Machine learning derived model for the prediction of bleeding in dual antiplatelet therapy patients.

Objective: This study aimed to develop a predictive model for assessing bleeding risk in dual antiplatelet therapy (DAPT) patients. Methods: A total of 18,408 DAPT patients were included. Data on patients' demographics, clinical features, underlying diseases, past history, and laboratory examinations were collected from Affiliated Dongyang Hospital of Wenzhou Medical University. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. LASSO regression, multivariate logistic regression, and six machine learning models, including random forest (RF), k-nearest neighbor imputing (KNN), decision tree (DT), extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), calibration curves, decision curve analysis (DCA), clinical impact curve (CIC), and net reduction curve (NRC). Results: The XGBoost model demonstrated the highest AUC. The model features were comprised of seven clinical variables, including: HGB, PLT, previous bleeding, cerebral infarction, sex, Surgical history, and hypertension. A nomogram was developed based on seven variables. The AUC of the model was 0.861 (95% CI 0.847-0.875) in the development cohort and 0.877 (95% CI 0.856-0.898) in the validation cohort, indicating that the model had good differential performance. The results of calibration curve analysis showed that the calibration curve of this nomogram model was close to the ideal curve. The clinical decision curve also showed good clinical net benefit of the nomogram model. Conclusions: This study successfully developed a predictive model for estimating bleeding risk in DAPT patients. It has the potential to optimize treatment planning, improve patient outcomes, and enhance resource utilization.

Risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia.

Objective: To identify risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia and to construct a nomogram predictive model. Methods: This retrospective study included 292 patients who underwent gastrointestinal endoscopy under acupuncture anesthesia at the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from June 2020 to May 2023. Logistic regression analysis was used to identify risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia. A nomogram prediction model was constructed using the RMS package of R4.1.2 software based on the independent risk factors identified. The predictive performance of the model was assessed using consistency index (C-index), calibration curve, and receiver operating characteristic (ROC) curve. Results: Seventy-five patients (25.68%) had complications. Body mass index (BMI), history of cardiovascular diseases, fasting time, history of respiratory diseases, and Sedation-Agitation Scale (SAS) score were identified as risk factors for complications. Based on this risk, a nomogram predictive model was constructed. The C-index of the nomogram model was 0.927. Calibration curve showed a good consistency between actual observations and nomogram predictions. The ROC curve area under curve (AUC) was 0.927 (95% CI: 0.895-0.959), indicating a certain predictive value for the occurrence of complications. When the optimal cut-off value was selected, the sensitivity and specificity of the model were 77.0% and 92.0%, respectively, indicating that the predictive model was effective. Conclusions: BMI, history of cardiovascular disease, fasting time, history of respiratory disease, and SAS score are independent risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia. The constructed nomogram predictive model has a good performance in predicting the occurrence of complications in patients undergoing gastrointestinal endoscopy with under acupuncture anesthesia.

Developing a rapid predictive model for falls in older hospitalized patients.

Background: This study was aimed to identify the independent risk factors for falls n hospitalized older patients and develop a corresponding predictive model. Methods: A retrospective observational study design was adopted, comprising 440 older patients with falls history and 510 older patients without falls history during hospitalization. Data collected included demographic information, vital signs, comorbidities, psychiatric disorder, function absent, current medication, other clinical indicators. Results: Mobility disability, high-risk medications use, frequency of hospitalizations, psychiatric disorder, visual impairment are independent risk factors for falls in older patients. The A-M2-HPV scoring system was developed. The AUC value of the nomogram was 0.884, indicating the model has excellent discriminative ability. The AUC value of the A-M2-HPV score was 0.788, demonstrating better discrimination and stratification capabilities. Conclusion: The A-M2-HPV scoring system provides a valuable tool to assess the risk of falls in hospitalized older patients and to aid in the implementation of preventive measures.

Construction of nomogram prediction model using heart rate and pulse perfusion variability index as predictors for hypotension in cervical cancer patients with spinal epidural anesthesia.

The prevention and treatment strategies for cervical cancer patients undergoing spinal epidural anesthesia have increasingly focused on early screening for high-risk factors associated with potential hypotension. We analyze the general conditions and preoperative examination results of 312 cervical cancer patients who received spinal epidural anesthesia, in order to identify independent risk factors for hypotension, assess their predictive efficacy, and construct a nomogram. 312 patients with cervical cancer received spinal epidural anesthesia were included in this study. Among them, 164 patients with hypotension after hysterectomy with spinal epidural anesthesia were in a hypotension group. Important risk predictors of hypotension after hysterectomy with spinal epidural anesthesia were identified using univariate and multivariate analyses, then a clinical nomogram was constructed. The predictive accuracy was assessed by unadjusted concordance index (C-index) and calibration plot. Univariate and multivariate regression analysis identified basal HR (≥95) (95% CI 0.831-0.900; P = 0.000) and basal PVI (95% CI 0.679-0.877; P = 0.000) were the independent risk factors for hypotension in cervical cancer patients with spinal epidural anesthesia. Those risk factors were used to construct a clinical predictive nomogram. The regression equation model based on the above factors was logit (P) = -6.820 + 0.216 * basal HR + basic PVI * 0.312. The calibration curves for hypotension risk revealed excellent accuracy of the predictive nomogram model. Decision curve analysis showed that the predictive model could be applied clinically when the threshold probability was 20 to 75%. We surmised that the basal HR values and PVI values are the independent risk factors for hypotension in cervical cancer patients with spinal epidural anesthesia. The construction of nomograms is beneficial in predicting the risk of hypotension in these patients.

Nomogram-based prognostic model construction for progression to castration-resistant prostate cancer in patients with high tumor burden and osseous metastatic prostate cancer.

This study aims to construct a Nomogram model to predict the risk of developing castration-resistant prostate cancer (CRPC) in patients with high tumor burden (HTB) and osseous metastatic prostate cancer (PCa), and to identify key prognostic factors. A retrospective analysis was conducted on patients with HTB and osseous metastatic PCa treated at The Sixth Affiliated Hospital, School of Medicine, South China University of Technology and the Second Affiliated Hospital of Guangzhou Medical University from January 2018 to February 2022. Patients' baseline data and laboratory indexes were collected. Cox regression analysis identified neural invasion (NI; P<0.001, HR: 2.371, 95% CI: 1.569-3.582), Gleason score (P=0.002, HR: 1.787, 95% CI: 1.241-2.573), initial PSA (P=0.004, HR: 1.677, 95% CI: 1.174-2.396), and lactate dehydrogenase (LDH; P<0.001, HR: 2.729, 95% CI: 1.855-4.014) as significant prognostic factors for progression to CRPC. The constructed Nomogram model exhibited high accuracy in predicting one- and two-year progression to CRPC, with external validation confirming its predictive performance. Time-dependent receiver operating characteristic (ROC) curves indicated that the areas under the curves (AUCs) of the model for one- and two-year progression to CRPC were 0.81 and 0.76, respectively. This model demonstrates high predictive performance, aiding clinical decision-making and providing personalized treatment strategies for patients with HTB and osseous metastatic PCa.

A prediction model for the walking and balance milestone in Parkinson's disease.

BACKGROUND: Walking and balance impairments, represented by freezing of gait and falls, are significant contributors to disability in advanced Parkinson's disease (PD) patients. However, the composite measure of the Walking and Balance Milestone (WBMS) has not been thoroughly investigated. METHODS: This study included 606 early-stage PD patients from the Parkinson's Progression Markers Initiative (PPMI) database, with a disease duration of less than 2 years and no WBMS at baseline. Patients were divided into a model development cohort (70 %) and a validation cohort (30 %) according to the enrollment site. Longitudinal follow-up data over a period of 12 years were analyzed. RESULTS: Among all 606 patients, the estimated probability of being WBMS-free at the 5th and 10th year was 88 % and 60 %, respectively. Five clinical variables (Age, Symbol Digit Modalities Test (SDMT), postural instability and gait difficulty (PIGD) score, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part I (MDS-UPDRS-I) score, and REM Sleep Behavior Disorder (RBD) were used to construct the Cox predictive model. The C-index of the model was 0.75 in the development cohort and 0.76 in the validation cohort. By optimizing the PIGD and MDS-UPDRS-I variables, an easy-to-use model was achieved with comparable predictive performance. CONCLUSION: A predictive model based on five baseline clinical measures (Age, SDMT, PIGD score, MDS-UPDRS-I score, RBD) could effectively estimate the risk of the WBMS in early PD patients. This model is valuable for prognostic counseling and clinical intervention trials for gait and balance impairment.

Nomogram for prediction of severe postoperative complications in elective hepato-pancreato-biliary surgery after COVID-19 breakthrough infection: A large multicenter study.

BACKGROUND: Currently, there is a deficiency in a strong risk prediction framework for precisely evaluating the likelihood of severe postoperative complications in patients undergoing elective hepato-pancreato-biliary surgery subsequent to experiencing breakthrough infection of coronavirus disease 2019 (COVID-19). This study aimed to find factors predicting postoperative complications and construct an innovative nomogram to pinpoint patients who were susceptible to developing severe complications following breakthrough infection of COVID-19 after undergoing elective hepato-pancreato-biliary surgery. METHODS: This multicenter retrospective cohort study included consecutive patients who underwent elective hepato-pancreato-biliary surgeries between January 3 and April 1, 2023 from four hospitals in China. All of these patients had experienced breakthrough infection of COVID-19 prior to their surgeries. Additionally, two groups of patients without preoperative COVID-19 infection were included as comparative controls. Surgical complications were meticulously documented and evaluated using the comprehensive complication index (CCI), which ranged from 0 (uneventful course) to 100 (death). A CCI value of 20.9 was identified as the threshold for defining severe complications. RESULTS: Among 2636 patients who were included in this study, 873 were included in the reference group I, 941 in the reference group II, 389 in the internal cohort, and 433 in the external validation cohort. Multivariate logistic regression analysis revealed that completing a full course of COVID-19 vaccination > 6 months before surgery, undergoing surgery within 4 weeks of diagnosis of COVID-19 breakthrough infection, operation duration of 4 h or longer, cancer-related surgery, and major surgical procedures were significantly linked to a CCI > 20.9. A nomogram model was constructed utilizing CCI > 20.9 in the training cohort [area under the curve (AUC): 0.919, 95% confidence interval (CI): 0.881-0.957], the internal validation cohort (AUC: 0.910, 95% CI: 0.847-0.973), and the external validation cohort (AUC: 0.841, 95% CI: 0.799-0.883). The calibration curve for the probability of CCI > 20.9 demonstrated good agreement between the predictions made by the nomogram and the actual observations. CONCLUSIONS: The developed model holds significant potential in aiding clinicians with clinical decision-making and risk stratification for patients who have experienced breakthrough infection of COVID-19 prior to undergoing elective hepato-pancreato-biliary surgery.

Clinical data and MRI features-based nomogram for differentiation of central nervous system infection and central nervous system involvement in hematological malignancy.

Central nervous system leukemia (CNSL) and central nervous system infection (CNSI) are the most important complications in patients with acute leukemia (AL). However, the differential diagnosis could represent a major challenge since the two disorders are all heterogeneous entities with overlapping clinical characteristics and radiological appearances. In this paper, we conduct a retrospective study to develop a model based on clinical data and magnetic resonance imaging (MRI) to distinguish CNSL from CNSI. A total of 108 patients with AL who underwent cranial MRI between January 2020 and December 2023 in our hospital were included. Univariate and multivariate logistic regression analyses were used to determine the independent predictors. A nomogram was developed based on the predictors, and the performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve. The validation cohort was used to test the predictive model. Hyperleukocytosis at initial diagnosis, marrow state, fever, conscious disturbance, coinfection in other sites and MRI (parenchyma type) were identified as independent factors. A nomogram was constructed and the discrimination was presented as AUC = 0.947 (95% CI 0.9105-0.984). Calibration of the nomogram showed that the predicted probability matched the actual probability well.

Uroflowmetry: nomograms in healthy young Pakistani men.

OBJECTIVE: To create a nomogram based on urine volume and flow of males without lower urinary tract symptoms. METHODS: The prospective, cross-sectional study was conducted at the Department of Urological Surgery and Transplantation, Jinnah Postgraduate Medical Centre, Karachi, from November 1, 2020, to October 31, 2022, and comprised healthy young males without lower urinary tract dysfunction who were recruited from the hospital as well as a large textile mill. They were asked to void on their normal desire. Uroflowmetry was done to determine maximum flow rate, average flow rate, and void volume values. A best-fit regression model was used to formulate uroflowmetry nomogram using average and maximum urine flow rate over voided volume. The sample size was calculated using PASS 2020 Power Analysis and Sample Size Software (2020). NCSS, LLC. Kaysville, Utah, USA. The database was developed on NCSS 2020 Statistical Software (2020). NCSS, LLC. Kaysville, Utah, USA for the data analysis. RESULTS: Of the 468 male subjects enrolled, data was analysed related to 432(92.3%). The mean age was 25.59±4.32 years. Mean maximum flow rate, average flow rate and void volume were 25.28±8.70mL/s, 14.77±4.79mL/s and 405.48±163.86mL, respectively. The association of age was noted with maximum flow rate (r=0.1435, p=0.004), average flow rate (r=0.1135, p=0.004) and void volume (r=0.0619, p=0.004). The best-fitted model for maximum and average flow rate was subsequently developed which was statistically significant (p<0.05). CONCLUSIONS: The nomograms developed could reliably predict the maximal flow rate in young Pakistani men.

The value of nomogram based on MRI functional imaging in differentiating cerebral alveolar echinococcosis from brain metastases.

OBJECTIVE: This study aims to evaluate the effectiveness of a nomogram model constructed using Diffusion Kurtosis Imaging (DKI) and 3D Arterial Spin Labeling (3D-ASL) functional imaging techniques in distinguishing between cerebral alveolar echinococcosis (CAE) and brain metastases (BM). METHODS: Prospectively collected were 24 cases (86 lesions) of patients diagnosed with CAE and 16 cases (69 lesions) of patients diagnosed with BM at the affiliated hospital of Qinghai University from 2018 to 2023, confirmed either pathologically or through comprehensive diagnosis. Both patient groups underwent DKI and 3D-ASL scanning. DKI parameters (Kmean, Dmean, FA, ADC) and cerebral blood flow (CBF) were analyzed for the parenchymal area, edema area, and symmetrical normal brain tissue area in both groups. There were 155 lesions in total in the two groups of patients. We used SPSS to randomly select 70% as the training set (108 lesions) and the remaining 30% as the test set (47 lesions) and performed a difference analysis between the two groups. The independent factors distinguishing CAE from BM were identified using univariate and multivariate logistic regression analyses. Based on these factors, a diagnostic model was constructed and expressed as a nomogram. RESULT: Univariate and multivariate logistic regression analyses identified nDmean1 and nCBF1 in the lesion parenchyma area, as well as nKmean2 and nDmean2 in the edema area, as independent factors for distinguishing CAE from BM. The model's performance, measured by the area under the ROC curve (AUC), had values of 0.942 and 0.989 for the training and test sets, respectively. Calibration curves demonstrated that the predicted probabilities were highly consistent with the actual values, and DCA confirmed the model's high clinical utility. CONCLUSION: The nomogram model, which incorporates DKI and 3D-ASL functional imaging, effectively distinguishes CAE from BM. It offers an intuitive, accurate, and non-invasive method for differentiation, thus providing valuable guidance for subsequent clinical decisions.

Development of a nomogram for deep vein thrombosis in patients with tibial plateau fractures based on systemic Immune-inflammation index.

In recent years, the incidence of tibial plateau fractures (TPF) has been on the rise. Deep vein thrombosis (DVT) may lead to poor prognosis in patients. The systemic immune-inflammation index(SII) are novel biomarkers of inflammation, and this study aims to verify their predictive effect and construct the nomogram model. This study used binary logistic regression analysis to predict the predictive effect of SII on the occurrence of DVT in tibial plateau fracture patients. And use R studio to construct nomogram model. The results showed that Age (1.03 [1, 1.06], p = 0.032), SII (3.57 [1.68, 7.61], p = 0.04), and NC (7.22 [3.21, 16.26], p < 0.001) were independent predictive factors for DVT. The nomogram demonstrated good predictive performance with small errors in both the training and validation groups, and most clinical patients could benefit from them. The nomogram constructed based on SII can assist clinicians in early assessment of the probability of DVT occurrence.

Risk factors and predictive model for nosocomial infections by extensively drug-resistant Acinetobacter baumannii.

Background: Extensively drug-resistant Acinetobacter baumannii (XDRAB) has become a significant pathogen in hospital environments, particularly in intensive care units (ICUs). XDRAB's resistance to conventional antimicrobial treatments and ability to survive on various surfaces pose a substantial threat to patient health, often resulting in severe infections such as ventilator-associated pneumonia (VAP) and bloodstream infections (BSI). Methods: We retrospectively analyzed clinical data from 559 patients with XDRAB infections admitted to Jinhua Central Hospital between January 2021 and December 2023. Patients were randomly divided into a training set (391 cases) and a testing set (168 cases). Variables were selected using Lasso regression and logistic regression analysis, and a predictive model was constructed and validated internally and externally. Model performance and clinical utility were evaluated using the Hosmer-Lemeshow test, C-index, ROC curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: Lasso regression analysis was used to screen 35 variables, selecting features through 10-fold cross-validation. We chose lambda.1se=0.03450 (log(lambda.1se)=-3.367), including 10 non-zero coefficient features. These features were then included in a multivariate logistic regression analysis, identifying 8 independent risk factors for XDRAB infection: ICU stay of 1-7 days (OR=3.970, 95%CI=1.586-9.937), ICU stay >7 days (OR=12.316, 95%CI=5.661-26.793), hypoproteinemia (OR=3.249, 95%CI=1.679-6.291), glucocorticoid use (OR=2.371, 95%CI=1.231-4.564), urinary catheterization (OR=2.148, 95%CI=1.120-4.120), mechanical ventilation (OR=2.737, 95%CI=1.367-5.482), diabetes mellitus (OR=2.435, 95%CI=1.050-5.646), carbapenem use (OR=6.649, 95%CI=2.321-19.048), and ß-lactamase inhibitor use (OR=4.146, 95%CI=2.145-8.014). These 8 factors were used to construct a predictive model visualized through a nomogram. The model validation showed a C-index of 0.932 for the training set and 0.929 for the testing set, with a Hosmer-Lemeshow test p-value of 0.47, indicating good calibration. Furthermore, the DCA curve demonstrated good clinical decision-making performance, and the CIC curve confirmed the model's reliable clinical impact. Conclusion: Regression analysis identified ICU stay duration, hypoproteinemia, glucocorticoid use, urinary catheterization, mechanical ventilation, diabetes mellitus, carbapenem use, and ß-lactamase inhibitor use as independent risk factors for XDRAB infection. The corresponding predictive model demonstrated high accuracy and stability.

Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy.

Sepsis-associated encephalopathy (SAE) is a frequent and severe complication in septic patients, characterized by diffuse brain dysfunction resulting from systemic inflammation. Accurate prediction of long-term mortality in these patients is critical for improving clinical outcomes and guiding treatment strategies. We conducted a retrospective cohort study using the MIMIC IV database to identify adult patients diagnosed with SAE. Patients were randomly divided into a training set (70%) and a validation set (30%). Least absolute shrinkage and selection operator regression and multivariate logistic regression were employed to identify significant predictors of 1-year mortality, which were then used to develop a prognostic nomogram. The model's discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. A total of 3,882 SAE patients were included in the analysis. The nomogram demonstrated strong predictive performance with AUCs of 0.881 (95% CI: 0.865, 0.896) in the training set and 0.859 (95% CI: 0.830, 0.888) in the validation set. Calibration plots indicated good agreement between predicted and observed 1-year mortality rates. The decision curve analysis showed that the nomogram provided greater net benefit across a range of threshold probabilities compared to traditional scoring systems such as Glasgow Coma Scale and Sequential Organ Failure Assessment. Our study presents a robust and clinically applicable nomogram for predicting 1-year mortality in SAE patients. This tool offers superior predictive performance compared to existing severity scoring systems and has significant potential to enhance clinical decision-making and patient management in critical care settings.

Nomograms predicting benefit after immunotherapy in oral bifidobacteria supplementation ICC patients: a retrospective study.

PURPOSE: The objective of this study was to develop nomograms for predicting outcomes following immunotherapy in patients diagnosed with intrahepatic cholangiocarcinoma (ICC). PATIENTS AND METHODS: A retrospective analysis was conducted on data from 75 ICC patients who received immunotherapy at Jinling Hospital and Drum Hospital. The discriminative power, accuracy, and clinical applicability of the nomograms were assessed using the concordance index (C-index), calibration curve, and decision curve analysis (DCA). The predictive performance of the nomograms for overall survival (OS) and progression-free survival (PFS) was evaluated using the area under the receiver operating characteristic (ROC) curve. Kaplan-Meier curves were also generated for validation purposes. RESULTS: Multivariable analysis identified independent prognostic factors for OS, including CA19-9 levels, portal vein tumor thrombus (PVTT) grade, bifidobacteria administration, and surgery. The C-index of the nomogram for OS prediction was 0.722 (95% confidence interval [CI]: 0.661-0.783). Independent prognostic factors for PFS included CA19-9 levels, albumin, and bilirubin, with a C-index of 0.678 (95% CI: 0.612-0.743) for the nomogram predicting PFS. Calibration curves demonstrated strong concordance between predicted and observed outcomes, while DCA and Kaplan-Meier curves further supported the clinical utility of the nomogram. CONCLUSION: The nomogram developed in this study demonstrated favorable performance in predicting the prognosis of ICC patients undergoing immunotherapy. Additionally, our findings, for the first time, identified probiotics as a potential prognostic marker for immunotherapy. This prognostic model has the potential to enhance patient selection for immunotherapy and improve clinical decision-making.

A Nomogram for Predicting Infertility Risk in Patients With Varicocele Using Inflammatory Markers.

The purpose of this study was to develop a nomogram using hemogram inflammatory markers to predict the risk of infertility in patients with varicocele (VC). Patients with VC from March 2022 to June 2024 were retrospectively investigated. We divided the patients into two groups based on their fertility status. A total of 162 patients were enrolled: 81 in the infertile group and 81 in the fertile group. Statistical differences were observed between the two groups in lymphocyte, monocyte, erythrocyte, red cell distribution width (RDW), mean erythrocyte volume (MCV), mean platelet volume (MPV), platelet distribution width (PDW), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), MPV/platelet ratio (MPVPR), and systemic inflammation response index (SIRI) (p < .05). The 162 patients were divided into a modeling cohort and a validation cohort in a 7:3 ratio. A predictive nomogram was constructed based on independent influencing factors identified through univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis, calibration curve, and decision curve analysis were used to assess the model's performance. Multivariate logistic regression analysis indicated that erythrocyte count, PDW, NLR, and SIRI were independent influencing factors. The area under the curve for the nomogram predicting the risk of infertility in patients with VC was 0.869 in the validation cohort. The nomogram demonstrated good predictive performance. In this study, we developed an effective predictive nomogram for assessing the risk of infertility in VC patients using inflammatory markers. However, further external validation is crucial.

Development and validation of a clinical-radiomics nomogram for the early prediction of Klebsiella pneumoniae liver abscess.

BACKGROUND AND AIM: Pyogenic liver abscess (PLA) is a devastating and potentially life-threatening disease globally, with Klebsiella pneumoniae liver abscess (KPLA) being the most prevalent in Asia. This study aims to develop an effective and comprehensive nomogram combining clinical and radiomics features for early prediction of KPLA. METHODS: 255 patients with PLA from 2013 to 2023 were enrolled and randomly divided into the training and validation cohorts at a 7:3 ratio. The differences between the two cohorts of patients were assessed via univariate analysis. The radiomics features were extracted from imaging data from enhanced CT of liver abscesses. The optimal radiomics features were filtered using the independent sample t-test and least absolute shrinkage and selection operator, and a radiomics score (Rad-score) was calculated by weighting their respective coefficients. Clinically independent predictors were identified from the clinical data and combined with the Rad-score to develop a nomogram by multivariate logistic regression. The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and clinical decision curve. RESULTS: The nomogram incorporated four clinical features of diabetes mellitus, cryptogenic liver abscess, C-reactive protein level, and splenomegaly, and the Rad-score that was constructed based on seven optimal radiomics features. It had an AUC of 0.929 (95% CI, 0.894-0.964) and 0.923 (95% CI, 0.864-0.981) in the training and validation cohorts, respectively. The calibration and decision curves showed that the nomogram had good agreement and clinical applicability. CONCLUSIONS: The clinical-radiomics nomogram performed well in predicting KPLA, hopefully serving as a reference for early diagnosis of KPLA.

Prediction model for early diagnosis of late-onset sepsis in preterm newborns.

AIM: Late-onset neonatal sepsis has a high mortality rate in premature infants. To date, no single test in the evaluation of neonatal sepsis has been demonstrated to be both sensitive and specific enough to assist in timely decision making. The aim of our study is to develop a predictive model that can be applied to all premature babies, using clinical and laboratory findings in premature babies, to recognize late-onset neonatal sepsis. STUDY DESIGN: 65 premature patients diagnosed with culture-proven late-onset neonatal sepsis and hospitalized in Dr. Behcet Uz Pediatric Diseases and Surgery Training and Research Hospital neonatal intensive care unit between January 2018 and December 2020, and 65 premature newborns of similar age and gender who did not have sepsis were included in the study retrospectively. RESULTS: In our study, feeding difficulties, worsening in clinical appearance and fever were found to be significant among clinical findings, while thrombocytopenia and high C-reactive protein among laboratory findings are the strongest data supporting late-onset neonatal sepsis. In multiple regression analysis, thrombocytopenia, mean platelet volume, C-reactive protein, lymphocyte count and feeding difficulties had the highest odds ratio (p < 0.05). By converting these data into a scoring system, a nomogram was created that can be easily used by all clinicians. CONCLUSION: In our study, we developed a scoring system that can be easily applied to all premature patients by evaluating the clinical and laboratory findings in late-onset neonatal sepsis. We think that it will help in recognizing late-onset neonatal sepsis and strengthening the treatment decision. Predicting the individual probability of sepsis in preterm newborns may provide benefits for uninfected newborns to be exposed to less antibiotics, not to be separated from mother and baby, and to reduce healthcare system expenditures. The nomogram can be used to assess the likelihood of sepsis and guide treatment decision.

A non-invasive predictive model based on multimodality ultrasonography images to differentiate malignant from benign focal liver lesions.

We have developed a non-invasive predictive nomogram model that combines image features from Sonazoid contrast-enhanced ultrasound (SCEUS) and Sound touch elastography (STE) with clinical features for accurate differentiation of malignant from benign focal liver lesions (FLLs). This study ultimately encompassed 262 patients with FLLs from the First Hospital of Shanxi Medical University, covering the period from March 2020 to April 2023, and divided them into training set (n = 183) and test set (n = 79). Logistic regression analysis was used to identify independent indicators and develop a predictive model based on image features from SCEUS, STE, and clinical features. The area under the receiver operating characteristic (AUC) curve was determined to estimate the diagnostic performance of the nomogram with CEUS LI-RADS, and STE values. The C-index, calibration curve, and decision curve analysis (DCA) were further used for validation. Multivariate and LASSO logistic regression analyses identified that age, ALT, arterial phase hyperenhancement (APHE), enhancement level in the Kupffer phase, and Emean by STE were valuable predictors to distinguish malignant from benign lesions. The nomogram achieved AUCs of 0.988 and 0.978 in the training and test sets, respectively, outperforming the CEUS LI-RADS (0.754 and 0.824) and STE (0.909 and 0.923) alone. The C-index and calibration curve demonstrated that the nomogram offers high diagnostic accuracy with predicted values consistent with actual values. DCA indicated that the nomogram could increase the net benefit for patients. The predictive nomogram innovatively combining SCEUS, STE, and clinical features can effectively improve the diagnostic performance for focal liver lesions, which may help with individualized diagnosis and treatment in clinical practice.

Nomogram for predicting short-term response to anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration: An observational study.

BACKGROUND: Anti-vascular endothelial growth factor (anti-VEGF) therapy is critical for managing neovascular age-related macular degeneration (nAMD), but understanding factors influencing treatment efficacy is essential for optimizing patient outcomes. AIM: To identify the risk factors affecting anti-VEGF treatment efficacy in nAMD and develop a predictive model for short-term response. METHODS: In this study, 65 eyes of exudative AMD patients after anti-VEGF treatment for ≥ 1 mo were observed using optical coherence tomography angiography. Patients were classified into non-responders (n = 22) and responders (n = 43). Logistic regression was used to determine independent risk factors for treatment response. A predictive model was created using the Akaike Information Criterion, and its performance was assessed with the area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA) with 500 bootstrap re-samples. RESULTS: Multivariable logistic regression analysis identified the number of junction voxels [odds ratio = 0.997, 95% confidence interval (CI): 0.993-0.999, P = 0.010] as an independent predictor of positive anti-VEGF treatment outcomes. The predictive model incorporating the fractal dimension, number of junction voxels, and longest shortest path, achieved an area under the curve of 0.753 (95%CI: 0.622-0.873). Calibration curves confirmed a high agreement between predicted and actual outcomes, and DCA validated the model's clinical utility. CONCLUSION: The predictive model effectively forecasts 1-mo therapeutic outcomes for nAMD patients undergoing anti-VEGF therapy, enhancing personalized treatment planning.

The 2012 Briganti nomogram predicts disease progression after surgery in high-risk prostate cancer patients.

Objectives: We tested whether the 2012 Briganti nomogram for the risk of pelvic lymph node invasion (PLNI) may represent a predictor of disease progression after surgical management in high-risk (HR) prostate cancer (PCa) patients according to the European Association of Urology. Methods: Between January 2013 and December 2021, HR PCa patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND) were identified. The 2012 Briganti nomogram was evaluated as a continuous and categorical variable, which was dichotomized using the median. The risk of disease progression, defined as the event of biochemical recurrence and/or local recurrence/distant metastases was assessed by Cox regression models. Results: Overall, 204 patients were identified. The median 2012 Briganti nomogram score resulted 12.0% (IQR: 6.0-22.0%). PLNI was detected in 57 (27.9%) cases. Compared to patients who had preoperatively a 2012 Briganti nomogram score ≤12%, those with a score >12% were more likely to present with higher percentage of biopsy positive cores, palpable tumors at digital rectal examination, high-grade cancers at prostate biopsies, and unfavorable pathology in the surgical specimen. At multivariable Cox regression analyses, disease progression, which occurred in 85 (41.7%) patients, was predicted by the 2012 Briganti nomogram score (HR: 1.02; 95%CI: 1.00-1.03; p = 0.012), independently by tumors presenting as palpable (HR: 1.78; 95%CI: 1.10.2.88; p = 0.020) or the presence of PLNI in the surgical specimen (HR: 3.73; 95%CI: 2.10-5.13; p = 0.012). Conclusions: The 2012 Briganti nomogram represented an independent predictor of adverse prognosis in HR PCa patients treated with RARP and ePLND. As the score increased, so patients were more likely to experience disease progression, independently by the occurrence of PLNI. The association between the nomogram, unfavorable pathology and tumor behavior might turn out to be useful for selecting a subset of patients needing different treatment paradigms in HR disease.