UNASSIGNED: Non-suicidal self-injury (NSSI) is a significant social issue, especially among adolescents with major depressive disorder (MDD). This study aimed to construct a risk prediction model using machine learning (ML) algorithms, such as XGBoost and random forest, to identify interventions for healthcare professionals working with adolescents with MDD.
UNASSIGNED: This study investigated 488 adolescents with MDD. Adolescents was randomly divided into 75% training set and 25% test set to testify the predictive value of risk prediction model. The prediction model was constructed using XGBoost and random forest algorithms. We evaluated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, recall, F Score of the two models for comparing the performance of the two models.
UNASSIGNED: There were 161 (33.00%) participants having NSSI. Compared without NSSI, there were statistically significant differences in gender (P=0.035), age (P=0.036), depressive symptoms (P=0.042), sleep quality (P=0.030), dysfunctional attitudes (P=0.048), childhood trauma (P=0.046), interpersonal problems (P=0.047), psychoticism (P) (P=0.049), neuroticism (N) (P=0.044), punishing and Severe (F2) (P=0.045) and Overly-intervening and Protecting (M2) (P=0.047) with NSSI. The AUC values for random forest and XGBoost were 0.780 and 0.807, respectively. The top five most important risk predictors identified by both machine learning methods were dysfunctional attitude, childhood trauma, depressive symptoms, F2 and M2.
UNASSIGNED: The study demonstrates the suitability of prediction models for predicting NSSI behavior in Chinese adolescents with MDD based on ML. This model improves the assessment of NSSI in adolescents with MDD by health care professionals working. This provides a foundation for focused prevention and interventions by health care professionals working with these adolescents.

UNASSIGNED: There are almost 2 million adult patients with congenital heart disease in China, and the number of moderate and severe patients is increasing. However, few studies have investigated the risk of serious adverse events (SAE) after catheterization among them. The aim of this study was to identify risk factors for SAE related to cardiac catheterization and to provide the risk scoring model for predicting SAE.
UNASSIGNED: A total of 690 patients with moderate and severe adult patients with congenital heart disease (ACHD) who underwent cardiac catheterization in Wuhan Asian Heart Hospital Affiliated to Wuhan University of Science and Technology from January 2018 to January 2022 were retrospectively collected and subsequently divided into a modeling group and a verification group. A univariate analysis was performed on the identified SAE risk factors, and then significant factors were included in the multivariate logistic regression model to screen for independent predictors of SAE. The receiver operating characteristic curve (ROC) and the Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model, respectively.
UNASSIGNED: A SAE occurred in 69 (10.0%) of the 690 catheterization procedures meeting inclusion criteria. The established SAE risk calculation formula was logit(p) = -6.134 + 0.992 × pulmonary artery hypertension (yes) + 1.459 × disease severity (severe) + 2.324 × procedure type (diagnostic and interventional) + 1.436 × cTnI ( ≥ 0.028 μ g/L) + 1.537 × NT-proBNP ( ≥ 126.65 pg/mL). The total score of the final risk score model based on the effect size of each predictor was 0 to 7, involving pulmonary artery hypertension (1 point), disease severity (1 point), procedure type (2 points), cTnI (1 point) and NT-proBNP (2 points), and the score greater than 3 means high risk. The C-statistic of the area under the ROC curve was 0.840 and 0.911 for the derivation and validation cohorts, respectively. According to the Hosmer-Lemeshow test, the p values in the modeling group and the verification group were 0.064 and 0.868, respectively.
UNASSIGNED: The risk prediction model developed in this study has high discrimination and calibration, which can provide reference for clinical prediction and evaluation of SAE risk after cardiac catheterization in patients with moderate and severe ACHD.

OBJECTIVE: The main aim of this study is to synthesize the prevalent predictive models for pressure injuries in hospitalized patients, with the goal of identifying common predictive factors linked to pressure injuries in hospitalized patients. This endeavour holds the potential to provide clinical nurses with a valuable reference for providing targeted care to high-risk patients.
BACKGROUND: Pressure injuries (PIs) are a frequently occurring health problem throughout the world. There are mounting studies about risk prediction model of PIs reported and published. However, the prediction performance of the models is still unclear.
METHODS: Systematic review and meta-analysis: The Cochrane Library, PubMed, Embase, CINAHL, Web of Science and Chinese databases including CNKI (China National Knowledge Infrastructure), Wanfang Database, Weipu Database and CBM (China Biology Medicine).
METHODS: This systematic review was conducted following PRISMA recommendations. The databases of Cochrane Library, PubMed, Embase, CINAHL, Web of Science, and CNKI, Weipu Database, Wanfang Database and CBM were searched for all studies published before September 2023. We included studies with cohort, case-control designs, reporting the development of risk model and have been validated externally and internally among the hospitalized patients. Two researchers selected the retrieved studies according to the inclusion and exclusion criteria, and critically evaluated the quality of studies based on the CHARMS checklist. The PRISMA guideline was used to report the systematic review and meta-analysis.
RESULTS: Sixty-two studies were included, which contained 99 pressure injuries risk prediction models. The AUC (area under ROC curve) of modelling in 32 prediction models were reported ranged from .70 to .99, while the AUC of verification in 38 models were reported ranged from .70 to .98. Gender (OR = 1.41, CI: .99 ~ 1.31), age (WMD = 8.81, CI: 8.11 ~ 9.57), diabetes mellitus (OR = 1.64, CI: 1.36 ~ 1.99), mechanical ventilation (OR = 2.71, CI: 2.05 ~ 3.57), length of hospital stay (WMD = 7.65, CI: 7.24 ~ 8.05) were the most common predictors of pressure injuries.
CONCLUSIONS: Studies of PIs risk prediction model in hospitalized patients had high research quality, and the risk prediction models also had good predictive performance. However, some of the included studies lacked of internal or external validation in modelling, which affected the stability and extendibility. The aged, male patient in ICU, albumin, haematocrit, low haemoglobin level, diabetes, mechanical ventilation and length of stay in hospital were high-risk factors for pressure injuries in hospitalized patients. In the future, it is recommended that clinical nurses, in practice, select predictive models with better performance to identify high-risk patients based on the actual situation and provide care targeting the high-risk factors to prevent the occurrence of diseases.
CONCLUSIONS: The risk prediction model is an effective tool for identifying patients at the risk of developing PIs. With the help of risk prediction tool, nurses can identify the high-risk patients and common predictive factors, predict the probability of developing PIs, then provide specific preventive measures to improve the outcomes of these patients.
UNASSIGNED: CRD42023445258.

OBJECTIVE: To conduct a systematic review to assess drug exposure handling in diabetic retinopathy (DR) risk prediction models, a network-meta-analysis to identify drugs associated with DR and a meta-analysis to determine which drugs contributed to enhanced model performance.
METHODS: Systematic review and meta-analysis.
METHODS: We included studies presenting DR models incorporating drug exposure as a predictor. We searched EMBASE, MEDLINE, and SCOPUS from inception to December 2023. We evaluated the quality of studies using the Prediction model Risk of Bias Assessment Tool and certainty using GRADE. We conducted network meta-analysis and meta-analysis to estimate the odds ratio (OR) and pooled C-statistic, respectively, and 95% confidence intervals (CI) (PROSPERO: CRD42022349764).
RESULTS: Of 5,653 records identified, we included 28 studies of 678,837 type 1 or 2 diabetes participants, of which 38,579 (5.7%) had DR. A total of 19, 3, and 7 studies were at high, unclear, and low risk of bias, respectively. Drugs included in models as predictors were: insulin (n = 24), antihypertensives (n = 5), oral antidiabetics (n = 12), lipid-lowering drugs (n = 7), antiplatelets (n = 2). Drug exposure was modelled primarily as a categorical variable (n = 23 studies). Two studies handled drug exposure as time-varying covariates, and one as a time-dependent covariate. Insulin was associated with an increased risk of DR (OR = 2.50; 95% CI: 1.61-3.86). Models that included insulin (n = 9) had a higher pooled C-statistic (C-statistic = 0.84, CI: 0.80-0.88), compared to models (n = 9) that incorporated a combination of drugs alongside insulin (C-statistic = 0.79, CI: 0.74-0.84), as well as models (n = 3) not including insulin (C-statistic = 0.70, CI: 0.64-0.75). Limitations include the high risk of bias and significant heterogeneity in reviewed studies.
CONCLUSIONS: This is the first review assessing drug exposure handling in DR prediction models. Drug exposure was primarily modelled as a categorical variable, with insulin associated with improved model performance. However, due to suboptimal drug handling, associations between other drugs and model performance may have been overlooked. This review proposes the following for future DR prediction models: (1) evaluation of drug exposure as a variable, (2) use of time-varying methodologies, and (3) consideration of drug regimen details. Improving drug exposure handling could potentially unveil novel variables capable of significantly enhancing the predictive capability of prediction models.

BACKGROUND: Identifying patients at risk of ventilator-associated pneumonia through prediction models can facilitate medical decision-making. Our objective was to evaluate the current models for ventilator-associated pneumonia in patients with mechanical ventilation.
METHODS: Nine databases systematically retrieved from establishment to March 6, 2024. Two independent reviewers performed study selection, data extraction, and quality assessment, respectively. The Prediction Model Risk of Bias Assessment Tool was used to evaluate the risk of model bias and applicability. Stata 17.0 was used to conduct a meta-analysis of discrimination of model validation.
RESULTS: The total of 34 studies were included, with reported 52 prediction models. The most frequent predictors in the models were mechanical ventilation duration, length of intensive care unit stay, and age. Each study was essentially considered having a high risk of bias. A meta-analysis of 17 studies containing 33 models with validation was performed with a pooled area under the receiver-operating curve of 0.80 (95% confidence interval: 0.78-0.83).
CONCLUSIONS: Despite the relatively excellent performance of the models, there is a high risk of bias of the model development process. Enhancing the methodological quality, especially the external validation, practical application, and optimization of the models need urgent attention.

UNASSIGNED: This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models.
UNASSIGNED: We searched databases including China National Knowledge Infrastructure (CNKI), Wanfang Data, VIP Chinese Science and Technology Journal Database, Chinese Biomedical Literature Database (CBM), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, up until 28 December 2023. Two researchers independently screened the literature and extracted and evaluated information according to a data extraction form and bias risk assessment tool for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software.
UNASSIGNED: A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction models have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective nature of most studies, unreasonable sample sizes, and studies conducted in a single center. Meta-analysis of the models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predictive performance.
UNASSIGNED: Research on DKD risk prediction models for patients with type 2 diabetes in China is still in its initial stages, with a high overall risk of bias and a lack of clinical application. Future efforts could focus on constructing high-performance, easy-to-use prediction models based on interpretable machine learning methods and applying them in clinical settings.
UNASSIGNED: This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recognized guideline for such research.
UNASSIGNED: https://www.crd.york.ac.uk/prospero/, identifier CRD42024498015.

BACKGROUND: Intensive care unit (ICU)-acquired weakness (ICU-AW) is a critical complication that significantly worsens patient prognosis. It is widely thought that risk prediction models can be harnessed to guide preventive interventions. While the number of ICU-AW risk prediction models is increasing, the quality and applicability of these models in clinical practice remain unclear.
OBJECTIVE: The objective of this study was to systematically review published studies on risk prediction models for ICU-AW.
METHODS: We searched electronic databases (PubMed, Web of Science, The Cochrane Library, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Periodical Database (VIP), and Wanfang Database) from inception to October 2023 for studies on ICU-AW risk prediction models. Two independent researchers screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies.
RESULTS: A total of 2709 articles were identified. After screening, 25 articles were selected, encompassing 25 risk prediction models. The area under the curve for these models ranged from 0.681 to 0.926. Evaluation of bias risk indicated that all included models exhibited a high risk of bias, with three models demonstrating poor applicability. The top five predictors among these models were mechanical ventilation duration, age, Acute Physiology and Chronic Health Evaluation II score, blood lactate levels, and the length of ICU stay. The combined area under the curve of the ten validation models was 0.83 (95% confidence interval: 0.77-0.88), indicating a strong discriminative ability.
CONCLUSIONS: Overall, ICU-AW risk prediction models demonstrate promising discriminative ability. However, further optimisation is needed to address limitations, including data source heterogeneity, potential biases in study design, and the need for robust statistical validation. Future efforts should prioritise external validation of existing models or the development of high-quality predictive models with superior performance.
BACKGROUND: The protocol for this study is registered with the International Prospective Register of Systematic Reviews (registration number: CRD42023453187).

BACKGROUND: Diabetic foot ulcer is one of the most prevalent, serious, and costly consequences of diabetes, often associated with peripheral neuropathy and peripheral arterial disease. These ulcers contribute to high disability and mortality rates in patients and pose a major challenge to clinical management.
OBJECTIVE: To systematically review the risk prediction models for post-healing recurrence in diabetic foot ulcer (DFU) patients, so as to provide a reference for clinical staff to choose appropriate prediction models.
METHODS: The authors searched five databases (Cochrane Library, PubMed, Web of Science, EMBASE, and Chinese Biomedical Database) from their inception to September 23, 2023, for relevant literature. After data extraction, the quality of the literature was evaluated using the Predictive Model Research Bias Risk and Suitability Assessment tool (PROBAST). Meta-analysis was performed using STATA 17.0 software.
RESULTS: A total of 9 studies involving 5956 patients were included. The recurrence rate after DFU healing ranged from 6.2 % to 41.4 %. Nine studies established 15 risk prediction models, and the area under the curve (AUC) ranged from 0.660 to 0.940, of which 12 models had an AUC≥0.7, indicating good prediction performance. The combined AUC value of the 9 validation models was 0.83 (95 % confidence interval: 0.79-0.88). Hosmer-Lemeshow test was performed for 10 models, external validation for 5 models, and internal validation for 6 models. Meta-analysis showed that 14 predictors, such as age and living alone, could predict post-healing recurrence in DFU patients (p < 0.05).
CONCLUSIONS: To enhance the quality of these risk prediction models, there is potential for future improvements in terms of follow-up duration, model calibration, and validation processes.

Background: Despite being the most commonly performed valvular intervention, risk prediction for aortic valve replacement in patients with severe aortic stenosis by currently used risk scores remains challenging. The study aim was to develop a biomarker-based risk score by means of a neuronal network. Methods: In this multicenter study, 3595 patients were divided into test and validation cohorts (70% to 30%) by random allocation. Input variables to develop the ABC-AS score were age, the cardiac biomarker high-sensitivity troponin T, and a patient history of cardiac decompensation. The validation cohort was used to verify the scores\' value and for comparison with the Society of Thoracic Surgery Predictive Risk of Operative Mortality score. Results: Receiver operating curves demonstrated an improvement in prediction by using the ABC-AS score compared to the Society of Thoracic Surgery Predictive Risk of Operative Mortality (STS prom) score. Although the difference in predicting cardiovascular mortality was most notable at 30-day follow-up (area under the curve of 0.922 versus 0.678), ABC-AS also performed better in overall follow-up (0.839 versus 0.699). Furthermore, univariate analysis of ABC-AS tertiles yielded highly significant differences for all-cause (p < 0.0001) and cardiovascular mortality (p < 0.0001). Head-to-head comparison between both risk scores in a multivariable cox regression model underlined the potential of the ABC-AS score (HR per z-unit 2.633 (95% CI 2.156-3.216), p < 0.0001), while the STS prom score failed to reach statistical significance (p = 0.226). Conclusions: The newly developed ABC-AS score is an improved risk stratification tool to predict cardiovascular outcomes for patients undergoing aortic valve intervention.

UNASSIGNED: Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication during pregnancy. We aimed to evaluate a risk prediction model of GDM based on traditional and genetic factors.
UNASSIGNED: A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to gather general data. Serum test results were collected from the laboratory information system. Independent risk factors for GDM were identified using univariate and multivariate logistic regression analyses. A GDM risk prediction model was constructed and evaluated with the Hosmer-Lemeshow goodness-of-fit test, goodness-of-fit calibration plot, receiver operating characteristic curve and area under the curve.
UNASSIGNED: Among traditional factors, age ≥30 years, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms (SNPs) (rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657) were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk.
UNASSIGNED: Both genetic and traditional factors contribute to the risk of GDM in women, operating through diverse mechanisms. Strengthening the risk prediction of SNPs for postpartum DM among women with GDM history is crucial for maternal and child health protection.
We aimed to evaluate a risk prediction model of gestational diabetes mellitus (GDM) based on traditional and genetic factors. A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to collect general data. Among traditional factors, age ≥30 years old, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. Both genetic and traditional factors increase the risk of GDM in women.