Clinical risk factors and predictive tool of bacteremia in patients with cirrhosis

Objective We aimed to analyze the risk factors and to establish a predictive tool for the occurrence of bloodstream infections (BSI) in patients with cirrhosis. Methods A total of 2888 patients with cirrhosis were retrospectively included. Multivariate analysis for risk factors of BSI were tested using logistic regression. Multivariate logistic regression was validated using five-fold cross-validation. Results Variables that were independently associated with incidence of BSI were white blood cell count (odds ratio [OR] = 1.094, 95% confidence interval [CI] 1.063–1.127)], C-reactive protein (OR = 1.005, 95% CI 1.002–1.008), total bilirubin (OR = 1.003, 95% CI 1.002–1.004), and previous antimicrobial exposure (OR = 4.556, 95% CI 3.369–6.160); albumin (OR = 0.904, 95% CI 0.883–0.926), platelet count (OR = 0.996, 95% CI 0.994–0.998), and serum creatinine (OR = 0.989, 95% CI 0.985–0.994) were associated with lower odds of BSI. The area under receiver operating characteristic (ROC) curve of the risk assessment scale was 0.850, and its sensitivity and specificity were 0.762 and 0.801, respectively. There was no significant difference between the ROC curves of cross-validation and risk assessment. Conclusions We developed a predictive tool for BSI in patients with cirrhosis, which could help with early identification of such episodes at admission, to improve outcome in these patients.

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Genome-wide 5hmC profiles to enable cancer detection and tissue of origin classification in breast, colorectal, lung, ovarian, and pancreatic cancers.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.3044 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 3044-3044

Author(s):

David Haan ◽

Anna Bergamaschi ◽

Yuhong Ning ◽

William Gibb ◽

Michael Kesling ◽

...

Keyword(s):

Logistic Regression ◽

Cancer Detection ◽

Normal Tissue ◽

Cross Validation ◽

Tissue Type ◽

Normal Tissues ◽

Pancreatic Cancers ◽

Tissue Of Origin ◽

Fold Cross Validation

3044 Background: Epigenomics assays have recently become popular tools for identification of molecular biomarkers, both in tissue and in plasma. In particular 5-hydroxymethyl-cytosine (5hmC) method, has been shown to enable the epigenomic regulation of gene expression and subsequent gene activity, with different patterns, across several tumor and normal tissues types. In this study we show that 5hmC profiles enable discrete classification of tumor and normal tissue for breast, colorectal, lung ovary and pancreas. Such classification was also recapitulated in cfDNA from patient with breast, colorectal, lung, ovarian and pancreatic cancers. Methods: DNA was isolated from 176 fresh frozen tissues from breast, colorectal, lung, ovary and pancreas (44 per tumor per tissue type and up to 11 tumor tissues for each stage (I-IV)) and up to 10 normal tissues per tissue type. cfDNA was isolated from plasma from 783 non-cancer individuals and 569 cancer patients. Plasma-isolated cfDNA and tumor genomic DNA, were enriched for the 5hmC fraction using chemical labelling, sequenced, and aligned to a reference genome to construct features sets of 5hmC patterns. Results: 5hmC multinomial logistic regression analysis was employed across tumor and normal tissues and identified a set of specific and discrete tumor and normal tissue gene-based features. This indicates that we can classify samples regardless of source, with a high degree of accuracy, based on tissue of origin and also distinguish between normal and tumor status.Next, we employed a stacked ensemble machine learning algorithm combining multiple logistic regression models across diverse feature sets to the cfDNA dataset composed of 783 non cancers and 569 cancers comprising 67 breast, 118 colorectal, 210 Lung, 71 ovarian and 100 pancreatic cancers. We identified a genomic signature that enable the classification of non-cancer versus cancers with an outer fold cross validation sensitivity of 49% (CI 45%-53%) at 99% specificity. Further, individual cancer outer fold cross validation sensitivity at 99% specificity, was measured as follows: breast 30% (CI 119% -42%); colorectal 41% (CI 32%-50%); lung 49% (CI 42%-56%); ovarian 72% (CI 60-82%); pancreatic 56% (CI 46%-66%). Conclusions: This study demonstrates that 5hmC profiles can distinguish cancer and normal tissues based on their origin. Further, 5hmC changes in cfDNA enables detection of the several cancer types: breast, colorectal, lung, ovarian and pancreatic cancers. Our technology provides a non-invasive tool for cancer detection with low risk sample collection enabling improved compliance than current screening methods. Among other utilities, we believe our technology could be applied to asymptomatic high-risk individuals thus enabling enrichment for those subjects that most need a diagnostic imaging follow up.

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Central line associated bloodstream infections in cohort of abdominal surgical patients

Acta chirurgica iugoslavica ◽

10.2298/aci1304023m ◽

2013 ◽

Vol 60 (4) ◽

pp. 23-30

Author(s):

Vesna Mioljevic ◽

Miroslav Milicevic ◽

Vesna Bumbasirevic ◽

Vesna Suljagic

Keyword(s):

Risk Factors ◽

Amino Acids ◽

Intensive Care ◽

Bloodstream Infections ◽

Central Line ◽

Aseptic Technique ◽

Candida Spp ◽

Anatomic Site ◽

Study Results ◽

Significant Difference

INTRODUCTION: Central venous catheter (CVC) placement is an unavoidable part of the everyday medical practice. At the same time CVC application is associated with high risk of development of central line-associated bloodstream infections (CLABSIs). These infections are cause of icreased morbidity and mortality rates as well as higer costs of the inpatient treatment2. Risk factors for onset of CLABSIs include duration of catheterization, length of hospital stay before catheterization, anatomic site of placement, CVC placement at the intensive care units (ICU), parenteral nutrition (PN) and ommisions diring CVC placement, use and care. CLABSI incidence rates vary depending on distribution of different risk factors associated with CVC and patient him/herself. The most significant causative organisms of CLABSIs are coagulase-negative Staphylococcocae, Staphylococcus aureus (S.aures), Enterococcus spp. i Candida spp. CLABSIs prevention measures include compliance with the rules of the aseptic technique upon placement, use and care of CVCs, which, based on the study results, may enable prevention of 65% to 70% of cases of CLABSIs. METHODS: A prospective cohort study included 200 patients hospitalized at the intensive care and therapy units of the CCS Clinic of Digestive Surgery in the period November 30th, 2006-November 31st, 2007 in whom CVC was placed for more than 48 hours. All the data necessary for the study were obtained based on the review of the case histories and they were recorded into the individual questionnaires for each patient. The questionnarire included patient information (age, gender, underlying disease, presence of other infections), information related to diagnostic and therapeutic procedures to which the patient was exposed. The incidence of CRBSIs in ICU patients, Institute of Digestive Diseases, CCS over the study period was 10.08 per 1,000 catheter-days. During the study CLABSI more often present in the females. Additionally, application of albumin and amino acids, ICU stay longer that 7 days and CVC application longer than 15 days, significantly more often present in patients with CRBSIs than in the group of patients without CRBSIs. Independent factors for development of CRBSI are gender, administration of albumin and amino acids. The most common microorganisms isolated from hemocultures and CVC were S. aureus and Klebsiella spp., with 31,8% of isolates each. Statistically significant difference was evidenced in frequency of resistance of S. aureus isolates to methicillin in the group of patients with CRBSI in comparison to the group of patients without CLABSIs. CLABSIs prevention measurs include compliance with the rules of the aseptic technique upon placement, use and care of CVCs.

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Preoperative hemoglobin and uric acid levels as risk factors for acute kidney injury in cardiac surgery patients

Medicinski pregled ◽

10.2298/mpns2002005g ◽

2020 ◽

Vol 73 (1-2) ◽

pp. 5-12

Author(s):

Miodrag Golubovic ◽

Andrej Preveden ◽

Ranko Zdravkovic ◽

Jelena Vidovic ◽

Bojan Mihajlovic ◽

...

Keyword(s):

Risk Factors ◽

Acute Kidney Injury ◽

Uric Acid ◽

Cardiac Surgery ◽

Kidney Injury ◽

C Reactive Protein ◽

Reactive Protein ◽

Preoperative Hemoglobin ◽

Significant Difference ◽

Acute Kidney Injury Development

Introduction. Acute kidney injury associated with cardiac surgery is a common and significant postoperative complication. With a frequency of 9 - 39% according to different studies, it is the second most common cause of acute kidney injury in intensive care units, and an independent predictor of mortality. This study aimed to investigate the importance of preoperative hemoglobin and uric acid levels as risk factors for acute kidney injury in the postoperative period in cardiac surgery patients. Material and Methods. The study included a total of 118 patients who were divided into two groups. Each group included 59 patients; the fist group included patients who developed acute kidney injury and required renal replacement therapy, and the second included patients without acute kidney injury. Types of cardiac surgery included coronary, valvular, combined, aortic dissection, and others. All necessary data were collected from patient medical records and the electronic database. Results. A statistically significant difference was found between the groups in preoperative hemoglobin levels (108.0 vs. 143.0 g/l, p = 0.0005); postoperative urea (26.4 vs. 5.8 mmol/l, p = 0.0005) and creatinine (371.0 vs. 95.0 ?mol/l, p = 0.0005), acute phase inflammatory reactants C-reactive protein (119.4 vs. 78.9 mg/l, p = 0.002) and procalcitonin (7.0 vs. 0.2 ng/ml, p = 0.0005), creatine kinase myocardial band isoenzyme (1045.0 vs. 647.0 mg/l, p = 0.014); duration of extracorporeal circulation (103.5 vs. 76.0 min, p = 0.0005) and ascending aortic clamp during cardiac surgery (89.0 vs. 67.0 min, p = 0.0005). The exception was the preoperative uric acid level, where there was no statistically significant difference (382.0 vs. 364.0 ?mol/l, p = 0.068). There was a statistically significant correlation between the use of inotropic agents and acute kidney injury development. Conclusion. There is a correlation between the preoperative low hemoglobin levels and postoperative acute kidney injury. There is no statistically significant correlation between the preoperative levels of uric acid and postoperative acute kidney injury.

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Abstract P014: Cardiovascular Risk Factors Associated with Low Dehydroepiandrosterone Sulfate

Circulation ◽

10.1161/circ.125.suppl_10.ap014 ◽

2012 ◽

Vol 125 (suppl_10) ◽

Author(s):

Monik C Jimenez ◽

JoAnn E Manson ◽

Kathryn M Rexrode

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Heart Disease ◽

Dehydroepiandrosterone Sulfate ◽

Stepwise Logistic Regression ◽

Postmenopausal Hormone ◽

Reactive Protein ◽

Unit Increase ◽

History Of ◽

Hormone Use

Introduction: Low dehydroepiandrosterone sulfate (DHEAS) levels recently have been related to elevated risk of ischemic stroke. However, the association between DHEAS and traditional cardiovascular risk factors remains unclear. Methods: Blood samples were collected in 1989-1990 among 32,826 participants of the Nurses’ Health Study. Samples were assayed for DHEAS, lipids, and other biomarkers as part of a nested case control study evaluating risk of ischemic stroke and 340 stroke-free controls with complete data were available. Lifestyle covariates were ascertained in 1988. Stepwise logistic regression models were used to evaluate the association of between CVD risk factors and low DHEAS (<42 μ g/dL), while stepwise linear regression was used to evaluate the association with continuous DHEAS. Stepwise models utilized an entry threshold of α=0.20 and exit criterion of α=0.10. Results: The mean level of DHEAS was 78.38 μ g/dL (s.d. 50.02; median=67.03) in this population of women aged 43-69 years (median=62). Age was strongly associated with lower DHEAS. Women with history of heart disease and higher total/HDL cholesterol were more likely to have low DHEAS. In stepwise logistic regression analyses, age (OR=2.94; 95%CI: 1.73-5.00 for 10 yrs) and history of heart disease (OR=1.84; 95% CI: 0.91-3.70) were identified as risk factors for low DHEAS. In stepwise linear regression modeling, age, postmenopausal hormone use, history of heart disease and C-reactive protein (CRP) were associated with lower DHEAS levels while alcohol use was associated with higher DHEAS levels (Table 1). Body mass index, smoking, diabetes, glycosylated hemoglobin and lipids were not associated with low DHEAS. Conclusions: In this population of healthy women, lower levels of DHEAS were associated with older age, history of heart disease, postmenopausal hormone use, higher CRP and lower levels of alcohol consumption. Further research is needed to explore these associations. Table 1 Multivariable * adjusted estimates for DHEAS by cardiovascular disease risk factors DHEAS (continuous μ g/dL) β † 95%CI Age ‡ −28.40 −36.75, -20.05 History of Heart disease −18.76 −39.23, 1.71 Postmenopausal Hormone Therapy Use & −12.01 −21.99, -2.04 CRP £ (mg/L) −0.66 −1.37, 0.04 Alcohol # (g/day) 2.95 0.46, 5.45 * All variables mutually adjusted for one another † Estimated from stepwise logistic regression model ‡ per 10 year increase in age & Ref = No use of postmenopausal hormone therapy £ per 1 unit increase in C-reactive protein (CRP- mg/L) # per 5 unit increase in alcohol consumption (g/day)

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Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility

Remote Sensing ◽

10.3390/rs12203284 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3284

Author(s):

Paramita Roy ◽

Subodh Chandra Pal ◽

Alireza Arabameri ◽

Rabin Chakrabortty ◽

Biswajeet Pradhan ◽

...

Keyword(s):

Logistic Regression ◽

River Basin ◽

Cross Validation ◽

Gully Erosion ◽

Multivariate Adaptive Regression Spline ◽

Boosted Regression Tree ◽

Regression Spline ◽

Adaptive Regression ◽

Fold Cross Validation ◽

Very High

The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and maintaining the correspondence between different spheres of the environment. The major goal of this study is to evaluate the gully erosion susceptibility in the rugged topography of the Hinglo River Basin of eastern India, which ultimately contributes to sustainable land management practices. Due to the nature of data instability, the weakness of the classifier andthe ability to handle data, the accuracy of a single method is not very high. Thus, in this study, a novel resampling algorithm was considered to increase the robustness of the classifier and its accuracy. Gully erosion susceptibility maps have been prepared using boosted regression trees (BRT), multivariate adaptive regression spline (MARS) and spatial logistic regression (SLR) with proposed resampling techniques. The re-sampling algorithm was able to increase the efficiency of all predicted models by improving the nature of the classifier. Each variable in the gully inventory map was randomly allocated with 5-fold cross validation, 10-fold cross validation, bootstrap and optimism bootstrap, while each consisted of 30% of the database. The ensemble model was tested using 70% and validated with the other 30% using the K-fold cross validation (CV) method to evaluate the influence of the random selection of training and validation database. Here, all resampling methods are associated with higher accuracy, but SLR bootstrap optimism is more optimal than any other methods according to its robust nature. The AUC values of BRT optimism bootstrap, MARS optimism bootstrap and SLR optimism bootstrap are 87.40%, 90.40% and 90.60%, respectively. According to the SLR optimism bootstrap, the 107,771 km2 (27.51%) area of this region is associated with a very high to high susceptible to gully erosion. This potential developmental area of the gully was found primarily in the Hinglo River Basin, where lateral exposure was mainly observed with scarce vegetation. The outcome of this work can help policy-makers to implement remedial measures to minimize the damage caused by erosion of the gully.

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Risk factors for E. coli Susceptibility in Bloods Stream Infections in England Between 2013-2017

European Journal of Public Health ◽

10.1093/eurpub/ckz185.290 ◽

2019 ◽

Vol 29 (Supplement_4) ◽

Author(s):

S Aliabadi ◽

K Honeyford ◽

E Jauneikaite ◽

B Muller-Pebody ◽

C Costelloe

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Bloodstream Infections ◽

Visual Assessment ◽

Outcome Variable ◽

Antibiotic Prescribing ◽

Cross Sectional ◽

E Coli ◽

Routine Surveillance ◽

Potential Risk Factors

Abstract Antimicrobial resistance (AMR) is a significant threat to global health. Escherichia coli is a frequent cause of Gram-Negative Bloodstream Infections (GNBSIs) and a key organism that contributes to the burden of AMR. This was a cross-sectional surveillance study that looked at 154,791 isolates between 1st January 2013 and 31st December 2017. Analysis was performed using routine surveillance data from Public Health England (PHE) containing data on the incidence and susceptibility results of E. coli bacteraemia. Exposure variables extracted were potential risk factors for AMR. The outcome variable was resistance to at least one antibiotic. Descriptive statistics and graphs were used to summarise the data. Associations between variables and the resistance to at least one antibiotic were assessed using univariate logistic regression. A multivariable logistic regression examined adjusted associations between the variables and resistance to at least one antibiotic. The final model included variables that showed strong evidence of association with resistance to at least one antibiotic. 43.2% of isolates were resistant to at least one antibiotic. Logistic regression showed an association between resistance of E. coli isolates to at least one antibiotic and children of school age (1.39 OR, 95% CI: 1.18-1.64; p ≤ 0.001), isolates taken from patients in Greater Manchester (1.50 OR, 95% CI: 1.41-1.60; p ≤ 0.001) and isolates taken from male patients (1.14 OR, 95% CI: 1.11-1.17; p ≤ 0.001), on adjustment. Visual assessment of trend graphs showed a decrease in resistance for common carbapenems and piperacillin/tazobactam. Prevalence of resistance has increased for common cephalosporins, gentamicin, and co-amoxiclav. Initial analyses suggest an increase in rates of E. coli resistance to at least one antibiotic in GNBSIs between 2013 and 2017 in England. Findings of this study have implications for appropriate antibiotic prescribing guidelines and for directing future AMR policies. Key messages Initial analysis of the dataset suggests that rates of AMR of E. coli in BSIs have increased between 2013 and 2017. There is evidence of an increase in E.coli infections that are resistant to cephalosporins over time and a decrease in E.coli infections that are resistant to carbapenems.

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Pre-existing interstitial lung abnormalities are risk factors for immune checkpoint inhibitor-induced interstitial lung disease in non-NSCLC cancers.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e15171 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e15171-e15171

Author(s):

Kiyofumi Shimoji ◽

Takeshi Masuda ◽

Yu Nakanishi ◽

Kakuhiro Yamaguchi ◽

Shinjiro Sakamoto ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Risk Factor ◽

Interstitial Lung Disease ◽

Lung Disease ◽

Logistic Regression Analysis ◽

Independent Risk Factor ◽

Patient Characteristics ◽

Significant Difference

e15171 Background: Immune check point inhibitor (ICI) induced interstitial lung disease (ICI-ILD) is a clinically serious and life-threatening toxicity. Pre-existing ILD has been reported to be a risk factor for ICI-ILD in patients with non-small cell lung cancer (NSCLC). In addition, we have previously reported that interstitial lung abnormality (ILA) is also a risk factor for the ICI-ILD. Therefore, we investigated whether any patient characteristics, including ILA, were risk factors for ICI-ILD in patients with non-NSCLC cancers. Methods: Head and neck cancer, malignant melanoma, oral cavity cancer, renal cell carcinoma or gastric cancer patients who received anti PD-1 antibody (Nivolumab or Pembrolizumab) at Hiroshima University Hospital from December 2015 to May 2019 were enrolled. Information on patient characteristics before anti-PD-1 antibody administration, including chest CT findings and laboratory data, were obtained. Results: Two hundred patients were enrolled, and 20 (10%) developed ICI-ILD. Grade1 was observed in 15 patients, grade2 in 3, and grade3 and 5 in 1. There was no significant difference in the background factors between patients with and without ICI-ILD. On the other hand, the proportion of patients with ILA was significantly higher in the patients with ICI-ILD than those without (P < 0.01). Furthermore, univariate logistic regression analysis revealed ILA was the risk factor for ICI-ILD (p < 0.01), and multivariate logistic regression analysis showed that GGA or reticulation in ILA was an independent risk factor for ICI-ILD (p = 0.016, 0.011). Conclusions: Pre-existing ILA is a risk factor for ICI-ILD, and GGA or reticulation in ILA is an independent risk factor for ICI-ILD in patients with non-NSCLC cancers. Therefore, we should pay more attention to the development of ICI-ILD in patients with ILA, especially GGA or reticulation.

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Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study

Journal of Medical Internet Research ◽

10.2196/22555 ◽

2020 ◽

Vol 22 (12) ◽

pp. e22555

Author(s):

Yao Lu ◽

Tianshu Zhou ◽

Yu Tian ◽

Shiqiang Zhu ◽

Jingsong Li

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Logistic Regression Model ◽

Cross Validation ◽

Homomorphic Encryption ◽

Privacy Preserving ◽

Medical Data ◽

Multiple Sources ◽

Model Training ◽

Fold Cross Validation

Background Data sharing in multicenter medical research can improve the generalizability of research, accelerate progress, enhance collaborations among institutions, and lead to new discoveries from data pooled from multiple sources. Despite these benefits, many medical institutions are unwilling to share their data, as sharing may cause sensitive information to be leaked to researchers, other institutions, and unauthorized users. Great progress has been made in the development of secure machine learning frameworks based on homomorphic encryption in recent years; however, nearly all such frameworks use a single secret key and lack a description of how to securely evaluate the trained model, which makes them impractical for multicenter medical applications. Objective The aim of this study is to provide a privacy-preserving machine learning protocol for multiple data providers and researchers (eg, logistic regression). This protocol allows researchers to train models and then evaluate them on medical data from multiple sources while providing privacy protection for both the sensitive data and the learned model. Methods We adapted a novel threshold homomorphic encryption scheme to guarantee privacy requirements. We devised new relinearization key generation techniques for greater scalability and multiplicative depth and new model training strategies for simultaneously training multiple models through x-fold cross-validation. Results Using a client-server architecture, we evaluated the performance of our protocol. The experimental results demonstrated that, with 10-fold cross-validation, our privacy-preserving logistic regression model training and evaluation over 10 attributes in a data set of 49,152 samples took approximately 7 minutes and 20 minutes, respectively. Conclusions We present the first privacy-preserving multiparty logistic regression model training and evaluation protocol based on threshold homomorphic encryption. Our protocol is practical for real-world use and may promote multicenter medical research to some extent.

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Development of a Mortality Prediction Model Combined With time to Positivity for Solid Tumor Patients with Escherichia Coli Bacteremia

10.21203/rs.3.rs-42504/v1 ◽

2020 ◽

Author(s):

Qing Zhang ◽

Haoyang Gao ◽

Ding Li ◽

Changsen Bai ◽

Zheng Li ◽

...

Keyword(s):

Risk Factors ◽

Escherichia Coli ◽

Logistic Regression ◽

Cancer Patients ◽

Tumor Patients ◽

Scoring Model ◽

Time To Positivity ◽

Significant Difference ◽

Development And Validation ◽

Risk Of Cancer

Abstract Background: To develop a scoring model incorporating time to positivity (TTP) into clinical variables for predicting the mortality of tumor patients with Escherichia coli caused bloodstream infection (ECBSI).Methods: A retrospective single center study enrolling hospitalized cancer patients with ECBSI was conducted from 2013 to 2018. The patients were randomly divided into development and validation groups. Univariable and multivariable logistic regression analysis were used to identify risk factors for mortality. The scoring model was developed and validated based on logistic regression coefficients.Results: 315 and 194 patients with ECBSI were included in development and validation groups, respectively. Six significant risk factors for mortality were identified and included in the scoring model: fever ≥ 39℃, inappropriate antibiotic therapy, metastasis, acute respiratory distress (ARDS), blood transfusion, and TTP ≤ 8h. Patients were classified into low-risk (<10% mortality), medium-risk (10%-20% mortality) and high-risk (≥20% mortality) categories based on the predicted mortality rates in each score. The predicted mortality for the three categories was 4.38%, 15.39%, and 51.77%, respectively, in the development group, and 3.72%, 13.88%, and 50.09%, respectively, in the validation group. The model showed excellent discrimination and calibration for both groups, with AUC curves being 0.858 versus 0.835, respectively, and no significant difference in the Hosmer-Lemeshow test (6.709, P=0.48) and the Chi-square test (6.993, P=0.46). Sensitivity and negative predictive values (NPV) increased along with the decrease of cut-off values.Conclusion: The developed TTP-combined scoring model is feasible for clinicians to predict the mortality risk of cancer patients with ECBSI.

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Cancer Patients at Risk for Medication-Related Osteonecrosis of the Jaw. A Case and Control Study Analyzing Predictors of MRONJ Onset

Journal of Clinical Medicine ◽

10.3390/jcm10204762 ◽

2021 ◽

Vol 10 (20) ◽

pp. 4762

Author(s):

Antonia Marcianò ◽

Ylenia Ingrasciotta ◽

Valentina Isgrò ◽

Luca L’Abbate ◽

Saveria Serena Foti ◽

...

Keyword(s):

Risk Factors ◽

Risk Assessment ◽

Logistic Regression ◽

Regression Analysis ◽

Zoledronic Acid ◽

Cancer Patients ◽

Logistic Regression Analysis ◽

Osteonecrosis Of The Jaw ◽

P Value ◽

Multiple Variables

The goal of this investigation was to identify potential risk factors to predict the onset of medication-related osteonecrosis of the jaw (MRONJ). Through the identification of the multiple variables positively associated to MRONJ, we aim to write a paradigm for integrated MRONJ risk assessment built on the combined analysis of systemic and local risk factors. The characteristics of a cohort of cancer patients treated with zoledronic acid and/or denosumab were investigated; beyond the set of proven risk factors a new potential one, the intake of new molecules for cancer therapy, was addressed. Registered data were included in univariate and multivariate logistic regression analysis in order to individuate significant independent predictors of MRONJ; a propensity score-matching method was performed adjusting by age and sex. Univariate logistic regression analysis showed a significant effect of the parameters number of doses of zoledronic acid and/or denosumab (OR = 1.03; 95% CI = 1.01–1.05; p = 0.008) and chemotherapy (OR = 0.35; 95% CI = 0.17–0.71; p = 0.008). The multiple logistic regression model showed that breast, multiple myeloma, and prostate cancer involved a significantly higher risk compared to lung cancer; a significant effect of the combined variables number of doses of zoledronic acid and/or denosumab (OR = 1.03; 95% CI = 1.01–1.06); p-value = 0.03) and exposure to novel molecule treatment (OR = 34.74; 95% CI = 1.39–868.11; p-value = 0.03) was observed. The results suggest that a risk assessment paradigm is needed for personalized prevention strategies in the light of patient-centered care.

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