Osteoporosis contributes significantly to health and economic burdens worldwide. However, the development of osteoporosis-related prediction tools has been limited for lower-middle-income countries, especially Vietnam. This study aims to develop prediction models for the Vietnamese population as well as evaluate the existing tools to forecast the risk of osteoporosis and evaluate the contribution of covariates that previous studies have determined to be risk factors for osteoporosis. The prediction models were developed to predict the risk of osteoporosis using machine learning algorithms. The performance of the included prediction models was evaluated based on two scenarios; in the first one, the original test parameters were directly modeled, and in the second the original test parameters were transformed into binary covariates. The area under the receiver operating characteristic curve, the Brier score, precision, recall and F1-score were calculated to evaluate the models’ performance in both scenarios. The contribution of the covariates was estimated using the Permutation Feature Importance estimation. Four models, namely, Logistic Regression, Support Vector Machine, Random Forest and Neural Network, were developed through two scenarios. During the validation phase, these four models performed competitively against the reference models, with the areas under the curve above 0.81. Age, height and weight contributed the most to the risk of osteoporosis, while the correlation of the other covariates with the outcome was minor. Machine learning algorithms have a proven advantage in predicting the risk of osteoporosis among Vietnamese women over 50 years old. Additional research is required to more deeply evaluate the performance of the models on other high-risk populations.

Venous thromboembolism (VTE) is a frequent preventable complication among surgical patients. Precise risk assessment is a necessary step for providing appropriate thromboprophylaxis and reducing mortality as well as morbidity caused by VTE. We carried out this work to define the rate of VTE postoperatively, following a Caprini score, and to determine VTE risk factors through a modified Caprini risk scoring system. This multicenter, observational, cohort study involved 2,790,027 patients who underwent surgery in four Vietnamese hospitals from 01/2017 to 12/2018. All patients who were evaluated before surgery by using a Caprini risk assessment model (RAM) and monitored within 90 days after surgery. The endpoint of the study was ultrasound-confirmed VTE. Our data showed that the 90-day postoperative VTE was found in 3068 patients. Most of VTE (46.97%) cases were found in the highest risk group (Caprini score > 5). A total of 37.19% were observed in the high risk group, while the rest (15.84%) were from low to moderate risk groups. The likelihood of occurring VTE was heightened 2.83 times for patients with a Caprini score of 3–4, 4.83 times for a Caprini score of 5–6, 8.84 times for a score of 7–8, and 11.42 times for a score of >8, comparing to ones with a score of 0 to 2 (all p values < 0.05). Thus, the frequency of postoperative VTE rises substantially, according to the advanced Caprini score. Further categorizing patients among the highest risk group need delivering more appropriate thromboprophylaxis.

BACKGROUND: Deep venous thrombosis (DVT) is a prevalent complication of orthopedic surgery. According in many studies. The incidence of DVT may be up to 50% if thromboprophylaxis is not available. AIM: The objective of this study was to check the degree of disease, clinical characteristics and analyzed factors in vulnerabilities with lower-limp DVT after orthopedic surgery in a Vietnam teaching hospital. METHODS: Orthopedic patients who met criteria were recruited at our hospital between August 2017 and June 2018. Ultrasound was used to discovering lower-limp DVT in pre-surgery and 7 days after surgery in all patients. RESULTS: The incidence of DVT after orthopedic surgery was 7.2%. Patients with older age (> 60) have a risk of 2 times higher of DVT after surgery than normal people (p < 0.05). The incidence of postoperative DVT was higher in immobile individuals > 72 hours (p < 0.05). Patients with prolonged surgical time (>120 minutes) had a higher risk of postoperative DVT than non-surgical patients’ surgery (p < 0.05). CONCLUSIONS: DVT remains a common complication following orthopedic surgery. Older age, immobility status, and surgical time have been found to be risky factors for the development of postoperative lower-limp DVT in orthopedic patients.

Emerging from early of 2020, the COVID-19 pandemic has become one of the most serious health crisis globally. In response to such threat, a wide range of digital health applications has been deployed in Vietnam to strengthen surveillance, risk communication, diagnosis, and treatment of COVID-19. Digital health has brought enormous benefits to the fight against COVID-19, however, numerous constrains in digital health application remain. Lack of strong governance of digital health development and deployment; insufficient infrastructure and staff capacity for digital health application are among the main drawbacks. Despite several outstanding problems, digital health is expected to contribute to reducing the spread, improving the effectiveness of pandemic control, and adding to the dramatic transformation of the health system the post-COVID era.

Objective: To estimate the incubation period of Vietnamese confirmed COVID-19 cases. Methods: Only confirmed COVID-19 cases who are Vietnamese and locally infected with available data on date of symptom onset and clearly defined window of possible SARS-CoV-2 exposure were included. We used three parametric forms with Hamiltonian Monte Carlo method for Bayesian Inference to estimate incubation period for Vietnamese COVID-19 cases. Leave-one-out Information Criterion was used to assess the performance of three models. Results: A total of 19 cases identified from 23 Jan 2020 to 13 April 2020 was included in our analysis. Average incubation periods estimated using different distribution model ranged from 6.0 days to 6.4 days with the Weibull distribution demonstrated the best fit to the data. The estimated mean of incubation period using Weibull distribution model was 6.4 days (95% credible interval (CrI): 4.89–8.5), standard deviation (SD) was 3.05 (95%CrI 3.05–5.30), median was 5.6, ranges from 1.35 to 13.04 days (2.5th to 97.5th percentiles). Extreme estimation of incubation periods is within 14 days from possible infection. Conclusion: This analysis provides evidence for an average incubation period for COVID-19 of approximately 6.4 days. Our findings support existing guidelines for 14 days of quarantine of persons potentially exposed to SARS-CoV-2. Although for extreme cases, the quarantine period should be extended up to three weeks.

Venous thromboembolism (VTE) associated with surgery can cause serious comorbidities or death and imposes a substantial economic burden to society. The study examined VTE cases after surgery to determined how this condition imposed an economic burden on patients based on the national health insurance reimbursement database. Methods: This retrospective analysis adopted the public payer’s perspective. The direct medical cost was estimated using data from the national claims database of Vietnam from Jan 1, 2017 to Sep 31, 2018. Adult patients who underwent surgeries were recruited for the study. Patients with a diagnostic code of up to 90 days after surgery were considered VTE cases with the outcome measure being the surgery-related costs within 90 days. The 90-day cost of VTE patients was found to be US$2,939. The rate of readmission increased by 5.4 times, the rate of outpatient visits increased by 1.8 times and total costs over 90 days in patients with VTE undergoing surgery increased by 2.2 times. Estimation using propensity score matching method showed that an increase of US$1,019 in the 90-day cost of VTE patients. The VTE-related costs can be used to assess the potential economic benefit and cost-savings from prevention efforts.