Patient Datasets Research Articles

A multivariable prediction model for invasive pulmonary aspergillosis in immunocompromised patients with acute respiratory failure (IPA-GRRR-OH score).

Invasive pulmonary aspergillosis (IPA) is a life-threatening opportunistic infection in immunocompromised patients. The diagnosis is often made late, with mortality reaching 90% when mechanical ventilation is needed. We sought to develop and validate a risk prediction model for the diagnosis of IPA. We used two independent datasets of immunocompromised patients with acute respiratory failure admitted to 12 intensive care units (ICUs). The derivation dataset include 3262 patients. Factors associated with probable or proven IPA were identified, and a risk prediction model was developed. This model was then validated in a prospective dataset (776 patients). IPA prevalence was 4.5% (146/3262) and 3.3% (26/776), in the derivation and the validation cohorts, respectively. The final model included eight variables constitutive of the IPA-GRRR-OH score: type of immunosuppression, high-dose or long-term corticosteroids, neutropenia, the presence of structural lung disease, time from symptoms onset to ICU admission > 7 days, hemoptysis, focal alveolar pattern on the chest imaging, and viral co-infection. The median score [IQR] was 2 [1-3] in the derivation and 1 [0-3] in the validation cohort. The best cutoff score for IPA diagnosis was 4 (sensitivity 23.1%; specificity 90.5%; negative predictive value 91.4%). Discrimination and calibration were good in both the derivation (AUC 0.72 [0.68-0.76]) and the validation cohort (AUC 0.85 [0.76-0.93]). The IPA-GRRR-OH is a clinical score, easily available at ICU admission, which reliably predicts IPA in immunocompromised patients with acute respiratory failure. Studies to demonstrate benefits from the bedside implementation of this score are warranted.

Central or Peripheral Venoarterial Extracorporeal Membrane Oxygenation for Pediatric Sepsis: Outcomes Comparison in the Extracorporeal Life Support Organization Dataset, 2000-2021.

Small studies of extracorporeal membrane oxygenation (ECMO) support for children with refractory septic shock (RSS) suggest that high-flow (≥ 150 mL/kg/min) venoarterial ECMO and a central cannulation strategy may be associated with lower odds of mortality. We therefore aimed to examine a large, international dataset of venoarterial ECMO patients for pediatric sepsis to identify outcomes associated with flow and cannulation site. Retrospective analysis of the Extracorporeal Life Support Organization (ELSO) database from January 1, 2000, to December 31, 2021. International pediatric ECMO centers. Patients 18 years old young or younger without congenital heart disease (CHD) cannulated to venoarterial ECMO primarily for a diagnosis of sepsis, septicemia, or septic shock. None. Of 1242 pediatric patients undergoing venoarterial ECMO runs in the ELSO dataset, overall mortality was 55.6%. We used multivariable logistic regression analyses to evaluate explanatory factors associated with adjusted odds ratios (aORs) and 95% CI of mortality. In the regression analysis of data 4 hours after ECMO initiation, logarithm of the aOR, plotted against ECMO flow as a continuous variable, showed that higher flow was associated with lower aOR of mortality (p = 0.03). However, at 24 hours, we failed to find such a relationship. Finally, peripheral cannulation, as opposed to central cannulation, was independently associated with greater odds of mortality (odds ratio, 1.7 [95% CI, 1.1-2.6]). In this 2000-2021 international cohort of venoarterial ECMO for non-CHD children with sepsis, we have found that higher ECMO flow at 4 hours after support initiation, and central- rather than peripheral-cannulation, were both independently associated with lower odds of mortality. Therefore, flow early in the ECMO run and cannula location are two important factors to consider in future research in pediatric patients requiring cannulation to venoarterial ECMO for RSS.

Automated extraction of post-stroke functional outcomes from unstructured electronic health records.

Population level tracking of post-stroke functional outcomes is critical to guide interventions that reduce the burden of stroke-related disability. However, functional outcomes are often missing or documented in unstructured notes. We developed a natural language processing (NLP) model that reads electronic health records (EHR) notes to automatically determine the modified Rankin Scale (mRS). We included consecutive patients (⩾18 years) with acute stroke admitted to our center (2015-2024). mRS scores were obtained from the Get With the Guidelines registry and clinical notes (if documented), and used as the gold standard to compare against NLP-generated scores. We used text-based features from notes, along with age, sex, discharge status, and outpatient follow-up to train a logistic regression for prediction of good (0-2) versus poor (3-6) mRS, and a linear regression for the full range of mRS scores. The models were trained for prediction of mRS at hospital discharge and post-discharge. The models were externally validated in a dataset of patients with brain injuries from a different healthcare center. We included 5307 patients, 5006 in train and test and 301 in validation; average age was 69 (SD 15) and 65 (SD 17) years, respectively; 47% female. The logistic regression achieved an area under the receiver operating curve (AUROC) of 0.94 [CI 0.93-0.95] (test) and 0.94 [0.91-0.96] (validation), and the linear model a root mean squared error (RMSE) of 0.91 [0.87-0.94] (test) and 1.17 [1.06-1.28] (validation). The NLP-based model is suitable for use in large-scale phenotyping of stroke functional outcomes and population health research.

Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients

Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment. However, these studies are limited in their sample sizes and require advanced coding skills for exploration. Here, we have compiled eight scRNA-seq datasets from nine cancer types, encompassing 223 patients, 90,270 cancer cells, and 265,671 other cell types. This compilation forms a unique resource tailored to investigate how cancer cells respond to ICB treatment across cancer types. We meticulously curated, quality-checked, pre-processed, and analyzed the data, ensuring easy access for researchers. Moreover, we designed a user-friendly interface for seamless exploration. By sharing the code and data for creating these interfaces, we aim to assist fellow researchers. These resources offer valuable support to those interested in leveraging and exploring single-cell datasets across diverse cancer types, facilitating a comprehensive understanding of ICB responses.

Ferritin Outperforms Other Biomarkers in Predicting Bone Marrow Iron Stores in Hematological Patients.

Although iron deficiency anemia is common, interpreting iron laboratory test results can be challenging in patients with comorbidities. We aimed to study the accuracy of common iron biomarkers compared with bone marrow iron staining in a large retrospective dataset of hematological patients. We collected from 6610 patients (median age 66 years) results of iron staining, with their concurrent ferritin, transferrin saturation, soluble transferrin receptor, transferrin, hemoglobin, and mean red blood cell volume results from Helsinki University Hospital electronic health records. In receiver operating characteristics (ROC) analysis, ferritin had the highest area under curve (AUC) with 88% (95% CI 86-90%) for females and 89% (87-91%) for males in predicting reduced bone marrow iron. Using a ferritin cut-off of 30 µg/L resulted in high specificity rates of 97% in females and 99% in males. However, sensitivity rates were only 54% and 35%, respectively. Other studied biomarkers had inferior AUCs. Multivariate logistic regression models did not significantly perform better in prediction compared to ferritin alone. With 50% pre-probability for reduced iron stores, a ferritin of 30 µg/L (females) and 51 µg/L (males) had 95% positive predictive value for reduced iron stores. A 95% negative predictive value was achieved at 1750 µg/L (females) and 4967 µg/L (males). In our large population study, ferritin was the best single biomarker for iron deficiency in secondary care. Adding other blood tests in a multivariate model did not improve performance. However, in these hematological patients, even a high ferritin did not rule out iron deficiency with 95% certainty.

Engineered allogeneic stem cells orchestrate T lymphocyte driven immunotherapy in immunosuppressive leptomeningeal brain metastasis.

Immune-checkpoint inhibitors have shown clinical benefit in non-small cell lung cancer (NSCLC) derived brain metastasis (BM), however, their efficacy in lung to leptomeningeal brain metastasis (LLBM) remains poor. A paired matched RNA expression dataset of patients with NSCLCs and BMs was analyzed to idenfiy BM specific suppressive tumor microenvironment (TME) features. Next, we created immune-competent LLBM mouse models that mimic clinical LLBM. We evaluated the efficacy of intrathecal (IT) delivery of allogeneic stem cells (SCs) engineered to release single-chain variable fragment anti-PD-1 (scFvPD-1). To enhance tumor cell killing and subsequent modulation of the immune TME, we explored the therapeutic activity of dual SCs releasing oncolytic herpes simplex virus (oHSV) and scFvPD-1 and profiled immune and metabolic consequences. RNA sequencing analysis of primary NSCLCs and BMs revealed an immune-suppressive TME with reduced immune cells and increased PD-1+ T cells in BMs. We showed significantly decreased immune cells and increased PD-1+ T cells in the TME of LLBM compared to primary NSCLC tumors in LLBM mouse tumor models. Next, we showed that locoregional IT treatment with SC releasing scFvPD-1, but not conventional systemic injection of anti-PD-1 antibody, suppressed tumor growth and improved survival in our immune-competent LLBM models. Furthermore, dual SCs releasing oHSV and scFvPD-1 (SC-oHSV/scFvPD-1) enhanced therapeutic outcomes by inducing oHSV-mediated immunogenic cell death, activating anti-tumor T cell signaling, and disrupting oxidative phosphorylation, which sensitized tumors to cisplatin. Locoregional delivery of SC-oHSV/scFvPD-1 effectively targets the immune-suppressive TME in LLBM, providing a promising strategy for treating LLBM.

"Assessing the Effect of a Mobile Application on Cancer Risk Health Literacy: A Cross-Sectional Study Design".

The "Cancer Risk Calculator" mobile application aims to inform patients about their personal risks of cancer and their risk factors influencingsaid risks. The present analysis examines the responses to a questionnaire submitted by oncology patients treated with radiotherapy or their family members. The primary objective was to determine the effectof the app on the user's awareness and potential habit changes related to cancer risk. Further, the study aimed to discern any relationships between respondent characteristics and their questionnaire responses. A total of 162 patients were included in the analysis. Each patient's dataset comprised gender, date of birth, entry date, respondent type, type of cancer, and responses to 12 application-related questions. Statistical methods such as multiple regression models were employed to identify any effects of the respondent's characteristics on their responses. Statistical significance was set at p<0.05. Responding to the survey questions, 67.1% of respondents found the application useful, and 63.4% reported learning something new. More than half (52.5%) indicated a willingness to change their habits based on the information provided. Respondents also indicated that they were surprised by the number of risk factors shaping their risks and the large influence of some of these risk factors. Variables such as breast cancer diagnosis (p=0.044) and age (p=0.049) influenced specific question responses. The "Cancer Risk Calculator" app appears to have a significant utility in educating its users about cancer risk and potentially influencing habit change.

Genetic variants in PD-1 and its ligands, gene-gene and gene-environment interactions in allergic rhinitis.

The etiology of allergic rhinitis (AR), in which genetic and environmental factors are closely intertwined, has not yet been completely clarified. Programmed cell death 1 (PD-1) and its ligands (PD-L1 and PD-L2) regulate the immune and inflammatory responses during the development of immune-related and atopic diseases. To clarify the associations of genetic variants in PD-1, PD-L1 and PD-L2 with susceptibility to AR, gene-gene and gene-environment interactions were investigated. A total of 452 AR patients and 495 controls were enrolled in this hospital-based case-control study. Eight single nucleotide polymorphisms (SNPs) in the PDCD1, PDCD1LG1 and PDCD1LG2 genes were genotyped. The correlations between SNPs and AR incidence, as well as gene-gene and gene-environment interactions were explored. Differentially expressed genes were screened by the Limma package in two Gene Expression Omnibus (GEO) datasets of AR patients. Expression qualitative trait locus (eQTL) analysis was performed via the Genotype-Tissue Expression (GTEx) database. The rs2297136 (A/G) in PDCD1LG1 was associated with a significantly increased risk of AR, whereas the PDCD1LG2 rs16923189 G allele was associated with a reduced risk of AR. In the subgroups according to AR-related phenotypes, the rs2297136 G allele increased, while the rs16923189 G allele reduced AR risk. Gene-gene interactions and gene-environment interactions (e.g., PDCD1LG1 polymorphisms with factors such as smoke, main road and cooking fumes) were verified in AR patients, but they were not significant after Bonferroni correction. PDCD1LG1 rs2297136 and PDCD1LG2 rs16923189 are associated with susceptibility to AR in this Chinese population. The PD-1/PD-L1 and PD-1/PD-L2 signaling pathways may regulate gene-gene and gene-environment interactions in the pathogenesis of AR.

An integrated investigation of mitochondrial genes in COPD reveals the causal effect of NDUFS2 by regulating pulmonary macrophages

BackgroundDespite the increasing body of evidence that mitochondrial activities implicate in chronic obstructive pulmonary disease (COPD), we are still far from a causal-logical and mechanistic understanding of the mitochondrial malfunctions in COPD pathogenesis.ResultsDifferential expression genes (DEGs) from six publicly available bulk human lung tissue transcriptomic datasets of COPD patients were intersected with the known mitochondria-related genes from MitoCarta3.0 to obtain mitochondria-related DEGs associated with COPD (MitoDEGs). The 32 hub MitoDEGs identified from protein-protein interaction (PPI) networks demonstrated superior overall diagnostic efficacy to non-hub MitoDEGs. Random forest (RF) analysis, least absolute shrinkage and selection operator (LASSO) regression, and Mendelian Randomization (MR) analysis of hub MitoDEGs further nominated NDUFS2, CAT, and MRPL2 as causal MitoDEGs for COPD, whose predominate expressions in pulmonary macrophages were revealed by an independent single-cell transcriptomic dataset of COPD human lungs. Finally, NDUFS2 was evaluated as the top-ranked contributor to COPD in the nomogram model and its downregulation in pulmonary macrophages could result in pro-inflammatory secretion, enhanced intercellular communications, whereas depressed phagocytosis of macrophages as revealed by gene set variation analysis (GSVA) and cell-cell interaction (CCI) analysis of single-cell transcriptomic dataset of COPD human lungs, which was later confirmed in COPD mouse model and macrophage cell lines.ConclusionsOur study established the causal linkage between mitochondrial malfunctions and COPD, providing a potential therapeutic avenue to alleviate pulmonary inflammation accounting for COPD by targeting mitochondria-related genes. NDUFS2, a canonical component of mitochondrial electron respiratory chain, was highlighted instrumental for the susceptibility of risk-exposed individuals to COPD.

Patient-Specific Variability in Interleukin-6 and Myeloperoxidase Responses in Osteoarthritis: Insights from Synthetic Data and Clustering Analysis.

Objectives: This study investigated the inflammatory responses of fibroblast-like synoviocytes (FLS) isolated from osteoarthritis (OA) patients, stimulated with lipopolysaccharide (LPS) and interleukin-6 (IL-6). Both experimental and synthetic data were utilised to investigate the variability in IL-6 and myeloperoxidase (MPO) production and its implications for OA pathogenesis. Methods: Synovial biopsies were obtained from OA patients undergoing joint replacement surgery. FLS were isolated, cultured, and stimulated with varying concentrations of LPS and IL-6. The production of IL-6 and MPO was measured using enzyme-linked immunosorbent assays (ELISA). Synthetic data generation techniques expanded the dataset to support comprehensive statistical analyses. Results: The patterns of inflammatory responses revealed distinct patient subgroups, highlighting individual variability. The integration of synthetic data with experimental observations validated their reliability and demonstrated dose-dependent differences in IL-6 and MPO production across patients. Conclusions: The results highlighted the importance of patient-specific factors in OA inflammation and demonstrated the utility of combining experimental and synthetic data to model individual variability. The results support the development of personalised treatment strategies in OA. Future research should include larger patient datasets and an exploration of molecular mechanisms underlying these responses.

Cuproptosis related lncRNA signature as a prognostic and therapeutic biomarker in osteosarcoma immunity

Osteosarcoma is one of the most common malignant bone tumours in children. In this study, we aimed to construct a cuproptosis-related lncRNAs signature to predict the prognosis and immune landscape of osteosarcoma patients. Databases from TARGET were used to acquire osteosarcoma patient datasets, which included clinical information and RNA sequencing data. Cuproptosis-related lncRNAs was obtained by correlation analysis. Through univariate Cox regression analysis, prognosis-related lncRNAs were obtained. We used nonnegative matrix factorization clustering to identify potential molecular subgroups with different cuproptosis-related lncRNA expression patterns. The least absolute shrinkage and selection operator algorithm and multivariate Cox regression analysis were used to construct the prognostic signature. The ESTIMATE algorithm, Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes were applied to explore the underlying mechanisms in the immune landscape of osteosarcoma. We used gene set enrichment analysis to compare the different enrichments in the high-risk group and the low-risk group. Furthermore, we predicted the response to targeted drugs in patients with different risk groups. Using multivariable analysis, we developed a risk scoring model based on 7 long noncoding RNAs and calculated two molecular subgroups from osteosarcoma patients from the database. There is a better immune microenvironment in the low-risk group compared to the high-risk group. At the same time, the gene functional enrichment analysis based on the differently expressed genes obtained by grouping showed they were mainly related to immunity, indicating that cuproptosis-related lncRNAs may affect the prognosis of osteosarcoma by regulating immunity. Moreover, these patients in high-risk group were more susceptible to targeted drugs than the low-risk group. We identified a cuproptosis-related lncRNA prognostic signature for osteosarcoma and showed a close connection in terms of immunity. Moreover, we provided some potential targeted drugs for the treatment of osteosarcoma.

Machine Learning in Healthcare: A Comprehensive Review of Predictive Models for COVID-19 Transmission among Vaccinated Individuals

This review provides an in-depth exploration of machine learning (ML) applications in healthcare, focusing specifically on predictive models for COVID-19 transmission among vaccinated individuals. It underscores the pivotal role of ML in disease forecasting and prognosis, showcasing its potential to enhance healthcare outcomes in pandemic contexts. Key challenges of COVID-19, such as the high transmission rate of asymptomatic carriers and the effectiveness of containment strategies, are analyzed to highlight areas where ML can offer significant advantages. The study aims to develop an advanced forecasting model for COVID-19 transmission using diverse supervised ML regression techniques, including linear regression, LASSO, support vector machine, and exponential smoothing, applied to an extensive COVID-19 patient dataset. The insights generated from this review support efforts to combat COVID-19 and improve public health strategies, demonstrating ML's vital contribution to pandemic management and healthcare resilience.

Catestatin improves heart metabolic flexibility by promoting mitochondrial structure and function.

Hypertension, a major cause of cardiomyopathy, is one of the most critical risk factors for heart failure and mortality worldwide. Loss of metabolic flexibility of cardiomyocytes is one of the major causes of heart failure. Although Catestatin (CST) treatment is known to be both hypotensive and cardioprotective, its effect on cardiac metabolism is unknown. In this study, we undertook a transcriptomic approach to identify differentially expressed genes that were filtered using Boolean implication relationships to develop a model of gene regulation in saline or CST-supplemented CST knockout (CST-KO) mice. The analysis revealed a set of gene signatures (fibroblast, cardiomyocyte, and macrophage) rescued after CST supplemented CST-KO mice compared to wild-type. Furthermore, we independently validated these gene signature models using publicly available patient datasets. Since the gene signature includes genes related to glucose, fatty acid metabolism, and mitochondrial function, we assessed the glucose and fatty acid uptake after CST treatment. We found that CST treatment can restore the cardiac metabolic inflexibility in CST-KO heart due to the metabolic shift of glucose utilization to fatty acid as energy source. Binding studies after immunoprecipitation and mass spectrometry revealed CST binding with ATP synthase, supported by molecular simulation and computational modeling that predicted CST binding to α/β subunit of ATP synthase. Colocalization of CST with mitochondria and increased mitochondrial membrane potential and ATP production upon CST treatment in neonatal cardiomyocytes further exhibit CST as a key regulator of cardiac metabolism and mitochondrial function.

Does the FNA sperm retrieval failure prediction model work well for current NOA individuals undertaking risk screening before the operation? Model validation, high-risk population identification and potential alternative sperm retrieval exploration

BackgroundNon-obstructive azoospermia (NOA), the severe type of male infertility. The objective of this study was to evaluate the predictive accuracy of a prediction model of sperm retrieval failure with fine needle aspiration (FNA).MethodsThis study involved 769 NOA patients (dataset 1) undertaking FNA and 140 NOA patients undertaking mTESE (dataset 2). The previous model was validated and then reconstructed for more potential risk factors and better accuracy in dataset 1. The reconstructed model was evaluated in NOA patients with different new variables. The outcomes of the micro- testicular sperm extraction (mTESE) were compared with the predicted outcomes of FNA to evaluate its potential as an alternative surgical sperm retrieval (SSR) technique.Results307 (39.92%) males experienced sperm retrieval failure in FNA while 92 (65.7%) males experienced sperm retrieval failure in mTESE. The refined model has 80% overall agreement (n = 616). The reconstructed model had an AUROC of 0.876 (95% CI: 0.850–0.921). The mTESE has significantly higher success rate (34.29%) than the predicted success rate of FNA (5.71%).ConclusionsPrevious model shows good consistency. mTESE can be an alternative SSR method for NOA patients with a high predicted risk of sperm retrieval failure with FNA.

Completeness of repeated patient-reported outcome measures in adult rehabilitation: a randomized controlled trial in a diverse clinical population.

Data collection through patient-reported outcome measures (PROMs) is essential for the purpose of rehabilitation research and registries. Existing problems with incomplete PROM data may relate to the patient burden and data set length. This study aimed to analyse response patterns and degree of data completeness in systematic outcome assessments conducted within a clinical study in a multidisciplinary rehabilitation setting, comparing completeness of a brief and a longer set of PROMs. The Norwegian RehabNytte Cohort was developed to monitor patients' long-term benefit of rehabilitation and progress on PROMs. Adults admitted to one of 17 institutions providing mostly inpatient rehabilitation in secondary healthcare were included between January 2019 and March 2020, and followed for one year. For the purpose of the current randomized controlled trial, the Cohort-patients in 16/17 institutions were randomized to complete either a brief or a longer set of PROMs at admission, discharge, and after 3, 6 and 12months. The brief set comprised the EQ-5D-5L and additional generic PROMs commonly used in rehabilitation settings. The longer data set expanded upon the brief set by including the Patient-Specific Functional Scale and the 29-item version of the PROMIS Profile instruments. Completeness was measured as the extent of present applicable PROM data at each time point. In addition, we assessed response patterns in terms of dropout rates and intermittently missed assessment events. The RehabNytte study is registered under ClinicalTrial.gov (NCT03764982, first posted 05.12.2018). Of the 2904 patients included, 1455 were assigned to the brief data set and 1449 to the longer data set. The majority of patients were referred to rehabilitation for rheumatic and musculoskeletal diseases (39.3%) or cancer (26.9%). The data set completeness was significantly higher in the brief set compared to the longer (p < 0.001). From admission to 12months follow-up, differences in completeness between groups ranged from 6.5 to 12.6 percentage points, consistently favouring the brief set. Completeness was highest at admission, reaching 96.8% (95% CI 0.96-0.98) for the brief set and 84.2% (95% CI 0.82-0.86) for the longer set. The lowest completeness was observed at discharge, with 46.0% (95% CI 0.43-0.49) for the brief set and 39.5% (95% CI 0.37-0.42) for the longer one. Discharge was the only time point without automatic reminders to non-responders from the digital data collection system. Patients responding to the longer data set exhibited the highest dropout rates, while degree of intermittent missing data was comparable between groups. In both groups, only one-third of patients provided complete or partly responses at all five time points. This study demonstrated that a brief set of PROMs achieved higher data completeness compared to a longer set, when used for repeated measurements in a rehabilitation research setting.

Individual and prescription level factors associated with overdose in opioid naïve older people.

Opioid naïve older adults may be at risk of overdose after receiving an initial opioid prescription. This population-based cohort study from a linked dataset of patients in Oregon, linking all payer claims data to other administrative datasets, aimed to assess the prescription- and patient-level characteristics associated with increased odds of opioid overdose after an initial opioid prescription. Included patients were ≥65 years old and received an index pain-formulation opioid prescription between 2016 and 2019. The primary outcome was an index nonfatal or fatal overdose within 6- or 12-months following index prescription. Patient characteristics included age, sex, insurance plan, number of medical comorbidities, and presence of psychiatric comorbidities. Prescription characteristics included opioid type, duration of action, and days' supply. A logistic regression model was used to determine the association with opioid overdose. There were 223,799 individuals included for analysis (58.6% 65-74 years old, 53.9% female). There were 183 fatal or nonfatal opioid overdoses in 6 months and 232 in 12 months following the index prescription. Adults aged ≥85 years were less likely to experience an overdose versus those 65-74 years (6-month adjusted odds ratio (aOR) 0.35, [95% confidence interval, 0.20-0.59]; 12-month aOR 0.38 [0.24-0.60]). Multiple factors were associated with increased odds, including dually enrolled in Medicare/Medicaid compared to commercial insurance (6-month aOR 5.99, [1.93-19.65]; 12-month aOR 3.53, [1.58-7.90]), three or more comorbidities compared to none: (6-month aOR 3.69, [1.91-8.13]; 12-month aOR 4.24, [2.32-7.74]), history of depression: (6-month aOR 1.94, [1.34-2.81]; 12-month aOR 2.20, [1.60-3.04]), received long-acting opioids (6-month aOR 5.76, [1.56-21.22]; 12-month aOR 4.0, [1.39-11.55]) compared to short-acting. For older adults, there is an association between opioid overdose risk and factors including patient insurance type, patient comorbidities, and receiving a long-acting opioid prescription. Providers should be aware of the risks of opioids in this population.

Identification of Gender Differences in Acute Myocardial Infarction Presentation and Management at Aga Khan University Hospital-Pakistan: Natural Language Processing Application in a Dataset of Patients With Cardiovascular Disease.

Ischemic heart disease is a leading cause of death globally with a disproportionate burden in low- and middle-income countries (LMICs). Natural language processing (NLP) allows for data enrichment in large datasets to facilitate key clinical research. We used NLP to assess gender differences in symptoms and management of patients hospitalized with acute myocardial infarction (AMI) at Aga Khan University Hospital-Pakistan. The primary objective of this study was to use NLP to assess gender differences in the symptoms and management of patients hospitalized with AMI at a tertiary care hospital in Pakistan. We developed an NLP-based methodology to extract AMI symptoms and medications from 5358 discharge summaries spanning the years 1988 to 2018. This dataset included patients admitted and discharged between January 1, 1988, and December 31, 2018, who were older than 18 years with a primary discharge diagnosis of AMI (using ICD-9 [International Classification of Diseases, Ninth Revision], diagnostic codes). The methodology used a fuzzy keyword-matching algorithm to extract AMI symptoms from the discharge summaries automatically. It first preprocesses the free text within the discharge summaries to extract passages indicating the presenting symptoms. Then, it applies fuzzy matching techniques to identify relevant keywords or phrases indicative of AMI symptoms, incorporating negation handling to minimize false positives. After manually reviewing the quality of extracted symptoms in a subset of discharge summaries through preliminary experiments, a similarity threshold of 80% was determined. Among 1769 women and 3589 men with AMI, women had higher odds of presenting with shortness of breath (odds ratio [OR] 1.46, 95% CI 1.26-1.70) and lower odds of presenting with chest pain (OR 0.65, 95% CI 0.55-0.75), even after adjustment for diabetes and age. Presentation with abdominal pain, nausea, or vomiting was much less frequent but consistently more common in women (P<.001). "Ghabrahat," a culturally distinct term for a feeling of impending doom was used by 5.09% of women and 3.69% of men as presenting symptom for AMI (P=.06). First-line medication prescription (statin and β-blockers) was lower in women: women had nearly 30% lower odds (OR 0.71, 95% CI 0.57-0.90) of being prescribed statins, and they had 40% lower odds (OR 0.67, 95% CI 0.57-0.78) of being prescribed β-blockers. Gender-based differences in clinical presentation and medication management were demonstrated in patients with AMI at a tertiary care hospital in Pakistan. The use of NLP for the identification of culturally nuanced clinical characteristics and management is feasible in LMICs and could be used as a tool to understand gender disparities and address key clinical priorities in LMICs.

The impact of high-risk cytogenetic abnormalities in extramedullary multiple myeloma in the era of novel agents: insights from a multicenter study

PurposeThis study aimed to examine the impact of high-risk cytogenetic abnormalities (HRA) on the survival outcomes of multiple myeloma patients with extramedullary disease (EMD) in the era of novel agents, utilizing the largest dataset of extramedullary multiple myeloma patients in China.MethodsThis study included a total of 371 patients with EMD, comprising 113 patients with de novo EME and 258 patients with EMB.ResultsPatients with one HRA and those with ≥ 2 HRA demonstrated significantly worse overall survival (OS) (P < 0.01) and progression-free survival (PFS) (P < 0.01) compared to patients without HRA. Additionally, 1q21 gain/amplification (1q21 +) remained a predictor of poor prognosis in EMD. CD38 monoclonal antibody-based therapy and single transplantation were less effective in improving survival outcomes for EMD with ≥ 2 HRA. Multivariable analysis identified LDH levels > 250 U/L, creatinine levels > 177 μmol/L, extramedullary extraosseous (EME), 1 HRA, and ≥ 2 HRA as independent adverse prognostic factors in patients with EMD.ConclusionPatients with EMD who had ≥ 2 HRA experienced an extremely poor prognosis, which could not be improved by single transplantation or CD38 monoclonal antibody-based treatment. The number of HRA could serve as an important factor in guiding treatment choices and predicting prognosis in patients with EMD. Furthermore, 1q21 + remained a significant factor associated with worse survival outcomes in EMD.

Risk of long-term post-stroke dementia using a linked dataset of patients with ischemic stroke without a history of dementia.

Post-stroke dementia (PSD) is a common and disabling sequela of stroke. However, the long-term incidence of PSD after an ischemic stroke and factors which predict its occurrence are incompletely understood. Linkage of large health datasets is being increasing used to study long term outcomes after disease. We used large scale linked data from Korea to determine the long-term incidence of PSD after ischemic stroke, and identify which factors predicted it occurrence. From January 2008 to December 2014, patients with ischemic stroke (n=37,553) without a history of dementia were included in a linked dataset comprising the claims database of the Health Insurance Review and Assessment Service and the Clinical Research Center for Stroke registry data. The outcome measure was PSD after ischemic stroke. Clinical factors evaluated included vascular risk factors, acute stroke management including reperfusion therapy, antithrombotics, and statins, stroke severity, and educational levels, were evaluated. Results: Among 37,553 patients with ischemic stroke without a history of dementia (mean age: 64.9 years; 61.9% males), 6,052 (16.1%) experienced PSD during a median follow-up period of 5 (interquartile range 3.4-7.0) years. The 10 year estimated cumulative incidence of dementia was 23.5%. Age [hazard ratio (HR) 1.82 per 10 years, 95% confidence interval (CI) 1.75-1.88] and a lower educational level [illiteracy or no education HR 1.65 (CI, 1.44-1.88), 0-3 years 1.53 (CI, 1.31-1.79), 4-6 years 1.60 (CI, 1.43-1.80), 7-9 years 1.32 (CI, 1.16-1.49), 10-12 years 1.17 (CI, 1.04-1.32)] were independently associated with an elevated risk of PSD. Male sex was associated with a significantly lower risk of PSD (HR 0.86, CI 0.79-0.92). Diabetes mellitus (HR 1.21, CI 1.14-1.29), a history of stroke before index stroke (HR 1.31, CI 1.21-1.41), and initial National Institutes of Health Stroke Scale (HR 1.03, CI 1.03-1.04) were independent risk factors for PSD. Regarding medications, the use of anticoagulation and antipsychotic medications after stroke appeared to be associated with increased PSD risk whereas statin therapy was associated with a reduced risk. PSD is common with a 5 and 10 year incidence in patients with ischemic stroke without a history of dementia of 16.1% and 23.5% respectively. Factors associated with PSD include age, female sex, lower educational level, diabetes mellitus, initial stroke severity, antipsychotics and anticoagulants. Further studies are required to determine whether reducing those risk factors which are treatable reduces the incidence of PSD.

Joint quantile regression of longitudinal continuous proportions and time-to-event data: Application in medication adherence and persistence.

This study introduces a novel joint modeling framework integrating quantile regression for longitudinal continuous proportions data with Cox regression for time-to-event analysis, employing integrated nested Laplace approximation for Bayesian inference. Our approach facilitates an examination across the entire distribution of patient health metrics over time, including the occurrence of key health events and their impact on patient outcomes, particularly in the context of medication adherence and persistence. Integrated nested Laplace approximation's fast computational speed significantly enhances the efficiency of this process, making the model particularly suitable for applications requiring rapid data analysis and updates. Applying this model to a dataset of patients who underwent treatment with atorvastatin, we demonstrate the significant impact of targeted interventions on improving medication adherence and persistence across various patient subgroups. Furthermore, we have developed a dynamic prediction method within this framework that rapidly estimates persistence probabilities based on the latest medication adherence data, demonstrating integrated nested Laplace approximation's quick updates and prediction capability. The simulation study validates the reliability of our modeling approach, evidenced by minimal bias and appropriate credible interval coverage probabilities across different quantile levels.