Healthcare Databases Research Articles

Development and Validation of a Predictive Model for Maternal Cardiovascular Morbidity Events in Patients With Hypertensive Disorders of Pregnancy.

Hypertensive disorders of pregnancy (HDP) are a major contributor to maternal morbidity, mortality, and accelerated cardiovascular (CV) disease. Comorbid conditions are likely important predictors of CV risk in pregnant people. Currently, there is no way to predict which people with HDP are at risk of acute CV complications. We developed and validated a predictive model for all CV events and for heart failure, renal failure, and cerebrovascular events specifically after HDP. Models were created using the Premier Healthcare Database. The inclusion criteria for the model dataset were delivery with an HDP with discharge from October 1, 2015 to December 31, 2020. Machine learning methods were used to derive predictive models of CV events occurring during delivery hospitalization (Index Model) or during readmission (Readmission Model) using a training set (60%) to estimate model parameters, a validation set (20%) to tune model hyperparameters and select a final model, and a test set (20%) to evaluate final model performance. The total model cohort consisted of 553,658 deliveries with an HDP. A CV event occurred in 6501 (1.2%) of the delivery hospitalizations. Multilabel neural networks were selected for the Index Model and Readmission Model due to favorable performance compared to alternatives. This approach is designed for prediction of multiple events that share risk factors and may cooccur. The Index Model predicted all CV events with area under the receiver operating curve (AUROC) 0.878 and average precision (AP) 0.239 (cerebrovascular events: AUROC 0.941, heart failure: AUROC 0.898, and renal failure: AUROC 0.885). With a positivity threshold set to achieve ≥90% sensitivity, model specificity was 65.0%, 83.5%, 68.6%, and 65.6% for predicting all CV events, cerebrovascular events, heart failure, and renal failure, respectively. CV events within 1 year of delivery occurred in 3018 (0.6%) individuals. The Readmission Model predicted all CV events with AUROC 0.717 and AP 0.022 (renal failure: AUROC 0.748, heart failure: AUROC 0.734, and cerebrovascular events AUROC 0.698). Feature importance analysis indicated that the presence of chronic renal disease, cardiac disease, pulmonary hypertension, and preeclampsia with severe features had the greatest effect on the prediction of CV events. Among individuals with HDP, our multilabel neural network model predicted CV events at delivery admission with good classification and events within 1 year of delivery with fair classification.

Analysis of Adherence to AAO-HNSF Clinical Practice Guidelines for Sudden Hearing Loss.

To assess adherence to the 2019 American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNSF) guidelines for the diagnosis and management of sudden hearing loss (SHL) and sudden sensorineural hearing loss (SSNHL). Retrospective cohort. TriNetX, a de-identified healthcare database. Evaluation focused on the percentage of patients undergoing proper guideline-adherent workup and treatment for SHL. Key measures include audiogram testing within 2 weeks for distinguishing SSNHL from CHL. For SSNHL patients, MRI or auditory brainstem response (ABR) testing should be obtained, and steroids and/or hyperbaric oxygen may be offered within 2 weeks. There were 24,203 SHL patients, 59.1% (n = 14,309) of whom underwent recommended audiogram testing, with 35.8% (n = 8,674) completing it within 2 weeks. Overall, 3,107 were diagnosed with unilateral SSNHL, 104 with unilateral conductive hearing loss (CHL), 121 with mixed hearing loss, and 10,977 were lost to follow-up. Among 3,107 SSNHL cases, just 25.5% (n = 791) obtained MRI/ABR within 1 month, and vestibular schwannoma was diagnosed in 3.5% (n = 28). Additionally, steroids were prescribed to 54.5% (n = 1,692), and <0.3% (n ≤ 10) received hyperbaric oxygen. In accordance with strong recommendations against or insufficient evidence to support the following diagnostics and treatments, 2.0% (n = 63) underwent a CT scan, less than 0.3% (n ≤ 10) were prescribed vasodilators or thrombolytics, and 3.5% (n = 108) were on antivirals. There is a significant opportunity for improvement in evaluating patients with SHL, specifically SSNHL. Proper adherence to guidelines may improve screening, detection, and management of neurotologic pathologies, including vestibular schwannoma.

Modeling heterogeneity of Sudanese hospital stay in neonatal and maternal unit: non-parametric random effect models with Gamma distribution

ObjectiveStudies looking into patient and institutional variables linked to extended hospital stays have arisen as a result of the increased focus on severe maternal morbidity and mortality. Understanding the length of hospitalization of patients after delivery is important to gain insights into when hospitals will reach capacity and to predict corresponding staffing or equipment requirements. In Sudan, the distribution of the length of stay during delivery hospitalizations is heavily skewed, with the average length of stay of 2 to 3 days. This study aimed to investigate the use of non-parametric random effect model with Gamma distributed response for analyzing skewed hospital length of stay data in Sudan in neonatal and maternal unit.MethodsWe applied Gamma regression models with unknown random effects, estimated using the non-parametric maximum likelihood (NPML) technique [5]. The NPML reduces the heterogeneity in the distribution of the response and produce a robust estimation since it does not require any assumptions on the distribution. The same applies to the log–Gamma link that does not require any transformation for the data distribution and it can handle the outliers in the data points. In this study, the models are fitted with and without covariates and compared using AIC and BIC values.ResultsThe findings imply that in the context of health care database investigations, Gamma regression models with non–parametric random effect consistently reduce heterogeneity and improve model accuracy. The generalized linear model with covariates and random effect (k = 4) had the best fit, indicating that Sudanese hospital length of stay data could be classified into four groups with varying average stays influenced by maternal, neonatal, and obstetrics data.ConclusionIdentifying factors contributing to longer stays allows hospitals to implement strategies for improvement. Non-parametric random effect model with Gamma distributed response effectively accounts for unobserved heterogeneity and individual-level variability, leading to more accurate inferences and improved patient care. Including random effects can significantly affect variable significance in statistical models, emphasizing the need to consider unobserved heterogeneity when analyzing data containing potential individual-level variability. The findings emphasise the importance of making robust methodological choices in healthcare research in order to inform accurate policy decisions.

The Relationship Between Surgeon Volume and Major Surgical Complications After Total Shoulder Arthroplasty: An Evaluation of 3177 US Orthopedic Surgeons.

Total shoulder arthroplasty (TSA), which includes anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA), is a technically demanding procedure and limited data exist on the relationship between case volume and complications. We sought to identify volume thresholds for TSA, aTSA, and rTSA at which risk of a major surgical complication decreased and to compare complications of patients treated by high-volume surgeons with those of patients treated by low-volume surgeons. Primary, elective TSAs (aTSA and rTSA) from January 1, 2016, to December 31, 2019, were identified in the Premier Healthcare Database. Multivariable logistic regression with restricted cubic splines modeled the relationship between annual TSA, aTSA, and rTSA surgeon volume and 90-day risk of major surgical complications. The 90-day complications of patients treated by high- and low-volume surgeons were compared. From 2016 to 2019, 3177 surgeons performed 78,639 TSAs. Increasing annual volume was associated with decreasing major surgical complication risk (thresholds: 50 TSAs, 25 aTSAs, and 36 rTSAs). High- and low-volume surgeons performed 24,595 and 54,044 TSAs, respectively. Patients of high-volume surgeons had lower risk of major surgical complications (adjusted odds ratio [aOR], 0.69; 95% CI, 0.56-0.84), myocardial infarction (aOR, 0.59; 95% CI, 0.36-0.97), and readmission (aOR, 0.71; 95% CI, 0.62-0.81). Importantly, 74.9% of high-volume and 93.0% of low-volume surgeon-year units had major surgical complication rates below the mean of all recorded surgeons. While most high- and low-volume surgeons had major surgical complication rates below the cohort average, increasing TSA volume was associated with a decreased risk of complications. [Orthopedics. 202x;4x(x):xx-xx.].

Delirium risk and mortality in people with pre-existing severe mental illness: a retrospective cohort study using linked datasets in England.

Delirium is a severe neuropsychiatric syndrome caused by physical illness, associated with high mortality. Understanding risk factors for delirium is key to targeting prevention and screening. Whether severe mental illness (SMI) predisposes people to delirium is not known. We aimed to establish whether pre-existing SMI diagnosis is associated with higher risk of delirium diagnosis and mortality following delirium diagnosis. A retrospective cohort and nested case-control study using linked primary and secondary healthcare databases from 2000-2017. We identified people diagnosed with SMI, matched to non-SMI comparators. We compared incidence of delirium diagnoses between people with SMI diagnoses and comparators, and between SMI subtypes; schizophrenia, bipolar disorder and 'other psychosis'. We compared 30-day mortality following a hospitalisation involving delirium between people with SMI diagnoses and comparators, and between SMI subtypes. We identified 20 566 people with SMI diagnoses, matched to 71 374 comparators. Risk of delirium diagnosis was higher for all SMI subtypes, with a higher risk conferred by SMI in the under 65-year group, (aHR:7.65, 95% CI 5.45-10.7, ⩾65-year group: aHR:3.35, 95% CI 2.77-4.05). Compared to people without SMI, people with an SMI diagnosis overall had no difference in 30-day mortality following a hospitalisation involving delirium (OR:0.66, 95% CI 0.38-1.14). We found an association between SMI and delirium diagnoses. People with SMI may be more vulnerable to delirium when in hospital than people without SMI. There are limitations to using electronic healthcare records and further prospective study is needed to confirm these findings.

Temporal and age-related trends in risk factors for development of atrial fibrillation in peripartum patients

Abstract Background Incidence of atrial fibrillation (AF) in pregnancy has steadily increased over the past two decades and may be related to changes in rates of AF risk factors. Objective To characterize trends in the prevalence of AF risk factors among patients with peripartum AF. Methods TrinetX, a large national healthcare database, was used to identify patients with peripartum AF from 2017-2022. Inclusion criteria were female patients aged &amp;gt;18 years old who received a diagnosis of AF within one month prior to and one year after a diagnosis of pregnancy. Annual prevalence of pre-existing AF risk factors, including diabetes, chronic kidney disease (CKD), obesity, and hypertension, were obtained, normalized to the annual number of peripartum AF cases, and followed. In a second analysis, student’s t-test and standardized mean difference were used to compare the rates of AF risk factors between patients with peripartum AF and pregnant patients with no history of AF within the same time frame. Comparisons were performed across age ranges. Results From 2017-2022, there were 6,465 patients with peripartum AF and 2,852,348 pregnant patients with no AF. There was an increase in the proportion of peripartum AF patients with pre-existing obesity (16.7% to 18.5%) and hypertension (28.9% to 33.7%). In comparing pregnant patients with no AF history with peripartum AF patients, the standardized mean difference for rates of all risk factors increased with age group. The peripartum AF group had significantly higher rates of all risk factors across all age groups (p &amp;lt; 0.0001 for all groups). Conclusion Rates of pre-existing obesity and hypertension increased among patients with peripartum AF, which could be a factor in increasing rates of peripartum AF. Differences in rates of AF risk factors between peripartum AF patients and pregnant patients with no AF were more pronounced at older age groups.

Public Health Benefits of Applying Evidence-Based Best Practices in Managing Patients Hospitalized for COVID-19.

As COVID-19-related mortality remains a concern, optimal management of patients hospitalized for COVID-19 continues to evolve. We developed a population model based on real-world evidence to quantify the clinical impact of increased utilization of remdesivir, the effectiveness of which has been well established in hospitalized patients with COVID-19. The PINC AI healthcare database records for patients hospitalized for COVID-19 from January to December 2023 were stratified by those treated with or without remdesivir ("RDV" and "No RDV") and by supplemental oxygen requirements: no supplemental oxygen charges (NSOc), low-flow oxygen (LFO), and high-flow oxygen/non-invasive ventilation (HFO/NIV). Key vulnerable subgroups such as elderly and immunocompromised patients were also evaluated. The model applied previously published hazard ratios (HRs) to 28-day in-hospital mortality incidence to determine the number of potential lives saved if additional "No RDV" patients had been treated with remdesivir upon hospital admission. Of 84,810 hospitalizations for COVID-19 in 2023, 13,233 "No RDV" patients were similar in terms of characteristics and clinical presentation to the "RDV" patients. The model predicted that initiation of remdesivir in these patients could have saved 231 lives. Projected nationally, this translates to >800 potential lives saved (95% CI: 469-1,126). Eighty-nine percent of potential lives saved were elderly and 19% were immunocompromised individuals. Seventy-one percent were among NSOc or LFO patients. This public health model underscores the value of initiating remdesivir upon admission in patients hospitalized for COVID-19, in accordance with evidence-based best practices, to minimize lives lost due to SARS-CoV-2 infection.

The Association between a Recorded Diagnosis of Obesity and Clinically Significant Weight Loss in the Primary Care Setting: A Nationwide Registry.

Overweight and obesity (OW/OB) are underdiagnosed. The primary aim was to assess whether a diagnosis of OW/OB recorded by a primary care physician (PCP) is associated with clinically significant weight loss, compared to a missed diagnosis. The secondary aim was to investigate the association between OW/OB diagnosis and patient attendance at dietary consultations. This retrospective, observational cohort study was conducted using a nationwide healthcare database. The study included a random sample of 200 000 adults with BMI ≥ 25 kg/m2, recorded on a primary care visit, between 2014-2020. Patients with prior diagnosis of OW/OB or obesity-related complications were excluded. The independent variable was OW/OB diagnosis recorded by the PCP immediately after BMI measurement. The outcome variable was ≥5% weight loss at a second weight measurement within 9-15 months. Multivariate regression analysis was applied. Of the 200 00 people with OW/OB, 36 033 (18.0%) had a diagnosis of OW/OB, and 37 368 (18.7%) had a second body weight measurement, of which 7 635 (20.4%) lost ≥5% of their baseline body weight. The prescription rate of anti-obesity medication was 1.2% and did not differ between patients who achieved weight loss and those who did not. Those with a recorded diagnosis were 2.6 times more likely to visit a dietitian (OR 2.57, 95%CI 2.56-2.64) and 2.5 times more likely to achieve weight loss (OR 2.53, 95%CI 2.46-2.60). After adjusting for multiple confounders, including attendance at dietary consultation, people who received OW/OB diagnosis were 32% more likely to achieve weight loss (OR 1.32 95% CI 1.28-1.36, p&lt;0.001) compared to people with missed diagnosis. Recording a diagnosis of obesity among relatively healthy people is associated with clinically significant weight loss at 1 year follow up, independent of attendance at dietary consultation. Early obesity diagnosis is a significant opportunity to promote weight loss in the primary care setting and may affect weight trajectory.

Management of vulnerable patients hospitalized for COVID-19 with remdesivir: a retrospective comparative effectiveness study of mortality in US hospitals.

COVID-19 remains a major public health concern, with continued resurgences of cases and substantial risk of mortality for hospitalized patients. Remdesivir has become standard-of-care for hospitalized COVID-19 patients. Given the continued evolution of the disease, clinical management relies on evidence from the current endemic period. Using the PINC AI Healthcare database, effectiveness of remdesivir was evaluated among adults hospitalized with a primary diagnosis of COVID-19 between December 2021 and February 2024. Three cohorts were analysed: adults, elderly (≥65 years), and those with documented COVID-19 pneumonia. Analyses were stratified by oxygen requirements. Patients receiving remdesivir were matched to those not receiving remdesivir using propensity score matching. Cox proportional hazards models were used to examine in-hospital mortality. 169,965 adults hospitalized for COVID-19 were included, of which 94,129 (55.4%) initiated remdesivir in the first two days of hospitalization. Remdesivir was associated with a significantly lower mortality rate as compared to no remdesivir among patients with no supplemental oxygen charges (NSOc) (aHR [95% CI]: 14-day, 0.75 [0.69-0.82]; 28-day, 0.77 [0.72-0.83]) and among those with supplemental oxygen charges (SOc): 14-day, 0.76 [0.72-0.81]; 28-day, 0.79 [0.74-0.83]) (p<0.0001, for all). Similar findings were observed for elderly patients and those hospitalized with COVID-19 pneumonia. This evidence builds on learnings from randomized controlled trials from the pandemic era to inform clinical practices. Remdesivir was associated with significant reduction in mortality for hospitalized patients including the elderly and those with COVID-19 pneumonia.

Navigating the nexus of personalized medicine and healthcare management: Advancing paediatric pain management modalities in sickle cell disease through thematic analysis for ages 1–12

ABSTRACT Sickle cell disease (SCD) affects millions of people and is a significant public health concern due to the severe pain crises it causes, often requiring emergency care and leading to hospitalization. Effective pain management strategies are crucial to improving the quality of life of children with SCD, particularly those aged 1 to 12 years, who are especially vulnerable. This study evaluates the effectiveness and suitability of various healthcare management strategies, with a specific focus on pain management modalities for this paediatric population. Through a thematic analysis of 36 articles from major healthcare databases, five key themes were identified: cognitive behavioral therapy (CBT) and complementary alternative medicine (CAM), digital and technological pain management methods, pain medications, individualized pain plans and enhancement strategies aimed at improving protocols. The findings highlight critical opportunities to advance pain management approaches and address current care gaps. The study underscores the importance of integrating parents and caregivers into the care process and addressing health inequalities that affect treatment outcomes. Evidence reveals that existing research on paediatric SCD pain management remains inadequate, and future studies are urgently needed to establish clear guidelines that improve treatment efficacy and translate into actionable evidence-based practices for this vulnerable population.

Is Dexamethasone Administration During Total Hip and Knee Arthroplasty Safe in Diabetic Patients?

Dexamethasone is used extensively during total hip and knee arthroplasty total joint arthroplasty (TJA) to control pain and decrease the risk of nausea and vomiting. However, the safety of dexamethasone utilization in diabetic patients is poorly understood. Therefore, this study aims to evaluate complications associated with perioperative dexamethasone administration during primary TJA in diabetic patients. The Premier Healthcare Database was queried for all patients with diabetes mellitus who underwent primary elective TJA from 2015 to 2020. Patients who received intravenous dexamethasone on the day of surgery were compared with those who did not. Patient characteristics, hospital factors, and rates of medical comorbidities were assessed and compared between the cohorts. Multivariate logistic regression was done to assess the 90-day risk of infectious and noninfectious postoperative complications. Overall, 261,474 diabetic patients were included for analysis, 122,631 (46.9%) of whom received dexamethasone. The two cohorts were similar in patient demographics, hospital characteristics, and comorbidity burden (absolute range of differences: 0.00 to 2.33%). Diabetic patients who received dexamethasone had decreased odds of periprosthetic joint infection (adjusted odds ratio 0.82, 95%-CI: 0.75 to 0.90, P < 0.001) and sepsis (aOR: 0.80, 95%-CI: 0.72 to 0.89, P < 0.001) compared with those who did not. Patients who received dexamethasone had shorter length of stay compared with those who did not (1.87 ± 1.60 days vs. 2.27 ± 1.88 days, P < 0.001). The adjusted odds of postoperative hyperglycemia were markedly higher in the dexamethasone group (aOR: 1.14, 95%-CI: 1.10 to 1.18, P < 0.001). Use of perioperative dexamethasone was not associated with the increased risk of infectious complications among diabetic patients undergoing TJA, supporting its safety in this high-risk population.

Prevalence of schizophrenia spectrum disorders in the Lazio region, Italy: use of an algorithm based on health administrative databases

BackgroundMental healthcare provision is undergoing substantial reconfiguration in many regions of the world. Such changes require a broad, evidence-based approach incorporating epidemiological data and information on local needs. The objective of this study was to estimate the prevalence of schizophrenia spectrum disorders (SSD) in the Lazio region and its geographical distribution using regional administrative healthcare databases.MethodsCases of SSD (15–64 years old) were identified using an algorithm based on data from the hospital discharge registry (ICD IX CM: 295, 297, 298 [excl. 298.0], 299) and the ticket exemption database [code 044], between 2006 and 2019. We calculated crude, age- and gender-specific prevalence estimates on December 31, 2019. We also calculated age- and gender-adjusted prevalence to compare prevalence in different regional areas.ResultsWe identified 18,371 cases. The overall prevalence was 5.03 per 1000 population (95% CI 4.96–5.10). Age-adjusted prevalence estimates were 4.18 (95% CI 4.09–4.27) per 1000 for women and 5.92 (95% CI 5.81–6.04) per 1000 for men. The prevalence was higher among older age groups, in both genders. There were differences in prevalence within the region, ranging from 4.25/1000 in the province of Viterbo to 5.42/1000 in Rome and 6.02/1000 in the province of Frosinone. When we analysed the subcategories of SSD, the three most frequent conditions were schizophrenia, schizoaffective disorder, and psychosis NOS. In general, the prevalence was higher in men for all the conditions but delusional disorders and brief psychosis.ConclusionsOur results show that the overall prevalence of SSD among adults in the Lazio region is similar to those published in previous reviews, but an uneven regional distribution was observed. While possible underestimation must be considered, administrative databases represent a valuable source of information for epidemiological surveillance and healthcare planning.

A bibliometric analysis and typology of drug pricing policies across the globe.

Limited research exists on pricing policies from a bibliometric and visualization perspective, and there is a lack of understanding of their typology. This study aims to address these gaps in knowledge and provide a deeper understanding of the research topics and development trends in this field. A bibliometric study was conducted on drug pricing approaches in healthcare literature, published between 2000 and June 2023. The literature was identified through an extensive search of healthcare databases and was then classified based on the year of publication, research topics, corresponding authors, location of corresponding authors, and journal titles. The citation data analysis was conducted using Bibliometrix, which consisted of descriptive, geographical, and time-series analyses and visualization. Between 2000 and June 2023, 173 articles were disseminated across 98 distinct publication sources. During the analysis, we observed a significant and consistent rise in literature reports on drug pricing approaches in healthcare, especially in 2010. The research topics were distributed almost equally, discussing improvement or issues with drug pricing models and addressing drug pricing applications. Our analysis revealed that the top ten corresponding authors were responsible for 19% of the total articles, with those based in the United States being the most productive. Furthermore, the "Health Economics" journal ranked first among the top ten journals. These findings align with the overall publication trends of drug pricing methods reported in other fields. The current study offers a comprehensive overview of drug pricing techniques utilized in medicine through visualization and bibliometric techniques. Analysis of authors, journals, institutions, and countries could serve as a reference for new researchers and guide them differently. Researchers can also consider emerging trends when determining the focus of their studies.

Remdesivir-associated survival outcomes among immunocompromised patients hospitalized for COVID-19: real-world evidence from the Omicron dominant era.

Patients with immunocompromising conditions are at an increased risk for coronavirus disease 2019 (COVID-19)-related hospitalizations and mortality. Randomized clinical trials provide limited enrollment, if any, to inform outcomes of such patients treated with remdesivir. Using the US PINC AI Healthcare Database, we identified adult patients with immunocompromising conditions, hospitalized for COVID-19 between December 2021 and February 2024. Primary outcome was all-cause inpatient mortality examined in propensity score (PS) matched patients in remdesivir versus non-remdesivir groups. Subgroup analyses were performed for patients with cancer, hematologic malignancies, and solid organ/hematopoietic stem cell transplant recipients. Of 28,966 patients included in the study, 16,730 (58%) received remdesivir during first two days of hospitalization. After PS matching, 8,822 patients in remdesivir and 8,822 patients in non-remdesivir group were analyzed. Remdesivir was associated with a significantly lower mortality among patients with no supplemental oxygen (aHR [95% CI]: 14-day, 0.73 [0.62-0.86]; 28-day, 0.79 [0.68-0.91]) and among those with supplemental oxygen (14-day, 0.75 [0.67-0.85]; 28-day, 0.78 [0.70-0.86]). Remdesivir was also associated with lower mortality in subgroups of patients with cancer, hematological malignancies (including leukemia, lymphoma, and multiple myeloma), and solid organ/hematopoietic stem cell transplantation. In this large cohort of patients with immunocompromising conditions hospitalized for COVID-19, remdesivir was associated with significant improvement in survival, including patients with varied underlying immunocompromising conditions. The integration of current real-world evidence into clinical guideline recommendations can inform clinical communities to optimize treatment decisions in the evolving COVID-19 era, extending beyond the conclusion of the public health emergency declaration.

Remdesivir Effectiveness in Reducing the Risk of 30-day Readmission in Vulnerable Patients Hospitalized for COVID-19: A Retrospective US Cohort Study Using Propensity Scores.

Reducing hospital readmission offer potential benefits for patients, providers, payers, and policymakers to improve quality of healthcare, reduce cost, and improve patient experience. We investigated effectiveness of remdesivir in reducing 30-day COVID-19-related readmission during the Omicron era, including older adults and those with underlying immunocompromising conditions. This retrospective study utilized the US PINC AI Healthcare Database to identify adult patients discharged alive from an index COVID-19 hospitalization between December 01, 2021 and February 29, 2024. Odds of 30-day COVID-19-related readmission to the same hospital were compared between patients who received remdesivir vs those not, after balancing characteristics of two groups using inverse probability of treatment weighting (IPTW). Analyses were stratified by maximum supplemental oxygen requirement during index hospitalization. Of 326,033 patients hospitalized for COVID-19 during study period, 210,586 patients met the eligibility criteria. Of these, 109,551 (52%) patients were treated with remdesivir. After IPTW, lower odds of 30-day COVID-19-related readmission were observed in patients who received remdesivir vs those who did not, in the overall population (3.3% vs 4.2%, respectively; odds ratio [95% confidence interval]: 0.78 [0.75-0.80]), elderly population (3.7% vs 4.7%, respectively; 0.78 [0.75-0.81]), and those with underlying immunocompromising conditions (5.3% vs 6.2%, respectively; 0.86 [0.80-0.92]). These results were consistent irrespective of supplemental oxygen requirements. Treating patients hospitalized for COVID-19 with remdesivir was associated with a significantly lower likelihood of 30-day COVID-19-related readmission across all patients discharged alive from the initial COVID-19 hospitalization, including older adults and those with underlying immunocompromising conditions.

Health outcomes and economic burden among patients with a COVID-19-associated hospitalization in the United States during the predominance of the XBB and JN.1 omicron lineages

Objective Conduct a comprehensive analysis of the health and economic outcomes of patients with a COVID-19-associated hospitalization in the US during the predominance of the XBB and JN.1 Omicron lineages. Methods This analysis used data from the PINC AI Healthcare Database (PHD) for all patients with a hospital admission date occurring between February 4, 2023, and February 29, 2024 with an ICD-10-CM code U07.1 “COVID-19” in any position. The data were used to estimate the mean and median length of stay (LOS), mean and median hospitalization cost, and proportion of patients that died in the hospital, by age and level of care (normal ward, intensive care [ICU], invasive mechanical ventilation [IMV]). Results LOS, hospitalization costs, and inpatient mortality increased with both the level of care and age. Patients not receiving ICU care had the shortest LOS, lowest inpatient mortality, and lowest hospitalization costs. LOS, hospitalization costs, and inpatient mortality were higher for those receiving ICU care and highest for those receiving IMV in the ICU. Within each level of care (normal ward, ICU without IMV, and ICU with IMV), the LOS, inpatient mortality, and hospitalization cost generally increased with age, indicating that older adults with COVID-19 required a longer recovery period, have a higher likelihood of death, and accrue higher costs. However, the proportion of pediatric patients with an ICU admission and/or IMV usage remained high. Limitations The PHD data may not be representative of all hospitalized patients in the US. Conclusions These findings suggest that COVID-19 continues to have severe and costly consequences in all age groups, but particularly for older adults including long LOS, ICU admission, need for IMV, mortality, and high hospital costs.

Association of early follow-up visits with reduced hospital readmissions of newborns: a French population-based data-linkage study.

Newborn care guidelines recommend assessments within a week of discharge from maternity care. However, the optimal timing and effectiveness to prevent neonatal hospital readmission are debated. This study aimed to determine the association between early follow-up visits (EFVs) within threedays of postpartum discharge and hospital readmission of newborns up to 15days of life. This population-based data-linkage study used data from the French National Health Care Database. "Healthy" singleton term infants with a gestational age (GA) of ≥36weeks, born between January 1, 2017, and November 30, 2018, in Southern France, were included. A multivariate regression analysis was performed. Among the 67,359 included infants, 1519 (2.25%) were hospitalized. The principal causes of hospitalization were infectious diseases (42.4%) and jaundice or growth/nutrition disorders (36.1%). Hospitalized infants were more likely to be born to young and socioeconomically deprived mothers, to be male (54.4% vs. 50.6%, P < 0.001), or born early (GA < 38weeks; 28.5% vs. 20.5%, P < 0.001). They received 6% less EFVs than non-hospitalized infants (63.7% vs. 67.8%, P < 0.001). The multivariable regression logistic analysis revealed that infants who received EFVs had 13% lower odds of being hospitalized than infants without EFVs, with an adjusted odds ratio of 0.87 (95% confidence interval=0.78-0.96; P < 0.01). EFVs within threedays were associated with a 13% reduction in the rate of neonatal hospital readmission. Therefore, EFVs should be implemented to improve infant health and reduce healthcare costs.

Are all robotic technologies created equal? Comparing one of the latest image-free robotic technologies to all other robotic systems for total knee arthroplasty

BackgroundRobotic-assisted technologies have been developed to increase surgical precision and reduce surgical variability in total knee arthroplasty (TKA). Several different robotic systems have been introduced in the last decade for TKA. The DePuy Synthes VELYS™ Robotic-Assisted Solution (VRAS) is an imageless system designed to eliminate the need for preoperative CT scans and is one of the latest entrants in the rapidly evolving field of robotic technology in TKA. This study compared the clinical and economic outcomes associated with VRAS and other robotic-assisted technologies for primary TKA.MethodsA retrospective cohort study using the Premier Healthcare Database included patients who underwent primary TKA with VRAS or other robotic-assisted technologies from January 1, 2022, to April 30, 2023. The primary outcome for the study was hospital follow-up visits (revisits) within 90 days post-TKA. Secondary outcomes included readmission and revision rates within 90 days post-TKA, operating room time, length of stay, discharge status and hospital costs. Cohorts were balanced using propensity score fine stratification, and generalized linear models were constructed to evaluate outcomes.ResultsThis study included 827 VRAS TKA patients and 16,428 TKA patients treated with other robotic-assisted technologies. The 90-day all-cause and knee-related revisit rates were significantly lower for VRAS than for other robotic-assisted technologies (all-cause 13.9% vs. 22.8% and knee-related 2.8% vs. 5.4%, respectively; p value < 0.01). The all-cause and knee-related 90-day readmission rates were also lower for VRAS, although the differences were not statistically significant. The 90-day revision rates were similar for VRAS and other robotic-assisted technologies (0.48% vs. 0.45%), as was the operating room time (138 vs. 137 min). The 90-day knee-related cost for the VRAS cohort was $15,048 compared to $16,867 for other robotic technologies.ConclusionsThis database study demonstrated that early postoperative revisit rates and total cost of care are lower for VRAS than for all other robotic-assisted technologies for TKA, while operating room time and discharge status were similar. These are important findings in ever-evolving healthcare systems that are increasingly cost conscious and cognizant of principles associated with value-based care.

The prevalence of multisystem diagnoses among young patients with hypermobile Ehlers-Danlos syndrome and hypermobility spectrum disorder: A retrospective analysis using a large healthcare claims database.

Clinical features of hypermobile Ehlers-Danlos syndrome (hEDS) and hypermobility syndrome (HMS) have classically focused on dysfunctions related to the musculoskeletal system. A growing body of literature suggests substantial multisystemic involvement, although this has not been recapitulated in a pediatric/young adult population. Leveraging a large United States healthcare claim database illuminates multisystem disorders among patients diagnosed with hEDS and HMS in the age range of 10 to 24. This was a retrospective review of patient records within the de-identified healthcare claims database, PearlDiver. Patients with a diagnosis of hEDS or HMS, and those without these diagnoses who were seen for their annual physical examination, between the ages of 10 and 24, were queried for the presence of additional medical conditions. Descriptive statistics were used to define the frequency of multisystem diagnoses. Nineteen thousand seven hundred ninety hEDS patients, 17,509 HMS patients, and 4,959,713 patients from the general population were analyzed. Within 2 years following hEDS or HMS diagnosis, digestive disorders were the most prevalent diagnosis, followed by cardiovascular conditions. Digestive disorders occurred in 54.6% of patients with hEDS and 41.6% of patients with HMS, compared to 28.5% of the general population. Cardiovascular disorders occurred in 43.6% of patients with hEDS and 21.8% of patients with HMS compared to 10.3% of the general population. Anxiety, respiratory disorders, and developmental disorders occurred in approximately 25% of the hEDS group and 20% of the HMS group, compared to ~15% of the general population, all statistically significantly higher in the hEDS and HMS groups. This study highlights multisystem diagnoses within the pediatric hEDS/HMS populations. hEDS patients had higher rates of multisystem diagnoses compared to HMS patients. These results suggest a high multisystem disease burden for young hEDS/HMS patients. Future research is needed to understand the timing and presentation of clinical symptoms for this population.

Finding the new potential research on diabetic kidney disease and hemodialysis in healthcare insurance databases: A bibliometric analysis

To the best of our knowledge, bibliometric analysis has not been performed for studies related to diabetic kidney disease (DKD) and hemodialysis using healthcare big data. Herein, the aim of this bibliometric analysis was to identify emerging research trends in DKD and hemodialysis within healthcare insurance databases by exploring authors, co-author networks, and countries to discover new potential research areas. A bibliometric study was conducted, utilizing data obtained from the Scopus database. Keywords such as diabetic kidney disease, hemodialysis, insurance or big data, and prediction were employed. Inclusion criteria were original articles and review articles written in English published between 2010 and 2022. VOSviewer and the Bibliometrix package in R were used for comprehensive bibliometric analysis. VOSviewer facilitated keyword co-occurrence analysis to identify clusters and visualize relationships among keywords, emphasizing distinct research themes, keyword density, and network visualization. Meanwhile, Bibliometrix allowed exploration of key metrics such as prolific authors and institutions, publication trends, co-authorship networks, citations, document types, emerging trends through keyword analysis, and network visualizations, including co-authorship and keyword co-occurrence. Results from both tools were integrated for a thorough analysis. The present study yielded 2,199 articles, which was reduced to 1,828 after removing duplicates and applying inclusion criteria. This bibliometric analysis found that machine learning and artificial intelligence are emerging yet remain relatively under-researched in the context of hemodialysis and DKD. The prominence of topics such as diabetic nephropathy, non-insulin treatments, and lifestyle modifications highlighted ongoing research priorities in DKD and hemodialysis. Taiwan's dominance in publications suggested robust research activity in this field, while international collaboration underscored global interest and the potential for diverse research perspectives. The need for similar research development in Indonesia, leveraging big data and machine learning, indicates opportunities for advancing the understanding and management of DKD and hemodialysis within the region.