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

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning

Objective To explore the expression of coagulation indexes in rheumatoid arthritis (RA) and dry syndrome (pSS) and their relationships with inflammation and immune function. Methods A total of 61 patients with RA who were hospitalized in the Department of Rheumatology of Anhui Provincial Hospital of Traditional Chinese Medicine from March 12 to September 9, 2024 were selected as the RA group. And 61 patients with pSS who were hospitalized in the Department of Rheumatology of the same hospital September 4, 2023, to August 17, 2024, were selected as the pSS group. 61 healthy individuals who underwent routine medical checkups at the Physical Examination Center of Anhui Provincial Hospital of Traditional Chinese Medicine during the same period were included as the control group. Baseline clinical indexes before treatment were collected from patients in each group, including prothrombin time(PT), international normalized ratio(INR), thrombia time(TT), fibrinogen(FBG), activated partial thromboplastin time(APTT), fibrin (ogen) degradation products(FDP) and D-Dimer(D-D). Results The expression levels of PT, FBG, TT, FDP, and D-D in the RA group, the pSS group, and the normal group were significantly different. The expression levels of PT, FBG, FDP, and D-D in the RA group were all higher than those in the pSS group and the control group, respectively. And the expression level of TT in the pSS group was lower than that in control group. ROC curve analysis showed that the AUC of PT was 0.638, the AUC of FBG was 0.899, the AUC of FDP was 0.866, and the AUC of D-D was 0.919 in the RA group compared with the normal group. And the AUC of coagulation indexes for joint diagnosis of RA was higher than that of the indexes detected individually. pSS group had an AUC of PT of 0.618 compared with that of the normal group. The AUC of TT was 0.645, and the AUC of coagulation indexes for the joint diagnosis of pSS was higher than the AUC of each index detected separately. Association rule analysis showed that elevated D-D in RA patients had a significant correlation with elevated hs-CRP, CCP and RF, and elevated FBG had a significant correlation with elevated hs-CRP, ESR, RF and CCP. Elevated D-D in pSS patients had a correlation with elevated hs-CRP and anti-SSA, and elevated INR has correlation with elevated hs-CRP, anti-SSA and anti-SSB. Correlation analysis showed that PT, INR, FBG, FDP, and D-D were positively correlated with CRP and ESR, and TT was negatively correlated with CRP and ESR in the RA group. FBG, FDP, and D-D were positively correlated with CRP and ESR in the pSS group. Moreover, coagulation indexes were positively correlated with immune indexes in RA group and pSS group which were all significant. The results of multiple linear regression analysis showed that FBG was a positive correlate of hs-CRP and ESR in RA patients. For PSS patients, FBG and FDP were positive correlates of hs-CRP. APTT and FBG were positive correlates of ESR. Conclusion Compared with pSS, coagulation indexes (especially PT, FBG, FDP and D-D) are more informative for the early diagnosis of RA and the judgment of the degree of the disease, and can be used as an important predictor for the confirmation of the diagnosis of RA.
Humans ; Female ; Male ; Arthritis, Rheumatoid/diagnosis* ; Middle Aged ; Fibrin Fibrinogen Degradation Products/analysis* ; Blood Coagulation ; Adult ; Fibrinogen/metabolism* ; Partial Thromboplastin Time ; Prothrombin Time ; Aged ; Inflammation/immunology* ; ROC Curve

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

PURPOSE: To screen laboratory markers with predictive value in early spinal surgical site infections (SSI) that are diagnosed within 30 days postoperatively. METHODS: Patients who underwent surgical treatment for internal spinal fixation between March 2022 and March 2023 in our hospital were retrospectively studied. The inclusion criteria were aged >18 years, undergoing internal fixation surgery, complete medical records with >30 days of postoperative follow-up, diagnosis was made within 30 days postoperatively, and an informed consent form was obtained. The exclusion criteria were abnormal white blood cell count or neutrophil percentage in the preoperative blood routine and combined diseases that may affect the C-reactive protein (CRP) or procalcitonin (PCT) values, including lower respiratory tract infection, renal insufficiency, and liver disease. We collected patients' personal information, surgical information, and blood laboratory data, including CRP, PCT, lymphocyte-neutrophil ratio, platelet-neutrophil ratio, and routine blood tests on preoperative and postoperative days 3, 5, and 7, from these patients. These data were statistically analyzed to determine which laboratory markers were statistically significant. The diagnostic value and optimal diagnostic threshold of these laboratory markers were further determined by receiver operating characteristic curve analysis. RESULTS: A total of 106 patients were enrolled in this study, of whom 8 patients were diagnosed with early SSI. A total of 4 laboratory markers were screened, namely, CRP on postoperative day 7 (optimal diagnostic threshold of ≥64.1 mg/L, sensitivity of 100%, specificity of 76.5%, area under the curve (AUC) of 0.908), PCT on postoperative day 7 (optimal diagnostic threshold of ≥0.2 ng/mL, sensitivity of 87.5%, specificity of 94.1%, AUC of 0.967), lymphocyte count on postoperative day 5 (optimal diagnostic threshold of ≤0.67 × 10⁹/L, sensitivity of 50%, specificity of 95.9%, AUC of 0.760), and lymphocyte count on postoperative day 7 (optimal diagnostic threshold of ≤1.32 × 10⁹/L, sensitivity of 87.5%, specificity of 55.1%, AUC of 0.721). CONCLUSION We concluded that CRP and PCT levels on postoperative day 7 and lymphocyte counts on postoperative days 5 and 7 are useful markers in screening for early spinal SSI.
Humans ; Retrospective Studies ; Male ; Female ; Biomarkers/blood* ; Middle Aged ; C-Reactive Protein/analysis* ; Surgical Wound Infection/blood* ; Procalcitonin/blood* ; Adult ; Aged ; Postoperative Period ; ROC Curve ; Predictive Value of Tests ; Spine/surgery*

PURPOSE: To analyze risk factors for severe trauma and establish a nomogram for early risk prediction, to improve the early identification of severe trauma. METHODS: This study was conducted on the patients treated in 81 trauma treatment institutions in Gansu province from 2020 to 2022. Patients were grouped by year, with 5364 patients from 2020 to 2021 as the training set and 1094 newly admitted patients in 2020 as the external validation set. Based on the injury severity score (ISS), patients in the training set were classified into 2 subgroups of the severe trauma group (n = 478, ISS scores ≥25) and the non-severe trauma group (n = 4886, ISS scores <25). Univariate and binary logistic regression analyses were employed to identify independent risk factors for severe trauma. Subsequently, a predictive model was developed using the R software environment. Furthermore, the model was subjected to internal and external validation via the Hosmer-Lemeshow test and receiver operating characteristic curve analysis. RESULTS: In total, 6458 trauma patients were included in this study. Initially, this study identified several independent risk factors for severe trauma, including multiple traumatic injuries (polytrauma), external hemorrhage, elevated shock index, elevated respiratory rate, decreased peripheral oxygen saturation, and decreased Glasgow coma scale score (all p < 0.05). For internal validation, the area under the receiver operating characteristic curve was 0.914, with the sensitivity and specificity of 88.4% and 87.6%, respectively; while for external validation, the area under the receiver operating characteristic curve was 0.936, with the sensitivity and specificity of 84.6% and 93.7%, respectively. In addition, a good model fitting was observed through the Hosmer-Lemeshow test and calibration curve analysis (p > 0.05). CONCLUSION This study establishes a nomogram for early risk prediction of severe trauma, which is suitable for primary healthcare institutions in underdeveloped western China. It facilitates early triage and quantitative assessment of trauma severity by clinicians prior to clinical interventions.
Humans ; Nomograms ; Male ; Female ; Wounds and Injuries/diagnosis* ; Risk Factors ; Middle Aged ; Adult ; Injury Severity Score ; Risk Assessment ; ROC Curve ; Aged ; Logistic Models ; China ; Glasgow Coma Scale

Parameters of peripheral blood cell have been shown as the potential predictors of erectile dysfunction (ED). To investigate the clinical significance of hematological parameters for predicting the risk of rapid ejaculation, we established a rat copulatory model on the basis of ejaculation distribution theory. Blood samples from different ejaculatory groups were collected for peripheral blood cell counts and serum serotonin (5-HT) tests. Meanwhile, the relationship between hematological parameters and ejaculatory behaviors was assessed. Final analysis included 11 rapid ejaculators, 10 normal ejaculators, and 10 sluggish ejaculators whose complete data were available. The platelet (PLT) count in rapid ejaculators was significantly lower than that in normal and sluggish ejaculators, whereas the platelet distribution width (PDW) and mean platelet volume (MPV) were significantly greater in rapid ejaculators. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis showed that the PLT was an independent protective factor for rapid ejaculation. Meanwhile, rapid ejaculators were found to have the lowest serum 5-HT compared to normal and sluggish ejaculators ( P < 0.001). Furthermore, there was a positive correlation between the PLT and serum 5-HT ( r = 0.662, P < 0.001), indicating that the PLT could indirectly reflect the serum 5-HT concentration. In addition, we assessed the association between the PLT and ejaculatory parameters. There was a negative correlation between ejaculation frequency (EF) and the PLT ( r = -0.595, P < 0.001), whereas there was a positive correlation between ejaculation latency (EL) and the PLT ( r = 0.740, P < 0.001). This study indicated that the PLT might be a useful and convenient diagnostic marker for predicting the risk of rapid ejaculation.
Male ; Animals ; Ejaculation/physiology* ; Rats ; Platelet Count ; Pilot Projects ; Serotonin/blood* ; Biomarkers/blood* ; Mean Platelet Volume ; Rats, Sprague-Dawley ; ROC Curve ; Erectile Dysfunction/physiopathology*

We propose a strategy to reduce unnecessary prostate biopsies in Chinese patients with total prostate-specific antigen (tPSA) >10 ng ml -1 and Prostate Imaging Reporting and Data System (PI-RADS) scores between 1 and 3. Clinical data derived from 517 patients of The First Affiliated Hospital of USTC (Hefei, China) from January 2020 to December 2023 who met the screening criteria for the study were retrospectively collected. Independent predictors were identified via univariate and multivariate logistic regression analysis. The diagnostic capacity of clinical variables was evaluated using the receiver operating characteristic (ROC) curves and area under the curve (AUC). A prostate biopsy strategy was developed via risk stratification. Of the 517 patients, 17/348 (4.9%) with PI-RADS 1-2 were diagnosed with clinically significant prostate cancer (csPCa), and 27/169 (16.0%) patients with PI-RADS 3 were diagnosed with csPCa. The appropriate prostate-specific antigen density (PSAD) cut-off values were 0.45 ng ml -2 for PI-RADS 1-2 patients and 0.3 ng ml -2 for PI-RADS 3 patients. The appropriate prostate volume (PV) cut-off values were 40 ml for PI-RADS 1-2 patients and 50 ml for PI-RADS 3 patients. The prostate biopsy strategy based on PSAD and PV developed in this study can reduce unnecessary prostate biopsies in patients with tPSA >10 ng ml -1 and PI-RADS 1-3. In the study, 66.5% (344/517) patients did not need to undergo prostate biopsy, at the expense of missing only 1.7% (6/344) patients with csPCa.
Humans ; Male ; Prostatic Neoplasms/diagnostic imaging* ; Prostate-Specific Antigen/blood* ; Aged ; Middle Aged ; Retrospective Studies ; Prostate/diagnostic imaging* ; Unnecessary Procedures/statistics & numerical data* ; Biopsy/statistics & numerical data* ; China ; ROC Curve

OBJECTIVES: To investigate the risk factors for plastic bronchitis (PB) in children with macrolide-unresponsive Mycoplasma pneumoniae pneumonia (MUMPP) and to establish a nomogram prediction model. METHODS: A retrospective analysis was conducted on 178 children with MUMPP who underwent bronchoscopy from January to December 2023. According to the presence or absence of PB, the children were divided into a PB group (49 children) and a non-PB group (129 children). The predictive factors for the development of PB in children with MUMPP were analyzed, and a nomogram prediction model was established. The model was assessed in terms of discriminatory ability, accuracy, and clinical effectiveness. RESULTS: The multivariate logistic regression analysis showed that older age and higher levels of lactate dehydrogenase and fibrinogen were closely associated with the development of PB in children with MUMPP (P<0.05). A nomogram model established based on these factors had an area under the receiver operating characteristic curve of 0.733 (95%CI: 0.651-0.816, P<0.001) and showed a good discriminatory ability. The Hosmer-Lemeshow goodness-of-fit test indicated that the predictive model had a good degree of fit (P>0.05), and the decision curve analysis showed that the model had a good clinical application value. CONCLUSIONS The risk nomogram model established based on age and lactate dehydrogenase and fibrinogen levels has good discriminatory ability, accuracy, and predictive efficacy for predicting the development of PB in children with MUMPP.
Retrospective Studies ; Risk Factors ; Nomograms ; Mycoplasma pneumoniae/isolation & purification* ; Pneumonia, Mycoplasma/microbiology* ; Bronchitis/microbiology* ; Macrolides/therapeutic use* ; Drug Resistance, Bacterial ; Bronchoscopy ; Area Under Curve ; ROC Curve ; Fibrinogen/analysis* ; Age Factors ; Humans ; Male ; Female ; Infant ; Child, Preschool ; Child ; Adolescent ; L-Lactate Dehydrogenase/blood*