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Objective: To compare the predictive efficacy of the PADUA and Caprini models for pulmonary embolism (PE) in gynecological inpatients, analyze the risk factors for PE, and validate whether both models can effectively predict mortality rates. Methods: A total of 355 gynecological inpatients who underwent computed tomography pulmonary angiography (CTPA) were included in the retrospective analysis. The comparative assessment of the predictive capabilities for PE between the PADUA and Caprini was carried out using receiver operating characteristic (ROC) curves. Logistic regression analysis was used to identify risk factors associated with PE. Additionally, Kaplan–Meier survival analysis plots were generated to validate the predictive efficacy for mortality rates. Results: Among 355 patients, the PADUA and Caprini demonstrated the area under the curve (AUC) values of 0.757 and 0.756, respectively. There was no statistically significant difference in the AUC between the two models (P = 0.9542). Multivariate logistic analysis revealed immobility (P < 0.001), history of venous thromboembolism (VTE) (P = 0.002), thrombophilia (P < 0.001), hormonal treatment (P = 0.022), and obesity (P = 0.019) as independent risk factors for PE. Kaplan–Meier survival analysis demonstrated the reliable predictive efficacy of both the Caprini (P = 0.00051) and PADUA (P = 0.00031) for mortality. ROC for the three- and six-month follow-ups suggested that the Caprini model exhibited superior predictive efficacy for mortality. Conclusions: The PADUA model can serve as a simple and effective tool for stratifying high-risk gynecological inpatients before undergoing CTPA. The Caprini model demonstrated superior predictive efficacy for mortality rates. [ABSTRACT FROM AUTHOR]

Background: Delayed extubation and transfer to the intensive care unit (ICU) in children undergoing major scoliosis surgery may increase postoperative complications, prolong hospital stay, and increase medical expenses; however, whether a child will require delayed extubation or transfer to the ICU after scoliosis orthopedic surgery is not fully understood. In this study, we reviewed the risk factors for delayed extubation and transfer to the ICU after scoliosis orthopedic surgery in children. Method: The electronic medical records of pediatric patients (≤ 18 years) who underwent posterior spinal fusion surgery between January 2018 and November 2021 were reviewed and analyzed. Patient characteristics (age, sex, body mass index, American Society of Anesthesiologists, ASA, grade, preoperative lung function, and congenital heart disease), preoperative Cobb angle, scoliosis type, correction rate, vertebral fusion segments, pedicle screws, surgical osteotomy, intraoperative bleeding, intraoperative allogeneic transfusion, intraoperative hemoglobin changes, intraoperative mean arterial pressure changes, intraoperative tidal volume (ml/kg predicted body weight), surgical time, postoperative extubation, and transfer to the ICU were collected. The primary outcomes were delayed extubation and transfer to the ICU. Multivariate logistic regression models were used to determine the risk factors for delayed extubation and ICU transfer. Results: A total of 246 children who satisfied the inclusion criteria were enrolled in this study, of whom 23 (9.3%) had delayed extubation and 81 (32.9%) were transferred to the ICU after surgery. High ASA grade (odds ratio [OR] 5.42; 95% confidence interval [CI] 1.49–19.78; p = 0.010), high Cobb angle (OR 1.04; 95% CI 1.02–1.07; p < 0.001), moderate to severe pulmonary dysfunction (OR 10.9; 95% CI 2.00–59.08; p = 0.006) and prolonged surgical time (OR 1.01; 95% CI 1.00–1.03; p = 0.040) were risk factors for delayed extubation. A high Cobb angle (OR 1.02; 95% CI 1.01–1.04; p = 0.004), high intraoperative bleeding volume (OR 1.06; 95% CI 1.03–1.10; p = 0.001), allogeneic transfusion (OR 3.30; 95% CI 1.24–8.83; p = 0.017) and neuromuscular scoliosis (OR 5.38; 95% CI 1.59–18.25; p = 0.007) were risk factors for transfer to the ICU. A high Cobb angle was a risk factor for both delayed extubation and ICU transfer. Age, sex, body mass index, number of vertebral fusion segments, correction rate, and intraoperative tidal volume were not associated with delayed postoperative extubation and ICU transfer. Conclusion: The most common risk factor for delayed extubation and ICU transfer in pediatric patients who underwent posterior spinal fusion was a high Cobb angle. Determining risk factors for a poor prognosis may help optimize perioperative respiratory management strategies and planning of postoperative care for children undergoing complicated spinal surgery. [ABSTRACT FROM AUTHOR]

Studies have highlighted the potential importance of modeling interactions for suicide attempt prediction. This case-cohort study identified risk factors for suicide attempts among persons with depression in Denmark using statistical approaches that do (random forests) or do not (least absolute shrinkage and selection operator regression [LASSO]) model interactions. Cases made a nonfatal suicide attempt (n  = 6032) between 1995 and 2015. The comparison subcohort was a 5% random sample of all persons in Denmark on January 1, 1995 (n  = 11 963). We used random forests and LASSO for sex-stratified prediction of suicide attempts from demographic variables, psychiatric and somatic diagnoses, and treatments. Poisonings, psychiatric disorders, and medications were important predictors for both sexes. Area under the receiver-operating characteristic curve (AUC) values were higher in LASSO models (in men, 0.85, 95% CI, 0.84-0.86; in women, 0.89, 95% C, 0.88-0.90) than random forests (in men, 0.76, 95% CI, 0.74-0.78; in women, 0.79, 95% CI = 0.78-0.81). Automatic detection of interactions via random forests did not result in better model performance than LASSO models that did not model interactions. Due to the complex nature of psychiatric comorbidity and suicide, modeling interactions may not always be the optimal statistical approach to enhancing suicide attempt prediction in high-risk samples. This article is part of a Special Collection on Mental Health. [ABSTRACT FROM AUTHOR]

Objectives Prediction models based on traditional risk factors underestimate cardiovascular (CV) risk in systemic lupus erythematosus (SLE). In a large sample of unselected SLE patients, we investigated cross-sectional associations of NT-proBNP with cardiovascular damage (CVD). Methods Serum NT-proBNP was measured in SLE patients enrolled in the MUHC Lupus Clinic registry. Serum was collected between March 2022 and April 2023 at annual research visits. The primary outcome was CVD identified on the SLICC Damage Index. Factors associated with CVD and NT-proBNP levels were determined. Results Overall, 270 SLE patients [female 91%, median age 50.7 (first quartile to third quartile: 39.6–62.1) years] were analysed for the primary outcome. Among them, 33 (12%) had CVD. The ROC curve for NT - proBNP demonstrated strong associations with CVD (AUC 0.78, 95% CI 0.69–0.87) with a threshold of 133 pg/ml providing the best discrimination for those with/without CVD. Hypertension (OR 3.3, 95% CI 1.2–9.0), dyslipidaemia (OR 3.6, 95% CI 1.3–9.6) and NT-proBNP >133 pg/ml (OR 7.0, 95% CI, 2.6–19.1) were associated with CVD in the multivariable logistic regression model. Increased NT-proBNP levels were associated with age (OR 4.2, 95% CI 2.2–8.3), ever smoking (OR 1.9, 95% CI 1.0–3.5), reduced eGFR (4.1, 95% CI 1.3–13.1), prior pericarditis/pleuritis (OR 2.5, 95% CI 1.4–4.5) and aPL antibodies (OR 2.6, 95% CI 1.4–4.9). Conclusion NT-proBNP is a biomarker for CV damage in SLE. The novel associations of NT-proBNP levels with prior pericarditis/pleuritis and aPL antibodies suggest new avenues for research to better understand what drives CV risk in SLE. [ABSTRACT FROM AUTHOR]

Objectives To assess the ability of two risk prediction models in interstitial lung disease (ILD) to predict death or lung transplantation in a cohort of patients with interstitial pneumonia with autoimmune features (IPAF). Methods We performed a retrospective cohort study of adults with IPAF at an academic medical centre. The primary outcome was a composite of lung transplantation or death. We applied the patient data to the previously described Gender–Age–Physiology (GAP) and ILD-GAP models to determine the ability of these models to predict the composite outcome. Model discrimination was assessed using the c-index, and model calibration was determined by comparing the incidence ratios of observed vs expected deaths. Results Ninety-four patients with IPAF were included. Mean (s. d.) age was 58 (13.5) years and the majority were female (62%). The majority met serologic and morphologic criteria for IPAF (94% and 91%, respectively). The GAP model had a c-index of 0.664 (95% CI 0.547–0.781), while the ILD-GAP model had a c-index of 0.569 (95% CI 0.440–0.697). In those with GAP stage 1 or GAP stage 2 disease, calibration of the GAP model was satisfactory at 2 and 3 years for the cumulative end point of lung transplantation or death. Conclusion In patients with IPAF, the GAP model performed well as a predictor of lung transplantation or death at 2 years and 3 years from ILD diagnosis in patients with GAP stage 1 and GAP stage 2 disease. [ABSTRACT FROM AUTHOR]

Aim: As the size of the elderly population gradually increases, musculoskeletal disorders, such as sarcopenia, are increasing. Diagnostic techniques such as X‐rays, computed tomography, and magnetic resonance imaging are used to predict and diagnose sarcopenia, and methods using machine learning are gradually increasing. This study aimed to create a model that can predict sarcopenia using physical characteristics and activity‐related variables without medical diagnostic equipment, such as imaging equipment, for the elderly aged 60 years or older. Methods: A sarcopenia prediction model was constructed using public data obtained from the Korea National Health and Nutrition Examination Survey. Models were built using Logistic Regression, Support Vector Machine (SVM), XGBoost, LightGBM, RandomForest, and Multi‐layer Perceptron Neural Network (MLP) algorithms, and the feature importance of the models trained with the algorithms, except for SVM and MLP, was analyzed. Results: The sarcopenia prediction model built with the LightGBM algorithm achieved the highest test accuracy, of 0.848. In constructing the LightGBM model, physical characteristic variables such as body mass index, weight, and waist circumference showed high importance, and activity‐related variables were also used in constructing the model. Conclusions: The sarcopenia prediction model, which consisted of only physical characteristics and activity‐related factors, showed excellent performance. This model has the potential to assist in the early detection of sarcopenia in the elderly, especially in communities with limited access to medical resources or facilities. Geriatr Gerontol Int 2024; 24: 595–602. [ABSTRACT FROM AUTHOR]

Background: Pitch counts are only one measure of the true workload of baseball pitchers. Newer research indicates that workload measurement and prevention of injury must include additional factors. Thus, current monitoring systems gauging pitcher workload may be considered inadequate. Purpose/Hypothesis: The purpose of this study was to develop a novel method to determine workload in baseball pitchers and improve processes for prevention of throwing-related injuries. It was hypothesized that our pitching workload model would better predict throwing-related injuries occurring throughout the baseball season than a standard pitch count model. Study Design: Cohort study; Level of evidence, 2. Methods: This prospective observational study was conducted at an academic medical center and community baseball fields during the 2019 to 2023 seasons. Pitchers aged 13 to 18 years were monitored for pitching-related injuries and workload (which included pitching velocity; intensity, using preseason and in-season velocity as a marker of effort; and pitch counts). Results: A total of 71 pitchers had 313 recorded pitcher outings, 11 pitching-related injuries, and 24,228 pitches thrown. Gameday pitch counts for all pitchers ranged from 19 to 219 (mean, 77.5 ± 41.0). Velocity ranged from 46.8 to 85.7 mph (mean, 71.3 ± 5.8 mph). Intensity ranged from 0.7 to 1.3 (mean, 1.0 ± 0.08). The mean workload was 74.7 ± 40.1 for all pitchers. Risk factors significant for injury included throwing at a higher velocity in game (P =.001), increased intensity (eg, an increase in mean velocity thrown from preseason to in-season; P <.001), and being an older pitcher (P =.014). No differences were found for workload between injured and noninjured pitchers because the analysis was underpowered. Conclusion: Our workload model indicated that throwing at a higher velocity, throwing at a higher intensity, and older age were risk factors for injury. Thus, this novel workload model should be considered as a means to identify pitchers who may be at greater risk for injury. [ABSTRACT FROM AUTHOR]

Fear of relapse into psychosis is an independent risk factor for future relapse events, indicating its importance as a novel intervention target. Twenty-five participants responded to daily ecological momentary assessment prompts assessing common early warning signs of relapse and self-reported positive experiences like feeling supported by others. We conducted multilevel vector auto-regression using common symptoms assessed in early warning signs monitoring relapse prevention while controlling for positive self-reported experiences like feeling supported by others to estimate three networks (to explore concurrent, temporal and overall relationships). Reporting fear of relapse was positively associated (within the same cross-sectional time window) with hearing voices, alongside anxiety, negative affect and sleep change. Fear of relapse appeared to predict anxiety, negative affect and greater fear of relapse on the next consecutive day. However, none of the typical early warning signs predicted fear of relapse within the temporal window, and the observed relationships were small. Early warning signs appeared to be poor predictors of experiencing fear of relapse in this study. Fear of relapse predicts later anxiety and negative affect and may be a valuable intervention target within the daily life of people diagnosed with schizophrenia. [ABSTRACT FROM AUTHOR]

Prescribed opioids are a mainstay pain treatment after traumatic injury, but a subgroup of patients may be at risk for continued opioid use. We evaluated the predictive utility of a traditional screening tool, the Opioid Risk Tool (ORT), and two other measures: average in-hospital milligram morphine equivalents (MME) per day and an assessment of opioid demand in predicting pain outcomes. Assessments of pain-related outcomes (pain intensity, interference, injury-related stress, and need for additional pain treatment) were administered at 2 weeks and 12 months post-discharge in a sample of 34 patients hospitalized for traumatic injury. Bayesian linear models were used to evaluate changes in responses over time as a function of predictors. High-risk ORT, higher MME per day, and greater opioid demand predicted less change in outcomes over time. This report provides first evidence that malleable factors of opioid and opioid demand have utility in predicting pain outcomes following traumatic injury. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore whether fibrinogen (Fib) can be used as a predictor of postpartum hemorrhage (PPH) in parturients with vaginal delivery, and the value of combining Fib with other indexes to predict postpartum hemorrhage in vaginal delivery. A total of 207 parturients who delivered via vagina were divided into PPH group (n=102) and non-PPH group (n=105). The PPH group was further divided into mild PPH group and severe PPH group. The differences of Fib, platelet (PLT), mean platelet volume (MPV), platelet distribution width (PDW), D-dimer (D-D), hemoglobin (HGB) and neonatal weight (Nw) between the two groups were compared to explore the significance of these indexes in predicting PPH. Fib, PLT and PDW in PPH group were significantly lower than those in non-PPH group, while D-D and Nw in PPH group were significantly higher than those in non-PPH group. In the binary logistic regression model, we found that Fib, D-D and Nw were independently related to PPH. The risk of PPH increased by 9.87 times for every 1 g/L decrease in Fib. The cut-off value of Fib is 4.395 (sensitivity 0.705, specificity 0.922). The AUC value of PPH predicted by Fib combined with D-D and Nw was significantly higher than that of PPH predicted by Fib (p<0.05, 95 % CI 0.00313–0.0587). Fib, D-D and Nw have good predictive value for PPH of vaginal delivery, among which Fib is the best. The combination of three indexes of Fib, D-D and Nw can predict PPH more systematically and comprehensively, and provide a basis for clinical prevention and treatment of PPH. [ABSTRACT FROM AUTHOR]

Objective: The Interpersonal Theory of Suicide (ITS) could help identify differences in groups of suicidal adolescents and inform treatment. Method: Latent Profile Analysis (LPA) using thwarted belongingness (TB), perceived burdensomeness (PB), hopelessness, and capability was conducted on data from an at‐risk clinical sample (N = 500). The ITS prediction that changes in TB and PB are associated with changes in suicidal ideation was tested using admission and discharge data. Results: Latent Profile Analysis identified three profiles with increasing complexity and severity on ITS factors. The profiles were labelled low‐severity (7.6% of participants), moderate‐severity (45.2%), and high‐severity (47.2%). ITS predictions were partially supported for the full sample and only for the high‐severity and moderate‐severity subgroups, whereby changes in TB were significantly associated with changes in suicidal ideation over the course of treatment. However, changes in PB were only significant in the moderate‐severity subgroup, and none of the ITS predictions were supported in the low‐severity subgroup. Additionally, effect sizes for changes in TB and PB were modest in all analyses. Conclusions: Our findings highlight the importance reducing low belongingness in youth, which is a component of all supported interventions of youth suicide prevention. However, given the modest association of changes in ITS variables had with changes in suicidal ideation, it may be fruitful to elaborate on the relative importance on types of low belongingness or include other non‐ITS variables. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes exposes the body to a state of high blood sugar for a long time and causes varying degrees of hardening of the arteries, making it more prone to cardiovascular emergencies. Objective: The aim of the study was to explore the risk factors of early arterial stiffness in patients with type 2 diabetes mellitus (T2DM). Methods: A retrospective study was conducted on 316 T2DM patients without macroangiopathy in The First Hospital of Qinhuangdao. Early arterial stiffness was evaluated by brachial-ankle pulse wave velocity (baPWV). Results: Ninety patients (28.5%) had baPWV≥1800cm/s. baPWV showed positive correlation with systolic blood pressure (r=0.456, p<0.001), diastolic blood pressure (r=0.133, p=0.018), urine albumin-creatinine ratio (UACR) (r=0.232, p<0.001), neutrophil lymphocyte ratio (NLR) (r=0.185, p=0.001), and visceral fat area (r=0.139, p=0.014). In multiple linear regression analysis, systolic blood pressure (β=6.240, p<0.001), UACR (β=3.805, p=0.019), NLR (β=43.722, p=0.013), and visceral fat area (β=0.778, p=0.030) were significant independent predictors for baPWV. Conclusion: The decline of arterial elasticity was common in T2DM patients without macroangiopathy. Elevated blood pressure, microangiopathy, chronic inflammation, and visceral fat accumulation were the risk factors of early arterial stiffness in patients with T2DM. [ABSTRACT FROM AUTHOR]

Background: This study aims to analyze the influencing factors of postoperative Low Anterior Resection Syndrome (LARS) in patients with middle and low rectal cancer who underwent robotic surgery. It also seeks to predict the probability of LARS through a visual, quantitative, and graphical nomogram. This approach is expected to lower the risk of postoperative LARS in these patients and improve their quality of life through effective prevention and early intervention. Patients and methods: This research involved patients with middle and low rectal cancer who underwent robotic surgery in the Department of Gastrointestinal Surgery at the First Affiliated Hospital of Nanchang University from January 2015 to October 2022. A series of intestinal dysfunction symptoms arising from postoperative rectal cancer were diagnosed and graded using LARS scoring criteria. After the initial screening of all variables related to LARS with Lasso regression, they were included in logistic regression for further univariate and multivariate analysis to identify independent risk factors for LARS. A prediction model was then constructed. Results: The study included 358 patients. The parameters identified by Lasso regression included obstruction, BMI, tumor localization, maximum tumor diameter, AJCC stage, stoma, neoadjuvant therapy (NAT), and postoperative adjuvant therapy (AT). Univariate and multivariate analyses indicated that a higher BMI, lower tumor localization, higher AJCC stage, neoadjuvant therapy, and postoperative adjuvant therapy were independent risk factors for total LARS. The AUC of the prediction nomogram was 0.834, with a sensitivity of 0.825 and specificity of 0.741. The calibration curve demonstrated excellent concordance with the nomogram, indicating the prediction curve fit the diagonal well. Conclusion: Higher BMI, lower tumor localization, higher AJCC stage, neoadjuvant therapy, and adjuvant therapy were identified as independent risk factors for total LARS. A new predictive nomogram for postoperative LARS in patients with middle and low rectal cancer undergoing robotic surgery was developed, proving to be stable and reliable. This tool will assist clinicians in managing the postoperative treatment of these patients, facilitating better clinical decision-making and maximizing patient benefits. [ABSTRACT FROM AUTHOR]

Hyperactivity of coagulation is common in exertional heatstroke (EHS). Disseminated intravascular coagulation (DIC) is the most severe form of coagulation dysfunction and associated with poor outcome. DIC, temperature and Glasgow coma scale score were identified as independent risk factors for in-hospital mortality by multivariate logistic regression analysis, and we developed a nomogram for predicting in-hospital mortality in a 13-year EHS patient cohort. The nomogram was assessed by calibration curves and bootstrap with 1,000 resamples. The receiver operating characteristic curve was constructed, and the area under the curve (AUC) was compared. Two hundred and ten patients were included. The in-hospital mortality was 9.0%, and the incidence of DIC was 17.6%. The AUC of the nomogram was 0.897 (95% CI 0.848–0.935, p <.0001) and was non-inferior to SOFA and APACHE II scores but superior to SIRS score, which were widely-used score systems of disease severity. The nomogram contributed to the adverse outcome prediction of EHS. [ABSTRACT FROM AUTHOR]

To evaluate the component patterns and risk stratification in patients with mixed malignant ovarian germ cell tumors (mMOGCT). A retrospective study of 70 mMOGCT patients treated in our hospital between 2000 and 2022 was conducted. The recurrence-free survival (RFS), disease-specific survival (DSS), and risk stratification systems based on scoring the identified prognostic factors were assessed. Yolk sac tumor component was the most common type (80%), followed by dysgerminoma (50%), immature teratoma (40%), embryonic carcinoma (27.1%), and chorionic carcinoma (15.7%). The 5-year RFS and DSS rates were 77.9% and 87.9%, respectively. International federation of gynecology and obstetrics (FIGO) stage III-IV (RR 3.253, P = 0.029) and normalization of tumor marker (TM) ≤ 3 cycles of chemotherapy (RR 6.249, P = 0.017) were risk factors for RFS and DSS, respectively. Significant DSS (RR 8.268, P = 0.006) was also noted between patients who had normalized TM ≤ 4 and ≥5 cycles of chemotherapy. FIGO stages I–II and stages III–IV were scored as 0 and 2, respectively. AFP normalization ≤3, 4, and ≥5 cycles of chemotherapy were scored as 0, 1, and 4, respectively. A total score of 0, 1–2, and ≥3 stratified patients into low-risk (43 patients), intermediate-risk (13 patients), and high-risk groups (14 patients), respectively. Patients in three risk stratifications manifested significant differences in DSS (P = 0.010) but not in RFS (P > 0.05). Distinct different component patterns existed among mMOGCT patients, and predicting survival outcomes in a universal model was challenging. • The mMOGCT patients manifested complex component combination modes, of which YST, DG, and IT were the most frequented types. • The 5-year RFS and 5-year DSS was 77.9% and 87.9%, respectively, and FIGO stage and the interval between treatment intervention and normalization of TM were the prognostic predictors. • Distinct different component patterns existed among mMOGCT patients, and predicting survival outcomes in a universal model was challenging. [ABSTRACT FROM AUTHOR]

Objective: Work‐related violence remains a significant problem in healthcare settings, including EDs. Violence risk assessment tools have been developed to improve risk mitigation in this setting; however, incorporation of these tools into standard hospital processes remains scarce. This research aimed to explore nurses' perspectives on the Bröset Violence Checklist used in routine violence risk assessment and their recommendations for additional items. Methods: Thirty nursing staff who used the Bröset Violence Checklist (BVC) as standard practice for 5 years participated in two focus groups where 23 violence risk factors were presented. Using multiple methods, participants were asked to select and elaborate from a pre‐determined list what they considered most useful in violence risk assessment in respect to descriptors and terminology. Results: Quantitative data showed most risk factors presented to the group were considered to be predictive of violence. Ten were regarded as associated with risk, and overt behaviours received the highest votes. The terms 'shouting and demanding' was preferred over 'boisterous', and 'cognitive impairment' over 'confusion'. Patient clinical characteristics and staff perceptions of harm, inability to observe subtle behaviour, imposed restrictions and interventions and environmental conditions and impact were also important considerations. Conclusions: We recommend that violence risk assessment include: history of violence, cognitive impairment, psychotic symptoms, drug and alcohol influence, shouting and demanding, verbal abuse/hostility, impulsivity, agitation, irritability and imposed restrictions and interventions. These violence risk factors fit within the four categories of historical, clinical, behavioural and situational. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia is associated with increased morbidity and mortality. The systemic immune-inflammation index (SII) has been correlated to a variety of disorders. The present study conducted a systematic review and meta-analysis to investigate the relationship between SII and sarcopenia. Methods: A literature search was performed in Web of Science, PubMed, Embase, Cochrane Library, CINAHL, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, Wanfang Database, and VIP Chinese Science and Technology Database, from inception to March 2024. Then, the literature quality was assessed. After the heterogeneity test, a random effects or fixed effects model was applied to establish the forest plot, and investigate the relationship between SII and sarcopenia. Then, the sensitivity analysis and publication bias were examined. Results: Nine articles, which included 18,634 adults, were analyzed. Sarcopenic adults had higher SII levels, when compared to non-sarcopenic adults (standardized mean difference [SMD] = 0.66, 95% confidence interval [CI] = 0.22 − 0.19, p = 0.003). The high SII level was associated to the increased risk of sarcopenia (odds ratio = 1.52, 95% CI = 1.09–2.13, p = 0.01). In addition, the subgroup analysis revealed that the SII levels were higher in the sarcopenic group, when compared to the non-sarcopenic group, in elderly adults, as well as in adults with or without gastrointestinal disorders. The analysis was robust with a low risk of publication bias. Conclusions: SII is closely associated to sarcopenia. Sarcopenic adults had elevated SII levels. The high SII level increased the risk of sarcopenia. Large scale multi-center prospective studies are required to validate these study findings. [ABSTRACT FROM AUTHOR]

Introduction: This article mainly studies the risk factors for postoperative acute myocardial infarction (AMI) in elderly hip fracture patients combined with coronary heart disease (CHD), constructs a prediction model, and evaluates the prognosis of all the patients. Methods: This article retrospectively collected elderly patients with hip fracture and CHD who underwent hip fracture surgery at the Third Hospital of Hebei Medical University from January 2019 to December 2021. Demographic data, laboratory indicators, and imaging examination results were collected from the medical case system. The risk factors of postoperative AMI were determined by univariate and multivariate logistic regression, and a nomogram prediction model was established. The ROC curve, calibration curve and DCA decision curve were plotted by R language software. The patients in the training set were followed up for 2 years to evaluate their survival situation. Results: 1094 eligible patients were divided into a training set (n = 824 from January 1, 2019 to September 31, 2021) and a validation set (n = 270 from October 1, 2021 to December 31, 2022). In the training set, women accounted for 58.6%; The average age of the patients was 79.45 years old; The main type of fracture was intertrochanteric fracture. There were 64.7% patients taken B receptor blockers; A total of 166 (20.1%) patients underwent percutaneous coronary intervention (PCI); Hypertension accounted for 55.5%; 520 (63.1%) patients had a preoperative waiting time greater than 3 days; The average hemoglobin value upon admission was 101.36 g/L; The average intraoperative bleeding volume was 212.42 ml; The average surgical time was 2.5 ± 0.3 h; Reginal anesthesia accounted for 29.7%; 63 (68.5%) AMI patients had no obvious clinical symptoms; 68 (73.9%) AMI patients did not show ST-segment elevation in ECG; The risk factors of postoperative AMI were age, hemoglobin at admission, diabetes, chronic kidney disease, intraoperative bleeding, and reginal anesthesia. The AUC of the nomogram prediction model was 0.729. The AUC in the validation set was 0.783. Survival analysis showed a significant statistical difference in 2-year mortality between patients with AMI and without AMI, among all the patients with AMI, patients with ECG ST-segment elevation has higher mortality than patients without ECG ST-segment elevation. Conclusion: Our research results found that the incidence of postoperative AMI in elderly patients with hip fractures and CHD was 11.1%. Age, diabetes, hemoglobin at admission, regional anesthesia, chronic kidney disease, and intraoperative bleeding are risk factors. The AUC of the nomogram in training set is 0.729. The 2-year mortality rate of the patients with AMI is higher than that of patients without AMI. [ABSTRACT FROM AUTHOR]

Background: To examine the risk factors associated with mortality in individuals suffering from acute diquat poisoning and to develop an effective prediction model using clinical data. Methods: A retrospective review was conducted on the clinical records of 107 individuals who were hospitalized for acute diquat poisoning at a tertiary hospital in Sichuan Province between January 2017 and September 30, 2023, and further categorized into survivor and nonsurvivor groups based on their mortality status within 30 days of poisoning. The patient's demographic information, symptoms within 24 h of admission, and details of the initial clinical ancillary examination, as well as the APACHE II score, were documented. The model was developed using backward stepwise logistic regression, and its performance was assessed using receiver operating characteristic curves, calibration curves, Brier scores, decision curve analysis curves, and bootstrap replicates for internal validation. Results: Multifactorial logistic regression analysis revealed that blood pressure (hypertension, OR 19.73, 95% CI 5.71–68.16; hypotension, OR 61.38, 95% CI 7.40–509.51), white blood count (OR 1.35, 95% CI 1.20–1.52), red cell distribution width-standard deviation (OR 1.22, 95% CI 1.08–1.38), and glomerular filtration rate (OR 0.96, 95% CI 0.94–0.97) were identified as independent risk factors for mortality in patients with diquat. Subsequently, a nomogram with an area under the curve of 0.97 (95% CI: 0.93–1) was developed. Internal bootstrap resampling (1000 repetitions) confirmed the model's adequate discriminatory power, with an area under the curve of 0.97. Decision curve analysis demonstrated greater net gains for the nomogram, while the clinical impact curves indicated greater predictive validity. Conclusion: The nomogram model developed in this study using available clinical data enhances the prediction of risk for DQ patients and has the potential to provide valuable clinical insights to guide patient treatment decisions. [ABSTRACT FROM AUTHOR]

Background: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in literature include incomplete reporting of model performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinical settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. Methods: Our cohort consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer between July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradient Boosting (XGBoost) and aligned to our envisioned use-case: "Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior." The model was trained with 75% of the dataset (n = 39,416 outpatient encounters) and validated on a 25% hold-out dataset (n = 13,122 outpatient encounters). To explain model outputs, we used Shapley Additive Explanations (SHAP) values. Clinical characteristics, laboratory tests and treatment data were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), while model calibration was assessed using the Brier score. Results: In total, 17,149 of the 52,538 prediction points (32.6%) had a mortality event within the 365-day prediction window. The model demonstrated an AUROC of 0.861 (95% CI 0.856–0.867) and AUPRC of 0.771. The Brier score was 0.147, indicating slight overestimations of mortality risk. Explanatory diagrams utilizing SHAP values allowed visualization of feature impacts on predictions at both the global and individual levels. Conclusion: Our machine learning model demonstrated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer. It has the potential to provide personalized mortality predictions and facilitate earlier integration of palliative care. [ABSTRACT FROM AUTHOR]

Background. Children and adolescents with type 1 diabetes mellitus (T1DM) are frequently hospitalised for severe hypoglycaemia, hyperglycaemia, and diabetic ketoacidosis (DKA). While several risk factors have been recognised, clinically identifying these children at high risk of acute decompensation remains challenging. Objective. To develop a risk prediction model to accurately estimate the risk of acute healthcare utilisation due to severe hypoglycaemia, hyperglycaemia, and DKA in children and adolescents with T1DM. Materials and Methods. Using a retrospective dataset, baseline demographic and clinical data were collected from patients (<18 years) seen at a regional paediatric diabetes clinic from 1 January 2018 to 1 January 2020. The outcome was the number of emergency department presentations or hospital admissions for severe hypoglycaemia, hyperglycaemia, and DKA across the study period. Variables that were significant in univariate analysis were entered into a multivariable model. Receiver operator characteristic (ROC) curves assessed the model's discrimination and generated cut-offs for risk group stratification (low, medium, and high). Kaplan–Meier survival analysis measured time to acute healthcare utilisation across the risk groups. Results. Our multivariable risk prediction model consisted of five predictors (continuous glucose monitoring device, previous acute healthcare utilisation, missed appointments, and child welfare services involvement and socioeconomic status). The model exhibited good discrimination (area under the ROC = 0.81), accurately stratified children into low-, medium-, and high-risk groups, and demonstrated significant differences between median time to healthcare utilisation. Conclusion. Our model identified patients at an increased risk of acute healthcare utilisation due to severe hypoglycaemia, hyperglycaemia, and DKA. [ABSTRACT FROM AUTHOR]

Background: Jugulo-omohyoid lymph nodes (JOHLN) metastasis has proven to be associated with lateral lymph node metastasis (LLNM). This study aimed to reveal the clinical features and evaluate the predictive value of JOHLN in PTC to guide the extent of surgery. Methods: A total of 550 patients pathologically diagnosed with PTC between October 2015 and January 2020, all of whom underwent thyroidectomy and lateral lymph node dissection, were included in this study. Results: Thyroiditis, tumor location, tumor size, extra-thyroidal extension, extra-nodal extension, central lymph node metastasis (CLNM), and LLMM were associated with JOHLN. Male, upper lobe tumor, multifocality, extra-nodal extension, CLNM, and JOHLN metastasis were independent risk factors from LLNM. A nomogram based on predictors performed well. Nerve invasion contributed the most to the prediction model, followed by JOHLN metastasis. The area under the curve (AUC) was 0.855, and the p-value of the Hosmer-Lemeshow goodness of fit test was 0.18. Decision curve analysis showed that the nomogram was clinically helpful. Conclusion: JOLHN metastasis could be a clinically sensitive predictor of further LLM. A high-performance nomogram was established, which can provide an individual risk assessment of LNM and guide treatment decisions for patients. [ABSTRACT FROM AUTHOR]

Background: Breastfeeding resets insulin resistance caused by pregnancy however, studies on the association between breastfeeding and diabetes mellitus (DM) have reported inconsistent results. Therefore, we aimed to investigate the risk of DM according to breastfeeding duration in large-scale population-based retrospective study. In addition, machine-learning prediction models for DM and hemoglobin A1c (HbA1c) were developed to further evaluate this association. Methods: We used the Korean National Health and Nutrition Examination Surveys database, a nationwide and population-based health survey from 2010 to 2020. We included 15,946 postmenopausal women with a history of delivery, whom we divided into three groups according to the average breastfeeding duration: (1) no breastfeeding, (2) < 12 months breastfeeding, and (3) ≥ 12 months breastfeeding. Prediction models for DM and HbA1c were developed using an artificial neural network, decision tree, logistic regression, Naïve Bayes, random forest, and support vector machine. Results: In total, 2248 (14.1%) women had DM and 14,402 (90.3%) had a history of breastfeeding. The prevalence of DM was the lowest in the < 12 breastfeeding group (no breastfeeding vs. < 12 months breastfeeding vs. ≥ 12 months breastfeeding; 161 [10.4%] vs. 362 [9.0%] vs. 1,725 [16.7%], p < 0.001). HbA1c levels were also the lowest in the < 12 breastfeeding group (HbA1c: no breastfeeding vs. < 12 months breastfeeding vs. ≥ 12 months breastfeeding; 5.9% vs. 5.9% vs. 6.1%, respectively, p < 0.001). After adjustment for covariates, the risk of DM was significantly increased in both, the no breastfeeding (adjusted odds ratio [aOR] 1.29; 95% CI 1.29, 1.62]) and ≥ 12 months of breastfeeding groups (aOR 1.18; 95% CI 1.01, 1.37) compared to that in the < 12 months breastfeeding group. The accuracy and the area under the receiver-operating-characteristic curve of the DM prediction model were 0.93 and 0.95, respectively. The average breastfeeding duration was ranked among the top 15 determinants of DM, which supported the strong association between breastfeeding duration and DM. This association was also observed in a prediction model for HbA1c. Conclusions: Women who did not breasted had a higher risk of developing DM than those who breastfed for up to 12 months. [ABSTRACT FROM AUTHOR]

Background: Tertiary lymphoid structures (TLSs) affect the prognosis and efficacy of immunotherapy in patients with non‐small cell lung cancer (NSCLC), but the underlying mechanisms are not well understood. Methods: TLSs were identified and categorized online from the Cancer Digital Slide Archive (CDSA). Overall survival (OS) and disease‐free survival (DFS) were analyzed. GSE111414 and GSE136961 datasets were downloaded from the GEO database. GSVA, GO and KEGG were used to explore the signaling pathways. Immune cell infiltration was analyzed by xCell, ssGSEA and MCP‐counter. The analysis of WGCNA, Lasso and multivariate cox regression were conducted to develop a gene risk score model based on the SU2C‐MARK cohort. Results: TLS‐positive was a protective factor for OS according to multivariate cox regression analysis (p = 0.029). Both the TLS‐positive and TLS‐mature groups exhibited genes enrichment in immune activation pathways. The TLS‐mature group showed more activated dendritic cell infiltration than the TLS‐immature group. We screened TLS‐related genes using WGCNA. Lasso and multivariate cox regression analysis were used to construct a five‐genes (RGS8, RUF4, HLA‐DQB2, THEMIS, and TRBV12‐5) risk score model, the progression free survival (PFS) and OS of patients in the low‐risk group were markedly superior to those in the high‐risk group (p < 0.0001; p = 0.0015, respectively). Calibration and ROC curves indicated that the combined model with gene risk score and clinical features could predict the PFS of patients who have received immunotherapy more accurately than a single clinical factor. Conclusions: Our data suggested a pivotal role of TLSs formation in survival outcome and immunotherapy response of NSCLC patients. Tumors with mature TLS formation showed more activated immune microenvironment. In addition, the model constructed by TLS‐related genes could predict the response to immunotherapy and is meaningful for clinical decision‐making. [ABSTRACT FROM AUTHOR]

Simple Summary: Currently, a versatile and useful predictive model for post-hepatectomy liver failure (PHLF) remains elusive. Therefore, this study aimed to develop predictive models for PHLF based on type IV collagen 7s domain (7s collagen) in patients with hepatocellular carcinoma. We retrospectively collected data from 972 patients. PHLF grades B or C were identified in 104 patients (11%): 98 (10%) and 6 (1%) PHLF grades B and C, respectively. The preoperative serum level of 7s collagen was significantly associated with a proportional increase in PHLF risk. A nomogram was developed based on 7s collagen, with a concordance index of 0.768. The inclusion of 7s collagen values in the predictive model increased the prediction accuracy. Our novel nomogram using 7s collagen may be useful for predicting the risk of PHLF. Background: Previous studies have attempted to establish predictive models for post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) undergoing liver resection. However, a versatile and useful predictive model for PHLF remains to be developed. Therefore, we aimed to develop predictive models for PHLF based on type IV collagen 7s domain (7s collagen) in patients with HCC. Methods: We retrospectively collected data from 972 patients with HCC who had undergone initial curative liver resection between February 2000 and December 2020 at our hospital. Multivariate logistic regression analysis using a restricted cubic spline was performed to evaluate the effect of 7s collagen on the incidence of PHLF. A nomogram was developed based on 7s collagen. Results: PHLF grades B or C were identified in 104 patients (11%): 98 (10%) and 6 (1%) PHLF grades B and C, respectively. Multivariate logistic regression analysis revealed that the preoperative serum level of 7s collagen was significantly associated with a proportional increase in the risk of PHLF, which was confirmed in both laparoscopic and open liver resections. A nomogram was developed based on 7s collagen, with a concordance index of 0.768. The inclusion of 7s collagen values in the predictive model increased the predictive accuracy. Conclusion: The findings highlight the efficacy of the serum level of 7s collagen as a predictive factor for PHLF. Our novel nomogram using 7s collagen may be useful for predicting the risk of PHLF. [ABSTRACT FROM AUTHOR]

Simple Summary: We developed an attention-based whole slide image (WSI)-level classification deep learning model employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The area under the curve of our model was 0.781–0.824, higher than that of previous artificial intelligence (AI) studies with only WSIs. Our AI model, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current JSCCR guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC. According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1–25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758–0.830 in the training set and 0.781–0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC. [ABSTRACT FROM AUTHOR]

Simple Summary: External dose rate measurements for I-125 permanent radioactive source implantation brachytherapy for prostate cancer requires standardized external dose rate measurements and radiation safety procedures. We developed predictive models for external dose rates to ensure compliance with radiation safety guidelines. We used Monte Carlo simulation and experimental measurements to construct an external dose rate model. The model's accuracy was validated against external dose rate measurements from clinical patients. This model facilitates the calculation and minimization of family members' radiation dose, aiding in the development of personalized radiation protection strategies. This study uses Monte Carlo simulation and experimental measurements to develop a predictive model for estimating the external dose rate associated with permanent radioactive source implantation in prostate cancer patients. The objective is to estimate the accuracy of the patient's external dose rate measurement. First, I-125 radioactive sources were implanted into Mylar window water phantoms to simulate the permanent implantation of these sources in patients. Water phantom experimental measurement was combined with Monte Carlo simulation to develop predictive equations, whose performance was verified against external clinical data. The model's accuracy in predicting the external dose rate in patients with permanently implanted I-125 radioactive sources was high (R2 = 0.999). A comparative analysis of the experimental measurements and the Monte Carlo simulations revealed that the maximum discrepancy between the measured and calculated values for the water phantom was less than 5.00%. The model is practical for radiation safety assessments, enabling the evaluation of radiation exposure risks to individuals around patients with permanently implanted I-125 radioactive sources. [ABSTRACT FROM AUTHOR]

Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies. [ABSTRACT FROM AUTHOR]

Background: The Center for Medicare and Medicaid Services (CMS) imposes payment penalties for readmissions following total joint replacement surgeries. This study focuses on total hip, knee, and shoulder arthroplasty procedures as they account for most joint replacement surgeries. Apart from being a burden to healthcare systems, readmissions are also troublesome for patients. There are several studies which only utilized structured data from Electronic Health Records (EHR) without considering any gender and payor bias adjustments. Methods: For this study, dataset of 38,581 total knee, hip, and shoulder replacement surgeries performed from 2015 to 2021 at Novant Health was gathered. This data was used to train a random forest machine learning model to predict the combined endpoint of emergency department (ED) visit or unplanned readmissions within 30 days of discharge or discharge to Skilled Nursing Facility (SNF) following the surgery. 98 features of laboratory results, diagnoses, vitals, medications, and utilization history were extracted. A natural language processing (NLP) model finetuned from Clinical BERT was used to generate an NLP risk score feature for each patient based on their clinical notes. To address societal biases, a feature bias analysis was performed in conjunction with propensity score matching. A threshold optimization algorithm from the Fairlearn toolkit was used to mitigate gender and payor biases to promote fairness in predictions. Results: The model achieved an Area Under the Receiver Operating characteristic Curve (AUROC) of 0.738 (95% confidence interval, 0.724 to 0.754) and an Area Under the Precision-Recall Curve (AUPRC) of 0.406 (95% confidence interval, 0.384 to 0.433). Considering an outcome prevalence of 16%, these metrics indicate the model's ability to accurately discriminate between readmission and non-readmission cases within the context of total arthroplasty surgeries while adjusting patient scores in the model to mitigate bias based on patient gender and payor. Conclusion: This work culminated in a model that identifies the most predictive and protective features associated with the combined endpoint. This model serves as a tool to empower healthcare providers to proactively intervene based on these influential factors without introducing bias towards protected patient classes, effectively mitigating the risk of negative outcomes and ultimately improving quality of care regardless of socioeconomic factors. [ABSTRACT FROM AUTHOR]

Background. Polycystic ovary syndrome (PCOS) is an endocrine disease attributed to multiple genetic variants and environmental factors. We aimed to find the causal association of homocysteine (Hcy) with PCOS. Methods. A two-sample Mendelian randomization (MR) analysis was performed. We selected 14 single-nucleotide polymorphisms (SNPs) as instrumental variables to predict the risk of PCOS from genome-wide association studies (GWAS). The summary statistics of PCOS were obtained from 3 large genome-wide association studies in the European population, involving 4,138 cases and 20,129 controls, 3,609 cases and 229,788 controls, 994 cases and 165,817 controls, separately. Results. The IVM analyses revealed that plasma Hcy levels were not causally associated with the risk of PCOS in the meta-analysis (combined effect = 1.032, 95% confidence interval (CI): 0.885–1.203, p = 0.688). Conclusions. There was no sufficient evidence to support the causal association of the Hcy with the risk of PCOS. [ABSTRACT FROM AUTHOR]

Background: Acute upper gastrointestinal bleeding (AUGIB) is a major cause of morbidity and mortality. This presentation however is not universally high risk as only 20–30% of bleeds require urgent haemostatic therapy. Nevertheless, the current standard of care is for all patients admitted to an inpatient bed to undergo endoscopy within 24 h for risk stratification which is invasive, costly and difficult to achieve in routine clinical practice. Objectives: To develop novel non-endoscopic machine learning models for AUGIB to predict the need for haemostatic therapy by endoscopic, radiological or surgical intervention. Design: A retrospective cohort study Method: We analysed data from patients admitted with AUGIB to hospitals from 2015 to 2020 (n = 970). Machine learning models were internally validated to predict the need for haemostatic therapy. The performance of the models was compared to the Glasgow-Blatchford score (GBS) using the area under receiver operating characteristic (AUROC) curves. Results: The random forest classifier [AUROC 0.84 (0.80–0.87)] had the best performance and was superior to the GBS [AUROC 0.75 (0.72–0.78), p < 0.001] in predicting the need for haemostatic therapy in patients with AUGIB. A GBS cut-off of ⩾12 was associated with an accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.74, 0.49, 0.81, 0.41 and 0.85, respectively. The Random Forrest model performed better with an accuracy, sensitivity, specificity, PPV and NPV of 0.82, 0.54, 0.90, 0.60 and 0.88, respectively. Conclusion: We developed and validated a machine learning algorithm with high accuracy and specificity in predicting the need for haemostatic therapy in AUGIB. This could be used to risk stratify high-risk patients to urgent endoscopy. [ABSTRACT FROM AUTHOR]

Introduction Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14–56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop a machine learning (ML) model for POD prediction in older patients, in close cooperation with the PAWEL (patient safety, cost-effectiveness and quality of life in elective surgery) project. Methods The model was trained on the PAWEL study's dataset of 878 patients (no intervention, age ≥ 70, 209 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical considerations and a recursive feature elimination. A logistic regression and a linear support vector machine (SVM) were trained, and evaluated using receiver operator characteristics (ROC). Results The selected features were American Society of Anesthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular filtration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cognitive assessment subscores 'memory', 'orientation' and 'verbal fluency', pre-existing dementia, clinical frailty scale, age, recent falls, post-operative isolation and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78–0.85] in the training set, 0.81 [95% CI 0.71–0.88] in the test set and 0.76 [95% CI 0.71–0.79] in a cross-centre validation. Conclusion We present a clinically useful and explainable ML model for POD prediction. The model will be deployed in the Supporting SURgery with GEriatric Co-Management and AI project. [ABSTRACT FROM AUTHOR]

Objective Moving into a long-term care facility (LTCF) requires substantial personal, societal and financial investment. Identifying those at high risk of short-term mortality after LTCF entry can help with care planning and risk factor management. This study aimed to: (i) examine individual-, facility-, medication-, system- and healthcare-related predictors for 90-day mortality at entry into an LTCF and (ii) create risk profiles for this outcome. Design Retrospective cohort study using data from the Registry of Senior Australians. Subjects Individuals aged ≥ 65 years old with first-time permanent entry into an LTCF in three Australian states between 01 January 2013 and 31 December 2016. Methods A prediction model for 90-day mortality was developed using Cox regression with the purposeful variable selection approach. Individual-, medication-, system- and healthcare-related factors known at entry into an LTCF were examined as predictors. Harrell's C-index assessed the predictive ability of our risk models. Results 116,192 individuals who entered 1,967 facilities, of which 9.4% (N  = 10,910) died within 90 days, were studied. We identified 51 predictors of mortality, five of which were effect modifiers. The strongest predictors included activities of daily living category (hazard ratio [HR] = 5.41, 95% confidence interval [CI] = 4.99–5.88 for high vs low), high level of complex health conditions (HR = 1.67, 95% CI = 1.58–1.77 for high vs low), several medication classes and male sex (HR = 1.59, 95% CI = 1.53–1.65). The model out-of-sample Harrell's C-index was 0.773. Conclusions Our mortality prediction model, which includes several strongly associated factors, can moderately well identify individuals at high risk of mortality upon LTCF entry. [ABSTRACT FROM AUTHOR]

Introduction Predicting risk of care home admission could identify older adults for early intervention to support independent living but require external validation in a different dataset before clinical use. We systematically reviewed external validations of care home admission risk prediction models in older adults. Methods We searched Medline, Embase and Cochrane Library until 14 August 2023 for external validations of prediction models for care home admission risk in adults aged ≥65 years with up to 3 years of follow-up. We extracted and narratively synthesised data on study design, model characteristics, and model discrimination and calibration (accuracy of predictions). We assessed risk of bias and applicability using Prediction model Risk Of Bias Assessment Tool. Results Five studies reporting validations of nine unique models were included. Model applicability was fair but risk of bias was mostly high due to not reporting model calibration. Morbidities were used as predictors in four models, most commonly neurological or psychiatric diseases. Physical function was also included in four models. For 1-year prediction, three of the six models had acceptable discrimination (area under the receiver operating characteristic curve (AUC)/ c s tatistic 0.70–0.79) and the remaining three had poor discrimination (AUC < 0.70). No model accounted for competing mortality risk. The only study examining model calibration (but ignoring competing mortality) concluded that it was excellent. Conclusions The reporting of models was incomplete. Model discrimination was at best acceptable, and calibration was rarely examined (and ignored competing mortality risk when examined). There is a need to derive better models that account for competing mortality risk and report calibration as well as discrimination. [ABSTRACT FROM AUTHOR]

Introduction: Cardiogenic shock (CS) is a critical condition and the leading cause of mortality after coronary artery bypass grafting (CABG). To define the risk factors for CS in patients who undergo CABG and create a risk-predictive model is crucial. Methods: In this observational study, we retrospectively evaluated consecutive patients who underwent CABG between January 2018 and October 2022 at Beijing Anzhen Hospital. A total of 496 patients were enrolled and categorized into the training (396 cases) and internal test (100 cases) sets. The variables significantly associated with mortality (p < 0.05) were analyzed using logistic regression analyses. Results: The E/A ratio at admission, postoperative brain natriuretic peptide, postoperative arterial lactate, two or more arrhythmias at the same time after CABG, and carotid artery stenosis at admission were identified as independent prognostic factors for in-hospital mortality after multivariate logistic regression analysis. The CS after CABG score (ACCS) was established and three classes of ACCS, named classes I (ACCS, <20), II (ACCS, 20–30), and III (ACCS, >30), made up the risk model. The ACCS showed better discrimination with an AUROC of 0.937 (95% confidence interval, 0.982–0.892) and calibration with the Hosmer–Lemeshow test (X2 = 5.854 with 8 df; p = 0.664). In addition, tenfold cross-validation demonstrated that the mean misdiagnosis rate was 5.56% and the lowest misdiagnosis rate was 6.38%. Conclusion: The ACCS score represents a risk-predictive model for in-hospital mortality of patients with CS after CABG in acute care settings. Patients identified as class III may have a worse prognosis. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to externally validate a reported model for identifying patients requiring extended stay following lower limb arthroplasty in a new setting. Design: External validation of a previously reported prognostic model, using retrospective data. Setting: Medium-sized hospital orthopaedic department, Australia. Participants: Electronic medical records were accessed for data collection between Sep-2019 and Feb-2020 and retrospective data extracted from 200 randomly selected total hip or knee arthroplasty patients. Intervention: Participants received total hip or knee replacement between 2-Feb-16 and 4-Apr-19. This study was a non-interventional retrospective study. Main measures: Model validation was assessed with discrimination, calibration on both original and adjusted forms of the candidate model. Decision curve analysis was conducted on the outputs of the adjusted model to determine net benefit at a predetermined decision threshold (0.5). Results: The original model performed poorly, grossly overestimating length of stay with mean calibration of −3.6 (95% confidence interval −3.9 to −3.2) and calibration slope of 0.52. Performance improved following adjustment of the model intercept and model coefficients (mean calibration 0.48, 95% confidence interval 0.16 to 0.80 and slope of 1.0), but remained poorly calibrated at low and medium risk threshold and net benefit was modest (three additional patients per hundred identified as at-risk) at the a-priori risk threshold. Conclusions: External validation demonstrated poor performance when applied to a new patient population and would provide limited benefit for our institution. Implementation of predictive models for arthroplasty should include practical assessment of discrimination, calibration and net benefit at a clinically acceptable threshold. [ABSTRACT FROM AUTHOR]

Background: Most fractures occur in women aged ≥80 years but competing mortality unrelated to fracture may limit the benefit of osteoporosis drug therapy for some women in late life. Our primary aim was to develop separate prediction models for non‐spine fracture (NSF) and mortality before fracture to identify subsets of women with varying fracture versus mortality risks. Methods: Separate prediction models were developed for NSF and mortality before NSF for 4895 women aged ≥80 years enrolled in the Study of Osteoporotic Fractures (SOF) or the Health Aging and Body Composition (HABC) study. Proportional hazards models modified to account for competing mortality were used to identify candidate risk factors for each outcome. Predictors associated with NSF or mortality (p < 0.2) were included in separate competing risk models to estimate the cumulative incidence of NSF and mortality before NSF during 5 years of follow‐up. This process was repeated to develop separate prediction models for hip fracture and mortality before hip fracture. Results: Significant predictors of NSF (race, total hip BMD, grip strength, prior fracture, falls, and use of selective serotonin reuptake inhibitors, benzodiazepines, or oral/transdermal estrogen) differed from predictors of mortality before NSF (age, walking speed, multimorbidity, weight change, shrinking, smoking, self‐rated health, dementia, and use of warfarin). Within nine subsets of women defined by tertiles of risk, 5‐year outcomes varied from 28% NSF and 8% mortality in the high‐risk NSF/low‐risk mortality subset, to 9% NSF and 22% mortality in the low‐risk NSF/high‐risk mortality subset. Similar results were seen for predictors of hip fracture and mortality before hip fracture. Conclusion: Considerable variation in 5‐year competing mortality risk is present among women in late life with similar 5‐year NSF risk. Both fracture risk and life expectancy should inform shared clinical decision‐making regarding initiation or continuation of osteoporosis drug therapy for women aged ≥80 years. [ABSTRACT FROM AUTHOR]

Background: Postoperative pancreatic fistula (POPF) is the most prevalent complications following minimally invasive pancreaticoduodenectomy (MIPD). Only one model related to MIPD exists, and previous POPF scoring prediction methods are based on open pancreaticoduodenectomy patients. Our objectives are to determine the variables that may increase the probability of pancreatic fistula following MIPD and to develop and validate a POPF predictive risk model. Methods: Data from 432 patients who underwent MIPD between July 2015 and May 2022 were retrospectively collected. A nomogram prediction model was created using multivariate logistic regression analysis to evaluate independent factors for POPF in patients undergoing MIPD in the modeling cohort. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) and the calibration curve were used to verify the nomogram prediction model internally and externally within the modeling cohort and the verification cohort. Results: Multivariate logistic regression analysis showed that body mass index (BMI), albumin, triglycerides, pancreatic duct diameter, pathological diagnosis and intraoperative bleeding were independent variables for POPF. On the basis of this information, a model for the prediction of risks associated with POPF was developed. In accordance with the ROC analysis, the modeling cohort's AUC was 0.819 (95% CI 0.747–0.891), the internal validation cohort's AUC was 0.830 (95% CI 0.747–0.912), and the external validation cohort's AUC was 0.793 (95% CI 0.671–0.915). Based on the calibration curve, the estimated values of POPF have a high degree of concordance with the actual values that were measured. Conclusions: This model for predicting the probability of pancreatic fistula following MIPD has strong predictive capacity and can provide a trustworthy predictive method for the early screening of high-risk patients with pancreatic fistula after MIPD and timely clinical intervention. [ABSTRACT FROM AUTHOR]

Background: The first step in preventing pressure injuries (PIs), which represent a significant burden on intensive care unit (ICU) patients and the health care system, is to assess the risk for developing PIs. A valid risk assessment scale is essential to evaluate the risk and avoid PIs. Objectives: To compare the predictive validity of the Braden scale and Waterlow scale in ICUs. Design: A multicentre, prospective and cross‐sectional study. Methods: We conducted this study among 6416 patients admitted to ICUs in Gansu province of China from April 2021 to October 2022. The incidence and characteristics of PIs were collected. The risk assessment of PIs was determined using the Braden and Waterlow scale. The sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve of the two scales were compared. Results: Out of 5903 patients, 72 (1.2%) developed PIs. The sensitivity, specificity, positive and negative predictive, and the area under the curve of the Braden scale were 77.8%, 50.9%, 0.014 and 0.996, and 0.689, respectively. These values for the Waterlow scale were 54.2%, 71.1%, 0.017, 0.994 and 0.651. Conclusions: Both scales could be used for risk assessment of PIs in ICU patients. However, the accuracy of visual inspection for assessment of skin colour, nursing preventive measures for patients and scales inter‐rater inconsistency may limited the predictive validity statistics. Relevance to Clinical Practice: Both scales could be used for PIs risk assessment. The low specificity of the Braden scale and low sensitivity of the Waterlow scale remind medical staff to use them in combination with clinical judgement and other objective indicators. Patient or Public Contribution: This study was designed to enhance the management of PIs. Patients and the general public were not involved in the study design, analysis, and interpretation of the data or manuscript preparation. [ABSTRACT FROM AUTHOR]

Background: Individuals with Down syndrome (DS) have a heightened risk for various co‐occurring health conditions, including congenital heart disease (CHD). In this two‐part study, electronic medical records (EMRs) were leveraged to examine co‐occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2). Methods: De‐identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one‐by‐two sex and age matched with samples of case–controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome‐disease association study (PheDAS) strategy was employed to reveal co‐occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case–controls. Data were included in predictive models and assessed which model‐based health conditions, when more prevalent, would increase the likelihood of surgical intervention. Results: In Study 1, relative to case–controls and those with other IDDs, co‐occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co‐occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non‐rheumatic aortic valve disorders and heart failure (circulatory); acid–base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention. Conclusions: Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision‐care development. [ABSTRACT FROM AUTHOR]

Background: Dementia is a significant cause of death in the older population and is becoming an important public health issue as the population ages and the prevalence of dementia increases. The Braden score is one of the most commonly used clinical tools to assess the risk of skin pressure injury in patients, and some studies have reported that it may reflect the state of frailty of patients. The present study attempted to explore the association between Braden score and 90‐day mortality, pressure injury, and aspiration pneumonia in older patients with dementia in the intensive care unit (ICU). Methods: The study involved extracting crucial data from the Medical Information Market for Intensive Care IV (MIMIC‐IV) database using Structured Query Language, with a license certificate obtained after completing the necessary training and examination available on the MIMIC‐IV website. A retrospective analysis was performed on older patients with dementia, aged 65 or older, who were first admitted to the ICU. Ninth and tenth revision International Classification of Diseases codes were used to identify patients with dementia. The primary outcome was 90‐day mortality. Cox proportional hazards models were used to determine the association between Braden score and death, and hazard ratios (HR) and 95% confidence intervals (CI) were calculated. Propensity score matching and E‐value assessments were employed for sensitivity analysis. Results: A total of 2892 patients with a median age of approximately 85 years (interquartile range 78.74–89.59) were included, of whom 1625 were female (56.2%). Patients had a median Braden score of 14 (interquartile range 12–15) at ICU admission. Braden score at ICU admission was inversely associated with 90‐day mortality risk after adjustment for demographics, severity of illness, treatment and medications, delirium, and sepsis (adjusted HR: 0.92, 95% CI: 0.87–0.98, p = 0.006). Patients were divided into two groups with a cut‐off value of 15: high‐risk group and low‐risk group. Compared to the low‐risk group (Braden score >15), the risk of 90‐day mortality was significantly increased in the high‐risk group (Braden score ≤15) (adjusted HR: 1.52, 95% CI: 1.10–2.09, p = 0.011, E‐value: 2.01), the risk of pressure injury (adjusted OR: 2.62, 95% CI: 2.02–3.43, E‐value: 2.62) and aspiration pneumonia (adjusted OR: 2.55, 95% CI: 1.84–3.61, E‐value: 2.57) was also significantly higher. Conclusions: The Braden score may be a quick and simple screening tool to identify the risk of adverse outcomes in critically ill older adults with dementia. Key points: The Braden score, which is commonly used to assess the risk of pressure injury, may reflect frailty in patients.The Braden score at ICU admission was inversely associated with 90‐day mortality risk, suggesting its potential as a mortality predictor.The findings suggest that the Braden score could be a valuable tool for quickly identifying critically ill older adults with dementia who are at risk of adverse outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Interest in stratification of prognosis for patients with colorectal liver metastases is growing. Numerous clinical prediction models have been developed for this purpose in recent years, either with the aid of traditional statistical methods or by using the aid of artificial intelligence techniques. We herein provide an overview of relevant studies discussing the different types of predictors proven to be of importance and critically assess the variable model development and validation techniques as well as the performance of the reported models. Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of "high-risk" patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to analyse clinical and radiological features (phenotypes) to predict revision risk after functional endoscopic sinus surgery in patients with chronic rhinosinusitis with nasal polyps. Method: Phenotype characteristics of the patients with chronic rhinosinusitis with nasal polyps who underwent functional endoscopic sinus surgery were analysed retrospectively. Results: The rates of asthma, aspirin sensitivity, smoking and a positive prick test result were significantly higher in revision functional endoscopic sinus surgery cases (p < 0.001, 0.001, < 0.001 and < 0.001, respectively). Multivariate analysis demonstrated that only gender, pre-operative nasal steroid use, pre-operative systemic steroid use, intra-operative systemic steroid use and smoking were significant risk factors for revision functional endoscopic sinus surgery (p = 0.034, 0.001, 0.010, 0.007 and 0.001, respectively). In addition, only eosinophilia and aspirin sensitivity were significant risk factors for multiple revision functional endoscopic sinus surgery procedures (p = 0.043 and 0.005, odds ratio = 2.4 and 5.2). Conclusion: Beyond the endotype of chronic rhinosinusitis with nasal polyps, defining clinical and radiological factors enables a valid prediction of patients at high risk of revision functional endoscopic sinus surgery. [ABSTRACT FROM AUTHOR]

Background: Older adults with hypertension have a high risk of disability, while an accurate risk prediction model is still lacking. This study aimed to construct interpretable disability prediction models for older Chinese with hypertension based on multiple time intervals. Methods: Data were collected from the Chinese Longitudinal Healthy Longevity and Happy Family Study for 2008–2018. A total of 1602, 1108, and 537 older adults were included for the periods of 2008–2012, 2008–2014, and 2008–2018, respectively. Disability was measured by basic activities of daily living. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning algorithms combined with LASSO set and full‐variable set were used to predict 4‐, 6‐, and 10‐year disability risk, respectively. Area under the receiver operating characteristic curve was used as the main metric for selection of the optimal model. SHapley Additive exPlanations (SHAP) was used to explore important predictors of the optimal model. Results: Random forest in full‐variable set and XGBoost in LASSO set were the optimal models for 4‐year prediction. Support vector machine was the optimal model for 6‐year prediction on both sets. For 10‐year prediction, deep neural network in full variable set and logistic regression in LASSO set were optimal models. Age ranked the most important predictor. Marital status, body mass index, score of Mini‐Mental State Examination, and psychological well‐being score were also important predictors. Conclusions: Machine learning shows promise in screening out older adults at high risk of disability. Disability prevention strategies should specifically focus on older patients with unfortunate marriage, high BMI, and poor cognitive and psychological conditions. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs). Methods: Sixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression]. The performance of this model was assessed using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was further applied to interpret the findings of the best-performing model. Results: The LightGBM model exhibited the highest AUC (0.849; 95% CI, 0.814–0.883). Additionally, the SHAP plot per the LightGBM depicted that age, heart failure, hypertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and amylase can help identify PLHIV who were at a high or low risk of developing CHD. Conclusion: This study developed a CHD risk prediction model for PLHIV utilizing ML techniques and EMR data. The LightGBM model exhibited improved comprehensive performance and thus had higher reliability in assessing the risk predictors of CHD. Hence, it can potentially facilitate the development of clinical management techniques for PLHIV care in the era of EMRs. [ABSTRACT FROM AUTHOR]

Background: The main cause of anesthesia-related deaths is the failure to manage difficult airways. Difficult laryngoscopic exposure is a major cause of unsuccessful management of difficult airways. Inadequate preoperative airway assessment hinders the clinical management of difficult airways cases, emphasizing the critical need for accurate identification of difficult airways. Currently, no definitive and reliable indicators are available to predict a difficult airway. Our study aims to predict laryngoscope exposure risk factors by combining ultrasonically measured upper airway anatomic parameters with physical examination indicators. Methods: Patients aged 18 to 75 years, classified as American Standards Association (ASA) I-III, and scheduled for elective general anesthesia with endotracheal intubation were included. All patients received the upper airway and ultrasonographic measurements. After anesthesia induction, laryngoscope exposure was analyzed using the Cormack-Lehane grading system, with Grades III and IV as indicative of difficult laryngoscopy. Univariate and multivariate logistic regression analyses were performed to identify reliable indicators for predicting difficult laryngoscopy. Receiver Operating Characteristic (ROC) curve analysis was utilized to assess the predictive performance of each indicator. Results: A total of 1120 patients finished the study, with 710 cases found in Grade I laryngoscopic exposure group, 360 cases in Grade II group, and 50 cases in Grade III group. There was no case observed in Grade IV group, thereby resulting in an incidence of difficult laryngoscopy of 4.46%. Univariate logistic regression analysis revealed that several parameters including age, Body Mass Index (BMI), neck circumference, neck mobility, snoring intensity, as well as ultrasound measurements of the pre-epiglottic space and thyromental distance were identified as significant risk factors for difficult laryngoscopy (P < 0.05). Among these, BMI, and neck circumference exhibited notable predictive value, with Area Under The Curve (AUC) values of 0.746 (95%CI 0.649–0.842) and 0.732 (95%CI 0.638–0.827), respectively. Neck mobility was also identified as an independent risk factor for predicting difficult laryngoscopy (P = 0.009) in multivariate logistic regression analysis, with an AUC of 0.672 (0.562–0.782) in the ROC curve. Conclusions: Our findings revealed a direct correlation between difficult laryngoscopy and age, BMI, neck circumference, neck mobility, snoring intensity, as well as ultrasound measurements of the pre-epiglottic space and thyromental distance. Furthermore, neck mobility was identified as an independent predictive factor. Trial registration: The trial was registered prior to patient enrollment at clinicaltrials.gov (register no. ChiCTR2100053826, Date of registration: November 30, 2021). [ABSTRACT FROM AUTHOR]

Background: Patients with acute myeloid leukemia (AML) are at increased risk of venous thromboembolic events (VTE). However, thromboprophylaxis is largely underused. Objectives: This study aimed to determine possible VTE development risk factors and to develop a novel predictive model. Methods: We conducted a retrospective cohort study of adult patients with newly diagnosed AML. We used univariate and multivariable logistic regression to estimate binary outcomes and identify potential predictors. Based on our final model, a dynamic nomogram was constructed with the goal of facilitating VTE probability calculation. Results: Out of 626 eligible patients with AML, 72 (11.5%) developed VTE during 6 months of follow-up. Six parameters were independent predictors: male sex (odds ratio [OR] 1.82, 95% confidence interval [CI]: 1.077–2.065), prior history of thrombotic events (OR 2.27, 95% CI: 1.4–4.96), international normalized ratio (OR 0.21, 95% CI: 0.05–0.95), Eastern Cooperative Oncology Group performance status (OR 0.71, 95% CI: 0.53–0.94), and intensive therapy (OR 2.05, 95% CI: 1.07–3.91). The C statistics for the model was 0.68. The model was adequately calibrated and internally validated. The decision-curve analysis suggested the use of thromboprophylaxis in patients with VTE risks between 8 and 20%. Conclusion: We developed a novel and convenient tool that may assist clinicians in identifying patients whose VTE risk is high enough to warrant thromboprophylaxis. Essentials: Acute myeloid leukemia patients are at increased risk of venous thromboembolism (VTE). Predictive model for VTE development in acute myeloid leukemia patients was created. Six parameters were included in the model: male sex, prior history of thrombotic events, international normalized ratio (iNR), Eastern Cooperative Oncology Group performance status and intensive therapy approach. This model could identify patients whose VTE risk is high enough to warrant thromboprophylaxis. [ABSTRACT FROM AUTHOR]

Background: Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling. Methods: A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created. Results: Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence (P <.05). The combined model based on these above factors had significantly higher predictive performance (AUC = 0.895, 95% confidence interval [CI] = [0.839-0.937]) than the clinical data model (AUC = 0.735, 95% CI = [0.6 62-0.800]) and radiomics model (AUC = 0.842, 95% CI = [0.779-0.894]). Decision curve analysis also confirmed the higher clinical net benefit of the combined model, and the same results were validated in the test set. The novel nomogram developed based on the combined model helps predict the recurrence of MH-GIST. Conclusions: The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making. [ABSTRACT FROM AUTHOR]

Objective To assess whether neonatal morbidities evident by the time of hospital discharge are associated with subsequent cerebral palsy (CP) or death. Study Design This is a secondary analysis of data from a multicenter placebo-controlled trial of magnesium sulfate for the prevention of CP. The association between prespecified intermediate neonatal outcomes (n = 11) and demographic and clinical factors (n = 10) evident by the time of discharge among surviving infants (n = 1889) and the primary outcome of death or moderate/severe CP at age 2 (n = 73) was estimated, and a prediction model was created. Results Gestational age in weeks at delivery (odds ratio [OR]: 0.74, 95% confidence interval [CI]: 0.67–0.83), grade III or IV intraventricular hemorrhage (IVH) (OR: 5.3, CI: 2.1–13.1), periventricular leukomalacia (PVL) (OR: 46.4, CI: 20.6–104.6), and male gender (OR: 2.5, CI: 1.4–4.5) were associated with death or moderate/severe CP by age 2. Outcomes not significantly associated with the primary outcome included respiratory distress syndrome, bronchopulmonary dysplasia, seizure, necrotizing enterocolitis, neonatal hypotension, 5-minute Apgar score, sepsis, and retinopathy of prematurity. Using all patients, the receiver operating characteristic curve for the final prediction model had an area under the curve of 0.84 (CI: 0.78–0.89). Using these data, the risk of death or developing CP by age 2 can be calculated for individual surviving infants. Conclusion IVH and PVL were the only neonatal complications evident at discharge that contributed to an individual infant's risk of the long-term outcomes of death or CP by age 2. A model that includes these morbidities, gestational age at delivery, and gender is predictive of subsequent neurologic sequelae. Key Points Factors known at hospital discharge are identified which are independently associated with death or CP by age 2. A model was created and validated using these findings to counsel parents. The risk of death or CP can be calculated at the time of hospital discharge. [ABSTRACT FROM AUTHOR]

Background Numerous prognostic scores have been published to support risk stratification for patients with coronavirus disease 2019 (COVID-19). Methods We performed a systematic review to identify the scores for confirmed or clinically assumed COVID-19 cases. An in-depth assessment and risk of bias (ROB) analysis (Prediction model Risk Of Bias ASsessment Tool [PROBAST]) was conducted for scores fulfilling predefined criteria ([I] area under the curve [AUC)] ≥ 0.75; [II] a separate validation cohort present; [III] training data from a multicenter setting [≥2 centers]; [IV] point-scale scoring system). Results Out of 1522 studies extracted from MEDLINE/Web of Science (20/02/2023), we identified 242 scores for COVID-19 outcome prognosis (mortality 109, severity 116, hospitalization 14, long-term sequelae 3). Most scores were developed using retrospective (75.2%) or single-center (57.1%) cohorts. Predictor analysis revealed the primary use of laboratory data and sociodemographic information in mortality and severity scores. Forty-nine scores were included in the in-depth analysis. The results indicated heterogeneous quality and predictor selection, with only five scores featuring low ROB. Among those, based on the number and heterogeneity of validation studies, only the 4C Mortality Score can be recommended for clinical application so far. Conclusions The application and translation of most existing COVID scores appear unreliable. Guided development and predictor selection would have improved the generalizability of the scores and may enhance pandemic preparedness in the future. [ABSTRACT FROM AUTHOR]