Empathy in undergraduate medical students: a multi-center cross-sectional study in China.

BACKGROUND: Fostering empathy has been continuously emphasized in the global medical education. Empathy is crucial to enhance patient-physician relationships, and is associated with medical students' academic and clinical performance. However, empathy level of medical students in China and related influencing factors are not clear. METHODS: This was a cross-sectional study among medical students in 11 universities. We used the Jefferson Scale of Empathy Student-version of Chinese version to measure empathy level of medical students. Factors associated with empathy were identified by the univariate and multivariate logistic regression analyses. Based on the variables identified above, the nomogram was established to predict high empathy probability of medical students. Receiver operating characteristic curve, calibration plot and decision curve analysis were used to evaluate the discrimination, calibration and educational utility of the model. RESULTS: We received 10,901 samples, but a total of 10,576 samples could be used for further analysis (effective response rate of 97.02%). The mean empathy score of undergraduate medical students was 67.38 (standard deviation = 9.39). Six variables including gender, university category, only child or not, self-perception doctor-patient relationship in hospitals, interest of medicine, Kolb learning style showed statistical significance with empathy of medical students (P < 0.05). Then, the nomogram was established based on six variables. The validation suggested the nomogram model was well calibrated and had good utility in education, as well as area under the curve of model prediction was 0.65. CONCLUSIONS: We identify factors influencing empathy of undergraduate medical students. Moreover, increasing manifest and hidden curriculums on cultivating empathy of medical students may be needed among medical universities or schools in China.

A nomogram predicting pneumonia after cardiac surgery: a retrospective modeling study.

BACKGROUND: Postoperative pneumonia (POP) is the most prevalent of all nosocomial infections in patients who underwent cardiac surgery. The aim of this study was to identify independent risk factors for pneumonia after cardiac surgery, from which we constructed a nomogram for prediction. METHODS: The clinical data of patients admitted to the Department of Cardiothoracic Surgery of Nanjing Drum Tower Hospital from October 2020 to September 2021 who underwent cardiac surgery were retrospectively analyzed, and the patients were divided into two groups according to whether they had POP: POP group (n=105) and non-POP group (n=1083). Preoperative, intraoperative, and postoperative indicators were collected and analyzed. Logistic regression was used to identify independent risk factors for POP in patients who underwent cardiac surgery. We constructed a nomogram based on these independent risk factors. Model discrimination was assessed via area under the receiver operating characteristic curve (AUC), and calibration was assessed via calibration plot. RESULTS: A total of 105 events occurred in the 1188 cases. Age (>55 years) (OR: 1.83, P=0.0225), preoperative malnutrition (OR: 3.71, P<0.0001), diabetes mellitus(OR: 2.33, P=0.0036), CPB time (Cardiopulmonary Bypass Time) > 135 min (OR: 2.80, P<0.0001), moderate to severe ARDS (Acute Respiratory Distress Syndrome )(OR: 1.79, P=0.0148), use of ECMO or IABP or CRRT (ECMO: Extra Corporeal Membrane Oxygenation; IABP: Intra-Aortic Balloon Pump; CRRT: Continuous Renal Replacement Therapy )(OR: 2.60, P=0.0057) and MV( Mechanical Ventilation )> 20 hours (OR: 3.11, P<0.0001) were independent risk factors for POP. Based on those independent risk factors, we constructed a simple nomogram with an AUC of 0.82. Calibration plots showed good agreement between predicted probabilities and actual probabilities. CONCLUSION: We constructed a facile nomogram for predicting pneumonia after cardiac surgery with good discrimination and calibration. The model has excellent clinical applicability and can be used to identify and adjust modifiable risk factors to reduce the incidence of POP as well as patient mortality.

Nomogram using intratumoral and peritumoral radiomics for the preoperative prediction of visceral pleural invasion in clinical stage IA lung adenocarcinoma.

BACKGROUND: Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. METHODS: A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. RESULTS: The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. CONCLUSIONS: The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.

Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large population-based cohort study.

BACKGROUND: Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model. METHODS: A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort. CONCLUSIONS: We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.

[Construction and evaluation of weight retention nomogram prediction model for women 1 year after delivery].

OBJECTIVE: To analyze the influencing factors of body weight retention in woman at 1 year postpartum, and to construct and evaluate a nomogram prediction model for postpartum 1-year weight retention. METHODS: From September 2010 to February 2011, 468 pregnant women in the third trimester were recruited from Yuexiu District and Baiyun District Maternal and Child Health Hospital in Guangzhou, and followed up to 1 year postpartum. The basic demographic information of pregnant women was collected by self-made questionnaire. Dietary intake in the third trimester was investigated by 3-day 24-hour dietary review. The weight of women before delivery and one year after delivery were measured. According to whether the weight retention at 1 year postpartum is greater than 0 kg, the study subjects were divided into the 1-year postpartum weight retention group and weight recovery group. Logistic regression analysis were used to screen the influencing factors of weight retention at 1 year postpartum. R 4.2.3 software was used to construct the nomogram prediction model. The subject working characteristic curve, calibration curve, Hosmer-Lemeshow goodness of fit test and clinical decision curve were used to evaluate the model's differentiation, accuracy and clinical applicability. RESULTS: Among 329 subjects in the model training set, the 1-year postpartum weight retention was 68.09%, and the median and quartile levels of retained body weight were 5.0(3.0, 10.0)kg. After Logistic analysis, a nomogram prediction model was constructed based on five factors: pre-pregnancy body mass index(BMI), pregnancy weight gain, parity, gravitity, 0-6 months postpartum feeding pattern. The model had good discrimination(AUC_(training)=0.778, AUC_(testing)=0.767) and accuracy(Hosmer-Lemeshow test: P_(training)=0.946, P_(testing)=0.891). CONCLUSION: The 1-year postnatal weight retention nomogram model based on women's pre-pregnancy BMI, pregnancy weight gain, parity, gravitity, 0-6 months postpartum feeding pattern has good differentiation, accuracy and clinical applicability.

Constructing and validating a predictive nomogram for osteoporosis risk among Chinese single-center male population using the systemic immune-inflammation index.

Osteoporosis (OP) is a bone metabolism disease that is associated with inflammatory pathological mechanism. Nonetheless, rare studies have investigated the diagnostic effectiveness of immune-inflammation index in the male population. Therefore, it is interesting to achieve early diagnosis of OP in male population based on the inflammatory makers from blood routine examination. We developed a prediction model based on a training dataset of 826 Chinese male patients through a retrospective study, and the data was collected from January 2022 to May 2023. All participants underwent the dual-energy X-ray absorptiometry (DXEA) and blood routine examination. Inflammatory markers such as systemic immune-inflammation index (SII) and platelet-to-lymphocyte ratio (PLR) was calculated and recorded. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to optimize feature selection. Multivariable logistic regression analysis was applied to construct a predicting model incorporating the feature selected in the LASSO model. This predictive model was displayed as a nomogram. Receiver operating characteristic (ROC) curve, C-index, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance. Internal validation was test by the bootstrapping method. This study was approved by the Ethic Committee of the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine (Ethic No. JY2023012) and conducted in accordance with the relevant guidelines and regulations. The predictive factors included in the prediction model were age, BMI, cardiovascular diseases, cerebrovascular diseases, neuropathy, thyroid diseases, fracture history, SII, PLR, C-reactive protein (CRP). The model displayed well discrimination with a C-index of 0.822 (95% confidence interval: 0.798-0.846) and good calibration. Internal validation showed a high C-index value of 0.805. Decision curve analysis (DCA) showed that when the threshold probability was between 3 and 76%, the nomogram had a good clinical value. This nomogram can effectively predict the incidence of OP in male population based on SII and PLR, which would help clinicians rapidly and conveniently diagnose OP with men in the future.

Integrated analysis of the role of PR/SET domain 14 in gastric cancer.

BACKGROUND: Gastric cancer is one of the most common tumors worldwide, and most patients are deprived of treatment options when diagnosed at advanced stages. PRDM14 has carcinogenic potential in breast and non-small cell lung cancer. however, its role in gastric cancer has not been elucidated. METHODS: We aimed to elucidate the expression of PRDM14 using pan-cancer analysis. We monitored the expression of PRDM14 in cells and patients using quantitative polymerase chain reaction, western blotting, and immunohistochemistry. We observed that cell phenotypes and regulatory genes were influenced by PRDM14 by silencing PRDM14. We evaluated and validated the value of the PRDM14-derived prognostic model. Finally, we predicted the relationship between PRDM14 and small-molecule drug responses using the Connectivity Map and The Genomics of Drug Sensitivity in Cancer databases. RESULTS: PRDM14 was significantly overexpressed in gastric cancer, which identified in cell lines and patients' tissues. Silencing the expression of PRDM14 resulted in apoptosis promotion, cell cycle arrest, and inhibition of the growth and migration of GC cells. Functional analysis revealed that PRDM14 acts in epigenetic regulation and modulates multiple DNA methyltransferases or transcription factors. The PRDM14-derived differentially expressed gene prognostic model was validated to reliably predict the patient prognosis. Nomograms (age, sex, and PRDM14-risk score) were used to quantify the probability of survival. PRDM14 was positively correlated with sensitivity to small-molecule drugs such as TPCA-1, PF-56,227, mirin, and linsitinib. CONCLUSIONS: Collectively, our findings suggest that PRDM14 is a positive regulator of gastric cancer progression. Therefore, it may be a potential therapeutic target for gastric cancer.

Significance of metastatic lymph nodes ratio in overall survival for patients with resected nonsmall cell lung cancer: a retrospective cohort study.

OBJECTIVE: The tumor, node and metastasis stage is widely applied to classify lung cancer and is the foundation of clinical decisions. However, increasing studies have pointed out that this staging system is not precise enough for the N status. In this study, we aim to build a convenient survival prediction model that incorporates the current items of lymph node status. METHODS: We performed a retrospective cohort study and collected the data from resectable nonsmall cell lung cancer (NSCLC) (IA-IIIB) patients from the Surveillance, Epidemiology, and End Results database (2006-2015). The x-tile program was applied to calculate the optimal threshold of metastatic lymph node ratio (MLNR). Then, independent prognostic factors were determined by multivariable Cox regression analysis and enrolled to build a nomogram model. The calibration curve as well as the Concordance Index (C-index) were selected to evaluate the nomogram. Finally, patients were grouped based on their specified risk points and divided into three risk levels. The prognostic value of MLNR and examined lymph node numbers (ELNs) were presented in subgroups. RESULTS TOTALLY,: 40853 NSCLC patients after surgery were finally enrolled and analyzed. Age, metastatic lymph node ratio, histology type, adjuvant treatment and American Joint Committee on Cancer 8th T stage were deemed as independent prognostic parameters after multivariable Cox regression analysis. A nomogram was built using those variables, and its efficiency in predicting patients' survival was better than the conventional American Joint Committee on Cancer stage system after evaluation. Our new model has a significantly higher concordance Index (C-index) (training set, 0.683 v 0.641, respectively; P < 0.01; testing set, 0.676 v 0.638, respectively; P < 0.05). Similarly, the calibration curve shows the nomogram was in better accordance with the actual observations in both cohorts. Then, after risk stratification, we found that MLNR is more reliable than ELNs in predicting overall survival. CONCLUSION: We developed a nomogram model for NSCLC patients after surgery. This novel and useful tool outperforms the widely used tumor, node and metastasis staging system and could benefit clinicians in treatment options and cancer control.

CT-based muscle and adipose measurements predict prognosis in patients with digestive system malignancy.

The role of skeletal muscle and adipose tissue in the progression of cancer has been gradually discussed, but it needs further exploration. The objective of this study was to provide an in-depth analysis of skeletal muscle and fat in digestive malignancies and to construct novel predictors for clinical management. This is a retrospective study that includes data from Cancer Center, the First Hospital of Jilin University. Basic characteristic information was analyzed by T tests. Correlation matrices were drawn to explore the relationship between CT-related indicators and other indicators. Cox risk regression analyses were performed to analyze the association between the overall survivals (OS) and various types of indicators. A new indicator body composition score (BCS) was then created and a time-dependent receiver operating characteristic curve was plotted to analyze the efficacy of the BCS. Finally, a nomogram was produced to develop a scored-CT system based on BCS and other indicators. C-index and calibration curve analyses were performed to validate the predictive accuracy of the scored-CT system. A total of 575 participants were enrolled in the study. Cox risk regression model revealed that VFD, L3 SMI and VFA/SFA were associated with prognosis of cancer patients. After adjustment, BCS index based on CT was significantly associated with prognosis, both in all study population and in subgroup analysis according to tumor types (all study population: HR 2.036, P < 0.001; colorectal cancer: HR 2.693, P < 0.001; hepatocellular carcinoma: HR 4.863, P < 0.001; esophageal cancer: HR 4.431, P = 0.008; pancreatic cancer: HR 1.905, P = 0.016; biliary system malignancies: HR 23.829, P = 0.035). The scored-CT system was constructed according to tumor type, stage, KPS, PG-SGA and BCS index, and it was of great predictive validity. This study identified VFD, L3 SMI and VFA/SFA associated with digestive malignancies outcomes. BCS was created and the scored-CT system was established to predict the OS of cancer patients.

Can a nomogram predict apical prostate cancer pathology upgrade from fusion biopsy to final pathology? A multicenter study.

BACKGROUND: This study evaluates the efficacy of a nomogram for predicting the pathology upgrade of apical prostate cancer (PCa). METHODS: A total of 754 eligible patients were diagnosed with apical PCa through combined systematic and magnetic resonance imaging (MRI)-targeted prostate biopsy followed by radical prostatectomy (RP) were retrospectively identified from two hospitals (training: 754, internal validation: 182, internal-external validation: 148). A nomogram for the identification of apical tumors in high-risk pathology upgrades through comparing the results of biopsy and RP was established incorporating statistically significant risk factors based on univariable and multivariable logistic regression. The nomogram's performance was assessed via the receiver operating characteristic (ROC) curve, calibration plots, and decision curve analysis (DCA). RESULTS: Univariable and multivariable analysis identified age, targeted biopsy, number of targeted cores, TNM stage, and the prostate imaging-reporting and data system score as significant predictors of apical tumor pathological progression. Our nomogram, based on these variables, demonstrated ROC curves for pathology upgrade with values of 0.883 (95% CI, 0.847-0.929), 0.865 (95% CI, 0.790-0.945), and 0.840 (95% CI, 0.742-0.904) for the training, internal validation and internal-external validation cohorts respectively. Calibration curves showed good consistency between the predicted and actual outcomes. The validation groups also showed great generalizability with the calibration curves. DCA results also demonstrated excellent performance for our nomogram with positive benefit across a threshold probability range of 0-0.9 for the training and internal validation group, and 0-0.6 for the internal-external validation group. CONCLUSION: The nomogram, integrating clinical, radiological, and pathological data, effectively predicts the risk of pathology upgrade in apical PCa tumors. It holds significant potential to guide clinicians in optimizing the surgical management of these patients.

[Development and validation of a nomogram for predicting 3-month mortality risk in patients with sepsis-associated acute kidney injury].

OBJECTIVE: To develop and evaluate a nomogram prediction model for the 3-month mortality risk of patients with sepsis-associated acute kidney injury (S-AKI). METHODS: Based on the American Medical Information Mart for Intensive Care- IV (MIMIC- IV), clinical data of S-AKI patients from 2008 to 2021 were collected. Initially, 58 relevant predictive factors were included, with all-cause mortality within 3 months as the outcome event. The data were divided into training and testing sets at a 7 : 3 ratio. In the training set, univariate Logistic regression analysis was used for preliminary variable screening. Multicollinearity analysis, Lasso regression, and random forest algorithm were employed for variable selection, combined with the clinical application value of variables, to establish a multivariable Logistic regression model, visualized using a nomogram. In the testing set, the predictive value of the model was evaluated through internal validation. The receiver operator characteristic curve (ROC curve) was drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of nomogram model and Oxford acute severity of illness score (OASIS), sequential organ failure assessment (SOFA), and systemic inflammatory response syndrome score (SIRS). The calibration curve was used to evaluate the calibration, and decision curve analysis (DCA) was performed to assess the net benefit at different probability thresholds. RESULTS: Based on the survival status at 3 months after diagnosis, patients were divided into 7 768 (68.54%) survivors and 3 566 (31.46%) death. In the training set, after multiple screenings, 7 variables were finally included in the nomogram model: Logistic organ dysfunction system (LODS), Charlson comorbidity index, urine output, international normalized ratio (INR), respiratory support mode, blood urea nitrogen, and age. Internal validation in the testing set showed that the AUC of nomogram model was 0.81 [95% confidence interval (95%CI) was 0.80-0.82], higher than the OASIS score's 0.70 (95%CI was 0.69-0.71) and significantly higher than the SOFA score's 0.57 (95%CI was 0.56-0.58) and SIRS score's 0.56 (95%CI was 0.55-0.57), indicating good discrimination. The calibration curve demonstrated that the nomogram model's calibration was better than the OASIS, SOFA, and SIRS scores. The DCA curve suggested that the nomogram model's clinical net benefit was better than the OASIS, SOFA, and SIRS scores at different probability thresholds. CONCLUSIONS: A nomogram prediction model for the 3-month mortality risk of S-AKI patients, based on clinical big data from MIMIC- IV and including seven variables, demonstrates good discriminative ability and calibration, providing an effective new tool for assessing the prognosis of S-AKI patients.

[Construction and validation of a predictive model for early occurrence of lower extremity deep venous thrombosis in ICU patients with sepsis].

OBJECTIVE: To investigate the risk factors of lower extremity deep venous thrombosis (LEDVT) in patients with sepsis during hospitalization in intensive care unit (ICU), and to construct a nomogram prediction model of LEDVT in sepsis patients in the ICU based on the critical care scores combined with inflammatory markers, and to validate its effectiveness in early prediction. METHODS: 726 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2015 to December 2021 were retrospectively included as the training set to construct the prediction model. In addition, 213 sepsis patients admitted to the ICU of the Affiliated Hospital of Jining Medical University from January 2022 to June 2023 were retrospectively included as the validation set to verify the performance of the prediction model. Clinical data of patients were collected, such as demographic information, vital signs at the time of admission to the ICU, underlying diseases, past history, various types of scores within 24 hours of admission to the ICU, the first laboratory indexes of admission to the ICU, lower extremity venous ultrasound results, treatment, and prognostic indexes. Lasso regression analysis was used to screen the influencing factors for the occurrence of LEDVT in sepsis patients, and the results of Logistic regression analysis were synthesized to construct a nomogram model. The nomogram model was evaluated by receiver operator characteristic curve (ROC curve), calibration curve, clinical impact curve (CIC) and decision curve analysis (DCA). RESULTS: The incidence of LEDVT after ICU admission was 21.5% (156/726) in the training set of sepsis patients and 21.6% (46/213) in the validation set of sepsis patients. The baseline data of patients in both training and validation sets were comparable. Lasso regression analysis showed that seven independent variables were screened from 67 parameters to be associated with the occurrence of LEDVT in patients with sepsis. Logistic regression analysis showed that the age [odds ratio (OR) = 1.03, 95% confidence interval (95%CI) was 1.01 to 1.04, P < 0.001], body mass index (BMI: OR = 1.05, 95%CI was 1.01 to 1.09, P = 0.009), venous thromboembolism (VTE) score (OR = 1.20, 95%CI was 1.11 to 1.29, P < 0.001), activated partial thromboplastin time (APTT: OR = 0.98, 95%CI was 0.97 to 0.99, P = 0.009), D-dimer (OR = 1.03, 95%CI was 1.01 to 1.04, P < 0.001), skin or soft-tissue infection (OR = 2.53, 95%CI was 1.29 to 4.98, P = 0.007), and femoral venous cannulation (OR = 3.72, 95%CI was 2.50 to 5.54, P < 0.001) were the independent influences on the occurrence of LEDVT in patients with sepsis. The nomogram model was constructed by combining the above variables, and the ROC curve analysis showed that the area under the curve (AUC) of the nomogram model for predicting the occurrence of LEDVT in patients with sepsis was 0.793 (95%CI was 0.746 to 0.841), and the AUC in the validation set was 0.844 (95%CI was 0.786 to 0.901). The calibration curve showed that its predicted probability was in good agreement with the actual probabilities were in good agreement, and both CIC and DCA curves suggested a favorable net clinical benefit. CONCLUSIONS: The nomogram model based on the critical illness scores combined with inflammatory markers can be used for early prediction of LEDVT in ICU sepsis patients, which helps clinicians to identify the risk factors for LEDVT in sepsis patients earlier, so as to achieve early treatment.

[Analysis of 28 day-mortality risk factors in sepsis patients and construction and validation of predictive model].

OBJECTIVE: To construct and validate a nomogram model for predicting the risk of 28-day mortality in sepsis patients. METHODS: A retrospective cohort study was conducted. 281 sepsis patients admitted to the department of intensive care unit (ICU) of the 940th Hospital of the Joint Logistics Support Force of PLA from January 2017 to December 2022 were selected as the research subjects. The patients were divided into a training set (197 cases) and a validation set (84 cases) according to a 7 : 3 ratio. The general information, clinical treatment measures and laboratory examination results within 24 hours after admission to ICU were collected. Patients were divided into survival group and death group based on 28-day outcomes. The differences in various data were compared between the two groups. The optimal predictive variables were selected using Lasso regression, and univariate and multivariate Logistic regression analyses were performed to identify factors influencing the mortality of sepsis patients and to establish a nomogram model. Receiver operator characteristic curve (ROC curve), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the nomogram model. RESULTS: Out of 281 cases of sepsis, 82 cases died with a mortality of 29.18%. The number of patients who died in the training and validation sets was 54 and 28, with a mortality of 27.41% and 33.33% respectively. Lasso regression, univariate and multivariate Logistic regression analysis screened for 5 independent predictors associated with 28-day mortality. There were use of vasoactive drugs [odds ratio (OR) = 5.924, 95% confidence interval (95%CI) was 1.244-44.571, P = 0.043], acute physiology and chronic health evaluation II (APACHE II: OR = 1.051, 95%CI was 1.000-1.107, P = 0.050), combined with multiple organ dysfunction syndrome (MODS: OR = 17.298, 95%CI was 5.517-76.985, P < 0.001), neutrophil count (NEU: OR = 0.934, 95%CI was 0.879-0.988, P = 0.022) and oxygenation index (PaO2/FiO2: OR = 0.994, 95%CI was 0.988-0.998, P = 0.017). A nomogram model was constructed using the independent predictive factors mentioned above, ROC curve analysis showed that the AUC of the nomogram model was 0.899 (95%CI was 0.856-0.943) and 0.909 (95%CI was 0.845-0.972) for the training and validation sets respectively. The C-index was 0.900 and 0.920 for the training and validation sets respectively, with good discrimination. The Hosmer-Lemeshoe tests both showed P > 0.05, indicating good calibration. Both DCA and CIC plots demonstrate the model's good clinical utility. CONCLUSIONS: The use of vasoactive, APACHE II score, comorbid MODS, NEU and PaO2/FiO2 are independent risk factors for 28-day mortality in patients with sepsis. The nomogram model based on these 5 indicators has a good predictive ability for the occurrence of mortality in sepsis patients.

Risk stratification and survival time of patients with gram-negative bacillary pneumonia in the intensive care unit.

Introduction: Pneumonia is a common infection in the intensive care unit (ICU), and gram-negative bacilli are the most common bacterial cause. The purpose of the study was to investigate the risk factors for 30-day mortality in patients with gram-negative bacillary pneumonia in the ICU, construct a predictive model, and stratify patients based on risk to assess their short-term survival. Methods: Patients admitted to the ICU with gram-negative bacillary pneumonia at Fujian Medical University Affiliated First Hospital between January 2018 and September 2020 were selected. Patients were divided into deceased and survivor groups based on whether death occurred within 30 days. Multifactorial logistic regression analysis was used to identify independent risk factors for 30-day mortality in these patients, and a predictive nomogram model was constructed based on these factors. Patients were categorized into low-, medium-, and high-risk groups according to the model's predicted probability, and Kaplan-Meier survival curves were plotted to assess short-term survival. Results: The study included 305 patients. Lactic acid (odds ratio [OR], 1.524, 95% CI: 1.057-2.197), tracheal intubation (OR: 4.202, 95% CI: 1.092-16.169), and acute kidney injury (OR:4.776, 95% CI: 1.632-13.978) were identified as independent risk factors for 30-day mortality. A nomogram prediction model was established based on these three factors. Internal validation of the model showed a Hosmer-Lemeshow test result of X2=5.770, P=0.834, and an area under the ROC curve of 0.791 (95% CI: 0.688-0.893). Bootstrap resampling of the original data 1000 times yielded a C-index of 0.791, and a decision curve analysis indicated a high net benefit when the threshold probability was between 15%-90%. The survival time for low-, medium-, and high-risk patients was 30 (30, 30), 30 (16.5, 30), and 17 (11, 27) days, respectively, which were significantly different. Conclusion: Lactic acid, tracheal intubation, and acute kidney injury were independent risk factors for 30-day mortality in patients in the ICU with gram-negative bacillary pneumonia. The predictive model constructed based on these factors showed good predictive performance and helped assess short-term survival, facilitating early intervention and treatment.

Development and validation of nomograms to predict survival of neuroendocrine carcinoma in genitourinary system: A population-based retrospective study.

Neuroendocrine carcinoma (NEC) is a rare yet potentially perilous neoplasm. The objective of this study was to develop prognostic models for the survival of NEC patients in the genitourinary system and subsequently validate these models. A total of 7125 neuroendocrine neoplasm (NEN) patients were extracted. Comparison of survival in patients with different types of NEN before and after propensity score-matching (PSM). A total of 3057 patients with NEC, whose information was complete, were extracted. The NEC influencing factors were chosen through the utilization of the least absolute shrinkage and selection operator regression model (LASSO) and the Fine & Gary model (FGM). Furthermore, nomograms were built. To validate the accuracy of the prediction, the efficiency was verified using bootstrap self-sampling techniques and receiver operating characteristic curves. LASSO and FGM were utilized to construct three models. Confirmation of validation was achieved by conducting analyses of the area under the curve and decision curve. Moreover, the FGS (DSS analysis using FGM) model produced higher net benefits. To maximize the advantages for patients, the FGS model disregarded the influence of additional occurrences. Patients are expected to experience advantages in terms of treatment options and survival assessment through the utilization of these models.

Real-time predictive model of extrauterine growth retardation in preterm infants with gestational age less than 32 weeks.

The aim of this study was to develop a real-time risk prediction model for extrauterine growth retardation (EUGR). A total of 2514 very preterm infants were allocated into a training set and an external validation set. The most appropriate independent variables were screened using univariate analysis and Lasso regression with tenfold cross-validation, while the prediction model was designed using binary multivariate logistic regression. A visualization of the risk variables was created using a nomogram, while the calibration plot and receiver operating characteristic (ROC) curves were used to calibrate the prediction model. Clinical efficacy was assessed using the decision curve analysis (DCA) curves. Eight optimal predictors that namely birth weight, small for gestation age (SGA), hypertensive disease complicating pregnancy (HDCP), gestational diabetes mellitus (GDM), multiple births, cumulative duration of fasting, growth velocity and postnatal corticosteroids were introduced into the logistic regression equation to construct the EUGR prediction model. The area under the ROC curve of the training set and the external verification set was 83.1% and 84.6%, respectively. The calibration curve indicate that the model fits well. The DCA curve shows that the risk threshold for clinical application is 0-95% in both set. Introducing Birth weight, SGA, HDCP, GDM, Multiple births, Cumulative duration of fasting, Growth velocity and Postnatal corticosteroids into the nomogram increased its usefulness for predicting EUGR risk in very preterm infants.

Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study.

OBJECTIVE: To develop a nomogram based on tumor and peritumoral edema (PE) radiomics features extracted from preoperative multiparameter MRI for predicting brain invasion (BI) in atypical meningioma (AM). METHODS: In this retrospective study, according to the 2021 WHO classification criteria, a total of 469 patients with pathologically confirmed AM from three medical centres were enrolled and divided into training (n = 273), internal validation (n = 117) and external validation (n = 79) cohorts. BI was diagnosed based on the histopathological examination. Preoperative contrast-enhanced T1-weighted MR images (T1C) and T2-weighted MR images (T2) for extracting meningioma features and T2-fluid attenuated inversion recovery (FLAIR) sequences for extracting meningioma and PE features were obtained. The multiple logistic regression was applied to develop separate multiparameter radiomics models for comparison. A nomogram was developed by combining radiomics features and clinical risk factors, and the clinical usefulness of the nomogram was verified using decision curve analysis. RESULTS: Among the clinical factors, PE volume and PE/tumor volume ratio are the risk of BI in AM. The combined nomogram based on multiparameter MRI radiomics features of meningioma and PE and clinical indicators achieved the best performance in predicting BI in AM, with area under the curve values of 0.862 (95% CI, 0.819-0.905) in the training cohort, 0.834 (95% CI, 0.780-0.908) in the internal validation cohort and 0.867 (95% CI, 0.785-0.950) in the external validation cohort, respectively. CONCLUSIONS: The nomogram based on tumor and PE radiomics features extracted from preoperative multiparameter MRI and clinical factors can predict the risk of BI in patients with AM.

Identifying and assessing a prognostic model based on disulfidptosis-related genes: implications for immune microenvironment and tumor biology in lung adenocarcinoma.

Introduction: Lung cancer, with the highest global mortality rate among cancers, presents a grim prognosis, often diagnosed at an advanced stage in nearly 70% of cases. Recent research has unveiled a novel mechanism of cell death termed disulfidptosis, which is facilitated by glucose scarcity and the protein SLC7A11. Methods: Utilizing the least absolute shrinkage and selection operator (LASSO) regression analysis combined with Cox regression analysis, we constructed a prognostic model focusing on disulfidptosis-related genes. Nomograms, correlation analyses, and enrichment analyses were employed to assess the significance of this model. Among the genes incorporated into the model, CHRNA5 was selected for further investigation regarding its role in LUAD cells. Biological functions of CHRNA5 were assessed using EdU, transwell, and CCK-8 assays. Results: The efficacy of the model was validated through internal testing and an external validation set, with further evaluation of its robustness and clinical applicability using a nomogram. Subsequent correlation analyses revealed associations between the risk score and infiltration of various cancer types, as well as oncogene expression. Enrichment analysis also identified associations between the risk score and pivotal biological processes and KEGG pathways. Our findings underscore the significant impact of CHRNA5 on LUAD cell proliferation, migration, and disulfidptosis. Conclusion: This study successfully developed and validated a robust prognostic model centered on disulfidptosis-related genes, providing a foundation for predicting prognosis in LUAD patients.

Development and validation of a preoperative CT­based radiomics nomogram to differentiate tuberculosis granulomas from lung adenocarcinomas: an external validation study.

BACKGROUND: An accurate and non-invasive approach is urgently needed to distinguish tuberculosis granulomas from lung adenocarcinomas. This study aimed to develop and validate a nomogram based on contrast enhanced-compute tomography (CE-CT) to preoperatively differentiate tuberculosis granuloma from lung adenocarcinoma appearing as solitary pulmonary solid nodules (SPSN). METHODS: This retrospective study analyzed 143 patients with lung adenocarcinoma (mean age: 62.4 ± 6.5 years; 54.5% female) and 137 patients with tuberculosis granulomas (mean age: 54.7 ± 8.2 years; 29.2% female) from two centers between March 2015 and June 2020. The training and internal validation cohorts included 161 and 69 patients (7:3 ratio) from center No.1, respectively. The external testing cohort included 50 patients from center No.2. Clinical factors and conventional radiological characteristics were analyzed to build independent predictors. Radiomics features were extracted from each CT-volume of interest (VOI). Feature selection was performed using univariate and multivariate logistic regression analysis, as well as the least absolute shrinkage and selection operator (LASSO) method. A clinical model was constructed with clinical factors and radiological findings. Individualized radiomics nomograms incorporating clinical data and radiomics signature were established to validate the clinical usefulness. The diagnostic performance was assessed using the receiver operating characteristic (ROC) curve analysis with the area under the receiver operating characteristic curve (AUC). RESULTS: One clinical factor (CA125), one radiological characteristic (enhanced-CT value) and nine radiomics features were found to be independent predictors, which were used to establish the radiomics nomogram. The nomogram demonstrated better diagnostic efficacy than any single model, with respective AUC, accuracy, sensitivity, and specificity of 0.903, 0.857, 0.901, and 0.807 in the training cohort; 0.933, 0.884, 0.893, and 0.892 in the internal validation cohort; 0.914, 0.800, 0.937, and 0.735 in the external test cohort. The calibration curve showed a good agreement between prediction probability and actual clinical findings. CONCLUSION: The nomogram incorporating clinical factors, radiological characteristics and radiomics signature provides additional value in distinguishing tuberculosis granuloma from lung adenocarcinoma in patients with a SPSN, potentially serving as a robust diagnostic strategy in clinical practice.

Risk factor analysis and nomogram for predicting poor symptom control in smoking asthmatics.

BACKGROUND: Smoking induces and modifies the airway immune response, accelerating the decline of asthmatics' lung function and severely affecting asthma symptoms' control level. To assess the prognosis of asthmatics who smoke and to provide reasonable recommendations for treatment, we constructed a nomogram prediction model. METHODS: General and clinical data were collected from April to September 2021 from smoking asthmatics aged ≥14 years attending the People's Hospital of Zhengzhou University. Patients were followed up regularly by telephone or outpatient visits, and their medication and follow-up visits were recorded during the 6-months follow-up visit, as well as their asthma control levels after 6 months (asthma control questionnaire-5, ACQ-5). The study employed R4.2.2 software to conduct univariate and multivariate logistic regression analyses to identify independent risk factors for 'poorly controlled asthma' (ACQ>0.75) as the outcome variable. Subsequently, a nomogram prediction model was constructed. Internal validation was used to test the reproducibility of the model. The model efficacy was evaluated using the consistency index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and decision curve. RESULTS: Invitations were sent to 231 asthmatics who smoked. A total of 202 participants responded, resulting in a final total of 190 participants included in the model development. The nomogram established five independent risk factors (P<0.05): FEV1%pred, smoking index (100), comorbidities situations, medication regimen, and good or poor medication adherence. The area under curve (AUC) of the modeling set was 0.824(95%CI 0.765-0.884), suggesting that the nomogram has a high ability to distinguish poor asthma control in smoking asthmatics after 6 months. The calibration curve showed a C-index of 0.824 for the modeling set and a C-index of 0.792 for the self-validation set formed by 1000 bootstrap sampling, which means that the prediction probability of the model was consistent with reality. Decision curve analysis (DCA) of the nomogram revealed that the net benefit was higher when the risk threshold probability for poor asthma control was 4.5 - 93.9%. CONCLUSIONS: FEV1%pred, smoking index (100), comorbidities situations, medication regimen, and medication adherence were identified as independent risk factors for poor asthma control after 6 months in smoking asthmatics. The nomogram established based on these findings can effectively predict relevant risk and provide clinicians with a reference to identify the poorly controlled population with smoking asthma as early as possible, and to select a better therapeutic regimen. Meanwhile, it can effectively improve the medication adherence and the degree of attention to complications in smoking asthma patients.