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

Exploring the mechanism of luteolin improving immune and inflammatory responses in systemic sclerosis based on systems biology and cell experiments

There is a growing trend of applying traditional Chinese medicine (TCM) to treat immune diseases. This study reveals the possible mechanism of luteolin, an active ingredient in the core prescription of TCM, in alleviating systemic sclerosis (SSc) inflammation. Bibliometrics was performed to retrieve the core keywords of SSc inflammation. The key inflammatory indicators in the serum samples of 50 SSc patients were detected by ELISA. Data mining was applied for correlation analysis, association rule analysis, and binary logistic regression analysis on the clinical indicators and medication of 50 SSc patients before and after treatment to determine the core prescription. Network pharmacology was used for identifying candidate genes and pathways; molecular docking was conducted to determine the core monomer components of the prescription, providing a basis for subsequent in vitro molecular mechanism research. The effect of luteolin on SSc-human dermal fibroblasts (HDF) viability and inflammatory factors was evaluated by means of ELISA, RT-PCR, and Western blot. The role of TNF in inflammation was explored by using a TNF overexpression vector, NF-κB inhibitor (PKM2), and SSc-HDF. The involvement of TNF/NF-κB pathway was validated by RT-PCR, Western blot, and immunofluorescence. TCM treatment partially corrected the inflammatory changes in SSc patients, indicating its anti-inflammatory effects in the body. Atractylodes, Yam, Astragalus root, Poria cocos, Pinellia ternata, Salvia miltiorrhiza, Safflower, Cassia twig, and Angelica were identified as the core prescriptions for improving inflammatory indicators. Luteolin was the main active ingredient in the prescription and showed a strong binding energy with TNF and NF-κB. Luteolin exerted anti-inflammatory effects in vitro by reducing inflammatory cytokines in SSc-HDF and inhibiting the activation of TNF/NF-κB. Mechanistically, luteolin inhibited the activation of the TNF/NF-κB pathway in SSc-HDF, as manifested by an increase in extranuclear p-P65 and TNF but a decrease in intranuclear p-P65. Interestingly, the addition of PKM2 augmented the therapeutic function of luteolin against inflammation in SSc-HDF. Our study showed the TCM alleviates the inflammatory response of SSc by inhibiting the activation of the TNF/NF-κB pathway and is an effective therapeutic agent for the treatment of SSc.

Differential expression of long non-coding RNAs in colon cancer: Insights from transcriptomic analysis

BACKGROUND

Colon Cancer (CC) incidence has sharply grown in recent years. Long non-coding RNAs (lncRNA) are produced by a group of non-protein-coding genes, and have important functions in controlling gene expression and impacting the biological features of various malignancies including CC.

METHODS

Our research focused on examining the function of lncRNAs in the development of colon cancer. To this end, we selected and analyzed a dataset (GSE104836) from the GEO database, which contained information about the expression of mRNAs and lncRNAs in both colon cancer tissues and normal adjacent paired tumor tissues. The DESeq2 R package in Bioconductor was used to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) that showed differences in expression levels. Next, by literature review of previous studies, we chose two lncRNAs (FENDRR and LINC00092) for additional studies. To validate our findings, a series of tests were performed on a total of 31 tumor tissues and normal paired adjacent tumor tissues. The lncRNA expression levels were assessed in tumor tissues as well as in surrounding normal tumor tissues.

RESULTS

The data confirmed that just two particular lncRNAs, FENDRR and LINC00092, had considerably decreased expression levels throughout all stages of cancer. In addition, the survival assay was conducted using the GEPIA2 software, revealing that a reduced expression of FENDRR is correlated with a reduced overall survival. Furthermore, our investigation using receiver operating characteristic (ROC) methodology revealed that these two lncRNAs had significant discriminatory ability between colon cancer and normal tissues. To determine the cause of the decrease in the activity of these two long non-coding RNAs (lncRNAs), we used methylation-specific PCR (MSP) to examine the methylation pattern of their promoter regions. Our investigation revealed hypermethylation in the promoter regions of FENDRR and LINC00092 within tumor tissues compared to normal adjacent tumor tissues.

CONCLUSION

Taken together, our findings revealed the lncRNAs signatures as potential therapeutic targets and molecular diagnostic biomarkers in colon cancer. Furthermore, the evidence provided substantiates the important role of promoter methylation in regulating the expression levels for both of these lncRNAs.

High cardiovascular mortality risk among older merkel cell carcinoma patients

OBJECTIVE

Previous research has primarily focused on the incidence and mortality rates of Merkel cell carcinoma (MCC), neglecting the examination of cardiovascular mortality (CVM) risk among survivors, particularly older patients. This study aims to assess the risk of CVM in older individuals diagnosed with MCC.

METHODS

Data pertaining to older MCC patients were obtained from the Surveillance, Epidemiology, and End Results database (SEER). CVM risk was measured using standardized mortality ratio (SMR) and cumulative mortality. Multivariate Fine-Gray's competing risk model was utilized to evaluate the risk factors contributing to CVM.

RESULTS

Among the study population of 2,899 MCC patients, 465 (16.0%) experienced CVM during the follow-up period. With the prolongation of the follow-up duration, the cumulative mortality rate for CVM reached 27.36%, indicating that cardiovascular disease (CVD) became the second most common cause of death. MCC patients exhibited a higher CVM risk compared to the general population (SMR: 1.69; 95% CI: 1.54-1.86, p < 0.05). Notably, the SMR for other diseases of arteries, arterioles, and capillaries displayed the most significant elevation (SMR: 2.69; 95% CI: 1.16-5.29, p < 0.05). Furthermore, age at diagnosis and disease stage were identified as primary risk factors for CVM, whereas undergoing chemotherapy or radiation demonstrated a protective effect.

CONCLUSION

This study emphasizes the significance of CVM as a competing cause of death in older individuals with MCC. MCC patients face a heightened risk of CVM compared to the general population. It is crucial to prioritize cardiovascular health starting from the time of diagnosis and implement personalized CVD monitoring and supportive interventions for MCC patients at high risk. These measures are essential for enhancing survival outcomes.

External validation of the iScore, ASTRAL score, DRAGON score, and THRIVE score and development of a nomogram to predict outcome in patients with large vessel occlusion-acute ischemic stroke

OBJECTIVE

This study aimed to validate the iScore, ASTRAL score, DRAGON score, and THRIVE score for assessing large vessel occlusion-acute ischemic stroke (AIS-LVO) and establish a predictive model for AIS-LVO patients that has better performance to guide clinical practice.

METHODS

We retrospectively included 439 patients with AIS-LVO and collected baseline data from all of them. External validation of the iScore, ASTRAL score, DRAGON score, and THRIVE score was performed. All variables were compared between groups via univariate analysis, and the results are expressed as ORs and 95 % CIs. Independent variables with P < 0.25 were included in the multivariate logistic analysis, and statistically significant differences (P < 0.05) were identified as risk factors for prognosis in AIS-LVO patients. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive value of our model.

RESULTS

Our external validation resulted in an iScore under the curve (AUC) of 0.8475, an ASTRAL AUC of 0.8324, a DRAGON AUC of 0.8196, and a THRIVE AUC of 0.8039. In our research, multivariate Cox regression revealed 8 independent predictors. We used a nomogram to visualize the results of the data analysis. The AUC for the training cohort was 0.8855 (95 % CI, 0.8487-0.9222), and that in the validation cohort was 0.8992 (95 % CI, 0.8496-0. 9488).

CONCLUSIONS

In this study, we verified that the above scores have excellent efficacy in predicting the prognosis of AIS-LVO patients. The nomogram we developed was able to predict the prognosis of AIS-LVO more accurately and may contribute to personalized clinical decision-making and treatment for future clinical work.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period

BACKGROUND

Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period.

METHODS

We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values.

RESULTS

A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality.

CONCLUSIONS

The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Secular trends in incidence and mortality of non-Hodgkin's lymphoma in China, 1990-2019, and predictions to 2030: Outlook for the future burden of disease

BACKGROUND

The aim of this study was to analyze the trend of non-Hodgkin's lymphoma incidence and mortality in China from 1990 to 2019, along with assessing the effects of age, period, and cohort, as well as to predict future trends.

MATERIAL AND METHODS

Using data from the Global Burden of Disease Study 2019 we calculated the estimated annual percentage changes in the incidence and mortality of non-Hodgkin's lymphoma. Age-period-cohort analysis was used to assess the independent effects of these elements. Incidence and mortality until 2030 were predicted using a Bayesian age-period-cohort approach.

RESULTS

During 1990-2019, there was a significant increase in the age-standardized incidence and mortality rate in non-Hodgkin's lymphoma. Strong effects of birth cohort and period on non-Hodgkin's lymphoma incidence and mortality were observed. In terms of prediction, future non-Hodgkin's lymphoma incidence and mortality in China will continue to increase, while the mortality rate will decrease; for women, both the rates are projected to rise, but they will remain lower than men.

CONCLUSIONS

Currently, the non-Hodgkin's lymphoma burden is high in China, and it is expected to continue increasing in the future. Policymakers need to prioritize addressing the factors contributing to sex differences in disease burden, including variations in environmental exposures and lifestyles among men and women.

Analyzing risk factors for second malignancies in early gastric carcinoma from the SEER database

This retrospective study analyzed a large population of gastric cancer (GC) patients treated between 2010 and 2015 to investigate the clinical features and predictive risk factors for developing secondary primary malignancies (SPMs). The cumulative incidence of SPM was assessed using Kaplan-Meier analysis. Competing risk analyses adjusted for mortality were conducted using stratified Cox proportional hazard regression models and multivariate analyses to identify independent predictors of SPM. A total of 3289 out of 167,747 GC patients were included in the analytic cohort, with 155 patients diagnosed with SPM. Patients whose histologic type other than adenocarcinomas (AC) and signet ring cell carcinoma (SRCC) emerged as an independent risk factor for developing SPM (hazard ratio [HR] 2.262, 95% confidence interval [CI] 1.146-4.465, P = 0.019) in multivariate Cox regression analysis. The surgical method, including biopsy/local excision (HR 2.3, [CI] 1.291-4.095, P = 0.005) and subtotal/total resection ([HR] 1.947, [CI] 1.028-3.687, P = 0.041), chemotherapy ([HR] 1.527, [CI] 1.006-2.316, P = 0.047), and histologic type ([HR] 2.318, [CI] 1.193-4.504, P = 0.013)), were identified as independent risk factors in the competitive risk model. Subgroup analyses, stratified by chemotherapy, revealed an increased risk of SPM among older patients. Furthermore, a nomogram was developed and internally validated to predict the cumulative incidence of SPM in GC patients (C-index = 0.73 for 72 months). These findings suggested that in specific histologic types of GC, the lymph node infiltration region missed after local surgical resection, and concomitant chemotherapy would have an increased risk of SPM for cancer survivors.

Prognostic nomogram for predicting the overall survival rate of patients with uterine clear-cell carcinoma: Based on SEER database

OBJECTIVE

To evaluate the risk factors for uterine clear-cell carcinoma (UCCC) and construct nomograms predicting 1-, 3-, and 5-year overall survival rates of patients with UCCC.

METHODS

The demographic and clinical information of 1674 patients diagnosed with UCCC between 2004 and 2015, including age, race, marital status, tumor size, American Joint Committee on Cancer (AJCC) stage, and details of surgery and radiotherapy/chemotherapy, was collected from the Surveillance, Epidemiology, and End Results (SEER) database. After excluding patients with unknown AJCC stage, race, marital status, or lymph node information, 1469 patients remained. Risk factors were determined using univariate and multivariate analyses, and nomograms were developed to predict 1-, 3-, and 5-year overall survival of UCCC. Various indicators were used to evaluate the performance of the nomogram, such as the C-index, net classification improvement (NRI) and decision curve analysis (DCA).

RESULTS

Age, log odds of positive lymph nodes, AJCC stage, surgery status, and chemotherapy status were independent risk factors for UCCC. The C-indexes of the training group and AJCC stage groups were 0.771 and 0.697, respectively. The results for the area under the receiver operating characteristics curve, NRI, and calibration curves indicated that the nomogram had good predictive ability. DCA revealed that the nomogram had greater clinical applicability than AJCC stage alone. Internal validation using the validation cohort also demonstrated that this nomogram had good predictive performance.

CONCLUSION

A new nomogram comprising a combination of demographic and clinical characteristics provided better survival predictions than the AJCC staging system alone, which will facilitate prognostic assessments and clinical decision-making.

Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer

Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal cancer. Limited research has been conducted on how CRLM develops. RNA sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Four machine learning algorithms were used to screen the hub CRLM-specific genes, including Least Absolute Shrinkage and Selection Operator (Lasso), Random forest, SVM-RFE, and XGboost. The model for identifying CRLM was developed using stepwise logistic regression and was validated using internal and independent datasets. The prognostic value of hub CRLM-specific genes was assessed using the Lasso-Cox method. The in vitro experiments were performed using SW620 cells. The CRLM identification model was developed based on four CRLM-specific genes (SPP1, ZG16, P2RY14, and PRKAR2B), and the model efficacy was validated using GSE41258 and three external cohorts. Five CRLM-specific prognostic hub genes, SPP1, ZG16, P2RY14, CYP2E1, and C5, were identified using the Lasso-Cox algorithm, and a risk score was constructed. The risk score was validated using the GSE39582 cohort. Three genes have both efficacy in identifying CRLM and prognostic value: ZG16, P2RY14, and SPP1. Immune infiltration and enrichment analyses demonstrated that SPP1 was associated with M2 macrophage polarization and extracellular matrix remodeling. In vitro experiments indicated that SPP1 may act as a cancer-promoting factor. The hub CRLM-specific gene SPP1 can help determine the diagnosis, prognosis, and immune infiltration of patients with CRLM.

Establishment of nomogram to predict overall survival and cancer-specific survival of local tumor resection in patients with colorectal cancer liver metastasis with unresectable metastases: a large population-based analysis

BACKGROUND AND PURPOSE

The tumour-node metastasis (TNM) classification is a common model for evaluating the prognostic value of tumour patients. However, few models have been used to predict the survival outcomes of patients with colorectal cancer liver metastasis (CRLM) with unresectable metastases who received the primary local surgery. Thus, we utilized the Surveillance, Epidemiology, and End Results (SEER) database to establish novel nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of these patients.

METHODS

Extracted primary data on CRLM patients by local surgery from SEER database. All prognostic factors of OS and CSS were determined by Cox regression analysis. The concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were used to further evaluate the accuracy and discrimination of these nomograms. Decision curve analysis (DCA) was executed to evaluate the nomograms for the clinical net benefit. Risk stratification analysis (RSA) was used to evaluate the reliability of them in clinical.

RESULTS

3622 eligible patients were screened and assigned to training cohort (1812) or validation cohort (1810). The age, chemotherapy, tumour grade, primary tumour site, tumour size, lymph node positive rate (LNR), marital status, and carcinoembryonic antigen (CEA) were independent prognostic factors of OS. Additionally, the age, chemotherapy, tumour grade, primary tumour site, tumour size, LNR, and CEA were independent prognostic factors of CSS. The results of C-indexes and ROC curves indicated that the established nomograms exhibited better discrimination power than TNM classification. The calibration curves demonstrated excellent agreement between the predicted and actual survival rates for 1-, 3-, and 5 year OS and CSS. Meanwhile, the validation cohort demonstrated similar results. Background the clinic context, the DCA showed that these nomograms have higher net benefits, and the RSA showed that patients were further divided into low risk, medium risk, and high risk groups according to the predicted scores from nomograms. And, the Kaplan-Meier curve and log-rank test showed that the survival differences among the three groups are statistically significant.

CONCLUSIONS

The prognostic nomograms showed very high accuracy, identifiability, and clinical practicality in predicting the OS and CSS of CRLM patients with unresectable metastases treated by local surgery at 1-, 3-, and 5 years, which might improve individualized predictions of survival risks and help clinicians formulate treatment plans.

Braden score predicts 30-day mortality risk in patients with ischaemic stroke in the ICU: A retrospective analysis based on the MIMIC-IV database

BACKGROUND

Ischaemic stroke remains a significant global health challenge, associated with high mortality rates. While the Braden Scale is traditionally employed to assess pressure ulcer risk, its potential to predict mortality among the intensive care unit (ICU) patients with ischaemic stroke has not been thoroughly investigated.

AIM/S

This study evaluates the predictive value of the Braden Scale for 30-day mortality among patients with ischaemic stroke admitted to ICU.

STUDY DESIGN

We conducted a retrospective analysis of 4710 adult patients with ischaemic stroke from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The association between the Braden Scale scores and 30-day mortality was assessed using receiver operating characteristic (ROC) curve analysis, Cox regression models and Kaplan-Meier survival estimates.

RESULTS

Patients with Braden Scale scores ≤ 15.5 showed significantly higher 30-day mortality rates (p-value < 0.001; hazard ratio (HR): 2.08, 95% confidence interval (CI): 1.71-2.53). The area under the ROC curve (AUC) was 0.71, demonstrating good predictive performance. Multivariate analysis confirmed the Braden Scale as an independent predictor of mortality, after adjusting for age, gender and comorbidities.

CONCLUSIONS

The Braden Scale effectively identifies high-risk ischaemic stroke patients in ICU settings, endorsing its integration into routine assessments to facilitate early intervention strategies.

RELEVANCE TO CLINICAL PRACTICE

Integrating the Braden Scale into routine ICU evaluations can enhance mortality risk stratification and improve patient care tailoring.

Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database

BACKGROUND

Mitral valve disorder (MVD) stands as the most prevalent valvular heart disease. Presently, a comprehensive clinical index to predict mortality in MVD remains elusive. The aim of our study is to construct and assess a nomogram for predicting the 28-day mortality risk of MVD patients.

METHODS

Patients diagnosed with MVD were identified via ICD-9 code from the MIMIC-III database. Independent risk factors were identified utilizing the LASSO method and multivariate logistic regression to construct a nomogram model aimed at predicting the 28-day mortality risk. The nomogram's performance was assessed through various metrics including the area under the curve (AUC), calibration curves, Hosmer-Lemeshow test, integrated discriminant improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

RESULTS

The study encompassed a total of 2771 patients diagnosed with MVD. Logistic regression analysis identified several independent risk factors: age, anion gap, creatinine, glucose, blood urea nitrogen level (BUN), urine output, systolic blood pressure (SBP), respiratory rate, saturation of peripheral oxygen (SpO2), Glasgow Coma Scale score (GCS), and metastatic cancer. These factors were found to independently influence the 28-day mortality risk among patients with MVD. The calibration curve demonstrated adequate calibration of the nomogram. Furthermore, the nomogram exhibited favorable discrimination in both the training and validation cohorts. The calculations of IDI, NRI, and DCA analyses demonstrate that the nomogram model provides a greater net benefit compared to the Simplified Acute Physiology Score II (SAPSII), Acute Physiology Score III (APSIII), and Sequential Organ Failure Assessment (SOFA) scoring systems.

CONCLUSION

This study successfully identified independent risk factors for 28-day mortality in patients with MVD. Additionally, a nomogram model was developed to predict mortality, offering potential assistance in enhancing the prognosis for MVD patients. It's helpful in persuading patients to receive early interventional catheterization treatment, for example, transcatheter mitral valve replacement (TMVR), transcatheter mitral valve implantation (TMVI).

Construction and Validation of Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Patients with Primary Anaplastic Oligodendroglioma

OBJECTIVE

Anaplastic oligodendroglioma (AOD) is a rare high-grade central nervous system tumor. The current research on prognostic prediction of AOD remains limited. This study aimed to identify prognostic factors and establish the nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for patients with AOD.

METHODS

Patients diagnosed with AOD between 1992 and 2020 were extracted from the Surveillance, Epidemiology, and End Result database. We performed univariate and multivariate Cox regression analyses to identify independent prognostic factors based on the training group. Kaplan-Meier survival curves were used to compare the impact of various independent factors on patient prognosis. For OS and CSS, the nomograms were constructed and verified by the validation group. Harrell''s concordance index, receiver operating characteristic curves, calibration curves, and decision curve analyses were used to assess the discrimination, consistency, and clinical value of the nomograms.

RESULTS

A total of 1202 AOD patients were enrolled, being randomly divided into training (n = 841) and validation (n = 361) groups (7:3 ratio). Univariate and multivariate Cox analysis identified 4 significant independent factors (tumor site, age, surgery, and chemotherapy). For OS and CSS, Harrell''s concordance index were 0.731 (0.705-0.757) and 0.728 (0.701-0.754) in the training group, 0.688 (0.646-0.731) and 0.684 (0.639-0.729) in the validation group, respectively. Receiver operating characteristic curves and Calibration curves showed good discrimination and consistency, respectively. In addition, the decision curve analyses curves showed the nomograms have good clinical benefits.

CONCLUSIONS

We successfully established the nomograms to predict the OS and CSS for AOD patients. The nomograms showed good performance in prognostic prediction, assisting clinicians in evaluating patient prognosis and personalizing treatment plans.

Construction of neural network model for exercise load monitoring based on yoga training data and rehabilitation therapy

The Internet of Things is based on the traditional Internet and its purpose is to achieve information exchange between users and devices, as well as between devices. The rapid development of sensor technology, communication network technology, and computer technology has enriched the coverage of the Internet of Things, including a wide range of intelligent applications such as healthcare, smart cities, and smart homes. The development of high-performance computing and machine learning technologies has promoted the wide application of intelligent auxiliary systems in sports medicine. With the rapid development of yoga in the field of sports, athletes can play the various functions of yoga, improve their physical strength and quality, and improve their strength, flexibility, etc., cultivate positive, optimistic, and healthy emotions, and these are conducive to rehabilitation treatment after sports injuries. Therefore, it is feasible and feasible to introduce yoga training into the monitoring of the exercise load of athletes. In this paper, neural network technology was used to break the traditional training method based on experience. Based on yoga training data, through experimental exercise research, it could explore a new effective way to monitor exercise load and rehabilitation treatment, and build an exercise load monitoring model of the Ant Colony Optimization (ACO) neural network. By sorting out the data, statistics and analysis of the data, this article confirmed the effect of yoga training on reducing fatigue after exercise. The experimental results showed that the prediction value obtained by the ACO neural network model was 9.106, and the error was only -0.003 compared to the actual detection value of 9.109. This result showed that the ACO neural network model can perfectly fit the functional relationship between yoga training level and exercise load and has high prediction accuracy. This also marked that the development of high-performance computing systems has entered a new journey in the field of sports and health.

Age-specific differences in association of glycosylated hemoglobin levels with the prevalence of cardiovascular diseases among nondiabetics: the National Health and Nutrition Examination Survey 2005-2018

BACKGROUND

Previous research has supported the presence of an association between high glycated hemoglobin (HbA1c) levels and cardiovascular disease (CVD). The objective of the present study was to determine whether increased HbA1c levels are associated with high CVD prevalence among nondiabetics. Furthermore, we aimed to explore the possible interaction of HbA1c levels and age in regard to CVD.

METHODS

This cross-sectional study analyzed data of 28,534 adult participants in the National Health and Nutrition Examination Survey 2005-2018. The association between HbA1c and CVD was assessed using univariate and multivariate logistic regression models. Propensity score matching was used to reduce selection bias. Subgroup analysis and restricted cubic spline (RCS) were used to further characterize the association between HbA1c levels and CVD. We modeled additive interactions to further assess the relationship between HbA1c levels and age.

RESULTS

In the multivariate logistic regression model, a positive association was found between CVD and increased HbA1c levels (highest quartile [Q4] vs. lowest quartile [Q1]: odds ratio [OR] = 1.277, 95% confidence interval [CI] = 1.111-1.469, P = 0.001). In the stratified analyses, the adjusted association between HbA1c and CVD was significant for those younger than 55 years (Q4 vs. Q1: OR = 1.437, 95% CI = 1.099-1.880, P = 0.008). RCS did not reveal a nonlinear relationship between HbA1c levels and CVD among nondiabetics (P for nonlinearity = 0.609). Additionally, a high HbA1c level was favorably connected with old age on CVD, with a synergistic impact.

CONCLUSIONS

Increased HbA1c levels were associated with high CVD prevalence among nondiabetics. However, we still need to carefully explain the effect of age on the relationship between HbA1c and CVD in nondiabetic population. Given the correlations of HbA1c with CVDs and CV events, HbA1c might be a useful indicator for predicting CVDs and CV events in the nondiabetic population.

Association between modified frailty index and postoperative delirium in patients after cardiac surgery: A cohort study of 2080 older adults

AIM

To evaluate the association between frailty and postoperative delirium (POD) in elderly cardiac surgery patients.

METHODS

A retrospective study was conducted of older patients admitted to the intensive care unit after cardiac surgery at a tertiary academic medical center in Boston from 2008 to 2019. Frailty was measured using the Modified Frailty Index (MFI), which categorized patients into frail (MFI ≥3) and non-frail (MFI = 0-2) groups. Delirium was identified using the confusion assessment method for the intensive care unit and nursing notes. Logistic regression models were used to examine the association between frailty and POD, and odds ratios (OR) with 95% confidence intervals (CI) were calculated.

RESULTS

Of the 2080 patients included (median age approximately 74 years, 30.9% female), 614 were frail and 1466 were non-frail. The incidence of delirium was significantly higher in the frail group (29.2% vs. 16.4%, p < 0.05). After adjustment for age, sex, race, marital status, Acute Physiology Score III (APSIII), sequential organ failure assessment (SOFA), albumin, creatinine, hemoglobin, white blood cell count, type of surgery, alcohol use, smoking, cerebrovascular disease, use of benzodiazepines, and mechanical ventilation, multivariate logistic regression indicated a significantly increased risk of delirium in frail patients (adjusted OR: 1.61, 95% CI: 1.23-2.10, p < 0.001, E-value: 1.85).

CONCLUSIONS

Frailty is an independent risk factor for POD in older patients after cardiac surgery. Further research should focus on frailty assessment and tailored interventions to improve outcomes.

ICU admission Braden score independently predicts delirium in critically ill patients with ischemic stroke

BACKGROUND

Delirium is a common and severe complication in intensive care unit (ICU) patients with acute ischemic stroke, exacerbating cognitive and physical impairments. It prolongs hospitalization, increases healthcare costs, and raises mortality risk. Early prediction is crucial because it facilitates prompt interventions that could possibly reverse or alleviate the detrimental consequences of delirium. Braden scores, traditionally used to assess pressure injury risk, could also signal frailty, providing an early warning of delirium and aiding in prompt and effective patient management.

OBJECTIVE

To examine the association between the Braden score and delirium.

METHODS

A retrospective analysis of adult ischemic stroke patients in the ICU of a tertiary academic medical center in Boston from 2008 to 2019 was performed. Braden scores were obtained on admission for each patient. Delirium, the primary study outcome, was assessed using the Confusion Assessment Method for Intensive Care Unit and a review of nursing notes. The association between Braden score and delirium was determined using Cox proportional hazards modeling, with hazard ratios (HR) and 95% confidence intervals (CI) calculated.

RESULTS

The study included 3,680 patients with a median age of 72 years, of whom 1,798 were women (48.9 %). The median Braden score at ICU admission was 15 (interquartile range 13-17). After adjustment for demographics, laboratory tests, severity of illness, and comorbidities, the Braden score was inversely associated with the risk of delirium (adjusted HR: 0.94, 95 % CI: 0.92-0.96, P < 0.001).

CONCLUSIONS

The Braden score may serve as a convenient and simple screening tool to identify the risk of delirium in ICU patients with ischemic stroke.

IMPLICATION FOR CLINICAL PRACTICE

The use of the Braden score as a predictor of delirium in ischemic stroke patients in the ICU allows early identification of high-risk patients. This facilitates timely intervention, thereby improving patient outcomes and potentially reducing healthcare costs.

Can admission Braden skin score predict delirium in older adults in the intensive care unit? Results from a multicenter study

AIMS AND OBJECTIVES

To investigate whether a low Braden Skin Score (BSS), reflecting an increased risk of pressure injury, could predict the risk of delirium in older patients in the intensive care unit (ICU).

BACKGROUND

Delirium, a common acute encephalopathy syndrome in older ICU patients, is associated with prolonged hospital stay, long-term cognitive impairment and increased mortality. However, few studies have explored the relationship between BSS and delirium.

DESIGN

Multicenter cohort study.

METHODS

The study included 24,123 older adults from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and 1090 older adults from the eICU Collaborative Research Database (eICU-CRD), all of whom had a record of BSS on admission to the ICU. We used structured query language to extract relevant data from the electronic health records. Delirium, the primary outcome, was primarily diagnosed by the Confusion Assessment Method for the ICU or the Intensive Care Delirium Screening Checklist. Logistic regression models were used to validate the association between BSS and outcome. A STROBE checklist was the reporting guide for this study.

RESULTS

The median age within the MIMIC-IV and eICU-CRD databases was approximately 77 and 75 years, respectively, with 11,195 (46.4%) and 524 (48.1%) being female. The median BSS at enrollment in both databases was 15 (interquartile range: 13, 17). Multivariate logistic regression showed a negative association between BSS on ICU admission and the prevalence of delirium. Similar patterns were found in the eICU-CRD database.

CONCLUSIONS

This study found a significant negative relationship between ICU admission BSS and the prevalence of delirium in older patients.

RELEVANCE TO CLINICAL PRACTICE

The BSS, which is simple and accessible, may reflect the health and frailty of older patients. It is recommended that BSS assessment be included as an essential component of delirium management strategies for older patients in the ICU.

NO PATIENT OR PUBLIC CONTRIBUTION

This is a retrospective cohort study, and no patients or the public were involved in the design and conduct of the study.

Prognostic role of urinary ketone body in patients with sepsis-associated encephalopathy without hepatic failure: a retrospective cohort study

Sepsis-associated encephalopathy (SAE) is defined as a dysfunction of the central nervous system experienced during sepsis with variable clinical features. The study aims to identify the prognostic role of urinary ketone bodies in relation to clinical outcomes in patients with SAE. The Medical Information Mart for Intensive Care III (MIMIC-III) database was used to conduct a retrospective cohort study. We recruited 427 patients with SAE admitted to the intensive care unit (ICU) from the MIMIC-III database. Patients with SAE were divided into a survival group (380 patients) and a non-survival group (47 patients). We used the Wilcoxon signed-rank test and the multivariate logistic regression analysis to analyze the relationship between the level of urinary ketone bodies and the clinical prognosis in patients with SAE. The primary outcome was the relationship between urinary ketone body levels and 28-day mortality of SAE. The secondary outcomes were the relationship between urinary ketone body levels and length of ICU stays, Simplified Acute Physiology Score II, Sequential Organ Failure Assessment (SOFA), Glasgow Coma Scale, mechanical ventilation, renal replacement therapy, and the use of vasopressors. The 28-day mortality of patients with SAE was 11.0%. Urinary ketone body levels were not significantly associated with the 28-day mortality of patients with SAE. Urinary ketone body levels were associated with SOFA score and the use of vasopressors in patients with SAE. The SOFA score was an independent risk factor for the 28-day mortality in patients with SAE. Urinary ketone body levels were significantly associated with SOFA score and the use of vasopressors in patients with SAE. Furthermore, the SOFA score can predict the prognosis of short-term outcomes of patients with SAE. Therefore, we should closely monitor the changes of urinary ketone bodies and SOFA score and intervene in time.

Blood Urine Nitrogen Trajectories of Acute Pancreatitis Patients in Intensive Care Units

Objective

To identify subclasses of acute pancreatitis (AP) patients in the intensive care unit (ICU) by analyzing blood urea nitrogen (BUN) trajectories.

Methods

AP patients in West China Hospital System (development cohort) and three public databases in the United States (validation cohort) were included. Latent class trajectory modelling was used to identify subclasses based on BUN trajectories within the first 21 days after ICU admission. Clinical characteristics and outcomes were compared, and results were externally validated.

Results

The study comprised 2971 and 930 patients in the development and validation cohorts, respectively, with five subclasses: Class 1 ("Moderate-azotemia, slow decreasing"), Class 2 ("Non-azotemia"), Class 3 ("Severe-azotemia, slow decreasing"), Class 4 ("Moderate-azotemia, rapid increasing"), and Class 5 ('Moderate-azotemia, slow increasing) identified. Azotemia patients showed significantly higher 30-day mortality risk in development and validation cohorts. Specifically, Class 4 patients exhibited notably highest mortality risk in both the development cohort (HR 5.32, 95% CI 2.62-10.82) and validation cohort (HR 6.23, 95% CI 2.93-13.22). Regarding clinical characteristics, AP patients in Class 4 showed lower mean arterial pressure and a higher proportion of renal disease. We also created an online early classification model to further identify Class 4 patients among all patients with moderate azotemia at baseline.

Conclusion

This multinational study uncovers heterogeneity in BUN trajectories among AP patients. Patients with "Moderate-azotemia, rapid increasing" trajectory, had a higher mortality risk than patients with severe azotemia at baseline. This finding complements studies that solely rely on baseline BUN for risk stratification and enhanced our understanding of longitudinal progression of AP.

Joinpoint regression analysis of recent trends in desmoplastic malignant melanoma incidence and mortality: 15-year multicentre retrospective study

BACKGROUND

Recent data reveal a marked rise in the detection and mortality rates of Desmoplastic Malignant Melanoma (DMM). This trend underscores the imperative for an in-depth analysis of DMM's epidemiology, which is crucial for the formulation of precise medical and public health strategies. This investigation seeks to elucidate the variations in the incidence and mortality of DMM over a 15-year period (2005-2019).

METHODS

Data on DMM patients was sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Both incidence and incidence-based mortality rates (IBM) were directly extracted from the SEER database. Joinpoint regression was used to analyze and calculate the average annual percent change (AAPC) and its 95% confidence interval (CI).

RESULTS

Between 2005 and 2019, 3,384 DMM cases were identified, boasting an age-adjusted incidence rate of 36.3 cases per 1000,000 person-years (95% CI 3.51-3.76) and an IBM of 1.65cases per 1000,000 person-years (95% CI 1.57-1.74). Of these, 2,353 were males (69.53%) and 1,031 were females (30.47%). There were 1894 patients (55.97%) who were over 70 years old. Predominantly, DMM lesions manifested in exposed areas: Limbs (955, 28.22%), Face (906, 26.77%), and Scalp and Neck (865, 25.56%). The incidence of DMM increased significantly at a rate of APC = 0.9% during 2005-2019, while the incidence-based mortality showed a significant upward trend (APC = 7%) during 2005-2012, and slowly increasing trend (APC = 0.6%) during 2012-2019. In contrast to the modest upward trajectory in female incidence and mortality, male incidence initially surged, later declining, while male mortality peaked and stabilized post-2012. The primary sites for incidence and mortality were chronically sun-exposed areas: Face, Scalp and Neck, and Limbs.

CONCLUSIONS

In recent years, the incidence and incidence-based mortality of DMM have significantly increased. Each subgroup analysis has different trends, and these trends can provide better support for our exploration of DMM.

A nomogram for predicting survival in patients with gastrointestinal stromal tumor: a study based on the surveillance, epidemiology, and end results database

Purpose

The objective of this investigation was to construct and validate a nomogram for prognosticating cancer-specific survival (CSS) in patients afflicted with gastrointestinal stromal tumor (GIST) at 3-, 5-, and 8-years post-diagnosis.

Methods

Data pertaining to patients diagnosed with GIST were acquired from the Surveillance, Epidemiology, and End Results (SEER) database. Through random selection, a training cohort (70%) and a validation cohort (30%) were established from the patient population. Employing a backward stepwise Cox regression model, independent prognostic factors were identified. Subsequently, these factors were incorporated into the nomogram to forecast CSS rates at 3-, 5-, and 8-years following diagnosis. The nomogram's performance was assessed using indicators such as the consistency index (C-index), the area under the time-dependent receiver operating characteristic curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration curves, and decision-curve analysis (DCA).

Results

This investigation encompassed a cohort of 3,062 GIST patients. By analyzing the Cox regression model within the training cohort, nine prognostic factors were identified: age, sex, race, marital status, AJCC (American Joint Committee on Cancer) stage, surgical status, chemotherapy status, radiation status, and income status. The nomogram was subsequently developed and subjected to both internal and external validation. The nomogram exhibited favorable discrimination abilities, as evidenced by notably high C-indices and AUC values. Calibration curves confirmed the nomogram's reliability. Moreover, the nomogram outperformed the AJCC model, as demonstrated by enhanced NRI and IDI values. The DCA curves validated the clinical utility of the nomogram.

Conclusion

The present study has successfully constructed and validated the initial nomogram for predicting prognosis in GIST patients. The nomogram's performance and practicality suggest its potential utility in clinical settings. Nevertheless, further external validation is warranted.

Effect of a fall within three months of admission on delirium in critically Ill elderly patients: a population-based cohort study

BACKGROUND

Delirium is common among elderly patients in the intensive care unit (ICU) and is associated with prolonged hospitalization, increased healthcare costs, and increased risk of death. Understanding the potential risk factors and early prevention of delirium is critical to facilitate timely intervention that may reverse or mitigate the harmful consequences of delirium.

AIM

To clarify the effects of pre-admission falls on ICU outcomes, primarily delirium, and secondarily pressure injuries and urinary tract infections.

METHODS

The study relied on data sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Statistical tests (Wilcoxon rank-sum or chi-squared) compared cohort characteristics. Logistic regression was employed to investigate the association between a history of falls and delirium, as well as secondary outcomes, while Kaplan-Meier survival curves were used to assess short-term survival in delirium and non-delirium patients.

RESULTS

Study encompassed 22,547 participants. Delirium incidence was 40%, significantly higher in patients with a history of falls (54.4% vs. 34.5%, p < 0.001). Logistic regression, controlling for confounders, not only confirmed that a history of falls elevates the odds of delirium (OR: 2.11; 95% CI: 1.97-2.26; p < 0.001) but also showed it increases the incidence of urinary tract infections (OR:1.50; 95% CI:1.40-1.62; p < 0.001) and pressure injuries (OR:1.36; 95% CI:1.26-1.47; p < 0.001). Elderly delirium patients exhibited lower 30-, 180-, and 360-day survival rates than non-delirium counterparts (all p < 0.001).

CONCLUSIONS

The study reveals that history of falls significantly heighten the risk of delirium and other adverse outcomes in elderly ICU patients, leading to decreased short-term survival rates. This emphasizes the critical need for early interventions and could inform future strategies to manage and prevent these conditions in ICU settings.

Prognostic nomogram to predict cancer-specific survival with small-cell carcinoma of the prostate: a multi-institutional study

Objective

The aim of this study is to examine the predictive factors for cancer-specific survival (CSS) in patients diagnosed with Small-Cell Carcinoma of the Prostate (SCCP) and to construct a prognostic model.

Methods

Cases were selected using the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method was utilized to calculate survival rates, while Lasso and Cox regression were employed to analyze prognostic factors. An independent prognostic factor-based nomogram was created to forecast CSS at 12 and 24 months. The model's predictive efficacy was assessed using the consistency index (C-index), calibration curve, and decision curve analysis (DCA) in separate tests.

Results

Following the analysis of Cox and Lasso regression, age, race, Summary stage, and chemotherapy were determined to be significant risk factors (P < 0.05). In the group of participants who received training, the rate of 12-month CSS was 44.6%, the rate of 24-month CSS was 25.5%, and the median time for CSS was 10.5 months. The C-index for the training cohort was 0.7688 ± 0.024. As for the validation cohort, it was 0.661 ± 0.041. According to the nomogram, CSS was accurately predicted and demonstrated consistent and satisfactory predictive performance at both 12 months (87.3% compared to 71.2%) and 24 months (80.4% compared to 71.7%). As shown in the external validation calibration plot, the AUC for 12- and 24-month is 64.6% vs. 56.9% and 87.0% vs. 70.7%, respectively. Based on the calibration plot of the CSS nomogram at both the 12-month and 24-month marks, it can be observed that both the actual values and the nomogram predictions indicate a predominantly stable CSS. When compared to the AJCC staging system, DCA demonstrated a higher level of accuracy in predicting CSS through the use of a nomogram.

Conclusion

Clinical prognostic factors can be utilized with nomograms to forecast CSS in Small-Cell Carcinoma of the Prostate (SCCP).

The role of big data in financial technology toward financial inclusion

In the rapidly evolving landscape of financial technology (FinTech), big data stands as a cornerstone, driving significant transformations. This study delves into the pivotal role of big data in FinTech and its implications for financial inclusion. Employing a comprehensive literature review methodology, we analyze diverse sources including academic journals, industry reports, and online articles. Our findings illuminate how big data catalyzes the development of novel financial products and services, enhances risk management, and boosts operational efficiency, thereby fostering financial inclusion. Particularly, big data's capability to offer insightful customer behavior analytics is highlighted as a key driver for creating inclusive financial services. However, challenges such as data privacy and security, and the need for ethical algorithmic practices are also identified. This research contributes valuable insights for policymakers, regulators, and industry practitioners, suggesting a need for balanced regulatory frameworks to harness big data's potential ethically and responsibly. The outcomes of this study underscore the transformative power of big data in FinTech, indicating a pathway toward a more inclusive financial ecosystem.

Venous thromboembolism prophylaxis and mortality in patients with spinal fractures in ICUs

BACKGROUND

Spinal fracture is a common traumatic condition in orthopaedics, accounting for 5%-6% of total body fractures, and is a high-risk factor for venous thromboembolism (VTE), which seriously affects patient prognosis.

AIM

The aim of this study was to determine the impact of VTE prophylaxis on the prognosis of patients with spinal fractures in intensive care units (ICUs) and to provide a scientific basis for clinical treatment and nursing.

DESIGN

A retrospective study of patients with spinal fractures from the multicenter eICU Collaborative Research Database.

METHOD

The outcomes of this study were ICU mortality and in-hospital mortality. Patients were divided into the VTE prophylaxis (VP) and no VTE prophylaxis (NVP) groups according to whether they had undergone VTE prophylaxis during their ICU admission. The association between groups and outcomes were analysed using Kaplan-Meier (KM) survival curve, log-rank test and the Cox proportional-hazards regression model.

RESULTS

This study included 1146 patients with spinal fractures: 330 in the VP group and 816 in the NVP group. KM survival curves and log-rank tests revealed that both ICU and in-hospital survival probabilities in the VP group were significantly higher than in the NVP group. After the Cox model was adjusted for all covariates, the hazard ratio for ICU mortality in the VP group was 0.38 (0.19-0.75); the corresponding value for in-hospital mortality in the VP group was 0.38 (0.21-0.68).

CONCLUSIONS

VTE prophylaxis is associated with reduced ICU and in-hospital mortality in patients with spinal fractures in ICUs. More research is necessary to further define specific strategies and optimal timing for VTE prophylaxis.

RELEVANCE TO CLINICAL PRACTICE

This study provides the basis that VTE prophylaxis may be associated with improved prognosis in patients with spinal fractures in ICUs. In clinical practice, an appropriate modality should be selected for VTE prophylaxis in such patients.

Competing risk nomogram predicting cause-specific mortality in older patients with testicular germ cell tumors

Background

Testicular germ cell tumor (TGCT) is the most common type of malignancy in young men, but rarely in older adults. We aimed to construct a competing risk model to predict the prognosis for older patients with TGCT.

Methods

We collected TGCT patients aged 50 years or older diagnosed between 2004 and 2015 from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. We estimated the cumulative incidences of cause-specific death (CSD) and other causes of death and established a nomogram predicting cause-specific mortality in older patients with TGCT by Fine-Gray competing risk regression. The concordance index (C-index), calibration curves, area under the receiver operating characteristic curve (AUC), and decision analysis curves (DCA) were used to evaluate the differentiation, accuracy, and clinical significance of the nomogram.

Results

A total of 2,751 older TGCT patients were included in the study. The 3-, 5-, and 10-year cumulative incidences were 4.4, 5.0 and 6.1%, respectively, for cause-specific death, and 3.8, 6.2, 13.1%, respectively, for other causes of death. Predictors of cause-specific mortality in older TGCT included age, marital status, annual household income, histology, tumor size, stage and surgery. In the training and validation sets, the C-indexes were greater than 0.8, indicating that the nomogram had good discrimination. The AUC revealed the same result. The calibration curves showed good agreement between the predicted and observed results of the nomogram. DCA curves indicated that the nomogram had more clinical significance than the conventional American Joint Committee on Cancer (AJCC) staging. Based on the total nomogram score of each case, all patients were categorized into low-risk and high-risk groups, and risk categorization allowed the identification of cases with a high risk of death.

Conclusion

We established a competing risk nomogram with good performance that may help clinicians accurately predict the prognosis of older TGCT patients.

Associations of Folate/Folic Acid Supplementation Alone and in Combination With Other B Vitamins on Dementia Risk and Brain Structure: Evidence From 466 224 UK Biobank Participants

Previous researchers have tried to explore the association between folate/folic acid intake and dementia incidence, but the results remain controversial. We evaluated the associations of folate/folic acid supplementation alone and in combination with other B vitamins on dementia risk and brain structure. A total of 466 224 UK Biobank participants were investigated. Cox proportional hazards models were used to assess the associations between folate/folic acid supplementation status and the risk of Alzheimer's disease (AD) and vascular dementia (VD). Multivariable linear regression models were employed to evaluate the association between folate/folic acid supplementation status and brain structure. In the final model, folate/folic acid supplementation alone was significantly associated with a higher risk of AD (hazard ratio [HR] = 1.34, 95% confidence interval [CI] = 1.06-1.69, p = .015) and VD (HR = 1.61, 95% CI = 1.21-2.13, p = .001). Folate/folic acid supplementation alone was associated with a reduction in the hippocampus (β = -95.25 mm3, 95% CI = -165.31 to -25.19 mm3, p = .014) and amygdala (β = -51.85 mm3, 95% CI = -88.02 to -15.68 mm3, p = .012). The risk of AD and VD, as well as brain structure, in the group with combined folate/folic acid supplementation and other B vitamins did not show a statistically significant difference compared to the reference group (all p > .05). Folate/folic acid supplementation alone is significantly associated with a higher risk of AD and VD, as well as adverse alterations in brain structure. However, when combined with other B vitamins, these detrimental effects can be counteracted.

Calcium administration appears not to benefit acute pancreatitis patients with hypocalcemia

OBJECTIVE

Hypocalcemia occurs commonly among patients with acute pancreatitis (AP) in the intensive care unit (ICU). Calcium therapy could be used to correct hypocalcemia and maintain calcium levels, but its impact on the prognosis has not been demonstrated. Our study aimed to determine whether calcium therapy could benefit the multiple outcomes of AP patients with hypocalcemia.

METHODS

We extracted 807 AP patients with hypocalcemia from the Beth Israel Deaconess Medical Center (MIMIC-IV) database and performed retrospective analyses. The outcomes were in-hospital, 28 days, ICU mortality, and the length of stay (LOS) in the hospital and ICU. We performed propensity matching (PSM) and inverse probability weighting (IPTW) to balance the baseline differences and conducted multivariate regression to investigate the impact of calcium therapy.

RESULTS

A total of 620 patients (76.8%) received calcium treatment (calcium group) during hospitalization, while 187 patients (non-calcium group) did not. Patients in the calcium group did not present significant survival differences between groups before and after matching. After including covariates, calcium administration had no association with patients' in-hospital (HR: 1.03, 95% Cl: 0.47-2.27, p = .942), 28 days and ICU mortality and was significantly associated with prolonged length of stay in the hospital (effect estimate: 6.18, 95% Cl: 3.27-9.09, p < .001) and ICU (effect estimate: 1.72, 95% Cl: 0.24-3.20, p < .001). Calcium therapy could not benefit patients in subgroups with exclusive parenteral infusion, early calcium therapy (<48 h), or various degrees of hypocalcemia.

CONCLUSION

AP patients with hypocalcemia could not benefit from calcium administration, which has no association with multiple mortality and is significantly associated with prolonged LOS in the hospital and ICU.

Association between trough serum vancomycin concentration and vancomycin-associated acute kidney injury and 30-day mortality in critically ill elderly adults

BACKGROUND

Vancomycin-associated acute kidney injury (VA-AKI) is the most clinically relevant side effect of vancomycin. The objective of this study was to investigate the association between VTC and VA-AKI as well as 30-day mortality in critically ill elderly adults.

METHOD

Elderly patients with trough serum vancomycin concentration records(VTC) in the Medical Information Mart-IV (MIMIC-IV) and eICU databases were retrospectively studied.

RESULTS

A total of 3,146 critically ill elderly adults were finally enrolled. The incidence of VA-AKI in the elderly population was 76.5%. Logistic regression analysis revealed significant relationships between VA-AKI and various factors, including VTC, comorbidities, and laboratory indicators, and SOFA, and GCS score. For each mg/L increase, the OR for VA-AKI increased by 2.5%. The association between VTC and 30-day mortality was found to be statistically significant (odds ratio (OR): 1.021, 95% CI: 1.010-1.031), P < 0.001). The Restricted cubic splines (RCS) curves revealed that VTC ranged of 19.67 to 35.72 mg/l for AKI and 19.17 to 42.86 mg/l for 30-day mortality exhibit OR with 95% CI above 1, indicating statistically significant associations with an increased risk of AKI and 30-day mortality, respectively. In the subgroup analysis, VTC was identified as a risk factor for VA-AKI in specific patient groups, including white individuals, female patients, those with shock, patients with SOFA > 6, patients with baseline creatinine > 1.2 mg/dl and patients with or without exposed to other nephrotoxic medications.

CONCLUSION

This study found the significant association between VTC and the incidence of VA-AKI and 30-day mortality in critically ill elderly adults. The RCS curves indicated concentration ranges for AKI (19.67-35.72 mg/L) and 30-day mortality (19.17-42.86 mg/L), signifying increased risk.

Prognostic nomograms for predicting long-term overall survival in spindle cell melanoma: a population-based study

Background

There are few research findings on the survival prognosis of spindle cell melanoma (SCM), which is an unusual kind of melanoma. The purpose of this study was to develop a thorough nomogram for predicting the overall survival (OS) of patients with SCM and to assess its validity by comparing it with the conventional American Joint Committee on Cancer (AJCC) staging system.

Methods

The Surveillance, Epidemiology, and End Results database was searched, and 2,015 patients with SCM were selected for the analysis. The patients were randomly divided into training (n = 1,410) and validation (n = 605) cohorts by using R software. Multivariate Cox regression was performed to identify predictive factors. A nomogram was established based on these characteristics to predict OS in SCM. The calibration curve, concordance index (C-index), area under the receiver operating characteristic curve, and decision-curve analysis were utilized to assess the accuracy and reliability of the model. The net reclassification improvement and integrated discrimination improvement were also applied in this model to evaluate its differences with the AJCC model.

Results

The developed nomogram suggests that race, AJCC stage, chemotherapy status, regional node examination status, marital status, and sex have the greatest effects on OS in SCM. The nomogram had a higher C-index than the AJCC staging system (0.751 versus 0.633 in the training cohort and 0.747 versus 0.650 in the validation cohort). Calibration plots illustrated that the model was capable of being calibrated. These criteria demonstrated that the nomogram outperforms the AJCC staging system alone.

Conclusion

The nomogram developed in this study is sufficiently reliable for forecasting the risk and prognosis of SCM, which may facilitate personalized treatment recommendations in upcoming clinical trials.

Geriatric nutritional risk index independently predicts delirium in older patients in intensive care units: A multicenter cohort study

BACKGROUND

Malnutrition is associated with clinical outcomes in several diseases. However, the role of malnutrition in the risk of delirium in elderly patients in intensive care units (ICU) remains unclear. This study aimed to elucidate the association between the Geriatric Nutritional Risk Index (GNRI) and the risk of delirium in elderly patients in the ICU.

METHODS

Elderly patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD) were included in this study, totaling 11,310 and 5,627, respectively. All subjects were stratified according to their GNRI score: those with no nutritional risk (GNRI > 98), mild nutritional risk (92 ≤ GNRI ≤ 98), moderate nutritional risk (82 ≤ GNRI ≤ 92), and severe nutritional risk (GNRI < 82). The primary outcome was delirium.

RESULTS

The GNRI in MIMIC-IV and eICU-CRD were 93.82 (84.88, 101.26) and 83.39 (75.95, 89.35), respectively. Compared to the cohort without nutritional risk, there was an increased risk of delirium in patients with mild nutritional risk (OR: 1.39, 95 % CI: 1.23-1.58), moderate nutritional risk (OR: 1.72, 95 % CI: 1.52-1.95), and severe nutritional risk (OR: 2.07, 95 % CI: 1.79-2.39). Inclusion of the GNRI score in the multivariate models improved the prediction of delirium. Similar results were found in the eICU-CRD database.

CONCLUSIONS

Nutritional assessment using the GNRI may be an independent predictor of the development of delirium in elderly patients in the ICU. The addition of the GNRI score to the delirium prediction model improves its predictive accuracy.

Improved prediction of sepsis-associated encephalopathy in intensive care unit sepsis patients with an innovative nomogram tool

Background

Sepsis-associated encephalopathy (SAE) occurs as a result of systemic inflammation caused by sepsis. It has been observed that the majority of sepsis patients experience SAE while being treated in the intensive care unit (ICU), and a significant number of survivors continue suffering from cognitive impairment even after recovering from the illness. The objective of this study was to create a predictive nomogram that could be used to identify SAE risk factors in patients with ICU sepsis.

Methods

We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We defined SAE as a Glasgow Coma Scale (GCS) score of 15 or less, or delirium. The patients were randomly divided into training and validation cohorts. We used least absolute shrinkage and selection operator (LASSO) regression modeling to optimize feature selection. Independent risk factors were determined through a multivariable logistic regression analysis, and a prediction model was built. The performance of the nomogram was evaluated using various metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results

Among the 4,476 sepsis patients screened, 2,781 (62.1%) developed SAE. In-hospital mortality was higher in the SAE group compared to the non-SAE group (9.5% vs. 3.7%, p < 0.001). Several variables were analyzed, including the patient's age, gender, BMI on admission, mean arterial pressure, body temperature, platelet count, sodium level, and use of midazolam. These variables were used to create and validate a nomogram. The nomogram's performance, assessed by AUC, NRI, IDI, and DCA, was found to be superior to the conventional SOFA score combined with delirium. Calibration plots and the Hosmer-Lemeshow test confirmed the accuracy of the nomogram. The enhanced NRI and IDI values demonstrated that our scoring system outperformed traditional diagnostic approaches. Additionally, the DCA curve indicated the practicality of the nomogram in clinical settings.

Conclusion

This study successfully identified autonomous risk factors associated with the emergence of SAE in sepsis patients and utilized them to formulate a predictive model. The outcomes of this investigation have the potential to serve as a valuable clinical resource for the timely detection of SAE in patients.

Elevated platelet count is associated with decreased mortality from hemorrhagic stroke in hospital: a multi-center retrospective cohort study

This study aimed to investigate the relationship between platelet count (PC) and mortality in patients with hemorrhagic stroke (HS). The research reviewed data from 10,466 patients hospitalized in 208 hospitals in the United States from January 1, 2014, to December 31, 2015. Of these, 3262 HS patients were included in the primary analysis for those admitted to the intensive care unit (ICU). The average age of these patients was 67.05 years, with 52.79% being male. The median PC was (221.67 ± 73.78) × 109/L. Multivariate logistic regression analysis revealed that PC was a protective factor for mortality in HS patients (OR = 0.98, 95% CI 0.97-1.00, P < 0.05). Additionally, a non-linear association between PC and mortality in HS patients was found using a generalized additive model (GAM) and smooth curve fitting (penalty spline method). For the first time, a recursive algorithm identified the inflection point of platelet count as 194 × 109/L. On the left side of the inflection point, for every increase of 10 units in platelet count, the mortality rate of HS patients decreases by 10%. The study demonstrates a non-linear relationship between PC and the risk of mortality in HS patients. A platelet counts higher than the inflection point (194 × 109/L) may be a significant intervention to reduce mortality in HS patients.

Traditional Chinese Medicine is Associated with the Reduction in Endpoint Events in Patients with Gouty Arthritis: Cohort Study and Association Rule Analysis

Objective

This study aimed to evaluate the effect of traditional Chinese medicine (TCM) on endpoint events in patients with gouty arthritis (GA).

Methods and Materials

A total of 2091 hospitalized GA patients were followed up by telephone, and propensity score matching (PSM) was used to reduce potential bias in the study design. Cox proportional risk model and Kaplan-Meier survival curve were utilized to analyze the impact and time effect of factors on the readmission of GA patients. The differences of laboratory indexes before and after treatment between the low and high exposure groups were compared, and the types and frequencies of medicines in all patients were counted. Association rule analysis was performed to investigate the association between TCM and test indexes or endpoint events.

Results

After 1:1 PSM, 187 patients were enrolled in the TCM group and 187 patients in the non-TCM group. The incidence of readmission, new tophus, and all-cause death was lower in the TCM group than that in the non-TCM group (P < 0.05). Cox proportional risk regression analysis showed that TCM, NSAIDs and uric acid lowering drug were independent protective factors for GA readmission. The protective effect was enhanced by the prolongation of TCM treatment and the drug combinations. Kaplan-Meier survival curves indicated a significantly lower readmission rate in the high exposure group than in the low exposure group (P < 0.01). Compared with before treatment, NLR, hs-CRP, UA, TC and other laboratory indexes of the low and high exposure groups were improved after treatment (P < 0.01); The improvement of TG and TC in the high exposure group was more significant than the low exposure group (P < 0.01). The analysis of medicines used by all patients identified the top 20 Chinese herbal medicines and the top 2 Chinese patent medicines. The core drugs identified through association rule analysis that can improve test index and reduce the incidence of endpoint events include Yiyiren, Danshen, and HQC, among others. The network diagram of association rule analysis intuitively shows the relationship between core drugs and "improvement of indicators" and "the absence of endpoint events".

Conclusion

TCM is associated with a reduced incidence of endpoint events in patients with GA.

New findings in prognostic factor assessment for adenocarcinoma of transverse colon: a comparison study between competing-risk and COX regression analysis

Purpose

Competing-risk analysis was used to accurately assess prognostic factors for cancer-specific death in patients with adenocarcinoma of transverse colon (ATC), and the results were compared with those from a conventional Cox regression analysis.

Materials and Methods

Patients diagnosed with ATC between 2000 and 2019 were selected from the Surveillance, Epidemiology, and End Results database. The crude mortality rates of patients with ATC were calculated and their differences were tested using the Gray's test, respectively. In performing multivariate analysis, the Cox regression model and the subdistribution hazard function (SD) in competing risk analysis were utilized, respectively.

Results

This study included 21,477 eligible patients. The SD model indicated that age, etc. are actual independent prognostic factors. In contrast to previous recognition, the results of the Cox regression showed false-positives for sex and Carcinoembryonic antigen, and underestimated point-estimates in the stage and American Joint Committee on Cancer stage due to competing events. A detailed comparison of treatment revealed that the larger surgical scopes were prognostic risk factors compared with the smaller scope of local tumor excision, partial colectomy, or segmental resection. Patients treated with external proton beam radiotherapy had an increased risk compared with those with no radiotherapy and internal radiotherapy.

Conclusions

After comparing the results of the two methods and mitigating the significant bias introduced by Cox regression, we found independent factors that really affect the prognosis of ATC. On the other hand, in terms of ATC, a larger surgical scope and external proton beam radiotherapy may not improve the long-term survival of patients. Therefore, when faced with ATC patients, these differences should be noted and treated differently from common colorectal cancer patients. Thus, clinicians are able to give more targeted treatment plans and prognostic assessments.

Does ICU admission dysphagia independently contribute to delirium risk in ischemic stroke patients? Results from a cohort study

BACKGROUND

Delirium is prevalent in ischemic stroke patients, particularly those in the intensive care unit (ICU), and it poses a significant burden on patients and caregivers, leading to increased mortality rates, prolonged hospital stays, and impaired cognitive function. Dysphagia, a common symptom in critically ill patients with ischemic stroke, further complicates their condition. However, the association between dysphagia and delirium in this context remains unclear. The objective of this study was to investigate the correlation between dysphagia and delirium in ICU patients with ischemic stroke.

METHODS

A retrospective analysis was conducted on adult patients diagnosed with ischemic stroke at a medical center in Boston. Ischemic stroke cases were identified using the ninth and tenth revisions of the International Classification of Diseases. Dysphagia was defined as a positive bedside swallowing screen performed by medical staff on the day of ICU admission, while delirium was assessed using the ICU Confusion Assessment Method and review of nursing notes. Logistic regression models were used to explore the association between dysphagia and delirium. Causal mediation analysis was employed to identify potential mediating variables.

RESULTS

The study comprised 1838 participants, with a median age of approximately 70 years, and 50.5% were female. Among the total study population, the prevalence of delirium was 43.4%, with a higher prevalence observed in the dysphagia group (60.7% vs. 40.8%, p < 0.001) compared to the non-dysphagia group. After adjusting for confounding factors including age, sex, race, dementia, depression, sedative medications, history of falls, visual or hearing deficit, sequential organ failure score, and Glasgow coma score, multifactorial logistic regression analysis demonstrated a significant association between dysphagia and an increased likelihood of delirium (odds ratio [OR]: 1.48; 95% confidence interval [CI]: 1.07-2.05; p = 0.018; E-value = 1.73). Causal mediation analysis revealed that serum albumin levels partially mediated the association between dysphagia and delirium in critically ill patients with ischemic stroke (average causal mediated effect [ACME]: 0.02, 95% CI: 0.01 to 0.03; p < 0.001).

CONCLUSION

ICU admission dysphagia may independently contribute to the risk of delirium in patients with ischemic stroke. Early identification and intervention in ischemic stroke patients with dysphagia may help mitigate the risk of delirium and improve patient prognosis.

Study from the United States: increased prevalence of kidney stones in patients with high weight-adjusted waist index

Objective

Using data from NHANES 2007-2018, to examine the association between WWI (weight-adjusted waist index) index and prevalence of kidney stones.

Methods

Using multiple logistic regression analysis of the National Health and Nutrition Examination Survey (NHANES) 2007-2018, we evaluated the association between WWI index and the prevalence of kidney stones, followed by subgroup analysis of sensitive populations. Smooth curve fitting was used to determine whether there was a non-linear relationship between the WWI index and kidney stone prevalence, and threshold effect analysis was used to test this relationship.

Results

Among 29,280 participants, 2,760 self-reported renal calculi. After adjustment for all confounders, there was a positive association between WWI and kidney stone prevalence (OR = 1.20, 95% CI: 1.12, 1.28), and this positive association was stronger with increasing WWI (and P = 0.01 for trend). Our results indicate a non-linear positive correlation between WWI index and kidney stones, with the saturation threshold effect analysis and the most important threshold value at 11.02. According to subgroup analysis, WWI showed the strongest association with kidney stone prevalence in participants aged 20-39 years, males, other US ethnic groups, and participants without hypertension and diabetes.

Conclusion

Increased WWI is positively associated with increased incidence of kidney stones, and increased WWI is a high risk for kidney stones that should be treated with caution. This association should be more pronounced in people between the ages of 20 and 39 years, in men, in other US ethnic populations, and in participants who do not have hypertension or diabetes.

Clinical Feature Vector Generation using Unsupervised Graph Representation Learning from Heterogeneous Medical Records

The diversity of patient information recorded on electronic medical records generally, presents a challenge for converting it into fixed-length vectors that align with clinical characteristics. To address this issue, this study aimed to utilize an unsupervised graph representation learning method to transform the unstructured inpatient information from electronic medical records into a fixed-length vector. Infograph, one of the unsupervised graph representation learning algorithms was applied to the graphed inpatient information, resulting in embedded vectors of fixed length. The embedded vectors were then evaluated for whether the clinical information was preserved in it. The results indicated that the embedded representation contained information that could predict readmission within 30 days, demonstrating the feasibility of using unsupervised graph representation learning to transform patient information into fixed-length vectors that retain clinical characteristics.

The Development of a Clinical Registry Digital Database on Invasive Fungal Infections in India: Advancing Epidemiological Understanding and Patient Care

A well-structured digital database is essential for any national priority project as it can provide real-time data analysis and facilitate quick decision making. In recent times, particularly after the COVID-19 pandemic, invasive fungal infections (IFIs) have emerged as a significant public health challenge in India, affecting vulnerable population, including immunocompromised individuals. The lack of comprehensive and well-structured data on IFIs has hindered efforts to understand their true burden and optimize patient care. To address this critical knowledge gap, the ICMR has undertaken a Pan-India pioneer initiative to develop a network of Advanced Mycology Diagnostic research centres in different geographical zones of the country (ICMR-MycoNet). Under the aegis of this project, a clinical registry on IFIs in the ICUs is initiated. This process paper presents a detailed account of the steps involved in the establishment of a web-based data entering and monitoring platform to capture data electronically, ensuring robust and secure data collection and management. This system not only allows participating ICMR-MycoNet centres to enter patient information directly into the database using standardized Case Report Form (CRF) but also includes data validation checks to ensure the accuracy and completeness of entered data. It is complemented by a real-time, web-based, and adaptable data visualization platform. This registry aims to provide crucial epidemiological insights, promote evidence-based hospital infection control programs, and ultimately improve patient outcomes in the face of this formidable healthcare challenge.

Prognostic nomograms for breast cancer with lung metastasis: a SEER-based population study

BACKGROUND

Lung metastasis is a significant adverse predictor of prognosis in patients with breast cancer. Accurate estimation for the prognosis of patients with lung metastasis and population-based validation for the models are lacking. In the present study, we aimed to establish the nomogram to identify prognostic factors correlated with lung metastases and evaluate individualized survival in patients with lung metastasis based on SEER (Surveillance, Epidemiology, and End Results) database.

METHODS

We selected 1197 patients diagnosed with breast cancer with lung metastasis (BCLM) from the SEER database and randomly assigned them to the training group (n = 837) and the testing group (n = 360). Based on univariate and multivariate Cox regression analysis, we evaluated the effects of multiple variables on survival in the training group and constructed a nomogram to predict the 1-, 2-, and 3-year survival probability of patients. The nomogram were verified internally and externally by Concordance index (C-index), Net Reclassification (NRI), Integrated Discrimination Improvement (IDI), Decision Curve Analysis (DCA), and calibration plots.

RESULTS

According to the results of multi-factor Cox regression analysis, age, histopathology, grade, marital status, bone metastasis, brain metastasis, liver metastasis, human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR), surgery, neoadjuvant therapy and chemotherapy were considered as independent prognostic factors for patients with BCLM. The C-index in the training group was 0.719 and the testing group was 0.695, respectively. The AUC values of the 1-, 2-, and 3-year prognostic nomogram in the training group were 0.798, 0.790 and 0.793, and the corresponding AUC values in the testing group were 0.765, 0.761 and 0.722. The calculation results of IDI and NRI were shown. The nomograms significantly improved the risk reclassification for 1-, 2-, and 3-year overall mortality prediction compared with the AJCC 7th staging system. According to the calibration plot, nomograms showed good consistency between predicted and actual overall survival (OS) values for the patients with BCLM. DCA showed that nomograms had better net benefits at different threshold probabilities at different time points compared with the AJCC 7th staging system.

CONCLUSIONS

Nomograms that predicted 1-, 2-, and 3-year OS for patients with BCLM were successfully constructed and validated to help physicians in evaluating the high risk of mortality in breast cancer patients.

Adverse Impact of Sodium Bicarbonate Administration on Multiple Outcomes in Acute Pancreatitis Patients With Hyperlactatemia

OBJECTIVE

Hyperlactatemia is likely to occur among patients with acute pancreatitis (AP). Sodium bicarbonate (SB) therapy could be applied to correct potential detrimental acidic disturbances, but the exact impact of SB treatment is unknown. This study aims to investigate the impact of SB on AP patients complicated with hyperlactatemia.

METHODS

The study was conducted based on the database named Medical Information Mart for Intensive Care-IV (MIMIC-IV). Propensity matching (PSM) and inverse probability weighting (IPTW) were used to balance the baseline differences. Multivariate regression and marginal structural Cox models were performed to investigate the association between SB and multiple outcomes.

RESULTS

Three hundred fifty-three AP patients with hyperlactatemia (initial serum lactate, >2.0 mmol/L) were extracted from the MIMIC-IV database. We found that SB treatment was significantly associated with worse multi-outcomes of AP patients with hyperlactatemia (in-hospital mortality: hazard ratio, 2.46; 95% confidence interval, 1.38-4.39; P < 0.01). Further analysis through marginal structural Cox models showed that SB had adverse impact on in-hospital prognosis of patients with severe lactic acidosis (pH < 7.15,lactate > 2.0 mmol/L).

CONCLUSION

Sodium bicarbonate might not be an appropriate treatment for AP patients with hyperlactatemia (lactate > 2.0 mmol/L) or with severe lactic acidosis (pH < 7.15, lactate > 2.0 mmol/L).

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

Evaluation and Prediction Analysis of 3- and 5-Year Relative Survival Rates of Patients with Cervical Cancer: A Model-Based Period Analysis

BACKGROUND

Cervical cancer remains a threat to female health due to high mortality. Clarification of the long-term trend of survival rate over time and the associated risk factors would be greatly informative to improve the prognosis of cervical cancer patients.

METHODS

This retrospective study was based on data extracted from the Surveillance, Epidemiology, and End Results (SEER) database of the United States. The 3-year and 5-year overall survival rates of patients with cervical cancer during 2002-2006, 2007-2011, and 2012-2016 were analyzed. Period analysis was used to assess the variation in survival rate stratified by age, race, and socioeconomic status during the 15-year study period and then predicted the relative survival rate in the following period from 2017 to 2021.

RESULTS

During 2002-2016, the 3-year relative survival rate of cervical cancer patients increased from 73.1% to 73.5% with a high jump between 2007 and 2011. This upward trend is expected to continue to 74.3% between 2017 and 2021. Patients older than 60 years, black ethnicity, or medium and high poverty status were likely to have a lower relative survival rate.

CONCLUSION

This study confirmed the increased relative survival rate of cervical cancer patients over years and identified relevant risk factors. Targeted initiatives for elderly and socially underprivileged individuals may be able to mitigate inequality.

Global, Regional, and National Burden of Ectopic Pregnancy: A 30-Year Observational Database Study

Objective

To estimate global, regional, and national trends due to ectopic pregnancy as part of the 2019 Global Burden of Disease study.

Methods

We systematically reviewed trends in ectopic pregnancy burden using data from the Global Burden of Disease (GBD) database, including 21 regions, 195 countries, and territories over the past 30 years. The trends of ectopic pregnancy-related incidence, mortality, and disability-adjusted life years (DALYs) attributable to all known risk factors were also analyzed. Age-standardized rates (ASRs) and their estimated annual percentage changes (EAPCs) were also calculated.

Results

Incident cases, deaths, and DALYs of ectopic pregnancy increased worldwide in the past 30 years. The age-standardized incidence rate (ASIR) was decreasing (EAPC = -1.14, 95% confidence interval (CI): -1.29 to -0.98), and the age-standardized death (EAPC = -0.9, 95% CI: -1.03 to -0.76) and DALY rate decreased generally (EAPC = -0.83, 95% CI: -0.98 to -0.68). In addition, the burden of ectopic pregnancy is lower in areas with higher socioeconomic development, and significant positive correlations between ASRs and sociodemographic index (SDI) were observed, especially among low-middle SDI, and low SDI quintiles carried the majority burden of ectopic pregnancy.

Conclusion

Globally, the incidence, mortality, and DALY rate of ectopic pregnancy had been decreasing from 1990 to 2019. Compared with lower and decreasing ASIR in the high SDI region, ASIR in the low SDI region was always high, indicating the need for ectopic pregnancy treatment improvement and the establishment of more targeted and specific strategies in low SDI countries to reduce the incidence of ectopic pregnancy.

Development and validation of a nomogram for predicting overall survival in patients with early-onset endometrial cancer

BACKGROUND

This study aimed to investigate the differences in the clinicopathological characteristics of younger and older patients with endometrial cancer (EC) and develop a nomogram to assess the prognosis of early onset EC in terms of overall survival.

METHODS

Patients diagnosed with EC from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were selected. Clinicopathological characteristics were compared between younger and older patients, and survival analysis was performed for both groups. Prognostic factors affecting overall survival in young patients with EC were identified using Cox regression. A nomogram was created and internal validation was performed using the consistency index, decision curve analysis, receiver operating characteristic curves, and calibration curves. External validation used data from 70 patients with early onset EC. Finally, Kaplan-Meier curves were plotted to compare survival outcomes across the risk subgroups.

RESULTS

A total of 1042 young patients and 12,991 older patients were included in this study. Younger patients were divided into training (732) and validation (310) cohorts in a 7:3 ratio. Cox regression analysis identified age, tumorsize, grade, FIGO stage(International Federation of Gynecology and Obstetrics) and surgery as independent risk factors for overall survival, and a nomogram was constructed based on these factors. Internal and external validations demonstrated the good predictive power of the nomogram. In particular, the C-index for the overall survival nomogram was 0.832 [95% confidence interval (0.797-0.844)] in the training cohort and 0.839 (0.810-0.868) in the internal validation cohort. The differences in the Kaplan-Meier curves between the different risk subgroups were statistically significant.

CONCLUSIONS

In this study, a nomogram for predicting overall survival of patients with early onset endometrial cancer based on the SEER database was developed to help assess the prognosis of patients and guide clinical treatment.

Competing-risks analysis for evaluating the prognosis of patients with microinvasive cutaneous squamous cell carcinoma based on the SEER database

BACKGROUND

Utilizing the traditional Cox regression model to identify the factors affecting the risk of mortality due to microinvasive cutaneous squamous cell carcinoma (micSCC) may produce skewed results. Since cause-specific mortality can guide clinical decision-making, this study employed the Fine-Gray model based on the Surveillance, Epidemiology, and End Results (SEER) database to identify significant predictive variables for the risk of micSCC-related mortality.

METHODS

This study used the information of patients with micSCC who were listed in the SEER database during 2000-2015. Cox regression and Fine-Gray models were utilized for the multivariable analysis, and Gray's test and the cumulative incidence function were used for the univariable analyses.

RESULTS

There were 100 patients who died from other reasons and 38 who died from micSCC among the 1259 qualified patients with micSCC. Most were female, white, married, had localized metastasis, etc. According to the univariable Gray's test (P < 0.05), the cumulative incidence rate for events of interest was strongly associated with age, sex, marital status, American Joint Committee on Cancer staging, radiation status, summary stage, chemotherapy status, surgery status, and tumor size. Multivariable Cox regression analysis and multivariable competing-risks analysis indicated that age, tumor size, and income were independent risk variables for the prognosis of patients with micSCC. In both age and tumor size variables, the competing-risks model showed a slight decrease in the hazard ratio and a slight narrowing of the 95% confidence interval compared with the Cox regression model. However, this pattern is not evident in the income variable.

CONCLUSIONS

This study established a Fine-Gray model for identifying the independent risk factors that influence the risk of mortality among patients with micSCC. This study uncovers that, in the context of competing risks, age, tumor size, and income serve as independent risk factors influencing the risk of mortality due to micSCC among patients. Our findings have the potential to provide more accurate risk assessments for patient outcomes and contribute to the development of individualized treatment plans.