Early Deterioration and Long-Term Prognosis of Patients With Intracerebral Hemorrhage Along With Hematoma Volume More Than 20 ml: Who Needs Surgery?

Background and Purpose: The treatment of patients with intracerebral hemorrhage along with moderate hematoma and without cerebral hernia is controversial. This study aimed to explore risk factors and establish prediction models for early deterioration and poor prognosis.Methods: We screened patients from the prospective intracerebral hemorrhage (ICH) registration database (RIS-MIS-ICH, ClinicalTrials.gov Identifier: NCT03862729). The enrolled patients had no brain hernia at admission, with a hematoma volume of more than 20 ml. All patients were initially treated by conservative methods and followed up ≥ 1 year. A decline of Glasgow Coma Scale (GCS) more than 2 or conversion to surgery within 72 h after admission was defined as early deterioration. Modified Rankin Scale (mRS) ≥ 4 at 1 year after stroke was defined as poor prognosis. The independent risk factors of early deterioration and poor prognosis were determined by univariate and multivariate regression analysis. The prediction models were established based on the weight of the independent risk factors. The accuracy and value of models were tested by the receiver operating characteristic (ROC) curve.Results: After screening 632 patients with ICH, a total of 123 legal patients were included. According to statistical analysis, admission GCS (OR, 1.43; 95% CI, 1.18–1.74; P < 0.001) and hematoma volume (OR, 0.9; 95% CI, 0.84–0.97; P = 0.003) were the independent risk factors for early deterioration. Hematoma location (OR, 0.027; 95% CI, 0.004–0.17; P < 0.001) and hematoma volume (OR, 1.09; 95% CI, 1.03–1.15; P < 0.001) were the independent risk factors for poor prognosis, and island sign had a trend toward significance (OR, 0.5; 95% CI, 0.16-1.57; P = 0.051). The admission GCS and hematoma volume score were combined for an early deterioration prediction model with a score from 2 to 5. ROC curve showed an area under the curve (AUC) was 0.778 and cut-off point was 3.5. Combining the score of hematoma volume, island sign, and hematoma location, a long-term prognosis prediction model was established with a score from 2 to 6. ROC curve showed AUC was 0.792 and cutoff point was 4.5.Conclusions: The novel early deterioration and long-term prognosis prediction models are simple, objective, and accurate for patients with ICH along with a hematoma volume of more than 20 ml.

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A clinical prediction model for estimating the risk of developing uveitis in patients with juvenile idiopathic arthritis

Rheumatology ◽

10.1093/rheumatology/keaa733 ◽

2020 ◽

Author(s):

Joeri W van Straalen ◽

Gabriella Giancane ◽

Yasmine Amazrhar ◽

Nikolay Tzaribachev ◽

Calin Lazar ◽

...

Keyword(s):

Risk Factors ◽

Prediction Model ◽

Protective Factor ◽

Prediction Models ◽

Characteristic Curve ◽

Multivariable Analysis ◽

Information Criterion ◽

Logistic Regression Models ◽

Adjusted Risk ◽

Independent Risk Factors

Abstract Objective To build a prediction model for uveitis in children with JIA for use in current clinical practice. Methods Data from the international observational Pharmachild registry were used. Adjusted risk factors as well as predictors for JIA-associated uveitis (JIA-U) were determined using multivariable logistic regression models. The prediction model was selected based on the Akaike information criterion. Bootstrap resampling was used to adjust the final prediction model for optimism. Results JIA-U occurred in 1102 of 5529 JIA patients (19.9%). The majority of patients that developed JIA-U were female (74.1%), ANA positive (66.0%) and had oligoarthritis (59.9%). JIA-U was rarely seen in patients with systemic arthritis (0.5%) and RF positive polyarthritis (0.2%). Independent risk factors for JIA-U were ANA positivity [odds ratio (OR): 1.88 (95% CI: 1.54, 2.30)] and HLA-B27 positivity [OR: 1.48 (95% CI: 1.12, 1.95)] while older age at JIA onset was an independent protective factor [OR: 0.84 (9%% CI: 0.81, 0.87)]. On multivariable analysis, the combination of age at JIA onset [OR: 0.84 (95% CI: 0.82, 0.86)], JIA category and ANA positivity [OR: 2.02 (95% CI: 1.73, 2.36)] had the highest discriminative power among the prediction models considered (optimism-adjusted area under the receiver operating characteristic curve = 0.75). Conclusion We developed an easy to read model for individual patients with JIA to inform patients/parents on the probability of developing uveitis.

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A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

Frontiers in Pediatrics ◽

10.3389/fped.2021.756095 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jie Liu ◽

Jian Zhang ◽

Haodong Huang ◽

Yunting Wang ◽

Zuyue Zhang ◽

...

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Retrospective Study ◽

Kawasaki Disease ◽

Prediction Model ◽

Prediction Models ◽

Ivig Resistance ◽

Independent Risk Factors

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms.Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models.Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067–1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270–1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008–1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996–1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575).Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.

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Acute hematoma expansion after spontaneous intracerebral hemorrhage: risk factors and impact on long-term prognosis

Neurological Sciences ◽

10.1007/s10072-020-04356-y ◽

2020 ◽

Vol 41 (9) ◽

pp. 2503-2509

Author(s):

Agnese Sembolini ◽

Michele Romoli ◽

Umberto Pannacci ◽

Giulio Gambaracci ◽

Piero Floridi ◽

...

Keyword(s):

Risk Factors ◽

Intracerebral Hemorrhage ◽

Hematoma Expansion ◽

Spontaneous Intracerebral Hemorrhage ◽

Hemorrhage Risk ◽

Long Term Prognosis

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Relationship between magnetic resonance imaging findings and prognosis of intracranial tuberculosis

Acta Radiologica ◽

10.1177/02841851211062083 ◽

2021 ◽

pp. 028418512110620

Author(s):

Xuan Wu ◽

Xiuhong Yang ◽

Bo Wang ◽

Nan Yin ◽

Xiaohui Mao ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Risk Factors ◽

Resonance Imaging ◽

Short Term ◽

Enhanced Mri ◽

Cranial Mri ◽

Independent Risk Factors ◽

Long Term Prognosis ◽

The Relationship

Background Intracranial tuberculosis (TB) is an intracranial infection caused by Mycobacterium tuberculosis. Magnetic resonance imaging (MRI), in particular enhanced MRI scan, has the ability to detect characteristic lesions of tuberculous meningitis or cerebral parenchymal TB. Purpose To analyze the relationship between MRI findings and prognosis of patients with intracranial TB. Material and Methods In this retrospective study, a total of 60 patients were confirmed with intracranial TB in the hospital from May 2019 to December 2020. All enrolled patients underwent TB-related laboratory examinations, cranial MRI, and contrast-enhanced MRI. Laboratory tests were analyzed and the relationship between clinical prognosis and cranial MRI features was evaluated. Results Of the 60 patients, 28 (46.67%) had disseminated TB complications, 20 (36.67%) had secondary TB complications, and the remaining 10 (16.66%) had lymphatic TB or spinal TB complications. Of the patients, 25 had good short-term prognosis and 35 had poor short-term prognosis; 44 patients had good long-term prognosis and 16 had poor long-term prognosis. The incidence of cerebral parenchymal tuberculomas on enhanced MRI was significantly higher in the group with good prognosis compared to that in the group with poor prognosis ( P < 0.05). Logistic analysis suggested that hydrocephalus (odds ratio [OR] = 0.057, 95% confidence interval [CI] = 0.003–0.444; P = 0.018) and cistern involvement (OR = 0.100, 95% CI = 0.011–0.581; P = 0.017) were independent risk factors for poor short-term prognosis. Conclusion MRI can display the pathological changes of intracranial TB in detail; hydrocephalus and cistern involvement were independent risk factors for poor short-term prognosis.

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Development of a prediction model for 1-year poor prognosis in patients with acute ischemic stroke

Journal of Investigative Medicine ◽

10.1136/jim-2018-000883 ◽

2019 ◽

Vol 67 (6) ◽

pp. 957-963 ◽

Cited By ~ 1

Author(s):

Xia Ling ◽

Bo Shen ◽

Kangzhi Li ◽

Lihong Si ◽

Xu Yang

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Ischemic Stroke ◽

Prediction Model ◽

Acute Ischemic Stroke ◽

Poor Prognosis ◽

Multivariate Logistic Regression ◽

Lemeshow Test ◽

Independent Risk Factors ◽

Auc Value

The goals of this study were to develop a new prediction model to predict 1-year poor prognosis (death or modified Rankin scale score of ≥3) in patients with acute ischemic stroke (AIS) and to compare the performance of the new prediction model with other prediction scales. Baseline data of 772 patients with AIS were collected, and univariate and multivariate logistic regression analyses were performed to identify independent risk factors for 1-year poor prognosis in patients with AIS. The area under the receiver operating characteristics curve (AUC) value of the new prediction model and the THRIVE, iScore and ASTRAL scores was compared. The Hosmer-Lemeshow test was used to assess the goodness of fit of the model. We identified 196 (25.4%) patients with poor prognosis at 1-year follow-up, and of these 68 (68/196, 34.7%) had died. Multivariate logistic regression and receiver operating characteristic curve analyses showed that age ≥70 years, consciousness (lethargy or coma), history of stroke or transient ischemic attack, cancer, abnormal fasting blood glucose levels ≥7.0 mmol/L, and National Institutes of Health Stroke Scale score were independent risk factors for 1-year poor prognosis in patients with AIS. Scores were assigned for each variable by rounding off β coefficient to the integer score, and a new prediction model with a maximum total score of 9 points was developed. The AUC value of the new prediction model was higher than the THRIVE score (p<0.05). The χ2 value for the Hosmer-Lemeshow test was 7.337 (p>0.05), suggesting that the prediction model had a good fit. The new prediction model can accurately predict 1-year poor prognosis in Chinese patients with AIS.

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Modified Clavien-Dindo Classification to Analyze the Risk Factors of Early Complications after Radical Gastrectomy and the Impact of Complications on Long-Term Prognosis

10.21203/rs.3.rs-96107/v1 ◽

2020 ◽

Author(s):

Jun Ma ◽

Ying Wang ◽

Shui-hong Yu ◽

Chao-pin Zhou ◽

Da-tian Wang ◽

...

Keyword(s):

Risk Factors ◽

Postoperative Complications ◽

Blood Loss ◽

Postoperative Hospital Stay ◽

Radical Gastrectomy ◽

Early Complications ◽

Independent Risk Factors ◽

Long Term Prognosis ◽

The Impact

Abstract Background The modified Clavien-Dindo classification system was employed to investigate the occurrence of early complications along with the related risk factors following a radical gastrectomy procedure, with the view of conducting an analysis into the impact of complications on long-term prognosis. Methods The clinical data of 525 patients who had previously undergone a radical gastrectomy procedure for gastric cancer were analyzed in a retrospective fashion. Results Postoperative hospital stay: Complication group (17.88±8.472) days, severe complications group (23.10±7.594) days, significantly higher than non-complication group (10.26±1.973) days and non-severe complications group (11.47±4.712) days (P=0.000<0.05).Multivariate analysis: age (OR = 1.781, P = 0.013), preoperative comorbidity (OR = 1.765, P = 0.020), blood loss (OR = 2.153, P = 0.001), surgical approach (OR = 3.137, P = 0.000) were identified as an independent risk factor associated with early complications. Blood loss (OR=13.053, P=0.013), type of resection (OR=7.936, P=0.047) and nerve involvement (OR=3.670, P=0.009) were identified to be independent risk factors for severe complications.Severe postoperative complications (HR=1.595, P=0.107) and postoperative complications (HR=1.078, P=0.670) were not independent risk factors affecting the 5-year over survival rate. Conclusion Complications following radical gastrectomy were closely related to age, preoperative comorbidity, blood loss, and surgical approach; severe complications were closely related to blood loss, total gastrectomy, and nerve involvement; complications and severe complications were not found to be independent risk factors associated with long-term survival, that being said, they were significantly prolonged postoperative hospital stay.

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Establishment and validation of a predictive model for mortality within 30 days in patients with sepsis-induced blood pressure drop: A retrospective analysis

PLoS ONE ◽

10.1371/journal.pone.0252009 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0252009

Author(s):

Bin Wang ◽

Jianping Chen

Keyword(s):

Risk Factors ◽

Blood Pressure ◽

Pressure Drop ◽

Prediction Model ◽

Roc Curve ◽

Cardiac Insufficiency ◽

Validation Group ◽

Model Group ◽

Risk Of Death ◽

Independent Risk Factors

Objectives To establish and validate an individualized nomogram to predict the probability of death within 30 days in patients with sepsis-induced blood pressure drop would help clinical physicians to pay attention to those with higher risk of death after admission to wards. Methods A total of 1023 patients who were admitted to the Dongyang People’s Hospital, China, enrolled in this study. They were divided into model group (717 patients) and validation group (306 patients). The study included 13 variables. The independent risk factors leading to death within 30 days were screened by univariate analyses and multivariate logistic regression analyses and used for Nomogram. The discrimination and correction of the prediction model were assessed by the area under the Receiver Operating Characteristic (ROC) curve and the calibration chart. The clinical effectiveness of the prediction model was assessed by the Decision Curve Analysis (DCA). Results Seven variables were independent risk factors, included peritonitis, respiratory failure, cardiac insufficiency, consciousness disturbance, tumor history, albumin level, and creatinine level at the time of admission. The area under the ROC curve of the model group and validation group was 0.834 and 0.836. The P value of the two sets of calibration charts was 0.702 and 0.866. The DCA curves of the model group and validation group were above the two extreme (insignificant) curves. Conclusions The model described in this study could effectively predict the death of patients with sepsis-induced blood pressure drop.

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Pulmonary infection after cardiopulmonary bypass surgery in children: a risk estimation model in China

Journal of Cardiothoracic Surgery ◽

10.1186/s13019-021-01450-w ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Chunnian Ren ◽

Chun Wu ◽

Zhengxia Pan ◽

Quan Wang ◽

Yonggang Li

Keyword(s):

Risk Factors ◽

Cardiopulmonary Bypass ◽

Prediction Model ◽

Congenital Heart ◽

Bypass Surgery ◽

Pulmonary Infection ◽

Regression Analyses ◽

Cardiopulmonary Bypass Surgery ◽

Independent Risk Factors ◽

Individualized Prediction

Abstract Objectives The occurrence of pulmonary infection after congenital heart disease (CHD) surgery can lead to significant increases in intensive care in cardiac intensive care unit (CICU) retention time, medical expenses, and risk of death risk. We hypothesized that patients with a high risk of pulmonary infection could be screened out as early after surgery. Hence, we developed and validated the first risk prediction model to verify our hypothesis. Methods Patients who underwent CHD surgery from October 2012 to December 2017 in the Children’s Hospital of Chongqing Medical University were included in the development group, while patients who underwent CHD surgery from December 2017 to October 2018 were included in the validation group. The independent risk factors associated with pulmonary infection following CHD surgery were screened using univariable and multivariable logistic regression analyses. The corresponding nomogram prediction model was constructed according to the regression coefficients. Model discrimination was evaluated by the area under the receiver operating characteristic curve (ROC) (AUC), and model calibration was conducted with the Hosmer-Lemeshow test. Results The univariate and multivariate logistic regression analyses identified the following six independent risk factors of pulmonary infection after cardiac surgery: age, weight, preoperative hospital stay, risk-adjusted classification for congenital heart surgery (RACHS)-1 score, cardiopulmonary bypass time and intraoperative blood transfusion. We established an individualized prediction model of pulmonary infection following cardiopulmonary bypass surgery for CHD in children. The model displayed accuracy and reliability and was evaluated by discrimination and calibration analyses. The AUCs for the development and validation groups were 0.900 and 0.908, respectively, and the P-values of the calibration tests were 0.999 and 0.452 respectively. Therefore, the predicted probability of the model was consistent with the actual probability. Conclusions Identified the independent risk factors of pulmonary infection after cardiopulmonary bypass surgery. An individualized prediction model was developed to evaluate the pulmonary infection of patients after surgery. For high-risk patients, after surgery, targeted interventions can reduce the risk of pulmonary infection.

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Abstract T P231: Does Intraventricular Hemorrhage Lead to a Normal Pressure Hydrocephalus-Like Syndrome?

Stroke ◽

10.1161/str.45.suppl_1.tp231 ◽

2014 ◽

Vol 45 (suppl_1) ◽

Author(s):

Andrew J Kruger ◽

Matthew Flaherty ◽

Padmini Sekar ◽

Mary Haverbusch ◽

Charles J Moomaw ◽

...

Keyword(s):

Risk Factors ◽

Normal Pressure Hydrocephalus ◽

Barthel Index ◽

Intraventricular Hemorrhage ◽

Normal Pressure ◽

Stroke Study ◽

Increased Risk ◽

Independent Risk Factors ◽

Poor Outcomes

Background: Intracerebral hemorrhage (ICH) has the highest short and long-term morbidity and mortality rates of stroke subtypes. While increased intracranial pressure due to the presence of intraventricular hemorrhage (IVH) may relate to early poor outcomes, the mechanism of reduced 3-month outcome with IVH is unclear. We hypothesized that IVH may cause symptoms similar to normal pressure hydrocephalus (NPH), specifically urinary incontinence and gait disturbance. Methods: We used interviewed cases from the Genetic and Environmental Risk Factors for Hemorrhagic Stroke Study (7/1/08-12/31/12) that had 3-month follow-ups available. CT images were analyzed for ICH volume and location, and IVH presence and volume. Incontinence and dysmobility were defined by Barthel Index at 3 months. We chose a Barthel Index score of bladder less than 10 and mobility less than 15 to define incontinence and dysmobility, respectively. Multivariate analysis was used to assess independent risk factors for incontinence and dysmobility. ICH and IVH volumes were log transformed because of non-normal distributions. Results: Barthel Index was recorded for 308 ICH subjects, of whom 106 (34.4%) had IVH. Presence of IVH was independently associated with both incontinence (OR 2.7; 95% CI 1.4-5.2; p=.003) and dysmobility (OR 2.5; 95% CI 1.4-4.8; p=.003). The Table shows that increasing IVH volume was also independently associated with both incontinence and dysmobility after controlling for ICH location, ICH volume, age, baseline mRS, and admission GCS. Conclusion: Our data show that patients with IVH after ICH are at an increased risk for developing the NPH-like symptoms of incontinence and dysmobility. This may explain the worse long-term outcomes of patients who survive ICH with IVH than those who had ICH alone. Future studies are needed to confirm this finding, and to determine the effect of IVH interventions such as shunt or intraventricular thrombolysis.

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Are Prolonged Ventricular Pauses in Atrial Fibrillation a Marker of Poor Prognosis?

Cardiology ◽

10.1159/000520183 ◽

2021 ◽

Author(s):

Dorte Marie Stavnem ◽

Rakin Hadad ◽

Bjørn Strøier Larsen ◽

Olav Wendelboe Nielsen ◽

Mark Aplin Frederiksen ◽

...

Keyword(s):

Atrial Fibrillation ◽

Poor Prognosis ◽

Pacemaker Implantation ◽

Real Life ◽

Increased Risk ◽

All Cause Mortality ◽

Long Term Prognosis ◽

Rate Limiting

Background: In patients with atrial fibrillation (AF), the long-term prognosis of long electrocardiographic pauses in the ventricular action is not well-studied. Methods: Consecutive Holter recordings in patients with AF (n=200) between 2009-2011 were evaluated, focusing on pauses of at least 2.5 s. Outcomes of interest were all-cause mortality and pacemaker implantation. Results: Forty-three patients (21.5%) had pauses with a mean of 3.2 s and SD of 0.9 s. After a median follow-up of 99 months (ranging 89-111), 47% (20/43) of the patients with, and 45% (70/157) without pauses were deceased. Pauses of ≥ 2.5 s did not constitute a risk of increased mortality: HR = 0.75; (95% CI: 0.34 - 1.66); p = 0.48. Neither did pauses of ≥ 3.0 s: HR = 0.43; (95% CI: 0.06 - 3.20); p = 0.41. Sixteen percent of patients with pauses underwent pacemaker implantation during follow-up. Only pauses in patients referred to Holter due to syncope and/or dizzy spells were associated with an increased risk of pacemaker treatment: HR = 4.7 (95% CI: 1.4-15.9), p = 0.014, adjusted for age, sex and rate-limiting medication. Conclusion: In patients with AF, prolonged electrocardiographic pauses of ≥ 2.5 s or ≥ 3.0 s are not a marker for increased mortality in this real-life clinical study.

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