Prediction the survival of patients with breast cancer using random survival forests for competing risks

Abstract Objectives: Breast cancer (BC) is the most common cause of cancer death in Iranian women. Sometimes death from other causes precludes the event of interest and makes the analysis complicated. The purpose of this study was to identify important prognosis factors associated with survival duration among patients with BC using random survival forests (RSF) model in presence of competing risks. Also, its performance was compared with cause-specific hazard model. Methods: This retrospective cohort study assessed 222 patients with BC who admitted in Ayatollah Khansari hospital, Arak. The cause-specific Cox proportional hazards and RSF models were employed to determine the important risk factors for survival of the patients. Results: The mean and median survival duration of the patients were 90.71 (95%CI: 83.8- 97.6) and 100.73 (95%CI: 89.2-- 121.5) months, respectively. The cause-specific model indicated that type of surgery and HER2 had statistically significant effects on the risk of death of BC. Moreover, the RSF model identified that HER2 was the most important variable for the event of interest. Conclusion: According to the results of this study, the performance of the RSF model was better than the cause-specific hazard model. However, HER2 was the most important variable for death of BC in both of the models

Forecasting New Cases of Bipolar Disorder Using Poisson Hidden Markov Model

Background: Bipolar disorder (BD) is a major public health problem. In time series count data there may be over dispersion, and serial dependency. In such situation some models that can consider the dependency are needed. The purpose current research was to use Poisson hidden Markov model to forecast new monthly BD instances.Methods: In current study the dataset including the frequency of new instances of BD from October 2008 to March 2015 in Hamadan Province, the west of Iran were used. We used Poisson hidden Markov with different number of conditions to determine the best model according to Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Then we used final model to forecast for the next 24 months.Results: Poisson hidden Markov with two states were chosen as the final model. Each component of dependent mixture model explained one of the states. The results showed that the new BD cases is increase over time and due to forecasting results number of patients for the next 24 months comforted in state two with mean 85.15. The forecast interval was approximately (56, 100).Conclusion: As the Poisson hidden Markov models was not used to forecast the future states in other prior researches, the findings of this study set forward a forecasting strategy as an alternative to common methods, by considering its deficiencies

Longitudinal Machine Learning Model for Predicting Systolic Blood Pressure in Patients with Heart Failure

Objective: Systolic blood pressure (SBP) is a powerful prognostic factor in heart failure (HF) patients, which is associated with death and readmission. Therefore, control of blood pressure is an important element for managing these patients. The goal of this study was to compare the performance of classical and machine learning models for predicting SBP and identify important variables related to SBP changes over time. Methods: The information of 483 HF patients was analyzed in this retrospective cohort study. These patients were hospitalized at least twice in Farshchian Heart Center Hamadan province, the west of Iran, between October 2015 and July 2019. We applied a linear mixed-effects model (LMM) and mixed-effects least-square support vector regression (MLS-SVR) for predicting SBP. The performance of both models was assessed by mean absolute error, and root mean squared error. Results: Based on LMM results, there was a significant association between sex, body mass index (BMI), sodium, time, and history of hypertension with SBP changes over time (P-value <0.05). Also, MLS-SVR indicated that the four most important variables were history of hypertension, sodium, BMI, and triglyceride. The performance of MLS-SVR compared to LMM was better in both training and testing datasets. Conclusions: According to our results, BMI, sodium, and history of hypertension were the important variables on SBP changes in both LMM and MLS-SVR models. Also, it seems that MLS-SVR can be used as an alternative for classical longitudinal models for predicting SBP in HF patients

Recurrence in Patients with Bipolar Disorder and its Risk Factors

Objective: The aim of this study was to identify prognosis factors associated with recurrence in patients ‎with bipolar disorder.‎‎ Method: This retrospective cohort study was conducted in Hamadan Province, the west of Iran. All ‎patients (n = 400) with bipolar disorder who were hospitalized for the second time or more ‎during April 2008 to September 2014 were included in this study. Ordinal logistic regression ‎analysis was employed to determine the effective factors in each recurrence, and odds ratio (OR) and 95% confidence intervals (CI) were obtained.‎ Results: The mean (SD) age of the participants at the entrance to the study was 34.62 (11.68) years. ‎There was an association between recurrence and type of bipolar disorder (P = 0.033). The ‎OR of recurrence was 0.28 (95% CI: 0.09, 0.90) for bipolar disorder II; 0.35 (95% CI: 0.13, ‎‎0.92) for the patients‎‏ ‏with college education; 0.39 (95% CI: 0.25, 0.60) for employed ‎patients; 0.55 (95% CI: 0.35, 0.87) for patients who received both drugs and ‎electroconvulsive therapy, and 1.89 (95% CI: 1.23, 2.92) for patients who stopped using ‎drugs. In addition, a non-significant association was found between recurrence and age, sex, ‎marital status, place of residence, season, mood classification and family history of mood ‎disorder.‎ Conclusion: Type of bipolar disorder and cessation of medication were the leading causes of an increase in ‎the relapse of the disease. Furthermore, patients who received both drugs and ‎electroconvulsive therapy had a fewer risk of recurrence.

Evaluation of Bipolar Disorder in Several Recurrences over the Time Using Generalized Estimating Equations

Objective: Bipolar disorder is characterized by periods of manic, depression, or mixed episodes. The purpose of this study was to assess the development of bipolar disorder episode over the time as well as determining the risk factors affecting bipolar disorder. Methods: This retrospective cohort study was conducted in Hamadan Province, the west of Iran, from April 2008 to September 2014 including 124 patients with bipolar disorder. All patients had experienced four recurrences. Generalized Estimating Equation (GEE) as a longitudinal modeling approach was used due to longitudinally recording of bipolar disorders. A significant level of 0.05 was considered for the tests. Results: The mean (SD) age of the 124 patients was 33.2 (11.55). GEE showed that the odds of manic males than depressed or mixed (as well as manic or depressed than mixed female patients according to proportionality of odds) is 1.99 times than those of females. The odds ratio of mania than depression or mixed (as well as mania or depression than mixed) is 0.441 for patients who used both drugs and psychotherapy comparing to only drugs. As well, the odds ratios comparing spring to winter and autumn are 2.01 and 1.82, respectively Conclusion: The results from this study using GEE method showed that in an Iranian bipolar disorder patients, mania is much more prevalent than depression or mixed as well as mania and depression than mixed. Sex, treatments and the seasons of recurrence can determine the episode of bipolar disorder

Effect of Postoperative Trendelenburg Positioning in Reducing Shoulder Pain after Laparoscopic Cholecystectomy

Background and Objective: Shoulder pain is one of the most common complaints of patients after laparoscopic cholecystectomy, which seems to be closely related to the residual volume of carbon dioxide gas used during surgery. Therefore, this study was conducted with the aim of investigating the effect of Trendelenburg positioning after surgery in reducing shoulder pain in patients. Materials and Methods: This study was conducted as a randomized clinical trial in 58 patients who were eligible for laparoscopic cholecystectomy. Patients were enrolled in the study by simple random sampling and randomly assigned to supine (control) and Trendelenburg (intervention) positions. After complete awakening in the recovery room, the patients in the intervention group were placed in the Trendelenburg position (30 degrees), while the patients in the control group were placed in the supine position for 30 minutes. The level of pain was measured 6, 12, 24and 48 hours after completion of the procedure using the NRS scale. Data analysis was performed using SPSS version 24 software. Results: The results of this study show that there is a significant difference between the pain intensity 6, 12, 24and 48 hours after the end of the intervention between the two intervention and control groups (P >0.05). The pain in the intervention group is therefore lower than in the control group. Conclusion: As a simpleeffective, and inexpensive technique, the Trendelenburg position can reduce shoulder pain in patients undergoing laparoscopic cholecystectom

Prediction of Breast Cancer Metastasis Via Decision Tree Modeling

Background: Nowadays, breast cancer (BC) metastasis is a nightmare for women and one of the main challenges among researchers worldwide. Unlike traditional statistical methods that are not able to handle and take into account the complexity of effects and existence of interactions among predictor variables, the decision trees can overcome these problems. This study aimed to predict and identify the main prognostic factors of BC metastasis status (binary response) using decision tree modeling. Methods: This retrospective cohort study was conducted on 375 patients with BC who had registered with the Comprehensive Cancer Control Center from 1998 to 2013. Some demographic features related to the conditions of the Person’s disease and the type of treatment received were recorded. We applied a tree-based approach using the Gini index as the homogeneity criterion to explore the factors affecting metastasis occurrence in BC patients. Results: The mean (SD) age of BC patients with and without metastasis was 55.7 (12.4) and 43.1 (7.2) years, respectively (P<0.001). The rate of metastasis was 33.3. The five most important risk factors for metastasis of tumor proposed by tree diagram were age at diagnosis, grade of tumor, type of surgery, number of deliveries, and axillary surgery. The prediction accuracy of the proposed model was 84.3, and its sensitivity and specificity were 66.4 and 93.2, respectively. Conclusion: Age at diagnosis was the most important factor for predicting breast cancer metastasis, so that breast cancer patients aged over 54 were at high risk of metastasis

Prediction of Hepatitis Disease Using Ensemble Learning Methods: Prediction of Hepatitis Disease

Objective: Hepatitis is one of the chronic diseases that can lead to liver cirrhosis and hepatocellular carcinoma, which cause deaths around the world. Hence, early diagnosis is needed to control, treat, and reduce the effects of this disease. This study's main goal was to compare the performance of traditional and ensemble learning methods for predicting hepatitis B virus (HBV), and hepatitis C virus (HCV). Also, important variables related to HBV and HCV were identified. Methods: This case-control study was conducted in Hamadan Province, Western Iran, between 2018 and 2019. It included 534 subjects (267 cases and 267 controls). The bagging, random forest, AdaBoost, and logistic regression were used for predicting HBV and HCV. These methods' performance was evaluated using accuracy. Results: According to the results, the accuracy of bagging, random forest, Adaboost, and logistic regression were 0.65±0.03, 0.66±0.03, 0.62±0.04, and 0.64±0.03, respectively, with random forest showing the best performance for predicting HBV. This method showed that ALT was the most important variable for predicting HBV. The accuracy of random forest was 0.77±0.03 for predicting HCV. Also, the random forest showed that the order of variable importance has belonged to AST, ALT, and age for predicting HCV. Conclusion: This study showed that random forest performed better than other methods for predicting HBV and HCV