Development of a diagnostic model for detecting mild cognitive impairment in young and middle-aged patients with obstructive sleep apnea: a prospective observational study.

Objectives: Obstructive sleep apnea (OSA) is a common sleep-disordered breathing condition linked to the accelerated onset of mild cognitive impairment (MCI). However, the prevalence of undiagnosed MCI among OSA patients is high and attributable to the complexity and specialized nature of MCI diagnosis. Timely identification and intervention for MCI can potentially prevent or delay the onset of dementia. This study aimed to develop screening models for MCI in OSA patients that will be suitable for healthcare professionals in diverse settings and can be effectively utilized without specialized neurological training. Methods: A prospective observational study was conducted at a specialized sleep medicine center from April 2021 to September 2022. Three hundred and fifty consecutive patients (age: 18-60 years) suspected OSA, underwent the Montreal Cognitive Assessment (MoCA) and polysomnography overnight. Demographic and clinical data, including polysomnographic sleep parameters and additional cognitive function assessments were collected from OSA patients. The data were divided into training (70%) and validation (30%) sets, and predictors of MCI were identified using univariate and multivariate logistic regression analyses. Models were evaluated for predictive accuracy and calibration, with nomograms for application. Results: Two hundred and thirty-three patients with newly diagnosed OSA were enrolled. The proportion of patients with MCI was 38.2%. Three diagnostic models, each with an accompanying nomogram, were developed. Model 1 utilized body mass index (BMI) and years of education as predictors. Model 2 incorporated N1 and the score of backward task of the digital span test (DST_B) into the base of Model 1. Model 3 expanded upon Model 1 by including the total score of digital span test (DST). Each of these models exhibited robust discriminatory power and calibration. The C-statistics for Model 1, 2, and 3 were 0.803 [95% confidence interval (CI): 0.735-0.872], 0.849 (95% CI: 0.788-0.910), and 0.83 (95% CI: 0.763-0.896), respectively. Conclusion: Three straightforward diagnostic models, each requiring only two to four easily accessible parameters, were developed that demonstrated high efficacy. These models offer a convenient diagnostic tool for healthcare professionals in diverse healthcare settings, facilitating timely and necessary further evaluation and intervention for OSA patients at an increased risk of MCI.

Predictors of early neurological deterioration in patients with acute ischemic stroke.

Background: The present study aimed to develop a reliable and straightforward Nomogram by integrating various parameters to accurately predict the likelihood of early neurological deterioration (END) in patients with acute ischemic stroke (AIS). Methods: Acute ischemic stroke patients from Shaoxing People's Hospital, Shanghai Yangpu District Shidong Hospital, and Shanghai Fifth People's Hospital were recruited based on specific inclusion and exclusion criteria. The primary outcome was END. Using the LASSO logistic model, a predictive Nomogram was generated. The performance of the Nomogram was evaluated using the ROC curve, the Hosmer-Lemeshow test, and a calibration plot. Additionally, the decision curve analysis was conducted to assess the effectiveness of the Nomogram. Results: It was found that the Nomogram generated in the present study showed strong discriminatory performance in both the training and the internal validation cohorts when their ROC-AUC values were 0.715 (95% CI 0.648-0.782) and 0.725 (95% CI 0.631-0.820), respectively. Similar results were observed in two external validation cohorts when their ROC-AUC values were 0.685 (95% CI 0.541-0.829) and 0.673 (95% CI 0.545-0.800), respectively. In addition, CAD, SBP, neutrophils, TBil, and LDL were found to be positively correlated with the occurrence of END post-stroke, while lymphocytes and UA were negatively correlated. Conclusion: Our study developed a novel Nomogram that includes CAD, SBP, neutrophils, lymphocytes, TBil, UA, and LDL and it demonstrated strong discriminatory performance in identifying AIS patients who are likely to develop END.

Risk factors and prediction model for osteonecrosis of the femoral head in female systemic lupus erythematosus.

Background: Osteonecrosis of the femoral head (ONFH) is a severe complication of systemic lupus erythematosus (SLE) and occurs more frequently in SLE patients than in other autoimmune diseases, which can influence patients' life quality. The objective of this research was to analyze risk factors for the occurrence of ONFH in female SLE patients, construct and validate a risk nomogram model. Methods: Clinical records of SLE patients who fulfilled the 1997 American College of Rheumatology SLE classification criteria were retrospectively analyzed. The Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis were used to summarize the independent risk factors of ONFH in female SLE patients, which were used to develop a nomogram. The predictive performance of the nomogram was assessed using the receiver characteristic (ROC) curve, calibration curves and decision curve analysis (DCA). Results: 793 female SLE patients were ultimately included in this study, of which 87 patients (10.9%) developed ONFH. Ten independent risk factors including disease duration, respiratory involvement, menstrual abnormalities, Sjögren's syndrome, osteoporosis, anti-RNP, mycophenolate mofetil, cyclophosphamide, biologics, and the largest daily glucocorticoid (GC) were identified to construct the nomogram. The area under the ROC curve of the nomogram model was 0.826 (95% CI: 0.780-0.872) and its calibration for forecasting the occurrence of ONFH was good (χ2 = 5.589, P = 0.693). DCA showed that the use of nomogram prediction model had certain application in clinical practice when the threshold was 0.05 to 0.95. In subgroup analysis, we found that the risk of ONFH was significantly increased in age at SLE onset of ≤ 50 years old, largest daily GC dose of ≥50 mg and the therapy of GC combined with immunosuppressant patients with menstrual abnormalities. Conclusion: Menstrual abnormalities were the first time reported for the risk factors of ONFH in female SLE patients, which remind that clinicians should pay more attention on female SLE patients with menstrual abnormalities and take early interventions to prevent or slow the progression of ONFH. Besides, the nomogram prediction model could provide an insightful and applicable tool for physicians to predict the risk of ONFH.

Research progress on predictive models for malnutrition in cancer patients.

Disease-related malnutrition is a prevalent issue among cancer patients, affecting approximately 40-80% of those undergoing treatment. This condition is associated with numerous adverse outcomes, including extended hospitalization, increased morbidity and mortality, delayed wound healing, compromised muscle function and reduced overall quality of life. Moreover, malnutrition significantly impedes patients' tolerance of various cancer therapies, such as surgery, chemotherapy, and radiotherapy, resulting in increased adverse effects, treatment delays, postoperative complications, and higher referral rates. At present, numerous countries and regions have developed objective assessment models to predict the risk of malnutrition in cancer patients. As advanced technologies like artificial intelligence emerge, new modeling techniques offer potential advantages in accuracy over traditional methods. This article aims to provide an exhaustive overview of recently developed models for predicting malnutrition risk in cancer patients, offering valuable guidance for healthcare professionals during clinical decision-making and serving as a reference for the development of more efficient risk prediction models in the future.

The clinical outcome of minor changes in serum creatinine for patients after curative gastrectomy: a prospective study.

Introduction: Patients with renal insufficiency are more prone to postoperative complications (PCs). Studies have shown that minor changes in serum creatinine (SCr), immediately post-surgery, can aid in assessing patients' renal function. This study aimed to explore the relationship between the changes in SCr and PCs in patients with gastric cancer (GC). Materials and methods: We prospectively collected data regarding the SCr of 530 GC patients, within 2 weeks before surgery and within 24 hours after surgery in our hospital (2014-2016). The patients were divided into three groups according to the level of SCr change after surgery: reduced (<10%), normal (10%), and elevated (>10%) creatinine groups. Univariate and multivariate logistic analysis were performed to evaluate its correlation with short-term PCs in the patients. The R language was used to construct a nomogram. Results: 83, 217, and 230 patients were assigned to the elevated, reduced, and normal SCr groups, respectively. Multivariate analysis showed that the reduced and elevated SCr groups were independently associated with the occurrence of PCs and severe postoperative complications (SPCs), respectively. Additionally, postsurgical SCr change, age, hypoalbuminemia, total gastrectomy, combined resection, and laparoscopy, were independently related to PCs. Combining the above influential factors, the predictive model can distinguish patients with PCs more reliably (c-index is 0.715). Conclusion: Post-surgery, reduced SCr is a protective factor for PCs, while elevated serum creatinine is an independent risk factor for SPCs. Our nomogram can identify GC patients with high risks of PCs.

Nomogram construction and evaluation for predicting non-remission after a single radioactive iodine therapy for Graves' hyperthyroidism: a retrospective cohort study.

Background: Radioactive iodine (RAI) therapy is a widely used treatment for Graves' Hyperthyroidism (GH). However, various factors can impact the non-remission rate of GH after single RAI therapy. This study aimed to develop an online dynamic nomogram to assist physicians in providing personalized therapy for GH. Methods: Data from 454 GH patients who received RAI therapy were retrospectively reviewed and included in the present study. The univariate and multivariate analysis were conducted to investigate and identify independent influencing factors. The nomogram was developed based on the training cohort to explore non-remission rates. Finally, the reliability and accuracy of the constructed nomogram model were verified in the validation cohort via the calibration, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results: 24-hours radioactive iodine uptake (RAIU24h), effective half-life (Teff), total iodine dose (TID) and iodine dose per gram of thyroid tissue (IDPG) were independent predictors. The nomogram had a high C-index 0.922 (95% CI: 0.892-0.953), for predicting non-remission. The calibration curves demonstrated excellent consistency between the predicted and the actual probability of non-remission. ROC analysis showed that the AUC of the nomogram model and the four independent factors in the training cohort were 0.922, 0.673, 0.760, 0.761, and 0.786, respectively. The optimal cutoff value for the total nomogram scores was determined to be 155. A total score of ≥155 indicates a higher likelihood of non-remission after a single RAI therapy for GH, whereas a score below 155 suggests a greater likelihood of remission. Additionally, the DCA curve indicated that this nomogram had good clinical utility in predicting non-remission. Conclusion: An online nomogram was constructed with good predictive performance, which can be used as a practical approach to predict and assist physicians in making personalized therapy decisions for GH patients.

Prognostic Nomograms for Patients With NF-Pan-NET After Pancreatectomy: A Retrospective Analysis Based on SEER Database.

AIMS: Surgical resection is the primary treatment option for patients diagnosed with nonfunctional pancreatic neuroendocrine tumors (NF-Pan-NETs). However, the postoperative prognostic evaluation for NF-Pan-NET patients remains obscure. This study aimed to construct an efficient model to predict the prognosis of NF-Pan-NET patients who have received surgical resection. METHODS: NF-Pan-NET patients after pancreatectomy were retrieved from the SEER database for the period of 2010 to 2019. A total of 2844 patients with NF-Pan-NET from SEER database were included in our study. After careful screening, six clinicopathological variables including age, grade, AJCC T stage, AJCC N stage, AJCC M stage, and chemotherapy were selected to develop nomograms to predict overall survival (OS) and cancer-specific survival (CSS) respectively of the patients. RESULTS: The novel models demonstrated high accuracy and discrimination in prognosticating resected NF-Pan-NET through various validation methods. Furthermore, the risk subgroups classified by the newly developed risk stratification systems based on the nomograms exhibited significant differences in both OS and CSS, surpassing the efficacy of the AJCC 8th TNM staging system. Novel nomograms and corresponding risk classification systems were developed to predict OS and CSS in patients with NF-Pan-NET after pancreatectomy. CONCLUSION: The models demonstrated superior performance compared to traditional staging systems, providing clinicians with more accurate and personalized guidance for postoperative surveillance and treatment.

Development and validation of a nomogram for individualizing fall risk in patients with hematologic malignancies.

Inpatient falls are common adverse events especially for patients with hematologic malignancies. A fall-risk prediction model for patients with hematologic malignancies are still needed. Here we conducted a multicenter study that prospectively included 516 hospitalized patients with hematologic malignancies, and developed a nomogram for fall risk prediction. Patients were divided into the modeling group (n = 389) and the validation group (n = 127). A questionnaire containing sociodemographic factors, general health factors, disease-related factors, medication factors, and physical activity factors was administered to all patients. Logistic regression analysis revealed that peripheral neuropathy, pain intensity, Morse fall scale score, chemotherapy courses, and myelosuppression days were risk factors for falls in patients with hematologic malignancies. The nomogram model had a sensitivity of 0.790 and specificity of 0.800. The calibration curves demonstrated acceptable agreement between the predicted and observed outcomes. Therefore, the nomogram model has promising accuracy in predicting fall risk in patients with hematologic malignancies.

Molecular subtypes and nomogram for predicting the prognosis of cervical cancer based on a matrix-immune signature.

Cervical cancer is a kind of tumor related to chronic HPV infection. Currently, the treatment of cervical cancer is guided mainly by clinicopathological factors. The role of tumor microenvironment in the prognosis and treatment of cervical cancer has been ignored. We aimed to use bioinformatics to identify the molecular subtypes in cervical cancer and construct a predictive nomogram combining a matrix-immune signature (MIS) and clinicopathological factors to support treatment decisions. Two cervical cancer subtypes with different prognoses were identified based on matrix- and immune-genes in TCGA-CESC. The MIS was developed using Cox regression and Lasso algorithm and verified in the Cancer Genome Characterization Initiative (CGCI) using time-dependent receiver operating characteristic (ROC) curve analysis. Multivariable analysis identified lymph node metastases, lymphovascular space invasion, and the MIS as independent prognostic factors, which were used to construct the predictive nomogram. The areas under the ROC curve of the model were 0.872, 0.879, and 0.803 for the 1-, 3-, and 5-year periods, respectively. The C-index was 0.845. Calibration curves confirmed the excellent prognosis prediction of the nomogram. The nomogram indicted a 3-year survival rate of > 90% in patients with a total score > 110.1. The constructed predictive nomogram has significant implications for prognostic assessment and treatment selection in cervical cancer.

SEMA7A as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltrates in Breast Cancer.

Background: The role of Semaphorin 7a (SEMA7A) in the initiation and progression of different types of cancerous lesions has been extensively studied. However, the prognostic significance of SEMA7A, specifically in breast cancer (BC), lacks clarity. Methods: We conducted an evaluation on the relationship between SEMA7A and the prognosis, immune invasion and tumor mutation burden in different types of cancer by analyzing data from The Cancer Genome Atlas database. The present study focused on investigating the expression level, mutation, immune correlation and coexpression of SEMA7A in BC, utilizing various databases such as the University of Alabama at Birmingham Cancer data analysis portal, cBioPortal and tumor immune estimation resource. Survival analysis was carried out using the Kaplan-Meier Plotter. Furthermore, we employed the R software package to generate receiver operating characteristic (ROC) curves and nomograms. Notably, P<0.05 was considered to indicate statistical significance. Results: Using pancancer analysis, it has been observed that the expression of SEMA7A is elevated in various types of cancer and is strongly correlated with the prognosis of different cancer types. SEMA7A also exhibits a significant association with the tumor mutation burden of diverse types of cancer. Moreover, SEMA7A displays a notable increase in BC cases, and was indicated to have a substantial association with the abundance of immune infiltration. In-depth survival analysis demonstrated that elevated levels of SEMA7A expression are notably linked to shorter overall survival and distant metastasis-free survival among patients with BC. The efficiency of SEMA7A as a reliable prognostic biomarker for BC has been substantiated by the validation of ROC curves and nomograms. Conclusion: SEMA7A has the potential to function as a prognostic indicator for BC, and its correlation with immune infiltration in BC is significant.

Establishment and validation of a nomogram model for prediction of clinical outcomes in patients with amanita phalloides poisoning.

Amanita phalloides poisoning, renowned for its high mortality rates, is one of the most serious food safety issue in certain regions worldwide. Assessment of prognosis and development of more efficacious therapeutic strategies are critical importance for amanita phalloides poisoning patients. The aim of the study is to establish a nomogram to predict the clinical outcome of amanita phalloides poisoning patients based on the independent risk factor for prognosis. Herein, between January 2013 and September 2023, a cohort of 149 patients diagnosed with amanita phalloides poisoning was enrolled and randomly allocated into training and validation cohorts, comprising 102 and 47 patients, respectively. Multivariate logistic regression analysis was performed to identify the independent risk factors for morality of amanita phalloides poisoning patients in training cohort. Subsequently, a nomogram model was constructed to visually display the risk prediction model. The predictive accuracy of nomogram was verified by the validation cohort. The C index, the area under the receiver operating characteristic curve (AUC), and calibration plots were used to assessed the performance of nomogram. The clinical utility was evaluated by decision curve analysis (DCA). In the present study, the results showed that hepatic encephalopathy (HE), upper gastrointestinal bleeding (UGB), AST, and PT were the independent risk factors associated with the mortality of amantia phalloides poisoning patients. We constructed a new nomogram to evaluate the probability of death induced by amantia phalloides poisoning. The AUC for the prediction accuracy of the nomogram was 0.936 for the training cohort and 0.929 for the validation cohort. The calibration curves showed that the predicted probability matched the actual likelihood. The results of the DCA suggested that the nomogram has a good potential for clinical application. In summary, we developed a new nomogram to assess the probability of mortality for amanita phalloides poisoning patients. This nomogram might facilitate clinicians in making more efficacious treatment strategies for patients with amanita phalloides poisoning.

A nomogram to predict the risk of death during hospitalization in Chinese neonates with respiratory failure.

Background: Neonatal respiratory failure (NRF) is a critical condition with high morbidity and mortality rates. This study aimed to develop a nomogram prediction model to early predict the risk of death in Chinese neonates with NRF. Methods: A retrospective analysis was conducted on NRF neonates from 21 tertiary neonatal intensive care units (NICUs) across 13 prefecture-level cities in Jiangsu Province, China, from March 2019 to March 2022. NRF neonates from one random NICU were selected as the external validation set, while those from the remaining 20 NICUs were divided into the training set and the internal validation set at a 7:3 ratio. Death was the primary outcome. LASSO regression and multivariate logistic regression were used to identify the predictive factors from the training set and then the nomogram was constructed. Results: A total of 5387 neonates with NRF were included in the analysis. Among them, 3444 were in the training set, 1470 were in the internal validation set, and 473 were in the external validation set. The nomogram was constructed based on the eight predictors of the 1-min Apgar score, birth weight, gestational age, the relationship between birth weight and gestational age, mode of first respiratory support, inhaled nitric oxide, antenatal corticosteroids, and vasoactive drugs. The area under the curve of the nomogram in the training set, internal validation set, and external validation set was 0.763, 0.733, and 0.891, respectively. The P-values of the Hosmer-Lemeshow goodness of fit test were 0.638, 0.273, and 0.253, respectively. Brier scores were 0.066, 0.072, and 0.037, respectively. The decision curve analysis demonstrated a significant net benefit in all cases. These data indicate the good performance of the nomogram. Conclusions: This nomogram can serve as a reference for clinicians to identify high-risk neonates early and reduce the incidence of neonatal mortality.

Development and validation of a nomogram for predicting the placement of nasointestinal tubes in critically ill patients based on abdominal radiography: A single-center, retrospective study.

Background: Enteral nutrition administered via the nasointestinal tube (NET) is a prevalent nutritional modality among critically ill patients, and abdominal radiographs hold significant value in accurately ascertaining the precise positioning of the NET subsequent to its placement. Therefore, we propose an innovative approach to construct a clinical prediction model based on NET's configuration within the gastrointestinal tract in abdominal radiography. This model aims to enhance the accuracy of determining the position of NETs after their placement. Methods: Patients admitted to the intensive care unit of Zhejiang Provincial People's Hospital between October 2017 and October 2021 were included to constitute the training cohort for retrospective analysis, and nomogram was constructed. Consecutively enrolled patients admitted to the same hospital from October 2021 to October 2023 were included as the validation cohort. The training cohort underwent a univariate analysis initially, followed by a multivariate logistic regression approach to analyze and identify the most appropriate model. Subsequently, nomogram was generated along with receiver operator characteristic curves, calibration curves, and decision curves for both the training and validation cohorts to evaluate the predictive performance of the model. Results: The training and validation cohorts comprised 574 and 249 patients, respectively, with successful tube placement observed in 60.1 % and 76.3 % of patients, correspondingly. The predictors incorporated in the prediction maps encompass the "C-shape," the height of "inverse C-shape," showing the duodenojejunal flexure, and the location of the head end of the NET. The model demonstrated excellent predictive efficacy, achieving an AUC of 0.883 (95 % CI 0.855-0.911) and good calibration. Furthermore, when applied to the validation cohort, the nomogram exhibited strong discrimination with an AUC of 0.815 (95 % CI 0.750-0.880) and good calibration. Conclusion: The combination of abdominal radiography and NET's configuration within the gastrointestinal tract enables accurate determination of NET placement in critically ill patients.

Development and validation of an MRI and clinicopathological factors prediction model for low anterior resection syndrome in anterior resection of middle and low rectal cancer.

Objective: To validate the predictive power of newly developed magnetic resonance (MR) morphological and clinicopathological risk models in predicting low anterior resection syndrome (LARS) 6 months after anterior resection of middle and low rectal cancer (MLRC). Methods: From May 2018 to January 2021, 236 patients with MLRC admitted to two hospitals (internal and external validation) were included. MR images, clinicopathological data, and LARS scores (LARSS) were collected. Tumor morphology data included longitudinal involvement length, maximum tumor diameter, proportion of tumor to circumference of the intestinal wall, tumor mesorectal infiltration depth, circumferential margin status, and distance between the tumor and anal margins. Pelvic measurements included anorectal angle, mesenterial volume (MRV), and pelvic volume. Univariate and multivariate logistic regression was used to obtain independent risk factors of LARS after anterior resection Then, the prediction model was constructed, expressed as a nomogram, and its internal and external validity was assessed using receiver operating characteristic curves. Results: The uni- and multivariate analysis revealed distance between the tumor and anal margins, MRV, pelvic volume, and body weight as significant independent risk factors for predicting LARS. From the nomogram, the area under the curve (AUC), sensitivity, and specificity were 0.835, 75.0 %, and 80.4 %, respectively. The AUC, sensitivity, and specificity in the external validation group were 0.874, 83.3 %, and 91.7 %, respectively. Conclusion: This study shows that MR imaging and clinicopathology presented by a nomogram can strongly predict LARSS, which can then individually predict LARS 6 months after anterior resection in patients with MLRC and facilitate clinical decision-making. Clinical relevance statement: We believe that our study makes a significant contribution to the literature. This method of predicting postoperative anorectal function by preoperative measurement of MRV provides a new tool for clinicians to study LARS.

Development and validation of a clinical nomogram for predicting in-hospital mortality in patients with traumatic brain injury prehospital: A retrospective study.

Objective: Traumatic brain injury (TBI) is among the leading causes of death and disability globally. Identifying and assessing the risk of in-hospital mortality in traumatic brain injury patients at an early stage is challenging. This study aimed to develop a model for predicting in-hospital mortality in TBI patients using prehospital data from China. Methods: We retrospectively included traumatic brain injury patients who sustained injuries due to external forces and were treated by pre-hospital emergency medical services (EMS) at a tertiary hospital. Data from the pre-hospital emergency database were analyzed, including demographics, trauma mechanisms, comorbidities, vital signs, clinical symptoms, and trauma scores. Eligible patients were randomly divided into a training set (241 cases) and a validation set (104 cases) at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were employed to identify independent risk factors. Analyzed the discrimination, calibration, and net benefit of the nomogram across both groups. Results: 17.40 % (42/241) of TBI patients died in the hospital in the training set, while 18.30 % (19/104) in the validation set. After analysis, chest trauma (odds ratio [OR] = 4.556, 95 % confidence interval [CI] = 1.861-11.152, P = 0.001), vomiting (OR = 2.944, 95%CI = 1.194-7.258, P = 0.019), systolic blood pressure (OR = 0.939, 95%CI = 0.913-0.966, P < 0.001), SpO2 (OR = 0.778, 95%CI = 0.688-0.881, P < 0.001), and heart rate (OR = 1.046, 95%CI = 1.015-1.078, P = 0.003) were identified as independent risk factors for in-hospital mortality in TBI patients. The nomogram based on the five factors demonstrated well-predictive power, with an area under the curve (AUC) of 0.881 in the training set and 0.866 in the validation set. The calibration curve and decision curve analysis showed that the predictive model exhibited good consistency and covered a wide range of threshold probabilities in both sets. Conclusion: The nomogram based on prehospital data demonstrated well-predictive performance for in-hospital mortality in TBI patients, helping prehospital emergency physicians identify and assess severe TBI patients earlier, thereby improving the efficiency of prehospital emergency care.

An age-related nomogram for antral follicle count: an observational study of 3821 women.

RESEARCH QUESTION: What is the relationship between antral follicle count (AFC) and chronological age, and what are the expected values for AFC? DESIGN: This was a retrospective cohort study at a specialist gynaecological ultrasound centre in London, UK. Women presenting to a gynaecology diagnostic unit for investigation of symptoms or routine check-up, and undergoing transvaginal ultrasound (TVUS) examinations, between 1 January 2017 and 22 September 2022 were included in this study. RESULTS: In total, 8821 TVUS records from 7573 patients were analysed. The relationship between AFC and age was estimated to develop an AFC nomogram independent of the stage in the menstrual cycle. AFC corresponding to the 10th, 25th, 50th, 75th and 90th centiles for each patient and age group were calculated. Both nomogram and condition-specific populations displayed a Gaussian relationship between AFC and age. For the nomogram population (scans n = 4256, patients n = 3821), a peak median AFC of 30 was observed between 21-23 years of age. The AFC distribution of the group with previous ovarian cystectomy (scans n = 534, patients n = 443) was found to be significantly different compared with the nomogram population (P < 0.0001). However, the same did not hold true for those on hormonal contraception (scans n = 566, patients n = 534) (P = 0.43). CONCLUSIONS: An AFC nomogram reporting median and interquartile values for AFC by chronological age across the reproductive years was developed. This is a useful tool for providing counselling for those undergoing ovarian reserve assessments, and can be taken any time in the menstrual cycle, including in women on hormonal contraceptives or who have undergone previous ovarian cystectomy.

Development and validation of a prognostic nomogram in patients aged ≥65 years with stage I-II non-small cell lung cancer treated with stereotactic body radiotherapy.

INTRODUCTION: This study aims to discern the efficacy and toxicity of stereotactic body radiotherapy (SBRT) in older adults with stage I-II non-small cell lung cancer (NSCLC) and establish a prognostic nomogram for these patients. MATERIALS AND METHODS: One hundred forty-two patients (aged ≥65 years) with clinically-confirmed stage I-II NSCLC treated with SBRT from 2009 to 2020 were enrolled in the study. Primary end points included overall survival (OS), progression free survival (PFS), cumulative incidences of local failure (LF), regional failure (RF), distant failure (DF), and toxicity. A nomogram for OS was developed and validated internally using one thousand bootstrap resamplings. RESULTS: The median times to LF, RF, and DF were 22.1 months, 26.9 months and 24.1 months, respectively. The 1-, 3-, and 5-year PFS rates from the start of SBRT were 79.4 %, 53.1 %, and 38.9 %, respectively. Performance status, pre-SBRT platelet to lymphocyte ratio (PLR), and planning tumor volume (PTV) were predictive of PFS. The 1-, 3-, and 5-year OS rates from the start of SBRT were 90.8 %, 67.9 % and 47.6 %, respectively. In multivariate analysis, good performance status, a low level of pre-SBRT PLR, and small tumor size were associated with better prognosis, all of which were included in the nomogram. The model showed optimal discrimination, with a C-index of 0.651 and good calibration. The most common adverse reactions were grade 1-2, such as anemia, cough, and fatigue. DISCUSSION: SBRT is a reasonable treatment modality for early-stage NSCLC in older adults. It achieved good survival outcomes and low toxicity. The proposed nomogram may be able to estimate individual outcomes for these patients.

Novel nomogram for the prediction of sepsis-induced coagulopathy in the PICU: A multicentre retrospective study.

INTRODUCTION: Sepsis-induced coagulopathy (SIC) is a severe complication of sepsis, characterized by poor prognosis and high mortality. However, the predictors of SIC in pediatric patients have yet to be identified. Our aim was to develop a user-friendly and efficient nomogram for predicting SIC in sepsis patients admitted to the pediatric intensive care unit (PICU). MATERIALS AND METHODS: We screened 948 sepsis patients admitted to the PICU in three hospitals located in Shandong, China. Least absolute shrinkage and selector operation (LASSO) regression was used in the training cohort for variable selection and regularization. The selected variables were utilized to construct a nomogram for predicting the risk of SIC among sepsis patients admitted to the PICU. RESULTS: Overall, SIC was observed in 324 (40.3 %) patients. The morbidity of SIC in sepsis patients is associated with age, fibrinogen, prothrombin time, C-reactive protein, lactate and the pediatric sequential organ failure assessment score. We developed a nomogram for the early identification of SIC in the training cohort (area under the curve [AUC] 0.869, 95 % confidence interval [CI] 0.830-0.907, sensitivity 75.7 %, specificity 84.8 %) and validation cohorts (validation cohort 1: AUC 0.854, 95 % CI 0.805-0.903, sensitivity 72.0 %, specificity 86.9 %; validation cohort 2: AUC 0.853, 95 % CI 0.796-0.910, sensitivity 70.1 %, specificity 87.8 %). The calibration plots of the nomogram demonstrated a high level of concordance in the SIC probabilities between the observed and predicted values. CONCLUSIONS: The novel nomogram showed excellent predictive performance for the morbidity of SIC among sepsis patients admitted to the PICU, potentially assisting healthcare professionals in early identification and intervention for SIC.

Development and validation of a machine-learning model for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage.

Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis and increased mortality. The aim of this study was to develop a predictive model for postoperative pneumonia (POP) in patients with aSAH. A retrospective analysis was conducted on 308 patients with aSAH who underwent surgery at the Neurosurgery Department of the First Affiliated Hospital of Soochow University. Univariate and multivariate logistic regression and lasso regression analysis were used to analyze the risk factors for POP. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the constructed model. Finally, the effectiveness of modeling these six variables in different machine learning methods was investigated. In our patient cohort, 23.4% (n = 72/308) of patients experienced POP. Univariate, multivariate logistic regression analysis and lasso regression analysis revealed age, Hunt-Hess grade, mechanical ventilation, leukocyte count, lymphocyte count, and platelet count as independent risk factors for POP. Subsequently, these six factors were used to build the final model. We found that age, Hunt-Hess grade, mechanical ventilation, leukocyte count, lymphocyte count, and platelet count were independent risk factors for POP in patients with aSAH. Through validation and comparison with other studies and machine learning models, our novel predictive model has demonstrated high efficacy in effectively predicting the likelihood of pneumonia during the hospitalization of aSAH patients.

A comprehensive predictive model for postoperative joint function in robot-assisted total hip arthroplasty patients: combining radiomics and clinical indicators.

Total hip arthroplasty (THA) effectively treats various end-stage hip conditions, offering pain relief and improved joint function. However, surgical outcomes are influenced by multifaceted factors. This research aims to create a predictive model, incorporating radiomic and clinical information, to forecast post-surgery joint function in robot-assisted THA (RA-THA) patients. The study set comprised 136 patients who underwent unilateral RA-THA, which were subsequently partitioned into a training set (n = 95) and a test set (n = 41) for analysis. Preoperative CT imaging was employed to derive 851 radiomic characteristics, selecting those with an intra-class correlation coefficient > 0.75 for analysis. Least absolute shrinkage and selection operator regression reduced redundancy to six significant radiomic features. Clinical data including preoperative Visual Analog Scale (VAS), Harris Hip Score (HHS), and Western Ontario and McMaster University Osteoarthritis Index (WOMAC) score were collected. Logistic regression identified significant predictors, and three models were developed. Receiver operating characteristic and decision curves evaluated the models. Preoperative VAS, HHS, WOMAC score, and radiomics feature scores were significant predictors. In the training set, the AUCs were 0.835 (clinical model), 0.757 (radiomic model), and 0.891 (combined model). In the test set, the AUCs were 0.777 (clinical model), 0.824 (radiomic model), and 0.881 (combined model). The constructed nomogram prediction model combines radiological features with relevant clinical data to accurately predict functional outcomes 3 years after RA-THA. This model has significant prediction accuracy and broad clinical application prospects and can provide a valuable reference for formulating personalized treatment plans and optimizing patient management strategies.