Classification And Regression Tree Analysis Research Articles

Predicting toxicity-related docetaxel discontinuation and overall survival in metastatic castration-resistant prostate cancer: a pooled analysis of open phase 3 clinical trial data.

Docetaxel is widely used in metastatic castration-resistant prostate cancer (mCRPC), however its optimal use remains unclear in the current treatment landscape. Biomarkers to predict Docetaxel toxicity may help optimize treatment selection. We aimed to create a predictive model for toxicity-related Docetaxel discontinuation (TRDD). Through Project Data Sphere, we accessed individual patient data from the control arms of three frontline mCRPC trials: ASCENT2, VENICE, and MAINSAIL. The inclusion criteria for these trials were all similar and included patients with chemotherapy-naïve mCRPC. The primary outcome was occurrence of TRDD. A competing risks regression (CRR) was used to predict TRDD, after accounting for the occurrence of competing events (death or progression). The output of the model was used as the dependent variable on a classification and regression tree (CART) to identify risk groups for TRDD. Overall, 1568 patients were considered. Pooled CI of TRDD was 19% after accounting for competing events (death: 474; progression: 59) within 12 months of starting treatment. To build a risk calculator we relied on a CRR that ultimately included age, ECOG performance status, AST, bilirubin, use of analgesics, and presence of diabetes and chronic kidney disease. The CART analysis identified three risk groups that were named: low (model-derived TRDD risk ≤24%), intermediate (25-64%), and high (≥65%) risk group. In each risk group, probability of TRDD during treatment was 14%, 58%, and 79%, and median OS was 24 months, 20 months, and 13 months, respectively (p < 0.001). Treatment selection in mCRPC remains a challenge. Our model can help clinicians balance Docetaxel toxicity and efficacy. The three risk categories that we identified correlated with OS and this is particularly useful for an optimal shared decision-making process.

Application of survival classification and regression tree analysis for identification of subgroups of risk in patients with heart failure and reduced left ventricular ejection fraction.

The aim of this study was to identify by classification and regression tree (CART) analysis groups of patients with different survival patterns in a population of patients with heart failure and reduced left ventricular ejection fraction (HFrEF) by using standard methods of heart function assessment, as well as well as utilizing non-traditional approaches for determining hydration and nutritional status in HF patients-lung ultrasonography (LUS) and bioimpedance spectroscopy (BIS) analysis. Eligible patients with a left ventricular ejection fraction (LVEF) below 45% were identified via the daily echocardiography assessments. LUS was performed with patients in the supine position, for a total of 28 sites per complete examination. The hydration state and the body composition were assessed using a portable whole-body BIS device. Our study included 151 patients (69.2% males) with a mean age of 67.1years. During the follow-up 53 (35.1%) patients died. Using the CART algorithm, we identified five groups based on serum sodium, the severity of NYHA class, serum urea and systolic blood pressure. When comparing the two models, the model derived from the CART analysis showed better predictive power than the conventional Cox model (c-index 0.790, 95% CI 0.723-0.857 vs. 0.736, 95%CI 0.664-0.807, p < 0.05). The application of CART analysis allowed us to identify different groups of risk for all-cause mortality in patients with HFrEF. The use of this type of modelling showed better prediction capabilities over that of using more conventional statistical approach.ClinicalTrials.gov Identifier: NCT02764073.

A diagnostic strategy for Parkinsonian syndromes using quantitative indices of DAT SPECT and MIBG scintigraphy: an investigation using the classification and regression tree analysis

PurposeWe aimed to evaluate the diagnostic performances of quantitative indices obtained from dopamine transporter (DAT) single-photon emission computed tomography (SPECT) and 123I-metaiodobenzylguanidine (MIBG) scintigraphy for Parkinsonian syndromes (PS) using the classification and regression tree (CART) analysis.MethodsWe retrospectively enrolled 216 patients with or without PS, including 80 without PS (NPS) and 136 with PS [90 Parkinson’s disease (PD), 21 dementia with Lewy bodies (DLB), 16 progressive supranuclear palsy (PSP), and 9 multiple system atrophy (MSA). The striatal binding ratio (SBR), putamen-to-caudate ratio (PCR), and asymmetry index (AI) were calculated using DAT SPECT. The heart-to-mediastinum uptake ratio (H/M) based on the early (H/M [Early]) and delayed (H/M [Delay]) images and cardiac washout rate (WR) were calculated from MIBG scintigraphy. The CART analysis was used to establish a diagnostic decision tree model for differentiating PS based on these quantitative indices.ResultsThe sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 87.5, 96.3, 93.3, 92.9, and 93.1 for NPS; 91.1, 78.6, 75.2, 92.5, and 83.8 for PD; 57.1, 95.9, 60.0, 95.4, and 92.1 for DLB; and 50.0, 98.0, 66.7, 96.1, and 94.4 for PSP, respectively. The PCR, WR, H/M (Delay), and SBR indices played important roles in the optimal decision tree model, and their feature importance was 0.61, 0.22, 0.11, and 0.05, respectively.ConclusionThe quantitative indices showed high diagnostic performances in differentiating NPS, PD, DLB, and PSP, but not MSA. Our findings provide useful guidance on how to apply these quantitative indices in clinical practice.

Association Between Physical Function and Neighborhood Environment in Healthy, Older Adults: An Exploratory Study Using Regression Tree Analysis.

The aim of this study was to perform an exploratory investigation of the individual characteristics of older adults that affect the relationships between physical function and neighborhood environment. A total of 624 community-dwelling older adults living independently, aged ≥65 years, participated in this cross-sectional study. Physical function was assessed by muscle strength (grip strength and knee extension strength) and physical performance (5-m walking time and Timed Up and Go Test). The neighborhood environment was assessed using the International Physical Activity Questionnaire Environmental Module. The individual characteristics that affect the association between both were analyzed using multiple regression analysis and Classification and Regression tree (CaRT) analysis. In both older men and women, multiple regression analysis showed that neighborhood environment was significantly associated with physical function. On the other hand, on CaRT analyses, older men ≤80 years of age without low back pain and depressive symptoms and perceived good access to recreational facilities had the shortest 5-m walking time. However, CaRT analyses found no relationship between physical function and neighborhood environment in older women. The relationships between physical function and neighborhood environment may be altered by sex, age, and physical and mental health conditions.

Predicting ACL Size in Adolescents

The purpose of this study was to evaluate the predictive value of MRI in determining hamstring autograft size preoperatively for pediatric ACL reconstructions. Children and adolescents from 10-20 years old that had undergone ACL reconstruction using hamstring autograft were identified from 2017-2018. Semitendinosus and gracilis cross-sectional area and diameter were measured on preoperative knee MRIs using the field of interest (FOI) and distance measurement tools. The slice used for measurement was identified as the largest section by the measuring surgeon. Two surgeons independently measured all MRIs. Preoperative demographic data (height, weight, body mass index (BMI), gender) and operative data (graft size) was collected. Data was analyzed by an independent statistician. Ordinal regression analysis was performed to determine the relationship between demographic data and graft size. A Classification and Regression Tree (CART) was constructed to identify predictors for the pre-determined graft sizes of 8 mm. Receiver operating characteristics (ROC) analysis were then created to assess the performance of the predictive model and determine sensitivity and specificity. All analyses were performed using SPSS v. 24 with alpha set at p<0.05 to declare significance. One hundred and ten children (54 boys, 56 girls) were included in this study with an average age of 15.93 years (range 11.81-20.22) and average BMI of 25.98 (range 16.02-40.08). CART analysis determined that if the sum of the cross-sectional areas of the semitendinosus and gracilis tendons were greater than 31.17 mm then 87.5% of children had graft sizes of 8 mm or greater. The ROC analysis determined the model to have an 80% sensitivity and 74% specificity for predicting a graft size of 8 mm or larger. A preoperative summation of the cross-sectional areas of the semitendinosus and gracilis tendons greater than 31.17 mm predicts an intraoperative graft size of 8 mm or larger in 87.5% of children.

Identifying Acute Lumbar Spondylolysis in Young Athletes with Low Back Pain: Retrospective Classification and Regression Tree Analysis.

Case-control study. The aim of this study was to establish an algorithm to distinguish acute lumbar spondylolysis (LS) from nonspecific low back pain (NSLBP) among patients in junior high school by classification and regression tree (CART) analysis. Rapid identification of acute LS is important because delayed diagnosis may result in pseudarthrosis in the pars interarticularis. To diagnose acute LS, magnetic resonance imaging (MRI) or computed tomography is necessary. However, not all adolescent patients with low back pain (LBP) can access these technologies. Therefore, a clinical algorithm that can detect acute LS is needed. The medical records of 223 junior high school-aged patients with diagnosed acute NSLBP or LS verified by MRI were reviewed. A total of 200 patients were examined for establishing the algorithm and 23 were employed for testing the performance of the algorithm. CART analysis was applied to establish the algorithm using the following data; age, sex, school grades, days after symptom onset, history of LBP, days of past LBP, height, passive straight leg raising test results, hours per week spent in sports activities, existence of spina bifida, lumbar lordosis angle, and lumbosacral joint angle. Sensitivity and specificity of the algorithm and the area under the ROC curve were calculated to assess algorithm performance. The algorithm revealed that sex, days after symptom onset, days of past LBP, hours per week spent in sports activities, and existence of spina bifida were key predictors for identifying acute LS versus NSLBP. Algorithm sensitivity was 0.64, specificity was 0.92, and the area under the ROC curve was 0.79. The algorithm can be used in clinical practice to distinguish acute LS from NSLBP in junior high school athletes, although referral to MRI may be necessary for definitive diagnosis considering the algorithm's sensitivity.Level of Evidence: 4.

Predictors of Turnover Intention in U.S. Federal Government Workforce: Machine Learning Evidence That Perceived Comprehensive HR Practices Predict Turnover Intention

This study aims to identify important predictors of turnover intention and to characterize subgroups of U.S. federal employees at high risk for turnover intention. Data were drawn from the 2018 Federal Employee Viewpoint Survey (FEVS, unweighted N = 598,003), a nationally representative sample of U.S. federal employees. Machine learning Classification and Regression Tree (CART) analyses were conducted to predict turnover intention and accounted for sample weights. CART analyses identified six at-risk subgroups. Predictor importance scores showed job satisfaction was the strongest predictor of turnover intention, followed by satisfaction with organization, loyalty, accomplishment, involvement in decisions, likeness to job, satisfaction with promotion opportunities, skill development opportunities, organizational tenure, and pay satisfaction. Consequently, Human Resource (HR) departments should seek to implement comprehensive HR practices to enhance employees’ perceptions on job satisfaction, workplace environments and systems, and favorable organizational policies and supports and make tailored interventions for the at-risk subgroups.

Optimal combinations of systolic and diastolic blood pressure in Korea: A nationwide population-based cohort study.

We investigated the optimal combinations of systolic blood pressure (SBP) and diastolic blood pressure (DBP) levels for lowest mortality in participants not taking hypertensive medication at the study baseline using nationwide representative databases. Survival rates and hazard ratios (HRs) were calculated using Kaplan‐Meier curves and multivariable Cox regression analyses. The discriminatory ability for clinical outcomes was assessed by Harrell's C‐index analysis. A survival spline curve was presented, and Classification and Regression Tree (CART) analysis was performed. SBP ≥ 140 group and DBP ≥ 90 group had the highest risk of mortality. Within SBP < 120, the HR (95% CIs) for all‐cause mortality (ACM) was the lowest for DBP 70‐79. Within SBP 120‐139, the HR (95% CIs) for ACM was significantly lower for DBP 70‐79. Within SBP ≥ 140, the HR (95% CIs) for ACM was significantly lower for DBP 80‐89. Conversely, within SBP ≥ 140, DBP < 70 showed the highest risk for ACM. Similar relationships were observed when survival spline curves and CART analysis were used. The combination of SBP and DBP discriminated better than SBP or DBP alone for mortality. The effect of DBP on mortality varies according to the SBP range. It is more effective to evaluate the effect of SBP and DBP jointly for clinical outcomes.

Association between serum albumin and cognitive dysfunction in hepatic encephalopathy: An exploratory data analysis.

Background and AimBecause covert hepatic encephalopathy (CHE) has been shown to affect the prognosis of cirrhotic patients, early diagnosis of hepatic encephalopathy (HE) is a prerequisite for the preservation of patients' quality of life and for prophylaxis of overt HE. The aim of this study was to identify a clinical parameter to predict impairment of cognitive function in cirrhotic patients with early‐stage HE.MethodsWe investigated the data from 172 patients with cirrhotic or idiopathic portosystemic shunt (PSS) in phase II/III trials of rifaximin in Japan. Classification and regression trees (CARTs) were constructed to identify clinical profiles related to cognitive dysfunction as indicated by the prolongation of time required for the Number Connection Test (NCT‐B).ResultsCART analysis detected age 65 years as the variable for the initial split, and serum albumin level was selected as the variable for the second split among patients aged ≤65 years. In 27 cirrhotic patients aged ≤65 years without PSS, receiver operating characteristic curve analysis revealed that the optimal albumin level cutoff point was 3.05 g/dL, and the area under the curve was 0.80 for the prolongation of NCT‐B time, which was higher than that of the branched‐chain amino acids‐to‐tyrosine ratio (0.46), the prothrombin time–international normalized ratio (PT‐INR) (0.68), serum ammonia (0.61), and total bilirubin (0.69).ConclusionsLower serum albumin level as a clinical biomarker associated with impaired cognitive function may be available as a screening examination for early‐stage HE in cirrhotic patients aged ≤65 years without PSS before undergoing neuropsychological tests.

Quality of life: Investigating the impact of two major mental disorders, anxiety and depression, in patients with age-related macular degeneration

Purpose: The purpose of this study was to investigate the impact on the quality of life of patients with age-related macular degeneration (AMD) in relation to common mental disorders, namely anxiety and depression, and determine specific factors (e.g., thoughts/feelings, sociodemographic characteristics) that may be used to refer such patients to psychiatrists. Materials–Methods: To classify the patients into different categories, regarding the development of the mental disorders under consideration, the “Hospital Anxiety and Depression Scale” (HADS) has been utilized. The main statistical methodologies applied are classification and regression trees (CART) techniques and logistic regression. For the purposes of the aforementioned statistical analysis, the R software package has been used. Results: Both depression and anxiety scores varied considerably across visual acuity (VA) severity. Patients with severe visual acuity problems tend to have also higher HADS scores. Women were more likely to be affected by depression; sociodemographic factors did not have any significant effect. According to the performed CART analysis, responses to two HADS items (namely, “I can enjoy a good book or radio or television program” and “I have lost interest in my appearance”) identified the vast majority of severely to moderately depressed patients. Furthermore, the level of VA severity was found as a main driver for diagnosing an AMD patient with depression. Conclusions: VA impairment (or decline) severity level was found to be the main factor associated with depression in patients with AMD. Moreover, specific thoughts/feelings present in patients with AMD have been found as significant regarding the level of their mental disorders under consideration and could be asked by the ophthalmologist to refer (or not) them to psychiatrists.

Comprehensive Analysis of Factors Predictive for Time to Transformation and Risk of Transformation in Patients (pts) with Mantle Cell Lymphoma

Comprehensive Analysis of Factors Predictive for Time to Transformation and Risk of Transformation in Patients (pts) with Mantle Cell Lymphoma

Mental state of central sterile supply department staff during COVID-19 epidemic and CART analysis

BackgroundDuring the COVID-19 epidemic, the central sterile supply department (CSSD) staff handled many devices, implements and non-disposable protective articles used by suspected or confirmed COVID-19 patients. As a result, the CSSD staff may have experienced psychological stress, however, the mental state of the CSSD staff during the COVID-19 epidemic has been rarely studied. We aim to investigate the mental state of the CSSD staff and relevant influencing factors experienced during the COVID-19 epidemic.MethodsThe survey utilising a general information questionnaire, Chinese perceived stress scale (CPSS), self-rating anxiety scale (SAS), and Connor-Davidson resilience scale (CD-RISC) was conducted with 423 CSSD staff members from 35 hospitals in Sichuan Province, China. Data was analysed in SPSS24.0. Classification and regression tree (CART) was utilised to analyse variables and find variation between groups. A chi-square test was performed on enumeration data, and t-test and analysis of variance were performed on measurement data.ResultsThe CSSD staff’s SAS score was 37.39 ± 8.458, their CPSS score was 19.21 ± 7.265, and their CD-RISC score was 64.26 ± 15.129 (Tenacity factor score: 31.70 ± 8.066, Strength factor score: 21.60 ± 5.066, Optimism factor scores: 10.96 ± 3.189). The CPSS score was positively correlated with the SAS score (r = 0.66; P < 0.01), the CPSS score was negatively correlated with the CD-RISC score (r = − 0.617, P < 0.01), and the SAS score was negatively correlated with the CD-RISC score (r = − 0.477, P < 0.01). The job position, age, and political status of the CSSD staff were the main factors affecting their mental state; for example, the CPSS score and SAS score of the CSSD nurses were significantly different from those of the CSSD logistic staff (P < 0.01).ConclusionDuring the epidemic, the CSSD staff’s psychological resilience was at a low level; the anxiety level of the CSSD nurses was higher than that of the CSSD logistic staff. Therefore, more attention should be given to the mental health of the CSSD staff, including taking protective measures regarding the risk factors to ensure they can maintain a healthy mental state.

Identifying sociodemographic profiles of veterans at risk for high-dose opioid prescribing using classification and regression trees.

To identify sociodemographic profiles of patients prescribed high-dose opioids. Cross-sectional cohort study. Veterans dually-enrolled in Veterans Health Administration and Medicare Part D, with ≥1 opioid pre-scription in 2012. We identified five patient-level demographic characteristics and 12 community variables re-flective of region, socioeconomic deprivation, safety, and internet connectivity. Our outcome was the proportion of vet-erans receiving >120 morphine milligram equivalents (MME) for ≥90 consecutive days, a Pharmacy Quality Alliance measure of chronic high-dose opioid prescribing. We used classification and regression tree (CART) methods to identify risk of chronic high-dose opioid prescribing for sociodemographic subgroups. Overall, 17,271 (3.3 percent) of 525,716 dually enrolled veterans were prescribed chronic high-dose opioids. CART analyses identified 35 subgroups using four sociodemographic and five community-level measures, with high-dose opioid prescribing ranging from 0.28 percent to 12.1 percent. The subgroup (n = 16,302) with highest frequency of the outcome included veterans who were with disability, age 18-64 years, white or other race, and lived in the Western Census region. The subgroup (n = 14,835) with the lowest frequency of the outcome included veterans who were with-out disability, did not receive Medicare Part D Low Income Subsidy, were >85 years old, and lived in communities within the second and sixth to tenth deciles of community public assistance. Using CART analyses with sociodemographic and community-level variables only, we identified sub-groups of veterans with a 43-fold difference in chronic high-dose opioid prescriptions. Interactions among disability, age, race/ethnicity, and region should be considered when identifying high-risk subgroups in large populations.

Predictors of Overall Survival among Hormone Receptor Positive Patients with Oligometastatic Breast Cancer

It is proposed that in some patients, isolated metastatic disease may represent an oligometastatic state as opposed to the first of many inevitable metastatic lesions. Breast cancer receptor subtype surrogates are clearly associated with varied natural history. Subtype surrogate-specific data to distinguish breast cancer patients in whom apparent oligometastatic disease (OMD) is associated with the short-term development of widely metastatic cancer are limited. Furthermore, no study has explored oligometastatic outcomes in concordance with hormone receptor (HR) positivity as a non-dichotomous variable. In this cohort of patients with metastatic breast cancer and a positive HR status quantified along a range from 0-100%, we propose using improved overall survival (OS) as a surrogate for a genuine oligometastatic state in order to identify predictors of OMD and, thus, patients who may benefit from ablative radiotherapy. We reviewed 765 patients diagnosed with metastatic, hormone receptor (HR)-positive breast cancer and treated at MD Anderson Cancer Center between August 1987 and March 2012. Classification and Regression Trees (CART) analysis was used to determine survival risk groups among patient and tumor characteristics including inflammatory breast cancer, race, age at diagnosis, estrogen receptor (ER) and progesterone receptor (PR) status, HER2 status, isolated versus multiple metastasis, visceral metastasis, and performance status. Final CART node size was limited to 20 patients or greater. Once the initial tree was formed, all identified nodes were ordered by the relative hazard ratios, and an additional CART analysis was performed on the ordered nodes to combine nodes into groups with similar risk. The model divided patients into 3 risk groups (p<0.001). The low-risk group (91.7% OS at 2 years) consists of patients with good performance status, a single metastatic site, and either PR% >1 or ER% >70. The moderate-risk group (79.8% OS at 2 years) consists of 2 distinct groups: 1) Pre-menopausal patients with good performance status, a single metastatic site, and PR% ≤1, ER%≤70 or 2) Non-black patients less than 72 years of age with good performance status, multiple metastases, and ER% >10. The high-risk group includes all other patients (51.0% OS at 2 years). HER2 status and a diagnosis of inflammatory breast cancer were not found to meaningfully divide patients into risk groups. Quantitative HR profile may help identify HR-positive patients with good PS and isolated metastases who are optimal candidates for ablation in addition to systemic and traditional hormonal therapy. Validation using prospective clinical data when available from ongoing randomized trials is warranted.

COVID-19 in the Emergency Department (ED): Patient Characteristics, Outcome and Parameters for Hospitalization

Background: The Emergency Department (ED) is directly involved in diagnosis and initial treatment of all stages of COVID-19. One major goal of the ED is to decide, whether patients need to be hospitalized or can be sent home safely. These decisions must usually be made without knowing the SARS-CoV-2 test result.Methods: We retrieved all patients who were treated at the ED of the Charité (Berlin, Germany) during the pandemic peak between March 1 and April 15, 2020 and who underwent SARS-CoV-2 testing using an oro-naso-pharyngeal swab. All patients with positive test results were characterized in detail and underwent a follow-up in hospital or outpatient setting after at least 7 days via telehealth. Logistic regression and classification and regression tree (CART) analyses were performed to identify predictors, which either justify hospital admission or confirm safe discharge. The results were validated using an independent second multiple-center cohort collated from data of four other university EDs from the ReCoVer registry. The clinical endpoint was all cause mortality or need for mechanical ventilation during index stay or after re-admission.Results: The primary test population of suspected COVID-19 consisted of n=1,255 cases, 45.2% were female (n=567). Of these, n=110 tested positive for SARS-CoV-2 (8.8%). The median age of SARS-CoV-2 positive cases was 45 years (IQR: 33-66 years), whereas the median age of the group tested negative for SARS-CoV-2 was 42 years (IQR: 30-60 years) (p=0.096). 43.6% were directly admitted to hospital care (n=48). The factors, which were significantly associated with hospitalization in univariate analysis were oxygen saturation (2-satuaration below 95%. The validation cohort confirmed the safety of discharge (1 event) and revealed an incidence of the clinical endpoint in different risk categories from 14.3% in patients with CV-disease only, 9.4% in patients with dyspnea and 18.2% in patients with O2-satuaration below 95%.Conclusions: Based on easily available variables like dyspnea, oxygen saturation, history of CV disease, approximately 25% of patients subsequently confirmed with COVID-19 can be identified for safe discharge. Further prospective studies are needed to identify more patients, who can be treated out of hospital.Funding Statement: There was no third party funding for this research.Declaration of Interests: The authors declare no conflicts of interest related to this work.Ethics Approval Statement: The Ethics Committee of the Charité – Universitätsmedizin Berlin approved the project as a sub-study of an influenza study. (EA2/204/19).

Determinants of Use of Long-term Continuous Electrocardiographic Monitoring for Acute Ischemic Stroke Patients without Atrial Fibrillation at Baseline.

Atrial fibrillation (AF) is the most common cardiac rhythm disorder associated with stroke. Increased risk of stroke is the same regardless of whether the AF is permanent or paroxysmal. However, detecting paroxysmal AF is challenging and resource intensive. We aimed to develop a predictive model for AF in patients with acute ischemic stroke, which could improve the detection rate of paroxysmal AF. We analyzed 10,034 adult patients with acute ischemic stroke. Differences in clinical characteristics between the patients with and without AF were analyzed in order to develop a predictive model of AF. The associated factors for AF were analyzed using multivariate logistic regression and classification and regression tree (CART) analyses. We used another dataset, which enrolled 860 acute ischemic stroke patients without AF at baseline, to test whether the developed model could improve the detection rate of paroxysmal AF. Among the study population, 1,658 patients (16.5%) had AF. Multivariate logistic regression revealed that sex, age, body weight, hypertension, diabetes mellitus, hyperlipidemia, pulse rate at admission, respiratory rate at admission, systolic blood pressure at admission, diastolic blood pressure at admission, National Institute of Health Stroke Scale (NIHSS) score at admission, total cholesterol level, triglyceride level, aspartate transaminase level, and sodium level were major factors associated with AF. CART analysis identified NIHSS score at admission, age, triglyceride level, and aspartate transaminase level as important factors for AF to classify the patients into subgroups. When selecting the high-risk group of patients (with an NIHSS score >12 and age >64.5 years, or with an NIHSS score ≤12, age >71.5 years, and triglyceride level ≤61.5 mg/dL) according to the CART model, the detection rate of paroxysmal AF was approximately double in the acute ischemic stroke patients without AF at baseline.

Preoperative C-reactive protein-to-albumin ratio and clinical outcomes after resection of colorectal liver metastases

PurposeAccumulating evidence suggests that the inflammatory tumor microenvironment can potentiate tumor progression and metastasis. The C-reactive protein-to-albumin ratio (CAR) is a novel inflammation-based prognostic score. This study was performed to examine the associations of the preoperative CAR with clinical outcomes in patients with colorectal liver metastases (CRLM) after curative resection. MethodsWe retrospectively assessed the preoperative CAR in 184 patients who underwent curative resection for CRLM from November 2001 to January 2018 at Kumamoto University (Kumamoto, Japan). The optimal cutoff level of the preoperative CAR was determined by survival classification and regression tree (CART) analysis. We compared clinicopathological factors and prognoses between the high-CAR and low-CAR groups. A Cox proportional hazards model was used to calculate hazard ratios (HRs), controlling for potential confounders. ResultsA higher preoperative CAR was associated with worse overall survival (OS) (p < 0.0001) and recurrence-free survival (RFS) (p = 0.003). Applying survival CART analysis, the high-CAR group comprised 33 patients (17.9%). In the multivariate analyses, a high CAR was independently associated with shorter OS (HR, 2.82; 95% confidence interval, 1.63–4.72; p = 0.0004) and RFS (HR, 1.62; 95% confidence interval, 1.02–2.49; p = 0.040). A high CAR was associated with a large tumor size, high serum carcinoembryonic antigen and carbohydrate antigen 19-9 levels, high intraoperative blood loss, and more postoperative complications. ConclusionA high preoperative CAR is associated with shorter OS and RFS and might serve as a prognostic marker for patients with CRLM after curative resection.

Comparative Population Pharmacokinetics of Darunavir in SARS-CoV-2 Patients vs. HIV Patients: The Role of Interleukin-6.

BackgroundDarunavir is an anti-HIV protease inhibitor repurposed for SARS-CoV-2 treatment.ObjectiveThe aim of this study was to assess the population pharmacokinetics of darunavir in SARS-CoV-2 patients compared with HIV patients.MethodsTwo separate models were created by means of a nonlinear mixed-effect approach. The influence of clinical covariates on each basic model was tested and the association of significant covariates with darunavir parameters was assessed at multivariate regression and classification and regression tree (CART) analyses. Monte Carlo simulation assessed the influence of covariates on the darunavir concentration versus time profile.ResultsA one-compartment model well-described darunavir concentrations in both groups. In SARS-CoV-2 patients (n = 30), interleukin (IL)-6 and body surface area were covariates associated with darunavir oral clearance (CL/F) and volume of distribution (Vd), respectively; no covariates were identified in HIV patients (n = 25). Darunavir CL/F was significantly lower in SARS-CoV-2 patients compared with HIV patients (4.1 vs. 10.3 L/h; p < 0.001). CART analysis found that an IL-6 level of 18 pg/mL may split the SARS-CoV-2 population in patients with low versus high darunavir CL/F (mean ± standard deviation 3.47 ± 1.90 vs. 8.03 ± 3.24 L/h; proportion of reduction in error = 0.46). Median (interquartile range) darunavir CL/F was significantly lower in SARS-CoV-2 patients with IL-6 levels ≥ 18 pg/mL than in SARS-CoV-2 patients with IL-6 levels < 18 pg/mL or HIV patients (2.78 [2.16–4.47] vs. 7.24 [5.88–10.38] vs. 9.75 [8.45–13.79] L/h, respectively; p < 0.0001). Increasing IL-6 levels affected darunavir concentration versus time simulated profiles. We hypothesized that increases in IL-6 levels associated with severe SARS-CoV-2 disease may downregulate the cytochrome P450 (CYP) 3A4-mediated metabolism of darunavir.ConclusionsThis is a proof-of-concept of SARS-CoV-2 disease–drug interactions, and may support the need for optimal dose selection of sensitive CYP3A4 substrates in severe SARS-CoV-2 patients.Electronic supplementary materialThe online version of this article (10.1007/s40262-020-00933-8) contains supplementary material, which is available to authorized users.

Abstract 3526: A novel prognostic model demonstrates social support has a beneficial impact on penile carcinoma outcomes

Abstract Introduction: Penile carcinoma is a rare malignancy accounting for 1% of cancers in men in the U.S. Although the overall 5-year survival rate is 83% for those with localized disease, this survival rate is reduced to 48% for those with regional lymph node involvement. Thus, there is an unmet need to identify prognostic factors for those at high risk of poor outcomes. Methods: Demographic and clinical information was obtained from electronic medical records among 230 men diagnosed with penile carcinoma between 1986 and 2013. The log-rank test was used to test for differences in overall survival (censored at 12, 36, and 60 months) by demographic and clinical variables. Overall survival was defined as date of cancer diagnosis to date of event or date of last follow-up. Modeling building approaches were utilized to identify the most informative variables associated with overall survival including backward elimination analysis and classification and regression tree (CART) analysis. Results: Following univariable analyses, a full multivariable model was generated and reduced to the most informative covariates using backward elimination analysis. The reduced model revealed penile tumors located on the penis glans (HR =0.48, 95% CI=0.27-0.85) or NOS (HR=0.45, 95% CI=0.25-0.84) and undetermined tumor differentiation (HR=0.48, 95% CI=0.27-0.86) was associated with a reduced risk of dying and late stage disease (III/IV) was associated with an increased risk of death (HR=2.83, 95% CI=1.68-4.75). To identify potential novel prognostic factors, the full model was subjected to CART analysis which identified four subgroups based on three covariates: tumor stage, tumor site, and marital status. Compared to risk-group 1 (Stage 0-II and penis glans/NOS), men in risk-group 3 (Stage 0-II, prepuce/penis corpus/overlapping lesion, single/widowed/divorced) exhibited a 4.9-fold increased risk of death (HR =4.87; 95% CI 2.28-10.42) and men in risk-group 4 (Stages III/IV) had a slightly attenuated increased risk of death (HR =4.62; 95% CI 2.68-7.98). When CART risk-groups were included into a single model and adjusted for alcohol use, ethnicity, and tumor differentiation, the point estimates were slightly somewhat attenuated but the overall trend was the same. Conclusion: This analysis of a cohort of patients diagnosed with penile cancer identified prognostic models with the most informative covariates significantly associated with overall survival. Using a decision tree approach, we identified four novel patient risk subgroups that demonstrated early stage patients who were not married had significantly worse 5-year survival (18.2%) compared to late stage patients (38.2%). These data may suggest that social support network has a beneficial impact on patient outcomes even among late stage patients. Citation Format: Monica E. Reyes, Heloise Borges, Muhamed S. Adjao, Nisha Vijayakumar, Philippe E. Spiess, Matthew B. Schabath. A novel prognostic model demonstrates social support has a beneficial impact on penile carcinoma outcomes [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3526.

Determining the CA19-9 concentration that best predicts the presence of CT-occult unresectable features in patients with pancreatic cancer: A population-based analysis

BackgroundSerum CA19-9 concentration may be useful in triaging patients with pancreatic cancer for more intensive staging investigations. Our aim was to identify the CA19-9 cut-point with the greatest accuracy for detecting unresectable features not identified by CT scan, and to examine the performance of this and other cut-points in predicting the outcome of staging laparoscopy (SL). MethodsPatients with pancreatic cancer were drawn from two state-wide cancer registries between 2009 and 2011. We used classification and regression tree (CART) analysis to identify the CA19-9 cut-point which best predicted the presence of imaging-occult unresectable features, and compared its performance with that of a number of alternative cut-points. We then used logistic regression to test the association between CA19-9 concentration and detection of unresectable features in patients who underwent SL. ResultsFrom the CART analysis, the optimal CA19-9 cut-point was 440 U/mL. CA19-9 ≥ 150 U/mL had a similar Youden Index, but greater sensitivity (69% versus 47%). This remained true for those who had obstructive jaundice at the time of CA19-9 sampling. CA19-9 concentration greater than or equal to 110 U/mL, 150 U/mL and 200 U/mL was associated with significantly greater odds of unresectable features being detected during SL. ConclusionElevated serum CA19-9 concentration is a valid marker for CT-occult unresectable features; the most clinically appropriate cut-point appears to be ≥ 150 U/mL irrespective of the presence of jaundice. Clinical trials which evaluate the value of CA19-9 in the staging algorithm for pancreatic cancer are needed before it is routinely used in clinical practice.