A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes.

The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods.

Assessment of the Tumor-Stroma Ratio and Tumor-Infiltrating Lymphocytes in Colorectal Cancer: Inter-Observer Agreement Evaluation.

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.

Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran.

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, O2 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).

Caregiver burden and coping strategies in caregivers of older patients with stroke.

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.

What is Digital Health? Review of Definitions.

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.

Association between rs2303861 polymorphism in CD82 gene and non-alcoholic fatty liver disease: a preliminary case-control study.

AIM: To investigate the genetic factors involved in the development of non-alcoholic fatty liver disease (NAFLD) and its sequelae in a Middle Eastern population. METHODS: This 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. RESULTS: The 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). CONCLUSION: A variant on CD82 gene and a miR-193b expression dysregulation may have a role in the development and progression of NAFLD and its sequelae.

Identifying Factors That Affect Patient Survival After Orthotopic Liver Transplant Using Machine-Learning Techniques.

OBJECTIVES: Survival after liver transplant depends on pretransplant, peritransplant, and posttransplant factors. Identifying effective factors for patient survival after transplant can help transplant centers make better decisions. MATERIALS AND METHODS: Our study included 902 adults who received livers from deceased donors from March 2011 to March 2014 at the Shiraz Organ Transplant Center (Shiraz, Iran). In a 3-step feature selection method, effective features of 6-month survival were extracted by (1) F statistics, Pearson chi-square, and likelihood ratio chi-square and by (2) 5 machine-learning techniques. To evaluate the performance of the machine-learning techniques, Cox regression was applied to the data set. Evaluations were based on the area under the receiver operating characteristic curve and sensitivity of models. (3) We also constructed a model using all factors identified in the previous step. RESULTS: The model predicted survival based on 26 identified effective factors. In the following order, graft failure, Aspergillus infection, acute renal failure and vascular complications after transplant, as well as graft failure diagnosis interval, previous diabetes mellitus, Model for End-Stage Liver Disease score, donor inotropic support, units of packed cell received, and previous recipient dialysis, were found to be predictive factors in patient survival. The area under the receiver operating characteristic curve and model sensitivity were 0.90 and 0.81, respectively. CONCLUSIONS: Data mining analyses can help identify effective features of patient survival after liver transplant and build models with equal or higher performance than Cox regression. The order of influential factors identified with the machine-learning model was close to clinical experiments.