Nomogram for Stability Stratification of Small Intracranial Aneurysm Based on Clinical and Morphological Risk Factors

Background and Purpose: Stability stratification of intracranial aneurysms (IAs) is crucial for individualized clinical management, especially for small IAs. We aim to develop and validate a nomogram based on clinical and morphological risk factors for individualized instability stratification of small IAs.Methods: Six hundred fifty-eight patients with unstable (n = 293) and stable (n = 416) IAs <7 mm were randomly divided into derivation and validation cohorts. Twelve clinical risk factors and 18 aneurysm morphological risk factors were extracted. Combined with important risk factors, a clinical-morphological predictive nomogram was developed. The nomogram performance was evaluated in the derivation and the validation cohorts in terms of discrimination, calibration, and clinical usefulness.Results: Five independent instability-related risk factors were included in the nomogram: location, irregularity, side/bifurcation type, flow angle, and height-to-width ratio. In the derivation cohort, the area under the curve (95% CI) of the nomogram was 0.803 (95% CI, 0.764–0.842), and good agreement between predicted instability risk and actual instability status could be detected in the calibration plot. The nomogram also exhibited good discriminations and calibration in the validation cohort: the area under the curve (95% CI) was 0.744 (95% CI, 0.677–0.812). Small IAs with scores <90 were considered to have low risk of instability, and those with scores of 90 or greater were considered to have high risk of instability.Conclusions: The nomogram based on clinical and morphological risk factors can be used as a convenient tool to facilitate individualized decision-making in the management of small IAs.

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Clinical Risk Factors and Prognostic Model for Idiopathic Inflammatory Demyelinating Diseases after Haploidentical Hematopoietic Stem Cell Transplantation in Patients with Hematological Malignancies

Blood ◽

10.1182/blood-2021-148957 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 2902-2902

Author(s):

Rui-Xin Deng ◽

Yun He ◽

Xiao-Lu Zhu ◽

Hai-Xia Fu ◽

Xiao-Dong Mo ◽

...

Keyword(s):

Risk Factors ◽

Prognostic Model ◽

Validation Cohort ◽

Regression Coefficient ◽

Risk Group ◽

Area Under The Curve ◽

Demyelinating Diseases ◽

Decision Curve Analysis ◽

Derivation Cohort ◽

Risk Of Death

Abstract Introduction As a neurological complication following haploidentical haematopoietic stem cell transplantation (haplo-HSCT), immune-mediated demyelinating diseases (IIDDs) of the central nervous system (CNS) are rare, but they seriously affect a patient's quality of life (J Neurooncol, 2012). Although several reports have demonstrated that IIDDs have a high mortality rate and a poor prognosis (J Neurooncol, 2012; Neurology 2013), a method to predict the outcome of CNS IIDDs after haplo-HSCT is not currently available. Here, we reported the largest research on CNS IIDDs post haplo-HSCT, and we developed and validated a prognostic model for predicting the outcome of CNS IIDDs after haplo-HSCT. Methods We retrospectively evaluated 184 consecutive CNS IIDD patients who had undergone haplo-HSCT at a single center between 2008 and 2019. The derivation cohort included 124 patients receiving haplo-HSCT from 2014 to 2019, and the validation cohort included 60 patients receiving haplo-HSCT from 2008 to 2013. The diagnosis of CNS IIDDs was based on the clinical manifestations and exclusion of other aetiologies, including infection, neurotoxicity, metabolic encephalopathy, ischaemic demyelinating disorders, and tumor infiltration. The final prognostic model selection was performed by backward stepwise logistic regression using the Akaike information criterion. The final model was internally and externally validated using the bootstrap method with 1000 repetitions. We assessed the prognostic model performance by evaluating the discrimination [area under the curve (AUC)], calibration (calibration plot), and net benefit [decision curve analysis (DCA)]. Results In total, 184 of 4532 patients (4.1%) were diagnosed with CNS IIDDs after transplantation. Among them, 120 patients had MS, 53 patients had NMO, 7 patients had ADEM, 3 patients had Schilder's disease, and 1 patient had Marburg disease. Grades II to IV acute graft-versus-host disease (aGVHD) (p<0.001) and chronic GVHD (cGVHD) (p<0.001) were identified as risk factors for developing IIDDs after haplo-HSCT. We also tested immune reconstitution by measuring the following parameters 30, 60, and 90 days after haplo-HSCT: proportions of CD19+ B cells, CD3+ T cells and CD4+ T cells; counts of lymphocytes and monocytes; and levels of immunoglobulins A, G, and M. These parameters showed no significant differences between patients with and without IIDD. CNS IIDDs were significantly associated with higher mortality and a poor prognosis (p＜0.001). In a/the multivariate logistic analysis of the derivation cohort, four candidate predictors were entered into the final prognostic model: cytomegalovirus (CMV) infection, Epstein-Barr virus (EBV) infection, the cerebrospinal fluid (CSF) IgG synthesis index (IgG-Syn), and spinal cord lesions. The value assignment was completed according to the regression coefficient of each identified independent prognostic factor for CNS IIDDs in the derivation cohort to establish the CELS risk score model. According to the regression coefficient, point values were given to each factor based on the log scale, and 1 point was awarded for each variable. These 4 factors determined the total risk score, ranging from 0 to 4. There was a higher risk of death in IIDD patients with higher CELS scores and we, therefore, defined three levels of risk of death in IIDD patients: a low-risk group for patients with a score of 0, a medium-risk group for patients with a total score of 1 or 2, and a high-risk group for patients with a total score of 3 or 4. The prognostic model had an area under the curve of 0.864 (95% CI: 0.803-0.925) in the internal validation cohort and 0.871 (95% CI: 0.806-0.931) in the external validation cohort. The calibration plots showed a high agreement between the predicted and observed outcomes. Decision curve analysis indicated that IIDD patients could benefit from the clinical application of the prognostic model. Conclusion s We identified the risk factors for IIDD onset after haplo-HSCT, and we also developed and validated a reliable prediction model, namely, the CELS, to accurately assess the outcome of IIDD patients after haplo-HSCT. Identifying IIDD patients who are at a high risk of death can help physicians treat them in advance, which will improve patient survival and prognosis. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

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Development of a delirium predictive model for adult trauma patients in an emergency and critical care center: a retrospective study

Trauma Surgery & Acute Care Open ◽

10.1136/tsaco-2021-000827 ◽

2021 ◽

Vol 6 (1) ◽

pp. e000827

Author(s):

Ayaka Matsuoka ◽

Toru Miike ◽

Mariko Miyazaki ◽

Taku Goto ◽

Akira Sasaki ◽

...

Keyword(s):

Risk Factors ◽

Scoring System ◽

Validation Cohort ◽

Care Center ◽

Univariate Analysis ◽

Area Under The Curve ◽

Predictive Score ◽

Calibration Plot ◽

Trauma Patients ◽

Time Of Admission

BackgroundDelirium has been shown to prolong the length of intensive care unit stay, hospitalization, and duration of ventilatory control, in addition to increasing the use of sedatives and increasing the medical costs. Although there have been a number of reports referring to risk factors for the development of delirium, no model has been developed to predict delirium in trauma patients at the time of admission. This study aimed to create a scoring system that predicts delirium in trauma patients.MethodsIn this single-center, retrospective, observational study, trauma patients aged 18 years and older requiring hospitalization more than 48 hours were included and divided into the development and validation cohorts. Univariate analysis was performed in the development cohort to identify factors significantly associated with prediction of delirium. The final scoring system for predicting delirium was developed using multivariate analysis and internal validation was performed.ResultsOf the 308 patients in the development cohort, 91 developed delirium. Clinical Frailty Score, fibrin/fibrinogen degradation products, low body mass index, lactate level, and Glasgow Coma Scale score were independently associated with the development of delirium. We developed a scoring system using these factors and calculated the delirium predictive score, which had an area under the curve of 0.85. In the validation cohort, 46 of 206 patients developed delirium. The area under the curve for the validation cohort was 0.86, and the calibration plot analysis revealed the scoring system was well calibrated in the validation cohort.DiscussionThis scoring system for predicting delirium in trauma patients consists of only five risk factors. Delirium prediction at the time of admission may be useful in clinical practice.Level of evidencePrognostic and epidemiological, level III.

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Role of gut microbiota in travel-related acquisition of extended spectrum β-lactamase-producing Enterobacteriaceae

Journal of Travel Medicine ◽

10.1093/jtm/taab022 ◽

2021 ◽

Author(s):

Ye Peng ◽

Suisha Liang ◽

Kanchana Poonsuk ◽

Hilda On ◽

Sze Wang Li ◽

...

Keyword(s):

Risk Factors ◽

Random Forest ◽

Gut Microbiota ◽

Area Under The Curve ◽

International Travel ◽

Resistant Bacteria ◽

Extended Spectrum ◽

Related Risk ◽

Associated Risk Factors

Abstract Background International travel could facilitate the spread of antimicrobial-resistant bacteria including extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-E). Previous studies, which attempted to understand the role of gut microbiota in the acquisition of antimicrobial resistant bacteria during international travels, are limited to western travelers. Methods We established a prospective cohort of 90 Hong Kong travelers to investigate gut microbiota determinants and associated risk factors for the acquisition of ESBL-E. Baseline characteristics and travel-associated risk factors were gathered through questionnaires. Fecal samples were collected in 3–4 days before and after travel. Antimicrobial susceptibility of ESBL-E isolates was tested, and gut microbiota were profiled by 16S rDNA amplicon sequencing. Non-parametric tests were used to detect potential associations, and logistic regression models were used to quantify the associations. Random forest models were constructed to identify microbial predictors for ESBL-E acquisition. Results 49 (54.4%) participants were tested negative for ESBL-E colonization before travel and were followed up after travel. A total of 60 ESBL-E isolates were cultured from 20 (40.8%) participants. Having low Actinobacteria richness and low abundance of short-chain fatty acid-producing bacteria in the gut microbiota before travel increased the risk of acquiring ESBL-E and the risk can be further exacerbated by eating raw seafood during travel. Besides, post-travel ESBL-E positive participants had increased abundances of several opportunistic pathogens such as Staphylococcus, Enterococcus, Escherichia/Shigella and Klebsiella. The random forest model integrating pre-travel microbiota and the identified travel-related risk factor could predict ESBL-E acquisition with an area under the curve of 75.4% (95% confidence interval: 57.9%–93.0%). Conclusions In this study, we identified both travel-related risk factors and microbiota predictors for the risk of ESBL-E acquisition. Our results provide foundational knowledge for future developments of microbiota-based interventions to prevent ESBL-E acquisition during international travels.

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The risk factors of postoperative delirium in patients with hip fracture: implication for clinical management

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-04091-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Weifang Xu ◽

Haiping Ma ◽

Wang Li ◽

Chen Zhang

Keyword(s):

Risk Factors ◽

Hip Fracture ◽

Postoperative Delirium ◽

Area Under The Curve ◽

Hip Fracture Surgery ◽

Related Risk ◽

History Of ◽

Laboratory Examinations ◽

Independent Risk Factors ◽

Epidemiological Characteristics

Abstract Background Delirium is a common complication of hip surgery patients. It is necessary to investigate the epidemiological characteristics and related risk factors of delirium after hip fracture surgery, to provide evidence supports for the prevention and management of delirium. Methods Hip fracture patients admitted to our hospital for surgical treatment from March 2018 to March 2020 were identified as participants. The characteristics and laboratory examinations in patients with and without postoperative delirium were compared and analyzed. Logistic regression analyses were conducted to ascertain the independent risk factors, and the area under the curve (AUC) were calculated to analyze the predictive value. Results A total of 568 postoperative patients with hip fracture were included, the incidence of delirium in postoperative patients with hip fracture was 14.44 %. The preoperative albumin (OR 4.382, 2.501 ~ 5.538), history of delirium (OR 2.197, 1.094 ~ 3.253), TSH (OR1.245, 1.077 ~ 1.638), the resting score on the first postoperative day (OR1.235, 0.944 ~ 1.506) and age(OR1.185, 0.065 ~ 1.814) were the independent risk factors for the postoperative delirium in patients with hip fracture(all p < 0.05). The AUC of albumin, history of delirium, TSH, the resting score on the first postoperative day and age were 0.794, 0.754, 0.746, 0.721 and 0.689 respectively. Conclusions The incidence of delirium in postoperative patients with hip fracture is rather high, especially for patients with old age and history of delirium. Monitoring albumin, TSH and resting score may be beneficial to the management of postoperative delirium.

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The 2CAN Score

Stroke ◽

10.1161/strokeaha.118.022130 ◽

2018 ◽

Vol 49 (12) ◽

pp. 2866-2871 ◽

Cited By ~ 2

Author(s):

Philip Chang ◽

Ilana Ruff ◽

Scott J. Mendelson ◽

Fan Caprio ◽

Deborah L. Bergman ◽

...

Keyword(s):

Risk Factors ◽

Predictive Value ◽

Validation Cohort ◽

Vital Signs ◽

Area Under The Curve ◽

Clinical Score ◽

Final Diagnosis ◽

Operating Characteristics ◽

Derivation Cohort ◽

Academic Center

Background and Purpose— A quarter of acute strokes occur in patients hospitalized for another reason. A stroke recognition instrument may be useful for non-neurologists to discern strokes from mimics such as seizures or delirium. We aimed to derive and validate a clinical score to distinguish stroke from mimics among inhospital suspected strokes. Methods— We reviewed consecutive inpatient stroke alerts in a single academic center from January 9, 2014, to December 7, 2016. Data points, including demographics, stroke risk factors, stroke alert reason, postoperative status, neurological examination, vital signs and laboratory values, and final diagnosis, were collected. Using multivariate logistic regression, we derived a weighted scoring system in the first half of patients (derivation cohort) and validated it in the remaining half of patients (validation cohort) using receiver operating characteristics testing. Results— Among 330 subjects, 116 (35.2%) had confirmed stroke, 43 (13.0%) had a neurological mimic (eg, seizure), and 171 (51.8%) had a non-neurological mimic (eg, encephalopathy). Four risk factors independently predicted stroke: clinical deficit score (clinical deficit score 1: 1 point; clinical deficit score ≥2: 3 points), recent cardiac procedure (1 point), history of atrial fibrillation (1 point), and being a new patient (<24 hours from admission: 1 point). The score showed excellent discrimination in the first 165 patients (derivation cohort, area under the curve=0.93) and remaining 165 patients (validation cohort, area under the curve=0.88). A score of ≥2 had 92.2% sensitivity, 69.6% specificity, 62.2% positive predictive value, and 94.3% negative predictive value for identifying stroke. Conclusions— The 2CAN score for recognizing inpatient stroke performs well in a single-center study. A future prospective multicenter study would help validate this score.

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Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention

European Heart Journal ◽

10.1093/eurheartj/ehaa648 ◽

2020 ◽

Vol 41 (41) ◽

pp. 3998-4007 ◽

Cited By ~ 1

Author(s):

Renate M Hoogeveen ◽

João P Belo Pereira ◽

Nick S Nurmohamed ◽

Veronica Zampoleri ◽

Michiel J Bom ◽

...

Keyword(s):

Risk Factors ◽

Primary Prevention ◽

Risk Model ◽

Control Sample ◽

Area Under The Curve ◽

Clinical Implementation ◽

Derivation Cohort ◽

Clinical Risk ◽

Model Combining ◽

Traditional Risk Factors

Abstract Aims In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort. Methods and results Using the proximity extension assay, 368 proteins were measured in a nested case–control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001). Conclusion In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention.

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Role of pulse oximetric monitoring during gastrointestinal endoscopy. Prospective multicenter study of the Gastroenterological Working Group of the Veszprém Regional Committee of the Hungarian Academy of Sciences (VEAB)

Orvosi Hetilap ◽

10.1556/oh.2013.29613 ◽

2013 ◽

Vol 154 (21) ◽

pp. 825-833

Author(s):

Zoltán Döbrönte ◽

Mária Szenes ◽

Beáta Gasztonyi ◽

Lajos Csermely ◽

Márta Kovács ◽

...

Keyword(s):

Risk Factors ◽

Multicenter Study ◽

Hungarian Academy ◽

Prospective Multicenter Study ◽

Standard Requirement ◽

Long Duration ◽

Related Risk ◽

American Society ◽

Academy Of Sciences ◽

Endoscopic Investigation

Introduction: Recent guidelines recommend routine pulse oximetric monitoring during endoscopy, however, this has not been the common practice yet in the majority of the local endoscopic units. Aims: To draw attention to the importance of the routine use of pulse oximetric recording during endoscopy. Method: A prospective multicenter study was performed with the participation of 11 gastrointestinal endoscopic units. Data of pulse oximetric monitoring of 1249 endoscopic investigations were evaluated, of which 1183 were carried out with and 66 without sedation. Results: Oxygen saturation less than 90% was observed in 239 cases corresponding to 19.1% of all cases. It occurred most often during endoscopic retrograde cholangiopancreatography (31.2%) and proximal enteroscopy (20%). Procedure-related risk factors proved to be the long duration of the investigation, premedication with pethidine (31.3%), and combined sedoanalgesia with pethidine and midazolam (34.38%). The age over 60 years, obesity, consumption of hypnotics or sedatives, severe cardiopulmonary state, and risk factor scores III and IV of the American Society of Anestwere found as patient-related risk factors. Conclusion: To increase the safety of patients undergoing endoscopic investigation, pulse oximeter and oxygen supplementation should be the standard requirement in all of the endoscopic investigation rooms. Pulse oximetric monitoring is advised routinely during endoscopy with special regard to the risk factors of hypoxemia. Orv. Hetil., 2013, 154, 825–833.

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