Aetiology and predictors of outcome in non-shockable in-hospital cardiac arrest: A retrospective cohort study from the Swedish Registry for Cardiopulmonary Resuscitation.

BACKGROUND: Non-shockable in-hospital cardiac arrest (IHCA) is a condition with diverse aetiology, predictive factors, and outcome. This study aimed to compare IHCA with initial asystole or pulseless electrical activity (PEA), focusing specifically on their aetiologies and the significance of predictive factors. METHODS: Using the Swedish Registry of Cardiopulmonary Resuscitation, adult non-shockable IHCA cases from 2018 to 2022 (n = 5788) were analysed. Exposure was initial rhythm, while survival to hospital discharge was the primary outcome. A random forest model with 28 variables was used to generate permutation-based variable importance for outcome prediction. RESULTS: Overall, 60% of patients (n = 3486) were male and the median age was 75 years (IQR 67-81). The most frequent arrest location (46%) was on general wards. Comorbidities were present in 79% of cases and the most prevalent comorbidity was heart failure (33%). Initial rhythm was PEA in 47% (n = 2702) of patients, and asystole in 53% (n = 3086). The most frequent aetiologies in both PEA and asystole were cardiac ischemia (24% vs. 19%, absolute difference [AD]: 5.4%; 95% confidence interval [CI] 3.0% to 7.7%), and respiratory failure (14% vs. 13%, no significant difference). Survival was higher in asystole (24%) than in PEA (17%) (AD: 7.3%; 95% CI 5.2% to 9.4%). Cardiopulmonary resuscitation (CPR) durations were longer in PEA, 18 vs 15 min (AD 4.9 min, 95% CI 4.0-5.9 min). The duration of CPR was the single most important predictor of survival across all subgroup and sensitivity analyses. Aetiology ranked as the second most important predictor in most analyses, except in the asystole subgroup where responsiveness at cardiac arrest team arrival took precedence. CONCLUSIONS: In this nationwide registry study of non-shockable IHCA comparing asystole to PEA, cardiac ischemia and respiratory failure were the predominant aetiologies. Duration of CPR was the most important predictor of survival, followed by aetiology. Asystole was associated with higher survival compared to PEA, possibly due to shorter CPR durations and a larger proportion of reversible aetiologies.

The Survival and Financial Benefit of Investigator-Initiated Trials Conducted by Korean Cancer Study Group.

Purpose: The Korean Cancer Study Group (KCSG) is a nationwide cancer clinical trial group dedicated to advancing investigator-initiated trials (IITs) by conducting and supporting clinical trials. This study aims to review IITs conducted by KCSG and quantitatively evaluate the survival and financial benefits of IITs for patients. Materials and Methods: We reviewed IITs conducted by KCSG from 1998 to 2023, analyzing progression-free survival (PFS) and overall survival (OS) gains for participants. PFS and OS benefits were calculated as the difference in median survival times between the intervention and control groups, multiplied by the number of patients in the intervention group. Financial benefits were assessed based on the cost of investigational products provided. Results: From 1998 to 2023, KCSG conducted 310 IITs, with 133 completed and published. Of these, 21 were included in the survival analysis. The analysis revealed that 1,951 patients in the intervention groups gained a total of 2,558.4 months (213.2 years) of PFS and 2,501.6 months (208.5 years) of OS, with median gains of 1.31 months in PFS and 1.58 months in OS per patient. When analyzing only statistically significant results, PFS and OS gain per patients was 1.69 months and 3.02 months, respectively. Investigational drug cost analysis from 6 available IITs indicated that investigational products provided to 252 patients were valued at 10,400,077,294 won (approximately 8,046,481 US dollars), averaging about 41,270,148 won (approximately 31,930 US dollars) per patient. Conclusion: Our findings, based on analysis of published research, suggest that IITs conducted by KCSG led to survival benefits for participants and, in some studies, may have provided financial benefits by providing investment drugs.

Association of Serum Proteases and Acute Phase Factors Levels with Survival Outcomes in Patients with Colorectal Cancer.

Colorectal cancer (CRC) represents a substantial burden on global healthcare, contributing to significant morbidity and mortality worldwide. Despite advances in screening methodologies, its incidence remains high, necessitating continued efforts in early detection and treatment. Neoplastic invasion and metastasis are primary determinants of CRC lethality, emphasizing the urgency of understanding underlying mechanisms to develop effective therapeutic strategies. This study aimed to explore the potential of serum biomarkers in predicting survival outcomes in CRC patients, with a focus on cathepsin B (CB), leukocytic elastase (LE), total sialic acid (TSA), lipid-associated sialic acid (LASA), antitrypsin activity (ATA), C-reactive protein (CRP), and cystatin C (CC). We recruited 185 CRC patients and 35 healthy controls, assessing demographic variables, tumor characteristics, and 7 serum biomarker levels, including (1) CB, (2) LE, (3) TSA, (4) LASA, (5) ATA, (6) CRP, and (7) CC. Statistical analyses included ANOVA with Tukey's post hoc tests and MANOVA for continuous variables. Student's t-test was used for dependent samples, while non-parametric tests like Mann-Whitney U and Wilcoxon signed-rank tests were applied for variables deviating from the normal distribution. Categorical variables were assessed using chi-square and Kruskal-Wallis tests. Spearman's rank correlation coefficient was utilized to examine variable correlations. Survival analysis employed the Kaplan-Meier method with a log-rank test for comparing survival times between groups. Significant associations were observed between CB (p = 0.04), LE (p = 0.01), and TSA (p = 0.008) levels and survival outcomes in CRC patients. Dukes' classification stages also showed a significant correlation with survival (p = 0.001). However, no significant associations were found for LASA, ATA, CRP, and CC. Multivariate analysis of LE, TSA, and ATA demonstrated a notable correlation with survival (p = 0.041), notwithstanding ATA's lack of significance in univariate analysis (p = 0.13). CB, LE, and TSA emerged as promising diagnostic markers with prognostic value in CRC, potentially aiding in early diagnosis and treatment planning. Further research is needed to validate these findings and explore additional prognostic indicators.

Case-cohort design as an efficient approach to evaluating COVID-19 vaccine effectiveness, waning, heterologous immune effect and optimal dosing interval.

Though widely applied in other epidemiological fields, the case-cohort study design has seen little application in the field of vaccinology. Case-cohort studies use probabilistic sampling and reweighting to draw inferences about effects (in this case vaccine efficacy) at the population level in an efficient manner. The SARS-CoV-2 pandemic was met with high vaccine uptake, and high rates of population testing prior to the emergence of Omicron variants of concern, in Ontario, Canada, providing an ideal environment for application of case-cohort methodology. We combined a population-based case line list and vaccination database for the province of Ontario between December 2020 and October 2021. Risk of infection after vaccination was evaluated in all laboratory-confirmed vaccinated SARS-CoV-2 cases, and a 2 % sample of vaccinated controls, evaluated using survival analytic methods, including construction of Cox proportional hazards models. Vaccination status was treated as a time-varying covariate. First and second doses of SARS-CoV-2 vaccine markedly reduced risk of infection (first dose efficacy 68 %, 95 % CI 67 %-69 %; second dose efficacy 88 %, 95 % CI 87-88 %). In multivariable models, extended dosing intervals were associated with lowest risk of breakthrough infection (HR for redosing 0.64 (95 % CI 0.61-0.67) at 6-8 weeks). Heterologous vaccine schedules that mixed viral vector vaccine first doses with mRNA second doses were significantly more effective than mRNA only vaccines. Risk of infection largely vanished during the time period 4-6 months after the second vaccine dose, but rose markedly thereafter. We conclude that a case-cohort design provided an efficient means to identify strong protective effects associated with SARS-CoV-2 vaccination in real time, and also served to quantify the timing and magnitude of infection breakthrough risk in the same cohort. Heterologous vaccination and extended dosing intervals improved the durability of immune response.

Reproductive potential and implant loss in female hamadryas baboons (Papio hamadryas) previously contracepted with melengestrol acetate contraceptive implants at AZA institutions.

Melengestrol acetate (MGA) implants are a progestin-based reversible contraceptive used to manage fertility in animals. MGA implants are recommended for replacement every 2 years; however, reproduction may be suppressed longer if implants are not removed. In this study, we investigated whether the probability of reproducing (pR) differed among nonimplanted females, females with MGA implants removed, and females whose implants were not removed. In addition, since implant loss in hamadryas baboons is a concern, we explored whether female age, institution, implant placement year, implant location, or implant placement type (intramuscular vs. subcutaneous) differed for females whose implants were lost compared to those that were not. The pR differed significantly across all three treatment conditions with the nonimplanted group having the highest pR. The pR plateaued at 63% after 40 months for the implant-removed group compared to 96% after 84 months in the nonimplanted group. There was no reproduction after contraception if implants were not removed (7.83-45.53 months). In the nonimplanted group, pR was significantly higher for older and parous females. In terms of implant loss, we found that implant placement type was significantly associated with implant loss, such that there were fewer losses when implants were placed intramuscularly (IM) as compared to subcutaneously. Our results suggest that placing MGA implants IM is likely to reduce loss. When loss is prevented, MGA implants are an effective form of contraception and are reliably reversibly in most individuals when removed. However, if not removed, they can prevent reproduction longer than 2 years.

Well-defined survival outcome disparity across age cutoffs at 45 and 60 for medullary thyroid carcinoma: a long-term retrospective cohort study of 3601 patients.

Background: Medullary thyroid cancer (MTC) is a challenging malignancy. The survival outcome of MTC based on AJCC staging system does not render a discriminant classifier among early stages. Methods: 3601 MTC patients from 2000 to 2018 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Smooth curve fitting, Cox proportional hazard regression and competing risk analysis were applied. Results: A linear correlation between age and log RR (relative risk of overall death) was detected. Overlaps were observed between K-M curves representing patients aged 45-50, 50-55, and 55-60. The study cohort was divided into 3 subgroups with 2 age cutoffs set at 45 and 60. Each further advanced age cutoff population resulted in a roughly "5%" increase in MTC-specific death risks and an approximately "3 times" increase in non-MTC-specific death risks. Conclusions: The survival outcome disparity across age cutoffs at 45 and 60 for MTC has been well defined.

MYBL2 is a Novel Independent Prognostic Biomarker and Correlated with TMB in pancreatic cancer.

Background: Pancreatic cancer continues to pose a significant threat due to its high mortality rate. While MYB family genes have been identified as oncogenes in certain cancer types, their role in pancreatic cancer remains largely unexplored. Methods: The mRNA and protein expression of MYB family genes in pancreatic cancer samples was analyzed using TNMplot, HPA, and TISBID online bioinformatics tools, sourced from the TCGA and GETx databases. The relationship between MYB family gene expression and survival time was assessed through Kaplan-Meier analysis, while the prognostic impact of MYB family gene expression was evaluated using the Cox proportional hazards model. Additionally, Spearman's correlation analysis was employed to investigate the correlation between MYB family genes and TMB/MSI. Results: The integration of data from various databases demonstrated that all MYB family genes exhibited dysregulated expression in pancreatic cancer. However, only the expression of the MYBL2 gene displayed a notable association with the grade and stage of pancreatic cancer. Furthermore, the MYBL2 gene exhibited significant variations in both univariate and multivariate factor analyses.Subsequent functional analyses revealed a significant correlation between MYBL2 expression in pancreatic cancers and various biological processes, such as DNA replication, tumor proliferation, G2M checkpoint regulation, pyrimidine metabolism, and the P53 pathway. Additionally, a notable positive association was observed between MYBL2 expression and tumor mutational burden (TMB), a predictive indicator for response to PD1 antibody treatment. Conclusion: MYBL2 may be a double marker for independent diagnosis and PD1 antibody response prediction of pancreatic cancer patients.

Brain natriuretic peptide is a long-term cardiovascular predictor in carotid endarterectomy.

BACKGROUND: In noncardiac surgery, several biomarkers are known to play a role in predicting long-term complications, such as major adverse cardiovascular events (MACE), myocardial infarction, or death. Carotid endarterectomy (CEA) is considered a low to medium-risk surgery for carotid stenosis aimed at preventing stroke events. Brain natriuretic peptide (BNP) is a biomarker with potential prognostic value regarding MACE. Since its role in patients undergoing CEA is unknown, this study aims to assess the potential role of BNP as a short and long-term predictor of all-cause mortality and MACE in patients undergoing CEA. METHODS: From a prospective database, patients who underwent CEA under regional anesthesia (RA) at a tertiary hospital center were enrolled, and a post hoc analysis was conducted. Patients on which BNP levels were measured up to fifteen days before surgery, and two groups based on the BNP threshold (200 pg/mL) were defined and compared. Kaplan Meier survival curves and adjusted hazard ratios (aHR) were assessed by multivariable Cox regression. The primary outcome was the incidence of long-term MACE and all-cause mortality. Secondary outcomes included the incidence of AMI and AHF. RESULTS: A total of 89 patients were evaluated. The mean age of the cohort was 71.2 ± 8.7 years, with 71 (79.8%) males, and presented a median follow-up of 30 [13.5-46.4] months. BNP > 200 pg/mL has demonstrated positive predictive value for MACE (aHR: 5.569, confidence interval (CI): 2.441-12.7, p < 0.001) and all-cause mortality (aHR: 3.469, CI: 1.315-9.150, p = 0.018). CONCLUSION: BNP has been demonstrated to independently predict long-term all-cause mortality, MACE and AMI following CEA. It serves as a low-cost, ready-to-use biomarker, although further studies are necessary.

ctGAN: combined transformation of gene expression and survival data with generative adversarial network.

Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, are indispensable. However, deep learning models are plagued by overfitting owing to the limited sample size relative to the large number of genes. Consequently, the latest style-transfer deep generative models have been implemented to generate gene expression data. However, these models are limited in their applicability for clinical purposes because they generate only transcriptomic data. Therefore, this study proposes ctGAN, which enables the combined transformation of gene expression and survival data using a generative adversarial network (GAN). ctGAN improves survival analysis by augmenting data through style transformations between breast cancer and 11 other cancer types. We evaluated the concordance index (C-index) enhancements compared with previous models to demonstrate its superiority. Performance improvements were observed in nine of the 11 cancer types. Moreover, ctGAN outperformed previous models in seven out of the 11 cancer types, with colon adenocarcinoma (COAD) exhibiting the most significant improvement (median C-index increase of ~15.70%). Furthermore, integrating the generated COAD enhanced the log-rank p-value (0.041) compared with using only the real COAD (p-value = 0.797). Based on the data distribution, we demonstrated that the model generated highly plausible data. In clustering evaluation, ctGAN exhibited the highest performance in most cases (89.62%). These findings suggest that ctGAN can be meaningfully utilized to predict disease progression and select personalized treatments in the medical field.

Retrospective analysis of the efficacy and survival associated with cTACE and DEB-TACE in the palliative treatment of hepatocellular carcinoma: experience of a tertiary care hospital in southern Brazil.

Objective: To compare conventional transarterial chemoembolization (cTACE) and drug-eluting bead TACE (DEB-TACE) in terms of efficacy, survival, and adverse effects in patients with hepatocellular carcinoma who are not candidates for curative therapy. Materials and Methods: This was a retrospective study of patients with hepatocellular carcinoma who underwent cTACE or DEB-TACE for palliative treatment between January 2009 and December 2021. The Kaplan-Meier method was used for survival analysis. Values of p < 0.05 were considered statistically significant. Results: We evaluated 268 patients, of whom 70 underwent DEB-TACE and 198 underwent cTACE. There was no significant difference between the groups regarding sex, age, or etiology of cirrhosis. The proportion of patients achieving a complete response on imaging examinations was higher in the cTACE group (31.8% vs. 16.1%), whereas that of patients achieving a partial response was higher in the DEB-TACE group (33.9% vs.19.7%), and the differences were significant (p = 0.014). The mortality rate was similar between the groups. The survival rate in the DEB-TACE and cTACE groups, respectively, was 87.0% and 87.9% at one year, 35.1% and 32.9% at three years, and 20.5% and 18.1% at five years (p = 0.661). There was no significant difference between the DEB-TACE and cTACE groups in terms of the frequency of adverse events (7.1% vs. 17.8%; p = 0.052). The most common complication in both groups was post-embolization syndrome. Conclusion: Although a complete response was more common among the patients who underwent cTACE, there was no difference in survival between the groups and the frequency of adverse events was similar.

Objetivo: Comparar a eficácia, sobrevida e efeitos adversos entre cTACE e DEB-TACE em pacientes com carcinoma hepatocelular não candidatos a terapia curativa. Materiais e Métodos: Estudo retrospectivo de pacientes com carcinoma hepatocelular submetidos a cTACE ou DEB-TACE para tratamento paliativo entre janeiro de 2009 e dezembro de 2021. Foi utilizado o método Kaplan-Meier para análise de sobrevida. Valor de p < 0,05 foi considerado estatisticamente significante. Resultados: Foram avaliados 268 pacientes, dos quais 70 foram submetidos a DEB-TACE e 198 foram submetidos a cTACE. Não houve diferença em relação ao sexo, idade e etiologia da cirrose. O grupo cTACE apresentou maior porcentual de resposta completa em exames de imagem (31,8% vs. 16,1%) e o grupo DEB-TACE apresentou maior porcentual de resposta parcial (33,9% vs.19,7%), com valor de p = 0,014. A mortalidade foi semelhante. As taxas de sobrevivência para os grupos DEB-TACE e cTACE foram 87,0% e 87,9% em um ano, 35,1% e 32,9% em três anos e 20,5% e 18,1% em cinco anos, respectivamente (p = 0,661). Em relação à frequência de eventos adversos, não houve diferença significativa entre os grupos (7,1% na DEB-TACE vs. 17,8% na cTACE; p = 0,052). A complicação mais comum, em ambos os grupos, foi a síndrome pós-embolização. Conclusão: Embora tenha sido observada maior frequência de resposta completa em pacientes submetidos a cTACE, não houve diferença na sobrevida dos pacientes entre os grupos. A taxa de eventos adversos também foi semelhante.

SurvdigitizeR: an algorithm for automated survival curve digitization.

BACKGROUND: Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan-Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves. METHODS: The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm's performance was also evaluated by Bland-Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves. RESULTS: The automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement. CONCLUSIONS: The algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/ .

A Bayesian Survival Analysis on Long COVID and non Long COVID patients: A Cohort Study Using National COVID Cohort Collaborative (N3C) Data.

Since the outbreak of COVID-19 pandemic in 2020, numerous researches and studies have focused on the long-term effects of COVID infection. The Centers for Disease Control (CDC) implemented an additional code into the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for reporting 'Post COVID-19 condition, unspecified (U09.9)' effective on October 1st 2021, representing that Long COVID is a real illness with potential chronic conditions. The National COVID Cohort Collaborative (N3C) provides researchers with abundant electronic health records (EHR) data by aggregating and harmonizing EHR data across different clinical organizations in the United States, making it convenient to build up a survival analysis on Long COVID patients and non Long COVID patients among large amounts of COVID positive patients.

Identification of prognostic imaging biomarkers in H3 K27-altered diffuse midline gliomas in adults: impact of tumor oxygenation imaging biomarkers on survival.

PURPOSE: To investigate prognostic markers for H3 K27-altered diffuse midline gliomas (DMGs) in adults with clinical, qualitative and quantitative imaging phenotypes, including tumor oxygenation characteristics. METHODS: Retrospective chart and imaging reviews were conducted on 32 adults with H3 K27-altered DMGs between 2017 and 2023. Clinical and qualitative imaging characteristics were analyzed. Quantitative imaging assessment was performed from the tumor mask via automatic segmentation to calculate normalized cerebral blood volume (nCBV), capillary transit time heterogeneity (CTH), oxygen extraction fraction (OEF), relative cerebral metabolic rate of oxygen (rCMRO2), and mean ADC values. Leptomeningeal metastases (LM) was diagnosed with imaging. Cox analyses were conducted to determine predictors of overall survival (OS) in entire patients and a subgroup of patients with contrast-enhancing (CE) tumor. RESULTS: The median patient age was 40.5 years (range 19.9-75.7), with an OS of 30.3 months (interquartile range 11.3-32.3). In entire patients, the presence of LM was the only independent predictor of OS (hazard ratio [HR] = 6.01, P = 0.009). In the subgroup of 23 (71.9%) patients with CE tumors, rCMRO2 of CE tumor (HR = 1.08, P = 0.019) and the presence of LM (HR = 5.92, P = 0.043) were independent predictors of OS. CONCLUSION: The presence of LM was independently associated with poor prognosis in adult patients with H3 K27-altered DMG. In patients with CE tumors, higher rCMRO2 of CE tumor, which may reflect higher metabolic activity in the tumor oxygenation microenvironment, may be a useful imaging biomarker to predict poor prognosis.

Survival among people with HIV and their families in Denmark 1995-2021: a nationwide population-based cohort study.

Background: Survival among people with HIV (PWH) has vastly improved globally over the last few decades but remains lower than among the general population. We aimed to estimate time trends of survival among PWH and their families from 1995 to 2021. Methods: We conducted a registry-based, nationwide, population-based, matched cohort study. We included all Danish-born PWH from 1995 to 2021 who had been on antiretroviral therapy for 90 days, did not report intravenous drug use, and were not co-infected with hepatitis C (n = 4168). We matched population controls from the general population 10:1 to PWH by date of birth and sex (n = 41,680). For family cohorts, we identified siblings, mothers, and fathers of PWH and population controls. From Kaplan-Meier tables with age as time scale, we estimated survival from age 25. We compared PWH with population controls and families of PWH with families of population controls to calculate mortality rate ratios adjusted for sex, age, comorbidities, and education (aMRR). Findings: The median age of death among PWH increased from 27.5 years in 1995-1997 to 73.9 years (2010-2014), but thereafter survival increased only marginally. From 2015 to 2021, mortality was increased among PWH (aMRR 1.87 (95% CI: 1.65-2.11)) and siblings (aMRR: 1.25 (95% CI: 1.07-1.47)), mothers (aMRR: 1.30 (95% CI: 1.17-1.43)), and fathers (aMRR: 1.15 (95% CI: 1.03-1.29)) of PWH compared to their respective control cohorts. Mortality among siblings of PWH who reported heterosexual route of HIV transmission (aMRR: 1.51 (95% CI: 1.16-1.96)) was higher than for siblings of PWH who reported men who have sex with men as route of HIV transmission (aMRR 1.19 (95% CI: 0.98-1.46)). Interpretation: Survival among PWH improved substantially until 2010, after which it increased only marginally. This may partly be due to social and behavioural factors as PWH families also had higher mortality. Funding: Preben and Anna Simonsen's Foundation and Independent Research Fund Denmark.

Survival Analysis of Patients with Severe Acute Malnutrition Admitted at the In-patient Therapeutic Care of the Bicol Regional Training and Teaching Hospital.

Background: Severe acute malnutrition (SAM) in children under five years remains a major global health concern. It carries a burden to the overall health of a child, contributes to mortality, and adds financial strain to the family and the hospital. The Philippine Integrated Management of Acute Malnutrition was established to address acute malnutrition in Filipino children. Objective: This study aimed to determine the factors affecting survival of patients admitted at Bicol Regional Training and Teaching Hospital (BRTTH) In-patient Therapeutic Care (ITC). Methods: This is a retrospective cohort study design utilizing survival analysis. Accrual period was from January 1, 2018 to December 31, 2018. Follow-up ended on March 31, 2019. There were 154 admissions and excluded 17 missing charts. Survival analysis was done utilizing STATA 14. Results: The prevalence of SAM requiring ITC admission was 3.0 percent. Majority belonged to 6-59 months of age (63%), with equal predilection for both sexes (1:1) and 71% came from the home province, Albay. Most of patients' caretakers had middle educational attainment. Sixty-eight percent (68%) were new patients, 16% readmitted, 15% transferred from the Out-patient Therapeutic Care (OTC) and <1% relapsed. The top three most common complications and co-morbidities include: pneumonia, low electrolytes, and fever. Sixty-three percent (63%) of patients at the ITC had a desirable treatment outcome, of which, 8% were cured and 55% transferred to OTC. Undesirable outcomes accounted for 37% of the cases which included non-cured, defaulter, and died at 12%, 8%, and 17%, respectively. The risk of dying was higher in SAM patients with parents having middle and low educational attainment as compared to those with high educational attainment (2-5 folds to 100-200 folds). SAM patients presenting with hypovolemic shock were likely to die by 1.5-19 times (1.5-19x) as compared to those without. SAM patients with malignancy were more likely to die 4-44 folds as compared to patients without malignancy. Conclusion and Recommendations: Educational attainment of parents, malignancy, and hypovolemic shock were significant predictors of mortality. We recommend prompt intervention by educating families, strengthen policies targeting socio-economic determinants, capacitate medical staff, refine current clinical practice guidelines and treatment pathways to reduce the number of children who die from severe acute malnutrition.

A deep learning approach for overall survival prediction in lung cancer with missing values.

BACKGROUND AND OBJECTIVE: In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored patients who have experienced the event of interest and censored patients who have not, by embedding a specialized technique within our AI model, not commonly utilized in other AI tasks. Through the realization of these objectives, our model aims to provide precise OS predictions for non-small cell lung cancer (NSCLC) patients, thus overcoming these significant challenges. METHODS: We present a novel approach to survival analysis with missing values in the context of NSCLC, which exploits the strengths of the transformer architecture to account only for available features without requiring any imputation strategy. More specifically, this model tailors the transformer architecture to tabular data by adapting its feature embedding and masked self-attention to mask missing data and fully exploit the available ones. By making use of ad-hoc designed losses for OS, it is able to account for both censored and uncensored patients, as well as changes in risks over time. RESULTS: We compared our method with state-of-the-art models for survival analysis coupled with different imputation strategies. We evaluated the results obtained over a period of 6 years using different time granularities obtaining a Ct-index, a time-dependent variant of the C-index, of 71.97, 77.58 and 80.72 for time units of 1 month, 1 year and 2 years, respectively, outperforming all state-of-the-art methods regardless of the imputation method used. CONCLUSIONS: The results show that our model not only outperforms the state-of-the-art's performance but also simplifies the analysis in the presence of missing data, by effectively eliminating the need to identify the most appropriate imputation strategy for predicting OS in NSCLC patients.

Impact of reproductive traits on productive life in Iranian Holstein dairy cows.

Enhancing reproductive performance is a key strategy to mitigate involuntary culling rates, thereby extending productive life (PL) and ultimately improving profitability in dairy cattle herds. A piecewise Weibull proportional hazards model was used to investigate the effect of several important reproductive traits on PL in Holstein dairy cows. Data comprised 200,747 lactation records from 82,505 cows sired by 1952 bulls across 36 dairy herds. PL was defined as the number of days from the first calving to the last milk record or censoring. The statistical model accounted for the time-dependent fixed effects of changes in herd size, year-season, milk production, fat and protein contents, and the time-independent fixed effect of age at first calving. Herd-year and sire effects were also included as random effects. Reproductive traits include calving traits such as calving ease (CE), calf size (CZ), and calf survival (CS), as well as female fertility traits such as number of inseminations per conception (NI), days from calving to first service (CFS), days from first service to conception (FSC), and days open (DO). All reproductive traits had a significant effect on PL (p < 0.001). Each reproductive trait was analysed separately. The relative risk (RR) of being culled increased as the severity of calving difficulties increased in both primiparous and multiparous cows. Cows that calved small or large calves showed a higher risk of being culled compared with those that calved medium size calves. The increased RR of culling was observed only for primiparous cows that gave birth to dead calves. In addition, cows that required more NI, a longer CFS, FSC, and DO had shorter longevity. These insights can deepen our comprehension of the factors affecting PL and provide information for refining management and breeding strategies, which could lead to increased profitability and sustainability in Iranian dairy farming.

Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease.

The landscape of survival analysis is constantly being revolutionized to answer biomedical challenges, most recently the statistical challenge of censored covariates rather than outcomes. There are many promising strategies to tackle censored covariates, including weighting, imputation, maximum likelihood, and Bayesian methods. Still, this is a relatively fresh area of research, different from the areas of censored outcomes (i.e., survival analysis) or missing covariates. In this review, we discuss the unique statistical challenges encountered when handling censored covariates and provide an in-depth review of existing methods designed to address those challenges. We emphasize each method's relative strengths and weaknesses, providing recommendations to help investigators pinpoint the best approach to handling censored covariates in their data.

Comprehensive transcriptomic analysis identifies SLC25A4 as a key predictor of prognosis in osteosarcoma.

Background: Osteosarcoma (OS) is highly malignant and prone to local infiltration and distant metastasis. Due to the poor outcomes of OS patients, the study aimed to identify differentially expressed genes (DEGs) in OS and explore their role in the carcinogenesis and progression of OS. Methods: RNA sequencing was performed to identify DEGs in OS. The functions of the DEGs in OS were investigated using bioinformatics analysis, and DEG expression was verified using RT-qPCR and Western blotting. The role of SLC25A4 was evaluated using gene set enrichment analysis (GSEA) and then investigated using functional assays in OS cells. Results: In all, 8353 DEGs were screened. GO and KEGG enrichment analyses indicated these DEGs showed strong enrichment in the calcium signaling pathway and pathways in cancer. Moreover, the Kaplan-Meier survival analysis showed ten hub genes were related to the outcomes of OS patients. Both SLC25A4 transcript and protein expression were significantly reduced in OS, and GSEA suggested that SLC25A4 was associated with cell cycle, apoptosis and inflammation. SLC25A4-overexpressing OS cells exhibited suppressed proliferation, migration, invasion and enhanced apoptosis. Conclusion: SLC25A4 was found to be significantly downregulated in OS patients, which was associated with poor prognosis. Modulation of SLC25A4 expression levels may be beneficial in OS treatment.

Application of Mixture and Non-mixture Cure Models in Survival Analysis of Patients With COVID-19.

Background Due to the emergence of new COVID-19 mutations and an increase in re-infection rates, it has become an important priority for the medical community to identify the factors affecting the short- and long-term survival of patients. This study aimed to determine the risk factors of short- and long-term survival in patients with COVID-19 based on mixture and non-mixture cure models. Methodology In this study, the data of 880 patients with COVID-19 confirmed with polymerase chain reaction in Fereydunshahr city (Isfahan, Iran) from February 20, 2020, to December 21, 2021, were gathered, and the vital status of these patients was followed for at least one year. Due to the high rate of censoring, mixture and non-mixture cure models were applied to estimate the survival. Akaike information criterion values were used to evaluate the fit of the models. Results In this study, the mean age of the patients was 48.9 ± 21.23 years, and the estimated survival rates on the first day, the fourth day, the first week, the first month, and at one year were 0.997, 0.982, 0.973, 0.936, and 0.928, respectively. Among the parametric models, the log-logistic mixed cure model with the logit link, which showed the lowest Akaike information criterion value, demonstrated the best fit. The variables of age and prescribed medication type were significant predictors of long-term survival, while occupation was influential in the short-term survival of patients. Conclusions According to the results of this study, it can be concluded that elderly patients should observe health protocols more strictly and consider receiving booster vaccine doses. The log-logistic cure model with a logit link can be used for survival analysis in COVID-19 patients, a fraction of whom have long-term survival.