Novel Score for Stratifying Risk of Critical Care Needs in Intracerebral Hemorrhage Patients: Critical Care Needs in ICH

Objective:To develop a risk prediction score identifying intracerebral hemorrhage (ICH) patients at low risk for critical care.Methods:We retrospectively analyzed data of 451 ICH patients between 2010-2018. The sample was randomly divided in a development and a validation cohort. Logistic regression was used to develop a risk score by weighting independent predictors of ICU needs based on strength of association. The risk score was tested in the validation cohort, and externally validated in a dataset from another institution.Results:The rate of ICU interventions was 80.3%. Systolic blood pressure (SBP), Glasgow Coma Scale (GCS), intraventricular hemorrhage (IVH), and ICH volume were independent predictors of critical care, resulting in the following point assignments for the INtensive care TRiaging IN Spontaneous IntraCerebral hemorrhage (INTRINSIC) score: SBP 160-190 mm Hg (1 point), SBP >190 mm Hg (3 points); GCS 8-13 (1 point), GCS <8 (3 points); ICH volume 16-40 cm3 (1 point), ICH volume >40 cm3 (2 points); and presence of IVH (1 point), with values ranging between 0-9. Among patients with a score of 0 and no ICU needs during their emergency department stay, 93.6% remained without critical care needs. In an external validation cohort of ICH patients, the INTRINSIC score achieved an AUC of 0.823 (95% CI 0.782-0.863). A score <2 predicted absence of critical care needs with 48.5% sensitivity and 88.5% specificity, and a score <3 predicted absence of critical care needs with 61.7% sensitivity and 83.0% specificity.Conclusion:The INTRINSIC score identifies ICH patients at low risk for critical care interventions.Classification of Evidence:This study provides Class II evidence that the INTRINSIC score identifies ICH patients at low risk for critical care interventions.

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A grading scale for surgically treated patients with spontaneous supratentorial intracerebral hemorrhage: the Surgical Swedish ICH Score

Journal of Neurosurgery ◽

10.3171/2019.5.jns19622 ◽

2020 ◽

Vol 133 (3) ◽

pp. 800-807 ◽

Cited By ~ 3

Author(s):

Andreas Fahlström ◽

Henrietta Nittby Redebrandt ◽

Hugo Zeberg ◽

Jiri Bartek ◽

Andreas Bartley ◽

...

Keyword(s):

Type 2 Diabetes ◽

Intracerebral Hemorrhage ◽

External Validation ◽

Grading Scale ◽

Ich Score ◽

Gcs Score ◽

Strength Of Association ◽

Point Type ◽

Clinical Grading

OBJECTIVEThe authors aimed to develop the first clinical grading scale for patients with surgically treated spontaneous supratentorial intracerebral hemorrhage (ICH).METHODSA nationwide multicenter study including 401 ICH patients surgically treated by craniotomy and evacuation of a spontaneous supratentorial ICH was conducted between January 1, 2011, and December 31, 2015. All neurosurgical centers in Sweden were included. All medical records and neuroimaging studies were retrospectively reviewed. Independent predictors of 30-day mortality were identified by logistic regression. A risk stratification scale (the Surgical Swedish ICH [SwICH] Score) was developed using weighting of independent predictors based on strength of association.RESULTSFactors independently associated with 30-day mortality were Glasgow Coma Scale (GCS) score (p = 0.00015), ICH volume ≥ 50 mL (p = 0.031), patient age ≥ 75 years (p = 0.0056), prior myocardial infarction (MI) (p = 0.00081), and type 2 diabetes (p = 0.0093). The Surgical SwICH Score was the sum of individual points assigned as follows: GCS score 15–13 (0 points), 12–5 (1 point), 4–3 (2 points); age ≥ 75 years (1 point); ICH volume ≥ 50 mL (1 point); type 2 diabetes (1 point); prior MI (1 point). Each increase in the Surgical SwICH Score was associated with a progressively increased 30-day mortality (p = 0.0002). No patient with a Surgical SwICH Score of 0 died, whereas the 30-day mortality rates for patients with Surgical SwICH Scores of 1, 2, 3, and 4 were 5%, 12%, 31%, and 58%, respectively.CONCLUSIONSThe Surgical SwICH Score is a predictor of 30-day mortality in patients treated surgically for spontaneous supratentorial ICH. External validation is needed to assess the predictive value as well as the generalizability of the Surgical SwICH Score.

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Risk score for predicting mortality including urine lipoarabinomannan detection in hospital inpatients with HIV-associated tuberculosis in sub-Saharan Africa: Derivation and external validation cohort study

PLoS Medicine ◽

10.1371/journal.pmed.1002776 ◽

2019 ◽

Vol 16 (4) ◽

pp. e1002776 ◽

Cited By ~ 9

Author(s):

Ankur Gupta-Wright ◽

Elizabeth L. Corbett ◽

Douglas Wilson ◽

Joep J. van Oosterhout ◽

Keertan Dheda ◽

...

Keyword(s):

Cohort Study ◽

Risk Score ◽

Validation Cohort ◽

External Validation ◽

Sub Saharan Africa ◽

Sub Saharan ◽

Hospital Inpatients

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Construction and Validation of a Universal Applicable Prognostic Signature for Gastric Cancer Based on Seven Immune-Related Gene Correlated With Tumor Associated Macrophages

Frontiers in Oncology ◽

10.3389/fonc.2021.635324 ◽

2021 ◽

Vol 11 ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

Risk Score ◽

Critical Role ◽

External Validation ◽

Low Risk ◽

Prognostic Signature ◽

Tumor Associated Macrophages ◽

Cox Regression Analysis ◽

Significant Difference

BackgroundTumor-associated macrophages (TAMs) play a critical role in the progression of malignant tumors, but the detailed mechanism of TAMs in gastric cancer (GC) is still not fully explored.MethodsWe identified differentially expressed immune-related genes (DEIRGs) between GC samples with high and low macrophage infiltration in The Cancer Genome Atlas datasets. A risk score was constructed based on univariate Cox analysis and Lasso penalized Cox regression analysis in the TCGA cohort (n=341). The optimal cutoff determined by the 5-year time-dependent receiver operating characteristic (ROC) curve was considered to classify patients into groups with high and low risk. We conducted external validation of the prognostic signature in four independent cohorts (GSE84437, n=431; GSE62254, n=300; GSE15459, n=191; and GSE26901, n=109) from the Gene Expression Omnibus (GEO) database.ResultsThe signature consisting of 7 genes (FGF1, GRP, AVPR1A, APOD, PDGFRL, CXCR4, and CSF1R) showed good performance in predicting overall survival (OS) in the 5 independent cohorts. The risk score presented an obviously positive correlation with macrophage abundance (cor=0.7, p<0.001). A significant difference was found between the high- and low-risk groups regarding the overall survival of GC patients. The high-risk group exhibited a higher infiltration level of M2 macrophages estimated by the CIBERSORT algorithm. In the five independent cohorts, the risk score was highly positively correlated with the stromal cell score, suggesting that we can also evaluate the infiltration of stromal cells in the tumor microenvironment according to the risk score.ConclusionOur study developed and validated a general applicable prognostic model for GC from the perspective of TAMs, which may help to improve the precise treatment strategy of GC.

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Development and external validation of a prognostic tool for COVID-19 critical disease

PLoS ONE ◽

10.1371/journal.pone.0242953 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242953

Author(s):

Daniel S. Chow ◽

Justin Glavis-Bloom ◽

Jennifer E. Soun ◽

Brent Weinberg ◽

Theresa Berens Loveless ◽

...

Keyword(s):

Critical Care ◽

Prognostic Model ◽

Validation Cohort ◽

External Validation ◽

Vital Signs ◽

Clinical Tool ◽

Derivation Cohort ◽

Laboratory Values ◽

Increased Risk ◽

Care Capacity

Background The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. Methods This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. Results Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21–88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27–88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87–1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease. Conclusions and relevance We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.

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Development and external validation of an acute kidney injury risk score for use in the general population

Clinical Kidney Journal ◽

10.1093/ckj/sfaa072 ◽

2020 ◽

Vol 13 (3) ◽

pp. 402-412

Author(s):

Samira Bell ◽

Matthew T James ◽

Chris K T Farmer ◽

Zhi Tan ◽

Nicosha de Souza ◽

...

Keyword(s):

Acute Kidney Injury ◽

Logistic Regression ◽

Risk Score ◽

Validation Cohort ◽

External Validation ◽

Kidney Injury ◽

Routinely Collected Data ◽

C Statistic ◽

Increased Risk ◽

The Impact

Abstract Background Improving recognition of patients at increased risk of acute kidney injury (AKI) in the community may facilitate earlier detection and implementation of proactive prevention measures that mitigate the impact of AKI. The aim of this study was to develop and externally validate a practical risk score to predict the risk of AKI in either hospital or community settings using routinely collected data. Methods Routinely collected linked datasets from Tayside, Scotland, were used to develop the risk score and datasets from Kent in the UK and Alberta in Canada were used to externally validate it. AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine–based criteria. Multivariable logistic regression analysis was performed with occurrence of AKI within 1 year as the dependent variable. Model performance was determined by assessing discrimination (C-statistic) and calibration. Results The risk score was developed in 273 450 patients from the Tayside region of Scotland and externally validated into two populations: 218 091 individuals from Kent, UK and 1 173 607 individuals from Alberta, Canada. Four variables were independent predictors for AKI by logistic regression: older age, lower baseline estimated glomerular filtration rate, diabetes and heart failure. A risk score including these four variables had good predictive performance, with a C-statistic of 0.80 [95% confidence interval (CI) 0.80–0.81] in the development cohort and 0.71 (95% CI 0.70–0.72) in the Kent, UK external validation cohort and 0.76 (95% CI 0.75–0.76) in the Canadian validation cohort. Conclusion We have devised and externally validated a simple risk score from routinely collected data that can aid both primary and secondary care physicians in identifying patients at high risk of AKI.

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Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

Journal of Translational Medicine ◽

10.1186/s12967-021-02846-x ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

T Cells ◽

Risk Score ◽

External Validation ◽

Disease Free Survival ◽

Low Risk ◽

Immune Gene ◽

Free Survival ◽

Gene Pairs

Abstract Background Growing attention have been paid to the relationship between TP53 and tumor immunophenotype, but there are still lacking enough search on the field of gastric cancer (GC). Materials and methods We identified differential expressed immune-related genes (DEIRGs) between the TP53-altered GC samples (n = 183) and without TP53-altered GC samples (n = 192) in The Cancer Genome Atlas and paired them. In the TCGA cohort (n = 350), a risk score was determined through univariate and multivariate cox regression and Lasso regression analysis. Patients were divided into two groups, high-risk and low-risk, based on the median risk score. Four independent cohorts (GSE84437,n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26901, n = 100) from the Gene Expression Omnibus (GEO) database were used to validate the reliability and universal applicability of the model. Results The signature contained 11 gene pairs showed good performance in predicting progression-free survival (PFS), disease-free survival (DFS), disease special survival (DSS), and the overall survival (OS) for GC patients in the TCGA cohort. The subgroup analysis showed that the signature was suitable for GC patients with different characteristics. The signature could capable of distinguish GC patients with good prognosis and poor prognosis in all four independent external validation cohorts. The high- and low-risk groups differed significantly in the proportion of several immune cell infiltration, especially for the T cells memory resting, T cells memory activated and follicular helper, and Macrophage M0, which was also related to the prognosis of GC patients. Conclusion The present work proposed an innovative system for evaluating the prognosis of gastric cancer. Considering its stability and general applicability, which may become a widely used tool in clinical practice.

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Derivation and external validation of a risk prediction algorithm to estimate future risk of cardiovascular death among patients with type 2 diabetes and incident diabetic nephropathy: prospective cohort study

BMJ Open Diabetes Research & Care ◽

10.1136/bmjdrc-2019-000735 ◽

2019 ◽

Vol 7 (1) ◽

pp. e000735 ◽

Cited By ~ 1

Author(s):

Dahai Yu ◽

Jin Shang ◽

Yamei Cai ◽

Zheng Wang ◽

Xiaoxue Zhang ◽

...

Keyword(s):

Type 2 Diabetes ◽

Diabetic Nephropathy ◽

Risk Score ◽

Cardiovascular Mortality ◽

Validation Cohort ◽

External Validation ◽

Cardiovascular Death ◽

Individual Risk ◽

Derivation Cohort

ObjectiveTo derive, and externally validate, a risk score for cardiovascular death among patients with type 2 diabetes and newly diagnosed diabetic nephropathy (DN).Research design and methodsTwo independent prospective cohorts with type 2 diabetes were used to develop and externally validate the risk score. The derivation cohort comprised 2282 patients with an incident, clinical diagnosis of DN. The validation cohort includes 950 patients with incident, biopsy-proven diagnosis of DN. The outcome was cardiovascular death within 2 years of the diagnosis of DN. Logistic regression was applied to derive the risk score for cardiovascular death from the derivation cohort, which was externally validated in the validation cohort. The score was also estimated by applying the United Kingdom Prospective Diabetes Study (UKPDS) risk score in the external validation cohort.ResultsThe 2-year cardiovascular mortality was 12.05% and 11.79% in the derivation cohort and validation cohort, respectively. Traditional predictors including age, gender, body mass index, blood pressures, glucose, lipid profiles alongside novel laboratory test items covering five test panels (liver function, serum electrolytes, thyroid function, blood coagulation and blood count) were included in the final model.C-statistics was 0.736 (95% CI 0.731 to 0.740) and 0.747 (95% CI 0.737 to 0.756) in the derivation cohort and validation cohort, respectively. The calibration slope was 0.993 (95% CI 0.974 to 1.013) and 1.000 (95% CI 0.981 to 1.020) in the derivation cohort and validation cohort, respectively.The UKPDS risk score substantially underestimated cardiovascular mortality.ConclusionsA new risk score based on routine clinical measurements that quantified individual risk of cardiovascular death was developed and externally validated. Compared with the UKPDS risk score, which underestimated the cardiovascular disease risk, the new score is a more specific tool for patients with type 2 diabetes and DN. The score could work as a tool to identify individuals at the highest risk of cardiovascular death among those with DN.

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Development and Validation of a Lncrnas-Based Nomogram for Survival Prediction in Neuroblastoma Patients

10.21203/rs.3.rs-448031/v1 ◽

2021 ◽

Author(s):

Yong Lv ◽

ShuGuang Jin ◽

Bo Xiang

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Validation Cohort ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment

Abstract BackgroundTreatment of neuroblastoma is evolving toward precision medicine. LncRNAs can be used as prognostic biomarkers in many types of cancer.MethodsBased on the RNA-seq data from GSE49710, we built a lncRNAs-based risk score using the least absolute shrinkage and selection operation (LASSO) regression. Cox regression, receiver operating characteristic curves were used to evaluate the association of the LASSO risk score with overall survival. Nomograms were created and then validated in an external cohort from TARGET database. Gene set enrichment analysis was performed to identify the significantly changed biological pathways. ResultsThe 16-lncRNAs-based LASSO risk score was used to separate patients into high-risk and low-risk groups. In GSE49710 cohort, the high-risk group exhibited a poorer OS than those in the low-risk group (P<0.001). Moreover, multivariate Cox regression analysis demonstrated that LASSO risk score was an independent risk factor (HR=6.201;95%CI:2.536-15.16). The similar prognostic powers of the 16-lncRNAs were also achieved in the external cohort and in stratified analysis. In addition, a nomogram was established and worked well both in the internal validation cohort (C-index=0.831) and external validation cohort (C-index=0.773). The calibration plot indicated the good clinical utility of the nomogram. Gene set enrichment analysis (GSEA) indicated that high-risk group was related with cancer recurrence, metastasis and inflammatory associated pathways.ConclusionThe lncRNA-based LASSO risk score is a promising and potential prognostic tool in predicting the survival of patients with neuroblastoma. The nomogram combined the lncRNAs and clinical parameters allows for accurate risk assessment in guiding clinical management.

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