Development and Validation of a Small for Gestational Age Screening Model at 21-24 Weeks Based on the Real-World Clinical Data.

BACKGROUND: Small for gestational age (SGA) is a condition in which fetal birthweight is below the 10th percentile for the gestational age, which increases the risk of perinatal morbidity and mortality. Therefore, early screening for each pregnant woman is of great interest. We aimed to develop an accurate and widely applicable screening model for SGA at 21-24 gestational weeks of singleton pregnancies. METHODS: This retrospective observational study included medical records of 23,783 pregnant women who gave birth to singleton infants at a tertiary hospital in Shanghai between 1 January 2018 and 31 December 2019. The obtained data were nonrandomly classified into training (1 January 2018 to 31 December 2018) and validation (1 January 2019 to 31 December 2019) datasets based on the year of data collection. The study variables, including maternal characteristics, laboratory test results, and sonographic parameters at 21-24 weeks of gestation were compared between the two groups. Further, univariate and multivariate logistic regression analyses were performed to identify independent risk factors for SGA. The reduced model was presented as a nomogram. The performance of the nomogram was assessed in terms of its discrimination, calibration, and clinical usefulness. Moreover, its performance was assessed in the preterm subgroup of SGA. RESULTS: Overall, 11,746 and 12,037 cases were included in the training and validation datasets, respectively. The developed SGA nomogram, comprising 12 selected variables, including age, gravidity, parity, body mass index, gestational age, single umbilical artery, abdominal circumference, humerus length, abdominal anteroposterior trunk diameter, umbilical artery systolic/diastolic ratio, transverse trunk diameter, and fasting plasma glucose, was significantly associated with SGA. The area under the curve value of our SGA nomogram model was 0.7, indicating a good identification ability and favorable calibration. Regarding preterm SGA fetuses, the nomogram achieved a satisfactory performance, with an average prediction rate of 86.3%. CONCLUSIONS: Our model is a reliable screening tool for SGA at 21-24 gestational weeks, especially for high-risk preterm fetuses. We believe that it will help clinical healthcare staff to arrange more comprehensive prenatal care examinations and, consequently, provide a timely diagnosis, intervention, and delivery.

Prediction of prognosis in COVID-19 patients using machine learning: A systematic review and meta-analysis.

BACKGROUND: Accurate prediction of prognostic outcomes in patients with COVID-19 could facilitate clinical decision-making and medical resource allocation. However, little is known about the ability of machine learning (ML) to predict prognosis in COVID-19 patients. OBJECTIVE: This study aimed to systematically examine the prognostic value of ML in patients with COVID-19. METHODS: A systematic search was conducted in PubMed, Web of Science, Embase, Cochrane Library, and IEEE Xplore up to December 15, 2021. Studies predicting the prognostic outcomes of COVID-19 patients using ML were eligible for inclusion. Risk of bias was evaluated by a tailored checklist based on Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pooled sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated to evaluate model performance. RESULTS: A total of 33 studies that described 35 models were eligible for inclusion, with 27 models presenting mortality, four intensive care unit (ICU) admission, and four use of ventilation. For predicting mortality, ML gave a pooled sensitivity of 0.86 (95% CI, 0.79-0.90), a specificity of 0.87 (95% CI, 0.80-0.92), and an AUC of 0.93 (95% CI, 0.90-0.95). For the prediction of ICU admission, ML had a sensitivity of 0.86 (95% CI, 0.78-0.92), a specificity of 0.81 (95% CI, 0.66-0.91), and an AUC of 0.91 (95% CI, 0.88-0.93). For the prediction of ventilation, ML had a sensitivity of 0.81 (95% CI, 0.68-0.90), a specificity of 0.78 (95% CI, 0.66-0.87), and an AUC of 0.87 (95% CI, 0.83-0.89). Meta-regression analyses indicated that algorithm, population, study design, and source of dataset influenced the pooled estimate. CONCLUSION: This meta-analysis demonstrated the satisfactory performance of ML in predicting prognostic outcomes in patients with COVID-19, suggesting the potential value of ML to support clinical decision-making. However, improvements to methodology and validation are still necessary before its application in routine clinical practice.

Machine learning for the prediction of atherosclerotic cardiovascular disease during 3-year follow up in Chinese type 2 diabetes mellitus patients.

AIMS/INTRODUCTION: Clinical guidelines for the management of individuals with type 2 diabetes mellitus endorse the systematic assessment of atherosclerotic cardiovascular disease risk for early interventions. In this study, we aimed to develop machine learning models to predict 3-year atherosclerotic cardiovascular disease risk in Chinese type 2 diabetes mellitus patients. MATERIALS AND METHODS: Clinical records of 4,722 individuals with type 2 diabetes mellitus admitted to 94 hospitals were used. The features included demographic information, disease histories, laboratory tests and physical examinations. Logistic regression, support vector machine, gradient boosting decision tree, random forest and adaptive boosting were applied for model construction. The performance of these models was evaluated using the area under the receiver operating characteristic curve. Additionally, we applied SHapley Additive exPlanation values to explain the prediction model. RESULTS: All five models achieved good performance in both internal and external test sets (area under the receiver operating characteristic curve >0.8). Random forest showed the highest discrimination ability, with sensitivity and specificity being 0.838 and 0.814, respectively. The SHapley Additive exPlanation analyses showed that previous history of diabetic peripheral vascular disease, older populations and longer diabetes duration were the three most influential predictors. CONCLUSIONS: The prediction models offer opportunities to personalize treatment and maximize the benefits of these medical interventions.

Theoretical Approach and Scale Construction of Patient Privacy Protection Behavior of Doctors in Public Medical Institutions in China: Pilot Development Study.

BACKGROUND: Considering the high incidence of medical privacy disclosure, it is of vital importance to study doctors' privacy protection behavior and its influencing factors. OBJECTIVE: We aim to develop a scale for doctors' protection of patients' privacy in Chinese public medical institutions, following construction of a theoretical model framework through grounded theory, and subsequently to validate the scale to measure this protection behavior. METHODS: Combined with the theoretical paradigm of protection motivation theory (PMT) and semistructured interview data, the grounded theory research method, followed by the Delphi expert and group discussion methods, a theoretical framework and initial scale for doctors in Chinese public medical institutions to protect patients' privacy was formed. The adjusted scale was collected online using a WeChat electronic survey measured using a 5-point Likert scale. Exploratory and confirmatory factor analysis (EFA and CFA) and tests to analyze reliability and validity were performed on the sample data. SPSS 19.0 and Amos 26.0 statistical analysis software were used for EFA and CFA of the sample data, respectively. RESULTS: According to the internal logic of PMT, we developed a novel theoretical framework of a "storyline," which was a process from being unaware of patients' privacy to having privacy protection behavior, that affected doctors' cognitive intermediary and changed the development of doctors' awareness, finally affecting actual privacy protection behavior in Chinese public medical institutions. Ultimately, we created a scale to measure 18 variables in the theoretical model, comprising 63 measurement items, with a total of 208 doctors participating in the scaling survey, who were predominantly educated to the master's degree level (n=151, 72.6%). The department distribution was relatively balanced. Prior to EFA, the Kaiser-Meyer-Olkin (KMO) value was 0.702, indicating that the study was suitable for factor analysis. The minimum value of Cronbach α for each study variable was .754, which met the internal consistency requirements of the scale. The standard factor loading value of each potential measurement item in CFA had scores greater than 0.5, which signified that all the items in the scale could effectively converge to the corresponding potential variables. CONCLUSIONS: The theoretical framework and scale to assess doctors' patient protection behavior in public medical institutions in China fills a significant gap in the literature and can be used to further the current knowledge of physicians' thought processes and adoption decisions.