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

PLoS ONE ◽  
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.

scholarly journals Development and External Validation of a Prognostic Tool for COVID-19 Critical Disease

2020 ◽  
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

AbstractBackgroundThe 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.MethodsThis 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.ResultsOf 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 RelevanceWe 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.

scholarly journals Development and Validation of a Prognostic Model for Transplant-Associated Thrombotic Microangiopathy Following Allogeneic Hematopoietic Stem Cell Transplantation

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 16-17
Author(s):  
Peng Zhao ◽  
Ye-Jun Wu ◽  
Qing-Yuan Qu ◽  
Shan Chong ◽  
Xiao-Wan Sun ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Model ◽  
Validation Cohort ◽  
Risk Group ◽  
External Validation ◽  
Internal Validation ◽  
Hematopoietic Stem ◽  
Derivation Cohort ◽  
Peking University ◽  
Score Model

Introduction Transplant-associated thrombotic microangiopathy (TA-TMA) is a potentially life-threatening complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT), which can result in multiorgan injury and increased risk for mortality. Renewed interest has emerged in the prognostication of TA-TMA with the development of novel diagnostic and management algorithms. Our previous study reported an adverse outcome in patients with TA-TMA and concomitant acute graft-versus-host disease (Eur J Haematol, 2018). However, information on markers for the early identification of severe cases remains limited. Therefore, this study is concentrated on the development and validation of a prognostic model for TA-TMA, which might facilitate risk stratification and contribute to individualized management. Methods Patients receiving allo-HSCT in Peking University People's Hospital with 1) a diagnosis of microangiopathic hemolytic anemia (MAHA) or 2) evidence of microangiopathy were retrospectively identified from 2010 to 2018. The diagnosis of TA-TMA was reviewed according to the Overall-TMA criteria (Transplantation, 2010). Patients without fulfillment of the diagnostic criteria or complicated with other causes of MAHA were excluded from analysis. Prognostic factors for TA-TMA were determined among patients receiving HSCT between 2010 and 2014 (derivation cohort). Candidate predictors (univariate P < 0.1) were included in the multivariate analysis using a backward stepwise logistic regression model. A risk score model was then established according to the regression coefficient of each independent prognostic factor. The performance of this predictive model was evaluated through internal validation (bootstrap method with 1000 repetitions) and external temporal validation performed on data from those who received HSCT between 2015 and 2018 (validation cohort). Results 5337 patients underwent allo-HSCT at Peking University Institute of Hematology from 2010 to 2018. A total of 1255 patients with a diagnosis of MAHA and/or evidence of microangiopathy were retrospectively identified, among whom 493 patients met the inclusion criteria for this analysis (269 in the derivation cohort and 224 in the validation cohort). The median age at the time of TA-TMA diagnosis was 28 (IQR: 17-41) years. The median duration from the time of transplantation to the diagnosis of TA-TMA was 63 (IQR: 38-121) days. The 6-month overall survival rate was 42.2% (208/493), and the 1-year overall survival rate was 45.0% (222/493). In the derivation cohort, patient age (≥35 years), anemia (hemoglobin <70 g/L), severe thrombocytopenia (platelet count <15,000/μL), elevated lactic dehydrogenase (serum LDH >800 U/L) and elevated total bilirubin (TBIL >1.5*ULN) were identified by multivariate analysis as independent prognostic factors for the 6-month outcome of TA-TMA. A risk score model was constructed according to the regression coefficients (Table 1), and patients were stratified into a low-risk group (0-1 points), an intermediate-risk group (2-4 points) and a high-risk group (5-6 points). The Kaplan-Meier estimations of overall survival separated well between these risk groups (Figure 1). The prognostic model showed significant discriminatory capacity, with a cross-validated c-index of 0.770 (95%CI, 0.714-0.826) in the internal validation and 0.768 (95%CI, 0.707-0.829) in the external validation cohort. The calibration plots also indicated a good correlation between model-predicted and observed probabilities. Conclusions A prognostic model for TA-TMA incorporating several baseline laboratory factors was developed and evaluated, which demonstrated significant predictive capacity through internal and external validation. This predictive model might facilitate prognostication of TA-TMA and contribute to early identification of patients at higher risk for adverse outcomes. Further study may focus on whether these high-risk patients could benefit from early application of specific management. Disclosures No relevant conflicts of interest to declare.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

scholarly journals Prediction model of bleeding after endoscopic submucosal dissection for early gastric cancer: BEST-J score

Gut ◽  
2020 ◽  
pp. gutjnl-2019-319926 ◽  
Author(s):  
Waku Hatta ◽  
Yosuke Tsuji ◽  
Toshiyuki Yoshio ◽  
Naomi Kakushima ◽  
Shu Hoteya ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Early Gastric Cancer ◽  
Prediction Model ◽  
Endoscopic Submucosal Dissection ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Derivation Cohort ◽  
Submucosal Dissection

ObjectiveBleeding after endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) is a frequent adverse event after ESD. We aimed to develop and externally validate a clinically useful prediction model (BEST-J score: Bleeding after ESD Trend from Japan) for bleeding after ESD for EGC.DesignThis retrospective study enrolled patients who underwent ESD for EGC. Patients in the derivation cohort (n=8291) were recruited from 25 institutions, and patients in the external validation cohort (n=2029) were recruited from eight institutions in other areas. In the derivation cohort, weighted points were assigned to predictors of bleeding determined in the multivariate logistic regression analysis and a prediction model was established. External validation of the model was conducted to analyse discrimination and calibration.ResultsA prediction model comprised 10 variables (warfarin, direct oral anticoagulant, chronic kidney disease with haemodialysis, P2Y12 receptor antagonist, aspirin, cilostazol, tumour size >30 mm, lower-third in tumour location, presence of multiple tumours and interruption of each kind of antithrombotic agents). The rates of bleeding after ESD at low-risk (0 to 1 points), intermediate-risk (2 points), high-risk (3 to 4 points) and very high-risk (≥5 points) were 2.8%, 6.1%, 11.4% and 29.7%, respectively. In the external validation cohort, the model showed moderately good discrimination, with a c-statistic of 0.70 (95% CI, 0.64 to 0.76), and good calibration (calibration-in-the-large, 0.05; calibration slope, 1.01).ConclusionsIn this nationwide multicentre study, we derived and externally validated a prediction model for bleeding after ESD. This model may be a good clinical decision-making support tool for ESD in patients with EGC.

scholarly journals Prognostic prediction model for glioblastoma: a metabolic gene signature and independent external validation

2020 ◽  
Author(s):  
Chuxiang Lei ◽  
Wenlin Chen ◽  
Yuekun Wang ◽  
Binghao Zhao ◽  
Penghao Liu ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Validation Cohort ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Normal Brain ◽  
Protective Effects ◽  
Lasso Regression ◽  
Metabolic Genes

Abstract Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001). Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients. Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001).Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients.

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

Blood ◽  
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&lt;0.001) and chronic GVHD (cGVHD) (p&lt;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.

scholarly journals Machine learning identifies clinical parameters to predict mortality in patients undergoing transcatheter mitral valve repair

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
E Zweck ◽  
M Spieker ◽  
P Horn ◽  
C Iliadis ◽  
C Metze ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Trials ◽  
Mitral Valve ◽  
Risk Stratification ◽  
Mitral Valve Repair ◽  
Validation Cohort ◽  
External Validation ◽  
Valve Repair ◽  
Derivation Cohort ◽  
Transcatheter Mitral Valve Repair

Abstract Background Transcatheter Mitral Valve Repair (TMVR) with MitraClip is an important treatment option for patients with severe mitral regurgitation. The lack of appropriate, validated and specific means to risk stratify TMVR patients complicates the evaluation of prognostic benefits of TMVR in clinical trials and practice. Purpose We aimed to develop an optimized risk stratification model for TMVR patients using machine learning (ML). Methods We included a total of 1009 TMVR patients from three large university hospitals, of which one (n=317) served as an external validation cohort. The primary endpoint was all-cause 1-year mortality, which was known in 95% of patients. Model performance was assessed using receiver operating characteristics. In the derivation cohort, different ML algorithms, including random forest, logistic regression, support vectors machines, k nearest neighbors, multilayer perceptron, and extreme gradient boosting (XGBoost) were tested using 5-fold cross-validation in the derivation cohort. The final model (Transcatheter MITral Valve Repair MortALIty PredicTion SYstem; MITRALITY) was tested in the validation cohort with respect to existing clinical scores. Results XGBoost was selected as the final algorithm for the MITRALITY Score, using only six baseline clinical features for prediction (in order of predictive importance): blood urea nitrogen, hemoglobin, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), mean arterial pressure, body mass index, and creatinine. In the external validation cohort, the MITRALITY Score's area under the curve (AUC) was 0.783, outperforming existing scores which yielded AUCs of 0.721 and 0.657 at best. 1-year mortality in the MITRALITY Score quartiles across the total cohort was 0.8%, 1.3%, 10.5%, and 54.6%, respectively. Odds of mortality in MITRALITY Score quartile 4 as compared to quartile 1 were 143.02 [34.75; 588.57]. Survival analyses showed that the differences in outcomes between the MITRALITY Score quartiles remained even over a timeframe of 3 years post intervention (log rank: p&lt;0.005). With each increase by 1% in the MITRALITY score, the respective proportional hazard ratio for 3-year survival was 1.06 [1.05, 1.07] (Cox regression, p&lt;0.05). Conclusion The MITRALITY Score is a novel, internally and externally validated ML-based tool for risk stratification of patients prior to TMVR. These findings may potentially allow for more precise design of future clinical trials, may enable novel treatment strategies tailored to populations of specific risk and thereby serve future daily clinical practice. FUNDunding Acknowledgement Type of funding sources: None. Summary Figure

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

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.

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

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000011927
Author(s):  
Roland Faigle ◽  
Bridget J. Chen ◽  
Rachel Krieger ◽  
Elisabeth B. Marsh ◽  
Ayham Alkhachroum ◽  
...  
Keyword(s):  
Critical Care ◽  
Intracerebral Hemorrhage ◽  
Risk Score ◽  
Validation Cohort ◽  
External Validation ◽  
Low Risk ◽  
Care Needs ◽  
Emergency Department Stay ◽  
Strength Of Association

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.

Triage Scoring System Based on Early Post-Transplant Complications for Patients with Myelodysplastic Syndrome Requiring ICU after Allo-HCT: An SFGM-TC Study

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3470-3470
Author(s):  
Alexis Caulier ◽  
Elodie Drumez ◽  
Marie Robin ◽  
Didier Blaise ◽  
Yves Beguin ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Survival Probability ◽  
Scoring System ◽  
Research Funding ◽  
Prognostic Model ◽  
Validation Cohort ◽  
Derivation Cohort ◽  
Post Transplant ◽  
Life Threatening ◽  
Transplant Complications

Abstract INTRODUCTION Allogeneic hematopoietic cell transplantation (allo-HCT) is the only curative option for high-risk myelodysplastic syndrome (MDS). Yet, allo-HCT is associated with potentially life-threatening complications such as conditioning-regimen toxicity, graft-versus-host disease (GVHD) and relapse. The ever-rising number of patients undergoing allo-HCT yields an increase of those requiring admission to an intensive care unit (ICU). Despite ICU outcome improvement, ICU admission is not the solution for all patients. Although ICU triage policies are intended to identify patients more likely to recover from life-threatening complications, they may lack of specificity as they often include very heterogeneous cohorts of patients with regard of underlying disease, patient's characteristics and transplantation modalities. It is therefore crucial to more accurately identify prognostic factors that affect the overall survival (OS) of patients treated with allo-HCT. We hereby aimed to establish a prognostic scoring system for OS inclusive of early post-transplant complications suitable to guide clinicians when ICU admission is pondered. PATIENTS AND METHODS The SFGM-TC database (PROMISE) was used to retrieve data from patients who underwent allo-HCT for MDS. A derivation cohort comprised data from January 1999 to December 2009. A validation cohort comprised data from January 2010 to December 2013. We included patients above 18 years of age, receiving a first sibling or HLA-matched unrelated allo-HCT at the allele level (so-called 10/10) and surviving more than 100 days after HCT. Patients could receive either bone marrow or peripheral blood stem cells. We excluded patients who received allo-HCT from an HLA-mismatched, haplo-identical donor, or ex-vivo T-cell depleted graft. To identify prognostic factors of 3-year OS, disease characteristics, donor and patients characteristics, transplantation modalities and early post-transplant complications were analyzed using a multivariable Cox model. Discrimination and calibration performance of the modelwas assessed by calculating c-index and comparing predicted and observed survivals. Finally, to favor daily use in clinical routine, we turned our prediction model into a point scoring system, in which each predictable variable was weighted by the nearest approximation of hazard ratio. RESULTS The derivation cohort included 393 patients and the validation cohort included 391 patients. The median follow-up from transplantationwas 3.8 years (range, 0.3 to 11.8 years) and 2.9 years (range, 0.4 to 5.5 years), respectively. The backward stepwise regression analysis revealed 3 independent predictors of 3-year OS: (i) the grade of acute GVHD (0/I vs. II vs. III/IV), (ii) the relapse before day 100 and (iii) the lack of platelet recovery before day 100 (Table 1). After over-optimism correction, the discrimination of the selected prognostic model was 0.67 (95%CI, 0.63-0.71) with a shrinkage factor of 0.903. A similar discrimination value was found in the validation cohort 0.65 (95%CI, 0.61-0.69) The point scoring system ranged from 0 to 8, discriminating low- (0), intermediate- (1 to 3), and high-risk (4 to 8) patients, according to survival prognosis (Table 2). The observed 3-year OS after transplantation in patients with low, intermediate and high scores was 70% (95%CI, 63% to 76%), 46% (95%CI, 38% to 55%) and 6% (95%CI, 2% to 16%) respectively (Figure 1). CONCLUSION We created then validated the first triage prognostic score based on early post-transplant complications, to quickly and simply estimate the survival probability after day 100 when ICU is to be considered. Our findings support the robustness, the reliability and the reproducibility of this scoring system. Additional studies are required to assess whether this scoring system may be suitable for hematologic malignancies other than MDS. Calibration of survival probability for the continuous prognostic model in the derivation cohort. Calibration of survival probability for the continuous prognostic model in the derivation cohort. Figure Figure. Disclosures Michallet: Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Astellas Pharma: Consultancy, Honoraria; MSD: Consultancy, Honoraria; Genzyme: Consultancy, Honoraria. Peffault De Latour:Novartis: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Alexion: Consultancy, Honoraria, Research Funding; Amgen: Research Funding.

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