Hematologic Abnormalities in Patients With Hidradenitis Suppurativa Receiving Adalimumab.

This post hoc analysis of PIONEER I and II randomized clinical trials assessed whether receiving adalimumab was associated with decreased hematologic abnormalities and increased clinical improvement in patients with hidradenitis suppurativa.

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients. MATERIALS AND METHODS: We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment. RESULTS: The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user. DISCUSSION: Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings. CONCLUSION: EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.

Spontaneous Tumor Lysis Syndrome Secondary to Metastatic Pancreatic Adenocarcinoma.

Spontaneous tumor lysis syndrome (STLS) secondary to metastatic pancreatic adenocarcinoma is a rare clinical phenomenon. An 86-year-old woman with a history of pancreatic cysts presented to the emergency department with progressive fatigue, transaminitis, elevated lactate dehydrogenase, and acute kidney injury of unclear etiology. Abdominal imaging and celiac lymph node biopsy were consistent with metastatic pancreatic adenocarcinoma. Her clinical status deteriorated requiring intensive care unit transfer, and her laboratory results were found to be consistent with STLS. Despite treatment, she entered multisystem organ failure and died shortly after. This case adds to the literature of STLS in pancreatic adenocarcinomas.

Machine learning natural language processing for identifying venous thromboembolism: Systematic review and meta-analysis.

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual chart review and diagnosis code interpretation. Natural language processing (NLP) can automate the process. Rule-based NLP methods are effective but time consuming. Machine learning (ML)-NLP methods present a promising solution. We conducted a systematic review and meta-analysis of studies published before May 2023 that use ML-NLP to identify VTE diagnoses in the electronic health records. Four reviewers screened all manuscripts, excluding studies that only used a rule-based method. A meta-analysis evaluated the pooled performance of each study's best performing model that evaluated for pulmonary embolism (PE) and/or deep vein thrombosis (DVT). Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with confidence interval (CI) were calculated by DerSimonian and Laird method using a random-effects model. Study quality was assessed using an adapted TRIPOD tool. Thirteen studies were included in the systematic review and 8 had data available for meta-analysis. Pooled sensitivity was 0.931 (95% CI 0.881-0.962), specificity 0.984 (95% CI 0.967-0.992), PPV 0.910 (95% CI 0.865-0.941) and NPV 0.985 (95% CI 0.977-0.990). All studies met at least 13 of the 21 NLP-modified TRIPOD items, demonstrating fair quality. The highest performing models used vectorization rather than bag-of-words, and deep learning techniques such as convolutional neural networks. There was significant heterogeneity in the studies and only four validated their model on an external dataset. Further standardization of ML studies can help progress this novel technology towards real-world implementation.

Artificial intelligence in the prediction of venous thromboembolism: A systematic review and pooled analysis.

BACKGROUND: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. AIMS: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. METHODS: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). RESULTS: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. CONCLUSION: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.

Venous thromboembolism prophylaxis for hospitalized adult patients: a survey of US health care providers on attitudes and practices.

Background: Venous thromboembolism (VTE) is a leading cause of preventable mortality among hospitalized patients, but appropriate risk assessment and thromboprophylaxis remain underutilized or misapplied. Objectives: We conducted an electronic survey of US health care providers to explore attitudes, practices, and barriers related to thromboprophylaxis in adult hospitalized patients and at discharge. Results: A total of 607 US respondents completed the survey: 63.1% reported working in an academic hospital, 70.7% identified as physicians, and hospital medicine was the most frequent specialty (52.1%). The majority of respondents agreed that VTE prophylaxis is important (98.8%; 95% CI: 97.6%-99.5%) and that current measures are safe (92.6%; 95% CI: 90.2%-94.5%) and effective (93.8%; 95% CI: 91.6%-95.6%), but only half (52.0%; 95% CI: 47.9%-56.0%) believed that hospitalized patients at their institution are on appropriate VTE prophylaxis almost all the time. One-third (35.4%) reported using a risk assessment model (RAM) to determine VTE prophylaxis need; 44.9% reported unfamiliarity with RAMs. The most common recommendation for improving rates of appropriate thromboprophylaxis was to leverage technology. A majority of respondents (84.5%) do not reassess a patient's need for VTE prophylaxis at discharge, and a minority educates patients about the risk (16.2%) or symptoms (18.9%) of VTE at discharge. Conclusion: Despite guideline recommendations to use RAMs, the majority of providers in our survey do not use them. A majority of respondents believed that technology could help improve VTE prophylaxis rates. A majority of respondents do not reassess the risk of VTE at discharge or educate patients about this risk of VTE at discharge.

Updated COVID-19 clearance time among patients with cancer in the Delta and Omicron waves.

BACKGROUND: COVID-19 infection delays therapy and in-person evaluation for oncology patients, but clinic clearance criteria are not clearly defined. METHODS: We conducted a retrospective review of oncology patients with COVID-19 at a tertiary care center during the Delta and Omicron waves and compared clearance strategies. RESULTS: Median clearance by two consecutive negative tests was 32.0 days (Interquartile Range [IQR] 22.0-42.5, n = 153) and was prolonged in hematologic malignancy versus solid tumors (35.0 days for hematologic malignancy, 27.5 days for solid tumors, p = 0.01) and in patients receiving B-cell depletion versus other therapies. Median clearance by single negative test was reduced to 23.0 days (IQR 16.0-33.0), with recurrent positive rate 25.4% in hematologic malignancy versus 10.6% in solid tumors (p = 0.02). Clearance by a predefined waiting period required 41 days until an 80% negative rate. CONCLUSIONS: COVID-19 clearance remains prolonged in oncology patients. Single-negative test clearance can balance delays in care with risk of infection in patients with solid tumors.

A systematic review of federated learning applications for biomedical data.

OBJECTIVES: Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology. METHODS: We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool. RESULTS: 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data. CONCLUSION: Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.

Attitudes, experiences, and safety behaviours of adolescents and young adults who read visit notes: Opportunities to engage patients early in their care.

The Cures Act made access to electronic visit notes ('open notes') nearly universal across the USA, and efforts to share open notes with patients are underway worldwide. This landmark policy change provides an opportunity to engage adolescents and young adults (AYA) early in their care, yet little is known about their attitudes related to reading notes. We compared the responses of 332 AYA (13-25 years old) and 6,914 adults (>25 years old) in a 2016 survey at two USA academic adult and paediatric hospitals. Over 85% of AYA and adults with available notes reported reading at least one note in the prior year. AYA reported similar benefits from note-reading to adults in 15 outcomes related to engagement, relational effects and safety behaviours, supporting efforts to engage AYA as partners in their care using open notes.

A Value-Added Health Systems Science Intervention Based on My Life, My Story for Patients Living with HIV and Medical Students: Translating Narrative Medicine from Classroom to Clinic.

In 2018-2019, at the Keck School of Medicine of the University of Southern California (KSOM), we developed and piloted a narrative-based health systems science intervention for patients living with HIV and medical students in which medical students co-wrote patients' life narratives for inclusion in the electronic health record. The pilot study aimed to assess the acceptability of the "life narrative protocol" (LNP) from multiple stakeholder positions and characterize participants' experiences of the clinical and pedagogical implications of the LNP. Students were recruited from KSOM. Patients and staff were recruited from the Maternal, Child, and Adolescent/Adult Center for Infectious Disease and Virology (MCA) at Los Angeles County+USC Medical Center. Ten patients, seventeen students, and ten MCA staff participated in the pilot study. Qualitative methods were used to gather data from students', patients', and staff's perspectives. Three themes emerged from the thematic analysis: (1) patients' life narratives conveyed their unique life experiences and voices; (2) the protocol could result in wide-ranging effects on HIV care; (3) the LNP enabled students to contribute value to patients' healthcare. Across groups, participants considered the LNP an acceptable intervention. The LNP, its limitations, and implications for HIV care, narrative medicine, and health information technology are presented.

Speaking up about patient-perceived serious visit note errors: Patient and family experiences and recommendations.

BACKGROUND: Open notes invite patients and families to read ambulatory visit notes through the patient portal. Little is known about the extent to which they identify and speak up about perceived errors. Understanding the barriers to speaking up can inform quality improvements. OBJECTIVE: To describe patient and family attitudes, experiences, and barriers related to speaking up about perceived serious note errors. METHODS: Mixed method analysis of a 2016 electronic survey of patients and families at 2 northeast US academic medical centers. Participants had active patient portal accounts and at least 1 note available in the preceding 12 months. RESULTS: 6913 adult patients (response rate 28%) and 3672 pediatric families (response rate 17%) completed the survey. In total, 8724/9392 (93%) agreed that reporting mistakes improves patient safety. Among 8648 participants who read a note, 1434 (17%) perceived ≥1 mistake. 627/1434 (44%) reported the mistake was serious and 342/627 (56%) contacted their provider. Participants who self-identified as Black or African American, Asian, "other," or "multiple" race(s) (OR 0.50; 95% CI (0.26,0.97)) or those who reported poorer health (OR 0.58; 95% CI (0.37,0.90)) were each less likely to speak up than white or healthier respondents, respectively. The most common barriers to speaking up were not knowing how to report a mistake (61%) and avoiding perception as a "troublemaker" (34%). Qualitative analysis of 476 free-text suggestions revealed practical recommendations and proposed innovations for partnering with patients and families. CONCLUSIONS: About half of patients and families who perceived a serious mistake in their notes reported it. Identified barriers demonstrate modifiable issues such as establishing clear mechanisms for reporting and more challenging issues such as creating a supportive culture. Respondents offered new ideas for engaging patients and families in improving note accuracy.

A Retrospective Review of 193 Cases of Severe Eosinophilia in the Northeast United States.

BACKGROUND: This retrospective review evaluated the causes of severe eosinophilia (≥5000 eosinophils/L). Higher eosinophilia levels are more likely to cause tissue damage and may reflect disease severity. METHODS: We reviewed 193 cases of patients seen at Beth Israel Deaconess Medical Center in Boston, Massachusetts, and at the University of Vermont Medical Center in Burlington, Vermont, between January 2015 to May 2020 who had a peak absolute eosinophil count of at least 5000/µL. RESULTS: Thirty-nine percent of cases were attributable to a hematologic or oncologic cause. These cases had the highest mean peak absolute eosinophil count at 11,698/µL. Twenty percent of cases were secondary to drug reactions, of which 90% took place in an inpatient setting. Three percent of cases were from helminthic infection, the majority of which were in returning travelers. CONCLUSIONS: In our region of study, hematologic and oncologic cases are important causes of severe eosinophilia, drug reactions are a common etiology in the inpatient setting, and infections are a rare cause.

Increased use-dependent plasticity in chronic insomnia.

STUDY OBJECTIVES: During normal sleep several neuroplasticity changes occur, some of which are considered to be fundamental to strengthen memories. Given the evidence linking sleep to neuroplasticity, it is conceivable that individuals with chronic sleep disruption, such as patients with chronic insomnia (CI), would experience abnormalities in neuroplastic processes during daytime. Protocols testing use-dependent plasticity (UDP), one of the mechanisms underlying formation of motor memories traces, provide a sensitive measure to assess neuroplasticity in the context of motor training. DESIGN AND PARTICIPANTS: A well-established transcranial magnetic stimulation (TMS) paradigm was used to evaluate the ability of patients with CI and age-matched good sleeper controls to undergo UDP. We also investigated the effect of insomnia on intracortical motor excitability measures reflecting GABAergic and glutamatergic mechanisms. SETTING: Human Brain Physiology Laboratory, Johns Hopkins Medical Institutions. MEASUREMENTS AND RESULTS: We found that patients with CI experienced increased UDP changes relative to controls. This effect was not due to differences in motor training. In addition, patients with CI showed enhanced intracortical facilitation relative to controls, in the absence of changes in intracortical inhibitory measures. CONCLUSION: This study provides the first evidence that patients with chronic insomnia have an increased plasticity response to physical exercise, possibly due to larger activation of glutamatergic mechanisms. This suggests a heightened state of neuroplasticity, which may reflect a form of maladaptive plasticity, similar to what has been described in dystonia patients and chronic phantom pain after amputation. These results could lead to development of novel treatments for chronic insomnia.