Insights into the performance of PREDICT tool in a large Mainland Chinese breast cancer cohort: a comparative analysis of versions 3.0 and 2.2.

BACKGROUND: PREDICT is a web-based tool for forecasting breast cancer outcomes. PREDICT version 3.0 was recently released. This study aimed to validate this tool for a large population in mainland China and compare v3.0 with v2.2. METHODS: Women who underwent surgery for nonmetastatic primary invasive breast cancer between 2010 and 2020 from the First Affiliated Hospital of Wenzhou Medical University were selected. Predicted and observed 5-year overall survival (OS) for both v3.0 and v2.2 were compared. Discrimination was compared using receiver-operator curves and DeLong test. Calibration was evaluated using calibration plots and chi-squared test. A difference greater than 5% was deemed clinically relevant. RESULTS: A total of 5424 patients were included, with median follow-up time of 58 months (IQR 38-89 months). Compared to v2.2, v3.0 did not show improved discriminatory accuracy for 5-year OS (AUC: 0.756 vs 0.771), same as ER-positive and ER-negative patients. However, calibration was significantly improved in v3.0, with predicted 5-year OS deviated from observed by -2.0% for the entire cohort, -2.9% for ER-positive and -0.0% for ER-negative patients, compared to -7.3%, -4.7% and -13.7% in v2.2. In v3.0, 5-year OS was underestimated by 9.0% for patients older than 75 years, and 5.8% for patients with micrometastases. Patients with distant metastases postdiagnosis was overestimated by 10.6%. CONCLUSIONS: PREDICT v3.0 reliably predicts 5-year OS for the majority of Chinese patients with breast cancer. PREDICT v3.0 significantly improved the predictive accuracy for ER-negative groups. Furthermore, caution is advised when interpreting 5-year OS for patients aged over 70, those with micrometastases or metastases postdiagnosis.

PD-1+ T lymphocyte proportions and hospitalized exacerbation of COPD: a prospective cohort study.

OBJECTIVE: To evaluate the predictive value of PD-1 expression in T lymphocytes for rehospitalization due to acute exacerbations of COPD (AECOPD) in discharged patients. METHODS: 115 participants hospitalized with COPD (average age 71.8 ± 6.0 years) were recruited at Fujian Provincial Hospital. PD1+T lymphocytes proportions (PD1+T%), baseline demographics and clinical data were recorded at hospital discharge. AECOPD re-admission were collected at 1-year follow-up. Kaplan-Meier analysis compared the time to AECOPD readmissions among groups stratified by PD1+T%. Multivariable Cox proportional hazards regression and stratified analysis determined the correlation between PD1+T%, potential confounders, and AECOPD re-admission. ROC and DCA evaluated PD1+T% in enhancing the clinical predictive values of Cox models, BODE and CODEX. RESULTS: 68 participants (59.1%) were AECOPD readmitted, those with AECOPD readmission exhibited significantly elevated baseline PD-1+CD4+T/CD4+T% and PD-1+CD8 + T/CD8 + T% compared to non-readmitted counterparts. PD1+ T lymphocyte levels statistically correlated with BODE and CODEX indices. Kaplan-Meier analysis demonstrated that those in Higher PD1+ T lymphocyte proportions had reduced time to AECOPD readmission (logRank p < 0.05). Cox analysis identified high PD1+CD4+T and PD1+CD8+T ratios as risk factors of AECOPD readmission, with hazard ratios of 1.384(95%CI [1.043-1.725]) and 1.401(95%CI [1.013-1.789]), respectively. Notably, in patients aged < 70 years and with fewer than twice AECOPD episodes in the previous year, high PD1+T lymphocyte counts significantly increased risk for AECOPD readmission(p < 0.05). The AECOPD readmission predictive model, incorporating PD1+T% exhibited superior discrimination to the Cox model, BODE index and CODEX index, AUC of ROC were 0.763(95%CI [0.633-0.893]) and 0.734(95%CI [0.570-0.899]) (DeLong's test p < 0.05).The DCA illustrates that integrating PD1+T% into models significantly enhances the utility in aiding clinical decision-making. CONCLUSION: Evaluation of PD1+ lymphocyte proportions offer a novel perspective for identifying high-risk COPD patients, potentially providing insights for COPD management. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR, URL: www.chictr.org.cn/ ), Registration number: ChiCTR2200055611 Date of Registration: 2022-01-14.

A novel real-time model for predicting acute kidney injury in critically ill patients within 12 hours.

BACKGROUND: A major challenge in prevention and early treatment of acute kidney injury (AKI) is the lack of high-performance predictors in critically ill patients. Therefore, we innovatively constructed U-AKIpredTM for predicting AKI in critically ill patients within 12 h of panel measurement. METHODS: The prospective cohort study included 680 patients in the training set and 249 patients in the validation set. After performing inclusion and exclusion criteria, 417 patients were enrolled in the training set and 164 patients were enrolled in the validation set finally. AKI was diagnosed by Kidney Disease Improving Global Outcomes (KDIGO) criteria. RESULTS: Twelve urinary kidney injury biomarkers (mALB, IgG, TRF, α1MG, NAG, NGAL, KIM-1, L-FABP, TIMP2, IGFBP7, CAF22 and IL-18) exhibited good predictive performance for AKI within 12 h in critically ill patients. U-AKIpredTM, combined with three crucial biomarkers (α1MG, L-FABP and IGFBP7) by multivariate logistic regression analysis, exhibited better predictive performance for AKI in critically ill patients within 12 h than the other twelve kidney injury biomarkers. The area under the curve (AUC) of the U-AKIpredTM, as a predictor of AKI within 12 h, was 0.802 (95% CI: 0.771-0.833, P < 0.001) in the training set and 0.844 (95% CI: 0.792-0.896, P < 0.001) in validation cohort. A nomogram based on the results of the training and validation sets of U-AKIpredTM was developed which showed optimal predictive performance for AKI. The fitting effect and prediction accuracy of U-AKIpredTM was evaluated by multiple statistical indicators. To provide a more flexible predictive tool, the dynamic nomogram (https://www.xsmartanalysis.com/model/U-AKIpredTM) was constructed using a web-calculator. Decision curve analysis (DCA) and a clinical impact curve were used to reveal that U-AKIpredTM with the three crucial biomarkers had a higher net benefit than these twelve kidney injury biomarkers respectively. The net reclassification index (NRI) and integrated discrimination index (IDI) were used to improve the significant risk reclassification of AKI compared with the 12 kidney injury biomarkers. The predictive efficiency of U-AKIpredTM was better than the NephroCheck® when testing for AKI and severe AKI. CONCLUSION: U-AKIpredTM is an excellent predictive model of AKI in critically ill patients within 12 h and would assist clinicians in identifying those at high risk of AKI.

Scoring model based on cardiac CT and clinical factors to predict early good mitral valve repair in rheumatic mitral disease.

OBJECTIVE: We aimed to evaluate the mitral valve calcification and mitral structure detected by cardiac computed tomography (cardiac CT) and establish a scoring model based on cardiac CT and clinical factors to predict early good mitral valve repair (EGMR) and guide surgical strategy in rheumatic mitral disease (RMD). MATERIALS AND METHODS: This is a retrospective bi-center cohort study. Based on cardiac CT, mitral valve calcification and mitral structure in RMD were quantified and evaluated. The primary outcome was EGMR. A logical regression algorithm was applied to the scoring model. RESULTS: A total of 579 patients were enrolled in our study from January 1, 2019, to August 31, 2022. Of these, 443 had baseline cardiac CT scans of adequate quality. The calcification quality score, calcification and thinnest part of the anterior leaflet clean zone, and papillary muscle symmetry were the independent CT factors of EGMR. Coronary artery disease and pulmonary artery pressure were the independent clinical factors of EGMR. Based on the above six factors, a scoring model was established. Sensitivity = 95% and specificity = 95% were presented with a cutoff value of 0.85 and 0.30 respectively. The area under the receiver operating characteristic of external validation set was 0.84 (95% confidence interval [CI] 0.73-0.93). CONCLUSIONS: Mitral valve repair is recommended when the scoring model value > 0.85 and mitral valve replacement is prior when the scoring model value < 0.30. This model could assist in guiding surgical strategies for RMD. CLINICAL RELEVANCE STATEMENT: The model established in this study can serve as a reference indicator for surgical repair in rheumatic mitral valve disease. KEY POINTS: • Cardiac CT can reflect the mitral structure in detail, especially for valve calcification. • A model based on cardiac CT and clinical factors for predicting early good mitral valve repair was established. • The developed model can help cardiac surgeons formulate appropriate surgical strategies.

Prediction of symptomatic anastomotic leak after rectal cancer surgery: A machine learning approach.

INTRODUCTION: Anastomotic leakage (AL) remains the most dreaded and unpredictable major complication after low anterior resection for mid-low rectal cancer. The aim of this study is to identify patients with high risk for AL based on the machine learning method. METHODS: Patients with mid-low rectal cancer undergoing low anterior resection were enrolled from West China Hospital between January 2008 and October 2019 and were split by time into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) method and stepwise method were applied for variable selection and predictive model building in the training cohort. The area under the receiver operating characteristic curve (AUC) and calibration curves were used to evaluate the performance of the models. RESULTS: The rate of AL was 5.8% (38/652) and 7.2% (15/208) in the training cohort and validation cohort, respectively. The LASSO-logistic model selected almost the same variables (hypertension, operating time, cT4, tumor location, intraoperative blood loss) compared to the stepwise logistic model except for tumor size (the LASSO-logistic model) and American Society of Anesthesiologists score (the stepwise logistic model). The predictive performance of the LASSO-logistics model was better than the stepwise-logistics model (AUC: 0.790 vs. 0.759). Calibration curves showed mean absolute error of 0.006 and 0.013 for the LASSO-logistics model and stepwise-logistics model, respectively. CONCLUSION: Our study developed a feasible predictive model with a machine-learning algorithm to classify patients with a high risk of AL, which would assist surgical decision-making and reduce unnecessary stoma diversion. The involved machine learning algorithms provide clinicians with an innovative alternative to enhance clinical management.

Tumor budding is an optimal indictor of occult cervical metastasis in clinical early-stage buccal mucosa squamous cell carcinoma.

BACKGROUND: Buccal mucosa squamous cell carcinoma (BMSCC) is an aggressive disease. This study investigated the clinicopathological significance of tumor budding (TB), depth of invasion (DOI), and mode of invasion (MOI) on occult cervical metastasis (CM) of BMSCC. METHODS: Seventy-one cT1-2N0 BMSCC patients were included in this retrospective study. TB, DOI, MOI, and other clinicopathological features were reviewed. Risk factors for occult CM, locoregional recurrence-free survival (LRRFS), and overall survival (OS) were analyzed using logistic regression and Cox's proportional hazard models, respectively. RESULTS: Multivariate analysis with the logistic regression model revealed that MOI, DOI, and TB were significantly associated with occult CM in early-stage BMSCC after adjusting for variates. However, multivariate analysis with the Cox's proportional hazard model found only TB to be a prognostic factor for LRRFS (hazard ratio 15.03, 95% confidence interval [CI] 1.94-116.66; p = 0.01; trend test p = 0.03). No significant association was found between MOI, DOI, or TB and OS. CONCLUSIONS: The optimal predictor of occult CM and prognosis of early-stage BMSCC is TB, which may assist clinicians in identifying patients at high risk of cervical metastasis.

Predictive Value of Jugulo-omohyoid Lymph Nodes in Lateral Lymph Node Metastasis of Papillary Thyroid Cancer.

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.

Three new discovery effector proteins from Candidatus Liberibacter asiaticus psy62 inhibit plant defense through interaction with AtCAT3 and AtGAPA.

KEY MESSAGE: We identify three SDEs that inhibiting host defence from Candidatus Liberibacter asiaticus psy62, which is an important supplement to the pathogenesis of HLB. Candidatus Liberibacter asiaticus (CLas) is the main pathogen of citrus Huanglongbing (HLB). 38 new possible sec-dependent effectors (SDEs) of CLas psy62 were predicted by updated predictor SignalP 5.0, which 12 new SDEs were found using alkaline phosphate assay. Among them, SDE4310, SDE4435 and SDE4955 inhibited hypersensitivity reactions (HR) in Arabidopsis thaliana (Arabidopsis, At) and Nicotiana benthamiana leaves induced by pathogens, which lead to a decrease in cell death and reactive oxygen species (ROS) accumulation. And the expression levels of SDE4310, SDE4435, and SDE4955 genes elevated significantly in mild symptom citrus leaves. When SDE4310, SDE4435 and SDE4955 were overexpressed in Arabidopsis, HR pathway key genes pathogenesis-related 2 (PR2), PR5, nonexpressor of pathogenesis-related 1 (NPR1) and isochorismate synthase 1 (ICS1) expression significantly decreased and the growth of pathogen was greatly increased relative to control with Pst DC3000/AvrRps4 treatment. Our findings also indicated that SDE4310, SDE4435 and SDE4955 interacted with AtCAT3 (catalase 3) and AtGAPA (glyceraldehyde-3-phosphate dehydrogenase A). In conclusion, our results suggest that SDE4310, SDE4435 and SDE4955 are CLas psy62 effector proteins that may have redundant functions. They inhibit ROS burst and cell death by interacting with AtCAT3 and AtGAPA to negatively regulate host defense.

Variables that predict hospital stay and the outcome of Fournier gangrene at King Abdulaziz University Hospital: a retrospective study.

BACKGROUND: The aggressive nature of Fournier gangrene and the associated health issues can result in a more complex clinical course and potentially a longer hospital stay. This study aimed to assess factors that affect the length of hospital stay (LHS) and its relation to the outcome of Fournier gangrene patients. METHODS: A retrospective study was performed at King Abdulaziz University Hospital (KAUH), Saudi Arabia, on patients diagnosed with Fournier gangrene between 2017 and 2023. Data about length of hospital stay (LHS), age, BMI, clinical and surgical data and outcome was obtained. RESULTS: The mean age of the studied patients was 59.23 ± 11.19 years, the mean body mass index (BMI) was 26.69 ± 7.99 kg/m2, and the mean duration of symptoms was 10.27 ± 9.16 days. The most common presenting symptoms were swelling or induration (64%), 88% had comorbidities with diabetes mellitus (DM) (84%), and 76% had uncontrolled DM. of patients, 24% had a poly-microbial infection, with E. coli being the most common (52%). The mean length of hospital stay (LHS) was 54.56 ± 54.57 days, and 24% of patients had an LHS of more than 50 days. Longer LHS (> 50 days) was associated with patients who did not receive a compatible initial antibiotic, whereas shorter LHS was associated with patients who received Impenem or a combination of vancomycin and meropenem as alternative antibiotics following incompatibility. Reconstruction patients had significantly longer LHS and a higher mean temperature. However, none of the studied variables were found to be predictors of long LHS in the multivariate regression analysis. CONCLUSION: Knowledge of the values that predict LHS allows for patient-centered treatment and may be useful in predicting more radical treatments or the need for additional treatment in high-risk patients. Future multicenter prospective studies with larger sample sizes are needed to assess the needed variables and predictors of long LHS.

Global incidence and mortality trends of gastric cancer and predicted mortality of gastric cancer by 2035.

OBJECTIVE: To study the historical global incidence and mortality trends of gastric cancer and predicted mortality of gastric cancer by 2035. METHODS: Incidence data were retrieved from the Cancer Incidence in Five Continents (CI5) volumes I-XI, and mortality data were obtained from the latest update of the World Health Organization (WHO) mortality database. We used join-point regression analysis to examine historical incidence and mortality trends and used the package NORDPRED in R to predict the number of deaths and mortality rates by 2035 by country and sex. RESULTS: More than 1,089,000 new cases of gastric cancer and 769,000 related deaths were reported in 2020. The average annual percent change (AAPC) in the incidence of gastric cancer from 2003 to 2012 among the male population, South Korea, Japan, Malta, Canada, Cyprus, and Switzerland showed an increasing trend (P > 0.05); among the female population, Canada [AAPC, 1.2; (95%Cl, 0.5-2), P < 0.05] showed an increasing trend; and South Korea, Ecuador, Thailand, and Cyprus showed an increasing trend (P > 0.05). AAPC in the mortality of gastric cancer from 2006 to 2015 among the male population, Thailand [3.5 (95%cl, 1.6-5.4), P < 0.05] showed an increasing trend; Malta Island, New Zealand, Turkey, Switzerland, and Cyprus had an increasing trend (P > 0.05); among the male population aged 20-44, Thailand [AAPC, 3.4; (95%cl, 1.3-5.4), P < 0.05] showed an increasing trend; Norway, New Zealand, The Netherlands, Slovakia, France, Colombia, Lithuania, and the USA showed an increasing trend (P > 0.05). It is predicted that the mortality rate in Slovenia and France's female population will show an increasing trend by 2035. It is predicted that the absolute number of deaths in the Israeli male population and in Chile, France, and Canada female population will increase by 2035. CONCLUSION: In the past decade, the incidence and mortality of gastric cancer have shown a decreasing trend; however, there are still some countries showing an increasing trend, especially among populations younger than 45 years. Although mortality in most countries is predicted to decline by 2035, the absolute number of deaths due to gastric cancer may further increase due to population growth.

Prenatal anaemia and risk of postpartum haemorrhage: a cohort analysis of data from the Predict-PPH study.

BACKGROUND: Most previous clinical studies investigating the connection between prenatal anaemia and postpartum haemorrhage (PPH) have reported conflicting results. OBJECTIVES: We examined the association between maternal prenatal anaemia and the risk of PPH in a large cohort of healthy pregnant women in five health institutions in Lagos, Southwest Nigeria. METHODS: This was a prospective cohort analysis of data from the Predict-PPH study that was conducted between January and June 2023. The study enrolled n = 1222 healthy pregnant women giving birth in five hospitals in Lagos, Nigeria. The study outcome, WHO-defined PPH, is postpartum blood loss of at least 500 milliliters. We used a multivariable logistic regression model with a backward stepwise conditional approach to examine the association between prenatal anaemia of increasing severity and PPH while adjusting for confounding factors. RESULTS: Of the 1222 women recruited to the Predict-PPH study between January and June 2023, 1189 (97·3%) had complete outcome data. Up to 570 (46.6%) of the enrolled women had prenatal anaemia while 442 (37.2%) of those with complete follow-up data had WHO-defined PPH. After controlling for potential confounding factors, maternal prenatal anaemia was independently associated with PPH (adjusted odds ratio = 1.37, 95% confidence interval: 1.05-1.79). However, on the elimination of interaction effects of coexisting uterine fibroids and mode of delivery on this association, a sensitivity analysis yielded a lack of significant association between prenatal anaemia and PPH (adjusted odds ratio = 1.27, 95% confidence interval: 0.99-1.64). We also recorded no statistically significant difference in the median postpartum blood loss in women across the different categories of anaemia (P = 0.131). CONCLUSION: Our study revealed that prenatal anaemia was not significantly associated with PPH. These findings challenge the previously held belief of a suspected link between maternal anaemia and PPH. This unique evidence contrary to most previous studies suggests that other factors beyond prenatal anaemia may contribute more significantly to the occurrence of PPH. This highlights the importance of comprehensive assessment and consideration of various maternal health factors in predicting and preventing this life-threatening obstetric complication.

Usefulness of lactate to albumin ratio for predicting in-hospital mortality in atrial fibrillation patients admitted to the intensive care unit: a retrospective analysis from MIMIC-IV database.

BACKGROUND: High lactate to albumin ratio (LAR) has been reported to be associated to with poor prognosis in patients admitted to the intensive care unit (ICU). However, its role in predicting in-hospital mortality in AF patients admitted to ICU has not been explored. METHODS: The Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was used to retrieve information on patients who had been diagnosed with AF. X-tile software was utilized to determine the optimal cut-off LAR. Area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA) were conducted to assess the prediction performance of LAR for in-hospital mortality. RESULTS: Finally, 8,287 AF patients were included and 1,543 death (18.6%) occurred. The optimal cut-off value of LAR is 0.5. Patients in lower LAR (< 0.5) group showed a better in-hospital survival compared to patients in higher LAR (≥ 0.5) group (HR: 2.67, 95%CI:2.39-2.97, P < 0.001). A nomogram for in-hospital mortality in patients with AF was constructed based on multivariate Cox analysis including age, CCI, ß blockers usage, APSIII, hemoglobin and LAR. This nomogram exhibited excellent discrimination and calibration abilities in predicting in-hospital mortality for critically ill AF patients. CONCLUSION: LAR, as a readily available biomarker, can predict in-hospital mortality in AF patients admitted to the ICU. The nomogram that combined LAR with other relevant variables performed exceptionally well in terms of predicting in-hospital mortality.

Could emergency admission plasma D-dimer level predict first pass effect of stent retriever thrombectomy in acute ischemic stroke?

BACKGROUND: Evidence on plasma biomarkers to identify first pass effect (FPE) in patients with acute ischemic stroke (AIS) with large vessel occlusion (LVO) treated with thrombectomy is limited. PURPOSE: To evaluate whether plasma D-dimer could predict FPE. MATERIAL AND METHODS: Consecutive patients with LVO who underwent first-line stent retriever thrombectomy at our center between January 2018 and August 2021 were enrolled. Patients were classified into the FPE (modified Thrombolysis in Cerebral Infarction [mTICI] ≥2c) group or non-FPE (mTICI 0-2b) group based on angiographic outcomes. Logistic regression analysis was performed to determine the predictors of FPE. The overall ability of D-dimer levels in predicting FPE was evaluated using receiver operating characteristic (ROC) curves. RESULTS: In total, 313 patients were included; 88 (28.1%) patients achieved FPE. Compared to those with non-FPE, patients with FPE had more diabetes mellitus history, lower D-dimer levels, higher clot burden score, a higher proportion of M1 middle cerebral artery, and a higher proportion of main stem occlusion pattern (P <0.05). After adjusting for potential variables, D-dimer levels (OR=0.81, 95% CI=0.52-0.96), clot burden score (OR=1.76, 95% CI=1.38-2.87), and main stem occlusion pattern (OR=1.85, 95% CI=1.19-2.62) remained independently associated with FPE. Based on the ROC analysis, the D-dimer as a predictor for predicting FPE presented with a specificity of 79%, a negative predictive value of 87%, and an area under the curve of 0.761. CONCLUSION: Low emergency admission plasma D-dimer level is an independent predictor of FPE in patients with AIS treated with stent retriever thrombectomy.

A Computed Tomography-Based Fracture Prediction Model With Images of Vertebral Bones and Muscles by Employing Deep Learning: Development and Validation Study.

BACKGROUND: With the progressive increase in aging populations, the use of opportunistic computed tomography (CT) scanning is increasing, which could be a valuable method for acquiring information on both muscles and bones of aging populations. OBJECTIVE: The aim of this study was to develop and externally validate opportunistic CT-based fracture prediction models by using images of vertebral bones and paravertebral muscles. METHODS: The models were developed based on a retrospective longitudinal cohort study of 1214 patients with abdominal CT images between 2010 and 2019. The models were externally validated in 495 patients. The primary outcome of this study was defined as the predictive accuracy for identifying vertebral fracture events within a 5-year follow-up. The image models were developed using an attention convolutional neural network-recurrent neural network model from images of the vertebral bone and paravertebral muscles. RESULTS: The mean ages of the patients in the development and validation sets were 73 years and 68 years, and 69.1% (839/1214) and 78.8% (390/495) of them were females, respectively. The areas under the receiver operator curve (AUROCs) for predicting vertebral fractures were superior in images of the vertebral bone and paravertebral muscles than those in the bone-only images in the external validation cohort (0.827, 95% CI 0.821-0.833 vs 0.815, 95% CI 0.806-0.824, respectively; P<.001). The AUROCs of these image models were higher than those of the fracture risk assessment models (0.810 for major osteoporotic risk, 0.780 for hip fracture risk). For the clinical model using age, sex, BMI, use of steroids, smoking, possible secondary osteoporosis, type 2 diabetes mellitus, HIV, hepatitis C, and renal failure, the AUROC value in the external validation cohort was 0.749 (95% CI 0.736-0.762), which was lower than that of the image model using vertebral bones and muscles (P<.001). CONCLUSIONS: The model using the images of the vertebral bone and paravertebral muscle showed better performance than that using the images of the bone-only or clinical variables. Opportunistic CT screening may contribute to identifying patients with a high fracture risk in the future.

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.

BACKGROUND: Clinical notes contain contextualized information beyond structured data related to patients' past and current health status. OBJECTIVE: This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data. METHODS: Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors. RESULTS: The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments. CONCLUSIONS: The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study.

BACKGROUND: Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability. OBJECTIVE: The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers. METHODS: We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods. RESULTS: Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910). CONCLUSIONS: RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis).

The objective of the present study was to evaluate the relationship between body weight (BW) and hip width (HW) in dairy buffaloes (Bubalus bubalis). HW was measured in 215 Murrah buffaloes with a BW of 341 ± 161.6 kg, aged between three months and five years, and raised in southeastern Mexico. Linear and non-linear regressions were used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). Additionally, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was assessed based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The relationship between BW and HW showed a high correlation coefficient (r = 0.96, P < 0.001). The chosen fitted model to predict BW was: -176.33 (± 40.83***) + 8.74 (± 1.79***) × HW + 0.04 (± 0.01*) × HW2, because it presented the lowest MSE, RMSE, and AIC values, which were 1228.64, 35.05 and 1532.41, respectively. Therefore, with reasonable accuracy, the quadratic model using hip width may be suitable for predicting body weight in buffaloes.

Distinct risk factors of lateral lymph node metastasis in patients with papillary thyroid cancer based on age stratification.

INTRODUCTION: Studies have revealed that age is associated with the risk of lateral lymph node metastasis (LLNM) in papillary thyroid cancer (PTC). This study aimed to identify the optimal cut point of age for a more precise prediction model of LLNM and to reveal differences in risk factors between patients of distinct age stages. METHODS: A total of 499 patients who had undergone thyroidectomy and lateral neck dissection (LND) for PTC were enrolled. The locally weighted scatterplot smoothing (LOWESS) curve and the 'changepoint' package were used to identify the optimal age cut point using R. Multivariate logistic regression analysis was performed to identify independent risk factors of LLNM in each group divided by age. RESULTS: Younger patients were more likely to have LLNM, and the optimal cut points of age to stratify the risk of LLNM were 30 and 45 years old. Central lymph node metastasis (CLNM) was a prominent risk factor for further LNM in all patients. Apart from CLNM, sex(p = 0.033), tumor size(p = 0.027), and tumor location(p = 0.020) were independent predictors for patients younger than 30 years old; tumor location(p = 0.013), extra-thyroidal extension(p < 0.001), and extra-nodal extension(p = 0.042) were independent risk factors for patients older than 45 years old. CONCLUSIONS: Our study could be interpreted as an implication for a change in surgical management. LND should be more actively performed when CLNM is confirmed; for younger patients with tumors in the upper lobe and older patients with extra-thyroidal extension tumors, more aggressive detection of the lateral neck might be considered.

Independent predictors and clinical predictive score of postanesthetic reintubation after general anesthesia: A time-matched, case control study.

Background and Aims: Postanesthetic reintubation is associated with increased morbidities and mortality; however, it can be reduced with defined predictors and using a score as a tool. This study aimed to identify independent predictors and develop a reliable predictive score. Material and Methods: A retrospective, time-matched, case control study was conducted on patients who underwent general anesthesia between October 2017 and September 2021. Using stepwise multivariable logistic regression analysis, predictors were determined and the predictive score was developed and validated. Results: Among 230 patients, 46 were in the reintubated group. Significant independent predictors included age >65 years (odds ratio [OR] 2.96 [95% confidence interval {CI} 1.23, 7.10]), the American Society of Anesthesiologists physical status III-IV (OR 6.60 [95%CI 2.50 17.41]), body mass index (BMI) ≥30 kg/m2 (OR 4.91 [95% CI 1.55, 15.51]), and head and neck surgery (OR 4.35 [95% CI 1.46, 12.87]). The predictive model was then developed with an area under the receiver operating characteristic curve (AUC) of 0.84 (95% CI 0.78, 0.90). This score ranged from 0 to 29 and was classified into three subcategories for clinical practicability, in which the positive predictive values were 6.01 (95% CI 2.63, 11.50) for low risk, 18.64 (95% CI 9.69, 30.91) for moderate risk, and 71.05 (95% CI 54.09, 84.58) for high risk. Conclusion: The independent predictors for postanesthetic reintubation according to this simplified risk-based scoring system designed to aid anesthesiologists before extubation were found to be advanced age, higher American Society of Anesthesiologists physical status, obesity, and head and neck surgery.

Evaluation and comparison of different breast cancer prognosis scores based on gene expression data.

BACKGROUND: Breast cancer is one of the three most common cancers worldwide and is the most common malignancy in women. Treatment approaches for breast cancer are diverse and varied. Clinicians must balance risks and benefits when deciding treatments, and models have been developed to support this decision-making. Genomic risk scores (GRSs) may offer greater clinical value than standard clinicopathological models, but there is limited evidence as to whether these models perform better than the current clinical standard of care. METHODS: PREDICT and GRSs were adapted using data from the original papers. Univariable Cox proportional hazards models were produced with breast cancer-specific survival (BCSS) as the outcome. Independent predictors of BCSS were used to build multivariable models with PREDICT. Signatures which provided independent prognostic information in multivariable models were incorporated into the PREDICT algorithm and assessed for calibration, discrimination and reclassification. RESULTS: EndoPredict, MammaPrint and Prosigna demonstrated prognostic power independent of PREDICT in multivariable models for ER-positive patients; no score predicted BCSS in ER-negative patients. Incorporating these models into PREDICT had only a modest impact upon calibration (with absolute improvements of 0.2-0.8%), discrimination (with no statistically significant c-index improvements) and reclassification (with 4-10% of patients being reclassified). CONCLUSION: Addition of GRSs to PREDICT had limited impact on model fit or treatment received. This analysis does not support widespread adoption of current GRSs based on our implementations of commercial products.