OBJECTIVE: To explore the efficacy of a Thalassemia risk assessment software for the screening of thalassemia mutation carriers and distribution of thalassemia genotypes detected by screening. METHODS: A total of 6 040 individuals were evaluated at Leshan Maternal and Child Health Care Hospital between 2022 and 2024 using the commonly used clinical thalassemia risk assessment method and the thalassemia screening software, respectively, and the performance indicators of the two methods were compared and analyzed against the result of thalassemia gene testing. This study was approved by the Ethics Committee of our hospital (Ethics No.: LfyLL[2022]005). RESULTS: The high-risk rate by the thalassemia screening software was 11.19%, with a sensitivity of 95.12%, specificity of 93.28%, positive predictive value of 43.20%, negative predictive value of 99.72%, and the area under the ROC curve (AUC) was 0.942. The thalassemia gene detection rate of the high-risk samples screened was 4.83%. The high-risk screening rate of the conventional method was 2.50%, with a sensitivity of 51.22%, specificity of 93.28%, positive predictive value of 80.79%, negative predictive value of 97.40%, and the AUC was 0.754. The thalassemia gene detection rate of the high-risk samples was 2.02%. CONCLUSION The software can effectively detect thalassemia carriers and significantly reduce the missed detection compared with conventional method, thereby significantly improve the efficacy of screening.
Humans ; Thalassemia/diagnosis* ; Software ; Female ; Genetic Testing/methods* ; Male ; Mutation ; Adult ; Genotype ; ROC Curve ; Risk Assessment

OBJECTIVES: Cardiovascular disease (CVD) poses a major threat to global health. Evaluating atherosclerosis in asymptomatic individuals can help identify those at high risk of CVD. This study aims to establish an individualized nomogram prediction model to estimate the risk of vascular plaque formation in asymptomatic individuals. METHODS: A total of 5 655 participants who underwent CVD screening at the Health Management Center of The Third Xiangya Hospital, Central South University, between January 2022 and June 2024 we retrospectively enrolled. Using simple random sampling, participants were divided into a training set (n=4 524) and a validation set (n=1 131) in an 8꞉2 ratio. Demographic and clinical data were collected and compared between groups. Multivariate logistic regression analysis was used to identify independent factors associated with vascular plaques and to construct a nomogram prediction model. The predictive performance and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow goodness-of-fit test, calibration plots, and decision curve analysis (DCA). RESULTS: The mean age of participants was 52 years old. There were 3 400 males (60.12%). The overall detection rate of vascular plaque in the screening population was 49.87% (2 820/5 655). No statistically significant differences were observed in clinical indicators between the training and validation sets (all P>0.05). Multivariate Logistic regression analysis identified age, systolic blood pressure, high-density lipoprotein (HDL), low-density lipoprotein (LDL), lipoprotein(a), male sex, smoking history, hypertension history, and diabetes history as independent risk factors for vascular plaque in asymptomatic individuals (all P<0.05). The area under the curve (AUC) of the nomogram model for predicting vascular plaque risk were 0.778 (95% CI 0.765 to 0.791, P<0.001) in the training set and 0.760 (95% CI 0.732 to 0.787, P<0.001) in the validation set. The Hosmer-Lemeshow goodness-of-fit test indicated good model calibration (training set: P=0.628; validation set: P=0.561). The calibration curve plotted using the Bootstrap method demonstrated good agreement between predicted probabilities and actual probabilities. DCA showed that the nomogram provided a clinical net benefit for predicting vascular plaque risk when the threshold probability ranged from 0.02 to 0.99. CONCLUSIONS The nomogram prediction model for vascular plaque risk, constructed using readily available and cost-effective physical examination indicators, exhibited good predictive performance. This model can assist in the early identification and intervention of asymptomatic individuals at high risk for cardiovascular disease.
Humans ; Male ; Middle Aged ; Female ; Nomograms ; Retrospective Studies ; Risk Factors ; Plaque, Atherosclerotic/diagnosis* ; Aged ; Adult ; Physical Examination ; Logistic Models ; Cardiovascular Diseases/epidemiology* ; ROC Curve

OBJECTIVES: The diagnostic value of ultrasonographic quantitative indicators of umbilical cord coiling, such as the umbilical coiling index (UCI) and pitch value, in identifying hypercoiling and predicting adverse pregnancy outcomes remains controversial. This study aims to evaluate the predictive value of UCI, pitch value, and the cerebroplacental ratio in pregnancies complicated by umbilical cord hypercoiling. METHODS: Pregnant women with densely coiled umbilical cords identified by routine obstetric ultrasound at Changsha Maternal and Child Health Hospital between November 2022 and November 2024 were enrolled. Complete clinical data, including UCI, pitch value, and cerebroplacental ratio (CPR), were collected. Pregnancy outcome scores were calculated, and newborns were categorized into the normal outcome group (n=177) and adverse outcome group (n=85), with the latter further subdivided into mild (n=51), moderate (n=19), and severe (n=15) subgroups. Differences in baseline data, UCI, pitch value, and incidence of CRP<1 were compared between groups and among subgroups. Correlations between UCI, pitch value, and adverse pregnancy outcomes were analyzed. Receiver operating characteristic (ROC) curve were used to assess the predictive performance of UCI, pitch value, CPR<1, and their combinations. RESULTS: Compared with the normal outcome group, the adverse outcome group had higher age, parity, parity, incidence of CPR<1, and UCI, while gestational age at delivery and pitch values were lower (all P<0.05). The incidence of obesity, gestational diabetes mellitus, and hypertensive disorders of pregnancy did not differ significantly between the 2 groups (all P>0.05). The normal outcome group showed lower UCI and higher pitch values than all 3 adverse outcome subgroups (all P<0.05), while differences among the 3 adverse subgroups were not significant (all P>0.05). UCI was positively correlated with adverse pregnancy outcomes (r_s=0.350, P<0.05), whereas pitch value was negatively correlated (r_s=-0.286, P<0.05). ROC curve analysis showed that the area under the curve (AUC) values for predicting adverse outcomes were 0.837 for UCI, 0.886 for pitch value, and 0.610 for CPR<1, with sensitivities of 77.6%, 82.4%, and 27.1% and specificities of 78.5%, 83.6%, and 94.9%, respectively. The combined UCI+CPR<1 and pitch value+CPR<1 models yielded AUCs of 0.841 and 0.886, with sensitivities of 78.8% and 81.2% and specificities of 78.5% and 84.2%, respectively. No significant differences were found between the AUCs of UCI and pitch value (P>0.05), but both outperformed CPR<1 alone (both P<0.001). The combined models showed no significant improvement over UCI or pitch value alone (both P>0.05), though both were superior to CPR<1 alone (both P<0.001). CONCLUSIONS Most umbilical cord hypercoiling cases had favorable outcomes, with UCI, pitch value, CPR<1 and their combinations demonstrating significant predictive value for adverse pregnancy outcomes.
Humans ; Female ; Pregnancy ; Pregnancy Outcome ; Adult ; Ultrasonography, Prenatal/methods* ; Umbilical Cord/diagnostic imaging* ; Hemodynamics ; Predictive Value of Tests ; Infant, Newborn ; ROC Curve

Objective:Inflammation has been confirmed to play an important role in the occurrence and development of sudden sensorineural hearing loss（SSNHL）, and the neutrophil-to-lymphocyte ratio（NLR） is a biomarker positively correlated with the degree of inflammation. This study aims to identify the difference in serum NLR between patients with SSNHL and normal population, and to evaluate the predictive efficacy of NLR for the occurrence and prognosis of SSNHL, thereby guiding the clinical diagnosis and treatment of SSNHL. Methods:In this study, 96 patients diagnosed with SSNHL admitted to our department from January 2023 to March 2024 and 96 patients diagnosed with vocal cord polyps admitted to our department during the same period were recruited as a control group. Multivariate Logistic regression was used to evaluate independent related factors, and a nomogram was constructed to predict the probability of SSNHL. The receiver operating characteristic（ROC） curve and calibration curve were used to evaluate the accuracy of prediction. Results:Multivariate logistic regression analysis showed that a high level NLR（OR2.215; 95%CI1.597-3.073; P<0.001） were independently associated with the presence of SSNHL. High age（OR1.036; 95%CI1.009-1.067; P=0.012）, high FIB（OR2.35; 95%CI1.176-4.960; P=0.019） were the risk factor for SSNHL. Incorporating these 3 factors, a forest plot and a nomogram were generated. The ROC curve, nomogram and calibration curve showed that the model had good clinical practicability. A low NLR（OR0.598; 95%CI0.439-0.816; P<0.001） was significantly associated with a favorable prognosis of SSNHL. Conclusion:Elevated NLR can serve as an promising biomarker for assessing the risk of SSNHL. The nomograms calculation model may be utilized as a tool to estimate the probability of SSNHL. Low level NLR is significantly associated with a good prognosis of SSNHL.
Humans ; Neutrophils ; Female ; Male ; Lymphocytes ; Hearing Loss, Sensorineural/blood* ; Hearing Loss, Sudden/diagnosis* ; Middle Aged ; Prognosis ; Nomograms ; ROC Curve ; Adult ; Logistic Models ; Biomarkers/blood* ; Lymphocyte Count ; Inflammation/blood* ; Clinical Relevance

Objective:To development a deep learning（DL） model based on conventional MRI for automatic segmentation and differential diagnosis of nasopharyngeal carcinoma（NPC） and nasopharyngeal lymphoma（NPL）. Methods:The retrospective study included 142 patients with NPL and 292 patients with NPC who underwent conventional MRI at Renmin Hospital of Wuhan University from June 2012 to February 2023. MRI from 80 patients were manually segmented to train the segmentation model. The automatically segmented regions of interest（ROIs） formed four datasets: T1 weighted images（T1WI）, T2 weighted images（T2WI）, T1 weighted contrast-enhanced images（T1CE）, and a combination of T1WI and T2WI. The ImageNet-pretrained ResNet101 model was fine-tuned for the classification task. Statistical analysis was conducted using SPSS 22.0. The Dice coefficient loss was used to evaluate performance of segmentation task. Diagnostic performance was assessed using receiver operating characteristic（ROC） curves. Gradient-weighted class activation mapping（Grad-CAM） was imported to visualize the model's function. Results:The DICE score of the segmentation model reached 0.876 in the testing set. The AUC values of classification models in testing set were as follows: T1WI: 0.78（95%CI 0.67-0.81）, T2WI: 0.75（95%CI 0.72-0.86）, T1CE: 0.84（95%CI 0.76-0.87）, and T1WI+T2WI: 0.93（95%CI 0.85-0.94）. The AUC values for the two clinicians were 0.77（95%CI 0.72-0.82） for the junior, and 0.84（95%CI 0.80-0.89） for the senior. Grad-CAM analysis revealed that the central region of the tumor was highly correlated with the model's classification decisions, while the correlation was lower in the peripheral regions. Conclusion:The deep learning model performed well in differentiating NPC from NPL based on conventional MRI. The T1WI+T2WI combination model exhibited the best performance. The model can assist in the early diagnosis of NPC and NPL, facilitating timely and standardized treatment, which may improve patient prognosis.
Humans ; Nasopharyngeal Carcinoma/diagnostic imaging* ; Deep Learning ; Magnetic Resonance Imaging ; Retrospective Studies ; Nasopharyngeal Neoplasms/diagnostic imaging* ; Lymphoma/diagnostic imaging* ; Diagnosis, Differential ; ROC Curve ; Male ; Female ; Middle Aged ; Adult

Objective:To explore the influencing factors related to olfactory dysfunction secondary to different types of chronic rhinosinusitis（CRS）. Methods:A retrospective analysis was conducted on 185 CRS patients treated at the Department of Otolaryngology-Head and Neck Surgery of Shanxi Provincial People's Hospital from July 2023 to July 2024. Based on the presence or absence of nasal polyps, CRS was divided into two groups: chronic rhinosinusitis with nasal polyps（CRSwNP） and chronic rhinosinusitis without nasal polyps（CRSsNP）. Further, based on whether olfactory dysfunction was present, the CRSwNP and CRSsNP groups were divided into subgroups with olfactory dysfunction and normal olfaction. General data, laboratory tests, and modified sinus CT scores were compared between the subgroups. Logistic regression analysis was conducted to identify independent influencing factors based on the results of univariate analysis combined with clinical significance, and two nomogram models were established. The area under the curve of the receiver operating characteristic（ROC） curve, calibration curves, and decision curve analysis were used to assess the diagnostic performance, calibration, and clinical utility of the predictive model. Results:The proportion of blood eosinophils, blood urea nitrogen, and total modified CT scores of the bilateral olfactory region were identified as independent influencing factors in the CRSwNP group; the proportion of blood monocytes and modified CT scores of the bilateral posterior region were independent influencing factors in the CRSsNP group. The nomogram prediction model showed good diagnostic performance, calibration, and clinical utility in both the CRSwNP and CRSsNP groups. Conclusion:Olfactory dysfunction in CRSwNP patients is closely related to the proportion of blood eosinophils, blood urea nitrogen, and total modified CT scores of the bilateral olfactory region, while olfactory dysfunction in CRSsNP patients is closely related to the proportion of blood monocytes and modified CT scores of the bilateral posterior region. Moreover, the predictive model established in this study demonstrates good clinical performance and can be used for early identification and risk prediction of olfactory dysfunction secondary to CRS.
Humans ; Sinusitis/complications* ; Chronic Disease ; Retrospective Studies ; Olfaction Disorders/etiology* ; Nasal Polyps/complications* ; Rhinitis/complications* ; Female ; Male ; Logistic Models ; Middle Aged ; Smell ; Adult ; ROC Curve ; Nomograms ; Eosinophils ; Tomography, X-Ray Computed

Objective:To explore the influencing factors of olfactory impairment in patients with obstructive sleep apnea（OSA） and establish a nomogram prediction model. Methods:A total of 100 OSA patients were enrolled. Snap&Sniff olfactory test was used to evaluate the olfactory identification function and olfactory threshold of the patients. According to the scoring criteria, either olfactory identification scores below 14 points or olfactory threshold scores below 3 points was defined as olfactory impairment. Multivariate logistic regression analysis was used to explore the influencing factors of olfactory impairment in OSA. The nomogram model was constructed by using the R 4.4.2 software package. ROC curve, calibration curve and decision curve were used to evaluate the predictive efficacy, consistency and clinical utility of the model. Results:A total of 55 of 100 OSA patients had olfactory impairment. The results of multivariate logistic regression analysis showed that age, ESS score, MoCA score, and apnea-hypopnea index（AHI） were the influencing factors of olfactory impairment in OSA. Based on the above parameters, a nomogram model was established. The ROC curve analysis showed that the AUC was 0.897（95%CI 0.834-0.961）, indicating that the model had good predictive ability. The calibration curve showed that the predicted probability of the model fits the actual probability well. Decision curve analysis showed that when the threshold probability was in the range of 0-0.9, the model had a high clinical net benefit rate. Conclusion:Age, ESS score, MoCA score and AHI are the influencing factors of olfactory impairment in patients with OSA. The nomogram model constructed based on the above factors has good predictive value, which is conducive to the clinical multi-angle understanding of OSA and the formulation of scientific prevention and treatment measures.
Humans ; Sleep Apnea, Obstructive/physiopathology* ; Nomograms ; Olfaction Disorders/etiology* ; Logistic Models ; Middle Aged ; Male ; Female ; ROC Curve ; Adult ; Aged

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

BACKGROUND: Concurrent chemo-radiotherapy (CCRT) is the standard treatment for locally advanced cervical cancer (LACC), but there are still many patients who suffer tumor recurrence. However, valuable predictors of treatment outcomes remain limited. This study aimed to assess the value of the serum immune biomarkers to predict the prognosis. METHODS: We reviewed cervical cancer patients treated with CCRT between January 2014 and May 2018 at Peking Union Medical College Hospital. The systemic immune inflammation index (SII), systemic inflammation response index (SIRI), and lactate dehydrogenase (LDH) were calculated using blood samples. The relationship between immune markers and the treatment outcome was analyzed. The area under the receiver operating characteristic (ROC) curve was used to evaluate the predictive efficiency. The Cox proportional hazards model and log-rank were used to predict overall survival (OS) and disease-free survival (DFS). RESULTS: This study included 667 patients. Among them, 195 (29.2%) patients were defined as treatment failure, including 127 (19.0%) patients with pelvic failure, 94 (14.1%) distant failure, and 25 (3.7%) concurrent pelvic and distant failure. It revealed that the tumor stage, size, metastatic lymph nodes (MLNs), and serum immune biomarkers, such as SII, SIRI, and LDH, were significantly related to treatment outcomes. We demonstrated that the optimal cut-off of the SII, SIRI, and LDH were 970.4 × 10 9 /L, 1.3 × 10 9 /L, and 207.52 U/L, respectively. Importantly, this study presented that LDH level had the highest OR (OR = 4.2; 95% CI [2.3-10.8]). Furthermore, the OS and DFS for patients with pre-SII ≥970.5 × 10 9 /L were significantly worse than those with pre-SII <970.5 × 10 9 /L. Similarly, pre-SIRI ≥1.25 × 10 9 /L and pre-LDH ≥207.5 U/L were related to poor survival outcomes. CONCLUSIONS This study demonstrated that the baseline SII, SIRI, and LDH levels can be used to accurately and effectively predict the treatment outcomes after CCRT and long-term prognosis. Our results may offer additional prognostic information in clinical, which helps to detect the potential recurrent metastasis in time.
Humans ; Female ; Uterine Cervical Neoplasms/drug therapy* ; Middle Aged ; Adult ; Aged ; Chemoradiotherapy/methods* ; L-Lactate Dehydrogenase/blood* ; Treatment Outcome ; Disease-Free Survival ; Prognosis ; ROC Curve ; Biomarkers, Tumor/blood* ; Proportional Hazards Models

INTRODUCTION: Obstructive sleep apnoea (OSA) is a serious but underdiagnosed condition. Demand for the gold standard diagnostic polysomnogram (PSG) far exceeds its availability. More efficient diagnostic methods are needed, even in tertiary settings. Machine learning (ML) models have strengths in disease prediction and early diagnosis. We explored the use of ML with oximetry, demographic and anthropometric data to diagnose OSA. METHODS: A total of 2,996 patients were included for modelling and divided into test and training sets. Seven commonly used supervised learning algorithms were trained with the data. Sensitivity (recall), specificity, positive predictive value (PPV) (precision), negative predictive value, area under the receiver operating characteristic curve (AUC) and F1 measure were reported for each model. RESULTS: In the best performing four-class model (neural network model predicting no, mild, moderate or severe OSA), a prediction of moderate and/or severe disease had a combined PPV of 94%; one out of 335 patients had no OSA and 19 had mild OSA. In the best performing two-class model (logistic regression model predicting no-mild vs. moderate-severe OSA), the PPV for moderate-severe OSA was 92%; two out of 350 patients had no OSA and 26 had mild OSA. CONCLUSION Our study showed that the prediction of moderate-severe OSA in a tertiary setting with an ML approach is a viable option to facilitate early identification of OSA. Prospective studies with home-based oximeters and analysis of other oximetry variables are the next steps towards formal implementation.
Humans ; Oximetry/methods* ; Sleep Apnea, Obstructive/diagnosis* ; Male ; Female ; Middle Aged ; Machine Learning ; Polysomnography ; Adult ; Anthropometry ; ROC Curve ; Aged ; Algorithms ; Predictive Value of Tests ; Sensitivity and Specificity ; Neural Networks, Computer ; Demography