Development and validation of a clinical predictive model for high-volume lymph node metastasis of papillary thyroid carcinoma.

The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815-0.878). In the validation and test sets, the AUCs were 0.831 (0.783-0.879) and 0.845 (0.789-0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757-0.85) and 0.747 (0.709-0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720-0.881) and 0.837 (0.806-0.867), multifocal and solitary cases as 0.803 (0.767-0.838) and 0.809 (0.769-0.849), and Hashimoto's thyroiditis (HT) and non-HT cases as 0.845 (0.793-0.897) and 0.845 (0.819-0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.

Ideal serum non-ceruloplasmin bound copper prediction for long-term treated patients with Wilson disease: a nomogram model.

Purpose: This study aimed to explore the factors associated with the optimal serum non-ceruloplasmin bound copper (NCBC) level and develop a flexible predictive model to guide lifelong therapy in Wilson disease (WD) and delay disease progression. Methods: We retrospectively collected clinical data from 144 patients hospitalized in the Encephalopathy Center of the first affiliated hospital of Anhui University of Chinese Medicine between May 2012 and April 2023. Independent variables were selected using variate COX and LASSO regressions, followed by multivariate COX regression analysis. A predictive nomogram was constructed and validated using the concordance index (C-index), calibration curves, and clinical decision curve analysis, of which nomogram pictures were utilized for model visualization. Results: A total of 61 (42.36%) patients were included, with an average treatment duration of 55.0 (range, 28.0, 97.0) months. Multivariate regression analysis identified several independent risk factors for serum NCBC level, including age of diagnosis, clinical classification, laminin liver stiffness measurement, and copper to zinc ratio in 24-h urinary excretion. The C-index indicated moderate discriminative ability (48 months: 0.829, 60 months: 0.811, and 72 months: 0.819). The calibration curves showed good consistency and calibration; clinical decision curve analysis demonstrated clinically beneficial threshold probabilities at different time intervals. Conclusion: The predictive nomogram model can predict serum NCBC level; consequently, we recommend its use in clinical practice to delay disease progression and improve the clinical prognosis of WD.

Novel model predicts diastolic cardiac dysfunction in type 2 diabetes.

OBJECTIVE: Diabetes mellitus complicated with heart failure has high mortality and morbidity, but no reliable diagnoses and treatments are available. This study aimed to develop and verify a new model nomogram based on clinical parameters to predict diastolic cardiac dysfunction in patients with Type 2 diabetes mellitus (T2DM). METHODS: 3030 patients with T2DM underwent Doppler echocardiography at the First Affiliated Hospital of Shenzhen University between January 2014 and December 2021. The patients were divided into the training dataset (n = 1701) and the verification dataset (n = 1329). In this study, a predictive diastolic cardiac dysfunction nomogram is developed using multivariable logical regression analysis, which contains the candidates selected in a minor absolute shrinkage and selection operator regression model. Discrimination in the prediction model was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The calibration curve was applied to evaluate the calibration of the alignment nomogram, and the clinical decision curve was used to determine the clinical practicability of the alignment map. The verification dataset was used to evaluate the prediction model's performance. RESULTS: A multivariable model that included age, body mass index (BMI), triglyceride (TG), creatine phosphokinase isoenzyme (CK-MB), serum sodium (Na), and urinary albumin/creatinine ratio (UACR) was presented as the nomogram. We obtained the model for estimating diastolic cardiac dysfunction in patients with T2DM. The AUC-ROC of the training dataset in our model was 0.8307, with 95% CI of 0.8109-0.8505. Similar to the results obtained with the training dataset, the AUC-ROC of the verification dataset in our model was 0.8083, with 95% CI of 0.7843-0.8324, thus demonstrating robust. The function of the predictive model was as follows: Diastolic Dysfunction = -4.41303 + 0.14100*Age(year)+0.10491*BMI (kg/m2) +0.12902*TG (mmol/L) +0.03970*CK-MB (ng/mL) -0.03988*Na(mmol/L) +0.65395 * (UACR > 30 mg/g) + 1.10837 * (UACR > 300 mg/g). The calibration plot diagram of predicted probabilities against observed DCM rates indicated excellent concordance. Decision curve analysis demonstrated that the novel nomogram was clinically useful. CONCLUSION: Diastolic cardiac dysfunction in patients with T2DM can be predicted by clinical parameters. Our prediction model may represent an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in T2DM patients and provide a reliable method for early screening of T2DM patients with cardiac complications.KEY MESSAGESThis study used clinical parameters to predict diastolic cardiac dysfunction in patients with T2DM. This study established a nomogram for predicting diastolic cardiac dysfunction by multivariate logical regression analysis. Our predictive model can be used as an effective tool for large-scale epidemiological study of diastolic cardiac dysfunction in patients with T2DM and provides a reliable method for early screening of cardiac complications in patients with T2DM.

Development and validation of a nomogram to predict in-hospital cardiac arrest in critically ill patients / 中华急诊医学杂志

Objective:To explore the independent risk factors of in-hospital cardiac arrest (IHCA) in critically ill patients and construct a nomogram model to predict the risk of IHCA based on the identified risk factors.Methods:Patients who were admitted to the intensive care units (ICUs) from 2008 to 2019 were retrospectively enrolled from the Medical Information Mart for Intensive Care -Ⅳ database. The patients were excluded if they (1) were younger than 18 years old, (2) had repeated ICU admission records, or (3) had an ICU stay shorter than 24 h. The patients were randomly divided into the training and internal validation cohorts (7 : 3). Univariate and multivariate logistic regression models were used to identify independent risk factors of IHCA, and a nomogram was constructed based on these independent risk factors. Calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the nomogram model. Finally, the nomogram was externally validated using the emergency ICU collaborative research database.Results:A total of 41,951 critically ill patients were enrolled (training cohort, n=29 366; internal validation cohort, n=12 585). Multivariate analysis showed that myocardial infarction, pulmonary heart disease, cardiogenic shock, respiratory failure, acute kidney injury, respiratory rate, glucose, hematocrit, sodium, anion gap, vasoactive drug use, and invasive mechanical ventilation were independent risk factors of IHCA. Based on the above risk factors, a nomogram for predicting IHCA was constructed. The area under the ROC curve (AUC) of the nomogram was 0.817 (95% CI: 0.785–0.847). The calibration curve showed that the predicted and actual probabilities of the nomogram were consistent. Moreover, DCA showed that the nomogram had clinical benefits for predicting IHCA. In the internal validation cohort, the nomogram had a similar predictive value of IHCA (AUC=0.807, 95% CI: 0.760–0.862). In an external validation cohort of 87,626 critically ill patients, the nomogram had stable ability for predicting IHCA (AUC=0.804, 95% CI: 0.786–0.822). In addition, the nomogram also had predictive value for in-hospital mortality (AUC=0.818, 95% CI: 0.802-0.834). Conclusions:The nomogram is constructed based on identified independent risk factors, which has good predictive value for IHCA. Moreover, the performance of the nomogram in the external validation cohort is robust. The study findings may help clinicians to assess the risk of IHCA in critically ill patients.

Development and validation of a clinical predictive model for 1-year prognosis in coronary heart disease patients combine with acute heart failure.

Background: The risk factors for acute heart failure (AHF) vary, reducing the accuracy and convenience of AHF prediction. The most common causes of AHF are coronary heart disease (CHD). A short-term clinical predictive model is needed to predict the outcome of AHF, which can help guide early therapeutic intervention. This study aimed to develop a clinical predictive model for 1-year prognosis in CHD patients combined with AHF. Materials and methods: A retrospective analysis was performed on data of 692 patients CHD combined with AHF admitted between January 2020 and December 2020 at a single center. After systemic treatment, patients were discharged and followed up for 1-year for major adverse cardiovascular events (MACE). The clinical characteristics of all patients were collected. Patients were randomly divided into the training (n = 484) and validation cohort (n = 208). Step-wise regression using the Akaike information criterion was performed to select predictors associated with 1-year MACE prognosis. A clinical predictive model was constructed based on the selected predictors. The predictive performance and discriminative ability of the predictive model were determined using the area under the curve, calibration curve, and clinical usefulness. Results: On step-wise regression analysis of the training cohort, predictors for MACE of CHD patients combined with AHF were diabetes, NYHA ≥ 3, HF history, Hcy, Lp-PLA2, and NT-proBNP, which were incorporated into the predictive model. The AUC of the predictive model was 0.847 [95% confidence interval (CI): 0.811-0.882] in the training cohort and 0.839 (95% CI: 0.780-0.893) in the validation cohort. The calibration curve indicated good agreement between prediction by nomogram and actual observation. Decision curve analysis showed that the nomogram was clinically useful. Conclusion: The proposed clinical prediction model we have established is effective, which can accurately predict the occurrence of early MACE in CHD patients combined with AHF.

Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma.

Immune checkpoint inhibitors have emerged as a novel therapeutic strategy for many different tumors, including clear cell renal cell carcinoma (ccRCC). However, these drugs are only effective in some ccRCC patients, and can produce a wide range of immune-related adverse reactions. Previous studies have found that ccRCC is different from other tumors, and common biomarkers such as tumor mutational burden, HLA type, and degree of immunological infiltration cannot predict the response of ccRCC to immunotherapy. Therefore, it is necessary to further research and construct corresponding clinical prediction models to predict the efficacy of Immune checkpoint inhibitors. We integrated PBRM1 mutation data, transcriptome data, endogenous retrovirus data, and gene copy number data from 123 patients with advanced ccRCC who participated in prospective clinical trials of PD-1 inhibitors (including CheckMate 009, CheckMate 010, and CheckMate 025 trials). We used AI to optimize mutation data interpretation and established clinical prediction models for survival (for overall survival AUC: 0.931; for progression-free survival AUC: 0.795) and response (ORR AUC: 0.763) to immunotherapy of ccRCC. The models were internally validated by bootstrap. Well-fitted calibration curves were also generated for the nomogram models. Our models showed good performance in predicting survival and response to immunotherapy of ccRCC.

Risk assessment and prediction model of renal damage in childhood immunoglobulin A vasculitis.

Objectives: To explore the risk factors for renal damage in childhood immunoglobulin A vasculitis (IgAV) within 6 months and construct a clinical model for individual risk prediction. Methods: We retrospectively analyzed the clinical data of 1,007 children in our hospital and 287 children in other hospitals who were diagnosed with IgAV. Approximately 70% of the cases in our hospital were randomly selected using statistical product service soltions (SPSS) software for modeling. The remaining 30% of the cases were selected for internal verification, and the other hospital's cases were reviewed for external verification. A clinical prediction model for renal damage in children with IgAV was constructed by analyzing the modeling data through single-factor and multiple-factor logistic regression analyses. Then, we assessed and verified the degree of discrimination, calibration and clinical usefulness of the model. Finally, the prediction model was rendered in the form of a nomogram. Results: Age, persistent cutaneous purpura, erythrocyte distribution width, complement C3, immunoglobulin G and triglycerides were independent influencing factors of renal damage in IgAV. Based on these factors, the area under the curve (AUC) for the prediction model was 0.772; the calibration curve did not significantly deviate from the ideal curve; and the clinical decision curve was higher than two extreme lines when the prediction probability was ~15-82%. When the internal and external verification datasets were applied to the prediction model, the AUC was 0.729 and 0.750, respectively, and the Z test was compared with the modeling AUC, P > 0.05. The calibration curves fluctuated around the ideal curve, and the clinical decision curve was higher than two extreme lines when the prediction probability was 25~84% and 14~73%, respectively. Conclusion: The prediction model has a good degree of discrimination, calibration and clinical usefulness. Either the internal or external verification has better clinical efficacy, indicating that the model has repeatability and portability. Clinical trial registration: www.chictr.org.cn, identifier ChiCTR2000033435.

Creation and Internal Validation of a Clinical Predictive Model for Fluconazole Resistance in Patients With Candida Bloodstream Infection.

Background: Fluconazole is recommended as first-line therapy for candidemia when risk of fluconazole resistance (fluc-R) is low. Lack of methods to estimate resistance risk results in extended use of echinocandins and prolonged hospitalization. This study aimed to develop a clinical predictive model to identify patients at low risk for fluc-R where initial or early step-down fluconazole would be appropriate. Methods: Retrospective analysis of hospitalized adult patients with positive blood culture for Candida spp from 2013 to 2019. Multivariable logistic regression model was performed to identify factors associated with fluc-R. Stepwise regression was performed on bootstrapped samples to test individual variable stability and estimate confidence intervals (CIs). We used receiver operating characteristic curves to assess performance across the probability spectrum. Results: We identified 539 adults with candidemia and 72 Candida isolates (13.4%) were fluc-R. Increased risk of fluc-R was associated with older age, prior bacterial bloodstream infection (odds ratio [OR], 2.02 [95% CI, 1.13-3.63]), myelodysplastic syndrome (OR, 3.09 [95% CI, 1.13-8.44]), receipt of azole therapy (OR, 5.42 [95% CI, 2.90-10.1]) within 1 year of index blood culture, and history of bone marrow or stem cell transplant (OR, 2.81 [95% CI, 1.41-5.63]). The model had good discrimination (optimism-corrected c-statistic 0.771), and all of the selected variables were stable. The prediction model had a negative predictive value of 95.7% for the selected sensitivity cutoff of 90.3%. Conclusions: This model is a potential tool for identifying patients at low risk for fluc-R candidemia to receive first-line or early step-down fluconazole.

[A Prognostic Nomogram Based on Response to Bortezomib and BTK Expression for Treatment-Experienced Multiple Myeloma Patients].

OBJECTIVE: To establish a prognostic nomogram based on response to bortezomib and BTK expression for treatment-experienced multiple myeloma patients. METHODS: The Oncomine database was utilized to determine BTK expression, sex, age, albumin, Mayo index, response to bortezomib treatment, follow-up time and survival status in multiple myeloma(MM) patients. Cut-off point for BTK expression was calculated using R software. Univariate and multivariate analyses by Cox proportional hazards regression were then performed. Significant prognostic factors were combined to build a nomogram. The discrimination ability and predictive accuracy of the nomogram were evaluated using the index of concordance (C-index) and calibration curves. RESULTS: Multivariate analysis showed that response to bortezomib, BTK expression and sex were independent risk factors for prognosis. The C-index value of the nomogram made according to the independent risk factors was 0.729 (95%CI, 0.642-0.8164). The calibration curves showed good consistency between predicted and actual survivals for 1-year and 2-year overall survival. CONCLUSION: The proposed nomogram is accurate in predicting the prognosis of patients with MM.

Clinical predictive models of invasive Candida infection: A systematic literature review.

Clinical predictive models (CPM) serve to identify and categorize patients into risk categories to assist in treatment and intervention recommendations. Predictive accuracy and practicality of models varies depending on methods used for their development, and should be evaluated. The aim of this study was to summarize currently available CPM for invasive candidiasis, analyze their performance, and assess their suitability for use in clinical decision making. We identified studies that described the construction of a CPM for invasive candidiasis from PubMed/MEDLINE, EMBASE, SCOPUS, Web of Science, Cochrane Library databases, and Clinicaltrials.gov. Data extracted included: author, data source, study design, recruitment period, characteristics of study population, outcome types, predictor types, number of study participants and outcome events, modelling method, and list of predictors used in the final model. Calibration and discrimination in the derivative datasets were used to assess the performance of each model. Ten articles were identified in our search and included for full text review. Five models were developed using data from ICUs, and five models included all hospitalized patients. The findings of this review highlight the limitations of currently available models to predict invasive candidiasis, including lack of generalizability, difficulty in everyday clinical use, and overly optimistic performance. There are significant concerns regarding predictive performance and usability in every day practice of existing CPM to predict invasive candidiasis.

Clinical predictive models may assist in early identification of patients at risk for invasive candidiasis to initiate appropriate treatment. The findings of this systematic review highlight the limitations of currently available models to predict invasive candidiasis.

Predictive Value of Limb Artery Indices and Endothelial Functional Tests for the Degree of Coronary Artery Stenosis in a Diabetic Population.

OBJECTIVE: To investigate the correlation between limb artery indices (brachial-ankle pulse wave velocity and ankle-brachial index), endothelial function index (FMD value), and the degree of coronary artery stenosis in diabetic patients and analyze their values in predicting the degree of coronary artery stenosis. METHODS: The study included 151 patients with type 2 diabetes mellitus and suspected coronary atherosclerotic heart disease. The patients were divided into "coronary atherosclerotic heart disease" (N=94) and "non-coronary atherosclerotic heart disease" (N=57) groups based on the coronary angiographic findings. Within the coronary atherosclerotic heart disease group, the patients were further divided into "low stenosis" (N=47) and "high stenosis" (N=47) subgroups according to their Gensini score. Indicators such as brachial-ankle pulse wave velocity, ankle-brachial index, and FMD value were measured and correlated with the degree of coronary artery stenosis. Logistic regression models were constructed and receiver operating characteristic curves plotted to assess the predictive ability of limb artery and endothelial functional indices for the degree of coronary artery stenosis. RESULTS: In a diabetic population, FMD value (P=0.003), ankle-brachial index (P=0.004), and brachial-ankle pulse wave velocity (P=0.003) were different in patients with and without coronary atherosclerotic heart disease. In the population with both diabetes mellitus and coronary atherosclerotic heart disease, the ankle-brachial index and FMD value were both independently associated with the degree of coronary artery stenosis (P=0.003). The area under the receiver operating characteristic curve plotted from the combined coefficients of ankle-brachial index and FMD value was 0.773, which is predictive of coronary artery stenosis in diabetic patients. CONCLUSION: Ankle-brachial index and FMD value are indicative of the degree of coronary artery stenosis in diabetic patients, and predictive efficacy can be improved by combining the two tests.

Predicting Mechanical Ventilation Using the EGRIS in Guillain-Barré Syndrome in a Latin American Country.

BACKGROUND: Up to one fifth of patients with Guillain-Barré syndrome (GBS) require mechanical ventilation (MV). The Erasmus GBS Respiratory Insufficiency Score (EGRIS) is a clinical predictive model developed in Europe to predict MV requirements among patients with GBS. However, there are significant differences between the Latin American and European population, especially in the distribution of GBS subtypes. Therefore, determining if the EGRIS is able to predict MV in a Latin American population is of clinical significance. METHODS: We retrospectively analyzed clinical and laboratory data of 177 patients with GBS in three Peruvian hospitals. We performed a multivariate logistic regression of the factors making up the EGRIS. Finally, we evaluated the EGRIS discrimination through a receiver operating characteristic curve and determined its calibration through a calibration curve and a Hosmer-Lemeshow test, a test used to determine the goodness of fit. RESULTS: We found that 14.1% of our patients required MV. One predictive factor of a patient's need for early MV was the number of days between the onset of motor symptoms and hospitalization. The Medical Research Council sum score did not alter the likelihood of early MV. Bulbar weakness increased the likelihood without showing statistical significance. In contrast, facial weakness was a protective factor of it. The EGRIS was significantly higher in patients who required early MV than in those who did not (P = 0.018). It showed an area under the curve (AUC) of 0.63, with an insignificant Hosmer-Lemeshow test result. CONCLUSIONS: Although the EGRIS was higher in patients who required early MV than in those who did not, it only showed a moderate discrimination capacity (AUC = 0.63). Facial weakness, an item of the EGRIS, was not found to be a predictive factor in our population. We suggest assessing whether these findings are due to subtype predominance and whether a modified version of the EGRIS could improve performance.

A Scoring System for Surgical Site Infection after Pancreaticoduodenectomy Using Clinical Data.

Object: To analyze the factors influencing surgical site infection (SSI) after pancreaticoduodenectomy and to establish a scoring system for predicting such infections. Methods: Patients who underwent pancreaticoduodenectomy in the Department of Hepatobiliary Surgery of the Second Affiliated Hospital of Chongqing Medical University from January 2015 to March 2019 were divided randomly into a model group and a test group in a proportion of 3:1. According to whether an SSI occurred after operation, the model group was divided into an incision-infection group and a non-infection group. Univariable analysis and multivariable regression analysis were used to analyze factors related to post-operative incision infection and to establish a clinical predictive scoring system. The scoring system was evaluated for the test group. Results: A total of 236 patients, 177 in the model group and 59 in the test group, were included. In the model group, univariable and logistic regression analysis showed that tumor nature (benign versus malignant), post-operative albumin concentration, pancreatic fistula formation, post-operative cough, and peri-operative blood transfusion were the independent risk factors for incision infection. Then we established a clinical predictive scoring system. In the test group, the area under the receiver operator characteristic curve of the system was 0.768 (p < 0.001, with sensitivity = 59.1% and specificity = 94.6%). Conclusion: The scoring system had good clinical prediction ability and high specificity, so it was worth using in the clinic.

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma.

Background: Metabolic syndrome (MetS) is associated with the development of esophageal squamous cell carcinoma (ESCC), and long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms of MetS and tumor. This study will explore the prognostic effect of MetS and the associated lncRNA signature on ESCC. Methods: Our previous RNA-chip data (GSE53624, GSE53622) for 179 ESCC patients were reanalyzed according to MetS. The recurrence-free survival (RFS) was collected for these patients. The status of the MetS-related tumor microenvironment was analyzed with the CIBERSORT and ESTIMATE algorithms. A lncRNA signature was established with univariate and multivariate Cox proportional hazards regression (PHR) analysis and verified using the Kaplan-Meier survival curve analysis and time-dependent receiver operating characteristic (ROC) curves. A clinical predictive model was constructed based on multiple risk factors, evaluated using C-indexes and calibration curves, and verified using data from the GEO and TCGA databases. Results: The results showed that MetS was an independent risk factor for ESCC patients conferring low OS and RFS. Tumor microenvironment analysis indicated that patients with MetS have high stromal scores and M2 macrophage infiltration. A six-lncRNA signature was established by 60 ESCC patients randomly selected from GSE53624 and identified with an effective predictive ability in validation cohorts (59 patients from GSE53624 and 60 patients from GSE53622), subgroup analysis, and ESCC patients from TCGA. MetS and the six-lncRNA signature could be regarded as independent risk factors and enhanced predictive ability in the clinical predictive model. Conclusions: Our results indicated that MetS was associated with poor prognosis in ESCC patients, and the possible mechanism was related to changes in the tumor microenvironment. MetS and the six-lncRNA signature could also serve as independent risk factors with available clinical application value.

Creation and Assessment of a Clinical Predictive Calculator and Mortality Associated With Candida krusei Bloodstream Infections.

BACKGROUND: Candida krusei bloodstream infection (CK BSI) is associated with high mortality, but whether this is due to underlying comorbidities in affected patients or the organism itself is unknown. Identifying patient characteristics that are associated with CK BSI is crucial for clinical decision-making and prognosis. METHODS: We conducted a retrospective analysis of hospitalized patients with Candida BSI at our institution between 2002 and 2015. Data were collected on demographics, comorbidities, medications, procedures, central lines, vital signs, and laboratory values. Multivariable logistic and Cox regression were used to identify risk factors associated with CK and mortality, respectively. RESULTS: We identified 1873 individual patients who developed Candida BSI within the study period, 59 of whom had CK BSI. CK BSI was predicted by hematologic malignancy, gastric malignancy, neutropenia, and the use of prophylactic azole antifungals, monoclonal antibodies, and ß-lactam/ß-lactamase inhibitor combinations. The C-statistic was 0.86 (95% confidence interval, 0.81-0.91). The crude mortality rates were 64.4% for CK BSI and 41.4% for non-CK BSI. Although CK was associated with higher mortality in univariable Cox regression, this relationship was no longer significant with the addition of the following confounders: lymphoma, neutropenia, glucocorticoid use, chronic liver disease, and elevated creatinine. CONCLUSIONS: Six patient comorbidities predicted the development of CK BSI with high accuracy. Although patients with CK BSI have higher crude mortality rates than patients with non-CK BSI, this difference is not significant when accounting for other patient characteristics.

Regional Validation and Recalibration of Clinical Predictive Models for Patients With Acute Heart Failure.

BACKGROUND: Heart failure clinical practice guidelines recommend applying validated clinical predictive models (CPMs) to support decision making. While CPMs are now widely available, the generalizability of heart failure CPMs is largely unknown. METHODS AND RESULTS: We identified CPMs derived in North America that predict mortality for patients with acute heart failure and validated these models in different world regions to assess performance in a contemporary international clinical trial (N=4133) of patients with acute heart failure treated with guideline-directed medical therapy. We performed independent external validations of 3 CPMs predicting in-hospital mortality, 60-day mortality, and 1-year mortality, respectively. CPM discrimination decreased in all regional validation cohorts. The median change in area under the receiver operating curve was -0.09 (range -0.05 to -0.23). Regional calibration was highly variable (90th percentile of absolute difference between smoothed observed and predicted values range <1% to >50%). Calibration remained poor after global recalibrations; however, region-specific recalibration procedures significantly improved regional performance (recalibrated 90th percentile of absolute difference range <1% to 5% across all regions and all models). CONCLUSIONS: Acute heart failure CPM discrimination and calibration vary substantially across different world regions; region-specific (as opposed to global) recalibration techniques are needed to improve CPM calibration.

Predictive model for inflammation grades of chronic hepatitis B: Large-scale analysis of clinical parameters and gene expressions.

BACKGROUND: Liver biopsy is the gold standard to assess pathological features (eg inflammation grades) for hepatitis B virus-infected patients although it is invasive and traumatic; meanwhile, several gene profiles of chronic hepatitis B (CHB) have been separately described in relatively small hepatitis B virus (HBV)-infected samples. We aimed to analyse correlations among inflammation grades, gene expressions and clinical parameters (serum alanine amino transaminase, aspartate amino transaminase and HBV-DNA) in large-scale CHB samples and to predict inflammation grades by using clinical parameters and/or gene expressions. METHODS: We analysed gene expressions with three clinical parameters in 122 CHB samples by an improved regression model. Principal component analysis and machine-learning methods including Random Forest, K-nearest neighbour and support vector machine were used for analysis and further diagnosis models. Six normal samples were conducted to validate the predictive model. RESULTS: Significant genes related to clinical parameters were found enriching in the immune system, interferon-stimulated, regulation of cytokine production, anti-apoptosis, and etc. A panel of these genes with clinical parameters can effectively predict binary classifications of inflammation grade (area under the ROC curve [AUC]: 0.88, 95% confidence interval [CI]: 0.77-0.93), validated by normal samples. A panel with only clinical parameters was also valuable (AUC: 0.78, 95% CI: 0.65-0.86), indicating that liquid biopsy method for detecting the pathology of CHB is possible. CONCLUSIONS: This is the first study to systematically elucidate the relationships among gene expressions, clinical parameters and pathological inflammation grades in CHB, and to build models predicting inflammation grades by gene expressions and/or clinical parameters as well.

Tufts PACE Clinical Predictive Model Registry: update 1990 through 2015.

BACKGROUND: Clinical predictive models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision-making and individualize care. The Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry is a comprehensive database of cardiovascular disease (CVD) CPMs. The Registry was last updated in 2012, and there continues to be substantial growth in the number of available CPMs. METHODS: We updated a systematic review of CPMs for CVD to include articles published from January 1990 to March 2015. CVD includes coronary artery disease (CAD), congestive heart failure (CHF), arrhythmias, stroke, venous thromboembolism (VTE), and peripheral vascular disease (PVD). The updated Registry characterizes CPMs based on population under study, model performance, covariates, and predicted outcomes. RESULTS: The Registry includes 747 articles presenting 1083 models, including both prognostic (n = 1060) and diagnostic (n = 23) CPMs representing 183 distinct index condition/outcome pairs. There was a threefold increase in the number of CPMs published between 2005 and 2014, compared to the prior 10-year interval from 1995 to 2004. The majority of CPMs were derived from either North American (n = 455, 42%) or European (n = 344, 32%) populations. The database contains 265 CPMs predicting outcomes for patients with coronary artery disease, 196 CPMs for population samples at risk for incident CVD, and 158 models for patients with stroke. Approximately two thirds (n = 701, 65%) of CPMs report a c-statistic, with a median reported c-statistic of 0.77 (IQR, 0.05). Of the CPMs reporting validations, only 333 (57%) report some measure of model calibration. Reporting of discrimination but not calibration is improving over time (p for trend < 0.0001 and 0.39 respectively). CONCLUSIONS: There is substantial redundancy of CPMs for a wide spectrum of CVD conditions. While the number of CPMs continues to increase, model performance is often inadequately reported and calibration is infrequently assessed. More work is needed to understand the potential impact of this literature.