Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures

Using clustering analysis for early vital signs, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Phenotyping using early vital signs has proven challenging, as vital signs are typically sampled sporadically. We proposed a novel, deep temporal interpolation and clustering network to simultaneously extract latent representations from irregularly sampled vital signs and derive phenotypes. Four distinct clusters were identified. Phenotype A (18%) had the greatest prevalence of comorbid disease with increased prevalence of prolonged respiratory insufficiency, acute kidney injury, sepsis, and long-term (3-year) mortality. Phenotypes B (33%) and C (31%) had a diffuse pattern of mild organ dysfunction. Phenotype B's favorable short-term clinical outcomes were tempered by the second highest rate of long-term mortality. Phenotype C had favorable clinical outcomes. Phenotype D (17%) exhibited early and persistent hypotension, high incidence of early surgery, and substantial biomarker incidence of inflammation. Despite early and severe illness, phenotype D had the second lowest long-term mortality. After comparing the sequential organ failure assessment scores, the clustering results did not simply provide a recapitulation of previous acuity assessments. This tool may impact triage decisions and have significant implications for clinical decision-support under time constraints and uncertainty.

Are we ready for "green surgery" to promote environmental sustainability in the operating room? Results from the WSES STAR investigation

BACKGROUND

The importance of environmental sustainability is acknowledged in all sectors, including healthcare. To meet the United Nations Sustainable Development Goals 2030 Agenda, healthcare will need a paradigm shift toward more environmentally sustainable practices that will also impact clinical decision-making. The study investigates trauma and emergency surgeons' perception, acceptance, and employment of environmentally friendly habits.

METHODS

An online survey based on the most recent literature regarding environmental sustainability in healthcare and surgery was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to the 917 WSES members through the society's website and Twitter/X profile.

RESULTS

450 surgeons from 55 countries participated in the survey. Results underline both a generally positive attitude toward environmental sustainability but also a lack of knowledge about several concepts and practices, especially concerning the potential contribution to patient care.

DISCUSSION

The topic of environmental sustainability in healthcare and surgery is still in its infancy. There is a clear lack of salient guidance and knowledge, and there is a critical need for governments, institutions, health agencies, and scientific societies to promote, disseminate, and report environmentally friendly initiatives and their potential impacts while employing an interdisciplinary approach.

Surgeons' perspectives on artificial intelligence to support clinical decision-making in trauma and emergency contexts: results from an international survey

BACKGROUND

Artificial intelligence (AI) is gaining traction in medicine and surgery. AI-based applications can offer tools to examine high-volume data to inform predictive analytics that supports complex decision-making processes. Time-sensitive trauma and emergency contexts are often challenging. The study aims to investigate trauma and emergency surgeons' knowledge and perception of using AI-based tools in clinical decision-making processes.

METHODS

An online survey grounded on literature regarding AI-enabled surgical decision-making aids was created by a multidisciplinary committee and endorsed by the World Society of Emergency Surgery (WSES). The survey was advertised to 917 WSES members through the society's website and Twitter profile.

RESULTS

650 surgeons from 71 countries in five continents participated in the survey. Results depict the presence of technology enthusiasts and skeptics and surgeons' preference toward more classical decision-making aids like clinical guidelines, traditional training, and the support of their multidisciplinary colleagues. A lack of knowledge about several AI-related aspects emerges and is associated with mistrust.

DISCUSSION

The trauma and emergency surgical community is divided into those who firmly believe in the potential of AI and those who do not understand or trust AI-enabled surgical decision-making aids. Academic societies and surgical training programs should promote a foundational, working knowledge of clinical AI.

Decision analysis and reinforcement learning in surgical decision-making

BACKGROUND

Surgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.

METHODS

Cochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.

RESULTS

Requirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.

CONCLUSION

Traditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

Intelligent, Autonomous Machines in Surgery

Surgeons perform two primary tasks: operating and engaging patients and caregivers in shared decision-making. Human dexterity and decision-making are biologically limited. Intelligent, autonomous machines have the potential to augment or replace surgeons. Rather than regarding this possibility with denial, ire, or indifference, surgeons should understand and steer these technologies. Closer examination of surgical innovations and lessons learned from the automotive industry can inform this process. Innovations in minimally invasive surgery and surgical decision-making follow classic S-shaped curves with three phases: (1) introduction of a new technology, (2) achievement of a performance advantage relative to existing standards, and (3) arrival at a performance plateau, followed by replacement with an innovation featuring greater machine autonomy and less human influence. There is currently no level I evidence demonstrating improved patient outcomes using intelligent, autonomous machines for performing operations or surgical decision-making tasks. History suggests that if such evidence emerges and if the machines are cost effective, then they will augment or replace humans, initially for simple, common, rote tasks under close human supervision and later for complex tasks with minimal human supervision. This process poses ethical challenges in assigning liability for errors, matching decisions to patient values, and displacing human workers, but may allow surgeons to spend less time gathering and analyzing data and more time interacting with patients and tending to urgent, critical-and potentially more valuable-aspects of patient care. Surgeons should steer these technologies toward optimal patient care and net social benefit using the uniquely human traits of creativity, altruism, and moral deliberation.

Opportunities for machine learning to improve surgical ward safety

BACKGROUND

Delayed recognition of decompensation and failure-to-rescue on surgical wards are major sources of preventable harm. This review assimilates and critically evaluates available evidence and identifies opportunities to improve surgical ward safety.

DATA SOURCES

Fifty-eight articles from Cochrane Library, EMBASE, and PubMed databases were included.

CONCLUSIONS

Only 15-20% of patients suffering ward arrest survive. In most cases, subtle signs of instability often occur prior to critical illness and arrest, and underlying pathology is reversible. Coarse risk assessments lead to under-triage of high-risk patients to wards, where surveillance for complications depends on time-consuming manual review of health records, infrequent patient assessments, prediction models that lack accuracy and autonomy, and biased, error-prone decision-making. Streaming electronic heath record data, wearable continuous monitors, and recent advances in deep learning and reinforcement learning can promote efficient and accurate risk assessments, earlier recognition of instability, and better decisions regarding diagnosis and treatment of reversible underlying pathology.

Should Physicians Consider the Environmental Effects of Prescribing Antibiotics?

Pharmaceuticals are beginning to receive attention as a source of pollution in aquatic environments. Yet the impact of physician prescription patterns on water resources is not often discussed in clinical decision making. Here, we comment on a case in which empiric antibiotic treatment might benefit a patient while simultaneously being detrimental to the aquatic environment. We first highlight the potential harm caused by this prescription from its production to its disposal. We then suggest that Van Rensselaer Potter's original conceptualization of bioethics can be used to balance clinicians' obligations to protect individual, public, and environmental health.

Survey of hepatitis B knowledge and stigma among chronically infected patients and uninfected persons in Beijing, China

BACKGROUND & AIMS

Hepatitis B virus (HBV) infection carries substantial stigma in China. We surveyed HBV knowledge and stigma among chronic hepatitis B (CHB) patients and persons without HBV infection in Beijing, China.

METHODS

Four hundred and thirty five CHB patients and 801 controls at Peking University People's Hospital were surveyed.

RESULTS

Chronic hepatitis B patients were older (mean 46 vs. 39 years) and more often men (71 vs. 48%) than controls. Mean knowledge score was 11.9/15 for CHB and 9.3/15 for control patients (P < 0.001). Average stigma score was 22.1/39 for CHB and 19.2/30 for control patients. Controls expressed discomfort with close contact (45%) or sharing meals with CHB patients (39%) and believed CHB patients should not be allowed to work in restaurants (58%) or childcare (44%). Chronic hepatitis B patients felt that they were undesirable as spouses (33 vs. 17%) and brought trouble to their families (58 vs. 34%) more often than controls. Despite legal prohibitions, 40% of CHB patients were required to undergo pre-employment HBV testing, and 29% of these individuals thought that they lost job opportunities because of their disease status. 16% of CHB patients regretted disclosing their HBV status and disclosure was inversely associated with stigma. Higher stigma was associated with older age, lower education and lower knowledge score among controls; and with lower education, younger age, having undergone pre-employment HBV testing and regret disclosing their HBV status among CHB patients.

CONCLUSION

Despite high prevalence of CHB in China, our study shows knowledge is limited and there is significant societal and internalized stigma associated with HBV infection.

Effects of molecular weight on the diffusion coefficient of aquatic dissolved organic matter and humic substances

In situ measurements of labile metal species using diffusive gradients in thin films (DGT) passive samplers are based on the diffusion rates of individual species. Although most studies have dealt with chemically isolated humic substances, the diffusion of dissolved organic matter (DOM) across the hydrogel is not well understood. In this study, the diffusion coefficient (D) and molecular weight (MW) of 11 aquatic DOM and 4 humic substances (HS) were determined. Natural, unaltered aquatic DOM was capable of diffusing across the diffusive gel membrane with D values ranging from 2.48×10(-6) to 5.31×10(-6) cm(2) s(-1). Humic substances had diffusion coefficient values ranging from 3.48×10(-6) to 6.05×10(-6) cm(2) s(-1), congruent with previous studies. Molecular weight of aquatic DOM and HS samples (∼500-1750 Da) measured using asymmetrical flow field-flow fractionation (AF4) strongly influenced D, with larger molecular weight DOM having lower D values. No noticeable changes in DOM size properties were observed during the diffusion process, suggesting that DOM remains intact following diffusion across the diffusive gel. The influence of molecular weight on DOM mobility will assist in further understanding and development of the DGT technique and the uptake and mobility of contaminants associated with DOM in aquatic environments.