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

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

Development and validation of prognostic nomogram for malignant pleural mesothelioma / 中华肿瘤杂志

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

Construction of blood transfusion prediction model for patients with severe traumatic brain injury / 中国输血杂志

【Objective】 To construct a blood transfusion prediction model for patients with severe traumatic brain injury (TBI), in order to predict the risk of blood transfusion and guide blood transfusion decision-making. 【Methods】 The clinical data of 756 patients with severe TBI admitted to the hospital from January 1, 2015 to June 30, 2021 were retrospectively analyzed. According to whether the patients were transfused with red blood cells after admission, the patients were divided into blood transfusion group (n=354) and non-blood transfusion group (n=402). The basic clinical data and prognostic indicators of the two groups were compared. Logistic regression algorithm was used to screen the risk factors related to blood transfusion in hospital to establish a nomogram prediction model, and the performance of the model was evaluated. 【Results】 No significant differences were noticed in gender, age, body temperature, cause of injury, ABO blood group, Rh blood group, serum Na and K concentrations between the two groups (P>0.05). Significant differences were found in Glasgow coma score (GCS), heart rate (HR), systolic blood pressure (SP), diastolic blood pressure (DP), shock index (SI), respiratory rate (RR), clinical diagnosis, treatment, hemoglobin concentration (Hb), hematocrit (Hct), platelet count (Plt) and coagulation function between the two groups (P0.05). Multivariate logistic regression analysis showed that surgical treatment, skull fracture, hemorrhagic shock, decreased Plt, decreased Hct and increased INR were independent risk factors for blood transfusion. A nomogram prediction model was constructed and the area under the ROC curve of the training set and the test set was 0.819(95% CI: 0.784-0.854) and 0.866(95% CI: 0.818-0.910), respectively, which had good predictive performance. 【Conclusion】 Surgical treatment, skull fracture, hemorrhagic shock, decreased Plt, decreased Hct and increased INR are independent risk factors for blood transfusion in adult patients with severe traumatic brain injury. The nomogram prediction model can better predict the blood transfusion demand of TBI patients and has high application value.

Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning / 生物医学工程学杂志

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

Risk factors of early postoperative complications after intersphincteric resection for low rectal cancer and construction of a nomogram prediction model / 中华普通外科杂志

Objective:To identify the risk factors for postoperative early complications of low rectal cancer treated with intersphincteric resection (ISR).Methods:The perioperative data of 82 patients with low rectal cancer undergoing ISR at the General Surgery Department of Shaanxi Provincial People's Hospital between Jan 2017 to Dec 2021 were retrospectively analyzed. Univariate, Logistic regression and multivariate analysis were used to analyze the risk factors for early complications after ISR, and a nomogram prediction model was drawn. Predictive models are validated.Results:There were 22 patients (27%) with complications. Univariate analysis showed that early complications were related to diabetes (0.021), serum albumin (<0.001), nutritional prognosis index (0.003), neoadjuvant chemoradiotherapy (<0.001), and operation time (<0.001). By multivariate analysis, diabetes ( OR=4.853, 95% CI: 1.059-22.241, P=0.042), low serum albumin ( OR=0.672, 95% CI: 0.468-0.966, P=0.032), neoadjuvant chemoradiotherapy ( OR=4.482, 95% CI: 1.117-17.979, P=0.034) and longer operation time ( OR=1.015, 95% CI: 1.001-1.029, P=0.037) were independent risk factors A nomogram prediction model was thus constructed, and the area under the curve of the nomogram prediction model was 0.888 (95% CI: 0.812-0.965). Conclusion:Diabetes mellitus, low serum albumin, neoadjuvant chemoradiotherapy, and longer operation time are independent risk factors of early postoperative complications for low rectal cancer undergoing ISR.

Construction of a nomogram prediction model for pathological complete response (pCR) of ipsilateral supraclavicular lymph node after neoadjuvant chemotherapy for breast cancer with first diagnosis of ipsilateral supraclavicular lymph node metastasis / 中华肿瘤杂志

Objective: To develop a predictive model for pathologic complete response (pCR) of ipsilateral supraclavicular lymph nodes (ISLN) after neoadjuvant chemotherapy for breast cancer and guide the local treatment. Methods: Two hundred and eleven consecutive breast cancer patients with first diagnosis of ipsilateral supraclavicular lymph node metastasis who underwent ipsilateral supraclavicular lymph node dissection and treated in the Breast Department of Henan Cancer Hospital from September 2012 to May 2019 were included. One hundred and forty two cases were divided into the training set while other 69 cases into the validation set. The factors affecting ipsilateral supraclavicular lymph node pCR (ispCR)of breast cancer after neoadjuvant chemotherapy were analyzed by univariate and multivariate logistic regression analyses, and a nomogram prediction model of ispCR was established. Internal and external validation evaluation of the nomogram prediction model were conducted by receiver operating characteristic (ROC) curve analysis and plotting calibration curves. Results: Univariate logistic regression analysis showed that Ki-67 index, number of axillary lymph node metastases, breast pCR, axillary pCR, and ISLN size after neoadjuvant chemotherapy were associated with ispCR of breast cancerafter neoadjuvant chemotherapy (P<0.05). Multivariate logistic regression analysis showed that the number of axillary lymph node metastases (OR=5.035, 95%CI: 1.722-14.721, P=0.003), breast pCR (OR=4.662, 95%CI: 1.456-14.922, P=0.010) and ISLN size after neoadjuvant chemotherapy (OR=4.231, 95%CI: 1.194-14.985, P=0.025) were independent predictors of ispCR of breast cancer after neoadjuvant chemotherapy. A nomogram prediction model of ispCR of breast cancer after neoadjuvant chemotherapy was constructed using five factors: number of axillary lymph node metastases, Ki-67 index, breast pCR, axillary pCR and size of ISLN after neoadjuvant chemotherapy. The areas under the ROC curve for the nomogram prediction model in the training and validation sets were 0.855 and 0.838, respectively, and the difference was not statistically significant (P=0.755). The 3-year disease-free survival rates of patients in the ispCR and non-ispCR groups after neoadjuvant chemotherapy were 64.3% and 54.8%, respectively, with statistically significant differences (P=0.024), the 3-year overall survival rates were 83.8% and 70.2%, respectively, without statistically significant difference (P=0.087). Conclusions: Disease free survival is significantly improved in breast cancer patients with ispCR after neoadjuvant chemotherapy. The constructed nomogram prediction model of ispCR of breast cancer patients after neoadjuvant chemotherapy is well fitted. Application of this prediction model can assist the development of local management strategies for the ipsilateral supraclavicular region after neoadjuvant chemotherapy and predict the long-term prognosis of breast cancer patients.

Construction of risk model of dry eye after cataract surgery in type 2 diabetic patients / 国际眼科杂志(Guoji Yanke Zazhi)

@#AIM: To explore the risk factors of xerophthalmia after cataract surgery in patients with type 2 diabetes mellitus, and to construct a risk prediction model.METHODS: A total of 212 patients(212 eyes)with type 2 diabetes who underwent cataract surgery in our hospital from April 2019 to April 2021 were selected. The patients were divided into dry eye group(43 cases, 43 eyes)and non-xerophthalmia eye(169 cases, 169 eyes). The general data, laboratory examination index and quality of life score of the two groups were compared; multivariate Logistic regression analysis was used to analyze the risk factors of postoperative xerophthalmia; constructed a line chart prediction model and evaluated its prediction accuracy. RESULTS: There were significant differences in the history of keratoconjunctival disease, pterygium, meibomian gland dysfunction, lens nucleus hardness, disease cognition, postoperative anxiety, postoperative depression, surgical incision, medication compliance, and the levels of interleukin-1β(IL-1β), interleukin-6(IL-6), tumor necrosis factor-α(TNF-α)and HbA1c at 1wk after operation between the two groups(<i>P</i><0.05). The results of multivariate Logistic regression analysis showed that postoperative anxiety, postoperative depression, 3.0mm of surgical incision, IL-1β>31.26ng/mL, IL-6>29.42ng/mL, TNF-α>77.68ng/mL and HbA1c≥6.50% were risk factors for postoperative xerophthalmia(<i>P</i><0.05). The calibration curve and standard curve of the nomogram prediction model were fit well, and the prediction probabilities were mostly distributed around 0 and 1, with high accuracy.The visual function evaluation, environmental trigger factors, ocular discomfort symptoms and ocular surface disease index(OSDI)score in the dry eye group was significantly higher than those in the non-xerophthalmia group(<i>P</i><0.05). CONCLUSION: Surgical incision, postoperative anxiety, depression, medication compliance, serum inflammatory factors and HbA1c are all related to xerophthalmia after cataract surgery in patients with type 2 diabetes. Early identification of risk factors and timely intervention are helpful to reduce the incidence of postoperative xerophthalmia and improve the quality of life.