Cost-effectiveness Analysis of Colorectal Cancer Screening Strategies Using Active Learning and Monte Carlo Simulation.

INTRODUCTION: Detection of colorectal cancer (CRC) in the early stages through available screening tests increases the patient's survival chances. Multimodal screening policies can benefit patients by providing more diverse screening options and balancing the risks and benefits of screening tests. We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies. METHODS: We developed a Monte Carlo simulation framework to model CRC dynamics. We proposed an innovative calibration process using machine learning models to estimate age- and size-specific adenomatous polyps' progression and regression rates. The proposed approach significantly expedites the model parameter space search. RESULTS: Two multimodal proposed policies (i.e., 1] colonoscopy at 50 y and fecal occult blood test annually between 60 and 75 y and 2] colonoscopy at 50 and 60 y and fecal immunochemical test annually between 70 and 75 y) are identified as efficient frontier policies. Both policies are cost-effective at a willingness to pay of $50,000. Sensitivity analyses were performed to assess the sensitivity of results to a change in screening test costs as well as adherence behavior. The sensitivity analysis results suggest that the proposed policies are mostly robust to the considered changes in screening test costs, as there is a significant overlap between the efficient frontier policies of the baseline and the sensitivity analysis cases. However, the efficient frontier policies were more sensitive to changes in adherence behavior. CONCLUSION: Generally, combining stool-based tests with visual tests will benefit patients with higher life expectancy and a lower expected cost compared with unimodal screening policies. Colonoscopy at younger ages (when the colonoscopy complication risk is lower) and stool-based tests at older ages are shown to be more effective. HIGHLIGHTS: We propose a detailed Markov model to capture the colorectal cancer (CRC) dynamics. The proposed Markov model presents the detailed dynamics of adenomas progression to CRC.We use more than 44,000 colonoscopy reports and available data in the literature to calibrate the proposed Markov model using an innovative approach that leverages machine learning models to expedite the calibration process.We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies and compare their performances with the current in-practice policies.

Tailoring Surveillance Colonoscopy in Patients With Advanced Adenomas.

BACKGROUND & AIMS: Patients with advanced colorectal adenomas (AAs) are directed to undergo intensive surveillance. However, the benefit derived from surveillance may be outweighed by the risk of death from non-colorectal cancer (CRC) causes, leading to uncertainty on how best to individualize follow-up. The aim of this study was to derive a risk prediction model and risk index that estimate and stratify the risk for non-CRC cancer mortality (NCM) subsequent to diagnosis and removal of AA. METHODS: We conducted a retrospective cohort study of veterans ≥40 years old who had colonoscopy for diagnostic or screening indications at 13 Veterans Affairs Medical Centers between 2002 and 2009 and had 1 or more AAs. The primary outcome was NCM using a fixed follow-up time period of 5 years. Logistic regression using the lasso technique was used to identify factors independently associated with NCM, and an index based on points from regression coefficients was constructed to estimate risk of 5-year NCM. RESULTS: We identified 2943 veterans with AA (mean age [standard deviation] 63 [8.6] years, 98% male, 74% white), with an overall 5-year mortality of 16.7%, which was nearly all due to NCM (16.6%). Age, comorbidity burden, specific comorbid conditions, and hospitalization within the preceding year were independently associated with NCM. The risk prediction model had a goodness of fit (calibration) P value of .41 and c-statistic (discrimination) of 0.74 (95% confidence interval, 0.71-0.76). On the basis of comparable 5-year risks of NCM, the scores comprised 3 risk categories: low (score of 0-1), intermediate (score of 2-4), and high (score of ≥5), in which NCM occurred in 6.5%, 14.1%, and 33.2%, respectively. CONCLUSIONS: We derived a risk prediction model that identifies veterans with advanced adenomas who are at high risk of NCM within 5 years, and who are thus unlikely to benefit from further surveillance.

Prevalence of Advanced Colorectal Neoplasia in Veterans: Effects of Age, Sex, and Race/Ethnicity.

GOAL: We sought to quantify the independent effects of age, sex, and race/ethnicity on risk of colorectal cancer (CRC) and advanced neoplasia (AN) in Veterans. STUDY: We conducted a retrospective, cross-sectional study of Veterans aged 40 to 80 years who had diagnostic or screening colonoscopy between 2002 and 2009 from 1 of 14 Veterans Affairs Medical Centers. Natural language processing identified the most advanced finding and location (proximal, distal). Logistic regression was used to examine the adjusted, independent effects of age, sex, and race, both overall and in screening and diagnostic subgroups. RESULTS: Among 90,598 Veterans [mean (SD) age 61.7 (9.4) y, 5.2% (n=4673) were women], CRC and AN prevalence was 1.3% (n=1171) and 8.9% (n=8081), respectively. Adjusted CRC risk was higher for diagnostic versus screening colonoscopy [odds ratio (OR)=3.79; 95% confidence interval (CI), 3.19-4.50], increased with age, was numerically (but not statistically) higher for men overall (OR=1.53; 95% CI, 0.97-2.39) and in the screening subgroup (OR=2.24; 95% CI, 0.71-7.05), and was higher overall for Blacks and Hispanics, but not in screening. AN prevalence increased with age, and was present in 9.2% of men and 3.9% of women [adjusted OR=1.90; 95% CI, 1.60-2.25]. AN risk was 11% higher in Blacks than in Whites overall (OR=1.11; 95% CI, 1.04-1.20), was no different in screening, and was lower in Hispanics (OR=0.74; 95% CI, 0.55-0.98). Women had more proximal CRC (63% vs. 39% for men; P=0.03), but there was no difference in proximal AN (38.3% for both genders). CONCLUSIONS: Age and race were associated with AN and CRC prevalence. Blacks had a higher overall prevalence of both CRC and AN, but not among screenings. Men had increased risk for AN, while women had a higher proportion of proximal CRC. These findings may be used to tailor when and how Veterans are screened for CRC.

Prediction of the number of COVID-19 confirmed cases based on K-means-LSTM.

COVID-19 is a pandemic disease that began to rapidly spread in the US, with the first case detected on January 19, 2020, in Washington State. March 9, 2020, and then quickly increased with total cases of 25,739 as of April 20, 2020. Although most people with coronavirus 81%, according to the U.S. Centers for Disease Control and Prevention (CDC), will have little to mild symptoms, others may rely on a ventilator to breathe or not at all. SEIR models have broad applicability in predicting the outcome of the population with a variety of diseases. However, many researchers use these models without validating the necessary hypotheses. Far too many researchers often "overfit" the data by using too many predictor variables and small sample sizes to create models. Models thus developed are unlikely to stand validity check on a separate group of population and regions. The researcher remains unaware that overfitting has occurred, without attempting such validation. In the paper, we present a combination algorithm that combines similar days features selection based on the region using Xgboost, K-Means, and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., K-Means-LSTM) for short-term COVID-19 cases forecasting in Louisana state USA. The weighted k-means algorithm based on extreme gradient boosting is used to evaluate the similarity between the forecasts and past days. The results show that the method with K-Means-LSTM has a higher accuracy with an RMSE of 601.20 whereas the SEIR model with an RMSE of 3615.83.

Evaluating the Cost-Effective Use of Follow-Up Colonoscopy Based on Screening Findings and Age.

INTRODUCTION: Colorectal cancer (CRC), if not detected early, can be costly and detrimental to one's health. Colonoscopy can identify CRC early as well as prevent the disease. The benefit of screening colonoscopy has been established, but the optimal frequency of follow-up colonoscopy is unknown and may vary based on findings from colonoscopy screening and patient age. METHODS: A partially observed Markov process (POMP) was used to simulate the effects of follow-up colonoscopy on the development of CRC. The POMP uses adenoma and CRC growth models to calculate the probability of a patient having colorectal adenomas and CRC. Then, based on mortality, quality of life, and the costs associated with diagnosis, treatment, and surveillance of colorectal cancer, the overall costs and increase in quality-adjusted life years (QALYs) are calculated for follow-up colonoscopy scenarios. RESULTS: At the $100,000/QALY gained threshold, only one follow-up colonoscopy is cost-effective only after screening at age 50 years. The optimal follow-up is 8.5 years, which gives 84.0 QALYs gained/10,000 persons. No follow-up colonoscopy was cost-effective at the $50,000 and $75,000/QALY gained thresholds. The intervals were insensitive to the findings at screening colonoscopy. CONCLUSION: Follow-up colonoscopy is cost-effective following screening at age 50 years but not if screening occurs later. Following screening at age 50 years, the optimal follow-up interval is close to the currently recommended 10 years for an average risk screening but does not vary by colonoscopy result.

Closed-Loop Intravenous Drug Administration Using Photoplethysmography.

An optically-based injection control system has been developed for preclinical use for an intravenous drug delivery application. Current clinical drug delivery for oncology typically provides for intravenous administration without an awareness of achieved plasma concentration, yet interpatient variability produces consequences ranging from toxicity to ineffectual treatments. We report a closed-loop injection system integrating a pulse-photoplethysmograph to measure the concentration of an injected agent in the circulating blood system using a previously described technique. A proportional-derivative (PD) controller manages the injection rate in real-time. The target function for the controller is the population estimate of the pharmacokinetic model developed using Bayesian statistics describing the injection phase of a calibration set of 22 injections in mice. The controlled set of eight injections showed a reduction in variance from the target injection phase concentration profile of 74.8%.

Patterns of computed tomography surveillance in survivors of colorectal cancer at Veterans Health Administration facilities.

BACKGROUND: Annual computed tomography (CT) scans are a component of the current standard of care for the posttreatment surveillance of survivors of colorectal cancer (CRC) after curative-intent resection. The authors conducted a retrospective study with the primary aim of assessing patient, physician, and organizational characteristics associated with the receipt of CT surveillance among veterans. METHODS: The Department of Veterans Affairs Central Cancer Registry was used to identify patients diagnosed with AJCC collaborative stage I to III CRC between 2001 and 2009. Patient sociodemographic and clinical (ie, CRC stage and comorbidity) characteristics, provider specialty, and organizational characteristics were measured. Hierarchical multivariable logistic regression models were used to assess the association between patient, provider, and organizational characteristics on receipt of 1) consistently guideline-concordant care (at least 1 CT every 12 months for both of the first 2 years of CRC surveillance) versus no CT receipt and 2) potential overuse (>1 CT every 12 months during the first 2 years of CRC surveillance) of CRC surveillance using CT. The authors also analyzed the impact of the 2005 American Society of Clinical Oncology update in CRC surveillance guidelines on care received over time. RESULTS: For 2263 survivors of stage II/III CRC who were diagnosed after 2005, 19.4% of patients received no surveillance CT, whereas potential overuse occurred in both surveillance years for 14.9% of patients. Guideline-concordant care was associated with younger age, higher stage of disease (stage III vs stage II), and geographic region. In adjusted analyses, younger age and higher stage of disease (stage III vs stage II) were found to be associated with overuse. There was no significant difference in the annual rate of CT scanning noted across time periods (year ≤ 2005 vs year > 2005). CONCLUSIONS: Among a minority of veteran survivors of CRC, both underuse and potential overuse of CT surveillance were present. Patient factors, but no provider or organizational characteristics, were found to be significantly associated with patterns of care. The 2005 change in American Society of Clinical Oncology guidelines did not appear to have an impact on rates of surveillance CT. Cancer 2017;123:2338-2351. © 2017 American Cancer Society.

The accuracy and completeness for receipt of colorectal cancer care using Veterans Health Administration administrative data.

BACKGROUND: The National Comprehensive Cancer Network and the American Society of Clinical Oncology have established guidelines for the treatment and surveillance of colorectal cancer (CRC), respectively. Considering these guidelines, an accurate and efficient method is needed to measure receipt of care. METHODS: The accuracy and completeness of Veterans Health Administration (VA) administrative data were assessed by comparing them with data manually abstracted during the Colorectal Cancer Care Collaborative (C4) quality improvement initiative for 618 patients with stage I-III CRC. RESULTS: The VA administrative data contained gender, marital, and birth information for all patients but race information was missing for 62.1% of patients. The percent agreement for demographic variables ranged from 98.1-100%. The kappa statistic for receipt of treatments ranged from 0.21 to 0.60 and there was a 96.9% agreement for the date of surgical resection. The percentage of post-diagnosis surveillance events in C4 also in VA administrative data were 76.0% for colonoscopy, 84.6% for physician visit, and 26.3% for carcinoembryonic antigen (CEA) test. CONCLUSIONS: VA administrative data are accurate and complete for non-race demographic variables, receipt of CRC treatment, colonoscopy, and physician visits; but alternative data sources may be necessary to capture patient race and receipt of CEA tests.

Suppression of the reticuloendothelial system using λ-carrageenan to prolong the circulation of gold nanoparticles.

AIM: Gold nanoparticles are employed for imaging and treatment of surgically inaccessible tumors owing to their inherent optical absorption and ability to extravasate through intravenous distribution. These nanoparticles are cleared from the blood by the reticuloendothelial system (RES) as expected given their size. MATERIALS & METHODS: This study demonstrates the effects of RES blockade through the intravenous administration of λ-carrageenan, resulting in a decrease in the median clearance rate from 18.9 (95% CrI: 15.9-22.6) to 11.2 (95% CrI: 8.8-13.9) µl/min and an increase in nanoparticle circulation half-life t(½)( = 264 ± 73 vs 160 ± 22 min; p < 0.01). RESULTS: This 59.3% decrease in clearance is greater than the 15% previously reported for liposomes [ 1 ]. CONCLUSION: The primary benefit of nontoxic RES blockade is to increase the circulation time, where traditional particle modification is ineffective or impractical.

Multi-center colonoscopy quality measurement utilizing natural language processing.

BACKGROUND: An accurate system for tracking of colonoscopy quality and surveillance intervals could improve the effectiveness and cost-effectiveness of colorectal cancer (CRC) screening and surveillance. The purpose of this study was to create and test such a system across multiple institutions utilizing natural language processing (NLP). METHODS: From 42,569 colonoscopies with pathology records from 13 centers, we randomly sampled 750 paired reports. We trained (n=250) and tested (n=500) an NLP-based program with 19 measurements that encompass colonoscopy quality measures and surveillance interval determination, using blinded, paired, annotated expert manual review as the reference standard. The remaining 41,819 nonannotated documents were processed through the NLP system without manual review to assess performance consistency. The primary outcome was system accuracy across the 19 measures. RESULTS: A total of 176 (23.5%) documents with 252 (1.8%) discrepant content points resulted from paired annotation. Error rate within the 500 test documents was 31.2% for NLP and 25.4% for the paired annotators (P=0.001). At the content point level within the test set, the error rate was 3.5% for NLP and 1.9% for the paired annotators (P=0.04). When eight vaguely worded documents were removed, 125 of 492 (25.4%) were incorrect by NLP and 104 of 492 (21.1%) by the initial annotator (P=0.07). Rates of pathologic findings calculated from NLP were similar to those calculated by annotation for the majority of measurements. Test set accuracy was 99.6% for CRC, 95% for advanced adenoma, 94.6% for nonadvanced adenoma, 99.8% for advanced sessile serrated polyps, 99.2% for nonadvanced sessile serrated polyps, 96.8% for large hyperplastic polyps, and 96.0% for small hyperplastic polyps. Lesion location showed high accuracy (87.0-99.8%). Accuracy for number of adenomas was 92%. CONCLUSIONS: NLP can accurately report adenoma detection rate and the components for determining guideline-adherent colonoscopy surveillance intervals across multiple sites that utilize different methods for reporting colonoscopy findings.

Clinical trial simulation to evaluate population pharmacokinetics and food effect: capturing abiraterone and nilotinib exposures.

The objectives of this study were to determine (1) the accuracy with which individual patient level exposure can be determined and (2) whether a known food effect can be identified in a trial simulation of a typical population pharmacokinetic trial. Clinical trial simulations were undertaken using NONMEM VII to assess a typical oncology pharmacokinetic trial design. Nine virtual trials for each compound were performed for combinations of different levels of between-occasion variability, number of patients in the trial, and magnitude of a food covariate on oral clearance. Less than 5% and 20% bias and precision were obtained in individual clearance estimated for both abiraterone and nilotinib using this design. This design resulted in biased and imprecise population clearance estimates for abiraterone. The between-occasion variability in most trials was captured with less than 30% of percent bias and precision. The food effect was detectable as a statistically significant covariate on oral clearance for abiraterone and nilotinib with percent bias and precision of the food covariate less than 20%. These results demonstrate that clinical trial simulation can be used to explore the ability of specific trial designs to evaluate the power to identify individual and population level exposures, covariate, and variability effects.

A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection.

The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection.

A risk index for advanced neoplasia on the second surveillance colonoscopy in patients with previous adenomatous polyps.

BACKGROUND: Predicting the risk of advanced colorectal neoplasia on the second surveillance colonoscopy could help tailor surveillance. OBJECTIVE: To derive and validate a risk index for advanced neoplasia on the second surveillance colonoscopy. DESIGN: Retrospective cohort. SETTING: Single-specialty practice; Veterans Affairs Medical Center. PATIENTS: A total of 965 patients with baseline adenomatous polyps, 2 surveillance colonoscopies, and no reported family history of colorectal cancer; validation cohort of 372. INTERVENTIONS: Multivariable logistic regression including demographics and previous colonoscopy results; derivation and validation of a risk index. MAIN OUTCOME MEASUREMENTS: Advanced adenoma (≥1 cm in size, villous histology, or high-grade dysplasia) on the second surveillance colonoscopy. RESULTS: Mean age was 57.8 ± 9.8 years, 62% were men, and 36% had an advanced adenoma on the index colonoscopy. Associated with advanced adenoma on the second surveillance colonoscopy were age at index colonoscopy (scored 0 for younger than 55 years of age, 1 for 55-59 years of age, 2 for 60-64 years of age, and 3 for older than 65 years of age) and previous findings (non-neoplastic, nonadvanced, advanced [scored 0, 1, and 2, respectively]) on index colonoscopy and the first surveillance colonoscopy, with scores ranging from 1 to 7. Risks of advanced adenoma on the second surveillance colonoscopy with scores of 5 or less and more than 5 were 4.8% (95% confidence interval, 3.5%-6.4%) and 14.9% (95% confidence interval, 7.4%-25.7%), respectively, comprising 93% and 7%, respectively, of the cohort. Corresponding results in the validation cohort were 5.6% and 19.2%, respectively, comprising 86.1% and 13.9%, respectively, of the cohort. LIMITATIONS: Retrospective study with potential for selection bias. CONCLUSION: This index stratifies the risk of advanced adenoma on the second surveillance colonoscopy. If validated independently, it may be useful for tailoring surveillance.

A predictive model of longitudinal, patient-specific colonoscopy results.

We suggest a model framework, in which an individual patient's risk for colonic neoplasia varies based on findings from his previous colonoscopies, to predict longitudinal colonoscopy results. The neoplasia natural history model describes progression through four neoplasia development states with patient age. Multiple natural history model parameter sets are assumed to act concurrently on the colon and parameter set prevalence combinations, whose a priori likelihoods are a function of patient sex, provide a basis set for patient-level predictions. The novelty in this approach is that after a colonoscopy, both the parameter set combination likelihoods and their model predictions can adjust in a Bayesian manner based on the results and conditions of the colonoscopy. The adjustment of model predictions operationalizes the clinical knowledge that multiple or advanced neoplasia at baseline colonoscopy is an independent predictor of multiple or advanced neoplasia at follow-up colonoscopy--and vice versa for negative colonoscopies--and the adjustment of parameter set combination likelihoods accounts for the possibility that patients may have different neoplasia development rates. A model that accurately captures serial colonoscopy results could potentially be used to design and evaluate post-colonoscopy treatment strategies based on the risk of individual patients. To support model identification, observational longitudinal colonoscopy results, procedure details, and patient characteristics were collected for 4084 patients. We found that at least two parameter sets specific to each sex with model adjustments was required to capture the longitudinal colonoscopy data and inclusion of multiple possible parameter set combinations, which account for random variations within the population, was necessary to accurately predict the second-time colonoscopy findings for patients with a history of advanced adenomas. Application of this model to predict CRC risks for patients adhering to guideline recommended follow-up colonoscopy intervals found that there are significant differences in risk with patient age, gender, and preparation quality and demonstrates the need for a more rigorous investigation into these recommendations.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q1 = 4.9 % and q3 = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

Population pharmacokinetic analysis of 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) in adult patients with solid tumors.

PURPOSE: To identify sources of exposure variability for the tumor growth inhibitor 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG) using a population pharmacokinetic analysis. METHODS: A total 67 solid tumor patients at 2 centers were given 1 h infusions of 17-DMAG either as a single dose, daily for 3 days, or daily for 5 days. Blood samples were extensively collected and 17-DMAG plasma concentrations were measured by liquid chromatography/mass spectrometry. Population pharmacokinetic analysis of the 17-DMAG plasma concentration with time was performed using nonlinear mixed effect modeling to evaluate the effects of covariates, inter-individual variability, and between-occasion variability on model parameters using a stepwise forward addition then backward elimination modeling approach. The inter-individual exposure variability and the effects of between-occasion variability on exposure were assessed by simulating the 95 % prediction interval of the AUC per dose, AUC(0-24 h), using the final model and a model with no between-occasion variability, respectively, subject to the five day 17-DMAG infusion protocol with administrations of the median observed dose. RESULTS: A 3-compartment model with first order elimination (ADVAN11, TRANS4) and a proportional residual error, exponentiated inter-individual variability and between occasion variability on Q2 and V1 best described the 17-DMAG concentration data. No covariates were statistically significant. The simulated 95% prediction interval of the AUC(0-24 h) for the median dose of 36 mg/m(2) was 1,059-9,007 mg/L h and the simulated 95 % prediction interval of the AUC(0-24 h) considering the impact of between-occasion variability alone was 2,910-4,077 mg/L h. CONCLUSIONS: Population pharmacokinetic analysis of 17-DMAG found no significant covariate effects and considerable inter-individual variability; this implies a wide range of exposures in the population and which may affect treatment outcome. Patients treated with 17-DMAG may require therapeutic drug monitoring which could help achieve more uniform exposure leading to safer and more effective therapy.

The effect of colonoscopy preparation quality on adenoma detection rates.

BACKGROUND: Colonoscopy reduces the risk of colorectal cancer mortality by removing precancerous adenomas. The detection rate of subcentimeter (<10 mm) polyps is lower for procedures with inadequate preparation quality. OBJECTIVE: To compare the adenoma detection rates of small (6-9 mm) and diminutive (≤ 5 mm) adenomas in patients with poor and fair quality preparations with those with adequate quality preparations. DESIGN: Cross-sectional study and multivariable, hierarchical model. SETTING: Roudebush Veterans Affairs Medical Center. PATIENTS: This study involved 8800 colonoscopies performed from 2001 to 2010. MAIN OUTCOME MEASUREMENTS: Preparation quality rating, polyp size, and polyp histology. RESULTS: Preparation quality was rated as fair in 2809 (31.9%) and poor in 829 (9.4%) colonoscopies. In patients with poor compared with adequate quality, the detection rate was lower for diminutive adenomas (odds ratio [OR] 0.57; 95% CI, 0.47-0.70) but not for small adenomas (OR 0.84; 95% CI, 0.65-1.07). There were no differences in the detection rate of diminutive (OR 1.08; 95% CI, 0.94-1.24]) or small (OR 1.09; 95% CI, 0.94-1.27) adenomas in patients with fair compared with adequate quality preparation. Detection of advanced histology in patients with poor preparation quality was lower than in those with adequate quality (P = .027; 3.3% vs 5.0%), but there was no difference in those with fair compared with adequate quality (P = .893; 4.9% vs 5.0%). LIMITATIONS: Single-center study; no standardization of preparation quality or size measurements. CONCLUSIONS: A fair preparation quality rating does not decrease the detection rate for adenomas of any size or for advanced histology, suggesting that fair quality may be considered adequate and that follow-up intervals may not need to be shortened. Poor preparation quality decreases the detection rate of diminutive adenomas and advanced histology, suggesting substandard colonoscopy performance.

Provider acceptance, safety, and effectiveness of a computer-based decision tool for colonoscopy preparation.

PURPOSE: To assess provider acceptance of recommendations by a decision tool that scans the electronic medical record and determines whether sodium phosphate may be taken. In addition, to determine decision tool effects on a composite outcome of colonoscopies canceled, rescheduled, aborted, or repeated sooner than recommended due to preparation (prep) quality; prep quality; colonoscopy duration; and patient satisfaction with and tolerance of the preparation. METHODS: We used 4 alternating 4-week periods to compare the decision tool with usual care for outpatient colonoscopy. All decision tool decisions were reviewed in real-time by gastroenterology nurses and/or physicians. Patients completed a survey about the prep process. Endoscopists blindly rated prep quality. Colonoscopy duration and findings were recorded. RESULTS: Of 354 persons in the decision tool group, 4 prep decisions were overridden because of patient preference or prior prep failure, but none for medical reasons. Sodium phosphate was used more frequently in the decision tool group (73% vs. 41%; P < 0.01). There was no difference between the decision tool and usual care groups in the composite outcome (26% vs. 30%, respectively; P = 0.29), acceptable prep quality (62% vs. 56%; P = 0.22), colonoscopy duration (28 vs. 30 min; P = 0.17), patient satisfaction (P = 0.38), or preparation tolerance (P = 0.37). CONCLUSIONS: An electronic medical record-based decision tool can safely and effectively tailor the prep for colonoscopy and may improve colonoscopy efficiency and patient satisfaction. LIMITATIONS: This study was performed at a single VA medical center and endoscopy unit, relies on the presence of relevant medical conditions and laboratory data in the electronic medical record, and had a higher than expected use of sodium phosphate during usual care.