Deep Survival Analysis for Interpretable Time-Varying Prediction of Preeclampsia Risk.

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 2015-2023. 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.

Performance Characteristics of Sepsis Screening Tools During Delivery Admissions.

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.

Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies.

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.

On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology.

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.

Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models.

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.

Investigation of the Optimum Baseline Blood Pressure for Spinal Anesthesia to Guide Vasopressor Management for Elective Cesarean Delivery: A Case-Control Design.

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.

An Interpretable Longitudinal Preeclampsia Risk Prediction Using Machine Learning.

Background: Preeclampsia is a pregnancy-specific disease characterized by new onset hypertension after 20 weeks of gestation that affects 2-8% of all pregnancies and contributes to up to 26% of maternal deaths. Despite extensive clinical research, current predictive tools fail to identify up to 66% of patients who will develop preeclampsia. We sought to develop a tool to longitudinally predict preeclampsia risk. Methods: In this retrospective model development and validation study, we examined a large cohort of patients who delivered at six community and two tertiary care hospitals in the New England region between 02/2015 and 06/2023. We used sociodemographic, clinical diagnoses, family history, laboratory, and vital signs data. We developed eight datasets at 14, 20, 24, 28, 32, 36, 39 weeks gestation and at the hospital admission for delivery. We created linear regression, random forest, xgboost, and deep neural networks to develop multiple models and compared their performance. We used Shapley values to investigate the global and local explainability of the models and the relationships between the predictive variables. Findings: Our study population (N=120,752) had an incidence of preeclampsia of 5.7% (N=6,920). The performance of the models as measured using the area under the curve, AUC, was in the range 0.73-0.91, which was externally validated. The relationships between some of the variables were complex and non-linear; in addition, the relative significance of the predictors varied over the pregnancy. Compared to the current standard of care for preeclampsia risk stratification in the first trimester, our model would allow 48.6% more at-risk patients to be identified. Interpretation: Our novel preeclampsia prediction tool would allow clinicians to identify patients at risk early and provide personalized predictions, as well as longitudinal predictions throughout pregnancy. Funding: National Institutes of Health, Anesthesia Patient Safety Foundation. RESEARCH IN CONTEXT: Evidence before this study: Current tools for the prediction of preeclampsia are lacking as they fail to identify up to 66% of the patients who develop preeclampsia. We searched PubMed, MEDLINE, and the Web of Science from database inception to May 1, 2023, using the keywords "deep learning", "machine learning", "preeclampsia", "artificial intelligence", "pregnancy complications", and "predictive models". We identified 13 studies that employed machine learning to develop prediction models for preeclampsia risk based on clinical variables. Among these studies, six included biomarkers such as serum placental growth factor, pregnancy-associated plasma protein A, and uterine artery pulsatility index, which are not routinely available in our clinical practice; two studies were in diverse cohorts of more than 100 000 patients, and two studies developed longitudinal predictions using medical records data. However, most studies have limited depth, concerns about data leakage, overfitting, or lack of generalizability.Added value of this study: We developed a comprehensive longitudinal predictive tool based on routine clinical data that can be used throughout pregnancy to predict the risk of preeclampsia. We tested multiple types of predictive models, including machine learning and deep learning models, and demonstrated high predictive power. We investigated the changes over different time points of individual and group variables and found previously known and novel relationships between variables such as red blood cell count and preeclampsia risk.Implications of all the available evidence: Longitudinal prediction of preeclampsia using machine learning can be achieved with high performance. Implementation of an accurate predictive tool within the electronic health records can aid clinical care and identify patients at heightened risk who would benefit from aspirin prophylaxis, increased surveillance, early diagnosis, and escalation in care. These results highlight the potential of using artificial intelligence in clinical decision support, with the ultimate goal of reducing iatrogenic preterm birth and improving perinatal care.

Zero-shot Interpretable Phenotyping of Postpartum Hemorrhage Using Large Language Models.

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 i nstructions. We investigated the per-formance 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 achieved strong performance in extracting 24 granular concepts associated with PPH. Identifying these granular concepts accurately allowed the development of inter-pretable, complex phenotypes and subtypes. The Flan-T5 model achieved 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 outperformed 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 lan-guage modelling approach enables rapid phenotyping without the need for any manually annotated training data across multiple clinical use cases.

A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research.

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.

Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores.

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=1,125 pregnant individuals who delivered between 05/2015-05/2022 at Mass General Brigham hospitals with available electronic health record (EHR) data and linked genetic data. Using clinical EHR data and systolic blood pressure polygenic risk scores (SBP PRS) derived from a large genome-wide association study, we developed machine learning (xgboost) and linear regression models to predict preeclampsia risk. Results: Pregnant individuals with an SBP PRS in the top quartile had higher blood pressures throughout pregnancy compared to patients within the lowest quartile SBP PRS. In the first trimester, the most predictive model was xgboost, with an area under the curve (AUC) of 0.73. Adding the SBP PRS to the models improved the performance only of the linear regression model from AUC 0.70 to 0.71; the predictive power of other models remained unchanged. In late pregnancy, with data obtained up to the delivery admission, the best performing model was xgboost using clinical variables, which achieved an AUC of 0.91. Conclusions: Integrating clinical and genetic 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 in clinical practice to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.

Development and implementation of databases to track patient and safety outcomes.

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.

A survey of pregnant patients' perspectives on the implementation of artificial intelligence in clinical care.

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.

Less stress, better success: a scoping review on the effects of anxiety on anesthetic and analgesic consumption.

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.

A Contemporary Analysis of Medicolegal Issues in Obstetric Anesthesia Between 2005 and 2015.

BACKGROUND: Detailed reviews of closed malpractice claims have provided insights into the most common events resulting in litigation and helped improve anesthesia care. In the past 10 years, there have been multiple safety advancements in the practice of obstetric anesthesia. We investigated the relationship among contributing factors, patient injuries, and legal outcome by analyzing a contemporary cohort of closed malpractice claims where obstetric anesthesiology was the principal defendant. METHODS: The Controlled Risk Insurance Company (CRICO) is the captive medical liability insurer of the Harvard Medical Institutions that, in collaboration with other insurance companies and health care entities, contributes to the Comparative Benchmark System database for research purposes. We reviewed all (N = 106) closed malpractice cases related to obstetric anesthesia between 2005 and 2015 and compared the following classes of injury: maternal death and brain injury, neonatal death and brain injury, maternal nerve injury, and maternal major and minor injury. In addition, settled claims were compared to the cases that did not receive payment. χ, analysis of variance, Student t test, and Kruskal-Wallis tests were used for comparison between the different classes of injury. RESULTS: The largest number of claims, 54.7%, involved maternal nerve injury; 77.6% of these claims did not receive any indemnity payment. Cases involving maternal death or brain injury comprised 15.1% of all cases and were more likely to receive payment, especially in the high range (P = .02). The most common causes of maternal death or brain injury were high neuraxial blocks, embolic events, and failed intubation. Claims for maternal major and minor injury were least likely to receive payment (P = .02) and were most commonly (34.8%) associated with only emotional injury. Compared to the dropped/denied/dismissed claims, settled claims more frequently involved general anesthesia (P = .03), were associated with delays in care (P = .005), and took longer to resolve (3.2 vs 1.3 years; P < .0001). CONCLUSIONS: Obstetric anesthesia remains an area of significant malpractice liability. Opportunities for practice improvement in the area of severe maternal injury include timely recognition of high neuraxial block, availability of adequate resuscitative resources, and the use of advanced airway management techniques. Anesthesiologists should avoid delays in maternal care, establish clear communication, and follow their institutional policy regarding neonatal resuscitation. Prevention of maternal neurological injury should be directed toward performing neuraxial techniques at the lowest lumbar spine level possible and prevention/recognition of retained neuraxial devices.

Gene-Centric Analysis of Preeclampsia Identifies Maternal Association at PLEKHG1.

The genetic susceptibility to preeclampsia, a pregnancy-specific complication with significant maternal and fetal morbidity, has been poorly characterized. To identify maternal genes associated with preeclampsia risk, we assembled 498 cases and 1864 controls of European ancestry from preeclampsia case-control collections in 5 different US sites (with additional matched population controls), genotyped samples on a cardiovascular gene-centric array composed of variants from ≈2000 genes selected based on prior genetic studies of cardiovascular and metabolic diseases and performed case-control genetic association analysis on 27 429 variants passing quality control. In silico replication testing of 9 lead signals with P<10-4 was performed in independent European samples from the SOPHIA (Study of Pregnancy Hypertension in Iowa) and Inova cohorts (212 cases, 456 controls). Multiethnic assessment of lead signals was then performed in samples of black (26 cases, 136 controls), Hispanic (132 cases, 468 controls), and East Asian (9 cases, 80 controls) ancestry. Multiethnic meta-analysis (877 cases, 3004 controls) revealed a study-wide statistically significant association of the rs9478812 variant in the pleiotropic PLEKHG1 gene (odds ratio, 1.40 [1.23-1.60]; Pmeta=5.90×10-7). The rs9478812 effect was even stronger in the subset of European cases with known early-onset preeclampsia (236 cases diagnosed <37 weeks, 1864 controls; odds ratio, 1.59 [1.27-1.98]; P=4.01×10-5). PLEKHG1 variants have previously been implicated in genome-wide association studies of blood pressure, body weight, and neurological disorders. Although larger studies are required to further define maternal preeclampsia heritability, this study identifies a novel maternal risk locus for further investigation.

Predictors of Achieving Recommended Daily Physical Activity Among Anesthesiologists at a Large Tertiary Care Academic Center.

BACKGROUND: The goal of the current study was to determine if the daily work patterns of anesthesiologists meet the recommended daily levels of activity. METHODS: Attending and resident anesthesiologists at a tertiary academic center were invited to participate. The subjects wore a pedometer during five regular clinical days at work and recorded the number of steps walked. The participants also completed the International Physical Activity Questionnaire (IPAQ) during one regular week. The results were analyzed using analysis of variance, Chi-square test and multivariate linear regression using STATA 12.1. RESULTS: During work, attending, compared with senior and junior resident, anesthesiologists had the most steps (5,953 ± 1,213, 5,153 ± 905, and 5,710 ± 1,513 steps, respectively, P = 0.2). Outside work, senior residents had the highest level of activity (3,592 ± 1,626 metabolic equivalent of task (MET)-minutes/week) compared to junior residents (1,788 ± 1,089 MET-minutes/week) and attending (2,104 ± 1,594 MET-minutes/week, P = 0.005); the percentage of recommended daily level of activity represented by this outside activity was senior residents (78.5%), junior residents (27%) and attending (21%) anesthesiologists (P = 0.002). When activity at and outside work was combined, most anesthesiologists met the recommended 10,000 steps daily, P < 0.009. CONCLUSIONS: The daily physical activity of faculty and trainee anesthesiologists at work in a busy tertiary care is low active. However, when additional physical activity is pursued outside of work, most anesthesiologists met recommended daily levels of activity. These results highlight the inadequacy of daily activity at work, and the need to pursue additional physical activity outside of work; such awareness can assist in promoting a healthy lifestyle.

Acute Kidney Injury After Craniotomy Is Associated With Increased Mortality: A Cohort Study.

BACKGROUND: Acute kidney injury (AKI) is a serious postoperative complication. OBJECTIVE: To determine whether AKI in patients after craniotomy is associated with heightened 30-day mortality. METHODS: We performed a 2-center, retrospective cohort study of 1656 craniotomy patients who received critical care between 1998 and 2011. The exposure of interest was AKI defined as meeting RIFLE (Risk, Injury, Failure, Loss of Kidney Function, and End-stage Kidney Disease) class risk, injury, and failure criteria, and the primary outcome was 30-day mortality. Adjusted odds ratios were estimated by multivariable logistic regression models with inclusion of covariate terms thought to plausibly interact with both AKI and mortality. Additionally, mortality in craniotomy patients with AKI was analyzed with a risk-adjusted Cox proportional hazards regression model and propensity score matching as a sensitivity analysis. RESULTS: The incidences of RIFLE class risk, injury, and failure were 5.7%, 2.9%, and 1.3%, respectively. The odds of 30-day mortality in patients with RIFLE class risk, injury, or failure fully adjusted were 2.79 (95% confidence interval [CI], 1.76-4.42), 7.65 (95% CI, 4.16-14.07), and 14.41 (95% CI, 5.51-37.64), respectively. Patients with AKI experienced a significantly higher risk of death during follow-up; hazard ratio, 1.82 (95% CI, 1.34-2.46), 3.37 (95% CI, 2.36-4.81), and 5.06 (95% CI, 2.99-8.58), respectively, fully adjusted. In a cohort of propensity score-matched patients, RIFLE class remained a significant predictor of 30-day mortality. CONCLUSION: Craniotomy patients who suffer postoperative AKI are among a high-risk group for mortality. The severity of AKI after craniotomy is predictive of 30-day mortality. ABBREVIATIONS: AKI, acute kidney injuryAPACHE II, Acute Physiology and Chronic Health Evaluation IICI, confidence intervalCPT, Current Procedural TerminologyICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical ModificationRIFLE, risk, injury, failure, loss of kidney function, and end-stage kidney diseaseRPDR, Research Patient Data Registry.

A Randomized, Double-blinded Trial of a "Rule of Threes" Algorithm versus Continuous Infusion of Oxytocin during Elective Cesarean Delivery.

BACKGROUND: The administration of uterotonic agents during cesarean delivery is highly variable. The authors hypothesized a "rule of threes" algorithm, featuring oxytocin 3 IU, timed uterine tone evaluations, and a systematic approach to alternative uterotonic agents, would reduce the oxytocin dose required to obtain adequate uterine tone. METHODS: Sixty women undergoing elective cesarean delivery were randomized to receive a low-dose bolus or continuous infusion of oxytocin. To blind participants, the rule group simultaneously received intravenous oxytocin (3 IU/3 ml) and a "wide-open" infusion of 0.9% normal saline (500 ml); the standard care group received intravenous 0.9% normal saline (3 ml) and a "wide-open" infusion of oxytocin (30 IU in 0.9% normal saline/500 ml). Uterine tone was assessed at 3, 6, 9, and 12 min, and if inadequate, additional uterotonic agents were administered. Uterine tone, total dose and timing of uterotonic agent use, maternal hemodynamics, side effects, and blood loss were recorded. RESULTS: Adequate uterine tone was achieved with lower oxytocin doses in the rule versus standard care group (mean, 4.0 vs. 8.4 IU; point estimate of the difference, 4.4 ± 1.0 IU; 95% CI, 2.60 to 6.15; P < 0.0001). No additional oxytocin or alternative uterotonic agents were needed in either group after 6 min. No differences in the uterine tone, maternal hemodynamics, side effects, or blood loss were observed. CONCLUSION: A "rule of threes" algorithm using oxytocin 3 IU results in lower oxytocin doses when compared with continuous-infusion oxytocin in women undergoing elective cesarean delivery.

Raising gestational choline intake alters gene expression in DMBA-evoked mammary tumors and prolongs survival.

Choline is an essential nutrient that serves as a donor of metabolic methyl groups used during gestation to establish the epigenetic DNA methylation patterns that modulate tissue-specific gene expression. Because the mammary gland begins its development prenatally, we hypothesized that choline availability in utero may affect the gland's susceptibility to cancer. During gestational days 11-17, pregnant rats were fed a control, choline-supplemented, or choline-deficient diet (8, 36, and 0 mmol/kg of choline, respectively). On postnatal day 65, the female offspring received 25 mg/kg of a carcinogen 7,12-dimethylbenz[alpha]anthracene. Approximately 70% of the rats developed mammary adenocarcinomas; prenatal diet did not affect tumor latency, incidence, size, and multiplicity. Tumor growth rate was inversely related to choline content in the prenatal diet, resulting in 50% longer survival until euthanasia, determined by tumor size, of the prenatally choline-supplemented rats compared with the prenatally choline-deficient rats. This was accompanied by distinct expression patterns of approximately 70 genes in tumors derived from the three dietary groups. Tumors from the prenatally choline-supplemented rats overexpressed genes that confer favorable prognosis in human cancers (Klf6, Klf9, Nid2, Ntn4, Per1, and Txnip) and underexpressed those associated with aggressive disease (Bcar3, Cldn12, Csf1, Jag1, Lgals3, Lypd3, Nme1, Ptges2, Ptgs1, and Smarcb1). DNA methylation within the tumor suppressor gene, stratifin (Sfn, 14-3-3sigma), was proportional to the prenatal choline supply and correlated inversely with the expression of its mRNA and protein in tumors, suggesting that an epigenetic mechanism may underlie the altered molecular phenotype and tumor growth. Our results suggest a role for adequate maternal choline nutrition during pregnancy in prevention/alleviation of breast cancer in daughters.