Serum trough levels of infliximab are not associated with peripheral arthralgia activity in patients with inflammatory bowel disease.

BACKGROUND: Infliximab is an efficacious therapy for inflammatory bowel disease and may play a role in management of some extraintestinal manifestations. While higher trough levels of infliximab are associated with higher rates of disease remission, the association between trough levels of infliximab and arthralgia activity characterised as an extraintestinal manifestation has yet to be defined. OBJECTIVE: We aimed to assess the association between serum trough levels of infliximab and peripheral arthralgia activity in patients with inflammatory bowel disease. DESIGN: In this cross-sectional study, we identified patients with inflammatory bowel disease on infliximab therapy with known history of arthralgias attributed to an extraintestinal manifestation. Collected variables included disease phenotype, medications (such as thiopurines or methotrexate), Harvey Bradshaw Index, partial Mayo score, C reactive protein, trough levels of infliximab and anti-infliximab antibodies. The primary outcome was active patient-reported arthralgia. RESULTS: Out of 267 patients included, 65 (24.4%) had active arthralgias at the time the trough level of infliximab was measured. No significant differences in trough levels were seen between those patients with and without arthralgias. Patients on combination therapy with methotrexate or thiopurines or those with detectable anti-infliximab antibodies were not more likely to have inactive arthralgias (OR 0.99, 95% CI 0.57 to 1.74, p=0.99 and OR 1.94, 95% CI 0.9 to 4.1, p=0.09, respectively). CONCLUSIONS: This study suggests that although therapeutic drug monitoring of infliximab can have a role in the management of Crohn's disease and ulcerative colitis, it does not seem to be useful in managing arthralgias associated with inflammatory bowel disease.

Endoscopic ultrasound guided fine-needle aspiration vs core needle biopsy for solid pancreatic lesions: Comparison of diagnostic accuracy and procedural efficiency.

BACKGROUND: Endoscopic ultrasound (EUS) guided core needle biopsies (CNB) are increasingly being performed to diagnose solid pancreatic lesions. However, studies have been conflicting in terms of CNB improving diagnostic accuracy and procedural efficiency vs fine-needle aspiration (FNA), which this study aims to elucidate. METHODS: Data were prospectively collected on consecutive patients with solid pancreatic or peripancreatic lesions at a single tertiary care center from November 2015 to November 2016 that underwent either FNA or CNB. Patient demographics, characteristics of lesions, diagnostic accuracy, final and follow-up pathology, use of rapid on-site evaluation (ROSE), complications, and procedure variables were obtained. RESULTS: A total of 75 FNA and 48 CNB were performed; of these, 13 patients had both. Mean passes were lower with CNB compared to FNA (2.4 vs 2.9, P = .02). Use of ROSE was higher for FNA (97.3% vs 68.1%, P = .001). Mean procedure time was shorter with CNB (34.1 minutes vs 51.2 minutes, P = .02) and diagnostic accuracy was similar (89.2% vs 89.4%, P = .98). There was no difference in diagnostic accuracy when ROSE was performed for CNB vs not performed (93.5% vs 85.7%, P = .58). Additionally, diagnostic accuracy of combined FNA and CNB procedures was 92.3%, which was comparable to FNA (P = .73) or CNB (P = .52) alone. CONCLUSION: FNA and CNB had comparable safety and diagnostic accuracy. Use of CNB resulted in less number of passes and shorter procedure time as compared to FNA. Moreover, diagnostic accuracy for CNB with or without ROSE was similar.

Performance of a Multidisciplinary Pancreatic Cancer Conference in Predicting and Managing Resectable Pancreatic Cancer.

OBJECTIVES: Surgery is the curative treatment for pancreatic ductal adenocarcinoma (PDA). Guidelines recommend utilizing a multidisciplinary pancreatic cancer conference (MDPC) in treatment; however, data are limited. The objective of this study was to assess the accuracy of an MDPC. METHODS: Patients with PDA presented at an MDPC were prospectively collected from April 2013 to August 2016. Patients were included if the MDPC predicted them to have resectable PDA and underwent upfront surgery. Secondary aims were to compare differences in tumor characteristics, time to surgery, and resection rates with patients prior to MDPC implementation (pre-MDPC). RESULTS: A total of 278 patients were presented at the MDPC. After excluding borderline and nonresectable cases, 91 patients were predicted as resectable on evaluation, and 70 were fit for surgery. The MDPC predicted resection in 91.4%. The MDPC had larger tumor size (32.6 vs 24.0 mm), greater proportion of stage II tumor, and a shorter time from diagnosis to resection (27.3 vs 35.5 days) compared with the pre-MDPC. Microscopically negative resections were similar between MDPC and pre-MDPC (85.9% vs 88.0%) despite advanced tumor size and stage. CONCLUSIONS: The MDPC demonstrates a high resection rate. Compared with a pre-MDPC, MDPC provides shorter time to surgery and selects for advanced tumors.

Esophageal Lichen Planus: Understanding a Potentially Severe Stricturing Disease.

A 67-year-old woman with a long-standing history of recurrent dysphagia and esophageal strictures failed to respond to aggressive antireflux management. She required multiple dilations for symptomatic strictures that were discovered throughout the esophagus. Intralesional, topical, and systemic glucocorticoid therapies were utilized without resolution in symptoms. Several years after initial presentation, histopathology ultimately demonstrated lichenoid features and a diagnosis of esophageal lichen planus (ELP) was confirmed. However, as her symptoms had already become significantly disabling with severe strictures that carried an increased risk of endoscopic complications with dilation, she ultimately decided to undergo an esophagectomy for definitive treatment. Moreover, ELP may often go unrecognized for several years. Clinicians should consider ELP in the differential for dysphagia in middle- to elderly-aged women with or without a known history of lichen planus (LP) especially for those with findings of multiple or proximal strictures.

Failure of Recombinant Activated Factor VII in Treatment of Diffuse Alveolar Hemorrhage due to Cryoglobulinemic Vasculitis.

Diffuse alveolar hemorrhage (DAH) is a serious complication of the small vessel vasculitis syndromes and carries a high mortality. Recombinant activated factor VII (rFVIIa) is used to treat bleeding in patients with hemophilia and antibodies to factor VIII or IX. It is increasingly being used in life-threatening hemorrhage in a variety of other settings in which conventional therapy is unsuccessful. Randomized controlled trials of rFVIIa in DAH are lacking. However, several case reports have described a complete or sustained control of DAH using rFVIIa after patients failed to respond to medical treatment. There are no case reports in the literature describing the use or the failure of rFVIIa in DAH associated with cryoglobulinemic vasculitis. We here report the failure of rFVIIa to control DAH in a patient with CD5+ B-cell non-Hodgkin's lymphoma and cryoglobulinemic vasculitis.

A new, accurate predictive model for incident hypertension.

OBJECTIVE: Data mining represents an alternative approach to identify new predictors of multifactorial diseases. This work aimed at building an accurate predictive model for incident hypertension using data mining procedures. METHODS: The primary study population consisted of 1605 normotensive individuals aged 20-79 years with 5-year follow-up from the population-based study, that is the Study of Health in Pomerania (SHIP). The initial set was randomly split into a training and a testing set. We used a probabilistic graphical model applying a Bayesian network to create a predictive model for incident hypertension and compared the predictive performance with the established Framingham risk score for hypertension. Finally, the model was validated in 2887 participants from INTER99, a Danish community-based intervention study. RESULTS: In the training set of SHIP data, the Bayesian network used a small subset of relevant baseline features including age, mean arterial pressure, rs16998073, serum glucose and urinary albumin concentrations. Furthermore, we detected relevant interactions between age and serum glucose as well as between rs16998073 and urinary albumin concentrations [area under the receiver operating characteristic (AUC 0.76)]. The model was confirmed in the SHIP validation set (AUC 0.78) and externally replicated in INTER99 (AUC 0.77). Compared to the established Framingham risk score for hypertension, the predictive performance of the new model was similar in the SHIP validation set and moderately better in INTER99. CONCLUSION: Data mining procedures identified a predictive model for incident hypertension, which included innovative and easy-to-measure variables. The findings promise great applicability in screening settings and clinical practice.

Leveraging rich annotations to improve learning of medical concepts from clinical free text.

Information extraction from clinical free text is one of the key elements in medical informatics research. In this paper we propose a general framework to improve learning-based information extraction systems with the help of rich annotations (i.e., annotators provide the medical assertion as well as evidences that support the assertion). A special graphical interface was developed to facilitate the annotation process, and we show how to implement this framework with a state-of-the-art context-based question answering system. Empirical studies demonstrate that with about 10% longer annotation time, we can significantly improve the accuracy of the system. An approach to provide supporting evidence for test documents is also briefly discussed with promising preliminary results.

Development and validation of a prognostic model using blood biomarker information for prediction of survival of non-small-cell lung cancer patients treated with combined chemotherapy and radiation or radiotherapy alone (NCT00181519, NCT00573040, and NCT00572325).

PURPOSE: Currently, prediction of survival for non-small-cell lung cancer patients treated with (chemo)radiotherapy is mainly based on clinical factors. The hypothesis of this prospective study was that blood biomarkers related to hypoxia, inflammation, and tumor load would have an added prognostic value for predicting survival. METHODS AND MATERIALS: Clinical data and blood samples were collected prospectively (NCT00181519, NCT00573040, and NCT00572325) from 106 inoperable non-small-cell lung cancer patients (Stages I-IIIB), treated with curative intent with radiotherapy alone or combined with chemotherapy. Blood biomarkers, including lactate dehydrogenase, C-reactive protein, osteopontin, carbonic anhydrase IX, interleukin (IL) 6, IL-8, carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1, were measured. A multivariate model, built on a large patient population (N = 322) and externally validated, was used as a baseline model. An extended model was created by selecting additional biomarkers. The model's performance was expressed as the area under the curve (AUC) of the receiver operating characteristic and assessed by use of leave-one-out cross validation as well as a validation cohort (n = 52). RESULTS: The baseline model consisted of gender, World Health Organization performance status, forced expiratory volume, number of positive lymph node stations, and gross tumor volume and yielded an AUC of 0.72. The extended model included two additional blood biomarkers (CEA and IL-6) and resulted in a leave-one-out AUC of 0.81. The performance of the extended model was significantly better than the clinical model (p = 0.004). The AUC on the validation cohort was 0.66 and 0.76, respectively. CONCLUSIONS: The performance of the prognostic model for survival improved markedly by adding two blood biomarkers: CEA and IL-6.

The importance of patient characteristics for the prediction of radiation-induced lung toxicity.

PURPOSE: Extensive research has led to the identification of numerous dosimetric parameters as well as patient characteristics, associated with lung toxicity, but their clinical usefulness remains largely unknown. We investigated the predictive value of patient characteristics in combination with established dosimetric parameters. PATIENTS AND METHODS: Data from 438 lung cancer patients treated with (chemo)radiation were used. Lung toxicity was scored using the Common Toxicity Criteria version 3.0. A multivariate model as well as two single parameter models, including either V(20) or MLD, was built. Performance of the models was expressed as the AUC (Area Under the Curve). RESULTS: The mean MLD was 13.5 Gy (SD 4.5 Gy), while the mean V(20) was 21.0% (SD 7.3%). Univariate models with V(20) or MLD both yielded an AUC of 0.47. The final multivariate model, which included WHO-performance status, smoking status, forced expiratory volume (FEV(1)), age and MLD, yielded an AUC of 0.62 (95% CI: 0.55-0.69). CONCLUSIONS: Within the range of radiation doses used in our clinic, dosimetric parameters play a less important role than patient characteristics for the prediction of lung toxicity. Future research should focus more on patient-related factors, as opposed to dosimetric parameters, in order to identify patients at high risk for developing radiation-induced lung toxicity more accurately.

Development and external validation of prognostic model for 2-year survival of non-small-cell lung cancer patients treated with chemoradiotherapy.

PURPOSE: Radiotherapy, combined with chemotherapy, is the treatment of choice for a large group of non-small-cell lung cancer (NSCLC) patients. Recent developments in the treatment of these patients have led to improved survival. However, the clinical TNM stage is highly inaccurate for the prediction of survival, and alternatives are lacking. The objective of this study was to develop and validate a prediction model for survival of NSCLC patients, treated with chemoradiotherapy. PATIENTS AND METHODS: The clinical data from 377 consecutive inoperable NSCLC patients, Stage I-IIIB, treated radically with chemoradiotherapy were collected. A prognostic model for 2-year survival was developed, using 2-norm support vector machines. The performance of the model was expressed as the area under the curve of the receiver operating characteristic and assessed using leave-one-out cross-validation, as well as two external data sets. RESULTS: The final multivariate model consisted of gender, World Health Organization performance status, forced expiratory volume in 1 s, number of positive lymph node stations, and gross tumor volume. The area under the curve, assessed by leave-one-out cross-validation, was 0.74, and application of the model to the external data sets yielded an area under the curve of 0.75 and 0.76. A high- and low-risk group could be clearly identified using a risk score based on the model. CONCLUSION: The multivariate model performed very well and was able to accurately predict the 2-year survival of NSCLC patients treated with chemoradiotherapy. The model could support clinicians in the treatment decision-making process.

Mining time-dependent patient outcomes from hospital patient records.

We describe REMIND, a data mining framework that accurately infers missing clinical information by reasoning over the entire patient record. Hospitals collect computerized patient records (CPR's) in structured (database tables) and unstructured (free text) formats. Structured clinical data in the CPR's is often poorly recorded, and information may be missing about key outcomes and processes. For instance, for a population of 344 colon cancer patients, important clinical outcomes, such as disease state and its evolution, are stored only as unstructured data (doctors' dictations) in the CPR. Raw evidence (extracted directly from the CPR) is not a good predictor of disease state. Yet by combining this evidence in a principled fashion (using methods from uncertain and temporal reasoning), REMIND accurately infers disease state sequences for recurrence, a complex time-varying outcome, for these patients. These outcomes can now be added back into the CPR in structured form.