Patient Views on Surgeon-specific Outcome Reporting in Vascular Surgery: Novel Validated Patient Questionnaire Study.

BACKGROUND: SSMD are used to enhance transparency, improve quality and facilitate patient choice. The use of SSMD is controversial, but patients' views on such data are largely unknown. OBJECTIVES: The aim of this study was therefore to explore the views of patients and to identify their priorities for outcome reporting in vascular surgery. METHODS: A prospective questionnaire study of 165 patients receiving care in a single academic vascular unit was performed. Data on patients' current understanding and use of SSMD, together with future priorities were collected. RESULTS: Of the 165 patients 80% were unaware of SSMD. 72% thought they should be made aware of the data, although 63% thought they were likely to misinterpret the results. The majority recognized the utility of SSMD to inform treatment (60%) and surgeon (53%) choice. The majority prioritize the patient-surgeon relationship (90%) and past experiences of care (71%) when making treatment decisions. A significant majority (66% vs 49%; P < 0.005) would favour hospital-level to surgeon-level data. The main patient priorities for future outcome reporting were waiting list length (56%), the quality of hospital facilities (55%), and patient satisfaction (54%). CONCLUSIONS: The aims of SSMD reporting are not currently being met, and both patients and healthcare professionals have shared concerns over the nature and usefulness of the data. Patients express a preference for hospital-level outcomes and prioritize the experience of receiving care over outcomes when making treatment decisions. Future outcome reporting should include patient-directed hospital-level metrics that are readily accessible and understood by all.

Real-World Outcomes of Patients with Advanced Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Cancer in Canada Using Data Extracted by Large Language Model-Based Artificial Intelligence.

Real-world evidence for patients with advanced EGFR-mutated non-small cell lung cancer (NSCLC) in Canada is limited. This study's objective was to use previously validated DARWENTM artificial intelligence (AI) to extract data from electronic heath records of patients with non-squamous NSCLC at University Health Network (UHN) to describe EGFR mutation prevalence, treatment patterns, and outcomes. Of 2154 patients with NSCLC, 613 had advanced disease. Of these, 136 (22%) had common sensitizing EGFR mutations (cEGFRm; ex19del, L858R), 8 (1%) had exon 20 insertions (ex20ins), and 338 (55%) had EGFR wild type. One-year overall survival (OS) (95% CI) for patients with cEGFRm, ex20ins, and EGFR wild type tumours was 88% (83, 94), 100% (100, 100), and 59% (53, 65), respectively. In total, 38% patients with ex20ins received experimental ex20ins targeting treatment as their first-line therapy. A total of 57 patients (36%) with cEGFRm received osimertinib as their first-line treatment, and 61 (39%) received it as their second-line treatment. One-year OS (95% CI) following the discontinuation of osimertinib was 35% (17, 75) post-first-line and 20% (9, 44) post-second-line. In this real-world AI-generated dataset, survival post-osimertinib was poor in patients with cEGFR mutations. Patients with ex20ins in this cohort had improved outcomes, possibly due to ex20ins targeting treatment, highlighting the need for more effective treatments for patients with advanced EGFRm NSCLC.

Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center.

PURPOSE: This study documents the creation of automated, longitudinal, and prospective data and analytics platform for breast cancer at a regional cancer center. This platform combines principles of data warehousing with natural language processing (NLP) to provide the integrated, timely, meaningful, high-quality, and actionable data required to establish a learning health system. METHODS: Data from six hospital information systems and one external data source were integrated on a nightly basis by automated extract/transform/load jobs. Free-text clinical documentation was processed using a commercial NLP engine. RESULTS: The platform contains 141 data elements of 7,019 patients with newly diagnosed breast cancer who received care at our regional cancer center from January 1, 2014, to June 3, 2022. Daily updating of the database takes an average of 56 minutes. Evaluation of the tuning of NLP jobs found overall high performance, with an F1 of 1.0 for 19 variables, with a further 16 variables with an F1 of > 0.95. CONCLUSION: This study describes how data warehousing combined with NLP can be used to create a prospective data and analytics platform to enable a learning health system. Although upfront time investment required to create the platform was considerable, now that it has been developed, daily data processing is completed automatically in less than an hour.

Automating Access to Real-World Evidence.

Introduction: Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer. Methods: Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported. Results: Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%-100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%-94%; Manual accuracy: 78%-94%; concordance: 71%-82%). Concurrent medications (86%-100%) and comorbid conditions (96%-100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%-100%) and highly concordant (83%-99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%-98%), although detection of biomarker test results was more variable (accuracy 84%-100%, concordance 84%-99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%-99%; manual: 71%-100%; concordance: 70%-99%) with the exception of lung and lymph node metastases (NLP: 66%-71%; manual: 87%-92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition. Conclusions: Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction.

Negative mucosal synergy between Herpes simplex type 2 and HIV in the female genital tract.

OBJECTIVE: There is substantial epidemiological evidence that infection by Herpes simplex virus type 2 (HSV2) enhances both HIV susceptibility and subsequent sexual transmission. Both infections are extremely common in female sex workers (FSWs) in sub-Saharan Africa, and up to 80% of new HIV infections in urban men in the region are acquired via transactional sex. The present study aimed to elucidate the mucosal immune interactions between HIV and HSV2 in the genital tract. METHODS: Endocervical immune cell populations, cytokine/chemokine protein levels in cervico-vaginal secretions and cervical immune gene expression profiles were measured in a well-defined cohort of HIV-infected and uninfected Kenyan FSWs. Associations between the genital immune milieu and infection by and/or shedding of common genital co-pathogens were examined. RESULTS: HIV-infected FSWs were much more likely to be infected by HSV2, and to shed HSV2 DNA in the genital tract. There was also a profound negative 'mucosal synergy' between these viruses. In HIV uninfected FSWs, HSV2 infection was associated with a ten-fold increase in cervical immature dendritic cells (iDC) expressing DC-SIGN, and a three-fold increase in cervical CD4+ T cells expressing CCR5. HIV infection was associated with iDC depletion in the cervix, and with increased HSV2 genital reactivation, which in turn was associated with HIV shedding levels. CONCLUSIONS: The findings suggest a mucosal vicious circle in which HSV2 infection increases HIV target cells in the genital mucosa, subsequent HIV infection impairs HSV2 mucosal immune control, and local HSV2 reactivation enhances both HSV2 and HIV transmission.

National Survey of UK Consultant Surgeons' Opinions on Surgeon-Specific Mortality Data in Cardiothoracic Surgery.

BACKGROUND: In the United Kingdom, cardiothoracic surgeons have led the outcome reporting revolution seen over the last 20 years. The objective of this survey was to assess cardiothoracic surgeons' opinions on the topic, with the aim of guiding future debate and policy making for all subspecialties. METHODS AND RESULTS: A questionnaire was developed using interviews with experts in the field. In January 2015, the survey was sent out to all consultant cardiothoracic surgeons in the United Kingdom (n=361). Logistic regression, bivariate correlation, and the χ(2) test were used to assess whether there was a relationship between answers and demographic variables. Free-text responses were analyzed using the grounded theory approach. The response rate was 73% (n=264). The majority of respondents (58.1% oppose, 34.1% favor, and 7.8% neither) oppose the public release of surgeon-specific mortality data and associate it with several adverse consequences. These include risk-averse behavior, gaming of data, and misinterpretation of data by the public. Despite this, the majority overwhelmingly supports publication of team-based measures of outcome. The free-text responses suggest that this is because most believe that quality of care is multifactorial and not represented by an individual's mortality rate. CONCLUSIONS: There is evident opposition to surgeon-specific mortality data among UK cardiothoracic surgeons who associate this with several unintended consequences. Policy makers should refine their strategy behind publication of surgeon-specific mortality data and possibly consider shift toward team-based results for which there will be the required support. Stakeholder feedback and inclusive strategy should be completed before introducing major initiatives to avoid unforeseen consequences and disagreements.

Bacterial vaginosis in HIV-infected women induces reversible alterations in the cervical immune environment.

BACKGROUND: Bacterial vaginosis (BV) has been associated with increased HIV cervicovaginal shedding. We hypothesized that this might relate to BV-associated increases in mucosal activated CD4 T cells, which could enhance local HIV replication. METHODS: Vaginal flora, cytokine/chemokine levels, and mucosal immune cell populations collected by cervical cytobrush were analyzed in 15 HIV-infected Kenyan female sex workers, before and after BV therapy with oral metronidazole. RESULTS: Therapy reduced the Nugent score in all but 1 participant, and BV elimination was associated with reduced genital levels of interleukin 1beta(IL1beta), interleukin 8 (IL-8), and Regulated Upon Activation Normal T-cell Expressed and Secreted (RANTES). In addition, BV elimination reduced the total number of cervical CD4 T cells, including those expressing the HIV coreceptor CCR5 and the activation marker CD69. CONCLUSIONS: BV induces significant and reversible alterations in cervical immune cell populations and local inflammatory cytokines that would be expected to enhance local HIV replication.