Optimizing the Combination of Common Clinical Concussion Batteries to Predict Persistent Postconcussion Symptoms in a Prospective Cohort of Concussed Youth

BACKGROUND

Studies have evaluated individual factors associated with persistent postconcussion symptoms (PPCS) in youth concussion, but no study has combined individual elements of common concussion batteries with patient characteristics, comorbidities, and visio-vestibular deficits in assessing an optimal model to predict PPCS.

PURPOSE

To determine the combination of elements from 4 commonly used clinical concussion batteries and known patient characteristics and comorbid risk factors that maximize the ability to predict PPCS.

STUDY DESIGN

Cohort study; Level of evidence, 2.

METHODS

We enrolled 198 concussed participants-87 developed PPCS and 111 did not-aged 8 to 19 years assessed within 14 days of injury from a suburban high school and the concussion program of a tertiary care academic medical center. We defined PPCS as a Post-Concussion Symptom Inventory (PCSI) score at 28 days from injury of ≥3 points compared with the preinjury PCSI score-scaled for younger children. Predictors included the individual elements of the visio-vestibular examination (VVE), Sport Concussion Assessment Tool, 5th Edition (SCAT-5), King-Devick test, and PCSI, in addition to age, sex, concussion history, and migraine headache history. The individual elements of these tests were grouped into interpretable factors using sparse principal component analysis. The 12 resultant factors were combined into a logistic regression and ranked by frequency of inclusion into the combined optimal model, whose predictive performance was compared with the VVE, initial PCSI, and the current existing predictive model (the Predicting and Prevention Postconcussive Problems in Pediatrics (5P) prediction rule) using the area under the receiver operating characteristic curve (AUC).

RESULTS

A cluster of 2 factors (SCAT-5/PCSI symptoms and VVE near point of convergence/accommodation) emerged. A model fit with these factors had an AUC of 0.805 (95% CI, 0.661-0.929). This was a higher AUC point estimate, with overlapping 95% CIs, compared with the PCSI (AUC, 0.773 [95% CI, 0.617-0.912]), VVE (AUC, 0.736 [95% CI, 0.569-0.878]), and 5P Prediction Rule (AUC, 0.728 [95% CI, 0.554-0.870]).

CONCLUSION

Among commonly used clinical assessments for youth concussion, a combination of symptom burden and the vision component of the VVE has the potential to augment predictive power for PPCS over either current risk models or individual batteries.

Handling time-varying treatments in observational studies: A scoping review and recommendations

OBJECTIVE

Time-varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time-varying treatments. This study aimed to examine the current methods for dealing with time-varying treatments in observational studies and developed practical recommendations.

METHODS

We searched PubMed from 2000 to 2021 for methodological articles about time-varying treatments, and qualitatively summarized the current methods for handling time-varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts.

RESULTS

Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time-varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time-varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time-independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention-to-treat, per-protocol, or as-treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings.

CONCLUSIONS

There is no consensus process for assessing treatment effects in observational studies with time-varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice.

Relevance of Protein Intake for Weaning in the Mechanically Ventilated Critically Ill: Analysis of a Large International Database

OBJECTIVES

The association between protein intake and the need for mechanical ventilation (MV) is controversial. We aimed to investigate the associations between protein intake and outcomes in ventilated critically ill patients.

DESIGN

Analysis of a subset of a large international point prevalence survey of nutritional practice in ICUs.

SETTING

A total of 785 international ICUs.

PATIENTS

A total of 12,930 patients had been in the ICU for at least 96 hours and required MV by the fourth day after ICU admission at the latest.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We modeled associations between the adjusted hazard rate (aHR) of death in patients requiring MV and successful weaning (competing risks), and three categories of protein intake (low: < 0.8 g/kg/d, standard: 0.8-1.2 g/kg/d, high: > 1.2 g/kg/d). We compared five different hypothetical protein diets (an exclusively low protein intake, a standard protein intake given early (days 1-4) or late (days 5-11) after ICU admission, and an early or late high protein intake). There was no evidence that the level of protein intake was associated with time to weaning. However, compared with an exclusively low protein intake, a standard protein intake was associated with a lower hazard of death in MV: minimum aHR 0.60 (95% CI, 0.45-0.80). With an early high intake, there was a trend to a higher risk of death in patients requiring MV: maximum aHR 1.35 (95% CI, 0.99-1.85) compared with a standard diet.

CONCLUSIONS

The duration of MV does not appear to depend on protein intake, whereas mortality in patients requiring MV may be improved by a standard protein intake. Adverse effects of a high protein intake cannot be excluded.

Comprehensive study of mechanical power in controlled mechanical ventilation: Prevalence of elevated mechanical power and component analysis

OBJECTIVE

To determine the prevalence of elevated mechanical power (MP) values (>17J/min) used in routine clinical practice.

DESIGN

Observational, descriptive, cross-sectional, analytical, multicenter, international study conducted on November 21, 2019, from 8:00 AM to 3:00 PM. NCT03936231.

SETTING

One hundred thirty-three Critical Care Units.

PATIENTS

Patients receiving invasive mechanical ventilation for any cause.

INTERVENTIONS

None.

MAIN VARIABLES OF INTEREST

Mechanical power.

RESULTS

A population of 372 patients was analyzed. PM was significantly higher in patients under pressure-controlled ventilation (PC) compared to volume-controlled ventilation (VC) (19.20±8.44J/min vs. 16.01±6.88J/min; p<0.001), but the percentage of patients with PM>17J/min was not different (41% vs. 35%, respectively; p=0.382). The best models according to AICcw expressing PM for patients in VC are described as follows: Surrogate Strain (Driving Pressure) + PEEP+Surrogate Strain Rate (PEEP/Flow Ratio) + Respiratory Rate. For patients in PC, it is defined as: Surrogate Strain (Expiratory Tidal Volume/PEEP) + PEEP+Surrogate Strain Rate (Surrogate Strain/Ti) + Respiratory Rate+Expiratory Tidal Volume+Ti.

CONCLUSIONS

A substantial proportion of mechanically ventilated patients may be at risk of experiencing elevated levels of mechanical power. Despite observed differences in mechanical power values between VC and PC ventilation, they did not result in a significant disparity in the prevalence of high mechanical power values.

Validation of the Hospital Frailty Risk Score in older adults hospitalized with community-acquired pneumonia

AIM

Frailty results from age-associated declines in physiological reserve and function and is prevalent in older people. Our aim is to examine the association of the Hospital Frailty Risk Score (HFRS) with adverse events in older patients hospitalized with community-acquired pneumonia (CAP) and hypothesise that frailty is a comparable predictor of outcomes in CAP versus traditional severity indices such as CURB-65.

METHODS

Retrospective review of electronic medical records in patients ≥65 years with CAP admitted to a tertiary hospital from 1 January to 30 April 2021. Patients were identified using ICD codes for CAP and categorized as high risk (>15), intermediate risk (5-15) and low risk (<5) of frailty using the HFRS.

RESULTS

Of 429 patients with CAP, 53.8% male, mean age of 82.9 years, older patients (85 vs. 79.7 years, P < 0.001) were at higher risk of frailty. Using the HFRS, 47.6% were deemed at high risk, 35.9% at intermediate risk, and 16.6% at low risk of frailty. Multivariate logistic regression shows that HFRS was more strongly associated (≥7 days, OR 1.042, CI 1.017-1.069) than CURB-65 (OR 0.995, CI 0.810-1.222) with long hospital length of stay (LOS), while CURB-65 (Confusion, Urea >7mmol/L, Respiratory rate >30, Blood pressure, age => 65 years old) was more strongly associated with mortality at 30, 90 and 365 days, compared with the HFRS. Comparing the values for the area under the receiver operator characteristic curve, the HFRS was found to be a better predictor of long LOS, while CURB-65 remains a better predictor of mortality.

CONCLUSIONS

Patients with high risk of frailty have higher healthcare utilization and HFRS is a better predictor of long LOS than CURB-65 but CURB-65 was a better predictor of mortality. Geriatr Gerontol Int 2024; 24: 135-141.

Deep Learning-Enhanced Hand Grip and Release Test for Degenerative Cervical Myelopathy: Shortening Assessment Duration to 6 Seconds

OBJECTIVE

Hand clumsiness and reduced hand dexterity can signal early signs of degenerative cervical myelopathy (DCM). While the 10-second grip and release (10-s G&R) test is a common clinical tool for evaluating hand function, a more accessible method is warranted. This study explores the use of deep learning-enhanced hand grip and release test (DL-HGRT) for predicting DCM and evaluates its capability to reduce the duration of the 10-s G&R test.

METHODS

The retrospective study included 508 DCM patients and 1,194 control subjects. Propensity score matching (PSM) was utilized to minimize the confounding effects related to age and sex. Videos of the 10-s G&R test were captured using a smartphone application. The 3D-MobileNetV2 was utilized for analysis, generating a series of parameters. Additionally, receiver operating characteristic curves were employed to assess the performance of the 10-s G&R test in predicting DCM and to evaluate the effectiveness of a shortened testing duration.

RESULTS

Patients with DCM exhibited impairments in most 10-s G&R test parameters. Before PSM, the number of cycles achieved the best diagnostic performance (area under the curve [AUC], 0.85; sensitivity, 80.12%; specificity, 74.29% at 20 cycles), followed by average grip time. Following PSM for age and gender, the AUC remained above 0.80. The average grip time achieved the highest AUC of 0.83 after 6 seconds, plateauing with no significant improvement in extending the duration to 10 seconds, indicating that 6 seconds is an adequate timeframe to efficiently evaluate hand motor dysfunction in DCM based on DL-HGRT.

CONCLUSION

DL-HGRT demonstrates potential as a promising supplementary tool for predicting DCM. Notably, a testing duration of 6 seconds appears to be sufficient for accurate assessment, enhancing the test more feasible and practical without compromising diagnostic performance.

Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records

BACKGROUND

Systemic inflammatory response syndrome (SIRS) and sepsis are the most common causes of in-hospital death. However, the characteristics associated with the improvement in the patient conditions during the ICU stay were not fully elucidated for each population as well as the possible differences between the two.

GOAL

The aim of this study is to highlight the differences between the prognostic clinical features for the survival of patients diagnosed with SIRS and those of patients diagnosed with sepsis by using a multi-variable predictive modeling approach with a reduced set of easily available measurements collected at the admission to the intensive care unit (ICU).

METHODS

Data were collected from 1,257 patients (816 non-sepsis SIRS and 441 sepsis) admitted to the ICU. We compared the performance of five machine learning models in predicting patient survival. Matthews correlation coefficient (MCC) was used to evaluate model performances and feature importance, and by applying Monte Carlo stratified Cross-Validation.

RESULTS

Extreme Gradient Boosting (MCC = 0.489) and Logistic Regression (MCC = 0.533) achieved the highest results for SIRS and sepsis cohorts, respectively. In order of importance, APACHE II, mean platelet volume (MPV), eosinophil counts (EoC), and C-reactive protein (CRP) showed higher importance for predicting sepsis patient survival, whereas, SOFA, APACHE II, platelet counts (PLTC), and CRP obtained higher importance in the SIRS cohort.

CONCLUSION

By using complete blood count parameters as predictors of ICU patient survival, machine learning models can accurately predict the survival of SIRS and sepsis ICU patients. Interestingly, feature importance highlights the role of CRP and APACHE II in both SIRS and sepsis populations. In addition, MPV and EoC are shown to be important features for the sepsis population only, whereas SOFA and PLTC have higher importance for SIRS patients.

Emerging importance of multidrug-resistant Stenotrophomonas maltophilia infections in neonatal intensive care unit in a tertiary center in Turkey

OBJECTIVE

Stenotrophomonas maltophilia is an emerging multi-drug resistant, opportunistic pathogen in the neonatal intensive care unit (NICU). In this study, we aimed to assess the incidence, clinical features, antibiotic susceptibility, and treatment options of S. maltophilia infection among the healthcare-associated infections (HAIs) in the neonatal unit.

METHODS

In this study, the patients who were hospitalized in the NICU between January 2020 and December 2021 with S. maltophilia isolated from clinical samples were included. Demographic, clinic features, and microbiological findings of the patients were retrospectively evaluated by using the medical records. The samples (lower respiratory tract, urine, peritoneal fluid) were first examined microscopically by gram preparation and cultured. Antibiotic susceptibility tests were performed according to the recommendations of The European Committee on Antimicrobial Susceptibility Testing (EUCAST) for TMP-SMX.

RESULTS

S. maltophilia was isolated in 38 clinical samples of the 20 patients who were hospitalized at the NICU between January 2020 and December 2021. A total of 40 % (n = 8) of samples from different patients were accepted as colonization. Ventilator-associated pneumonia was determined in 55 % (n = 11), and urinary tract infection in 5 % (n = 1). S. maltophilia-associated bacteremia was not detected in any of the cases. The TMP-SMX susceptibilities of the strains were as it follows: 3 (15 %) were resistant (R), 7 (28 %) were susceptible (S), and 10 (47 %) were susceptible-increased exposure (I). Three of these patients were given dual antibiotics therapy (levofloxacin plus TMP-SMX) and nine of them were given only TMP-SMX. The most common hospital-acquired infectious agents are Gram negative microorganisms (51 %), followed by coagulase negative staphylococci (CNS), Staphylococcus aureus (24 %) and S. maltophilia (24 %).

CONCLUSION

Increasing TMP-SMX resistance and specific drug and dosage-related problems in the neonatal unit are important problems in treatment management.

Relationship between work-related psychosocial factors and self-leadership in advanced nurse practitioners: A cross-sectional study

AIM

The aim of this study was to investigate the relationship between advanced nurse practitioners' self-leadership and commitment to the workplace, work engagement and influence at work.

BACKGROUND

The concept of self-leadership is particularly suited to ANPs, who are required to take responsibility for their work roles. An optimum balance between the ANPs' psychosocial work environment and self-leadership may positively impact work ability in this group and can be compromised by interactions between and among these variables.

DESIGN

A cross-sectional correlational study was conducted from July 2020 to August 2020 on 153 ANPs across a national health service.

METHODS

The survey was distributed to respondents online. The revised self-leadership questionnaire was used to measure self-leadership, and three scales from the Copenhagen Psychosocial Questionnaire were used to measure commitment to the workplace, work engagement and influence at work. Multiple linear regression was used to examine the association between self-leadership and the psychosocial variables.

RESULTS

ANPs with high levels of self-leadership reported high levels of work engagement and commitment to the workplace. No relationship was found between self-leadership and influence at work.

CONCLUSION

Improving self-leadership among ANPs by involving them in strategic leadership activities at an organizational level could be an effective strategy for optimizing the role and facilitating ANPs to contribute at an organizational level beyond the clinical interface. However, organizational support is required to ensure that ANPs practise to the full potential of their training and capability.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

IMPACT

This study provided new evidence of a relationship between ANPs' self-leadership and psychosocial factors. This study found that ANPs with high levels of self-leadership reported high levels of work engagement and commitment to the workplace. Policymakers and organizational leaders can optimize the ANP role and facilitate ANPs to contribute strategically to improve care systems. This study identifies a relationship between ANPs' self-leadership and specific psychosocial variables.

Perceived Quality of Life in Intensive Care Medicine Physicians: A French National Survey

Purpose: There is a growing interest in the quality of work life (QWL) of healthcare professionals and staff well-being. We decided to measure the perceived QWL of ICU physicians and the factors that could influence their perception. Methods: We performed a survey coordinated and executed by the French Trade Union of Intensive Care Physicians (SMR). QWL was assessed using the French version of the Work-Related Quality of Life (WRQoL) scale, perceived stress using the French version of 10 item-Perceived Stress Scale (PSS-10) and group functioning using the French version of the Reflexivity Scale, the Social Support at Work Questionnaire (QSSP-P). Results: 308 French-speaking ICU physicians participated. 40% perceived low WRQoL, mainly due to low general well-being, low satisfaction with working conditions and low possibility of managing the articulation between their private and professional lives. Decreased QWL was associated with being a woman (p = .002), having children (p = .022) and enduring many monthly shifts (p = .022). Conclusions: This work highlights the fact that ICU physicians feel a significant imbalance between the demands of their profession and the resources at their disposal. Communication and exchanges within a team and quality of social support appear to be positive elements to maintain and/or develop within our structures.

Prognostic prediction of sepsis patient using transformer with skip connected token for tabular data

Sepsis is known as a common syndrome in intensive care units (ICU), and severe sepsis and septic shock are among the leading causes of death worldwide. The purpose of this study is to develop a deep learning model that supports clinicians in efficiently managing sepsis patients in the ICU by predicting mortality, ICU length of stay (>14 days), and hospital length of stay (>30 days). The proposed model was developed using 591 retrospective data with 16 tabular data related to a sequential organ failure assessment (SOFA) score. To analyze tabular data, we designed the modified architecture of the transformer that has achieved extraordinary success in the field of languages and computer vision tasks in recent years. The main idea of the proposed model is to use a skip-connected token, which combines both local (feature-wise token) and global (classification token) information as the output of a transformer encoder. The proposed model was compared with four machine learning models (ElasticNet, Extreme Gradient Boosting [XGBoost]), and Random Forest) and three deep learning models (Multi-Layer Perceptron [MLP], transformer, and Feature-Tokenizer transformer [FT-Transformer]) and achieved the best performance (mortality, area under the receiver operating characteristic (AUROC) 0.8047; ICU length of stay, AUROC 0.8314; hospital length of stay, AUROC 0.7342). We anticipate that the proposed model architecture will provide a promising approach to predict the various clinical endpoints using tabular data such as electronic health and medical records.

Emergency department prediction model for 30-day mortality after hip fracture: the Spanish National Hip Fracture Registry (RNFC) cohort

INTRODUCTION

The aim of this study was to design and validate a predictive model for 30-day mortality in a cohort of patients from the Spanish National Hip Fracture Registry (RNFC) with variables collected at the Emergency Department.

METHODS

Retrospective study of a prospective database of hip fracture patients ⩾75 years old between 1 January 2017 and 30 September 2019. Patient characteristics, type of fracture and osteoprotective medication were collected at the Emergency Department. Univariate analysis compared the results between patients alive and deceased 30 days after hospital discharge. The variables associated with 30-day mortality in the regression analysis were age >85 years, male sex, indoors pre-fracture mobility, dementia, ASA score >3, pathological fracture, and vitamin D intake. A score scale was created with these variables. Discriminative performance was assessed using the area under the curve (AUC), calibration was assessed by applying Hosmer-Lemeshow goodness-of-fit test and predicted-to-observed mortality was compared.

RESULTS

A total of 29,875 hip fracture cases were included in the study. The 30-day mortality of the overall cohort was 7.7%. A scale of 0-9 points was created, with a cut-off point of 4 points for the determination of patients at high risk of mortality. The AUC was 0.886. RNFC score presented good level of calibration (p = 0.139). The predicted-to-observed ratio was 1.09.

CONCLUSIONS

The RNFC predictive model with variables collected at the Emergency Department showed an excellent predictive capacity for 30-day mortality in patients after hip fracture.

Merkel cell carcinoma recurrence risk estimation is improved by integrating factors beyond cancer stage: A multivariable model and web-based calculator

BACKGROUND

Merkel cell carcinoma (MCC) recurs in 40% of patients. In addition to stage, factors known to affect recurrence risk include: sex, immunosuppression, unknown primary status, age, site of primary tumor, and time since diagnosis.

PURPOSE

Create a multivariable model and web-based calculator to predict MCC recurrence risk more accurately than stage alone.

METHODS

Data from 618 patients in a prospective cohort were used in a competing risk regression model to estimate recurrence risk using stage and other factors.

RESULTS

In this multivariable model, the most impactful recurrence risk factors were: American Joint Committee on Cancer stage (P < .001), immunosuppression (hazard ratio 2.05; P < .001), male sex (1.59; P = .003) and unknown primary (0.65; P = .064). Compared to stage alone, the model improved prognostic accuracy (concordance index for 2-year risk, 0.66 vs 0.70; P < .001), and modified estimated recurrence risk by up to 4-fold (18% for low-risk stage IIIA vs 78% for high-risk IIIA over 5 years).

LIMITATIONS

Lack of an external data set for model validation.

CONCLUSION/RELEVANCE

As demonstrated by this multivariable model, accurate recurrence risk prediction requires integration of factors beyond stage. An online calculator based on this model (at merkelcell.org/recur) integrates time since diagnosis and provides new data for optimizing surveillance for MCC patients.

Nomogram to predict risk of early mortality following definitive or adjuvant radiation and systemic therapy for head and neck cancer

Purpose/Objectives

We sought to create nomograms to predict individual risk of early mortality, which can identify patients who require interventions to prevent early death.

Methods

We included patients in the National Cancer Database with non-metastatic squamous cell carcinoma of the head and neck who received radiation and systemic therapy between 2004 and 2017 in the definitive or adjuvant setting. Early mortality was defined as any death less than 90 days after starting radiation. Multivariable logistic regression was used to assess the relationship between covariates and early mortality. Nomograms to predict the risk of early death were created for both the definitive and adjuvant settings.

Results

Among 84,563 patients in the definitive group and 18,514 patients in the adjuvant group, rates of early mortality were 3.5 % (95 % CI 3.4-3.7 %) and 2.2 %, (95 % CI 1.9-2.4 %), respectively. Patients above the age of 70 had an early mortality rate of 7.8 % (95 % CI 7.3-8.2 %) in the definitive group and 4.4 % (95 % CI 3.6-5.4 %) in the adjuvant group. In the multivariable analysis, age, comorbidity, T and N category, and tumor site were associated with early mortality in both cohorts (p < 0.05 for all). Nomograms including age, comorbidity, T and N category and tumor site performed better than age alone at predicting early mortality (AUC for definitive group: 0.70 vs 0.66; AUC for adjuvant group: 0.71 vs 0.61).

Conclusion

Nomograms including age, comorbidity, T and N category and tumor site were developed to predict the risk of early death following definitive or adjuvant chemoradiation.

The Role of Biomarkers in the Diagnosis and Management of Pneumonia

Biomarkers are used in the diagnosis, severity determination, and prognosis for patients with community-acquired pneumonia (CAP). Selected biomarkers may indicate a bacterial infection and need for antibiotic therapy (C-reactive protein, procalcitonin, soluble triggering receptor expressed on myeloid cells). Biomarkers can differentiate CAP patients who require hospital admission and severe CAP requiring intensive care unit admission. Biomarker-guided antibiotic therapy may limit antibiotic exposure without compromising outcome and thus improve antibiotic stewardship. The authors discuss the role of biomarkers in diagnosing, determining severity, defining the prognosis, and limiting antibiotic exposure in CAP and ventilator-associated pneumonia patients.

Comparison of eight prehospital early warning scores in life-threatening acute respiratory distress: a prospective, observational, multicentre, ambulance-based, external validation study

BACKGROUND

A myriad of early warning scores (EWSs) exist, yet there is a need to identify the most clinically valid score to be used in prehospital respiratory assessments to estimate short-term and midterm mortality, intensive-care unit admission, and airway management in life-threatening acute respiratory distress.

METHODS

This is a prospective, observational, multicentre, ambulance-based, external validation study performed in 44 ambulance services and four hospitals across three Spanish provinces (ie, Salamanca, Segovia, and Valladolid). We identified adults (ie, those aged 18 years and older) discharged to the emergency department with suspected acute respiratory distress. The primary outcome was 2-day all-cause in-hospital mortality, for all the patients or according to prehospital respiratory conditions, including dyspnoea, chronic obstructive pulmonary disease (COPD), COVID-19, other infections, and other conditions (asthma exacerbation, haemoptysis, and bronchoaspirations). 30-day mortality, intensive-care unit admission, and invasive and non-invasive mechanical ventilation were secondary outcomes. Eight EWSs, namely, the National Early Warning Score 2, the Modified Rapid Emergency Medicine Score, the Rapid Acute Physiology Score, the Quick Sequential Organ Failure Assessment Score, the CURB-65 Severity Score for Community-Acquired Pneumonia, the BAP-65 Score for Acute Exacerbation of COPD, the Quick COVID-19 Severity Index, and the Modified Sequential Organ Failure Assessment (mSOFA), were explored to determine their predictive validity through calibration, clinical net benefit as determined through decision curve analysis, and discrimination analysis (area under the curve of the receiver operating characteristic [AUROC], compared with Delong's test).

FINDINGS

Between Jan 1, 2020, and Nov 31, 2022, 902 patients were enrolled. The global 2-day mortality rate was 87 (10%); in proportion to various respiratory conditions, the rates were 35 (40%) for dyspnoea, nine (10%) for COPD, 13 (15%) for COVID-19, 28 (32%) for other infections, and two (2%) for others conditions. mSOFA showed the best calibration, a higher net benefit, and the best discrimination (AUROC 0·911, 95% CI 0·86-0·95) for predicting 2-day mortality, and its discrimination was statistically significantly more accurate (p<0·0001) compared with the other scores. The performance of mSOFA for predicting 2-day mortality was higher than the other scores when considering the prehospital respiratory conditions, and was also higher for the secondary outcomes, except for non-invasive mechanical ventilation.

INTERPRETATION

Our results showed that mSOFA outperformed other EWSs. The inclusion of mSOFA in prehospital decision making will entail a quick identification of patients in acute respiratory distress at high risk of deterioration, allowing prioritisation of resources and patient care.

FUNDING

Gerencia Regional de Salud, Public Health System of Castilla y León (GRS Spain).

TRANSLATION

For the Spanish translation of the abstract see Supplementary Materials section.

Critical roles of S100A12, MMP9, and PRTN3 in sepsis diagnosis: Insights from multiple microarray data analyses

BACKGROUND

Sepsis, characterized by systemic inflammatory response syndrome and life-threatening organ dysfunction, remains a significant global cause of disability and death. Despite its impact, reliable biomarkers for sepsis diagnosis are yet to be identified.

OBJECTIVE

This study aims to investigate and identify key genes and pathways in sepsis through the analysis of multiple microarray datasets, providing potential treatment targets for future clinical trials.

METHODS

Two independent gene expression profiles (GSE54514 and GSE69528) were downloaded from the Gene Expression Omnibus (GEO) database. After merging and batch normalization, differentially expressed genes (DEGs) were obtained using the "limma" package. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were performed using "R" software. A Protein-Protein Interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING). The top 10 hub genes were identified using Cytoscape. A Nomogram model for predicting sepsis occurrence was constructed and evaluated.

RESULTS

Bioinformatic analysis of 210 sepsis and 91 control blood samples identified 72 DEGs. GO analyses revealed associations with immune response processes. GSEA indicated involvement in key signaling pathways. S100A12, MMP9, and PRTN3 were identified as independent risk factors for sepsis.

CONCLUSION

This study unveils critical genes and pathways in sepsis through bioinformatic methods. S100A12, MMP9, and PRTN3 may play essential roles in the immune response to infection, influencing sepsis prognosis.

Influence of epicardial adipose tissue inflammation and adipocyte size on postoperative atrial fibrillation in patients after cardiovascular surgery

Epicardial adipose tissue (EAT) is an active endocrine organ that is closely associated with occurrence of atrial fibrillation (AF). However, the role of EAT in the development of postoperative AF (POAF) remains unclear. We aimed to investigate the association between EAT profile and POAF occurrence in patients who underwent cardiovascular surgery. We obtained EAT samples from 53 patients to evaluate gene expression, histological changes, mitochondrial oxidative phosphorylation (OXPHOS) capacity in the EAT, and protein secretion in EAT-conditioned medium. EAT volume was measured using computed tomography scan. Eighteen patients (34%) experienced POAF within 7 days after surgery. Although no significant difference was observed in EAT profile between patients with and without POAF, logistic regression analysis identified that the mRNA expression levels of tumor necrosis factor-alpha (TNF-α) were positively correlated and adipocyte size in the EAT was inversely correlated with onset of POAF, respectively. Mitochondrial OXPHOS capacity in the EAT was not associated with POAF occurrence; however, it showed an inverse correlation with adipocyte size and a positive correlation with adiponectin secretion. In conclusion, changes in the secretory profile and adipocyte morphology of the EAT, which represent qualitative aspects of the adipose tissue, were present before the onset of AF.

Placebo and Nocebo Responses in Pharmacological Trials of Tic Disorders: A Meta-Analysis

BACKGROUND

Clinical trials of new drugs for tic disorders (TD) often fail to yield positive results. Placebo and nocebo responses play a vital role in interpreting the outcomes of randomized controlled trials (RCTs), yet these responses in RCTs of TD remain unexplored.

OBJECTIVE

The aim was to assess the magnitude of placebo and nocebo responses in RCTs of pharmacological interventions for TD and identify influencing factors.

METHODS

A systematic search of the Embase, Medline, Cochrane Central Register of Controlled Trials, and PsycINFO databases was conducted. Eligible studies were RCTs that compared active pharmacological agents with placebos. Placebo response was defined as the change from baseline in TD symptom severity in the placebo group, and nocebo response as the proportion experiencing adverse events (AEs) in this group. Subgroup analysis and meta-regression were performed to explore modifying factors.

RESULTS

Twenty-four trials involving 2222 participants were included in this study. A substantial placebo response in TD symptom severity was identified, with a pooled effect size of -0.79 (95% confidence interval [CI] -0.99 to -0.59; I2 = 67%). Forty-four percent (95% CI 27% to 63%; I2 = 92%) of patients experienced AEs while taking inert pills. Sample size, study design, and randomization ratio were correlated with changes in placebo and nocebo responses.

CONCLUSION

There were considerable placebo and nocebo responses in TD clinical trials. These results are of great relevance for the design of future trials and for clinical practice in TD.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration ID CRD42023388397. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients

Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performance internally and externally in critically ill patients. A total of 21,163 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (BIDMC) were first included in building the model. Two external validation populations included 3025 patients from the Philips eICU Research Institute and 2625 patients from Zhongda Hospital Southeast University. A total of 152 intelligently engineered predictors were extracted on an hourly basis. The prediction model referred to as DeepAKI was designed with the basic framework of squeeze-and-excitation networks with dilated causal convolution embedded. The integrated gradients method was utilized to explain the prediction model. When performed on the internal validation set (3175 [15 %] patients from BIDMC) and the two external validation sets, DeepAKI obtained the area under the curve of 0.799 (95 % CI 0.791-0.806), 0.763 (95 % CI 0.755-0.771) and 0.676 (95 % CI 0.668-0.684) for continuousAKI prediction, respectively. For model interpretability, clinically relevant important variables contributing to the model prediction were informed, and individual explanations along the timeline were explored to show how AKI risk arose. The potential threats to generalisability in deep learning-based models when deployed across health systems in real-world settings were analyzed.

Increased Proteolytic Activity of Serratia marcescens Clinical Isolate HU1848 Is Associated with Higher eepR Expression

Serratia marcescens is a global opportunistic pathogen. In vitro cytotoxicity of this bacterium is mainly related to metalloprotease serralysin (PrtS) activity. Proteolytic capability varies among the different isolates. Here, we characterized protease production and transcriptional regulators at 37°C of two S. marcescens isolates from bronchial expectorations, HU1848 and SmUNAM836. As a reference strain the insect pathogen S. marcescens Db10 was included. Zymography of supernatant cultures revealed a single (SmUNAM836) or double proteolytic zones (HU1848 and Db10). Mass spectrometry confirmed the identity of PrtS and the serralysin-like protease SlpB from supernatant samples. Elevated proteolytic activity and prtS expression were evidenced in the HU1848 strain through azocasein degradation and qRT-PCR, respectively. Evaluation of transcriptional regulators revealed higher eepR expression in HU1848, whereas cpxR and hexS transcriptional levels were similar between studied strains. Higher eepR expression in HU1848 was further confirmed through an in vivo transcriptional assay. Moreover, two putative CpxR binding motifs were identified within the eepR regulatory region. EMSA validated the interaction of CpxR with both motifs. The evaluation of eepR transcription in a cpxR deletion strain indicated that CpxR negatively regulates eepR. Sequence conservation suggests that regulation of eepR by CpxR is common along S. marcescens species. Overall, our data incorporates CpxR to the complex regulatory mechanisms governing eepR expression and associates the increased proteolytic activity of the HU1848 strain with higher eepR transcription. Based on the global impact of EepR in secondary metabolites production, our work contributes to understanding virulence factors variances across S. marcescens isolates.

Predictive value of serum albumin and calcium levels in burn patients with Pseudomonas aeruginosa infection: A comprehensive analysis of clinical outcomes

In the ongoing challenge to reduce burn-associated mortality rates, this study explores the predictive capacity of clinical factors in burn patients, focusing on vitamin D, calcium, and serum albumin levels during hospitalisation in cases with Pseudomonas aeruginosa infection. Our research involves a comprehensive analysis of 100 burn patients, encompassing crucial clinical parameters such as the burn severity index, serum albumin, vitamin D, and calcium levels at admission. Data were meticulously entered into IBM Statistics SPSS software version 28 and subjected to statistical analysis. The study reveals an average patient age of 39.75 years and a notable 34% mortality rate. Additionally, the average lengths of hospital and intensive care unit (ICU) stays are determined to be 11.33 and 7.79 days, respectively. Significantly, a correlation between calcium and albumin variables and treatment outcomes is established, showcasing their potential to predict variable changes in patient mortality rates. Furthermore, a noteworthy association is observed between serum calcium levels and the duration of ICU hospitalisation. In conclusion, albumin and calcium variables emerge as sensitive and specific indicators for predicting outcomes in burn patients. Importantly, the independence of these factors from the physician's experience and diagnosis reduces human error and thus increases the accuracy of mortality prediction in this patient population.

Impact of AML1/ETO Fusion on the Efficacy of Venetoclax Plus Hypomethylating Agents in Newly Diagnosed Acute Myeloid Leukemia

BACKGROUND

AML1/ETO fusion confers favorable prognosis in acute myeloid leukemia (AML) treated with intensive chemotherapy (IC). However, the impact of AML1/ETO fusion on the efficacy of venetoclax in the treatment of AML is unclear.

OBJECTIVE

The aim of this study was to evaluate the efficacy of venetoclax plus hypomethylating agents (VEN/HMAs) in patients with AML1/ETO-positive AML.

PATIENTS AND METHODS

Patients with newly diagnosed AML in two centers were reviewed and divided into three cohorts: AML1/ETO-positive AML treated with frontline VEN/HMA (Cohort A), AML1/ETO-negative AML treated with frontline VEN/HMA (Cohort B), or AML1/ETO-positive AML treated with frontline IC (Cohort C). The response and survival were compared between the cohorts.

RESULTS

A total of 260 patients were included in the study. Patients in Cohort A had a significantly lower overall response rate (ORR) than patients in Cohort B (40.9% vs 71.2%, p = 0.005). The median event-free survival (EFS) in Cohort A and Cohort B was 2.7 months and 7.7 months, respectively, with no significant difference. The ORR and median EFS in Cohort C were 80.8% and 14.9 months, respectively, which were significantly superior to those in Cohort A, and the advantages remained significant after propensity score matching. ORR and EFS in KIT-mutated patients with AML1/ETO-positive AML receiving VEN/HMA were much inferior to those in KIT wild-type patients (ORR 0.0% vs 81.8%, p = 0.001; EFS 1.2 months vs not reached, p < 0.001).

CONCLUSIONS

Newly diagnosed AML patients with AML1/ETO fusion had a poor response to frontline VEN/HMA treatment. When determining induction therapy for patients with AML1/ETO-positive AML, IC should be preferred over VEN/HM.

The Effect of Body Temperature Changes on the Course of Treatment in Patients With Pneumonia and Sepsis: Results of an Observational Study

BACKGROUND

Traditionally, patients who are critically ill with infection and fever have been treated with antipyretics or even physically cooled. Presumed benefits of the reduction of body temperature are mostly based on decreased metabolic demands. However, it has been shown that decreasing body temperature in patients who are critically ill is not associated with improvement in treatment outcomes. Additionally, there is some data to support the use of temperature modulation (therapeutic hyperthermia) as an adjuvant treatment strategy in patients with infection.

OBJECTIVE

This study aims to determine the effect of body temperature on the course of intensive care unit (ICU) treatment of patients who are mechanically ventilated with pneumonia, sepsis, and positive tracheal aspirates on admission.

METHODS

We performed a single-center retrospective study. Core body temperature was measured in all patients. We analyzed associations between average temperatures in the first 48 hours after admission to ICU and ICU treatment parameters. Additionally, patients were divided into three groups: patients with negative tracheal aspirates 1 week after ICU admission (P-N group), patients with a different pathogen in tracheal aspirates 1 week after ICU admission (P-HAP group), and patients with a persisting pathogen in tracheal aspirates 1 week after ICU admission (P-P group). Differences in body temperature and interventions aimed at temperature modulation were determined.

RESULTS

We observed a significantly higher average temperature in the first 48 hours after admission to ICU in patients who survived to hospital discharge compared to nonsurvivors (mean 37.2 °C, SD 1 °C vs mean 36.9 °C, SD 1.6 °C; P=.04). We observed no associations between average temperatures in the first 48 hours after ICU admission and days of mechanical ventilation in the first 7 days of treatment (ρ=-0.090; P=.30), the average maximum daily requirement for noradrenaline in the first 7 days of treatment (ρ=-0.029; P=.80), average maximum FiO2 in the first 7 days of ICU treatment (ρ=0.040; P=.70), and requirement for renal replacement therapy in the first 7 days of ICU treatment (mean 37.3 °C, SD 1.4 °C vs mean 37.0 °C, SD 1.3 °C; P=.23). In an additional analysis, we observed a significantly greater use of paracetamol in the P-N group (mean 1.0, SD 1.1 g vs mean 0.4, SD 0.7 g vs mean 0.4, SD 0.8 g; P=.009), a trend toward greater use of active cooling in the first 24 hours after ICU admission in the P-N group (n=11, 44% vs n=14, 33.3% vs n=16, 32%; P=.57), and no other significant differences in parameters of ICU treatment between patient groups.

CONCLUSIONS

We observed better survival in patients who developed higher body temperatures in the first 48 hours after admission to the ICU; however, we observed no changes in other treatment parameters. Similarly, we observed greater use of paracetamol in patients with negative tracheal aspirates 1 week after ICU admission. Our results support the strategy of temperature tolerance in patients who are intubated with pneumonia and sepsis.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study

INTRODUCTION

Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models.

MATERIAL AND METHODS

We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed.

RESULTS

A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods.

CONCLUSIONS

The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

Can Patients with Electrolyte Disturbances Be Safely and Effectively Treated in a Hospital-at-Home, Telemedicine-Controlled Environment? A Retrospective Analysis of 267 Patients

BACKGROUND

Morbidities indicated for hospital-at-home (HAH) treatment include infectious diseases and exacerbations of chronic conditions. Electrolyte disturbances are not included per se. However, their rate is high. We aimed to describe our experience via the monitoring and treatment of such patients.

METHODS

This was a retrospective analysis of patients in the setting of telemedicine-controlled HAH treatment. We collected data from the electronic medical records of patients who presented electrolyte disturbances.

RESULTS

For 14 months, we treated 267 patients in total in HAH settings, with a mean age of 72.2 + 16.4, 44.2% for males. In total, 261 (97.75%) patients were flagged with electrolyte disturbances, of whom 149 had true electrolyte disturbances. Furthermore, 67 cases (44.96%) had hyponatremia, 9 (6.04%) had hypernatremia after correction for hyperglycemia, 20 (13.42%) had hypokalemia and 27 (18.12%) had hyperkalemia after the exclusion of hemolytic samples. Ten (6.09%) patients had hypocalcemia and two (1.34%) had hypercalcemia corrected to albumin levels. Thirteen (8.72%) patients had hypomagnesemia and one (0.67%) had hypermagnesemia. Patients with electrolyte disturbances suffered from more chronic kidney disease (24.2% vs. 12.2%; p = 0.03) and malignancy (6.3% vs. 0.6%; p = 0.006), and were more often treated with diuretics (12.6% vs. 4.1%; p = 0.016). No patient died or suffered from clinically significant cardiac arrhythmias.

CONCLUSIONS

The extent of electrolyte disturbances amongst HAH treatment patients is high. The monitoring and treatment of such patients can be conducted safely in this setting.

Association of coping mechanisms with medication adherence among young People living with HIV (PLHIV) in Klang Valley

Background

As young People Living with HIV (PLHIV) will need to take antiretroviral therapy (ART) for life, there is a need to understand their coping mechanisms in living with the disease. Lack of coping mechanisms leads to poor medication adherence and hospital follow-up, poor health outcomes and shortened life expectancy.

Objectives

This study aimed to determine the pattern of coping mechanisms in young PLHIV and its association with medication adherence.

Methods

This study was a cross-sectional study amongst young PLHIV patients (aged 20-39 years old) attending two HIV clinics in Klang Valley. Data was collected between February to August 2022. The pattern of coping strategies was assessed using the 28-item Brief Coping Orientation to Problems Experienced (COPE) questionnaire in English and Malay language, which was validated and found to have good internal consistency. Self-reported medication adherence was measured using the one-item Medical Outcomes Study (MOS) Specific Adherence Scale. Statistical analysis included descriptive statistics, single and multiple logistic regression.

Results

A total of 395 respondents were recruited for the study. The mean scores for each coping mechanism were: 1) problem-focused coping 2.98 (SD 0.62), 2) emotion-focused coping 2.40 (SD 0.48), 3) dysfunctional coping 1.84 (SD 0.44) and 4) religion/spirituality coping 3.07 (SD 0.97). The majority of the respondents (66.8%) were adherent to their ART. Respondents who had a longer duration of medication [OR:1.014 (95% CI: 1.002,1.026)] and those who adopted less religion/spirituality coping mechanisms [OR: 0.495 (95% CI:0.246, 0.997)] were found to be significantly associated with medication adherence.

Conclusion

This study revealed an overall medication adherence rate of 66.8%. Patients with longer ART duration and who adopted less religion or spirituality coping had better medication adherence. These study findings provide input into the design of intervention by clinicians and healthcare policy makers for young PLHIV in clinical practice.

The Predictive Role of Lactate in the Emergency Department in Patients with Severe Dyspnea

Objective

An accurate identification of patients at the need for prioritized diagnostics and care are crucial in the emergency department (ED). Blood gas (BG) analysis is a widely available laboratory test, which allows to measure vital parameters, including markers of ventilation and perfusion. The aim of our analysis was to assess whether blood gas parameters in patients with dyspnea at an increased risk of respiratory failure admitted to the ED can predict short-term outcomes.

Methods

The study group eventually consisted of 108 patients, with available BG analysis. The clinical and laboratory parameters were retrospectively evaluated, and three groups were distinguished-arterial blood gas (ABG), venous blood gas (VBG), and mixed blood gas. The primary endpoint was short-term, all-cause mortality during the follow-up of median (quartile 1-quartile 3) 2 (1-4) months. The independent risk factors for mortality that could be obtained from blood gas sampling were evaluated.

Results

The short-term mortality was 35.2% (38/108). Patients who died were more frequently initially assigned to the red triage risk group, more burdened with comorbidities, and the median SpO2 on admission was significantly lower than in patients who survived the follow-up period. In the multivariable analysis, lactate was the strongest independent predictor of death, with 1 mmol/L increasing all-cause mortality by 58% in ABG (95% CI: 1.01-2.47), by 80% in VBG (95% CI: 1.13-2.88), and by 68% in the mixed blood gas analysis (95% CI: 1.22-2.31), what remained significant in VBG and mixed group after correction for base excess. In each group, pH, pO2, and pCO2 did not predict short-term mortality.

Conclusions

In patients admitted to the ED due to dyspnea, at risk of respiratory failure, lactate levels in arterial, venous, and mixed blood samples are independent predictors of short-term mortality.

A bibliometric analysis of brachial plexus injury from 1980 to 2022

Background

Brachial plexus injury is a common severe peripheral nerve injury with high disability. At present, the bibliometric analysis of brachial plexus injury is basically unknown.

Methods

This article analyzes the data retrieved to the web of science and uses the R language (version 4.2), Citespace (version 6.1.R3 Advanced), Vosviewer (Lei deng university) to make a scientific map. Specifically, we analyze the main publication countries, institutions, journals where the article is published, and the cooperative relationship between different institutions, the relationship between authors, main research directions in this field, and current research hotspots.

Results

From 1980 to 2022, the total number of publications is 1542. In terms of countries where articles were published, 551 records were published in the United States, accounting for 35% of the total. With 74 articles, Fudan University ranks first in the world in terms of the number of articles issued by the institution, followed by 72 articles from Mayo Clinic. The magazine with the largest number of articles is JOURNAL OF HAND SURGERY-AMERICAN VOLUME, which has published 87 articles in total. GU YD (Gu Yu-Dong) team (Fudan University) and spinner RJ (Robert J Spinner) team (Mayo clinic) are in a leading position in this field. Nerve transfer and nerve reconstruction have been a hot topic of brachial plexus injury. "Spinal nerve root repair and reimplantation of avulsed ventral roots into the spinal cord after brachial plexus injury" has the strongest citation bursts.

Conclusion

Research on brachial plexus injury shows a trend of increasing heat. At present, there is a lack of communication and cooperation between scholars from different countries. Nerve transfer and nerve reconstruction are the current and future research directions in the treatment of brachial plexus injury.

Association between red blood cell distribution width-to-platelet ratio and post-discharge readmission rate in patients with heart failure: A retrospective cohort study

Background

To date, no studies have investigated the association between red blood cell distribution width (RDW)-to-platelet ratio (RPR) and readmission rates among patients with heart failure (HF). As such, the present study aimed to examine the relationship between RPR and readmission rates in patients with HF.

Methods

Data for this study were obtained from the Fourth People's Hospital (Zigong, Sichuan Province, China). Patients were diagnosed with HF in accordance with European Society of Cardiology criteria. The primary outcome was the 28-day readmission rate. Various logistic regression models were constructed to explore the association between RPR and the 28-day readmission rate.

Results

The study comprised 1978 patients with HF, with a 28-day readmission rate of 6.98%. RPR emerged as an independent risk factor for 28-day readmission, evidenced by consistent results across the various regression-adjusted models. The covariate-adjusted propensity score model demonstrated that every 0.1 increase in RPR was associated with an 8.2% increase in 28-day readmission rate (odds ratio [OR] 1.082 [95% confidence interval (CI) 1.012-1.158]; P = 0.0212). Similarly, each 0.1 change in RPR was associated with a 9.8% (OR 1.098 [95% CI 1.014-1.188]) and 7.3% (OR 1.073 [95% CI 0.991-1.161]) increase in 3- and 6-month readmission rates, respectively. However, RPR was not statistically associated with the 6-month readmission rate. Curve fit plots illustrated a nonlinear positive correlation between RPR and 28-day, and 3- and 6-month readmissions. Moreover, the effects of RPR on 28-day, and 3- and 6-month readmission rates remained robust across subgroup variables in stratified analysis. Finally, the effect sizes of pooled multiply imputed data were consistent with the original data, suggesting robust results.

Conclusion

RPR was an independent risk factor for 28-day readmission among patients with HF and also demonstrated modest predictive value for readmissions at 3 and 6 months, despite being non-significant for the 6-month readmission rate. Early identification of patients with HF with elevated RPR would facilitate management and may confer favorable effects on prognosis.

Conditional survival analysis and dynamic survival prediction for intracranial solitary-fibrous tumor/hemangiopericytoma

BACKGROUND

As the form of World Health Organization Central Nervous System (WHO CNS) tumor classifications is updated, there is a lack of research on outcomes for intracranial combined solitary-fibrous tumor and hemangiopericytoma (SFT/HPC). This study aimed to explore conditional survival (CS) pattern and develop a survival prediction tool for intracranial SFT/HPC patients.

METHODS

Data of intracranial SFT/HPC patients was gathered from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. The patients were split into training and validation groups at a 7:3 ratio for our analysis. CS is defined as the likelihood of surviving for a specified period of time (y years), given that the patient has survived x years after initial diagnosis. Then, we used this definition of CS to analyze the intracranial SFT/HPC patients. The least absolute shrinkage and selection operator (LASSO) regression and best subset regression (BSR) were employed to identify predictive factors. The Multivariate Cox regression analysis was applied to establish a novel CS-based nomogram, and a risk stratification system was developed using this model.

RESULTS

From the SEER database, 401 patients who were diagnosed with intracranial SFT/HPC between 2000 and 2019 were identified. Among them, 280 were included in the training group and 121 were included in the internal validation group for analysis. Our study revealed that in intracranial SFT/HPC, 5-year survival rates saw significant improvement ranging from 78% at initial diagnosis to rates of 83%, 87%, 90%, and 95% with each successive year after surviving for 1-4 years. The LASSO regression and BSR identified patient age, tumor behavior, surgery and radiotherapy as predictors of CS-based nomogram development. A risk stratification system was also successfully constructed to facilitate the identification of high-risk patients.

CONCLUSION

The CS pattern of intracranial SFT/HPC patients was outlined, revealing a notable improvement in 5-year survival rates after an added period of survival. Our newly-established CS-based nomogram and risk stratification system can provide a real-time dynamic survival estimation and facilitate the identification of high-risk patients, allowing clinicians to better guide treatment decision for these patients.

Optimizing sequence design strategies for perturbation MPRAs: a computational evaluation framework

The advent of perturbation-based massively parallel reporter assays (MPRAs) technique has facilitated the delineation of the roles of non-coding regulatory elements in orchestrating gene expression. However, computational efforts remain scant to evaluate and establish guidelines for sequence design strategies for perturbation MPRAs. In this study, we propose a framework for evaluating and comparing various perturbation strategies for MPRA experiments. Within this framework, we benchmark three different perturbation approaches from the perspectives of alteration in motif-based profiles, consistency of MPRA outputs, and robustness of models that predict the activities of putative regulatory motifs. While our analyses show very similar results across multiple benchmarking metrics, the predictive modeling for the approach involving random nucleotide shuffling shows significant robustness compared with the other two approaches. Thus, we recommend designing sequences by randomly shuffling the nucleotides of the perturbed site in perturbation-MPRA, followed by a coherence check to prevent the introduction of other variations of the target motifs. In summary, our evaluation framework and the benchmarking findings create a resource of computational pipelines and highlight the potential of perturbation-MPRA in predicting non-coding regulatory activities.

Emergency department utilisation and treatment for trauma-related presentations of adolescents aged 16-18: a retrospective cross-sectional study

BACKGROUND

A recent study conducted at our tertiary hospital emergency department (ED) reviewed ED consultations and found that adolescents aged 16-18 years present significantly more often for trauma and psychiatric problems than adults over 18 years. Accidental injuries are one of the greatest health risks for children and adolescents. In view of the increased vulnerability of the adolescent population, this study aimed to further analyse trauma-related presentations in adolescents.

METHODS

We conducted a single-centre, retrospective, cross-sectional study of all adolescent trauma patients aged 16 to 18 years presenting to the adult ED at the University Hospital (Inselspital) in Bern, Switzerland, from January 2013 to July 2017. We analysed presentation data as well as inpatient treatment and cost-related data. Data of female and male patients were compared by univariable analysis. A comparison group was formed consisting of 200 randomly chosen patients aged 19-25 years old with the same presentation characteristics. Predictive factors for surgical treatment were obtained by multivariable analysis.

RESULTS

The study population included a total of 1,626 adolescent patients aged 16-18 years. The predominant causes for ED presentation were consistent within case and comparison groups for sex and age and were sports accidents, falls and violence. Male patients were more likely to need surgical treatment (OR 1.8 [95% CI: 1.2-2.5], p = 0.001) and consequently inpatient treatment (OR 1.5 [95% CI: 1.1-2.1], p = 0.01), associated with higher costs (median 792 Swiss francs [IQR: 491-1,598]). Other independent risk factors for surgical treatment were violence-related visits (OR 2.1 [95% CI: 1.3-3.5, p = 0.004]) and trauma to the upper extremities (OR 2.02 [95% CI: 1.5-2.8], p < 0.001). Night shift (OR 0.56 [95% CI: 0.37-0.86], 0.008) and walk-in consultations (OR 0.3 [95% CI: 0.2; 0.4, < 0.001] were preventive factors for surgical treatment.

CONCLUSIONS

Male adolescents account for the majority of emergency visits and appear to be at higher risk for accidents as well as for surgical treatment and/or inpatient admission due to sports accidents or injuries from violence. We suggest that further preventive measures and recommendations should be implemented and that these should focus on sport activities and injuries from violence.

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.

Exploring the latency period in Chagas disease: duration and determinants in a cohort from Colombia

BACKGROUND

Chagas disease has a varying latency period, the time between infection and onset of cardiac symptoms, due to multiple factors. This study seeks to identify and understand these factors to enhance our knowledge of the disease.

METHODS

A retrospective follow-up study was conducted in Colombia on patients with indeterminate chronic Chagas disease. Medical files were examined to evaluate the disease latency time using time ratios (TRs) and the AFT Weibull model.

RESULTS

The study followed 578 patients, of whom 309 (53.5%) developed cardiac disease, with a median latency period of 18.5 (95% CI 16 to 20) y for the cohort. Those with the TcISyl genotype (TR 0.72; 95% CI 0.61 to 0.80), individuals who lived 5-15 y (TR 0.80; 95% CI 0.67 to 0.95), 15-30 y (TR 0.63; 95% CI 0.53 to 0.74) or >30 y (vs 5 y) in areas with high disease prevalence had shorter latency periods. On the other hand, undergoing treatment increased the latency period (TR: 1.74; 95% CI 1.52 to 1.87).

CONCLUSIONS

The latency period of Chagas disease was found to be independently related to male gender, receipt of etiological treatment, length of time spent in an endemic area and the TcISyl genotype. The implications of these findings are discussed.

The Role of Base in Reaction Performance of Photochemical Synthesis of Thiazoles: An Integrated Theoretical and Experimental Study

Artificial intelligence (AI)/machine learning (ML) is emerging as pivotal in synthetic chemistry, offering revolutionary potential in retrosynthetic analysis, reaction conditions and reaction prediction. We have combined chemical descriptors, primarily based on Density Functional Theory (DFT) calculations, with various AI/ML tools such as Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict the synthesis of 2-arylbenzothiazole in photoredox reactions. Significantly, our models underscore the critical role of the molecular structure and physicochemical characteristics of the base, especially the total atomic polarizabilities, in the rate-determining steps involving cyclohexyl and phenethyl moieties of the substrate. Moreover, we validated our findings in articles through experimental studies. It showcases the power of AI/ML and quantum chemistry in shaping the future of organic chemistry.

Acute dissection of a syphilitic saccular aneurysm of the ascending aorta and arch in a hypertensive patient - a rare phenomenon

We report the case of a 77-year-old male who suffered from hypertension and died suddenly. At autopsy, he was found to have hypertensive cardiomegaly and a dissecting syphilitic saccular aneurysm of the ascending aorta and arch with tamponade. Chronic aortic regurgitation, which is often seen in syphilitic aortitis, produces an additive effect to the concentric left ventricular hypertrophy seen in hypertension.

A multi-center randomized controlled trial to investigate potential effects of exercise therapy on renal function stratified by renal disorders and renal pathology: beneficial or harmful effect in immunoglobulin a nephropathy

BACKGROUND

The effects of exercise therapy (ET) on renal function in chronic kidney disease (CKD) remain unclear.

METHODS

In a randomized controlled trial (UMIN-CTR number: UMIN000038415), we investigated whether ET affects renal function in CKD; eligible patients had undergone renal biopsy in the past 3 months. We stratified patients by disease (immunoglobulin A [IgA] nephropathy, n = 16; diabetic nephropathy, n = 4; benign nephrosclerosis, n = 13; and other CKD types, n = 13) and randomized them to 12 weeks' observation and 24 weeks' ET comprising home-based aerobic exercise 3×/week and resistance training 2×/week (intervention group) or usual care (non-intervention group). Primary endpoint was creatinine-based estimated glomerular filtration rate (eGFR) or serum cystatin C-based eGFR (eGFRcys). Secondary endpoints included urinary protein and exercise tolerance.

RESULTS

Seventy patients were enrolled, 50 fulfilled the inclusion criteria, but 4 discontinued before randomization. No items significantly differed between week 0 to 24 in either group (intervention group, n = 23; non-intervention group, n = 23) or between groups at week 24 (intention-to-treat population) in the total study population. The eGFRcys slope showed no significant intergroup difference in the observation period, but eGFRcys improved significantly in IgA nephropathy patients (n = 16) in the intervention group (stratified comparison; week 0, 48.3 ± 18.2; week 24, 51.6 ± 17.6; p = 0.043). In these patients, urinary protein was significantly worse at week 24 in the non-intervention group (p = 0.046) and worsened significantly less in the intervention group (p = 0.039).

CONCLUSION

ET did not improve renal function overall in CKD patients but might help maintain renal function in patients with IgA nephropathy.

AbImmPred: An immunogenicity prediction method for therapeutic antibodies using AntiBERTy-based sequence features

Due to the unnecessary immune responses induced by therapeutic antibodies in clinical applications, immunogenicity is an important factor to be considered in the development of antibody therapeutics. To a certain extent, there is a lag in using wet-lab experiments to test the immunogenicity in the development process of antibody therapeutics. Developing a computational method to predict the immunogenicity at once the antibody sequence is designed, is of great significance for the screening in the early stage and reducing the risk of antibody therapeutics development. In this study, a computational immunogenicity prediction method was proposed on the basis of AntiBERTy-based features of amino sequences in the antibody variable region. The AntiBERTy-based sequence features were first calculated using the AntiBERTy pre-trained model. Principal component analysis (PCA) was then applied to reduce the extracted feature to two dimensions to obtain the final features. AutoGluon was then used to train multiple machine learning models and the best one, the weighted ensemble model, was obtained through 5-fold cross-validation on the collected data. The data contains 199 commercial therapeutic antibodies, of which 177 samples were used for model training and 5-fold cross-validation, and the remaining 22 samples were used as an independent test dataset to evaluate the performance of the constructed model and compare it with other prediction methods. Test results show that the proposed method outperforms the comparison method with 0.7273 accuracy on the independent test dataset, which is 9.09% higher than the comparison method. The corresponding web server is available through the official website of GenScript Co., Ltd., https://www.genscript.com/tools/antibody-immunogenicity.

Association between systemic inflammation response index and chronic kidney disease: a population-based study

Introduction

Our objective was to explore the potential link between systemic inflammation response index (SIRI) and chronic kidney disease (CKD).

Methods

The data used in this study came from the National Health and Nutrition Examination Survey (NHANES), which gathers data between 1999 and 2020. CKD was diagnosed based on the low estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m2 or albuminuria (urinary albumin-to-creatinine ratio (ACR) of more than 30 mg/g). Using generalized additive models and weighted multivariable logistic regression, the independent relationships between SIRI and other inflammatory biomarkers (systemic immune-inflammation index (SII), monocyte/high-density lipoprotein ratio (MHR), neutrophil/high-density lipoprotein ratio (NHR), platelet/high-density lipoprotein ratio (PHR), and lymphocyte/high-density lipoprotein ratio (LHR)) with CKD, albuminuria, and low-eGFR were examined.

Results

Among the recruited 41,089 participants, males accounted for 49.77% of the total. Low-eGFR, albuminuria, and CKD were prevalent in 8.30%, 12.16%, and 17.68% of people, respectively. SIRI and CKD were shown to be positively correlated in the study (OR = 1.24; 95% CI: 1.19, 1.30). Furthermore, a nonlinear correlation was discovered between SIRI and CKD. SIRI and CKD are both positively correlated on the two sides of the breakpoint (SIRI = 2.04). Moreover, increased SIRI levels were associated with greater prevalences of low-eGFR and albuminuria (albuminuria: OR = 1.27; 95% CI: 1.21, 1.32; low-eGFR: OR = 1.11; 95% CI: 1.05, 1.18). ROC analysis demonstrated that, compared to other inflammatory indices (SII, NHR, LHR, MHR, and PHR), SIRI exhibited superior discriminative ability and accuracy in predicting CKD, albuminuria, and low-eGFR.

Discussion

When predicting CKD, albuminuria, and low-eGFR, SIRI may show up as a superior inflammatory biomarker when compared to other inflammatory biomarkers (SII, NHR, LHR, MHR, and PHR). American adults with elevated levels of SIRI, SII, NHR, MHR, and PHR should be attentive to the potential risks to their kidney health.

Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis

The poly (ADP-ribose) polymerase-1 (PARP-1) enzyme is an important target in the treatment of breast cancer. Currently, treatment options include the drugs Olaparib, Niraparib, Rucaparib, and Talazoparib; however, these drugs can cause severe side effects including hematological toxicity and cardiotoxicity. Although in silico models for the prediction of PARP-1 activity have been developed, the drawbacks of these models include low specificity, a narrow applicability domain, and a lack of interpretability. To address these issues, a comprehensive machine learning (ML)-based quantitative structure-activity relationship (QSAR) approach for the informed prediction of PARP-1 activity is presented. Classification models built using the Synthetic Minority Oversampling Technique (SMOTE) for data balancing gave robust and predictive models based on the K-nearest neighbor algorithm (accuracy 0.86, sensitivity 0.88, specificity 0.80). Regression models were built on structurally congeneric datasets, with the models for the phthalazinone class and fused cyclic compounds giving the best performance. In accordance with the Organization for Economic Cooperation and Development (OECD) guidelines, a mechanistic interpretation is proposed using the Shapley Additive Explanations (SHAP) to identify the important topological features to differentiate between PARP-1 actives and inactives. Moreover, an analysis of the PARP-1 dataset revealed the prevalence of activity cliffs, which possibly negatively impacts the model's predictive performance. Finally, a set of chemical transformation rules were extracted using the matched molecular pair analysis (MMPA) which provided mechanistic insights and can guide medicinal chemists in the design of novel PARP-1 inhibitors.

Association of prehospital lactate levels with base excess in various emergencies - a retrospective study

OBJECTIVES

Blood gas analysis, including parameters like lactate and base excess (BE), is crucial in emergency medicine but less commonly utilized prehospital. This study aims to elucidate the relationship between lactate and BE in various emergencies in a prehospital setting and their prognostic implications.

METHODS

We conducted a retrospective analysis of prehospital emergency patients in Graz, Austria, from October 2015 to November 2020. Our primary aim was to assess the association between BE and lactate. This was assessed using Spearman's rank correlation and fitting a multiple linear regression model with lactate as the outcome, BE as the primary covariate of interest and age, sex, and medical emergency type as confounders.

RESULTS

In our analysis population (n=312), lactate and BE levels were inversely correlated (Spearman's ρ, -0.75; p<0.001). From the adjusted multiple linear regression model (n=302), we estimated that a 1 mEq/L increase in BE levels was associated with an average change of -0.35 (95 % CI: -0.39, -0.30; p<0.001) mmol/L in lactate levels. Lactate levels were moderately useful for predicting mortality with notable variations across different emergency types.

CONCLUSIONS

Our study highlights a significant inverse association between lactate levels and BE in the prehospital setting, underscoring their importance in early assessment and prognosis in emergency care. Additionally, the findings from our secondary aims emphasize the value of lactate in diagnosing acid-base disorders and predicting patient outcomes. Recognizing the nuances in lactate physiology is essential for effective prehospital care in various emergency scenarios.

Validation of SeptiCyte RAPID to Discriminate Sepsis from Non-Infectious Systemic Inflammation

(1) Background: SeptiCyte RAPID is a molecular test for discriminating sepsis from non-infectious systemic inflammation, and for estimating sepsis probabilities. The objective of this study was the clinical validation of SeptiCyte RAPID, based on testing retrospectively banked and prospectively collected patient samples. (2) Methods: The cartridge-based SeptiCyte RAPID test accepts a PAXgene blood RNA sample and provides sample-to-answer processing in ~1 h. The test output (SeptiScore, range 0-15) falls into four interpretation bands, with higher scores indicating higher probabilities of sepsis. Retrospective (N = 356) and prospective (N = 63) samples were tested from adult patients in ICU who either had the systemic inflammatory response syndrome (SIRS), or were suspected of having/diagnosed with sepsis. Patients were clinically evaluated by a panel of three expert physicians blinded to the SeptiCyte test results. Results were interpreted under either the Sepsis-2 or Sepsis-3 framework. (3) Results: Under the Sepsis-2 framework, SeptiCyte RAPID performance for the combined retrospective and prospective cohorts had Areas Under the ROC Curve (AUCs) ranging from 0.82 to 0.85, a negative predictive value of 0.91 (sensitivity 0.94) for SeptiScore Band 1 (score range 0.1-5.0; lowest risk of sepsis), and a positive predictive value of 0.81 (specificity 0.90) for SeptiScore Band 4 (score range 7.4-15; highest risk of sepsis). Performance estimates for the prospective cohort ranged from AUC 0.86-0.95. For physician-adjudicated sepsis cases that were blood culture (+) or blood, urine culture (+)(+), 43/48 (90%) of SeptiCyte scores fell in Bands 3 or 4. In multivariable analysis with up to 14 additional clinical variables, SeptiScore was the most important variable for sepsis diagnosis. A comparable performance was obtained for the majority of patients reanalyzed under the Sepsis-3 definition, although a subgroup of 16 patients was identified that was called septic under Sepsis-2 but not under Sepsis-3. (4) Conclusions: This study validates SeptiCyte RAPID for estimating sepsis probability, under both the Sepsis-2 and Sepsis-3 frameworks, for hospitalized patients on their first day of ICU admission.

Causes of death among people living with metastatic cancer

Studying survivorship and causes of death in patients with advanced or metastatic cancer remains an important task. We characterize the causes of death among patients with metastatic cancer, across 13 cancer types and 25 non-cancer causes and predict the risk of death after diagnosis from the diagnosed cancer versus other causes (e.g., stroke, heart disease, etc.). Among 1,030,937 US (1992-2019) metastatic cancer survivors, 82.6% of patients (n = 688,529) died due to the diagnosed cancer, while 17.4% (n = 145,006) died of competing causes. Patients with lung, pancreas, esophagus, and stomach tumors are the most likely to die of their metastatic cancer, while those with prostate and breast cancer have the lowest likelihood. The median survival time among patients living with metastases is 10 months; our Fine and Gray competing risk model predicts 1 year survival with area under the receiver operating characteristic curve of 0.754 (95% CI [0.754, 0.754]). Leading non-cancer deaths are heart disease (32.4%), chronic obstructive and pulmonary disease (7.9%), cerebrovascular disease (6.1%), and infection (4.1%).

Leveraging big data for causal understanding in mental health: a research framework

Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.

Magnetic Resonance Imaging Frequency After Stereotactic Body Radiation Therapy for Spine Metastases

PURPOSE

Stereotactic body radiation therapy (SBRT) is increasingly being used to treat spine metastases. Current post-SBRT imaging surveillance strategies in this patient population may benefit from a more data-driven and personalized approach. The objective of this study was to develop risk-stratified post-SBRT magnetic resonance imaging (MRI) surveillance strategies using quantitative methods.

METHODS AND MATERIALS

Adult patients with bony spine metastases treated with SBRT between 2008 and 2021 and who had at least 2 follow-up spine MRIs were reviewed retrospectively. A recursive partitioning analysis model was developed to separate patients into different risk categories for post-SBRT progression anywhere within the spine. Imaging intervals were derived for each risk category using parametric survival regression based on multiple expected spine progression rates per scan.

RESULTS

A total of 446 patients and 1039 vertebral segments were included. Cumulative incidence of spine progression was 19.2% at 1 year, 26.7% at 2 years, and 35.3% at 4 years. The internally validated risk stratification model was able to divide patients into 3 risk categories based on epidural disease, paraspinal disease, and Spinal Instability Neoplastic Score category. The 4-year risk of spine progression was 23.4%, 39.0%, and 51.8%, respectively, for the low-, intermediate-, and high-risk groups. Using an expected per-scan spine progression rate of 3.75%, the low-risk group would require follow-up scans every 6.0 months (95% CI, 4.9-7.6) and the intermediate-risk group would require surveillance every 3.1 months (95% CI, 2.6-3.7). At an expected spine progression rate of 5%, the high-risk group would require surveillance every 1.3 months (95% CI, 1.1-1.6) during the first 13.2 months after SBRT and every 5.9 months thereafter (95% CI, 2.8-12.3).

CONCLUSIONS

Data-driven follow-up MRI surveillance intervals at a range of expected spine progression rates have been determined for patients at different risks of spine progression based on an internally validated, single-institution risk stratification model.

Effect of early dexamethasone on outcomes of COVID-19: A quasi-experimental study using propensity score matching

BACKGROUND

The RECOVERY trial demonstrated that the use of dexamethasone is associated with a 36% lower 28-day mortality in hospitalized patients with COVID-19 on invasive mechanical ventilation. Nevertheless, the optimal timing to start dexamethasone remains uncertain.

METHODS

We conducted a quasi-experimental study at National Taiwan University Hospital (Taipei, Taiwan) using propensity score matching to simulate a randomized controlled trial to receive or not to receive early dexamethasone (6 mg/day) during the first 7 days following the onset of symptoms. Treatment was standard protocol-based, except for the timing to start dexamethasone, which was left to physicians' decision. The primary outcome is 28-day mortality. Secondary outcomes include secondary infection within 60 days and fulfilling the criteria of de-isolation within 20 days.

RESULTS

A total of 377 patients with COVID-19 were enrolled. Early dexamethasone did not decrease 28-day mortality in all patients (adjusted odds ratio [aOR], 1.03; 95% confidence interval [CI], 0.97-1.10) or in patients who required O2 for severe/critical disease at admission (aOR, 1.05; 95%CI, 0.94-1.18); but is associated with a 24% increase in superinfection in all patients (aOR, 1.24; 95% CI, 1.12-1.37) and a 23% increase in superinfection in patients of O2 for several/critical disease at admission (aOR, 1.23; 95% CI, 1.02-1.47). Moreover, early dexamethasone is associated with a 42% increase in likelihood of delayed clearance of SARS-CoV-2 virus (adjusted hazard ratio, 1.42; 95% CI, 1.01-1.98).

CONCLUSION

An early start of dexamethasone (within 7 days after the onset of symptoms) could be harmful to hospitalized patients with COVID-19.

Integration of basement membrane-related genes in a risk signature for prognosis in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is characterized by high heterogeneity and recurrence rates, posing significant challenges for stratification and treatment. Basement membrane-related genes (BMGs) play a crucial role in tumor initiation and progression. Clinical and transcriptomic data of ccRCC patients were extracted from TCGA and GEO databases. We employed univariate regression and LASSO-Cox stepwise regression analysis to construct a BMscore model based on BMGs expression level. A nomogram combining clinical features and BMscore was constructed to predict individual survival probabilities. Further enrichment analysis and immune-related analysis were conducted to explore the enriched pathways and immune features associated with BMGs. High-risk individuals predicted by BMscore exhibited poorer overall survival, which was consistent with the validation dataset. BMscore was identified as an independent risk factor for ccRCC. Functional analysis revealed that BMGs were related to cell-matrix and tumor-associated signaling pathways. Immune profiling suggests that BMGs play a key role in immune interactions and the tumor microenvironment. BMGs serve as a novel prognostic predictor for ccRCC and play a role in the immune microenvironment and treatment response. Targeting the BM may represent an alternative therapeutic approach for ccRCC.