Accurate diagnosis of acute appendicitis in the emergency department: an artificial intelligence-based approach.

The diagnosis of abdominal pain in emergency departments is challenging, and appendicitis is a common concern. Atypical symptoms often delay diagnosis. Although the Alvarado score aids in decision-making, its low specificity can lead to unnecessary surgeries. By leveraging machine learning, we aim to enhance diagnostic accuracy by predicting appendicitis and distinguishing it from other causes of abdominal pain in the emergency department. Data were collected from 534 patients who presented with acute abdominal pain. Patient characteristics, laboratory results, and causes of pain were recorded. Machine learning algorithms (support vector classifier, random forest classifier, gradient boosting classifier, and Gaussian naive Bayes) were used to predict the cause of pain. Model calibration was assessed using the Brier score. The mean age was 46.89 (20.3) years, with an almost equal sex distribution (49% male, 51% female). Cholecystitis was the most prevalent outcome (37.07%), followed by appendicitis (25.84%). The Gaussian naive Bayes model exhibited superior performance in terms of accuracy (95.03% 95% CI 90.44-97.83%), sensitivity (87.18% 95% CI 72.57-95.70%), and specificity (97.54% 95% CI 92.98-99.49%), while the random forest model showed a sensitivity of 79.49%, specificity of 96.72%, and accuracy of 92.55%. The gradient boosting algorithm achieved a sensitivity, specificity, and accuracy of 89.74%, 95.90%, and 94.41%, respectively. The support vector classifier demonstrated a sensitivity of 89.74%, specificity of 92.62%, and accuracy of 91.93%. The use of modern machine learning methods aids in the accurate diagnosis of appendicitis.

Thy-DAMP: deep artificial neural network model for prediction of thyroid cancer mortality.

PURPOSE: Despite the rising incidence of differentiated thyroid cancer (DTC), mortality rates have remained relatively low yet crucial for effective patient management. This study aims to develop a deep neural network capable of predicting mortality in patients with differentiated thyroid cancer. METHODS: Leveraging data from the Surveillance, Epidemiology, and End Results (SEER) database, we developed Thy-DAMP (Deep Artificial Neural Network Model for Prediction of Thyroid Cancer) to forecast mortality in DTC patients. The dataset comprised demographic, histologic, and staging information. Following data normalization and feature encoding, the dataset was partitioned into training, testing, and validation subsets, with model hyperparameters fine-tuned via cross-validation. RESULTS: Among the 63,513 patients, the mean age was 48.22 years (SD = 14.8), with 77.32% being female. Papillary carcinoma emerged as the predominant subtype, representing 62.94% of cases. The majority presented with stage I disease (73.96%). Thy-DAMP demonstrated acceptable performance metrics on both the test and validation sets. Sensitivity was 83.24% (95% CI 76.95-88.40%), specificity was 93.53% (95% CI 93.01-94.02%), and accuracy stood at 93.33% (95% CI 92.82-93.83%). The model exhibited a positive predictive value of 19.76% (95% CI 18.20-21.42%) and a negative predictive value of 99.66% (95% CI 99.53-99.75%). Additionally, Thy-DAMP demonstrated a positive likelihood ratio of 12.86 (95% CI 11.62-14.23), a negative likelihood ratio of 0.18 (95% CI 0.13-0.25), and an area under the receiver operating characteristic curve (AUROC) of 0.95. The model was externally validated on a separate dataset with nearly identical performance. CONCLUSION: Thy-DAMP showcases considerable promise in accurately predicting mortality in DTC patients, leveraging limited set of patient data.

Assessment of endocarditis prophylaxis knowledge and attitudes among healthcare practitioners in Tehran and Hamadan.

BACKGROUND: Having knowledge of the dental procedures that necessitate endocarditis prophylaxis is of high importance. Therefore, the aim of the present study was to determine the knowledge level and attitudes of general medical and dental practitioners, dental specialists, and cardiologists in Tehran and Hamadan about endocarditis. METHODS: This cross-sectional study was carried out on 420 general medical and dental practitioners, dental specialists, and cardiologists in Tehran and Hamadan provinces in 2015. The questionnaire used in this research consisted of three parts as follows: part one: information on cardiac diseases; part two: dental procedures requiring endocarditis prophylaxis; part three: antibiotic diet in endocarditis prophylaxis. Independent t-test, one-way ANOVA, and chi-square tests were conducted to analyze the data. All the analyses were performed in SPSS version 16. RESULTS: The results showed that 86.7 had a relatively favorable and 10.5% of subjects had a favorable level of knowledge about endocarditis. Also, 58.6% of subjects had a poor attitude toward endocarditis prophylaxis. There was a significant relationship between knowledge and attitude, age, gender, and work experience (P < 0.001). There was a significant relationship between knowledge and attitude, and job groups; dental specialists had a more favorable knowledge and positive attitude than others (P < 0.001). CONCLUSION: We recommended developing more practical training programs in dental schools on cardiac diseases, and dental procedures requiring endocarditis prophylaxis and antibiotic diets.

Antibacterial and antibiofilm potentials of vancomycin-loaded niosomal drug delivery system against methicillin-resistant Staphylococcus aureus (MRSA) infections.

The threat of methicillin-resistant Staphylococcus aureus (MRSA) is increasing worldwide, making it significantly necessary to discover a novel way of dealing with related infections. The quick spread of MRSA isolates among infected individuals has heightened public health concerns and significantly limited treatment options. Vancomycin (VAN) can be applied to treat severe MRSA infections, and the indiscriminate administration of this antimicrobial agent has caused several concerns in medical settings. Owing to several advantageous characteristics, a niosomal drug delivery system may increase the potential of loaded antimicrobial agents. This work aims to examine the antibacterial and anti-biofilm properties of VAN-niosome against MRSA clinical isolates with emphasis on cytotoxicity and stability studies. Furthermore, we aim to suggest an effective approach against MRSA infections by investigating the inhibitory effect of formulated niosome on the expression of the biofilm-associated gene (icaR). The thin-film hydration approach was used to prepare the niosome (Tween 60, Span 60, and cholesterol), and field emission scanning electron microscopy (FE-SEM), an in vitro drug release, dynamic light scattering (DLS), and entrapment efficiency (EE%) were used to investigate the physicochemical properties. The physical stability of VAN-niosome, including hydrodynamic size, polydispersity index (PDI), and EE%, was analyzed for a 30-day storage time at 4 °C and 25 °C. In addition, the human foreskin fibroblast (HFF) cell line was used to evaluate the cytotoxic effect of synthesized niosome. Moreover, minimum inhibitory and bactericidal concentrations (MICs/MBCs) were applied to assess the antibacterial properties of niosomal VAN formulation. Also, the antibiofilm potential of VAN-niosome was investigated by microtiter plate (MTP) and real-time PCR methods. The FE-SEM result revealed that synthesized VAN-niosome had a spherical morphology. The hydrodynamic size and PDI of VAN-niosome reported by the DLS method were 201.2 nm and 0.301, respectively. Also, the surface zeta charge of the prepared niosome was - 35.4 mV, and the EE% ranged between 58.9 and 62.5%. Moreover, in vitro release study revealed a sustained-release profile for synthesized niosomal formulation. Our study showed that VAN-niosome had acceptable stability during a 30-day storage time. Additionally, the VAN-niosome had stronger antibacterial and anti-biofilm properties against MRSA clinical isolates compared with free VAN. In conclusion, the result of our study demonstrated that niosomal VAN could be promising as a successful drug delivery system due to sustained drug release, negligible toxicity, and high encapsulation capacity. Also, the antibacterial and anti-biofilm studies showed the high capacity of VAN-niosome against MRSA clinical isolates. Furthermore, the results of real-time PCR exhibited that VAN-niosome could be proposed as a powerful strategy against MRSA biofilm via down-regulation of icaR gene expression.

Identification of Risk Factors for Relapse in Childhood Leukemia Using Penalized Semi-parametric Mixture Cure Competing Risks Model.

BACKGROUND: Leukemia is the most common childhood malignancy. Identifying prognostic factors of patient survival and relapse using more reliable statistical models instead of traditional variable selection methods such as stepwise regression is of great importance. The present study aimed to apply a penalized semi-parametric mixture cure model to identify the prognostic factors affecting short-term and long-term survival of childhood leukemia in the presence of competing risks. The outcome of interest in this study was time to relapse. Study Design: A retrospective cohort study. METHODS: A total of 178 patients (0‒15 years old) with leukemia participated in this study (September 1997 to September 2016, followed up to June 2021) at Golestan University of Medical Sciences, Iran. Demographic, clinical, and laboratory data were collected, and then a penalized semi-parametric mixture cure competing risk model with smoothly clipped absolute deviation (SCAD) and least absolute shrinkage and selection operator (LASSO) regularizations was used to analyze the data. RESULTS: Important prognostic factors of relapse patients selected by the SCAD regularization method were platelets (150000‒400000 vs.>400000; odds ratio=0.31) in the cure part and type of leukemia (ALL vs. AML, hazard ratio (HR)=0.08), mediastinal tumor (yes vs. no, HR=16.28), splenomegaly (yes vs. no; HR=2.94), in the latency part. In addition, significant prognostic factors of death identified by the SCAD regularization method included white blood cells (<4000 vs.>11000, HR=0.25) and rheumatoid arthritis signs (yes vs. no, HR=5.75) in the latency part. CONCLUSION: Several laboratory factors and clinical side effects were associated with relapse and death, which can be beneficial in treating the disease and predicting relapse and death time.

Family-based association of HLA-DRB1 and DQB1 alleles and haplotypes in a group of Iranian Type 1 diabetes children.

This family-based study was conducted in a group of Iranians with Type 1 diabetes (T1D) to investigate the transmission from parents of risk and non-risk HLA alleles and haplotypes, and to estimate the genetic risk score for this disease within this population. A total of 240 T1D subjects including 111 parent-child trios (111 children with T1D, 133 siblings, and 222 parents) and 330 ethnically matched healthy individuals were recruited. High-resolution HLA typing for DRB1/DQB1 loci was performed for all study subjects (n = 925) using polymerase chain reaction-sequence-specific oligonucleotide probe method. The highest predisposing effect on developing T1D was conferred by the following haplotypes both in all subjects and in probands compared to controls: DRB1*04:05-DQB1*03:02 (Pc = 2.97e-06 and Pc = 6.04e-10, respectively), DRB1*04:02-DQB1*03:02 (Pc = 5.94e-17 and Pc = 3.86e-09, respectively), and DRB1*03:01-DQB1*02:01 (Pc = 8.26e-29 and Pc = 6.56e-16, respectively). Conversely, the major protective haplotypes included DRB1*13:01-DQB1*06:03 (Pc = 6.99e-08), DRB1*15:01-DQB1*06:02 (Pc = 2.97e-06) in the cases versus controls. Also, DRB1*03:01-DQB1*02:01/DRB1*04:02|05-DQB1*03:02 and DRB1*03:01-DQB1*02:01/DRB1*03:01-DQB1*02:01 diplotypes conferred the highest predisposing effect in the cases (Pc = 8.65e-17 and Pc = 6.26e-08, respectively) and in probands (Pc = 5.4e-15 and Pc = 0.001, respectively) compared to controls. Transmission disequilibrium test showed that the highest risk was conferred by DRB1*04:02-DQB1*03:02 (Pc = 3.26e-05) and DRB1*03:01-DQB1*02:01 (Pc = 1.78e-12) haplotypes and the highest protection by DRB1*14:01-DQB1*05:03 (Pc = 8.66e-05), DRB1*15:01-DQB1*06:02 (Pc = 0.002), and DRB1*11:01-DQB1*03:01 (Pc = 0.0003) haplotypes. Based on logistic regression analysis, carriage of risk haplotypes increased the risk of T1D development 24.5 times in the Iranian population (p = 5.61e-13). Also, receiver operating characteristic curve analysis revealed a high predictive power of those risk haplotypes in discrimination of susceptible from healthy individuals (area under curve: 0.88, p = 5.5e-32). Our study highlights the potential utility of genetic risk assessment based on HLA diplotypes for predicting T1D risk in individuals, particularly among family members of affected children in our population.

Increasing Prevalence and High Survival Rate of Liver Transplanted Patients with NASH and PSC Cirrhosis.

BACKGROUND: Epidemiological studies on liver transplant (LT) patients can provide valuable information about the etiology and trends of cirrhosis. The present study aimed to investigate the prevalence and trend of different etiologies and survival rates of LT patients at the Namazi Transplant Center in Shiraz, Iran, between 2001 and 2018. METHODS: In this single-center, retrospective cohort study, the demographic and clinical characteristics of 3751 patients who underwent LT and met the study inclusion criteria, including age, gender, blood group, body mass index, model for end-stage liver disease (MELD) score, cause of cirrhosis, and diabetes, were extracted from patients' physical or electronic medical records between 2001 and 2018. RESULTS: The MELD scores of LT patients with primary sclerosing cholangitis (PSC), hepatitis B virus (HBV), and non-alcoholic steatohepatitis (NASH) cirrhosis significantly decreased over the study period (P<0.001). Among the LT patients, HBV infection had the highest frequency (21.09%), followed by cryptogenic (17.33%) and PSC (17.22%). The proportion of patients with PSC and NASH (both P<0.001) cirrhosis was significantly increased, so that PSC cirrhosis (2016: 19.4%, 2018: 18.8%) surpassed HBV (2016: 18.4%, 2018: 13.5%), autoimmune hepatitis (2016: 11.7%, 2018: 12.7%), and cryptogenic cirrhosis (2016: 16.1%, 2018:14%) as the leading indication for LT from 2016 to the end of the study period. Fortunately, these patients had a better survival rate than other common diseases (HR: 0.53, CI: 0.43‒0.66; P<0.001). CONCLUSION: The proportion of NASH and PSC cirrhosis significantly increased during the 18 years of study. However, these patients had an improved survival rate. Therefore, health organizations should pay more attention to non-communicable diseases, especially fatty liver disease and cholangitis.

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.

Vancomycin and nisin-modified magnetic Fe3O4@SiO2 nanostructures coated with chitosan to enhance antibacterial efficiency against methicillin resistant Staphylococcus aureus (MRSA) infection in a murine superficial wound model.

BACKGROUND: The objective of this research was to prepare some Fe3O4@SiO2@Chitosan (CS) magnetic nanocomposites coupled with nisin, and vancomycin to evaluate their antibacterial efficacy under both in vitro and in vivo against the methicillin-resistant Staphylococcus. aureus (MRSA). METHODS: In this survey, the Fe3O4@SiO2 magnetic nanoparticles (MNPs) were constructed as a core and covered the surface of MNPs via crosslinking CS by glutaraldehyde as a shell, then functionalized with vancomycin and nisin to enhance the inhibitory effects of nanoparticles (NPs). X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), field emission scanning electron microscope (FE-SEM), vibrating sample magnetometer (VSM), and dynamic light scattering (DLS) techniques were then used to describe the nanostructures. RESULTS: Based on the XRD, and FE-SEM findings, the average size of the modified magnetic nanomaterials were estimated to be around 22-35 nm, and 34-47 nm, respectively. The vancomycin was conjugated in three polymer-drug ratios; 1:1, 2:1 and 3:1, with the percentages of 45.52%, 35.68%, and 24.4%, respectively. The polymer/drug ratio of 1:1 exhibited the slowest release rate of vancomycin from the Fe3O4@SiO2@CS-VANCO nanocomposites during 24 h, which was selected to examine their antimicrobial effects under in vivo conditions. The nisin was grafted onto the nanocomposites at around 73.2-87.2%. All the compounds resulted in a marked reduction in the bacterial burden (P-value < 0.05). CONCLUSION: The vancomycin-functionalized nanocomposites exhibited to be more efficient in eradicating the bacterial cells both in vitro and in vivo. These findings introduce a novel bacteriocin-metallic nanocomposite that can suppress the normal bacterial function on demand for the treatment of MRSA skin infections.

Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling.

Colorectal cancer is a prevalent malignancy with global significance. This retrospective study aimed to investigate the influence of stage and tumor site on survival outcomes in 284 colorectal cancer patients diagnosed between 2001 and 2017. Patients were categorized into four groups based on tumor site (colon and rectum) and disease stage (early stage and advanced stage). Demographic characteristics, treatment modalities, and survival outcomes were recorded. Bayesian survival modeling was performed using semi-competing risks illness-death models with an accelerated failure time (AFT) approach, utilizing R 4.1 software. Results demonstrated significantly higher time ratios for disease recurrence (TR = 1.712, 95% CI 1.489-2.197), mortality without recurrence (TR = 1.933, 1.480-2.510), and mortality after recurrence (TR = 1.847, 1.147-2.178) in early-stage colon cancer compared to early-stage rectal cancer. Furthermore, patients with advanced-stage rectal cancer exhibited shorter survival times for disease recurrence than patients with early-stage colon cancer. The interaction effect between the disease site and cancer stage was not significant. These findings, derived from the optimal Bayesian log-normal model for terminal and non-terminal events, highlight the importance of early detection and effective management strategies for colon cancer. Early-stage colon cancer demonstrated improved survival rates for disease recurrence, mortality without recurrence, and mortality after recurrence compared to other stages. Early intervention and comprehensive care are crucial to enhance prognosis and minimize adverse events in colon cancer patients.

Spatial inequalities and non-linear association of continuous variables with mortality risk of liver transplantation in Iran: a retrospective cohort study.

Liver transplantation is the second most common solid organ transplant and the best option for liver failure. Of course, patient survival after transplantation depends on many risk factors. The aim of this study was to investigate the spatial and non-linear effects of continuous risk factors on patient survival after liver transplantation. This retrospective cohort study (n = 3148) used data on liver transplantation in Iran (2004-2019). A generalized additive model with spatial effects and non-linear effects of age and Model for End-Stage Liver Disease (MELD) score variables by penalized spline was used. The majority of patients were male (63.3%), with a mean (SD) age of 42.65 (13.31) and a mean (SD) MELD score of 24.43 (6.72). The 1, 5, and 10-year survival rates were 88.2%, 84.6%, and 82.5% respectively. The non-linear effect showed a steeper slope of the age effect on the hazard of death after the age of 50 (p < 0.05), and the MELD score had a direct but non-linear relationship with the hazard of death (p < 0.05). In the spatial pattern, the provinces with a greater distance from the transplant center had significantly fewer old patients than other provinces. Also, more distant provinces with an older transplant age had higher post-transplant mortality rates. Our study showed that it is better to take age and MELD score into account in postoperative care. The spatial pattern of mortality risk reflects inequalities in access to transplantation and public health services after transplantation.

The impact of telenursing on level of depression, stress and anxiety in discharged patients after coronary artery bypass graft surgery: A randomized clinical trial.

BACKGROUND: After coronary artery bypass graft surgery patients are susceptible to mental disorders such as stress, anxiety, and depression following discharge from the hospital and often require post-discharge support and follow-up. Telenursing is an accessible method that may reduce stress, anxiety, and depression experienced by patients. This study aimed to investigate the impact of telenursing on depression, stress, and anxiety in discharged patients after CABG surgery. MATERIALS AND METHODS: A randomized clinical trial was carried out. Eligible patients were divided randomly into intervention (n=40) and control (n=40) groups. Depression Anxiety Stress Scale-21 (DASS21) questionnaire was filled out by both groups one day before discharge from the hospital. The intervention group received SMS reminders of their treatment plan, referrals to the cardiac rehabilitation clinic, and routine care three times a week for six weeks, while the control group received routine care provided by the hospital only. In the week following the completion of the intervention period, both groups returned to fill out the questionnaire, and the data were analyzed using SPSS version 16.0, descriptive and inferential statistics, and independent and paired T-tests. RESULT: The mean scores of depression, stress, and anxiety before intervention in the intervention group were 11.95, 18.75, and 18.17, and in the control group were 11.55, 18.37, and 17.4 respectively. The mean scores of depression, stress, and anxiety after intervention in the intervention group were 7.85, 10.5, and 10.45, and in the control group were 10.56, 17.9, and 16.5 respectively. No significant differences were seen between the two groups before the intervention (P>0.05), but the results showed significant differences between the two groups' mean scores of depression, stress, and anxiety after intervention (P<0.001). CONCLUSION: Telenursing can reduce stress, anxiety, and depression in discharged patients after coronary artery bypass graft surgery by providing proper and cost-effective follow-up.

Survival prediction and prognostic factors in colorectal cancer after curative surgery: insights from cox regression and neural networks.

Medical research frequently relies on Cox regression to analyze the survival distribution of cancer patients. Nonetheless, in specific scenarios, neural networks hold the potential to serve as a robust alternative. In this study, we aim to scrutinize the effectiveness of Cox regression and neural network models in assessing the survival outcomes of patients who have undergone treatment for colorectal cancer. We conducted a retrospective study on 284 colorectal cancer patients who underwent surgery at Imam Khomeini clinic in Hamadan between 2001 and 2017. The data was used to train both Cox regression and neural network models, and their predictive accuracy was compared using diagnostic measures such as sensitivity, specificity, positive predictive value, accuracy, negative predictive value, and area under the receiver operating characteristic curve. The analyses were performed using STATA 17 and R4.0.4 software. The study revealed that the best neural network model had a sensitivity of 74.5% (95% CI 61.0-85.0), specificity of 83.3% (65.3-94.4), positive predictive value of 89.1% (76.4-96.4), negative predictive value of 64.1% (47.2-78.8), AUC of 0.79 (0.70-0.88), and accuracy of 0.776 for death prediction. For recurrence, the best neural network model had a sensitivity of 88.1% (74.4-96.0%), specificity of 83.7% (69.3-93.2%), positive predictive value of 84.1% (69.9-93.4%), negative predictive value of 87.8% (73.8-95.9%), AUC of 0.86 (0.78-0.93), and accuracy of 0.859. The Cox model had comparable results, with a sensitivity of 73.6% (64.8-81.2) and 85.5% (78.3-91.0), specificity of 89.6% (83.8-93.8) and 98.0% (94.4-99.6), positive predictive value of 84.0% (75.6-90.4) and 97.4% (92.6-99.5), negative predictive value of 82.0% (75.6-90.4) and 88.8% (0.83-93.1), AUC of 0.82 (0.77-0.86) and 0.92 (0.89-0.95), and accuracy of 0.88 and 0.92 for death and recurrence prediction, respectively. In conclusion, the study found that both Cox regression and neural network models are effective in predicting early recurrence and death in patients with colorectal cancer after curative surgery. The neural network model showed slightly better sensitivity and negative predictive value for death, while the Cox model had better specificity and positive predictive value for recurrence. Overall, both models demonstrated high accuracy and AUC, indicating their usefulness in predicting these outcomes.

Joint Modeling of Longitudinal Outcome and Competing Risks: Application to HIV/AIDS Data.

BACKGROUND: Tuberculosis (TB) and human immunodeficiency virus (HIV) are major public health challenges globally, and the number of TB infections and death caused by HIV are high because of HIV/ TB co-infection. On the other hand, CD4 count plays a significant role in TB/HIV co-infections. We used a joint model of longitudinal outcomes and competing risks to identify the potential risk factors and the effect of CD4 cells on TB infection and death caused by HIV in HIV-infected patients. STUDY DESIGN: This was a retrospective cohort study. METHODS: The current study was performed on 1436 HIV+patients referred to Behavioral Diseases Counseling Centers in Kermanshah Province during 1998-2019. In this study, joint modeling was used to identify the effect of potential risk factors and CD4 cells on TB and death caused by HIV. RESULTS: The results demonstrated that the decreasing CD4 cell count was significantly associated with an increased risk of death, while it had no significant relation with the risk of TB. In addition, patients with TB were at a higher risk of death. Based on the results, a significant relationship was found between CD4 count and sex, marital status, education level, antiretroviral therapy (ART), time, and the interaction between time and ART. Further, people infected with HIV through sexual relationships were at higher risk of TB, while those with a history of imprisonment who received ART or were infected with HIV through drug injection had a lower risk of TB. CONCLUSION: The findings revealed that the decreasing CD4 count had a significant association with an increased risk of death caused by HIV. However, it was not significantly related to the risk of TB. Finally, patients with TB were at higher risk of death caused by HIV.

Estimation of treatment effect in presence of noncompliance and competing risks: a simulation study.

A randomized controlled trial is commonly designed to assess the treatment effect in survival studies, in which patients are randomly assigned to the standard or the experimental treatment group. Upon disease progression, patients who have been randomized to standard treatment are allowed to switch to the experimental treatment. Treatment switching in a randomized controlled trial refers to a situation in which patients switch from their randomized treatment to another treatment. Often, the switchis from the control group to the experimental treatment. In this case, the treatment effect estimate is adjusted using either convenient naive methods such as intention-to-treat, per-protocol or advanced methods such as rank preserving structural failure time (RPSFT) models. In previous simulation studies performed so far, there was only one possible outcome for patients. However, in oncology in particular, multiple outcomes are potentially possible. These outcomes are called competing risks. This aspect has not been considered in previous studies when determining the effect of a treatment in the presence of noncompliance. This study aimed to extend the RPSFT method using a two-dimensional G-estimation in the presence of competing risks. The RPSFT method was extended for two events, the event of interest and the competing event. For this purpose, the RPSFT method was applied based on the cause-specific hazard approach, the result of which is compared to the naive methods used in simulation studies. The results show that the proposed method has a good performance compared to other methods.

The estimation of long and short term survival time and associated factors of HIV patients using mixture cure rate models.

BACKGROUND: HIV is one of the deadliest epidemics and one of the most critical global public health issues. Some are susceptible to die among people living with HIV and some survive longer. The aim of the present study is to use mixture cure models to estimate factors affecting short- and long-term survival of HIV patients. METHODS: The total sample size was 2170 HIV-infected people referred to the disease counseling centers in Kermanshah Province, in the west of Iran, from 1998 to 2019. A Semiparametric PH mixture cure model and a mixture cure frailty model were fitted to the data. Also, a comparison between these two models was performed. RESULTS: Based on the results of the mixture cure frailty model, antiretroviral therapy, tuberculosis infection, history of imprisonment, and mode of HIV transmission influenced short-term survival time (p-value < 0.05). On the other hand, prison history, antiretroviral therapy, mode of HIV transmission, age, marital status, gender, and education were significantly associated with long-term survival (p-value < 0.05). The concordance criteria (K-index) value for the mixture cure frailty model was 0.65 whereas for the semiparametric PH mixture cure model was 0.62. CONCLUSION: This study showed that the frailty mixture cure models is more suitable in the situation where the studied population consisted of two groups, susceptible and non-susceptible to the event of death. The people with a prison history, who received ART treatment, and contracted HIV through injection drug users survive longer. Health professionals should pay more attention to these findings in HIV prevention and treatment.

Recurrence and Postoperative Death in Patients with Colorectal Cancer: A New Perspective via Semi-competing Risk Framework.

BACKGROUND/AIMS: Cancer studies suffer from an overestimation of prediction of survival when both recurrence and death are of interest. This longitudinal study aimed to mitigate this problem utilizing a semi-competing risk approach evaluating the factors affecting recurrence and postoperative death in patients with colorectal cancer. MATERIALS AND METHODS: This longitudinal prospective study was conducted in 284 patients with resected colorectal cancer who were referred to the Imam Khomeini Clinic in Hamadan, Iran, during 2001-2017. Primary outcomes were postoperative outcomes and patient survival, including time to recurrence (of colorectal cancer), time to death, and time to death after recurrence. All patients who were alive at the end of the study were censored for death and who did not experience recurrence of colorectal cancer were also censored for recurrent colorectal cancer. The relationship between underlying demographics and clinical factors and the outcomes was assessed using a semicompeting risk approach. RESULTS: The results of the multivariable analysis showed that having metastasis to other sites (hazard ratio = 36.03; 95% CI = 19.48- 66.64) and higher pathological node (pN) stage (hazard ratio = 2.46; 95% CI = 1.32-4.56) were associated with a raised hazard of recurrence. The fewer chemotherapies (hazard ratio = 0.39; 95% CI = 0.17-0.88) and higher pN stages (hazard ratio = 4.32; 95% CI = 1.27-14.75) showed significantly higher hazards of death without recurrence. Having metastasis to other sites (hazard ratio = 2.67; 95% CI = 1.24-5.74) and higher pN stages (hazard ratio = 1.91; 95% CI = 1.02-3.61) were linked with the higher hazard of death after recurrence. CONCLUSION: Considering findings on death /recu rrenc e-spe cific predictors obtained in this study to manage the outcomes in patients with colorectal cancer, tailored strategies for preventive and interventional plans should be deliberated.

Epidemiological pattern of trauma patients based on the mechanisms of trauma: trends of a regional trauma center in Midwest of Iran.

INTRODUCTION: Trauma is one of the important issues in public health because it is responsible for 90% of mortality in Low and Middle-Income Countries (LIMCs). The present study aimed to determine the epidemiological pattern of trauma patients in a regional trauma center in the Midwest of Iran from 2014 to 2020. METHODS: This study was a retrospective study that was performed on 29,804 trauma patients admitted to Be'sat Hospital in Hamadan from January 2014 to December 2020. Data was collected using Health Information Management (HIM) Center of the Be'sat Hospital. For investigating the relationship of the characteristics of trauma patients and the mechanisms of trauma, Multiple Multinomial Logistic Regression (MMNLR) model was used. All statistical analyses were performed using the IBM SPSS Statistics version 24. RESULTS: The mean age of all patients was 35.4 (SD = 21.9) years. Most of them were men (71.7%). The most common mechanism of trauma was road traffic accidents (RTAs) (39.6%) followed by falls (30.2%), other (19.7%), violence (6.2%), and burn (4.4%). 1.5% of the trauma patients expired. The results of multiple multinomial logistic regression indicated that significant affected factor on odds referring because of RTAs compared to other mechanism were: season and hospital length of stay (LOS); in falls and violence: age, sex, season, and LOS; and in burn: age, sex, season, evening time, and LOS (p < 0.05). CONCLUSION: Based on the investigation of 29,804 trauma patients, in Iran as a developing country, RTAs and falls were two common mechanisms of trauma. It seems that as a short-term plan, it is possible to focus on road safety, to improve the quality of vehicles, to hold training courses for drivers. Also, as a long-term goal, considering that the elderly population in Iran is increasing, it is necessary to pay attention to fall reduction programs.

Improving service efficiency and throughput of cardiac surgery patients using Monte Carlo simulation: a queueing setting.

Bed occupancy rate (BOR) is important for healthcare policymakers. Studies showed the necessity of using simulation approach when encountering complex real-world problems to plan the optimal use of resources and improve the quality of services. So, the aim of the present study is to estimate average length of stay (LOS), BOR, bed blocking probability (BBP), and throughput of patients in a cardiac surgery department (CSD) using simulation models. We studied the behavior of a CSD as a complex queueing system at the Farshchian Hospital. In the queueing model, customers were patients and servers were beds in intensive care unit (ICU) and post-operative ward (POW). A computer program based on the Monte Carlo simulation, using Python software, was developed to evaluate the behavior of the system under different number of beds in ICU and POW. The queueing simulation study showed that, for a fixed number of beds in ICU, BOR in POW decreases as the number of beds in POW increases and LOS in ICU increases as the number of beds in POW decreases. Also, based on the available data, the throughput of patients in the CSD during 800 days was 1999 patients. Whereas, the simulation results showed that, 2839 patients can be operated in the same period. The results of the simulation study clearly demonstrated the behavior of the CSD; so, it must be mentioned, hospital administrators should design an efficient plan to increase BOR and throughput of patients in the future.

A bayesian approach to model the underlying predictors of early recurrence and postoperative death in patients with colorectal Cancer.

OBJECTIVE: This study aimed at utilizing a Bayesian approach semi-competing risks technique to model the underlying predictors of early recurrence and postoperative Death in patients with colorectal cancer (CRC). METHODS: In this prospective cohort study, 284 patients with colorectal cancer, who underwent surgery, referred to Imam Khomeini clinic in Hamadan from 2001 to 2017. The primary outcomes were the probability of recurrence, the probability of Mortality without recurrence, and the probability of Mortality after recurrence. The patients 'recurrence status was determined from patients' records. The Bayesian survival modeling was carried out by semi-competing risks illness-death models, with accelerated failure time (AFT) approach, in R 4.1 software. The best model was chosen according to the lowest deviance information criterion (DIC) and highest logarithm of the pseudo marginal likelihood (LPML). RESULTS: The log-normal model (DIC = 1633, LPML = -811), was the optimal model. The results showed that gender(Time Ratio = 0.764: 95% Confidence Interval = 0.456-0.855), age at diagnosis (0.764: 0.538-0.935 ), T3 stage (0601: 0.530-0.713), N2 stage (0.714: 0.577-0.935 ), tumor size (0.709: 0.610-0.929), grade of differentiation at poor (0.856: 0.733-0.988), and moderate (0.648: 0.503-0.955) levels, and the number of chemotherapies (1.583: 1.367-1.863) were significantly related to recurrence. Also, age at diagnosis (0.396: 0.313-0.532), metastasis to other sites (0.566: 0.490-0.835), T3 stage (0.363: 0.592 - 0.301), T4 stage (0.434: 0.347-0.545), grade of differentiation at moderate level (0.527: 0.387-0.674), tumor size (0.595: 0.500-0.679), and the number of chemotherapies (1.541: 1.332-2.243) were the significantly predicted the death. Also, age at diagnosis (0.659: 0.559-0.803), and the number of chemotherapies (2.029: 1.792-2.191) were significantly related to mortality after recurrence. CONCLUSION: According to specific results obtained from the optimal Bayesian log-normal model for terminal and non-terminal events, appropriate screening strategies and the earlier detection of CRC leads to substantial improvements in the survival of patients.