We retrospectively analyzed 65 patients with refractory/relapsed (r/r) ALL who were treated with blinatumomab for predictors of leukemia response as well as clinical patterns of relapse and resistance with particular focus on downregulation of CD19 expression and extramedullary disease (EM-ALL). The complete remission (CR) rate was 51%, and 15 (45%) responders underwent allogeneic hematopoietic cell transplantation (HCT) in CR. High leukemia burden (bone marrow blasts >50%) (P = .02), history of prior EM-ALL (P = .005), and active EM-ALL at the time of initiating blinatumomab (P = .05) predicted lower CR rate. Among refractory cases, 13 (41%) had evidence of EM-ALL progression, and CD19 expression was negative or dim in 18% and 23%, respectively. Among responders, 20 (61%) subsequently relapsed among whom EM-ALL relapse occurred in 8 (40%) patients, and CD19 expression was negative or dim in 35 and 6% of evaluable cases, respectively. Pretreatment moderate/strong CD19 expression (P = .01) and history of prior EM-ALL during ALL course (P = .04) were risk factors for developing EM-ALL at progression/relapse. However, no pretreatment factors predicted progression/relapse with CD19-negative ALL. Overall-survival (OS) and even-free survival were improved for patients underwent allogeneic HCT compared to responders who did not. Furthermore, OS was superior for patients responded to blinatumomab compared to those who did not. Extramedullary and CD19-negative disease are common during blinatumomab failure in r/r ALL. In addition to high leukemia burden, concurrent or prior history EM-ALL were associated with lower response to blinatumomab. Higher CD19 expression as well as prior history of EM-ALL were associated with EM-ALL at the time of blinatumomab failure.
Background: Up to 50% of child abuse (CA) victims exhibit evidence of traumatic facial or intraoral injuries. Dental health professionals (DHPs) are therefore well-positioned to detect and report incidences of CA. This study aimed to assess the knowledge and attitudes of Western Australian DHPs towards identifying and reporting CA. Methods: General dentists, specialists, hygienists and oral health therapists completed an online questionnaire which assessed their knowledge and experience in identifying and reporting CA. Results: A total of 228 participants completed the questionnaire (representing 7% of DHPs, 60% of paediatric dentists and 11% of all dental hygienists and therapists in Western Australia). The majority of participants (66.2%, P < 0.05) felt that they were unlikely to recognize a patient with physical abuse, or detect signs of sexual abuse (90.8%, P < 0.001). Uncertainty around diagnosing abuse was a barrier towards reporting cases (86.4%, P < 0.05) and most participants (78.0%, P < 0.05) felt that they did not have adequate safeguarding training to report CA. Conclusions: Self-reported confidence in identifying and reporting CA cases was low; with the majority of the dental professionals participating in this study unlikely to recognize signs of CA. Inadequate training and knowledge around correct reporting protocols were identified as barriers, which warrants an appropriate change to improve child safeguarding.
Introduction:Valuable research data is limited in its use when it is unstructured and not stored in discrete meaningful fields. Reports of the bone marrow (BM) aspirate and biopsies performed in patients with suspected or confirmed myeloid neoplasms typically include blood counts, peripheral blood (PB) and BM aspirate/touch preparation differential counts, morphological interpretation of aspirate and core biopsy and ancillary data such as karyotype, fluorescent in situ hybridization (FISH) and molecular mutations. Final BM reports are typically reported in a semi-structured document that are sufficient for a single patient review but inadequate for large scale queries to identify patients with a specific diagnosis or capture important diagnostic data. Manual extraction of these fields is expensive, time consuming and error prone. The aim of this study is to develop a customized algorithm for automated extraction of data from bone marrow biopsy reports and generate a framework that allows us to perform large-scale queries. Methods:We randomly identified 148 patients with a diagnosis of a myeloid neoplasm: chronic myeloid leukemia (n=45), chronic myelomonocytic leukemia (n=54) and acute myeloid leukemia (n=57). Seven patients included in this analysis were initially diagnosed as CMML and subsequently transformed to acute myeloid leukemia. Total number of reports evaluated was 524. Numerical and text diagnostic data were extracted manually from the entire cohort selected, which is considered a gold standard. A customized rule based algorithm was developed for each data attribute using Natural Language Processing (I2E Text Mining platform, Linguamatics Ltd, Cambridge, UK). Numerical data captured included differential counts from peripheral blood, bone marrow aspirate or touch preparation. Diagnostic data was captured as included diagnostic interpretation of peripheral blood smear and bone marrow aspirate. The algorithms for extracting the data were previously trained on a separate cohort. Precision and recall calculated for each data attribute utilizing R programing language and statistical computing environment. The calculation of precision can be defined as an index to measure the accuracy or closeness of a measured value to a known value (also known as positive predictive value). Recall can be defined as a measure of ability to capture all data points of interest (true positive rate or sensitivity). F-measure combines precision and recall as a harmonic mean. Results:Overall accuracy for the data captured was precision n = 0.9117 and recall n =0.7951. Precision and recall values for numerical and text data is reported in Table 1 and Figure 1. Conclusion:Extraction of relevant diagnostic data from unstructured bone marrow biopsy reports through automated approach is feasible and accurate. This method saves time and can be utilized for automated extraction of unstructured pathology reports from patients with different hematologic malignancies. Capturing data and storing in structured formats will allow researchers to perform large-scale queries. At the Huntsman Cancer Institute, this data is stored in easily accessible database and linked to other databases such as tissue banking. This approach will allow physicians and translational researchers to find samples with specific diagnosis or molecular mutation, for example identifying AML patients with mutated FLT3 gene. Data on extraction of karyotype, FISH and molecular mutations is being analyzed for accuracy and will be presented at the meeting. Future work involves identifying and improving accuracy and expanding the algorithms to extract additional fields in bone marrow biopsies and apply these algorithms to other hematologic malignancies. Disclosures Deininger: Blueprint: Consultancy; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees.
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