Updated outcomes using clockwise technique for laparoscopic distal pancreatectomy:Optimal treatment of benign and malignant disease of the left pancreas

Background:Laparoscopic distal pancreatectomy(LDP)has become the preferred approach for surgical management of left sided pancreas pathology.Our institution previously published its experience with distal pancreatectomies using a clockwise technique with good outcomes.We now reexamine our outcomes across a longer time interval.Methods:From August 2008 to November 2020,364 patients underwent LDP by hepatobiliary surgeons(HA and JS).All procedures were performed using the same clockwise approach,which includes the stepwise slow compression technique.Retrospective descriptive analysis of patient demographic,clinical,operative,and pathologic data was conducted.Results:Of the 364 patients who underwent LDP using this technique,clinically significant postoperative pancreatic fistula(POPF)was noted in 26(7.1%)patients,while major morbidity and mortality were reported in 9.9%and 0.3%,respectively.Hand-assisted method was required for 18(4.9%)patients and unplanned conversion in 20(5.5%)patients.In a subset analysis of patients with pancreatic adenocarcinoma(n¼90),POPF was noted in 13(14.4%),with minor complications occurring in 34.4%and major morbidity in 14.4%.Conclusion:LDP with a clockwise approach for dissection,combined with the stepwise slow compression technique results in excellent outcomes,with even lower POPF rates than originally reported.Subset analysis of patients with pancreatic adenocarcinoma shows acceptable perioperative outcomes with this technique.

Critical care practices in the world:Results of the global intensive care unit need assessment survey 2020

BACKGROUND There is variability in intensive care unit(ICU)resources and staffing worldwide.This may reflect variation in practice and outcomes across all health systems.AIM To improve research and quality improvement measures administrative leaders can create long-term strategies by understanding the nature of ICU practices on a global scale.METHODS The Global ICU Needs Assessment Research Group was formed on the basis of diversified skill sets.We aimed to survey sites regarding ICU type,availability of staffing,and adherence to critical care protocols.An international survey‘Global ICU Needs Assessment’was created using Google Forms,and this was distributed from February 17^(th),2020 till September 23^(rd),2020.The survey was shared with ICU providers in 34 countries.Various approaches to motivating healthcare providers were implemented in securing submissions,including use of emails,phone calls,social media applications,and WhatsApp^(TM).By completing this survey,providers gave their consent for research purposes.This study was deemed eligible for category-2 Institutional Review Board exempt status.RESULTS There were a total 121 adult/adult-pediatrics ICU responses from 34 countries in 76 cities.A majority of the ICUs were mixed medical-surgical[92(76%)].108(89%)were adult-only ICUs.Total 36 respondents(29.8%)were 31-40 years of age,with 79(65%)male and 41(35%)female participants.89 were consultants(74%).A total of 71(59%)respondents reported having a 24-h inhouse intensivist.A total of 87(72%)ICUs were reported to have either a 2:1 or≥2:1 patient/nurse ratio.About 44%of the ICUs were open and 76%were mixed type(medical-surgical).Protocols followed regularly by the ICUs included sepsis care(82%),ventilator-associated pneumonia(79%);nutrition(76%),deep vein thrombosis prophylaxis(84%),stress ulcer prophylaxis(84%),and glycemic control(89%).CONCLUSION Based on the findings of this international,multi-dimensional,needs-assessment survey,there is a need for increased recruitment and staffing in critical care facilities,along with improved patientto-nurse ratios.Future research is warranted in this field with focus on implementing appropriate health standards,protocols and resources for optimal efficiency in critical care worldwide.

Predictive modeling in neurocritical care using causal artificial intelligence

Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the development of Archimedes,an AI model of diabetes,in 2003.More recently,AI models have been applied to the fields of cardiology,endocrinology,and undergraduate medical education.The use of digital twins and AI thus far has focused mainly on chronic disease management,their application in the field of critical care medicine remains much less explored.In neurocritical care,current AI technology focuses on interpreting electroencephalography,monitoring intracranial pressure,and prognosticating outcomes.AI models have been developed to interpret electroencephalograms by helping to annotate the tracings,detecting seizures,and identifying brain activation in unresponsive patients.In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.

Pregnancy Outcomes Associated with Ovarian Hyperstimulation Syndrome:A Retrospective Cohort Study of Infertile Women

Objective To assess the relationship between ovarian hyperstimulation syndrome(OHSS)and adverse outcomes using population-based data in the United States.The hypothesis is that patients with OHSS were more likely to deliver preterm and more likely to have hypertensive disorders.Methods This retrospective cohort study identified 94 patients with OHSS and 183 matched referents in eight counties in Minnesota.Data were collected regarding pregnancy history,infertility treatment,and pregnancy outcomes.Using the Rochester Epidemiology Project,study subjects were identified from female patients,aged 18 to 49 years,who were diagnosed with infertility from January 2,1995 to December 1,2017,and had a pregnancy greater than 20 weeks'gestation.The primary outcome was preterm delivery or hypertensive disorder of pregnancy incidence in the OHSS group when compared with control patients.Chi-squared test,t test,and multivariate logistic models were used where appropriate.Results Patients with OHSS were more likely to deliver preterm(odds ratio,2.14;95%confidence interval,1.26–3.65;P<0.01),and their neonates were more likely to be small for gestational age(odds ratio,4.78;95%confidence interval,1.61–14.19;P<0.01).No significant differences between the groups were observed in any other outcome.Patients with OHSS are more likely to deliver preterm if they undergo fresh transfer compared with a freeze all and subsequent frozen transfer(odds ratio,3.03,95%confidence interval,1.20–7.66,P=0.02).Conclusion OHSS may lead to preterm birth and small-for-gestational-age neonates,which changes patient counseling and leads to arranging specialized obstetrical care for these patients with OHSS.

Clinical longitudinal evaluation of COVID-19 patients and prediction of organ-specific recovery using artificial intelligence

Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease,and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become overwhelmed.To this end,we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from COVID-19.We trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal,pulmonary,immune,cardiac,and hepatic systems.To greatly enhance the speed and utility of our model,we applied an artificial intelligence method to segment and classify regions on CT imaging,from which interpretable data could be directly fed into the predictive machine learning model for overall recovery.Across all organ systems we achieved validation set area under the receiver operator characteristic curve(AUC)values for organ-specific recovery ranging from 0.80 to 0.89,and significant overall recovery prediction in Kaplan-Meier analyses.This demonstrates that the synergistic use of an artificial intelligence(AI)framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.

Probing Patient Messages Enhanced by Natural Language Processing:A Top-Down Message Corpus Analysis

Background.Patients increasingly use asynchronous communication platforms to converse with care teams.Natural language processing(NLP)to classify content and automate triage of these messages has great potential to enhance clinical efficiency.We characterize the contents of a corpus of portal messages generated by patients using NLP methods.We aim to demonstrate descriptive analyses of patient text that can contribute to the development of future sophisticated NLP applications.Methods.We collected approximately 3,000 portal messages from the cardiology,dermatology,and gastroenterology departments at Mayo Clinic.After labeling these messages as either Active Symptom,Logistical,Prescription,or Update,we used NER(named entity recognition)to identify medical concepts based on the UMLS library.We hierarchically analyzed the distribution of these messages in terms of departments,message types,medical concepts,and keywords therewithin.Results.Active Symptom and Logistical content types comprised approximately 67%of the message cohort.The“Findings”medical concept had the largest number of keywords across all groupings of content types and departments.“Anatomical Sites”and“Disorders”keywords were more prevalent in Active Symptom messages,while“Drugs”keywords were most prevalent in Prescription messages.Logistical messages tended to have the lower proportions of“Anatomical Sites,”,“Disorders,”,“Drugs,”,and“Findings”keywords when compared to other message content types.Conclusions.This descriptive corpus analysis sheds light on the content and foci of portal messages.The insight into the content and differences among message themes can inform the development of more robust NLP models.