A clinical tool to predict overall survival of elderly patients with soft tissue sarcoma after surgical resection.

With the aging world population, the incidence of soft tissue sarcoma (STS) in the elderly gradually increases and the prognosis is poor. The primary goal of this research was to analyze the relevant risk factors affecting the postoperative overall survival in elderly STS patients and to provide some guidance and assistance in clinical treatment. The study included 2,353 elderly STS patients from the Surveillance, Epidemiology, and End Results database. To find independent predictive variables, we employed the Cox proportional risk regression model. R software was used to develop and validate the nomogram model to predict postoperative overall survival. The performance and practical value of the nomogram were evaluated using calibration curves, the area under the curve, and decision curve analysis. Age, tumor primary site, disease stage, tumor size, tumor grade, N stage, and marital status, are the risk variables of postoperative overall survival, and the prognostic model was constructed on this basis. In the two sets, both calibration curves and receiver operating characteristic curves showed that the nomogram had high predictive accuracy and discriminative power, while decision curve analysis demonstrated that the model had good clinical usefulness. A predictive nomogram was designed and tested to evaluate postoperative overall survival in elderly STS patients. The nomogram allows clinical practitioners to more accurately evaluate the prognosis of individual patients, facilitates the progress of individualized treatment, and provides clinical guidance.

A bibliometric analysis of speech and language impairments in Parkinson's disease based on Web of Science.

Many individuals with Parkinson's disease suffer from speech and language impairments that significantly impact their quality of life. Despite several studies on these disorders, there is a lack of relevant bibliometric analyses. This paper conducted a bibliometric analysis of 3,610 papers on speech and language impairments in Parkinson's disease patients from January 1961 to November 2023, based on the Web of Science Core Collection database. Using Citespace software, the analysis focused on annual publication volume, cooperation among countries and institutions, author collaborations, journals, co-citation references, and keywords, aiming to explore the current research status, hotspots, and frontiers in this field. The number of annual publications related to speech and language impairment in Parkinson's disease have been increasing over the years. The USA leads in the number of publications. Research hotspots include the mechanism underlying speech and language impairments, clinical symptoms, automated diagnosis and classification of patients with PD using linguistic makers, and rehabilitation interventions.

The survival prediction of advanced colorectal cancer received neoadjuvant therapy-a study of SEER database.

PURPOSE: The aim of study was to screen factors associated with the overall survival of colorectal cancer patients with lymph nodes metastasis who received neoadjuvant therapy and construct a nomogram model. METHODS: All enrolled subjects of the SEER database were randomly assigned to the training and testing group in a ratio of 3:2. The patients of Tangdu Hospital were seemed as validation group. Univariate cox regression analysis, lasso regression and random forest survival were used to screen variables related to the survival of advanced CRC patients received neoadjuvant therapy in the training group. Area under curves were adopted to evaluate the 1,3,5-year prediction value of the optimal model in three cohorts. Calibration curves were drawn to observe the prediction accuracy of the nomogram model. Decision curve analysis was used to assess the potential clinical value of the nomogram model. RESULTS: A total of 1833 subjects were enrolled in this study. After random allocation, 1055 cases of the SEER database served as the training group, 704 cases as the testing group and 74 patients from our center as the external validation group. Variables were screened by univariate cox regression used to construct a nomogram survival prediction model, including M, age, chemotherapy, CEA, perineural invasion, tumor size, LODDS, liver metastasis and radiation. The AUCs of the model for predicting 1-year OS in the training group, testing and validation group were 0.765 (0.703,0.827), 0.772 (0.697,0.847) and 0.742 (0.601,0.883), predicting 3-year OS were 0.761 (0.725,0.780), 0.742 (0.699,0.785), 0.733 (0.560,0.905) and 5-year OS were 0.742 (0.711,0.773), 0.746 (0.709,0.783), 0.838 (0.670,0.980), respectively. The calibration curves showed the difference between prediction probability of the model and the actual survival was not significant in three cohorts and the decision curve analysis revealed the practice clinical application value. And the prediction value of model was better for young CRC than older CRC patients. CONCLUSION: A nomogram model including LODDS for the prognosis of advanced CRC received neoadjuvant therapy was constructed and verified based on the SEER database and single center practice. The accuracy and potential clinical application value of the model performed well, and the model had better predictive value for EOCRC than LOCRC.

A DPC Database Study on the Safety of Atezolizumab/Bevacizumab/Carboplatin/Paclitaxel in Non-small Cell Lung Cancer in Japanese Patients.

INTRODUCTION: Atezolizumab, bevacizumab, carboplatin, and paclitaxel (ABCP) combination therapy is a standard of care for advanced non-squamous non-small cell lung cancer (NSQ-NSCLC); however, the lack of safety data limits its clinical application in Japan. METHODS: This study compared the safety of ABCP with that of bevacizumab, carboplatin, and paclitaxel (BCP) combination for the treatment of advanced NSQ-NSCLC in Japanese patients by evaluating the clinical background and incidence of adverse events (AEs) based on data extracted from the Diagnosis Procedure Combination (DPC) database. Incidence rates and restricted mean survival times (RMSTs) for up to 1 year were analyzed for 19 clinically important AEs. Covariates were adjusted using the inverse probability weighting method. RESULTS: A search conducted using the International Statistical Classification of Diseases and Related Health Problems 10th Revision codes identified 350,987 patients, of whom 202 were included in the ABCP cohort and 232 in the BCP cohort. Among the 19 AEs, the incidence of skin disorder and febrile neutropenia (FN) was significantly higher in the ABCP cohort versus the BCP cohort. The adjusted incidence rate ratios were 2.65 [95% confidence interval (CI) 1.43-4.91] for skin disorder and 1.70 (95% CI 1.01-2.85) for FN. The adjusted RMST differences were - 64.2 days (95% CI - 93.0 to - 35.4 days) and - 46.0 days (95% CI - 73.5 to - 18.5 days) for skin disorder and FN, respectively. These results were comparable to those of other pivotal clinical trials. CONCLUSIONS: The findings of this DPC database study highlight the safety of ABCP in Japanese clinical practice, and this methodology may facilitate more efficient research in real-world settings. TRIAL REGISTRATION: UMIN Clinical Trials Registry ID UMIN000041507.

Unveiling the untreated: development of a database algorithm to identify potential Fabry disease patients in Germany.

BACKGROUND: Fabry disease (FD), an X-linked lysosomal storage disorder, is caused by mutations in the gene encoding α-galactosidase A, resulting in lysosomal accumulation of globotriaosylceramide and other glycosphingolipids. Early detection of FD is challenging, accounting for delayed diagnosis and treatment initiation. This study aimed to develop an algorithm using a logistic regression model to facilitate early identification of patients based on ICD-10-GM coding using a German Sickness Fund Database. METHODS: The logistic regression model was fitted on a binary outcome variable based on either a treated FD cohort or a control cohort (without FD). Comorbidities specific to the involved organs were used as covariates to identify potential FD patients with ICD-10-GM E75.2 diagnosis but without any FD-specific medication. Specificity and sensitivity of the model were optimized to determine a likely threshold. The cut-point with the largest values for the Youden index and concordance probability method and the lowest value for closest to (0,1) was identified as 0.08 for each respective value. The sensitivity and specificity for this cut-point were 80.4% and 79.8%, respectively. Additionally, a sensitivity analysis of the potential FD patients with at least two codes of E75.2 diagnoses was performed. RESULTS: A total of 284 patients were identified in the potential FD cohort using the logistic regression model. Most potential FD patients were < 30 years old and female. The identification and incidence rates of FD in the potential FD cohort were markedly higher than those of the treated FD cohort. CONCLUSIONS: This model serves as a tool to identify potential FD patients using German insurance claims data.

Analysis of cancer-specific survival in patients with metastatic colorectal cancer: A evidence-based medicine study.

BACKGROUND: Metastatic colorectal cancer (mCRC) is a common malignancy whose treatment has been a clinical challenge. Cancer-specific survival (CSS) plays a crucial role in assessing patient prognosis and treatment outcomes. However, there is still limited research on the factors affecting CSS in mCRC patients and their correlation. AIM: To predict CSS, we developed a new nomogram model and risk grading system to classify risk levels in patients with mCRC. METHODS: Data were extracted from the United States Surveillance, Epidemiology, and End Results database from 2018 to 2023. All eligible patients were randomly divided into a training cohort and a validation cohort. The Cox proportional hazards model was used to investigate the independent risk factors for CSS. A new nomogram model was developed to predict CSS and was evaluated through internal and external validation. RESULTS: A multivariate Cox proportional risk model was used to identify independent risk factors for CSS. Then, new CSS columns were developed based on these factors. The consistency index (C-index) of the histogram was 0.718 (95%CI: 0.712-0.725), and that of the validation cohort was 0.722 (95%CI: 0.711-0.732), indicating good discrimination ability and better performance than tumor-node-metastasis staging (C-index: 0.712-0.732). For the training set, 0.533, 95%CI: 0.525-0.540; for the verification set, 0.524, 95%CI: 0.513-0.535. The calibration map and clinical decision curve showed good agreement and good potential clinical validity. The risk grading system divided all patients into three groups, and the Kaplan-Meier curve showed good stratification and differentiation of CSS between different groups. The median CSS times in the low-risk, medium-risk, and high-risk groups were 36 months (95%CI: 34.987-37.013), 18 months (95%CI: 17.273-18.727), and 5 months (95%CI: 4.503-5.497), respectively. CONCLUSION: Our study developed a new nomogram model to predict CSS in patients with synchronous mCRC. In addition, the risk-grading system helps to accurately assess patient prognosis and guide treatment.

'X' marks the spot! Utilising factor Xa inhibitors to optimise thromboprophylaxis in multiple myeloma.

Venous thromboembolism (VTE) remains a significant cause of morbidity and mortality among multiple myeloma patients. Chang and colleagues' findings indicate that factor Xa inhibitors are as effective as warfarin in preventing VTE without raising the risk of gastrointestinal or intracranial bleeding complications. Commentary on: Chang et al. The comparative efficacy and safety of factor Xa inhibitors and warfarin for primary thromboprophylaxis in multiple myeloma patients undergoing immunomodulatory therapy. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19612.

Changing clinical practice and prognosis for severe respiratory failure over time: A nationwide inpatient database study.

BACKGROUND: Severe respiratory failure requires numerous interventions and its clinical implementation changes over time. We aimed to clarify the clinical practice and prognosis of severe respiratory failure and its changes over time. METHODS: In a nationwide Japanese administrative database from 2016 to 2019, we identified nonoperative patients with severe respiratory failure without congestive heart failure as the main diagnosis who received mechanical ventilation (MV) for more than four days. We examined trends in patient characteristics, adjunctive interventions, and prognosis. RESULTS: Among 66,905 patients included in this study, patients received antibiotics (90%), high-dose corticosteroids (14%), low-dose corticosteroids (18%), and 51% were admitted to the critical care unit. Hospital mortality was 35%. Median mechanical ventilation lasted 10 days. Tracheostomy occurred in 23% of cases. Median critical care and hospital stays were 10 and 25 days, respectively. Among survivors, 23% had mechanical ventilation dependency at hospital discharge. Large relative changes in adjunctive therapies included fentanyl (30%-38%), rocuronium (4.4%-6.7%), vasopressin (3.8%-6.0%), early rehabilitation (27%-38%), extracorporeal membrane oxygenation (0.7%-1.2%), dopamine (15%-10%), and sivelestat (8.6%-3.5%). No notable changes were seen in mechanical ventilation duration, tracheostomy, critical care unit stay, hospital stay, or ventilator dependency at discharge, except for a slight reduction in hospital mortality (36%-34%). CONCLUSIONS: Several adjunctive therapies for severe respiratory failure changed from 2016 to 2019, with an increase in evidence-based practices and a slight decrease in hospital mortality.

Reduction in the need for surgery and mortality after early administration of fibrinolytics following empyema drainage.

OBJECTIVES: Although intrapleural administration of fibrinolytics is an important treatment option for the management of empyema, the addition of fibrinolytics failed to reduce the need for surgery and mortality in previous randomized controlled trials. This study aimed to investigate the effect of administrating fibrinolytics in the early phase (within 3 days of chest tube insertion) of empyema compared with late administration or no administration. METHODS: We used the Japanese Diagnosis Procedure Combination inpatient database to identify patients aged ≥ 16 years who were hospitalized and underwent chest tube drainage for empyema. A 1:2 propensity score matching and stabilized inverse probability of treatment weighting were conducted. RESULTS: Among the 16,265 eligible patients, 3,082 and 13,183 patients were categorized into the early and control group, respectively. The proportion of patients who underwent surgery was significantly lower in the early fibrinolytics group than in the control group; the odds ratio (95% confidence interval) was 0.69 (0.54-0.88) in the propensity score matching (P = 0.003) and 0.64 (0.50-0.80) in the stabilized inverse probability of treatment weighting analysis (P < 0.001). All-cause 30-day in-hospital mortality, length of hospital stay, duration of chest tube drainage, and total hospitalization costs were also more favorable in the early fibrinolytic group. CONCLUSIONS: The early administration of fibrinolytics may reduce the need for surgery and death in adult patients with empyema.

Association between frailty index based on laboratory tests and all-cause mortality in critically ill patients with heart failure.

BACKGROUND: The frailty index based on laboratory tests (FI-lab) can identify individuals at increased risk for adverse health outcomes. The association between the FI-lab and all-cause mortality in patients with heart failure (HF) in the intensive care unit (ICU) remains unknown. This study aimed to determine the correlation between FI-lab and all-cause mortality to evaluate the impact of FI-lab on the prognosis of critically ill patients with HF. METHODS: This retrospective observational study utilized data extracted from the Medical Information Mart for Intensive Care IV database. The FI-lab, which consists of 33 laboratory tests, was constructed. Patients were then grouped into quartiles (Q1-Q4) based on their FI-lab scores. Kaplan-Meier analysis was used to compare all-cause mortality among the four groups. A Cox proportional hazard analysis was conducted to examine the association between the FI-lab score and all-cause mortality. The incremental predictive value of adding FI-lab to classical disease severity scores was assessed using Harrell's C statistic, integrated discrimination improvement (IDI) and net reclassification improvement (NRI). RESULTS: Among 3021 patients, 838 (27.74%) died within 28 days, and 1400 (46.34%) died within a 360 day follow-up period. Kaplan-Meier analysis indicated that patients with higher FI-lab scores had significantly higher risks of all-cause mortality (log-rank P < 0.001). Multivariable Cox regression suggested that FI-lab, evaluated as a continuous variable (for each 0.01 increase), was associated with increased 28 day mortality [hazard ratio (HR) 1.02, 95% confidence interval (CI) (1.01-1.03), P < 0.001] and 360 day mortality [HR 1.02, 95% CI (1.01-1.02), P < 0.001]. When assessed in quartiles, the 28 day mortality risk [HR 1.66, 95% CI (1.28-2.15), P < 0.001] and 360 day mortality risk [HR 1.48, 95% CI (1.23-1.8), P < 0.001] were significantly higher for FI-lab Q4 compared with FI-lab Q1. FI-lab significantly improved the predictive capability of classical disease severity scores for 28 and 360 day mortality. CONCLUSIONS: In ICU patients diagnosed with HF, the FI-lab is a potent predictor of short-term and long-term mortality in critically ill patients with HF. The active use of FI-lab to identify high-risk groups among critically ill HF patients and initiate timely interventions may have significant value in improving the prognosis of critically ill patients with HF.

Census-Dependent Mortality of Ventilated Patients With COVID-19 in Israel: Noninterventional Observational Cohort Study.

BACKGROUND: The COVID-19 pandemic led to several surges in the mass hospitalization rate. Extreme increases in hospital admissions without adequate medical resources may increase mortality. No study has addressed the impact of daily census of ventilated patients on mortality in the context of the pandemic in a nationwide setting. OBJECTIVE: This study aimed to determine whether daily census of ventilated patients affected COVID-19 mortality rates nationwide in Israel. METHODS: We conducted a cohort study using nationwide, public-domain, population-based COVID-19 data of hospitalized patients from an Israeli database from March 11, 2020, until February 11, 2021. We included all COVID-19 hospital admissions, classified as mild to severe per the Centers for Diseases Control and Prevention classification irrespective of whether they were mechanically ventilated. Outcome measures were daily death rates and death rates expressed as a percentage of ventilated patients. RESULTS: During the study period (338 days from March 11, 2020, to February 11, 2021), 715,743 patients contracted and were clinically confirmed as having COVID-19. Among them, 5577 (0.78%) patients died. In total, 3398 patients were ventilated because of severe COVID-19. Daily mortality correlated with daily census of ventilated patients (R2=0.828, P<.001). The daily percent mortality of ventilated patients also correlated with the daily census of ventilated patients (R2=0.365, P<.001)-backward multiple regression analysis demonstrated that this positive correlation was still highly significant even when correcting for the average age or gender of ventilated patients (R2=0.4328, P<.001) or for the surge in their number. Overall, 40% of the variation in mortality was explained by variations in the daily census of ventilated patients. ANOVA revealed that at less than 50 ventilated patients per day, the daily mortality of ventilated patients was slightly above 5%, and it nearly doubled (10%) with 50-149 patients; moreover, in all categories of ≥200 patients ventilated per day, it more than tripled at ≥15% (P<.001). CONCLUSIONS: Daily mortality rates per ventilated patient increased with an increase in the number of ventilated patients, suggesting the saturation of medical resources. Policy makers should be aware that expanding medical services without adequate resources may increase mortality. Governments should perform similar analyses to provide indicators of system saturation, although further validation of these results might be needed to use this indicator to drive public policy.

Normalized lactate load as an independent prognostic indicator in patients with cardiogenic shock.

BACKGROUND: Early prognosis evaluation is crucial for decision-making in cardiogenic shock (CS) patients. Dynamic lactate assessment, for example, normalized lactate load, has been a better prognosis predictor than single lactate value in septic shock. Our objective was to investigate the correlation between normalized lactate load and in-hospital mortality in patients with CS. METHODS: Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The calculation of lactate load involved the determination of the cumulative area under the lactate curve, while normalized lactate load was computed by dividing the lactate load by the corresponding period. Receiver Operating Characteristic (ROC) curves were constructed, and the evaluation of areas under the curves (AUC) for various parameters was performed using the DeLong test. RESULTS: Our study involved a cohort of 1932 CS patients, with 687 individuals (36.1%) experiencing mortality during their hospitalization. The AUC for normalized lactate load demonstrated significant superiority compared to the first lactate (0.675 vs. 0.646, P < 0.001), maximum lactate (0.675 vs. 0.651, P < 0.001), and mean lactate (0.675 vs. 0.669, P = 0.003). Notably, the AUC for normalized lactate load showed comparability to that of the Sequential Organ Failure Assessment (SOFA) score (0.675 vs. 0.695, P = 0.175). CONCLUSION: The normalized lactate load was an independently associated with the in-hospital mortality among CS patients.

PCMD: A multilevel comparison database of intra- and cross-species metabolic profiling in 530 plant species.

Comparative metabolomics plays a crucial role in investigating gene function, exploring metabolite evolution, and accelerating crop genetic improvement. However, a systematic platform for comparing intra- and cross-species metabolites is currently lacking. Here, we report the Plant Comparative Metabolome Database (PCMD; http://yanglab.hzau.edu.cn/PCMD), a multilevel comparison database based on predicted metabolic profiles in 530 plant species. PCMD serves as a platform for comparing metabolite characteristics at various levels, including species, metabolites, pathways, and biological taxonomy. The database also provides a series of user-friendly online tools, such as Species-comparison, Metabolites-enrichment, and ID conversion, enabling users to perform comparisons and enrichment analyses of metabolites across different species. In addition, PCMD establishes a unified system based on existing metabolite-related databases by standardizing metabolite numbering. PCMD is the most species-rich comparative plant metabolomics database currently available, and a case study demonstrated its capability to provide new insights into understanding plant metabolic diversity.

Association of Definitive Radiotherapy for Esophageal Cancer and the Incidence of Secondary Head and Neck Cancers: A SEER Population-Based Study.

Background: Impact of radiotherapy (RT) for esophageal cancer (EC) patients on the development of secondary head and neck cancer (SHNC) remains equivocal. The objective of this study was to investigate the link between definitive RT used for EC treatment and subsequent SHNC. Methods: This study was conducted using the Surveillance, Epidemiology, and End Results (SEER) database to collect the data of primary EC patients. Fine-Gray competing risk regression and standardized incidence ratio (SIR) and propensity score matching (PSM) method were used to match SHNC patients with only primary head and neck cancer (HNC) patients. Overall survival (OS) rates were applied by Kaplan-Meier analysis. Results: In total, 14,158 EC patients from the SEER database were included, of which 9,239 patients (65.3%) received RT and 4,919 patients (34.7%) received no radiation therapy (NRT). After a 12-month latency period, 110 patients (1.2%) in the RT group and 36 patients (0.7%) in the NRT group experienced the development of SHNC. In individuals with primary EC, there was an increased incidence of SHNC compared to the general US population (SIR = 5.95, 95% confidence interval (CI): 5.15 - 6.84). Specifically, the SIR for SHNC was 8.04 (95% CI: 6.78 - 9.47) in the RT group and 3.51 (95% CI: 2.64 - 4.58) in the NRT group. Patients who developed SHNC after RT exhibited significantly lower OS compared to those after NRT. Following PSM, the OS of patients who developed SHNC after RT remained significantly lower than that of matched patients with only primary HNC. Conclusion: An association was discovered between RT for EC and increased long-term risk of SHNC. This work enables radiation oncologists to implement mitigation strategies to reduce the long-term risk of SHNC in patients who have received RT following primary EC.

AI as a Medical Device Adverse Event Reporting in Regulatory Databases: Protocol for a Systematic Review.

BACKGROUND: The reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals. OBJECTIVE: The systematic review outlined in this protocol aims to yield insights into the frequency and severity of AEs while characterizing the events using existing regulatory guidance. METHODS: Publicly accessible AE databases will be searched to identify AE reports for AIaMD. Scoping searches have identified 3 regulatory territories for which public access to AE reports is provided: the United States, the United Kingdom, and Australia. AEs will be included for analysis if an artificial intelligence (AI) medical device is involved. Software as a medical device without AI is not within the scope of this review. Data extraction will be conducted using a data extraction tool designed for this review and will be done independently by AUK and a second reviewer. Descriptive analysis will be conducted to identify the types of AEs being reported, and their frequency, for different types of AIaMD. AEs will be analyzed and characterized according to existing regulatory guidance. RESULTS: Scoping searches are being conducted with screening to begin in April 2024. Data extraction and synthesis will commence in May 2024, with planned completion by August 2024. The review will highlight the types of AEs being reported for different types of AI medical devices and where the gaps are. It is anticipated that there will be particularly low rates of reporting for indirect harms associated with AIaMD. CONCLUSIONS: To our knowledge, this will be the first systematic review of 3 different regulatory sources reporting AEs associated with AIaMD. The review will focus on real-world evidence, which brings certain limitations, compounded by the opacity of regulatory databases generally. The review will outline the characteristics and frequency of AEs reported for AIaMD and help regulators and policy makers to continue developing robust safety monitoring processes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48156.

Association between lactate/albumin ratio and prognosis in critically ill patients with acute kidney injury undergoing continuous renal replacement therapy.

BACKGROUND: The primary objective was to examine the association between the lactate/albumin ratio (LAR) and the prognosis of patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT). METHODS: Utilizing the Medical Information Mart for Intensive Care IV (MIMIC-IV, v2.0) database, we categorized 703 adult AKI patients undergoing CRRT into survival and non-survival groups based on 28-day mortality. Patients were further grouped by LAR tertiles: low (< 0.692), moderate (0.692-1.641), and high (> 1.641). Restricted cubic splines (RCS), Least Absolute Shrinkage and Selection Operator (LASSO) regression, inverse probability treatment weighting (IPTW), and Kaplan-Meier curves were employed. RESULTS: In our study, the patients had a mortality rate of 50.07% within 28 days and 62.87% within 360 days. RCS analysis revealed a non-linear correlation between LAR and the risk of mortality at both 28 and 360 days. Cox regression analysis, which was adjusted for nine variables identified by LASSO, confirmed that a high LAR (>1.641) served as an independent predictor of mortality at these specific time points (p < 0.05) in AKI patients who were receiving CRRT. These findings remained consistent even after IPTW adjustment, thereby ensuring a reliable and robust outcome. Kaplan-Meier survival curves exhibited a gradual decline in cumulative survival rates at both 28 and 360 days as the LAR values increased (log-rank test, χ2 = 48.630, p < 0.001; χ2 = 33.530, p < 0.001). CONCLUSION: A high LAR (>1.641) was found to be an autonomous predictor of mortality at both 28 and 360 days in critically ill patients with AKI undergoing CRRT.

Disproportionality Analysis of Nusinersen in the Food and Drug Administration Adverse Event Reporting System: A Real-World Postmarketing Pharmacovigilance Assessment.

BACKGROUND: Nusinersen is the first drug for precise targeted therapy of spinal muscular atrophy, a rare disease that occurs in one of 10,000 to 20,000 live births. Therefore, thorough and comprehensive reports on the safety of nusinersen in large, real-world populations are necessary. This study aimed to mine the adverse event (AE) signals related to nusinersen through the Food and Drug Administration Adverse Event Reporting System (FAERS) database. METHODS: We extracted reports of AEs with nusinersen as the primary suspect from FAERS between December 2016 and March 2023. Reporting odds ratio (ROR) and Bayesian confidence propagation neural network (BCPNN) were used for AE signal detection. RESULTS: We extracted a total of 4807 suspected AE cases with nusinersen as the primary suspect from the FAERS database. Among them, 106 positive signals were obtained using the ROR and BCPNN. The highest frequency reported systemic organ class was general disorders and administration site conditions. Common clinical AEs of nusinersen were detected in the FAERS database, such as pneumonia, vomiting, back pain, headache, pyrexia, and post-lumbar puncture syndrome. In addition, we identified potential unexpected serious AEs through disproportionality analysis, including sepsis, seizure, epilepsy, brain injury, cardiorespiratory arrest, and cardiac arrest. CONCLUSIONS: Analyzing large amounts of real-world data from the FAERS database, we identified potential new AEs of nusinersen by disproportionate analysis. It is advantageous for health care professionals and pharmacists to concentrate on effectively managing high-risk AEs of nusinersen, improve medication levels in clinical settings, and uphold patient medication safety.

[Microbial metaproteomics--From sample processing to data acquisition and analysis].

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

A Real-world Study of Denosumab For Reducing Refracture Risk after Percutaneous Vertebral Augmentation.

OBJECTIVE: To investigate the use of anti-osteoporotic agents and refracture incidence in patients with osteoporotic vertebral compression fracture (OVCF) following percutaneous vertebral augmentation (PVA) and to evaluate the real-world treatment of patients using denosumab following PVA. This study aims to provide spine surgeons with empirical insights derived from real-world scenarios to enhance the management of bone health in OVCF patients. METHODS: This retrospective cohort study was based on data from the MarketScan and Optum databases from the USA. Female patients aged 55-90 years who underwent PVA for OVCF between January 2013 and March 2020 were included and followed up from the day after surgery. Patients who received at least one dose of denosumab were included in the denosumab cohort and were further divided into the on-treatment and off-treatment groups according to whether they received a second dose of denosumab, with follow-up beginning on the index day (225 days after the first denosumab dose). In this study, the off-treatment group was considered as the control group. Refracture incidence after PVA, the proportion of patients using anti-osteoporotic agents in the total study population, and refracture incidence after the index day in the denosumab cohort were analyzed. RESULTS: A total of 13,451 and 21,420 patients from the MarketScan and Optum databases, respectively, were included. In the denosumab cohort, the cumulative incidence of clinical osteoporotic fractures within 3 years after the index day was significantly lower in the on-treatment group than in the off-treatment group (MarketScan database: 23.0% vs 39.0%, p = 0.002; Optum database: 28.2% vs 40.0%, p = 0.023). The cumulative incidence of clinical vertebral fractures was also lower in the on-treatment group than in the off-treatment group, with a significant difference in the MarketScan database (14.4% vs 25.5%, p = 0.002) and a numerical difference was found in the Optum database (20.2% vs 27.5%, p = 0.084).The proportion of patients using anti-osteoporotic agents was low at 6 months postoperatively, with only approximately 7% using denosumab and 13%-15% taking oral bisphosphonates. CONCLUSION: Postmenopausal women have a high refracture rate and a low proportion of anti-osteoporotic drug use after PVA. Continued denosumab treatment after PVA is associated with a lower risk of osteoporotic and clinical vertebral fractures. Therefore, denosumab may be a treatment option for patients with osteoporosis after PVA.

Perioperative complications and cost of posterior decompression with fusion in thoracic spine for ossification of the posterior longitudinal ligament and ossification of the ligamentum flavum -a comparative study using a national inpatient database.

BACKGROUND: Although posterior decompression with fusion (PDF) are effective for treating thoracic myelopathy, surgical treatment has a high risk of various complications. There is currently no information available on the perioperative complications in thoracic ossification of the longitudinal ligament (T-OPLL) and thoracic ossification of the ligamentum flavum (T-OLF). We evaluate the perioperative complication rate and cost between T-OPLL and T-OLF for patients underwent PDF. METHODS: Patients undergoing PDF for T-OPLL and T-OLF from 2012 to 2018 were detected in Japanese nationwide inpatient database. One-to-one propensity score matching between T-OPLL and T-OLF was performed based on patient characteristics and preoperative comorbidities. We examined systemic and local complication rate, reoperation rate, length of hospital stays, costs, discharge destination, and mortality after matching. RESULTS: In a total of 2,660 patients, 828 pairs of T-OPLL and T-OLF patients were included after matching. The incidence of systemic complications did not differ significantly between the T-OPLL and OLF groups. However, local complications were more frequently occurred in T-OPLL than in T-OLF groups (11.4% vs. 7.7% P = 0.012). Transfusion rates was also significantly higher in the T-OPLL group (14.1% vs. 9.4%, P = 0.003). T-OPLL group had longer hospital stay (42.2 days vs. 36.2 days, P = 0.004) and higher medical costs (USD 32,805 vs. USD 25,134, P < 0.001). In both T-OPLL and T-OLF, the occurrence of perioperative complications led to longer hospital stay and higher medical costs. While fewer patients in T-OPLL were discharged home (51.6% vs. 65.1%, P < 0.001), patients were transferred to other hospitals more frequently (47.5% vs. 33.5%, P = 0.001). CONCLUSION: This research identified the perioperative complications of T-OPLL and T-OLF in PDF using a large national database, which revealed that the incidence of local complications was higher in the T-OPLL patients. Perioperative complications resulted in longer hospital stays and higher medical costs.