Anti-tuberculosis activity of morusin: a promising flavonoid from white mulberry

BACKGROUND: TB has remained a significant public health concern from historical times to the present day. Each year, growing drug resistance problems necessitate the discovery of new drugs and drug precursors for TB treatment. Morusin is an important flavone found in the bark of white mulberry (Morus alba L.) with anti-oxidant, antimicrobial, anti-tumour, anti-inflammatory and antiallergic activity.OBJECTIVE: To determine the anti-TB efficacy of morusin on Mycobacterium tuberculosis strains.DESIGN: Anti-TB efficacy of morusin was tested on H37Ra (American Type Culture Collection [ATCC] 25177), H37Rv (ATCC 27294), ATCC 35822 (isoniazid [INH] resistant), ATCC 35838 (rifampicin [RIF] resistant), and ATCC 35820 (streptomycin [SM] resistant) standard strains and its efficacy was determined using nitrate reductase assay (NRA).RESULTS: The minimum inhibitory concentration (MIC) of morusin was tested in the range of 53.83â-"0.21 μg/ml. The MIC for H37Ra (ATCC 25177), H37Rv (ATCC 27294) and ATCC 35838 (RIF-resistant) strains were found to be 6.72 μg/ml, and this was 13.45 μg/ml for the ATCC 35822 (INHresistant) and ATCC 35820 (SM-resistant) strains.CONCLUSION: To consider morusin as a viable alternative or precursor drug for TB treatment, it is imperative to conduct an exhaustive examination of its mechanism of action and conduct in vitro studies using clinical isolates.

Disparities in Preoperative Goals of Care Documentation in Veterans

Importance

Preoperative goals of care discussion and documentation are important for patients undergoing surgery, a major health care stressor that incurs risk.

Objective

To assess the association of race, ethnicity, and other factors, including history of mental health disability, with disparities in preoperative goals of care documentation among veterans.

Design, Setting, and Participants

This retrospective cross-sectional study assessed data from the Veterans Healthcare Administration (VHA) of 229 737 veterans who underwent surgical procedures between January 1, 2017, and October 18, 2022.

Exposures

Patient-level (ie, race, ethnicity, medical comorbidities, history of mental health comorbidity) and system-level (ie, facility complexity level) factors.

Main Outcomes and Measures

Preoperative life-sustaining treatment (LST) note documentation or no LST note documentation within 30 days prior to or on day of surgery. The standardized mean differences were calculated to assess the magnitude of differences between groups. Odds ratios (ORs) and 95% CIs were estimated with logistic regression.

Results

In this study, 13 408 patients (5.8%) completed preoperative LST from 229 737 VHA patients (209 123 [91.0%] male; 20 614 [9.0%] female; mean [SD] age, 65.5 [11.9] years) who received surgery. Compared with patients who did complete preoperative LST, patients tended to complete preoperative documentation less often if they were female (19 914 [9.2%] vs 700 [5.2%]), Black individuals (42 571 [19.7%] vs 2416 [18.0%]), Hispanic individuals (11 793 [5.5%] vs 631 [4.7%]), or from rural areas (75 637 [35.0%] vs 4273 [31.9%]); had a history of mental health disability (65 974 [30.5%] vs 4053 [30.2%]); or were seen at lowest-complexity (ie, level 3) facilities (7849 [3.6%] vs 78 [0.6%]). Over time, despite the COVID-19 pandemic, patients undergoing surgical procedures completed preoperative LST increasingly more often. Covariate-adjusted estimates of preoperative LST completion demonstrated that patients of racial or ethnic minority background (Black patients: OR, 0.79; 95% CI, 0.77-0.80; P <.001; patients selecting other race: OR, 0.78; 95% CI, 0.74-0.81; P <.001; Hispanic patients: OR, 0.78; 95% CI, 0.76-0.81; P <.001) and patients from rural regions (OR, 0.91; 95% CI, 0.90-0.93; P <.001) had lower likelihoods of completing LST compared with patients who were White or non-Hispanic and patients from urban areas. Patients with any mental health disability history also had lower likelihood of completing preoperative LST than those without a history (OR, 0.93; 95% CI, 0.92-0.94; P = .001).

Conclusions and Relevance

In this cross-sectional study, disparities in documentation rates within a VHA cohort persisted based on race, ethnicity, rurality of patient residence, history of mental health disability, and access to high-volume, high-complexity facilities.

Accomplished women leaders in informatics: insights about successful careers

We sought to learn from the experiences of women leaders in informatics by interviewing women in Informatics leadership roles. Participants reported career challenges, how they built confidence, advice to their younger selves, and suggestions for attracting and retaining additional women. Respondents were 16 women in leadership roles in academia (n = 9) and industry (n = 7). We conducted a thematic analysis revealing: (1) careers in informatics are serendipitous and nurtured by supportive communities, (2) challenges in leadership were profoundly related to gender issues, (3) "Big wins" in informatics careers were about making a difference, and (4) women leaders highlighted resilience, excellence, and personal authenticity as important for future women leaders. Sexism is undeniably present, although not all participants reported overt gender barriers. Confidence and authenticity in leadership point to the value offered by individual leaders. The next step is to continue to foster an informatics culture that encourages authenticity across the gender spectrum.

Evaluation of race/ethnicity-specific survival machine learning models for Hispanic and Black patients with breast cancer

OBJECTIVES

Survival machine learning (ML) has been suggested as a useful approach for forecasting future events, but a growing concern exists that ML models have the potential to cause racial disparities through the data used to train them. This study aims to develop race/ethnicity-specific survival ML models for Hispanic and black women diagnosed with breast cancer to examine whether race/ethnicity-specific ML models outperform the general models trained with all races/ethnicity data.

METHODS

We used the data from the US National Cancer Institute's Surveillance, Epidemiology and End Results programme registries. We developed the Hispanic-specific and black-specific models and compared them with the general model using the Cox proportional-hazards model, Gradient Boost Tree, survival tree and survival support vector machine.

RESULTS

A total of 322 348 female patients who had breast cancer diagnoses between 1 January 2000 and 31 December 2017 were identified. The race/ethnicity-specific models for Hispanic and black women consistently outperformed the general model when predicting the outcomes of specific race/ethnicity.

DISCUSSION

Accurately predicting the survival outcome of a patient is critical in determining treatment options and providing appropriate cancer care. The high-performing models developed in this study can contribute to providing individualised oncology care and improving the survival outcome of black and Hispanic women.

CONCLUSION

Predicting the individualised survival outcome of breast cancer can provide the evidence necessary for determining treatment options and high-quality, patient-centred cancer care delivery for under-represented populations. Also, the race/ethnicity-specific ML models can mitigate representation bias and contribute to addressing health disparities.

Considerations for the Use of Machine Learning Extracted Real-World Data to Support Evidence Generation: A Research-Centric Evaluation Framework

A vast amount of real-world data, such as pathology reports and clinical notes, are captured as unstructured text in electronic health records (EHRs). However, this information is both difficult and costly to extract through human abstraction, especially when scaling to large datasets is needed. Fortunately, Natural Language Processing (NLP) and Machine Learning (ML) techniques provide promising solutions for a variety of information extraction tasks such as identifying a group of patients who have a specific diagnosis, share common characteristics, or show progression of a disease. However, using these ML-extracted data for research still introduces unique challenges in assessing validity and generalizability to different cohorts of interest. In order to enable effective and accurate use of ML-extracted real-world data (RWD) to support research and real-world evidence generation, we propose a research-centric evaluation framework for model developers, ML-extracted data users and other RWD stakeholders. This framework covers the fundamentals of evaluating RWD produced using ML methods to maximize the use of EHR data for research purposes.

Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage

Background

Explicit documentation of stage is an endorsed quality metric by the National Quality Forum. Clinical and pathological cancer staging is inconsistently recorded within clinical narratives but can be derived from text in the Electronic Health Record (EHR). To address this need, we developed a Natural Language Processing (NLP) solution for extraction of clinical and pathological TNM stages from the clinical notes in prostate cancer patients.

Methods

Data for patients diagnosed with prostate cancer between 2010 and 2018 were collected from a tertiary care academic healthcare system's EHR records in the United States. This system is linked to the California Cancer Registry, and contains data on diagnosis, histology, cancer stage, treatment and outcomes. A randomly selected sample of patients were manually annotated for stage to establish the ground truth for training and validating the NLP methods. For each patient, a vector representation of clinical text (written in English) was used to train a machine learning model alongside a rule-based model and compared with the ground truth.

Results

A total of 5,461 prostate cancer patients were identified in the clinical data warehouse and over 30% were missing stage information. Thirty-three to thirty-six percent of patients were missing a clinical stage and the models accurately imputed the stage in 21–32% of cases. Twenty-one percent had a missing pathological stage and using NLP 71% of missing T stages and 56% of missing N stages were imputed. For both clinical and pathological T and N stages, the rule-based NLP approach out-performed the ML approach with a minimum F1 score of 0.71 and 0.40, respectively. For clinical M stage the ML approach out-performed the rule-based model with a minimum F1 score of 0.79 and 0.88, respectively.

Conclusions

We developed an NLP pipeline to successfully extract clinical and pathological staging information from clinical narratives. Our results can serve as a proof of concept for using NLP to augment clinical and pathological stage reporting in cancer registries and EHRs to enhance the secondary use of these data.

Analyzing real world data of blood transfusion adverse events: Opportunities and challenges

BACKGROUND

Blood transfusions are a vital component of modern healthcare, yet adverse reactions to blood product transfusions can cause morbidity, and rarely result in mortality. Therefore, accurate reporting of transfusion related adverse events (TRAEs) is paramount to improved transfusion practice. This study aims to investigate real-world data (RWD) on TRAEs by evaluating differences between ICD 9/10-based electronic health records (EHR) and blood bank-specific reporting.

STUDY DESIGN AND METHODS

TRAE data were retrospectively collected from a blood bank-specific database between Jan 2015 and June 2019 as the reference data source and compared it to ICD 9/10 diagnostic codes corresponding to various TRAEs. Seven reactions that have corresponding ICD 9/10 diagnostic codes were evaluated: Transfusion related circulatory overload (TACO), transfusion related acute lung injury (TRALI), febrile non-hemolytic reaction (FNHTR), transfusion-related anaphylactic reaction (TRA), acute hemolytic transfusion reaction (AHTR), delayed hemolytic transfusion reaction (DHTR), and delayed serologic reaction (DSTR). These accounted for 33% of the TRAEs at an academic institution during the study period.

RESULTS

Among 18637 adult blood transfusion recipients, there were 229 unique patients with 263 TRAE related ICD codes in the EHR, while there were 191 unique patients with 287 TRAEs identified in the blood bank database. None of the categories of reaction we investigated had perfect alignment between ICD 9/10 codes and blood bank specific diagnoses.

DISCUSSION

Multiple systemic challenges were identified that hinder effective reporting of TRAEs. Identifying factors causing inconsistent reporting between blood banks and EHRs is paramount to developing effective workability between these electronic systems, as well as across clinical and laboratory teams.

Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model

As artificial intelligence (AI) makes continuous progress to improve quality of care for some patients by leveraging ever increasing amounts of digital health data, others are left behind. Empirical evaluation studies are required to keep biased AI models from reinforcing systemic health disparities faced by minority populations through dangerous feedback loops. The aim of this study is to raise broad awareness of the pervasive challenges around bias and fairness in risk prediction models. We performed a case study on a MIMIC-trained benchmarking model using a broadly applicable fairness and generalizability assessment framework. While open-science benchmarks are crucial to overcome many study limitations today, this case study revealed a strong class imbalance problem as well as fairness concerns for Black and publicly insured ICU patients. Therefore, we advocate for the widespread use of comprehensive fairness and performance assessment frameworks to effectively monitor and validate benchmark pipelines built on open data resources.

Can peripheral blood monocyte percentage and lymphocyte monocyte ratio at diagnosis predict survival in pediatric neuroblastoma patients?

BACKGROUND

Previous studies have shown that the immune system plays a critical role in cancer pathogenesis. The lymphocyte monocyte ratio (LMR) and monocyte percentage (MP) have been found to be prognostic factors in various types of adult cancers. But studies about pediatric tumors are scarce and to our knowledge, there are no studies evaluating the immune system effect in pediatric neuroblastoma patients. The aim of this study was to assess whether LMR and MP at diagnosis may have an effect on prognosis in neuroblastoma patients.

METHODS

We retrospectively analyzed MP and LMR at diagnosis in 71 pediatric neuroblastoma patients treated between 2002 and 2016.

RESULTS

The optimal cut-off values of LMR and MP were determined using the receiver operating characteristics curves (ROC) and area under the curve (AUC). We found that a low LMR (≤3.5) and a high MP (≥7.5%) were correlated with worse overall survival and shorter event-free survival in univariate analysis. Multivariate analysis revealed that elevated LMR was an independent factor for better OS and EFS.

CONCLUSIONS

In conclusion, LMR and MP might be valuable prognostic factors for predicting OS in neuroblastoma patients. Multicenter and prospective studies are warranted to confirm this hypothesis.

Characterizing patient flow after an academic hospital merger and acquisition

OBJECTIVES

Hospital mergers and acquisitions are increasingly used as a strategy to facilitate value-based care. However, no studies have assessed health care utilization (HCU) and patient flow across merged institutions. We aim to evaluate patient population distribution, HCU, and patient flow across a recent hospital merger of an academic medical center (AMC), a primary and specialty care alliance (PSC), and a community-based medical center (CMC).

STUDY DESIGN

This was a retrospective observational study.

METHODS

The study used 2018 adult demographic and encounter data from electronic health records. Patients' parent health care institution was determined by the most frequently visited site of face-to-face visits. Differences in patient demographics and HCU (ie, emergency department [ED] visits, hospitalizations, primary care visits) were compared. Independent factors associated with utilization were identified using adjusted logistic regression models.

RESULTS

A total of 406,303 adult patients were identified in the cohort. The PSC setting, compared with the AMC and the CMC, had significantly more female (62.7% vs 54.4% and 58.5%, respectively), older (mean [SD] age, 52.0 [18.1] vs 51.1 [17.8] and 49.2 [17.8] years), and privately insured (63.6% vs 51.3% and 56.0%) patients. A higher proportion of patients at the CMC (27.5%) visited the ED compared with patients at the AMC (10.8%). Approximately 1645 primary care patients (7%) at the CMC setting went to the AMC for specialized care such as oncology, surgery, and neurology.

CONCLUSIONS

Hospital mergers are increasing across the United States, allowing AMCs to expand their reach. These findings suggest that patients mainly sought care at their parent health care institution, yet appropriately received specialized care at the AMC. These results provide insights for future mergers and guide resource allocation and opportunities for improving care delivery.

Association of treatment type with patient-reported quality of life in cancer distress screening

178

Background: Routine distress screening is recommended for all patients with cancer. In 2015, Stanford Cancer Center implemented such screening using a modified PROMIS-GH questionnaire. With the recent growth of oncology drugs, novel medications have been perceived as better tolerated than chemotherapy. We analyzed patient reported quality of life with global mental health (GMH) and global physical health (GPH) scores for different medication classes. Methods: Patients who completed a questionnaire at our center between 6/1/2015 and 12/31/2020 were included. Medications were classified as chemotherapy, targeted therapy, endocrine therapy, or immunotherapy using guidance from SEER.Rx ontology. Baseline (B) and treatment (Tx) questionnaires were completed before any treatment initiation of each medication type and within 3 months of each treatment, respectively. GPH and GMH scores were calculated using PROMIS T-scores stratified by medication type for B and Tx questionnaires. We analyzed for differences based on demographics and diagnoses. Clinically significant differences were defined as a 3-point difference in T-scores, which then prompted statistical comparison with t-tests to compare the B and Tx scores to each other and to the US population mean of 50. Results: We analyzed 28,180 questionnaires from 11,644 patients (59% women, median age 64; 23% stage I and II, 12% stage III, 23% stage IV, 51% missing). B and Tx mean GMH scores did not differ clinically compared to the US mean or to each other (baseline: 49.03 +/- 9.16, post: 48.5 +/- 9.1). However, both mean GPH scores were statistically and clinically lower (baseline: 44.2+/- 10.38, post: 42.4 +/- 10.1,) compared to the US mean (p < 0.001). Changes in scores by treatment category are shown in the table below. There was a statistically significant difference in post-treatment GPH scores for chemo-immunotherapy patients when compared to both corresponding baseline scores (p < 0.001) and post treatment chemotherapy alone scores (p < 0.001). There was no clinically significant difference in scores when stratified by age, sex, primary language, insurance, disease stage or type. Conclusions: In this large retrospective study, we found that patients being treated for cancer did not report worse GMH scores compared to the US mean population, but do report lower GPH scores. While most scores varied little relative to other treatment types, those receiving chemo-immunotherapy had lower GPH scores when comparing baseline to treatment and to the US mean, warranting further investigation, given increasing use.[Table: see text]

Diverse patient trajectories during cytotoxic chemotherapy: Capturing longitudinal patient-reported outcomes

Background

High‐value cancer care balances effective treatment with preservation of quality of life. Chemotherapy is known to affect patients’ physical and psychological well‐being negatively. Patient‐reported outcomes (PROs) provide a means to monitor declines in a patients’ well‐being during treatment.

Methods

We identified 741 oncology patients undergoing chemotherapy in our electronic health record (EHR) system who completed Patient‐Reported Outcomes Measurement Information System (PROMIS) surveys during treatment at a comprehensive cancer center, 2013–2018. PROMIS surveys were collected before, during, and after chemotherapy treatment. Linear mixed‐effects models were performed to identify predictors of physical and mental health scores over time. A k‐mean cluster analysis was used to group patient PROMIS score trajectories.

Results

Mean global physical health (GPH) scores were 48.7 (SD 9.3), 47.7 (8.8), and 48.6 (8.9) and global mental health (GMH) scores were 50.4 (8.6), 49.5 (8.8), and 50.6 (9.1) before, during, and after chemotherapy, respectively. Asian race, Hispanic ethnicity, public insurance, anxiety/depression, stage III cancer, and palliative care were predictors of GPH and GMH decline. The treatment time period was also a predictor of both GPH and GMH decline relative to pre‐treatment. Trajectory clustering identified four distinct PRO clusters associated with chemotherapy treatment.

Conclusions

Patient‐reported outcomes are increasingly used to help monitor cancer treatment and are now a part of care reimbursement. This study leveraged routinely collected PROMIS surveys linked to EHRs to identify novel patient trajectories of physical and mental well‐being in oncology patients undergoing chemotherapy and potential predictors. Supportive care interventions in high‐risk populations identified by our study may optimize resource deployment.

Novelty and impact

This study leveraged routinely collected patient‐reported outcome (PROMIS) surveys linked to electronic health records to characterize oncology patients’ quality of life during chemotherapy. Important clinical and demographic predictors of declines in quality of life were identified and four novel trajectories to guide personalized interventions and support. This work highlights the utility of monitoring patient‐reported outcomes not only before and after, but during chemotherapy to help advert adverse patient outcomes and improve treatment adherence.

MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care

The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present MINIMAR (MINimum Information for Medical AI Reporting), a proposal describing the minimum information necessary to understand intended predictions, target populations, and hidden biases, and the ability to generalize these emerging technologies. We call for a standard to accurately and responsibly report on AI in health care. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias.

Assessment of a Clinical Trial-Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer

IMPORTANCE

Randomized clinical trials (RCTs) are considered the criterion standard for clinical evidence. Despite their many benefits, RCTs have limitations, such as costliness, that may reduce the generalizability of their findings among diverse populations and routine care settings.

OBJECTIVE

To assess the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic castration-resistant prostate cancer (CRPC) when the model is applied to real-world data from electronic health records (EHRs).

DESIGN, SETTING, AND PARTICIPANTS

The RCT-trained model and patient data from the RCTs were obtained from the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge for prostate cancer, which occurred from March 16 to July 27, 2015. This challenge included 4 phase 3 clinical trials of patients with metastatic CRPC. Real-world data were obtained from the EHRs of a tertiary care academic medical center that includes a comprehensive cancer center. In this study, the DREAM challenge RCT-trained model was applied to real-world data from January 1, 2008, to December 31, 2019; the model was then retrained using EHR data with optimized feature selection. Patients with metastatic CRPC were divided into RCT and EHR cohorts based on data source. Data were analyzed from March 23, 2018, to October 22, 2020.

EXPOSURES

Patients who received treatment for metastatic CRPC.

MAIN OUTCOMES AND MEASURES

The primary outcome was the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic CRPC when the model is applied to real-world data. Model performance was compared using 10-fold cross-validation according to time-dependent integrated area under the curve (iAUC) statistics.

RESULTS

Among 2113 participants with metastatic CRPC, 1600 participants were included in the RCT cohort, and 513 participants were included in the EHR cohort. The RCT cohort comprised a larger proportion of White participants (1390 patients [86.9%] vs 337 patients [65.7%]) and a smaller proportion of Hispanic participants (14 patients [0.9%] vs 42 patients [8.2%]), Asian participants (41 patients [2.6%] vs 88 patients [17.2%]), and participants older than 75 years (388 patients [24.3%] vs 191 patients [37.2%]) compared with the EHR cohort. Participants in the RCT cohort also had fewer comorbidities (mean [SD], 1.6 [1.8] comorbidities vs 2.5 [2.6] comorbidities, respectively) compared with those in the EHR cohort. Of the 101 variables used in the RCT-derived model, 10 were not available in the EHR data set, 3 of which were among the top 10 features in the DREAM challenge RCT model. The best-performing EHR-trained model included only 25 of the 101 variables included in the RCT-trained model. The performance of the RCT-trained and EHR-trained models was adequate in the EHR cohort (mean [SD] iAUC, 0.722 [0.118] and 0.762 [0.106], respectively); model optimization was associated with improved performance of the best-performing EHR model (mean [SD] iAUC, 0.792 [0.097]). The EHR-trained model classified 256 patients as having a high risk of mortality and 256 patients as having a low risk of mortality (hazard ratio, 2.7; 95% CI, 2.0-3.7; log-rank P < .001).

CONCLUSIONS AND RELEVANCE

In this study, although the RCT-trained models did not perform well when applied to real-world EHR data, retraining the models using real-world EHR data and optimizing variable selection was beneficial for model performance. As clinical evidence evolves to include more real-world data, both industry and academia will likely search for ways to balance model optimization with generalizability. This study provides a pragmatic approach to applying RCT-trained models to real-world data.

Reporting of demographic data and representativeness in machine learning models using electronic health records

OBJECTIVE

The development of machine learning (ML) algorithms to address a variety of issues faced in clinical practice has increased rapidly. However, questions have arisen regarding biases in their development that can affect their applicability in specific populations. We sought to evaluate whether studies developing ML models from electronic health record (EHR) data report sufficient demographic data on the study populations to demonstrate representativeness and reproducibility.

MATERIALS AND METHODS

We searched PubMed for articles applying ML models to improve clinical decision-making using EHR data. We limited our search to papers published between 2015 and 2019.

RESULTS

Across the 164 studies reviewed, demographic variables were inconsistently reported and/or included as model inputs. Race/ethnicity was not reported in 64%; gender and age were not reported in 24% and 21% of studies, respectively. Socioeconomic status of the population was not reported in 92% of studies. Studies that mentioned these variables often did not report if they were included as model inputs. Few models (12%) were validated using external populations. Few studies (17%) open-sourced their code. Populations in the ML studies include higher proportions of White and Black yet fewer Hispanic subjects compared to the general US population.

DISCUSSION

The demographic characteristics of study populations are poorly reported in the ML literature based on EHR data. Demographic representativeness in training data and model transparency is necessary to ensure that ML models are deployed in an equitable and reproducible manner. Wider adoption of reporting guidelines is warranted to improve representativeness and reproducibility.

Phenotyping severity of patient-centered outcomes using clinical notes: A prostate cancer use case

Introduction

A learning health system (LHS) must improve care in ways that are meaningful to patients, integrating patient‐centered outcomes (PCOs) into core infrastructure. PCOs are common following cancer treatment, such as urinary incontinence (UI) following prostatectomy. However, PCOs are not systematically recorded because they can only be described by the patient, are subjective and captured as unstructured text in the electronic health record (EHR). Therefore, PCOs pose significant challenges for phenotyping patients. Here, we present a natural language processing (NLP) approach for phenotyping patients with UI to classify their disease into severity subtypes, which can increase opportunities to provide precision‐based therapy and promote a value‐based delivery system.

Methods

Patients undergoing prostate cancer treatment from 2008 to 2018 were identified at an academic medical center. Using a hybrid NLP pipeline that combines rule‐based and deep learning methodologies, we classified positive UI cases as mild, moderate, and severe by mining clinical notes.

Results

The rule‐based model accurately classified UI into disease severity categories (accuracy: 0.86), which outperformed the deep learning model (accuracy: 0.73). In the deep learning model, the recall rates for mild and moderate group were higher than the precision rate (0.78 and 0.79, respectively). A hybrid model that combined both methods did not improve the accuracy of the rule‐based model but did outperform the deep learning model (accuracy: 0.75).

Conclusion

Phenotyping patients based on indication and severity of PCOs is essential to advance a patient centered LHS. EHRs contain valuable information on PCOs and by using NLP methods, it is feasible to accurately and efficiently phenotype PCO severity. Phenotyping must extend beyond the identification of disease to provide classification of disease severity that can be used to guide treatment and inform shared decision‐making. Our methods demonstrate a path to a patient centered LHS that could advance precision medicine.

Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm

Radiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summarization and cancer response assessment. The purpose of this work is to develop and evaluate a natural language processing (NLP) pipeline that can extract measurements and their core descriptors, such as temporality, anatomical entity, imaging observation, RadLex descriptors, series number, image number, and segment from a wide variety of radiology reports (MR, CT, and mammogram). We created a hybrid NLP pipeline that integrates rule-based feature extraction modules and conditional random field (CRF) model for extraction of the measurements from the radiology reports and links them with clinically relevant features such as anatomical entities or imaging observations. The pipeline was trained on 1117 CT/MR reports, and performance of the system was evaluated on an independent set of 100 expert-annotated CT/MR reports and also tested on 25 mammography reports. The system detected 813 out of 806 measurements in the CT/MR reports; 784 were true positives, 29 were false positives, and 0 were false negatives. Similarly, from the mammography reports, 96% of the measurements with their modifiers were extracted correctly. Our approach could enable the development of computerized applications that can utilize summarized lesion measurements from radiology report of varying modalities and improve practice by tracking the same lesions along multiple radiologic encounters.

Acute pain after breast surgery and reconstruction: A two-institution study of surgical factors influencing short-term pain outcomes

BACKGROUND AND OBJECTIVES

Acute postoperative pain following surgery is known to be associated with chronic pain development and lower quality of life. We sought to analyze the relationship between differing breast cancer excisional procedures, reconstruction, and short-term pain outcomes.

METHODS

Women undergoing breast cancer excisional procedures with or without reconstruction at two systems: an academic hospital (AH) and Veterans Health Administration (VHA) were included. Average pain scores at the time of discharge and at 30-day follow-up were analyzed across demographic and clinical characteristics. Linear mixed effects modeling was used to assess the relationship between patient/clinical characteristics and interval pain scores with a random slope to account for differences in baseline pain.

RESULTS

Our study included 1402 patients at AH and 1435 at VHA, of which 426 AH and 165 patients with VHA underwent reconstruction. Pain scores improved over time and were found to be highest at discharge. Time at discharge, 30-day follow-up, and preoperative opioid use were the strongest predictors of high pain scores. Younger age and longer length of stay were independently associated with worse pain scores.

CONCLUSIONS

Younger age, preoperative opioid use, and longer length of stay were associated with higher levels of postoperative pain across both sites.

Natural Language Processing Approaches to Detect the Timeline of Metastatic Recurrence of Breast Cancer

PURPOSE

Electronic medical records (EMRs) and population-based cancer registries contain information on cancer outcomes and treatment, yet rarely capture information on the timing of metastatic cancer recurrence, which is essential to understand cancer survival outcomes. We developed a natural language processing (NLP) system to identify patient-specific timelines of metastatic breast cancer recurrence.

PATIENTS AND METHODS

We used the OncoSHARE database, which includes merged data from the California Cancer Registry and EMRs of 8,956 women diagnosed with breast cancer in 2000 to 2018. We curated a comprehensive vocabulary by interviewing expert clinicians and processing radiology and pathology reports and progress notes. We developed and evaluated the following two distinct NLP approaches to analyze free-text notes: a traditional rule-based model, using rules for metastatic detection from the literature and curated by domain experts; and a contemporary neural network model. For each 3-month period (quarter) from 2000 to 2018, we applied both models to infer recurrence status for that quarter. We trained the NLP models using 894 randomly selected patient records that were manually reviewed by clinical experts and evaluated model performance using 179 hold-out patients (20%) as a test set.

RESULTS

The median follow-up time was 19 quarters (5 years) for the training set and 15 quarters (4 years) for the test set. The neural network model predicted the timing of distant metastatic recurrence with a sensitivity of 0.83 and specificity of 0.73, outperforming the rule-based model, which had a specificity of 0.35 and sensitivity of 0.88 (P < .001).

CONCLUSION

We developed an NLP method that enables identification of the occurrence and timing of metastatic breast cancer recurrence from EMRs. This approach may be adaptable to other cancer sites and could help to unlock the potential of EMRs for research on real-world cancer outcomes.

Four distinct patient-reported outcome (PRO) trajectories in longitudinal responses collected before, during, and after chemotherapy

2012

Background: Cancer chemotherapy, whether given with curative or palliative intent, is toxic. Toxicity is routinely captured in clinical trials by investigator observation and increasingly by PRO. The ability to capture PRO in the routine treatment workflow has been standard at Stanford since 2015 (Roy et al ASCO 2020). Analysis of longitudinally captured, real world PRO and prospectively identifying patients (pts) whose quality of life (QOL) is at risk of deteriorating either permanently or temporarily is needed. Routine serial PRO measurement should enhance precision care delivery, precision toxicity detection and management. Methods: We identified patients undergoing chemotherapy at Stanford and analyzed PROMIS (PRO Measurement Information System) responses. Pts with PROMIS survey information at three intervals—pre-treatment, during chemotherapy and post chemotherapy—were identified. We evaluated global physical health (GPH) and global mental health (GMH). Pts with a clinically significant decrease (CSD) in GPH or GMH scores were identified. A k-median cluster analysis was used to identify patient trajectory clusters and a machine-learning model was applied to identify risk factors for CSD and predict CSD. Results: We identified 670 adult oncology patients undergoing chemotherapy who completed at least one PROMIS survey in each interval. GPH scores were 48.4 ± 9.1 before, 47.1 ± 8.5 during, and 48.5 ± 8.9 after chemotherapy and GMH scores were 50.5 ± 8.2, 49.1 ± 8.5, and 50.7 ± 9.0, respectively. The majority of patients did not have a CSD in GPH or GMH post treatment compared to pretreatment scores. Pretreatment scores were the strongest predictor of a CSD in GPH and GMH. Trajectory clustering identified four distinct trajectories: Temporary Improver, Temporary Deteriorator, Improver, Inexorable Deteriorators. We were not able to predict any cluster based on pre-treatment features. Conclusions: Using routinely collected PROMIS surveys in a real-world setting, we are able to predict patients with post-treatment decreases in their physical and mental well-being. We further defined four novel patient trajectories during chemotherapy, which could guide personalized supportive interventions to improve patient’s chemotherapy experience. Identification of patients at risk for deterioration and the patterns of deterioration could help guide efficient deployment of toxicity mitigating and supportive care interventions to patients most in need.

Oral health related qualıty of lıfe and dısease severıty ın autoımmune bullous dıseases

Background

There is an increased risk of long-term dental and periodontal disease in autoimmune bullous diseases (AIBD).

Aims

In this cross-sectional study, we aimed to determine whether the oral health-related quality of life status (OHRQoL) was associated with disease severity and activity in patients with AIBD.

Subjects and Methods

67 patients with AIBD were enrolled in this study. Autoimmune Bullous Skin Disorder Intensity Score (ABSIS) was used to evaluate the disease severity. The score was categorized as a significant course (≥17) and moderate course (<17). Oral health impact profile-14 (OHIP-14) questionnaire was filled to assess the OHRQoL. Self-reported oral health status and oral lesion related pain score were also evaluated in the study group.

Results

OHIP-14 score was significantly higher in active patients (42.28 ± 13.66) than inactive patients (29.08 ± 12.25) (P = 0.004) and it was correlated with the pain score (6.33 ± 2.78; r = 0.409, P = 0.013). Furthermore, OHIP-14 score was higher in patients with a significant disease course (45.18 ± 15.08) (P = 0.010) than in patients with a moderate course (36.09 ± 9.73).

Conclusions

OHRQoL may be useful in the disease management and treatment. Since it can be affected by both presence of oral erosions and disease severity, a collaboration between dermatologists and dentists could be crucial to the disease management in AIBD.

Knowledge, attitudes and medical practice regarding hepatitis B prevention and management among healthcare workers in Northern Vietnam

BACKGROUND AND AIM

Vietnam's burden of liver cancer is largely due to its high prevalence of chronic hepatitis B virus (HBV) infection. This study aimed to examine healthcare workers' (HCWs) knowledge, attitude and practices regarding HBV prevention and management.

METHODS

A cross-sectional survey among health care workers working at primary and tertiary facilities in two Northern provinces in Vietnam in 2017. A standardized questionnaire was administered to randomly selected HCWs. Multivariate regression was used to identify predictors of the HBV knowledge score.

RESULTS

Among the 314 participants, 75.5% did not know HBV infection at birth carries the highest risk of developing chronic infection. The median knowledge score was 25 out of 42 (59.5%). About one third (30.2%) wrongly believed that HBV can be transmitted through eating or sharing food with chronic hepatitis B patients. About 38.8% did not feel confident that the hepatitis B vaccine is safe. Only 30.1% provided correct answers to all the questions on injection safety. Up to 48.2% reported they consistently recap needles with two hands after injection, a practice that would put them at greater risk of needle stick injury. About 24.2% reported having been pricked by a needle at work within the past 12 months. More than 40% were concerned about having casual contact or sharing food with a person with chronic hepatitis B infection (CHB). In multivariate analysis, physicians scored significantly higher compared to other healthcare professionals. Having received training regarding hepatitis B within the last two years was also significantly associated with a better HBV knowledge score.

CONCLUSIONS

Findings from the survey indicated an immediate need to implement an effective hepatitis B education and training program to build capacity among Vietnam's healthcare workers in hepatitis B prevention and control and to dispel hepatitis B stigma.

Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer

Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health records (EHRs). Therefore, we evaluated three supervised machine learning methods (Support Vector Machine, Decision Trees, Gradient Boosting) to classify free-text pathology reports for prostate cancer into T, N and M stage groups.

Is it possible to automatically assess pretreatment digital rectal examination documentation using natural language processing? A single-centre retrospective study

OBJECTIVES

To develop and test a method for automatic assessment of a quality metric, provider-documented pretreatment digital rectal examination (DRE), using the outputs of a natural language processing (NLP) framework.

SETTING

An electronic health records (EHR)-based prostate cancer data warehouse was used to identify patients and associated clinical notes from 1 January 2005 to 31 December 2017. Using a previously developed natural language processing pipeline, we classified DRE assessment as documented (currently or historically performed), deferred (or suggested as a future examination) and refused.

PRIMARY AND SECONDARY OUTCOME MEASURES

We investigated the quality metric performance, documentation 6 months before treatment and identified patient and clinical factors associated with metric performance.

RESULTS

The cohort included 7215 patients with prostate cancer and 426 227 unique clinical notes associated with pretreatment encounters. DREs of 5958 (82.6%) patients were documented and 1257 (17.4%) of patients did not have a DRE documented in the EHR. A total of 3742 (51.9%) patient DREs were documented within 6 months prior to treatment, meeting the quality metric. Patients with private insurance had a higher rate of DRE 6 months prior to starting treatment as compared with Medicaid-based or Medicare-based payors (77.3%vs69.5%, p=0.001). Patients undergoing chemotherapy, radiation therapy or surgery as the first line of treatment were more likely to have a documented DRE 6 months prior to treatment.

CONCLUSION

EHRs contain valuable unstructured information and with NLP, it is feasible to accurately and efficiently identify quality metrics with current documentation clinician workflow.

A nomogram for decision-making of completion surgery in endometrial cancer diagnosed after hysterectomy

OBJECTIVE

Extrauterine tumor spread is one of the essential determinants of disease outcome in endometrial cancer. However; more than 30% of patients still undergo incomplete surgery at the initial attempt. Strategies regarding the management of patients with incompletely staged early-stage disease or patients with undebulked advanced-stage disease remain controversial. Depending on postoperative uterine features and findings on imaging, patients may be put on observation or receive adjuvant therapy or undergo re-staging or debulking surgery followed by adjuvant therapy. To identify patients who would most benefit from a completion surgery, either for restaging or for cytoreduction, we developed a nomogram for estimation of extrauterine disease based on findings of final hysterectomy specimen.

METHODS

Data of 336 patients whose extrauterine disease status was known were analyzed. A nomogram was constructed using patient characteristics including age, grade, myometrial invasion, lymphovascular space involvement, cervical involvement, and peritoneal cytology. The nomogram was internally validated in terms of discrimination, calibration and overall performance.

RESULTS

The nomogram showed good performance accuracy with an area under the receiver operating characteristic curve of 0.870, a specificity of 95.5%, and a positive predictive value of 73.9%. Decision curve analysis revealed that the use of the nomogram in decision-making for completion surgery leads to the equivalent of a net 18 true-positive results per 100 patients without an increase in the number of false-positive results.

CONCLUSIONS

Estimation of extrauterine disease from final hysterectomy specimen is possible with high predictive performance using the nomogram developed. The nomogram may help clinicians in decision-making for management of incomplete surgeries.

Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients

OBJECTIVE

Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches.

MATERIALS AND METHODS

Our cohort was divided into risk groups based on Electronic Health Records (EHR). Information on bone scan utilization was identified in both structured data and free text from clinical notes. Our pipeline annotated sentences with a combination of a rule-based method using the ConText algorithm (a generalization of NegEx) and a Convolutional Neural Network (CNN) method using word2vec to produce word embeddings.

RESULTS

A total of 5500 patients and 369,764 notes were included in the study. A total of 39% of patients were high-risk and 73% of these received a bone scan; of the 18% low risk patients, 10% received one. The accuracy of CNN model outperformed the rule-based model one (F-measure = 0.918 and 0.897 respectively). We demonstrate a combination of both models could maximize precision or recall, based on the study question.

CONCLUSION

Using structured data, we accurately classified patients' cancer risk group, identified bone scan documentation with two NLP methods, and evaluated guideline adherence. Our pipeline can be used to provide concrete feedback to clinicians and guide treatment decisions.

Distribution of global health measures from routinely collected PROMIS surveys in patients with breast cancer or prostate cancer

BACKGROUND

The collection of patient-reported outcomes (PROs) is an emerging priority internationally, guiding clinical care, quality improvement projects and research studies. After the deployment of Patient-Reported Outcomes Measurement Information System (PROMIS) surveys in routine outpatient workflows at an academic cancer center, electronic health record data were used to evaluate survey completion rates and self-reported global health measures across 2 tumor types: breast and prostate cancer.

METHODS

This study retrospectively analyzed 11,657 PROMIS surveys from patients with breast cancer and 4411 surveys from patients with prostate cancer, and it calculated survey completion rates and global physical health (GPH) and global mental health (GMH) scores between 2013 and 2018.

RESULTS

A total of 36.6% of eligible patients with breast cancer and 23.7% of patients with prostate cancer completed at least 1 survey, with completion rates lower among black patients for both tumor types (P < .05). The mean T scores (calibrated to a general population mean of 50) for GPH were 48.4 ± 9 for breast cancer and 50.6 ± 9 for prostate cancer, and the GMH scores were 52.7 ± 8 and 52.1 ± 9, respectively. GPH and GMH were frequently lower among ethnic minorities, patients without private health insurance, and those with advanced disease.

CONCLUSIONS

This analysis provides important baseline data on patient-reported global health in breast and prostate cancer. Demonstrating that PROs can be integrated into clinical workflows, this study shows that supportive efforts may be needed to improve PRO collection and global health endpoints in vulnerable populations.

Automatic inference of BI-RADS final assessment categories from narrative mammography report findings

We propose an efficient natural language processing approach for inferring the BI-RADS final assessment categories by analyzing only the mammogram findings reported by the mammographer in narrative form. The proposed hybrid method integrates semantic term embedding with distributional semantics, producing a context-aware vector representation of unstructured mammography reports. A large corpus of unannotated mammography reports (300,000) was used to learn the context of the key-terms using a distributional semantics approach, and the trained model was applied to generate context-aware vector representations of the reports annotated with BI-RADS category (22,091). The vectorized reports were utilized to train a supervised classifier to derive the BI-RADS assessment class. Even though the majority of the proposed embedding pipeline is unsupervised, the classifier was able to recognize substantial semantic information for deriving the BI-RADS categorization not only on a holdout internal testset and also on an external validation set (1900 reports). Our proposed method outperforms a recently published domain-specific rule-based system and could be relevant for evaluating concordance between radiologists. With minimal requirement for task specific customization, the proposed method can be easily transferable to a different domain to support large scale text mining or derivation of patient phenotype.

An Automated Feature Engineering for Digital Rectal Examination Documentation using Natural Language Processing

Digital rectal examination (DRE) is considered a quality metric for prostate cancer care. However, much of the DRE related rich information is documented as free-text in clinical narratives. Therefore, we aimed to develop a natural language processing (NLP) pipeline for automatic documentation of DRE in clinical notes using a domain-specific dictionary created by clinical experts and an extended version of the same dictionary learned by clinical notes using distributional semantics algorithms. The proposed pipeline was compared to a baseline NLP algorithm and the results of the proposed pipeline were found superior in terms of precision (0.95) and recall (0.90) for documentation of DRE. We believe the rule-based NLP pipeline enriched with terms learned from the whole corpus can provide accurate and efficient identification of this quality metric.

Evaluation of Ear, Nose, and Throat Involvement in Pemphigus Vulgaris in Comparison with Pemphigus Severity Scoring Systems: A Cross-sectional Study

Pemphigus vulgaris (PV) frequently affects the mucous membranes of the ear, nose, and throat (ENT). Since ENT examination is not a routinely performed procedure, the exact involvement of PV remains unrecognized. The available severity scoring systems (Pemphigus Disease Area Index (PDAI) and Autoimmune Bullous Skin Disorder Intensity Score (ABSIS)) for PV do not include a full ENT examination. This study was designed to evaluate the real extent of PV in ENT areas and to find out the specific scores which indicate the need for ENT examination. The patients were evaluated for ENT manifestations by endoscopic examination whether or not they exhibited symptoms. PDAI, ABSIS, and ENT scores were calculated, and the results were compared for correlation and significance. The mucosal involvement was more severe when scored by ENT examination than when assessed by PDAI or ABSIS. The ENT score was significantly associated with symptoms and endoscopic findings, especially when PDAI ≥15 and/or ABSIS ≥17. ENT endoscopic examination could result in more accurate grading in PV. In particular, performing such an examination should be considered in patients, especially when PDAI ≥15 and/or ABSIS ≥17, regardless of ENT symptoms.

The role of F-18 FDG PET/CT in differentiating benign from malignant pulmonary masses and accompanying lymph nodes

Introduction

The aim of this study was to evaluate the usefulness of SUV_max and lesion size to differentiate benign and malignant lesions of the lung and accompanying mediastinal lymph node on F-18 FDG PET/CT imaging.

Materials and Methods

A retrospective analysis was carried out on 100 patients with suspected lung cancer who were recommended for PET/CT scans for diagnosis and staging. The results of the SUV_max, lesion size and patient's age were compared with histopathology which was considered to be the 'gold standard' and sensitivity and specificity were calculated respectively. Lymph nodes greater than 1 cm in patients with benign pathology were evaluated and the SUV_max values were recorded.

Result

Of the 100 patients, 38 were found to have benign, whereas 62 had malignant on histopathology. The SUV_max was significantly more elevated in malign masses (13.1 ± 6.4) than in benign masses (8 ± 5.7) (p< 0.05). The dimensions of malignant masses (4.5 ± 2.5 cm) were larger than benign ones (3 ± 1.6 cm) (p< 0.05). SUV_max of 7.6 was determined as the cut-off value, while the sensitivity and specificity were 82% and 55% respectively. The sensitivity was 87% and specificity was 45% for the lesion sizes in differentiation of the malignant and benign lesions.

Conclusions

There are significant overlaps between benign and malignant lesions and specialists must be aware of the various pathological conditions that can give false positives and negatives.

The clinical impact of serous tubal intraepithelial carcinoma on outcomes of patients with high-grade serous carcinoma of the ovary, fallopian tube, and peritoneum

Aims

To investigate whether the presence of serous tubal intraepithelial carcinoma (STIC) is associated with clinical outcomes in a nonselected (unknown BRCA status) cohort of patients with a high-grade serous carcinoma (HGSC) of the ovary, fallopian tube, and peritoneum.

Settings and Design

A prospective case-series with planned data collection.

Subjects and Methods

The study was conducted in a total of 131 patients, who underwent primary cytoreductive surgery between 2007 and 2012. Histological examination of the fallopian tubes included the "sectioning and extensively examining the fimbriated end" protocol. The diagnosis of STIC was based on the combination of morphology and immunohistochemistry. The patients were divided into two groups according to the absence or presence of STIC and compared clinicopathologically.

Statistical Analysis Used

Analyses were performed using PASW 18 (SPSS/IBM, Chicago, IL, USA) software. The primary outcome was progression-free survival (PFS), and the secondary outcome was overall survival (OS).

Results

STIC was identified in 20.6% of patients. Median follow-up time was 49.5 months for the STIC-positive group and 38.0 months for the STIC-negative group. Study groups were comparable in terms of clinicopathological characteristics with the exception that patients with STIC had less lymph node involvement (55.0% vs. 65.4%, P = 0.001), and more diagnosis of primary tubal carcinoma (29.6% vs. 3.8%, P = 0.001) compared to those without STIC. No statistically significant differences in terms of PFS (P = 0.462) and OS (P = 0.501) were observed between the groups.

Conclusions

The absolute identification of the origin of tumor cell does not seem to significantly affect the clinical course of the patients with HGSC.

Expanding a radiology lexicon using contextual patterns in radiology reports

Objective

Distributional semantics algorithms, which learn vector space representations of words and phrases from large corpora, identify related terms based on contextual usage patterns. We hypothesize that distributional semantics can speed up lexicon expansion in a clinical domain, radiology, by unearthing synonyms from the corpus.

Materials and Methods

We apply word2vec, a distributional semantics software package, to the text of radiology notes to identify synonyms for RadLex, a structured lexicon of radiology terms. We stratify performance by term category, term frequency, number of tokens in the term, vector magnitude, and the context window used in vector building.

Results

Ranking candidates based on distributional similarity to a target term results in high curation efficiency: on a ranked list of 775 249 terms, >50% of synonyms occurred within the first 25 terms. Synonyms are easier to find if the target term is a phrase rather than a single word, if it occurs at least 100× in the corpus, and if its vector magnitude is between 4 and 5. Some RadLex categories, such as anatomical substances, are easier to identify synonyms for than others.

Discussion

The unstructured text of clinical notes contains a wealth of information about human diseases and treatment patterns. However, searching and retrieving information from clinical notes often suffer due to variations in how similar concepts are described in the text. Biomedical lexicons address this challenge, but are expensive to produce and maintain. Distributional semantics algorithms can assist lexicon curation, saving researchers time and money.

Relationships of nuclear, architectural and International Federation of Gynecology and Obstetrics grading systems in endometrial cancer

Objective:

To examine correlations among nuclear, architectural, and International Federation of Gynecology and Obstetrics (FIGO) grading systems, and their relationships with lymph node (LN) involvement in endometrioid endometrial cancer.

Material and Methods:

Histopathology slides of 135 consecutive patients were reviewed with respect to tumor grade and LN metastasis. Notable nuclear atypia was defined as grade 3 nuclei. FIGO grade was established by raising the architectural grade (AG) by one grade when the tumor was composed of cells with nuclear grade (NG) 3. Correlations between the grading systems were analyzed using Spearman’s rank correlation coefficients, and relationships of grading systems with LN involvement were assessed using logistic regression analysis.

Results:

Correlation analysis revealed a significant and strongly positive relationship between FIGO and architectural grading systems (r=0.885, p=0.001); however, correlations of nuclear grading with the architectural (r=0.535, p=0.165) and FIGO grading systems (r=0.589, p=0.082) were moderate and statistically non-significant. Twenty-five (18.5%) patients had LN metastasis. LN involvement rates differed significantly between tumors with AG 1 and those with AG 2, and tumors with FIGO grade 1 and those with FIGO grade 2. In contrast, although the difference in LN involvement rates failed to reach statistical significance between tumors with NG 1 and those with NG 2, it was significant between NG 2 and NG 3 (p=0.042). Although all three grading systems were associated with LN involvement in univariate analyses, an independent relationship could not be established after adjustment for other confounders in multivariate analysis.

Conclusion:

Nuclear grading is significantly correlated with neither architectural nor FIGO grading systems. The differences in LN involvement rates in the nuclear grading system reach significance only in the setting of tumor cells with NG 3; however, none of the grading systems was an independent predictor of LN involvement.

Practice-based evidence for factors associated with urinary incontinence following prostate cancer care

106

Background: Urinary incontinence (UI) is a common complication following treatment for localized prostate cancer. Past studies evaluating UI risk factors use surveys or chart abstraction, which may be costly and lack generalizability. Electronic health records (EHR) allow us to examine UI at a population level. We applied data mining methods to EHR data to: (1) evaluate rates of UI following prostate cancer treatment; and (2) evaluate potential risk factors for posttreatment UI. Methods: We conducted a retrospective analysis of patients undergoing prostatectomy or radiation therapy for localized prostate cancer between 2009-2016, and who received follow-up care at our medical center. Our cohort was constructed from the institutional EHR and the California Cancer Registry. The primary outcome was the presence of UI, measured in three-month intervals from the start of first-line treatment. The secondary outcome was UI 12-24 months following treatment (“late UI”). UI was assessed using natural language processing of EHR clinician notes. UI was also assessed with the EPIC-26 quality of life survey, which a subset of patients had prospectively completed. Results: Our cohort consisted of 2783 men, of whom 1907 (69%) underwent surgery and the remainder received radiation; of this cohort, 609 (22%) had data on late UI status. UI prevalence was higher among surgery than radiation patients across all posttreatment time points, and 278 of 434 (64%) surgery patients had late UI compared to 78 of 175 (45%) radiation patients (p < 0.001). Univariable analyses showed an association between pretreatment and late UI among surgery patients as measured in the EHR (OR 2.5, 95% CI 1.0-6.5, p = 0.05) and by EPIC-26 (OR 8.1, 95% CI 1.8-36.5, p = 0.01). Only surgery (compared to radiation) was a significant predictor of late UI (OR 5.8, 95% CI 1.1-32.3, p = 0.05) in multivariable regression with EHR data. Conclusions: Using EHR data, we found that treatment modality was a significant predictor of late UI among prostate cancer patients who underwent prostatectomy or radiation therapy. These results suggest the utility of EHRs in patient-centered outcomes research in prostate cancer care, and should be validated at other sites.

Comparison of positional and rapid eye movement-dependent sleep apnea syndromes

AIM

We aimed to compare the clinical, epidemiological, and polysomnographic features of rapid eye movement (REM)-dependent obstructive sleep apnea syndrome (OSAS) and positional OSAS which are two separate clinical entities.

METHODS

Between January 2014 and December 2015, at the Akdeniz University Medical Faculty Hospital, patients who were diagnosed REM-dependent and positional OSAS with polysomnography were retrospectively studied.

RESULTS

In this study, 1727 patients were screened consecutively. Five hundred and eighty-four patients were included in the study. Of the patients, 24.6% (140) were diagnosed with REM-dependent OSAS and 75.4% (444) were diagnosed as positional OSAS. Female predominance was found in REM-dependent OSAS (P < 0.001). The mean total apnea-hypopnea index (AHI), non-REM AHI, and supine AHI in REM-dependent OSAS were 14.73, 9.24, and 17.73, respectively, and these values were significantly lower when compared with positional OSAS (P < 0.001). Patients diagnosed with REM-dependent OSAS had a statistically significant tendency to be overweight (P < 0.001). For REM-dependent OSAS, total pulse rate, supine pulse rate, and REM pulse rate were statistically higher than positional OSAS (P < 0.001).

CONCLUSION

Positional OSAS is a clinical entity that is more common than REM-dependent OSAS. OSAS severity is higher in positional OSAS than REM-dependent OSAS. REM-dependent OSAS is observed more commonly in women.

Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomno graphy Resources?

Objectives: The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination.

Methods: In order to produce classification models for OSA severity, five different machine learning methods (Bayesian network, Decision Tree, Random Forest, Neural Networks and Logistic Regression) were trained while relevant variables and their relationships were derived empirically from observed data. Each model was trained and evaluated using 10-fold cross-validation and to evaluate classification performances of all methods, true positive rate (TPR), false positive rate (FPR), Positive Predictive Value (PPV), F measure and Area Under Receiver Operating Characteristics curve (ROC-AUC) were used.

Results: Results of 10-fold cross validated tests with different variable settings promisingly indicated that the OSA severity of suspected OSA patients can be classified, using non-polysomnographic features, with 0.71 true positive rate as the highest and, 0.15 false positive rate as the lowest, respectively. Moreover, the test results of different variables settings revealed that the accuracy of the classification models was significantly improved when physical examination variables were added to the model.

Conclusions: Study results showed that machine learning methods can be used to estimate the probabilities of no, mild, moderate, and severe obstructive sleep apnea and such approaches may improve accurate initial OSA screening and help referring only the suspected moderate or severe OSA patients to sleep laboratories for the expensive tests.