Deliberate self-harm (DSH) along with old age, physical disability, and low socioeconomic status are well-known contributors to suicide-related deaths. In recent years, South Korea has the highest suicide death rate among all Organization for Economic Co-operation and Development countries. Owing to the difficulty of accessing data of individuals with DSH behavior who died by suicide, the factors associated with suicide death in these high-risk individuals have not been sufficiently explored. There have been conflicting findings with regard to the relationship between previous psychiatric visits and suicidal death.We aimed to address the following 3 questions: Are there considerable differences in demographics, socioeconomic status, and clinical features in individuals who received psychiatric diagnosis (either before DSH or after DSH event) and those who did not? Does receiving a psychiatric diagnosis from the Department of Psychiatry, as opposed to other departments, affect survival? and Which factors related to DSH contribute to deaths by suicide?We used the Korean National Health Insurance Service Database to design a cohort of 5640 individuals (3067/5640, 54.38% women) who visited the hospital for DSH (International Classification of Diseases codes X60-X84) between 2002 and 2020. We analyzed whether there were significant differences among subgroups of individuals with DSH behavior based on psychiatric diagnosis status (whether they had received a psychiatric diagnosis, either before or after the DSH event) and the department from which they had received the psychiatric diagnosis. Another main outcome of the study was death by suicide. Cox regression models yielded hazard ratios (HRs) for suicide risk. Patterns were plotted using Kaplan-Meier survival curves.There were significant differences in all factors including demographic, health-related, socioeconomic, and survival variables among the groups that were classified according to psychiatric diagnosis status (P<.001). The group that did not receive a psychiatric diagnosis had the lowest survival rate (867/1064, 81.48%). Analysis drawn using different departments from where the individual had received a psychiatric diagnosis showed statistically significant differences in all features of interest (P<.001). The group that had received psychiatric diagnoses from the Department of Psychiatry had the highest survival rate (888/951, 93.4%). These findings were confirmed using the Kaplan-Meier survival curves (P<.001). The severity of DSH (HR 4.31, 95% CI 3.55-5.26) was the most significant contributor to suicide death, followed by psychiatric diagnosis status (HR 1.84, 95% CI 1.47-2.30).Receiving psychiatric assessment from a health care professional, especially a psychiatrist, reduces suicide death in individuals who had deliberately harmed themselves before. The key characteristics of individuals with DSH behavior who die by suicide are male sex, middle age, comorbid physical disabilities, and higher socioeconomic status.
BACKGROUND Fever is one of the most common symptoms in children and is the physiological response of the human immune system to external pathogens. However, effectiveness studies of single and combined antipyretic therapy are relatively few due to lack of data. In this study, we used large-scale patient-generated health data from mobile apps to compare antipyretic affects between single and combination antipyretics. OBJECTIVE We aimed to establish combination patterns of antipyretics and compare antipyretic affects between single and combination antipyretics using large-scale patient-generated health data from mobile apps. METHODS This study was conducted using medical records of feverish children from July 2015 to June 2017 using the Fever Coach mobile app. In total, 3,584,748 temperature records and 1,076,002 antipyretic records of 104,337 children were analyzed. Antipyretic efficacy was measured by the mean difference in the area under the temperature change curve from baseline for 6 hours, 8 hours, 10 hours, and 12 hours after antipyretic administration in children with a body temperature of ≥38.0 ℃ between single and combination groups. RESULTS The single antipyretic and combination groups comprised 152,017 and 54,842 cases, respectively. Acetaminophen was the most commonly used single agent (60,929/152,017, 40.08%), and acetaminophen plus dexibuprofen was the most common combination (28,065/54,842, 51.17%). We observed inappropriate use, including triple combination (1205/206,859, 0.58%) and use under 38 ℃ (11,361/206,859, 5.50%). Combination antipyretic use increased with temperature; 23.82% (33,379/140,160) of cases were given a combination treatment when 38 ℃ ≤ temperature < 39 ℃, while 41.40% (1517/3664) were given a combination treatment when 40 ℃ ≤ temperature. The absolute value of the area under the curve at each hour was significantly higher in the single group than in the combination group; this trend was consistently observed, regardless of the type of antipyretics. In particular, the delta fever during the first 6 hours between the two groups showed the highest difference. The combination showed the lowest delta fever among all cases. CONCLUSIONS Antipyretics combination patterns were analyzed using large-scale data. Approximately 75% of febrile cases used single antipyretics, mostly acetaminophen, but combination usage became more frequent as temperature increased. However, combination antipyretics did not show definite advantages over single antipyretics in defervescence, regardless of the combination. Single antipyretics are effective in reducing fever and relieving discomfort in febrile children.
BACKGROUND Deliberate self-harm (DSH) along with old age, physical disability, and low socioeconomic status are well-known contributors to suicide-related deaths. In recent years, South Korea has the highest suicide death rate among all Organization for Economic Co-operation and Development countries. Owing to the difficulty of accessing data of individuals with DSH behavior who died by suicide, the factors associated with suicide death in these high-risk individuals have not been sufficiently explored. There have been conflicting findings with regard to the relationship between previous psychiatric visits and suicidal death. OBJECTIVE We aimed to address the following 3 questions: Are there considerable differences in demographics, socioeconomic status, and clinical features in individuals who received psychiatric diagnosis (either before DSH or after DSH event) and those who did not? Does receiving a psychiatric diagnosis from the Department of Psychiatry, as opposed to other departments, affect survival? and Which factors related to DSH contribute to deaths by suicide? METHODS We used the Korean National Health Insurance Service Database to design a cohort of 5640 individuals (3067/5640, 54.38% women) who visited the hospital for DSH (International Classification of Diseases codes X60-X84) between 2002 and 2020. We analyzed whether there were significant differences among subgroups of individuals with DSH behavior based on psychiatric diagnosis status (whether they had received a psychiatric diagnosis, either before or after the DSH event) and the department from which they had received the psychiatric diagnosis. Another main outcome of the study was death by suicide. Cox regression models yielded hazard ratios (HRs) for suicide risk. Patterns were plotted using Kaplan-Meier survival curves. RESULTS There were significant differences in all factors including demographic, health-related, socioeconomic, and survival variables among the groups that were classified according to psychiatric diagnosis status (<i>P</i><.001). The group that did not receive a psychiatric diagnosis had the lowest survival rate (867/1064, 81.48%). Analysis drawn using different departments from where the individual had received a psychiatric diagnosis showed statistically significant differences in all features of interest (<i>P</i><.001). The group that had received psychiatric diagnoses from the Department of Psychiatry had the highest survival rate (888/951, 93.4%). These findings were confirmed using the Kaplan-Meier survival curves (<i>P</i><.001). The severity of DSH (HR 4.31, 95% CI 3.55-5.26) was the most significant contributor to suicide death, followed by psychiatric diagnosis status (HR 1.84, 95% CI 1.47-2.30). CONCLUSIONS Receiving psychiatric assessment from a health care professional, especially a psychiatrist, reduces suicide death in individuals who had deliberately harmed themselves before. The key characteristics of individuals with DSH behavior who die by suicide are male sex, middle age, comorbid physical disabilities, and higher socioeconomic status.
Abstract Background: Adverse drug reactions (ADRs) are unintended negative drug-induced responses. Determining the association between drugs and ADRs is crucial, and several methods have been proposed to demonstrate this association. This systematic review aimed to examine the analytical tools by considering original articles that utilized statistical and machine learning methods for detecting ADRs. Methods: A systematic literature review was conducted based on articles published between 2015 and 2020. The keywords used were statistical, machine learning, and deep learning methods for detecting ADR signals. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines. Results: We reviewed 72 articles, of which 51 and 21 addressed statistical and machine learning methods, respectively. Electronic medical record (EMR) data were exclusively analyzed using the regression method. For FDA Adverse Event Reporting System (FAERS) data, components of the disproportionality method were preferable. DrugBank was the most used database for machine learning. Other methods accounted for the highest and supervised methods accounted for the second highest. Conclusions: Using the 72 main articles, this review provides guidelines on which databases are frequently utilized and which analysis methods can be connected. For statistical analysis, >90% of the cases were analyzed by disproportionate or regression analysis with each spontaneous reporting system (SRS) data or electronic medical record (EMR) data; for machine learning research, however, there was a strong tendency to analyze various data combinations. Only half of the DrugBank database was occupied, and the k-nearest neighbor method accounted for the greatest proportion.
The aims of this study were to document our single-center experience with pediatric acute fulminant myocarditis (AFM) and to investigate its clinical features and short-term outcomes.We performed a retrospective chart review of all children <18 years old who were diagnosed with AFM between October 2008 and February 2013. Data about patient demographics, initial symptoms, investigation results, management, and outcomes between survivors and nonsurvivors were collected.Seventeen of 21 patients (80.9%) with myocarditis were diagnosed with AFM. Eleven patients (64.7%) survived to discharge, and 6 (35.3%) died. Electrocardiography on admission revealed dysrhythmia in 10 patients (58.8%); of these, all 7 patients with a complete atrioventricular block survived. Fractional shortening upon admission was significantly different between the survivors (16%) and nonsurvivors (8.5%) (P=0.01). Of the serial biochemical markers, only the initial brain natriuretic peptide (P=0.03) and peak blood urea nitrogen levels (P=0.02) were significantly different. Of 17 patients, 4 (23.5%) required medical treatment only. Extracorporeal membrane oxygenation (ECMO) was performed in 13 patients (76.5%); the survival rate in these patients was 53.8%. ECMO support was initiated >24 hours after admission in 4 of the 13 patients (30.7%), and 3 of those 4 patients (75%) died.AFM outcomes may be associated with complete atrioventricular block upon hospital admission, left ventricular fractional shortening at admission, time from admission to the initiation of ECMO support, initial brain natriuretic peptide level, and peak blood urea nitrogen level.
BACKGROUND Caregivers are often advised to give additional antipyretic doses if fever persists or recurs before the next dose time. In previous studies, there is no consistent evidence. In clinical guideline, there is no recommended doses in alternative antipyretics treatment. OBJECTIVE To evaluate more appropriate time intervals for alternative therapy using large-scale patient-generated health data. METHODS Participants were youth (aged 6-144 months) and their caregiver used the Fever Coach mobile application between February 2015 to December 2019. One case was referred to a single record for 72 hours after the first antipyretic record input. Baseline means the temperature record closest to the first antipyretic dose. In total, 138,117 cases with alternative antipyretics were selected for final analysis. Area under the curve (AUC) calculated by the area under the temperature curve from baseline for certain hours was used for efficacy analysis. We counted cases with low body temperature records (<36.0℃) to estimate adverse effects. RESULTS In total 138,117 cases, mean age was 29.58 months, and mean baseline temperature was 38.77℃. The time interval between the first and the second antipyretics was 2-3 h in 44,669 (32.34%), 3-4 h in 48,472 (35.09%), and 4-5 h in 44,976 (32.56%) cases. Within 2 h of the first dose, the 2-3 h interval group continued to have fever >38.0℃. The reduction in body temperature from baseline was -0.33℃, -0.54℃, and -0.62℃ in the 2-3 h, 3-4 h, and 4-5 h interval groups, respectively (P < .001, Effect Size 0.041). Within 6 h, the AUC was -201.59 at 2-3 h interval, -165.62 in 3-4 h interval, and -164.32 in 4-5 h intervals (P < .001, Effect Size 0.014). The area under the curve for alternative therapy with 2-3 h intervals was significantly higher than other interval. The mean body temperature of each hour was drawn and acetaminophen with ibuprofen/dexibuprofen showed the fastest and largest antipyretic effects. Within 12 h, 0.89%, 0.50%, and 0.40% cases had low body temperature (<36.0℃) in the 2-3 h, 3-4 h, and 4-5 h interval groups, respectively (P < .001, Effect Size 0.001). CONCLUSIONS In this study, using large-scale patient-generated health data, antipyretic effects were higher at 2-3h interval in alternating therapy. However, education programs and proper care are needed to avoid overdosing.
Abstract Caregivers are often advised to use additional antipyretic agents if fever persists before the next dosing time. However, no consistent evidence or guidelines have been reported. This study was to evaluate appropriate time intervals for alternating antipyretic therapy based on the antipyretic effect and incidence of low body temperature using large-scale patient-generated health data. Participants were children and their caregivers, who used the Fever Coach mobile application between February 2015 and December 2019. One case was referred to as a single record for up to 72 h after the first antipyretic record. Of the total 138,117 cases, the mean age was 29.58 months, and the mean baseline temperature was 38.77°C. The reduction in body temperature from baseline was -0.33°C, -0.54°C, and -0.62°C in the 2-3 hours, 3-4 hours, and 4-5 hours interval groups, respectively (P<.001, effect size 0.041). Within 6 hours, the area under the temperature curve from baseline was -201.59 in the 2-3 hours interval, -165.62 in the 3-4 hours interval, and -164.32 in the 4-5 hours interval groups (P<.001, effect size 0.014). In this study using large-scale patient-generated health data, antipyretic effects were greatest at the 2-3 hours interval for alternating therapy. Digitalized patient-generated health data could be used as a proper reference for real-world health guidelines.
Purpose Recognition of cardiogenic syncope caused by acute myocarditis masquerading as febrile seizures (FS) in children can be difficult in the emergency department (ED) before a cardiac work-up. We aimed to identify clinical and laboratory characteristics of children with seizure-like activity and fever caused by myocarditis that would enable their condition to be distinguished from benign FS. Methods We identified seven children who visited the ED for paroxysmal seizure-like activity with fever and were diagnosed with acute myocarditis between 2012 and 2015, as well as 204 children who were diagnosed with benign FS during the same period. A detailed retrospective review of the medical charts of both groups was conducted. Results Age at onset of seizure-like activity was much higher in the myocarditis group than in the FS group (4.4±1.9 years vs. 2.4±1.1 years, P=0.033). Body temperature at seizure-like activity onset was significantly lower in the myocarditis group than in the FS group (37.9°C±0.2°C vs. 38.7°C±0.6°C, P<0.001). Prodromal symptoms were significantly different, with nausea/vomiting (85.7% vs. 1.5%, P<0.001), abdominal pain (42.9% vs. 0.0%, P=0.021), and lethargic mentality (57.10% vs. 0.0%, P=0.015) being more frequent in the myocarditis group. The initial laboratory findings significantly differed between the two groups, with higher levels of liver enzymes, lactate dehydrogenase, creatinine, uric acid, creatine kinase, and potassium in the myocarditis group. Conclusion Prodromal symptoms and initial laboratory results were significantly different between the myocarditis and FS groups. A good clinical history and laboratory findings can be helpful for differentiating cardiogenic syncope from benign FS. Keywords: Child; Myocarditis; Seizures, febrile; Syncope; Arrhythmias, cardiac
Background Securing the representativeness of study populations is crucial in biomedical research to ensure high generalizability. In this regard, using multi-institutional data have advantages in medicine. However, combining data physically is difficult as the confidential nature of biomedical data causes privacy issues. Therefore, a methodological approach is necessary when using multi-institution medical data for research to develop a model without sharing data between institutions. Objective This study aims to develop a weight-based integrated predictive model of multi-institutional data, which does not require iterative communication between institutions, to improve average predictive performance by increasing the generalizability of the model under privacy-preserving conditions without sharing patient-level data. Methods The weight-based integrated model generates a weight for each institutional model and builds an integrated model for multi-institutional data based on these weights. We performed 3 simulations to show the weight characteristics and to determine the number of repetitions of the weight required to obtain stable values. We also conducted an experiment using real multi-institutional data to verify the developed weight-based integrated model. We selected 10 hospitals (2845 intensive care unit [ICU] stays in total) from the electronic intensive care unit Collaborative Research Database to predict ICU mortality with 11 features. To evaluate the validity of our model, compared with a centralized model, which was developed by combining all the data of 10 hospitals, we used proportional overlap (ie, 0.5 or less indicates a significant difference at a level of .05; and 2 indicates 2 CIs overlapping completely). Standard and firth logistic regression models were applied for the 2 simulations and the experiment. Results The results of these simulations indicate that the weight of each institution is determined by 2 factors (ie, the data size of each institution and how well each institutional model fits into the overall institutional data) and that repeatedly generating 200 weights is necessary per institution. In the experiment, the estimated area under the receiver operating characteristic curve (AUC) and 95% CIs were 81.36% (79.37%-83.36%) and 81.95% (80.03%-83.87%) in the centralized model and weight-based integrated model, respectively. The proportional overlap of the CIs for AUC in both the weight-based integrated model and the centralized model was approximately 1.70, and that of overlap of the 11 estimated odds ratios was over 1, except for 1 case. Conclusions In the experiment where real multi-institutional data were used, our model showed similar results to the centralized model without iterative communication between institutions. In addition, our weight-based integrated model provided a weighted average model by integrating 10 models overfitted or underfitted, compared with the centralized model. The proposed weight-based integrated model is expected to provide an efficient distributed research approach as it increases the generalizability of the model and does not require iterative communication.
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