Early Identification Of Patients Research Articles

The Predictive Value of Aortic Calcification on Computed Tomography for Major Cardiovascular Events.

As the prevalence of cardiovascular disease continues to increase, early identification of patients at high risk of major adverse cardiovascular events (MACE) using reliable diagnostic modalities is important. Transcatheter aortic valve implantation (TAVI) is a minimally invasive percutaneous procedure used to replace the aortic valve with a bioprosthetic one, often without the need for surgery. Extra coronary calcification in the ascending and/or descending thoracic aorta, aortic arch, and abdominal aorta has recently been identified as a method to quantify the extent of atherosclerotic cardiovascular disease. However, its definitive role in the prediction of MACE remains unclear. We performed a comprehensive review to summarize the current literature on the diagnostic and predictive value of thoracic and abdominal aortic calcification, as quantified in computed tomography, for the association, risk stratification, and prediction of MACE and after TAVI procedures. Despite increasing evidence, the predictive role of thoracic calcification still remains unproven, with a need for carefully tailored studies to confirm these findings.

Establishment and validation of a prediction model for apnea on bronchiolitis.

The objective of this study is to examine the risk factors associated with apnea in hospitalized patients diagnosed with bronchiolitis and to develop a nomogram prediction model for the early identification of patients who are at risk of developing apnea. The clinical data of patients diagnosed with acute bronchiolitis and hospitalized at the Children's Hospital of Nanjing Medical University between February 2018 and May 2021 were retrospectively analyzed. LASSO regression and logistic regression analysis were used to determine the risk factors for apnea in these patients. A nomogram was constructed based on variables selected through multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve and calibration curve were used to assess the accuracy and discriminative ability of the nomogram model, and decision curve analysis (DCA) was performed to evaluate the model's performance and clinical effectiveness. A retrospective analysis was conducted on 613 children hospitalized with bronchiolitis, among whom 53 (8.6%) experienced apnea. The results of Lasso regression and Logistic regression analyses showed that underlying diseases, feeding difficulties, tachypnea, WBC count, and lung consolidation were independent risk factors for apnea. A nomogram prediction model was constructed based on the five predictors mentioned above. After internal validation, the nomogram model demonstrated an AUC of 0.969 (95% CI 0.951-0.987), indicating strong predictive performance for apnea in bronchiolitis. Calibration curve analysis confirmed that the nomogram prediction model had good calibration, and the clinical decision curve analysis (DCA) indicated that the nomogram was clinically useful in estimating the net benefit to patients. In this study, a nomogram model was developed to predict the risk of apnea in hospitalized children with bronchiolitis. The model showed good predictive performance and clinical applicability, allowing for timely identification and intensified monitoring and treatment of high-risk patients to improve overall clinical prognosis.

NEWS2, S/F-ratio and ROX-index at emergency department for the prediction of adverse outcomes in COVID-19 patients: An external validation study

BackgroundIn the context of the COVID-19 pandemic, the early and accurate identification of patients at risk of deterioration was crucial in overcrowded and resource-limited emergency departments. This study conducts an external validation for the evaluation of the performance of the National Early Warning Score 2 (NEWS2), the S/F ratio, and the ROX index at ED admission in a large cohort of COVID-19 patients from Colombia, South America, assessing the net clinical benefit with decision curve analysis. MethodsA prospective cohort study was conducted on 6907 adult patients with confirmed COVID-19 admitted to a tertiary care ED in Colombia. The study evaluated the diagnostic performance of NEWS2, S/F ratio, and ROX index scores at ED admission using the area under the receiver operating characteristic curve (AUROC) for discrimination, calibration, and decision curve analysis for the prediction of intensive care unit admission, invasive mechanical ventilation, and in-hospital mortality. ResultsWe included 6907 patients who presented to the ED with confirmed SARS-CoV-2 infection from March 2020 to November 2021. Mean age was 51 (35–65) years and 50.4% of patients were males. The rate of intensive care unit admission was 28%, and in-hospital death was 9.8%. All three scores have good discriminatory performance for the three outcomes based on the AUROC. S/F ratio showed miscalibration at low predicted probabilities and decision curve analysis indicated that the NEWS2 score provided a greater net benefit compared to other scores across at a 10% threshold to decide ED admission at a high-level of care facility. ConclusionsThe NEWS2, S/F ratio, and ROX index at ED admission have good discriminatory performances in COVID-19 patients for the prediction of adverse outcomes, but the NEWS2 score has a higher net benefit underscoring its clinical utility in optimizing patient management and resource allocation in emergency settings.

Prevalence and determinants of post-stroke psychosis in Aswan: a prospective study

BackgroundPost-stroke psychosis (PSP) is a rare but serious neuropsychiatric condition characterized by delusions and/or hallucinations following a stroke. Despite its impact on prognosis and quality of life, PSP remains underdiagnosed and undertreated, with limited data on its prevalence and risk factors. The purpose of this study is to assess the prevalence and determinants of post-stroke psychosis (PSP), as well as to compare PSP and non-PSP patients in terms of improvement and daily living outcomes. This nested case–control study included 2,624 acute stroke patients from a university hospital between May 2017 and April 2022. Patients who developed PSP within 6 months post-stroke were identified as cases (n = 108), and 119 patients without PSP were randomly selected as controls. Comprehensive assessments included clinical, laboratory, and imaging evaluations at baseline. After 6 months, follow-up evaluations were conducted, including neurological examinations, psychiatric assessments, and stroke severity assessments using the Barthel index (BI). The psychiatric assessments included the Hamilton Depression Rating Scale for depression and the Mini-Mental State Examination for cognitive status.ResultsThe prevalence of PSP was 5.4%. risk factors significantly associated with PSP included older age, male patients, lower education level (≤ 5 years), hemiplegia, sphincter affection, cortical lesion, brain atrophy, small vessel disease, ischemic stroke, post-stroke dementia, and seizures. Non-specified psychosis and delusional disorder were the most common psychosis subtypes. There was a significantly higher percentage of excellent patients in the non-PSP group compared to those who had PSP regarding the observed improvement in the patient's condition. Also, there is a higher percentage of deteriorated patients in the PSP group (46.6%) compared to another group (18.9%) regarding BI.ConclusionPSP is a prevalent post-stroke complication associated with distinct risk factors and poor functional outcomes. Early screening and identification of high-risk patients, along with multidisciplinary management strategies, are crucial for optimizing recovery and quality of life in stroke survivors.

Development and validation of a clinical predictive model for high-volume lymph node metastasis of papillary thyroid carcinoma

The central lymph node metastasis (CLNM) status in the cervical region serves as a pivotal determinant for the extent of surgical intervention and prognosis in papillary thyroid carcinoma (PTC). This paper seeks to devise and validate a predictive model based on clinical parameters for the early anticipation of high-volume CLNM (hv-CLNM, > 5 nodes) in high-risk patients. A retrospective analysis of the pathological and clinical data of patients with PTC who underwent surgical treatment at Medical Centers A and B was conducted. The data from Center A was randomly divided into training and validation sets in an 8:2 ratio, with those from Center B serving as the test set. Multifactor logistic regression was harnessed in the training set to select variables and construct a predictive model. The generalization ability of the model was assessed in the validation and test sets. The model was evaluated through the receiver operating characteristic area under the curve (AUC) to predict the efficiency of hv-CLNM. The goodness of fit of the model was examined via the Brier verification technique. The incidence of hv-CLNM in 5897 PTC patients attained 4.8%. The occurrence rates in males and females were 9.4% (128/1365) and 3.4% (156/4532), respectively. Multifactor logistic regression unraveled male gender (OR = 2.17, p < .001), multifocality (OR = 4.06, p < .001), and lesion size (OR = 1.08 per increase of 1 mm, p < .001) as risk factors, while age emerged as a protective factor (OR = 0.95 per an increase of 1 year, p < .001). The model constructed with four predictive variables within the training set exhibited an AUC of 0.847 ([95%CI] 0.815–0.878). In the validation and test sets, the AUCs were 0.831 (0.783–0.879) and 0.845 (0.789–0.901), respectively, with Brier scores of 0.037, 0.041, and 0.056. Subgroup analysis unveiled AUCs for the prediction model in PTC lesion size groups (≤ 10 mm and > 10 mm) as 0.803 (0.757–0.85) and 0.747 (0.709–0.785), age groups (≤ 31 years and > 31 years) as 0.778 (0.720–0.881) and 0.837 (0.806–0.867), multifocal and solitary cases as 0.803 (0.767–0.838) and 0.809 (0.769–0.849), and Hashimoto’s thyroiditis (HT) and non-HT cases as 0.845 (0.793–0.897) and 0.845 (0.819–0.871). Male gender, multifocality, and larger lesion size are risk factors for hv-CLNM in PTC patients, whereas age serves as a protective factor. The clinical predictive model developed in this research facilitates the early identification of high-risk patients for hv-CLNM, thereby assisting physicians in more efficacious risk stratification management for PTC patients.

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

BackgroundFabry 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.MethodsThe 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.ResultsA 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.ConclusionsThis model serves as a tool to identify potential FD patients using German insurance claims data.

Taking care of inpatients with fragility hip fractures: the hip-padua osteosarcopenia (Hip-POS) fracture liaison service model.

Osteoporotic fragility fractures (FF), particularly those affecting the hip, represent a major clinical and socio-economic concern. These fractures can lead to various adverse outcomes, which may be exacerbated by the presence of sarcopenia, especially among older and frail patients. Early identification of patients with FF is crucial for implementing effective diagnostic and therapeutic strategies to prevent subsequent fractures and their associated consequences. The Hip-POS program, implemented at Azienda Ospedale-Università Padova, is a Fracture Liaison Service (FLS) program to evaluate patients aged > 50years old admitted with fragility hip fractures, involving an interdisciplinary team. After the identification of patients with hip fractures in the Emergency Department, a comprehensive evaluation is conducted to identify risk factors for further fractures, and to assess the main domains of multidimensional geriatric assessment, including muscle status. Patients are then prescribed with anti-fracture therapy, finally undergoing periodic follow-up visits. During the first five months, a total of 250 patients were evaluated (70.4% women, median age 85years). Following assessment by the Hip-POS team, compared to pre-hospitalization, the proportion of patients not receiving antifracture therapy decreased significantly from 60 to 21%. The prescription rates of vitamin D and calcium increased markedly from 29.6% to 81%. We introduced the Hip-POS program for the care of older adults with hip fractures. We aspire that our model will represent a promising approach to enhancing post-fracture care by addressing the multifactorial nature of osteoporosis and its consequences, bridging the gap in secondary fracture prevention, and improving patient outcomes.

A retrospective analysis of the clinical profile and factors associated with mortality and poor hospital outcomes in adult Guillain–Barre syndrome patients

Guillain–Barré syndrome (GBS) is an acute autoimmune polyneuropathy with substantial geographic variations in demography, antecedent events, clinical manifestations, electrophysiological sub-types, diagnostic findings, treatment modalities, and prognostic indicators. However, there is limited contemporary data on GBS patient profiles and prognostic factors from low-resource settings like Ethiopia. The objective of this study is to investigate the clinical profile, factors associated with mortality, and hospital outcomes among GBS patients admitted to Tikur Anbessa Specialized Hospital (TASH) in Addis Ababa, Ethiopia. A retrospective cross-sectional study was conducted among 60 GBS patients admitted to TASH from January 2018 to December 2022. Data on demographics, clinical features, treatments, complications, and outcomes were extracted from medical records. Bivariate and multivariate logistic regression analyses identified factors associated with mortality and poor hospital outcomes. The cohort had a mean age of 28.5 years, with 76.7% aged 14–34 years. Males comprised 61.7% of cases. Ascending paralysis (76.7%) was the predominant presentation. Absent or reduced reflexes were seen in 91.7% of patients. The most common antecedent event was gastroenteritis (26.7%), followed by upper respiratory tract infection (URTI) (15%) and vaccination (11.7%). The mean interval from symptom onset to hospital presentation was 8.77 days, and the peak symptom severity was 4.47 days. The axonal variant (75.5%) was the most common subtype, followed by the demyelinating variant (24.5%). Intravenous immunoglobulin was administered to 41.7% of patients. Respiratory failure requiring invasive mechanical ventilator (MV) support occurred in 26.7% of cases. The mortality rate was 10%, with mechanical ventilation being the only factor significantly associated with mortality (95% CI 2.067–184.858; P < 0.010). At discharge, 55% had a good outcome, and 45% had a poor outcome, according to the Hughes Functional Disability Scale (HFDS). Mechanical ventilation (AOR 0.024, 95% CI 0.001–0.607) and a GBS disability score > 3 (AOR 0.106, 95% CI 0.024–0.467) were factors significantly associated with poor hospital outcomes. GBS in this cohort primarily affected individuals of young age, commonly preceded by gastroenteritis and characterized by a high frequency of the axonal variant. Mechanical ventilation was found to be significantly linked to mortality. Alongside mechanical ventilation requirements, severe disability upon presentation emerged as a crucial determinant of poor outcomes upon discharge, underscoring the importance of early identification of high-risk patients and prompt interventions.

Facing Life in Old Age: Exploring Resilience in Older Adults with Bipolar Disorder.

Background: Older adults with bipolar disorder (OABD) are individuals aged 50 years and older with bipolar disorder (BD). People with BD may have fewer coping strategies or resilience. A long duration of the disease, as seen in this population, could affect the development of resilience strategies, but this remains under-researched. Therefore, this study aims to assess resilience levels within the OABD population and explore associated factors, hypothesizing that resilience could improve psychosocial functioning, wellbeing and quality of life of these patients. Methods: This study sampled 33 OABD patients from the cohort at the Bipolar and Depressive Disorders Unit of the Hospital Clinic of Barcelona. It was an observational, descriptive and cross-sectional study. Demographic and clinical variables as well as psychosocial functioning, resilience and cognitive reserve were analyzed. Resilience was measured using the CD-RISC-10. Non-parametric tests were used for statistical analysis. Results: The average CD-RISC-10 score was 25.67 points (SD 7.87). Resilience negatively correlated with the total number of episodes (p = 0.034), depressive episodes (p = 0.001), and the FAST (p < 0.001). Participants with normal resilience had a lower psychosocial functioning (p = 0.046), a higher cognitive reserve (p = 0.026), and earlier onset (p = 0.037) compared to those with low resilience. Conclusions: OABD individuals may have lower resilience levels which correlate with more psychiatric episodes, especially depressive episodes and worse psychosocial functioning and cognitive reserve. Better understanding and characterization of resilience could help in early identification of patients requiring additional support to foster resilience and enhance OABD management.

C-Reactive Protein as an Early Marker of Severity and Outcome in Patients with SARS-CoV-2 Infection

Abstract Background SARS CoV-2 infection is a major cause of morbidity and mortality worldwide. The incidence of SARS CoV-2 infection is rising with millions of people getting infected daily. Mortality rate varies from a country to the other(0.3-6%), most of which are attributed to respiratory failure. Several studies have been performed to identify biomarkers for use in the early identification of patients at risk. Aim of the work This study was done to estimate and evaluate the plasma levels of CRP in patients with SARS CoV-2 and to study the association of CRP levels with severity of disease progression and prognosis of those patients. Patients and Methods This retrospective cohort study was conducted on 100 patients admitted to El-Obour Ain Shams University Specialized Hospital for Isolation during the period between January and October 2020. Results Regarding history patients whom were old or have any of the following comorbid conditons as hypertension, Diabetes mellitus, chronic liver disease and active cancer were statistically more prone to not surviving. Regarding complete blood picture, there was significant decrease in haemoglobin and or increase in total leukocytic count in those who did not survive. Serum Ferritin and D-Dimer were significantly high in those who did not survive. As regard CRP, it was significantly higher in the non-survivor group, Also it correlated with severity as ICU admission and need of respiratory support. Regarding the ROC curve result the best cut-off of CRP to predict mortality was &amp;gt;129mg/l, with Area under the curve = 0.981, Sensitivity = 90.91%, Specificity= 97.75%, positive predictive value = 83.3% and negative predictive value =98.9%. Regarding the ROC curve result the best cut-off of CRP to predict need of respiratory support was &amp;gt;55.3mg/L, with Area under the curve = 0.976, Sensitivity = 100%, Specificity= 86.21%, positive predictive value = 52.0% and negative predictive value =100.0%. Regarding the ROC curve result the best cut-off of CRP to predict need of ICU admission was &amp;gt;55.3mg/L, with Area under the curve = 0.987, Sensitivity = 100.0%, Specificity= 93.75%, positive predictive value = 80.0% and negative predictive value =100.0%. Conclusion Laboratory and clinical medicine groups should actively collaborate and optimize their individual expertise to potentially prove standardized biomarkers assays that will optimize patient care.

New Pro-inflammatory Cytokines in Children and Adolescents with Type 1 Diabetes Patients with Microvascular Complications

Abstract Background Type 1 diabetes mellitus has become a global health burden, and poor glycemic control, in combination with prolonged diabetes duration, leads to the early development of microvascular complications, including diabetic kidney disease, retinopathy, and neuropathy. Therefore, it is urgent to establish new biomarkers for the early identification of at-risk patients. Objectives To investigate the role of inflammatory proteins (human growth differentiation factor 15 and interleukin 29) in the course of microvascular complications and glycemic control in T1DM. Patients and Methods Our study included 40 children with T1DM (18 T1DM patients had microvascular complications and 22 T1DM patients were free of these complications; their ages ranged from 8 to 18 years); there was also another T1DM subgroup based on glycemic control (11 T1DM patients had controlled HbA1c &amp;lt; 7% and 29 patients had uncontrolled HbA1C &amp;gt; 7%). 40 children of the same age were selected as a control group. The diagnosis of TIDM was made based on the criteria of the International Society of Pediatric and Adolescent Diabetes (ISPAD) (2022). A detailed medical history was collected, including the age of onset of diabetes, disease duration, and symptoms of diabetic complications. In the study, GDF15 and IL29 values in serum blood were investigated using the ELISA method. Results The mean age was 14.05 years in the diabetic group and 13.25 years in the control group (P = 0.178). In the T1DM patient group, the GDF15 and IL29 values were found to be significantly higher than those in the control group (P = 0.041 and 0.013, respectively). In diabetic patients, GDF15 was significantly higher in the complicated T1DM group versus the uncomplicated group (P = 0.001). Furthermore, GDF15 levels were significantly higher in uncontrolled patients with HbA1C &amp;gt; 7% than in the control group with HbA1C &amp;lt; 7% (P = 0.004); however, no significant statistical differences in IL29 levels were found between diabetic patient subgroups (P &amp;gt; 0.05). Conclusion The data from this study showed that GDF15 has an impact on glycemic control and that the presence of microvascular complications in the course of T1DM is very possible, but IL29 does not appear to play a role in the course of microvascular complications.

Understanding the value of monocyte distribution width (MDW) in acutely ill medical patients presenting to the emergency department: a prospective single center evaluation

The monocyte distribution width (MDW) has emerged as a promising biomarker for accurate and early identification of patients with potentially life-threatening infections. Here we tested the diagnostic performance of MDW in adult patients requiring hospital admission for community-acquired infections and sepsis, evaluated sources of heterogeneity in the estimates of diagnostic accuracy, and assessed the meaning of MDW in a patient population presenting to the emergency department (ED) for acute non-infectious conditions. 1925 consecutive patients were categorized into three groups: non-infection (n = 1507), infection (n = 316), and sepsis/septic shock (n = 102). Diagnostic performance for infection or sepsis of MDW alone or in combination with components of SOFA was tested using AUC of ROC curves, sensitivity, and specificity. The relationship between MDW and different pathogens as well as the impact of non-infectious conditions on MDW values were explored. For the prediction of infection, the AUC/ROC of MDW (0.84) was nearly overlapping that of procalcitonin (0.83), and C-reactive protein (0.89). Statistical optimal cut-off value for MDW was 21 for predicting infection (sensitivity 73%, specificity 82%) and 22 for predicting sepsis (sensitivity 79%, specificity 83%). The best threshold to rule out infection was MDW ≤ 17 (NPV 96.9, 95% CI 88.3–100.0), and ≤ 18 (NPV 99.5, 95% CI 98.3–100.0) to rule out sepsis. The combination of MDW with markers of organ dysfunction (creatinine, bilirubin, platelets) substantially improved the AUC (0.96 (95% CI 0.94–0.97); specificity and sensitivity of 88% and 94%, respectively). In conclusion, MDW has a good diagnostic performance in diagnosing infection and sepsis in patients presenting in ED. Its use as an infection marker even increases when combined with other markers of organ dysfunction. Understanding the impact of interactions of non-infectious conditions and comorbidities on MDW and its diagnostic accuracy requires further elucidation.

Prediction of in-hospital Mortality of Intensive Care Unit Patients with Acute Pancreatitis Based on an Explainable Machine Learning Algorithm.

Acute pancreatitis (AP) is potentially fatal. Therefore, early identification of patients at a high mortality risk and timely intervention are essential. This study aimed to establish an explainable machine-learning model for predicting in-hospital mortality of intensive care unit (ICU) patients with AP. Data on patients with AP, including demographics, vital signs, laboratory tests, comorbidities, treatment, complication, and severity scores, were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU collaborative research database (eICU-CRD). Based on the data from MIMIC-IV, we used the least absolute shrinkage and selection operator algorithm to select variables and then established 9 machine-learning models and screened the optimal model. Data from the eICU-CRD were used for external validation. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, decision curve, and calibration plots were used to assess the models' efficacy. Shapley's additive explanation values were used to explain the model. Gaussian naive Bayes (GNB) model had the best performance on the data from MIMIC-IV, with an AUC, accuracy, sensitivity, and specificity of 0.840, 0.787, 0.839, and 0.792, respectively. The GNB model also performed well on the data from the eICU-CRD, with an AUC, accuracy, sensitivity, and specificity of 0.862, 0.833, 0.848, and 0.763, respectively. According to Shapley's additive explanation values, the top 4 predictive factors were maximum red cell distribution width, minimum saturation of blood oxygen, maximum blood urea nitrogen, and the Sequential Organ Failure Assessment score. The GNB model demonstrated excellent performance and generalizability in predicting mortality in ICU patients with AP. Therefore, it can identify patients at a high mortality risk.

Is palliative care a utopia for older patients with organ failure, dementia or frailty? A qualitative study through the prism of emergency department admission

BackgroundNearly three out of four older people will use the emergency department (ED) during their last year of life. However, most of them do not benefit from palliative care. Providing palliative care is a real challenge for ED clinicians who are trained in acute, life-saving medicine. Our aim is to understand the ED’s role in providing palliative care for this population.MethodsWe designed a qualitative study based on 1) interviews – conducted with older patients (≥ 75 years) with a palliative profile and their informal caregivers – and 2) focus groups – conducted with ED and primary care nurses and physicians. Palliative profiles were defined by the Supportive and Palliative Indicators tool (SPICT). Qualitative data was collected in French-speaking Belgium between July 2021 and July 2022. We used a constant inductive and comparative analysis.ResultsFive older patients with a palliative profile, four informal caregivers, 55 primary and ED caregivers participated in this study. A priori, the participants did not perceive any role for the ED in palliative care. In fact, there is widespread discomfort with caring for older patients and providing palliative care. This is explained by multiple areas of tensions. Palliative care is an approach fraught with pitfalls, i.e.: knowledge and know-how gaps, their implementation depends on patients’(co)morbidity profile and professional values, experiences and type of practice. In ED, there are constant tensions between emergency and palliative care requirements, i.e.: performance, clockwork and needs for standardised procedures versus relational care, time and diversity of palliative care projects. However, even though the ED’s role in palliative care is not recognised at first sight, we highlighted four roles assumed by ED caregivers: 1) Investigator, 2) Objectifier, 3) Palliative care provider, and 4) Decision-maker on the intensity of care. A common perception among participants was that ED caregivers can assist in the early identification of patients with a palliative profile.ConclusionsCurrently, there is widespread discomfort regarding ED caregivers caring for older patients and providing palliative care. Nonetheless, ED caregivers play four roles in palliative care for older patients. In the future, ED caregivers might also perform the role of early identifier.

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in 18F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

Development of a Machine Learning-Based Model for Predicting the Incidence of Peripheral Intravenous Catheter-Associated Phlebitis.

Early and accurate identification of high-risk patients with peripheral intravascular catheter (PIVC)-related phlebitis is vital to prevent medical device-related complications. This study aimed to develop and validate a machine learning-based model for predicting the incidence of PIVC-related phlebitis in critically ill patients. Four machine learning models were created using data from patients ≥ 18 years with a newly inserted PIVC during intensive care unit admission. Models were developed and validated using a 7:3 split. Random survival forest (RSF) was used to create predictive models for time-to-event outcomes. Logistic regression with least absolute reduction and selection operator (LASSO), random forest (RF), and gradient boosting decision tree were used to develop predictive models that treat outcome as a binary variable. Cox proportional hazards (COX) and logistic regression (LR) were used as comparators for time-to-event and binary outcomes, respectively. The final cohort had 3429 PIVCs, which were divided into the development cohort (2400 PIVCs) and validation cohort (1029 PIVCs). The c-statistic (95% confidence interval) of the models in the validation cohort for discrimination were as follows: RSF, 0.689 (0.627-0.750); LASSO, 0.664 (0.610-0.717); RF, 0.699 (0.645-0.753); gradient boosting tree, 0.699 (0.647-0.750); COX, 0.516 (0.454-0.578); and LR, 0.633 (0.575-0.691). No significant difference was observed among the c-statistic of the four models for binary outcome. However, RSF had a higher c-statistic than COX. The important predictive factors in RSF included inserted site, catheter material, age, and nicardipine, whereas those in RF included catheter dwell duration, nicardipine, and age. The RSF model for the survival time analysis of phlebitis occurrence showed relatively high prediction performance compared with the COX model. No significant differences in prediction performance were observed among the models with phlebitis occurrence as the binary outcome.

Tricuspid Annular Plane Systolic Excursion-to-Systolic Pulmonary Artery Pressure Ratio as a Prognostic Factor in Heart Transplant Patients.

Background and Objectives: Heart transplant is currently the final step in treating patients with heart failure. The success of this procedure is strongly connected to potential complications such as postoperative heart failure, infections, graft rejection, graft vasculopathy, and kidney failure. Thus, identifying potential prognostic factors for patients' outcome is of utmost importance. We investigated the prognostic role of the postoperative ratio between the tricuspid annular plane systolic excursion (TAPSE) and systolic pulmonary artery pressure (sPAP) in patients who underwent heart transplantation in our center. Materials and Methods: The study included 46 adult patients from the Emergency Institute for Cardiovascular Diseases and Transplant of Târgu Mureș, who underwent heart transplant between January 2011 and April 2023. By the use of receiver operating characteristic (ROC) analysis, we determined an optimal cut-off value for TAPSE/sPAP with regard to survival at 6 months. Differences in central tendencies of baseline characteristics in those who had a value lower than the cut-off value of TAPSE/sPAP and those who presented a value above it were investigated using the corresponding parametric or nonparametric tests. Results: A value for TAPSE/sPAP above 0.47 mm/mmHg was associated with 6-month survival (OR: 59.5, CI: 5.7-616.0). No significant differences in central tendencies for baseline characteristics were found between the patients who had a TAPSE/sPAP ratio below the cut-off and those who had a ratio above it. Conclusions: The TAPSE/sPAP ratio might prove to be valuable in the early identification of at-risk heart transplant patients. Further prospective studies with larger cohorts are required for validation.

Construction and validation of a risk prediction model for postoperative delirium in patients with off‑pump coronary artery bypass grafting.

Compared with cardiopulmonary bypass surgery, off-pump coronary artery bypass grafting (OPCABG) reduces trauma to the body. However, there is still a risk of neurological complications, including postoperative delirium (POD). To date, few studies have been conducted on the risk of POD in OPCABG patients, and no standardized prediction model has been established. Thus, this study sought to analyze the factors influencing POD in OPCABG patients and to construct a risk prediction model. A total of 1,258 patients with OPCABG were enrolled and divided into the training set for model construction (944 cases) and the test set for model validation (314 cases). A risk prediction model for POD in OPCABG patients was established by least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression, and a nomogram was drawn. The discrimination and calibration degree of the model was evaluated by the receiver operator characteristic (ROC) curve and calibration curve. Eight variables [i.e., age, tissue oxygen saturation, mean arterial pressure (MAP), carotid stenosis, the anterior-posterior diameter of the aortic sinus, ventricular septum thickness, left ventricular ejection fraction (LVEF), and Mini-Mental State Examination (MMSE) scores] were screen out by the LASSO regression and multivariate logistic regression, and the model was constructed. The area under the ROC curve of the training set was 0.702 [95% confidence interval (CI): 0.662-0.743], and that of the test set was 0.658 (95% CI: 0.585-0.730). The results of the Hosmer-Lemeshow goodness-of-fit test showed that the predicted POD risk of OPCABG patients in the training and test sets was consistent with the actual POD risk (χ2=5.154, P=0.74). The occurrence of POD in OPCABG patients is related to age, tissue oxygen saturation, MAP, carotid artery stenosis, the anterior-posterior diameter of aortic sinus, ventricular septal thickness, LVEF, and MMSE scores. The prediction model constructed with the above variables had high predictive performance, and thus may be helpful in the early identification of such patients.

Diagnostic Performance of Cardiac Stress Testing Following Exclusion of Acute Myocardial Infarction with a 0/1-Hour, High-Sensitivity Cardiac Troponin Protocol

Background: Rapid exclusion of acute myocardial infarction (AMI) is critical for patients presenting to emergency departments (EDs) with chest pain or other anginal equivalents. High-sensitivity cardiac troponin (hs-cTn) protocols have been widely adopted in the United States for this purpose. These protocols allow for early identification and exclusion of patients with AMI using a 0 and 1-hour hs-cTn measurement. However, little is known about the use of cardiac stress testing in patients who ruled-out for AMI within 1 hour with very low hs-cTn values. This study analyzed the diagnostic performance of cardiac stress tests in this population. Methods: We performed a secondary analysis of the RACE-IT trial, a stepped-wedge cluster randomized trial performed across 9 EDs in a large metropolitan health system from July 2020 through March 2021. The eligibility criteria for the trial mirrored the real-world use of hs-cTn testing, including both patients complaining of chest pain and/or other anginal equivalents. All adults with a hs-cTnI and electrocardiogram (ECG) completed in the ED were enrolled, while patients with ST- segment elevation AMI, trauma, or pregnancy were excluded. In the interventional arm of the trial, AMI was excluded if hs-cTnI was &lt;4 ng/L at presentation or =4 ng/L at presentation with a 1-hour value &lt; 8 ng/L. The trial followed all patients through 30 days to assess for AMI or death and captured all cardiac testing. We compared stress testing results to invasive coronary imaging with or without revascularization. Results: 10,444 study patients (43.61%) ruled out for AMI in the ED within 1 hour and were included in this analysis. There were 320 (3.0%) patients who had a stress test within 30 days, with few ischemic findings (25, 0.24%) or revascularization procedures (5, 0.05%). The positive predictive value of stress testing in this population to identify the need for revascularization was 10.1% (95% CI 2.8% - 29.4%). Table 1 displays the proportion of ischemic stress tests and overall test performance in this population. The rate of 30-day death or AMI was low (17, 0.20%) among those discharged from the ED or placed in observation (n=8,553). Conclusions: Our study highlights the infrequent use and low diagnostic yield of stress testing in patients who have been ruled out for AMI within 1 hour using an accelerated hs-cTn protocol in the ED.

Machine learning algorithms for predicting COVID-19 mortality in Ethiopia

BackgroundCoronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia.MethodsOur study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC).ResultsThe study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18–79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features.ConclusionOur study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management.Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills.This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.