Increasing SARS-CoV-2 seroprevalence among UK pediatric patients on dialysis and kidney transplantation between January 2020 and August 2021

BACKGROUND

Coronavirus disease 2019 (COVID-19) was officially declared a pandemic by the World Health Organisation (WHO) on 11 March 2020, as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly across the world. We investigated the seroprevalence of anti-SARS-CoV-2 antibodies in pediatric patients on dialysis or kidney transplantation in the UK.

METHODS

Excess sera samples were obtained prospectively during outpatient visits or haemodialysis sessions and analysed using a custom immunoassay calibrated with population age-matched healthy controls. Two large pediatric centres contributed samples.

RESULTS

In total, 520 sera from 145 patients (16 peritoneal dialysis, 16 haemodialysis, 113 transplantation) were analysed cross-sectionally from January 2020 until August 2021. No anti-SARS-CoV-2 antibody positive samples were detected in 2020 when lockdown and enhanced social distancing measures were enacted. Thereafter, the proportion of positive samples increased from 5% (January 2021) to 32% (August 2021) following the emergence of the Alpha variant. Taking all patients, 32/145 (22%) were seropositive, including 8/32 (25%) with prior laboratory-confirmed SARS-CoV-2 infection and 12/32 (38%) post-vaccination (one of whom was also infected after vaccination). The remaining 13 (41%) seropositive patients had no known stimulus, representing subclinical cases. Antibody binding signals were comparable across patient ages and dialysis versus transplantation and highest against full-length spike protein versus spike subunit-1 and nucleocapsid protein.

CONCLUSIONS

Anti-SARS-CoV-2 seroprevalence was low in 2020 and increased in early 2021. Serological surveillance complements nucleic acid detection and antigen testing to build a greater picture of the epidemiology of COVID-19 and is therefore important to guide public health responses. A higher resolution version of the Graphical abstract is available as Supplementary information.

Human IPSC-Derived PreBötC-Like Neurons and Development of an Opiate Overdose and Recovery Model

Opioid overdose is the leading cause of drug overdose lethality, posing an urgent need for investigation. The key brain region for inspiratory rhythm regulation and opioid-induced respiratory depression (OIRD) is the preBötzinger Complex (preBötC) and current knowledge has mainly been obtained from animal systems. This study aims to establish a protocol to generate human preBötC neurons from induced pluripotent cells (iPSCs) and develop an opioid overdose and recovery model utilizing these iPSC-preBötC neurons. A de novo protocol to differentiate preBötC-like neurons from human iPSCs is established. These neurons express essential preBötC markers analyzed by immunocytochemistry and demonstrate expected electrophysiological responses to preBötC modulators analyzed by patch clamp electrophysiology. The correlation of the specific biomarkers and function analysis strongly suggests a preBötC-like phenotype. Moreover, the dose-dependent inhibition of these neurons' activity is demonstrated for four different opioids with identified IC50's comparable to the literature. Inhibition is rescued by naloxone in a concentration-dependent manner. This iPSC-preBötC mimic is crucial for investigating OIRD and combating the overdose crisis and a first step for the integration of a functional overdose model into microphysiological systems.

Developing and validating a natural language processing algorithm to extract preoperative cannabis use status documentation from unstructured narrative clinical notes

OBJECTIVE

This study aimed to develop a natural language processing algorithm (NLP) using machine learning (ML) techniques to identify and classify documentation of preoperative cannabis use status.

MATERIALS AND METHODS

We developed and applied a keyword search strategy to identify documentation of preoperative cannabis use status in clinical documentation within 60 days of surgery. We manually reviewed matching notes to classify each documentation into 8 different categories based on context, time, and certainty of cannabis use documentation. We applied 2 conventional ML and 3 deep learning models against manual annotation. We externally validated our model using the MIMIC-III dataset.

RESULTS

The tested classifiers achieved classification results close to human performance with up to 93% and 94% precision and 95% recall of preoperative cannabis use status documentation. External validation showed consistent results with up to 94% precision and recall.

DISCUSSION

Our NLP model successfully replicated human annotation of preoperative cannabis use documentation, providing a baseline framework for identifying and classifying documentation of cannabis use. We add to NLP methods applied in healthcare for clinical concept extraction and classification, mainly concerning social determinants of health and substance use. Our systematically developed lexicon provides a comprehensive knowledge-based resource covering a wide range of cannabis-related concepts for future NLP applications.

CONCLUSION

We demonstrated that documentation of preoperative cannabis use status could be accurately identified using an NLP algorithm. This approach can be employed to identify comparison groups based on cannabis exposure for growing research efforts aiming to guide cannabis-related clinical practices and policies.

Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond

There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication. These non-diagnostic workflow and process optimization tasks are an important part of the arsenal of potential AI tools that can streamline day to day clinical practice and patient care.

Cysteine-Selective Modification of Peptides and Proteins via Desulfurative C-C Bond Formation

The site-selective modification of peptides and proteins facilitates the preparation of targeted therapeutic agents and tools to interrogate biochemical pathways. Among the numerous bioconjugation techniques developed to install groups of interest, those that generate C(sp3 )-C(sp3 ) bonds are significantly underrepresented despite affording proteolytically stable, biogenic linkages. Herein, a visible-light-mediated reaction is described that enables the site-selective modification of peptides and proteins via desulfurative C(sp3 )-C(sp3 ) bond formation. The reaction is rapid and high yielding in peptide systems, with comparable translation to proteins. Using this chemistry, a range of moieties is installed into model systems and an effective PTM-mimic is successfully integrated into a recombinantly expressed histone.

Building an automated, machine learning-enabled platform for predicting post-operative complications

Objective. In 2019, the University of Florida College of Medicine launched theMySurgeryRiskalgorithm to predict eight major post-operative complications using automatically extracted data from the electronic health record.Approach. This project was developed in parallel with our Intelligent Critical Care Center and represents a culmination of efforts to build an efficient and accurate model for data processing and predictive analytics.Main Results and Significance. This paper discusses how our model was constructed and improved upon. We highlight the consolidation of the database, processing of fixed and time-series physiologic measurements, development and training of predictive models, and expansion of those models into different aspects of patient assessment and treatment. We end by discussing future directions of the model.

Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System

BACKGROUND

In single-institution studies, overtriaging low-risk postoperative patients to ICUs has been associated with a low value of care; undertriaging high-risk postoperative patients to general wards has been associated with increased mortality and morbidity. This study tested the reproducibility of an automated postoperative triage classification system to generating an actionable, explainable decision support system.

STUDY DESIGN

This longitudinal cohort study included adults undergoing inpatient surgery at two university hospitals. Triage classifications were generated by an explainable deep learning model using preoperative and intraoperative electronic health record features. Nearest neighbor algorithms identified risk-matched controls. Primary outcomes were mortality, morbidity, and value of care (inverted risk-adjusted mortality/total direct costs).

RESULTS

Among 4,669 ICU admissions, 237 (5.1%) were overtriaged. Compared with 1,021 control ward admissions, overtriaged admissions had similar outcomes but higher costs ($15.9K [interquartile range $9.8K to $22.3K] vs $10.7K [$7.0K to $17.6K], p < 0.001) and lower value of care (0.2 [0.1 to 0.3] vs 1.5 [0.9 to 2.2], p < 0.001). Among 8,594 ward admissions, 1,029 (12.0%) were undertriaged. Compared with 2,498 control ICU admissions, undertriaged admissions had longer hospital length-of-stays (6.4 [3.4 to 12.4] vs 5.4 [2.6 to 10.4] days, p < 0.001); greater incidence of hospital mortality (1.7% vs 0.7%, p = 0.03), cardiac arrest (1.4% vs 0.5%, p = 0.04), and persistent acute kidney injury without renal recovery (5.2% vs 2.8%, p = 0.002); similar costs ($21.8K [$13.3K to $34.9K] vs $21.9K [$13.1K to $36.3K]); and lower value of care (0.8 [0.5 to 1.3] vs 1.2 [0.7 to 2.0], p < 0.001).

CONCLUSIONS

Overtriage was associated with low value of care; undertriage was associated with both low value of care and increased mortality and morbidity. The proposed framework for generating automated postoperative triage classifications is reproducible.

Postoperative Overtriage to an Intensive Care Unit Is Associated With Low Value of Care

OBJECTIVE

We test the hypothesis that for low-acuity surgical patients, postoperative intensive care unit (ICU) admission is associated with lower value of care compared with ward admission.

BACKGROUND

Overtriaging low-acuity patients to ICU consumes valuable resources and may not confer better patient outcomes. Associations among postoperative overtriage, patient outcomes, costs, and value of care have not been previously reported.

METHODS

In this longitudinal cohort study, postoperative ICU admissions were classified as overtriaged or appropriately triaged according to machine learning-based patient acuity assessments and requirements for immediate postoperative mechanical ventilation or vasopressor support. The nearest neighbors algorithm identified risk-matched control ward admissions. The primary outcome was value of care, calculated as inverse observed-to-expected mortality ratios divided by total costs.

RESULTS

Acuity assessments had an area under the receiver operating characteristic curve of 0.92 in generating predictions for triage classifications. Of 8592 postoperative ICU admissions, 423 (4.9%) were overtriaged. These were matched with 2155 control ward admissions with similar comorbidities, incidence of emergent surgery, immediate postoperative vital signs, and do not resuscitate order placement and rescindment patterns. Compared with controls, overtraiged admissions did not have a lower incidence of any measured complications. Total costs for admission were $16.4K for overtriage and $15.9K for controls ( P =0.03). Value of care was lower for overtriaged admissions [2.9 (2.0-4.0)] compared with controls [24.2 (14.1-34.5), P <0.001].

CONCLUSIONS

Low-acuity postoperative patients who were overtriaged to ICUs had increased total costs, no improvements in outcomes, and received low-value care.

Fairness in the prediction of acute postoperative pain using machine learning models

Introduction

Overall performance of machine learning-based prediction models is promising; however, their generalizability and fairness must be vigorously investigated to ensure they perform sufficiently well for all patients.

Objective

This study aimed to evaluate prediction bias in machine learning models used for predicting acute postoperative pain.

Method

We conducted a retrospective review of electronic health records for patients undergoing orthopedic surgery from June 1, 2011, to June 30, 2019, at the University of Florida Health system/Shands Hospital. CatBoost machine learning models were trained for predicting the binary outcome of low (≤4) and high pain (>4). Model biases were assessed against seven protected attributes of age, sex, race, area deprivation index (ADI), speaking language, health literacy, and insurance type. Reweighing of protected attributes was investigated for reducing model bias compared with base models. Fairness metrics of equal opportunity, predictive parity, predictive equality, statistical parity, and overall accuracy equality were examined.

Results

The final dataset included 14,263 patients [age: 60.72 (16.03) years, 53.87% female, 39.13% low acute postoperative pain]. The machine learning model (area under the curve, 0.71) was biased in terms of age, race, ADI, and insurance type, but not in terms of sex, language, and health literacy. Despite promising overall performance in predicting acute postoperative pain, machine learning-based prediction models may be biased with respect to protected attributes.

Conclusion

These findings show the need to evaluate fairness in machine learning models involved in perioperative pain before they are implemented as clinical decision support tools.

Multi-dimensional patient acuity estimation with longitudinal EHR tokenization and flexible transformer networks

Transformer model architectures have revolutionized the natural language processing (NLP) domain and continue to produce state-of-the-art results in text-based applications. Prior to the emergence of transformers, traditional NLP models such as recurrent and convolutional neural networks demonstrated promising utility for patient-level predictions and health forecasting from longitudinal datasets. However, to our knowledge only few studies have explored transformers for predicting clinical outcomes from electronic health record (EHR) data, and in our estimation, none have adequately derived a health-specific tokenization scheme to fully capture the heterogeneity of EHR systems. In this study, we propose a dynamic method for tokenizing both discrete and continuous patient data, and present a transformer-based classifier utilizing a joint embedding space for integrating disparate temporal patient measurements. We demonstrate the feasibility of our clinical AI framework through multi-task ICU patient acuity estimation, where we simultaneously predict six mortality and readmission outcomes. Our longitudinal EHR tokenization and transformer modeling approaches resulted in more accurate predictions compared with baseline machine learning models, which suggest opportunities for future multimodal data integrations and algorithmic support tools using clinical transformer networks.

Perioperative Use of Gabapentinoids and Risk for Postoperative Long-Term Opioid Use in Older Adults Undergoing Total Knee or Hip Arthroplasty

BACKGROUND

Gabapentinoids are recommended by guidelines as a component of multimodal analgesia to manage postoperative pain and reduce opioid use. It remains unknown whether perioperative use of gabapentinoids is associated with a reduced or increased risk of postoperative long-term opioid use (LTOU) after total knee or hip arthroplasty (TKA/THA).

METHODS

Using Medicare claims data from 2011 to 2018, we identified fee-for-service beneficiaries aged ≥ 65 years who were hospitalized for a primary TKA/THA and had no LTOU before the surgery. Perioperative use of gabapentinoids was measured from 7 days preadmission through 7 days postdischarge. Patients were required to receive opioids during the perioperative period and were followed from day 7 postdischarge for 180 days to assess postoperative LTOU (ie, ≥90 consecutive days). A modified Poisson regression was used to estimate the relative risk (RR) of postoperative LTOU in patients with versus without perioperative use of gabapentinoids, adjusting for confounders through propensity score weighting.

RESULTS

Of 52,788 eligible Medicare older beneficiaries (mean standard deviation [SD] age 72.7 [5.3]; 62.5% females; 89.7% White), 3,967 (7.5%) received gabapentinoids during the perioperative period. Postoperative LTOU was 3.8% in patients with and 4.0% in those without perioperative gabapentinoids. After adjusting for confounders, the risk of postoperative LTOU was similar comparing patients with versus without perioperative gabapentinoids (RR = 1.07; 95% confidence interval [CI] = 0.91-1.26, P = .408). Sensitivity and bias analyses yielded consistent results.

CONCLUSION

Among older Medicare beneficiaries undergoing a primary TKA/THA, perioperative use of gabapentinoids was not associated with a reduced or increased risk for postoperative LTOU.

Phenotype clustering in health care: A narrative review for clinicians

Human pathophysiology is occasionally too complex for unaided hypothetical-deductive reasoning and the isolated application of additive or linear statistical methods. Clustering algorithms use input data patterns and distributions to form groups of similar patients or diseases that share distinct properties. Although clinicians frequently perform tasks that may be enhanced by clustering, few receive formal training and clinician-centered literature in clustering is sparse. To add value to clinical care and research, optimal clustering practices require a thorough understanding of how to process and optimize data, select features, weigh strengths and weaknesses of different clustering methods, select the optimal clustering method, and apply clustering methods to solve problems. These concepts and our suggestions for implementing them are described in this narrative review of published literature. All clustering methods share the weakness of finding potential clusters even when natural clusters do not exist, underscoring the importance of applying data-driven techniques as well as clinical and statistical expertise to clustering analyses. When applied properly, patient and disease phenotype clustering can reveal obscured associations that can help clinicians understand disease pathophysiology, predict treatment response, and identify patients for clinical trial enrollment.

Executive summary of the artificial intelligence in surgery series

As opportunities for artificial intelligence to augment surgical care expand, the accompanying surge in published literature has generated both substantial enthusiasm and grave concern regarding the safety and efficacy of artificial intelligence in surgery. For surgeons and surgical data scientists, it is increasingly important to understand the state-of-the-art, recognize knowledge and technology gaps, and critically evaluate the deluge of literature accordingly. This article summarizes the experiences and perspectives of a global, multi-disciplinary group of experts who have faced development and implementation challenges, overcome them, and produced incipient evidence thereof. Collectively, evidence suggests that artificial intelligence has the potential to augment surgeons via decision-support, technical skill assessment, and the semi-autonomous performance of tasks ranging from resource allocation to patching foregut defects. Most applications remain in preclinical phases. As technologies and their implementations improve and positive evidence accumulates, surgeons will face professional imperatives to lead the safe, effective clinical implementation of artificial intelligence in surgery. Substantial challenges remain; recent progress in using artificial intelligence to achieve performance advantages in surgery suggests that remaining challenges can and will be overcome.

HLA-DR polymorphism in SARS-CoV-2 infection and susceptibility to symptomatic COVID-19

SARS-CoV-2 infection results in different outcomes ranging from asymptomatic infection to mild or severe disease and death. Reasons for this diversity of outcome include differences in challenge dose, age, gender, comorbidity and host genomic variation. Human leukocyte antigen (HLA) polymorphisms may influence immune response and disease outcome. We investigated the association of HLAII alleles with case definition symptomatic COVID-19, virus-specific antibody and T-cell immunity. A total of 1364 UK healthcare workers (HCWs) were recruited during the first UK SARS-CoV-2 wave and analysed longitudinally, encompassing regular PCR screening for infection, symptom reporting, imputation of HLAII genotype and analysis for antibody and T-cell responses to nucleoprotein (N) and spike (S). Of 272 (20%) HCW who seroconverted, the presence of HLA-DRB1*13:02 was associated with a 6·7-fold increased risk of case definition symptomatic COVID-19. In terms of immune responsiveness, HLA-DRB1*15:02 was associated with lower nucleocapsid T-cell responses. There was no association between DRB1 alleles and anti-spike antibody titres after two COVID vaccine doses. However, HLA DRB1*15:01 was associated with increased spike T-cell responses following both first and second dose vaccination. Trial registration: NCT04318314 and ISRCTN15677965.

Concurrent use of prescription gabapentinoids with opioids and risk for fall-related injury among older US Medicare beneficiaries with chronic noncancer pain: A population-based cohort study

BACKGROUND

Gabapentinoids are increasingly prescribed to manage chronic noncancer pain (CNCP) in older adults. When used concurrently with opioids, gabapentinoids may potentiate central nervous system (CNS) depression and increase the risks for fall. We aimed to investigate whether concurrent use of gabapentinoids with opioids compared with use of opioids alone is associated with an increased risk of fall-related injury among older adults with CNCP.

METHODS AND FINDINGS

We conducted a population-based cohort study using a 5% national sample of Medicare beneficiaries in the United States between 2011 and 2018. Study sample consisted of fee-for-service (FFS) beneficiaries aged ≥65 years with CNCP diagnosis who initiated opioids. We identified concurrent users with gabapentinoids and opioids days' supply overlapping for ≥1 day and designated first day of concurrency as the index date. We created 2 cohorts based on whether concurrent users initiated gabapentinoids on the day of opioid initiation (Cohort 1) or after opioid initiation (Cohort 2). Each concurrent user was matched to up to 4 opioid-only users on opioid initiation date and index date using risk set sampling. We followed patients from index date to first fall-related injury event ascertained using a validated claims-based algorithm, treatment discontinuation or switching, death, Medicare disenrollment, hospitalization or nursing home admission, or end of study, whichever occurred first. In each cohort, we used propensity score (PS) weighted Cox models to estimate the adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) of fall-related injury, adjusting for year of the index date, sociodemographics, types of chronic pain, comorbidities, frailty, polypharmacy, healthcare utilization, use of nonopioid medications, and opioid use on and before the index date. We identified 6,733 concurrent users and 27,092 matched opioid-only users in Cohort 1 and 5,709 concurrent users and 22,388 matched opioid-only users in Cohort 2. The incidence rate of fall-related injury was 24.5 per 100 person-years during follow-up (median, 9 days; interquartile range [IQR], 5 to 18 days) in Cohort 1 and was 18.0 per 100 person-years during follow-up (median, 9 days; IQR, 4 to 22 days) in Cohort 2. Concurrent users had similar risk of fall-related injury as opioid-only users in Cohort 1(aHR = 0.97, 95% CI 0.71 to 1.34, p = 0.874), but had higher risk for fall-related injury than opioid-only users in Cohort 2 (aHR = 1.69, 95% CI 1.17 to 2.44, p = 0.005). Limitations of this study included confounding due to unmeasured factors, unavailable information on gabapentinoids' indication, potential misclassification, and limited generalizability beyond older adults insured by Medicare FFS program.

CONCLUSIONS

In this sample of older Medicare beneficiaries with CNCP, initiating gabapentinoids and opioids simultaneously compared with initiating opioids only was not significantly associated with risk for fall-related injury. However, addition of gabapentinoids to an existing opioid regimen was associated with increased risks for fall. Mechanisms for the observed excess risk, whether pharmacological or because of channeling of combination therapy to high-risk patients, require further investigation. Clinicians should consider the risk-benefit of combination therapy when prescribing gabapentinoids concurrently with opioids.

Aligning Patient Acuity With Resource Intensity After Major Surgery: A Scoping Review

OBJECTIVE

Develop unifying definitions and paradigms for data-driven methods to augment postoperative resource intensity decisions.

SUMMARY BACKGROUND DATA

Postoperative level-of-care assignments and frequency of vital sign and laboratory measurements (ie, resource intensity) should align with patient acuity. Effective, data-driven decision-support platforms could improve value of care for millions of patients annually, but their development is hindered by the lack of salient definitions and paradigms.

METHODS

Embase, PubMed, and Web of Science were searched for articles describing patient acuity and resource intensity after inpatient surgery. Study quality was assessed using validated tools. Thirty-five studies were included and assimilated according to PRISMA guidelines.

RESULTS

Perioperative patient acuity is accurately represented by combinations of demographic, physiologic, and hospital-system variables as input features in models that capture complex, non-linear relationships. Intraoperative physiologic data enriche these representations. Triaging high-acuity patients to low-intensity care is associated with increased risk for mortality; triaging low-acuity patients to intensive care units (ICUs) has low value and imparts harm when other, valid requests for ICU admission are denied due to resource limitations, increasing their risk for unrecognized decompensation and failure-to-rescue. Providing high-intensity care for low-acuity patients may also confer harm through unnecessary testing and subsequent treatment of incidental findings, but there is insufficient evidence to evaluate this hypothesis. Compared with data-driven models, clinicians exhibit volatile performance in predicting complications and making postoperative resource intensity decisions.

CONCLUSION

To optimize value, postoperative resource intensity decisions should align with precise, data-driven patient acuity assessments augmented by models that accurately represent complex, non-linear relationships among risk factors.

Serum levels of pro-inflammatory lipid mediators and specialised pro-resolving molecules are increased in SARS-CoV-2 patients and correlate with markers of the adaptive immune response

Background

Specialized proresolution molecules (SPMs) halt the transition to chronic pathogenic inflammation. We aimed to quantify serum levels of pro- and anti-inflammatory bioactive lipids in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients, and to identify potential relationships with innate responses and clinical outcome.

Methods

Serum from 50 hospital admitted inpatients (22 female, 28 male) with confirmed symptomatic SARS-CoV-2 infection and 94 age- and sex-matched controls collected prior to the pandemic (SARS-CoV-2 negative), were processed for quantification of bioactive lipids and anti-nucleocapsid and anti-spike quantitative binding assays.

Results

SARS-CoV-2 serum had significantly higher concentrations of omega-6–derived proinflammatory lipids and omega-6– and omega-3–derived SPMs, compared to the age- and sex-matched SARS-CoV-2–negative group, which were not markedly altered by age or sex. There were significant positive correlations between SPMs, proinflammatory bioactive lipids, and anti-spike antibody binding. Levels of some SPMs were significantly higher in patients with an anti-spike antibody value >0.5. Levels of linoleic acid and 5,6-dihydroxy-8Z,11Z,14Z-eicosatrienoic acid were significantly lower in SARS-CoV-2 patients who died.

Conclusions

SARS-CoV-2 infection was associated with increased levels of SPMs and other pro- and anti-inflammatory bioactive lipids, supporting the future investigation of the underlying enzymatic pathways, which may inform the development of novel treatments.

Physiologic signatures within six hours of hospitalization identify acute illness phenotypes

During the early stages of hospital admission, clinicians use limited information to make decisions as patient acuity evolves. We hypothesized that clustering analysis of vital signs measured within six hours of hospital admission would reveal distinct patient phenotypes with unique pathophysiological signatures and clinical outcomes. We created a longitudinal electronic health record dataset for 75,762 adult patient admissions to a tertiary care center in 2014-2016 lasting six hours or longer. Physiotypes were derived via unsupervised machine learning in a training cohort of 41,502 patients applying consensus k-means clustering to six vital signs measured within six hours of admission. Reproducibility and correlation with clinical biomarkers and outcomes were assessed in validation cohort of 17,415 patients and testing cohort of 16,845 patients. Training, validation, and testing cohorts had similar age (54-55 years) and sex (55% female), distributions. There were four distinct clusters. Physiotype A had physiologic signals consistent with early vasoplegia, hypothermia, and low-grade inflammation and favorable short-and long-term clinical outcomes despite early, severe illness. Physiotype B exhibited early tachycardia, tachypnea, and hypoxemia followed by the highest incidence of prolonged respiratory insufficiency, sepsis, acute kidney injury, and short- and long-term mortality. Physiotype C had minimal early physiological derangement and favorable clinical outcomes. Physiotype D had the greatest prevalence of chronic cardiovascular and kidney disease, presented with severely elevated blood pressure, and had good short-term outcomes but suffered increased 3-year mortality. Comparing sequential organ failure assessment (SOFA) scores across physiotypes demonstrated that clustering did not simply recapitulate previously established acuity assessments. In a heterogeneous cohort of hospitalized patients, unsupervised machine learning techniques applied to routine, early vital sign data identified physiotypes with unique disease categories and distinct clinical outcomes. This approach has the potential to augment understanding of pathophysiology by distilling thousands of disease states into a few physiological signatures.

Immunocompromised children and young people are at no increased risk of severe COVID-19

OBJECTIVES

We aimed to prospectively describe the incidence and clinical spectrum of SARS-CoV-2 infection in immunocompromised paediatric patients in the UK.

METHODS

From March 2020 to 2021 weekly questionnaires were sent to immunocompromised paediatric patients or their parents. Information, including symptom presentation and SARS-CoV-2 PCR test results, was collected from 1527 participants from 46 hospitals. Cross-sectional serology was investigated in February and March 2021.

RESULTS

Until the end of September 2020, no cases were reported. From September 28th 2020 to March 2021 a total of 38 PCR-detected SARS-CoV-2 infections were reported. Of these, four children were admitted to hospital but none had acute severe COVID-19. Increasing age in association with immunodeficiency increased reporting of SARS-CoV-2 infection. Worsening of fever, cough, and sore throat were associated with participants reporting SARS-CoV-2 infection. Serology data included 452 unvaccinated participants. In those reporting prior positive SARS-CoV-2 PCR, there were detectable antibodies in 9 of 18 (50%). In those with no prior report of infection, antibodies were detected in 32 of 434 (7•4%).

CONCLUSIONS

This study shows SARS-CoV-2 infections have occurred in immunocompromised children and young people with no increased risk of severe disease. No children died.

Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible

Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We propose a framework for ideal algorithms, including 6 desiderata: explainable (convey the relative importance of features in determining outputs), dynamic (capture temporal changes in physiologic signals and clinical events), precise (use high-resolution, multimodal data and aptly complex architecture), autonomous (learn with minimal supervision and execute without human input), fair (evaluate and mitigate implicit bias and social inequity), and reproducible (validated externally and prospectively and shared with academic communities). We present an ideal algorithms checklist and apply it to highly cited algorithms. Strategies and tools such as the predictive, descriptive, relevant (PDR) framework, the Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence (SPIRIT-AI) extension, sparse regression methods, and minimizing concept drift can help healthcare algorithms achieve these objectives, toward ideal algorithms in healthcare.

Cannabis use assessment and documentation in healthcare: Priorities for closing the gap

Several factors, including the lack of a systematic cannabis use assessment within healthcare systems, have led to significant under-documentation of cannabis use and its correlates in medical records, the unpreparedness of clinicians, and poor quality of cannabis-related electronic health record data, limiting its utilization in research. Multiple steps are required to overcome the existing knowledge gaps and accommodate the health needs implied by the increasing cannabis use prevalence. These steps include (1) enhancing clinician and patient education on the importance of cannabis use assessment and documentation, (2) implementing a standardized approach for comprehensive cannabis use assessment within and across healthcare systems, (3) improving documentation of cannabis use and its correlates in medical records and electronic health records by building in prompts, (4) developing and validating reliable computable phenotypes of cannabis use, (5) conducting research utilizing electronic health data to study a wide array of related health outcomes, (6) and establishing evidence-based guidelines to inform clinical practices and policies. Integrating comprehensive cannabis use assessment and documentation within healthcare systems is necessary to enhance patient care and improve the quality of electronic health databases. Employing electronic health record data in cannabis-related research is crucial to accelerate research in light of the existing knowledge gaps on a wide array of health outcomes. Thus, improving and modernizing cannabis use assessment and documentation in healthcare is an integral step on which research conduct and evidence generation primarily rely.

Effects of Patient and Surgery Characteristics on Persistent Postoperative Pain: A Mediation Analysis

OBJECTIVE

Acute postoperative pain intensity is associated with persistent postsurgical pain (PPP) risk. However, it remains unclear whether acute postoperative pain intensity mediates the relationship between clinical factors and persistent pain.

MATERIALS AND METHODS

Participants from a mixed surgical population completed the Brief Pain Inventory and Pain Catastrophizing Scale before surgery, and the Brief Pain Inventory daily after surgery for 7 days and at 30 and 90 days after surgery. We considered mediation models using the mean of the worst pain intensities collected daily on each of postoperative days (PODs) 1 to 7 against outcomes of worst pain intensity at the surgical site endpoints reflecting PPP (POD 90) and subacute pain (POD 30).

RESULTS

The analyzed cohort included 284 participants for the POD 90 outcome. For every unit increase of maximum acute postoperative pain intensity through PODs 1 to 7, there was a statistically significant increase of mean POD 90 pain intensity by 0.287 after controlling for confounding effects. The effects of female versus male sex (m=0.212, P=0.034), pancreatic/biliary versus colorectal surgery (m=0.459, P=0.012), thoracic cardiovascular versus colorectal surgery (m=0.31, P=0.038), every minute increase of anesthesia time (m=0.001, P=0.038), every unit increase of preoperative average pain score (m=0.012, P=0.015), and every unit increase of catastrophizing (m=0.044, P=0.042) on POD 90 pain intensity were mediated through acute PODs 1 to 7 postoperative pain intensity.

DISCUSSION

Our results suggest the mediating relationship of acute postoperative pain on PPP may be predicated on select patient and surgical factors.

Association of Postoperative Undertriage to Hospital Wards With Mortality and Morbidity

IMPORTANCE

Undertriaging patients who are at increased risk for postoperative complications after surgical procedures to low-acuity hospital wards (ie, floors) rather than highly vigilant intensive care units (ICUs) may be associated with risk of unrecognized decompensation and worse patient outcomes, but evidence for these associations is lacking.

OBJECTIVE

To test the hypothesis that postoperative undertriage is associated with increased mortality and morbidity compared with risk-matched ICU admission.

DESIGN, SETTING, AND PARTICIPANTS

This longitudinal cross-sectional study was conducted using data from the University of Florida Integrated Data Repository on admissions to a university hospital. Included patients were individuals aged 18 years or older who were admitted after a surgical procedure from June 1, 2014, to August 20, 2020. Data were analyzed from April through August 2021.

EXPOSURES

Ward admissions were considered undertriaged if their estimated risk for hospital mortality or prolonged ICU stay (ie, ≥48 hours) was in the top quartile among all inpatient surgical procedures according to a validated machine-learning model using preoperative and intraoperative electronic health record features available at surgical procedure end time. A nearest neighbors algorithm was used to identify a risk-matched control group of ICU admissions.

MAIN OUTCOMES AND MEASURES

The primary outcomes of hospital mortality and morbidity were compared among appropriately triaged ward admissions, undertriaged wards admissions, and a risk-matched control group of ICU admissions.

RESULTS

Among 12 348 postoperative ward admissions, 11 042 admissions (89.4%) were appropriately triaged (5927 [53.7%] women; median [IQR] age, 59 [44-70] years) and 1306 admissions (10.6%) were undertriaged and matched with a control group of 2452 ICU admissions. The undertriaged group, compared with the control group, had increased median [IQR] age (64 [54-74] years vs 62 [50-73] years; P = .001) and increased proportions of women (649 [49.7%] women vs 1080 [44.0%] women; P < .001) and admitted patients with do not resuscitate orders before first surgical procedure (53 admissions [4.1%] vs 27 admissions [1.1%]); P < .001); 207 admissions that were undertriaged (15.8%) had subsequent ICU admission. In the validation cohort, hospital mortality and prolonged ICU stay estimations had areas under the receiver operating characteristic curve of 0.92 (95% CI, 0.91-0.93) and 0.92 (95% CI, 0.92-0.92), respectively. The undertriaged group, compared with the control group, had similar incidence of prolonged mechanical ventilation (32 admissions [2.5%] vs 53 admissions [2.2%]; P = .60), decreased median (IQR) total costs for admission ($26 900 [$18 400-$42 300] vs $32 700 [$22 700-$48 500]; P < .001), increased median (IQR) hospital length of stay (8.1 [5.1-13.6] days vs 6.0 [3.3-9.3] days, P < .001), and increased incidence of hospital mortality (19 admissions [1.5%] vs 17 admissions [0.7%]; P = .04), discharge to hospice (23 admissions [1.8%] vs 14 admissions [0.6%]; P < .001), unplanned intubation (45 admissions [3.4%] vs 49 admissions [2.0%]; P = .01), and acute kidney injury (341 admissions [26.1%] vs 477 admissions [19.5%]; P < .001).

CONCLUSIONS AND RELEVANCE

This study found that admitted patients at increased risk for postoperative complications who were undertriaged to hospital wards had increased mortality and morbidity compared with a risk-matched control group of admissions to ICUs. Postoperative undertriage was identifiable using automated preoperative and intraoperative data as features in real-time machine-learning models.

Machine Learning Applications in Solid Organ Transplantation and Related Complications

The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.

Two doses of the SARS-CoV-2 BNT162b2 vaccine enhance antibody responses to variants in individuals with prior SARS-CoV-2 infection

Understanding the impact of prior infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on the response to vaccination is a priority for responding to the coronavirus disease 2019 (COVID-19) pandemic. In particular, it is necessary to understand how prior infection plus vaccination can modulate immune responses against variants of concern. To address this, we sampled 20 individuals with and 25 individuals without confirmed previous SARS-CoV-2 infection from a large cohort of health care workers followed serologically since April 2020. All 45 individuals had received two doses of the Pfizer-BioNTech BNT162b2 vaccine with a delayed booster at 10 weeks. Absolute and neutralizing antibody titers against wild-type SARS-CoV-2 and variants were measured using enzyme immunoassays and pseudotype neutralization assays. We observed antibody reactivity against lineage A, B.1.351, and P.1 variants with increasing antigenic exposure, through either vaccination or natural infection. This improvement was further confirmed in neutralization assays using fixed dilutions of serum samples. The impact of antigenic exposure was more evident in enzyme immunoassays measuring SARS-CoV-2 spike protein–specific IgG antibody concentrations. Our data show that multiple exposures to SARS-CoV-2 spike protein in the context of a delayed booster expand the neutralizing breadth of the antibody response to neutralization-resistant SARS-CoV-2 variants. This suggests that additional vaccine boosts may be beneficial in improving immune responses against future SARS-CoV-2 variants of concern.

Mutations in the binding site of TNFR1 PLAD reduce homologous interactions but can enhance antagonism of wild-type TNFR1 activity

The tumour necrosis factor receptor superfamily (TNFRSF) members contain cysteine-rich domains (CRD) in their extracellular regions, and the membrane-distal CRD1 forms homologous interactions in the absence of ligand. The CRD1 is therefore termed a pre-ligand assembly domain (PLAD). The role of PLAD-PLAD interactions in the induction of signalling as a consequence of TNF-TNFR binding led to the development of soluble PLAD domains as antagonists of TNFR activation. In the present study, we generated recombinant wild-type (WT) PLAD of TNFR1 and mutant forms with single alanine substitutions of amino acid residues thought to be critical for the formation of homologous dimers and/or trimers of PLAD (K19A, T31A, D49A and D52A). These mutated PLADs were compared with WT PLAD as antagonists of TNF-induced apoptosis or the activation of inflammatory signalling pathways. Unlike WT PLAD, the mutated PLADs showed little or no homologous interactions, confirming the importance of particular amino acids as contact residues in the PLAD binding region. However, as with WT PLAD, the mutated PLADs functioned as antagonists of TNF-induced TNFR1 activity leading to induction of cell death or NF-κB signalling. Indeed, some of the mutant PLADs, and K19A PLAD in particular, showed enhanced antagonistic activity compared with WT PLAD. This is consistent with the reduced formation of homologous multimers by these PLAD mutants effectively increasing the concentration of PLAD available to bind and antagonize WT TNFR1 when compared to WT PLAD acting as an antagonist. This may have implications for the development of antagonistic PLADs as therapeutic agents.

On kernel machine learning for propensity score estimation under complex confounding structures

Post marketing data offer rich information and cost-effective resources for physicians and policy-makers to address some critical scientific questions in clinical practice. However, the complex confounding structures (e.g., nonlinear and nonadditive interactions) embedded in these observational data often pose major analytical challenges for proper analysis to draw valid conclusions. Furthermore, often made available as electronic health records (EHRs), these data are usually massive with hundreds of thousands observational records, which introduce additional computational challenges. In this paper, for comparative effectiveness analysis, we propose a statistically robust yet computationally efficient propensity score (PS) approach to adjust for the complex confounding structures. Specifically, we propose a kernel-based machine learning method for flexibly and robustly PS modeling to obtain valid PS estimation from observational data with complex confounding structures. The estimated propensity score is then used in the second stage analysis to obtain the consistent average treatment effect estimate. An empirical variance estimator based on the bootstrap is adopted. A split-and-merge algorithm is further developed to reduce the computational workload of the proposed method for big data, and to obtain a valid variance estimator of the average treatment effect estimate as a by-product. As shown by extensive numerical studies and an application to postoperative pain EHR data comparative effectiveness analysis, the proposed approach consistently outperforms other competing methods, demonstrating its practical utility.

Classifying Non-Dementia and Alzheimer's Disease/Vascular Dementia Patients Using Kinematic, Time-Based, and Visuospatial Parameters: The Digital Clock Drawing Test

BACKGROUND

Advantages of digital clock drawing metrics for dementia subtype classification needs examination.

OBJECTIVE

To assess how well kinematic, time-based, and visuospatial features extracted from the digital Clock Drawing Test (dCDT) can classify a combined group of Alzheimer's disease/Vascular Dementia patients versus healthy controls (HC), and classify dementia patients with Alzheimer's disease (AD) versus vascular dementia (VaD).

METHODS

Healthy, community-dwelling control participants (n = 175), patients diagnosed clinically with Alzheimer's disease (n = 29), and vascular dementia (n = 27) completed the dCDT to command and copy clock drawing conditions. Thirty-seven dCDT command and 37 copy dCDT features were extracted and used with Random Forest classification models.

RESULTS

When HC participants were compared to participants with dementia, optimal area under the curve was achieved using models that combined both command and copy dCDT features (AUC = 91.52%). Similarly, when AD versus VaD participants were compared, optimal area under the curve was, achieved with models that combined both command and copy features (AUC = 76.94%). Subsequent follow-up analyses of a corpus of 10 variables of interest determined using a Gini Index found that groups could be dissociated based on kinematic, time-based, and visuospatial features.

CONCLUSION

The dCDT is able to operationally define graphomotor output that cannot be measured using traditional paper and pencil test administration in older health controls and participants with dementia. These data suggest that kinematic, time-based, and visuospatial behavior obtained using the dCDT may provide additional neurocognitive biomarkers that may be able to identify and tract dementia syndromes.

Forty-two Million Ways to Describe Pain: Topic Modeling of 200,000 PubMed Pain-Related Abstracts Using Natural Language Processing and Deep Learning-Based Text Generation

OBJECTIVE

Recent efforts to update the definitions and taxonomic structure of concepts related to pain have revealed opportunities to better quantify topics of existing pain research subject areas.

METHODS

Here, we apply basic natural language processing (NLP) analyses on a corpus of >200,000 abstracts published on PubMed under the medical subject heading (MeSH) of "pain" to quantify the topics, content, and themes on pain-related research dating back to the 1940s.

RESULTS

The most common stemmed terms included "pain" (601,122 occurrences), "patient" (508,064 occurrences), and "studi-" (208,839 occurrences). Contrarily, terms with the highest term frequency-inverse document frequency included "tmd" (6.21), "qol" (6.01), and "endometriosis" (5.94). Using the vector-embedded model of term definitions available via the "word2vec" technique, the most similar terms to "pain" included "discomfort," "symptom," and "pain-related." For the term "acute," the most similar terms in the word2vec vector space included "nonspecific," "vaso-occlusive," and "subacute"; for the term "chronic," the most similar terms included "persistent," "longstanding," and "long-standing." Topic modeling via Latent Dirichlet analysis identified peak coherence (0.49) at 40 topics. Network analysis of these topic models identified three topics that were outliers from the core cluster, two of which pertained to women's health and obstetrics and were closely connected to one another, yet considered distant from the third outlier pertaining to age. A deep learning-based gated recurrent units abstract generation model successfully synthesized several unique abstracts with varying levels of believability, with special attention and some confusion at lower temperatures to the roles of placebo in randomized controlled trials.

CONCLUSIONS

Quantitative NLP models of published abstracts pertaining to pain may point to trends and gaps within pain research communities.

Dual-trajectories of opioid and gabapentinoid use and risk of subsequent drug overdose among Medicare beneficiaries in the United States: a retrospective cohort study

BACKGROUND AND AIMS

Little is known about opioid and gabapentinoid (OPI-GABA) use duration and dose patterns' associations with adverse outcome risks. We examined associations between OPI-GABA dose and duration trajectories and subsequent drug overdose.

DESIGN

Retrospective cohort study.

SETTING

US Medicare.

PARTICIPANTS

Using a 5% sample (2011-16), we identified 71 005 fee-for-service Medicare beneficiaries with fibromyalgia, low back pain, neuropathy and/or osteoarthritis initiating OPIs and/or GABAs [mean age ± standard deviation (SD) = 65.5 ± 14.5 years, female = 68.1%, white = 76.8%].

MEASUREMENTS

Group-based multi-trajectory models identified distinct OPI-GABA use patterns during the year of OPI and/or GABA initiation, based on weekly average standardized daily dose (i.e. OPIs = morphine milligram equivalent, GABAs = minimum effective daily dose). We estimated models with three to 12 trajectories and selected the best model based on Bayesian information criterion (BIC) and Nagin's criteria. We estimated risk of time to first drug overdose diagnosis within 12 months following the index year, adjusting for socio-demographic and health factors using inverse probability of treatment weighted multivariable Cox proportional hazards models.

FINDINGS

We identified 10 distinct trajectories (BIC = -1 176 954; OPI-only = 3, GABA-only = 3, OPI-GABA = 4). Compared with OPI-only early discontinuers (40.6% of the cohort), 1-year drug overdose risk varied by trajectory group: consistent low-dose OPI-only users [16.6%; hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.19-1.82], consistent high-dose OPI-only users (1.8%; HR = 4.57, 95% CI = 2.99-6.98), GABA-only early discontinuers (12.5%; HR = 1.39, 95% CI = 1.09-1.77), consistent low-dose GABA-only users (11.0%; HR = 1.44, 95% CI = 1.12-1.85), consistent high-dose GABA-only users (3.1%; HR = 1.43, 95% CI = 0.94-2.17), early discontinuation of OPIs and consistent low-dose GABA users (6.9%; HR = 1.24, 95% CI = 0.90-1.69), consistent low-dose OPI-GABA users (3.4%; HR = 2.49, 95% CI = 1.76-3.52), consistent low-dose OPI and high-dose GABA users (3.2%; HR = 2.46, 95% CI = 1.71-3.53) and consistent high-dose OPI and moderate-dose GABA users (0.9%; HR = 7.22, 95% CI = 4.46-11.69).

CONCLUSIONS

Risk of drug overdose varied substantially among US Medicare beneficiaries on different use trajectories of opioids and gabapentinoids. High-dose opioid-only users and all consistent opioid and gabapentinoid users (regardless of doses) had more than double the risk of subsequent drug overdose compared with opioid-only early discontinuers.

Predicting long-term postsurgical pain by examining the evolution of acute pain

BACKGROUND

Increased acute postoperative pain intensity has been associated with the development of persistent postsurgical pain (PPP) in mechanistic and clinical investigations, but it remains unclear which aspects of acute pain explain this linkage.

METHODS

We analysed clinical postoperative pain intensity assessments using symbolic aggregate approximations (SAX), a graphical way of representing changes between pain states from one patient evaluation to the next, to visualize and understand how pain intensity changes across sequential assessments are associated with the intensity of postoperative pain at 1 (M1) and 6 (M6) months after surgery. SAX-based acute pain transition patterns were compared using cosine similarity, which indicates the degree to which patterns mirror each other.

RESULTS

This single-centre prospective cohort study included 364 subjects. Patterns of acute postoperative pain sequential transitions differed between the 'None' and 'Severe' outcomes at M1 (cosine similarity 0.44) and M6 (cosine similarity 0.49). Stratifications of M6 outcomes by preoperative pain intensity, sex, age group, surgery type and catastrophising showed significant heterogeneity of pain transition patterns within and across strata. Severe-to-severe acute pain transitions were common, but not exclusive, in patients with moderate or severe pain intensity at M6.

CONCLUSIONS

Clinically, these results suggest that individual pain-state transitions, even within patient or procedural strata associated with PPP, may not alone offer good predictive information regarding PPP. Longitudinal observation in the immediate postoperative period and consideration of patient- and surgery-specific factors may help indicate which patients are at increased risk of PPP.

SIGNIFICANCE

Symbolic aggregate approximations of clinically obtained, acute postoperative pain intraday time series identify different motifs in patients suffering moderate to severe pain 6 months after surgery.

Array-based measurements of aero-allergen-specific IgE correlate with skin-prick test reactivity in asthma regardless of specific IgG4 or total IgE measurements

Skin prick testing (SPT) and measurement of serum allergen-specific IgE (sIgE) are used to investigate asthma and other allergic conditions. Measurement of serum total IgE (tIgE) and allergen-specific IgG4 (sIgG4) may also be useful. The aim was to ascertain the correlation between these serological parameters and SPT. Sera from 60 suspected asthmatic patients and 18 healthy controls were assayed for sIgE and sIgG4 reactivity against a panel of 70 SPT allergen preparations, and for tIgE. The patients were also assessed by skin prick tests for reactivity to cat, dog, house dust mite and grass allergens. Over 50% of the patients had tIgE levels above the 75th percentile of the controls. 58% of patients and 39% of controls showed sIgE reactivity to ≥1 allergen. The mean number of allergens detected by sIgE was 3.1 in suspected asthma patients and 0.9 in controls. 58% of patients and 50% of controls showed sIgG4 reactivity to ≥1 allergen. The mean number of allergens detected by sIgG4 was 2.5 in patients and 1.7 in controls. For the patients, a strong correlation was observed between clinical SPT reactivity and serum sIgE levels to cat, dog, house dust mite (HDM) and grass allergens. SPT correlations using sIgE/sIgG4 or sIgE/tIgE ratios were not markedly higher. The measurement of serum sIgE by microarray using SPT allergen preparations showed good correlation with clinical SPT reactivity to cat, dog, HDM and grass allergens. This concordance was not improved by measuring tIgE or sIgG4.

Association Between Dual Trajectories of Opioid and Gabapentinoid Use and Healthcare Expenditures Among US Medicare Beneficiaries

OBJECTIVES

Little is known about relationships between opioid- and gabapentinoid-use patterns and healthcare expenditures that may be affected by pain management and risk of adverse outcomes. This study examined the association between patients' opioid and gabapentinoid prescription filling/refilling trajectories and direct medical expenditures in US Medicare.

METHODS

This cross-sectional study included a 5% national sample (2011-2016) of fee-for-service beneficiaries with fibromyalgia, low back pain, neuropathy, or osteoarthritis newly initiating opioids or gabapentinoids. Using group-based multitrajectory modeling, this study identified patients' distinct opioid and gabapentinoid (OPI-GABA) dose and duration patterns, based on standardized daily doses, within a year of initiating opioids and/or gabapentinoids. Concurrent direct medical expenditures within the same year were estimated using inverse probability of treatment weighted multivariable generalized linear regression, adjusting for sociodemographic and health status factors.

RESULTS

Among 67 827 eligible beneficiaries (mean age ± SD = 63.6 ± 14.8 years, female = 65.8%, white = 77.1%), 11 distinct trajectories were identified (3 opioid-only, 4 gabapentinoid-only, and 4 concurrent OPI-GABA trajectories). Compared with opioid-only early discontinuers ($13 830, 95% confidence interval = $13 643-14 019), gabapentinoid-only early discontinuers and consistent low-dose and moderate-dose gabapentinoid-only users were associated with 11% to 23% lower health expenditures (adjusted mean expenditure = $10 607-$11 713). Consistent low-dose opioid-only users, consistent high-dose opioid-only users, consistent low-dose OPI-GABA users, consistent low-dose opioid and high-dose gabapentinoid users, and consistent high-dose opioid and moderate-dose gabapentinoid users were associated with 14% to 106% higher healthcare expenditures (adjusted mean expenditure = $15 721-$28 464).

CONCLUSIONS

Dose and duration patterns of concurrent OPI-GABA varied substantially among fee-for-service Medicare beneficiaries. Consistent opioid-only users and all concurrent OPI-GABA users were associated with higher healthcare expenditures compared to opioid-only discontinuers.

Normative References for Graphomotor and Latency Digital Clock Drawing Metrics for Adults Age 55 and Older: Operationalizing the Production of a Normal Appearing Clock

BACKGROUND

Relative to the abundance of publications on dementia and clock drawing, there is limited literature operationalizing 'normal' clock production.

OBJECTIVE

To operationalize subtle behavioral patterns seen in normal digital clock drawing to command and copy conditions.

METHODS

From two research cohorts of cognitively-well participants age 55 plus who completed digital clock drawing to command and copy conditions (n = 430), we examined variables operationalizing clock face construction, digit placement, clock hand construction, and a variety of time-based, latency measures. Data are stratified by age, education, handedness, and number anchoring.

RESULTS

Normative data are provided in supplementary tables. Typical errors reported in clock research with dementia were largely absent. Adults age 55 plus produce symmetric clock faces with one stroke, with minimal overshoot and digit misplacement, and hands with expected hour hand to minute hand ratio. Data suggest digitally acquired graphomotor and latency differences based on handedness, age, education, and anchoring.

CONCLUSION

Data provide useful benchmarks from which to assess digital clock drawing performance in Alzheimer's disease and related dementias.

Multiobjective optimization challenges in perioperative anesthesia: A review

Physicians use perioperative decision-support tools to mitigate risks and maximize benefits to achieve the most successful outcome for patients. Contemporary risk-assessment practices augment surgeons' judgement and experience with decision-support algorithms driven by big data and machine learning. These algorithms accurately assess risk for a wide range of postoperative complications by parsing large datasets and performing complex calculations that would be cumbersome for busy clinicians. Even with these advancements, large gaps in perioperative risk assessment remain; decision-support algorithms often cannot account for risk-reduction therapies applied during a patient's perioperative course and do not quantify tradeoffs between competing goals of care (eg, balancing postoperative pain control with the risk of respiratory depression or balancing intraoperative volume resuscitation with the risk for complications from pulmonary edema). Multiobjective optimization solutions have been applied to similar problems successfully but have not yet been applied to perioperative decision support. Given the large volume of data available via electronic medical records, including intraoperative data, it is now feasible to successfully apply multiobjective optimization in perioperative care. Clinical application of multiobjective optimization would require semiautomated pipelines for analytics and reporting model outputs and a careful development and validation process. Under these circumstances, multiobjective optimization has the potential to support personalized, patient-centered, shared decision-making with precision and balance.

Pain and Physical Activity Association in Critically Ill Patients

Critical care patients experience varying levels of pain during their stay in the intensive care unit, often requiring administration of analgesics and sedation. Such medications generally exacerbate the already sedentary physical activity profiles of critical care patients, contributing to delayed recovery. Thus, it is important not only to minimize pain levels, but also to optimize analgesic strategies in order to maximize mobility and activity of ICU patients. Currently, we lack an understanding of the relation between pain and physical activity on a granular level. In this study, we examined the relationship between nurse assessed pain scores and physical activity as measured using a wearable accelerometer device. We found that average, standard deviation, and maximum physical activity counts are significantly higher before high pain reports compared to before low pain reports during both daytime and nighttime, while percentage of time spent immobile was not significantly different between the two pain report groups. Clusters detected among patients using extracted physical activity features were significant in adjusted logistic regression analysis for prediction of pain report group.

Phenotyping Cognitive Impairment using Graphomotor and Latency Features in Digital Clock Drawing Test

The Clock Drawing Test, where the participant is asked to draw a clock from memory and copy a model clock, is widely used for screening of cognitive impairment. The digital version of the clock test, the digital clock drawing test (dCDT), employs accelerometer and pressure sensors of a digital pen to capture time and pressure information from a participant's performance in a granular digital format. While visual features of the clock drawing test have previously been studied, little is known about the relationship between demographic and cognitive impairment characteristics with dCDT latency and graphomotor features. Here, we examine dCDT feature clusters with respect to sociodemographic and cognitive impairment outcomes. Our results show that the clusters are not significantly different in terms of age and gender, but did significantly differ in terms of education, Mini-Mental State Exam scores, and cognitive impairment diagnoses.This study shows that features extracted from digital clock drawings can provide important information regarding cognitive reserve and cognitive impairments.

Joint Distribution and Transitions of Pain and Activity in Critically Ill Patients

Pain and physical function are both essential indices of recovery in critically ill patients in the Intensive Care Units (ICU). Simultaneous monitoring of pain intensity and patient activity can be important for determining which analgesic interventions can optimize mobility and function, while minimizing opioid harm. Nonetheless, so far, our knowledge of the relation between pain and activity has been limited to manual and sporadic activity assessments. In recent years, wearable devices equipped with 3-axis accelerometers have been used in many domains to provide a continuous and automated measure of mobility and physical activity. In this study, we collected activity intensity data from 57 ICU patients, using the Actigraph GT3X device. We also collected relevant clinical information, including nurse assessments of pain intensity, recorded every 1-4 hours. Our results show the joint distribution and state transition of joint activity and pain states in critically ill patients.

Artificial Intelligence and Surgical Decision-making

Importance

Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.

Observations

Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.

Conclusions and Relevance

Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.

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.

Trends, Patient and Prescriber Characteristics in Gabapentinoid Use in a Sample of United States Ambulatory Care Visits from 2003 to 2016

Increasing gabapentinoid use has raised concerns of misuse and abuse in the United States (US). Little is known about the characteristics of gabapentinoid use in general clinical practice over time. This cross-sectional study used data from the National Ambulatory Medical Care Survey. We examined the trends of patient and prescriber characteristics and the diagnoses associated with US ambulatory care visits involving gabapentinoids for adult visits from 2003 to 2016. Using multivariable logistic regression, we estimated the adjusted proportion of gabapentinoid-involved visits among all visits and tested for trend significance. Among the weighted estimate of 260.1 million gabapentinoid-involved visits (aged 18–64 years: 61.8%; female: 61.9%; white: 85.5%), the adjusted annual proportion of gabapentinoid-involved visits nearly quadrupled from 2003 to 2016 (9.1 to 34.9 per 1000 visits; P_trend < 0.0001), driven mainly by gabapentin. Nearly half had concurrent use with opioids (32.9%) or benzodiazepines (15.3%). Primary care physicians (45.8%), neurologists (8.2%), surgeons (6.2%), and psychiatrists (4.8%) prescribed two-thirds of the gabapentinoids. Most (96.6%) of the gabapentinoid visits did not have an approved indication for gabapentinoids among the first three diagnoses. Among US ambulatory care visits from 2003 to 2016, gabapentinoid use increased substantially, commonly prescribed by primary care physicians.

Cigarette smoking differentially affects immunoglobulin class levels in serum and saliva: An investigation and review

The aim of the present study was to compare concentrations of IgG, IgA, IgM and IgD in both serum and saliva samples from smoking and non-smoking individuals using a protein microarray assay. The findings were also compared to previous studies. Serum and saliva were collected from 48 smoking male individuals and 48 age-matched never-smoker male individuals. The protein microarray assays for detection of human IgG, IgM, IgA and IgD were established and optimized using Ig class-specific affinity-purified goat anti-human Ig-Fc capture antibodies and horseradish peroxidase (HRP)-conjugated goat anti-human Ig-Fc detection antibodies. The Ig class specificity of the microarray assays was verified, and the optimal dilutions of serum and saliva samples were determined for quantification of Ig levels against standard curves. We found that smoking is associated with reduced IgG concentrations and enhanced IgA concentrations in both serum and saliva. By contrast, smoking differentially affected IgM concentrations-causing increased concentrations in serum, but decreased concentrations in saliva. Smoking was associated with decreased IgD concentrations in serum and did not have a significant effect on the very low IgD concentrations in saliva. Thus, cigarette smoking differentially affects the levels of Ig classes systemically and in the oral mucosa. Although there is variation between the results of different published studies, there is a consensus that smokers have significantly reduced levels of IgG in both serum and saliva. A functional antibody deficiency associated with smoking may compromise the body's response to infection and result in a predisposition to the development of autoimmunity.

Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning

Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring in the Intensive Care Unit (ICU). As an exemplary prevalent condition, we characterized delirious patients and their environment. We used wearable sensors, light and sound sensors, and a camera to collect data on patients and their environment. We analyzed collected data to detect and recognize patient's face, their postures, facial action units and expressions, head pose variation, extremity movements, sound pressure levels, light intensity level, and visitation frequency. We found that facial expressions, functional status entailing extremity movement and postures, and environmental factors including the visitation frequency, light and sound pressure levels at night were significantly different between the delirious and non-delirious patients. Our results showed that granular and autonomous monitoring of critically ill patients and their environment is feasible using a noninvasive system, and we demonstrated its potential for characterizing critical care patients and environmental factors.

Patients with tumour necrosis factor (TNF) receptor-associated periodic syndrome (TRAPS) are hypersensitive to Toll-like receptor 9 stimulation

Tumour necrosis factor receptor-associated periodic syndrome (TRAPS) is a hereditary autoinflammatory disorder characterized by recurrent episodes of fever and inflammation. It is associated with autosomal dominant mutations in TNFRSF1A, which encodes tumour necrosis factor receptor 1 (TNF-R1). Our aim was to understand the influence of TRAPS mutations on the response to stimulation of the pattern recognition Toll-like receptor (TLR)-9. Peripheral blood mononuclear cells (PBMCs) and serum were isolated from TRAPS patients and healthy controls: serum levels of 15 proinflammatory cytokines were measured to assess the initial inflammatory status. Interleukin (IL)-1β, IL-6, IL-8, IL-17, IL-22, tumour necrosis factor (TNF)-α, vascular endothelial growth factor (VEGF), interferon (IFN)-γ, monocyte chemoattractant protein 1 (MCP-1) and transforming growth factor (TGF)-β were significantly elevated in TRAPS patients' sera, consistent with constitutive inflammation. Stimulation of PBMCs with TLR-9 ligand (ODN2006) triggered significantly greater up-regulation of proinflammatory signalling intermediates [TNF receptor-associated factor (TRAF 3), IL-1 receptor-associated kinase-like 2 (IRAK2), Toll interacting protein (TOLLIP), TRAF6, phosphorylated transforming growth factor-β-activated kinase 1 (pTAK), transforming growth factor-β-activated kinase-binding protein 2 (TAB2), phosphorylated TAK 2 (pTAB2), IFN-regulatory factor 7 (IRF7), receptor interacting protein (RIP), nuclear factor kappa B (NF-κB) p65, phosphorylated NF-κB p65 (pNF-κB p65) and mitogen-activated protein kinase kinase (MEK1/2)] in TRAPS patients' PBMCs. This up-regulation of proinflammatory signalling intermediates and raised serum cytokines occurred despite concurrent anakinra treatment and no overt clinical symptoms at time of sampling. These novel findings further demonstrate the wide-ranging nature of the dysregulation of innate immune responses underlying the pathology of TRAPS and highlights the need for novel pathway-specific therapeutic treatments for this disease.