Target trial emulation using hospital-based observational data: demonstration and application in COVID-19

Methodological biases are common in observational studies evaluating treatment effectiveness. The objective of this study is to emulate a target trial in a competing risks setting using hospital-based observational data. We extend established methodology accounting for immortal time bias and time-fixed confounding biases to a setting where no survival information beyond hospital discharge is available: a condition common to coronavirus disease 2019 (COVID-19) research data. This exemplary study includes a cohort of 618 hospitalized patients with COVID-19. We describe methodological opportunities and challenges that cannot be overcome applying traditional statistical methods. We demonstrate the practical implementation of this trial emulation approach via clone–censor–weight techniques. We undertake a competing risk analysis, reporting the cause-specific cumulative hazards and cumulative incidence probabilities. Our analysis demonstrates that a target trial emulation framework can be extended to account for competing risks in COVID-19 hospital studies. In our analysis, we avoid immortal time bias, time-fixed confounding bias, and competing risks bias simultaneously. Choosing the length of the grace period is justified from a clinical perspective and has an important advantage in ensuring reliable results. This extended trial emulation with the competing risk analysis enables an unbiased estimation of treatment effects, along with the ability to interpret the effectiveness of treatment on all clinically important outcomes.This research was funded by the German Research Foundation (original: Deutsche Forschungsgemeinschaft), grant number WO 1746/5-1.Peer ReviewedPostprint (published version

Post-discharge health assessment in survivors of coronavirus disease: a time-point analysis of a prospective cohort study

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.PURPOSE: The objective of this study was to quantitatively evaluate psychological and quality of life-related complications at three months following discharge in hospitalized coronavirus disease 2019 (COVID-19) patients during the pandemic in Iran. METHODS: In this time-point analysis of prospective cohort study data, adult patients hospitalized with symptoms suggestive of COVID-19 were enrolled. Patients were stratifed in analyses based on severity. The primary outcomes consisted of psychological problems and pulmonary function tests (PFTs) in the three months following discharge, with Health-related quality of life (HRQoL) as the secondary outcome. Exploratory predictors were determined for both primary and secondary outcomes. Results 283 out of 900 (30%) eligible patients were accessible for the follow-up assessment and included in the study. The mean age was 53.65±13.43 years, with 68% experiencing a severe disease course. At the time of the fnal follow-up, participants still reported persistent symptoms, among which fatigue, shortness of breath, and cough were the most common. Based on the regression-adjusted analysis, lower levels of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio was associated with higher levels of depression (standardized ß=- 0.161 (SE=0.042), P=0.017) and stress levels (standardized ß=- 0.110 (SE=0.047), P=0.015). Furthermore, higher levels of anti-SARS-CoV-2 immunoglobulinM (IgM) were associated with signifcantly lower levels of depression (standardized ß=- 0.139 (SE=0.135), P=0.031). CONCLUSIONS: There is an association between lung damage during COVID-19 and the reduction of pulmonary function for up to three months from acute infection in hospitalized patients. Varying degrees of anxiety, depression, stress, and low HRQoL frequently occur in patients with COVID-19. More severe lung damage and lower COVID-19 antibodies were associated with lower levels of psychological health.Isfahan University of Medical Sciences, IR.MUI.MED.REC.1399.517, Ramin SamiPeer ReviewedPostprint (published version

PARS risk charts: A 10-year study of risk assessment for cardiovascular diseases in Eastern Mediterranean Region

This study was designed to develop a risk assessment chart for the clinical management and prevention of the risk of cardiovascular disease (CVD) in Iranian population, which is vital for developing national prevention programs. The Isfahan Cohort Study (ICS) is a popu- lation-based prospective study of 6504 Iranian adults 35 years old, followed-up for ten years, from 2001 to 2010. Behavioral and cardiometabolic risk factors were examined every five years, while biennial follow-ups for the occurrence of the events was performed by phone calls or by verbal autopsy. Among these participants, 5432 (2784 women, 51.3%) were CVD free at baseline examination and had at least one follow-up. Cox proportional hazard regression was used to predict the risk of ischemic CVD events, including sudden cardiac death due to unstable angina, myocardial infarction, and stroke. The model fit statis- tics such as area under the receiver-operating characteristic (AUROC), calibration chi- square and the overall bias were used to assess the model performance. We also tested the Framingham model for comparison. Seven hundred and five CVD events occurred during 49452.8 person-years of follow-up. The event probabilities were calculated and presented color-coded on each gender-specific PARS chart. The AUROC and Harrell’s C indices were 0.74 (95% CI, 0.72–0.76) and 0.73, respectively. In the calibration, the Nam-D’Ago stino ¿ 2 was 10.82 (p = 0.29). The overall bias of the proposed model was 95.60%. PARS model was also internally validated using cross-validation. The Android app and the Web-based risk assessment tool were also developed as to have an impact on public health. In compari- son, the refitted and recalibrated Framingham models, estimated the CVD incidence with the overall bias of 149.60% and 128.23% for men, and 222.70% and 176.07% for women, respectively. In conclusion, the PARS risk assessment chart is a simple, accurate, and well- calibrated tool for predicting a 10-year risk of CVD occurrence in Iranian population and can be used in an attempt to develop national guidelines for the CVD management .Peer ReviewedPostprint (published version

Correspondence on “Electrocardiographic findings and prognostic values in patients hospitalised with COVID-19 in the World Heart Federation Global Study” by Pinto-Filho et al

© BMJ Publishing Group Ltd, 2023. Reuse of this manuscript version (excluding any databases, tables, diagrams, photographs and other images or illustrative material included where a another copyright owner is identified) is permitted strictly pursuant to the terms of the Creative Commons Attribution-Non Commercial 4.0 International (CC-BY-NC 4.0) http://creativecommons.orgThe manuscript by Martins Pinto-Filho et al., a multicentre study on the prognostic value of electrocardiographic findings, was evaluated. However, certain critical issues were highlighted. First, the authors didn't verify the randomness of missing data before implementing the Multiple Imputation by Chained Equations (MICE), which assumes randomness. Second, there was no use of external or internal validation despite the multicentre nature of the study. The study also lacks essential diagnostic measures, like pseudo-R2 statistics and the Hosmer-Lemeshow test. The time-dependent nature of ECG findings and the lack of consistent treatment strategy across different centers raises concerns. Furthermore, the study does not clarify how the imbalanced sample size influenced the conclusion. Lastly, the paper does not provide essential outputs and parameters like Receiver Operating Characteristic (ROC) and Harrell's C-index, which are important to avoid overfitting and ensure global applicability.This work was supported by the Beatriu de Pinós post- doctoral programme from the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia (Grant number: #2020 BP 00261).Peer ReviewedObjectius de Desenvolupament Sostenible::3 - Salut i BenestarObjectius de Desenvolupament Sostenible::3 - Salut i Benestar::3.d - Reforçar la capacitat de tots els països, en particular els països en desenvolupament, en matèria d’alerta primerenca, reducció de riscos i gestió dels riscos per a la salut nacional i mundialPostprint (author's final draft

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) using optimized ensemble learning

Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread tothe body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breastcancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breastcancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selectedusing statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) asthe inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combi-nation of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictorof breast cancerrecurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as sup-ported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selectedfeatures were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymphnodes, progesterone receptor expression,having hormone therapyand type of surgery.The minimum sensitivity,specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellentagreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, andtissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrencePeer ReviewedPostprint (author's final draft

Reliable diagnosis and prognosis of COVID-19

The 2019 novel coronavirus disease (COVID-19) epidemic was officially announced by the World Health Organization (WHO) as an international public health emergency. The medical research world is responding to the COVID-19 pandemic at breathtaking speed. Most of the studies related to this outbreak identify the epidemiology and clinical characteristics of infected patients and focus on its short-term effects. However, there are many studies with inappropriate study design, data mining, and statistical analysis. Proper design and reliability assessment of COVID-19 diagnosis systems (e.g., proper feature selection, classification, and performance assessment) must be performed. Also, advanced statistical methods (e.g., multistate and competing risk models) are required to avoid the risk of bias in prognosis systems. Moreover, many studies may be too small and poorly designed to be helpful, merely adding to the COVID-19 noise. Additionally, trials without a control group, non-randomized and imbalanced trials are common problems of experimental designs. Thus, in this chapter, we aim to address the critical methods to use in the diagnosis and prognosis of COVID-19.The research leading to this results has also received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement Nº 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia.Peer ReviewedPostprint (published version

Classification of psychiatric symptoms using deep interaction networks: the CASPIAN-IV study

Identifying the possible factors of psychiatric symptoms among children can reduce the risk of adverse psychosocial outcomes in adulthood. We designed a classification tool to examine the association between modifiable risk factors and psychiatric symptoms, defined based on the Persian version of the WHO-GSHS questionnaire in a developing country. Ten thousand three hundred fifty students, aged 6–18 years from all Iran provinces, participated in this study. We used feature discretization and encoding, stability selection, and regularized group method of data handling (GMDH) to classify the a priori specific factors (e.g., demographic, sleeping-time, life satisfaction, and birth-weight) to psychiatric symptoms. Self-rated health was the most critical feature. The selected modifiable factors were eating breakfast, screentime, salty snack for depression symptom, physical activity, salty snack for worriedness symptom, (abdominal) obesity, sweetened beverage, and sleep-hour for mild-to-moderate emotional symptoms. The area under the ROC curve of the GMDH was 0.75 (CI 95% 0.73–0.76) for the analyzed psychiatric symptoms using threefold cross-validation. It significantly outperformed the state-of-the-art (adjusted p¿<¿0.05; McNemar's test). In this study, the association of psychiatric risk factors and the importance of modifiable nutrition and lifestyle factors were emphasized. However, as a cross-sectional study, no causality can be inferred.The authors would like to thank the CASPIAN team working on this national project and all students and their families participating in this project. The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia (TECSPR18-1-0017).Peer ReviewedPostprint (published version

Prosthesis control using undersampled surface electromyographic signals

Amputations can result in disability, permanent physical injury, and even posttraumatic stress disorder. Upper extremity amputations are mostly work-related, and such injuries include about 7% of the total burden of disease. High-functional artificial limbs are not available to most amputees because of their high price and the lack of public health coverage. Thus, there has been a significant interest in the design and fabrication of low-cost active upper-limb prostheses. Such devices are usually controlled by surface electromyographic (sEMG) signals. Recently, portable, low-cost recording devices such as Thalmic Labs Myo Gesture Control Armband have been used in the movement detection. Such devices use undersampled sEMG signals. In this chapter, we discuss upper-limb prostheses and their control. We further provide the results of some experiments showing that such undersampled signals could be used for various applications required in advanced prosthesis control, e.g., force prediction, elbow angle prediction, movement detection, and time and frequency parameter extraction using undersampled sEMG signals. Finally, a low-cost controller for BRUNEL HAND 2.0 from Open Bionics is designed to link low-cost recording and prosthesis.This work was supported by the Ministry of Economy and Competitiveness (MINECO), Spain, under contract DPI2017-83989-R and the Ministry of Science and Innovation (MICINN), Spain, under contract PRE2018-085387. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. JF Alonso is a Serra Hunter Fellow. The research leading to this results has also received funding from the European's Union Horizon 2020 research and innovation program under the Marie Slodowska-Curie Grant Agreement No 712349 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia.Postprint (published version

Psychiatric disorders and cognitive impairment following COVID-19: a comprehensive review and its implications for smart healthcare design

Chapter 3 discusses the role of sensors in cases of moderate COVID-19 infection that may be linked to cognitive impairments; such prediction systems are promising for smart healthcare design.This work was supported by the Beatriu de Pinós post-doctoral programme from the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia (Grant number: #2020 BP 00261), and the TecnioSpring Industry fellowship (ACCIO, H2020-EU- EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions, #ACE026/21/000035).Peer ReviewedPostprint (author's final draft

Epileptic seizure prediction and onset zone localization using intracranial and scalp electroencephalographic and magnetoencephalographic signals

In this chapter, we discuss how intracranial or scalp electroencephalographic and magnetoencephalographic recordings could be used for epileptic seizure prediction and onset zone localization. The signal processing methods and the challenges of the state-of-the-art are discussed.Peer ReviewedPostprint (published version