Brief introduction of medical database and data mining technology in big data era

Application of miRNA Biomarkers in Predicting Overall Survival Outcomes for Lung Adenocarcinoma

Background

With the development of research, the importance of microRNAs (miRNAs) in the occurrence, metastasis, and prognosis of lung adenocarcinoma (LUAD) has attracted extensive attention. This study is aimed at predicting overall survival (OS) results through bioinformatics to identify novel miRNA biomarkers and hub genes.

Materials and Methods

The data of LUAD-related miRNA and mRNA samples was downloaded from The Cancer Genome Atlas (TCGA) database. Upon screening and pretreatment of initial data, TCGA data were analyzed using R platform and a series of analytical tools to identify biomarkers with high specificity and sensitivity.

Results

7 miRNAs and 13 hub genes that had strong relation to the overall surviving status were identified in patients with LUAD. The expression of seven miRNAs (hsa-miR-19a-3p, hsa-miR-126-5p, hsa-miR-556-3p, hsa-miR-671-5p, hsa-miR-937-3p, hsa-miR-4664-3p, and hsa-miR-4746-5p) could apparently improve the OS rate of patient with LUAD. The 13 hub genes, namely, CCT6A, CDK5R1, CEP55, DNAJB4, EGLN3, HDGF, HOXC8, LIMD1, MKI67, PCP4L1, PPIL1, SCAI, and STK32A, showed a correlation with the OS status.

Conclusion

7 miRNAs were identified as novel biomarkers for the prognosis of patients with LUAD. This study offered a deeper comprehension of LUAD treatment and prognosis from the molecular level and helped enhance the understanding of the pathogenesis and potential molecular events of LUAD.

Body mass index, genetic susceptibility, and Alzheimer's disease: a longitudinal study based on 475,813 participants from the UK Biobank

BACKGROUND

The association between body mass index (BMI) and Alzheimer's disease (AD) remains controversial. Genetic and environmental factors are now considered contributors to AD risk. However, little is known about the potential interaction between genetic risk and BMI on AD risk.

OBJECTIVE

To study the causal relationship between BMI and AD, and the potential interaction between AD genetic risk and BMI on AD risk.

METHODS AND RESULTS

Using the UK Biobank database, 475,813 participants were selected for an average follow-up time of more than 10 years.

MAIN FINDINGS

1) there was a nonlinear relationship between BMI and AD risk in participants aged 60 years or older (p for non-linear < 0.001), but not in participants aged 37-59 years (p for non-linear = 0.717) using restricted cubic splines; 2) for participants aged 60 years and older, compared with the BMI (23-30 kg/m²) group, the BMI (< 23 kg/m²) group was associated with a higher AD risk (HR = 1.585; 95% CI 1.304-1.928, p < 0.001) and the BMI (> 30 kg/m²) group was associated with a lower AD risk (HR = 0.741; 95% CI 0.618-0.888, p < 0.01) analyzed using the Cox proportional risk model; 3) participants with a combination of high AD genetic risk score (AD-GRS) and BMI (< 23 kg/m²) were associated with the highest AD risk (HR = 3.034; 95% CI 2.057-4.477, p < 0.001). In addition, compared with the BMI (< 23 kg/m²), the higher BMI was associated with a lower risk of AD in participants with the same intermediate or high AD-GRS; 4) there was a reverse causality between BMI and AD when analyzed using bidirectional Mendelian randomization (MR).

CONCLUSION

There was a reverse causality between BMI and AD analyzed using MR. For participants aged 60 years and older, the higher BMI was associated with a lower risk of AD in participants with the same intermediate or high AD genetic risk. BMI (23-30 kg/m²) may be a potential intervention for AD.

Using a machine learning model to predict the development of acute kidney injury in patients with heart failure

Background

Heart failure (HF) is a life-threatening complication of cardiovascular disease. HF patients are more likely to progress to acute kidney injury (AKI) with a poor prognosis. However, it is difficult for doctors to distinguish which patients will develop AKI accurately. This study aimed to construct a machine learning (ML) model to predict AKI occurrence in HF patients.

Materials and methods

The data of HF patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was retrospectively analyzed. A ML model was established to predict AKI development using decision tree, random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression (LR) algorithms. Thirty-nine demographic, clinical, and treatment features were used for model establishment. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were used to evaluate the performance of the ML algorithms.

Results

A total of 2,678 HF patients were engaged in this study, of whom 919 developed AKI. Among 5 ML algorithms, the RF algorithm exhibited the highest performance with the AUROC of 0.96. In addition, the Gini index showed that the sequential organ function assessment (SOFA) score, partial pressure of oxygen (PaO₂), and estimated glomerular filtration rate (eGFR) were highly relevant to AKI development. Finally, to facilitate clinical application, a simple model was constructed using the 10 features screened by the Gini index. The RF algorithm also exhibited the highest performance with the AUROC of 0.95.

Conclusion

Using the ML model could accurately predict the development of AKI in HF patients.

Machine learning models for predicting survival in patients with ampullary adenocarcinoma

Objective

The aim of this study was to predict the long-term survival probability of patients with ampullary adenocarcinoma (AAC), which would provide a theoretical basis for the long-term care of these patients.

Methods

Data on patients with AAC during 2004–2015 were obtained from the Surveillance, Epidemiology, and End Results database, which were split at a 7:3 ratio into two independent cohorts: training and testing cohorts. Differences in survival between the two groups were tested using the Kaplan–Meier estimator and log-rank test methods. We constructed six survival analysis methods: the American Joint Committee on Cancer TNM stage, Cox Proportional Hazards regression, CoxTime, DeepSurv, XGBoost Survival Embeddings, and Random Survival Forest. The performances of these models were evaluated using the C-index, receiver operating characteristic (ROC), and calibration curves.

Results

This study included 2,935 patients with AAC. Univariate Cox regression analyses of the training cohort indicated that race, marital status at diagnosis, scope of regional lymph node surgery, tumor grade, summary stage, American Joint Committee on Cancer stage, TNM stage T, and TNM stage N were important factors affecting survival (P ​< ​0.05). The results of the C-index indicated that DeepSurv performed the best among the six models, with the highest C-index of 0.731. The areas under the ROC curves of the DeepSurv model at the 1-year, 3-year, 5-year, and 10-year time points were 0.823, 0.786, 0.803, and 0.813, respectively. The calibration curve indicated that DeepSurv performed well, with good calibration.

Conclusions

Machine learning models such as DeepSurv have a stronger performance in the survival analysis of patients with AAC.

Medication Regularity of Traditional Chinese Medicine in the Treatment of Aplastic Anemia Based on Data Mining

Objective

Aplastic anemia (AA) is an uncommon disease, characterized by pancytopenia and hypocellular bone marrow, but it is common in the blood system. The medication rules of traditional Chinese medicine (TCM) in the treatment of AA are not clear, for which it is worth exploring the medication rules by data mining methods.

Methods

This study used SPSS Modeler 18.0 and SPSS statistics to analyze the cases of AA from Zhejiang Provincial Hospital of Chinese Medicine (ZJHCM) from March 1, 2019, to March 1, 2022. Data mining methods, including frequency analysis, cluster analysis, and association rule learning, were performed in order to explore the medication rules for AA.

Results

(1) A total of 859 prescriptions, which met the inclusion criteria, consisted of 255 herbs. In descending order of the frequency of herbal medicine, we have Danggui, Huangqi, Shudihuang, Fuling, Gancao, Shanyao, Shanzhuyu, Baizhu, Dangshen, and Xianhecao. (2) Frequency analysis of herb properties: the Four Qi of 255 kinds of TCMs are mainly warm and neutral medicines. The Five Flavors are mainly sweet medicines, followed by bitter medicines. The main meridians are the liver, spleen, and kidney. (3) Clustering of medications: TCMs with the top 20 frequencies are classified into 9 groups by cluster analysis. (4) Association rule analysis of high-frequency herbs: using the Apriori algorithm, the results showed that there were 3 herb pairs with support of over 0.3 and 12 herb pairs with confidence above 0.85.

Conclusion

The basic pathogenesis of AA (Sui Lao) is spleen and kidney essence deficiency, Qi deficiency, and blood stasis. The main herbs have warm and neutral properties, sweet tastes, and liver, spleen, and kidney meridian tropisms, whose purpose is to tonify the kidney and invigorate the spleen, tonify Qi, and promote blood circulation.

Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning

Background

This study aimed to develop machine learning models to predict Low Cardiac Output Syndrome (LCOS) in patients following cardiac surgery using machine learning algorithms.

Methods

The clinical data of cardiac surgery patients in Nanjing First Hospital between June 2019 and November 2020 were retrospectively extracted from the electronic medical records. Six conventional machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting and light gradient boosting machine, were employed to construct the LCOS predictive models with all predictive features (full models) and selected predictive features (reduced models). The discrimination of these models was evaluated by the area under the receiver operating characteristic curve (AUC) and the calibration of the models was assessed by the calibration curve. Shapley Additive explanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the predictive models.

Results

Data from 1,585 patients [982 (62.0%) were male, aged 18 to 88, 212 (13.4%) with LCOS] were employed to train and validate the LCOS models. Among the full models, the RF model (AUC: 0.909, 95% CI: 0.875–0.943; Sensitivity: 0.849, 95% CI: 0.724–0.933; Specificity: 0.835, 95% CI: 0.796–0.869) and the XGB model (AUC: 0.897, 95% CI: 0.859–0.935; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.809, 95% CI: 0.768–0.845) exhibited well predictive power for LCOS. Eleven predictive features including left ventricular ejection fraction (LVEF), first post-operative blood lactate (Lac), left ventricular diastolic diameter (LVDd), cumulative time of mean artery blood pressure (MABP) lower than 65 mmHg (MABP &lt; 65 time), hypertension history, platelets level (PLT), age, blood creatinine (Cr), total area under curve above threshold central venous pressure (CVP) 12 mmHg and 16 mmHg, and blood loss during operation were used to build the reduced models. Among the reduced models, RF model (AUC: 0.895, 95% CI: 0.857–0.933; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.806, 95% CI: 0.765–0.843) revealed the best performance. SHAP and LIME plot showed that LVEF, Lac, LVDd and MABP &lt; 65 time significantly contributed to the prediction model.

Conclusion

In this study, we successfully developed several machine learning models to predict LCOS after surgery, which may avail to risk stratification, early detection and management of LCOS after cardiac surgery.

Individualized assessment of risk and overall survival in patients newly diagnosed with primary osseous spinal neoplasms with synchronous distant metastasis

Background

The prognosis of patients with primary osseous spinal neoplasms (POSNs) presented with distant metastases (DMs) is still poor. This study aimed to evaluate the independent risk and prognostic factors in this population and then develop two web-based models to predict the probability of DM in patients with POSNs and the overall survival (OS) rate of patients with DM.

Methods

The data of patients with POSNs diagnosed between 2004 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistics regression analyses were used to study the risk factors of DM. Based on independent DM-related variables, we developed a diagnostic nomogram to estimate the risk of DM in patients with POSNs. Among all patients with POSNs, those who had synchronous DM were included in the prognostic cohort for investigating the prognostic factors by using Cox regression analysis, and then a nomogram incorporating predictors was developed to predict the OS of patients with POSNs with DM. Kaplan-Meier (K-M) survival analysis was conducted to study the survival difference. In addition, validation of these nomograms were performed by using receiver operating characteristic (ROC) curves, the area under curves (AUCs), calibration curves, and decision curve analysis (DCA).

Results

A total of 1345 patients with POSNs were included in the study, of which 238 cases (17.70%) had synchronous DM at the initial diagnosis. K-M survival analysis and multivariate Cox regression analysis showed that patients with DM had poorer prognosis. Grade, T stage, N stage, and histological type were found to be significantly associated with DM in patients with POSNs. Age, surgery, and histological type were identified as independent prognostic factors of patients with POSNs with DM. Subsequently, two nomograms and their online versions (https://yxyx.shinyapps.io/RiskofDMin/ and https://yxyx.shinyapps.io/SurvivalPOSNs/) were developed. The results of ROC curves, calibration curves, DCA, and K-M survival analysis together showed the excellent predictive accuracy and clinical utility of these newly proposed nomograms.

Conclusion

We developed two well-validated nomograms to accurately quantify the probability of DM in patients with POSNs and predict the OS rate in patients with DM, which were expected to be useful tools to facilitate individualized clinical management of these patients.

Nomograms and scoring system for forecasting overall and cancer-specific survival of patients with prostate cancer

BACKGROUND

Estimated life expectancy is one of the most important factors in determining treatment options for prostate cancer (PCa) patients. However, clinicians have few effective prognostic tools to individually assess survival in patients with PCa.

METHODS

We screened 283,252 patients diagnosed with PCa from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly divided them into the training and validation groups. We used univariate and multivariate Cox analyses to identify independent prognostic factors and further established nomograms to predict 1-, 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS) for PCa patients. The prediction performance of nomograms was tested and externally validated by Concordance index (C-index) and receiver operating characteristic (ROC) curve. Calibration curve and decision curve analysis (DCA) were used for internal validation. We further developed PCa prognostic scoring system based on the impact of available variables on survival.

RESULTS

The variables age, race, marital status, TNM stage, surgery method, radiotherapy, chemotherapy, PSA value, and Gleason score identified as independent prognostic factors were included in the survival nomograms. The results of training (C-index: OS = 0.776, CSS = 0.889; AUC value: OS = 0.772-0.802, CSS = 0.892-0.936) and external validation (C-index: OS = 0.759, CSS = 0.875) indicated our nomograms had good performance in predicting 1-, 3-, 5-, and 10-year OS and CSS prediction. Internal validation using the calibration curves and DCA curves demonstrated the effectiveness of the prediction models. The prognostic scoring system was more effective than the AJCC staging system in predicting the survival of PCa patients, especially for OS.

CONCLUSION

The prognostic nomograms and prognostic scoring system have favorable performance in predicting OS and CSS of PCa patients. These individualized survival prediction tools may contribute to clinical decisions.

Establishment of haemoglobin A2 reference intervals in Pretoria, South Africa: A retrospective secondary data analysis

Background

Haemoglobinopathies are one of the most common inherited diseases worldwide. Quantification of haemoglobin A₂ is necessary for the diagnosis of the beta thalassaemia trait. In this context, it is important to have a reliable reference interval for haemoglobin A₂ and a local reference range for South Africa has not been established.

Objective

This study aimed to establish reference intervals for haemoglobin A₂ using stored patient laboratory data.

Methods

This descriptive study used retrospective data to evaluate haemoglobin A₂ levels determined using high-performance liquid chromatography at the National Health Laboratory Service haematology laboratory in Pretoria, South Africa. All tests performed from 01 October 2012 to 31 December 2020 were screened for inclusion; of these, 144 patients’ data met the selection criteria. The reference interval was calculated using descriptive statistics (mean and standard deviation) with a 95% confidence interval.

Results

Analysed data from enrolled patients showed a normal distribution. The mean age of the patients was 40 years (range: 3–84 years). The reference interval for haemoglobin A₂ calculated from this data was 2.3% – 3.6%. The minimum haemoglobin A₂ was 2.3% and the maximum was 3.9% with a mean of 2.95% and a standard deviation of 0.357%.

Conclusion

A normal reference interval has been established for the population served by the laboratory that will assist with accurate diagnosis of the beta thalassaemia trait. This reference interval may also be useful to other laboratories that employ the same technology, especially smaller laboratories where obtaining a sufficiently large number of normal controls may be challenging.

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Background

Intracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.

Methods

ICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

A total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.

Conclusion

This study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

Relationship between hepatitis C and kidney stone in US females: Results from the National Health and Nutrition Examination Survey in 2007–2018

Background

The main objective of this study is to explore the effects of hepatitis C (HCV) on the prevalence rate of kidney stones in US women.

Method

Dates for HCV infection and kidney stones were collected from National Health and Nutrition Examination Survey (NHANES) database, a cross-sectional study. The analysis samples included adults aged ≥20 years and women from six consecutive cycles of the NHANES 2007–2018. The association between HCV infection and kidney stones was performed by using logistic regression models. Subgroup analyses were conducted to find sensitive crowds.

Results

A total of 13,262 participants were enrolled, including 201 infected with HCV. After adjustment for potential confounders, we revealed a positive relationship between HCV and kidney stones (OR = 1.70, 95%CI:1.13–2.56). The crowds' statistically significant difference was characterized by other races (OR = 8.17, 95%CI:1.62–41.22) and BMI within 25–29.9 kg/m2 (OR = 2.45, 95%CI:1.24–4.83).

Conclusions

HCV infection may affect the prevalence of urolithiasis in US women, even the causal relationship remains unclear, the relation deserves special attention. We considered such a study an ideal way to begin exploring the effects of HCV on kidney stones.

Competing risk analysis of cardiovascular death in patients with primary gallbladder cancer

BACKGROUND

Developments in medical technology are resulting in continuous decreases in the cancer mortality rate of patients with gallbladder cancer, while non-cancer deaths in cancer patients are becoming more common. The main cause of this is cardiovascular mortality (CVM). The purpose of this study was to determine the CVM risk in patients with primary gallbladder cancer (PGC).

METHODS

We extracted information on patients in the SEER database who were diagnosed with PGC from 2004 to 2015, compared CVM in patients with PGC with the general United States population, and calculated standardized mortality rates (SMRs) and the absolute excess risk. A competing risks model was used to identify and analyze the independent risk factors for cardiovascular death in patients with PGC.

RESULTS

This study included 5925 patients, 247 of whom died from cardiovascular disease. The SMR of cardiovascular death in patients with PGC was 15.84 (95% confidence interval: 15.83-15.85), and the SMR was slightly lower in male than female patients. The competing risks analysis indicated that age, marital status, cancer cell differentiation, chemotherapy status, and year of diagnosis were risk factors for cardiovascular death in patients with PGC.

CONCLUSIONS

The CVM risk is considerably higher in patients with PGC than in the general population. It is therefore very necessary to apply cardioprotective interventions to patients with PGC.

Sleep duration, genetic susceptibility, and Alzheimer's disease: a longitudinal UK Biobank-based study

BACKGROUND

Alzheimer's disease (AD) is the most frequently occurring type of dementia. Concurrently, inadequate sleep has been recognized as a public health epidemic. Notably, genetic and environmental factors are now considered contributors to AD progression.

OBJECTIVE

To assess the association between sleep duration, genetic susceptibility, and AD.

METHODS AND RESULTS

Based on 483,507 participants from the UK Biobank (UKB) with an average follow-up of 11.3 years, there was a non-linear relationship between AD incidence and sleep duration (P for non-linear < 0.001) by restricted cubic splines (RCS). Sleep duration was categorized into short sleep duration (< 6 h/night), normal sleep duration (6-9 h/night), and long sleep duration (> 9 h/night). No statistically significant interaction was identified between sleep duration and the AD-GRS (Alzheimer's disease genetic risk score, P for interaction = 0.45) using Cox proportional risk model. Compared with the participants who had a low AD-GRS and normal sleep duration, there was associated with a higher risk of AD in participants with a low AD-GRS and long sleep duration (HR = 3.4806; 95% CI 2.0011-6.054, p < 0.001), participants with an intermediate AD-GRS and long sleep duration (HR = 2.0485; 95% CI 1.3491-3.1105, p < 0.001), participants with a high AD-GRS and normal sleep duration (HR = 1.9272; 95% CI 1.5361-2.4176, p < 0.001), and participants with a high AD-GRS and long sleep duration (HR = 5.4548; 95% CI 3.1367-9.4863, p < 0.001).In addition, there was no causal association between AD and sleep duration using Two Sample Mendelian randomization (MR).

CONCLUSION

In the UKB population, though there was no causal association between AD and sleep duration analyzed using Two Sample MR, long sleep duration (> 9 h/night) was significantly associated with a higher risk of AD, regardless of high, intermediate or low AD-GRS. Prolonged sleep duration may be one of the clinical predictors of a higher risk of AD.

External validation based on transfer learning for diagnosing atelectasis using portable chest X-rays

Background

Although there has been a large amount of research focusing on medical image classification, few studies have focused specifically on the portable chest X-ray. To determine the feasibility of transfer learning method for detecting atelectasis with portable chest X-ray and its application to external validation, based on the analysis of a large dataset.

Methods

From the intensive care chest X-ray medical information market (MIMIC-CXR) database, 14 categories were obtained using natural language processing tags, among which 45,808 frontal chest radiographs were labeled as “atelectasis,” and 75,455 chest radiographs labeled “no finding.” A total of 60,000 images were extracted, including positive images labeled “atelectasis” and positive X-ray images labeled “no finding.” The data were categorized into “normal” and “atelectasis,” which were evenly distributed and randomly divided into three cohorts (training, validation, and testing) at a ratio of about 8:1:1. This retrospective study extracted 300 X-ray images labeled “atelectasis” and “normal” from patients in ICUs of The First Affiliated Hospital of Jinan University, which was labeled as an external dataset for verification in this experiment. Data set performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive values derived from transfer learning training.

Results

It took 105 min and 6 s to train the internal training set. The AUC, sensitivity, specificity, and accuracy were 88.57, 75.10, 88.30, and 81.70%. Compared with the external validation set, the obtained AUC, sensitivity, specificity, and accuracy were 98.39, 70.70, 100, and 86.90%.

Conclusion

This study found that when detecting atelectasis, the model obtained by transfer training with sufficiently large data sets has excellent external verification and acculturate localization of lesions.

The relationship between hematocrit and serum albumin levels difference and mortality in elderly sepsis patients in intensive care units-a retrospective study based on two large database

BACKGROUND

Sepsis still threatens the lives of more than 300 million patients annually and elderly patients with sepsis usually have a more complicated condition and a worse prognosis. Existing studies have shown that both Hematocrit (HCT) and albumin (ALB) can be used as potential predictors of sepsis, and their difference HCT-ALB has a significant capacity to diagnose infectious diseases. Currently, there is no relevant research on the relationship between HCT-ALB and the prognosis of elderly sepsis patients. Therefore, this study aims to explore the association between HCT-ALB and mortality in elderly patients with sepsis.

METHODS

This study was a multi-center retrospective study based on the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD) in elderly patients with sepsis. The optimal HCT-ALB cut-off point for ICU mortality was calculated by the Youden Index based on the eICU-CRD dataset, and multivariate logistic regressions were conducted to explore the association between HCT-ALB and ICU/hospital mortality in the two databases. Subgroup analyses were performed for different parameters and comorbidity status.

RESULTS

The number of 16,127 and 3043 elderly sepsis patients were selected from two large intensive care databases (eICU-CRD and MIMIC-IV, respectively) in this study. Depending on the optimal cut-off point, patients in both eICU-CRD and MIMIC-IV were independently divided into low HCT-ALB (< 6.7) and high HCT-ALB (≥ 6.7) groups. The odds ratio (95%confidence interval) [OR (95CI%)] of the high HCT-ALB group were 1.50 (1.36,1.65) and 1.71 (1.58,1.87) for ICU and hospital mortality in the eICU-CRD database after multivariable adjustment. Similar trends in the ICU and hospital mortality [OR (95%CI) 1.41 (1.15,1.72) and 1.27 (1.07,1.51)] were observed in MIMIC-IV database. Subgroup analysis showed an interaction effect with SOFA score in the eICU-CRD database however not in MIMIC-IV dataset.

CONCLUSIONS

High HCT-ALB (≥ 6.7) is associated with 1.41 and 1.27 times ICU and hospital mortality risk in elderly patients with sepsis. HCT-ALB is simple and easy to obtain and is a promising clinical predictor of early risk stratification for elderly sepsis patients in ICU.

Development and validation of tumor-size-stratified prognostic nomograms for patients with uterine sarcoma: A SEER database analysis

BACKGROUND

Tumor-size-stratified analysis on the prognosis of uterine sarcoma is insufficient. This study aimed to establish the tumor-size-stratified nomograms to predict the 3- and 5-year overall survival (OS) of patients with uterine sarcoma.

METHODS

The data analyzed in this study were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. We collected data from patients with uterine sarcoma diagnosed between 2004 and 2015. According to the median tumor size of 7.8 cm, the enrolled patients were divided into two tumor size (TS) groups: TS <7.8 cm and TS ≥7.8 cm. Patients in each group were randomly divided into the training and validation cohorts with a ratio of 7:3. Chi-square test was used to compare differences between categorical variables. Multivariate Cox regression models were used to identify significant predictors. We calculated the concordance index (C-index) and the area under the receiver operating characteristics curve (AUC) to validate the nomograms.

RESULTS

Compared with TS <7.8 cm group, TS ≥7.8 cm group had more patients of 45-64 years group, higher black race prevalence, higher proportion of myometrium tumor, higher stage, and higher grade; In the TS <7.8 cm training cohort, six variables (age, race, marital status, tumor primary site, stage, and grade) were identified as significantly associated with OS in multivariate analysis. However in the TS ≥7.8 cm training cohort, only four variables (surgery on primary site, tumor size, stage, and grade) were significantly identified; The C-index of two nomograms were 0.80 and 0.73 in training cohorts, respectively, and the AUC values for 3- and 5-year OS predictions in training cohorts were all above 0.80. Similar results were observed in validation cohorts.

CONCLUSIONS

This study found that the significant prognostic factors were different between two tumor size groups of uterine sarcoma patients. The tumor-size-stratified nomograms, which we constructed and validated, might be useful to predict the probability of survival for patients with uterine sarcoma.

Intraductal papillary carcinoma of breast with invasion: A nomogram and survival from the analysis of the SEER database

BACKGROUND

Intraductal papillary carcinoma (IPC) with invasion is a rare type of breast cancer. There have been few studies on its prognosis, and a nomogram that predicts the prognosis of the disease has not been described to date.

METHODS

Patients who were diagnosed with invasive IPC were screened from the Surveillance, Epidemiology, and End Results (SEER) database. The screened patients were randomly divided into a training cohort and a verification cohort at 7:3. A Cox proportional hazard regression model was performed to analyze the effects of different variables on the risk of death in invasive IPC. A nomogram was constructed to quantify the possibility of death. The concordance index (C-index), calibration plots, receiver operating characteristic (ROC) curves, and decision curves analysis (DCA) were used to verify the proposed model.

RESULTS

We included a total of 803 patients diagnosed with invasive IPC, including 563 patients in the training cohort and 240 patients in the validation cohort. The median follow-up times in the training cohort and validation cohort were 63 months (range, 2-155 months) and 61 months (range, 1-154 months), respectively. For all patients, the probability of death with invasive IPC was 1.4% within 5 years and 5.4% within 10 years. In multivariate analysis, sex, race, tumor size, lymph node status, type of treatment, and chemotherapy were related to the prognosis of invasive IPC. We constructed a nomogram to predict the possibility of death in patients with invasive IPC.

CONCLUSION

Patients with invasive IPC had a high survival rate. The proven nomogram was helpful to both patients and clinical decision makers.

Metformin Protects Cardiovascular Health in People With Diabetes

Background

Metformin is the most commonly used drug for patients with diabetes, but there is still some controversy about whether it has a protective effect on cardiovascular health. We therefore used the National Health and Nutritional Examination Survey (NHANES) database to analyze the impact of metformin use on cardiovascular health in patients with diabetes.

Methods

We extracted the demographic data and laboratory test results of all people with diabetes in the NHANES database from January 2017 to March 2020. The outcomes were seven indicators of cardiovascular health from the American Heart Association, each was scored as 0, 1, and 2 to represent poor, moderate, and ideal health statuses, respectively. The scores for the indicators (excluding diet and glycemic status) were summed, and the sum score was then considered to indicate unhealthy (0–5) or healthy (>5). Multivariate logistic regression analysis was used, and subgroup analyses were performed by age, alcohol consumption, education, and marital status.

Results

This study included 1,356 patients with diabetes, among which 606 were taking metformin. After adjusting for all included variables, oral metformin in patients with diabetes had a protective effect on the cardiovascular health of patients (OR = 0.724, 95% CI = 0.573–0.913, P = 0.007). Subgroup analysis indicated that metformin protects the cardiovascular health of people with diabetes more clearly in those who are young (OR = 0.655, 95% CI = 0.481–0.892, P = 0.007), married (OR = 0.633, 95% CI = 0.463–0.863, P = 0.003), and drink alcohol (OR = 0.742, 95% CI = 0.581–0.946, P = 0.016).

Conclusion

This study found that metformin has a protective effect on the cardiovascular health of patients with diabetes. The study findings support the general applicability of metformin.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

Early prediction of in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational cohort study

Objective

Congestive heart failure (CHF) is a clinical syndrome in which the heart disease progresses to a severe stage. Early diagnosis and risk assessment of death of patients with CHF are critical to prognosis and treatment. The purpose of this study was to establish a nomogram that predicts the in-hospital death of patients with CHF in the intensive care unit (ICU).

Design

A retrospective observational cohort study.

Setting and participants

Data for the study were from 30 411 patients with CHF in the Medical Information Mart for Intensive Care database and the eICU Collaborative Research Database (eICU-CRD).

Primary outcome

In-hospital mortality.

Methods

Univariate logistic regression analysis was used to select risk factors associated with in-hospital mortality of patients with CHF, and multivariate logistic regression was used to build the prediction model. Discrimination, calibration and clinical validity of the model were evaluated by AUC, calibration curve, Hosmer-Lemeshow χ² test and decision curve analysis, respectively. Finally, data from 15 503 patients with CHF in the multicentre eICU-CRD were used for external validation of the established nomogram.

Results

The inclusion criteria were met by 15 983 subjects, whose in-hospital mortality rate was 12.4%. Multivariate analysis determined that the independent risk factors were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, mechanical ventilation, intubation, hepatic failure (HepF), heart rate, respiratory rate, temperature, systolic blood pressure (SBP), anion gap (AG), blood urea nitrogen (BUN), creatinine, chloride, mean corpuscular volume (MCV), red blood cell distribution width (RDW) and white cell count (WCC). The C-index of the nomogram (0.767, 95% CI 0.759 to 0.779) was superior to that of the traditional Sequential Organ Failure Assessment, Acute Physiology Score III and Get With The Guidelines Heart Failure scores, indicating its discrimination power. Calibration plots demonstrated that the predicted results are in good agreement with the observed results. The decision curves of the derivation and validation sets both had net benefits.

Conclusion

The 20 independent risk factors for in-hospital mortality of patients with CHF were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, mechanical ventilation, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, RDW and WCC. The nomogram, which included these factors, accurately predicted the in-hospital mortality of patients with CHF. The novel nomogram has the potential for use in clinical practice as a tool to predict and assess mortality of patients with CHF in the ICU.

Additional Postoperative Radiotherapy Prolonged the Survival of Patients with I-IIA Small Cell Lung Cancer: Analysis of the SEER Database

Purpose

Complete resection and adjuvant chemotherapy are recommended as the standard strategy for patients with stage I-IIA small cell lung cancer (SCLC). However, the role of additional postoperative radiotherapy (PORT) in treatment remains controversial.

Methods

Patients with stage I-IIA SCLC undergoing surgery and adjuvant chemotherapy were extracted from the Surveillance, Epidemiology, and End Results database. Stage I-IIA, defined as T1-2N0M0, was recalculated according to the 8th AJCC TNM staging system. Propensity score matching (PSM) was conducted to identify the therapeutic impact of PORT. Univariate Cox hazards regression and least absolute shrinkage and selection operator regression were utilized for primary screening of prognostic variables for I-IIA SCLC disease. A nomogram to predict overall survival (OS) was constructed based on the multivariate Cox proportional hazards model, evaluated with area under the curve, calibration curve, and decision curve analysis, and validated with bootstrap resampling.

Results

Our results demonstrated that compared with no PORT, PORT significantly prolonged the median OS (8.58 vs. 5.17 years, HR = 0.61 [0.39–0.96], P = 0.032) and median cancer-specific survival (11.33 vs. 8.08, HR = 0.47 [0.27–0.82], P = 0.0086) after PSM. The 5-year OS rate was 61.56% vs. 46.60%. Five variables including age at diagnosis, gender, T stage, surgical type, and PORT were elucidated to impact on prognosis and included in a nomogram to predict 3-/5-/10-year OS probability. The area under the curve values were 0.72, 0.71, and 0.81, respectively. The nomogram also exhibited satisfactory accuracy and clinical usefulness.

Conclusion

PORT was verified to improve the OS of patients with T1-2N0M0 SCLC after surgery and chemotherapy. A prognostic nomogram was developed and validated for OS prediction for these patients.

The Association Between Bronchoscopy and the Prognoses of Patients With Ventilator-Associated Pneumonia in Intensive Care Units: A Retrospective Study Based on the MIMIC-IV Database

Background: In intensive care units (ICUs), the morbidity and mortality of ventilator-associated pneumonia (VAP) are relatively high, and this condition also increases medical expenses for mechanically ventilated patients, which will seriously affect the prognoses of critically ill patients. The purpose of this study was to determine the impact of bronchoscopy on the prognosis of patients with VAP undergoing invasive mechanical ventilation (IMV).

Methods: This was a retrospective study based on patients with VAP from the Medical Information Mart for Intensive Care IV database. The outcomes were ICU and in-hospital mortality. Patients were divided based on whether or not they had undergone bronchoscopy during IMV. Kaplan-Meier (KM) survival curves and Cox proportional-hazards regression models were used to analyze the association between groups and outcomes. Propensity score matching (PSM) and propensity score based inverse probability of treatment weighting (IPTW) were used to further verify the stability of the results. The effect of bronchoscopy on prognosis was further analyzed by causal mediation analysis (CMA).

Results: This study enrolled 1,560 patients with VAP: 1,355 in the no-bronchoscopy group and 205 in the bronchoscopy group. The KM survival curve indicated a significant difference in survival probability between the two groups. The survival probabilities in both the ICU and hospital were significantly higher in the bronchoscopy group than in the no bronchoscopy group. After adjusting all covariates as confounding factors in the Cox model, the HRs (95% CI) for ICU and in-hospital mortality in the bronchoscopy group were 0.33 (0.20–0.55) and 0.40 (0.26–0.60), respectively, indicating that the risks of ICU and in-hospital mortality were 0.67 and 0.60 lower than in the no-bronchoscopy group. The same trend was obtained after using PSM and IPTW. CMA showed that delta-red blood cell distribution width (RDW) mediated 8 and 7% of the beneficial effects of bronchoscopy in ICU mortality and in-hospital mortality.

Conclusion: Bronchoscopy during IMV was associated with reducing the risk of ICU and in-hospital mortality in patients with VAP in ICUs, and this beneficial effect was partially mediated by changes in RDW levels.

A Novel Risk-Scoring System to Identify the Potential Population Benefiting From Adjuvant Chemotherapy for Node-Negative TNBC Patients With Tumor Size Less Than 1 cm

Background and Objectives

Whether chemotherapy is needed in node-negative triple-negative breast cancer (TNBC) patients with tumor size less than 1 cm is still controversial. In our research, we constructed a novel risk-scoring system to identify the potential TNBC patients benefiting from adjuvant chemotherapy in T1miN0M0, T1aN0M0, and T1bN0M0 stages.

Methods

Relevant data were extracted from the SEER database. We applied Kaplan-Meier curves and the Cox hazards model for survival analysis and developed a nomogram of overall survival. The X-tile software was used for risk stratification. The information of TNBC patients treated in the First Affiliated Hospital of Xi’an Jiaotong University was used for the application of the model.

Results

A total of 4266 patients who met the criteria of our study were included. T stage, age, race, surgery, and radiotherapy state were used to create the nomogram of overall survival. According to the total risk score, the patients were divided into high-risk (score g 73), median-risk (38 ≤ score &lt; 73), and low-risk (score &lt;38) groups. Chemotherapy can prolong the overall survival of patients in the median-risk and high-risk groups, while patients in the low-risk group can be exempted from chemotherapy. In addition, we also used the risk-scoring system in real-world patients as application and verification.

Conclusion

We constructed a novel risk-scoring system that can be used as a chemotherapy decision-making tool for node-negative TNBC patients with tumor size less than 1 cm. Tumor size should not be the only criterion for chemotherapy treatment decision-making.

Thiamine May Be Beneficial for Patients With Ventilator-Associated Pneumonia in the Intensive Care Unit: A Retrospective Study Based on the MIMIC-IV Database

Background: Ventilator-associated pneumonia (VAP) is a common infection complication in intensive care units (ICU). It not only prolongs mechanical ventilation and ICU and hospital stays, but also increases medical costs and increases the mortality risk of patients. Although many studies have found that thiamine supplementation in critically ill patients may improve prognoses, there is still no research or evidence that thiamine supplementation is beneficial for patients with VAP. The purpose of this study was to determine the association between thiamine and the prognoses of patients with VAP.

Methods: This study retrospectively collected all patients with VAP in the ICU from the Medical Information Mart for Intensive Care-IV database. The outcomes were ICU and in-hospital mortality. Patients were divided into the no-thiamine and thiamine groups depending upon whether or not they had received supplementation. Associations between thiamine and the outcomes were tested using Kaplan-Meier (KM) survival curves and Cox proportional-hazards regression models. The statistical methods of propensity-score matching (PSM) and inverse probability weighting (IPW) based on the XGBoost model were also applied to ensure the robustness of our findings.

Results: The study finally included 1,654 patients with VAP, comprising 1,151 and 503 in the no-thiamine and thiamine groups, respectively. The KM survival curves indicated that the survival probability differed significantly between the two groups. After multivariate COX regression adjusted for confounding factors, the hazard ratio (95% confidence interval) values for ICU and in-hospital mortality in the thiamine group were 0.57 (0.37, 0.88) and 0.64 (0.45, 0.92), respectively. Moreover, the results of the PSM and IPW analyses were consistent with the original population.

Conclusion: Thiamine supplementation may reduce ICU and in-hospital mortality in patients with VAP in the ICU. Thiamine is an inexpensive and safe drug, and so further clinical trials should be conducted to provide more-solid evidence on whether it improves the prognosis of patients with VAP.

Infusion of Human Albumin on Acute Pancreatitis Therapy: New Tricks for Old Dog?

Objective: Human serum albumin (HSA) infusion is a common administration on acute pancreatitis therapy in the Intensive Care Unit (ICU), but its actual association with patients’ outcomes has not been confirmed. The study is aimed to determine whether the in-hospital prognosis of ICU patients with acute pancreatitis could benefit from HSA.

Methods: 950 acute pancreatitis patients diagnosed in 2008–2019 were extracted from the MIMIC-IV database as our primary study cohort. The primary outcome was in-hospital mortality. We also performed an external validation with a cohort of 104 acute pancreatitis patients after PSM matching from the eICU database.

Results: In MIMIC-IV, 228 acute pancreatitis patients received HSA infusion (Alb group) during their hospitalization, while 722 patients did not (non-Alb group). Patients in the Alb group presented a poorer survival curve than the non-Alb group, while this difference disappeared after PSM or IPTW matching (log-rank test: PSM: p = 0.660, IPTW: p = 0.760). After including covariates, no association was found between HSA infusion and patients’ in-hospital mortality before and after matching (original cohort: HR: 1.00, 95% CI: 0.66–1.52, p = 0.998). HSA infusion also did not benefit patients’ 28-days or ICU mortality, while it was significantly associated with a longer duration of hospital and ICU. In addition, the initial serum albumin levels, infections, the total amount, or the initial timing of infusion did not affect the conclusion. Similarly, in the eICU cohort, HSA infusion was still not a beneficial prognostic factor for patients’ in-hospital prognosis (p = 0.087).

Conclusion: Intravenous human serum albumin infusion could not benefit acute pancreatitis patients’ in-hospital prognosis and was associated with prolonged hospital and ICU duration.

Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury

Background

Sepsis-associated acute kidney injury (SA-AKI) is common in critically ill patients, which is associated with significantly increased mortality. Existing mortality prediction tools showed insufficient predictive power or failed to reflect patients' dynamic clinical evolution. Therefore, the study aimed to develop and validate machine learning-based models for real-time mortality prediction in critically ill patients with SA-AKI.

Methods

The multi-center retrospective study included patients from two distinct databases. A total of 12,132 SA-AKI patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) were randomly allocated to the training, validation, and internal test sets. An additional 3,741 patients from the eICU Collaborative Research Database (eICU-CRD) served as an external test set. For every 12 h during the ICU stays, the state-of-the-art eXtreme Gradient Boosting (XGBoost) algorithm was used to predict the risk of in-hospital death in the following 48, 72, and 120 h and in the first 28 days after ICU admission. Area under the receiver operating characteristic curves (AUCs) were calculated to evaluate the models' performance.

Results

The XGBoost models, based on routine clinical variables updated every 12 h, showed better performance in mortality prediction than the SOFA score and SAPS-II. The AUCs of the XGBoost models for mortality over different time periods ranged from 0.848 to 0.804 in the internal test set and from 0.818 to 0.748 in the external test set. The shapley additive explanation method provided interpretability for the XGBoost models, which improved the understanding of the association between the predictor variables and future mortality.

Conclusions

The interpretable machine learning XGBoost models showed promising performance in real-time mortality prediction in critically ill patients with SA-AKI, which are useful tools for early identification of high-risk patients and timely clinical interventions.

The Use of Antibiotics for Ventilator-Associated Pneumonia in the MIMIC-IV Database

Purpose: By analyzing the clinical characteristics, etiological characteristics and commonly used antibiotics of patients with ventilator-associated pneumonia (VAP) in intensive care units (ICUs) in the intensive care database. This study aims to provide guidance information for the clinical rational use of drugs for patients with VAP.

Method: Patients with VAP information were collected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, including their sociodemographic characteristics, vital signs, laboratory measurements, complications, microbiology, and antibiotic use. After data processing, the characteristics of the medications used by patients with VAP in ICUs were described using statistical graphs and tables, and experiences were summarized and the reasons were analyzed.

Results: This study included 2,068 patients with VAP. Forty-eight patient characteristics, including demographic indicators, vital signs, biochemical indicators, scores, and comorbidities, were compared between the survival and death groups of VAP patients. Cephalosporins and vancomycin were the most commonly used. Among them, fourth-generation cephalosporin (ForGC) combined with vancomycin was used the most, by 540 patients. First-generati49n cephalosporin (FirGC) combined with vancomycin was associated with the highest survival rate (86.7%). More than 55% of patients were infected with Gram-negative bacteria. However, patients with VAP had fewer resistant strains (&lt;25%). FirGC or ForGC combined with vancomycin had many inflammation-related features that differed significantly from those in patients who did not receive medication.

Conclusion: Understanding antibiotic use, pathogenic bacteria compositions, and the drug resistance rates of patients with VAP can help prevent the occurrence of diseases, contain infections as soon as possible, and promote the recovery of patients.

Deep transfer learning to quantify pleural effusion severity in chest X-rays

Purpose

The detection of pleural effusion in chest radiography is crucial for doctors to make timely treatment decisions for patients with chronic obstructive pulmonary disease. We used the MIMIC-CXR database to develop a deep learning model to quantify pleural effusion severity in chest radiographs.

Methods

The Medical Information Mart for Intensive Care Chest X-ray (MIMIC-CXR) dataset was divided into patients ‘with’ or ‘without’ chronic obstructive pulmonary disease (COPD). The label of pleural effusion severity was obtained from the extracted COPD radiology reports and classified into four categories: no effusion, small effusion, moderate effusion, and large effusion. A total of 200 datasets were randomly sampled to manually check each item and determine whether the tags are correct. A professional doctor re-tagged these items as a verification cohort without knowing their previous tags. The learning models include eight common network structures including Resnet, DenseNet, and GoogleNET. Three data processing methods (no sampling, downsampling, and upsampling) and two loss algorithms (focal loss and cross-entropy loss) were used for unbalanced data. The Neural Network Intelligence tool was applied to train the model. Receiver operating characteristic curves, Area under the curve, and confusion matrix were employed to evaluate the model results. Grad-CAM was used for model interpretation.

Results

Among the 8533 patients, 15,620 chest X-rays with clearly marked pleural effusion severity were obtained (no effusion, 5685; small effusion, 4877; moderate effusion, 3657; and large effusion, 1401). The error rate of the manual check label was 6.5%, and the error rate of the doctor’s relabeling was 11.0%. The highest accuracy rate of the optimized model was 73.07. The micro-average AUCs of the testing and validation cohorts was 0.89 and 0.90, respectively, and their macro-average AUCs were 0.86 and 0.89, respectively. The AUC of the distinguishing results of each class and the other three classes were 0.95 and 0.94, 0.76 and 0.83, 0.85 and 0.83, and 0.87 and 0.93.

Conclusion

The deep transfer learning model can grade the severity of pleural effusion.

Analysis of the Impacts on the Psychological Changes of Chinese Returning College Students After the Outbreak of the 2019 Coronavirus Disease

This work aims to analyze the impacts on the psychological changes of Chinese returning college students after the outbreak of the 2019 coronavirus disease (COVID-19). A questionnaire survey is used to take 1,482 college students who returned to school after the epidemic as the research objects. The Chinese college students' knowledge of the epidemic, alienation in physical education class, school happiness, and expectations for a healthy life in the future are investigated and analyzed. The research results manifest that Chinese returning college students have relatively poor awareness of COVID-19, and the overall degree of alienation in physical education classes after the epidemic is low, with an average score of 3.55 ± 1.018. The overall level of school happiness is high, with an average score of 4.94 ± 0.883; the overall level of expectation for a healthy life in the future is high, with an average score of 3.50 ± 0.840. It denotes that the epidemic has a great psychological impact on returning college students, and it is necessary to strengthen mental health education for college students after COVID-19. It provides a sustainable theoretical reference for the formulation of psychological intervention measures for returning college students.

A novel nomogram for predicting cancer-specific survival in women with uterine sarcoma: a large population-based study

Background

Uterine sarcoma (US) is a rare malignant uterine tumor with aggressive behavior and rapid progression. The purpose of this study was to constructa comprehensive nomogram to predict cancer-specific survival (CSS) of patients with US-based on the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

A retrospective population-based study was conducted using data from patients with US between 2010 and 2015 from the SEER database. They were randomly divided into a training cohort and a validation cohort ata 7-to-3 ratio. Multivariate Cox analysis was performed to identify independent prognostic factors. Subsequently, a nomogram was established to predict patient CSS. The discrimination and calibration of the nomogram were evaluated by the concordance index (C-index) and the area under the curve (AUC). Finally, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the benefits of the new prediction model.

Results

A total of 3861 patients with US were included in our study. As revealed in multivariate Cox analysis, age at diagnosis, race, marital status, insurance record, tumor size, pathology grade, histological type, SEER stage, AJCC stage, surgery status, radiotherapy status, and chemotherapy status were found to be independent prognostic factors. In our nomogram, pathology grade had strongest correlation with CSS, followed by age at diagnosis and surgery status. Compared to the AJCC staging system, the new nomogram showed better predictive discrimination with a higher C-index in the training and validation cohorts (0.796 and 0.767 vs. 0.706 and 0.713, respectively). Furthermore, the AUC value, calibration plotting, NRI, IDI, and DCA also demonstrated better performance than the traditional system.

Conclusion

Our study validated the first comprehensive nomogram for US, which could provide more accurate and individualized survival predictions for US patients in clinical practice.

Efficient big data security analysis on HDFS based on combination of clustering and data perturbation algorithm using health care database

In this manuscript proposes an efficient big data security analysis on HDFS based on the combination of Improved Deep K-means Clustering (IDFKM) algorithm and Modified 3D rotation data perturbation algorithm using health care database. To compile a similar group of data, an Improved Deep K-means Clustering (IDFKM) Algorithm is used as partitioning the medical data. After clustering, Modified 3D rotation data perturbation technique is used to satisfy the privacy requirement of the client. Modified 3D rotation Data Perturbation technique perturbs each and every sensitive data of the cluster and all the key parameters values used for clustering have warehoused in the database file sector. The proposed approach is executed by Java program, its efficiency is assessed by Health care database. The metrics under the study of memory usage attains higher accuracy 34.765%, 23.44%, 52.74%, 18.74%, lower execution time 35.23%, 23.76%, 27.86%, 27.76%, higher Efficiency 26.85%, 38.97%, 28.97%, 35.65%. then the proposed method is compared with the existing methods such asSecurity Analysis of SDN Applications for Big Data with spoofing identity, Tampering with data, Repudiation threats, Information disclosure, Denial of service and Elevation of privileges (STRIDE), Big Data Analysis-based Secure Cluster Management for using Ant Colony Optimization (ACA) Optimized Control Plane in Software-Defined Networks, System Architecture for Secure Authentication and Data Sharing in Cloud Enabled Big Data Environment using LemperlZivMarkow Algorithm (LZMA) and Density-based Clustering of Applications with Noise (DBSCAN), Big Data Based Security Analytics using data based security analytics (BDSA) approach for Protecting Virtualized Infrastructures in Cloud Computing respectively.

E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database

Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients' survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality prediction are identified, and the effects of changing each feature on the prediction are studied. We used supervised machine learning models and illness severity scoring systems to benchmark the mortality prediction. We also implemented a combination of SHAP, LIME, partial dependence, and individual conditional expectation plots to explain the predictions made by the best-performing model (CatBoost). We proposed E-CatBoost, an optimized and efficient patient mortality prediction model, which can accurately predict the patients' discharge status using only ten input features. We used eICU-CRD v2.0 to train and validate the models; the dataset contains information on over 200,000 ICU admissions. The patients were divided into twelve disease groups, and models were fitted and tuned for each group. The models' predictive performance was evaluated using the area under a receiver operating curve (AUROC). The AUROC scores were 0.86 [std:0.02] to 0.92 [std:0.02] for CatBoost and 0.83 [std:0.02] to 0.91 [std:0.03] for E-CatBoost models across the defined disease groups; if measured over the entire patient population, their AUROC scores were 7 to 18 and 2 to 12 percent higher than the baseline models, respectively. Based on SHAP explanations, we found age, heart rate, respiratory rate, blood urine nitrogen, and creatinine level as the most critical cross-disease features in mortality predictions.

Effects of Gastric Acid Secretion Inhibitors for Ventilator-Associated Pneumonia

Objective: This study analyzed the association of gastric acid secretion inhibitors (GASIs) [including proton pump inhibitors (PPIs) and histamine 2 receptor antagonists (H2RAs)] with the occurrence of ventilator-associated pneumonia (VAP) and in-hospital mortality in patients who received invasive mechanical ventilation (IMV).

Method: Patients who received IMV and used GASI were included based on records in the MIMIC-IV database. The relationships of GASIs with VAP and the in-hospital mortality were determined using univariate and multivariate logistic regression analyses. Also, the effects of GASIs in some subgroups of the population were further analyzed.

Results: A total of 18,669 patients were enrolled, including 9191 patients on H2RAs only, 6921 patients on PPIs only, and 2557 were on a combination of the two drugs. Applying logistic regression to the univariate and multivariate models revealed that compared with H2RAs, PPIs had no significant effect on the incidence of VAP, and the combination of H2RAs and PPIs was a risk factor for VAP. Compared with H2RAs, univariate logistic regression revealed that, PPIs and combine the two drugs were both risk factors for in-hospital mortality, but multivariate logistic regression showed that they were not significantly associated with in-hospital mortality. In subgroup analysis, there were interaction in different subgroups of age, PCO2, myocardial infarct, congestive heart failure (P for interaction<0.05).

Conclusion: Compared with H2RAs, PPIs did not have a significant association with either VAP or in-hospital mortality; the combination of H2RAs and PPIs was risk factor for VAP, but did not have a significantly associated with in-hospital mortality.

Development, validation, and visualization of a web-based nomogram for predicting the incidence of leiomyosarcoma patients with distant metastasis

BACKGROUND

Leiomyosarcoma (LMS) is one of the most common soft tissue sarcomas. LMS is prone to distant metastasis (DM), and patients with DM have a poor prognosis.

AIM

In this study, we investigated the risk factors of DM in LMS patients and the prognostic factors of LMS patients with DM.

METHODS AND RESULTS

LMS patients diagnosed between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Result (SEER) database. Patients were randomly divided into the training set and validation set. Univariate and multivariate logistic regression analyses were performed, and a nomogram was established. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram. Based on the nomogram, a web-based nomogram is established. The univariate and multivariate Cox regression analyses were used to assess the prognostic risk factors of LMS patients with DM. Eventually, 2184 patients diagnosed with LMS were enrolled, randomly divided into the training set (n = 1532, 70.14%) and validation set (n = 652, 29.86%). Race, primary site, grade, T stage, and tumor size were correlated with DM incidence in LMS patients. The AUC of the nomogram is 0.715 in training and 0.713 in the validation set. The calibration curve and DCA results showed that the nomogram performed well in predicting the DM risk. A web-based nomogram was established to predict DM's risk in LMS patients (https://wenn23.shinyapps.io/riskoflmsdm/). Epithelioid LMS, in uterus, older age, giant tumor, multiple organ metastasis, without surgery, and chemotherapy had a poor prognosis.

CONCLUSIONS

The established web-based nomogram (https://wenn23.shinyapps.io/riskoflmsdm/) is an accurate and personalized tool to predict the risks of LMS developing DM. Advanced age, larger tumor, multiple organ metastasis, epithelioid type, uterine LMS, no surgery, and no chemotherapy were associated with poor prognosis in LMS patients with DM.

Using Restricted Cubic Splines to Study the Duration of Antibiotic Use in the Prognosis of Ventilator-Associated Pneumonia

Background: Ventilator-associated pneumonia (VAP) is the most widespread and life-threatening nosocomial infection in intensive care units (ICUs). The duration of antibiotic use is a good predictor of prognosis in patients with VAP, but the ideal duration of antibiotic therapy for VAP in critically ill patients has not been confirmed. Research is therefore needed into the optimal duration of antibiotic use and its impact on VAP.

Methods: The Medical Information Mart for Intensive Care database included 1,609 patients with VAP. Chi-square or Student’s t-tests were used to compare groups, and Cox regression analysis was used to investigate the factors influencing the prognoses of patients with VAP. Nonlinear tests were performed on antibiotic use lasting <7, 7–10, and >10 days. Significant factors were included in the model for sensitivity analysis. For the subgroup analyses, the body mass indexes (BMIs) of patients were separated into BMI <30 kg/m2 and BMI ≥30 kg/m2, with the criterion of statistical significance set at p < 0.05. Restricted cubic splines were used to analyze the relationship between antibiotic use duration and mortality risk in patients with VAP.

Results: In patients with VAP, the effects of antibiotic use duration on the outcomes were nonlinear. Antibiotic use for 7–10 days in models 1–3 increased the risk of antibiotic use by 2.6020-, 2.1642-, and 2.3263-fold relative to for >10 days, respectively. The risks in models 1–3 for <7 days were 2.6510-, 1.9933-, and 2.5151-fold higher than those in models with >10 days of antibiotic use, respectively. These results were robust across the analyses.

Conclusions: The duration of antibiotic treatment had a nonlinear effect on the prognosis of patients with VAP. Antibiotic use durations of <7 days and 7–10 days both presented risks, and the appropriate duration of antibiotic use can ensure the good prognosis of patients with VAP.

Prognostic nomograms to predict overall survival and cause specific death in vulvar squamous cell carcinoma

Objective

The incidence of vulvar squamous cell carcinoma has been rising in recent decades. The prognosis of patients with vulvar squamous cell carcinoma was explored, and nomograms were constructed to predict survival rates.

Methods

Vulvar squamous cell carcinoma patient data were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into a training dataset and testing dataset. Univariable and multivariable Cox regression were used to identify risk factors affecting vulvar squamous cell carcinoma overall survival in the training dataset. Cumulative incidence function and Fine–Gray regression were used to analyze cancer specific death in the training dataset. Overall survival and cancer specific death nomograms were constructed and validated in the testing and whole datasets. Receiver operating characteristic and calibration were used to verify the predictive value and clinical applicability of the models.

Results

Age ≥60 years, grade 3, American Joint Committee on Cancer stages III and IV, TNM (tumor, nodes, metastasis) stages T2, T3, N1, and M1 had a negative effect on overall survival in vulvar cancer patients. Surgery (hazard ratio (HR)=0.416, 95% confidence interval (CI) 0.349 to 0.496, p<0.001) and chemotherapy (HR=0.637, 95% CI 0.544 to 0.746, p<0.001) may improve overall survival. Age, tumor grade, American Joint Committee on Cancer stage, T stage, N stage, M stage, surgery, and chemotherapy significantly affected vulvar cancer specific death. For area under the receiver operating characteristic curve, the predictive ability of the nomograms for overall survival and cancer specific death for 1 year (area under the curve (AUC)=0.862), 3 years (AUC=0.832), and 5 years (AUC=0.808) were all >0.800.

Conclusion

The nomograms established in our study had an excellent predictive ability for overall survival and cancer specific death in vulvar cancer patients.

Effect of First Trough Vancomycin Concentration on the Occurrence of AKI in Critically Ill Patients: A Retrospective Study of the MIMIC-IV Database

Background

Vancomycin can effectively inhibit Gram-positive cocci and is widely used in critically ill patients. This study utilized a large public database to explore the effect of patients' first vancomycin trough concentration (FVTC) on the occurrence of acute kidney injury (AKI) and mortality after receiving vancomycin treatment in intensive care unit (ICU).

Methods

Critically ill patients who used vancomycin in the Medical Information Mart for Intensive Care (MIMIC) IV have been retrospectively studied. The outcomes included the occurrence of AKI during the use of vancomycin or within 72 h of withdrawal, ICU mortality and hospital mortality. Restricted cubic splines (RCS) were used to analyze the linear relationship between FVTC and the outcomes. Multivariate logistic/Cox regression analysis was used to analyze the association between patient's FVTC and the occurrence of AKI, ICU mortality, and in-hospital mortality.

Results

The study ultimately included 3,917 patients from the MIMIC-IV database who had been treated with vancomycin for more than 48 h. First of all, the RCS proved the linear relationship between FVTC and the outcomes. After controlling for all covariates as confounders in logistic/Cox regression, FVTC was a risk factor with the occurrence of AKI (OR: 1.02; 95% CI: 1.01–1.04), ICU mortality (HR: 1.02; 95% CI: 1.01–1.03), and in-hospital mortality (HR: 1.02; 95% CI: 1.01–1.03). Moreover, patients were divided into four groups in the light of the FVTC value: group1 ≤ 10 mg/L, 10 <group 2 ≤ 15 mg/L, 15 <group 3 ≤ 20 mg/L, group4 > 20 mg/L. Categorical variables indicated that group 3 and group 4 had a significant relationship on the occurrence of AKI [group 3: (OR: 1.36; 95% CI: 1.02–1.81); group 4: (OR: 1.76; 95% CI: 1.32–2.35)] and ICU mortality [group 3: (HR: 1.47; 95% CI: 1.03–2.09); group 4: (HR: 1.87; 95% CI: 1.33–2.62)], compared to group 1, while group 4 had a significant effect on in-hospital mortality (HR: 1.48; 95% CI: 1.15–1.91).

Conclusions

FVTC is associated with the occurrence of AKI and increased ICU and in-hospital mortality in critically ill patients. Therefore, in clinical practice, patients in intensive care settings receiving vancomycin should be closely monitored for FVTC to prevent drug-related nephrotoxicity and reduce patient mortality.

A novel nomogram to predict mortality in patients with stroke: a survival analysis based on the MIMIC-III clinical database

Background

Stroke is a disease characterized by sudden cerebral ischemia and is the second leading cause of death worldwide. We aimed to develop and validate a nomogram model to predict mortality in intensive care unit patients with stroke.

Methods

All data involved in this study were extracted from the Medical Information Mart for Intensive Care III database (MIMIC-III). The data were analyzed using multivariate Cox regression, and the performance of the novel nomogram, which assessed the patient’s overall survival at 30, 180, and 360 days after stroke, was evaluated using Harrell’s concordance index (C-index) and the area under the receiver operating characteristic curve. A calibration curve and decision curve were introduced to test the clinical value and effectiveness of our prediction model.

Results

A total of 767 patients with stroke were randomly divided into derivation (n = 536) and validation (n = 231) cohorts at a 7:3 ratio. Multivariate Cox regression showed that 12 independent predictors, including age, weight, ventilation, cardiac arrhythmia, metastatic cancer, explicit sepsis, Oxford Acute Severity of Illness Score or OASIS score, diastolic blood pressure, bicarbonate, chloride, red blood cell and white blood cell counts, played a significant role in the survival of individuals with stroke. The nomogram model was validated based on the C-indices, calibration plots, and decision curve analysis results.

Conclusions

The plotted nomogram accurately predicted stroke outcomes and, thus may contribute to clinical decision-making and treatment as well as consultation services for patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01836-3.

Exploration of the Immuno-Inflammatory Potential Targets of Xinfeng Capsule in Patients with Ankylosing Spondylitis Based on Data Mining, Network Pharmacology, and Molecular Docking

Objective

This study aimed to ascertain the immuno-inflammatory molecular targets of Xinfeng capsules (XFC) in the treatment of ankylosing spondylitis (AS) based on data mining, network pharmacology, and molecular docking.

Methods

The efficacy of XFC in the treatment of AS was assessed by clinical data mining. Network pharmacology was utilized to establish a network of the targets for XFC active ingredients in the treatment of AS. The binding mode and affinity of XFC active ingredients to the key targets for AS were predicted using molecular docking.

Results

XFC significantly diminished immuno-inflammatory indicators of AS. In total, 208 targets of XFC were obtained from the TCMSP database and 629 disease targets of AS were screened from the GeneCards database, which were intersected to yield 57 targets of XFC in the treatment of AS. Protein-protein interaction, gene ontology, and Kyoto genome encyclopedia analyses showed that XFC might activate TNF and NF-κB signaling pathways. Quercetin, kaempferol, triptolide, and formononetin had free binding energies < -9 kcal/mol to inflammatory targets (TNF and PTGS2) in the molecular docking analysis of XFC-active ingredients, indicating that TNF and PTGS2 might be the targets of the action of XFC.

Conclusions

Collectively, XFC had a significant therapeutic effect on AS. Specifically, the active ingredients of XFC, including quercetin, kaempferol, triptolide, and formononetin, inhibited the inflammatory response in AS by downregulating TNF and PTGS2 in the TNF and NF-κB signaling pathways.

A Novel Nomogram for Predicting Morbidity Risk in Patients with Secondary Malignant Neoplasm of Bone and Bone Marrow: An Analysis Based on the Large MIMIC-III Clinical Database

Objective

Bone and bone marrow are the third most frequent sites of metastases from many cancers and are associated with low survival and high morbidity rates. Currently, there are no effective bedside tools to predict the morbidity risk of these patients in general intensive care units (ICUs). The main objective of this study was to establish and validate a nomogram to predict the morbidity risk of patients with bone and bone marrow metastases.

Methods

Data on patients with bone and bone marrow metastases were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The patients were divided into training and validation cohorts. The data were analyzed using univariate and multivariate Cox regression methods. Factors significantly and independently prognostic of survival were used to construct a nomogram predicting 30-day morbidity. The nomogram was validated by various methods, including Harrell’s concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, integrated discrimination improvement (IDI), net reclassification index (NRI), and decision curve analysis (DCA).

Results

The study included 610 patients in the training cohort and 262 in the validation cohort. Multivariate Cox regression analysis showed that temperature, SpO₂, Sequential Organ Failure Assessment (SOFA) score, Oxford Acute Severity of Illness Score (OASIS), comorbidities with coagulopathy, white blood cell count, heart rate, and respiratory rate were independent predictors of patient survival. The resulting nomogram had good discriminative ability, as shown by high AUCs, and was well calibrated, as demonstrated by calibration curves. Improvements in NRI and IDI values suggested that the nomogram was superior to the SOFA scoring system. DCA curves revealed that the nomogram showed good value in clinical applications.

Conclusion

This prognostic nomogram, based on demographic and laboratory parameters, was predictive of the 30-day morbidity rate in patients with secondary malignant neoplasms of the bone and bone marrow, suggesting its applicability in clinical practice.

Creation and Validation of a Survival Nomogram Based on Immune-Nutritional Indexes for Colorectal Cancer Patients

Nutritional and inflammatory status was associated with prognosis in various types of malignant cancer, including colorectal cancer (CRC). This clinical research was performed to estimate the prognostic role of immune-nutritional indexes CRC in patients and to set up a survival nomogram based on the significant immune-nutritional indexes. 1024 CRC patients underwent surgical resection from Wuhan Union Hospital were enrolled and divided into the test cohort (n = 717) and validation cohort (n = 307). A total of 19 immune-nutritional indexes were included into our analysis. The Cox regression analysis was utilized to identify the informative immune-nutritional indexes which were closely associated with overall survival (OS) and disease-free survival (DFS). Survival nomograms were created in the test set and further verified in the validation set. Td-ROC was curved to estimate the predictive performance of survival nomograms for CRC patients. Body mass index (BMI), chemotherapy, TNM stage, T stage, lactate dehydrogenase (LDH)/prealbumin (PA), monocytes (MON)/albumin (ALB), and prognostic nutritional index (PNI) were seven potent prognostic biomarkers of CRC patients. We created an OS-nomogram based on the seven risk indexes, and the predictive accuracy expressed with area under curve (AUC) was 0.826 for 1-year, 0.809 for 3-year, and 0.80 for 5-year OS rates in the test set and 0.795 for 1-year, 0.749 for 3-year, and 0.647 for 5-year OS rates in the validation set. TNM stage, T stage, LDH/ALB, and MON/ALB were risk factors for unfavorable DFS in CRC patients. We further built a DFS-nomogram based on the four risk factors, and the predictive performance presented with AUC was 0.806 for 1-year, 0.763 for 3-year, and 0.82 for 5-year DFS rates in the test set, and 0.704 for 1-year, 0.692 for 3-year, and 0.692 for 5-year DFS rates in the validation set. Our survival nomogram based on immune-nutritional indexes is a useful and potential prognostic tool in CRC patients.

Identification of miRNA signature for predicting the prognostic biomarker of squamous cell lung carcinoma

As explorations deepen, the role of microRNAs (miRNAs) in lung squamous cell carcinoma (LUSC), from its emergence to metastasis and prognosis, has elicited extensive concern. LUSC-related miRNA and mRNA samples were acquired from The Cancer Genome Atlas (TCGA) database. The data were initially screened and pretreated, and the R platform and series analytical tools were used to identify the specific and sensitive biomarkers. Seven miRNAs and 15 hub genes were found to be closely related to the overall survival of patients with LUSC. Determination of the expression of these miRNAs can help improve the overall survival of LUSC patients. The 15 hub genes correlated with overall survival (OS). The new miRNA markers were identified to predict the prognosis of LUSC. The findings of this study offer novel views on the evolution of precise cancer treatment approaches with high reliability.

Predicting ICU Mortality in Rheumatic Heart Disease: Comparison of XGBoost and Logistic Regression

Background

Rheumatic heart disease (RHD) accounts for a large proportion of Intensive Care Unit (ICU) deaths. Early prediction of RHD can help with timely and appropriate treatment to improve survival outcomes, and the XGBoost machine learning technology can be used to identify predictive factors; however, its use has been limited in the past. We compared the performance of logistic regression and XGBoost in predicting hospital mortality among patients with RHD from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

Methods

The patients with RHD in the MIMIC-IV database were divided into two groups retrospectively according to the availability of data and its clinical significance based on whether they survived or died. Backward stepwise regression was used to analyze the independent factors influencing patients with RHD, and to compare the differences between the two groups. The XGBoost algorithm and logistic regression were used to establish two prediction models, and the areas under the receiver operating characteristic curves (AUCs) and decision-curve analysis (DCA) were used to test and compare the models. Finally, DCA and the clinical impact curve (CIC) were used to validate the model.

Results

Data on 1,634 patients with RHD were analyzed, comprising 207 who died during hospitalization and 1,427 survived. According to estimated results for the two models using AUCs [0.838 (95% confidence interval = 0.786–0.891) and 0.815 (95% confidence interval = 0.765–0.865)] and DCA, the logistic regression model performed better. DCA and CIC verified that the logistic regression model had convincing predictive value.

Conclusions

We used logistic regression analysis to establish a more meaningful prediction model for the final outcome of patients with RHD. This model might be clinically useful for patients with RHD and help clinicians to provide detailed treatments and precise management.

Nomogram to predict cause-specific mortality of patients with rectal adenocarcinoma undergoing surgery: a competing risk analysis

BACKGROUND

Rectal adenocarcinoma is one of major public health problems, severely threatening people's health and life. Cox proportional hazard models have been applied in previous studies widely to analyze survival data. However, such models ignore competing risks and treat them as censored, resulting in excessive statistical errors. Therefore, a competing-risk model was applied with the aim of decreasing risk of bias and thereby obtaining more-accurate results and establishing a competing-risk nomogram for better guiding clinical practice.

METHODS

A total of 22,879 rectal adenocarcinoma cases who underwent primary-site surgical resection were collected from the SEER (Surveillance, Epidemiology, and End Results) database. Death due to rectal adenocarcinoma (DRA) and death due to other causes (DOC) were two competing endpoint events in the competing-risk regression analysis. The cumulative incidence function for DRA and DOC at each time point was calculated. Gray's test was applied in the univariate analysis and Gray's proportional subdistribution hazard model was adopted in the multivariable analysis to recognize significant differences among groups and obtain significant factors that could affect patients' prognosis. Next, A competing-risk nomogram was established predicting the cause-specific outcome of rectal adenocarcinoma cases. Finally, we plotted calibration curve and calculated concordance indexes (c-index) to evaluate the model performance.

RESULTS

22,879 patients were included finally. The results showed that age, race, marital status, chemotherapy, AJCC stage, tumor size, and number of metastasis lymph nodes were significant prognostic factors for postoperative rectal adenocarcinoma patients. We further successfully constructed a competing-risk nomogram to predict the 1-year, 3-year, and 5-year cause-specific mortality of rectal adenocarcinoma patients. The calibration curve and C-index indicated that the competing-risk nomogram model had satisfactory prognostic ability.

CONCLUSION

Competing-risk analysis could help us obtain more-accurate results for rectal adenocarcinoma patients who had undergone surgery, which could definitely help clinicians obtain accurate prediction of the prognosis of patients and make better clinical decisions.

A novel LASSO-derived prognostic model predicting survival for non-small cell lung cancer patients with M1a diseases

Introduction

The current American Joint Committee on Cancer (AJCC) M1a staging of non‐small cell lung cancer (NSCLC) encompasses a wide disease spectrum, showing diverse prognosis.

Methods

Patients who diagnosed in an earlier period formed the training cohort, and those who diagnosed thereafter formed the validation cohort. Kaplan–Meier analysis was performed for the training cohort by dividing the M1a stage into three subgroups: (I) malignant pleural effusion (MPE) or malignant pericardial effusion (MPCE); (II) separate tumor nodules in contralateral lung (STCL); and (III) pleural tumor nodules on the ipsilateral lung (PTIL). Gender, age, histologic, N stage, grade, surgery for primary site, lymphadenectomy, M1a groups, and chemotherapy were selected as independent prognostic factors using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. And a nomogram was constructed using Cox hazard regression analysis. Accuracy and clinical practicability were separately tested by Harrell's concordance index, the receiver operating characteristic (ROC) curve, calibration plots, residual plot, the integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

The concordance index (0.661 for the training cohort and 0.688 for the validation cohort) and the area under the ROC curve (training cohort: 0.709 for 1‐year and 0.727 for 2‐year OS prediction; validation cohort: 0.737 for 1‐year and 0.734 for 2‐year OS prediction) indicated satisfactory discriminative ability of the nomogram. Calibration curve and DCA presented great prognostic accuracy, and clinical applicability. Its prognostic accuracy preceded the AJCC staging with evaluated NRI (1‐year: 0.327; 2‐year: 0.302) and IDI (1‐year: 0.138; 2‐year: 0.130).

Conclusion

Our study established a nomogram for the prediction of 1‐ and 2‐year OS in patients with NSCLC diagnosed with stage M1a, facilitating healthcare workers to accurately evaluate the individual survival of M1a NSCLC patients. The accuracy and clinical applicability of this nomogram were validated.

Anatomic Subsites and Prognosis of Gastric Signet Ring Cell Carcinoma: A SEER Population-Based 1 : 1 Propensity-Matched Study

Background. The dismal prognosis of gastric signet ring cell carcinoma (GSRC) is a global problem. The current study is conducted to comprehensively evaluate clinicopathological features and survival outcomes in GSRC patients stratified by anatomic subsites. Then, predictive nomograms are constructed and validated to improve the effectiveness of personalized management. Method. The patients diagnosed with GSRC were recruited from the online SEER database. The influence of anatomic subsites on overall survival (OS) and cancer-specific survival (CSS) was evaluated using multivariate Cox regression and Kaplan-Meier analysis. Then, we employed propensity score matching (PSM) technique to decrease selection bias and balance patients’ epidemiological factors. Predictive nomograms were constructed and validated. Sensitivity analysis was performed to validate the conclusion. Results. Multivariate Cox regression demonstrated that the patients with overlapping gastric cancer (OGC) suffered the highest mortality risk for OS (HR, 1.29; 95% CI, 1.23-1.36; $P<0.001$ ) and CSS (HR, 1.33; 95% CI, 1.28-1.37; $P<0.001$ ). Age, TNM stage, tumor localization, tumor size, surgery, and chemotherapy presented a highly significant relationship with OS and CSS. Following subgroup and PSM analysis, OGC patients were confirmed to have the worst OS and CSS. Then, nomograms predicting 6-month, 12-month, and 36-month survival were constructed. The area under the curve (AUC) value in ROC was 0.775 (95% CI, 0.761-0.793) for 6-month survival, 0.789 (95% CI, 0.776-0.801) for 12-month survival, and 0.780 (95% CI, 0.765-0.793) for 36-month survival in the OS group, while in the CSS group, it was 0.771 (95% CI, 0.758-0.790) for 6-month survival, 0.781 (95% CI, 0.770-0.799) for 12-month survival, and 0.773 (95% CI, 0.762-0.790) for 36-month survival. Conclusion. We identified anatomic subsites as a predictor of survival in those with GSRC. Patients with OGC suffered the highest mortality risk. The proposed nomograms allowed a relatively accurate survival prediction for GSRC patients.

Research on the Role of Marriage Status Among Women Underwent Breast Reconstruction Following Mastectomy: A Competing Risk Analysis Model Based on the SEER Database, 1998–2015

Background

Marital status is an important foundation of social public relations in modern society, but little is known about the role of marriage status among women who underwent breast reconstruction following mastectomy. This research mainly aimed to investigate the prognostic value of marital status in breast cancer women who underwent breast reconstruction.

Methods

The demographic and clinical data of patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program database. The eligible population was assessed on overall survival (OS), breast cancer-specific survival (BCSS), and breast cancer-specific death (BCSD) through propensity score matching (PSM) method, multivariate Cox proportional hazards model analysis, competing risk model analysis, multivariate competing risk regression model analysis, and subgroup analysis.

Results

Of the 54,683 women included in the current study, a total of 38,110 participants were married patients (married group), and 16,573 participants were unmarried patients (unmarried group). Patients in the married group tended to have better OS (hazard ratio [HR] = 1.397, 95% CI: 1.319–1.479, p < 0.001), BCSS (HR = 1.332, 95% CI: 1.244–1.426, p < 0.001), cumulative BCSD incidence (Gray's test, p < 0.001), and other causes-specific death (OCSD) incidence (Gray's test, p < 0.001) than those in the unmarried group. In subgroup analysis, subjects with HR+/HER2– subtype breast cancer in the married group showed improved OS (1.589, 95% CI: 1.363–1.854, p < 0.001) and BCSS (HR = 1.512, 95% CI: 1.255–1.82, p < 0.001) than those in the unmarried group.

Conclusions

Our study demonstrated that the inexistence of marriage was associated with poorer OS and BCSS, especially for HR+/HER2– breast cancer women who underwent breast reconstruction.

Red Cell Distribution Width to Platelet Ratio Is Associated with Increasing In-Hospital Mortality in Critically Ill Patients with Acute Kidney Injury

Background. Inflammation plays a key role in the pathophysiology and progression of acute kidney injury (AKI). Red cell distribution width (RDW) to platelet ratio (RPR) is a novel inflammatory index, and its prognostic effect on critically ill patients with AKI is rarely investigated. This work is aimed at investigating the association between RPR and in-hospital mortality in these patients. Methods. Data were extracted from the Medical Information Mart for Intensive Care III database. All-cause death during hospitalization was selected as the primary outcome. Receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value, and the area under the curve (AUC) was applied to compare predictive ability among different indices. Cox proportional hazard models were utilized to assess the association between RPR and in-hospital mortality. Restricted cubic spline analysis for multivariate Cox model was performed to explore the shape of the relationship between RPR and mortality. Results. A total of 24,166 critically ill patients with AKI were included. The relationship of RPR and in-hospital mortality was nonlinear with a trend to rise rapidly and then gradually. For mortality prediction, RPR had the optimal cut-off value of 0.093, of which the AUC was 0.791 (95% confidence interval (CI): 0.773–0.810), which was higher than those of RDW, platelet, sequential organ failure assessment score, simplified acute physiology score II, neutrophil to lymphocyte ratio, and platelet to lymphocytes ratio. After adjustments for various confounders, high RPR showed a significant association with increased mortality with hazard ratios of 1.46 (95% CI: 1.40–1.55) for categorical variable and 1.88 (95% CI: 1.80–1.97) for continuous variables in the fully adjusted model. Conclusions. Elevated RPR on admission is substantially associated with high risk of in-hospital mortality in critically ill patients with AKI and thus may serve as a novel predictor of prognosis for these patients.

Midlife brain metastases in the United States: Is male at risk?

Background

Population‐based estimates of the impact of gender throughout the whole course of brain metastases (BMs) at the time of diagnosis of systemic malignancies are insufficient. We aimed to discover the influence of gender on the presence of BMs in newly diagnosed malignancies and the survival of those patients on a population‐based level.

Methods

Midlife patients (40 years ≤ age ≤60 years) with newly diagnosed malignancies and BMs at the time of diagnosis were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute. Clinical variables adjusted patient data. The LASSO regression was performed to exclude the possibility of collinearity. Univariable and multivariable logistic regression analyses were applied to find independent predictors for the presence of BMs, while univariable and multivariable Cox proportional hazard regression analyses were used to determine prognosticators of survival. K‐M curves were used to perform the survival analysis.

Result

276,327 population‐based samples met inclusion criteria between 2014 and 2016, and 5747 (2.08%) patients were diagnosed with BMs at the time of diagnosis of systematic malignancies. Among all midlife patients with cancer, 44.02% (121,634) were male, while 51.68% (2970) were male among patients with BMs at the time of diagnosis. The most frequent tumor type was breast cancer (23.11%), and lung cancer had the highest incidence proportion of BMs among the entire cohort (19.34%). The multivariable logistic regression model suggested that female (vs. male, odds ratio [OR] 1.07, 95% CI: 1.01–1.14, p < 0.001) was associated with a higher risk of the presence of BMs at the time of diagnosis. Moreover, in the multivariable Cox model for all‐cause mortality in individuals with BMs at diagnosis, female (vs. male, hazard ratio [HR], 0.86, 95% CI, 0.80–0.92, p < 0.001) was shown to have a lower risk of decreased all‐cause mortality.

Conclusion

The middle‐aged females were at increased risk of developing BMs, while the middle‐aged males with BMs were at higher risk of having poorer survival.