scholarly journals A logistic analysis prediction model of TMJ condylar erosion in patients with TMJ arthralgia

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rüdiger Emshoff ◽  
Annika Bertram ◽  
Linus Hupp ◽  
Ansgar Rudisch
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Univariate Analysis ◽  
Individual Risk ◽  
Multivariate Logistic Regression ◽  
Posterior Teeth ◽  
Disk Displacement ◽  
Axis I ◽  
Tmj Pain ◽  
Pain Patients

Abstract Background In terms of diagnostic and therapeutic management, clinicians should adequately address the frequent aspects of temporomandibular joint (TMJ) osteoarthritis (OA) associated with disk displacement. Condylar erosion (CE) is considered an inflammatory subset of OA and is regarded as a sign of progressive OA changes potentially contributing to changes in dentofacial morphology or limited mandibular growth. The purpose of this study was to establish a risk prediction model of CE by a multivariate logistic regression analysis to predict the individual risk of CE in TMJ arthralgia. It was hypothesized that there was a closer association between CE and magnetic resonance imaging (MRI) indicators. Methods This retrospective paired-design study enrolled 124 consecutive TMJ pain patients and analyzed the clinical and TMJ-related MRI data in predicting CE. TMJ pain patients were categorized according to the research diagnostic criteria for temporomandibular disorders (RDC/TMD) Axis I protocol. Each patient underwent MRI examination of both TMJs, 1–7 days following clinical examination. Results In the univariate analysis analyses, 9 influencing factors were related to CE, of which the following 4 as predictors determined the binary multivariate logistic regression model: missing posterior teeth (odds ratio [OR] = 1.42; P = 0.018), RDC/TMD of arthralgia coexistant with disk displacement without reduction with limited opening (DDwoR/wLO) (OR = 3.30, P = 0.007), MRI finding of disk displacement without reduction (OR = 10.96, P < 0.001), and MRI finding of bone marrow edema (OR = 11.97, P < 0.001). The model had statistical significance (chi-square = 148.239, Nagelkerke R square = 0.612, P < 0.001). Out of the TMJs, 83.9% were correctly predicted to be CE cases or Non-CE cases with a sensitivity of 81.4% and a specificity of 85.2%. The area under the receiver operating characteristic curve was 0.916. Conclusion The established prediction model using the risk factors of TMJ arthralgia may be useful for predicting the risk of CE. The data suggest MRI indicators as dominant factors in the definition of CE. Further research is needed to improve the model, and confirm the validity and reliability of the model.

Clinical prediction model for acute inpatient complications after traumatic cervical spinal cord injury: a subanalysis from the Surgical Timing in Acute Spinal Cord Injury Study

2012 ◽  
Vol 17 (Suppl1) ◽  
pp. 46-51 ◽  
Author(s):  
Jefferson R. Wilson ◽  
Paul M. Arnold ◽  
Anoushka Singh ◽  
Sukhvinder Kalsi-Ryan ◽  
Michael G. Fehlings
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Logistic Regression ◽  
Prediction Model ◽  
Univariate Analysis ◽  
High Energy ◽  
Acute Spinal Cord Injury ◽  
Multivariate Logistic Regression ◽  
Surgical Timing ◽  
Cord Injury

Object While the majority of existing reports focus on complications sustained during the chronic stages after traumatic spinal cord injury (SCI), the objective in the current study was to characterize and quantify acute inpatient complications. In addition, the authors sought to create a prediction model using clinical variables documented at hospital admission to predict acute complication development. Methods Analyses were based on data from the Surgical Timing in Acute Spinal Cord Injury Study (STASCIS) data registry, which contains prospective information on adult patients with cervical SCIs who were enrolled at 6 North American centers over a 7-year period. All patients who underwent a standardized American Spinal Injury Association (ASIA) neurological examination within 24 hours of injury and whose follow-up information was available at the acute hospital discharge were included in the study. For purposes of classification, complications were divided into 5 major categories: 1) cardiopulmonary, 2) surgical, 3) thrombotic, 4) infectious, and 5) decubitus ulcer development. Univariate statistical analyses were performed to determine the relationship between complication occurrence and individual demographic, injury, and treatment variables. Multivariate logistic regression was subsequently performed to create a complication prediction model. Model discrimination was judged according to the area under the receiver operating characteristic curve. Results Complete complication information was available for 411 patients at the acute care discharge. One hundred sixty patients (38.9%) experienced 240 complications. The mean age among those who experienced at least one complication was 45.9 years, as compared with 43.5 years among those who did not have a complication (p = 0.18). In the univariate analysis, patients with complications were less likely to receive steroids at admission (p = 0.01), had a greater severity of neurological injury as indicated by the ASIA Impairment Scale (AIS) grade at presentation (p < 0.01), and a higher frequency of significant comorbidity (p = 0.04). In a multivariate logistic regression model, a severe initial AIS grade (p < 0.01), a high-energy injury mechanism (p = 0.07), an older age (p = 0.05), the absence of steroid administration (p = 0.02), and the presence of comorbid illness (p = 0.02) were associated with a greater likelihood of complication development during the period of acute hospitalization. The area under the curve value for the full model was 0.75, indicating acceptable predictive discrimination. Conclusions These results will help clinicians to identify patients with cervical SCIs at greatest risk for complication development and thus allowing for the institution of aggressive complication prevention measures.

scholarly journals Risk factors and prediction model construction of post-operative atrial fibrillation after thoracic surgery in Chinese patients

2021 ◽  
Author(s):  
Yunling Lin ◽  
Jianmin Sun ◽  
Xun Yuan ◽  
Hui Liu
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Logistic Regression ◽  
Thoracic Surgery ◽  
Prediction Model ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Chinese Patients ◽  
Multivariate Logistic Regression ◽  
Post Operative Atrial Fibrillation

IntroductionThe purpose of this study was to analyze the risk factors of post-operative atrial fibrillation (POAF) after thoracic surgery, and to build a predictive model for accurate preoperative identification of high-risk patients.Material and methodsIn this study, data of 2072 patients with pulmonary masses and esophageal cancer who attended our hospital in the period from January 1, 2017 to December 31, 2018 were analyzed retrospectively. According to whether AF occurred after the operation, the patients were divided into atrial fibrillation (AF) and non-AF (NAF) groups. The general information (age, sex, height, etc.), previous medical history (chronic lung disease, hypertension, etc.), medication history, preoperative ultrasound and cardiogram results, and preoperative and postoperative electrocardiogram (ECG) were collected. The operation mode, resection scope, histopathology and hospitalization were recorded. Univariate and multivariate logistic regression were used to screen out the risk factors of AF and establish a prediction model.ResultsThe incidence of POAF was 5.98%. Univariate analysis showed that sex, age, body mass index, left atrial diameter and operation organ were the risk factors of POAF. The above factors were included in the multivariate logistic regression analysis, and the results showed that male sex, age, anteroposterior diameter of left atrium and surgical organs were related to POAF. On this basis, a POAF prediction model was constructed, which had good discrimination and calibration. The area under the curve (AUC) is 0.784 with 95% CI: 0.746–0.822.ConclusionsThe prediction model of POAF based on the risk factors selected in this study can accurately predict the occurrence of AF after thoracic surgery.

MO558ROLE OF ANEMIA ON PROGRESSION AND CLINICAL OUTCOMES OF CKD: A PATIENT-LEVEL, RETROSPECTIVE, COHORT ANALYSIS OF A CKD OUTPATIENTS POPULATION

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Sara Fernandes ◽  
Beatriz Donato ◽  
Adriana Paixão Fernandes ◽  
Luís Falcão ◽  
Mário Raimundo ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Cohort Analysis ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Ckd Progression ◽  
Patient Level

Abstract Background and Aims Anemia is a well-know complication of Chronic Kidney Disease (CKD) and it seems to contribute for deterioration of kidney function. Experimental data suggest that anemia produces hypoxia of tubular cells which leads to tubulointerstitial damage resulting on CKD progression. Other mechanism described is that red blood cells have antioxidant properties that prevent the damage of tubulointerstitial cells and glomerulosclerosis from oxidative stress. There aren’t many observational studies that evaluated the association between anemia and progression of CKD. Therefore, our aim was to evaluate the association of anemia and CKD progression and its association outcomes in an outpatient ND-CKD population. Method We conduct a retrospective, patient-level, cohort analysis of all adult ND-CKD patients evaluated in an outpatient nephrology clinic over a 6 years period. The follow up time was at least 12 months. Anemia was defined according to the WHO definition (hemoglobin [hb] &lt; 13.0 g/dL in men and 12.0 g/dL in women). Progression of CKD was defined by one of the following criteria: decline in eGFR (CKD-EPI) superior to 5 ml/min/1.73 m2/year; duplication of serum creatinine or the need renal replacement therapy. Demographics and clinical data were also accessed. Results Out of 3008 patients referred to the nephrology clinic, 49.9% had anemia (mean age 71.9±15.9 years; 50.4% male; 92% white; mean follow-up time of 2.3±1.2 years). The mean Hb was 11.8 ±1.9 g/dL. Important cardiovascular comorbidities in patients with anemia were arterial hypertension (86.7%), obesity (65.5%), Diabetes Mellitus (DM) (52%) and dyslipidemia (46%). In univariate analysis, mortality was associated with anemia (36.9 vs 13.0%, p&lt;0.001), obesity (30.1 vs 21.8%, p&lt;0.001) and DM (30.1 vs 21.1%, p&lt;0.001). Of the patients with anemia, 738 met the criteria for CKD progression. In univariate analysis, CKD progression was associated with anemia (49.6 vs 43.9%, p=0.002), male gender (49.5 vs 43.6% p= 0.001); DM (49.6 vs 44.8 % p=0.009) and hypertension (47.9 vs 42.3% p=0.0018). In multivariate logistic regression analysis, anemia emerged was an independent predictor of CKD progression (OR 1.435, CI 95% 1.21-1.71, p&lt;0,001). Comparing hb values intervals (hb ≤10g/dl; hb10-12 g/dL; hb ≥12 g/dL), in the multivariate logistic regression analysis, hb ≤10g/dl was not associated with CKD progression and hb value between 10-12 g/dL was associated (OR 1,486, CI 95% 1.23-1.80, p&lt;0,001), when compared with the group with hb ≥12g/dL. In multivariate logistic regression analysis, the independent predictors of mortality were: older age (OR per 1 year increase: 1.048, 95% CI 95% 1.04-1.06, p&lt;0.001); arterial hypertension (OR 0.699 CI 95% 0.51-0.96, p=0.0029); obesity (OR 0.741, CI 95% 0.60-0.91, p=0.004) and hb value (OR per 1 g/dL decrease: 1.301, CI 95% 1.23-1.38, p&lt;0.001). Cardiovascular events were correlated with Hb levels between 10-12 g/dL (univariate analysis: OR 2.021, CI 95% 1.27-3.22, P=0.003), but not with the group with hb≤10 g/dL (univariate analysis: OR 1.837, CI 95% 0.96-3.51, P=0.066), having the group with hb ≥12g/dL was reference. Anemia was strongly associated with hospitalizations (multivariate logistic regression analysis: OR per 1 g/dL of Hb decrease: 1.256 CI 95% 1.12-1.32 p&lt;0.001), and this strong association was also observed on the groups with hb hb≤10 g/dL (multivariate logistic regression analysis: OR 3.591 CI 95% 32.67-4.84 p&lt;0.001) and between 10-12 g/dL (multivariate logistic regression analysis: OR 1.678 CI 95% 1.40-2.02, p&lt;0.001) Conclusion Our study suggests that anemia, at first consultation, increases the risk for rapid CKD progression and global mortality. This study could guide us on the development of futures studies in order to prove if anemia correction can slow the progression of CKD.

scholarly journals Risk Factors for and Outcomes Associated with Clinical Isolates ofEscherichia coliandKlebsiellaSpecies Resistant to Extended-Spectrum Cephalosporins among Patients Admitted to Canadian Hospitals

2009 ◽  
Vol 20 (3) ◽  
pp. e43-e48 ◽  
Author(s):  
Marianna Ofner-Agostini ◽  
Andrew Simor ◽  
Michael Mulvey ◽  
Alison McGeer ◽  
Zahir Hirji ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Care Facility ◽  
P Value ◽  
Third Generation ◽  
Multivariate Logistic Regression ◽  
Extended Spectrum ◽  
Third Generation Cephalosporins

BACKGROUND: Clinical features associated with Gram-negative bacterial isolates with extended-spectrum beta-lactamase (ESBL)- and AmpC-mediated resistance identified in Canadian hospitals is largely unknown. The objective of the present study was to determine the demographics, risk factors and outcomes of patients with ESBL- or AmpC-mediated resistant organisms in Canadian hospitals.METHODS: Patients with clinical cultures ofEscherichia coliorKlebsiellaspecies were matched with patients with a similar organism but susceptible to third-generation cephalosporins. Molecular identification of the AmpC or ESBL was determined using a combination of polymerase chain reaction and sequence analysis. Univariate and multivariate logistic regression analysis was performed to identify variables associated with becoming a case.RESULTS: Eight Canadian hospitals identified 106 cases (ESBL/AmpC) and 106 controls. All risk factors identified in the univariate analysis as a predictor of being an ESBL/AmpC cases at the 0.20 P-value were included in the multivariate analysis. No significant differences in outcomes were observed (unfavourable responses 17% versus 15% and mortality rates 13% versus 7%, P not significant). Multivariate logistic regression found an association of becoming an ESBL/AmpC case with: previous admission to a nursing home (OR 8.28, P=0.01) or acute care facility (OR 1.96, P=0.03), length of stay before infection (OR 3.05, P=0.004), and previous use of first-generation cephalosporins (OR 2.38, P=0.02) or third-generation cephalosporins (OR 4.52, P=0.01). Appropriate antibiotics were more likely to be given to controls (27.0% versus 13.3%, P=0.05) and number of days to appropriate antibiotics was longer for cases (median 2.8 days versus 1.2 days, P=0.05).CONCLUSION: The importance of patient medical history, present admission and antibiotic use should be considered for allE coliorKlebsiellaspecies patients pending susceptibility testing results.

scholarly journals Risk factors for periprocedural ischemic stroke following endovascular treatment of intracranial aneurysms

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yisen Zhang ◽  
Chao Wang ◽  
Zhongbin Tian ◽  
Wei Zhu ◽  
Wenqiang Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Treatment Modality ◽  
Intracranial Aneurysms ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Flow Diverter ◽  
Multivariate Logistic Regression

Abstract Background The aim of this study was to comprehensively evaluate the risk factors of periprocedural ischemic stroke associated with endovascular treatment of intracranial aneurysms using a real-world database. Methods From August 2016 to March 2017, 167 patients were enrolled. Univariate analysis and multivariate logistic regression analysis were used to examine the risk factors for periprocedural ischemic stroke. Results Among the 167 cases, periprocedural ischemic stroke occurred in 20 cases (11.98%). After univariate analysis, the ischemic group had a higher proportion of large (≥ 10 mm) aneurysms than the control group (45.0% vs. 23.1%, p = 0.036). The incidence of periprocedural ischemic stroke was higher in cases treated by flow diverter (21.6%) or stent-assisted coiling (11.8%) than in cases treated by coiling only (2.7%), and the differences were statistically significant (p = 0.043). After multivariate logistic regression analysis, treatment modality was the independent risk factor for periprocedural ischemic stroke. Compared with the coiling-only procedure, flow diverter therapy was associated with a significantly higher rate of periprocedural ischemic stroke (OR 9.931; 95% CI 1.174–84.038; p = 0.035). Conclusions Aneurysm size and treatment modality were associated with periprocedural ischemic stroke. Larger aneurysms were associated with increased risk of periprocedural ischemic stroke. Flow diverter therapy was associated with significantly more periprocedural ischemic stroke than the coiling procedure alone.

scholarly journals Sero-epidemiological Investigation of the Major Abortive Bacterial Agents in Ewes of M’Sila Governorate, Algeria

2020 ◽  
Vol 77 (2) ◽  
pp. 24
Author(s):  
Laatra ZEMMOURI ◽  
Mohamed BESBACI ◽  
Adel MAMMERI ◽  
Mohamed LAFRI
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Univariate Analysis ◽  
Economic Losses ◽  
Epidemiological Investigation ◽  
Multivariate Logistic Regression ◽  
Serum Samples ◽  
Chlamydia Abortus ◽  
Breeding Management

The aim of this study was to evaluate the seroprevalence, risk factors and zoonotic threats of the major abortive bacterial agents in sheep of M’Sila Governorate. A total of 184 serum samples were collected from ewes among 16 sheep flocks and tested for Coxiella burnetti, Chlamydia abortus, Brucella spp. and Salmonella abortusovis via ELISA. Simultaneously, a questionnaire was used to collect breeding management data. Seropositive results were as follows : Coxiella burnetti (27.9%), Salmonella abortusovis (15.9%), Chlamydia abortus (10.9%) and Brucella spp. (3.8%).The use of univariate analysis and multivariate logistic regression showed a highly significant correlation between Coxiella burnetti seropositivity and presence of cats in farms (OR = 5.75; 95% CI = 1.86-19.9; p= 0.001), while promiscuity with newly introduced animals was associated to Chlamydia abortus seropositivity (OR=3.37; 95%CI=1.01-14.9; p= 0.04). Additionally to the economic losses, the dissemination of Coxiella burnetti and Brucella spp. presents uncontrollable zoonotic hazards.

scholarly journals Mitral valve thickening in acute rheumatic fever as a predictor of late valvar dysfunction

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259737
Author(s):  
Telêmaco Luis da Silva ◽  
Antonio Pazin-Filho ◽  
Minna M. D. Romano ◽  
Virgínia P. L. Ferriani ◽  
José A. Marin-Neto ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Mitral Valve ◽  
Rheumatic Fever ◽  
Odds Ratio ◽  
Acute Rheumatic Fever ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression ◽  
Valve Dysfunction ◽  
Logistic Regression Models ◽  
Important Health

Background Rheumatic heart disease (RHD) complicating acute rheumatic fever (ARF) remains an important health problem in developing countries. No definitive diagnostic test for ARF exists and the role of Doppler echocardiography (DEC) for long-term prognostic evaluation following ARF is not well established. Objective To investigate the prognostic value of DEC in patients with ARF as a predictor of chronic valve dysfunction. Methods Prospectively enrolled patients with clinical ARF had a DEC performed soon after diagnosis and repeated at 1, 3, 6 and 12 months and thereafter at every 1–2 years. We defined chronic valve dysfunction by ≥ 3 of the following: increased valve thickening, commissure fusion, subvalvular thickening, reduced leaflet mobility, non-trivial mitral and/or aortic regurgitation. We performed univariate analysis and developed multivariate logistic regression models to identify variables that may influence evolution to RHD. p <0.05 was considered significant. Results We evaluated 70(57% men) patients, 10.8±5.6 years-old during the ARF episode and followed for 95±26 months. Chronic valve dysfunction was identified in 36(51.4%) which fulfilled criteria for RHD and 10(27.8%) of them died or underwent valve surgery. Univariate analysis showed that mitral valve thickening and presence of mitral regurgitation at baseline DEC, were associated with RHD(p<0.01). Multivariate logistic regression showed that only mitral valve thickness either as a continuous (Odds-Ratio:5.8;95%CI:1.7–19.7) or as a categorical variable (Odds-Ratio:4.04;95%CI:1.06–15.3) was an independent predictor of chronic valve dysfunction. Conclusions Mitral leaflets thickening documented at the time of diagnosis of ARF is a consistent prognostic marker for the subsequent evolution to RHD.

scholarly journals Development and Validation of a Dynamic Nomogram to Predict the Risk of Neonatal White Matter Damage

2021 ◽  
Vol 14 ◽  
Author(s):  
Wenjun Cao ◽  
Chenghan Luo ◽  
Mengyuan Lei ◽  
Min Shen ◽  
Wenqian Ding ◽  
...  
Keyword(s):  
Logistic Regression ◽  
White Matter ◽  
Confidence Interval ◽  
Prediction Model ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Risk Prediction Model ◽  
Multivariate Logistic Regression ◽  
White Matter Damage ◽  
Web Based

PurposeWhite matter damage (WMD) was defined as the appearance of rough and uneven echo enhancement in the white matter around the ventricle. The aim of this study was to develop and validate a risk prediction model for neonatal WMD.Materials and MethodsWe collected data for 1,733 infants hospitalized at the Department of Neonatology at The First Affiliated Hospital of Zhengzhou University from 2017 to 2020. Infants were randomly assigned to training (n = 1,216) or validation (n = 517) cohorts at a ratio of 7:3. Multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model and web-based risk calculator based on the training cohort data. The predictive accuracy of the model was verified in the validation cohort.ResultsWe identified four variables as independent risk factors for brain WMD in neonates by multivariate logistic regression and LASSO analysis, including gestational age, fetal distress, prelabor rupture of membranes, and use of corticosteroids. These were used to establish a risk prediction nomogram and web-based calculator (https://caowenjun.shinyapps.io/dynnomapp/). The C-index of the training and validation sets was 0.898 (95% confidence interval: 0.8745–0.9215) and 0.887 (95% confidence interval: 0.8478–0.9262), respectively. Decision tree analysis showed that the model was highly effective in the threshold range of 1–61%. The sensitivity and specificity of the model were 82.5 and 81.7%, respectively, and the cutoff value was 0.099.ConclusionThis is the first study describing the use of a nomogram and web-based calculator to predict the risk of WMD in neonates. The web-based calculator increases the applicability of the predictive model and is a convenient tool for doctors at primary hospitals and outpatient clinics, family doctors, and even parents to identify high-risk births early on and implementing appropriate interventions while avoiding excessive treatment of low-risk patients.

scholarly journals Risk Factors of Thromboembolism in Lymphoma Patients Undergoing Chemotherapy and its Clinical Significance

2021 ◽  
Vol 27 ◽  
pp. 107602962110379
Author(s):  
Xiao Li ◽  
Shu-Ling Hou ◽  
Xi Li ◽  
Li Li ◽  
Ke Lian ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Clinical Significance ◽  
Logistic Regression Analysis ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Stage Iii ◽  
Multivariate Logistic Regression ◽  
Ann Arbor

This study investigated the risk factors of thromboembolism (TE) in lymphoma patients undergoing chemotherapy and its clinical significance. A total of 304 lymphoma patients who received chemotherapy from January 2012 to July 2019 were retrospectively analyzed, including 111 patients with and 193 patients without TE. The clinical characteristics and related laboratory test results were compared between the 2 groups using univariate analysis, while the risk factors for TE in lymphoma patients undergoing chemotherapy were analyzed using multivariate logistic regression analysis. Univariate analysis revealed an increase in the risk of TE among lymphoma patients with chemotherapy in the following categories: female patients, patients with body mass index <18.5 or > 24, patients aged ≥60 years, those with platelet abnormality before chemotherapy, single hospital-stay patients, and Ann Arbor stage III/IV patients. Multivariate logistic regression analysis revealed that for platelet count abnormality before chemotherapy, Ann Arbor stage III/IV and female patients represented independent risk factors for TE among lymphoma patients after chemotherapy ( P < .05). For lymphoma patients treated with chemotherapy, the risk of TE occurring in women, patients with platelet abnormalities before chemotherapy, and patients at Ann Arbor stage III/IV was significantly higher compared with other patients. For these patients, we recommend prophylactic anticoagulant therapy.

scholarly journals Development and validation of nomograms to intraoperatively predict metastatic patterns in regional lymph nodes in patients diagnosed with esophageal cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Zhao ◽  
Rong-Xin Lu ◽  
Jin-Yuan Liu ◽  
Jun Fan ◽  
Hao-Ran Lin ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Prediction Model ◽  
Validation Cohort ◽  
Regional Lymph Node ◽  
Univariate Analysis ◽  
Clinical Usefulness ◽  
Multivariable Logistic Regression ◽  
Good Calibration

Abstract Background An accurate intraoperative prediction of lymph node metastatic risk can help surgeons in choosing precise surgical procedures. We aimed to develop and validate nomograms to intraoperatively predict patterns of regional lymph node (LN) metastasis in patients with esophageal cancer. Methods The prediction model was developed in a training cohort consisting of 487 patients diagnosed with esophageal cancer who underwent esophagectomy with complete LN dissection from January 2016 to December 2016. Univariate and multivariable logistic regression were used to identify independent risk factors that were incorporated into a prediction model and used to construct a nomogram. Contrast-enhanced computed tomography reported LN status and was an important comparative factor of clinical usefulness in a validation cohort. Nomogram performance was assessed in terms of calibration, discrimination, and clinical usefulness. An independent validation cohort comprised 206 consecutive patients from January 2017 to December 2017. Results Univariate analysis and multivariable logistic regression revealed three independent predictors of metastatic regional LNs, three independent predictors of continuous regional LNs, and two independent predictors of skipping regional LNs. Independent predictors were used to build three individualized prediction nomograms. The models showed good calibration and discrimination, with area under the curve (AUC) values of 0.737, 0.738, and 0.707. Application of the nomogram in the validation cohort yielded good calibration and discrimination, with AUC values of 0.728, 0.668, and 0.657. Decision curve analysis demonstrated that the three nomograms were clinically useful in the validation cohort. Conclusion This study presents three nomograms that incorporate clinicopathologic factors, which can be used to facilitate the intraoperative prediction of metastatic regional LN patterns in patients with esophageal cancer.

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