Cardiopulmonary effects of apneustic anesthesia ventilation in anesthetized pigs: a new mode of ventilation for anesthetized veterinary species.

Objective: To compare the cardiopulmonary effects of apneustic anesthesia ventilation (AAV) and conventional mechanical ventilation (CMV) in anesthetized pigs and to describe a new mode of ventilation for anesthetized veterinary species. Study design: Randomized, crossover design without washout. Animals: Twelve healthy, female white Landrace pigs. Methods: Following ketamine-midazolam premedication and anesthetic induction with propofol, the trachea was intubated, and each pig was positioned in dorsal recumbency. Anesthesia was maintained with propofol and sufentanil infusions. Pigs were instrumented and their lungs were sequentially ventilated with each mode, in random order, for 1 h according to predefined criteria [fraction of inspired oxygen (FiO2) = 0.21, 10 mL kg-1 tidal volume (VT), and arterial carbon dioxide tension (PaCO2) within 40-45 mmHg]. Cardiopulmonary data were collected at baseline, 30 and 60 min. In 8 pigs, thoracic computed tomography (CT) was performed following the 60 min time point for each mode of ventilation and images were analyzed to quantify lung aeration. The effects of ventilation mode, time, and order were analyzed using repeated measures ANOVA. Paired t-tests were used to compare lung aeration between modes. Significance was defined as p < 0.05. Results: Data from 12 pigs were analyzed. A significant effect of mode was found for heart rate, mean arterial pressure (MAP), pulmonary artery occlusion pressure, cardiac index (CI), stroke volume index, systemic vascular resistance, pulmonary vascular resistance, oxygen delivery index (DO2I), oxygen extraction ratio (O2ER), VT, arterial oxygen tension, arterial hemoglobin saturation, PaCO2, end-tidal carbon dioxide tension, alveolar dead space (VDalv/VTalv), venous admixture ( Q . s / Q . t ), mean airway pressure, and dynamic compliance index (CRSI). Order effects were also observed for some cardiovascular and respiratory variables. For the eight pigs that underwent thoracic CT, AAV resulted in significantly larger proportions of normally and hyperaerated lung while CMV resulted in larger proportions of hypoaerated and atelectatic lung. Conclusions: In dorsally recumbent anesthetized pigs, ventilated with FiO2 = 0.21, both modes of ventilation supported adequate oxygenation while AAV resulted in higher CRSI, and lower VDalv/VTalv and Q . s / Q . t , compared with CMV. AAV was also associated with lower MAP, CI, and DO2I and higher O2ER compared with CMV. Further investigation of AAV in anesthetized animals is warranted.

Weight Management with Orlistat in Type 2 Diabetes - An Electronic Healthcare Records Study.

AIMS: Orlistat is recommended as an adjunct to diet and exercise for weight loss in type 2 diabetes mellitus (T2DM). The aims were to explore (i) associations between patient characteristics and orlistat prescribing and to determine (ii) associations of orlistat with weight loss in T2DM/prediabetes. METHODS: This cohort study used anonymised records of T2DM/prediabetes (2016-2017) patients ≥18 years, from the UK Clinical Practice Research Datalink (CPRD) database. Multivariable logistic regression models determined associations with starting orlistat and stopping it early (<12 weeks of prescriptions) and orlistat's associations with weight loss in those not prescribed 2nd line anti-diabetic medications. RESULTS: Out of 100,552 patients with incident T2DM/prediabetes, 655 (0.8%) T2DM and 128 (0.7%) prediabetes patients were prescribed orlistat. Younger people, females, those in more deprived regions, current smokers, co-prescribed metformin and recorded with hypertension were significantly more likely to be prescribed orlistat while higher baseline HbA1c levels were associated with early stopping. Those who continued orlistat for >12 weeks were more likely to lose ≥5% weight (adjusted OR: 1.69; 95% CI: 1.07, 2.67) than those not on orlistat, but those who stopped orlistat early were less likely (adjusted OR: 0.56; 95% CI: 0.29, 1.09). CONCLUSIONS: Orlistat was significantly associated with weight loss in T2DM/prediabetes when taken for at least 12 weeks. However, orlistat is infrequently prescribed and often taken for <12 weeks. Orlistat may be a useful adjunct to lifestyle modifications in T2DM/prediabetes however barriers to continuing orlistat means it may not be effective for everyone in managing weight loss.

Risk of adverse outcomes during gabapentinoid therapy and factors associated with increased risk in UK primary care using the clinical practice research datalink: a cohort study.

ABSTRACT: Growing evidence from pharmacovigilance data and postmortem toxicology reports highlights the misuse potential of gabapentinoids. This study aimed to investigate the risk of serious adverse outcomes (drug misuse, overdose, major trauma), and their risk factors, in primary care patients who are prescribed gabapentinoids. Using the UK Clinical Practice Research Datalink, a matched cohort study calculated adverse event rates separately for gabapentinoid-exposed and unexposed cohorts. In the exposed cohort, event rates for exposure to a range of potential risk factors were calculated. Event rates were compared using Cox proportional hazards models, adjusted for age, sex, deprivation, previous mental health diagnosis, and coprescribing with potentially interacting medicines. Substance misuse (gabapentin adjusted hazard ratio [95% CI]: 2.40 [2.25-2.55]), overdose (2.99 [2.56-3.49]), and major trauma (0-2.5 years: 1.35 [1.28-1.42]; 2.5 to 10 years: 1.73 [1.56-1.95]) were more common among patients prescribed gabapentinoids than matched individuals who were not. The association with overdose was stronger for pregabalin than gabapentin. All adverse outcomes were significantly associated with smoking, history of substance misuse, overdose, or a mental health condition and prescription of opioids, benzodiazepines, antidepressants, and Z-drug hypnotics (eg, gabapentin hazard ratios for association of concurrent opioid use: misuse 1.49 [1.47-1.51]; overdose 1.87 [1.78-1.96]; major trauma 1.28 [1.26-1.30]). Our findings highlight the importance of careful patient selection when prescribing gabapentinoids and the need to educate prescribers about the risks of these drugs, particularly in combination with other central nervous system depressants.

Apneustic anesthesia ventilation improves pulmonary function in anesthetized bottlenose dolphins (Tursiops truncatus).

Introduction: Use of mechanical ventilation during general anesthesia is a necessary practice in the anesthetization of small cetaceans as spontaneous ventilation fails to provide adequate gas exchange. Currently available methods of ventilation do not account for the intermittent breathing strategy of representative species within this infraorder of fully aquatic mammals and may have a significant effect on cardiac and respiratory physiology. Methods: To understand the impact of mechanical ventilation on cardiopulmonary function in one small species of cetacean, the bottlenose dolphin (Tursiops truncatus), we compared controlled mechanical ventilation (CMV) to a novel ventilation method known as apneustic anesthesia ventilation (AAV). AAV simulates the normal inspiratory breath-hold pattern of dolphins. Ten anesthetic procedures (dental procedure, n = 9; bronchoscopy, n = 2) were performed on nine dolphins (age range: 10-42 years; mean = 32 years; median = 37 years; female = 3, 40%; male = 6, 60%). In a cross-over study design, dolphins were instrumented and randomly assigned to AAV or CMV as the initial mode of ventilation, then switched to the alternate mode. Baseline cardiopulmonary data were collected and again after 30 min on each mode of ventilation. Cardiac index, stroke volume index, systemic vascular resistance, alveolar dead space, alveolar-arterial oxygen tension gradient, arterial oxygen content, oxygen delivery index, and dynamic respiratory system compliance index were calculated at each of the four time points. Results: During AAV, dolphins had higher arterial oxygen tension, higher mean airway pressure, reduced alveolar dead space ventilation and lower alveolar-arterial oxygen difference. Cardiovascular performance was not statistically different between the two modes. Discussion: Our study suggests AAV, which more closely resembles the conscious intermittent respiratory pattern phenotype of dolphins, improves ventilation and pulmonary function in the anesthetized dolphin. Future studies should evaluate the cardiopulmonary effects of neutral buoyancy and cardiopulmonary sparing drug protocols to reduce the need for hemodynamic support of current protocols.

Using neural networks to autonomously assess adequacy in intraoperative cholangiograms.

BACKGROUND: Intraoperative cholangiography (IOC) is a contrast-enhanced X-ray acquired during laparoscopic cholecystectomy. IOC images the biliary tree whereby filling defects, anatomical anomalies and duct injuries can be identified. In Australia, IOC are performed in over 81% of cholecystectomies compared with 20 to 30% internationally (Welfare AIoHa in Australian Atlas of Healthcare Variation, 2017). In this study, we aim to train artificial intelligence (AI) algorithms to interpret anatomy and recognise abnormalities in IOC images. This has potential utility in (a) intraoperative safety mechanisms to limit the risk of missed ductal injury or stone, (b) surgical training and coaching, and (c) auditing of cholangiogram quality. METHODOLOGY: Semantic segmentation masks were applied to a dataset of 1000 cholangiograms with 10 classes. Classes corresponded to anatomy, filling defects and the cholangiogram catheter instrument. Segmentation masks were applied by a surgical trainee and reviewed by a radiologist. Two convolutional neural networks (CNNs), DeeplabV3+ and U-Net, were trained and validated using 900 (90%) labelled frames. Testing was conducted on 100 (10%) hold-out frames. CNN generated segmentation class masks were compared with ground truth segmentation masks to evaluate performance according to a pixel-wise comparison. RESULTS: The trained CNNs recognised all classes.. U-Net and DeeplabV3+ achieved a mean F1 of 0.64 and 0.70 respectively in class segmentation, excluding the background class. The presence of individual classes was correctly recognised in over 80% of cases. Given the limited local dataset, these results provide proof of concept in the development of an accurate and clinically useful tool to aid in the interpretation and quality control of intraoperative cholangiograms. CONCLUSION: Our results demonstrate that a CNN can be trained to identify anatomical structures in IOC images. Future performance can be improved with the use of larger, more diverse training datasets. Implementation of this technology may provide cholangiogram quality control and improve intraoperative detection of ductal injuries or ductal injuries.

Building Statewide Quality Improvement Capacity to Improve Cardiovascular Care and Health Equity: Lessons from the Tennessee Heart Health Network.

DRIVING FORCES: Many states with high rates of cardiovascular disease (CVD) lack statewide quality improvement (QI) infrastructure (for example, resources, leadership, community) to address relevant health needs of the population. Academic health centers are well positioned to play a central role in addressing this deficiency. This article describes early experience and lessons learned in building statewide QI infrastructure through the Tennessee Heart Health Network (Network). APPROACH: A statewide, multistakeholder network composed of primary care practices (PCPs), health systems, health plans, QI organizations, patients, and academic institutions was led by the University of Tennessee Health Science Center (UTHSC), an academic health center, to improve cardiovascular health by supporting dissemination and implementation of patient-centered outcomes research (PCOR) evidence-based interventions in primary care. PCPs were required to select and implement at least one of three interventions (health coaching, tailored health-related text messaging, and pharmacist-physician collaboration). OUTCOMES AND KEY INSIGHTS: Thirty statewide organizational partners joined the Network in year one, including 18 health systems representing 77 PCPs (30.0% of 257 potentially eligible PCPs identified) with approximately 300,000 patients. The organizational partners share EHRs for the ongoing tracking and reporting of key health metrics, including hypertension control and delivery of tobacco cessation counseling. Of the 77 PCPs, 62 continue participation after year two (80.5% retention). Main barriers to participation and reasons for discontinuing participation included reluctance to share data and changes in leadership at the health system level. These 62 PCPs selected the following interventions to implement: health coaching (41.9%), tailored health-related text messages (48.4%), and pharmacist-physician collaboration (40.3%). CONCLUSION AND WHAT'S NEXT: Academic health centers have broad reach and high acceptability by diverse stakeholders. Tennessee's experience illustrates how academic health centers can serve as platforms for building a statewide infrastructure for disseminating, implementing, and sustaining QI interventions at the practice level. Assessment of Network impact is ongoing.

Association of Conjunctival Ulceration With Pembrolizumab.

This case report describes a patient with conjunctival ulceration following pembrolizumab therapy for breast cancer.

Pre-existing musculoskeletal pain and its association with mortality in newly diagnosed co-morbid conditions: an electronic health record cohort study.

Objective: Musculoskeletal pain is a common risk factor for co-morbid conditions and might increase the risk of poor outcomes. The objective was to determine whether patients with pre-existing musculoskeletal pain have an increased risk for mortality following a new diagnosis of a co-morbid condition. Methods: Patients aged ≥45 years with a new diagnosis of acute coronary syndrome (ACS), stroke, cancer, dementia or pneumonia recorded in a UK electronic primary care database linked to hospital and mortality records were examined. The association of mortality with musculoskeletal pain (inflammatory conditions, OA and regional pain) was determined. Results: The sample size varied from 128 649 (stroke) to 406 289 (cancer) by cohort, with 22-31% having pre-existing musculoskeletal conditions. In the ACS cohort, there was a higher rate of mortality for all musculoskeletal types. There were also higher unadjusted mortality rates in patients with inflammatory arthritis compared with those without musculoskeletal pain in the stroke, cancer and dementia cohorts and for patients with OA in the stroke and cancer cohorts. After adjustment for the number of prescribed medications and age, the increased risk of mortality remained only for patients with inflammatory arthritis in the ACS cohort (adjusted hazard ratio = 1.07; 95% CI 1.03, 1.10). Conclusion: Older adults with inflammatory arthritis and OA have increased risk of mortality when they develop a new condition, which seems to be related to the prescription of multiple medicines. Pre-existing musculoskeletal pain is an indicator of a complex patient who is at risk of poorer outcomes at the onset of new illnesses.

Bulk Osteochondral Allograft for a Firearm Injury to the Medial Femoral Condyle in a Military Service Member: A Case Report.

A 27-year-old U.S. military active duty male sustained an accidental, self-inflicted left knee gunshot injury with an unsalvageable medial femoral condyle injury. The patient underwent bulk osteochondral allograft transplantation. Nine months post-operation, the patient was fit for full military duties with no reported functional limitations and remained on active duty. Severe knee medial femoral condyle bone loss after accidental firearm injury is uncommon. Bulk knee osteochondral allograft transplantation to the medial femoral condyle provided a successful treatment option for an active duty U.S. military member with multicompartment osteochondral defects and severe medial femoral condyle bone loss due to a gunshot injury.

Racial differences in healthcare expenditures for prevalent multimorbidity combinations in the USA: a cross-sectional study.

BACKGROUND: We aimed to model total charges for the most prevalent multimorbidity combinations in the USA and assess model accuracy across Asian/Pacific Islander, African American, Biracial, Caucasian, Hispanic, and Native American populations. METHODS: We used Cerner HealthFacts data from 2016 to 2017 to model the cost of previously identified prevalent multimorbidity combinations among 38 major diagnostic categories for cohorts stratified by age (45-64 and 65 +). Examples of prevalent multimorbidity combinations include lipedema with hypertension or hypertension with diabetes. We applied generalized linear models (GLM) with gamma distribution and log link function to total charges for all cohorts and assessed model accuracy using residual analysis. In addition to 38 major diagnostic categories, our adjusted model incorporated demographic, BMI, hospital, and census division information. RESULTS: The mean ages were 55 (45-64 cohort, N = 333,094) and 75 (65 + cohort, N = 327,260), respectively. We found actual total charges to be highest for African Americans (means $78,544 [45-64], $176,274 [65 +]) and lowest for Hispanics (means $29,597 [45-64], $66,911 [65 +]). African American race was strongly predictive of higher costs (p < 0.05 [45-64]; p < 0.05 [65 +]). Each total charge model had a good fit. With African American as the index race, only Asian/Pacific Islander and Biracial were non-significant in the 45-64 cohort and Biracial in the 65 + cohort. Mean residuals were lowest for Hispanics in both cohorts, highest in African Americans for the 45-64 cohort, and highest in Caucasians for the 65 + cohort. Model accuracy varied substantially by race when multimorbidity grouping was considered. For example, costs were markedly overestimated for 65 + Caucasians with multimorbidity combinations that included heart disease (e.g., hypertension + heart disease and lipidemia + hypertension + heart disease). Additionally, model residuals varied by age/obesity status. For instance, model estimates for Hispanic patients were highly underestimated for most multimorbidity combinations in the 65 + with obesity cohort compared with other age/obesity status groupings. CONCLUSIONS: Our finding demonstrates the need for more robust models to ensure the healthcare system can better serve all populations. Future cost modeling efforts will likely benefit from factoring in multimorbidity type stratified by race/ethnicity and age/obesity status.

ARTERIAL CATHETERIZATION IN GREAT APES.

Anesthesia is frequently required to provide appropriate medical care to captive great apes. Anesthetic safety can be optimized through placement of an arterial catheter, which allows direct measurement of arterial blood pressure and easy sampling of arterial blood for blood-gas analysis. Arterial catheterization in great apes can be achieved through palpation-guided or ultrasound-guided placement with or without a modified Seldinger technique. Potential sites for arterial catheterization include the anterior tibial artery, caudal tibial and posterior saphenous arteries, dorsal pedal artery, femoral artery, radial artery, and brachial artery. Arterial catheterization is recommended for lengthy great ape anesthetic procedures or those involving invasive procedures.

Estimating the direct healthcare utilization and cost of musculoskeletal pain among people with comorbidity: a retrospective electronic health record study.

OBJECTIVE: To investigate the impact of pre-existing painful musculoskeletal conditions on healthcare utilization and costs among patients with five common conditions: acute coronary syndrome (ACS), stroke, cancer, dementia and pneumonia. METHODS: Using primary and secondary care services data from electronic health records, a negative binomial regression model was used to compare resource use while a two-part model was used to compare costs across the five conditions, between those with and without a pre-existing musculoskeletal pain. RESULTS: The study included 760,792 patients (144,870 with ACS, 121,208 with stroke, 231,702 with cancer, 134,638 with dementia, and 128,374 with pneumonia) in the complete case analysis. Pre-existing musculoskeletal pain had an incident rate ratio of above one for most healthcare resources over the follow-up period and an adjusted additional mean cumulative total healthcare costs per patient of £674.59 (95%CI 570.30 to 778.87) for ACS; £613.34 (95%CI 496.87 to 729.82) for stroke; £459.26 (95%CI 376.60 to 541.91) for cancer; and £766.23 (95%CI 655.06 to 877.39) for dementia over five years after diagnosis; and £200.85 (95%CI 104.16 to 297.55) for pneumonia over one year after diagnosis compared to those without musculoskeletal pain. CONCLUSION: This study highlights that individuals with painful musculoskeletal conditions have higher healthcare utiliszation and costs than those without painful musculoskeletal conditions. Given the high occurrence of musculoskeletal pain in patients with other conditions, effective management strategies are needed to reduce the burden on healthcare resources.

Graph structure reforming framework enhanced by commute time distance for graph classification.

As a graph data mining task, graph classification has high academic value and wide practical application. Among them, the graph neural network-based method is one of the mainstream methods. Most graph neural networks (GNNs) follow the message passing paradigm and can be called Message Passing Neural Networks (MPNNs), achieving good results in structural data-related tasks. However, it has also been reported that these methods suffer from over-squashing and limited expressive power. In recent years, many works have proposed different solutions to these problems separately, but none has yet considered these shortcomings in a comprehensive way. After considering these several aspects comprehensively, we identify two specific defects: information loss caused by local information aggregation, and an inability to capture higher-order structures. To solve these issues, we propose a plug-and-play framework based on Commute Time Distance (CTD), in which information is propagated in commute time distance neighborhoods. By considering both local and global graph connections, the commute time distance between two nodes is evaluated with reference to the path length and the number of paths in the whole graph. Moreover, the proposed framework CTD-MPNNs (Commute Time Distance-based Message Passing Neural Networks) can capture higher-order structural information by utilizing commute paths to enhance the expressive power of GNNs. Thus, our proposed framework can propagate and aggregate messages from defined important neighbors and model more powerful GNNs. We conduct extensive experiments using various real-world graph classification benchmarks. The experimental performance demonstrates the effectiveness of our framework. Codes are released on https://github.com/Haldate-Yu/CTD-MPNNs.

Sociodemographic variations in the uptake of faecal immunochemical tests in primary care: a retrospective study.

BACKGROUND: Faecal immunochemical test (FIT) usage for symptomatic patients is increasing, but variations in use caused by sociodemographic factors are unknown. A clinical pathway for colorectal cancer (CRC) was introduced in primary care for symptomatic patients in November 2017. The pathway was commissioned to provide GPs with direct access to FITs. AIM: To identify whether sociodemographic factors affect FIT return in symptomatic patients. DESIGN AND SETTING: A retrospective study was undertaken in Nottingham, UK, following the introduction of FIT as triage tool in primary care. It was mandated for all colorectal referrals (except rectal bleeding or mass) to secondary care. FIT was used, alongside full blood count and ferritin, to stratify CRC risk. METHOD: All referrals from November 2017 to December 2021 were retrospectively reviewed. Sociodemographic factors affecting FIT return were analysed by multivariate logistic regression. RESULTS: A total of 35 289 (90.7%) patients returned their index FIT, while 3631 (9.3%) did not. On multivariate analysis, males were less likely to return an FIT (odds ratio [OR] 1.11, 95% confidence interval [CI] = 1.03 to 1.19). Patients aged ≥65 years were more likely to return an FIT (OR 0.78 for non-return, 95% CI = 0.72 to 0.83). Unreturned FIT more than doubled in the most compared with the least deprived quintile (OR 2.20, 95% CI = 1.99 to 2.43). Patients from Asian (OR 1.82, 95% CI = 1.58 to 2.10), Black (OR 1.21, 95% CI = 0.98 to 1.49), and mixed or other ethnic groups (OR 1.29, 95% CI = 1.05 to 1.59) were more likely to not return an FIT compared with patients from a White ethnic group. A total of 599 (1.5%) CRCs were detected; 561 in those who returned a first FIT request. CONCLUSION: FIT return in those suspected of having CRC varied by sex, age, ethnic group, and socioeconomic deprivation. Strategies to mitigate effects on FIT return and CRC detection should be considered as FIT usage expands.

Analgesic prescribing in patients with inflammatory arthritis in England: observational studies in the Clinical Practice Research Datalink.

OBJECTIVES: Despite little evidence that analgesics are effective in inflammatory arthritis (IA), studies report substantial opioid prescribing. The extent this applies to other analgesics is uncertain. We undertook a comprehensive evaluation of analgesic prescribing in patients with IA in the Clinical Practice Research Datalink Aurum to evaluate this. METHODS: From 2004 to 2020, cross-sectional analyses evaluated analgesic prescription annual prevalence in RA, PsA and axial spondyloarthritis (axSpA), stratified by age, sex, ethnicity, deprivation and geography. Joinpoint regression evaluated temporal prescribing trends. Cohort studies determined prognostic factors at diagnosis for chronic analgesic prescriptions using Cox proportional hazards models. RESULTS: Analgesic prescribing declined over time but remained common: 2004 and 2020 IA prescription prevalence was 84.2/100 person-years (PY) (95% CI 83.9, 84.5) and 64.5/100 PY (64.2, 64.8), respectively. In 2004, NSAIDs were most prescribed (56.1/100 PY; 55.8, 56.5), falling over time. Opioids were most prescribed in 2020 (39.0/100 PY; 38.7, 39.2). Gabapentinoid prescribing increased: 2004 prevalence 1.1/100 PY (1.0, 1.2); 2020 prevalence 9.9/100 PY (9.7, 10.0). Most opioid prescriptions were chronic (2020 prevalence 23.4/100 PY [23.2, 23.6]). Non-NSAID analgesic prescribing was commoner in RA, older people, females and deprived areas/northern England. Conversely, NSAID prescribing was commoner in axSpA/males, varying little by deprivation/geography. Peri-diagnosis was high-risk for starting chronic opioid/NSAID prescriptions. Prognostic factors for chronic opioid/gabapentinoid and NSAID prescriptions differed, with NSAIDs having no consistently significant association with deprivation (unlike opioids/gabapentinoids). CONCLUSION: IA analgesic prescribing of all classes is widespread. This is neither evidence-based nor in line with guidelines. Peri-diagnosis is an opportune moment to reduce chronic analgesic prescribing.

Trends in consultations and prescribing for rheumatic and musculoskeletal diseases: an electronic primary care records study.

BACKGROUND: Rheumatic and musculoskeletal diseases (RMDs) are common and generally managed in primary care through supported self-care, physiotherapy, analgesia, and specialist referral where indicated. The COVID-19 pandemic led to abrupt changes in primary care delivery, including moves to remote consulting, pauses on group-based self-care, and restricted referrals. AIM: To describe how patterns of UK primary healthcare consultations and analgesic prescribing relating to RMDs changed during the COVID-19 pandemic. DESIGN AND SETTING: Observational study using routinely collected national primary care electronic health record data from the Clinical Practice Research Datalink between 1 April 2017 and 1 October 2021. METHOD: RMD and analgesic SNOMED-CT codes were derived through consensus and published work. Prevalent and incident RMD-related consultations were determined, and RMD consultations matched to prevalent and incident analgesia prescriptions. Joinpoint regression was used to describe trends over time. RESULTS: Prevalent and incident RMD consultations steadily increased until March 2020 when a substantial drop occurred as pandemic- related restrictions were introduced; levels had not recovered to pre-pandemic highs by October 2021. While incident and prevalent analgesic prescribing also reduced around March 2020, the proportion of patients with an RMD consultation prescribed any analgesic increased from 27.72% in February 2020 to 38.15% in April 2020, with increases across all analgesic groups. A higher proportion of strong opioid prescriptions was seen in the most deprived areas. CONCLUSION: Pandemic-associated restrictions led to fewer primary care consultations and relative increases in analgesic prescribing, including strong opioids, for RMDs in the UK. Policymakers must consider the impact of these changes in future healthcare resource planning.

Patients' Views on AI for Risk Prediction in Shared Decision-Making for Knee Replacement Surgery: Qualitative Interview Study.

BACKGROUND: The use of artificial intelligence (AI) in decision-making around knee replacement surgery is increasing, and this technology holds promise to improve the prediction of patient outcomes. Ambiguity surrounds the definition of AI, and there are mixed views on its application in clinical settings. OBJECTIVE: In this study, we aimed to explore the understanding and attitudes of patients who underwent knee replacement surgery regarding AI in the context of risk prediction for shared clinical decision-making. METHODS: This qualitative study involved patients who underwent knee replacement surgery at a tertiary referral center for joint replacement surgery. The participants were selected based on their age and sex. Semistructured interviews explored the participants' understanding of AI and their opinions on its use in shared clinical decision-making. Data collection and reflexive thematic analyses were conducted concurrently. Recruitment continued until thematic saturation was achieved. RESULTS: Thematic saturation was achieved with 19 interviews and confirmed with 1 additional interview, resulting in 20 participants being interviewed (female participants: n=11, 55%; male participants: n=9, 45%; median age: 66 years). A total of 11 (55%) participants had a substantial postoperative complication. Three themes captured the participants' understanding of AI and their perceptions of its use in shared clinical decision-making. The theme Expectations captured the participants' views of themselves as individuals with the right to self-determination as they sought therapeutic solutions tailored to their circumstances, needs, and desires, including whether to use AI at all. The theme Empowerment highlighted the potential of AI to enable patients to develop realistic expectations and equip them with personalized risk information to discuss in shared decision-making conversations with the surgeon. The theme Partnership captured the importance of symbiosis between AI and clinicians because AI has varied levels of interpretability and understanding of human emotions and empathy. CONCLUSIONS: Patients who underwent knee replacement surgery in this study had varied levels of familiarity with AI and diverse conceptualizations of its definitions and capabilities. Educating patients about AI through nontechnical explanations and illustrative scenarios could help inform their decision to use it for risk prediction in the shared decision-making process with their surgeon. These findings could be used in the process of developing a questionnaire to ascertain the views of patients undergoing knee replacement surgery on the acceptability of AI in shared clinical decision-making. Future work could investigate the accuracy of this patient group's understanding of AI, beyond their familiarity with it, and how this influences their acceptance of its use. Surgeons may play a key role in finding a place for AI in the clinical setting as the uptake of this technology in health care continues to grow.

Comparing hospital costs and length of stay for cancer patients in New York State Comprehensive Cancer Centers versus nondesignated academic centers and community hospitals.

OBJECTIVE: To explore differences in costs and lengths of stay for cancer patients admitted to National Cancer Institute-designated Comprehensive Cancer Centers, nondesignated academic medical centers, and community hospitals in New York State. DATA SOURCES: We use patient-level data from the New York State Statewide Planning and Research Cooperative System Hospital Inpatient Discharges dataset for the years 2017-2019. STUDY DESIGN: We employ ordinary least squares and Poisson regressions to compare hospital costs and length of stay for cancer patients, controlling for hospital type, patient demographics, and patient health. Our key outcomes are differences in costs and lengths of stay. DATA COLLECTION: We use data on patient demographics, total treatment costs, and lengths of stay for patients discharged from New York hospitals with cancer-related diagnoses between 2017 and 2019. PRINCIPAL FINDINGS: We determine that inpatient costs were 27% higher (95% CI 0.252, 0.285), but length of stay was 12% shorter (95% CI -0.131, -0.100), in Comprehensive Cancer Centers relative to community hospitals. CONCLUSIONS: The results imply that, in New York State, Comprehensive Cancer Centers are a magnet for more complex oncology cases and administer more expensive treatments. That expertise, however, seems to be responsible for more efficient care delivery and thorough discharge planning, allowing for shorter average lengths of stay.