Human ST3Gal II and ST6GalNAc IV genes increase human serum-mediated cytotoxicity to xenogeneic cells.

Carbohydrate-antigens widely existed on glycoproteins and glycosphingolipids of all mammalian cells play a crucial role in self-defense and immunity. Xeno-reactive antibodies included in natural human sera play a protecting role in an acute phase-rejection of xenotransplantation. In this study, we investigated the effect of an alteration of glycosylation-pattern, caused by human sialyltransferases such as hST3Gal II or hST6GalNAc IV, on human serum mediated cytotoxicity in pig kidney PK15 cells. From LDH cytotoxicity assay, cytotoxicity to human serum was significantly increased in hST3Gal II and hST6GalNAc IV-transfected PK15 cells, as compared to the control. In the hST6Gal I-carrying cells, the cytotoxicity to human serum was rather decreased. Moreover, flow cytometry analysis revealed that an alteration of pig glycosylation-pattern by hST3Gal II or hST6GalNAc IV influences on a binding of human IgM or IgG, respectively, in pig kidney cells, regardless of Gal antigen alteration. Finally, we found that hST6GalNAc IV contributed to increase of terminal disialylated tetrasaccharide structure, disialyl T antigen, as evidenced by increase of the MAL II lectin binding capacity in the hST6GalNAc IV-transfected PK15 cells, compared with control. Therefore, our results suggest that carbohydrate antigens, such as disialyl T antigen, newly synthesized by the ST3Gal II- and ST6GalNAc IV are potentially believed to be new xeno-reactive elements.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

Influencing Factors on Career Preparation Behavior of Nursing Students in the Post COVID-19 Era.

This study aims to determine the factors influencing the career preparation behavior of nursing students in the post-COVID-19 era and to provide a basis for preparation strategies to enhance nursing students' nursing professionalism and career preparation behaviors. This is a descriptive cross-sectional study that measures major satisfaction, self-efficacy, nursing image, nursing professionalism, nursing image and intuition, and career preparation to identify factors influencing nursing students' career preparation behavior in the post-COVID-19 era. An online survey was conducted to collect the data. The data were analyzed using descriptive statistics, Pearson's correlation, and multiple regression analysis using the SPSS/WIN 25.0 program. A total of 240 students were included; most of them were female (86.3%) and between 21 and 25 years old (80%). The level of motivation to pursue nursing (F = 12.34, p < 0.001) and clinical practice satisfaction (F = 11.37, p < 0.001) showed statistically significant differences in career preparation behavior. Self-efficacy (r = 0.32), major satisfaction (r = 0.32), nursing image (r = 0.32), and nursing professionalism (r = 0.32) were positively correlated with career preparation behavior and significant (p < 0.001). According to the findings, nursing professionalism and image can be enhanced by providing career planning and counseling based on the student's degree of comprehension and cognitive behaviors to nurture the professional and positive attitudes that are essential for a successful nursing career. Nursing schools need to incorporate a job portal, facilities, and a mentorship program to help nursing students prepare for their careers.

The current state of training in pain medicine fellowships: An Association of Pain Program Directors (APPD) survey of program directors.

INTRODUCTION: The Accreditation Council for Graduate Medical Education (ACGME) approved the first pain medicine fellowship programs over three decades ago, designed around a pharmacological philosophy. Following that, there has been a rise in the transition of pain medicine education toward a multidisciplinary interventional model based on a tremendous surge of contemporaneous literature in these areas. This trend has created variability in clinical experience and education amongst accredited pain medicine programs with minimal literature evaluating the differences and commonalities in education and experience of different pain medicine fellowships through Program Director (PD) experiences. This study aims to gather insight from pain medicine fellowship program directors across the country to assess clinical and interventional training, providing valuable perspectives on the future of pain medicine education. METHODS: This study involved 56 PDs of ACGME-accredited pain fellowship programs in the United States. The recruitment process included three phases: advanced notification, invitation, and follow-up to maximize response rate. Participants completed a standard online questionnaire, covering various topics such as subcategory fields, online platforms for supplemental education, clinical experience, postgraduate practice success, and training adequacy. RESULTS: Surveys were completed by 39/56 (69%) standing members of the Association of Pain Program Directors (APPD). All PDs allowed fellows to participate in industry-related and professional society-related procedural workshops, with 59% encouraging these workshops. PDs emphasized the importance of integrity, professionalism, and diligence for long-term success. Fifty-four percent of PDs expressed the need for extension of fellowship training to avoid supplemental education by industry or pain/spine societies. CONCLUSION: This study highlights the challenge of providing adequate training in all Pain Medicine subtopics within a 12-month pain medicine fellowship. PDs suggest the need for additional training for fellows and discuss the importance of curriculum standardization.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions: Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

Nurse-led self-care interventions for older adults with multiple chronic conditions: A protocol for a systematic review and network meta-analysis.

The importance of self-care interventions is progressively recognized, marked by a rapidly aging population that results in growing demands on costly tertiary and institutional care services, placing substantial pressure on health and social care systems. Against this background, in this study, we will provide evidence for the impact of self-care interventions on health-related quality of life by focusing on interventions led by nurses who can employ integrated approaches. Several databases, including Ovid MEDLINE, Cochrane, CINAHL, Embase, and PubMed, will be searched along with gray literature to prevent biased results. There will be no time restrictions, and all literature with English abstracts will be included. Using the Template for Intervention Description and Replication framework, we will evaluate intervention characteristics. The primary outcome will be health-related quality of life, and the secondary outcomes will be symptom burden, physical function, and cost-effectiveness. Pairwise and network meta-analyses will be performed, and network geometry and the surface under the cumulative ranking curve will be used to determine which type of nurse-led self-care intervention is best for health-related quality of life for older adults with multiple chronic conditions. We will secure study quality through PRISMA, AMSTAR 2, RoB 2.0, and the GRADE checklist. To ensure the quality of network meta-analysis, similarity will be checked based on the PICO framework. The consistency of the network meta-analysis results will be checked to ensure transitivity by using the Bayesian hierarchical framework. The study protocol is registered with PROSPERO (CRD42022365467).

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables.

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

The Impact of the Rapid Blood Culture Identification Panel on Antibiotic Treatment and Clinical Outcomes in Bloodstream Infections, Particularly Those Associated with Multidrug-Resistant Micro-Organisms.

We evaluated the impact of the FilmArray blood culture identification (BCID) panel on the time taken to administer effective antibiotics and the clinical outcomes of bloodstream infections. We retrospectively screened patients with bloodstream infections who underwent BCID testing and compared them to a historical control group that received conventional culture testing. A total of 144 and 214 patients who underwent BCID and conventional cultures, respectively, were compared. The 30-day mortality (BCID: 9.7% vs. conventional method: 10.7%, p = 0.755), time to effective antibiotic administration (3 h for both BCID and conventional method, p = 0.789), and time to appropriate antibiotic administration did not differ significantly between the groups. BCID was not significantly associated with 30-day mortality after adjusting for the Pitt bacteremia score and the Charlson comorbidity index (adjusted OR = 0.833, CI; 0.398-1.743). Compared with conventional methods, BCID reduced the time to administration of effective antibiotics in cases of carbapenem-resistant Enterobacterales (CRE) (39 h vs. 93 h, p = 0.012) and vancomycin-resistant enterococci (VRE) (50 h vs. 92 h, p < 0.001) bacteremia. BCID did not affect the clinical outcomes of overall bloodstream infections; however, it contributed to the early administration of effective antibiotics in cases of CRE and VRE bacteremia.

Understanding the molecular mechanisms of odorant binding and activation of the human OR52 family.

Structural and mechanistic studies on human odorant receptors (ORs), key in olfactory signaling, are challenging because of their low surface expression in heterologous cells. The recent structure of OR51E2 bound to propionate provided molecular insight into odorant recognition, but the lack of an inactive OR structure limited understanding of the activation mechanism of ORs upon odorant binding. Here, we determined the cryo-electron microscopy structures of consensus OR52 (OR52cs), a representative of the OR52 family, in the ligand-free (apo) and octanoate-bound states. The apo structure of OR52cs reveals a large opening between transmembrane helices (TMs) 5 and 6. A comparison between the apo and active structures of OR52cs demonstrates the inward and outward movements of the extracellular and intracellular segments of TM6, respectively. These results, combined with molecular dynamics simulations and signaling assays, shed light on the molecular mechanisms of odorant binding and activation of the OR52 family.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

Molecular basis for PHF7-mediated ubiquitination of histone H3.

The RING-type E3 ligase has been known for over two decades, yet its diverse modes of action are still the subject of active research. Plant homeodomain (PHD) finger protein 7 (PHF7) is a RING-type E3 ubiquitin ligase responsible for histone ubiquitination. PHF7 comprises three zinc finger domains: an extended PHD (ePHD), a RING domain, and a PHD. While the function of the RING domain is largely understood, the roles of the other two domains in E3 ligase activity remain elusive. Here, we present the crystal structure of PHF7 in complex with the E2 ubiquitin-conjugating enzyme (E2). Our structure shows that E2 is effectively captured between the RING domain and the C-terminal PHD, facilitating E2 recruitment through direct contact. In addition, through in vitro binding and functional assays, we demonstrate that the N-terminal ePHD recognizes the nucleosome via DNA binding, whereas the C-terminal PHD is involved in histone H3 recognition. Our results provide a molecular basis for the E3 ligase activity of PHF7 and uncover the specific yet collaborative contributions of each domain to the PHF7 ubiquitination activity.

Hemosiderin induced acute kidney injury requiring hemodialysis in patients with paroxysmal nocturnal hemoglobinuria: A case report.

RATIONALE: Paroxysmal nocturnal hemoglobinuria (PNH) is a rare hematopoietic stem cell disease with features of hemolytic anemia, thrombosis, and bone marrow failure. Due to intravascular hemolysis and hemoglobinuria, renal dysfunction is often accompanied in PNH patients. PATIENT CONCERNS: A 25-year old woman presenting gross hematuria after coronavirus disease 2019 infection was admitted to our medical center. She had mild nausea and headache. She was diagnosed with iron deficiency anemia few years ago and had no other underlying disease. Her laboratory findings showed acute kidney injury (AKI) and severe anemia, with evidences of hemolysis. DIAGNOSIS: Renal biopsy was done to determine the cause of renal failure and the result was acute tubular necrosis with deposition of golden pigments, hemosiderin. With pathologic result and laboratory finding of hemolysis, we did flow cytometry for PNH, and the patient was finally diagnosed with PNH. INTERVENTIONS: With AKI and oliguria, the patient started to take hemodialysis. OUTCOMES: After taking 5 sessions of hemodialysis, the patient's renal function was recovered from AKI. With diagnosis of PNH, the patient is now being treated with complement C5 inhibitor. LESSONS: This challenging case tells us that we should consider the first manifestation of PNH as a cause of severe AKI requiring hemodialysis in a patient with anemia and evidence of hemolysis.

Antibody response in patients undergoing chronic hemodialysis post-severe acute respiratory syndrome coronavirus 2 vaccination: A prospective observational study.

Vaccination is important for patients undergoing hemodialysis (HD) to prevent coronavirus disease 2019 (COVID-19) infection since they are more vulnerable. However, they exhibit a weak response to vaccines, underscoring the importance of understanding whether antibodies are sufficiently produced and their durability post-COVID-19 vaccination. This prospective observational study assessed the antibody response of Korean patients undergoing HD for 1 year. We compared the antibody responses of patients undergoing HD to the COVID-19 vaccine with those of healthy volunteers from 2021 to 2022. The patient and control groups received 2 doses of ChAdOx1 nCoV-19 and mRNA-1273, respectively. Immunoglobulin G (IgG) and neutralizing antibody levels were measured weeks or months apart after 2 doses for 1 year using enzyme-linked immunosorbent and fluorescence-based competitive severe acute respiratory syndrome coronavirus 2 neutralizing assays, respectively. We analyzed the third dose's effect on the patient group by categorizing the group into patients who received the third dose and those who did not since it was initiated midway through the study. In the control group, we enrolled participants who had completed 3 doses of mRNA-1273 since almost all participants received the third dose. Thirty-two patients undergoing HD and 15 healthy participants who received 2 doses of ChAdOx1 nCoV-19 and 3 of mRNA-1273, respectively, were enrolled. Although antibody production was weaker in the patient group than in the control group (P < .001), patients showed an increase in IgG levels (0.408 ± 0.517 optical density (OD) pre-vaccination, 2.175 ± 1.241 OD in patients with 2 doses, and 2.134 ± 1.157 OD in patients with 3 doses 1 year after the second dose) and neutralizing antibodies (23 ± 8% pre-vaccination, 87 ± 23% in patients with 2 doses, and 89 ± 18% in patients with 3 doses 1 year after the second dose) post-vaccination (P < .001). In the patient group, 19 patients received a third dose (BNT162b2 or mRNA-1273); however, it did not increase the antibody levels (P = 1.000). Furthermore, the antibodies produced by the vaccination did not wane until 1 year. Two doses of vaccination resulted in a significant antibody response in patients undergoing HD, and antibody levels did not wane until 1 year.

Antibody response to COVID-19 vaccination in patients on chronic hemodialysis.

Purpose: Since patients on hemodialysis (HD) are known to be vulnerable to coronavirus disease 2019 (COVID-19), many studies were conducted regarding the effectiveness of the COVID-19 vaccine in HD patients in Western countries. Here, we assessed antibody response of HD patients for 6 months post-vaccination to identify the duration and effectiveness of the COVID-19 vaccine in the Asian population. Materials and Methods: We compared antibody response of the COVID-19 vaccine in HD patients with healthy volunteers. Patient and control groups had two doses of ChAdOx1 nCoV-19 and mRNA-1273, respectively. Immunoglobulin G (IgG) was measured before vaccination, 2 weeks after the first dose, 2 and 4 weeks, 3 and 6 months after the second dose. Neutralizing antibody was measured before vaccination and at 2 weeks, 3 and 6 months after second dose. Since the third dose was started in the middle of the study, we analyzed the effect of the third dose as well. Results: Although antibody production was weaker than the control group (n=22), the patient group (n=39) showed an increase in IgG and neutralizing antibody after two doses. And, 21/39 patients and 14/22 participants had a third dose (BNT162b2 or mRNA-1273 in the patient group, mRNA-1273 in the control group), and it did not affect antibody response in both group. Trend analysis showed IgG and neutralizing antibody did not decrease over time. Age, sex, and HD vintage did not affect antibody production in HD patients. Patients with higher body mass index displayed better seroresponse, while those on immunosuppressants showed poor seroresponse. Conclusion: Two doses of vaccination led to significant antibody response in HD patients, and the antibody did not wane until 6 months.

[A Structural Equation Model for Posttraumatic Growth among Cured Patients with COVID-19].

PURPOSE: This study aimed to develop and test a model for posttraumatic growth among cured patients with COVID-19. This model was based on Calhoun and Tedeschi's Posttraumatic Growth model and a literature review. METHODS: The participants comprised 223 patients cured from COVID-19 who were ≥ 19 years of age. The data were collected through an online questionnaire from March 21 to 24, 2022. The assessment tools included the Impact of Event Scale: Revised Korean version, the Connor-Davidson Resilience Scale, the Distress Disclosure Index, the Multidimensional Scale of Perceived Social Support, the Korean version of the Event-related Rumination Inventory, and the Korean version of the Post-traumatic Growth Inventory. Data were analyzed using the IBM SPSS version 24.0 and IBM AMOS 26.0. RESULTS: The modified model showed appropriate goodness of fit (χ² = 369.90, χ²/degree of freedom = 2.09, SRMR = .09, RMESA = .07, CFI = .94, TLI = .93). The post-traumatic growth of cured patients with COVID-19 was explained through distress perception, self-disclosure, and deliberate rumination, with the explanatory power being 70.0%. CONCLUSION: This study suggests preparing a disaster psychology program involving experts who can activate deliberate rumination is necessary. Further, this study may serve as basic data for developing a program to enhance the post-traumatic growth of patients cured from COVID-19.

Latent classes based on fall risk factors in community-dwelling older adults: The 2017 National Survey of Older Persons.

AIMS: This work aimed to classify the elderly according to fall risk factors and to identify the characteristics of the latent classes. BACKGROUND: Falls mainly occur due to combinations of various risk factors, and each older adult has a different combination of risk factors. DESIGN: This was a secondary data analysis using data from the 2017 National Survey of Older Persons conducted by the Korean Ministry of Health and Welfare. METHODS: Latent class analysis and multiple logistic regression were performed using data from 1556 older adults who experienced at least one fall during 1 year (1 January 2016 to 31 December 2016). Indicator variables included eight fall risk factors. RESULTS: A 3-class solution was selected according to acceptable goodness of fit. The 'healthy falls risk class' included over half of the cohort, and the comprised older adults did not show typical health problems. The 'complex falls risk class' included older people with physical and mental problems, and the 'musculoskeletal falls risk class' included older people with osteoarthritis and back pain. CONCLUSION: The results identified combinations of fall risk factors and characteristics among community-dwelling older adults that may contribute to the planning of effective fall prevention programmes.