Survival analysis-part 2: Cox proportional hazards model

Prioritizing Disease Diagnosis in Neonatal Cohorts through Multivariate Survival Analysis: A Nonparametric Bayesian Approach

Understanding the intricate relationships between diseases is critical for both prevention and recovery. However, there is a lack of suitable methodologies for exploring the precedence relationships within multiple censored time-to-event data, resulting in decreased analytical accuracy. This study introduces the Censored Event Precedence Analysis (CEPA), which is a nonparametric Bayesian approach suitable for understanding the precedence relationships in censored multivariate events. CEPA aims to analyze the precedence relationships between events to predict subsequent occurrences effectively. We applied CEPA to neonatal data from the National Health Insurance Service, identifying the precedence relationships among the seven most commonly diagnosed diseases categorized by the International Classification of Diseases. This analysis revealed a typical diagnostic sequence, starting with respiratory diseases, followed by skin, infectious, digestive, ear, eye, and injury-related diseases. Furthermore, simulation studies were conducted to demonstrate CEPA suitability for censored multivariate datasets compared to traditional models. The performance accuracy reached 76% for uniform distribution and 65% for exponential distribution, showing superior performance in all four tested environments. Therefore, the statistical approach based on CEPA enhances our understanding of disease interrelationships beyond competitive methodologies. By identifying disease precedence with CEPA, we can preempt subsequent disease occurrences and propose a healthcare system based on these relationships.

Author Response

How to cite this article: Vijayakumar M, Selvam V, Renuka MK, Rajagopalan RE. Author Response. Indian J Crit Care Med 2024;28(5):515.

Author Response

How to cite this article: Vijayakumar M, Selvam V, Renuka MK, Rajagopalan RE. Author Response. Indian J Crit Care Med 2024;28(5):512.

Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model

Aims

Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique.

Methods and results

Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2.

Conclusion

Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.

Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models

Introduction

Colorectal cancer (CRC), also known as colorectal cancer, is a significant disease marked by high fatality rates, ranking as the third leading cause of global mortality. The main objective of this study was to assess the accuracy of predictive models in predicting both mortality events and the probability of disease recurrence.

Method

A retrospective analysis was conducted on a cohort of 284 individuals diagnosed with colorectal cancer between 2001 and 2017. Demographic and clinical data, including gender, disease stage, age at diagnosis, recurrence status, and treatment details, were meticulously recorded. We rigorously evaluated various predictive models, including Decision Trees, Random Forests, Random Survival Forests (RSF), Gradient Boosting, mboost, Deep Learning Neural Network (DLNN), and Cox regression. Performance metrics, such as sensitivity, positive predictive value (PPV), specificity, area under the receiver operating characteristic curve (ROC area), and overall accuracy, were calculated for each model to predict mortality and disease recurrence. The analysis was performed using R version 4.1.3 software and the Python programming language.

Results

For mortality prediction, the mboost model demonstrated the highest sensitivity at 96.9% (95% CI: 0.83-0.99) and an ROC area of 0.88. It also exhibited high specificity at 80% (95% CI: 0.59-0.93), a positive predictive value of 86.1% (95% CI: 0.70-0.95), and an overall accuracy of 89% (95% CI: 0.78-0.96). Random Forests showed perfect sensitivity of 100% (95% CI: 0.85-1) but had low specificity at 0% (95% CI: 0-0.52) and poor overall accuracy (50%). On the other hand, DLNN had the lowest performance metrics for mortality prediction, with a sensitivity of 24% (95% CI: 0.222-0.268), specificity of 75% (95% CI: 0.73-0.77), and a lower positive predictive value of 42% (95% CI: 0.38-0.45). The Gradient Boosting model showed the best performance in predicting recurrence, achieving perfect sensitivity of 100% (95% CI: 0.87-1) and high specificity at 92.9% (95% CI: 0.76-0.99). It also had a high positive predictive value of 93.3% (95% CI: 0.77-0.99). Gradient Boosting, with an ROC area of 96.4%, and mboost, with an ROC area of 75%, demonstrated remarkable performance. DLNN had the lowest performance metrics for recurrence prediction, with sensitivity at 1.75% (95% CI: 0.01-0.02), specificity at 98% (95% CI: 0.97-0.98), and a lower positive predictive value at 52.6% (95% CI: 0.39-0.65).

Conclusion

In summary, the mboost model demonstrated outstanding performance in predicting mortality, achieving exceptional results across various evaluation metrics. Random Forests exhibited perfect sensitivity but showed poor specificity and overall accuracy. The DLNN model displayed the lowest performance metrics for mortality prediction. In terms of recurrence prediction, the Gradient Boosting model outperformed other models with perfect sensitivity, high specificity, and positive predictive value. The DLNN model had the lowest performance metrics for recurrence prediction. Overall, the results emphasize the effectiveness of the mboost and Gradient Boosting models in predicting mortality and recurrence in colorectal cancer patients.

Clinical Characteristics, Treatment, and Survival Outcome of Ependymoma in Infants

BACKGROUND

Treatment modalities of ependymoma in infants remain controversial. Postoperative adjuvant radiotherapy could prolong overall survival but has the potential to affect nervous system development in infants. The role of adjuvant chemotherapy in prolonging overall survival for infants with ependymoma is still unclear. Therefore we designed this study to explore the effect of treatment modalities on survival time of infants with ependymoma.

METHODS

We studied 72 infants with ependymoma from the Surveillance, Epidemiology, and End Results database in this retrospective analysis. Univariate and multivariate Cox proportional hazard models were adopted to determine hazard ratios and compare overall survival.

RESULTS

Among 72 infants with ependymoma, 35 were male (48.6%) and 37 were female (51.4%). The 5-year overall survival of all patients was 67%. Forty-six infants (63.9%) received gross total resection, 20 (27.8%) received subtotal resection, and 6 (8.3%) did not receive surgical resection or only autopsy. Twenty-one infants (29.2%) received radiotherapy, and 45 (62.5%) received chemotherapy. Multivariate analysis revealed that patients accepted surgical resection (No vs. gross total resection, P < 0.001; No vs. subtotal resection, P = 0.026) and chemotherapy (No vs. Yes, P = 0.024) are the independent prognostic factors for overall survival.

CONCLUSIONS

Treatment modality is associated with survival time in infants with ependymoma. The extent of resection and chemotherapy were independent prognostic factors for infants with ependymoma.

Doing more with less: low-titer group O whole blood resulted in less total transfusions and an independent association with survival in adults with severe traumatic hemorrhage

BACKGROUND

Low-titer group O whole blood (LTOWB) or component therapy (CT) may be used to resuscitate hemorrhaging trauma patients. LTOWB may have clinical and logistical benefits and may improve survival.

OBJECTIVES

We hypothesized LTOWB would improve 24-hour survival in hemorrhaging patients and would be safe and equally efficacious in non-group O compared with group O patients.

METHODS

Adult trauma patients with massive transfusion protocol activations were enrolled in this observational study. The primary outcome was 24-hour mortality. Secondary outcomes included 72-hour total blood product use. A Cox regression determined the independent associations with 24-hour mortality.

RESULTS

In total, 348 patients were included (CT, n = 180; LTOWB, n = 168). Demographics were similar between cohorts. Unadjusted 24-hour mortality was reduced in LTOWB vs CT: 8% vs 19% (P = .003), but 6-hour and 28-day mortality were similar. In an adjusted analysis with multivariable Cox regression, LTOWB was independently associated with reduced 24-hour mortality (hazard ratio, 0.21; 95% CI, 0.07-0.67; P = .004). LTOWB patients received significantly less 72-hour total blood products (80.9 [41.6-139.3] mL/kg vs 48.9 [25.9-106.9] mL/kg; P < .001). In stratified 24-hour survival analyses, LTOWB was associated with improved survival for patients in shock or with coagulopathy. LTOWB use in non-group O patients was not associated with increased mortality, organ injury, or adverse events.

CONCLUSION

In this hypothesis-generating study, LTOWB use was independently associated with improved 24-hour survival, predominantly in patients with shock or coagulopathy. LTOWB also resulted in a 40% reduction in blood product use which equates to a median 2.4 L reduction in transfused products.

Perioperative mortality among trauma patients in Northwest Ethiopia: a prospective cohort study

Trauma is the leading cause of mortality in persons under 45 and a significant public health issue. Trauma is the most frequent cause of perioperative mortality among all surgical patients. Little is known about perioperative outcomes among trauma patients in low-income countries. This study aimed to assess the incidence and identify predictors of perioperative mortality among adult trauma victims at Tibebe Ghion Specialised Hospital. From June 1, 2019, to June 30, 2021, a prospective cohort study was conducted at Tibebe Ghion Specialized Hospital. Demographic, pre-hospital and perioperative clinical data were collected using an electronic data collection tool, Research Electronic Data Capture (REDCap). Cox proportional hazard model regression was used to assess the association between predictors and perioperative mortality among trauma victims. Crude and adjusted hazard ratio (HR) with a 95% confidence interval (CI) was computed; a p-value < 0.05 was a cutoff value to declare statistical significance. One thousand sixty-nine trauma patients were enrolled in this study. The overall incidence of perioperative mortality among trauma patients was 5.89%, with an incidence rate of 2.23 (95% CI 1.74 to 2.86) deaths per 1000 person-day observation. Age ≥ 65 years (AHR = 2.51, 95% CI: 1.04, 6.08), patients sustained blunt trauma (AHR = 3.28, 95% CI: 1.30, 8.29) and MVA (AHR = 2.96, 95% CI: 1.18, 7.43), trauma occurred at night time (AHR = 2.29, 95% CI: 1.15, 4.56), ASA physical status ≥ III (AHR = 3.84, 95% CI: 1.88, 7.82), and blood transfusion (AHR = 2.01, 95% CI: 1.08, 3.74) were identified as a significant predictor for perioperative mortality among trauma patients. In this trauma cohort, it was demonstrated that perioperative mortality is a healthcare burden. Risk factors for perioperative mortality among trauma patients were old age, patients sustaining blunt trauma and motor vehicle accidents, injuries at night, higher ASA physical status, and blood transfusion. Trauma care services need improvement in pre-hospital and perioperative care.

Estimation of the onset time of diabetic complications in type 2 diabetes patients in Thailand: a survival analysis

BACKGROUND

This study aimed to identify factors associated with the onset time of diabetic complications in patients with type 2 diabetes mellitus (T2DM) and determine the best-fitted survival model.

METHODS

A retrospective cohort study was conducted among T2DM patients enrolled from October 1, 2016 to July 15, 2020 at the National Health Security Office (NHSO). In total, 388 T2DM patients were included. Cox proportional-hazard and parametric models were used to identify factors related to the onset time of diabetic complications. The Akaike information criterion, Bayesian information criterion, and Cox-Snell residual were compared to determine the best-fitted survival model.

RESULTS

Thirty diabetic complication events were detected among the 388 patients (7.7%). A 90% survival rate for the onset time of diabetic complications was found at 33 months after the first T2DM diagnosis. According to multivariate analysis, a duration of T2DM ≥42 months (time ratio [TR], 0.56; 95% confidence interval [CI], 0.33-0.96; p=0.034), comorbid hypertension (TR, 0.30; 95% CI, 0.15-0.60; p=0.001), mildly to moderately reduced levels of the estimated glomerular filtration rate (eGFR) (TR, 0.43; 95% CI, 0.24-0.75; p=0.003) and an eGFR that was severely reduced or indicative of kidney failure (TR, 0.38; 95% CI, 0.16-0.88; p=0.025) were significantly associated with the onset time of diabetic complications (p<0.05).

CONCLUSION

Patients with T2DM durations of more than 42 months, comorbid hypertension, and decreased eGFR were at risk of developing diabetic complications. The NHSO should be aware of these factors to establish a policy to prevent diabetic complications after the diagnosis of T2DM.

Comparative effectiveness of dual antiplatelet therapy versus monotherapy in patients with ischemic stroke

OBJECTIVES

To compare the effectiveness of aspirin-clopidogrel dual antiplatelet therapy (DAPT) with aspirin or clopidogrel antiplatelet monotherapy (AM) in patients with ischemic stroke.

METHODS

It was a single-center, retrospective cross-sectional study of medical records of ischemic stroke patients admitted at King Abdulaziz University Hospital between January 2015 and October 2019. The primary endpoints were ischemic stroke recurrence, rehospitalization, and all-cause mortality between DAPT and AM. Kaplan-Meier and Cox proportional hazard analyses were employed in univariate and multivariate time-to-event analyses.

RESULTS

The median time to recurrence of ischemic stroke was 15.0 months (95% confidence interval [CI], 8.586-23.01) for DAPT and 20.4 months (95% CI, 9.872-30.928) for the AM. The median survival time until all-cause mortality was 8.0 months (95% CI, 2.893-13.107) for DAPT and 14.1 months (95% CI, 8.173-19.97) for the AM. No statistically significant reductions in the instantaneous risks of recurrence (hazard ratio [HR], 1.27; 95% CI, 0.59-2.72; p=0.54), re-hospitalization (HR, 0.95; 95% CI, 0.59-1.48; p= 0.77), and mortality (HR, 1.04; 95% CI, 0.48-2.26; p=0.92) were found between the DAPT and AM groups.

CONCLUSION

The DAPT was not superior to AM in reducing recurrence and mortality events in patients with ischemic stroke. Rehospitalization due to the sequelae of the composite of stroke, angina, and myocardial infarction was higher in the DAPT group.

Mortality Risk in Older People Who Drank Alcohol in the Past by Varying Duration of Alcohol Abstention

INTRODUCTION

This study aims to explore the mortality risk in older people who drank alcohol in the past by varying the duration of alcohol abstention.

METHODS

In total, 31,999 participants aged ≥65 years from the Chinese Longitudinal Healthy Longevity Survey (Waves 1998, 2000, 2002, 2005, 2008, 2011, 2014) were included. Duration of alcohol abstention was assessed by designed questions, and the study outcome was all-cause mortality. Cox proportional hazard models were used to examine the association. Analyses occurred from 2022 to 2023.

RESULTS

During a follow-up of 140,974.8 person-years, all-cause mortality occurred in 24,257 participants. Mortality significantly increased by 23% (adjusted hazard ratio=1.23, 95% CI=1.14, 1.33, p<0.001), by 17% (adjusted hazard ratio=1.17, 95% CI=1.06, 1.31, p=0.003), and by 17% (adjusted hazard ratio=1.17, 95% CI=1.07, 1.28, p=0.001) in people who drank alcohol in the past with ≤5 years, 5-10 years, 10-20 years of alcohol abstention, respectively, compared with that among those who drink alcohol at present. After 20 years of alcohol abstention, the increased mortality risk disappeared (adjusted hazard ratio=1.06, 95% CI=0.97, 1.15, p=0.204). Stratified and sensitivity analysis revealed similar results. In addition, compared with the risk of all-cause mortality among people who never drink alcohol, the risk of all-cause mortality in those who drank alcohol in the past also significantly increased in the following 20 years after they stop drinking, and then the increased risk disappeared afterward.

CONCLUSIONS

An increased risk of all-cause mortality in older people who drank alcohol in the past was observed, which disappeared after 20 years of alcohol abstention.

Integrated bioinformatics analysis of noncoding RNAs with tumor immune microenvironment in gastric cancer

In recent years, molecular and genetic research hotspots of gastric cancer have been investigated, including microRNAs, long noncoding RNAs (lncRNAs) and messenger RNA (mRNAs). The study on the role of lncRNAs may help to develop personalized treatment and identify potential prognostic biomarkers in gastric cancer. The RNA-seq and miRNA-seq data of gastric cancer were downloaded from the TCGA database. Differential analysis of RNA expression between gastric cancer samples and normal samples was performed using the edgeR package. The ceRNA regulatory network was visualized using Cytoscape. KEGG pathway analysis of mRNAs in the ceRNA network was performed using the clusterProfiler package. CIBERSORT was used to distinguish 22 immune cell types and the prognosis-related genes and immune cells were determined using Kaplan-Meier and Cox proportional hazard analyses. To estimate these nomograms, we used receiver operating characteristic and calibration curve studies. The ceRNA regulation network of gastric cancer was built in this study, and the genes in the network were analyzed for prognosis. A total of 980 lncRNAs were differentially expressed, of which 774 were upregulated and 206 were downregulated. A survival study identified 15 genes associated with gastric cancer prognosis, including VCAN-AS1, SERPINE1, AL139002.1, LINC00326, AC018781.1, C15orf54, hsa-miR-145. Monocytes and Neutrophils were associated with the survival rate of gastric cancer. Our research uncovers new ceRNA network for the detection, treatment, and monitoring of gastric cancer.

Orbital Exenteration for Craniofacial Lesions: A Systematic Review and Meta-Analysis of Patient Characteristics and Survival Outcomes

BACKGROUND

The outcomes of orbital exenteration (OE) in patients with craniofacial lesions (CFLs) remain unclear. The present review summarizes the available literature on the clinical outcomes of OE, including surgical outcomes and overall survival (OS).

METHODS

Relevant articles were retrieved from Medline, Scopus, and Cochrane according to PRISMA guidelines. A systematic review and meta-analysis were conducted on the clinical characteristics, management, and outcomes.

RESULTS

A total of 33 articles containing 957 patients who underwent OE for CFLs were included (weighted mean age: 64.3 years [95% CI: 59.9-68.7]; 58.3% were male). The most common lesion was squamous cell carcinoma (31.8%), and the most common symptom was disturbed vision/reduced visual acuity (22.5%). Of the patients, 302 (31.6%) had total OE, 248 (26.0%) had extended OE, and 87 (9.0%) had subtotal OE. Free flaps (33.3%), endosseous implants (22.8%), and split-thickness skin grafts (17.2%) were the most used reconstructive methods. Sino-orbital or sino-nasal fistula (22.6%), flap or graft failure (16.9%), and hyperostosis (13%) were the most reported complications. Regarding tumor recurrences, 38.6% were local, 32.3% were distant, and 6.7% were regional. The perineural invasion rate was 17.4%, while the lymphovascular invasion rate was 5.0%. Over a weighted mean follow-up period of 23.6 months (95% CI: 13.8-33.4), a weighted overall mortality rate of 39% (95% CI: 28-50%) was observed. The 5-year OS rate was 50% (median: 61 months [95% CI: 46-83]). The OS multivariable analysis did not show any significant findings.

CONCLUSIONS

Although OE is a disfiguring procedure with devastating outcomes, it is a viable option for carefully selected patients with advanced CFLs. A patient-tailored approach based on tumor pathology, extension, and overall patient condition is warranted.

Integrated Multi-Omic Analysis Reveals Immunosuppressive Phenotype Associated with Poor Outcomes in High-Grade Serous Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is characterized by a complex genomic landscape, with both genetic and epigenetic diversity contributing to its pathogenesis, disease course, and response to treatment. To better understand the association between genomic features and response to treatment among 370 patients with newly diagnosed HGSOC, we utilized multi-omic data and semi-biased clustering of HGSOC specimens profiled by TCGA. A Cox regression model was deployed to select model input features based on the influence on disease recurrence. Among the features most significantly correlated with recurrence were the promotor-associated probes for the NFRKB and DPT genes and the TREML1 gene. Using 1467 transcriptomic and methylomic features as input to consensus clustering, we identified four distinct tumor clusters-three of which had noteworthy differences in treatment response and time to disease recurrence. Each cluster had unique divergence in differential analyses and distinctly enriched pathways therein. Differences in predicted stromal and immune cell-type composition were also observed, with an immune-suppressive phenotype specific to one cluster, which associated with short time to disease recurrence. Our model features were additionally used as a neural network input layer to validate the previously defined clusters with high prediction accuracy (91.3%). Overall, our approach highlights an integrated data utilization workflow from tumor-derived samples, which can be used to uncover novel drivers of clinical outcomes.

Incidence and predictors of perioperative mortality in a low-resource country, Ethiopia: a prospective follow-up study

OBJECTIVE

This study aimed to assess the incidence and identify predictors of perioperative mortality among the adult age group at Tibebe Ghion Specialised Hospital.

DESIGN

A single-centre prospective follow-up study.

SETTING

A tertiary hospital in North West Ethiopia.

PARTICIPANTS

We enrolled 2530 participants who underwent surgery in the current study. All adults aged 18 and above were included except those with no telephone.

PRIMARY OUTCOME MEASURES

The primary outcome was time to death measured in days from immediate postoperative time up to the 28th day following surgery.

RESULT

A total of 2530 surgical cases were followed for 67 145 person-days. There were 92 deaths, with an incidence rate of 1.37 (95% CI 1.11 to 1.68) deaths per 1000 person-day observations. Regional anaesthesia was significantly associated with lower postoperative mortality (adjusted hazard ratio (AHR) 0.18, 95% CI 0.05 to 0.62). Patients aged ≥65 years (AHR 3.04, 95% CI 1.65 to 5.75), American Society of Anesthesiologist (ASA) physical status III (AHR 2.41, 95% CI 1.1.13 to 5.16) and IV (AHR 2.74, 95% CI 1.08 to 6.92), emergency surgery (AHR 1.85, 95% CI 1.02 to 3.36) and preoperative oxygen saturation <95% (AHR 3.14, 95% CI 1.85 to 5.33) were significantly associated with a higher risk of postoperative mortality.

CONCLUSION

The postoperative mortality rate at Tibebe Ghion Specialised Hospital was high. Age ≥65, ASA physical status III and IV, emergency surgery, and preoperative oxygen saturation <95% were significant predictors of postoperative mortality. Patients with the identified predictors should be offered targeted treatment.

A Gallbladder Cancer Survival Prediction Model Based on Multimodal Fusion Analysis

BACKGROUND

Gallbladder cancer is the sixth most common malignant gastrointestinal tumor. Radical surgery is currently the only effective treatment, but patient prognosis is poor, with a 5-year survival rate of only 5-10%. Establishing an effective survival prediction model for gallbladder cancer patients is crucial for disease status assessment, early intervention, and individualized treatment approaches. The existing gallbladder cancer survival prediction model uses clinical data-radiotherapy and chemotherapy, pathology, and surgical scope-but fails to utilize laboratory examination and imaging data, limiting its prediction accuracy and preventing sufficient treatment plan guidance.

AIMS

The aim of this work is to propose an accurate survival prediction model, based on the deep learning 3D-DenseNet network, integrated with multimodal medical data (enhanced CT imaging, laboratory test results, and data regarding systemic treatments).

METHODS

Data were collected from 195 gallbladder cancer patients at two large tertiary hospitals in Shanghai. The 3D-DenseNet network extracted deep imaging features and constructed prognostic factors, from which a multimodal survival prediction model was established, based on the Cox regression model and incorporating patients' laboratory test and systemic treatment data.

RESULTS

The model had a C-index of 0.787 in predicting patients' survival rate. Moreover, the area under the curve (AUC) of predicting patients' 1-, 3-, and 5-year survival rates reached 0.827, 0.865, and 0.926, respectively.

CONCLUSIONS

Compared with the monomodal model based on deep imaging features and the tumor-node-metastasis (TNM) staging system-widely used in clinical practice-our model's prediction accuracy was greatly improved, aiding the prognostic assessment of gallbladder cancer patients.

A retrospective study on the incidence, management and risk factors of skin rash in patients with advanced prostate cancer in Japan

BACKGROUND

Worldwide, prostate cancer (PC) is the second most diagnosed cancer and the fifth leading cause of cancer death in men. Hormonal therapies, commonly used for PC, are associated with a range of treatment-emergent adverse events (TEAEs). The population from Japan seems to be at higher risk of developing TEAEs of skin rash compared to the overall global population. This study was conducted to get a better insight into the incidence, management, and risk factors for skin rash during active treatment for advanced PC in Japan.

METHODS

A retrospective cohort of PC patients was identified and subsequently categorized, into non-metastatic and metastatic castration-resistant prostate cancer patients (nmCRPC and mCRPC), and metastatic castration-naïve prostate cancer patients (mCNPC). The analysis was based on a dataset from the Medical Data Vision (MDV) database. Descriptive statistics were determined, and a multivariate Cox proportional hazards model was used to the associated risk factors for the onset of rash.

RESULTS

Overall, 1,738 nmCRPC patients, 630 mCRPC patients, and 454 mCNPC patients were included in this analysis. The median age was 78 years old and similar across the three cohorts. The skin rash incidence was 19.97% for nmCRPC cohort, 28.89% for mCRPC cohort, and 28.85% for mCNPC cohort. The median duration of skin rash ranged from 29 to 42 days. Statistically significant risk factors for developing skin rash included a history of allergy or hypersensitivity (all cohorts), increased age (nmCRPC and mCRPC), a body mass index (BMI) of < 18.5 (nmCRPC and mCRPC), and a PSA level higher than the median (nmCRPC). Skin rash was commonly managed with systemic and topical corticosteroids which ranged from 41.76% to 67.03% for all cohorts. Antihistamines were infrequently used.

CONCLUSION

This study provides a better understanding of the real-world incidence, onset, duration, management and risk factors of skin rash in patients on active PC treatment in Japan. It was observed that approximately 20-30% of PC patients experience skin rash. Development of skin rash was associated with previous allergy or hypersensitivity, BMI of < 18.5, increased age and higher PSA levels, and was usually treated with corticosteroids.

Breast cancer survival analysis agents for clinical decision support

Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.

Borrowing Concurrent Information from Non-Concurrent Control to Enhance Statistical Efficiency in Platform Trials

A platform trial is a trial involving an innovative adaptive design with a single master protocol to efficiently evaluate multiple interventions. It offers flexible features such as dropping interventions for futility and adding new interventions to be evaluated during the course of a trial. Although there is a consensus that platform trials can identify beneficial interventions with fewer patients, less time, and a higher probability of success than traditional trials, there remains debate on certain issues, one of which is whether (and how) the non-concurrent control (NCC) (i.e., patients in the control group recruited prior to the new interventions) can be combined with the current control (CC) in the analysis, especially if there is a change of standard of care during the trial. Methods: In this paper, considering time-to-event endpoints under the proportional hazard model assumption, we introduce a new concept of NCC concurrent observation time (NCC COT), and propose to borrow NCC COT through left truncation. This assumes that the NCC COT and CC are comparable. If the protocol does not prohibit NCC patients to change the standard of care while on study, NCC COT and CC likely will share the same standard of care. A simulated example is provided to demonstrate the approach. Results: Using exponential distributions, the simulated example assumes that NCC COT and CC have the same hazard, and the treatment group has a lower hazard. The estimated HR comparing treatment to the pooled control group is 0.744 (95% CI 0.575, 0.962), whereas the comparison to the CC group alone is 0.755 (95% CI 0.566, 1.008), with corresponding p-values of 0.024 versus 0.057, respectively. This suggests that borrowing NCC COT can improve statistical efficiency when the exchangeability assumption holds. Conclusion: This article proposes an innovative approach of borrowing NCC COT to enhance statistical inference in platform trials under appropriate scenarios.

Survival benefits of oral anticoagulation therapy in acute kidney injury patients with atrial fibrillation: a retrospective study from the MIMIC-IV database

OBJECTIVE

To find out the effect of different oral anticoagulation therapies (OAC) on mortality rate in patients with acute kidney injury (AKI) and atrial fibrillation (AF).DesignA retrospective study.

SETTING

This study was conducted in the Medical Information Mart for Intensive Care IV database.

PARTICIPANTS

A total of 19 672 patients diagnosed with AKI.

MAIN OUTCOME MEASURES

Patients were categorised into three groups: (1) AF; (2) AKI and AF, OAC-; (3) AKI and AF, OAC+. The primary endpoint was 30-day mortality. Secondary endpoints were the length of stay (LOS) in the intensive care unit (ICU) and hospital. Propensity score matching (PSM) and Cox proportional hazards model adjusted confounding factors. Linear regression was applied to assess the associations between OAC treatment and LOS.

RESULTS

After PSM, 2042 pairs of AKI and AF patients were matched between the patients who received OAC and those without anticoagulant treatment. Cox regression analysis showed that, OAC significantly reduce 30-day mortality compared with non-OAC (HR 0.30; 95% CI 0.25 to 0.35; p<0.001). Linear regression analysis revealed that OAC prolong LOS in hospital (11.3 days vs 10.0 days; p=0.013) and ICU (4.9 days vs 4.4 days; p<0.001). OAC did not improve survival in patients with haemorrhage (HR 0.67; 95% CI 0.34 to 1.29; p=0.23). Novel OAC did not reduce mortality in acute-on-chronic renal injury (HR 2.03; 95% CI 1.09 to 3.78; p=0.025) patients compared with warfarin.

CONCLUSION

OAC administration was associated with improved short-term survival in AKI patients concomitant with AF.

An Empirical Study of Promotion Pressure among University Teachers in China Using Event History Analysis

OBJECTIVE

We sought to understand the status of promotion pressure among university teachers in China. This study explored the promotion duration and influencing factors among teachers in different disciplines of the social sciences.

METHODS

Using event history analysis, this study collected data regarding university teachers of China. The sample included 536 teachers who had been promoted from assistant to associate professor and 243 teachers promoted from associate to full professor. Our results revealed that the overall time required for promotion in the social sciences is relatively long. For those promoted from assistant to associate professor, the mean time for promotion was 14.155 years, with a median of 11 years, while for the transition from associate to full professor, the mean was 13.904 years with a median of nine years. Furthermore, in the survival function of the promotion duration, there is a stage pattern for both assistant to associate professor and associate to full professor. In addition, the Kaplan-Meier results showed that the mean promotion time in economics was the shortest. The Cox regression results indicated that males had a higher chance of promotion than females, and faculty members with doctoral degrees had a higher likelihood of promotion than those without. For those advancing from assistant to associate professor, the university of employment had significant positive effects on promotion. This paper provides empirical support for the current societal concerns regarding promotion pressure among university teachers.

Cox model and decision trees: an application to breast cancer data

Objective.

To evaluate, using semiparametric methodologies of survival analysis, the relationship between covariates and time to death of patients with breast cancer, as well as the determination discriminatory power in the conditional inference tree of patients who had cancer.

Methods.

A retrospective cohort study was conducted using data collected from medical records of women who had breast cancer and underwent treatment between 2005 and 2015 at the Hospital da Fundação de Assistencial da Paraíba in Campina Grande, State of Paraiba, Brazil. Survival curves were estimated using the Kaplan–Meier method, Cox regression, and conditional decision tree.

Results.

Women with triple-negative molecular subtypes had a shorter survival time compared to women with positive hormone receptors. The addition of hormone therapy reduced the risk of a patient dying by 5.5%, and the risk of a HER2-positive patient dying was 34.5% lower compared to those who were negative for this gene. Patients undergoing hormone therapy had a median survival time of 4 753 days.

Conclusions.

This paper shows a favorable scenario for the use of immunotherapy for patients with HER2 overexpression. Further studies could assess the effectiveness of immunotherapy in patients with other conditions, to favor the prognosis and better quality of life for the patient.