Modeling Infant Mortality Risk Factors using Logistic Regression Model and Spatial Analysis in Kenya

Globally, infant mortality is used as an important indicator for healthcare status hence an important tool for evaluation and planning of public health strategies. Despite of numerous interventions by governments aimed at reducing infant mortality, high rates are still reported in Kenya. A lot of resources are channeled towards its control leading to low productivity hence impacting the household economic welfare and national GD. The specific objective was to establish risk factors and the spatial variation of infant mortality in Kenya by analyzing the 2014 Kenya Demographic Health Survey data. A fully Bayesian paradigm and logistic regression model were used to determine infant mortality risk factors and spatial variation in Kenya. Demographic, socioeconomic and environmental factors were found to have significant effect on infant mortality. Counties from the northern parts of Kenya, Rift Valley, Central, Eastern, Nyanza, Coastal and Western parts of Kenya showed a high level of infant deaths. Infant mortality is high in arid and semi-arid areas and coastal areas due to high prevalence of infectious diseases and inadequate water supply, health facilities and low education levels. Infant mortality varies significantly across regions in Kenya due to cultural activities, and weather patterns hence exists spatial autocorrelation among neighboring regions.

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A comprehensive, contemporary assessment of the association between hepatosteatosis and coronary artery calcium scoring

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeaa356.234 ◽

2021 ◽

Vol 22 (Supplement_1) ◽

Author(s):

T Heseltine ◽

SW Murray ◽

RL Jones ◽

M Fisher ◽

B Ruzsics

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Coronary Artery ◽

Regression Model ◽

Coronary Artery Calcium ◽

Logistic Regression Model ◽

Prediction Models ◽

Multinomial Logistic Regression ◽

Cvd Risk ◽

Male Sex

Abstract Funding Acknowledgements Type of funding sources: None. onbehalf Liverpool Multiparametric Imaging Collaboration Background Coronary artery calcium (CAC) score is a well-established technique for stratifying an individual’s cardiovascular disease (CVD) risk. Several well-established registries have incorporated CAC scoring into CVD risk prediction models to enhance accuracy. Hepatosteatosis (HS) has been shown to be an independent predictor of CVD events and can be measured on non-contrast computed tomography (CT). We sought to undertake a contemporary, comprehensive assessment of the influence of HS on CAC score alongside traditional CVD risk factors. In patients with HS it may be beneficial to offer routine CAC screening to evaluate CVD risk to enhance opportunities for earlier primary prevention strategies. Methods We performed a retrospective, observational analysis at a high-volume cardiac CT centre analysing consecutive CT coronary angiography (CTCA) studies. All patients referred for investigation of chest pain over a 28-month period (June 2014 to November 2016) were included. Patients with established CVD were excluded. The cardiac findings were reported by a cardiologist and retrospectively analysed by two independent radiologists for the presence of HS. Those with CAC of zero and those with CAC greater than zero were compared for demographic and cardiac risks. A multivariate analysis comparing the risk factors was performed to adjust for the presence of established risk factors. A binomial logistic regression model was developed to assess the association between the presence of HS and increasing strata of CAC. Results In total there were 1499 patients referred for CTCA without prior evidence of CVD. The assessment of HS was completed in 1195 (79.7%) and CAC score was performed in 1103 (92.3%). There were 466 with CVD and 637 without CVD. The prevalence of HS was significantly higher in those with CVD versus those without CVD on CTCA (51.3% versus 39.9%, p = 0.007). Male sex (50.7% versus 36.1% p= <0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= <0.001) and diabetes (12.4% versus 6.9%, p = 0.04) were also significantly higher in the CAC group compared to the CAC score of zero. HS was associated with increasing strata of CAC score compared with CAC of zero (CAC score 1-100 OR1.47, p = 0.01, CAC score 101-400 OR:1.68, p = 0.02, CAC score >400 OR 1.42, p = 0.14). This association became non-significant in the highest strata of CAC score. Conclusion We found a significant association between the increasing age, male sex, diabetes and HS with the presence of CAC. HS was also associated with a more severe phenotype of CVD based on the multinomial logistic regression model. Although the association reduced for the highest strata of CAC (CAC score >400) this likely reflects the overall low numbers of patients within this group and is likely a type II error. Based on these findings it may be appropriate to offer routine CVD risk stratification techniques in all those diagnosed with HS.

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Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

Scientific Reports ◽

10.1038/s41598-021-86853-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Anping Guo ◽

Jin Lu ◽

Haizhu Tan ◽

Zejian Kuang ◽

Ying Luo ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Liver Disease ◽

Length Of Stay ◽

Regression Model ◽

Logistic Regression Model ◽

Hospital Length ◽

Clinical Treatment ◽

Hospital Length Of Stay ◽

Female Sex

AbstractTreating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

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Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

10.21203/rs.3.rs-390569/v1 ◽

2021 ◽

Author(s):

Li Lu Wei ◽

Yu jian

Keyword(s):

Neural Network ◽

Risk Factors ◽

Logistic Regression ◽

Regression Model ◽

Bp Neural Network ◽

Logistic Regression Model ◽

Classification Tree ◽

Overweight And Obesity ◽

Tree Model ◽

Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

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1244. Evaluation of Intraoperative Topical Vancomycin and the Incidence of Acute Kidney Injury

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.1107 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S448-S448

Author(s):

Alison L Blackman ◽

Sabeen Ali ◽

Xin Gao ◽

Rosina Mesumbe ◽

Carly Cheng ◽

...

Keyword(s):

Risk Factors ◽

Acute Kidney Injury ◽

Logistic Regression ◽

Serum Creatinine ◽

Regression Model ◽

Logistic Regression Model ◽

Kidney Injury ◽

Topical Formulation ◽

Multivariable Logistic Regression Model ◽

Multivariable Logistic Regression

Abstract Background The use of intraoperative topical vancomycin (VAN) is a strategy aimed to prevent surgical site infections (SSI). Although there is evidence to support its efficacy in SSI prevention following orthopedic spine surgeries, data describing its safety, specifically acute kidney injury (AKI) risk, is limited. The purpose of this study was to determine the AKI incidence associated with intraoperative topical VAN. Methods This is a retrospective cohort study reviewing patient encounters where intraoperative topical VAN was administered from February 2018 to July 2018. All adult patients ( ≥18 years) that received topical VAN in the form of powder, beads, rods, paste, cement spacers, or unspecified topical routes were included. Patient encounters were excluded for AKI or renal replacement therapy (RRT) at baseline, ≤ 2 serum creatinine values drawn after surgery, and/or if irrigation was the only topical formulation given. The primary outcome was the percentage of patients who developed AKI after intraoperative topical VAN administration. AKI was defined as an increase in serum creatinine (SCr) ≥50% from baseline, an increase in SCr >0.5 from baseline, or0 if RRT was initiated after topical VAN was given. Secondary outcomes included analysis of AKI risk factors and SSI incidence. AKI risk factors were analyzed using a multivariable logistic regression model. Results A total of 589 patient encounters met study criteria. VAN powder was the most common formulation (40.9%), followed by unspecified topical routes (30.7%) and beads (9.9%%). Nonspinal orthopedic surgeries were the most common procedure performed 46.7%. The incidence of AKI was 8.7%. In a multivariable logistic regression model, AKI was associated with concomitant systemic VAN (OR 3.39, [3.39–6.22]) and total topical VAN dose. Each doubling of the topical dose was associated with increased odds of developing AKI (OR = 1.42, [1.08–1.86]). The incidence of SSI was 5.3%. Conclusion AKI rates associated with intraoperative topical VAN are comparable to that of systemic VAN. Total topical vancomycin dose and concomitant systemic VAN was associated with an increased AKI risk. Additional analysis is warranted to compare these patients to a similar population that did not receive topical VAN. Disclosures All authors: No reported disclosures.

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A nomogram for predicting mortality in patients with COVID-19 and solid tumors: a multicenter retrospective cohort study

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-001314 ◽

2020 ◽

Vol 8 (2) ◽

pp. e001314

Author(s):

Chao Liu ◽

Li Li ◽

Kehan Song ◽

Zhi-Ying Zhan ◽

Yi Yao ◽

...

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Solid Tumors ◽

Neutrophil Count ◽

Mortality Risk ◽

Logistic Regression Model ◽

Multivariate Logistic Regression Model ◽

Multivariate Logistic Regression ◽

Lemeshow Test ◽

Wbc Count

BackgroundIndividualized prediction of mortality risk can inform the treatment strategy for patients with COVID-19 and solid tumors and potentially improve patient outcomes. We aimed to develop a nomogram for predicting in-hospital mortality of patients with COVID-19 with solid tumors.MethodsWe enrolled patients with COVID-19 with solid tumors admitted to 32 hospitals in China between December 17, 2020, and March 18, 2020. A multivariate logistic regression model was constructed via stepwise regression analysis, and a nomogram was subsequently developed based on the fitted multivariate logistic regression model. Discrimination and calibration of the nomogram were evaluated by estimating the area under the receiver operator characteristic curve (AUC) for the model and by bootstrap resampling, a Hosmer-Lemeshow test, and visual inspection of the calibration curve.ResultsThere were 216 patients with COVID-19 with solid tumors included in the present study, of whom 37 (17%) died and the other 179 all recovered from COVID-19 and were discharged. The median age of the enrolled patients was 63.0 years and 113 (52.3%) were men. Multivariate logistic regression revealed that increasing age (OR=1.08, 95% CI 1.00 to 1.16), receipt of antitumor treatment within 3 months before COVID-19 (OR=28.65, 95% CI 3.54 to 231.97), peripheral white blood cell (WBC) count ≥6.93 ×109/L (OR=14.52, 95% CI 2.45 to 86.14), derived neutrophil-to-lymphocyte ratio (dNLR; neutrophil count/(WBC count minus neutrophil count)) ≥4.19 (OR=18.99, 95% CI 3.58 to 100.65), and dyspnea on admission (OR=20.38, 95% CI 3.55 to 117.02) were associated with elevated mortality risk. The performance of the established nomogram was satisfactory, with an AUC of 0.953 (95% CI 0.908 to 0.997) for the model, non-significant findings on the Hosmer-Lemeshow test, and rough agreement between predicted and observed probabilities as suggested in calibration curves. The sensitivity and specificity of the model were 86.4% and 92.5%.ConclusionIncreasing age, receipt of antitumor treatment within 3 months before COVID-19 diagnosis, elevated WBC count and dNLR, and having dyspnea on admission were independent risk factors for mortality among patients with COVID-19 and solid tumors. The nomogram based on these factors accurately predicted mortality risk for individual patients.

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Surgical Site Infection in a Tertiary Neonatal Surgery Centre

European Journal of Pediatric Surgery ◽

10.1055/s-0038-1636916 ◽

2018 ◽

Vol 29 (03) ◽

pp. 260-265 ◽

Cited By ~ 6

Author(s):

Adiam Woldemicael ◽

Sarah Bradley ◽

Caroline Pardy ◽

Justin Richards ◽

Paolo Trerotoli ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Model ◽

Surgical Site Infection ◽

Hospital Stay ◽

Logistic Regression Model ◽

Length Of Hospital Stay ◽

Neonatal Surgery ◽

Site Infection ◽

Increased Risk

Introduction Surgical site infection (SSI) is a key performance indicator to assess the quality of surgical care. Incidence and risk factors for SSI in neonatal surgery are lacking in the literature. Aim To define the incidence of SSI and possible risk factors in a tertiary neonatal surgery centre. Materials and Methods This is a prospective cohort study of all the neonates who underwent abdominal and thoracic surgery between March 2012 and October 2016. The variables analyzed were gender, gestational age, birth weight, age at surgery, preoperative stay in neonatal intensive care unit, type of surgery, length of stay, and microorganisms isolated from the wounds. Statistical analysis was done with chi-square, Student's t- or Mann–Whitney U-tests. A logistic regression model was used to evaluate determinants of risk for SSI; variables were analyzed both with univariate and multivariate models. For the length of hospital stay, a logistic regression model was performed with independent variables. Results A total of 244 neonates underwent 319 surgical procedures. The overall incidence of SSIs was 43/319 (13.5%). The only statistical differences between neonates with and without SSI were preoperative stay (<4 days vs. ≥4 days, p < 0.01) and length of hospital stay (<30 days vs. ≥30 days, p < 0.01). A pre-operative stay longer than 4 days was associated with almost three times increased risk of SSI (odds ratio [OR] 2.96, 95% confidence interval [CI] 1.05–8.34, p = 0.0407). Gastrointestinal procedures were associated with more than ten times the risk of SSI compared with other procedures (OR 10.17, 95% CI 3.82–27.10, p < 0.0001). Gastroschisis closure and necrotizing enterocolitis (NEC) laparotomies had the highest incidence SSI (54% and 62%, respectively). The risk of longer length of hospital stay after SSI was more than three times higher (OR = 3.36, 95%CI 1.63–6.94, p = 0.001). Conclusion This is the first article benchmarking the incidence of SSI in neonatal surgery in the United Kingdom. A preoperative stay ≥4 days and gastrointestinal procedures were independent risk factors for SSI. More research is needed to develop strategies to reduce SSI in selected neonatal procedures.

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Maternal age as a risk factor for cerebral palsy

Anthropological Review ◽

10.2478/anre-2021-0009 ◽

2021 ◽

Vol 84 (2) ◽

pp. 117-131

Author(s):

Marta Sternal ◽

Barbara Kwiatkowska ◽

Krzysztof Borysławski ◽

Agnieszka Tomaszewska

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Cerebral Palsy ◽

Regression Model ◽

Maternal Age ◽

Logistic Regression Model ◽

Significant Risk ◽

Control Group ◽

Logistic Regression Models ◽

Pregnancy And Childbirth

Abstract The relationship between maternal age and the occurrence of cerebral palsy is still highly controversial. The aim of the study was to examine the effect of maternal age on the risk of CP development, taking into account all significant risk factors and the division into single, twin, full-term, and pre-term pregnancies. The survey covered 278 children with CP attending selected educational institutions in Poland. The control group consisted of data collected from the medical records of 435 children born at Limanowa county hospital, Poland. The analyses included socio-economic factors, factors related to pregnancy and childbirth, and factors related to the presence of comorbidities and diseases in the child. Constructed logistic regression models were used for statistical analyses. For all age categories included in the estimated models (assessing the effect of demographic factors on the development of CP), only the category of ≤24 years of age (in the group of all children) was significant. It was estimated that in this mother’s age category, the risk of CP is lower (OR 0.6, 95% CI: 0.3–1.0) in comparison to mothers aged 25-29 (p = 0.03). However, estimation with the use of a complex logistic regression model did not show any significant effect of maternal age on the incidence of CP in groups from different pregnancies types. It became apparent that maternal age is a weak predictor of CP, insignificant in the final logistic regression model. It seems correct to assume that the studies conducted so far, showing a significant effect of maternal age in this respect, may be associated with bias in the estimators used to assess the risk of CP due to the fact that other important risk factors for CP development were not included in the research.

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Prevalence of, and risk factors for, cognitive impairment in lacunar stroke

International Journal of Stroke ◽

10.1177/17474930211064965 ◽

2022 ◽

pp. 174749302110649

Author(s):

Laura Ohlmeier ◽

Stefania Nannoni ◽

Claudia Pallucca ◽

Robin B Brown ◽

Laurence Loubiere ◽

...

Keyword(s):

Diabetes Mellitus ◽

Risk Factors ◽

Logistic Regression ◽

Cognitive Impairment ◽

Regression Model ◽

Logistic Regression Model ◽

Full Time ◽

Lacunar Stroke ◽

Stroke Patients ◽

Increased Risk

Background: Small vessel disease (SVD) is associated with vascular cognitive impairment (VCI) but why VCI occurs in some, but not other patients, is uncertain. We determined the prevalence of, and risk factors for, VCI in a large cohort of patients with lacunar stroke. Methods: Participants with magnetic resonance imaging (MRI)-confirmed lacunar stroke were recruited in the multicenter DNA Lacunar 2 study and compared with healthy controls. A logistic regression model was used to determine which vascular risk factors and MRI parameters were independent predictors of VCI, assessed using the Brief Memory and Executive Test (BMET). Results: A total of 912 lacunar stroke patients and 425 controls were included, with mean ( SD) age of 64.6 (12.26) and 64.7 (12.29) years, respectively. VCI was detected in 38.8% lacunar patients and 13.4% controls. In a logistic regression model, diabetes mellitus (odds ratio (OR) = 1.98 (95% confidence interval (CI) = 1.40–2.80), p < 0.001) and higher body mass index (BMI) (OR = 1.03 (95% CI = 1.00–1.05), p = 0.029) were independently associated with increased risk of VCI, and years of full-time education with lower risk (OR = 0.92 (95% CI = 0.86–0.99), p = 0.018). When entering both lacune count and white matter hyperintensity (WMH) in the same logistic regression model, only WMH grade was significantly associated with VCI (OR = 1.46 (95% CI = 1.24–1.72), p < 0.001). Conclusion: VCI is common in lacunar stroke patients, affecting almost 40%. This prevalence suggests that it should be routinely screened for in clinical practice. Risk factors for VCI in patients with lacunar stroke include diabetes mellitus, depressive symptoms, higher BMI, and WMH severity, while education is protective.

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An Artificial Neural Network–Based Pediatric Mortality Risk Score: Development and Performance Evaluation Using Data From a Large North American Registry (Preprint)

10.2196/preprints.24079 ◽

2020 ◽

Author(s):

Niema Ghanad Poor ◽

Nicholas C West ◽

Rama Syamala Sreepada ◽

Srinivas Murthy ◽

Matthias Görges

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Mortality Risk ◽

Regression Models ◽

Logistic Regression Model ◽

Single Layer ◽

Ann Model ◽

Data Set ◽

Using Data ◽

Better Than

BACKGROUND In the pediatric intensive care unit (PICU), quantifying illness severity can be guided by risk models to enable timely identification and appropriate intervention. Logistic regression models, including the pediatric index of mortality 2 (PIM-2) and pediatric risk of mortality III (PRISM-III), produce a mortality risk score using data that are routinely available at PICU admission. Artificial neural networks (ANNs) outperform regression models in some medical fields. OBJECTIVE In light of this potential, we aim to examine ANN performance, compared to that of logistic regression, for mortality risk estimation in the PICU. METHODS The analyzed data set included patients from North American PICUs whose discharge diagnostic codes indicated evidence of infection and included the data used for the PIM-2 and PRISM-III calculations and their corresponding scores. We stratified the data set into training and test sets, with approximately equal mortality rates, in an effort to replicate real-world data. Data preprocessing included imputing missing data through simple substitution and normalizing data into binary variables using PRISM-III thresholds. A 2-layer ANN model was built to predict pediatric mortality, along with a simple logistic regression model for comparison. Both models used the same features required by PIM-2 and PRISM-III. Alternative ANN models using single-layer or unnormalized data were also evaluated. Model performance was compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC) and their empirical 95% CIs. RESULTS Data from 102,945 patients (including 4068 deaths) were included in the analysis. The highest performing ANN (AUROC 0.871, 95% CI 0.862-0.880; AUPRC 0.372, 95% CI 0.345-0.396) that used normalized data performed better than PIM-2 (AUROC 0.805, 95% CI 0.801-0.816; AUPRC 0.234, 95% CI 0.213-0.255) and PRISM-III (AUROC 0.844, 95% CI 0.841-0.855; AUPRC 0.348, 95% CI 0.322-0.367). The performance of this ANN was also significantly better than that of the logistic regression model (AUROC 0.862, 95% CI 0.852-0.872; AUPRC 0.329, 95% CI 0.304-0.351). The performance of the ANN that used unnormalized data (AUROC 0.865, 95% CI 0.856-0.874) was slightly inferior to our highest performing ANN; the single-layer ANN architecture performed poorly and was not investigated further. CONCLUSIONS A simple ANN model performed slightly better than the benchmark PIM-2 and PRISM-III scores and a traditional logistic regression model trained on the same data set. The small performance gains achieved by this two-layer ANN model may not offer clinically significant improvement; however, further research with other or more sophisticated model designs and better imputation of missing data may be warranted. CLINICALTRIAL

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Prospective analysis of UGT1A1 genotyping for predicting toxicities in advanced colorectal cancer (aCRC) treated with irinotecan (IRI)-based regimens: Interim safety analysis of a Japanese observational study.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.3535 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 3535-3535 ◽

Cited By ~ 1

Author(s):

Wataru Ichikawa ◽

Keisuke Uehara ◽

Keisuke Minamimura ◽

Chihiro Tanaka ◽

Yasumasa Takii ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Safety Analysis ◽

Severe Neutropenia ◽

Prospective Analysis ◽

Severe Diarrhea ◽

Associated Risk Factors ◽

Ugt1a1 Genotype

3535 Background: UGT1A1*6 and UGT1A1*28 are risk factors for severe IRI-related toxicities in Asians, but recommended IRI doses based on UGT1A1 genotypes and other risk factors are unclear. We conducted a prospective analysis to examine the correlation between UGT1A1 genotypes and the efficacy and safety of IRI-based regimens in Japanese aCRC patients (pts), (NCT 01039506). Methods: Pts who had histologically confirmed aCRC, PS of 0–2, received IRI-based regimens (FOLFIRI, IRI+S-1, IRI monotherapy), were UGT1A1 genotyped, and provided written informed consent were included. UGT1A1 polymorphisms were analyzed and categorized into 3 groups: wild (*1/*1), hetero (*1/*6, *1/*28), and homo (*6/*6, *6/*28, *28/*28). Detailed toxicities in the first 3 months of treatment were prospectively recorded. For interim safety analysis, incidences of grade 3–4 (severe) toxicities were compared among UGT1A1 genotypes and a logistic regression model was used to predict the risk of severe toxicities. Severe toxicities and associated risk factors were predicted using a nomogram and bootstrap validation was performed. Results: We enrolled 1376 pts between October 2009 and March 2012. At the time of abstract submission, toxicity data of 504 pts were available; 46% pts had wild, 44% hetero, and 11% homo polymorphisms. FOLFIRI was administered to 63% pts. Severe neutropenia developed during the first 3 months of treatment in 33% pts: 36% in hetero [OR, 1.5; 95% CI, 1.0–2.3], 47% in homo (OR, 2.3; 95% CI, 1.2–4.4), and 28% in wild. Severe diarrhea incidence was 5%, which did not correlate with UGT1A1 genotypes. Multiple logistic regression model included regimen, initial IRI dose, gender, age, UGT1A1 genotype, and PS as predictors of severe neutropenia in the first treatment cycle. The resulting nomogram demonstrated good accuracy in predicting severe neutropenia, with a bootstrap-corrected concordance index of 0.74. Conclusions: Considering UGT1A1 genotype along with other clinical factors is important for managing pts undergoing IRI-based regimens. Our presentation will provide analysis of data from more than 1000 pts. Clinical trial information: NCT01039506.

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