A risk score for predicting hospitalization for community-acquired pneumonia in ITP using nationally representative data

Abstract Infection is one of the primary causes of death from immune thrombocytopenia (ITP), and the lungs are the most common site of infection. We identified the factors associated with hospitalization for community-acquired pneumonia (CAP) in nonsplenectomized adults with ITP and established the ACPA prediction model to predict the incidence of hospitalization for CAP. This was a retrospective study of nonsplenectomized adult patients with ITP from 10 large medical centers in China. The derivation cohort included 145 ITP inpatients with CAP and 1360 inpatients without CAP from 5 medical centers, and the validation cohort included the remaining 63 ITP inpatients with CAP and 526 inpatients without CAP from the other 5 centers. The 4-item ACPA model, which included age, Charlson Comorbidity Index score, initial platelet count, and initial absolute lymphocyte count, was established by multivariable analysis of the derivation cohort. Internal and external validation were conducted to assess the performance of the model. The ACPA model had an area under the curve of 0.853 (95% confidence interval [CI], 0.818-0.889) in the derivation cohort and 0.862 (95% CI, 0.807-0.916) in the validation cohort, which indicated the good discrimination power of the model. Calibration plots showed high agreement between the estimated and observed probabilities. Decision curve analysis indicated that ITP patients could benefit from the clinical application of the ACPA model. To summarize, the ACPA model was developed and validated to predict the occurrence of hospitalization for CAP, which might help identify ITP patients with a high risk of hospitalization for CAP.

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

Scientific Reports ◽

10.1038/s41598-020-80474-z ◽

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.

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Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

Cancer Imaging ◽

10.1186/s40644-021-00395-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sijia Cui ◽

Tianyu Tang ◽

Qiuming Su ◽

Yajie Wang ◽

Zhenyu Shu ◽

...

Keyword(s):

Calibration Curve ◽

Main Pancreatic Duct ◽

Validation Cohort ◽

External Validation ◽

High Grade ◽

Intraductal Papillary Mucinous Neoplasms ◽

Training Cohort ◽

Independent Validation ◽

Medical Centers ◽

Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

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Bedside estimates of dead space using end-tidal CO2 are independently associated with mortality in ARDS

Critical Care ◽

10.1186/s13054-021-03751-x ◽

2021 ◽

Vol 25 (1) ◽

Author(s):

Paola Lecompte-Osorio ◽

Steven D. Pearson ◽

Cole H. Pieroni ◽

Matthew R. Stutz ◽

Anne S. Pohlman ◽

...

Keyword(s):

Dead Space ◽

Validation Cohort ◽

Univariate Analysis ◽

Multivariable Analysis ◽

Separate Analysis ◽

University Of Chicago ◽

Derivation Cohort ◽

Ventilator Mode ◽

The Difference ◽

End Tidal

Abstract Purpose In acute respiratory distress syndrome (ARDS), dead space fraction has been independently associated with mortality. We hypothesized that early measurement of the difference between arterial and end-tidal CO2 (arterial-ET difference), a surrogate for dead space fraction, would predict mortality in mechanically ventilated patients with ARDS. Methods We performed two separate exploratory analyses. We first used publicly available databases from the ALTA, EDEN, and OMEGA ARDS Network trials (N = 124) as a derivation cohort to test our hypothesis. We then performed a separate retrospective analysis of patients with ARDS using University of Chicago patients (N = 302) as a validation cohort. Results The ARDS Network derivation cohort demonstrated arterial-ET difference, vasopressor requirement, age, and APACHE III to be associated with mortality by univariable analysis. By multivariable analysis, only the arterial-ET difference remained significant (P = 0.047). In a separate analysis, the modified Enghoff equation ((PaCO2–PETCO2)/PaCO2) was used in place of the arterial-ET difference and did not alter the results. The University of Chicago cohort found arterial-ET difference, age, ventilator mode, vasopressor requirement, and APACHE II to be associated with mortality in a univariate analysis. By multivariable analysis, the arterial-ET difference continued to be predictive of mortality (P = 0.031). In the validation cohort, substitution of the arterial-ET difference for the modified Enghoff equation showed similar results. Conclusion Arterial to end-tidal CO2 (ETCO2) difference is an independent predictor of mortality in patients with ARDS.

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

Gut ◽

10.1136/gutjnl-2019-319926 ◽

2020 ◽

pp. gutjnl-2019-319926 ◽

Cited By ~ 1

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.

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

Blood ◽

10.1182/blood-2021-148957 ◽

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<0.001) and chronic GVHD (cGVHD) (p<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.

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

European Heart Journal ◽

10.1093/eurheartj/ehab724.2221 ◽

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<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<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

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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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DERIVATION AND VALIDATION OF A CLINICAL SCORE TO PREDICT DEATH AMONG NON-PALLIATIVE COVID-19 PATIENTS PRESENTING TO EMERGENCY DEPARTMENTS: THE CCEDRRN COVID MORTALITY SCORE

10.1101/2021.07.28.21261283 ◽

2021 ◽

Author(s):

Corinne M Hohl ◽

Rhonda J Rosychuk ◽

Patrick M Archambault ◽

Fiona O'Sullivan ◽

Murdoch Leeies ◽

...

Keyword(s):

Hospital Mortality ◽

Validation Cohort ◽

Area Under The Curve ◽

Clinical Score ◽

Severe Liver ◽

Derivation Cohort ◽

Response Network ◽

Sex Type ◽

Patient Arrival ◽

Severe Liver Disease

Background: Predicting mortality from coronavirus disease 2019 (COVID-19) using information available when patients present to the Emergency Department (ED) can inform goals-of-care decisions and assist with ethical allocation of critical care resources. Methods: We conducted an observational study to develop and validate a clinical score to predict ED and in-hospital mortality among consecutive non-palliative COVID-19 patients. We recruited from 44 hospitals participating in the Canadian COVID-19 ED Rapid Response Network (CCEDRRN) between March 1, 2020 and January 31, 2021. We randomly assigned hospitals to derivation or validation, and pre-specified clinical variables as candidate predictors. We used logistic regression to develop the score in a derivation cohort, and examined its performance in predicting ED and in-hospital mortality in a validation cohort. Results: Of 8,761 eligible patients, 618 (7·01%) died. The score included age, sex, type of residence, arrival mode, chest pain, severe liver disease, respiratory rate, and level of respiratory support. The area under the curve was 0·92 (95% confidence intervals [CI] 0·91—0·93) in derivation and 0·92 (95%CI 0·89—0·93) in validation. The score had excellent calibration. Above a score of 15, the observed mortality was 81·0% (81/100) with a specificity of 98·8% (95%CI 99·5—99·9%). Interpretation: The CCEDRRN COVID Mortality Score is a simple score that accurately predicts mortality with variables that are available on patient arrival without the need for diagnostic tests.

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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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Simple Scoring Tool to Estimate Risk of Hospitalization and Mortality in Ambulatory and Emergency Department Patients with COVID-19

10.1101/2021.02.22.21252171 ◽

2021 ◽

Author(s):

Brandon J. Webb ◽

Nicholas M. Levin ◽

Nancy Grisel ◽

Samuel M. Brown ◽

Ithan D. Peltan ◽

...

Keyword(s):

Monoclonal Antibodies ◽

High Risk ◽

Validation Cohort ◽

Characteristic Curve ◽

External Validation ◽

Receiver Operator Characteristic Curve ◽

Multivariable Logistic Regression Model ◽

White Race ◽

Derivation Cohort ◽

Scoring Tool

AbstractBackgroundAccurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality.MethodsAll consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality.Results22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n=16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81-0.84) for hospitalization and 0.91 (0.83-0.94) for 28-day mortality; in the validation cohort (n=6,786) AUROC for hospitalization was 0.8 (CI 0.78-0.82) and for mortality 0.8 (CI 0.69-0.9).ConclusionA prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

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