Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke.

BACKGROUND: Post-stroke infection is the most common complication of stroke and poses a huge threat to patients. In addition to prolonging the hospitalization time and increasing the medical burden, post-stroke infection also significantly increases the risk of disease and death. Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke (AIS) is of great significance. It can guide clinical practice to perform corresponding prevention and control work early, minimizing the risk of stroke-related infections and ensuring favorable disease outcomes. AIM: To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model. METHODS: The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected. Baseline data and post-stroke infection status of all study subjects were assessed, and the risk factors for post-stroke infection in patients with AIS were analyzed. RESULTS: Totally, 48 patients with AIS developed stroke, with an infection rate of 23.3%. Age, diabetes, disturbance of consciousness, high National Institutes of Health Stroke Scale (NIHSS) score at admission, invasive operation, and chronic obstructive pulmonary disease (COPD) were risk factors for post-stroke infection in patients with AIS (P < 0.05). A nomogram prediction model was constructed with a C-index of 0.891, reflecting the good potential clinical efficacy of the nomogram prediction model. The calibration curve also showed good consistency between the actual observations and nomogram predictions. The area under the receiver operating characteristic curve was 0.891 (95% confidence interval: 0.839-0.942), showing predictive value for post-stroke infection. When the optimal cutoff value was selected, the sensitivity and specificity were 87.5% and 79.7%, respectively. CONCLUSION: Age, diabetes, disturbance of consciousness, NIHSS score at admission, invasive surgery, and COPD are risk factors for post-stroke infection following AIS. The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

Development and validation of a nomogram for predicting heterotopic ossification following spinal cord injury.

OBJECTIVE: Heterotopic ossification (HO) following spinal cord injury (SCI) can severely compromise patient mobility and quality of life. Precise identification of SCI patients at an elevated risk for HO is crucial for implementing early clinical interventions. While the literature presents diverse correlations between HO onset and purported risk factors, the development of a predictive model to quantify these risks is likely to bolster preventive approaches. This study is designed to develop and validate a nomogram-based predictive model that estimates the likelihood of HO in SCI patients, utilizing recognized risk factors to expedite clinical decision-making processes. METHODS: We recruited a total of 145 patients with SCI and presenting with HO who were hospitalized at the China Rehabilitation Research Center, Beijing Boai Hospital, from June 2016 to December 2022. Additionally, 337 patients with SCI without HO were included as controls. Comprehensive data were collected for all study participants, and subsequently, the dataset was randomly partitioned into training and validation groups. Using Least Absolute Shrinkage and Selection Operator regression, variables were meticulously screened during the pretreatment phase to formulate the predictive model. The efficacy of the model was then assessed using metrics including receiver-operating characteristic (ROC) analysis, calibration assessment, and decision curve analysis. RESULTS: The final prediction model incorporated age, sex, complete spinal cord injury status, spasm occurrence, and presence of deep vein thrombosis (DVT). Notably, the model exhibited commendable performance in both the training and validation groups, as evidenced by areas under the ROC curve (AUCs) of 0.756 and 0.738, respectively. These values surpassed the AUCs obtained for single variables, namely age (0.636), sex (0.589), complete spinal cord injury (0.681), spasm occurrence (0.563), and DVT presence (0.590). Furthermore, the calibration curve illustrated a congruence between the predicted and actual outcomes, indicating the high accuracy of the model. The decision curve analysis indicated substantial net benefits associated with the application of the model, thereby underscoring its practical utility. CONCLUSIONS: HO following SCI correlates with several identifiable risk factors, including male gender, youthful age, complete SCI, spasm occurrence and DVT. Our predictive model effectively estimates the likelihood of HO development by leveraging these factors, assisting physicians in identifying patients at high risk. Subsequently, correct positioning to prevent spasm-related deformities and educating healthcare providers on safe lower limb mobilization techniques are crucial to minimize muscle injury risks from rapid iliopsoas muscle extension. Additionally, the importance of early DVT prevention through routine screening and anticoagulation is emphasized to further reduce the incidence of HO.

PAX1 methylation as a robust predictor: developing and validating a nomogram for assessing endocervical curettage (ECC) necessity in human papillomavirus16/18-positive women undergoing colposcopy.

OBJECTIVE: The major challenge in routine endocervical curettage (ECC) among Human Papillomavirus (HPV) 16/18-positive patients is that only a small fraction benefit. Nevertheless, current reported models often overestimate the validity and necessity of ECC, making it difficult to improve benefits for patients. This research hypothesized that assessing paired boxed gene 1 methylation levels (PAX1m) and clinical characteristics could enhance the predictive accuracy of detecting additional high-grade squamous intraepithelial lesions or worse (HSIL +) through ECC that were not identified by colposcopy-directed biopsy (CDB). METHODS: Data from 134 women with HPV16/18 positivity undergoing CDB and ECC between April 2018 and April 2022 were collected and analyzed. Quantitative methylation-specific polymerase chain reaction (qMSP) was utilized to measure PAX1m, expressed as ΔCp. Univariate and multivariate regression analyses were conducted to screen variables and select predictive factors. A nomogram was constructed using multivariate logistic regression to predict additional HSIL + detected by ECC. The discrimination, calibration, and clinical utility of the nomogram were evaluated using receiver operating characteristic curves (ROC) and the calibration plot. RESULTS: Age (odds ratio [OR], 5.654; 95% confidence interval [CI], 1.131-37.700), cytology (OR, 24.978; 95% CI, 3.085-540.236), and PAX1 methylation levels by grade (PAX1m grade) (OR, 7.801; 95% CI, 1.548-44.828) were independent predictive factors for additional detection of HSIL + by ECC. In HPV16/18-positive women, the likelihood of additional detection of HSIL + through ECC increased with the severity of cytological abnormalities, peaking at 43.8% for high-grade cytological lesions. Moreover, when cytological findings indicated low-grade lesions, PAX1 methylation levels were positively correlated with the additional detection of HSIL + by ECC (P value < 0.001). A nomogram prediction model was developed (area under curve (AUC) = 0.946; 95% CI, 0.901-0.991), demonstrating high sensitivity (90.9%) and specificity (90.5%) at the optimal cutoff point of 107. Calibration analysis confirmed the model's strong agreement between predicted and observed probabilities. CONCLUSION: The clinical nomogram presented promising predictive performance for the additional detection of HSIL + through ECC among women with HPV16/18 infection. PAX1 methylation level could serve as a valuable tool in guiding individualized clinical decisions regarding ECC for patients with HPV 16/18 infection, particularly in cases of low-grade cytological findings.

A nomogram of anastomotic stricture after rectal cancer: a retrospective cohort analysis.

BACKGROUND: Anastomotic stricture significantly impacts patients' quality of life and long-term prognosis. However, current clinical practice lacks accurate tools for predicting anastomotic stricture. This study aimed to develop a nomogram to predict anastomotic stricture in patients with rectal cancer who have undergone anterior resection. METHODS: A total of 1542 eligible patients were recruited for the study. Least absolute shrinkage selection operator (Lasso) analysis was used to preliminarily select predictors. A prediction model was constructed using multivariate logistic regression and presented as a nomogram. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration diagrams, and decision curve analysis (DCA). Internal validation was conducted by assessing the model's performance on a validation cohort. RESULTS: 72 (4.7%) patients were diagnosed with anastomotic stricture. Participants were randomly divided into training (n = 1079) and validation (n = 463) sets. Predictors included in this nomogram were radiotherapy, diverting stoma, anastomotic leakage, and anastomotic distance. The area under the ROC curve (AUC) for the training set was 0.889 [95% confidence interval (CI) 0.840-0.937] and for the validation set, it was 0.930 (95%CI 0.879-0.981). The calibration curve demonstrated a strong correlation between predicted and observed outcomes. DCA results showed that the nomogram had clinical value in predicting anastomotic stricture in patients after anterior resection of rectal cancer. CONCLUSION: We developed a predictive model for anastomotic stricture following anterior resection of rectal cancer. This nomogram could assist clinicians in predicting the risk of anastomotic stricture, thus improving patients' quality of life and long-term prognosis.

Development of risk prediction nomogram for neonatal sepsis in Group B Streptococcus-colonized mothers: a retrospective study.

Neonatal clinical sepsis is recognized as a significant health problem, This study sought to identify a predictive model of risk factors for clinical neonatal sepsis. A retrospective study was conducted from 1 October 2018 to 31 March 2023 in a large tertiary hospital in China. Neonates were divided into patients and controls based on the occurrence of neonatal sepsis. A multivariable model was used to determine risk factors and construct models.The utilization and assessment of model presentation were conducted using Norman charts and web calculators, with a focus on model differentiation, calibration, and clinical applicability (DCA). Furthermore, the hospital's data from 1 April 2023 to 1 January 2024 was utilized for internal validation. In the modelling dataset, a total of 339 pairs of mothers and their newborns were included in the study and divided into two groups: patients (n = 84, 24.78%) and controls (n = 255, 75.22%). Logistic regression analysis was performed to examine the relationship between various factors and outcome. The results showed that maternal age < 26 years (odds ratio [OR] = 2.16, 95% confidence interval [CI] 1.06-4.42, p = 0.034), maternal gestational diabetes (OR = 2.17, 95% CI 1.11-4.27, p = 0.024), forceps assisted delivery (OR = 3.76, 95% CI 1.72-5.21, p = 0.032), umbilical cord winding (OR = 1.75, 95% CI 1.32-2.67, p = 0.041) and male neonatal sex (OR = 1.59, 95% CI 1.00-2.62, p = 0.050) were identified as independent factors influencing the outcome of neonatal clinical sepsis. A main effects model was developed incorporating these five significant factors, resulting in an area under the curve (AUC) value of 0.713 (95% CI 0.635-0.773) for predicting the occurrence of neonatal clinical sepsis. In the internal validation cohort, the AUC value of the model was 0.711, with a 95% CI of 0.592-0.808. A main effects model incorporating the five significant factors was constructed to help healthcare professionals make informed decisions and improve clinical outcomes.

Design and clinical application of a risk prediction model for diabetic foot.

OBJECTIVE: To construct and evaluate a nomogram prediction model for the risk of diabetic foot in patients with type 2 diabetes based on their clinical data, and to assist clinical healthcare professionals in identifying high-risk factors and developing targeted intervention measures. METHODS: We retrospectively collected clinical data from 478 hospitalized patients with type 2 diabetes at the First Affiliated Hospital of Shantou University Medical College from January 2019 to December 2021. The patients were divided into a diabetic foot group (n=312) and a non-diabetic foot group (n=166) based on whether they had diabetic foot. The baseline data of both groups were collected. Univariate and multivariate analyses as well as logistic regression analysis were conducted to explore the risk factors for diabetic foot. A nomogram prediction model was established using the package "rms" version 4.3. The model was internally validated using the area under the receiver operating characteristic curve (AUC). Additionally, the decision curve analysis (DCA) was performed to evaluate the performance of the nomogram model. RESULTS: The results from the logistic regression analysis revealed that being male, smoking, duration of diabetes, glycated hemoglobin, hyperlipidemia, and atherosclerosis were influencing factors for diabetic foot (all P<0.05). The AUC of the model in predicting diabetic foot was 0.804, with a sensitivity of 75.3% and specificity of 74.4%. Harrell's C-index of the nomogram prediction model for diabetic foot was 0.804 (95% CI: 0.762-0.844), with a threshold value of >0.675. The DCA findings demonstrated that the nomogram model provided a net clinical benefit. CONCLUSION: The nomogram prediction model constructed in this study showed good predictive performance and can provide a basis for clinical workers to prevent and intervene in diabetic foot, thereby improving the overall diagnosis and treatment.

Prognostic Factors and Construction of Nomogram Prediction Model of Lung Cancer Patients Using Clinical and Blood Laboratory Parameters.

Objective: This work aimed to explore the prognostic risk factors of lung cancer (LC) patients and establish a line chart prediction model. Methods: A total of 322 LC patients were taken as the study subjects. They were randomly divided into a training set (n = 202) and a validation set (n = 120). Basic information and laboratory indicators were collected, and the progression-free survival (PFS) and overall survival (OS) were followed up. Single-factor and cyclooxygenase (COX) multivariate analyses were performed on the training set to construct a Nomogram prediction model, which was validated with 120 patients in the validation set, and Harrell's consistency was analyzed. Results: Single-factor analysis revealed significant differences in PFS (P<0.05) between genders, body mass index (BMI), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), squamous cell carcinoma antigen (SCCA), treatment methods, treatment response evaluation, smoking status, presence of pericardial effusion, and programmed death ligand 1 (PD-L1) at 0 and 1-50%. Significant differences in OS (P<0.05) were observed for age, tumor location, treatment methods, White blood cells (WBC), uric acid (UA), CA125, pro-gastrin-releasing peptide (ProGRP), SCCA, cytokeratin fragment 21 (CYFRA21), and smoking status. COX analysis identified male gender, progressive disease (PD) as treatment response, and SCCA > 1.6 as risk factors for LC PFS. The consistency indices of the line chart models for predicting PFS and OS were 0.782 and 0.772, respectively. Conclusion: Male gender, treatment response of PD, and SCCA > 1.6 are independent risk factors affecting the survival of LC patients. The PFS line chart model demonstrates good concordance.

Clinical nomogram prediction model to assess the risk of prolonged ICU length of stay in patients with diabetic ketoacidosis: a retrospective analysis based on the MIMIC-IV database.

BACKGROUND: The duration of hospitalization, especially in the intensive care unit (ICU), for patients with diabetic ketoacidosis (DKA) is influenced by patient prognosis and treatment costs. Reducing ICU length of stay (LOS) in patients with DKA is crucial for optimising healthcare resources utilization. This study aimed to establish a nomogram prediction model to identify the risk factors influencing prolonged LOS in ICU-managed patients with DKA, which will serve as a basis for clinical treatment, healthcare safety, and quality management research. METHODS: In this single-centre retrospective cohort study, we performed a retrospective analysis using relevant data extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Clinical data from 669 patients with DKA requiring ICU treatment were included. Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model. Subsequently, the selected variables were subjected to a multifactorial logistic regression analysis to determine independent risk factors for prolonged ICU LOS in patients with DKA. A nomogram prediction model was constructed based on the identified predictors. The multivariate variables included in this nomogram prediction model were the Oxford acute severity of illness score (OASIS), Glasgow coma scale (GCS), acute kidney injury (AKI) stage, vasoactive agents, and myocardial infarction. RESULTS: The prediction model had a high predictive efficacy, with an area under the curve value of 0.870 (95% confidence interval [CI], 0.831-0.908) in the training cohort and 0.858 (95% CI, 0.799-0.916) in the validation cohort. A highly accurate predictive model was depicted in both cohorts using the Hosmer-Lemeshow (H-L) test and calibration plots. CONCLUSION: The nomogram prediction model proposed in this study has a high clinical application value for predicting prolonged ICU LOS in patients with DKA. This model can help clinicians identify patients with DKA at risk of prolonged ICU LOS, thereby enhancing prompt intervention and improving prognosis.

Risk Factors Analysis and Nomogram Model Establishment of Hidden Blood Loss in Overweight and Obese Elderly Patients After Total Hip Arthroplasty.

Background: Total hip arthroplasty (THA) has become the first-choice treatment for elderly patients with end-stage hip disease. The high amount of hidden blood loss (HBL) in overweight and obese patients after THA not only affects rapid recovery, but also results in a greater economic burden. We aimed to identify risk factors that contribute to elevated HBL in overweight and obese patients after THA by retrospective analysis, and establish a nomogram prediction model for massive HBL in overweight and obese patients after THA. Methods: A total of 505 overweight and obese patients treated with THA were included and randomly divided into modeling and validation sets according to a 7:3 ratio. The demographic and relevant clinical data of the patients were collected. The independent risk factors affecting HBL after THA in overweight and obese patients were obtained by Pearson, independent sample T-test, and multiple linear regression analyses. R software was used to establish a nomogram prediction model for postoperative HBL, as well as a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: HBL was 911±438 mL, accounting for 79.5±13.1% of the total perioperative blood loss (1104±468 mL). A multiple linear regression analysis showed that HBL was associated with necrosis of the femoral head, absence of hypertension, longer operative time, higher preoperative erythrocytes, and higher preoperative D-dimer levels. The areas under the ROC curve (AUC) for the modeling and validation sets were 0.751 and 0.736, respectively, while the slope of the calibration curve was close to 1. The DCA curve demonstrated a better net benefit at a risk of HBL ≥1000 ml in both the training and validation groups. Conclusion: HBL was an important component of total blood loss (TBL) after THA in overweight and obese patients. Necrosis of the femoral head, absence of hypertension, longer operative time, higher preoperative erythrocytes, and higher preoperative D-dimer levels were independent risk factors for postoperative HBL in these patients. The predictive model constructed based these data had better discriminatory power and accuracy, and could result in better net benefit for patients.

The Influence of the Preoperative Albumin to Alkaline Phosphatase Ratio on Overall Survival in Post-Radical Surgery for Colorectal Cancer and the Construction of a Nomogram Prediction Model.

PURPOSE: The albumin to alkaline phosphatase ratio (AAPR) is a newly developed blood biomarker that has been reported to have prognostic value in several types of cancers. The aim of this study was to investigate the predictive value of AAPR in overall survival after radical colon cancer surgery in patients with stage I-III colorectal cancer (CRC). METHODS: The clinical data of 221 eligible patients with stage I ∼ III CRC were retrospectively analyzed. A series of survival analyses were performed to assess the prognostic value of AAPR. Univariate and multifactorial Cox analyses were performed to identify independent risk factors. Columnar graph prediction models were further constructed based on independent risk factors such as AAPR, and their predictive properties were validated. RESULTS: The optimal cutoff value of preoperative AAPR for postoperative overall survival (OS) in patients undergoing laparoscopic radical CRC was .495 as shown by univariate and multifactorial Cox regression analysis. The factors of age ≤65 years, Tumor-Node-Metastasis (TNM) stage I-II, tumor grading (high/medium differentiation), CEA ≤5, and AAPR ≥.495 were associated with better OS (P < .05). CONCLUSIONS: Preoperative AAPR level was a good predictor of postoperative survival in patients undergoing laparoscopic radical CRC surgery, and AAPR <.495 was an independent risk factor for decreased postoperative OS.

Construction of a nomogram risk prediction model for PICC-related venous thrombosis and its application.

OBJECTIVE: To explore the risk factors of the peripherally inserted central catheter (PICC)-related venous thrombosis and correspondingly construct a nomogram risk prediction model. METHODS: The clinical data of 401 patients receiving PICC catheterization in our hospital from June 2019 to June 2022 were retrospectively analyzed. The independent influencing factors for venous thrombosis were predicted using logistic regression analysis, and significant indicators were screened to construct a nomogram for predicting PICC-related venous thrombosis. The difference in predictive efficacy between simple clinical data and nomogram was analyzed using a receiver operating characteristic (ROC) curve, and the nomogram was internally validated. RESULTS: Single-factor analysis showed that catheter tip position, plasma D-dimer concentration, venous compression, malignant tumor, diabetes, history of thrombosis, history of chemotherapy, and history of PICC/CVC catheterization were correlated with PICC-related venous thrombosis. Further multi-factor analysis revealed that catheter tip position, plasma D-dimer elevation, venous compression, history of thrombosis and history of PICC/CVC catheterization were the risk factors for PICC-related venous thrombosis. Based on binary logistic regression analysis, a nomogram prediction model for PICC-related venous thrombosis was constructed. The area under the curve (AUC) was 0.876 (95%CI: 0.818-0.925), with a statistically significant difference (P < 0.01). CONCLUSION: The independent risk factors for PICC-related venous thrombosis are screened out, including catheter tip position, plasma D-dimer elevation, venous compression, history of thrombosis and history of PICC/CVC catheterization, and a nomogram prediction model with good effect is constructed to predict the risk of PICC-related venous thrombosis.

Establishment and validation of nomogram prediction model of cefoperazone/sulbactam-induced thrombocytopenia / 中国药房

OBJECTIVE To explore the predictive factors of cefoperazone/sulbactam-induced thrombocytopenia in adult inpatients， and to establish and validate the nomogram prediction model. METHODS Data of adult inpatients treated with cefoperazone/sulbactam in Xi’an Central Hospital from Jun. 30th， 2021 to Jun. 30th， 2023 were retrospectively collected. The training set and internal validation set were randomly constructed in a 7∶3 ratio. Singler factor and multifactor Logistic regression analysis were used to screen the independent predictors of cefoperazone/sulbactam-induced thrombocytopenia. The nomogram was drawn by using “RMS” of R 4.0.3 software， and the predictive performance of the model was evaluated by the receiver operating characteristic curve and C-index curve. Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration degree of the model. Using the same standard， the clinical data of hospitalized patients receiving cefoperazone/sulbactam in Xi’an First Hospital in the same period were collected for external validation of the nomogram prediction model. RESULTS A total of 1 045 patients in Xi’an Central Hospital were included in this study， among which 67 patients suffered from cefoperazone/sulbactam-induced thrombocytopenia， with an incidence of 6.41%. After the false positive patients were excluded， 473 patients were included finally， including 331 in the training set and 142 in theinternal validation set. Multifactor Logistic regression analysis showed that age [OR＝1.043， 95%CI （1.017， 1.070）]， estimated glomerular filtration rate （eGFR） [OR＝0.988，95%CI（0.977， 0.998）]， baseline platelet （PLT） [OR＝0.989， 95%CI（0.982， 0.996）]， nutritional risk [OR＝3.863， 95%CI（1.884， 7.921）] and cumulative defined daily doses （DDDs） [OR＝1.082， 95%CI（1.020， 1.147）] were independent predictors for cefoperazone/sulbactam-induced thrombocytopenia （P＜0.05）. The C-index values of the training set and the internal validation set were 0.824 [95%CI （0.759， 0.890）] and 0.828 [95%CI （0.749， 0.933）]， respectively. The results of the Hosmer-Lemeshow test showed that χ 2 values were 0.441 （P＝0.802） and 1.804 （P＝0.406）. In the external validation set， the C-index value was 0.808 [95%CI （0.672， 0.945）]， the χ 2 value of the Hosmer-Lemeshow test was 0.899 （P＝0.638）. CONCLUSIONS The independent predictors of cefoperazone/sulbactam-induced thrombocytopenia include age， baseline PLT， eGFR， nutritional risk and cumulative DDDs. The model has good predictive efficacy and extrapolation ability， which can help clinic identify the potential risk of cefoperazone/sulbactam-induced thrombocytopenia quickly and accurately.

Development and internal and external validation of a nomogram model for frailty risk among hospitalised older people using comprehensive geriatric assessment data.

BACKGROUND: Currently, there are few such studies about establishing the frailty prediction model on the basis of the research on the factors influencing frailty in older patients, which can better predict frailty and identify its risk factors, and then guide the formulation of intervention measures precisely, especially in the hospital setting in China. Meanwhile, comprehensive geriatric assessment (CGA) can provide measurable and substantial health improvements for frail older people. The study aimed to develop a nomogram model for frailty risk among hospitalised older people using CGA data and validated its predictive performance for providing a basis for medical staff to grasp the risk and risk factors of older inpatients' frailty conveniently and accurately, and to formulate reasonable nursing intervention plan. METHODS: We used CGA data of individuals over age 64. Demographic characteristics, geriatric syndrome assessment, and frailty assessment based on the FRAIL scale were included as potential predictors. Significant variables in univariate analysis were used to construct risk models by logistic regression analysis. We used the root mean square (rms) to develop the nomogram prediction model for frailty based on independent clinical factors. Nomogram performance was internally validated with Bootstrap resampling. The final model was externally validated using an independent validation data set and was assessed for discrimination and calibration. RESULTS: Data from 2226 eligible older inpatients were extracted. Five hundred sixty-two older inpatients (25.25%) suffered from frailty. The final prediction model included damaged skin, MNA-SF, GDS-15, Morse risk scores, hospital admission, ICI-Q-SF, Braden score, MMSE, BI scores, and Caprini scores. The prediction model displayed fair discrimination. The calibration curve demonstrated that the probabilities of frailty predicted by the nomogram were satisfactorily matched. CONCLUSIONS: The prediction model to identify hospitalised older people at high risk for frailty using comprehensive geriatric assessment data displayed fair discrimination and good predictive calibration. Therefore, it is inexpensive, easily applied, and accessible in clinical practice, containing variables routinely collected and readily available through consultation. It will be valuable for grasp older inpatients at high risk of frailty and risk factors in hospital setting to guide the formulation of intervention measures precisely for reversing and preventing frailty.

Construction and evaluation of a nomogram prediction model for aspiration pneumonia in patients with acute ischemic stroke.

Background: Aspiration Pneumonia (AP) is a leading cause of death in patients with Acute Ischemic Stroke (AIS). Early detection, diagnosis and effective prevention measures are crucial for improving patient prognosis. However, there is a lack of research predicting AP occurrence after AIS. This study aimed to identify risk factors and develop a nomogram model to determine the probability of developing AP after AIS. Method: A total of 3258 AIS patients admitted to Jinshan Hospital of Fudan University between January 1, 2016, and August 20, 2022, were included. Among them, 307 patients were diagnosed with AP (AP group), while 2951 patients formed the control group (NAP group). Univariate and multivariate logistic regression analyses were conducted to identify relevant risk factors for AP after AIS. These factors were used to establish a scoring system and develop a nomogram model using R software. Results: Univariate analysis revealed 20 factors significantly associated (P < 0.05) with the development of AP after AIS. These factors underwent multivariate logistic regression analysis, which identified age (elderly), National Institute of Health Stroke Scale (NIHSS) score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, elevated Fasting Blood Glucose (FBG), elevated C-Reactive Protein (CRP), elevated Neutrophil percentage (NEUT%), and decreased prealbumin as independent risk factors. A nomogram model incorporating these 11 risk factors was constructed, with a C-index of 0.872 (95 % CI: 0.845-0.899), indicating high accuracy. Calibration and clinical decision analyses demonstrated the model's reliability and clinical value. Conclusion: A nomogram model incorporating age, NIHSS score, dysphagia, atrial fibrillation, cardiac insufficiency, renal insufficiency, hepatic insufficiency, FBG, CRP, NEUT%, and prealbumin effectively predicts AP risk in AIS patients. This model provides guidance for early intervention strategies, enabling the identification of high-risk individuals for timely preventive measures.

Establishment of a prediction model for the impact of endometrial thickness on the day of embryo transfer on ectopic pregnancy in frozen-thawed embryo transfer cycle.

Objective: This study aims to investigate the factors affecting the ectopic pregnancy (EP) rate in the frozen-thawed embryo transfer (FET) cycle. Methods: This study retrospectively analyzed 5606 FET cycles, including 5496 cycles resulting in intrauterine pregnancy and 110 cycles resulting in EP. Smooth curve fitting and piece-wise linear regression were utilized to evaluate a non-linear association between endometrial thickness (EMT) and EP. Multiple logistic regression analysis was used to study the effect of EMT on the embryo transfer (ET) day and other indexes on EP rate after adjusting for confounding factors. A nomographic prediction model was employed to predict EP occurrence. The predictive efficacy of the model was assessed using the area under the receiver operating characteristic (ROC) curve (AUC), utilizing the bootstrap sampling method for internal validation. Results: After accounting for the confounding factors, the segmented linear regression analysis indicated that the EMT inflection point was 9 mm; the EP rate significantly decreased by 28% with each additional millimeter of EMT up to 9 mm (odds ratio (OR) = 0.72; 95% confidence interval (CI), 0.53-0.99; P = 0.0412) while insignificantly decreased when the EMT was greater than 9 mm (OR = 0.91; 95% CI, 0.76-1.08; P = 0.2487). Multivariate logistic regression analysis revealed that after adjusting for confounders, EP risk significantly increased in the number of previous EPs ≥ 1 (OR = 2.29; 95% CI, 1.26-4.16; P = 0.0064) and tubal factor infertility (OR = 3.86; 95% CI, 2.06-7.24; P < 0.0001). Conversely, EP risk was significantly reduced by the increased EMT (OR = 0.84; 95% CI, 0.74-0.96; P = 0.0078) and the blastocyst transfer (OR = 0.45; 95% CI, 0.27-0.76; P = 0.0027). These variables were used as independent variables in a nomogram prediction model, resulting in an AUC of 0.685. The nomination models were internally verified using self-sampling (bootstrap sampling resampling times = 500). This validation yielded an AUC of 0.689, with a sensitivity of 0.6915 and a specificity of 0.5790. The internal validation indicated minimal fluctuations in the AUC, signifying a relatively stable model. Conclusion: Undergoing EMT on the day of ET poses a separate EP risk in the FET cycle; to mitigate the EP incidence, the EMT should exceed 9 mm before ET. Furthermore, previous EPs and tubal factor infertility were additional factors independently increasing EP risk. Furthermore, implementing blastocyst transfer demonstrated that EP incidence was significantly reduced. Utilizing a nomogram predicting system enables EP risk evaluation before ET for individual patients, establishing a basis for devising clinical strategies for ET.

[Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning].

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.

Risk factors for postoperative pulmonary infection in patients with non-small cell lung cancer: analysis based on regression models and construction of a nomogram prediction model.

OBJECTIVE: This study was designed to analyze risk factors for postoperative pulmonary infection (PPI) in patients with non-small cell lung cancer (NSCLC) based on regression models and to construct a corresponding nomogram prediction model. METHODS: A total of 244 patients with NSCLC who received surgical treatment from June 2015 to January 2017 were retrospectively analyzed. According to the PPI, they were assigned to a pulmonary infection group (n=27) or non-pulmonary infection group (n=217). The independent risk factors for PPI in NSCLC patients were screened through least absolute shrinkage and selection operator (LASSO) and logistic regression analysis, and a corresponding nomogram prediction model was constructed. RESULTS: A total of 244 NSCLC patients were included, including 27 with PPI (11.06%). According to LASSO regression-based screening, age, diabetes mellitus (DM), tumor node metastasis (TNM) staging, chemotherapy regimen, chemotherapy cycle, post-chemotherapy albumin (g/L), pre-chemotherapy KPS and operation time were all significant and found to be the influencing factors for PPI. The risk model constructed based on LASSO was 0.0035770333 + (0.0020227686* age) + (0.057554487* DM) + (0.016365428* TNM staging) + (0.048514458* chemotherapy regimen) + (0.00871801* chemotherapy cycle) + (-0.002096683* post-chemotherapy albumin (g/L) + (-0.00090206* pre-chemotherapy Karnofsky performance score (KPS)) + (0.000296876* operation time). The pulmonary infection group got significantly higher risk scores than the non-pulmonary infection group (P<0.0001). According to receiver operating characteristic (ROC) curve-based analysis, the area under the curve (AUC) of risk score in predicting pulmonary infection was 0.894. Based on 4 independent predictors, a risk-prediction nomogram model was constructed to predict pulmonary infection in NSCLC patients after surgery. The internal verification C-index was 0.900 (95% CI: 0.839-0.961), and the calibration curves were well fitted with the ideal ones. CONCLUSION: The prediction model based on a regression model for PPI in NSCLC patients demonstrates good prediction efficiency, which is conducive to early screening of high-risk patients and further improvement of treatment regimen.

[Development and validation of prognostic nomogram for malignant pleural mesothelioma].

Objective: To development the prognostic nomogram for malignant pleural mesothelioma (MPM). Methods: Two hundred and ten patients pathologically confirmed as MPM were enrolled in this retrospective study from 2007 to 2020 in the People's Hospital of Chuxiong Yi Autonomous Prefecture, the First and Third Affiliated Hospital of Kunming Medical University, and divided into training (n=112) and test (n=98) sets according to the admission time. The observation factors included demography, symptoms, history, clinical score and stage, blood cell and biochemistry, tumor markers, pathology and treatment. The Cox proportional risk model was used to analyze the prognostic factors of 112 patients in the training set. According to the results of multivariate Cox regression analysis, the prognostic prediction nomogram was established. C-Index and calibration curve were used to evaluate the model's discrimination and consistency in raining and test sets, respectively. Patients were stratified according to the median risk score of nomogram in the training set. Log rank test was performed to compare the survival differences between the high and low risk groups in the two sets. Results: The median overall survival (OS) of 210 MPM patients was 384 days (IQR=472 days), and the 6-month, 1-year, 2-year, and 3-year survival rates were 75.7%, 52.6%, 19.7%, and 13.0%, respectively. Cox multivariate regression analysis showed that residence (HR=2.127, 95% CI: 1.154-3.920), serum albumin (HR=1.583, 95% CI: 1.017-2.464), clinical stage (stage Ⅳ: HR=3.073, 95% CI: 1.366-6.910) and the chemotherapy (HR=0.476, 95% CI: 0.292-0.777) were independent prognostic factors for MPM patients. The C-index of the nomogram established based on the results of Cox multivariate regression analysis in the training and test sets were 0.662 and 0.613, respectively. Calibration curves for both the training and test sets showed moderate consistency between the predicted and actual survival probabilities of MPM patients at 6 months, 1 year, and 2 years. The low-risk group had better outcomes than the high-risk group in both training (P=0.001) and test (P=0.003) sets. Conclusion: The survival prediction nomogram established based on routine clinical indicators of MPM patients provides a reliable tool for prognostic prediction and risk stratification.

Construction of predictive model for prognosis of patients after radical resection of colon cancer based on nomogram.

OBJECTIVE: To construct a predictive model for 3-year survival of patients after curative resection of colon cancer by nomogram. METHOD: A retrospective analysis was conducted to analyze the clinicopathologic data of 102 patients who underwent radical resection of colon cancer in Baoji Central Hospital from April 2015 to April 2017. The optimal cutoff values of preoperative CEA, CA125, and NLR for predicting overall survival were analyzed by receiver operating characteristic (ROC) curves. To observe the relationship between NLR, CEA and CA125 and clinicopathologic data, we performed multivariate Cox regression to analyze the independent factors affecting the prognosis of patients, and Kaplan-Meier test to identify the relationship between NLR, CEA and CA125 and patient survival. A nomogram prediction model was drawn for patients' 1-, 2-, and 3-year survival after radical resection of colon cancer, and the efficacy of the prediction model was evaluated. RESULTS: The area under the curve (AUC) of NLR, CEA and CA125 in predicting patient death was 0.784, 0.790 and 0.771, respectively. NLR was correlated with clinical stage, tumor diameter and differentiation (all P < 0.05); CEA was associated with clinical stage, tumor diameter, differentiation and lymph node metastasis (all P < 0.05); CA125 was only associated with tumor diameter in patients (P < 0.05). Differentiation, NLR, CEA and CA125 were independent risk factors affecting the prognosis of patients (all P < 0.05). The nomogram predicted a model C-index of 0.918 (95% CI 0.885-0.952), and the risk model score was found to have a high clinical value in the 3-year survival of preexisting patients. CONCLUSION: Preoperative NLR, CEA, CA125 and clinical stage are correlated with the prognosis of patients with colon cancer. The nomogram model constructed based on NLR, CEA, CA125 and clinical stage has good accuracy.

Prediction model of anastomotic leakage after anterior resection for rectal cancer-based on nomogram and multivariate analysis with 1995 patients.

BACKGROUND: Postoperative anastomotic leakage for rectal cancer shows higher morbidity with grievous concomitant symptoms. Accurate assessment of the incidence of anastomotic leakage, multivariate analysis, and establishment of a scientific prediction model can be useful to dispose of its possible severe clinical consequences. METHODS: This retrospective study collected 1995 consecutive patients who underwent anterior resection of rectal cancer with primary anastomosis at Northern Jiangsu People's Hospital between January 2016 and June 2022. Independent risk factors associated with anastomotic leakage were analyzed by univariate and multivariate logistic regression. The chosen independent risk factors were used to construct a nomogram risk prediction model whose availability was evaluated by using a bootstrapped-concordance index and calibration plots with R software. RESULTS: A total of 1995 patients who underwent anterior resection for rectal cancer were included while 120 patients were diagnosed with anastomotic leakage, an incidence of 6.0%. Univariate analysis and its concomitant multivariate cox regression analysis indicated that independent risk factors associated with anastomotic leakage included male gender (odds ratio (OR) = 2.873), diabetes (OR = 2.480), neoadjuvant therapy (OR = 5.283), tumor's distance from the anus verge < 5 cm (OR = 5.824), tumor size ≥ 5 cm (OR = 4.888), and the blood lose > 50 mL (OR = 9.606).We established a nomogram prediction model with proper applicability (concordance index, 0.83) and the calibration curve to justify its predictive ability that the predicted occurrence probability keeps a high degree of consistency with the actual occurrence probability. Meanwhile, the area under the receiver operating characteristic (ROC) curve was 0.83. CONCLUSIONS: The characteristics of patients and tumor surgery-related conditions can affect the incidence of anastomotic leakage. However, whether the surgical method will affect morbidity is still controversial. Our nomogram can be seen as an effective instrument to predict anastomotic leakage after anterior resection for rectal cancer precisely.