6-month Prognosis Research Articles

The prognostic impact of malnutrition on the outcomes of patients with vertebrobasilar artery occlusion following endovascular treatment

Background and purposeMalnutrition is associated with poor outcomes in different diseases. Our aim was to investigate whether measures of malnutrition could be used to predict 90-day outcomes in patients with vertebrobasilar artery occlusion (VBAO) undergoing endovascular treatment (EVT). MethodsWe retrospectively analyzed patients with VBAO who received EVT at three comprehensive stroke centers. Malnutrition was assessed using the controlling nutritional status (CONUT) score, geriatric nutritional risk index (GNRI), and prognostic nutritional index (PNI). Primary outcome was good functional outcome defined as modified Rankin Scale (mRS) 0–3 measured at 90 days. ResultsA total of 285 patients were enrolled, of which 260 (91.22 %) met the requirements. According to the CONUT, GNRI, and PNI scores, the proportions of patients classified as moderately or severely malnourished were 7.3 %, 3.08 %, and 35 %, respectively. In the multivariate regression model after adjusting for potential confounders, malnutrition (severe risk versus normal nutritional status) was significantly associated with an increased risk of poor prognosis for CONUT scores (adjusted odds ratio [OR]14.91, 95 %CI, 1.69 - 131.71; P = 0.015), GNRI scores (adjusted [OR] 10.67, 1.17 - 96.93; P = 0.036) and PNI scores (adjusted [OR] 4.61, 2.28 - 9.31; P < 0.001). Similar results were obtained when malnutrition scores were analyzed as continuous variables. Adding the 3 malnutrition measures to the risk reclassification that included traditional risk factors significantly improved the predictive value of 3-month poor prognosis. ConclusionsOur study showed that malnutrition may be associated with poor prognosis within 3 months of EVT in patients with VBAO.

Development of a short-term prognostic model for anti-N-methyl-D-aspartate receptor encephalitis in Chinese patients

BackgroundRecognizing the predictors of poor short-term prognosis after first-line immunotherapy in patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is essential for individualized treatment strategy. The objective of this study was to ascertain the factors that forecast short-term prognosis in patients with anti-NMDAR encephalitis, develop a prognostic prediction model, and authenticate its efficacy in an external validation cohort. Further, all patients were followed-up long-term to assess the factors of long-term outcome and relapses.MethodsA prospective enrollment of patients diagnosed with anti-NMDAR encephalitis was conducted across five clinical centers in China from June 2014 to Mar 2022. The enrolled patients were divided into the derivation and validation sets based on enrollment time. The short-term prognostic model was visualized using a nomogram. Further, all patients were followed-up long-term to assess the factors of long-term outcome.ResultsThis study found that poor short-term prognosis was a risk factor for poor long-term outcome (6-month prognosis, OR 29.792, 95%CI 6.507-136.398, p < 0.001; 12-month prognosis, OR 15.756, 95%CI 3.384–73.075, p < 0.001; 24-month prognosis, OR 5.500, 95%CI 1.045–28.955, p = 0.044). Abnormal behavior or cognitive dysfunction (OR 8.57, 95%CI 1.48–49.79, p = 0.017), consciousness impairment (OR19.32, 95%CI 3.03-123.09, p = 0.002), autonomic dysfunction or central hypoventilation (OR 5.66, 95%CI 1.25–25.75, p = 0.025), CSF pleocytosis (OR 4.33, 95%CI 1.48–12.65, p = 0.007), abnormal EEG (OR 5.48, 95% CI 1.09–27.54, p = 0.039) were independent predictors for a poor short-term prognosis after first-line immunotherapy. A nomogram that incorporated those factors showed good discrimination and calibration abilities. The area under the curve (AUC) for the prognostic model were 0.866 (95%CI: 0.798–0.934) with a sensitivity of 0.761 and specificity of 0.869.ConclusionWe established and validated a prognostic model that can provide individual prediction of short-term prognosis after first-line immunotherapy for patients with anti-NMDAR encephalitis. This practical prognostic model may help neurologists to predict the short-term prognosis early and potentially assist in adjusting appropriate treatment timely.

Effect of working hours on prognosis of acute ischemic stroke patients following alteplase intravenous thrombolysis.

To investigate the effect of hospital working hours on outcomes of patients with acute ischemic stroke 3 months after receiving alteplase intravenous thrombolysis. A retrospective analysis was performed on 254 individuals with acute ischemic stroke who received alteplase intravenous thrombolysis between January 2018 and December 2020 either during peak hospital working hours (08:00-17:59; Group A) or off-peak hours (18:00-07:59 the following day; Group B). Patients were also categorized according to which of four peak/off-peak-hour periods they received treatment in: Group 1 (08:00-11:59), Group 2 (12:00-17:59), Group 3 (18:00-21:59), Group 4 (22:00-07:59 the following day). Baseline data and 3-month prognosis were compared across groups. Logistic regression analysis was used to investigate the correlation between hospital working hours and 3-month prognosis. There were no significant differences in door-to-needle time, onset-to-needle time, 24-hour National Institutes of Health Stroke Scale (NIHSS) score, 7-day NIHSS score or Modified Rankin Score between Groups 1 to 4 or between Groups A and B. Whether treatment was administered during peak or off-peak hours did not significantly affect 3-month prognosis. At this hospital, differences in the time at which stroke patients were treated were not associated with outcomes.

Developing a nomogram model for 3-month prognosis in patients who had an acute ischaemic stroke after intravenous thrombolysis: a multifactor logistic regression model approach

ObjectivesThis study is to establish a nomination graph model for individualised early prediction of the 3-month prognosis of patients who had an acute ischaemic stroke (AIS) receiving intravenous thrombolysis with...

Establishment of a corneal ulcer prognostic model based on machine learning

Corneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different microorganisms, such as bacteria and fungi, worsen keratitis leading to corneal perforation even with optimal drug treatment. The cornea forms the main refractive surface of the eye. Diseases affecting the cornea can cause severe visual impairment. Therefore, it is crucial to analyze the risk of corneal perforation and visual impairment in corneal ulcer patients for making early treatment strategies. The modeling of a fully automated prognostic model system was performed in two parts. In the first part, the dataset contained 4973 slit lamp images of corneal ulcer patients in three centers. A deep learning model was developed and tested for segmenting and classifying five lesions (corneal ulcer, corneal scar, hypopyon, corneal descementocele, and corneal neovascularization) in the eyes of corneal ulcer patients. Further, hierarchical quantification was carried out based on policy rules. In the second part, the dataset included clinical data (name, gender, age, best corrected visual acuity, and type of corneal ulcer) of 240 patients with corneal ulcers and respective 1010 slit lamp images under two light sources (natural light and cobalt blue light). The slit lamp images were then quantified hierarchically according to the policy rules developed in the first part of the modeling. Combining the above clinical data, the features were used to build the final prognostic model system for corneal ulcer perforation outcome and visual impairment using machine learning algorithms such as XGBoost, LightGBM. The ROC curve area (AUC value) evaluated the model’s performance. For segmentation of the five lesions, the accuracy rates of hypopyon, descemetocele, corneal ulcer under blue light, and corneal neovascularization were 96.86, 91.64, 90.51, and 93.97, respectively. For the corneal scar lesion classification, the accuracy rate of the final model was 69.76. The XGBoost model performed the best in predicting the 1-month prognosis of patients, with an AUC of 0.81 (95% CI 0.63–1.00) for ulcer perforation and an AUC of 0.77 (95% CI 0.63–0.91) for visual impairment. In predicting the 3-month prognosis of patients, the XGBoost model received the best AUC of 0.97 (95% CI 0.92–1.00) for ulcer perforation, while the LightGBM model achieved the best performance with an AUC of 0.98 (95% CI 0.94–1.00) for visual impairment.

Acute-to-chronic glycemic ratio as an outcome predictor in ischemic stroke in patients with and without diabetes mellitus

ObjectiveElevated plasma glucose levels are common in patients suffering acute ischemic stroke (AIS), and acute hyperglycemia has been defined as an independent determinant of adverse outcomes. The impact of acute-to-chronic glycemic ratio (ACR) has been analyzed in other diseases, but its impact on AIS prognosis remains unclear. The main aim of this study was to assess whether the ACR was associated with a 3-month poor prognosis in patients with AIS.Research, design and methodsRetrospective analysis of patients admitted for AIS in Hospital del Mar, Barcelona. To estimate the chronic glucose levels (CGL) we used the formula eCGL= [28.7xHbA1c (%)]-46.7. The ACR (glycemic at admission / eCGL) was calculated for all subjects. Tertile 1 was defined as: 0.28–0.92, tertile 2: 0.92–1.13 and tertile 3: > 1.13. Poor prognosis at 3 months after stroke was defined as mRS score 3–6.Results2.774 subjects with AIS diagnosis were included. Age, presence of diabetes, previous disability (mRS), initial severity (NIHSS) and revascularization therapy were associated with poor prognosis (p values < 0.05). For each 0.1 increase in ACR, there was a 7% increase in the risk of presenting a poor outcome. The 3rd ACR tertile was independently associated with a poor prognosis and mortality. In the ROC curves, adding the ACR variable to the classical clinical model did not increase the prediction of AIS prognosis (0.786 vs. 0.781).ConclusionsACR was positively associated with a poor prognosis and mortality at 3-months follow-up after AIS. Subjects included in the 3rd ACR tertile presented a higher risk of poor prognosis and mortality. Baseline glucose or ACR did not add predictive value in comparison to only using classical clinical variables.

Machine learning is an effective method to predict the 3-month prognosis of patients with acute ischemic stroke

Background and objectivesUpwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients.MethodsA total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient’ demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants’ prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables.ResultsFeature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900.ConclusionThese findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.

Predictive value of monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) for poor prognosis after intravenous thrombolytic therapy for acute ischaemic stroke

IntroductionThe purpose of this study was to examine the relationship between monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) and poor short-term 3-month and long-term 6-month prognosis after intravenous thrombolysis in patients with acute ischaemic stroke.Material and methodsBy retrospective analysis, 763 eligible patients with acute ischaemic stroke with intravenous thrombolysis were included in the study, and the general data and clinical laboratory examination results of the patients were collected. The relationship between MHR and poor prognosis at 3 and 6 months in patients with intravenous thrombolysis was derived by stepwise regression using the R language, followed by 1:1 propensity score matching to determine the MHR threshold and to investigate the relationship between high and low MHR values and poor prognosis.ResultsMHR level was found to predict the prognosis of intravenous thrombolysis patients with acute ischaemic stroke, and it was an effective predictor of poor prognosis at 3 and 6 months after intravenous thrombolysis. MHR has a threshold of 0.584. High MHR levels were strongly associated with a poor 3-month prognosis of intravenous thrombolysis in patients with acute ischaemic stroke (OR = 5.657; 95% CI: 4.124–7.762; p &lt; 0.001). High MHR level was closely associated with poor prognosis of acute ischaemic stroke patients with intravenous thrombolysis at 6 months (OR = 4.923; 95% CI: 3.603–6.726; p &lt; 0.001).ConclusionsMHR level is a valid predictor for poor prognosis at 3-6 months after intravenous thrombolysis in patients in acute ischaemic stroke.

Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence-Based Prognosis for Patients With Advanced Solid Tumors.

Use of artificial intelligence (AI) in cancer care is increasing. What remains unclear is how best to design patient-facing systems that communicate AI output. With oncologist input, we designed an interface that presents patient-specific, machine learning-based 6-month survival prognosis information designed to aid oncology providers in preparing for and discussing prognosis with patients with advanced solid tumors and their caregivers. The primary purpose of this study was to assess patient and caregiver perceptions and identify enhancements of the interface for communicating 6-month survival and other prognosis information when making treatment decisions concerning anticancer and supportive therapy. This qualitative study included interviews and focus groups conducted between November and December 2022. Purposive sampling was used to recruit former patients with cancer and/or former caregivers of patients with cancer who had participated in cancer treatment decisions from Utah or elsewhere in the United States. Categories and themes related to perceptions of the interface were identified. We received feedback from 20 participants during eight individual interviews and two focus groups, including four cancer survivors, 13 caregivers, and three representing both. Overall, most participants expressed positive perceptions about the tool and identified its value for supporting decision making, feeling less alone, and supporting communication among oncologists, patients, and their caregivers. Participants identified areas for improvement and implementation considerations, particularly that oncologists should share the tool and guide discussions about prognosis with patients who want to receive the information. This study revealed important patient and caregiver perceptions of and enhancements for the proposed interface. Originally designed with input from oncology providers, patient and caregiver participants identified additional interface design recommendations and implementation considerations to support communication about prognosis.

Acute and Chronic Kidney Dysfunction and Prognosis following Thrombectomy for Ischemic Stroke

Introduction: Patients with chronic kidney disease (CKD) have an increased risk of stroke, and CKD seems associated with worse outcome after a stroke. The main objective of our study RISOTTO was to evaluate the influence of CKD and acute kidney injury (AKI) on the clinical outcome and mortality of ischemic stroke patients after thrombolysis and/or thrombectomy. Methods: This multicenter cohort study included patients in the acute phase of ischemic stroke due to large artery occlusion managed by thrombectomy. Functional outcome at 3 months was assessed by the modified Rankin Scale (mRS). Results: 280 patients were included in the analysis. Fifty-nine patients (22.6%) had CKD. At 3 months, CKD was associated with similar functional prognosis (mRS 3–6: 50.0% vs. 41.7%, p = 0.262) but higher mortality (24.2% versus 9.5%, p = 0.004). In univariate analysis, patients with CKD had a higher burden of white matter hyperintensities (Fazekas score: 1.7 ± 0.8 vs. 1.0 ± 0.8, p = 0.002), lower initial infarct volume with equivalent severity, and lower recanalization success (86.4% vs. 97.0%, p = 0.008) compared to non-CKD patients. Forty-seven patients (20.0%) developed AKI. AKI was associated with poorer 3-month functional outcome (mRS 3–6: 63.8% vs. 49.0%, p = 0.002) and mortality (23.4% versus 7.7%, p = 0.002). In multivariate analysis, AKI appeared as an independent risk factor for poor functional outcome (mRS 3–6: adjOR 2.79 [1.11–7.02], p = 0.029) and mortality (adjOR 2.52 [1.03–6.18], p = 0.043) at 3 months, while CKD was not independently associated with 3-month mortality and poor neurological outcome. Conclusions: AKI is independently associated with poorer functional outcome and increased mortality at 3 months. CKD was not an independent risk factor for 3-month mortality or poor functional prognosis.

An analysis of neutrophil-to-lymphocyte ratios and monocyte-to-lymphocyte ratios with six-month prognosis after cerebral contusions.

Neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) have been identified as potential prognostic markers in various conditions, including cancer, cardiovascular disease, and stroke. This study aims to investigate the dynamic changes of NLR and MLR following cerebral contusion and their associations with six-month outcomes. Retrospective data were collected from January 2016 to April 2020, including patients diagnosed with cerebral contusion and discharged from two teaching-oriented tertiary hospitals in Southern China. Patient demographics, clinical manifestations, laboratory test results (neutrophil, monocyte, and lymphocyte counts) obtained at admission, 24 hours, and one week after cerebral contusion, as well as outcomes, were analyzed. An unfavorable outcome was defined as a Glasgow Outcome Score (GOS) of 0-3 at six months. Logistic regression analysis was performed to identify independent predictors of prognosis, while receiver characteristic curve analysis was used to determine the optimal cutoff values for NLR and MLR. A total of 552 patients (mean age 47.40, SD 17.09) were included, with 73.19% being male. Higher NLR at one-week post-cerebral contusion (adjusted OR = 4.19, 95%CI, 1.16 - 15.16, P = 0.029) and higher MLR at admission and at 24h (5.80, 1.40 - 24.02, P = 0.015; 9.06, 1.45 - 56.54, P = 0.018, respectively) were significantly associated with a 6-month unfavorable prognosis after adjustment for other risk factors by multiple logistic regression. The NLR at admission and 24 hours, as well as the MLR at one week, were not significant predictors for a 6-month unfavorable prognosis. Based on receiver operating characteristic curve analysis, the optimal thresholds of NLR at 1 week and MLR at admission after cerebral contusion that best discriminated a unfavorable outcome at 6-month were 6.39 (81.60% sensitivity and 70.73% specificity) and 0.76 (55.47% sensitivity and 78.26% specificity), respectively. NLR measured one week after cerebral contusion and MLR measured at admission may serve as predictive markers for a 6-month unfavorable prognosis. These ratios hold potential as parameters for risk stratification in patients with cerebral contusion, complementing established biomarkers in diagnosis and treatment. However, further prospective studies with larger cohorts are needed to validate these findings.

Evaluation of ChatGPT in Predicting 6-Month Outcomes After Traumatic Brain Injury.

To evaluate the capacity of ChatGPT, a widely accessible and uniquely popular artificial intelligence-based chatbot, in predicting the 6-month outcome following moderate-to-severe traumatic brain injury (TBI). Single-center observational retrospective study. Data are from a neuro-ICU from a level 1 trauma center. All TBI patients admitted to ICU between September 2021 and October 2022 were included in a prospective database. None. Based on anonymized clinical, imaging, and biological information available at the patients' hospital admission and extracted from the database, clinical vignettes were retrospectively submitted to ChatGPT for prediction of patients' outcomes. The predictions of two intensivists (one neurointensivist and one non-neurointensivist) both from another level 1 trauma center (Beaujon Hospital), were also collected as was the International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) scoring. Each intensivist, as well as ChatGPT, made their prognostic evaluations independently, without knowledge of the others' predictions and of the patients' actual management and outcome. Both the intensivists and ChatGPT were given access to the exact same set of information. The main outcome was a 6-month-functional status dichotomized into favorable (Glasgow Outcome Scale Extended [GOSE] ≥ 5) versus poor (GOSE < 5). Prediction of intracranial hypertension management, pulmonary infectious risk, and removal of life-sustaining therapies was also investigated as secondary outcomes. Eighty consecutive moderate-to-severe TBI patients were included. For the 6-month outcome prognosis, area under the receiver operating characteristic curve (AUC-ROC) for ChatGPT, the neurointensivist, the non-neurointensivist, and IMPACT were, respectively, 0.62 (0.50-0.74), 0.70 (0.59-0.82), 0.71 (0.59-0.82), and 0.81 (0.72-0.91). ChatGPT had the highest sensitivity (100%), but the lowest specificity (26%). For secondary outcomes, ChatGPT's prognoses were generally less accurate than clinicians' prognoses, with lower AUC values for most outcomes. This study does not support the use of ChatGPT for prediction of outcomes after TBI.

Predictive value of neutrophil to apolipoprotein A1 ratio in patients with acute ischaemic stroke.

The neutrophil to apolipoprotein A1 ratio has emerged as a possible prognostic biomarker in different medical conditions. Nonetheless, the predictive potential of neutrophil to apolipoprotein A1 ratio in determining the 3-month prognosis of acute ischaemic stroke patients who undergo intravenous thrombolysis has yet to be fully acknowledged. In this study, 196 acute ischaemic stroke patients with recombinant tissue plasminogen activator and 133 healthy controls were included. Meanwhile, we incorporated a total of 386 non-thrombolytic acute ischaemic stroke patients. The acute ischaemic stroke patients with recombinant tissue plasminogen activator were divided into four groups based on quartiles of neutrophil to apolipoprotein A1 ratio. The association between neutrophil to apolipoprotein A1 ratio and the 3-month prognosis was evaluated through univariate and multivariate regression analyses. Additionally, subgroup analyses were conducted to investigate the predictive value of neutrophil to apolipoprotein A1 ratio in different patient populations. Adverse outcomes were defined as a modified Rankin Scale score of 3-6. The study findings revealed a significant association between elevated neutrophil to apolipoprotein A1 ratio levels and poor prognosis in acute ischaemic stroke patients. In the highest quartile of neutrophil to apolipoprotein A1 ratio levels (Q4), after controlling for age, gender, admission National Institutes of Health Stroke Scale score, blood urea nitrogen and stroke subtypes, the odds ratio for adverse outcomes at 3 months was 13.314 (95% confidence interval: 2.878-61.596, P = 0.001). An elevated neutrophil to apolipoprotein A1 ratio value was found to be associated with a poor prognosis in acute ischaemic stroke patients, regardless of whether they received recombinant tissue plasminogen activator treatment or not. The new model, which incorporating neutrophil to apolipoprotein A1 ratio into the conventional model, demonstrated a statistically significant improvement in discriminatory power and risk reclassification for 3-month poor outcomes in acute ischaemic stroke patients treated with recombinant tissue plasminogen activator. The new model exhibited a categorical net reclassification index (P = 0.035) of 12.9% and an integrated discrimination improvement (P = 0.013) of 5.2%. Subgroup analyses indicated that the predictive value of neutrophil to apolipoprotein A1 ratio differed across stroke subtypes. Neutrophil to apolipoprotein A1 ratio is a potential biomarker for predicting the prognosis of acute ischaemic stroke patients. The clinical implications of our findings are significant, as early identification and intervention in high-risk patients can improve their outcomes. However, further studies are required to validate our results and elucidate the underlying mechanisms of the association between neutrophil to apolipoprotein A1 ratio and poor prognosis in acute ischaemic stroke patients.

Curative effect analysis of robot-assisted drainage surgery in treatment of spontaneous hypertensive brainstem hemorrhage.

Spontaneous hypertensive brainstem hemorrhage (HBSH) is characterized by sudden onset, rapid progression and poor prognosis. There has been a growing tendency of surgical treatment for HBSH. This study aimed to investigate outcomes and potential factors associated with the prognosis of robot-assisted drainage surgery for HBSH treatment. Patients with HBSH from July 2016 to March 2023 at a single neurosurgery center were included and divided into conservative group and surgical groups. Baseline and clinical data, radiographic characteristics, complications, and outcome evaluations were recorded and analyzed. A total of 125 patients, with 74 in the conservative group and 51 in the surgical group, were enrolled in the study. Mortality at 6 months was 59/74 (79.7%) in the conservative group and 9/51 (17.6%) in the surgical group. Twenty-four patients (47.1%) achieved favorable outcomes in the surgical group, whereas this rate in the conservative group was only 5.4% (4/74). There was a significant difference in NIHSS, GCS, and mRS at 6 months between surviving patients in the conservative and surgical groups. In prognostic analysis in the surgical subgroup, initial GCS score [5 (IQR 4-7) vs. 3 (IQR 3-4), p < 0.001], NIHSS [36 (IQR 32-38) vs. 40 (IQR 38-40), p < 0.001], smoking history [45.8% (11/24) vs. 74.1% (20/27), p = 0.039], hematoma volume [6.9 (IQR 6.2-7.6) vs. 9.6 (IQR 7.3-11.4), p = 0.001], and hemorrhage location (p = 0.001) were potential risk factors for poor 6-month prognosis after robot-assisted surgery for HBSH. Based on the results of this study, robot-assisted minimally invasive drainage of brain stem hematoma may significantly reduce mortality and improve prognosis. Surgery should be conducted for selected patients.

Unveiling the negative association of Faecalibacterium prausnitzii with ischemic stroke severity, impaired prognosis and pro-inflammatory markers

BackgroundThe correlation between acute ischemic stroke (AIS) and gut microbiota has opened a promising avenue for improving stroke prognosis through the utilization of specific gut bacterial species. This study aimed to identify gut bacterial species in AIS patients and their correlation with stroke severity, 3-month prognosis, and inflammatory markers. Methods: We enrolled 59 AIS patients (from June 2021 to July 2022) and 31 age-matched controls with similar cerebrovascular risk profiles but no stroke history. Fecal samples were analyzed using 16 S rDNA V3–V4 sequencing to assess α and β diversity and identify significant microbiota differences. AIS cases were categorized based on the National Institute of Health Stroke Scale (NIHSS) scores and 3-month modified Rankin Scale (mRS) scores. Subgroup analyses were performed, and correlation analysis was used to examine associations between flora abundance, inflammatory markers and stroke outcome. ResultsSignificant differences in β-diversity were observed between case and control groups (P < 0.01). Bacteroides dominated AIS samples, while Clostridia, Lachnospirales, Lachnospiraceae, Ruminococcaceae, Faecalibacterium, and Faecalibacterium prausnitzii were prominent in controls. Faecalibacterium and Faecalibacterium prausnitzii were significantly reduced in non-minor stroke and 3-month poor prognosis groups compared to controls, while this difference was less pronounced in patients with minor stroke and 3-month good prognosis. Both Faecalibacterium and Faecalibacterium prausnitzii were negatively correlated with the NIHSS score on admission (r = −0.48, −0.48, P < 0.01) and 3-month mRS score (r = −0.48, −0.44, P < 0.01). Additionally, they showed negative correlations with pro-inflammatory factors and positive correlations with anti-inflammatory factors (both P < 0.01). ConclusionsFaecalibacterium prausnitzii is negatively associated with stroke severity, impaired prognosis, and pro-inflammatory markers, highlighting its potential application in AIS treatments.

Serum IL-31 is related to the severity and 3-month prognosis of patients with Intracerebral hemorrhage.

Interleukin (IL)-31/IL-33 axis has been proved to play an important role in the regulation of inflammation, and serum IL-33 was found to be a novel serum prognostic marker of intracerebral hemorrhage (ICH), while the value of serum IL-31 levels on prognosis in patients with ICH remains unknown. The present study was designed to study the value of serum IL-31 levels on prognosis in ICH patients. A total of 200 ICH patients and 50 healthy people were included in this study. We collected clinical data such as demographic data, laboratory data, admission disease scores and medical histories of these participants. We measured serum IL-31 levels using enzyme-linked immunosorbent assay, and assessed the prognosis of ICH patients 3 months after onset by mRS scale, and mRS > 2 was defined as a 3-month poor outcome. The level of IL-31 in ICH patients were significantly higher than that in healthy control people (211.91 ± 61.61 vs 167.64 ± 27.45 pg/mL, P < .001), and levels of IL-31 in ICH patients with 3-month good outcome were significantly lower than that in ICH patients with 3-month poor outcome (196.09 ± 50.84 vs 248.05 ± 41.41 pg/mL, P < .001). Results of correlation analysis suggested that the level of serum IL-31 was positively related to admission NIHSS score (r = 0.627, P < .001), hematoma volume (r = 0.352, P < .001), mRS score (r = 0.515, P < .001), high-density lipoprotein-cholesterol (r = 0.177, P = .012), serum C-reactive protein levels (r = 0.483, P < .001), and serum tumor necrosis factor α levels (r = 0.389, P < .001) in ICH patients, while the level of serum IL-31 was negatively related to the admission GCS score (r = -0.518, P < .001) and triglycerides (r = -0.147, P = .038). Results of multivariate regression analysis shows that serum IL-31 levels are an independent risk factor affecting NIHSS scores (OR = 1.023, 95% CI = 1.010-2.036) and 3-month prognosis (OR = 1.023, 95% CI = 0.982-1.747) in ICH patients. The receiver operating characteristic curve analysis showed that the sensitivity and specificity of serum IL-31 level in evaluating the prognosis of ICH were 85.2% and 76.7%, respectively. A cutoff value of serum IL-31 level > 185.30 pg/mL may indicate a poor prognosis for ICH. Serum IL-31 levels on admission in ICH patients are associated with patient prognosis, and higher serum IL-31 levels are associated with a higher risk of poor prognosis in ICH patients.

Predictive Analytics-Based Methodology Supported by Wireless Monitoring for the Prognosis of Roller-Bearing Failure

Roller mills are commonly used in the production of mining derivatives, since one of their purposes is to reduce raw materials to very small sizes and to combine them. This research evaluates the mechanical condition of a mill containing four rollers, focusing on the largest cylindrical roller bearings as the main component that causes equipment failure. The objective of this work is to make a prognosis of when the overall vibrations would reach the maximum level allowed (2.5 IPS pk), thus enabling planned replacements, and achieving the maximum possible useful life in operation, without incurring unscheduled corrective maintenance and unexpected plant shutdown. Wireless sensors were used to capture vibration data and the ARIMA (Auto-Regressive Integrated Moving Average) and Holt–Winters methods were applied to forecast vibration behavior in the short term. Finally, the results demonstrate that the Holt–Winters model outperforms the ARIMA model in precision, allowing a 3-month prognosis without exceeding the established vibration limit.

Systemic Immune-Inflammation Index is a Prognostic Predictor for Patients With Acute Ischemic Stroke Treated With Intravenous Thrombolysis.

To investigate whether baseline systemic immune-inflammation index (SII) is associated with 3-month poor prognosis and early neurological outcomes in patients with acute ischemic stroke treated with intravenous thrombolysis. A total of 221 consecutive patients were enrolled in the retrospective study. The primary endpoints were poor functional outcomes or death at 3 months. Secondary endpoints were early neurological deterioration (END) or symptomatic intracerebral hemorrhage within 24 hours. Receiver operating characteristic curve analyses was performed to assess the overall discriminative ability of SII in predicting the 4 endpoints. We also performed the Spearman correlation test to evaluate the relationship between SII and stroke severity. Univariable and multivariable logistic regression analyses were performed to evaluate the associations between SII and endpoints. The cutoff values of SII were 504.99×10 9 /L for predicting a 3-month poor prognosis (sensitivity, 70.9% and specificity, 69.6%), 524.47×10 9 /L for predicting 3-month death (sensitivity, 78.9% and specificity, 59.9%) and 504.99×10 9 /L for predicting END (sensitivity, 70.7% and specificity, 62.6%), respectively. A positive association between SII and the National Institutes of Health Stroke Scale was observed ( rs = 0.306, P < 0.001). Multivariable analyses indicated that SII was independently associated with 3-month poor prognosis [odds ratio (OR) = 5.384; 95% CI: 2.844-10.193; P < 0.001], 3-month death (OR = 2.592, 95% CI: 1.046-6.421, P = 0.040) and END (OR = 3.202, 95% CI: 1.796-5.707, P < 0.001). Increased baseline SII was associated with END and 3-month poor outcomes, and may act as a potential prognostic predictor for acute ischemic stroke patients treated with intravenous thrombolysis.

Clinical Value of Serum Secretoneurin Levels in Prediction of Delayed Cerebral Ischemia and Prognostic Analysis of Aneurysmal Subarachnoid Hemorrhage: A Prospective Cohort Study.

Secretoneurin is a neuropeptide with several neuroprotective properties. Here, we discuss the importance of serum secretoneurin in assessing severity and predicting delayed cerebral ischemia (DCI) and functional outcomes following aneurysmal subarachnoid hemorrhage (aSAH). A prospective cohort study of 167 patients with aSAH and 100 controls was performed to determine serum secretoneurin levels. Severity was reflected by the Hunt-Hess and modified Fisher scores. Prognostic parameters included DCI and poor 6-month prognosis (extended Glasgow outcome scale scores of 1-4). Univariate analysis followed by multivariate analysis was performed to determine the correlation between severity and prognosis. Compared to controls, patients exhibited a marked elevation in serum secretoneurin levels. Serum secretoneurin levels, which were independently correlated with Hunt-Hess scores and modified Fisher scores, independently predicted DCI and bad 6-month prognosis. Serum secretoneurin levels, which were linearly related to the risk of DCI and poor prognosis under a restricted cubic spline, effectively distinguished the risks under the receiver operating characteristic (ROC) curve. Subgroup analysis for prognosis or DCI prediction revealed no substantial interactions between serum secretoneurin levels and other variables, such as age, sex, hypertension, diabetes, alcohol consumption, and cigarette consumption. In addition, the prognosis model, in which serum secretoneurin, Hunt-Hess scale, and modified Fisher scale were merged, was graphically represented by a nomogram and performed well under the calibration, decision, and ROC curves. Serum secretoneurin levels significantly increased after aSAH, which was intimately correlated with disease severity and independently associated with DCI and worse outcomes, indicating that serum secretoneurin may be a potential prognostic biomarker of aSAH.

Risk factors and a model for prognosis prediction after intravenous thrombolysis with alteplase in acute ischemic stroke based on propensity score matching.

Background: Alteplase intravenous thrombolysis is effective for treating acute ischemic stroke (AIS) within 4.5 h. Nevertheless, the prognosis remains poor for some patients.Objective: To investigate the risk factors for poor prognosis in patients undergoing intravenous thrombolysis with alteplase following AIS based on propensity score matching and to develop a predictive model.Result: Multivariate logistic regression analysis showed that baseline blood glucose (OR = 1.20, 95%CI, 1.03-1.39), baseline NIH Stroke Scale score (OR = 1.23, 95%CI, 1.12-1.35), and hyperlipidemia (OR = 6.60, 95%CI 1.74-25.00) were risk factors for poor prognosis in patients with AIS undergoing alteplase intravenous thrombolysis. Using these factors, a nomogram model was constructed for predicting patient prognosis at 3 months. The areas under the receiver operating characteristic curve (AUCs) of the training and validation groups were 0.792 (95CI% 0.715-0.870) and 0.885 (95CI% 0.798-0.972), respectively, showing good differentiation. The Hosmer Lemeshow goodness-of-fit test showed that the model had good fit. The calibration curve fitted well with the ideal curve, and the decision curve analysis curve showed that the model had good clinical applicability when the threshold probability was between 10%-80%.Conclusion: The established nomogram could successfully predict the 3-month prognosis of patients with AIS after undergoing alteplase intravenous thrombolysis. The model thus has clinical application value.