Hojjat Zeraati, PhD.
Vol 7 No 3 (2021)
Introduction: In the present study, the goal was to estimate the hospital length of stay among patients admitted with COVID-19 in a hospital in Tehran.
Methods: We used retrospective data on 446 hospitalized patients with COVID-19 who admitted from 7 March to 8 Oct 2020 in a referral hospital in Tehran, Iran. The prognostic effects of variables, including age, gender, comorbidity status, and symptoms were analyzed by using Kaplan-Meier methods and a competing risk analysis. Length of stay in hospital was calculated using time of last status minus time of admission. All analyses performed using SPSS version 22.0 and STATA version 15.
Results: The mean age of cases was 57.09±16.85 years old. The median (IQR) of hospital length of stay among all patients was 7 (11-5) days. The length of Hospital stay, for >80 years’ patients (9days (15-5)) and females (7days (11-5)) was the longest. The most of cases (94 (21.1%)) were in 60–69 age group. In overall 267 (59.9%) of all cases were males and 179 (40.1%) were females. The most common symptom among patients was Respiratory distress 249 (55.8), Cough 233 (52.2) and fever 209 (46.9) respectively. Regarding having any comorbidities, 106 (23.8%) of COVID-19 cases had Cardiovascular disease, 114 (25.6%) had diabetes and 100 (22.4%) had hypertension. Most of deaths (21 (32.3%)) occurred in 70-79 years’ age group. The overall Case Fatality Rate (CFR) in under-studied cases was 14.6%.
Conclusion: Although the result of the present study showed that hospital length of stay in Iran is not higher than in other countries, but by applying some measures including the early detection of suspected cases and timely treatment and necessary funding on preparing required facilities, medicine and equipment, it could be shortened or at least prevented from increasing.
Introduction: This study aimed to assess the association between the survival of patients and outcomes in Brain Stroke (BS) in the presence of competing risks utilizing a Weibull parametric model.
Methods: In this longitudinal study, 332 patients with BS were attended from Imam Khomeini Hospital in Ardabil, Iran. The stroke was diagnosed according to the medical history, current symptoms, and brain imaging during June 2008 and 2018. The survival of the patients, as the primary outcome, was modeled utilizing the best-chosen Weibull model in the presence of competing risks, including stroke and other factors (heart disease, blood pressure, etc.).
Results: Older age at diagnosis (59-68 years: hazard ratio [HR]=2.27; 90% confidence interval [CI]: 1.65 to 3.12; 69-75 years: HR=4.79; 95% CI: 3.56 to 6.44; ≥76 years: HR, 4.92; 95% CI: 3.55 to 6.80), being a male (HR, 1.39; 95% CI: 1.11 to 1.75), being unemployed (HR, 1.44; 95% CI: 1.39 to 1.82), having heart disease (HR, 1.68; 95% CI: 1.38 to 2.06), and hemorrhagic stroke (HR, 2.21; 95% CI: 1.378to 2.75) were directly related to death from BS. Older age at diagnosis (59-68 years: HR, 18.01; 90% CI, 5.33 to 64.92; 75-69 years: HR, 18.56; 95% CI: 6.97 to 86.57; ≥76 years: HR, 28.90; 95% CI: 15.77 to 218.49), and urban residence (HR, 0.46; 90% CI, 0.28 to 0.77) were directly related to death from other causes.
Conclusion: The recognition of the influential factors on the mortality of BS patients can allow increasing their survival.
Introduction: Study of cancer incidence trends can provide better insight for decision-making and considering necessary interventions. The current study was focused on investigating the main patterns in the incidence of gynecological cancers among the provinces of Iran during the last decades.
Methods: We carried out an applied longitudinal study through the growth mixture model (GMM), with a concentration on the trajectory of incidence rates. Information about the rate of gynecological cancer incidence (per 100,000) in 31 provinces of Iran during the 1990-2016 period was extracted from the Data Visualization System. Taking into account the p-value of the likelihood ratio test (LRT), the number of main patterns was estimated by Mplus 7.4 software.
Results: Tehran province with the incidence of 2.00 per 100,000 was in the first rank in 1990, while in 2016 the highest rate was observed in Yazd province with 9.38 cases. Five main patterns were determined based on LRT. Tehran and Yazd provinces showed the sharpest rise, while Khuzestan, Fars, Esfahan, Semnan, East Azerbaijan, Razavi Khorasan, and Mazandaran provinces belonged to the pattern with a moderate-to-highrising trend. 10 provinces including Kerman, Kurdistan, Gilan, Lorestan, Alborz, Hamedan, Kermanshah, Markazi, Ardabil, and West Azerbaijan were on the other hand categorized in the moderate-rising trend. Sistan and Baluchestan and Hormozgan provinces had a slow-rising pattern, and finally, the remaining 10 provinces had the pattern with a slow-to-moderate upward trajectory.
Conclusion: Due to the considerable rising trend in most provinces in Iran, taking urgent and effective preventive actions seems necessary.
Introduction: Propensity score matching (PSM) is a method to reduce the impact of essential and confounders. When the number of confounders is high, there may be a problem of matching, in which, finding matched pairs for the case group is difficult, or impossible. The propensity score (PS) minimizes the effect of the confounders, and it is reduced to one dimension. There are various algorithms in the field of PSM. This study aimed to compared the nearest neighbor and caliper algorithms.
Methods: Data obtained in this study were from patients undergoing angiography at Ghaem Hospital in Mashhad, between 2011-12. The study was a retrospective case-control using PSM. In total, 604 patients were included in the case and control groups. A logistic regression model was used to calculate the propensity score and adjust the variables, such as age, gender, Body Mass Index (BMI), systolic blood pressure, smoking status, and triglyceride. Then, the Odds Ratios (ORs) with 95% Confidence Intervals (CIs) for the raw data and two matching algorithms were determined to examine the relationship between type 2 diabetes and coronary artery disease (CAD).
Results: Propensity score in the nearest neighbor and caliper algorithms matched the total number of 604 samples, 200 and 178 pairs, respectively. All variables were significantly different between the two groups before matching (P<0.05). The gender was significantly different between the two groups after matching using the nearest neighbor algorithm (P=0.002). No variables created a significant difference between the two groups after matching with the caliper algorithm.
Conclusion: Bias reduction in the caliper algorithm was greater than for the nearest neighbor algorithm for all variables except the triglyceride variable.
Introduction: One of the complications of hemodialysis treatment is hypotension, which can increase morbidity and mortality and compromise dialysis efficacy. Dialysate temperature is an important factor that contributes to hemodynamic stability during hemodialysis. This study investigated the effect of dialysate temperature on the patients' blood pressure and pulse rate. Model-based approaches were used to produce more reliable results compared with traditional methods.
Methods: A total of 30 patients were studied during 9 dialysis sessions. Dialysate temperatures were 37°C,36°C and 35° C. A joint longitudinal model was used to analyze both responses of blood pressure and pulserate, simultaneously.
Results: The results showed that low-dialysate temperature was not significantly associated with higher systolic blood pressure (p>0.05) or a higher pulse rate (p>0.05) either during or after dialysis. Pulse rate and blood pressure were higher for women during dialysate (p<0.001). However, increasing age was associated with higher blood pressure and a lower pulse rate (p<0.001).
Conclusion: Using several separate, repeated measure analysis of variances may produce misleading results, when there is more than one response variable measured over time, Multivariate statistical methods (including joint longitudinal models), should be used.
Introduction: Chronic Kidney Disease (CKD) is a disease in which damaged kidneys could not remove waste material from the blood which could result in other health problems. The aim of this analysis was to identify significant laboratory prognostic factors on death hazard due to CKD.
Methods: There were 109 patients with end-stage renal disease (ESRD) treated at Helal pharmaceutical and clinical complex. The survival time was set as the time interval from starting dialysis until death due to CKD. Age, gender and factors such as creatinine, cholesterol, uric acid, SGOT, SGPT, bilirubin, hemoglobin, potassium, ALP, HbA1C, ferritin, calcium, phosphorus, PTH and albumin were employed in this study. Weibull Distribution with non-Constant Shape Parameter versus constant Shape Parameter for the analysis were used.
Results: Death due to CKD occurred in 29 (26.6%) of the patients. Sixty-seven (61.5%) had uric acid higher than 6.8 (mg/dl) and 39(35%) had phosphorus higher than 4.7 (mg/dl) which were poor prognoses. The incidence of death was 48.4%. Calcium<8.5 (mg/dl) (p=0.002), Calcium > 9.5 (mg/dl) (p=0.003), Albumin 4-6.3 (g/dl) (p=0.034), Phosphorus (p=0.022), hemoglobin<10 (g/dl) (p=0.043), hemoglobin>12.5 (g/dl) (p=0.006) and iPTH (p<0.001) were significant variables which had an effect on death hazard rates.
Conclusion: The Weibull model with Non-Constant shape parameter was suggested to be more accurate for identifying risk factors, leading to more precise results, compared to constant shape parameter. Investigators mostly emphasize on the importance of Calcium, Albumin, Phosphorus, hemoglobin and iPTH for reducing hazard rates in CKD patients.
Introduction: Diabetes is a chronic, non-communicable disease characterized by elevated blood glucose levels. The purpose of this study was to jointly model the transition of diabetic patients in a series of clinical states and to assess the relationship between each state and different patient characteristics.
Methods: A hospital-based retrospective study was conducted on 524 patients with type II diabetes, aged 18 years or older, who attended their medication between January 1, 2005, and December 31, 2017. Multistate models with different assumptions were considered to explore the effects of different prognostic factors on the transition intensity of type II diabetes mellitus patients.
Results: During a median follow-up time of 7.4 years (Inter-Quartile Range=4.01), 54.8% of diabetic patients developed either microvascular or macrovascular complications, and 10.5% of them experienced both microand macrocomplications, and 16.66% of diabetes patients died. The assumption Markov was assessed by using the likelihood ratio test showed that Markov assumption was not held just for the transition. The transition rate of patients from the macrovascular state to the death state was affected by the residence of the patients (P=0.05) and age at diagnosis (p=0.01). The transition rates of patients with microvascular complications to death were significantly affected by baseline triglyceride level (P<0.001), age at first diagnosis (P=0.01), baseline glucose level (P=0.03, and baseline serum creatinine level (P=0.04).
Conclusion: The semi-Markov model fitted the data well and could be used as a convenient model for the analysis of time to diabetes-related complications or death.
Introduction: Reliability is an integral part of measuring the reproducibility of research information. Intra-cluster correlation coefficient (ICC) is one of the necessary indicators for reliability reporting, which can be misleading in terms of its diversity. The main purpose of this study was to introduce the types of reliability and appropriate ICC indices.
Methods: In this tutorial article, useful information about the types of reliability and indicators needed to report the results, as well as the types of ICC and its applications were explained for dummies.
Results: Three general types of reliability include inter-rater reliability, test-retest reliability, and intra-rater reliability was presented. 10 different types of ICC were also introduced and explained.
Conclusion: The research results may be misleading if any of the reliability types and calculation criteria types are chosen incorrectly. Therefore, to make the results of the study more accurate and valuable. Medical researchers must seek help from relevant guidelines such as this study before conducting reliability analysis.
Introduction: The quantitative information on the risk of infection in the COVID-19 pandemic is calculated currently exclusively on the base of new infections per day, which only contribute 6.60%±1.34% to the 100% contagious acute infections and are, therefore, not proportional to the risk of infection. All methods and results presented here are shown for data in Germany, but can be transferred to any other region worldwide.
Methods: More precise parameters as are used at present, are based on acute infections: stress index with information about the distance to the stress limit of the health system, the density of the sources of infection and the change in acute infections during the last 5 days are suggested here.
Results: The comparison of the results of the current and the new assessment parameters shows that large daily fluctuations in new infections of up to ±22% lead to unnecessary uncertainties. The new assessment parameters are correspondingly more precise. The 7-days incidence warning thresholds introduced by German law in November 2020 and April 2021 are defined on the base of new infections. As a result, the real infection risks can be incorrectly assessed due to the large fluctuations of the 7-days incidence values up to ±23%, so that legal conflicts can arise if legally prescribed protective measures are objectively unjustified or introduced too late.
Conclusion: By moving from new infections to acute infections as a base for calculation, infection risks can be described more precisely and even unjustified, expensive protective measures can be avoided.
Hojjat Zeraati, PhD.
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