2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC.

https://hull-repository.worktribe.com/preview/1797952/1-s2.0-S0140673618322797-main.pdf

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

世界経済・社会統計 = World development indicators

https://openresearch-repository.anu.edu.au/bitstream/1885/164632/2/01_Bin%2bSayeed_Global%252C_regional%252C_and_national_2018.pdf

Global, regional, and national incidence, prevalence, andyears lived with disability for 354 diseases and injuries for195 countries and territories, 1990–2017: a systematicanalysis for the Global Burden of Disease Study 2017

WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update

Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Patients' outcomes after cardiac arrest based on age: Methodological issues.

Re: Can Activities of Daily Living Predict Complications following Percutaneous Nephrolithotomy?: D. A. Leavitt, P. Motamedinia, S. Moran, M. Siev, P. T. Zhao, N. Theckumparampil, M. Fakhoury, S. Elsamra, D. Hoenig, A. Smith and Z. Okeke J Urol 2016;195:1805-1809.

First, it was not described how the multivariate logistic regression was built. The standard multivariate model building is conducted in two steps: (A) bivariate associations between the explanatory variables are initially examined to check for multicollinearity. (B) Explanatory variables with P value <0.2 are imported into the multivariate model to check whether imported variables are independently associated with studied outcome or not [2, 3]. Bayraktar and colleagues did not considered step A, whereas it seems that Right CCA YEM, Left CCA YEM, Right CCA stiffness, Left CCA stiffness, and Left CCA CIMT are highly associated with each other [4]. Therefore, the results of study conducted by Bayraktar et al. may be biased by multicollinearity between the explanatory variables in the multivariate model. Second, it was not explained how the systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured. SBP and DBP have within-individual fluctuations and these variables are proposed to be measured for three times and their mean should be used as study variable. Bayraktar et al. overlooked this point which can result in regression dilution bias in their study’s findings [5, 6]. As take home message for the readers is that the multivariate model building should be conducted more accurately to avoid any biased results.

Relationship between increased carotid artery stiffness and idiopathic subjective tinnitus: methodological issues

We were interested to read the paper by Kozaki and colleagues that was published in Archives of Gynecology and Obstetrics [1]. The authors aimed to examine the efficacy of the chemotherapy, predictive factors for the effects of chemotherapy, and prognostic factors in these patients. They concluded that therapy-free interval (TFI) has predictive value for response to chemotherapy and prognosis of patients with recurrent cervical cancer after definitive concurrent chemoradiotherapy (CCRT). Although the useful study has been conducted, some methodological issues need to be noted. First, the Cox proportional hazard (PH) model was used to find predictors of overall survival in uniand multi-variable analyses which should be more checked. The Cox PH model has an important assumption that must be evaluated before applying to the data. Otherwise, biased results may be reported. The Extended Cox can be used when this assumption is not established [2]. So, Kozaki and colleagues should consider this point and use appropriate model. Second, the author developed multivariable regression model but no information has been provided on how the independent variables were imported into the model. In fact, it is unclear based on which criteria the variables were considered for multivariable model. In addition, no multicollinearity is assessed between included variables. Scientists suggest that the Testimation bias and multicollinearity can appear in the multivariable models which have not followed standard model building process [3, 4]. Finally, Kozaki and colleagues stated that TFI has predictive value for response to chemotherapy and prognosis of patients which is questionable. The prediction performance of the developed model should be validated in their study using the Bootstrapping method or in an independent cohort through cross-validation; otherwise, their interpretation is optimistic [4]. The take-home message is that the assumption of regression models and standard process of model building should be considered in epidemiological studies. Also, the validation of prediction model should be evaluated before applying them into clinical practice.

Therapy-free interval has prognostic value in patients with recurrent cervical cancer treated with chemotherapy following definitive concurrent chemoradiotherapy: methodological issues.

We were interested to read the article by Kim et al. that was recently published in Seizure [1]. The authors aimed to identify predictive factors and to ascertain the characteristics of seizureinduced signal changes using an MRI (SCM) on patients with their first seizures. As one of the important results, they found that the epileptiform discharge on EEG was independently associated with the presence of SCM in patients following their first seizures [odds ratio (OR) = 29.7; 95% confidence interval (CI): 1.79–493.37], which is problematic. It has been stated that large measures of association, in this case the large OR, with very wide CIs are common in studies with sparse data [2,3]. Data sparsity means that the number of observations, for each combination of independent and dependent variables, was not sufficient to obtain an accurate reflection of the relationship between the variables. This issue has been named sparse data bias, which results in an inflated OR and extremely wide CIs [2,3]. In this particular research, the bias would be more obvious in the multivariable model, as the number of combinations between independent and dependent variables was increased and thus the data sparsity would be more severe [2]. Based on the data provided in Table 1 of Kim et al. article, the crude univariable OR for the association between epileptiform discharge and SCM was calculated [OR = 5.9; 95% CI: 1.40–24.98], which was substantially lower than the multivariable OR [OR = 29.7; 95% confidence interval (CI): 1.79–493.37] and had a narrower CI. This partially demonstrates the impact of sparse data bias on the OR and CI. Fortunately, the biased ORs can be corrected, in both the univariable and multivariable models using advanced statistical methods. Greenland et al. proposed the penalization via data augmentation method in 2016, which has been found to effectively decrease sparse data bias [2]. Hence, we respectfully suggest that Kim and colleagues re-estimate their adjusted OR for the association between the epileptiform discharge and SCM to obtain a valid and less biased OR. The take-home message for readers is that sparse data bias is common in medical research [4,5], but this issue can be resolved, or at least ameliorated, using advanced statistical techniques.

Saeid Safiri

Papers

Patients' outcomes after cardiac arrest based on age: Methodological issues.

Re: Can Activities of Daily Living Predict Complications following Percutaneous Nephrolithotomy?: D. A. Leavitt, P. Motamedinia, S. Moran, M. Siev, P. T. Zhao, N. Theckumparampil, M. Fakhoury, S. Elsamra, D. Hoenig, A. Smith and Z. Okeke J Urol 2016;195:1805-1809.

Relationship between increased carotid artery stiffness and idiopathic subjective tinnitus: methodological issues

Therapy-free interval has prognostic value in patients with recurrent cervical cancer treated with chemotherapy following definitive concurrent chemoradiotherapy: methodological issues.

Characteristics of seizure-induced signal changes on MRI in patients with first seizures: Comment on data sparsity.