scholarly journals A Novel Severity Score to Predict Inpatient Mortality in COVID-19 patients

2020 ◽  
Author(s):  
David Altschul ◽  
Santiago R Unda ◽  
Joshua Benton ◽  
Rafael de La Garza Ramos ◽  
Mark Mehler ◽  
...  
Keyword(s):  
Severity Score ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Validation Cohort ◽  
Clinical Decision ◽  
International Normalized Ratio ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Derivation Cohort ◽  
Mortality Incidence

Abstract IntroductionCOVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality.Methods4,711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n=2,355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2,356 patients.ResultsMortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814-0.851) and an AUC of 0.798 (95% CI 0.789-0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0-3), moderate (4-6) and high (7-10) COVID-19 severity score.ConclusionThis developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.

scholarly journals A novel severity score to predict inpatient mortality in COVID-19 patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
David J. Altschul ◽  
Santiago R. Unda ◽  
Joshua Benton ◽  
Rafael de la Garza Ramos ◽  
Phillip Cezayirli ◽  
...  
Keyword(s):  
Severity Score ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Validation Cohort ◽  
Clinical Decision ◽  
International Normalized Ratio ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Derivation Cohort ◽  
Mortality Incidence

Abstract COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality. 4711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n = 2355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2356 patients. Mortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814–0.851) and an AUC of 0.798 (95% CI 0.789–0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0–3), moderate (4–6) and high (7–10) COVID-19 severity score. This developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.

Prognostic nomogram for patients with minor stroke and transient ischaemic attack

2020 ◽  
pp. postgradmedj-2020-137680
Author(s):  
Zhihao Lei ◽  
Shuanglin Li ◽  
Hongye Feng ◽  
Yupeng Lai ◽  
Yanxia Zhou ◽  
...  
Keyword(s):  
Transient Ischaemic Attack ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Clinical Decision ◽  
Favourable Outcome ◽  
Minor Stroke ◽  
Derivation Cohort ◽  
Actual Observation ◽  
Ischaemic Attack

BackgroundIschaemic stroke and transient ischaemic attack (TIA) share a common cause. We aim to develop and validate a concise prognostic nomogram for patients with minor stroke and TIA.MethodsA total of 994 patients with minor stroke and TIA were included. They were split into a derivation (n=746) and validation (n=248) cohort. The modified Rankin Scale (mRS) scores 3 months after onset were used to assess the prognosis as unfavourable outcome (mRS≥2) or favourable outcome (mRS<2).ResultThe final model included seven independent predictors: gender, age, baseline National Institute of Health Stroke Scale (NIHSS), hypertension, diabetes mellitus, white blood cell and serum uric acid. The Harrell’s concordance index (C-index) of the nomogram for predicting the outcome was 0.775 (95% CI 0.735 to 0.814), which was confirmed by the validation cohort (C-index=0.787 (95% CI 0.722 to 0.853)). The calibration curve showed that the nomogram-based predictions were consistent with actual observation in both derivation cohort and validation cohort.ConclusionThe proposed nomogram showed favourable predictive accuracy for minor stroke and TIA. This has the potential to contribute to clinical decision-making.

Preoperative Score to Predict Postoperative Mortality (POSPOM)

2016 ◽  
Vol 124 (3) ◽  
pp. 570-579 ◽  
Author(s):  
Yannick Le Manach ◽  
Gary Collins ◽  
Reitze Rodseth ◽  
Christine Le Bihan-Benjamin ◽  
Bruce Biccard ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
Postoperative Mortality ◽  
Clinical Decision ◽  
Derivation Cohort ◽  
International Statistical Classification ◽  
Hospital Deaths ◽  
Preoperative Score

Abstract Background An accurate risk score able to predict in-hospital mortality in patients undergoing surgery may improve both risk communication and clinical decision making. The aim of the study was to develop and validate a surgical risk score based solely on preoperative information, for predicting in-hospital mortality. Methods From January 1, 2010, to December 31, 2010, data related to all surgeries requiring anesthesia were collected from all centers (single hospital or hospitals group) in France performing more than 500 operations in the year on patients aged 18 yr or older (n = 5,507,834). International Statistical Classification of Diseases, 10th revision codes were used to summarize the medical history of patients. From these data, the authors developed a risk score by examining 29 preoperative factors (age, comorbidities, and surgery type) in 2,717,902 patients, and then validated the risk score in a separate cohort of 2,789,932 patients. Results In the derivation cohort, there were 12,786 in-hospital deaths (0.47%; 95% CI, 0.46 to 0.48%), whereas in the validation cohort there were 14,933 in-hospital deaths (0.54%; 95% CI, 0.53 to 0.55%). Seventeen predictors were identified and included in the PreOperative Score to predict PostOperative Mortality (POSPOM). POSPOM showed good calibration and excellent discrimination for in-hospital mortality, with a c-statistic of 0.944 (95% CI, 0.943 to 0.945) in the development cohort and 0.929 (95% CI, 0.928 to 0.931) in the validation cohort. Conclusion The authors have developed and validated POSPOM, a simple risk score for the prediction of in-hospital mortality in surgical patients.

scholarly journals Predicting Risk Score for Mechanical Ventilation in Hospitalized Adult Patients Suffering from COVID-19

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Samira Kafan ◽  
Kiana Tadbir Vajargah ◽  
Mehrdad Sheikhvatan ◽  
Gholamreza Tabrizi ◽  
Ahmad Salimzadeh ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Risk Score ◽  
Scoring System ◽  
Clinical Decision Making ◽  
Wide Spectrum ◽  
Clinical Decision ◽  
Severe Illness ◽  
Mechanical Ventilators ◽  
Roc Curve Analysis ◽  
Logistic Analysis

Background: COVID-19 has become a pandemic since December 2019, causing millions of deaths worldwide. It has a wide spectrum of severity, ranging from mild infection to severe illness requiring mechanical ventilation. In the middle of a pandemic, when medical resources (including mechanical ventilators) are scarce, there should be a scoring system to provide the clinicians with the information needed for clinical decision-making and resource allocation. Objectives: This study aimed to develop a scoring system based on the data obtained on admission, to predict the need for mechanical ventilation in COVID-19 patients. Methods: This study included COVID-19 patients admitted to Sina Hospital, Tehran University of Medical Sciences from February 20 to May 29, 2020. Patients' data on admission were retrospectively recruited from Sina Hospital COVID-19 Registry (SHCo-19R). Multivariable logistic regression and receiver operating characteristic (ROC) curve analysis were performed to identify the predictive factors for mechanical ventilation. Results: A total of 681 patients were included in the study; 74 patients (10.9%) needed mechanical ventilation during hospitalization, while 607 (89.1%) did not. Multivariate logistic analysis revealed that age (OR,1.049; 95% CI:1.008-1.09), history of diabetes mellitus (OR,3.216; 95% CI:1.134-9.120), respiratory rate (OR,1.051; 95% CI:1.005-1.100), oxygen saturation (OR,0.928; 95% CI:0.872-0.989), CRP (OR,1.013; 95% CI:1.001-1.024) and bicarbonate level (OR,0.886; 95% CI:0.790-0.995) were risk factors for mechanical ventilation during hospitalization. Conclusions: A risk score has been developed based on the available data within the first hours of hospital admission to predict the need for mechanical ventilation. This risk score should be further validated to determine its applicability in other populations.

scholarly journals Laboratory predictors of death from coronavirus disease 2019 (COVID-19) in the area of Valcamonica, Italy

2020 ◽  
Vol 58 (7) ◽  
pp. 1100-1105 ◽  
Author(s):  
Graziella Bonetti ◽  
Filippo Manelli ◽  
Andrea Patroni ◽  
Alessandra Bettinardi ◽  
Gianluca Borrelli ◽  
...  
Keyword(s):  
Hospital Stay ◽  
Clinical Decision Making ◽  
Troponin I ◽  
Laboratory Data ◽  
High Sensitivity ◽  
Clinical Decision ◽  
International Normalized Ratio ◽  
Serum Glucose ◽  
Hospital Death ◽  
Laboratory Findings

AbstractBackgroundComprehensive information has been published on laboratory tests which may predict worse outcome in Asian populations with coronavirus disease 2019 (COVID-19). The aim of this study is to describe laboratory findings in a group of Italian COVID-19 patients in the area of Valcamonica, and correlate abnormalities with disease severity.MethodsThe final study population consisted of 144 patients diagnosed with COVID-19 (70 who died during hospital stay and 74 who survived and could be discharged) between March 1 and 30, 2020, in Valcamonica Hospital. Demographical, clinical and laboratory data were collected upon hospital admission and were then correlated with outcome (i.e. in-hospital death vs. discharge).ResultsCompared to patients who could be finally discharged, those who died during hospital stay displayed significantly higher values of serum glucose, aspartate aminotransferase (AST), creatine kinase (CK), lactate dehydrogenase (LDH), urea, creatinine, high-sensitivity cardiac troponin I (hscTnI), prothrombin time/international normalized ratio (PT/INR), activated partial thromboplastin time (APTT), D-dimer, C reactive protein (CRP), ferritin and leukocytes (especially neutrophils), whilst values of albumin, hemoglobin and lymphocytes were significantly decreased. In multiple regression analysis, LDH, CRP, neutrophils, lymphocytes, albumin, APTT and age remained significant predictors of in-hospital death. A regression model incorporating these variables explained 80% of overall variance of in-hospital death.ConclusionsThe most important laboratory abnormalities described here in a subset of European COVID-19 patients residing in Valcamonica are highly predictive of in-hospital death and may be useful for guiding risk assessment and clinical decision-making.

scholarly journals In-depth blood proteome profiling analysis revealed distinct functional characteristics of plasma proteins between severe and non-severe COVID-19 patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Joonho Park ◽  
Hyeyoon Kim ◽  
So Yeon Kim ◽  
Yeonjae Kim ◽  
Jee-Soo Lee ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Good Prognosis ◽  
Severe Illness ◽  
Proteome Profiling ◽  
T Cell Suppression ◽  
Cell Suppression ◽  
Profiling Analysis ◽  
Necrosis Factor

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected over forty million patients worldwide. Although most coronavirus disease 2019 (COVID-19) patients have a good prognosis, some develop severe illness. Markers that define disease severity or predict clinical outcome need to be urgently developed as the mortality rate in critical cases is approximately 61.5%. In the present study, we performed in-depth proteome profiling of undepleted plasma from eight COVID-19 patients. Quantitative proteomic analysis using the BoxCar method revealed that 91 out of 1222 quantified proteins were differentially expressed depending on the severity of COVID-19. Importantly, we found 76 proteins, previously not reported, which could be novel prognostic biomarker candidates. Our plasma proteome signatures captured the host response to SARS-CoV-2 infection, thereby highlighting the role of neutrophil activation, complement activation, platelet function, and T cell suppression as well as proinflammatory factors upstream and downstream of interleukin-6, interleukin-1B, and tumor necrosis factor. Consequently, this study supports the development of blood biomarkers and potential therapeutic targets to aid clinical decision-making and subsequently improve prognosis of COVID-19.

scholarly journals Validation of parsimonious prognostic models for patients infected with COVID-19

2021 ◽  
Vol 28 (1) ◽  
pp. e100267
Author(s):  
Keerthi Harish ◽  
Ben Zhang ◽  
Peter Stella ◽  
Kevin Hauck ◽  
Marwa M Moussa ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Clinical Decision ◽  
Prognostic Models ◽  
Clinical Prediction ◽  
Inclusion Criteria ◽  
Local Data ◽  
External Data ◽  
Operator Curve

ObjectivesPredictive studies play important roles in the development of models informing care for patients with COVID-19. Our concern is that studies producing ill-performing models may lead to inappropriate clinical decision-making. Thus, our objective is to summarise and characterise performance of prognostic models for COVID-19 on external data.MethodsWe performed a validation of parsimonious prognostic models for patients with COVID-19 from a literature search for published and preprint articles. Ten models meeting inclusion criteria were either (a) externally validated with our data against the model variables and weights or (b) rebuilt using original features if no weights were provided. Nine studies had internally or externally validated models on cohorts of between 18 and 320 inpatients with COVID-19. One model used cross-validation. Our external validation cohort consisted of 4444 patients with COVID-19 hospitalised between 1 March and 27 May 2020.ResultsMost models failed validation when applied to our institution’s data. Included studies reported an average validation area under the receiver–operator curve (AUROC) of 0.828. Models applied with reported features averaged an AUROC of 0.66 when validated on our data. Models rebuilt with the same features averaged an AUROC of 0.755 when validated on our data. In both cases, models did not validate against their studies’ reported AUROC values.DiscussionPublished and preprint prognostic models for patients infected with COVID-19 performed substantially worse when applied to external data. Further inquiry is required to elucidate mechanisms underlying performance deviations.ConclusionsClinicians should employ caution when applying models for clinical prediction without careful validation on local data.

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.

Orale Antikoagulation bei chronischer Beinischämie

VASA ◽  
2001 ◽  
Vol 30 (2) ◽  
pp. 83-88
Author(s):  
U. Mueller-Kolck
Keyword(s):  
Oral Anticoagulation ◽  
Clinical Decision Making ◽  
Risk Model ◽  
Anticoagulation Therapy ◽  
Limb Ischemia ◽  
Risk Groups ◽  
Clinical Decision ◽  
Arterial Disease ◽  
Bleeding Complications ◽  
Minor Bleeding

This review article summarizes clinical data on adjuvant long-term oral anticoagulation therapy (OAC) of peripheral arterial disease (PAD). It analyzes the underlying risk model of oral anticoagulation. Definitions of runoff patterns, of major and minor bleeding complications, of predictors of major bleedings as well as a classification of patient risk groups are described. The indication for OAC treatment of chronic limb ischemia remains still due to an individual decision. Clinical decision making is facilitated by the risk model. Improved oral anticoagulation control results in fewer bleeding complications. Studies on patient weekly self-testing and self-dosing which support this hypothesis are reviewed in the context of adjuvant OAC therapy.

A prospective validation cohort study of baseline plasma cell-free DNA (cfDNA) as a prognostic biomarker in newly diagnosed glioblastoma (GBM).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 2508-2508
Author(s):  
Stephen Joseph Bagley ◽  
Seyed Ali Nabavizadeh ◽  
Jacob Till ◽  
Aseel Abdalla ◽  
Hareena Sanga ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Performance Status ◽  
Survival Outcomes ◽  
Prognostic Impact ◽  
Newly Diagnosed ◽  
Roc Curve Analysis ◽  
Optimal Cutoff ◽  
Derivation Cohort ◽  
Mutational Status

2508 Background: Due to significant interpatient heterogeneity, survival outcomes vary widely in patients with GBM. Novel prognostic biomarkers are needed. We aimed to determine the prognostic impact of baseline plasma cfDNA concentration in patients with GBM. Methods: We analyzed 84 patients with newly diagnosed GBM and at least 7 months of follow-up time. The first 41 patients comprised a previously published derivation cohort (Bagley, Clin Cancer Res 2020). The subsequent 43 patients served as an independent validation cohort. cfDNA was extracted from plasma collected prior to initial surgical resection and quantified by qPCR for a 115 bp amplicon of the human ALU repeat element. Receiver operating characteristic (ROC) curve analysis was used in the derivation cohort to (1) assess the accuracy of plasma cfDNA concentration for predicting progression-free survival status at 7 months (PFS-7), a landmark based on the median PFS for newly diagnosed GBM (Stupp, N Engl J Med 2005), and (2) derive the optimal cutoff for dichotomizing patients into high- and low-cfDNA groups. In the validation cohort, logistic regression was used to measure the association of plasma cfDNA concentration (high vs. low) with PFS-7, adjusted for age, isocitrate dehydrogenase ( IDH) 1/2 mutational status, 0-6-methylguanine-methyltransferase ( MGMT) methylation, extent of resection, and performance status. Multivariate Cox regression was used for overall survival (OS) analysis. Results: In the derivation cohort, the optimal cutoff for plasma cfDNA was 25.0 ng/mL (area under the curve [AUC] = 0.663), with inferior PFS and OS in patients with cfDNA above this cutoff (PFS, median 4.9 vs. 9.5 months, log-rank p = 0.001; OS, median 8.5 vs. 15.5 months, log-rank p = 0.03). In the validation cohort, baseline plasma cfDNA concentration over the cutoff was independently associated with a lower likelihood of being alive and progression-free at 7 months (adjusted OR 0.13, 95% CI 0.02 – 0.75, p = 0.02). OS was also worse in in the validation cohort in patients with high plasma cfDNA (adjusted HR 3.0, 95% CI 1.1 – 8.0, p = 0.03). Conclusions: In patients with newly diagnosed GBM, high baseline plasma cfDNA concentration is associated with worse survival outcomes independent of other prognostic factors. Further validation in a larger, multicenter study is warranted.

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