Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

scholarly journals The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhichuang Lian ◽  
Yafang Li ◽  
Wenyi Wang ◽  
Wei Ding ◽  
Zongxin Niu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Underlying Diseases

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

scholarly journals Evaluation of the Ginsburg Scheme: Where Is Significant Prostate Cancer Missed?

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2502
Author(s):  
August Sigle ◽  
Cordula A. Jilg ◽  
Timur H. Kuru ◽  
Nadine Binder ◽  
Jakob Michaelis ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cancer Detection ◽  
Logistic Regression Analysis ◽  
Anterior Region ◽  
Targeted Biopsy ◽  
Detection Rates ◽  
Clinically Significant ◽  
Systematic Biopsy

Background: Systematic biopsy (SB) according to the Ginsburg scheme (GBS) is widely used to complement MRI-targeted biopsy (MR-TB) for optimizing the diagnosis of clinically significant prostate cancer (sPCa). Knowledge of the GBS’s blind sectors where sPCa is missed is crucial to improve biopsy strategies. Methods: We analyzed cancer detection rates in 1084 patients that underwent MR-TB and SB. Cancerous lesions that were missed or underestimated by GBS were re-localized onto a prostate map encompassing Ginsburg sectors and blind-sectors (anterior, central, basodorsal and basoventral). Logistic regression analysis (LRA) and prostatic configuration analysis were applied to identify predictors for missing sPCa with the GBS. Results: GBS missed sPCa in 39 patients (39/1084, 3.6%). In 27 cases (27/39, 69.2%), sPCa was missed within a blind sector, with 17/39 lesions localized in the anterior region (43.6%). Neither LRA nor prostatic configuration analysis identified predictors for missing sPCa with the GBS. Conclusions: This is the first study to analyze the distribution of sPCa missed by the GBS. GBS misses sPCa in few men only, with the majority localized in the anterior region. Adding blind sectors to GBS defined a new sector map of the prostate suited for reporting histopathological biopsy results.

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

scholarly journals Clinical diagnosis of prostate cancer using digital rectal examination and prostate-specific antigen tests: a systematic review and meta-analysis of sensitivity and specificity

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Nelson C. Okpua ◽  
Simon I. Okekpa ◽  
Stanley Njaka ◽  
Augusta N. Emeh
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Antigen Test ◽  
Rectal Examination ◽  
Prostate Specific Antigen Test ◽  
Prostate Cancers ◽  
Antigen Tests

Abstract Background Being diagnosed with cancer, irrespective of type initiates a serious psychological concern. The increasing rate of detection of indolent prostate cancers is a source of worry to public health. Digital rectal examination and prostate-specific antigen tests are the commonly used prostate cancer screening tests. Understanding the diagnostic accuracies of these tests may provide clearer pictures of their characteristics and values in prostate cancer diagnosis. This review compared the sensitivities and specificities of digital rectal examination and prostate-specific antigen test in detection of clinically important prostate cancers using studies from wider population. Main body We conducted literature search in PubMed, Medline, Science Direct, Wiley Online, CINAHL, Scopus, AJOL and Google Scholar, using key words and Boolean operators. Studies comparing the sensitivity and specificity of digital rectal examination and prostate-specific antigen tests in men 40 years and above, using biopsy as reference standard were retrieved. Data were extracted and analysed using Review manager (RevMan 5.3) statistical software. The overall quality of the studies was good, and heterogeneity was observed across the studies. The result comparatively shows that prostate-specific antigen test has higher sensitivity (P < 0.00001, RR 0.74, CI 0.67–0.83) and specificity (P < 0.00001, RR 1.81, CI 1.54–2.12) in the detection of prostate cancers than digital rectal examination. Conclusion Prostate-specific antigen test has higher sensitivity and specificity in detecting prostate cancers from men of multiple ethnic origins. However, combination of prostate-specific antigen test and standardized digital rectal examination procedure, along with patients history, may improve the accuracy and minimize over-diagnoses of indolent prostate cancers.

O-207 Which infertile patients mostly deserve to have a sperm DNA fragmentation index done? Findings from a cross-sectional study

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
E Pozzi ◽  
L Boeri ◽  
L Candela ◽  
D Cignoli ◽  
G Colandrea ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Dna Fragmentation ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Sperm Concentration ◽  
Testicular Volume ◽  
Sperm Dna Fragmentation ◽  
Sperm Dna ◽  
Increased Risk

Abstract Study question Current scientific guidelines do not clearly suggest which patients would benefit the most from a sperm DNA fragmentation (SDF) test. Summary answer We aimed to investigate potential predictive factors for altered SDF in a homogenous cohort of white-European men presenting for primary couple’s infertility. What is known already High SDF has been associated with reduced fertilization rates, reduced chances of natural conception and an increased risk of early pregnancy loss. Study design, size, duration Data from 478 consecutive men with normal or altered SDF were analysed. Infertility was defined according to the WHO criteria. Semen analysis, SDF (according to SCSA) and serum hormones were measured in every patient. Health significant comorbidities were scored with the Charlson Comorbidity Index (CCI). Altered SDF was considered with a threshold of &gt; 30%. Participants/materials, setting, methods Descriptive statistics compared the overall characteristics of patients with normal SDF and altered SDF. Logistic regression analysis tested potential predictors of altered SDF. ROC curve was used to test the accuracy of the model in predicting SDF alteration Main results and the role of chance Of 478 patients, 253 (57.7%) had altered SDF. Median (IQR) age and BMI of the whole cohort were 38 (35-42) years and 25.1 (23.3-27.1) kg/m2 respectively. Patients with altered SDF were older (median (IQR) age: 39 (36-43) vs. 37 (34-38) years, p &lt; 0.0001), had lower sperm concentration (5 (1.1–18) vs. 17 x106/mL (6–38.8), p &lt; 0.0001), testicular volume (15.1 (12 –20) vs. 16.8 (12 – 25) Prader, p = 0.0005), and total motile sperm count (TMSC) (1.8 (0.21–10.71) vs. 11.8x106 (2–37.26), p &lt; 0.0001). Conversely, men with altered SDF had higher FSH (6.1 (3.85–9.7) vs. 4.8 (3.85 – 7.9) mIU/mL, p &lt; 0.0001) and prolactin levels (9.8 (7.43–14.04) vs. 8.3 (6.6–11.3) pg/mL, p = 0.0004) than those with normal SDF. At multivariable logistic regression analysis, patients’ age &gt;35 years (OR: 2.45, p = 0.0009), FSH &gt; 8.0 mIU/mL (OR: 2.23, p &lt; 0.0001) and lower TMSC (OR: 2.04, p = 0.002) were identified as indipendent predictors of altered SDF, after adjusting for testicular volume and CCI≥1. ROC curve (Figure 1) revealed that the model has a good predictive ability to identify patients with SDF alteration (AUC: 0.72, 95%CI: 0.67 - 0.77). Limitations, reasons for caution It is a retrospective analysis at a single, tertiary-referral academic centre, thus raising the possibility of selection biases. In spite of this, all patients have been consistently analysed over time with a rigorous follow-up, thus limiting potential heterogeneity in terms of data reporting Wider implications of the findings Primary infertile men older than 35 years, with high serum FSH and low TMSC at baseline are the ones who mostly deserve a SDF test over their diagnostic work-up and that would potentially benefit the most of certain treatments to improve SDF value, thus increasing chances of conceiving. Trial registration number Not applicable

scholarly journals Prostate-Specific Antigen Density: A Measurement to Differentiate Benign Hypertrophy of Prostate from Prostate Carcinoma

2020 ◽  
Vol 12 (01) ◽  
pp. 44-48
Author(s):  
Chandan Kumar Nath ◽  
Bhupen Barman ◽  
Pranjal Phukan ◽  
Stephen L. Sailo ◽  
Biswajit Dey ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Prostate Volume ◽  
Specific Antigen ◽  
Prostate Specific Antigen Density ◽  
Significant Difference ◽  
The Mean ◽  
Set Up ◽  
The Individual

Abstract Background Determination of isolated prostate-specific antigen (PSA) in asymptomatic individuals has not demonstrated sufficient sensitivity and specificity to be useful in the routine evaluation of prostate disease. To enhance the accuracy of serum PSA we have used a proportion of serum PSA and prostate volume, which we refer to as prostate-specific antigen density (PSAD). Prostate volume in this study was calculated using transrectal ultrasonography (TRUS). Materials and Methods A total of 106 patients with prostatic disease clinically confined to the prostate glands were evaluated. Results and Observation The mean PSAD for prostate cancer was 0.15 ± 0.01 while that for benign hypertrophy of the prostate (BPH) was 0.11 ± 0.02 (p < 0.05). Significant difference (p < 0.05) was noted in the prostate volume in these two groups with the mean prostate volume measured by TRUS in the BPH to be 53.85 ± 9.71 mL compared with 58.14 ± 7.48 mL in the carcinoma. PSA density of 0.13 ng/mL can be used as a cutoff for the individual in our set-up who should go for prostate biopsy with sensitivity and specificity of over 90%. Conclusion These results suggest that PSAD may be useful in distinguishing BPH and prostate cancer.

scholarly journals Performance of cytokine models in predicting SLE activity

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Nopparat Ruchakorn ◽  
Pintip Ngamjanyaporn ◽  
Thanitta Suangtamai ◽  
Thanuchporn Kafaksom ◽  
Charin Polpanumas ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Disease Activity ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Lupus Erythematosus ◽  
Activity Index ◽  
Multiple Logistic Regression Analysis ◽  
Negative Relationship ◽  
Complement C3

Abstract Background Identification of universal biomarkers to predict systemic lupus erythematosus (SLE) flares is challenging due to the heterogeneity of the disease. Several biomarkers have been reported. However, the data of validated biomarkers to use as a predictor for lupus flares show variation. This study aimed to identify the biomarkers that are sensitive and specific to predict lupus flares. Methods One hundred and twenty-four SLE patients enrolled in this study and were prospectively followed up. The evaluation of disease activity achieved by the SLE disease activity index (SLEDAI-2K) and clinical SLEDAI (modified SLEDAI). Patients with active SLE were categorized into renal or non-renal flares. Serum cytokines were measured by multiplex bead-based flow cytometry. The correlation and logistic regression analysis were performed. Results Levels of IFN-α, MCP-1, IL-6, IL-8, and IL-18 significantly increased in active SLE and correlated with clinical SLEDAI. Complement C3 showed a weakly negative relationship with IFN-α and IL-18. IL-18 showed the highest positive likelihood ratios for active SLE. Multiple logistic regression analysis showed that IL-6, IL-8, and IL-18 significantly increased odds ratio (OR) for active SLE at baseline while complement C3 and IL-18 increased OR for active SLE at 12 weeks. IL-18 and IL-6 yielded higher sensitivity and specificity than anti-dsDNA and C3 to predict active renal and active non-renal, respectively. Conclusion The heterogeneity of SLE pathogenesis leads to different signaling mechanisms and mediates through several cytokines. The monitoring of cytokines increases the sensitivity and specificity to determine SLE disease activity. IL-18 predicts the risk of active renal SLE while IL-6 and IL-8 predict the risk of active non-renal. The sensitivity and specificity of these cytokines are higher than the anti-dsDNA or C3. We propose to use the serum level of IL-18, IL-6, and IL-8 to monitor SLE disease activity in clinical practice.

GIS-based Landslide Susceptibility Mapping Using Logistic Regression Analysis: A Case study in the Kadernath valley, Central Himalaya.

2020 ◽  
Author(s):  
Suman Das
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Sensor Data ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Anthropogenic Factors ◽  
Susceptibility Map

&lt;p&gt;Himalayan Terrain is highly susceptible to landslide events triggered by frequent earthquakes and heavy rainfall. In the recent past, cloud burst events are on rising, causing massive loss of life and property, mainly attributed to climate change and extensive anthropogenic activities in the mountain region as experienced in case of 2013 Kedarnath Tragedy. The study aimed to identify the potential landslide hazard zone in Mandakini valley by utilizing different types of data including Survey of India toposheet, geological (lithological and structural) maps, IRS-1D, LISS IV multispectral and PAN satellite sensor data and field observations. Relevant 18 thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, LULC, NDVI, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques. &amp;#160;A detailed landslide susceptibility map was produced using a logistic regression method with datasets developed in GIS. which has further been categorized into four landslide susceptibility zones from high to very low. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. ROC curve analysis showing an accuracy of 87.3 % for an independent set of test samples. The result also showed a strong agreement between the distribution of existing landslides and predicted landslide susceptibility zones. Consequently, this study could serve as an effective guide for further land-use planning and for the implementation of development.&lt;/p&gt;

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