Identifying patients at high risk for neutropenic complications during chemotherapy for metastatic breast cancer (MBC) with doxorubicin or pegylated liposomal doxorubicin: Development of a prediction model

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 6598-6598
Author(s):  
G. Reardon ◽  
D. Rayson ◽  
J. Chang ◽  
K. Gelmon ◽  
G. Dranitsaris
Keyword(s):  
High Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Metastatic Breast ◽  
Pegylated Liposomal Doxorubicin ◽  
Risk Scores ◽  
Patient Treatment ◽  
Poor Performance Status ◽  
P Value ◽  
Prediction Tool

6598 Background: Despite the effectiveness of anthracycline (ACH) therapy in the adjuvant and MBC settings, neutropenic complications (NC) remain a common and often unpredictable problem. Consequences may include dose reductions or delays in chemotherapy, or hospitalization for fever or infection. This study describes the development of a cycle-based risk prediction model for NC during chemotherapy with traditional doxorubicin (DOX) or a pegylated liposomal formulation (PLD) for MBC. Methods: Data analyzed was from a randomized clinical trial of MBC patients (n=509), who received chemotherapy with DOX (60 mg/m2 every 3 wks) or PLD (50 mg/m2 every 4 wks) [O'Brien, 2004]. NC were defined as an absolute neutrophil count (ANC) = 1.5 x106 cells/L, febrile neutropenia or neutropenia with infection. Patient, treatment and hematological factors potentially associated with NC were evaluated. Factors with a p-value of ≤ 0.25 within a cycle were included in a generalized estimating equations (GEE) regression model. Using backward elimination, we derived a risk scoring algorithm (range 0–63) from the final reduced model. Results: Risk factors retained in the model included poor performance status, ANC = 2.0 × 106 cells/L at some point in the previous cycle, the first cycle of chemotherapy, DOX vs. PLD and older age. A precycle risk score from = 25 to < 40 for a given patient was identified as being the optimal threshold for sensitivity (58.0%) and specificity (78.7%). Patients with a score at or beyond this threshold would be considered at high risk for developing NC in later cycles. Risk scores below, within, or above this threshold predict a 0.3%–2%, 3%–8% and a 9%–45% probability risk of NC, respectively. Conclusion: This risk prediction tool demonstrated acceptable internal validity and can be readily applied by the clinician prior to a given cycle of chemotherapy. The application of this prediction tool may allow for identification and targeted intervention (such as growth factor support or the use of PLD) for those most likely to experience NC during anthracycline-based chemotherapy for MBC. No significant financial relationships to disclose.

scholarly journals Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jun Miyoshi ◽  
Tsubasa Maeda ◽  
Katsuyoshi Matsuoka ◽  
Daisuke Saito ◽  
Sawako Miyoshi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ulcerative Colitis ◽  
Prediction Model ◽  
Clinical Features ◽  
Unmet Need ◽  
Patient Treatment ◽  
Clinical Activity ◽  
Prediction Tool ◽  
Real World Data ◽  
Predictive Values

AbstractPredicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (VDZ) before administration is an unmet need for optimizing individual patient treatment. We hypothesized that the machine-learning approach with daily clinical information can be a new, promising strategy for developing a drug-efficacy prediction tool. Random forest with grid search and cross-validation was employed in Cohort 1 to determine the contribution of clinical features at baseline (week 0) to steroid-free clinical remission (SFCR) with VDZ at week 22. Among 49 clinical features including sex, age, height, body weight, BMI, disease duration/phenotype, treatment history, clinical activity, endoscopic activity, and blood test items, the top eight features (partial Mayo score, MCH, BMI, BUN, concomitant use of AZA, lymphocyte fraction, height, and CRP) were selected for logistic regression to develop a prediction model for SFCR at week 22. In the validation using the external Cohort 2, the positive and negative predictive values of the prediction model were 54.5% and 92.3%, respectively. The prediction tool appeared useful for identifying patients with UC who would not achieve SFCR at week 22 during VDZ therapy. This study provides a proof-of-concept that machine learning using real-world data could permit personalized treatment for UC.

scholarly journals Development and Validation of a Clostridium difficile Infection Risk Prediction Model

2011 ◽  
Vol 32 (4) ◽  
pp. 360-366 ◽  
Author(s):  
Erik R. Dubberke ◽  
Yan Yan ◽  
Kimberly A. Reske ◽  
Anne M. Butler ◽  
Joshua Doherty ◽  
...  
Keyword(s):  
High Risk ◽  
Clostridium Difficile ◽  
Prediction Model ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Tertiary Care ◽  
Risk Prediction Model ◽  
Brier Score ◽  
Tertiary Care Medical Center

Objective.To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease.Design and Setting.Retrospective cohort study in a tertiary care medical center.Patients.Patients admitted to the hospital for at least 48 hours during the calendar year 2003.Methods.Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.Results.A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).Conclusions.The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.

Underperformance of clinical risk scores in identifying vascular ultrasound-based high cardiovascular risk in systemic lupus erythematosus

2020 ◽  
Vol 28 (3) ◽  
pp. 346-352
Author(s):  
George C Drosos ◽  
George Konstantonis ◽  
Petros P Sfikakis ◽  
Maria G Tektonidou
Keyword(s):  
Systemic Lupus Erythematosus ◽  
High Risk ◽  
Lupus Erythematosus ◽  
Risk Scores ◽  
P Value ◽  
Atherosclerotic Plaques ◽  
Systemic Lupus ◽  
Cvd Risk ◽  
Risk Models ◽  
Clinical Risk

Abstract Aims The aim of this study was to assess the performance of eight clinical risk prediction scores to identify individuals with systemic lupus erythematosus (SLE) at high cardiovascular disease (CVD) risk, as defined by the presence of atherosclerotic plaques. Methods CVD risk was estimated in 210 eligible SLE patients without prior CVD or diabetes mellitus (female: 93.3%, mean age: 44.8 ± 12 years) using five generic (Systematic Coronary Risk Evaluation (SCORE), Framingham Risk Score (FRS), Pooled Cohort Risk Equations (ASCVD), Globorisk, Prospective Cardiovascular Münster Study risk calculator (PROCAM)) and three ‘SLE-adapted’ (modified-SCORE, modified-FRS, QRESEARCH risk estimator, version 3 (QRISK3)) CVD risk scores, as well as ultrasound examination of the carotid and femoral arteries. Calibration, discrimination and classification measures to identify high CVD risk based on the presence of atherosclerotic plaques were assessed for all risk models. CVD risk reclassification was applied for all scores by incorporating ultrasound results. Results Moderate calibration (p-value range from 0.38 to 0.63) and discrimination (area under the curve 0.73–0.84), and low-to-moderate sensitivity (8.3–71.4%) and classification ability (Matthews correlation coefficient (MCC) 0.25–0.47) were observed for all risk models to identify patients with plaques at any arterial site as high-risk. MCC was improved for modified-FRS versus FRS (0.43 vs 0.36), but not for modified-SCORE versus SCORE (0.25 vs 0.25). Based on plaque presence, CVD risk was upgraded to high-risk in 10%, 16.1%, 20.5%, 21.5%, 24%, 28.2% and 28.6% of cases classified as non-high-risk by QRISK3, modified-FRS, Globorisk, FRS/PROCAM, ASCVD, modified-SCORE and SCORE, respectively. Conclusions Most of the five generic and three ‘SLE-adapted’ clinical risk scores underestimated high CVD risk defined by atherosclerotic plaque presence in patients with SLE.

scholarly journals A prospective study of acute kidney injury in the intensive care unit: development and validation of a risk prediction model

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Qi Wang ◽  
Yi Tang ◽  
Jiaojiao Zhou ◽  
Wei Qin
Keyword(s):  
Chronic Kidney Disease ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
High Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Kidney Injury ◽  
Risk Prediction Model ◽  
Risk Patients

Abstract Background Acute kidney injury (AKI) has high morbidity and mortality in intensive care units (ICU). It can also lead to chronic kidney disease (CKD), more costs and longer hospital stay. Early identification of AKI is important. Methods We conducted this monocenter prospective observational study at West China Hospital, Sichuan University, China. We recorded information of each patient in the ICU within 24 h after admission and updated every two days. Patients who reached the primary outcome were accepted into the AKI group. Of all patients, we randomly drew 70% as the development cohort and the remaining 30% as the validation cohort. Using binary logistic regression we got a risk prediction model of the development cohort. In the validation cohort, we validated its discrimination by the area under the receiver operator curve (AUROC) and calibration by a calibration curve. Results There were 656 patients in the development cohorts and 280 in the validation cohort. Independent predictors of AKI in the risk prediction model including hypertension, chronic kidney disease, acute pancreatitis, cardiac failure, shock, pH ≤ 7.30, CK > 1000 U/L, hypoproteinemia, nephrotoxin exposure, and male. In the validation cohort, the AUROC is 0.783 (95% CI 0.730–0.836) and the calibration curve shows good calibration of this prediction model. The optimal cut-off value to distinguish high-risk and low-risk patients is 4.5 points (sensitivity is 78.4%, specificity is 73.2% and Youden’s index is 0.516). Conclusions This risk prediction model can help to identify high-risk patients of AKI in ICU to prevent the development of AKI and treat it at the early stages. Trial registration TCTR, TCTR20170531001. Registered 30 May 2017, http://www.clinicaltrials.in.th/index.php?tp=regtrials&menu=trialsearch&smenu=fulltext&task=search&task2=view1&id=2573

scholarly journals Effects of semaglutide on risk of cardiovascular events across a continuum of cardiovascular risk: combined post hoc analysis of the SUSTAIN and PIONEER trials

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Mansoor Husain ◽  
Stephen C. Bain ◽  
Anders Gaarsdal Holst ◽  
Thomas Mark ◽  
Søren Rasmussen ◽  
...  
Keyword(s):  
Relative Risk ◽  
High Risk ◽  
Prediction Model ◽  
Risk Reduction ◽  
Risk Prediction ◽  
Risk Score ◽  
Relative Risk Reduction ◽  
Absolute Risk ◽  
Post Hoc Analysis ◽  
Post Hoc

Abstract Background Semaglutide is a glucagon-like peptide-1 (GLP-1) analog treatment for type 2 diabetes (T2D) available in subcutaneous (s.c.) and oral formulations. Two cardiovascular (CV) outcomes trials showed that in subjects with T2D at high risk of CV events there were fewer major adverse CV events (MACE; defined as CV death, non-fatal stroke, non-fatal myocardial infarction) with semaglutide than with placebo (hazard ratio [95% CI]: 0.74 [0.58;0.95] for once-weekly s.c. semaglutide and 0.79 [0.57;1.11] for once-daily oral semaglutide). However, there is little evidence for an effect of semaglutide on MACE in subjects not at high risk of CV events. This post hoc analysis examined CV effects of semaglutide in subjects across a continuum of baseline CV risk. Methods Data from the s.c. (SUSTAIN) and oral (PIONEER) semaglutide phase 3a clinical trial programs were combined according to randomized treatment (semaglutide or comparators) and analyzed to assess time to first MACE and its individual components. A CV risk model was developed with independent data from the LEADER trial (liraglutide vs placebo), considering baseline variables common to all datasets. Semaglutide data were analyzed to assess effects of treatment as a function of CV risk predicted using the CV risk prediction model. Results The CV risk prediction model performed satisfactorily when applied to the semaglutide data set (area under the curve: 0.77). There was a reduced relative and absolute risk of MACE for semaglutide vs comparators across the entire continuum of CV risk. While the relative risk reduction tended to be largest with low CV risk score, the largest absolute risk reduction was for intermediate to high CV risk score. Similar results were seen for relative risk reduction of the individual MACE components and also when only placebo comparator data were included. Conclusion Semaglutide reduced the risk of MACE vs comparators across the continuum of baseline CV risk in a broad T2D population. Trial registrations ClinicalTrials.gov identifiers: NCT02054897, NCT01930188, NCT01885208, NCT02128932, NCT02305381, NCT01720446, NCT02207374, NCT02254291, NCT02906930, NCT02863328, NCT02607865, NCT02863419, NCT02827708, NCT02692716, NCT02849080, NCT03021187, NCT03018028, NCT03015220.

Risk prediction of anthracycline-related cardiomyopathy (AC) in childhood cancer survivors (CCS): A COG-ALTE03N1 and CCSS report.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 10015-10015 ◽  
Author(s):  
Xuexia Wang ◽  
Yanjun Chen ◽  
Lindsey Hageman ◽  
Purnima Singh ◽  
Wendy Landier ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Characteristic Curve ◽  
Low Risk ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
Clinical Genetic ◽  
Final Model ◽  
Clinical Model ◽  
Gxe Interaction

10015 Background: CCS treated with anthracyclines are at risk for AC. While risk increases with dose, significant inter-patient variability in AC risk suggests a role for genetic predisposition in moderating the risk and provides an opportunity to identify patients at high or low risk. Methods: We curated candidate single nucleotide polymorphisms (SNPs) associated with AC from previous publications and used these to develop a risk prediction model, drawing upon COG-ALTE03N1 (CCS with AC [155 cases] matched with CCS without AC [256 controls]). Final Model (clinical + genetic) was obtained using backward variable selection guided by effect on area under receiver operating characteristic curve (AUC). Bootstrapping corrected for optimism of AUC. Regression coefficient estimates from Final Model were used to calculate risk scores, which were used to create risk groups. We validated the model in an independent sample from CCSS (229 cases; 5,360 controls). Results: Previously-published SNPs (rs1786814 [ CELF4], rs11864374 [ ABCC1], rs1800566 [ NQO1], rs4673 [ CYBA], rs2232228 [ HAS3]) were verified in COG-ALTE03N1 and were included, along with GxE interaction of rs1786814, rs4673, rs2232228 in a Final Model containing age at cancer, sex, race, cumulative anthracyclines (mg/m2), chest radiation, diabetes, hypertension, dyslipidemia. This yielded an optimism-corrected AUC = 0.8138, which was superior ( P= 0.0002) to the Clinical Model (corrected AUC = 0.7677). The sensitivity/specificity of the prediction model were 73.7%/ 81.3%. The prediction model was successfully replicated in CCSS (Final Model performed significantly better than the Clinical Model, P= 0.02). Conclusions: It is possible to identify CCS at high or low risk for AC on the basis of genetic and clinical information. This information can be used to inform interventions in CCS. [Table: see text]

scholarly journals Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

PLoS Medicine ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. e1003498
Author(s):  
Luanluan Sun ◽  
Lisa Pennells ◽  
Stephen Kaptoge ◽  
Christopher P. Nelson ◽  
Scott C. Ritchie ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Scores ◽  
Cvd Risk ◽  
Polygenic Risk ◽  
History Of ◽  
Predicted Risk ◽  
Conventional Risk Factors

Background Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD. Methods and findings Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703–0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009–0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40–75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to <10%) 10-year CVD risk could help prevent 1 additional CVD event for approximately every 340 individuals screened. Such a targeted strategy could help prevent 7% more CVD events than conventional risk prediction alone. Potential gains afforded by assessment of PRSs on top of conventional risk factors would be about 1.5-fold greater than those provided by assessment of C-reactive protein, a plasma biomarker included in some risk prediction guidelines. Potential limitations of this study include its restriction to European ancestry participants and a lack of health economic evaluation. Conclusions Our results suggest that addition of PRSs to conventional risk factors can modestly enhance prediction of first-onset CVD and could translate into population health benefits if used at scale.

scholarly journals Identification of risk factors for daptomycin-associated creatine phosphokinase elevation and development of a risk prediction model for incidence probability

2021 ◽  
Author(s):  
Masaru Samura ◽  
Naoki Hirose ◽  
Takenori Kurata ◽  
Keisuke Takada ◽  
Fumio Nagumo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Logistic Regression Analysis ◽  
Creatine Phosphokinase ◽  
Risk Prediction Model ◽  
Risk Scores

Abstract Background In this study, we investigated the risk factors for daptomycin-associated creatine phosphokinase (CPK) elevation and established a risk score for CPK elevation. Methods Patients who received daptomycin at our hospital were classified into the normal or elevated CPK group based on their peak CPK levels during daptomycin therapy. Univariable and multivariable analyses were performed, and a risk score and prediction model for the incidence probability of CPK elevation were calculated based on logistic regression analysis. Results The normal and elevated CPK groups included 181 and 17 patients, respectively. Logistic regression analysis revealed that concomitant statin use (odds ratio [OR] 4.45, 95% confidence interval [CI] 1.40–14.47, risk score 4), concomitant antihistamine use (OR 5.66, 95% CI 1.58–20.75, risk score 4), and trough concentration (Cmin) between 20 and &lt;30 µg/mL (OR 14.48, 95% CI 2.90–87.13, risk score 5) and ≥30.0 µg/mL (OR 24.64, 95% CI 3.21–204.53, risk score 5) were risk factors for daptomycin-associated CPK elevation. The predicted incidence probabilities of CPK elevation were &lt;10% (low risk), 10%–&lt;25% (moderate risk), and ≥25% (high risk) with the total risk scores of ≤4, 5–6, and ≥8, respectively. The risk prediction model exhibited a good fit (area under the receiving-operating characteristic curve 0.85, 95% CI 0.74–0.95). Conclusions These results suggested that concomitant use of statins with antihistamines and Cmin ≥20 µg/mL were risk factors for daptomycin-associated CPK elevation. Our prediction model might aid in reducing the incidence of daptomycin-associated CPK elevation.

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