Individual Hospitals Research Articles

Associations of pollen and cardiovascular disease morbidity in Atlanta during 1993-2018.

Pollen exposure is associated with substantial respiratory morbidity, but its potential impact on cardiovascular disease (CVD) remains less understood. This study aimed to investigate the associations between daily levels of 13 pollen types and emergency department (ED) visits for eight CVD outcomes over a 26-year period in Atlanta, GA. We acquired pollen data from Atlanta Allergy & Asthma, a nationally certified pollen counting station, and ED visit data from individual hospitals and the Georgia Hospital Association. We performed time-series analyses using quasi-Poisson distributed lag models, with primary analyses assessing 3-day (lag 0-2 days) pollen levels. Models controlled for temporally varying covariates, including air pollutants. During 1993-2018, there were 1,573,968 CVD ED visits. Most pairwise models of the 13 pollen types and eight CVD outcomes showed no association, with a few exceptions potentially due to chance. We found limited evidence of the impact of pollen on cardiovascular morbidity in Atlanta. Further study on pollen exposures in different climactic zones and exploration of pollen-pollution mixture effects is warranted.

Using Blood Wisely: lessons learnt in establishing a national implementation programme to reduce inappropriate red blood cell transfusion

BackgroundUp to 50% of blood is transfused inappropriately despite best evidence. In 2020, Choosing Wisely Canada launched a major national programme, ‘Using Blood Wisely’, the aim was to engage hospitals...

Practice Guidelines for Central Venous Access 2020 - Time for German Guidelines

The American Society of Anesthesiologists released practice guidelines for central venous access in 2020, and the entire world literature was examined for evidence on how to perform the entire process with best practice and minimal risk and harm to the patient. These guidelines may serve as a gold standard for individual procedural steps, allowing practitioners and hospital departments to critically question the own standard and improve upon them.We interpreted the guidelines for individual procedural steps on how to improve success of catheterization, minimize risks or adverse effects, enhance the management of accidental arterial punctures, adhere to evidence-based practices, and generally reduce the trauma of puncturing. In our opinion, the most needed recommendation for central venous access is to utilize ultrasound guidance, a practice that many international societies have already incorporated into their published national guidelines.In our view, it is time to implement a national guideline for central venous access using ultrasound in Germany. Doing so may improve success rates in the first attempt, reduce procedural time, decrease the number of needle insertions per patient, and lower the rate of arterial punctures. This approach represents best practice from ethical, insurance, civil rights, and patient security perspectives, and is supported by relevant societies.

Understanding the Occurrence of Psychiatric Disorders in Epilepsy in Brazil: An Epidemiological Investigation

IntroductionEpilepsy is one of the most common serious brain illness, with symptoms influenced by multiple risk factors and a strong genetic predisposition, rather than having a single expression and cause¹. Neuropsychiatric symptoms in epilepsy can encompass manifestations such as mood alterations, anxiety, sleep disturbances, psychosis, and behavioral disorders. While the motor and sensory manifestations of epileptic seizures are widely recognized, neuropsychiatric symptoms accompanying epilepsy are often underestimated. Therefore, it is essential to understand the most prevalent epidemiological profile of these patients to improve the diagnosis and management of these symptoms.ObjectivesOur goal was to evaluate the neuropsychiatric behavior of epilepsy patients in Brazilian over the past 3 years through hospitalization data in order to outline an epidemiological and behavioral profile.MethodsA cross-sectional, descriptive, retrospective, and quantitative study was conducted on hospitalizations of individuals simultaneously diagnosed with epilepsy, schizotypal and delusional disorders, and mood disorders in all five regions of Brazil (South, Southeast, Midwest, North, and Northeast) between February 2020 and December 2022. Data from January 2020 were not available. The data used were collected through the Department of Health Informatics of the Brazilian Unified Health System (DATASUS) in the “Hospital Information System of SUS” section, gathering information regarding the nature of care, age range, gender, and ethnicity of the patients.ResultsThe analysis covers the years 2020 to 2022, totaling 503,045 hospitalizations. In 2022, the highest number of cases occurred (≈ 37.55%), followed by 2021 (≈ 33.62%) and 2020 (≈ 28.81%). Urgent hospitalizations represented ≈ 90.85% of the total. The most affected age group was 30 to 39 years old (≈ 18.30%). Men were more affected than women (≈ 52.03% and ≈ 47.96%, respectively), and Caucasians accounted for ≈ 36.07% of the hospitalizations. The average length of stay was 19.1 days, and the mortality rate was 1.4%.ConclusionsThus, there is a gradual and annual increase in the number of hospitalizations during the observed period. While there is a minimal disparity between the affected genders, it is evident that the profile of male, caucasian, and adult patients is the most prevalent. Moreover, the predominantly urgent nature of hospitalizations points to an alarming scenario regarding this issue. From the analysis of the data obtained in the study, there is a clear need for interventions capable of reducing the prevalence of hospitalizations for neuropsychiatric symptoms in epilepsy patients in Brazil.Disclosure of InterestNone Declared

Genetic contribution to disease-course severity and progression in the SUPER-Finland study, a cohort of 10,403 individuals with psychotic disorders.

Genetic factors contribute to the susceptibility of psychotic disorders, but less is known how they affect psychotic disease-course development. Utilizing polygenic scores (PGSs) in combination with longitudinal healthcare data with decades of follow-up we investigated the contributing genetics to psychotic disease-course severity and diagnostic shifts in the SUPER-Finland study, encompassing 10 403 genotyped individuals with a psychotic disorder. To longitudinally track the study participants' past disease-course severity, we created a psychiatric hospitalization burden metric using the full-coverage and nation-wide Finnish in-hospital registry (data from 1969 and onwards). Using a hierarchical model, ranking the psychotic diagnoses according to clinical severity, we show that high schizophrenia PGS (SZ-PGS) was associated with progression from lower ranked psychotic disorders to schizophrenia (OR = 1.32 [1.23-1.43], p = 1.26e-12). This development manifested already at psychotic illness onset as a higher psychiatric hospitalization burden, the proxy for disease-course severity. In schizophrenia (n = 5 479), both a high SZ-PGS and a low educational attainment PGS (EA-PGS) were associated with increased psychiatric hospitalization burden (p = 1.00e-04 and p = 4.53e-10). The SZ-PGS and the EA-PGS associated with distinct patterns of hospital usage. In individuals with high SZ-PGS, the increased hospitalization burden was composed of longer individual hospital stays, while low EA-PGS associated with shorter but more frequent hospital visits. The negative effect of a low EA-PGS was found to be partly mediated via substance use disorder, a major risk factor for hospitalizations. In conclusion, we show that high SZ-PGS and low EA-PGS both impacted psychotic disease-course development negatively but resulted in different disease-course trajectories.

The influence of hospital volume and physician volume on early mortality in acute promyelocytic leukemia patients

Acute promyelocytic leukemia (APL) is a highly curable hematologic malignancy in the era of all-trans retinoic acid (ATRA) combination treatment. However, only a modest change in early mortality rate has been observed despite the wide availability of ATRA. In addition to the clinical characteristics of APL patients, studies on the hospital volume-outcome relationship and the physician volume-outcome relationship remained limited. We aim to evaluate the association between hospital and physician volume and the early mortality rate among APL patients. The patients were collected from Taiwan’s National Health Insurance Research Database (NHIRD). Early mortality is defined as death within 30 days of diagnosis. Patients were categorized into four groups according to individual cumulative hospital and physician volume. The risk of all-cause mortality in APL patients with different cumulative volume groups was compared using a Cox proportional hazard model. The probability of overall survival was estimated using the Kaplan–Meier method. All 741 patients were divided into four quartile volume groups. In the multivariate analysis, only physician volume was significantly associated with early mortality rate. The physician volume of the highest quartile was a protective factor for early mortality compared with the physician volume of the lowest quartile (HR 0.10, 95% CI 0.02–0.65). Hospital characteristics were not associated with early mortality. In the sensitivity analyses, the results remained consistent using two other different definitions of early mortality. Higher physician volume was independently associated with lower early mortality, while hospital volume was not. Enhancing the clinical expertise of low-volume physicians may ensure better outcomes.

Factors affecting minimally invasive surgery utilization during elective colectomies for diverticular disease in the United States

BackgroundCompared with open surgery, minimally invasive surgery (MIS) has been shown to have improved outcomes when treating diverticular disease. This study aimed to analyze recent trends in MIS utilization for elective colectomy for diverticular disease and to identify individual variables and hospital characteristics associated with MIS utilization. MethodsThis population-based study examined individuals from the National Inpatient Sample who underwent elective colectomy for diverticular disease from 2016 to 2019. ResultsHospitals in the Midwest used MIS less than those in other geographic regions. Rural hospitals used MIS less than urban hospitals. Hospital bed size and teaching status were not associated with differences in MIS utilization. Patients with private insurance were more likely to have an MIS operation. There was also a racial disparity in MIS utilization, even after adjusting for insurance status. ConclusionsWhile there is no longer any variance in MIS utilization based on hospital bed size or teaching status, disparities concerning patient race remain, even after adjusting for insurance status. Further investigation is needed to determine the roots of these disparities.

Institutional and Regional Variation in Opioid Prescribing for Hospitalized Infants in the US

High-risk infants, defined as newborns with substantial neonatal-perinatal morbidities, often undergo multiple procedures and require prolonged intubation, resulting in extended opioid exposure that is associated with poor outcomes. Understanding variation in opioid prescribing can inform quality improvement and best-practice initiatives. To examine regional and institutional variation in opioid prescribing, including short- and long-acting agents, in high-risk hospitalized infants. This retrospective cohort study assessed high-risk infants younger than 1 year from January 1, 2016, to December 31, 2022, at 47 children's hospitals participating in the Pediatric Health Information System (PHIS). The cohort was stratified by US Census region (Northeast, South, Midwest, and West). Variation in cumulative days of opioid exposure and methadone treatment was examined among institutions using a hierarchical generalized linear model. High-risk infants were identified by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes for congenital heart disease surgery, medical and surgical necrotizing enterocolitis, extremely low birth weight, very low birth weight, hypoxemic ischemic encephalopathy, extracorporeal membrane oxygenation, and other abdominal surgery. Infants with neonatal opioid withdrawal syndrome, in utero substance exposure, or malignant tumors were excluded. Any opioid exposure and methadone treatment. Regional and institutional variations in opioid exposure. Overall, 132 658 high-risk infants were identified (median [IQR] gestational age, 34 [28-38] weeks; 54.5% male). Prematurity occurred in 30.3%, and 55.3% underwent surgery. During hospitalization, 76.5% of high-risk infants were exposed to opioids and 7.9% received methadone. Median (IQR) length of any opioid exposure was 5 (2-12) cumulative days, and median (IQR) length of methadone treatment was 19 (7-46) cumulative days. There was significant hospital-level variation in opioid and methadone exposure and cumulative days of exposure within each US region. The computed intraclass correlation coefficient estimated that 16% of the variability in overall opioid prescribing and 20% of the variability in methadone treatment was attributed to the individual hospital. In this retrospective cohort study of high-risk hospitalized infants, institution-level variation in overall opioid exposure and methadone treatment persisted across the US. These findings highlight the need for standardization of opioid prescribing in this vulnerable population.

Understanding Pathways into Care-homes using Data (UnPiCD study): a two-part model to estimate inpatient and care-home costs using national linked health and social care data

BackgroundPathways into care-homes have been under-researched. Individuals who move-in to a care-home from hospital are clinically distinct from those moving-in from the community. However, it remains unclear whether the source of care-home admission has any implications in term of costs. Our aim was to quantify hospital and care-home costs for individuals newly moving-in to care homes to compare those moving-in from hospital to those moving-in from the community.MethodsUsing routinely-collected national social care and health data we constructed a cohort including people moving into care-homes from hospital and community settings between 01/04/2013-31/03/2015 based on records from the Scottish Care-Home Census (SCHC). Individual-level data were obtained from Scottish Morbidity Records (SMR01/04/50) and death records from National Records of Scotland (NRS). Unit costs were identified from NHS Scotland costs data and care-home costs from the SCHC. We used a two-part model to estimate costs conditional on having incurred positive costs. Additional analyses estimated differences in costs for the one-year period preceding and following care-home admission.ResultsWe included 14,877 individuals moving-in to a care-home, 8,472 (57%) from hospital, and 6,405 (43%) from the community. Individuals moving-in to care-homes from the community incurred higher costs at £27,117 (95% CI £ 26,641 to £ 27,594) than those moving-in from hospital with £24,426 (95% CI £ 24,037 to £ 24,814). Hospital costs incurred during the year preceding care-home admission were substantially higher (£8,323 (95% CI£8,168 to £8,477) compared to those incurred after moving-in to care-home (£1,670 (95% CI£1,591 to £1,750).ConclusionIndividuals moving-in from hospital and community have different needs, and this is reflected in the difference in costs incurred. The reduction in hospital costs in the year after moving-in to a care-home indicates the positive contribution of care-home residency in supporting those with complex needs. These data provide an important contribution to inform capacity planning on care provision for adults with complex needs and the costs of care provision.

Consequences of COVID-19 on Adipose Tissue Signatures.

Since the emergence of coronavirus disease-19 (COVID-19) in 2019, it has been crucial to investigate the causes of severe cases, particularly the higher rates of hospitalization and mortality in individuals with obesity. Previous findings suggest that adipocytes may play a role in adverse COVID-19 outcomes in people with obesity. The impact of COVID-19 vaccination and infection on adipose tissue (AT) is currently unclear. We therefore analyzed 27 paired biopsies of visceral and subcutaneous AT from donors of the Leipzig Obesity BioBank that have been categorized into three groups (1: no infection/no vaccination; 2: no infection but vaccinated; 3: infected and vaccinated) based on COVID-19 antibodies to spike (indicating vaccination) and/or nucleocapsid proteins. We provide additional insights into the impact of COVID-19 on AT biology through a comprehensive histological transcriptome and serum proteome analysis. This study demonstrates that COVID-19 infection is associated with smaller average adipocyte size. The impact of infection on gene expression was significantly more pronounced in subcutaneous than in visceral AT and mainly due to immune system-related processes. Serum proteome analysis revealed the effects of the infection on circulating adiponectin, interleukin 6 (IL-6), and carbonic anhydrase 5A (CA5A), which are all related to obesity and blood glucose abnormalities.

Delivery at an inadequate level of maternal care is associated with severe maternal morbidity

BackgroundImplementing levels of maternal care is one strategy proposed to reduce maternal morbidity and mortality. The levels of maternal care framework outline individual medical and obstetric comorbidities, along with hospital resources required for individuals with these different comorbidities to deliver safely. The overall goal is to match individuals to hospitals, so all birthing people get appropriate resources and personnel during delivery to reduce maternal morbidity. ObjectivesThis study examined the association between delivery in a hospital with an inappropriate level of maternal care and the risk of experiencing severe maternal morbidity. Study designThe 40 birthing hospitals in Massachusetts were surveyed using the Centers for Disease Control and Prevention’s Levels of Care Assessment Tool. We linked individual delivery hospitalizations from the Massachusetts Pregnancy to Early Life Longitudinal data system to hospital-level data from the Levels of Care Assessment Tool surveys. Level of maternal care guidelines were used to outline 16 high-risk conditions warranting delivery at hospitals with resources beyond those considered basic (level I) obstetric care. We then used the Levels of Care Assessment Tool assessed levels to determine whether delivery occurred at a hospital that had the resources to meet an individual’s needs (i.e., if a patient received risk-inappropriate care).We conducted our analyses in two stages. First, multivariable logistic regression models predicted whether an individual delivered in a hospital that did not have the resources for their risk condition. The main explanatory variable of interest was whether the hospital self-assessed their level of maternal care to be higher than their Levels of Care Assessment Tool -assessment. We then used logistic regression to examine the association between delivery at an inappropriate level and the presence of severe maternal morbidity at delivery. ResultsAmong 64,441 deliveries in Massachusetts from January 1 to December 31, 2019, 33.2% (21,415/64,441) had one or more of the 16 high-risk conditions suggesting delivery should occur at a center designated as a level I or higher. Of the 21,415 individuals with a high-risk condition, 13% (2,793/21,415) or 4% (2,793/64,441) of the entire sample delivered at an inappropriate level of maternal care. Birthing individuals with high-risk conditions delivering at a hospital with an inappropriate level had elevated odds (aOR: 3.34, 95% CI: 2.24, 4.96) of experiencing severe maternal morbidity after adjusting for patient comorbidities, demographics, average hospital severe maternal morbidity rate, hospital level of maternal care, and geographic region. ConclusionsBirthing people who delivered in a hospital with risk-inappropriate resources were significantly more likely to experience severe maternal morbidity. Delivery in a hospital with a discrepancy in their self-assessment and Levels of Care Assessment Tool assessment significantly predicted delivery in a hospital with an inappropriate level of maternal care, suggesting inadequate knowledge of hospitals’ resources and capabilities. Our data demonstrate the potential for the levels of maternal care paradigm to decrease severe maternal morbidity while highlighting the need for robust implementation and education to ensure everyone receives risk-appropriate care.

Building a unified communications centre to improve the distribution of EMS patients to a large multi-hospital health system

In 2023, hospital care-based models are faced with increasing patient volumes, limited physical space and limited resources. These constraints, felt in almost all acute-based care models, is leading to a crucial crossroads in acute care delivery. Balancing capacity and availability of hospital and system resources is almost impossible in this environment, as need greatly exceeds access to resources and patient care delivery can be significantly hindered. Historically, emergency medical services (EMS) brought patients to the nearest available emergency department (ED), and load balancing could only be accomplished after arrival in the ED. Intervening earlier in patient’s care by providing EMS with destination recommendations based on available resources optimises patient outcomes and decreases the burden on any individual hospital. This change can also greatly affect EMS processes to improve transport times and decrease wall time, the time that EMS crews spend at the hospital waiting to offload their patients into a hospital bed. Wall times can exceed several hours depending on location, time of day and patient resource needs. Reduction of this waiting time has the potential to profoundly improve throughput and patient-centred metrics like patient satisfaction, length of stay and admission rates, as well as reduce overall risk. This also allows health systems to maintain community resources by decreasing EMS crews’ idle time at the hospital. Through the creation of a unified communication centre (UCC), we sought to create a structure that appropriately stratified patients to the most appropriate system hospitals while still in the care of EMS. Our team’s goal was to optimise patient treatment, decrease wall time with EMS, and route patients to the most appropriate facility based on the patient’s medical complaints, hospital capacity and hospital capability in the community.

Modeling and investigating the spread of COVID-19 dynamics with Atangana-Baleanu fractional derivative: a numerical prospective

Fractional-order models have been used in the study of COVID-19 to incorporate memory and hereditary properties into the systems Moira and Xu (2003) Respirology 8 S9â14. These models have been applied to analyze the dynamics and behavior of the novel coronavirus. Various fractional-order models have been proposed, including the SIR and SEIR models, with the addition of compartments such as asymptomatic classes and virus repositories. Overall, fractional-order models have proven to be effective in studying the dynamics and spread of COVID-19. In this paper, we propose a nonlinear fractional model employing the Atangana-Baleanu derivative to describe COVID-19. To offer a clearer perspective, our investigation incorporates two distinct quarantine stages within the population. The first quarantine class consists of individuals who have not yet contracted the virus but have chosen to self-isolate at home. The second quarantine class encompasses individuals who are infected and are undergoing quarantine in hospitals. Additionally, we introduce a vaccination class, consisting of the portion of the population that has received the COVID-19 vaccination and is now at a reduced risk of infection. Fixed point theorems are employed to prove the existence and uniqueness of the solutions. The model’s threshold parameter R0 is calculated to investigate the pandemic’s future dynamics. The Toufik-Atangana scheme is applied to obtain numerical solutions for the fractional model. Further, we analyzed the data to assess how vaccination impacts the spread of the virus.The model includes two novel parameters to determine the speed and effectiveness of vaccination measures. To evaluate the accuracy of our model, we simulate the graphical results of this stage and compared them with integer-order derivative. The mathematical model for COVID-19 shows that both equilibrium points are locally stable. Moreover, to gain a deeper understanding of the disease, we conduct a sensitivity analysis to examine the effect of parameters on R0 . The study recommends continuing hospitalization and home isolation for infected individuals until virus transmission reduces sufficiently after vaccination. The given model provides useful insights into the pandemic’s dynamics and suggests measures for controlling its spread.

Association Between Past and Present Operative Mortality in Congenital Heart Surgery: An Analysis of The Society of Thoracic Surgeons Congenital Heart Surgery Database

BackgroundMortality after congenital heart surgery is an important metric across benchmarking, quality, and reporting initiatives. All rely on estimates from prior years, and how well these reflect current outcomes is unclear. MethodsIndex operations from The Society of Thoracic Surgeons Congenital Database (2014-2019) were included. Adjusted operative mortality in “past” 4-year and 1-year intervals vs the most recent year (“present”) was evaluated using Bayesian hierarchical logistic regression with results presented as odds ratios and 95% credible intervals (CrI). ResultsAmong 115,699 operations (106 hospitals), overall present observed mortality was 2.5%. Regression to the mean was evident, and individual hospital’s present vs past adjusted mortality were only weakly correlated (−0.11 vs past 1-year data, 0.22 vs past 4-year data). A significant relationship was found between past and present mortality only for the group of hospitals in the highest mortality quartile, most prominent for past 4-year data (adjusted odds ratio vs lowest mortality quartile, 2.04; 95% CrI, 1.44-2.80). The proportion of present hospital mortality variation explained by past mortality quartile was 52% (95% CrI, 20%-90%) using past 4-year data and only 27% (95% CrI, 92%-83%) using past 1-year data. Overall 66% of hospitals changed mortality quartiles from past to present (30% by ≥2 quartiles). ConclusionsPast mortality relates to present primarily for groups of hospitals at the extremes, with past 4-year data more informative than past 1-year data. For individual hospitals, past may differ from present, regression to the mean is common, and many change quartiles. Past mortality should be used thoughtfully and not as the sole factor informing present decision making.

Systematic development and validation of a predictive model for major postoperative complications in the Peri-operative Quality Improvement Project (PQIP) dataset.

Complications are common following major surgery and are associated with increased use of healthcare resources, disability and mortality. Continued reliance on mortality estimates risks harming patients and health systems, but existing tools for predicting complications are unwieldy and inaccurate. We aimed to systematically construct an accurate pre-operative model for predicting major postoperative complications; compare its performance against existing tools; and identify sources of inaccuracy in predictive models more generally. Complete patient records from the UK Peri-operative Quality Improvement Programme dataset were analysed. Major complications were defined as Clavien-Dindo grade ≥ 2 for novel models. In a 75% train:25% test split cohort, we developed a pipeline of increasingly complex models, prioritising pre-operative predictors using Least Absolute Shrinkage and Selection Operators (LASSO). We defined the best model in the training cohort by the lowest Akaike's information criterion, balancing accuracy and simplicity. Of the 24,983 included cases, 6389 (25.6%) patients developed major complications. Potentially modifiable risk factors (pain, reduced mobility and smoking) were retained. The best-performing model was highly complex, specifying individual hospital complication rates and 11 patient covariates. This novel model showed substantially superior performance over generic and specific prediction models and scores. We have developed a novel complications model with good internal accuracy, re-prioritised predictor variables and identified hospital-level variation as an important, but overlooked, source of inaccuracy in existing tools. The complexity of the best-performing model does, however, highlight the need for a step-change in clinical risk prediction to automate the delivery of informative risk estimates in clinical systems.

Epidemiology and Outcomes of Non–Small Cell Lung Cancer in South Korea

Valuable evidence regarding clinical characteristics, treatments, and outcomes for non-small cell lung cancer (NSCLC) is limited to individual hospital databases or national-level registries. The common data and federated analysis framework developed through the Extensible Platform for Observational Research in Lung Cancer (EXPLORE-LC) initiative allows for research across multiple high-quality data sources, which may provide a deeper understanding of the NSCLC landscape and identification of unmet needs of subpopulations. To describe clinical characteristics, initial treatment patterns, subsequent treatment, and overall survival (OS) of patients with NSCLC in South Korea. This multicenter cohort study included patients aged 18 years and older who were diagnosed with NSCLC between 2014 and 2019 and followed up until March 2020 at 3 tertiary hospitals in South Korea. Clinical data were collected using a common data model and clinical data warehouse. Patients who had an initial diagnosis of nonsquamous (NSQ) or squamous (SQ) NSCLC and who had received at least 1 treatment for NSCLC were included in the study. Data were analyzed from June through November 2022. The primary outcome was clinical OS for patients with NSCLC. Secondary outcomes were clinical characteristics and treatment patterns subsequent to the diagnosis of NSCLC. Among 22 101 patients with NSCLC who received anticancer treatment analyzed in this study, 17 350 patients (78.5%) had NSQ and 4751 patients (21.5%) had SQ NSCLC. Clinical characteristics and outcomes and treatment patterns were assessed for 13 084 patients with NSQ cancer who had known EGFR and ALK status (75.4%; mean [SD] 62.2 [10.5] years; 6552 males [50.1%]) and all 4751 patients with SQ cancer (mean [SD] age, 67.1 [8.6] years; 4427 males [93.2%]). More than half of patients with NSQ cancer were never smokers (7399 patients [56.6%]). Patients with SQ cancer were mostly males and former or current smokers (4235 patients [89.1%]) and were diagnosed at a later clinical stage than patients with NSQ cancer (eg, stage I: 1165 patients [24.5%] vs 5388 patients [41.2%]). Patients with EGFR-positive and ALK-positive NSQ cancer diagnosed between 2017 and 2019 had better median OS than similar patients diagnosed between 2014 and 2016 (EGFR-positive: not reached [95% CI, 35.9 months to not reached] vs 28.4 months [95% CI, 25.8 to 30.0 months]; P < .001; ALK-positive: not reached [95% CI, not reached] vs 49.5 months [95% CI, 35.1 months to not reached]; P < .001). No significant difference was observed in OS from first-line treatment for patients with SQ cancer. This study, which pooled medical data from multiple clinical data warehouses to produce a large study cohort, may provide meaningful insights into the clinical practice of NSCLC and underscores the value of a common data model approach. The analyzable dataset may hold great promise for future comprehensive identification of subpopulations and unmet needs.

Sex-based analysis of NSTEMI processes of care and outcomes by hospital: a nationwide cohort study.

Contemporary studies demonstrate that non-ST-segment elevation myocardial infarction (NSTEMI) processes of care vary according to sex. Little is known regarding variation in practice between geographical areas and centers. We identified 305014 NSTEMI admissions in the United Kingdom (UK) Myocardial Infarction National Audit Project (MINAP), 2010-2017, including female sex (110209). Hierarchical, multivariate logistic regression models were fitted assessing for differences in primary outcomes according to sex. Risk standardized mortality rates (RSMR) were calculated for individual hospitals to illustrate correlation with variables of interest. 'Heat-maps' were plotted to show regional and sex-based variation in opportunity-based quality-indicator score (surrogate for optimal processes of care). Women presented older (77y vs. 69y, P<0.001) and were more often Caucasian (93%vs. 91%, P<0.001). Women were less frequently managed with an invasive coronary angiogram (ICA) (58%vs. 75%, P<0.001) or percutaneous coronary intervention (PCI) (35%vs. 49%, P<0.001)). In our hospital-clustered analysis, we show positive correlation between the RSMR and increasing proportion of women treated for NSTEMI (R2=0.17, P<0.001). There was clear negative correlation between proportion of women who had an optimum OBQI score during their admission and RSMR (R2=0.22, P<0.001), with weaker correlation in men (R2=0.08, P<0.001). Heat-maps according to clinical commissioning group (CCG) demonstrate significant regional variation in OBQI score, with women receiving poorer quality care throughout the UK. There was a significant in variation of the management of patients with NSTEMI according to sex, with widespread geographical variation. Structural changes are required to enable improved care for women.

Reliability and validity of the online application of London Chest Activity of Daily Living scale in assessing dyspnea-related functional impairment in individuals after hospitalization for COVID-19

Purpose To investigate the test-retest reliability and construct validity of the LCADL scale applied via online form in individuals after hospitalization for COVID-19. Methods Methodological study. After hospitalization for COVID-19 individuals completed the LCADL via online form at two separate times. They also answered the post-COVID-19 Functional Status Scale (PCFS), dyspnea, fatigue, and health perception scales, modified Medical Research Council (MRCm), Short Form Health Survey 36 (SF-36). Hospitalization data were collected from the individual’s medical record. Results 104 individuals participated in the study (57 men, 45.2 ± 11.9 years). The LCADL showed moderately to high test-retest reliability (ICC: 0.73–0.86; p < 0.001), there was no difference in scores between test and retest (p > 0.05), the mean difference between the applications was smaller than the standard error of measurement and the internal consistency was adequate (Cronbach’s α = 0.70–0.94). In addition, it demonstrated adequate construct validity, showing correlations with PCFS, dyspnea perception, fatigue and health scales, mMRC, SF-36, and length of stay in the Intensive Care Unit (p < 0.05). The LCADL as percentage of the total score presented a significant floor effect (25%). Conclusion The LCADL applied online was reliable and valid for assessing limitations due to dyspnea in ADL in individuals after hospitalization for COVID-19.

Costs of End-of-Life Hospitalizations in the United States for People With Pulmonary Diseases

BackgroundLung transplant is a life-saving intervention for individuals with advanced lung disease but is costly and resource-intensive. To investigate the cost-effectiveness of lung transplant as a treatment option in pulmonary disease, we must understand costs attributable to end-of-life hospitalizations for end-stage lung disease. Research QuestionWhat are the costs associated with end-of-life hospitalizations for individuals with pulmonary disease and how have these trends changed over time? Study Design and MethodsAdults aged 18-74 with hospitalization data in the Cost and Utilization Project National Inpatient Sample data from 2009-2019 with a pulmonary disease admission were included in this analysis. Those with a history of lung transplant were excluded. ICD codes were used to identify pulmonary disease admissions, complications, and procedures and interventions. Total charges were calculated for hospitalizations and stratified by patient status at time discharge. Trends in charges over time were assessed by demographic and hospital factors. Results109,924 (4.1%) hospital admissions for pulmonary disease resulted in in-hospital mortality. Those with obstructive lung disease accounted for 94.1% of hospitalizations and 88.1% cases of in-hospital mortality. Estimated costs for end-of-life hospitalizations cost $29,981 on average with wide variation in cost by diagnosis and procedure utilization. Inpatient costs were highest for younger individuals who received more procedures. Among the most expensive admissions, mechanical ventilation accounted for the greatest proportion of interventions. Significant increases in the use of mechanical ventilation, extracorporeal membrane oxygenation, and dialysis occurred over the time period. The rate of hospital transfers increased with a proportionately greater increase across admissions resulting in in-hospital mortality. InterpretationCosts accrued during end-of-life hospitalizations vary across individuals but represent a significant healthcare cost that can be averted for selected individuals who undergo lung transplant. These costs should be considered in studies of cost-effectiveness in lung transplantation.

Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study.

The study aimed to use supervised machine learning models to predict the length and risk of prolonged hospitalization in PLWHs to help physicians timely clinical intervention and avoid waste of health resources. Regression models were established based on RF, KNN, SVM, and XGB to predict the length of hospital stay using RMSE, MAE, MAPE, and R2, while classification models were established based on RF, KNN, SVM, NN, and XGB to predict risk of prolonged hospital stay using accuracy, PPV, NPV, specificity, sensitivity, and kappa, and visualization evaluation based on AUROC, AUPRC, calibration curves and decision curves of all models were used for internally validation. In regression models, XGB model performed best in the internal validation (RMSE = 16.81, MAE = 10.39, MAPE = 0.98, R2 = 0.47) to predict the length of hospital stay, while in classification models, NN model presented good fitting and stable features and performed best in testing sets, with excellent accuracy (0.7623), PPV (0.7853), NPV (0.7092), sensitivity (0.8754), specificity (0.5882), and kappa (0.4672), and further visualization evaluation indicated that the largest AUROC (0.9779), AUPRC (0.773) and well-performed calibration curve and decision curve in the internal validation. This study showed that XGB model was effective in predicting the length of hospital stay, while NN model was effective in predicting the risk of prolonged hospitalization in PLWH. Based on predictive models, an intelligent medical prediction system may be developed to effectively predict the length of stay and risk of HIV patients according to their medical records, which helped reduce the waste of healthcare resources.