Publications (14)29.04 Total impact
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Article: Evidence-Based Guidelines for Precision Risk Stratifica- tion-Based Screening (PRSBS) for Colorectal Cancer: Lessons Learned from the US Armed Forces: Consensus and Future Directions
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ABSTRACT: Colorectal cancer (CRC) is the third most common cause of cancer-related death in the United States (U.S.), with estimates of 143,460 new cases and 51,690 deaths for the year 2012. Numerous organizations have published guidelines for CRC screening; however, these numerical estimates of incidence and disease-specific mortality have remained stable from years prior. Technological, genetic profiling, molecular and surgical advances in our modern era should allow us to improve risk stratification of patients with CRC and identify those who may benefit from preventive measures, early aggressive treatment, alternative treatment strategies, and/or frequent surveillance for the early detection of disease recurrence. To better negotiate future economic constraints and enhance patient outcomes, ultimately, we propose to apply the principals of personalized and precise cancer care to risk-stratify patients for CRC screening (Precision Risk Stratification- Based Screening, PRSBS). We believe that genetic, molecular, ethnic and socioeconomic disparities impact oncological outcomes in general, those related to CRC, in particular. This document highlights evidence-based screening recommendations and risk stratification methods in response to our CRC working group private-public consensus meeting held in March 2012. Our aim was to address how we could improve CRC risk stratification-based screening, and to provide a vision for the future to achieving superior survival rates for patients diagnosed with CRC.Journal of Cancer. 02/2013; 4:172-192. -
Article: Evidence-based Guidelines for Precision Risk Stratification-Based Screening (PRSBS) for Colorectal Cancer: Lessons learned from the US Armed Forces: Consensus and Future Directions.
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ABSTRACT: Colorectal cancer (CRC) is the third most common cause of cancer-related death in the United States (U.S.), with estimates of 143,460 new cases and 51,690 deaths for the year 2012. Numerous organizations have published guidelines for CRC screening; however, these numerical estimates of incidence and disease-specific mortality have remained stable from years prior. Technological, genetic profiling, molecular and surgical advances in our modern era should allow us to improve risk stratification of patients with CRC and identify those who may benefit from preventive measures, early aggressive treatment, alternative treatment strategies, and/or frequent surveillance for the early detection of disease recurrence. To better negotiate future economic constraints and enhance patient outcomes, ultimately, we propose to apply the principals of personalized and precise cancer care to risk-stratify patients for CRC screening (Precision Risk Stratification-Based Screening, PRSBS). We believe that genetic, molecular, ethnic and socioeconomic disparities impact oncological outcomes in general, those related to CRC, in particular. This document highlights evidence-based screening recommendations and risk stratification methods in response to our CRC working group private-public consensus meeting held in March 2012. Our aim was to address how we could improve CRC risk stratification-based screening, and to provide a vision for the future to achieving superior survival rates for patients diagnosed with CRC.Journal of Cancer. 01/2013; 4(3):172-92. -
Article: Clinical decision support systems: potential with pitfalls.
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ABSTRACT: Clinical Decision Support Systems (CDSS), an important part of clinical practice, are comprised of a: knowledge base; program for integrating patient-specific information with the knowledge-base; and, user-interface to allow clinicians to interact with the system and get the right information needed to make the right decision for the right patient at the right time. We review the common approaches to CDSS, their strengths and weaknesses and how they are evaluated and developed for clinical use.Journal of Surgical Oncology 04/2012; 105(5):502-10. · 2.10 Impact Factor -
Article: Using machine-learned bayesian belief networks to predict perioperative risk of clostridium difficile infection following colon surgery.
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ABSTRACT: Clostridium difficile (C-Diff) infection following colorectal resection is an increasing source of morbidity and mortality. We sought to determine if machine-learned Bayesian belief networks (ml-BBNs) could preoperatively provide clinicians with postoperative estimates of C-Diff risk. We performed a retrospective modeling of the Nationwide Inpatient Sample (NIS) national registry dataset with independent set validation. The NIS registries for 2005 and 2006 were used for initial model training, and the data from 2007 were used for testing and validation. International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes were used to identify subjects undergoing colon resection and postoperative C-Diff development. The ml-BBNs were trained using a stepwise process. Receiver operating characteristic (ROC) curve analysis was conducted and area under the curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) were calculated. From over 24 million admissions, 170,363 undergoing colon resection met the inclusion criteria. Overall, 1.7% developed postoperative C-Diff. Using the ml-BBN to estimate C-Diff risk, model AUC is 0.75. Using only known a priori features, AUC is 0.74. The model has two configurations: a high sensitivity and a high specificity configuration. Sensitivity, specificity, PPV, and NPV are 81.0%, 50.1%, 2.6%, and 99.4% for high sensitivity and 55.4%, 81.3%, 3.5%, and 99.1% for high specificity. C-Diff has 4 first-degree associates that influence the probability of C-Diff development: weight loss, tumor metastases, inflammation/infections, and disease severity. Machine-learned BBNs can produce robust estimates of postoperative C-Diff infection, allowing clinicians to identify high-risk patients and potentially implement measures to reduce its incidence or morbidity.Interactive journal of medical research. 01/2012; 1(2):e6. -
Article: Development of a Bayesian Belief Network Model for personalized prognostic risk assessment in colon carcinomatosis.
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ABSTRACT: Multimodality therapy in selected patients with peritoneal carcinomatosis is gaining acceptance. Treatment-directing decision support tools are needed to individualize care and select patients best suited for cytoreductive surgery +/- hyperthermic intraperitoneal chemotherapy (CRS +/- HIPEC). The purpose of this study is to develop a predictive model that could support surgical decisions in patients with colon carcinomatosis. Fifty-three patients were enrolled in a prospective study collecting 31 clinical-pathological, treatment-related, and outcome data. The population was characterized by disease presentation, performance status, extent of peritoneal cancer (Peritoneal Cancer Index, PCI), primary tumor histology, and nodal staging. These preoperative parameters were analyzed using step-wise machine-learned Bayesian Belief Networks (BBN) to develop a predictive model for overall survival (OS) in patients considered for CRS +/- HIPEC. Area-under-the-curve from receiver-operating-characteristics curves of OS predictions was calculated to determine the model's positive and negative predictive value. Model structure defined three predictors of OS: severity of symptoms (performance status), PCI, and ability to undergo CRS +/- HIPEC. Patients with PCI < 10, resectable disease, and excellent performance status who underwent CRS +/- HIPEC had 89 per cent probability of survival compared with 4 per cent for those with poor performance status, PCI > 20, who were not considered surgical candidates. Cross validation of the BBN model robustly classified OS (area-under-the-curve = 0.71). The model's positive predictive value and negative predictive value are 63.3 per cent and 68.3 per cent, respectively. This exploratory study supports the utility of Bayesian classification for developing decision support tools, which assess case-specific relative risk for a given patient for oncological outcomes based on clinically relevant classifiers of survival. Further prospective studies to validate the BBN model-derived prognostic assessment tool are warranted.The American surgeon 02/2011; 77(2):221-30. · 1.28 Impact Factor -
Article: Lymph node ratio as a quality and prognostic indicator in stage III colon cancer.
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ABSTRACT: The presence and number of nodal metastasis significantly impact colon cancer prognosis. Similarly, the number of resected/evaluated nodes impacts staging accuracy. This ratio of metastatic to examined nodes or lymph node ratio (LNR) may have independent prognostic value in colon carcinoma. : To evaluate the impact of LNR on overall survival in colon cancer patients with fewer than 12 or 12 examined nodes or more. Patients (n = 36,712) with node-positive nonmetastatic colon cancer diagnosed between 1992 and 2004 were identified from the Surveillance, Epidemiology, and End Results database and stratified according to LNR and number of nodes examined. Survival was estimated by Kaplan-Meier method, and differences analyzed by log-rank test. A Cox proportional hazards model was used for multivariate analysis. Patients with fewer than 12 nodes were older and male and had lower primary tumor stage, grade, and N stage (P < 0.01). Survival appeared greater with 12 total nodes examined or more (median 53 vs. 66 months, P < 0.001). Within each LNR stratum, survival with 12 nodes or more was improved for those with less than 10% of nodes positive for cancer, but was worse with higher LNRs (P < 0.01). Lymph node ratio was significantly associated with survival independent of total nodes (HR 1.24-5.12, P < 0.001). Other significant factors included age, race, tumor grade, stage, location, and N stage. Metastatic LNR independently estimates survival in Stage III colon cancer, irrespective of number of nodes examined. However, statistically significant differences in each LNR stratum between those with resection of fewer than 12 or 12 nodes or more would indicate that a 12-node minimum may still be necessary for accurate staging.Annals of surgery 01/2011; 253(1):82-7. · 7.90 Impact Factor -
Article: Development of a prognostic naive bayesian classifier for successful treatment of nonunions.
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ABSTRACT: predictive models permitting individualized prognostication for patients with fracture nonunion are lacking. The objective of this study was to train, test, and cross-validate a Bayesian classifier for predicting fracture-nonunion healing in a population treated with extracorporeal shock wave therapy. prospectively collected data from 349 patients with delayed fracture union or a nonunion were utilized to develop a naïve Bayesian belief network model to estimate site-specific fracture-nonunion healing in patients treated with extracorporeal shock wave therapy. Receiver operating characteristic curve analysis and tenfold cross-validation of the model were used to determine the clinical utility of the approach. predictors of fracture-healing at six months following shock wave treatment were the time between the fracture and the first shock wave treatment, the time between the fracture and the surgery, intramedullary stabilization, the number of bone-grafting procedures, the number of extracorporeal shock wave therapy treatments, work-related injury, and the bone involved (p < 0.05 for all comparisons). These variables were all included in the naïve Bayesian belief network model. a clinically relevant Bayesian classifier was developed to predict the outcome after extracorporeal shock wave therapy for fracture nonunions. The time to treatment and the anatomic site of the fracture nonunion significantly impacted healing outcomes. Although this study population was restricted to patients treated with shock wave therapy, Bayesian-derived predictive models may be developed for application to other fracture populations at risk for nonunion. prognostic Level II. See Instructions to Authors for a complete description of levels of evidence.The Journal of Bone and Joint Surgery 01/2011; 93(2):187-94. · 3.27 Impact Factor -
Article: Consensus recommendations for advancing breast cancer: risk identification and screening in ethnically diverse younger women.
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ABSTRACT: A need exists for a breast cancer risk identification paradigm that utilizes relevant demographic, clinical, and other readily obtainable patient-specific data in order to provide individualized cancer risk assessment, direct screening efforts, and detect breast cancer at an early disease stage in historically underserved populations, such as younger women (under age 40) and minority populations, who represent a disproportionate number of military beneficiaries. Recognizing this unique need for military beneficiaries, a consensus panel was convened by the USA TATRC to review available evidence for individualized breast cancer risk assessment and screening in young (< 40), ethnically diverse women with an overall goal of improving care for military beneficiaries. In the process of review and discussion, it was determined to publish our findings as the panel believes that our recommendations have the potential to reduce health disparities in risk assessment, health promotion, disease prevention, and early cancer detection within and in other underserved populations outside of the military. This paper aims to provide clinicians with an overview of the clinical factors, evidence and recommendations that are being used to advance risk assessment and screening for breast cancer in the military.Journal of Cancer. 01/2011; 2:210-27. -
Article: Combat Wound Initiative program.
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ABSTRACT: The Combat Wound Initiative (CWI) program is a collaborative, multidisciplinary, and interservice public-private partnership that provides personalized, state-of-the-art, and complex wound care via targeted clinical and translational research. The CWI uses a bench-to-bedside approach to translational research, including the rapid development of a human extracorporeal shock wave therapy (ESWT) study in complex wounds after establishing the potential efficacy, biologic mechanisms, and safety of this treatment modality in a murine model. Additional clinical trials include the prospective use of clinical data, serum and wound biomarkers, and wound gene expression profiles to predict wound healing/failure and additional clinical patient outcomes following combat-related trauma. These clinical research data are analyzed using machine-based learning algorithms to develop predictive treatment models to guide clinical decision-making. Future CWI directions include additional clinical trials and study centers and the refinement and deployment of our genetically driven, personalized medicine initiative to provide patient-specific care across multiple medical disciplines, with an emphasis on combat casualty care.Military medicine 07/2010; 175(7 Suppl):18-24. · 0.92 Impact Factor -
Article: Predictive model of outcome of targeted nodal assessment in colorectal cancer.
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ABSTRACT: Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making. To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions. Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model. Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively. This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.Annals of surgery 02/2010; 251(2):265-74. · 7.90 Impact Factor -
Article: Translational research in surgical disease.
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ABSTRACT: To review cutting-edge, novel, implemented and potential translational research and to provide a glimpse into rich, innovative, and brilliant approaches to everyday surgical problems. Scientific literature and unpublished results. Articles reviewed were chosen based on innovation and application to surgical diseases. Each section was written by a surgeon familiar with cutting-edge and novel research in their field of expertise and interest. Articles that met criteria were summarized in the manuscript. Multiple avenues have been used for the discovery of improved means of diagnosis, treatment, and overall management of patients with surgical diseases. These avenues have incorporated the use of genomics, electrical impedence, statistical and mathematical modeling, and immunology.Archives of surgery (Chicago, Ill.: 1960) 02/2010; 145(2):187-96. · 4.32 Impact Factor -
Article: Development of a Bayesian model to estimate health care outcomes in the severely wounded.
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ABSTRACT: Graphical probabilistic models have the ability to provide insights as to how clinical factors are conditionally related. These models can be used to help us understand factors influencing health care outcomes and resource utilization, and to estimate morbidity and clinical outcomes in trauma patient populations. Thirty-two combat casualties with severe extremity injuries enrolled in a prospective observational study were analyzed using step-wise machine-learned Bayesian belief network (BBN) and step-wise logistic regression (LR). Models were evaluated using 10-fold cross-validation to calculate area-under-the-curve (AUC) from receiver operating characteristics (ROC) curves. Our BBN showed important associations between various factors in our data set that could not be developed using standard regression methods. Cross-validated ROC curve analysis showed that our BBN model was a robust representation of our data domain and that LR models trained on these findings were also robust: hospital-acquired infection (AUC: LR, 0.81; BBN, 0.79), intensive care unit length of stay (AUC: LR, 0.97; BBN, 0.81), and wound healing (AUC: LR, 0.91; BBN, 0.72) showed strong AUC. A BBN model can effectively represent clinical outcomes and biomarkers in patients hospitalized after severe wounding, and is confirmed by 10-fold cross-validation and further confirmed through logistic regression modeling. The method warrants further development and independent validation in other, more diverse patient populations.Journal of Multidisciplinary Healthcare 01/2010; 3:125-35. -
Article: Development of a Bayesian classifier for breast cancer risk stratification: a feasibility study.
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ABSTRACT: Lifetime risk assessment tools are relatively limited in identifying breast cancer risk in younger women. The predictive value of mathematical models to estimate risk varies according to age, menopausal status, race/ethnicity, and family history. Current risk prediction models estimate population, not individual, levels of breast cancer risk; hence, individualized risk prediction models are needed to identify younger at-risk women who could benefit from timely risk reduction interventions. Clinical data collected as part of breast cancer screening studies may be modeled using Bayesian classification. To train a proof-of-concept Bayesian classifier for breast cancer risk stratification. We trained a Bayesian belief network (BBN) model on cohort data (including risk factors, demographic, electrical impedance scanning (EIS), breast imaging, and biopsy data) from a prospective pilot screening trial in younger women (N = 591). Receiver operating characteristic curve analysis and cross-validation of the model were used to derive preliminary guidance on the robustness of this approach and to gain insights into what a cross-validation exercise could provide in terms of risk stratification in a larger population. Independent predictors of biopsy outcome in the BBN model included personal breast disease history, breast size, EIS (low vs high risk) and imaging results, and Gail cutoff (5-year risk: <1.66% vs > or =1.66%). Area under the receiver operating characteristic curve and positive predictive value for benign and malignant biopsy outcomes were 0.88 and 97% and 0.97 and 42%, respectively. Patient-specific probability of biopsy outcome given positive EIS result and Gail model 5-year risk > or =1.66% indicated that the combined effect of these predictors on likelihood that a biopsy would prove malignant exceeded the sum of the individual effects; breast cancer likelihood is as follows: 3% (EIS negative and Gail model 5-year risk <1.66%) versus 9% (EIS positive and Gail model 5-year risk <1.66%) versus 27% (EIS negative and Gail model 5-year risk > or =1.66%) versus 45% (EIS positive and Gail model 5-year risk > or =1.66%). Clinical data collected as part of breast cancer screening studies can be modeled using Bayesian classification. The BBN model may be predictive and may provide clinically useful incremental risk information for individualized breast cancer risk assessment in younger women.Eplasty 01/2010; 10:e25. -
Article: Development of a clinical decision model for thyroid nodules.
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ABSTRACT: Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10-18 million people) has a palpable thyroid nodule, however the majority (>95%) of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm) with associated malignancy risk prevalence of 20-30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70-80%) of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery. Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US), electrical impedance scanning (EIS) and fine needle aspiration cytology (FNA) prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records) and a test set (10% of records). A receiver-operating-characteristics (ROC) curve of these predictions and area under the curve (AUC) were calculated to determine model robustness for predicting malignancy in thyroid nodules. Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of malignancy. Cross validation of the model created with Bayesian Network Analysis effectively predicted malignancy [AUC = 0.88 (95%CI: 0.82-0.94)] in thyroid nodules. The positive and negative predictive values of the model are 83% (95%CI: 76%-91%) and 79% (95%CI: 72%-86%), respectively. An integrated predictive decision model using Bayesian inference incorporating readily obtainable thyroid nodule measures is clinically relevant, as it effectively predicts malignancy in thyroid nodules. This model warrants further validation testing in prospective clinical trials.BMC Surgery 08/2009; 9:12. · 1.33 Impact Factor
Top co-authors
Top Journals
Institutions
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2013
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Uniformed Services University of the Health Sciences
Bethesda, MD, USA
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2012
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Madigan Army Medical Center
Tacoma, WA, USA
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2011
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University of California, Davis
- Department of Surgery
Davis, CA, USA
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2010
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Hebrew University of Jerusalem
- Department of Surgery
Jerusalem, Jerusalem District, Israel
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