Search

Importance: There are limited data on the association of sex with the incidence of postoperative atrial fibrillation (poAF) and subsequent long-term mortality after cardiac surgery., Objective: To evaluate whether the incidence of poAF and associated long-term mortality after cardiac surgery differ by sex., Design, Setting, and Participants: This retrospective cohort study was conducted at 2 tertiary care centers in Massachusetts from January 1, 2002, until October 1, 2016, with follow-up until December 1, 2022. Adult (aged >20 years) women and men undergoing coronary artery bypass graft surgery, aortic valve surgery, mitral valve surgery, and combined procedures with cardiopulmonary bypass were examined using medical records. Patients who had data on poAF were included in data analyses., Exposures: Sex and poAF., Main Outcomes and Measures: Primary outcomes were the incidence of poAF and all-cause mortality. poAF was defined as any atrial fibrillation detected on electrocardiogram (EKG) during the index hospitalization in patients presenting for surgery in normal sinus rhythm. Data on poAF were obtained from EKG reports and supplemented by information from the Society of Thoracic Surgeons Adult Cardiac Surgery Database. All-cause mortality was assessed via hospital records. The hypotheses were formulated prior to data analysis., Results: Among 21 568 patients with poAF data (mean [SD] age, 66.5 [12.4] years), 2694 of 6601 women (40.8%) and 5805 of 14 967 men (38.8%) developed poAF. In a multivariable logistic regression model, women had lower risk of poAF (odds ratio [OR], 0.85; 95% CI, 0.79-0.91; P < .001). During the follow-up study period, 1294 women (50.4%) and 2376 men (48.9%) in the poAF group as well as 1273 women (49.6%) and 2484 men (51.1%) in the non-poAF group died. Cox proportional hazards analysis found that the association between poAF and mortality was significantly moderated (ie, effect modified) by sex. Compared with same-sex individuals without poAF, men with poAF had a 17% higher mortality hazard (hazard ratio [HR], 1.17; 95% CI, 1.11-1.25; P < .001), and women with poAF had a 31% higher mortality hazard (HR, 1.31; 95% CI, 1.21-1.42; P < .001)., Conclusions and Relevance: In this retrospective cohort study of 21 568 patients who underwent cardiac surgery, women were less likely to develop poAF than men when controlling for other relevant characteristics; however, women who did develop poAF had a higher risk of long-term mortality than men who developed poAF. This observed elevated risk calls for a tailored approach to perioperative care in women undergoing cardiac surgery.

Objective: Survival analysis is widely utilized in healthcare to predict the timing of disease onset. Traditional methods of survival analysis are usually based on Cox Proportional Hazards model and assume proportional risk for all subjects. However, this assumption is rarely true for most diseases, as the underlying factors have complex, non-linear, and time-varying relationships. This concern is especially relevant for pregnancy, where the risk for pregnancy-related complications, such as preeclampsia, varies across gestation. Recently, deep learning survival models have shown promise in addressing the limitations of classical models, as the novel models allow for non-proportional risk handling, capturing nonlinear relationships, and navigating complex temporal dynamics., Methods: We present a methodology to model the temporal risk of preeclampsia during pregnancy and investigate the associated clinical risk factors. We utilized a retrospective dataset including 66,425 pregnant individuals who delivered in two tertiary care centers from 2015 to 2023. We modeled the preeclampsia risk by modifying DeepHit, a deep survival model, which leverages neural network architecture to capture time-varying relationships between covariates in pregnancy. We applied time series k-means clustering to DeepHit's normalized output and investigated interpretability using Shapley values., Results: We demonstrate that DeepHit can effectively handle high-dimensional data and evolving risk hazards over time with performance similar to the Cox Proportional Hazards model, achieving an area under the curve (AUC) of 0.78 for both models. The deep survival model outperformed traditional methodology by identifying time-varied risk trajectories for preeclampsia, providing insights for early and individualized intervention. K-means clustering resulted in patients delineating into low-risk, early-onset, and late-onset preeclampsia groups-notably, each of those has distinct risk factors., Conclusion: This work demonstrates a novel application of deep survival analysis in time-varying prediction of preeclampsia risk. Our results highlight the advantage of deep survival models compared to Cox Proportional Hazards models in providing personalized risk trajectory and demonstrating the potential of deep survival models to generate interpretable and meaningful clinical applications in medicine., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Vesela Kovacheva reports financial support was provided by National Institutes of Health. Kathryn Gray reports financial support was provided by National Institutes of Health. Vesela Kovacheva reports a relationship with Avania that includes: consulting or advisory. Kathryn Gray reports a relationship with Aetion, Roche, BillionToOne, and Janssen Global Services that includes: consulting or advisory. David Bates reports a relationship with EarlySense, CDI Negev, Valera Health, eCLEW, MDClone, AESOP Technology, FeelBetter, IBM Watson Health that includes: consulting or advisory, equity or stocks, and funding grants. Vesela Kovacheva reports a relationship with Anesthesia Patient Safety Foundation (APSF), BWH IGNITE that includes: funding grants. Vesela Kovacheva has patent issued to Mass General Brigham. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper]., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: To evaluate the screening performance characteristics of existing tools for the diagnosis of sepsis during delivery admissions., Methods: This was a case-control study using electronic health record data, including vital signs and laboratory results, for all delivery admissions of patients with sepsis from 59 nationally distributed hospitals. Patients with sepsis were matched by gestational age at delivery in a 1:4 ratio with patients without sepsis to create a comparison group. Patients with chorioamnionitis and sepsis were compared with a complete cohort of patients with chorioamnionitis without sepsis. Multiple screening criteria for sepsis were evaluated: the CMQCC (California Maternal Quality Care Collaborative), SIRS (Systemic Inflammatory Response Syndrome), the MEWC (the Maternal Early Warning Criteria), UKOSS (United Kingdom Obstetric Surveillance System), and the MEWT (Maternal Early Warning Trigger Tool). Sensitivity, false-positive rates, and C-statistics were reported for each screening tool. Analyses were stratified into cohort 1, which excluded patients with chorioamnionitis-endometritis, and cohort 2, which included those patients., Results: Delivery admissions at 59 hospitals were extracted for patients with sepsis. Cohort 1 comprised 647 patients with sepsis, including 228 with end-organ injury, matched with a control group of 2,588 patients without sepsis. Cohort 2 comprised 14,591 patients with chorioamnionitis-endometritis, of whom 1,049 had sepsis and 238 had end-organ injury. In cohort 1, the CMQCC and the UKOSS pregnancy-adjusted criteria had the lowest false-positive rates (6.9% and 9.6%, respectively) and the highest C-statistics (0.92 and 0.91, respectively). Although other screening criteria, such as SIRS and the MEWC, had similar sensitivities, it was at the cost of much higher false-positive rates (21.3% and 38.3%, respectively). In cohort 2, including all patients with chorioamnionitis-endometritis, the highest C-statistics were again for the CMQCC (0.67) and UKOSS (0.64). All screening tools had high false-positive rates, but the false-positive rates for the CMQCC and UKOSS were substantially lower than those for SIRS and the MEWC., Conclusion: During delivery admissions, the CMQCC and UKOSS pregnancy-adjusted screening criteria have the lowest false-positive results while maintaining greater than 90% sensitivity rates. Performance of all screening tools was degraded in the setting of chorioamnionitis-endometritis., Competing Interests: Financial Disclosure Melissa Bauer reports consulting fees from Institute for Healthcare Innovation. Vesela. Kovacheva reports consulting fees from Avania CRO unrelated to the current work. Paloma Toledo reports speaker fees from Pacira Biosciences, Inc. Kurt Wharton receives consulting fees from Molnlycke. The other authors did not report any potential conflicts of interest., (Copyright © 2023 by the American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.)

Background: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed., Methods: We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk., Results: Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score., Conclusions: Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes., Competing Interests: Disclosures V.P. Kovacheva and R.Y. Cohen report consulting fees from Avania CRO and patent WO2021119593A1 for control of a therapeutic delivery system assigned to the Mass General Brigham. K. Gray has served as a consultant to Illumina, Inc, Aetion, Roche, and BillionToOne. The other authors report no conflicts.

Many areas of medicine would benefit from deeper, more accurate phenotyping, but there are limited approaches for phenotyping using clinical notes without substantial annotated data. Large language models (LLMs) have demonstrated immense potential to adapt to novel tasks with no additional training by specifying task-specific instructions. Here we report the performance of a publicly available LLM, Flan-T5, in phenotyping patients with postpartum hemorrhage (PPH) using discharge notes from electronic health records (n = 271,081). The language model achieves strong performance in extracting 24 granular concepts associated with PPH. Identifying these granular concepts accurately allows the development of interpretable, complex phenotypes and subtypes. The Flan-T5 model achieves high fidelity in phenotyping PPH (positive predictive value of 0.95), identifying 47% more patients with this complication compared to the current standard of using claims codes. This LLM pipeline can be used reliably for subtyping PPH and outperforms a claims-based approach on the three most common PPH subtypes associated with uterine atony, abnormal placentation, and obstetric trauma. The advantage of this approach to subtyping is its interpretability, as each concept contributing to the subtype determination can be evaluated. Moreover, as definitions may change over time due to new guidelines, using granular concepts to create complex phenotypes enables prompt and efficient updating of the algorithm. Using this language modelling approach enables rapid phenotyping without the need for any manually annotated training data across multiple clinical use cases., (© 2023. The Author(s).)

Background: Current guidelines recommend prophylactic vasopressor administration during spinal anesthesia for cesarean delivery to maintain intraoperative blood pressure above 90% of the baseline value. We sought to determine the optimum baseline mean arterial pressure (MAP) reading to guide the management of spinal hypotension., Methods: We performed a secondary analysis of data collected from normotensive patients presenting for elective cesarean delivery in a tertiary care institution from October 2018 to August 2020. We compared the magnitude of hypotension in patients who reported nausea versus those who did not, using a case-control design. Baseline MAPs at last office visit, morning of surgery, or operating room (pre-spinal) were determined. We calculated the duration and degree of hypotension using the area under the curve (AUC) when the MAP of the respective patient was below 90% of each baseline., Results: The patients who experienced nausea (n=45) had longer and more profound periods of hypotension than those who did not develop nausea (n=240). A comparison of AUC using MAP baseline at the last office visit or on the morning of surgery showed a statistically significant between-group difference, P=0.02, and P=0.005, respectively, and no significant between-group difference when 90% of the MAP baseline in the operating room was used., Conclusions: Patients had the highest preoperative MAP in the operating room and the AUC was similar for those with and without nausea when the pre-spinal MAP baseline was used. Therefore, maintaining higher intraoperative blood pressure using individual pre-spinal MAP as baseline should reduce intraoperative maternal nausea., Competing Interests: Vesela P. Kovacheva reports patent #WO2021119593A1 for control of a therapeutic delivery system, which is assigned to the Mass General Brigham., (Copyright © 2023, Kovacheva et al.)

Purpose of Review: The purpose of this review is to summarize the current research and critically examine artificial intelligence (AI) technologies and their applicability to the daily practice of anesthesiologists., Recent Findings: Novel AI tools are developed using data from electronic health records, imaging, waveforms, clinical notes, and wearables. These tools can accurately predict the perioperative risk for adverse outcomes, the need for blood transfusion, and the risk of difficult intubation. Intraoperatively, AI models can assist with technical skill augmentation, patient monitoring, and management. Postoperatively, AI technology can aid in preventing complications and discharge planning. While further prospective validation is needed, these early applications demonstrate promise in every area of perioperative care., Summary: The practice of anesthesiology is at a precipice fueled by technological innovation. The clinical AI implementation would enable personalized and safer patient care by offering actionable insights from the wealth of perioperative data., Competing Interests: Conflict of Interest VPK reports consulting fees from Avania CRO unrelated to the current work and patent #WO2021119593A1 for control of a therapeutic delivery system.

Healthcare artificial intelligence (AI) holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tools for analysis. Collection and translation of electronic health record data, live data, and real-time high-resolution device data can be challenging and time-consuming. The development of clinically relevant AI tools requires overcoming challenges in data acquisition, scarce hospital resources, and requirements for data governance. These bottlenecks may result in resource-heavy needs and long delays in research and development of AI systems. We present a system and methodology to accelerate data acquisition, dataset development and analysis, and AI model development. We created an interactive platform that relies on a scalable microservice architecture. This system can ingest 15,000 patient records per hour, where each record represents thousands of multimodal measurements, text notes, and high-resolution data. Collectively, these records can approach a terabyte of data. The platform can further perform cohort generation and preliminary dataset analysis in 2-5 minutes. As a result, multiple users can collaborate simultaneously to iterate on datasets and models in real time. We anticipate that this approach will accelerate clinical AI model development, and, in the long run, meaningfully improve healthcare delivery.

Background: Coronavirus disease 2019 (COVID-19) is associated with adverse maternal and neonatal outcomes. Early studies suggested that COVID-19 was associated with a higher incidence of hypotension following neuraxial anesthesia in parturients. We explored the hemodynamic response to spinal anesthesia for cesarean delivery in pregnant severe respiratory distress syndrome-coronavirus-2 (SARS-CoV-2) positive patients, using a retrospective case-control design., Methods: We searched our electronic medical records for patients who received spinal anesthesia for cesarean delivery, and were SARS-CoV-2 positive or recovered at delivery, and used historical and SARS-CoV-2 negative controls from two tertiary care hospitals. We compared the demographic, clinical, and hemodynamic variables between patients who were SARS-CoV-2 positive at delivery, those who were positive during pregnancy and recovered before delivery, and controls. Analyses were stratified by normotensive versus hypertensive status of the patients at delivery., Results: We identified 22 SARS-CoV-2 positive, 73 SARS-CoV-2 recovered, and 1517 controls. The SARS-CoV-2 positive, and recovered pregnant patients, had on average 5.6 and 2.2 mmHg, respectively, higher post-spinal mean arterial pressures (MAPs) than control patients, adjusting for covariates. Additionally, the lowest post-spinal MAP was negatively correlated with the number of daysbetween the onset of COVID-19 symptoms and delivery in patients with hypertension (correlation -0.55, 95% CI -0.81 to -0.09)., Conclusions: Patients with SARS-CoV-2 infection during pregnancy exhibit less spinal hypotension than non-infected patients. While the clinical significance of this finding is unknown, it points to important cardiovascular effects of the virus., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Objective: To evaluate and understand pregnant patients' perspectives on the implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to improve healthcare technologies and healthcare delivery., Materials and Methods: We developed an anonymous survey and enrolled patients presenting to the labor and delivery unit at a tertiary care center September 2019-June 2020. We investigated the role and interplay of patient demographic factors, healthcare literacy, understanding of AI, comfort levels with various AI scenarios, and preferences for AI use in clinical care., Results: Of the 349 parturients, 57.6% were between the ages of 25-34 years, 90.1% reported college or graduate education and 69.2% believed the benefits of AI use in clinical care outweighed the risks. Cluster analysis revealed 2 distinct groups: patients more comfortable with clinical AI use (Pro-AI) and those who preferred physician presence (AI-Cautious). Pro-AI patients had a higher degree of education, were more knowledgeable about AI use in their daily lives and saw AI use as a significant advancement in medicine. AI-Cautious patients reported a lack of human qualities and low trust in the technology as detriments to AI use., Discussion: Patient trust and the preservation of the human physician-patient relationship are critical in moving forward with AI implementation in healthcare. Pregnant individuals are cautiously optimistic about AI use in their care., Conclusion: Our findings provide insights into the status of AI use in perinatal care and provide a platform for driving patient-centered innovations., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose of Review: Recent advancements in big data analytical tools and large patient databases have expanded tremendously the opportunities to track patient and safety outcomes.We discuss the strengths and limitations of large databases and implementation in practice with a focus on the current opportunities to use technological advancements to improve patient safety., Recent Findings: The most used sources of data for large patient safety observational studies are administrative databases, clinical registries, and electronic health records. These data sources have enabled research on patient safety topics ranging from rare adverse outcomes to large cohort studies of the modalities for pain control and safety of medications. Implementing the insights from big perioperative data research is augmented by automating data collection and tracking the safety outcomes on a provider, institutional, national, and global level. In the near future, big data from wearable devices, physiological waveforms, and genomics may lead to the development of personalized outcome measures., Summary: Patient safety research using large databases can provide actionable insights to improve outcomes in the perioperative setting. As datasets and methods to gain insights from those continue to grow, adopting novel technologies to implement personalized quality assurance initiatives can significantly improve patient care., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Preoperative anxiety has an incidence of 11-80% in patients undergoing surgical or interventional procedures. Understanding the role of preoperative anxiety on intraoperative anesthetic requirements and postoperative analgesic consumption would allow personalized anesthesia care. Over- or under-anesthetizing patients can lead to complications such as postoperative cognitive dysfunction in elderly patients, or procedural discomfort, respectively. Our scoping review focuses on the current evidence regarding the association between preoperative anxiety and intraoperative anesthetic and/or postoperative analgesic consumption in patients undergoing elective surgical or interventional procedures. Based on 44 studies that met the inclusion criteria, we found that preoperative anxiety has a significant positive correlation effect on intraoperative propofol and postoperative opioid consumption. The analysis of the literature is limited by the heterogeneity of preoperative anxiety tools used, study designs, data analyses, and outcomes. The use of shorter, validated preoperative anxiety assessment tools may help optimize the intraoperative anesthetic and postoperative analgesic regimen. Further research to determine the most feasible and clinically relevant preoperative anxiety tool and subsequent implementation has the potential to optimize perioperative care and improve patient outcomes., (© 2022. The Author(s) under exclusive licence to Japanese Society of Anesthesiologists.)