ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Risk Group ◽  
Intensive Therapy ◽  
Cardiovascular Death ◽  
Response To Therapy ◽  
Low Risk ◽  
Number Needed To Treat ◽  
Genome Wide Association Studies ◽  
Increased Risk

AbstractType 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction can lead to timely intervention and better outcomes. Through summary statistics of meta-analyses of published genome-wide association studies performed in over 1.2 million of individuals, we combined 9 PRS gathering genomic variants associated to cardiovascular and renal diseases and their key risk factors into one logistic regression model, to predict micro- and macrovascular endpoints of diabetes. Its clinical utility in predicting complications of diabetes was tested in 4098 participants with diabetes of the ADVANCE trial followed during a period of 10 years and replicated it in three independent non-trial cohorts. The prediction model adjusted for ethnicity, sex, age at onset and diabetes duration, identified the top 30% of ADVANCE participants at 3.1-fold increased risk of major micro- and macrovascular events (p=6.3×10−21 and p=9.6×10−31, respectively) and at 4.4-fold (p=6.8×10−33) increased risk of cardiovascular death compared to the remainder of T2D subjects. While in ADVANCE overall, combined intensive therapy of blood pressure and glycaemia decreased cardiovascular mortality by 24%, the prediction model identified a high-risk group in whom this therapy decreased mortality by 47%, and a low risk group in whom the therapy had no discernable effect. Patients with high PRS had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. This novel polygenic prediction model identified people with diabetes at low and high risk of complications and improved targeting those at greater benefit from intensive therapy while avoiding unnecessary intensification in low-risk subjects.

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.

scholarly journals Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients with Acute Heart Failure

2021 ◽  
Author(s):  
Faisal Rahman ◽  
Noam Finkelstein ◽  
Anton Alyakin ◽  
Nisha Gilotra ◽  
Jeff Trost ◽  
...  
Keyword(s):  
Heart Failure ◽  
High Risk ◽  
Cardiogenic Shock ◽  
Risk Model ◽  
Acute Decompensated Heart Failure ◽  
Clinical Care ◽  
Delayed Diagnosis ◽  
Low Risk ◽  
Cohort Identification ◽  
High Risk Cohort

Abstract Objective: Despite technological and treatment advancements over the past two decades, cardiogenic shock (CS) mortality has remained between 40-60%. A number of factors can lead to delayed diagnosis of CS, including gradual onset and nonspecific symptoms. Our objective was to develop an algorithm that can continuously monitor heart failure patients, and partition them into cohorts of high- and low-risk for CS.Methods: We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Healthcare system. Our cohort identification approach is based on logistic regression, and makes use of vital signs, lab values, and medication administrations recorded during the normal course of care. Results: Our algorithm identified patients at high-risk of CS. Patients in the high-risk cohort had 10.2 times (95% confidence interval 6.1-17.2) higher prevalence of CS than those in the low-risk cohort. Patients who experienced cardiogenic shock while in the high-risk cohort were first deemed high-risk a median of 1.7 days (interquartile range 0.8 to 4.6) before cardiogenic shock diagnosis was made by their clinical team. Conclusions: This risk model was able to predict patients at higher risk of CS in a time frame that allowed a change in clinical care. Future studies need to evaluate if CS analysis of high-risk cohort identification may affect outcomes.

scholarly journals A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

2020 ◽  
Author(s):  
Yi Ding ◽  
Tian Li ◽  
Min Li ◽  
Tuersong Tayier ◽  
MeiLin Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Clinical Information ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Positive Regulation ◽  
Risk Patients ◽  
Cox Analysis

Abstract Background: Autophagy and long non-coding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma.Methods: We downloaded RNA-sequencing data and clinical information of melanoma from The Cancer Genome Atlas. The co-expression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate COX regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups.Results: According to the results of the univariate COX analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate COX analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group (p<0.001). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism.Conclusion: The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNAs risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

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