Risk Prediction Model For Patients Research Articles

Prevalence and determinants of atrial fibrillation progression in paroxysmal atrial fibrillation

ObjectiveAtrial fibrillation (AF) often progresses from paroxysmal AF (PAF) to more permanent forms. To improve personalised medicine, we aim to develop a new AF progression risk prediction model in patients...

Readmission Risk Prediction Model for Patients with Chronic Heart Failure: A Systematic Review and Meta-Analysis

Background:The present systematic review and meta-analysis aimed to systematically evaluate a risk prediction model for the readmission of patients with CHF.Methods:The search was carried out in databases including PubMed, Embase, EBSCO, Web of Science, Cochrane Library and also domestic databases including Chinese Biomedical Literature Database, Chinese Academic Journal Full Text Database, Wanfang Database, and Vipu Chinese Journal Service Platform. All the original studies published by July 2021. Two researchers identified previous studies involving readmission risk prediction models that met our selection criteria. The quality of the included studies was evaluated based on the CHARMS checklist, and the prediction models were systematically evaluated.Results:Of the overall 4787 studies retrieved, nine studies—two prospective, seven retrospective—met our selection criteria. The area under the receiver operating characteristic curve exceeded 0.63 (0.63–0.80) for all the studies. The most common predictors in the model were B-type natriuretic peptide (BNP) or N-terminal pro-brain BNP (Odds Ratio 4.35; 95% confidence interval (CI) 2.53–7.49; P<0.001), renal insufficiency (Odds Ratio 1.60; 95%CI 1.24–2.08; P<0.001), comorbidities, and a history of hospitalization.Conclusion:The use of non-parametric statistical methods and assessment of large samples of electronic data improve the predictive abilities of the risk assessment models. It is necessary to calibrate and verify such models and promote the combined use of parametric and non-parametric methods to establish precise predictive models for clinical use.

Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score

ObjectiveTo use echocardiographic and clinical features to develop an explainable clinical risk prediction model in patients with aortic stenosis (AS), including those with low-gradient AS (LGAS), using machine learning (ML).MethodsIn...

MP45-07 ONCOLOGIC OUTCOMES OF TOTAL LENGTH GLEASON PATTERN 4 ON BIOPSY IN MEN WITH GRADE GROUP 2 PROSTATE CANCER

You have accessJournal of UrologyCME1 May 2022MP45-07 ONCOLOGIC OUTCOMES OF TOTAL LENGTH GLEASON PATTERN 4 ON BIOPSY IN MEN WITH GRADE GROUP 2 PROSTATE CANCER Marlon Perera, Melissa Assel, Nicole Benfante, Andrew Vickers, Victor Reuter, Sigrid Carlsson, Vincent Laudone, Karim Touijer, James Eastham, Peter Scardino, Samson Fine, and Behfar Ehdaie Marlon PereraMarlon Perera More articles by this author , Melissa AsselMelissa Assel More articles by this author , Nicole BenfanteNicole Benfante More articles by this author , Andrew VickersAndrew Vickers More articles by this author , Victor ReuterVictor Reuter More articles by this author , Sigrid CarlssonSigrid Carlsson More articles by this author , Vincent LaudoneVincent Laudone More articles by this author , Karim TouijerKarim Touijer More articles by this author , James EasthamJames Eastham More articles by this author , Peter ScardinoPeter Scardino More articles by this author , Samson FineSamson Fine More articles by this author , and Behfar EhdaieBehfar Ehdaie More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002611.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Gleason Score 7 prostate cancer (including Grade Group (GG) 2 and GG3) represents a widely heterogenous disease process, and precise sub-stratification of this cohort is paramount. Our group previously demonstrated that the total length of Gleason pattern 4 (GP4) across all biopsy cores has been shown to be associated with adverse pathologic outcomes after radical prostatectomy. We aimed to determine the association of GP4 length on prostate biopsy on post-prostatectomy oncologic outcomes. METHODS: We compared four GP4 quantification methods including: maximum %GP4 in any single core, overall %GP4 (mm of GP4/total mm of cancer), total length GP4 (mm) across all cores, and length GP4 (mm) in the core with the greatest volume of GP4. for prediction of biochemical recurrence–free survival after radical prostatectomy using multivariable Cox proportional hazards regression. RESULTS: A total of 457 men with Grade Group 2 prostate cancer on biopsy subsequently underwent radical prostatectomy at our institution. The 3-year biochemical recurrence–free survival probability was 85% (95% CI 81–88%). On multivariable analysis, all four GP4 quantification methods—maximum %GP4 (HR=1.30; 95% CI 1.07–1.59; p=0.009), overall %GP4 (HR=1.61; 95% CI 1.21–2.15; p=0.001), total length GP4 (HR=2.48; 95% CI 1.36–4.52; p=0.003), and total length GP4 in highest core (HR=1.32; 95% CI 1.11–1.57; p=0.001)—were significantly associated with BCR. Models including all methods of GP4 quantification resulted in a meaningful increase in discrimination of BCR risk, with similar gains in Harrell’s C-index ranging from 0.017 to 0.019. CONCLUSIONS: These findings further support routine reporting of and inclusion of GP4 quantification in pathology reports and risk prediction models for patients with Grade Group 2 prostate cancer. These data also support studying GP4 quantification as a surrogate endpoint for disease progression for trials, including those of men managed with active surveillance. Source of Funding: This work was supported by the Sidney Kimmel Center for Prostate and Urologic Cancers at MSK, the NIH/NCI grant P50 CA092629, and the NIH/NCI Cancer Center Support Grant to Memorial Sloan Kettering Cancer Center (P30 CA008748). Marlon Perera is sponsored by the Australian-America Fulbright Commission administered through a 2021-2022 Fulbright Future Scholarship funded by The Kinghorn Foundation. Sigrid Carlsson is supported by a NIH/NCI grant K22-CA234400 © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e764 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Marlon Perera More articles by this author Melissa Assel More articles by this author Nicole Benfante More articles by this author Andrew Vickers More articles by this author Victor Reuter More articles by this author Sigrid Carlsson More articles by this author Vincent Laudone More articles by this author Karim Touijer More articles by this author James Eastham More articles by this author Peter Scardino More articles by this author Samson Fine More articles by this author Behfar Ehdaie More articles by this author Expand All Advertisement PDF DownloadLoading ...

Translated article] Surgical Risk Following Anatomic Lung Resection in Thoracic Surgery: A Prediction Model Derived From a Spanish Multicenter Database

IntroductionThe aim of this study was to develop a surgical risk prediction model in patients undergoing anatomic lung resections from the registry of the Spanish Video-Assisted Thoracic Surgery Group (GEVATS). MethodsData were collected from 3533 patients undergoing anatomic lung resection for any diagnosis between December 20, 2016 and March 20, 2018.We defined a combined outcome variable: death or Clavien-Dindo grade IV complication at 90 days after surgery. Univariate and multivariate analyses were performed by logistic regression. Internal validation of the model was performed using resampling techniques. ResultsThe incidence of the outcome variable was 4.29% (95% CI 3.6–4.9). The variables remaining in the final logistic model were: age, sex, previous lung cancer resection, dyspnea (mMRC), right pneumonectomy, and ppo DLCO. The performance parameters of the model adjusted by resampling were: C-statistic 0.712 (95% CI 0.648–0.750), Brier score 0.042 and bootstrap shrinkage 0.854. ConclusionsThe risk prediction model obtained from the GEVATS database is a simple, valid, and reliable model that is a useful tool for establishing the risk of a patient undergoing anatomic lung resection.

Development and validation of a comprehensive early risk prediction model for patients with undifferentiated acute chest pain.

AimsExisting risk scores for undifferentiated chest pain focus on excluding coronary events and do not represent a comprehensive risk assessment if an alternate serious diagnosis is present. This study aimed to develop and validate an all-inclusive risk prediction model among patients with undifferentiated chest pain.MethodsWe developed and validated a multivariable logistic regression model for a composite measure of early all-inclusive risk (defined as hospital admission excluding a discharge diagnosis of non-specific pain, 30-day all-cause mortality, or 30-day myocardial infarction [MI]) among adults assessed by emergency medical services (EMS) for non-traumatic chest pain using a large population-based cohort (January 2015 to June 2019). The cohort was randomly divided into development (146,507 patients [70%]) and validation (62,788 patients [30%]) cohorts.ResultsThe composite outcome occurred in 28.4%, comprising hospital admission in 27.7%, mortality within 30-days in 1.8%, and MI within 30-days in 0.4%. The Early Chest pain Admission, MI, and Mortality (ECAMM) risk model was developed, demonstrating good discrimination in the development (C-statistic 0.775, 95% CI 0.772–0.777) and validation cohorts (C-statistic 0.765, 95% CI 0.761–0.769) with excellent calibration. Discriminatory performance for the composite outcome and individual components was higher than existing scores commonly used in undifferentiated chest pain risk stratification.ConclusionsThe ECAMM risk score model can be used as an all-inclusive risk stratification assessment of patients with non-traumatic chest pain without the limitation of a single diagnostic outcome. This model could be clinically useful to help guide decisions surrounding the need for non-coronary investigations and safety of early discharge.

Vasculink iPhone Application and Risk Prediction Model for Groin Complication in Vascular Surgery

Postoperative groin complication is a common cause of morbidity in vascular surgery. Prophylactic wound adjuncts addressing this issue have been shown to reduce complications in high-risk patients, but their widespread implementation is limited by their high cost. This study introduces a risk prediction model for patients at a high risk for groin complication which can be accessed through the iPhone application, Vasculink. A literature search identified risk prediction models for groin complication in vascular surgery. Odds ratios of risk factors that were present in at least 2 published models were calculated with a pooled effect size. The weighted risk for each factor was used to create our model and a cutoff point defining high risk patients was chosen. The initial model was assessed and validated using a split-sample methodology on a cohort identified via a retrospective chart review of all patients undergoing open vascular surgery at our institution between 2017 and 2020. Model performance was assessed using the C-statistic. Risk factors included in our model were female gender, body mass index ≥28kg/m2, ever-smoker, reoperation, use of prosthetic, emergency, and end-stage renal disease. Of 216 patients, 131 were at a high risk. The overall groin complication rate was 43%, and specific complication rates were 27% infection, 14.8% seroma, and 6.9% hematoma. Our model's sensitivity and specificity were 92.47% and 60.98%, respectively. The C-statistic is 0.768. By using risk factors identified in the literature we have been able to establish a highly sensitive risk prediction model for groin complication following open vascular surgery. By incorporating our model into an iPhone application, Vasculink, we hope to facilitate preoperative decision making regarding the use of prophylactic wound adjuncts.

Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records

BackgroundEstablished prediction models of Diabetic kidney disease (DKD) are limited to the analysis of clinical research data or general population data and do not consider hospital visits. Construct a 3-year diabetic kidney disease risk prediction model in patients with type 2 diabetes mellitus (T2DM) using machine learning, based on electronic medical records (EMR).MethodsData from 816 patients (585 males) with T2DM and 3 years of follow-up at the PLA General Hospital. 46 medical characteristics that are readily available from EMR were used to develop prediction models based on seven machine learning algorithms (light gradient boosting machine [LightGBM], eXtreme gradient boosting, adaptive boosting, artificial neural network, decision tree, support vector machine, logistic regression). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was used to interpret the results of the best performing model.ResultsThe LightGBM model had the highest AUC (0.815, 95% CI 0.747–0.882). Recursive feature elimination with random forest and SHAP plot based on LightGBM showed that older patients with T2DM with high homocysteine (Hcy), poor glycemic control, low serum albumin (ALB), low estimated glomerular filtration rate (eGFR), and high bicarbonate had an increased risk of developing DKD over the next 3 years.ConclusionsThis study constructed a 3-year DKD risk prediction model in patients with T2DM and normo-albuminuria using machine learning and EMR. The LightGBM model is a tool with potential to facilitate population management strategies for T2DM care in the EMR era.

Risk Prediction Models for Atherosclerotic Cardiovascular Disease in Patients with Chronic Kidney Disease: The CRIC Study.

Individuals with CKD may be at high risk for atherosclerotic cardiovascular disease (ASCVD). However, there are no ASCVD risk prediction models developed in CKD populations to inform clinical care and prevention. We developed and validated 10-year ASCVD risk prediction models in patients with CKD that included participants without self-reported cardiovascular disease from the Chronic Renal Insufficiency Cohort (CRIC) study. ASCVD was defined as the first occurrence of adjudicated fatal and nonfatal stroke or myocardial infarction. Our models used clinically available variables and novel biomarkers. Model performance was evaluated based on discrimination, calibration, and net reclassification improvement. Of 2604 participants (mean age 55.8 years; 52.0% male) included in the analyses, 252 had incident ASCVD within 10 years of baseline. Compared with the American College of Cardiology/American Heart Association pooled cohort equations (area under the receiver operating characteristic curve [AUC]=0.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P=0.03), achieving an AUC of 0.736 (95% confidence interval [CI], 0.649 to 0.826). The CRIC model developed using clinically available variables had an AUC of 0.760 (95% CI, 0.678 to 0.851). The CRIC biomarker-enriched model had an AUC of 0.771 (95% CI, 0.674 to 0.853), which was significantly higher than the clinical model (P=0.001). Both the clinical and biomarker-enriched models were well-calibrated and improved reclassification of nonevents compared with the pooled cohort equations (6.6%; 95% CI, 3.7% to 9.6% and 10.0%; 95% CI, 6.8% to 13.3%, respectively). The 10-year ASCVD risk prediction models developed in patients with CKD, including novel kidney and cardiac biomarkers, performed better than equations developed for the general population using only traditional risk factors.

A comparison of risk prediction models for patients with acute coronary syndromes.

A comparison of risk prediction models for patients with acute coronary syndromes.

POS-325 Prediction of 3-Year Risk of Diabetic Kidney Disease Using Machine Learning Based on Electronic Medical Records

Diabetic nephropathy (DN) is a leading cause of end-stage renal disease (ESRD), cardiovascular (CV) disease, and all-cause morbidity and mortality in patients with diabetes. To construct a 3-year diabetic kidney disease (DKD) risk prediction model in patients with type 2 diabetes mellitus (T2DM) and normo-albuminuria using machine learning, based on electronic medical records (EMR).

The best predictive model for hepatocellular carcinoma in patients with chronic hepatitis B infection.

Chronic hepatitis B (CHB) seriously threatens human health. About 820,000 deaths annually are due to related complications such as hepatitis B and hepatocellular carcinoma (HCC). Recently, the use of oral antiviral agents has significantly improved the prognosis of patients with CHB infection and reduced the risk of HCC. However, hepatitis B virus still remains a major factor in the development of HCC, raising many concerns. Therefore, numerous studies have been conducted to assess the risk of HCC in patients with CHB infection and many models have been proposed to predict the risk of developing HCC. However, as each study has different models for predicting HCC development that can be applied depending on the use of antiviral agents or the type of antiviral agents, it is necessary to properly understand characteristics of each model when using it for the evaluation of HCC in patients with CHB infection. In addition, because different variables such as host factor, viral activity, and cirrhosis are used to evaluate the risk of HCC development, it is necessary to assess the risk by carefully verifying which variables are used. Recently, studies have also evaluated the risk of HCC using risk prediction models through transient elastography and artificial intelligence (AI) system. These HCC risk predication models are also noteworthy. In this review, we aimed to compare HCC risk prediction models in patients with CHB infection reported to date to confirm variables used and specificity between each model to determine an appropriate HCC risk prediction method.

Analysing Biochemical Parameters and Developing Risk Prediction Models in Patients with Schizophrenia and Bipolar Disorder

Objectives: The aim of this study is to analyze biochemical parameters and develop risk prediction models in patients with schizophrenia and bipolar disorder. Methods: Biochemical values of 65 schizophrenia and 65 bipolar disorder patients were statistically compared by analyzing thyroid function tests (TSH, T3 and T4), 25-0H vitamin D3, calcium, ferritin, iron, folate, vitamin B12, and magnesium levels. Risk analysis of the control group was evaluated in line with the obtained findings. Results: Male patients diagnosed with schizophrenia were observed to have lower T3 level than that of control group (p<0.001). Vitamin B12 value and Ferritin levels of male patients diagnosed with bipolar disorder were higher than those of control group (p<0.05), while their T3 levels were lower comparing to control group (p<0.001). Female patients diagnosed with schizophrenia were observed to have higher Ferritin levels (p<0.05) and lower T3 levels comparing to control group (p<0.001). When female patients diagnosed with bipolar disorder were compared with control group, their Ferritin levels and T4 levels were observed to be higher (p<0.05). Conclusion: Unless an intervention is made by detecting vitamin deficiency, the treatment may fail with psychotropic medications. In psychotic symptoms, in addition to antipsychotic treatment, replacement of low serum vitamin level may have positive effects on recovery. Therefore, serum vitamin B12, T3, T4 and TSH hormones, and ferritin levels can be recommended to be used as routine screening tests especially in the first applications of psychiatric patients with cognitive disorders.

Reporting and Performance of Hepatocellular Carcinoma Risk Prediction Models: Based on TRIPOD Statement and Meta-Analysis

Background The performance of risk prediction models for hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) was uncertain. The aim of the study was to critically evaluate the reports of transparent and external validation performances of these prediction models based on system review and meta-analysis. Methods A systematic search of the Web of Science and PubMed was performed for studies published until October 17, 2020. The transparent reporting of a multivariable prediction model for the individual prognosis or diagnosis (TRIPOD) tool was used to critically evaluate the quality of external validation reports for six models (CU-HCC, GAG-HCC, PAGE-B, mPAGE-B, REACH-B, and mREACH-B). The area under the receiver operator characteristic curve (AUC) values was to estimate the pooled external validating performance based on meta-analysis. Subgroup analysis and metaregression were also performed to explore heterogeneity. Results Our meta-analysis included 22 studies published between 2011 and 2020. The compliance of the included studies to TRIPOD ranged from 59% to 90% (median, 74%; interquartile range (IQR), 70%, 79%). The AUC values of the six models ranged from 0.715 to 0.778. In the antiviral therapy subgroups, the AUC values of mREACH-B, GAG-HCC, and mPAGE-B were 0.785, 0.760, and 0.778, respectively. In the cirrhosis subgroup, all models had poor discrimination performance (AUC < 0.7). Conclusions A full report of calibration and handling of missing values would contribute to a greater improvement in the quality of external validation reports for CHB-related HCC risk prediction. It was necessary to develop a specific HCC risk prediction model for patients with cirrhosis.

A Predictive Model for the Risk of Cognitive Impairment in Patients with Gallstones.

Objectives Gallstones can cause malnutrition in patients and further lead to cognitive impairment. This study is aimed at constructing a validated clinical prediction model for evaluating the risk of developing cognitive impairment from gallstones. Methods The study was a single-centre crosssectional study. Four models or methods (SVM-RFE, random forest model, Lasso model, and logistics analysis) were analyzed and compared regarding their predictive performance. The model with the best classification performance and predictive power was selected. The AUC index, C-index, and calibration curves were applied to the chosen model to further evaluate its classification and prediction performance. Finally, the nomogram was plotted, and the clinical usability, efficacy, and safety of the nomogram were assessed using decision curve analysis (DCA). Results This study included a total of 294 patients with gallstones, of which 110 had cognitive impairment. Factors such as gender, age, education, place of birth, history of alcohol consumption, abdominal circumference, sarcopenia, diabetes, anaemia, depression, and Pittsburgh Sleep Quality Index (PSQI) were incorporated into the model for nomogram construction. The calibration curve showed that the nomogram had good classification performance. Furthermore, the C-index of the model was 0.778 (95% CI, 0.674-0.882) in the test group. The DCA curves indicated that the constructed model had strong clinical applicability, efficacy, and safety. Conclusions This study constructed a cognitive impairment risk prediction model for patients with gallstones with good classification and predictive power. The constructed predictive model allows us to screen patients with gallstones and at high risk of cognitive impairment. These efforts might also help to further increase patient compliance, assist healthcare professionals to better manage patients with gallstones, and ultimately improve their overall health status and quality of life. Future clinical studies should further evaluate the accuracy and clinical usability of this model.

Prediction of surgical risk in patients with endocarditis: Comparison of logistic EuroSCORE, EuroSCORE II and APORTEI score

ObjectivesAPORTEI score is a new risk prediction model for patients with infective endocarditis. It has been recently validated on a Spanish multicentric national cohort of patients. The aim of the present study is to compare APORTEI performances with logistic EuroSCORE and EuroSCORE II by testing calibration and discrimination on a local sample population underwent cardiac surgery because of endocarditis. MethodsWe tested three prediction scores on 111 patients underwent surgery from 2014 to 2020 at our Institution because of infective endocarditis. Area under the curves and Hosmer–Lemeshow test were used to analyze discrimination and calibration respectively of logistic EuroSCORE, EuroSCORE II and APORTEI score. ResultsThe overall observed one-month mortality rate was 21.6%. The observed-to-expected ratio was 1.27 for logistic EuroSCORE, 3.27 for EuroSCORE II and 0.94 for APORTEI. The area under the curve (AUC) value of APORTEI (0.88±0.05) was significantly higher than that one of logistic EuroSCORE (AUC 0.77±0.05; p 0.0001) and of EuroSCORE II (AUC 0.74±0.05; p 0.0005). Hosmer–Lemeshow test showed better calibration performance of the APORTEI, (logistic EuroSCORE: p 0.19; EuroSCORE II: p 0.11; APORTEI: p 0.56). ConclusionAPORTEI risk score shows significantly higher performances in term of discrimination and calibration compared with both logistic EuroSCORE and EuroSCORE II.

Survival risk prediction model for patients with pT1\u20133 N0M0 esophageal squamous cell carcinoma after R0 esophagectomy with two-field lymphadenectomy for therapeutic purposes

BackgroundThe overall survival (OS) remains unsatisfactory in patients with esophageal squamous cell carcinoma (ESCC) after extended esophagectomy with two-field lymphadenectomy. Therefore, this retrospective study aimed to identify the risk factors that contribute to the low survival of patients with pT1–3N0M0 ESCC.MethodsPatients with pT1–3N0M0 ESCC who only underwent R0 esophagectomy with two-field lymphadenectomy in our department from January 2008 to December 2012 were retrospectively enrolled in this study and medical records were reviewed. Postoperative OS, disease-free survival (DFS), recurrence-free survival (RFS), and locoregional recurrence-free survival (LRFS) were analyzed sequentially.ResultsThis study recruited a total of 488 patients, whose follow-up visits were completed at the end of December 2019. The five-year OS, DFS, RFS and LRFS rates were 62.1, 53.1, 58.3 and 65.6%, respectively. Multivariate Cox analysis identified patient age, site of the lesion, small mediastinal lymph nodes in CT imaging (SLNs in CT), dissected lymph nodes (LNs), and stage of esophageal malignancy as independent risk factors for OS of the patients. Of these factors, the site of the lesion, SLNs in CT and stage of the cancer were determined to be independent factors for DFS, RFS and LRFS. Based on all five factors, the recursive partitioning analysis (RPA) score system was developed to stratify the patients into low-, medium- and high-risk groups, which were found to possess significantly different rates of OS, DFS, RFS and LRFS (p < 0.001).ConclusionsSeveral factors were associated with the survival of patients with pT1–3 N0M0 ESCC who underwent extended esophagectomy with two-field lymphadenectomy. These factors contributed to the RPA scoring system, which could stratify the risk of postoperative survival and may expedite the initiation of postoperative adjuvant therapy.

Risk Stratification With the Use of Coronary Computed Tomographic Angiography in Patients With Nonobstructive Coronary Artery Disease

ObjectivesThe purpose of this study was to develop a risk prediction model for patients with nonobstructive CAD. BackgroundAmong stable chest pain patients, most cardiovascular (CV) events occur in those with nonobstructive coronary artery disease (CAD). Thus, developing tailored risk prediction approaches in this group of patients, including CV risk factors and CAD characteristics, is needed. MethodsIn PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) computed tomographic angiography patients, a core laboratory assessed prevalence of CAD (nonobstructive 1% to 49% left main or 1% to 69% stenosis any coronary artery), degree of stenosis (minimal: 1% to 29%; mild: 30% to 49%; or moderate: 50% to 69%), high-risk plaque (HRP) features (positive remodeling, low-attenuation plaque, and napkin-ring sign), segment involvement score (SIS), and coronary artery calcium (CAC). The primary end point was an adjudicated composite of unstable angina pectoris, nonfatal myocardial infarction, and death. Cox regression analysis determined independent predictors in nonobstructive CAD. ResultsOf 2,890 patients (age 61.7 years, 46% women) with any CAD, 90.4% (n = 2,614) had nonobstructive CAD (mean age 61.6 yrs, 46% women, atherosclerotic cardiovascular disease [ASCVD] risk 16.2%). Composite events were independently predicted by ASCVD risk (hazard ratio [HR]: 1.03; p = 0.001), degree of stenosis (30% to 69%; HR: 1.91; p = 0.011), and presence of ≥2 HRP features (HR: 2.40; p = 0.008). Addition of ≥2 HRP features to: 1) ASCVD and CAC; 2) ASCVD and SIS; or 3) ASCVD and degree of stenosis resulted in a statistically significant improvement in model fit (p = 0.0036; p = 0.0176; and p = 0.0318; respectively). Patients with ASCVD ≥7.5%, any HRP, and mild/moderate stenosis had significantly higher event rates than those who did not meet those criteria (3.0% vs. 6.2%; p = 0.007). ConclusionsAdvanced coronary plaque features have incremental value over total plaque burden for the discrimination of clinical events in low-risk stable chest pain patients with nonobstructive CAD. This may be a first step to improve prevention in this cohort with the highest absolute risk for CV events.

Risk Prediction Model Based on Biomarkers of Remodeling in Patients with Acute Anterior ST-Segment Elevation Myocardial Infarction.

BackgroundThe aim of the present study was to develop a risk prediction model in patients with acute anterior ST-segment elevation myocardial infarction (STEMI).Material/MethodsClinical data from 333 patients with acute anterior STEMI were retrospectively analyzed. Clinical echocardiographic and angiographic data from patients with left ventricular remodeling (LVR) and those without LVR were compared. Factors that influenced risk were identified using multivariate logistic regression analysis. The area under the curve (AUC) of the receiver operating characteristic curve was used to assess the diagnostic performance of the model.ResultsAfter 6-month follow-up, 135 of the patients experienced LVR (LVR group), whereas 198 did not (non-LVR group). Results of multivariate analysis showed that the number of stenosed coronary vessels, left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), transforming growth factor-beta (TGF-β) at admission, and cardiac troponin I 3 days after admission (3-d cTnI) were all factors predictive of LVR in patients with acute anterior STEMI (all P<0.05). The established prediction model was Y=−20.639+0.711×number of stenosed coronary vessels + 0.137×LVEDV-0.129×LVEF+0.026×TGF-β at admission + 0.162×3-d cTnI. The estimated AUC of this model was 0.978 (95% confidence interval [CI] 0.955–0.991), significantly superior to the single-factor numbers for stenosed coronary vessel of 0.650 (95% CI 0.597–0.702), LVEDV of 0.876 (95% CI 0.836–0.910), LVEF of 0.684 (95% CI 0.631–0.734), TGF-β at admission of 0.696 (95% CI 0.644–0.745), cTnI at admission of 0.913 (95% CI 0.877–0.941), and 3-d cTnI of 0.945 (95% CI 0.914–0.967).ConclusionsThe established model had excellent diagnostic accuracy for predicting LVR in patients with acute anterior STEMI.

Riesgo quirúrgico tras resección pulmonar anatómica en cirugía torácica. Modelo predictivo a partir de una base de datos nacional multicéntrica

IntroductionThe aim of this study was to develop a surgical risk prediction model in patients undergoing anatomic lung resections from the registry of the Spanish Video-Assisted Thoracic Surgery Group (GEVATS). MethodsData were collected from 3,533 patients undergoing anatomic lung resection for any diagnosis between December 20, 2016 and March 20, 2018.We defined a combined outcome variable: death or Clavien Dindo grade IV complication at 90 days after surgery. Univariate and multivariate analyses were performed by logistic regression. Internal validation of the model was performed using resampling techniques. ResultsThe incidence of the outcome variable was 4.29% (95% CI 3.6-4.9). The variables remaining in the final logistic model were: age, sex, previous lung cancer resection, dyspnea (mMRC), right pneumonectomy, and ppo DLCO. The performance parameters of the model adjusted by resampling were: C-statistic 0.712 (95% CI 0.648-0.750), Brier score 0.042 and bootstrap shrinkage 0.854. ConclusionsThe risk prediction model obtained from the GEVATS database is a simple, valid, and reliable model that is a useful tool for establishing the risk of a patient undergoing anatomic lung resection.