Predictive Logistic Regression Model Research Articles

Predicting Kawasaki disease shock syndrome in children.

Kawasaki disease shock syndrome (KDSS) is a critical manifestation of Kawasaki disease (KD). In recent years, a logistic regression prediction model has been widely used to predict the occurrence probability of various diseases. This study aimed to investigate the clinical characteristics of children with KD and develop and validate an individualized logistic regression model for predicting KDSS among children with KD. The clinical data of children diagnosed with KDSS and hospitalized between January 2021 and December 2023 were retrospectively analyzed. The best predictors were selected by logistic regression and lasso regression analyses. A logistic regression model was built of the training set (n = 162) to predict the occurrence of KDSS. The model prediction was further performed by logistic regression. A receiver operating characteristic curve was used to evaluate the performance of the logistic regression model. We built a nomogram model by visualizing the calibration curve using a 1000 bootstrap resampling program. The model was validated using an independent validation set (n = 68). In the univariate analysis, among the 24 variables that differed significantly between the KDSS and KD groups, further logistic and Lasso regression analyses found that five variables were independently related to KDSS: rash, brain natriuretic peptide, serum Na, serum P, and aspartate aminotransferase. A logistic regression model was established of the training set (area under the receiver operating characteristic curve, 0.979; sensitivity=96.2%; specificity=97.2%). The calibration curve showed good consistency between the predicted values of the logistic regression model and the actual observed values in the training and validation sets. Here we established a feasible and highly accurate logistic regression model to predict the occurrence of KDSS, which will enable its early identification.

Upper limb muscle strength and exercise endurance as predictors of successful extubation in mechanically ventilated patients

BackgroundFailed extubation increases the intensive care unit (ICU) length of stay, hospital length of stay, and financial costs and it reduces the patient’s functional ability. Avoiding failed extubation is of utmost importance, therefore predictors for successful extubation are paramount.ObjectiveTo determine if successful extubation in mechanically ventilated patients can be predicted by physiotherapists using upper limb muscle strength and exercise endurance.MethodsFifty-seven patients from the medical and trauma ICUs of a large academic hospital were eligible for testing. Muscle strength was evaluated using the Oxford grading scale, Medical Research Council score (MRC score), handgrip dynamometer, and maximum inspiratory pressure (MIP). Exercise endurance was tested while the patient was actively riding the MOTOmed® letto2 cycle ergometer for six minutes with the upper limbs.ResultsExercise endurance (time the patient rode actively) (P = 0.005), MRC-score (P = 0.007), and number of days ventilated (P = 0.005) were associated with successful extubation. The handgrip strength (P = 0.061), MIP (P = 0.095), and muscle strength of the sternocleidomastoid (P = 0.053) and trapezius muscles (P = 0.075) were marginally associated with successful extubation. Due to multicollinearity when developing the prediction equation, the final multivariable logistic regression prediction model included only exercise endurance and the number of days ventilated. The newly developed prediction equation conferred a sensitivity of 81.82% and a specificity of 77.14% to predict successful extubation.ConclusionSuccessful extubation of mechanically ventilated patients can be predicted by physiotherapists using the newly developed prediction equation consisting of exercise endurance and number of days ventilated.

Machine learning-based prediction models affecting the recovery of postoperative bowel function for patients undergoing colorectal surgeries

PurposeThe debate surrounding factors influencing postoperative flatus and defecation in patients undergoing colorectal resection prompted this study. Our objective was to identify independent risk factors and develop prediction models for postoperative bowel function in patients undergoing colorectal surgeries.MethodsA retrospective analysis of medical records was conducted for patients who undergoing colorectal surgeries at Peking University People’s Hospital from January 2015 to October 2021. Machine learning algorithms were employed to identify risk factors and construct prediction models for the time of the first postoperative flatus and defecation. The prediction models were evaluated using sensitivity, specificity, the Youden index, and the area under the receiver operating characteristic curve (AUC) through logistic regression, random forest, Naïve Bayes, and extreme gradient boosting algorithms.ResultsThe study included 1358 patients for postoperative flatus timing analysis and 1430 patients for postoperative defecation timing analysis between January 2015 and December 2020 as part of the training phase. Additionally, a validation set comprised 200 patients who undergoing colorectal surgeries from January to October 2021. The logistic regression prediction model exhibited the highest AUC (0.78) for predicting the timing of the first postoperative flatus. Identified independent risk factors influencing the time of first postoperative flatus were Age (p < 0.01), oral laxatives for bowel preparation (p = 0.01), probiotics (p = 0.02), oral antibiotics for bowel preparation (p = 0.02), duration of operation (p = 0.02), postoperative fortified antibiotics (p = 0.02), and time of first postoperative feeding (p < 0.01). Furthermore, logistic regression achieved an AUC of 0.72 for predicting the time of first postoperative defecation, with age (p < 0.01), oral antibiotics for bowel preparation (p = 0.01), probiotics (p = 0.01), and time of first postoperative feeding (p < 0.01) identified as independent risk factors.ConclusionsThe study suggests that he use of probiotics and early recovery of diet may enhance the recovery of bowel function in patients undergoing colorectal surgeries. Among the various analytical methods used, logistic regression emerged as the most effective approach for predicting the timing of the first postoperative flatus and defecation in this patient population.

- Mechanism of LDH and IL-8 Involved in pancreatic cancer Pain and the Correlation of Pain Degree

Objective: To observe the mechanism of lactate dehydrogenase (LDH) and interleukin 8 (IL-8) in pancreatic cancer pain and their correlation with pain degree. Methods: 126 patients with pancreatic cancer who visited our hospital from January 2021 to February 2023 were selected. The patients were divided into 58 patients with low pain (1~3 points) and 68 patients with high pain (4~10 points) by visual analog scale (VAS). And 50 health examinees in the same period were selected as the healthy control group. The serum LDH and IL-8 concentrations are analyzed by enzyme-linked immunosorbent assay, as well as the subjective pain grading method scoreis analyzed. The differences in LDH and IL-8 concentrations among the three groups of patients were compared. Pearson correlation analysis was used to investigate the correlation between LDH, IL-8 concentrations and patient pain. Binary logistic regression was used to determine independent risk factors for high pain, and ROC curves were used to analyze the diagnostic efficacy of each indicator. Result: The serum LDH and IL-8 concentrations in the high pain group were exceed the low pain group’s (P&lt;0.05). The serum LDH and IL-8 concentrations in the low pain groupexceed the healthy control group’s(P&lt;0.05). Pearson correlation analysis revealed a positive correlation between serum LDH concentration and pain grading (r=0.736, P=0.000). The serum IL-8 has positive correlation with pain grading (r=0.680, P=0.000). Serum LDH and IL-8 concentrations have positive correlation(r=0.589, P=0.000). LDH and IL-8 concentrations are independent risk factors for high pain levels (OR=1.033, 1.142, P&lt;0.05). The logistic regression prediction model formula was used: Y=constant+B1X1+B2X2+...+BnXn to set the joint diagnostic prediction model as -12.063+0.033×LDH+0.133×IL-8. The areas under the ROC curves of LDH, IL-8, and predictive model (LDH+IL-8) in patients with high pain were 0.925, 0.945, and 0.974, respectively. The relevant standards for LDH are&gt;190U/L, IL-8 is&gt;36pg/mL, and the relevant standards for prediction models are&gt;5.75. Conclusion: LDH and IL-8 participate in the pain aggravation process of pancreatic cancer, and are closely related to the pain grading. The combination of LDH and IL-8 can be used as a biological indicator to evaluate the pain severity of pancreatic cancer, and provide reference for clinical diagnosis and treatment.

Applying Machine Learning for Enhanced MicroRNA Analysis: A Companion Risk Tool for Oral Squamous Cell Carcinoma in Standard Care Incisional Biopsy.

Machine learning analyses within the realm of oral cancer outcomes are relatively underexplored compared to other cancer types. This study aimed to assess the performance of machine learning algorithms in identifying oral cancer patients, utilizing microRNA expression data. In this study, we implemented this approach using a panel of oral cancer-associated microRNAs sourced from standard incisional biopsy specimens to identify cases of oral squamous cell carcinomas (OSCC). For the model development process, we used a dataset comprising 30 OSCC and 30 histologically normal epithelium (HNE) cases. We initially trained a logistic regression prediction model using 70 percent of the dataset, while reserving the remaining 30 percent for testing. Subsequently, the model underwent hyperparameter tuning resulting in enhanced performance metrics. The hyperparameter-tuned model exhibited high accuracy (0.894) and ROC AUC (0.898) in predicting OSCC. Testing the model on cases of potentially malignant disorders (OPMDs) revealed that leukoplakia with mild dysplasia was predicted as having a high risk of progressing to OSCC, emphasizing machine learning's advantage over histopathology in detecting early molecular changes. These findings underscore the necessity for further refinement, incorporating a broader set of variables to enhance the model's predictive capabilities in assessing the risk of oral potentially malignant disorders.

Development and validation of a novel risk classification tool for predicting long length of stay in NICU blood transfusion infants

Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model’s calibration and predictability. The model’s clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (< 28 weeks), birth weight (BW) (< 1000 g), type of respiratory support, umbilical venous catheter (UVC), sepsis, and resuscitation frequency. The area under the receiver operating characteristic (ROC) curve (AUC) for the training set was 0.851 (95% CI 0.805–0.891), and for the validation set, it was 0.859 (95% CI 0.789–0.920). Fivefold cross-validation indicates that the model has good generalization ability. The calibration curve demonstrated a strong correlation between the predicted risk and the observed actual risk, indicating good consistency. When the intervention threshold was set at 2%, the decision curve analysis indicated that the model had greater clinical utility. The results of our study have led to the development of a novel nomogram that can assist clinicians in predicting the probability of long hospitalization in blood transfused infants with reasonable accuracy. Our findings indicate that GA (< 28 weeks), BW(< 1000 g), type of respiratory support, UVC, sepsis, and resuscitation frequency are associated with a higher likelihood of extended hospital stays among newborns who have received blood transfusions.

Diagnostic Efficiency of ACR-TIRADS Score for Differentiating Benign and Malignant Thyroid Nodules of Various Pathological Types.

BACKGROUND Thyroid nodule prevalence reaches 65% in the general population. Hence, appropriate ultrasonic examination is key in disease monitoring and management. We investigated the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) score for diagnosis of benign and malignant thyroid nodules and pathological types. MATERIAL AND METHODS A retrospective study was conducted. According to ultrasound images, ultrasonic characteristics of benign and malignant thyroid nodules and different pathological types were analyzed using ACR-TIRADS score, and diagnostic value was determined. AUCs were compared for tumor diagnosis and differentiation. RESULTS Overall, 1675 thyroid nodules from 1614 patients were included. AUC value of papillary thyroid carcinoma (PTC) diagnosed with ACR-TIRADS was highest (0.955 [95% CI=0.946-0.965]), while that of follicular thyroid carcinoma (FTC) was lowest (0.877 [95% CI=0.843-0.912]). FTC had the highest sensitivity (95.1%) and lowest specificity (64.8%). When the cut-off value was 5.5 points, accuracy of diagnosing PTC and anaplastic thyroid carcinoma (ATC) was highest, 80.5% and 78.7% respectively. Comparison of the multi-index prediction model constructed by multivariable logistic regression analysis and prediction model constructed by ACR-TIRADS score showed, when evaluating PTC and ATC, the multi-index model was better: AUCs of PTC were 0.966 vs 0.955, and AUCs of ATC were 0.982 vs 0.952, respectively, (P<0.05). CONCLUSIONS ACR-TIRADS score-based ultrasound examination of thyroid nodules aids diagnosis of benign and malignant thyroid nodules. TIRADS criteria favor diagnosis of PTC (and ATC) over FTC. ACR-TIRADS score can help clinicians diagnose thyroid nodules quickly and earlier, exhibits good clinical value, and can prevent missed diagnoses.

No-Reflow Prediction in Acute Coronary Syndrome During Percutaneous Coronary Intervention: The NORPACS Risk Score.

Suboptimal coronary reperfusion (no reflow) is common in acute coronary syndrome percutaneous coronary intervention (PCI) and is associated with poor outcomes. We aimed to develop and externally validate a clinical risk score for angiographic no reflow for use following angiography and before PCI. We developed and externally validated a logistic regression model for prediction of no reflow among adult patients undergoing PCI for acute coronary syndrome using data from the Melbourne Interventional Group PCI registry (2005-2020; development cohort) and the British Cardiovascular Interventional Society PCI registry (2006-2020; external validation cohort). A total of 30 561 patients (mean age, 64.1 years; 24% women) were included in the Melbourne Interventional Group development cohort and 440 256 patients (mean age, 64.9 years; 27% women) in the British Cardiovascular Interventional Society external validation cohort. The primary outcome (no reflow) occurred in 4.1% (1249 patients) and 9.4% (41 222 patients) of the development and validation cohorts, respectively. From 33 candidate predictor variables, 6 final variables were selected by an adaptive least absolute shrinkage and selection operator regression model for inclusion (cardiogenic shock, ST-segment-elevation myocardial infarction with symptom onset >195 minutes pre-PCI, estimated stent length ≥20 mm, vessel diameter <2.5 mm, pre-PCI Thrombolysis in Myocardial Infarction flow <3, and lesion location). Model discrimination was very good (development C statistic, 0.808; validation C statistic, 0.741) with excellent calibration. Patients with a score of ≥8 points had a 22% and 27% risk of no reflow in the development and validation cohorts, respectively. The no-reflow prediction in acute coronary syndrome risk score is a simple count-based scoring system based on 6 parameters available before PCI to predict the risk of no reflow. This score could be useful in guiding preventative treatment and future trials.

Mechanistic Investigation of Calcium Channel Regulation-Associated Genes in Pulmonary Arterial Hypertension and Signatures for Diagnosis.

Pulmonary arterial hypertension (PAH) is a severe cardiopulmonary disorder with complex causes. Calcium channel blockers have long been used in its treatment. Our study aimed to validate experimental results showing increased calcium ion concentration in PAH patients. We investigated the impact of genes related to calcium channel regulation on PAH development and developed an accurate diagnostic model. Clinical trial data from serum of 18 healthy individuals and 18 patients with PAH were retrospectively analyzed. Concentrations of calcium and potassium ions were determined and compared. Datasets were retrieved, selecting genes associated with calcium ion release. R packages processed the datasets, filtering 174 common genes, and conducting Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Six hub genes were identified, and nomogram and logistic regression prediction models were constructed. Random forest filtered cross genes, and a diagnostic model was developed and validated using an artificial neural network. The 174 intersection genes related to calcium ions showed significant correlations with biological processes, cellular components, and molecular functions. Six key genes were obtained by constructing a protein-protein interaction network. A diagnostic model with high accuracy (> 90%) and diagnostic capability (AUC = 0.98) was established using a neural network algorithm. This study validated the experimental results, identified key genes associated with calcium ions, and developed a highly accurate diagnostic model using a neural network algorithm. These findings provide insights into the role of calcium release genes in PAH and demonstrate the potential of the diagnostic model for clinical application. However, due to limitations in sample size and a lack of prognosis data, the regulatory mechanisms of calcium ions in PAH patients and their impact on the clinical prognosis of PAH patients still need further exploration in the future.

Prediction of adverse neonatal adaptation in fetuses with severe fetal growth restriction after 34 weeks of gestation

ObjectiveTo establish a predictive model for adverse immediate neonatal adaptation (INA) in fetuses with suspected severe fetal growth restriction (FGR) after 34 gestational weeks (GW). Methods. We conducted a retrospective observational study at the University Hospitals of Strasbourg between 2000 and 2020, including 1,220 women with a singleton pregnancy and suspicion of severe FGR who delivered from 34 GW. The primary outcome (composite) was INA defined as Apgar 5-minute score <7, arterial pH <7.10, immediate transfer to pediatrics, or the need for resuscitation at birth. We developed and tested a logistic regression predictive model. Results. Adverse INA occurred in 316 deliveries. The model included six features available before labor: parity, gestational age, diabetes, middle cerebral artery Doppler, cerebral-placental inversion, onset of labor. The model could predict individual risk of adverse INA with confidence interval at 95 %. Taking an optimal cutoff threshold of 32 %, performances were: sensitivity 66 %; specificity 83 %; positive and negative predictive values 60 % and 87 % respectively, and area under the curve 78 %. Discussion. The predictive model showed good performances and a proof of concept that INA could be predicted with pre-labor characteristics, and needs to be investigated further.

Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients.

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.

Distinguishing Primary Prevention From Secondary Prevention Implantable Cardioverter Defibrillators Using Administrative Health and Cardiac Device Registry Data

BackgroundAdministrative health data and cardiac device registries can be used to empirically evaluate outcomes and costs after implantable cardioverter defibrillator (ICD) implantation. These datasets often have incomplete information on the indication for implantation (primary vs secondary prevention of sudden cardiac death). MethodsWe used 16 years of population-based cardiac device registry and administrative health data from British Columbia, Canada, to derive and internally validate statistical models that predict the likely indication for ICD implantation. We used chart review data as the reference standard for ICD indication in the Cardiac Device Registry database (CDR; 2004-2012 [Cardiac Services BC]) and nonmissing indication as the reference standard in the Heart Information System registry database (HEARTis; 2013-2019 [Cardiac Services BC]). We created 3 logistic regression prediction models in each database: one using only registry data, one using only administrative data, and one using both registry and administrative data. We assessed the predictive performance of each model using standard metrics after optimism correction with 200 bootstrap resamples. ResultsModels that used registry data alone demonstrated excellent predictive performance (sensitivity ≥ 89%; specificity ≥ 87%). Models that used only administrative data performed well (sensitivity ≥ 84%; specificity ≥ 70%). Models that used both registry and administrative data showed modest gains over those that used registry data alone (sensitivity ≥ 90%; specificity ≥ 89%). ConclusionsAdministrative health data and cardiac device registry data can distinguish secondary prevention ICDs from primary prevention ICDs with acceptable sensitivity and specificity. Imputation of missing ICD indication might make these data resources more useful for research and health system monitoring.

Predictive models based on machine learning for early recurrence and metastasis in postoperative patients with colorectal cancer

Abstract Objectives: To construct and validate a prediction model based on machine learning algorithms for early recurrence and metastasis in patients with colorectal cancer after surgery. Methods: This study employed a prospective cohort design. A total of 498 postoperative patients with colorectal cancer, treated at an affiliated hospital of Qingdao University, were recruited using convenience sampling from June to December 2021. Data were collected during outpatient visits and hospitalizations. The risk factors for early recurrence and metastasis of colorectal cancer were determined through multivariate logistic regression analysis in SPSS 26.0 software. Using Python 3.7.0 software, 4 machine learning algorithms (logistic regression, Support Vector Machine, XGBoost, and LightGBM) were used to develop and validate prediction models for early recurrence and metastasis of colorectal cancer after surgery. Results: Of the 498 patients, 51 (10.24%) had early recurrence and metastasis. Multivariate logistic regression analysis showed that personal traits (family history of cancer, histologic type, degree of tumor differentiation, number of positive lymph nodes, and T stage), behavior and/or lifestyle (intake of refined grains, whole grains, fish, shrimp, crab, and nuts, as well as resilience), and interpersonal networks (social support) were all associated with early recurrence and metastasis of colorectal cancer (P&lt;0.05). The logistic regression prediction model showed the best prediction performance out of the 4 models, with an accuracy rate of 0.920, specificity of 0.982, F1 of 0.495, AUC of 0.867, Kappa of 0.056, and Brier score of 0.067. Conclusion: Our findings suggest that a prediction model based on logistic regression could accurately and scientifically predict which patients are likely to experience early recurrence and metastasis, helping to lessen the burden for both patients and the health care system.

MASH Resolution Index: development and validation of a non-invasive score to detect histological resolution of MASH

BackgroundDynamic changes in non-invasive tests, such as changes in alanine aminotransferase (ALT) and MRI proton-density-fat-fraction (MRI-PDFF), may help to detect metabolic dysfunction-associated steatohepatitis (MASH) resolution, but a combination of non-invasive...

Effect of variable selection strategy on the predictive models for adverse pregnancy outcomes of pre-eclampsia: A retrospective study

Objectives: The improvement of prediction for adverse pregnancy outcomes is quite essential to the women suffering from pre-eclampsia, while the collection of predictive indicators is the prerequisite. The traditional knowledge-based strategy for variable selection confronts challenge referring to dataset with high-dimensional or unfamiliar data. In this study, we employed five different automatic variable selection methods to screen out influential indicators, and evaluated the performance of constructed predictive models. Methods: Seven hundreds and thirty-three Han-Chinese women were enrolled and 56 clinical and laboratory variables were recorded. After grouping based on binary pregnancy outcomes, statistical description and analysis were performed. Then, utilizing forward stepwise logistic regression (FSLR) as the reference method, another four variable selection strategies were included for filtering contributing variables as the predictive subsets, respectively. Finally, the logistic regression prediction models were constructed by the five subsets and evaluated by the receiver operator characteristic curve. Results: The variables confirmed statistical significance between the adverse and satisfactory outcomes groups did not overlap with the variables selected by selection strategies. “Platelet” and “Creatinine clearance rate” were the most influential indicator to predict adverse maternal outcome, while “Birth weight of neonates” was the best indicator for predicting adverse neonatal outcome. In average, the predictive models for neonatal outcomes achieved better performance than models for maternal outcomes. “Mutual information” and “Recursive feature elimination” were the best strategy under current dataset and study design. Conclusions: Variable selection strategies may provide an alternative approach besides picking influential indicators by statistical significance. Future work will focus on applying different variable selection methods to the high-dimensional dataset, which includes novel or unfamiliar variables. This aims to identify the most appropriate collection of predictors that can enhance prediction ability and clinical decision-making.

Intravoxel Incoherent Motion Diffusion-weighted Magnetic Resonance Imaging combined with Texture Analysis in Predicting the Histological Grades of Rectal Adenocarcinoma.

To evaluate the predictive value of 3.0T MRI Intravoxel Incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) combined with texture analysis (TA) in the histological grade of rectal adenocarcinoma. Seventy-one patients with rectal adenocarcinoma confirmed by pathology after surgical resection were collected retrospectively. According to pathology, they were divided into a poorly differentiated group (n=23) and a moderately differentiated group (n=48). The IVIM-DWI parameters and TA characteristics of the two groups were compared, and a prediction model was constructed by multivariate logistic regression analysis. ROC curves were plotted for each individual and combined parameter. There were statistically significant differences in D and D* values between the two groups (P < 0.05). The three texture parameters SmallAreaEmphasis, Median, and Maximum had statistically significant differences between groups (P = 0.01, 0.004, 0.009, respectively). The logistic regression prediction model showed that D*, the median, and the maximum value were significant independent predictors, and the AUC of the regression prediction model was 0.860, which was significantly higher than other single parameters. 3.0T MRI IVIM-DWI parameters combined with TA can provide valuable information for predicting the histological grades of rectal adenocarcinoma one week before the operation.

Bioinformatics identification of potential biomarkers and therapeutic targets for ischemic stroke and vascular dementia

Ischemic stroke and vascular dementia, as common cerebrovascular diseases, with the former causing irreversible neurological damage and the latter causing cognitive and memory impairment, are closely related and have long received widespread attention. Currently, the potential causative genes of these two diseases have yet to be investigated, and effective early diagnostic tools for the diseases have not yet emerged. In this study, we screened new potential biomarkers and analyzed new therapeutic targets for both diseases from the perspective of immune infiltration. Two gene expression profiles on ischemic stroke and vascular dementia were obtained from the NCBI GEO database, and key genes were identified by LASSO regression and SVM-RFE algorithms, and key genes were analyzed by GO and KEGG enrichment. The CIBERSORT algorithm was applied to the gene expression profile species of the two diseases to quantify the 24 subpopulations of immune cells. Moreover, logistic regression modeling analysis was applied to illustrate the stability of the key genes in the diagnosis. Finally, the key genes were validated using RT-PCR assay. A total of 105 intersecting DEGs genes were obtained in the 2 sets of GEO datasets, and bioinformatics functional analysis of the intersecting DEGs genes showed that GO was mainly involved in the purine ribonucleoside triphosphate metabolic process，respiratory chain complex，DNA−binding transcription factor binding and active transmembrane transporter activity. KEGG is mainly involved in the Oxidative phosphorylation, cAMP signaling pathway. The LASSO regression algorithm and SVM-RFE algorithm finally obtained three genes, GAS2L1, ARHGEF40 and PFKFB3, and the logistic regression prediction model determined that the three genes, GAS2L1 (AUC: 0.882), ARHGEF40 (AUC: 0.867) and PFKFB3 (AUC: 0.869), had good diagnostic performance. Meanwhile, the two disease core genes and immune infiltration were closely related, GAS2L1 and PFKFB3 had the highest positive correlation with macrophage M1 (p < 0.001) and the highest negative correlation with mast cell activation (p = 0.0017); ARHGEF40 had the highest positive correlation with macrophage M1 and B cells naive (p < 0.001), the highest negative correlation with B cell memory highest correlation (p = 0.0047). RT-PCR results showed that the relative mRNA expression levels of GAS2L1, ARHGEF40, and PFKFB3 were significantly elevated in the populations of both disease groups (p < 0.05). Immune infiltration-based models can be used to predict the diagnosis of patients with ischemic stroke and vascular dementia and provide a new perspective on the early diagnosis and treatment of both diseases.

HB-EGF Plasmatic Level Contributes to the Development of Early Risk Prediction Nomogram for Severe COVID-19 Cases.

(1) Background: Heparin-Binding Epidermal Growth Factor-like Growth Factor (HB-EGF) is involved in wound healing, cardiac hypertrophy, and heart development processes. Recently, circulant HB-EGF was reported upregulated in severely hospitalized COVID-19 patients. However, the clinical correlations of HB-EGF plasma levels with COVID-19 patients' characteristics have not been defined yet. In this study, we assessed the plasma HB-EGF correlations with the clinical and paraclinical patients' data, evaluated its predictive clinical value, and built a risk prediction model for severe COVID-19 cases based on the resulting significant prognostic markers. (2) Methods: Our retrospective study enrolled 75 COVID-19 patients and 17 control cases from May 2020 to September 2020. We quantified plasma HB-EGF levels using the sandwich ELISA technique. Correlations between HB-EGF plasma levels with clinical and paraclinical patients' data were calculated using two-tailed Spearman and Point-Biserial tests. Significantly upregulated parameters for severe COVID-19 cases were identified and selected to build a multivariate logistic regression prediction model. The clinical significance of the prediction model was assessed by risk prediction nomogram and decision curve analyses. (3) Results: HB-EGF plasma levels were significantly higher in the severe COVID-19 subgroup compared to the controls (p = 0.004) and moderate cases (p = 0.037). In the severe COVID-19 group, HB-EGF correlated with age (p = 0.028), pulse (p = 0.016), dyspnea (p = 0.014) and prothrombin time (PT) (p = 0.04). The multivariate risk prediction model built on seven identified risk parameters (age p = 0.043, HB-EGF p = 0.0374, Fibrinogen p = 0.009, PT p = 0.008, Creatinine p = 0.026, D-Dimers p = 0.024 and delta miR-195 p < 0.0001) identifies severe COVID-19 with AUC = 0.9556 (p < 0.0001). The decision curve analysis revealed that the nomogram model is clinically relevant throughout a wide threshold probability range. (4) Conclusions: Upregulated HB-EGF plasma levels might serve as a prognostic factor for severe COVID-19 and help build a reliable risk prediction nomogram that improves the identification of high-risk patients at an early stage of COVID-19.

CT imaging features of lung ground-glass nodule patients with upgraded intraoperative frozen pathology

PurposeIntraoperative frozen section pathology (FS) is widely used to guide surgical strategies while the accuracy is relatively low. Underestimating the pathological condition may result in inadequate surgical margins. This study aims to identify CT imaging features related to upgraded FS and develop a predictive model.MethodsCollected data from 860 patients who underwent lung surgery from January to December 2019. We analyzed the consistency rate of FS and categorized the patients into three groups: Group 1 (n = 360) had both FS and Formalin-fixed Paraffin-embedded section (FP) as non-invasive adenocarcinoma (IAC); Group 2 (n = 128) had FS as non-IAC but FP as IAC; Group 3 (n = 372) had both FS and FP as IAC. Clinical baseline characteristics were compared and propensity score adjustment was used to mitigate the effects of these characteristics. Univariate analyses identified imaging features with inter-group differences. A multivariate analysis was conducted to screen independent risk factors for FS upgrade, after which a logistic regression prediction model was established and a receiver operating characteristic (ROC) curve was plotted.ResultsThe consistency rate of FS with FP was 84.19%. 26.67% of the patients with non-IAC FS diagnosis were upgraded to IAC. The predictive model’s Area Under Curve (AUC) is 0.785. Consolidation tumor ratio (CTR) ≤ 0.5 and smaller nodule diameter are associated with the underestimation of IAC in FS.ConclusionCT imaging has the capacity to effectively detect patients at risk of upstaging during FS.

Pendugaan Koefisien Regresi Logistik Biner Menggunakan Algoritma Least Angle Regression

Binary logistic regression is a statistical analysis method that aims to determine the relationship between variable which has two categories with the predictor variable that have categorical or continuous scale. The method that used to estimate logistic regression parameters is Maximum Likelihood Estimation (MLE) method. In estimating parameters, Least Angle Regression (LAR) algorithm is used to select the significant variables in order to get the best model from the estimation results of binary logistic regression coefficients. This LAR algorithm is applied to the risko of stunting data in two-year-old-babies at Buntu Batu Health Center working area, Enrekang Regency, South Sulawesi in 2019. This results obtained in the estimation of binary logistic regression prediction model using LAR algorithm, the standard error value is 0.018 smaller than the standard error value of binary logistic regression, which is 0.025. This shows that the binary logistic regression model using LAR algorithm is better than the usual binary logistic regression model on the risk of stunting data. Based on the results obtained, the variables that significantly affect the risk of stunting in two-year-old-babies on 2019 are father’s height, body length of birth, exclusive breastfeeding, history of infectious diseases, and history of immunization.