Kemal Nazaruddin Siregar

Purpose: To determine the accuracy of the fundus red reflex test using a smartphone and autorefractor to detect refractive errors in children.Methods: This cross-sectional study included 1302 eyes of 651 school-age children aged 6- 13 years with visual acuity worse than 6/9 in at least one eye, examined using the Snellen chart. Study participants were selected using purposive sampling, after which non-cycloplegic refractive measurement was performed using two instruments: a smartphone-based application (MD EyeCare) and an autorefractor. The detection of refractive errors using the online platform was compared with autorefraction as the reference standard.Results: Our study found that 46.10% of participants with abnormal red reflex had significant myopia, 41.20% had nonsignificant refractive errors, and 12.70% had significant hypermetropia. The mean difference between the smartphone-based application and the autorefractor was ≤−3.00 D (range: −2.88 to −2.63), with a sensitivity of 0.89 to 0.90 and a specificity of 0.86 to 0.87 for significant myopia, and the area under the curve was between 0.94 and 0.97. The mean difference between the smartphone-based application and the autorefractor was ≥+1.00 D (range: +0.13 to +0.38), with a sensitivity of 0.76 and a specificity of 0.97 for significant hypermetropia, and the area under the curve was between 0.88 and 0.96.Conclusion: A smartphone-based application could be used as a screening tool for refractive errors in children, especially in resource-limited settings.

This policy brief aims to identify existing policies related to heart failure management and support group interventions, and also to examine how these policies can be optimized by strengthening the role of nurses in Indonesia. Specifically, it examines the integration of nursing-led support groups into the healthcare system as a strategic approach to reducing hospital readmissions and enhancing patient continuity of care for those with heart failure. A policy analysis approach was used by combining a literature review, synthesis of national regulations, and evaluation of global best practices. The process included identifying critical gaps in current heart failure management policy, assessing the impact of support groups, and formulating evidence-based, context-specific policy recommendations. Key challenges identified include high rates of rehospitalization, increased financial burden on BPJS Kesehatan, caregiver stress, and lack of structured, multidisciplinary post-discharge interventions. The evidence shows that support groups effectively enhance treatment adherence, patient empowerment, and overall quality of life. Moreover, nursing-led initiatives have demonstrated significant potential in improving patient engagement and reducing healthcare disparities in diverse populations. Nurses must play active roles in facilitating peer education and providing psychosocial support to contribute to improved health outcomes and system efficiency. This brief emphasizes the necessity of policy frameworks that empower nurses with adequate training, resources, and authority to lead these groups effectively. Nursing-led support groups offer a cost-effective and scalable approach to reducing rehospitalization and promoting self-care in patients with heart failure. Furthermore, integrating these groups within existing clinical pathways can foster multidisciplinary collaboration, improve data collection for monitoring outcomes, and support sustainable healthcare financing models. This policy brief calls for their integration into clinical pathways, supported by regulation, cross-sector collaboration, and sustainable funding. Nurses, alongside interdisciplinary teams, play a key role in ensuring continuity of care through structured peer support at all levels of service.

Background: The risk of rehospitalization in patients with heart failure (HF) has initiated various efforts to prevent and simultaneously improve quality of life. Self-monitoring at home is one option, and technology is increasingly being used for this purpose. Objective: This pilot study aimed to evaluate the feasibility and preliminary effects of a digital home monitoring intervention on patient-reported outcomes and 30-day readmissions among patients with HF in Indonesia. Methods: A mixed methods pilot study was conducted, combining qualitative system development and quantitative evaluation. Patients were assigned to an intervention group (digital monitoring) or control group (standard care). Readmission rates were compared using chi-square tests and odds ratios. Changes in Kansas City Cardiomyopathy Questionnaire scores were analyzed using linear mixed-effects models. Results: A total of 60 patients were included (n=30, 50% in the intervention group; n=30, 50% in the control group). Readmission occurred in 20% (6/30) of patients in the intervention group and 43.3% (13/30) of patients in the control group (odds ratio 0.33, 95% CI 0.10‐1.09; P =.10). Linear mixed-effects analysis showed greater improvement in Kansas City Cardiomyopathy Questionnaire overall summary score in the intervention group ( P =.02). Improvements were observed in the physical limitation, symptom frequency, symptom burden, quality of life, and social limitation domains. During follow-up, 3.3% (1/30) of the patients in the intervention group died of non–HF-related causes, and 10% (3/30) of the patients in the control group died due to HF. Conclusions: This pilot study suggests that digital home monitoring is feasible and associated with improvements in patient-reported outcomes, with a potential signal toward reduced readmission. Larger studies are needed to confirm effectiveness.

BACKGROUND The risk of rehospitalization in heart failure (HF) patients has initiated various preventive efforts and to simultaneously improve the quality of life (QoL) of the patients. Self-monitoring at home is one option, and technology is increasingly being used for this purpose. OBJECTIVE To reduce the risk of hospital readmission and maintain the QoL of HF patients through self-monitoring with the assistance of a digital application. METHODS Starting with determining the need for HF services in National Cardiovascular Center Jakarta in 2024, an application called FineHeart was built and tested on HF patients in a quasi-experimental pilot study. After the User Acceptance Test was completed, HF patients were recruited as intervention group and control group participants. Quality of Life was measured with the Kansas City Cardiomyopathy Questionnaire (KCCQ) before and after the test. The readmission rate and 4 of the KCCQ parameters were analysed between the 2 groups. RESULTS Of the 72 patients who met the inclusion criteria, thirty participated in the intervention group and thirty in the control group who had signed the consent form. In the intervention group, after 30 days, 22 people complied using the FineHeart application prototype (73.3%), six were readmitted (20%), and one died not because of HF (3.3%). In the control group, 10 were readmitted (33.3%), and three died because of HF (10%). There was a significant relationship between adherence to application usage and an increase in Quality-of-Life scores (p<0.029) and Overall Summary Score (p<0.001)), but not in Self-efficacy and Social Limitation Scores. CONCLUSIONS The findings of this study provide a potential benefit for HF patients to perform self-monitoring at home in collaboration with the hospital team by using FineHeart app. Patient adherence may reduce the risk of readmission and improve QoL. Multicenter studies on larger groups of patients are needed to confirm these preliminary results.

Background: Heart failure leads to reduced quality of life, high hospitalization rates, mortality rates, and treatment costs where out-of-hospital self-monitoring can help with management and prevention of hospitalization and digital apps can help with this. The purpose of this review was getting all information of the studies regarding self-monitoring assisted by mobile health or digital application for heart failure patients.Subjects and Method: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols for a Scoping Review (PRISMA-ScR), literature searches were developed by searching the databases: PubMed.gov, ScienceDirect, SpringerLink, Google Scholar. Following the eligibility criteria, articles that included were analyzed to get the result.Results: Mostly the studies were conducted in 2016 and 2017 (20% each, n=5), less studies in 2018, 2019, 2020, and increased again in 2021 (16%, n=4). The studies were done 57% in USA (n=13), both Australia and Canada were 9% (n=2), and other countries. The designs of the studies were mostly RCT (74%, n=17). Sample size was variative mostly less than 50 participants (39%, n=9). There were 65% of the studies measured the daily body weight (n=15), others used vital signs 57%, medication adherence 39%, and other items. QoL was the most in the outcome (61%, n=14). Main findings mostly showed positive impact on self-monitoring with digital application.Conclusion: Using mobile apps for heart failure patients’ self-monitoring created the positive impacts in the expected outcomes, mostly for quality of life.

Adverse Pregnancy Outcome (APO) is the leading cause of infant mortality, particularly in low and middle-income countries. Despite no significant change in neonatal mortality rates in Indonesia over the past fifteen years, identifying the risk factors for APO can help healthcare professionals provide personalized interventions and support. This study aims to determine the determinants of APO risk in Indonesia. The cross-sectional study utilized the 2017 Indonesian Health Demographic Survey (IDHS), which included 9,752 women of reproductive age. APO, including low birth weight, early birth, and stillbirth, was the outcome of the study. The demographic characteristics and other risk factors were evaluated. To identify APO-risk-associated factors, we performed multiple logistic regression analysis. The results showed that low economic status (Adj. OR= 1.407; 1.153-1.716; p= 0.001)), living in rural areas (Adj. OR= 0.804; 0.663-0.976; p= 0.027), having a history of pregnancy or childbirth complications (Adj. OR= 4.563; 3.617-5.756; p= 0.0001), premature rupture of the membrane during pregnancy or after giving birth (Adj. OR= 1.351; 1.069-1.708; p= 0.012), antenatal visits <4 times (Adj. OR= 1.603; 1.123-2.288; p= 0.009), consumption of iron supplement tablets <90 days (Adj. OR= 1.275; 1.063-1.529; p= 0.009), and twin pregnancy (Adj. OR= 33.715; 18.587-61.153; p= 0.0001) significantly influenced the APO prevalence. The study concluded that APO risk is significantly impacted by poor obstetric history features, multiple births, low health service utilization, and sociodemographic factors. Early detection of these risk factors is essential for reducing neonatal death caused by APO.

Objectives: In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application.Methods: In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores.Results: Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF.Conclusions: The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.