[Prevalence and etiology of heart murmurs in 2-24-months-old infants Kerman, Iran]

Background and Aim: Congenital heart disease is one of the most common malformations at birth that require timely recognition and treatment. The aim of this study was to determine the prevalence and etiology of detected heart murmurs and association between congenital heart disease and heart murmurs. Recognition of murmurs etiology would help us to manage and treat them properly

Methods: In this cross-sectional study, 2757 infants between two to 24 months in Kerman city, Iran were enrolled. The infants who had heart murmurs in physical exam were referred to pediatric cardiologist for more evaluations such as echocardiography

Results: 2757 infants were screened for the presence of heart murmurs and murmurs were heard in 145 [5.29%] including 71 girls [49%] and 74 boys [51%]. Innocent and pathologic murmurs prevalence was 49 and 51 percent, respectively. Altogether, left to right shunts [ventricular ceptal defect [VSD], atrial ceptal defect [ASD], patent ductus arteriosus [PDA], atrioventricular septal defect [AVSD]] were the most common cardiac abnormalities in this study. The most common heart lesion was ventricular septal defect [21.6%]. There was a significant correlation between younger infants, lower weights and girl sex with congenital heart disease

Conclusion: On base of our study, the incidence of pathologic heart murmurs related to congenital heart disease is more than innocent murmurs in infants; with accurate heart exam and timely screening, we can prevent irreversible heart complication in these ages

[Immunity against influenza type a among 4-14-years-old children in Kerman, Iran]

Background and Aims: Influenza viruses are important causes of mortality and morbidity in children. The aim of this study was to assess the presence of antibodies [IgG] against Influenza A in children in Kerman, Iran

Methods: In a cross-sectional study, 200 children aged 4-14 years referred to Besaat Clinic and Afzalipour hospital for diseases other than influenza were enrolled. Sera were tested for anti influenza A IgG with NovaLisa ELISA kits [NOVATEC, Germany]

Results: Anti-Influenza virus A IgG was detected in 12% [24/200] of the sera. This group had the highest mean age [9.62 [7-12] years]. Among studied variables, only age was related to seropositivness for anti-Influenza A serotypes

Conclusion: The majority of children aged 4-14 years in Kerman had no immunity to Influenza A. So, they are at risk for influenza and its morbidity during possible epidemics of this infection

Assessment of internal validity of prognostic models through bootstrapping and multiple imputation of missing data

Prognostic models have clinical appeal to aid therapeutic decision making. Two main practical challenges in development of such models are assessment of validity of models and imputation of missing data. In this study, importance of imputation of missing data and application of bootstrap technique in development, simplification, and assessment of internal validity of a prognostic model is highlighted. Overall, 310 breast cancer patients were recruited. Missing data were imputed 10 times. Then to deal with sensitivity of the model due to small changes in the data [internal validity], 100 bootstrap samples were drawn from each of 10 imputed data sets leading to 1000 samples. A Cox regression model was fitted to each of 1000 samples. Only variables retained in more than 50% of samples were used in development of final model. Four variables retained significant in more than 50% [i.e. 500 samples] of bootstrap samples; tumour size [91%], tumour grade [64%], history of benign breast disease [77%], and age at diagnosis [59%]. Tumour size was the strongest predictor with inclusion frequency exceeding 90%. Number of deliveries was correlated with age at diagnosis [r=0.35, P<0.001]. These two variables together retained significant in more than 90% of samples. We addressed two important methodological issues using a cohort of breast cancer patients. The algorithm combines multiple imputation of missing data and bootstrapping and has the potential to be applied in all kind of regression modelling exercises so as to address internal validity of models

[Evaluation of submicron particle filtration efficiency of domestic and imported surgical masks]

Bioaerosols are one of the most important agents that cause post operating infections in hospitals. Surgical masks are recommended for prevention of bioaerosols transmition in operating rooms. This study aimed at evaluation of submicron particle filtration efficiency of domestic and imported surgical masks. In this cross sectional study, 5 types of surgical masks that have the most utilization in operating rooms of country's hospitals including domestic and imported surgical masks were tested. To evaluate all samples, the submicron particle filtration measurements were carried out based on ISIRI 6138 and American DOP standards. Filtration efficiency calculations and pressure drop measurements were performed and the results were analyzed using statistical tests. Results showed that particle filtration efficiency of domestic and imported masks were 56.130% [ +/- 10.7] and 31.906% [ +/- 7.062] respectively. Also, filtration efficiency in domestic masks were more than imported masks [P< 0.001]. Among all samples, Arman mask had the most filtration efficiency [66.5475% +/- 6.14951], where the least [27.8275% +/- 4.44152] filtration efficiency [P< 0.001] belongs to Blosom. The maximum mean of pressure drop in Arman mask [35 +/- 2. 58 Pa] and the least mean of pressure drop in Zist filter mask [11 +/- 1.82 Pa] were observed. According to the effect of filtration efficiency and pressure drop on general quality of mask, the quality factor of masks were also evaluated. Results showed that Zist filter mask had the most quality factor [0.068] while Blosom had the least quality factor [0.016]. This research showed that domestic surgical masks have a better quality toward imported surgical masks but can not obtained quality confirmed by standards, yet. To reduce respective infections and prevalence of diseases, it is recommended using filters with suitable physical characteristics and also carrying out test of surgical masks before supplying

Prevention of disease complications through diagnostic models: how to tackle the problem of missing data?

Diagnostic models are frequently used to assess the role of risk factors on disease complications, and therefore to avoid them. Missing data is an issue that challenges the model making. The aim of this study was to develop a diagnostic model to predict death in HIV/ AIDS patients when missing data exist. HIV patients [n=1460] referred to Voluntary Consoling and Testing Center [VCT] of Shiraz southern Iran during 2004-2009 were recruited. Univariate association between variables and death was assessed. Only variables which had univariate P< 0.25 were selected to be offered to the Multifactorial models. First, patients with missing data on candidate variables were deleted [C-C model]. Then, applying Multivariable Imputation via Chained Equations [MICE], missing data were imputed. Logistic regression was fitted to C-C and imputed data sets [MICE model]. Models were compared in terms of number of variables retained in the final model, width of confidence intervals, and discrimination ability. About 22% of data were lost in C-C model. Number of variables retained in the C-C and MICE models was 2 and 6 respectively. Confidence Intervals [C.I.] corresponding to C-C model was wider than that of MICE. The MICE model showed greater discrimination ability than C-C model [70% versus 64%]. The C-C analysis resulted to loss of power and wide CI's. Once missing data were imputed, more variables reached significance level and C.I.'s were narrower. Therefore, we do recommend the application of the imputation method for handling missing data

Does the missing data imputation method affect the composition and performance of prognostic models?

We already showed the superiority of imputation of missing data [via Multivariable Imputation via Chained Equations [MICE] method] over exclusion of them; however, the methodology of MICE is complicated. Furthermore, easier imputation methods are available. The aim of this study was to compare them in terms of model composition and performance. Three hundreds and ten breast cancer patients were recruited. Four approaches were applied to impute missing data. First we adopted an ad hoc method in which missing data for each variable was replaced by the median of observed values. Then 3 likelihood-based approaches were used. In the regression imputation, a regression model compared the variable with missing data to the rest of the variables. The regression equation was used to fill the missing data. The Expectation Maximum [E-M] algorithm was implemented in which missing data and regression parameters were estimated iteratively until convergence of regression parameters. Finally, the MICE method was applied. Models developed were compared in terms of variables significantly contributed to the multifactorial analysis, sensitivity and specificity. All candidate variables significantly contributed to the MICE model. However, grade of disease lost its effect in other three models. The MICE model showed the best performance followed by E-M model. Among imputation methods, final models were not the same, in terms of composition and performance. Therefore, modern imputation methods are recommended to recover the information

Dichotomisation of continuous data: review of methods, advantages, and disadvantages

In medical research, dichotomisation of continuous variables is a widespread use approach. However, it has been argued that dichotomization might be waste of information. The aim of this paper is to review the main methods to dichotomise continuous data, to address practical issues around dichotomization methods, and to investigate whether dichotomisation is always a bad idea. A total of 310 breast cancer patients were recruited. Information on 3 categorical and 1 continuous variable [age at diagnosis] was available. Missing data were imputed applying the Multivariable Imputation via Chained Equations [MICE] method. Then a minimum P-value method was applied to dichotomise the age variable. The Cox regression model was fitted to develop models in which dichotomised versus continuous version of the age variable plus other 3 variables were used. Results were compared in terms of discrimination ability, goodness of fit, and classification improvement. For the age variable, an optimal split at 47 was found. This split was close to menopause age of women in Shiraz [48] so had biological interpretability. The stability of optimal split was confirmed in bootstrap study. Model in which dichotomised version of age was used showed higher discrimination ability and goodness of fit. Furthermore, dichotomised model assigned 14% of live patients into a more appropriate risk group. Dichotomisation of continuous data is a contentious issue. We have shown that dichotomisation might improve performance of models when it has biological interpretation. More research is needed to understand situations in which dichotomisation might work

[How to estimate the sample size in special conditions? [part two]]

In the previous paper, the basic concepts of sample size calculation were presented. This paper explores main post-calculation adjustments of the sample size calculation in special circumstances such as multiple group comparisons, unbalanced studies [with unequal number of subjects in different groups]; sample size correction for missing data, and adjustment for finite population size. In addition, the concept of design effect in multi-stage sampling and its impact on the sample size are presented. We then focused on the sample size estimation when we have to use non-parametric statistical tests for data analysis. The concept of power-efficacy of parametric versus non-parametric methods and its use in the correction of sample size has been explained

Multiple imputation in survival models: applied on breast cancer data

Missing data is a common problem in cancer research. While simple methods such as complete-case [C-C] analysis are commonly employed for handling this problem, several studies have shown that these methods led to biased estimates. We aim to address the methodological issues in development of a prognostic model with missing data. Three hundred and ten breast cancer patients were enrolled. At first, patients with missing data on any of four candidate variables were omitted. Secondly, missing data were imputed 10 times. Cox regression model was fitted to the C-C and imputed data. Results were compared in terms of variables retained in the model, discrimination ability, and goodness of fit. Some variables lost their effect in complete-case analysis, due to loss in power, but reached significance level after imputation of missing data. Discrimination ability and goodness of fit of imputed data sets model was higher than that of complete-case model [C-index 76% versus 72%; Likelihood Ratio Test 51.19 versus 32.44]. Our findings showed inappropriateness of ad hoc complete-case analysis. This approach led to loss in power and imprecise estimates. Application of multiple imputation techniques to avid such problems is recommended

Survival models in breast cancer patients

Breast cancer is the most prevalent malignancy among Iranian women. Five and ten year survival is one of the indicators used for evaluation of the quality of care after surgery. In this study, we used several survival models to determine risk factors, survival times and life expectancies of different types of surgery. This study was performed on 310 patients who underwent surgery during a ten years period. Logistic regression and Cox regression models were used to analyze the factors leading to death. The Kaplan-Meier method [non-parametric] was used to estimate the survival rate. The log-rank test was used to compare survival in different groups. To compare life expectancy of different types of surgery, we used the actuarial life table method. Logistic regression showed that stage, grade, age and history of benign malignancy had significant relationship with death. Log-rank test showed that there was a significant difference between survival for patients with different stages, age and history of benign tumors. Cox regression model demonstrated that the variables of stage, grade, age and benign problems were the major risk factors. Actuarial life table model showed that the life expectancy for all patients was 10.03 years. This life expectancy in early stages of breast cancer for mastectomy and lumpectomy were 8.99 and 8.35 years, respectively, which was not significant. It can be concluded that the higher stage, grade, age and history of benign tumor were, the most important risk factors were correlated to mortality in breast cancer patients. This study showed that there was no significant difference between life expectancies of mastectomy and lumpectomy surgery