Background Survival analysis of patients on maintenance hemodialysis (HD) has been the subject of many studies. No study has evaluated the effect of different factors on the survival time of these patients. In this study, by using parametric survival models, we aimed to find the factors affecting survival and discover the effect of them on the survival time. Methods As a retrospective cohort study, we evaluated the data of 1408 HD patients. We considered the data of patients who had at least 3 months of HD and started HD from December 2011 to February 2016. The data were extracted from Shiraz University of Medical Sciences (SUMS) Special Diseases database. Primary event was death. We applied Cox-adjusted PH to find the variables with significant effect on risk of death. The effect of various parameters on the survival time was evaluated by a parametric survival model, the one found to have the best fit by Akaike Information Criterion (AIC). Results Of 428 HD patients eligible for the analysis, 221 (52%) experienced death. With the mean ± SD age of 60 ± 16 years and BMI of 23 ± 4.6 Kg/m, they comprised of 250 men (58%). The median of the survival time (95% CI) was 624 days (550 to 716). The overall 1, 2, 3, and 4-year survival rates for the patients undergoing HD were 74, 42, 25, and 17%; respectively. By using AIC, AFT log-normal model was recognized as the best functional form of the survival time. Cox-adjusted PH results showed that the amount of ultrafiltration volume (UF) (HR = 1.146, P = 0.049), WBC count (HR = 1.039, P = 0.001), RBC count (HR = 0.817, P = 0.044), MCHC (HR = 0.887, P = 0.001), and serum albumin (HR = 0.616, P < 0.001) had significant effects on mortality. AFT log-normal model indicated that WBC (ETR = 0.982, P = 0.018), RBC (ETR = 1.131, P = 0.023), MCHC (ETR = 1.067, P = 0.001), and serum albumin (ETR = 1.232, 0.002) had significant influence on the survival time. Conclusion Considering Cox and three parametric event-time models, the parametric AFT log-normal had the best efficiency in determining factors influencing HD patients survival. Resulting from this model, WBC and RBC count, MCHC and serum albumin are factors significantly affecting survival time of HD patients.

Background: More than 1.2 million scorpion stings occur annually worldwide, particularly in tropical regions. In the absence of proper medical care, mortality due to venomous scorpion stings is an important public health issue. The aim of the present study is to explore the temporal trend of scorpionism with time series models and determine the effective factors on this event using regression models. Methods: A retrospective cross sectional study was conducted on 853 scorpion stung patients. They were referred to Haji-Abad Hospital of Hormozgan University of Medical Sciences (HUMS), south Iran, from May 2012 to July 2016. A linear model to describe and predict the monthly trend of scorpion sting cases is fit with autoregressive moving average (ARMA) model.

Background Automated office blood pressure (AOBP) machines measure blood pressure (BP) multiple times over a brief period. We aimed to compare the results of manual office blood pressure (MOBP) and AOBP methods with ambulatory BP monitoring (ABPM) in patients with chronic kidney disease (CKD). Methods This study was performed on 64 patients with CKD (stages 3–4). A nurse manually measured the BP on both arms using a mercury sphygmomanometer, followed by AOBP of the arm with the higher BP and then ABPM. Mean BP readings were compared by paired t test and Bland–Altman graphs. Results The mean ± standard deviation (SD) age of participants was 59.3 ± 13.6. The mean ± SD awake systolic BP obtained by ABPM was 140.2 ± 19.0 mmHg, which was lower than the MOBP and AOBP methods (156.6 ± 17.8 and 148.8 ± 18.6 mmHg, respectively; P < 0.001). The mean ± SD awake diastolic BP was 78.6 ± 13.2 mmHg by ABPM which was lower than the MOBP and AOBP methods (88.9 ± 13.2 and 84.1 ± 14.0 mmHg, respectively; P < 0.001). Using Bland–Altman graphs, MOBP systolic BP readings showed a bias of 16.4 mmHg, while AOBP measurements indicated a bias of 8.6 mmHg compared with ABPM. Conclusion AOBP methods may be more reliable than MOBP methods for determining BP in patients with CKD. However, the significantly higher mean BPs recorded by AOBP method suggested that AOBPs may not be as accurate as ABPM in patients with CKD.

This study aimed to determine the prognostic factors influencing the overall survival (OS) of Iranian women with epithelial ovarian cancer (EOC). Methods: Information about newly diagnosed patients with confirmed EOC at Motahari Clinic, Shiraz, Iran, from January 1, 2001, to December 31, 2016, was retrospectively reviewed and analyzed. Cox-adjusted proportional hazards (PH) and stratified Cox (SC) models were used to determine the potential prognostic factors. Results: The mean (±SD) age at the diagnosis of 385 patients with EOC was 49.0 (±13.2) years old. Early-stage EOC (ESEOC) and advanced-stage EOC (ASEOC) were diagnosed in 34.3% and 65.7% of the total patients, respectively. The median (95% CI) OS was 35 (28−41) months. For ESEOC patients, a stage II-tumor led to a lower OS in the multivariable analysis compared to a lower stage tumor (P= 0.025). For ASEOC patients, age≥65 years at diagnosis (P=0.008) led to a lower OS. ASEOC patients with 2-5 parities (P=0.014) and >5 parity (P=0.001) demonstrated better OS than nulliparous women. Conclusion: Patients with ESEOC, higher tumor stage was associated with a shorter OS. The age at diagnosis harmed the OS of patients with ASEOC. More than one parity improved OS in ASEOC patients.

Background: Snakebite envenomation is a vital status necessitating immediate treatment following case detection. Many cases of snakebites are recorded every year due to the suitable climatic conditions for the existence and survival of snakes in south Iran. Methods: In the present retrospective cross-sectional study, 195 snake (Reptilia: Squamata: Viperidae; Echis carinatus sochureki) bite cases referred to 10 rural health centers, two health care stations and the Haji-Abad Central Hospital of Hormozgan University of Medical Sciences (HUMS) were surveyed during 2012-2016. Seasonal time series models were applied to fit a linear model to describe and predict the monthly trend of snakebite cases. Results: Among these patients, males (70%, 136) from rural areas (79.5%, 155) were mostly recorded. The mean (± SD) age of victims was 33 (± 17.0) years old and the most common age group was 20-29 years (32%). Most snakebites took place outdoors (80%), on hands and legs (97%), and among unemployed people and farmers (61.0%). Snakebites often happened between midnight and 6 am (32%); also 51% of them occurred during summer. Most (70%) patients had pain at the bite sites. The location of being bitten (indoors or outdoors) had a significant difference with patient's sex (χ 2 = 7.764, P = 0.021). Conclusions: Time series analysis proposed a mixed seasonal autoregressive moving average, ARMA × (1, 0) (1, 1) 12 as the best process for the monthly trend of snakebite and to predict the incidence of snakebites. Local residents should be more cautious on snakebites during warm seasons.

Background The most common type of ovarian cancer (OC) is epithelial ovarian cancer (EOC) which is the most lethal gynecologic malignancy in adult women. Aim This study aimed to determine the conditional disease‐free survival (CDFS) rates and their associated determinants in patients with EOC. Methods and results The clinical and demographic data of 335 patients with confirmed EOC at Motahari Clinic (Shiraz, Iran) were retrospectively reviewed and analyzed. Traditional DFS (TDFS) and CDFS were calculated using the Kaplan–Meier method and cumulative DFS estimates, respectively. To evaluate the effects of the prognostic determinants on the DFS of the patients, a multiple covariate Cox analysis using the landmarking method was applied. The 1‐ and 3‐year TDFSs were 81.1% and 47.0%, respectively, and decreased over time. At baseline, a higher stage tumor and endometrioid histology were associated with a higher risk of recurrence when compared to stage I and other histological subtypes, respectively. The hazard of recurrence for older women (age ≥55 years) was approximately twice and three times more than that of women aged <45 years at 1‐ and 3‐year landmark time points, respectively. Conclusion The age at diagnosis, defined by a cut‐off of 55 years, was a prognostic factor for the CDFS of EOC women. Moreover, patients with advanced‐stage EOC (ASEOC) (stages III and IV) and endometrioid histology had poorer CDFSs compared to those with early‐stage EOC (ESEOC) (stages I and II) and other histological types. In ESEOC patients with age at diagnosis of >55 years, CDFS gradually decreased in 3 years after remission which should be considered for follow‐up care decision‐making.

Background Narrowing a large set of features to a smaller one can improve our understanding of the main risk factors for in-hospital mortality in patients with COVID-19. This study aimed to derive a parsimonious model for predicting overall survival (OS) among re-infected COVID-19 patients using machine-learning algorithms. Methods The retrospective data of 283 re-infected COVID-19 patients admitted to twenty-six medical centers (affiliated with Shiraz University of Medical Sciences) from 10 June to 26 December 2020 were reviewed and analyzed. An elastic-net regularized Cox proportional hazards (PH) regression and model approximation via backward elimination were utilized to optimize a predictive model of time to in-hospital death. The model was further reduced to its core features to maximize simplicity and generalizability. Results The empirical in-hospital mortality rate among the re-infected COVID-19 patients was 9.5%. In addition, the mortality rate among the intubated patients was 83.5%. Using the Kaplan-Meier approach, the OS (95% CI) rates for days 7, 14, and 21 were 87.5% (81.6-91.6%), 78.3% (65.0-87.0%), and 52.2% (20.3-76.7%), respectively. The elastic-net Cox PH regression retained 8 out of 35 candidate features of death. Transfer by Emergency Medical Services (EMS) (HR=3.90, 95% CI: 1.63-9.48), SpO2≤85% (HR=8.10, 95% CI: 2.97-22.00), increased serum creatinine (HR=1.85, 95% CI: 1.48-2.30), and increased white blood cells (WBC) count (HR=1.10, 95% CI: 1.03-1.15) were associated with higher in-hospital mortality rates in the re-infected COVID-19 patients. Conclusion The results of the machine-learning analysis demonstrated that transfer by EMS, profound hypoxemia (SpO2≤85%), increased serum creatinine (more than 1.6 mg/dL), and increased WBC count (more than 8.5 (×109 cells/L)) reduced the OS of the re-infected COVID-19 patients. We recommend that future machine-learning studies should further investigate these relationships and the associated factors in these patients for a better prediction of OS.

Background:Patients who are identified to be at a higher risk of mortality from COVID-19 should receive better treatment and monitoring. This study aimed to propose a simple yet accurate risk assessment tool to help decision-making in the management of the COVID-19 pandemic. Methods: From Jul to Nov 2020, 5454 patients from Fars Province, Iran, diagnosed with COVID-19 were enrolled. A multiple logistic regression model was trained on one dataset (training set: n=4183) and its prediction performance was assessed on another dataset (testing set: n=1271). This model was utilized to develop the COVID-19 risk-score in Fars (CRSF). Results: Five final independent risk factors including gender (male: OR=1.37), age (60-80: OR=2.67 and >80: OR=3.91), SpO2 (≤85%: OR=7.02), underlying diseases (yes: OR=1.25), and pulse rate (<60: OR=2.01 and >120: OR=1.60) were significantly associated with in-hospital mortality. The CRSF formula was obtained using the estimated regression coefficient values of the aforementioned factors. The point values for the risk factors varied from 2 to 19 and the total CRSF varied from 0 to 45. The ROC analysis showed that the CRSF values of ≥15 (high-risk patients) had a specificity of 73.5%, sensitivity of 76.5%, positive predictive value of 23.2%, and negative predictive value (NPV) of 96.8% for the prediction of death (AUC=0.824, P<0.0001). Conclusion:This simple CRSF system, which has a high NPV,can be useful for predicting the risk of mortality in COVID-19 patients. It can also be used as a disease severity indicator to determine triage level for hospitalization.