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

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

INTRODUCTION: In this study, we aimed to compare the performance of a convolutional neural network (CNN)-based deep learning model that was trained on a dataset of normal and abnormal paediatric elbow radiographs with that of paediatric emergency department (ED) physicians on a binomial classification task. METHODS: A total of 1,314 paediatric elbow lateral radiographs (patient mean age 8.2 years) were retrospectively retrieved and classified based on annotation as normal or abnormal (with pathology). They were then randomly partitioned to a development set (993 images); first and second tuning (validation) sets (109 and 100 images, respectively); and a test set (112 images). An artificial intelligence (AI) model was trained on the development set using the EfficientNet B1 network architecture. Its performance on the test set was compared to that of five physicians (inter-rater agreement: fair). Performance of the AI model and the physician group was tested using McNemar test. RESULTS: The accuracy of the AI model on the test set was 80.4% (95% confidence interval [CI] 71.8%-87.3%), and the area under the receiver operating characteristic curve (AUROC) was 0.872 (95% CI 0.831-0.947). The performance of the AI model vs. the physician group on the test set was: sensitivity 79.0% (95% CI: 68.4%-89.5%) vs. 64.9% (95% CI: 52.5%-77.3%; P = 0.088); and specificity 81.8% (95% CI: 71.6%-92.0%) vs. 87.3% (95% CI: 78.5%-96.1%; P = 0.439). CONCLUSION The AI model showed good AUROC values and higher sensitivity, with the P-value at nominal significance when compared to the clinician group.
Humans ; Deep Learning ; Child ; Retrospective Studies ; Male ; Female ; Radiography/methods* ; ROC Curve ; Elbow/diagnostic imaging* ; Neural Networks, Computer ; Child, Preschool ; Elbow Joint/diagnostic imaging* ; Emergency Service, Hospital ; Adolescent ; Infant ; Artificial Intelligence

INTRODUCTION: The diagnosis of sarcopenia relies on key indicators such as handgrip strength, walking speed and muscle mass. Developing a composite index that integrates these measures could enhance clinical evaluation in older adults. This study aimed to standardise and combine these metrics to establish a z score for the sarcopenia composite index (ZoSCI) tailored for the ageing population. Additionally, we explore the risk factors associated with ZoSCI to provide insights into early prevention and intervention strategies. METHOD: This retrospective study analysed data between January 2017 and December 2021 from an elderly health programme in Taiwan, applying the Asian Working Group for Sarcopenia criteria to assess sarcopenia. ZoSCI was developed by standardising handgrip strength, walking speed and muscle mass into z scores and integrating them into a composite index. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values, and multiple regression analysis identified factors influencing ZoSCI. RESULTS: Among the 5047 participants, the prevalence of sarcopenia was 3.7%, lower than the reported global prevalence of 3.9-15.4%. ROC curve analysis established optimal cut-off points for distinguishing sarcopenia in ZoSCI: -1.85 (sensitivity 0.91, specificity 0.88) for males and -1.97 (sensitivity 0.93, specificity 0.88) for females. Factors associated with lower ZoSCI included advanced age, lower education levels, reduced exercise frequency, lower body mass index and creatinine levels. CONCLUSION This study introduces ZoSCI, a new compo-site quantitative indicator for identifying sarcopenia in older adults. The findings highlight specific risk factors that can inform early intervention. Future studies should validate ZoSCI globally, with international collaborations to ensure broader applicability.
Humans ; Sarcopenia/physiopathology* ; Male ; Aged ; Female ; Retrospective Studies ; Hand Strength ; Taiwan/epidemiology* ; ROC Curve ; Aged, 80 and over ; Risk Factors ; Walking Speed ; Geriatric Assessment/methods* ; Prevalence ; Muscle, Skeletal ; Middle Aged

OBJECTIVES: To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators. METHODS: Tabular data with missing laboratory indicators were collected from 393 patients with ovarian cancer and 1951 control patients. The missing ovarian cancer laboratory indicator features were projected to the latent space to obtain a classification model using the representational learning classification model based on discriminative learning and mutual information coupled with feature projection significance score consistency and missing location estimation. The proposed constraint term was ablated experimentally to assess the feasibility and validity of the constraint term by accuracy, area under the ROC curve (AUC), sensitivity, and specificity. Cross-validation methods and accuracy, AUC, sensitivity and specificity were also used to evaluate the discriminative performance of this classification model in comparison with other interpolation methods for processing of the missing data. RESULTS: The results of the ablation experiments showed good compatibility among the constraints, and each constraint had good robustness. The cross-validation experiment showed that for identification of ovarian cancer with missing laboratory indicators, the AUC, accuracy, sensitivity and specificity of the proposed multi-constraints representation-based learning classification model was 0.915, 0.888, 0.774, and 0.910, respectively, and its AUC and sensitivity were superior to those of other interpolation methods. CONCLUSIONS The proposed model has excellent discriminatory ability with better performance than other missing data interpolation methods for identification of ovarian cancer with missing laboratory indicators.
Female ; Humans ; Ovarian Neoplasms/diagnosis* ; Machine Learning ; ROC Curve

OBJECTIVES: To investigate the effect of serum advanced glycation endproducts (AGEs) on stenosis after first autologous arteriovenous fistula (AVF) in patients with end-stage renal disease (ESRD). METHODS: Patients with ESRD undergoing standard native arteriovenous fistula (AVF) for the first time in the Department of Nephrology, Affiliated Hospital of Guilin Medical University from February to June 2022 were prospectively enrolled. The preoperative general data, clinical examination results and ultrasound data of the operated limbs were collected. The patients with and without stenosis within 2 months after the operation were compared for preoperative serum AGEs levels detected using ELISA and the clinical parameters. Logistic regression analysis was used to analyze the independent risk factors of AVF stenosis, and the sensitivity and specificity of AGEs for predicting postoperative stenosis were analyzed using receiver-operating characteristic (ROC) curve. RESULTS: Of the 94 patients enrolled, 34 had postoperative arteriovenous stenosis and 60 had no stenosis. The number of diabetic patients differed significantly between stenosis group and non-stenosis group (P<0.001). Serum AGEs levels, which were negatively correlated with serum phosphorus level (P<0.05), were significantly higher in stenosis group than in non-stenosis group (Z=-2.837, P=0.005). Serum AGE level was an independent risk factor for postoperative stenosis after AVF (OR=1.251, 95% CI:1.096-1.423, P<0.001). For predicting AVF stenosis, the area under the ROC curve (AUC) of AGEs was 0.677 (P=0.007, 95% CI: 0.572-0.770), with a specificity of 90.00% and a sensitivity of 52.94% at the optimal cut-off value of 8.43 µg/mL; AGEs combined with fibrinogen had an AUC of 0.763 (P<0.001, 95% CI: 0.664-0.844), with a specificity of 73.33% and a sensitivity of 70.59% at the optimal cut-off value of 0.30. CONCLUSIONS Elevated serum AGEs level is an independent risk factor for postoperative AVF stenosis, and its combination with fibrinogen has a better efficacy for predicting postoperative AVF stenosis.
Humans ; Glycation End Products, Advanced/blood* ; Risk Factors ; Arteriovenous Shunt, Surgical/adverse effects* ; Kidney Failure, Chronic/blood* ; Male ; Constriction, Pathologic/etiology* ; Female ; Middle Aged ; Postoperative Complications/etiology* ; Renal Dialysis ; Aged ; Prospective Studies ; ROC Curve ; Adult

OBJECTIVE: To investigate the predictive value of early lactate/albumin ratio (LAR) combined with quick sequential organ failure assessment (qSOFA) for the 28-day prognosis of patients with sepsis caused by emergency community-acquired pneumonia (CAP). METHODS: The clinical data of patients with sepsis caused by CAP admitted to the department of emergency of Beijing Haidian Hospital from June 2021 to August 2022 were retrospectively analyzed, including gender, age, comorbidities, lactic acid (Lac), serum albumin (Alb), LAR, procalcitonin (PCT) within 1 hour, and 28-day prognosis. Patients were divided into two groups based on 28-day prognosis, and risk factors affecting patients' prognosis were analyzed using univariate and multivariate Cox regression methods. Patients were divided into two groups according to the best cut-off value of LAR, and Kaplan-Meier survival curves were used to analyze the 28-day cumulative survival of patients in each group. Time-dependent receiver operator characteristic curve (ROC curve) were plotted to analyze the predictive value of sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), and qSOFA+LAR score on the prognosis of patients with sepsis caused by CAP at 28 days. The area under the curve (AUC) was calculated and compared. RESULTS: A total of 116 patients with sepsis caused by CAP were included, of whom 80 survived at 28 days and 36 died, 28-day mortality of 31.0%. There were no statistically significant differences in age, gender, comorbidities, pH, platelet count, and fibrinogen between the survival and death groups, and there were significantly differences in blood urea nitrogen (BUN), white blood cell count (WBC), hemoglobin, Lac, Alb, PCT, D-dimer, LAR, as well as qSOFA score, SOFA score, and APACHE II score. Univariate Cox regression analyses showed that BUN, WBC, pH, Lac, Alb, PCT, LAR, qSOFA score, SOFA score, and APACHE II score were associated with mortality outcome. Multifactorial Cox regression analysis of the above variables showed that BUN, WBC, PCT, and APACHE II score were independent risk factors for 28-day death in the emergency department in patients with sepsis caused by CAP [hazard ratio (HR) were 1.081, 0.892, 1.034, and 1.135, respectively, all P < 0.05]. The best cut-off value of early LAR for predicting the 28-day prognosis of sepsis patients was 0.088, the Kaplan-Meier survival curve showed that the 28-day cumulative survival rate of sepsis patients in the LAR ≤ 0.088 group was significantly higher than that in the LAR > 0.088 group [82.9% (63/76) vs. 42.5% (17/40), Log-Rank test: χ² = 22.51, P < 0.001]. The qSOFA+LAR score was calculated based on the LAR cut-off value and qSOFA score, and ROC curve analysis showed that the AUCs of SOFA score, APACHE II score, and qSOFA+LAR score for predicting 28-day death of patients with sepsis caued by CAP were 0.741, 0.774, and 0.709, respectively, with the AUC of qSOFA+LAR score slightly lower than those of SOFA score and APACHE II score, but there were no significantly differences. When the best cut-off value of qSOFA+LAR score was 1, the sensitivity was 63.9% and the specificity was 80.0%. CONCLUSION The qSOFA+LAR score has predictive value for the 28-day prognosis of patients with sepsis caused by CAP in the emergency department, its predictive value is comparable to the SOFA score and the APACHE II score, and it is more convenient for early use in the emergency department.
Emergency Service, Hospital/statistics & numerical data* ; Sepsis/etiology* ; Prognosis ; Community-Acquired Pneumonia/mortality* ; Organ Dysfunction Scores ; Predictive Value of Tests ; Lactic Acid/blood* ; Serum Albumin, Human/analysis* ; Biomarkers/blood* ; Retrospective Studies ; Hospital Mortality ; Kaplan-Meier Estimate ; APACHE ; Procalcitonin/blood* ; ROC Curve ; Area Under Curve ; Humans

OBJECTIVE: To explore the prognostic value of C-reactive protein (CRP)/albumin (Alb) ratio combined with platelet count (PLT) and Glasgow coma score (GCS) in patients with heat stroke (HS). METHODS: A retrospective analysis was conducted on the clinical data of HS patients admitted to the department of intensive care unit (ICU) of Nanchong Central Hospital from May 1, 2020 to October 31, 2023. This included general information, admission GCS, laboratory indicators and 28-day prognosis. The differences in the above indicators were compared between two groups of patients with different prognoses. Statistically significant indicators from univariate analysis were included in multivariate Logistic regression analysis to screen for factors influencing 28-day mortality in HS patients. The predictive value of various influencing factors on the 28 days prognosis of HS patients were analyzed by receiver operator characteristic curve (ROC curve). RESULTS: A total of 73 HS patients were included, of whom 41 survived for 28-day and 32 died. There were no statistically significant differences in gender and age between the two groups of HS patients with different prognoses. The white blood cell count (WBC), neutrophil count (NEU), aspartate aminotransferase (AST), alanine aminotransferase (ALT), CRP, and CRP/Alb ratio in the death group were significantly higher than those of the survival group, and the admission GCS score, platelet count (PLT), total bilirubin (TBil) and Alb were significantly lower than the survival group [WBC (×10⁹/L): 14.80 (11.44, 17.15) vs. 11.96 (9.47, 14.82), NEU (×10⁹/L): 13.05 (8.56, 15.67) vs. 9.50 (6.68, 12.09), AST (U/L): 108.00 (52.70, 291.50) vs. 64.50 (38.25, 110.50), ALT (U/L): 62.00 (19.50, 159.00) vs. 34.50 (20.75, 70.75), CRP (mg/L): 22.49 (3.42, 58.93) vs. 3.68 (1.01, 11.46), CRP/Alb ratio: 0.53 (0.08, 1.77) vs. 0.08 (0.02, 0.44), GCS score: 7.0 (5.0, 8.0) vs. 8.5 (7.0, 11.0), PLT (×10⁹/L): 107.00 (73.50, 126.00) vs. 131.50 (107.50, 176.25), TBil (mmol/L): 15.60 (10.00, 25.30) vs. 21.40 (14.80, 30.05), Alb (g/L): 32.65 (32.53, 49.30) vs. 38.70 (36.20, 40.40), all P < 0.05]. Binary Logistic regression analysis showed that the GCS score [odds ratio (OR) = 0.686, 95% confidence interval (95%CI) was 0.491-0.959, P = 0.028], PLT (OR = 0.973, 95%CI was 0.954-0.992, P = 0.005), NEU (OR = 1.312, 95%CI was 1.072-1.606, P = 0.009) and CRP/Alb ratio (OR = 7.652, 95%CI was 1.632-35.881, P = 0.010) were independent influencing factors for 28-day mortality in HS patients. ROC curve analysis showed that the area under the curve (AUC) of GCS score, PLT, and CRP/Alb ratio for single prediction of 28-day prognosis in HS patients was 0.705, 0.752, and 0.729, and the combination of all three predicted the highest AUC of 28-day prognosis in HS patients (0.917), with a sensitivity and specificity of 86.2% and 81.2%, respectively. CONCLUSION CRP/Alb ratio, PLT, and GCS score are independent influencing factors affecting the prognosis of HS patients, and all of them have a certain predictive value for the prognosis of HS patients, in which the combination of the three has a higher predictive value for the prognosis of HS patients.
Humans ; C-Reactive Protein/analysis* ; Prognosis ; Glasgow Coma Scale ; Retrospective Studies ; Heat Stroke/diagnosis* ; Platelet Count ; Male ; Female ; Serum Albumin/analysis* ; Middle Aged ; Aged ; Adult ; ROC Curve

OBJECTIVE: To investigate the clinical characteristics and related risk factors of Clostridium difficile infection (CDI) in intensive care unit (ICU). METHODS: A retrospective study was conducted. Patients with diarrhea admitted to the ICU of the General Hospital of Ningxia Medical University from May 1 to August 30, 2023 were selected. Patients were divided into CDI group and non-CDI group based on the presence or absence of CDI. Clinical data from two groups of patients meeting the criteria were collected and compared, including gender, age, acute physiology and chronic health evaluation II (APACHE II), length of hospital stay, serum lactic acid, parenteral nutrition time, white blood cell count (WBC), procalcitonin (PCT), C-reactive protein (CRP), coagulation indicators, albumin, antibiotic exposure, etc. Multivariate Logistic regression analysis was performed to analyze the risk factors for CDI in ICU diarrhea patients. Receiver operator characteristic curve (ROC curve) was drawn to analyze the predictive value of each index for CDI in diarrhea patients. RESULTS: A total of 24 patients with diarrhea were enrolled, including 9 patients in the CDI group and 15 patients in the non-CDI group. The time of parenteral nutrition in the CDI group was significantly longer than that in the non-CDI group [days: 18.0 (13.5, 19.5) vs. 10.0 (4.0, 18.0)], the serum lactic acid level [mmol/L: 4.40 (3.00, 15.25) vs. 2.50 (1.90, 3.20)] and the ratio of serum lactic acid > 3.9 mmol/L [66.67% (6/9) vs. 6.67% (1/15)] were significantly higher than those in the non-CDI group, with statistical significance (all P < 0.05). Multivariate binary Logistic regression analysis showed that the serum lactic acid level of the patients was an independent risk factor for CDI [odds ratio (OR) = 3.193, 95% confidence interval (95%CI) was 1.011-10.080, P = 0.048]. ROC curve showed that serum lactic acid level had a high predictive value for CDI in ICU patients with diarrhea, and the area under the curve (AUC) was 0.815, respectively. When the cut-off value of serum lactic acid was 3.9 mmol/L, the sensitivity was 66.7% and the specificity was 93.3%. CONCLUSION Patients with diarrhea who have higher serum lactate levels (> 3.9 mmol/L) on admission are at increased risk of developing CDI.
Humans ; Retrospective Studies ; Risk Factors ; Intensive Care Units ; Clostridium Infections ; Clostridioides difficile ; Male ; Female ; Middle Aged ; Aged ; Diarrhea/microbiology* ; Logistic Models ; ROC Curve ; Adult

OBJECTIVE: To investigate the predictive value of inflammatory indicator and serum cystatin C (Cys C) for the prognosis of patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A prospective observational study was conducted. Patients with SA-AKI admitted to the intensive care unit (ICU) of the General Hospital of Ningxia Medical University from January 2022 to December 2023 were selected as the study subjects. General patient data, sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), inflammatory indicator, and serum Cys C levels were collected. The 28-day survival status of the patients was observed. A multivariate Logistic regression model was used to analyze the risk factors affecting the poor prognosis of SA-AKI patients. Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive efficacy of each risk factor for the prognosis of SA-AKI patients. RESULTS: A total of 111 SA-AKI patients were included, with 65 patients (58.6%) in the survival group and 46 patients (41.4%) in the death group. The SOFA score, APACHE II score, interleukin-6 (IL-6), procalcitonin (PCT), hypersensitive C-reactive protein (hs-CRP), and serum Cys C levels in the death group were significantly higher than those in the survival group [SOFA score: 15.00 (14.00, 17.25) vs. 14.00 (11.00, 16.00), APACHE II score: 26.00 (23.75, 28.00) vs. 23.00 (18.50, 28.00), IL-6 (ng/L): 3 731.00±1 573.61 vs. 2 087.93±1 702.88, PCT (μg/L): 78.19±30.35 vs. 43.56±35.37, hs-CRP (mg/L): 266.50 (183.75, 326.75) vs. 210.00 (188.00, 273.00), serum Cys C (mg/L): 2.01±0.61 vs. 1.62±0.50, all P < 0.05]. Multivariate Logistic regression analysis showed that SOFA score [odds ratio (OR) = 1.273, 95% confidence interval (95%CI) was 1.012-1.600, P = 0.039], IL-6 (OR = 1.000, 95%CI was 1.000-1.001, P = 0.043), PCT (OR = 1.018, 95%CI was 1.002-1.035, P = 0.030), and Cys C (OR = 4.139, 95%CI was 1.727-9.919, P = 0.001) were independent risk factors affecting the 28-day prognosis of SA-AKI patients. ROC curve analysis showed that the area under the curve (AUC) of SOFA score, IL-6, PCT, and Cys C in predicting the 28-day prognosis of SA-AKI patients were 0.682 (95%CI was 0.582-0.782, P = 0.001), 0.753 (95%CI was 0.662-0.843, P < 0.001), 0.765 (95%CI was 0.677-0.854, P < 0.001), and 0.690 (95%CI was 0.583-0.798, P = 0.001), respectively. The combined predictive value of these four indicators for the prognosis of SA-AKI patients were superior to that of any single indicator, with an AUC of 0.847 (95%CI was 0.778-0.916, P < 0.001), a sensitivity of 95.7%, and a specificity of 56.9%. CONCLUSION The combination of SOFA score, IL-6, PCT, and Cys C provides a reliable predictive value for the prognosis of SA-AKI patients.
Humans ; Acute Kidney Injury/mortality* ; APACHE ; C-Reactive Protein ; Cystatin C/blood* ; Interleukin-6/blood* ; Logistic Models ; Predictive Value of Tests ; Procalcitonin/blood* ; Prognosis ; Prospective Studies ; Risk Factors ; ROC Curve ; Sepsis/mortality*