Web-based Calculator Research Articles

Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome

BackgroundAcute kidney injury (AKI) can make cases of acute respiratory distress syndrome (ARDS) more complex, and the combination of the two can significantly worsen the prognosis. Our objective is to utilize machine learning (ML) techniques to construct models that can promptly identify the risk of AKI in ARDS patients.MethodWe obtained data regarding ARDS patients from the Medical Information Mart for Intensive Care III (MIMIC-III) and MIMIC-IV databases. Within the MIMIC-III dataset, we developed 11 ML prediction models. By evaluating various metrics, we visualized the importance of its features using Shapley additive explanations (SHAP). We then created a more concise model using fewer variables, and optimized it using hyperparameter optimization (HPO). The model was validated using the MIMIC-IV dataset.ResultA total of 928 ARDS patients without AKI were included in the analysis from the MIMIC-III dataset, and among them, 179 (19.3%) developed AKI after admission to the intensive care unit (ICU). In the MIMIC-IV dataset, there were 653 ARDS patients included in the analysis, and among them, 237 (36.3%) developed AKI. A total of 43 features were used to build the model. Among all models, eXtreme gradient boosting (XGBoost) performed the best. We used the top 10 features to build a compact model with an area under the curve (AUC) of 0.850, which improved to an AUC of 0.865 after the HPO. In extra validation set, XGBoost_HPO achieved an AUC of 0.854. The accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and F1 score of the XGBoost_HPO model on the test set are 0.865, 0.813, 0.877, 0.578, 0.957 and 0.675, respectively. On extra validation set, they are 0.724, 0.789, 0.688, 0.590, 0.851, and 0.675, respectively.ConclusionML algorithms, especially XGBoost, are reliable for predicting AKI in ARDS patients. The compact model maintains excellent predictive ability, and the web-based calculator improves clinical convenience. This provides valuable guidance in identifying AKI in ARDS, leading to improved patient outcomes.

ABO-adjusted cPRA metric for kidney allocation in an Asian-predominant population.

Recent studies showed that ABO-adjusted calculated panel reactive antibody (ABO-cPRA) may better reflect the histocompatibility level in a multi-ethnic population, but such data in Asians is not available. We developed an ABO-adjusted cPRA metric on a cohort of waitlist kidney transplant patients (n = 647, 99% Chinese) in Hong Kong, based on HLA alleles and ABO frequencies of local donors. The concordance between the web-based ABO-cPRA calculator and the impact on kidney allocation were evaluated. The blood group distribution for A, B, O and AB among waitlist kidney candidates were 26.2%, 27.5%, 40.1%, and 6.1%, and their chances of encountering incompatible blood group donors were 32.6%, 32.4%, 57.6%, and 0%, respectively. There is poor agreement between web-based ABO-cPRA calculator and our locally developed metrics. Over 90% of patients showed an increase in cPRA after ABO adjustment, most notably in those with cPRA between 70% and 79%. Blood group O patients had a much greater increase in cPRA scores after adjustment while patients of blood group A and B had similar increment. 10.6% of non-AB blood group waitlist patients had ABO-cPRA elevated to ≥80%. A local ABO-adjusted cPRA metric is required for Asian populations and may improve equity in kidney distribution for patients with disadvantageous blood groups. The result from the current study potentially helps other countries/localities in establishing their own unified ABO-cPRA metrics and predict the impact on kidney allocation.

MitraCox Score Predicts In-Hospital Mortality in Patients Admitted for Transcatheter Edge-to-Edge Mitral Valve Repair

As mitral valve transcatheter edge-to-edge repair (M-TEER) is evolving as an effective treatment for high-risk surgical patients with mitral regurgitation, there is a pressing need for cardiologists to optimize resources through risk stratification of in-hospital mortality for this patient population. Although current risk-prediction models have been shown to predict adverse outcomes with reasonable accuracy, models trained using the US nationwide population are lacking. This study aimed to identify clinical, demographic, and procedural features that predict in-hospital mortality, and to derive and validate an in-hospital mortality risk-prediction tool in patients who have undergone M-TEER. A total of 9,373 admissions from the Nationwide Readmissions Database of patients who underwent M-TEER between 2015 and 2018 were used to develop and validate the model. We first performed least absolute shrinkage and selection operator (LASSO) regularization of Cox regression (Coxnet) that is 10-fold cross-validated. The non-zero coefficients were multiplied with the respective values of each observation of the predictors to build the scoring formula. Out of 9,373 admissions, 196 patients (2%) died in-hospital during index admission. In descending order, the top variables that were most predictive of in-hospital mortality were higher age, presence of fluid and electrolyte disturbance, and large metropolitan location of the hospital. The validation C-statistic of the MitraCox score was 0.82. Using X-tile software (Yale School of Medicine, New Haven, Connecticut), 2 cutoffs of the score were determined on the basis of in-hospital mortality and length of stay, and the survival of the population was classified into 3 risk groups: low, intermediate, and high. The scoring system deployed online as a web-based calculator can be accessed at https://kathavs.shinyapps.io/Mitracox_Kapadia/. In conclusion, MitraCox score is easy to calculate and predicts in-hospital mortality depending on length of stay in a dynamic manner.

A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multicenter retrospective cohort study in the United States.

Heart failure (HF) is a significant cause of in-hospital mortality, especially for the elderly admitted to intensive care units (ICUs). This study aimed to develop a web-based calculator to predict 30-day in-hospital mortality for elderly patients with HF in the ICU and found a relationship between risk factors and the predicted probability of death. Data (N = 4450) from the MIMIC-III/IV database were used for model training and internal testing. Data (N = 2,752) from the eICU-CRD database were used for external validation. The Brier score and area under the curve (AUC) were employed for the assessment of the proposed nomogram. Restrictive cubic splines (RCSs) found the cutoff values of variables. The smooth curve showed the relationship between the variables and the predicted probability of death. A total of 7,202 elderly patients with HF were included in the study, of which 1,212 died. Multivariate logistic regression analysis showed that 30-day mortality of HF patients in ICU was significantly associated with heart rate (HR), 24-h urine output (24h UOP), serum calcium, blood urea nitrogen (BUN), NT-proBNP, SpO2, systolic blood pressure (SBP), and temperature (P < 0.01). The AUC and Brier score of the nomogram were 0.71 (0.67, 0.75) and 0.12 (0.11, 0.15) in the testing set and 0.73 (0.70, 0.75), 0.13 (0.12, 0.15), 0.65 (0.62, 0.68), and 0.13 (0.12, 0.13) in the external validation set, respectively. The RCS plot showed that the cutoff values of variables were HR of 96 bmp, 24h UOP of 1.2 L, serum calcium of 8.7 mg/dL, BUN of 30 mg/dL, NT-pro-BNP of 5121 pg/mL, SpO2 of 93%, SBP of 137 mmHg, and a temperature of 36.4°C. Decreased temperature, decreased SpO2, decreased 24h UOP, increased NT-proBNP, increased serum BUN, increased or decreased SBP, fast HR, and increased or decreased serum calcium increase the predicted probability of death. The web-based nomogram developed in this study showed good performance in predicting 30-day in-hospital mortality for elderly HF patients in the ICU.

Development and validation of a model to predict the risk of distant metastases from hepatocellular carcinoma: a real-world retrospective study.

This study aimed to construct a novel clinical prediction model to predict the risk of distant metastases (DM) in hepatocellular carcinoma (HCC). We included 3869 HCC patients, comprising 3076 patients from the Surveillance, Epidemiology, and End Results (SEER) database and 793 patients from a hospital in China. Variables with a P-value < 0.05 in the univariate logistic analysis were entered into the multivariate analysis to determine the independent predictive factors for DM in HCC. A nomogram was created based on the independent predictive factors. The predictive performance of the model was assessed using the receiver operating characteristics (ROCs) curve, decision curve analysis (DCA), calibration curves, and clinical impact curve analysis (CIC). Additionally, we developed a user-friendly web-based calculator based on the model. The multivariate logistic regression analysis revealed that tumor size (P < 0.001), type of treatment (P < 0.001), T stage (P = 0.001), N stage (P < 0.001), and grade (P = 0.043) were identified as independent predictive factors. A nomogram was constructed based on these factors. The area under the ROC curves (AUC) value was 0.845 (95% CI 0.815-0.874) for the training set, 0.818 (95% CI 0.774-0.863) for the internal validation set, and 0.823 (95% CI 0.770-0.876) for the external validation set. Moreover, DCA analysis, calibration curves, and CIC analysis demonstrated the favorable predictive performance of the nomogram. Finally, a more user-friendly web-based calculator was developed. We developed a nomogram and showed its favorable predictive performance in predicting DM in HCC. Furthermore, we developed a more user-friendly web-based calculator, which has the potential to aid clinicians in individualized diagnosis and make better clinical decisions for HCC patients.

Personalised decision making to predict absolute metastatic risk in cutaneous squamous cell carcinoma: development and validation of a clinico-pathological model

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer, affecting more than 2 million people worldwide yearly and metastasising in 2-5% of patients. However, current clinical staging systems do not provide estimates of absolute metastatic risk, hence missing the opportunity for more personalised treatment advice. We aimed to develop a clinico-pathological model that predicts the probability of metastasis in patients with cSCC. Nationwide cohorts from (1) all patients with a first primary cSCC in The Netherlands in 2007-2008 and (2) all patients with a cSCC in 2013-2015 in England were used to derive nested case-control cohorts. Pathology records of primary cSCCs that originated a loco-regional or distant metastasis were identified, and these cSCCs were matched to primary cSCCs of controls without metastasis (1:1 ratio). The model was developed on the Dutch cohort (n=390) using a weighted Cox regression model with backward selection and validated on the English cohort (n=696). Model performance was assessed using weighted versions of the C-index, calibration metrics, and decision curve analysis; and compared to the Brigham and Women's Hospital (BWH) and the American Joint Committee on Cancer (AJCC) staging systems. Members of the multidisciplinary Skin Cancer Outcomes (SCOUT) consortium were surveyed to interpret metastatic risk cutoffs in a clinical context. Eight out of eleven clinico-pathological variables were selected. The model showed good discriminative ability, with an optimism-corrected C-index of 0.80 (95% Confidence interval (CI) 0.75-0.85) in the development cohort and a C-index of 0.84 (95% CI 0.81-0.87) in the validation cohort. Model predictions were well-calibrated: the calibration slope was 0.96 (95% CI 0.76-1.16) in the validation cohort. Decision curve analysis showed improved net benefit compared to current staging systems, particularly for thresholds relevant for decisions on follow-up and adjuvant treatment. The model is available as an online web-based calculator (https://emc-dermatology.shinyapps.io/cscc-abs-met-risk/). This validated model assigns personalised metastatic risk predictions to patients with cSCC, using routinely reported histological and patient-specific risk factors. The model can empower clinicians and healthcare systems in identifying patients with high-risk cSCC and offering personalised care/treatment and follow-up. Use of the model for clinical decision-making in different patient populations must be further investigated. PPP Allowance made available by Health-Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships.

Diet quality and prodromal Parkinson's disease probability in isolated REM sleep behavior disorder

IntroductionIt is reported that a diet that lowers oxidative stress reduces the prodromal Parkinson's disease (pPD) probability as well as the risk of Parkinson's disease (PD). In this study, we evaluated whether the diet quality of patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) were associated with the pPD probability score, PD risk markers, or prodromal markers. MethodsPolysomnography (PSG)-confirmed iRBD patients from the Neurology Department at Seoul National University Hospital were enrolled. We calculated the pPD probability using the “Web-based Medical Calculator for Prodromal Risk in Parkinsonism” Diet quality was assessed using the Recommended Food Score (RFS). ResultsWe enrolled 101 patients with iRBD. The mean RFS score of patients with iRBD was 28.23 ± 9.29, which did not differ from the general population. Among patients with iRBD, the probability of pPD did not differ between the high and low RFS groups. In patients aged <70 years, although total RFS was not correlated with pPD probability (p = 0.529, Spearman rank correlation), legume consumption was negatively correlated with pPD probability (p = 0.032): furthermore, legume consumption was significantly higher in patients with fewer prodromal markers (p = 0.016). ConclusionDiet quality assessed by RFS did not differ between the general population and patients with iRBD in Korea. Further studies are needed to confirm these protective effects of legume consumption on iRBD, which may have strong implications for the prevention and management of PD.

A Web-Based Application for Risk Stratification and Optimization in Patients With Cardiovascular Disease: Pilot Study.

In addition to aspirin, angiotensin-converting enzyme inhibitors, statins, and lifestyle modification interventions, novel pharmacological agents have been shown to reduce morbidity and mortality in atherosclerotic cardiovascular disease patients, including new antithrombotics, antihyperglycemics, and lipid-modulating therapies. Despite their benefits, the uptake of these guideline-directed therapies remains a challenge. There is a need to develop strategies to support knowledge translation for the uptake of secondary prevention therapies. The goal of this study was to test the feasibility and usability of Stratification and Optimization in Patients With Cardiovascular Disease (STOP-CVD), a point-of-care application that was designed to facilitate knowledge translation by providing individualized risk stratification and optimization guidance. Using the REACH (Reduction of Atherothrombosis for Continued Health) Registry trial and predictive modeling (which included 67,888 patients), we designed a free web-based secondary risk calculator. Based on demographic and comorbidity profiles, the application was used to predict an individual's 20-month risk of cardiovascular events and cardiovascular mortality and provides a comparison to an age-matched control with an optimized cardiovascular risk profile to illustrate the modifiable residual risk. Additionally, the application used the patient's risk profile to provide specific guidance for possible therapeutic interventions based on a novel algorithm. During an initial 3-month adoption phase, 1-time invitations were sent through email and telephone to 240 physicians that refer to a regional cardiovascular clinic. After 3 months, a survey of user experience was sent to all users. Following this, no further marketing of the application was performed. Google Analytics was collected postimplementation from January 2021 to December 2021. These were used to tabulate the total number of distinct users and the total number of monthly uses of the application. During the 1-year pilot, 47 of the 240 invited clinicians used the application 1573 times, an average of 131 times per month, with sustained usage over time. All 24 postimplementation survey respondents confirmed that the application was functional, easy to use, and useful. This pilot suggests that the STOP-CVD application is feasible and usable, with high clinician satisfaction. This tool can be easily scaled to support the uptake of guideline-directed medical therapy, which could improve clinical outcomes. Future research will be focused on evaluating the impact of this tool on clinician management and patient outcomes.

A dynamic nomogram integrated with blood inflammation markers for predicting overall survival in patients with upper tract urothelial carcinoma.

Upper tract urothelial carcinoma (UTUC) is a relatively rare disease with a poor prognosis. A growing body of evidence demonstrates that inflammation and the inflammatory microenvironment play a crucial role in tumorigenesis and tumor progression. Our aim was to evaluate the prognostic value of blood inflammation markers and develop a prediction model that incorporates inflammation markers in order to predict overall survival (OS) of UTUC. We included 304 localized UTUC patients from two medical institutions who had undergone radical nephroureterectomy (RNU) (167 in the training cohort, 137 in the validation cohort). Univariate and multivariate Cox regression analyses were performed to screen the prognostic factors, and a nomogram and a web-based calculator were generated based on these predictors. The Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Independent predictors incorporated in the nomogram were pathological stage, surgical margin, albumin-to-globulin ratio (AGR), and hemoglobin-to-red cell distribution width ratio (HRR). The c-index value was 0.726 in the training cohort and 0.761 in the validation cohort. The area under the ROC of the nomogram at 1-, 3- and 5-year in the training and validation sets were 0.765, 0.755, 0.763, and 0.791, 0.833, 0.802, respectively. Both the internal and external validation calibration plots showed a subtle distinction between the predicted and the actual probabilities. And it appears to provide incremental benefits for clinical decision-making in comparison to the American Joint Committee of Cancer (AJCC) staging system. In patients with UTUC after RNU, lower preoperative AGR and HRR were independent predictors of inferior survival. In addition, we created a novel blood inflammation marker-based dynamic nomogram that may be useful for surgeons or oncologists in risk stratification and patient selection for more intensive therapy and closer follow-up.

Color Transformations of Photometric Measurements of Galaxies in Optical and Near-infrared Wide-field Imaging Surveys* *An online color transformation calculator is available at: https://colors.voxastro.org/.

Over the past 2 decades, wide-field photometric surveys in optical and infrared domains reached a nearly all-sky coverage thanks to numerous observational facilities operating in both hemispheres. However, subtle differences among exact realizations of Johnson and SDSS photometric systems require one to convert photometric measurements into the same system prior to analysis of composite data sets originating from multiple surveys. It turns out that the published photometric transformations lead to substantial biases when applied to integrated photometry of galaxies from the corresponding catalogs. Here we present photometric transformations based on piece-wise linear approximations of integrated photometry of galaxies in the optical surveys SDSS, DECaLS, BASS, MzLS, DES, DELVE, KiDS, VST ATLAS, and the near-infrared surveys UKIDSS, UHS, VHS, and VIKING. We validate our transformations by constructing k-corrected color–magnitude diagrams of non-active galaxies and measuring the position and tightness of the “red sequence.” We also provide transformations for aperture magnitudes and show how they are affected by the image quality difference among the surveys. We present the implementation of the derived transformations in python and idl and also a web-based color transformation calculator for galaxies. By comparing DECaLS and DES, we identified systematic issues in DECaLS photometry for extended galaxies, which we attribute to the photometric software package used by DECaLS. As an application of our method, we compiled two multi-wavelength photometric catalogs for over 200,000 low- and intermediate-redshift galaxies originating from CfA FAST and Hectospec spectral archives.

Demographics, Risk Factors, and Etiology of Posterior Cerebral Artery Stroke Causing Homonymous Hemianopia.

Posterior cerebral artery (PCA) strokes account for up to 10% of all ischemic strokes, often presenting with homonymous hemianopia. The proportion of these strokes attributed to various etiologies varies widely in previously published studies, owing largely to differing patient populations, definitions of stroke pathogenesis, and vascular territories involved. The Causative Classification System (CCS), an automated version of the Stop Stroke Study (SSS) Trial of Org 10,172 in Acute Stroke Treatment (TOAST) system, allows for a more rigorous assignment of stroke etiology. We excerpted clinical and imaging data on 85 patients who had PCA stroke with homonymous hemianopia examined at the University of Michigan. We compared the stroke risk factor profile of our PCA cohort with that of 135 patients with stroke in the distribution of the internal carotid artery (ICA) and middle cerebral artery (MCA) in an unpublished University of Michigan registry. We applied the CCS web-based calculator to our PCA cohort to determine stroke etiology. In our PCA cohort, 80.0% had at least 2 conventional stroke risk factors and 30.6% had 4 risk factors, most commonly systemic hypertension. The risk factor profile of our PCA cohort resembled that of our ICA/MCA cohort except that the mean age of our PCA cohort was more than a decade younger and had a significantly lower frequency of atrial fibrillation (AF) than our ICA/MCA cohort. In nearly half of the patients with AF in our PCA cohort, AF was diagnosed after the stroke. Among stroke etiologies in our PCA cohort, 40.0% were of undetermined cause, 30.6% were from cardioaortic embolism, 17.6% were from other determined causes, and only 11.8% were from supra-aortic large artery atherosclerosis. Strokes after endovascular or surgical interventions were prominent among other determined causes. Most patients in our PCA cohort had multiple conventional stroke risk factors, a finding not previously documented. Mean age at stroke onset and AF frequency were lower than in our ICA/MCA cohort, in agreement with previous studies. As some other studies have found, nearly 1/3 of strokes were attributed to cardioaortic embolism. Within that group, AF was often a poststroke diagnosis, a finding not previously highlighted. Compared with earlier studies, a relatively high portion of strokes were of undetermined etiology and of other determined etiologies, including stroke after endovascular or surgical interventions. Supra-aortic large artery atherosclerosis was a relatively uncommon explanation for stroke.

External validation of a clinical prediction tool for the use of manual therapy for patients with temporomandibular disorders: a protocol for a prospective observational study

IntroductionClinical guidelines recommend conservative treatment for the management of temporomandibular disorders (TMD), and manual therapy directed to temporomandibular structures is commonly applied to reduce pain and improve function. In a...

Developing Implementation Strategies to Support the Uptake of a Risk Tool to Aid Physicians in the Clinical Management of Patients With Syncope: Systematic Theoretical and User-Centered Design Approach.

The Canadian Syncope Risk Score (CSRS) was developed to improve syncope management in emergency department settings. Evidence-based tools often fail to have the intended impact because of suboptimal uptake or poor implementation. In this paper, we aimed to describe the process of developing evidence-based implementation strategies to support the deployment and use of the CSRS in real-world emergency department settings to improve syncope management among physicians. We followed a systematic approach for intervention development, including identifying who needs to do what differently, identifying the barriers and enablers to be addressed, and identifying the intervention components and modes of delivery to overcome the identified barriers. We used the Behaviour Change Wheel to guide the selection of implementation strategies. We engaged CSRS end users (ie, emergency medicine physicians) in a user-centered design approach to generate and refine strategies. This was achieved over a series of 3 qualitative user-centered design workshops lasting 90 minutes each with 3 groups of emergency medicine physicians. A total of 14 physicians participated in the workshops. The themes were organized according to the following intervention development steps: theme 1-identifying and refining barriers and theme 2-identifying the intervention components and modes of delivery. Theme 2 was subdivided into two subthemes: (1) generating high-level strategies and developing strategies prototypes and (2) refining and testing strategies. The main strategies identified to overcome barriers included education in the format of meetings, videos, journal clubs, and posters (to address uncertainty around when and how to apply the CSRS); the development of a web-based calculator and integration into the electronic medical record (to address uncertainty in how to apply the CSRS); a local champion (to address the lack of team buy-in); and the dissemination of evidence summaries and feedback through email communications (to address a lack of evidence about impact). The ability of the CSRS to effectively improve patient safety and syncope management relies on broad buy-in and uptake across physicians. To ensure that the CSRS is well positioned for impact, a comprehensive suite of strategies was identified to address known barriers.

A deep-learning-based clinical risk stratification for overall survival in adolescent and young adult women with breast cancer.

The objective of this study is to construct a novel clinical risk stratification for overall survival (OS) prediction in adolescent and young adult (AYA) women with breast cancer. From the Surveillance, Epidemiology, and End Results (SEER) database, AYA women with primary breast cancer diagnosed from 2010 to 2018 were included in our study. A deep learning algorithm, referred to as DeepSurv, was used to construct a prognostic predictive model based on 19 variables, including demographic and clinical information. Harrell's C-index, the receiver operating characteristic (ROC) curve, and calibration plots were adopted to comprehensively assess the predictive performance of the prognostic predictive model. Then, a novel clinical risk stratification was constructed based on the total risk score derived from the prognostic predictive model. The Kaplan-Meier method was used to plot survival curves for patients with different death risks, using the log-rank test to compared the survival disparities. Decision curve analyses (DCAs) were adopted to evaluate the clinical utility of the prognostic predictive model. Among 14,243 AYA women with breast cancer finally included in this study, 10,213 (71.7%) were White and the median (interquartile range, IQR) age was 36 (32-38) years. The prognostic predictive model based on DeepSurv presented high C-indices in both the training cohort [0.831 (95% CI 0.819-0.843)] and the test cohort [0.791 (95% CI 0.764-0.818)]. Similar results were observed in ROC curves. The excellent agreement between the predicted and actual OS at 3 and 5years were both achieved in the calibration plots. The obvious survival disparities were observed according to the clinical risk stratification based on the total risk score derived from the prognostic predictive model. DCAs also showed that the risk stratification possessed a significant positive net benefit in the practical ranges of threshold probabilities. Lastly, a user-friendly Web-based calculator was generated to visualize the prognostic predictive model. A prognostic predictive model with sufficient prediction accuracy was construct for predicting OS of AYA women with breast cancer. Given its public accessibility and easy-to-use operation, the clinical risk stratification based on the total risk score derived from the prognostic predictive model may help clinicians to make better-individualized management.

WED-155 - An online web-based calculator accurately diagnoses immune tolerant phase in chronic HBV-infected patients

WED-155 - An online web-based calculator accurately diagnoses immune tolerant phase in chronic HBV-infected patients

Predicting Micro-TESE among Heterogeneous Nonobstructive Azoospermic Patients: The Impact on Surgical Decision and ICSI

Background. Previously published predictive models for microdissection testicular sperm extraction (micro-TESE) were generally assumed patients with nonobstructive azoospermia (NOA) a homogenous population, i.e., the laboratory predictors were associated with sperm retrieval rate (SRR) in a similar way among different subpopulations. In addition, previous studies primarily regarded the success of sperm retrieval as the sole endpoint, although live birth is the ultimate goal for the couples. Objectives. The main objective is to develop and evaluate the clinical benefit of a model predicting the clinical outcome of micro-TESE in heterogeneous population with NOA. The outcome of pregnancy was taken into account via assessing the association between the predicted outcome of micro-TESE and pregnancy. Materials and Methods. A development cohort of 1,292 patients with NOA and an external validation cohort of 530 patients were included. Sperm retrieval was performed using micro-TESE. Clinical outcomes, including sperm retrieval, clinical pregnancy, and live birth, were collected. We developed a model using the machine learning method random forest and provided a web-based calculator. Results. The SRR was 38.1% (492/1,292) in the development cohort and 48.5% (257/530) in the validation cohort. The final model includes etiology, AMH, sperm retrieval surgical history, testicular volume, FSH, LH, and age as predictors (ordered by variable importance). The area under the curve of our model was 0.76 (0.74–0.79) in the development cohort and 0.75 (0.71–0.79) in the external validation cohort. The decision curve analysis showed that personalized model-based surgical decision provides additional clinical benefit. The clinical pregnancy rate (CPR) and cumulative live birth rate (CLBR) were 45.3% (405/895) and 57.6% (338/587), respectively, in the overall population. For patients of different SRR, the CPR and CLBR of whom had successful sperm retrieval were similar. Discussion and Conclusion. Our model predicting the SRR of micro-TESE was generalizable and easy to use. Predicted pregnancy outcomes like CPR and CLBR could also be derived from predicted SRR. A model-based surgical decision after personalized consultation would be beneficial to patients with NOA.

Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study.

Metastasis in the lungs is common in patients with rectal cancer, and it can have severe consequences on their survival and quality of life. Therefore, it is essential to identify patients who may be at risk of developing lung metastasis from rectal cancer. In this study, we utilized eight machine-learning methods to create a model for predicting the risk of lung metastasis in patients with rectal cancer. Our cohort consisted of 27,180 rectal cancer patients selected from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2017 for model development. Additionally, we validated our models using 1118 rectal cancer patients from a Chinese hospital to evaluate model performance and generalizability. We assessed our models' performance using various metrics, including the area under the curve (AUC), the area under the precision-recall curve (AUPR), the Matthews Correlation Coefficient (MCC), decision curve analysis (DCA), and calibration curves. Finally, we applied the best model to develop a web-based calculator for predicting the risk of lung metastasis in patients with rectal cancer. Our study employed tenfold cross-validation to assess the performance of eight machine-learning models for predicting the risk of lung metastasis in patients with rectal cancer. The AUC values ranged from 0.73 to 0.96 in the training set, with the extreme gradient boosting (XGB) model achieving the highest AUC value of 0.96. Moreover, the XGB model obtained the best AUPR and MCC in the training set, reaching 0.98 and 0.88, respectively. We found that the XGB model demonstrated the best predictive power, achieving an AUC of 0.87, an AUPR of 0.60, an accuracy of 0.92, and a sensitivity of 0.93 in the internal test set. Furthermore, the XGB model was evaluated in the external test set and achieved an AUC of 0.91, an AUPR of 0.63, an accuracy of 0.93, a sensitivity of 0.92, and a specificity of 0.93. The XGB model obtained the highest MCC in the internal test set and external validation set, with 0.61 and 0.68, respectively. Based on the DCA and calibration curve analysis, the XGB model had better clinical decision-making ability and predictive power than the other seven models. Lastly, we developed an online web calculator using the XGB model to assist doctors in making informed decisions and to facilitate the model's wider adoption (https://share.streamlit.io/woshiwz/rectal_cancer/main/lung.py). In this study, we developed an XGB model based on clinicopathological information to predict the risk of lung metastasis in patients with rectal cancer, which may help physicians make clinical decisions.

Conditional Cancer-Specific Survival for Inflammatory Breast Cancer: Analysis of SEER, 2010 to 2016

BackgroundConditional survival takes into account the time that has elapsed since diagnosis and may have additional informative value. Compared with the static traditional survival evaluation method, conditional survival predictions can be adapted to incorporate the dynamic changes during the disease and provide a more suitable way of identifying time-evolved prognoses. MethodsOf 3333 patients diagnosed with inflammatory breast cancer between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Results database. The trend of the hazard rate over time was represented by the kernel density smoothing curve. The traditional cancer-specific survival (CSS) rate was estimated by the Kaplan-Meier method. Conditional CSS assessment was defined as the probability that a patient will survive y years given the x years who already survived after diagnosis, and the formula is as follows: CS(y)=CSS(x + y)/CSS(x). 3-year cancer-specific survival (CSS3) and 3-year conditional cancer-specific survival (CS3) were estimated. The Fine-Gray proportional subdistribution hazard model was constructed to screen for time-dependent risk factors associated with cancer-specific death. Subsequently, a nomogram was applied to predict a 5-year survival rate based on the number of years already survived. ResultsOf 3333 patients, the cancer-specific survival (CSS) rate decreased from 57% in the 4th year to 49% in the 6th year, while the comparable 3-year CS (CS3) rate improved from 65% in the first year to 76% in the third year. Overall, the CS3 rate was superior to actuarial cancer-specific survival, which was also found in subgroup analysis, especially in patients with high-risk characteristics. The Fine-Gray's model indicated that remote organ metastasis (M stage), lymph node metastasis (N stage), and surgery all significantly impacted the prognosis for cancer-specific survival. The Fine-Gray's model-based nomogram was constructed to predict 5-year cancer-specific survival immediately after diagnosis and given survival for 1, 2, 3, and 4 years after diagnosis. ConclusionHigh-risk patients had a significantly improved cancer-specific survival prognosis after surviving for 1 or more years after diagnosis with inflammatory breast cancer. The probability of reaching 5-year cancer-specific survival following diagnosis improves with each additional year survived. More effective follow-up is required for patients diagnosed at an advanced N stage, remote organ metastasis, or not received surgery. Additionally, a nomogram and web-based calculator may be helpful for patients with inflammatory breast cancer during follow-up counseling (https://ibccondsurv.shinyapps.io/dynnomapp/).

Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule (OxSATS)

BackgroundAssessment of suicide risk in individuals who have self-harmed is common in emergency departments, but is often based on tools developed for other purposes.ObjectiveWe developed and validated a predictive model...

Developing a predictive nomogram and web-based survival calculator for locally advanced hypopharyngeal cancer: A propensity score-adjusted, population-based study.

Understanding the clinical features and accurately predicting the prognosis of patients with locally advanced hypopharyngeal squamous cell carcinoma (LA-HPSCC) is important for patient centered decision-making. This study aimed to create a multi-factor nomogram predictive model and a web-based calculator to predict post-therapy survival for patients with LA-HPSCC. A retrospective cohort study analyzing Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015 for patients diagnosed with LA-HPSCC was conducted and randomly divided into a training and a validation group (7:3 ratio). The external validation cohort included 276 patients from Sichuan Cancer Hospital, China. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis was used to identify independent factors associated with overall survival (OS) and cancer-specific survival (CSS), and nomogram models and web-based survival calculators were constructed. Propensity score matching (PSM) was used to compare survival with different treatment options. A total of 2526 patients were included in the prognostic model. The median OS and CSS for the entire cohort were 20 (18.6-21.3) months and 24 (21.7-26.2) months, respectively. Nomogram models integrating the seven factors demonstrated high predictive accuracy for 3-year and 5-year survival. PSM found that patients who received surgery-based curative therapy had better OS and CSS than those who received radiotherapy-based treatment (median survival times: 33 months vs 18 months and 40 months vs 22 months, respectively). The nomogram model accurately predicted patient survival from LA-HPSCC. Surgery with adjuvant therapy yielded significantly better survival than definitive radiotherapy. and should be prioritized over definitive radiotherapy.