The impact of the COVID-19 pandemic on infant obesity: A nationwide retrospective cohort study.

BACKGROUND: Lifestyle changes, such as those related to the COVID-19 pandemic, including alterations in physical activity and dietary habits, are known to affect pregnancy outcomes. In particular, suboptimal intrauterine conditions during pregnancy are known to influence not only fetal growth but also growth during infancy. However, research on the impact of the environmental changes caused by the COVID-19 pandemic on the growth of infants and children during their early years is lacking. To address this issue, this study evaluated the effect of the COVID-19 pandemic on obesity in infants. METHODS: This retrospective cohort study used the data collected from the Korea National Health Insurance (KNHI) claims database. The data of 1985,678 women who delivered infants between 2015 and 2021 were collected. Women who delivered during the pandemic and those who delivered during the pre-pandemic period were matched in a 1:1 frequency-matched pair procedure for factors such as age, hypertension, diabetes mellitus, preeclampsia, gestational diabetes mellitus, mode of delivery, gestational age at delivery, offspring sex, and birth weight. Finally, 197,580 women were enrolled. The weight and head circumference of infants (4-6 months of age) of the COVID-19 pandemic group were compared with those of the pre-pandemic group. RESULTS: The COVID-19 pandemic group infants exhibited significantly higher weight and prevalence of obesity at 4-6 months of age compared to infants in the pre-pandemic group. After adjustment for covariates, pandemic group infants had a higher risk of obesity (odds ratio: 1.54, 95% confidence interval: 1.51-1.57) compared to the pre-pandemic group infants. CONCLUSION: The COVID-19 pandemic has had a notable impact on the weight of infants aged 4-6 months. This suggests that pandemic conditions may influence the growth of newborns, underscoring the importance of monitoring and assessing trends in the growth of infants born during such crises.

Machine learning analysis for the association between breast feeding and metabolic syndrome in women.

This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010-2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age.

The Risk of Hypertension and Diabetes Mellitus According to Offspring's Birthweight in Women With Normal Body Mass Index: A Nationwide Population-Based Study.

BACKGROUND: Maladaptation to vascular, metabolic, and physiological changes during pregnancy can lead to fetal growth disorders. Moreover, adverse outcomes during pregnancy can further increase the risk of cardiovascular and metabolic diseases in mothers. Delivering a large-for-gestational-age (LGA) baby may indicate a pre-existing metabolic dysfunction, whereas delivering a small-for-gestational-age (SGA) baby may indicate a pre-existing vascular dysfunction. This study aims to assess the risk of hypertension (HTN) and diabetes mellitus (DM) in women with normal body mass index (BMI) scores who did not experience gestational DM or hypertensive disorders during pregnancy based on the offspring's birthweight. METHODS: This retrospective nationwide study included women with normal BMI scores who delivered a singleton baby after 37 weeks. Women with a history of DM or HTN before pregnancy and those with gestational DM or hypertensive disorders, were excluded from the study. We compared the risk of future maternal outcomes (HTN and DM) according to the offspring's birthweight. Multivariate analyses were performed to estimate the hazard ratio (HR) for the future risk of HTN or DM. RESULTS: A total of 64,037 women were included in the analysis. Of these, women who delivered very LGA babies (birthweight > 97th percentile) were at a higher risk of developing DM than those who delivered appropriate-for-gestational-age (AGA) babies (adjusted HR = 1.358 [1.068-1.727]), and women who delivered very SGA babies (birthweight < 3rd percentile) were at a higher risk of developing HTN than those who delivered AGA babies (adjusted HR = 1.431 [1.181-1.734]), even after adjusting for age, parity, gestational age at delivery, fetal sex, maternal BMI score, and a history of smoking. CONCLUSION: These findings provide a novel support for the use of the offspring's birthweight as a predictor of future maternal diseases such as HTN and DM.

Explainable Model Using Shapley Additive Explanations Approach on Wound Infection after Wide Soft Tissue Sarcoma Resection: "Big Data" Analysis Based on Health Insurance Review and Assessment Service Hub.

Background and Objectives: Soft tissue sarcomas represent a heterogeneous group of malignant mesenchymal tissues. Despite their low prevalence, soft tissue sarcomas present clinical challenges for orthopedic surgeons owing to their aggressive nature, and perioperative wound infections. However, the low prevalence of soft tissue sarcomas has hindered the availability of large-scale studies. This study aimed to analyze wound infections after wide resection in patients with soft tissue sarcomas by employing big data analytics from the Hub of the Health Insurance Review and Assessment Service (HIRA). Materials and Methods: Patients who underwent wide excision of soft tissue sarcomas between 2010 and 2021 were included. Data were collected from the HIRA database of approximately 50 million individuals' information in the Republic of Korea. The data collected included demographic information, diagnoses, prescribed medications, and surgical procedures. Random forest has been used to analyze the major associated determinants. A total of 10,906 observations with complete data were divided into training and validation sets in an 80:20 ratio (8773 vs. 2193 cases). Random forest permutation importance was employed to identify the major predictors of infection and Shapley Additive Explanations (SHAP) values were derived to analyze the directions of associations with predictors. Results: A total of 10,969 patients who underwent wide excision of soft tissue sarcomas were included. Among the study population, 886 (8.08%) patients had post-operative infections requiring surgery. The overall transfusion rate for wide excision was 20.67% (2267 patients). Risk factors among the comorbidities of each patient with wound infection were analyzed and dependence plots of individual features were visualized. The transfusion dependence plot reveals a distinctive pattern, with SHAP values displaying a negative trend for individuals without blood transfusions and a positive trend for those who received blood transfusions, emphasizing the substantial impact of blood transfusions on the likelihood of wound infection. Conclusions: Using the machine learning random forest model and the SHAP values, the perioperative transfusion, male sex, old age, and low SES were important features of wound infection in soft-tissue sarcoma patients.

Explainable artificial intelligence for predicting red blood cell transfusion in geriatric patients undergoing hip arthroplasty: Machine learning analysis using national health insurance data.

This study uses machine learning and population data to analyze major determinants of blood transfusion among patients with hip arthroplasty. Retrospective cohort data came from Korea National Health Insurance Service claims data for 19,110 patients aged 65 years or more with hip arthroplasty in 2019. The dependent variable was blood transfusion (yes vs no) in 2019 and its 31 predictors were included. Random forest variable importance and Shapley Additive Explanations were used for identifying major predictors and the directions of their associations with blood transfusion. The random forest registered the area under the curve of 73.6%. Based on random forest variable importance, the top-10 predictors were anemia (0.25), tranexamic acid (0.17), age (0.16), socioeconomic status (0.05), spinal anesthesia (0.05), general anesthesia (0.04), sex (female) (0.04), dementia (0.03), iron (0.02), and congestive heart failure (0.02). These predictors were followed by their top-20 counterparts including cardiovascular disease, statin, chronic obstructive pulmonary disease, diabetes mellitus, chronic kidney disease, peripheral vascular disease, liver disease, solid tumor, myocardial infarction and hypertension. In terms of max Shapley Additive Explanations values, these associations were positive, e.g., anemia (0.09), tranexamic acid (0.07), age (0.09), socioeconomic status (0.05), spinal anesthesia (0.05), general anesthesia (0.04), sex (female) (0.02), dementia (0.03), iron (0.04), and congestive heart failure (0.03). For example, the inclusion of anemia, age, tranexamic acid or spinal anesthesia into the random forest will increase the probability of blood transfusion among patients with hip arthroplasty by 9%, 7%, 9% or 5%. Machine learning is an effective prediction model for blood transfusion among patients with hip arthroplasty. The high-risk group with anemia, age and comorbid conditions need to be treated with tranexamic acid, iron and/or other appropriate interventions.

Ultrasonographic assessment of abnormal fetal growth related to uteroplacental-fetal biometrics and Doppler (U-AID) indices: Protocol for multicenter retrospective cohort study trial.

Fetal growth restriction (FGR) is one of the leading causes of perinatal morbidity and mortality. Many studies have reported an association between FGR and fetal Doppler indices focusing on umbilical artery (UA), middle cerebral artery (MCA), and ductus venosus (DV). The uteroplacental-fetal circulation which affects the fetal growth consists of not only UA, MCA, and DV, but also umbilical vein (UV), placenta and uterus itself. Nevertheless, there is a paucity of large-scale cohort studies that have assessed the association between UV, uterine wall, and placental thickness with perinatal outcomes in FGR, in conjunction with all components of the uteroplacental-fetal circulation. Therefore, this multicenter study will evaluate the association among UV absolute flow, placental thickness, and uterine wall thickness and adverse perinatal outcome in FGR fetuses. This multicenter retrospective cohort study will include singleton pregnant women who undergo at least one routine fetal ultrasound scan during routine antepartum care. Pregnant women with fetuses having structural or chromosomal abnormalities will be excluded. The U-AID indices (UtA, UA, MCA, and UV flow, placental and uterine wall thickness, and estimated fetal body weight) will be measured during each trimester of pregnancy. The study population will be divided into two groups: (1) FGR group (pregnant women with FGR fetuses) and (2) control group (those with normal growth fetus). We will assess the association between U-AID indices and adverse perinatal outcomes in the FGR group and the difference in U-AID indices between the two groups.

Machine learning analysis with population data for the associations of preterm birth with temporomandibular disorder and gastrointestinal diseases.

This study employs machine learning analysis with population data for the associations of preterm birth (PTB) with temporomandibular disorder (TMD) and gastrointestinal diseases. The source of the population-based retrospective cohort was Korea National Health Insurance claims for 489,893 primiparous women with delivery at the age of 25-40 in 2017. The dependent variable was PTB in 2017. Twenty-one predictors were included, i.e., demographic, socioeconomic, disease and medication information during 2002-2016. Random forest variable importance was derived for finding important predictors of PTB and evaluating its associations with the predictors including TMD and gastroesophageal reflux disease (GERD). Shapley Additive Explanation (SHAP) values were calculated to analyze the directions of these associations. The random forest with oversampling registered a much higher area under the receiver-operating-characteristic curve compared to logistic regression with oversampling, i.e., 79.3% vs. 53.1%. According to random forest variable importance values and rankings, PTB has strong associations with low socioeconomic status, GERD, age, infertility, irritable bowel syndrome, diabetes, TMD, salivary gland disease, hypertension, tricyclic antidepressant and benzodiazepine. In terms of max SHAP values, these associations were positive, e.g., low socioeconomic status (0.29), age (0.21), GERD (0.27) and TMD (0.23). The inclusion of low socioeconomic status, age, GERD or TMD into the random forest will increase the probability of PTB by 0.29, 0.21, 0.27 or 0.23. A cutting-edge approach of explainable artificial intelligence highlights the strong associations of preterm birth with temporomandibular disorder, gastrointestinal diseases and antidepressant medication. Close surveillance is needed for pregnant women regarding these multiple risks at the same time.

The Role of the Aryl Hydrocarbon Receptor in Vascular Factors Related to Preeclampsia in a Smoking Mouse Model.

Smoking cigarettes is known to lower the risk of preeclampsia. The objective of this study is to evaluate the effect of smoking on the expression of soluble FMS-like tyrosine kinase-1 (sFlt-1), vascular endothelial growth factor (VEGF), and endoglin (sEng)-1 and the role of the aryl hydrocarbon receptor (AhR) in pregnant mice. We developed a smoking mouse model using a gas-filling system. One or two cigarettes per day were exposed to each of the five pregnant mice for five days a week throughout pregnancy. AhR agonist and antagonist were injected. Serum levels and expression in the placenta of sFlt-1, VEGF, and sEng-1 were analyzed and compared among the cigarette smoke and no-exposure groups after delivery. Compared to the no-smoke exposure group, the serum level of sFlt-1 was significantly decreased in the two-cigarette-exposed group (p < 0.001). When the AhR antagonist was added to the two-cigarette-exposed group, sFlt-1 levels were significantly increased compared to the two-cigarette group (p = 0.002). The levels of sFlt-1 in the AhR antagonist group did not change regardless of two-cigarette exposure (p = 0.064). With the AhR agonist, sFlt-1 decreased significantly compared to the control (p = 0.001) and AhR antagonist group (p = 0.002). The sFlt-1 level was significantly decreased after the injection of the AhR agonist compared to the control group (p = 0.001). Serum levels of VEGF were significantly decreased in the one-cigarette-exposed group compared to the control group; however, there was no difference between the control and the two-cigarette-exposed groups. The placental expression of sFlt-1, VEGF, and sEng were inconsistent. This study offers insights into the potential role of AhR on antiangiogenic sFlt-1 associated with preeclampsia. It may support the invention of a new treatment strategy for preeclampsia using AhR activation.

Short-term neonatal and long-term neurodevelopmental outcome of children born term low birth weight.

This study aimed to examine the impact of term LBW on short-term neonatal and long-term neurodevelopmental outcomes in children 5-7 years of age. This is a population-based cohort study that merged national data from the Korea National Health Insurance claims and National Health Screening Program for Infants and Children. The participants were women who gave birth at a gestational age of ≥ 37 weeks between 2013 and 2015 in the Republic of Korea, and were tracked during 2020 for the neurodevelopmental surveillance of their children. Among 830,806 women who gave birth during the study period, 31,700 (3.8%) of their babies weighed less than 2500 g. By Cox proportional hazard analysis, children aged 5-7 years who had LBW were associated with any developmental, motor developmental delay, cognitive developmental delay, autism spectrum, attention deficit hyperactivity disorders, and epileptic and febrile seizures.Children born with term LBW were more vulnerable to neurodevelopmental disorders at 5-7 years of age than those with normal and large birth weights. This study further substantiates counseling parents regarding the long-term outcomes of children being born underweight.

Prepregnancy Glucose Levels Within Normal Range and Its Impact on Obstetric Complications in Subsequent Pregnancy: A Population Cohort Study.

BACKGROUND: We sought to identify the influence of prepregnancy glucose levels on obstetric complications in subsequent pregnancy. METHODS: Women in Republic of Korea who had given birth between January 1st, 2007 and December 31st, 2010 were enrolled. The database of the Health Insurance Review and Assessment Service and data from a national health screening program for infants and children were used. Subjects were divided into seven groups according to their fasting glucose levels. RESULTS: 59,619 women were included for analysis, and 10.4%, 13.7%, 19.1%, 21.5%, 16.0%, 11.6%, and 7.5% women had glucose levels of < 75, 75-79, 80-84, 85-89, 90-94, 95-100 and > 100 mg/dL. Each 5 mg/dL increase in prepregnancy fasting glucose levels was associated with increased risk of gestational diabetes and macrosomia in subsequent pregnancy. Adjusted risk ratio for gestational diabetes per standard deviation prepregnancy glucose > 100 mg/dL was 2.015 (95% confidence interval, 1.649-2.462) and for macrosomia an adjusted risk ratio 1.389 (95% confidence interval, 1.147-1.682). CONCLUSION: Higher prepregnancy glucose level within normal range was related to gestational diabetes and macrosomia in following pregnancy. Our results may aid in the identification of women at future risk of obstetric complications and may guide to stratify women into normal and intensified care. TWEETABLE ABSTRACT: Higher prepregnancy glucose in normal range is associated with gestational diabetes and macrosomia.

A Randomized, Multicenter, Double-Blind, Parallel, Non-Inferiority Clinical Study to Compare the Efficacy and Safety of Unicenta and Melsmon for Menopausal Symptom Improvement.

This study was conducted to evaluate the efficacy and safety of Unicenta in female subjects with menopausal symptoms by analyzing the changes in the Kupperman index (primary endpoint) and hormonal changes (secondary endpoint). It was a randomized, multi-center, double-blind, parallel, non-inferiority clinical study conducted at two different tertiary medical centers. A Unicenta injection was shown to be non-inferior to Melsmon based on the Kupperman index in both the intent-to-treat and per-protocol populations (p = 0.789 and p = 0.826, respectively). Additionally, there were no statistically significant differences in hormone levels (estradiol, follicular-stimulating hormone) or in the evaluation of facial flushes. There was no statistically significant difference in the incidence rate of adverse events between the two groups (p = 0.505). The study demonstrated that Unicenta is not inferior to Melsmon in terms of the change in the Kupperman index after 12 days of injection. The efficacy and safety of Unicenta were shown, resulting in the improvement of menopausal symptoms.

Real-time Classification of Fetal Status Based on Deep Learning and Cardiotocography Data.

This study uses convolutional neural networks (CNNs) and cardiotocography data for the real-time classification of fetal status in the mobile application of a pregnant woman and the computer server of a data expert at the same time (The sensor is connected with the smartphone, which is linked with the web server for the woman and the computer server for the expert). Data came from 5249 (or 4833) cardiotocography traces in Anam Hospital for the mobile application (or the computer server). 150 data cases of 5-minute duration were extracted from each trace with 141,001 final cases for the mobile application and for the computer server alike. The dependent variable was fetal status with two categories (Normal, Abnormal) for the mobile application and three categories (Normal, Middle, Abnormal) for the computer server. The fetal heart rate served as a predictor for the mobile application and the computer server, while uterus contraction for the computer server only. The 1-dimension (or 2-dimension) Resnet CNN was trained for the mobile application (or the computer server) during 800 epochs. The sensitivity, specificity and their harmonic mean of the 1-dimension CNN for the mobile application were 94.9%, 91.2% and 93.0%, respectively. The corresponding statistics of the 2-dimension CNN for the computer server were 98.0%, 99.5% and 98.7%. The average inference time per 1000 images was 6.51 micro-seconds. Deep learning provides an efficient model for the real-time classification of fetal status in the mobile application and the computer server at the same time.

Association between early preterm birth and maternal exposure to fine particular matter (PM10): A nation-wide population-based cohort study using machine learning.

Although preterm birth (PTB), a birth before 34 weeks of gestation accounts for only less than 3% of total births, it is a critical cause of various perinatal morbidity and mortality. Several studies have been conducted on the association between maternal exposure to PM and PTB, but the results were inconsistent. Moreover, no study has analyzed the risk of PM on PTB among women with cardiovascular diseases, even though those were thought to be highly susceptible to PM considering the cardiovascular effect of PM. Therefore, we aimed to evaluate the effect of PM10 on early PTB according to the period of exposure, using machine learning with data from Korea National Health Insurance Service (KNHI) claims. Furthermore, we conducted subgroup analysis to compare the risk of PM on early PTB among pregnant women with cardiovascular diseases and those without. A total of 149,643 primiparous singleton women aged 25 to 40 years who delivered babies in 2017 were included. Random forest feature importance and SHAP (Shapley additive explanations) value were used to identify the effect of PM10 on early PTB in comparison with other well-known contributing factors of PTB. AUC and accuracy of PTB prediction model using random forest were 0.9988 and 0.9984, respectively. Maternal exposure to PM10 was one of the major predictors of early PTB. PM10 concentration of 5 to 7 months before delivery, the first and early second trimester of pregnancy, ranked high in feature importance. SHAP value showed that higher PM10 concentrations before 5 to 7 months before delivery were associated with an increased risk of early PTB. The probability of early PTB was increased by 7.73%, 10.58%, or 11.11% if a variable PM10 concentration of 5, 6, or 7 months before delivery was included to the prediction model. Furthermore, women with cardiovascular diseases were more susceptible to PM10 concentration in terms of risk for early PTB than those without cardiovascular diseases. Maternal exposure to PM10 has a strong association with early PTB. In addition, in the context of PTB, pregnant women with cardiovascular diseases are a high-risk group of PM10 and the first and early second trimester is a high-risk period of PM10.

Patient blood management to minimize transfusions during the postpartum period.

Patient blood management is an evidence-based concept that seeks to minimize blood loss by maintaining adequate hemoglobin levels and optimizing hemostasis during surgery. Since the coronavirus disease 2019 pandemic, patient blood management has gained significance due to fewer blood donations and reduced amounts of blood stored for transfusion. Recently, the prevalence of postpartum hemorrhage (PPH), as well as the frequency of PPH-associated transfusions, has steadily increased. Therefore, proper blood transfusion is required to minimize PPH-associated complications while saving the patient's life. Several guidelines have attempted to apply this concept to minimize anemia during pregnancy and bleeding during delivery, prevent bleeding after delivery, and optimize recovery methods from anemia. This study systematically reviewed various guidelines to determine blood loss management in pregnant women.

Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance database.

BACKGROUND: Maternal heart disease is suspected to affect preterm birth (PTB); however, validated studies on the association between maternal heart disease and PTB are still limited. This study aimed to build a prediction model for PTB using machine learning analysis and nationwide population data, and to investigate the association between various maternal heart diseases and PTB. METHODS: A population-based, retrospective cohort study was conducted using data obtained from the Korea National Health Insurance claims database, that included 174,926 primiparous women aged 25-40 years who delivered in 2017. The random forest variable importance was used to identify the major determinants of PTB and test its associations with maternal heart diseases, i.e., arrhythmia, ischemic heart disease (IHD), cardiomyopathy, congestive heart failure, and congenital heart disease first diagnosed before or during pregnancy. RESULTS: Among the study population, 12,701 women had PTB, and 12,234 women had at least one heart disease. The areas under the receiver-operating-characteristic curves of the random forest with oversampling data were within 88.53 to 95.31. The accuracy range was 89.59 to 95.22. The most critical variables for PTB were socioeconomic status and age. The random forest variable importance indicated the strong associations of PTB with arrhythmia and IHD among the maternal heart diseases. Within the arrhythmia group, atrial fibrillation/flutter was the most significant risk factor for PTB based on the Shapley additive explanation value. CONCLUSIONS: Careful evaluation and management of maternal heart disease during pregnancy would help reduce PTB. Machine learning is an effective prediction model for PTB and the major predictors of PTB included maternal heart disease such as arrhythmia and IHD.

The Skin Antiseptic agents at Vaginal dElivery (SAVE) trial: study protocol for a randomized controlled trial.

BACKGROUND: Cleansing of the vulva and perineum is recommended during preparation for vaginal delivery, and special attention is paid to cleansing before episiotomy because episiotomy is known to increase the risk of perineal wound infection and/or dehiscence. However, the optimal method of perineal cleansing has not been established, including the choice of antiseptic agent. To address this issue, we designed a randomized controlled trial to examine whether skin preparation with chlorhexidine-alcohol is superior to povidone-iodine for the prevention of perineal wound infection after vaginal delivery. METHODS: In this multicenter randomized controlled trial, term pregnant women who plan to deliver vaginally after episiotomy will be enrolled. The participants will be randomly assigned to use antiseptic agents for perineal cleansing (povidone-iodine or chlorhexidine-alcohol). The primary outcome is superficial or deep perineal wound infection within 30 days after vaginal delivery. The secondary outcomes are the length of hospital stay, physician office visits, or hospital readmission for infection-related complications, endometritis, skin irritations, and allergic reactions. DISCUSSION: This study will be the first randomized controlled trial aiming to determine the optimal antiseptic agent for the prevention of perineal wound infections after vaginal delivery. TRIAL REGISTRATION: ClinicalTrials.gov NCT05122169. First submitted date on 8 November 2021. First posted date on 16 November 2021.

Associations of Preterm Birth with Dental and Gastrointestinal Diseases: Machine Learning Analysis Using National Health Insurance Data.

BACKGROUND: This study uses machine learning with large-scale population data to assess the associations of preterm birth (PTB) with dental and gastrointestinal diseases. METHODS: Population-based retrospective cohort data came from Korea National Health Insurance claims for 124,606 primiparous women aged 25-40 and delivered in 2017. The 186 independent variables included demographic/socioeconomic determinants, disease information, and medication history. Machine learning analysis was used to establish the prediction model of PTB. Random forest variable importance was used for identifying major determinants of PTB and testing its associations with dental and gastrointestinal diseases, medication history, and socioeconomic status. RESULTS: The random forest with oversampling data registered an accuracy of 84.03, and the areas under the receiver-operating-characteristic curves with the range of 84.03-84.04. Based on random forest variable importance with oversampling data, PTB has strong associations with socioeconomic status (0.284), age (0.214), year 2014 gastroesophageal reflux disease (GERD) (0.026), year 2015 GERD (0.026), year 2013 GERD (0.024), progesterone (0.024), year 2012 GERD (0.023), year 2011 GERD (0.021), tricyclic antidepressant (0.020) and year 2016 infertility (0.019). For example, the accuracy of the model will decrease by 28.4%, 2.6%, or 1.9% if the values of socioeconomic status, year 2014 GERD, or year 2016 infertility are randomly permutated (or shuffled). CONCLUSION: By using machine learning, we established a valid prediction model for PTB. PTB has strong associations with GERD and infertility. Pregnant women need close surveillance for gastrointestinal and obstetric risks at the same time.

Effect of a Patient Blood Management Program on the Appropriateness of Red Blood Cell Transfusion and Clinical Outcomes in Elderly Patients Undergoing Hip Fracture Surgery.

BACKGROUND: Elderly patients with hip fractures frequently receive perioperative transfusions, which are associated with increased morbidity and mortality. This study aimed to evaluate the impact of a patient blood management (PBM) program on the appropriateness of red blood cell (RBC) transfusion and clinical outcomes in geriatric patients undergoing hip fracture surgery. METHODS: In 2018, the revised PBM program was implemented at the Korea University Anam Hospital, Seoul, Republic of Korea. Elderly patients aged ≥ 65 years who underwent hip fracture surgery from 2017 to 2020 were evaluated. Clinical characteristics and outcomes were analyzed according to the timing of PBM implementation (pre-PBM, early-PBM, and late-PBM). Multiveriate regression analysis was used to evaluate the risk factors of the adverse outcomes, such as in-hospital mortality or 30-day readmission. RESULTS: A total of 884 elderly patients were included in this study. The proportion of patients who received perioperative RBC transfusions decreased significantly (43.5%, 40.1%, and 33.2% for pre-PBM, early-PBM, and late-PBM, respectively; P = 0.013). However, the appropriateness of RBC transfusion significantly increased (54.0%, 60.1%, and 94.7%, respectively; P < 0.001). The duration of in-hospital stay and 30-day readmission rates significantly decreased. Multivariable regression analysis revealed that RBC transfusion (odds ratio, 1.815; 95% confidence interval, 1.137-2.899; P = 0.013) was significantly associated with adverse outcomes. CONCLUSION: Implementing the PBM program increased the appropriateness of RBC transfusion without compromising transfusion quality and clinical outcomes. Therefore, adopting the PBM program may improve the clinical management of elderly patients following hip fracture surgery.

Blood Transfusion, All-Cause Mortality and Hospitalization Period in COVID-19 Patients: Machine Learning Analysis of National Health Insurance Claims Data.

This study presents the most comprehensive machine-learning analysis for the predictors of blood transfusion, all-cause mortality, and hospitalization period in COVID-19 patients. Data came from Korea National Health Insurance claims data with 7943 COVID-19 patients diagnosed during November 2019−May 2020. The dependent variables were all-cause mortality and the hospitalization period, and their 28 independent variables were considered. Random forest variable importance (GINI) was introduced for identifying the main factors of the dependent variables and evaluating their associations with these predictors, including blood transfusion. Based on the results of this study, blood transfusion had a positive association with all-cause mortality. The proportions of red blood cell, platelet, fresh frozen plasma, and cryoprecipitate transfusions were significantly higher in those with death than in those without death (p-values < 0.01). Likewise, the top ten factors of all-cause mortality based on random forest variable importance were the Charlson Comorbidity Index (53.54), age (45.68), socioeconomic status (45.65), red blood cell transfusion (27.08), dementia (19.27), antiplatelet (16.81), gender (14.60), diabetes mellitus (13.00), liver disease (11.19) and platelet transfusion (10.11). The top ten predictors of the hospitalization period were the Charlson Comorbidity Index, socioeconomic status, dementia, age, gender, hemiplegia, antiplatelet, diabetes mellitus, liver disease, and cardiovascular disease. In conclusion, comorbidity, red blood cell transfusion, and platelet transfusion were the major factors of all-cause mortality based on machine learning analysis. The effective management of these predictors is needed in COVID-19 patients.