Digital technologies are transforming the health sector all over the world, however various aspects of this emerging field of science is yet to be properly understood. Ambiguity in the definition of digital health is a hurdle for research, policy, and practice in this field. With the aim of achieving a consensus in the definition of digital health, we undertook a quantitative analysis and term mapping of the published definitions of digital health. After inspecting 1527 records, we analyzed 95 unique definitions of digital health, from both scholar and general sources. The findings showed that digital health, as has been used in the literature, is more concerned about the provision of healthcare rather than the use of technology. Wellbeing of people, both at population and individual levels, have been more emphasized than the care of patients suffering from diseases. Also, the use of data and information for the care of patients was highlighted. A dominant concept in digital health appeared to be mobile health (mHealth), which is related to other concepts such as telehealth, eHealth, and artificial intelligence in healthcare.

Background Coping strategies play a key role in modulating the physical and psychological burden on caregivers of stroke patients. The present study aimed to determine the relationship between the severity of burden of care and coping strategies amongst a sample of Iranian caregivers of older stroke patients. It also aimed to examine the differences of coping strategies used by male and female caregivers. Methods A total of 110 caregivers of older patients who previously had a stroke participated in this descriptive and cross-sectional study. The Zarit Burden Interview and Lazarus coping strategies questionnaires were used for data collection. Questionnaires were completed by the caregivers, who were selected using convenience sampling. The collected data were analyzed using Pearson's correlations and independent t-tests. Results The mean age of participants was 32.09 ± 8.70 years. The majority of the caregivers sampled reported mild to moderate (n = 74, 67.3%) burden. The most commonly used coping strategies reported were positive reappraisal and seeking social support. Results of the independent t-test showed that male caregivers used the positive reappraisal strategy (t(110) = 2.76; p = 0.007) and accepting responsibility (t(110) = 2.26; p = 0.026) significantly more than female caregivers. Pearson’s correlations showed a significant positive correlation between caregiver burden and emotional-focused strategies, including escaping (r = 0.245, p = 0.010) and distancing (r = 0.204, p = 0.032). Conclusions Caregivers with higher burden of care used more negative coping strategies, such as escape-avoidance and distancing. In order to encourage caregivers to utilize effective coping skills, appropriate programs should be designed and implemented to support caregivers. Use of effective coping skills to reduce the level of personal burden can improve caregiver physical health and psychological well-being.

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AimTo investigate the genetic factors involved in the development of non-alcoholic fatty liver disease (NAFLD) and its sequelae in a Middle Eastern population.MethodsThis genetic case-control association study, conducted in 2018, enrolled 30 patients with NAFLD and 30 control individuals matched for age, sex, and body mass index. After quality control measures, entire exonic regions of 3654 genes associated with human diseases were sequenced. Allelic association test and enrichment analysis of the significant genetic variants were performed.ResultsThe association analysis was conducted on 27 NAFLD patients and 28 controls. When Bonferroni correction was applied, NAFLD was significantly associated with rs2303861, a variant located in the CD82 gene (P = 2.49 × 10−7, adjusted P = 0.0059). When we used Benjamini-Hochberg adjustment for correction, NAFLD was significantly associated with six more variants. Enrichment analysis of the genes corresponding to all the seven variants showed significant enrichment for miR-193b-5p (P = 0.00004, adjusted P = 0.00922).ConclusionA variant on CD82 gene and a miR-193b expression dysregulation may have a role in the development and progression of NAFLD and its sequelae.

A bstract Background Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). Aims and objectives To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. Materials and methods In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models. Results A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O 2 saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors. Conclusion In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients’ early data, particularly in low- and middle-income countries where their resources are as limited as Iran. How to cite this article Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022;26(6):688–695. Ethics approval This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018).

Background: To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) assessment through conventional microscopy by comparing observers’ estimations. Methods: Intratumoral and tumor-front stromal TILs, and TSR, were assessed by three pathologists using 86 CRC HE slides. TSR and TILs were categorized using one and four different proposed cutoff systems, respectively, and agreement was assessed using the intraclass coefficient (ICC) and Cohen’s kappa statistics. Pairwise evaluation of agreement was performed using the Fleiss kappa statistic and the concordance rate and it was visualized by Bland–Altman plots. To investigate the association between biomarkers and patient data, Pearson’s correlation analysis was applied. Results: For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95% CI: 0.35–0.64) was obtained, kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95% CI: 0.32–0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95% CI: 0.35–0.60), the kappa value was 0.49 and the concordance rate was 0.76. We observed a significant correlation between tumor grade and the median of TSR (0.29 (95% CI: 0.032–0.51), p-value = 0.03). Conclusions: The agreement between pathologists in estimating these markers corresponds to poor-to-moderate agreement; implementing immune scores in daily practice requires more concentration in inter-observer agreements.

Background: Medication adherence is one of the most important challenges in chronic diseases. Objectives: In this study, we investigated medication adherence prevalence among children with chronic liver diseases. Methods: A total of 160 children with chronic liver disease were enrolled in our study. We evaluated medication adherence using the 8-item Morisky Medication Adherence Scale (MMAS-8) and classified them based on the scores (score < 6 = low adherence, scores 6 - 8 = medium adherence, and > 8 = high adherence). Logistic regression recognized final influencing variables on adherence. Results: Of 160 patients, 84 (52.5%) were female, and the mean age of patients was 11.2 ± 4.4 years. Also, 56 participants (35%) were high adherers, and 66 (41.25%) were low adherers. The most common reason for low adherence was forgetfulness in 37 patients (23.13%) and low access to medication in 21 subjects (13.13%). In multivariate logistic regression, age, housing status, and underlying disease were significantly associated with medication adherence. Conclusions: Almost half of the children with liver cirrhosis demonstrated low medication adherence. Age, housing status, and underlying disease were significantly associated with medication adherence. We should implement programs to reduce medication non-adherence among children with chronic liver disease.

Introduction: The popularity of mobile phone applications (Apps) and wearable devices for medical and health purposes is on the rise, but not all the mobile health (mHealth) innovative solutions that hit the news every day will sustain and have an impact on the health of people. The aim of this news-based horizon scanning study was to explore and identify new and emerging mobile technologies that are likely to impact the future of health and medical care.Methods: We conducted a systematic search on top ranking technology websites, according to Alexa Ranking, to identify health-related mobile-based technologies. We followed the EuroScan guide for horizon scanning, which recommends four steps: identification, filtering, prioritization, evaluation and conclusion. Technologies of interest were mHealth technologies regardless of their maturity level. The impact of technologies was assessed and scored in four areas: user, technology, safety, and cost.Results: Five hundred news articles were identified through the electronic search. After screening, 106 mHealth innovative technologies were included in this study. We categorized the included technologies into three groups: mobile apps (n=37), smart-connected devices (n=19), and wearables (n=50). mHealth technologies were most frequently developed for preventive health services, mental health services and rehabilitation services. There was no remarkable difference between the technology groups in terms of safety and adverse effects, but the groups were significantly different in terms of the target population, technology, and cost.Conclusion: An increasing number of solutions based on mobile technology is being developed by both public and private sectors but a low proportion of them undergo proper scientific evaluations. Despite the commercial availability of many innovative mobile apps, wearables, and smart connected devices, few of them have been actually used in clinics, hospitals, and health centers. There is a clear need for changes in healthcare service models to unlock the full potential of these innovative technologies.