LUCAS: A highly accurate yet simple risk calculator that predicts survival of COVID-19 patients using rapid routine tests

There is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission. Methods We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK contributed clinical data on SARS CoV-2 positive patients using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS CoV-2 positive patients, respectively. External validation of final model conducted on 741 Accident and Emergency admissions with suspected SARS CoV-2 infection from a separate NHS Trust which was not part of the initial NCCID data set. Findings The LUCAS mortality score included five strongest predictors (lymphocyte count, urea, CRP, age, sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the AUC-ROC in development cohort 0.765 (95% confidence interval (CI): 0.738 - 0.790), in internal validation cohort 0.744 (CI: 0.673 - 0.808), and in external validation cohort 0.752 (CI: 0.713 - 0.787). The discriminatory power of LUCAS mortality score was increased slightly when including the CXR image data (for normal versus abnormal): internal validation AUC-ROC 0.770 (CI: 0.695 - 0.836) and external validation AUC-ROC 0.791 (CI: 0.746 - 0.833). The discriminatory power of LUCAS and LUCAS + CXR performed in the upper quartile of pre-existing risk stratification scores with the added advantage of using only 5 predictors. Interpretation This simplified prognostic tool derived from objective parameters can be used to obtain valid predictions of mortality in patients within 60 days SARS CoV-2 RT-PCR results. This free-to-use simplified tool can be used to assist the triage of patients into low, moderate, high or very high risk of fatality and is available at https://mdscore.net/.

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Development and Validation of a Simple-to-Use Nomogram to Predict Early Death in Metastatic Pancreatic Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.729175 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhong Zhang ◽

Juan Pu ◽

Haijun Zhang

Keyword(s):

Risk Factors ◽

Pancreatic Adenocarcinoma ◽

Validation Cohort ◽

External Validation ◽

Significant Risk ◽

Early Death ◽

Seer Database ◽

Data Set ◽

Internal Validation ◽

Chinese Cohort

BackgroundPancreatic adenocarcinoma (PCa) is a highly aggressive malignancy with high risk of early death (survival time ≤3 months). The present study aimed to identify associated risk factors and develop a simple-to-use nomogram to predict early death in metastatic PCa patients.MethodsPatients diagnosed with metastatic PCa between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were collected for model construction and internal validation. An independent data set was obtained from China for external validation. Independent risk variables contributed to early death were identified by logistic regression models, which were then used to construct a nomogram. Internal and external validation was performed to evaluate the nomogram using calibration curves and the receiver operating characteristic curves.ResultsA total of 19,464 patients in the SEER cohort and 67 patients in the Chinese cohort were included. Patients from the SEER database were randomly divided into the training cohort (n = 13,040) and internal validation cohort (n = 6,424). Patients in the Chinese cohort were selected for the external validation cohort. Overall, 10,484 patients experienced early death in the SEER cohort and 35 in the Chinese cohort. A reliable nomogram was constructed on the basis of 11 significant risk factors. Internal validation and external validation of the nomogram showed high accuracy in predicting early death. Decision curve analysis demonstrated that this predictive nomogram had excellent and potential clinical applicability.ConclusionThe nomogram provided a simple-to-use tool to distinguish early death in patients with metastatic PCa, assisting clinicians in implementing individualized treatment regimens.

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Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

10.1101/2020.04.03.20052068 ◽

2020 ◽

Cited By ~ 4

Author(s):

Zhiyi Wang ◽

Jie Weng ◽

Zhongwang Li ◽

Ruonan Hou ◽

Lebin Zhou ◽

...

Keyword(s):

Predictive Factors ◽

Validation Cohort ◽

External Validation ◽

Clinical Impact ◽

Validation Data ◽

Data Set ◽

Internal Validation ◽

C Statistic ◽

Prediction Probability ◽

Wbc Count

BackgroundThe COVID-19 virus is an emerging virus rapidly spread worldwide This study aimed to establish an effective diagnostic nomogram for suspected COVID-19 pneumonia patients.METHODSWe used the LASSO aggression and multivariable logistic regression methods to explore the predictive factors associated with COVID-19 pneumonia, and established the diagnostic nomogram for COVID-19 pneumonia using multivariable regression. This diagnostic nomogram was assessed by the internal and external validation data set. Further, we plotted decision curves and clinical impact curve to evaluate the clinical usefulness of this diagnostic nomogram.RESULTSThe predictive factors including the epidemiological history, wedge- shaped or fan-shaped lesion parallel to or near the pleura, bilateral lower lobes, ground glass opacities, crazy paving pattern and white blood cell (WBC) count were contained in the nomogram. In the primary cohort, the C-statistic for predicting the probability of the COVID-19 pneumonia was 0.967, even higher than the C-statistic (0.961) in initial viral nucleic acid nomogram which was established using the univariable regression. The C-statistic was 0.848 in external validation cohort. Good calibration curves were observed for the prediction probability in the internal validation and external validation cohort. The nomogram both performed well in terms of discrimination and calibration. Moreover, decision curve and clinical impact curve were also beneficial for COVID- 19 pneumonia patients.CONCLUSIONOur nomogram can be used to predict COVID-19 pneumonia accurately and favourably.

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Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters

Journal of Intensive Care ◽

10.1186/s40560-021-00531-1 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Yue Gao ◽

Lingxi Chen ◽

Jianhua Chi ◽

Shaoqing Zeng ◽

Xikang Feng ◽

...

Keyword(s):

Machine Learning ◽

Critical Illness ◽

Prediction Model ◽

Validation Cohort ◽

External Validation ◽

Validation Dataset ◽

Support Vector ◽

Internal Validation ◽

Inflammatory Parameters ◽

Severity Prediction

Abstract Background Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. Methods We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. Results Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979–1.000) in internal validation cohort and 0.999 (95% CI 0.998–1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. Conclusions The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry (ChiCTR2000032161). Graphical abstracthelper lymphocytve vv

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Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

10.21203/rs.3.rs-22140/v1 ◽

2020 ◽

Author(s):

Zhiyi Wang ◽

Jie Weng ◽

Zhongwang Li ◽

Ruonan Hou ◽

Lebin Zhou ◽

...

Keyword(s):

Predictive Factors ◽

Validation Cohort ◽

External Validation ◽

Respiratory Illness ◽

Clinical Impact ◽

Validation Data ◽

Data Set ◽

Internal Validation ◽

C Statistic ◽

Prediction Probability

Abstract Background The COVID-19 virus is an emerging virus associated with severe respiratory illness first detected in December, 2019, and rapidly spread worldwide. The aim of this study was to establish an effective diagnostic nomogram for suspected COVID-19 pneumonia patients. Methods We used the LASSO aggression and multivariable logistic regression methods to explore the predictive factors associated with COVID-19 pneumonia, and established the diagnostic nomogram for COVID-19 pneumonia using multivariable regression. This diagnostic nomogram was assessed by the internal and external validation data set. Further, we plotted decision curves and clinical impact curve to evaluate the clinical usefulness of this diagnostic nomogram. Results The predictive factors including the epidemiological history, wedge-shaped or fan-shaped lesion parallel to or near the pleura, bilateral lower lobes, ground glass opacities, crazy paving pattern and white blood cell (WBC) count were contained in the nomogram. In the primary cohort, the C-statistic for predicting the probability of the COVID-19 pneumonia was 0.967, even higher than the C-statistic (0.961) in initial viral nucleic acid nomogram which was established using the univariable regression. The C-statistic was 0.848 in external validation cohort. Good calibration curves were observed for the prediction probability in the internal validation and external validation cohort. The nomogram both performed well in terms of discrimination and calibration. Moreover, decision curve and clinical impact curve were also beneficial for COVID-19 pneumonia patients. Conclusion Our nomogram can be used to predict COVID-19 pneumonia accurately and favourably.

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Age-adapted percentiles of measured glomerular filtration in healthy individuals: extrapolation to living kidney donors over 65 years

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2021-1011 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Pierre Delanaye ◽

François Gaillard ◽

Jessica van der Weijden ◽

Geir Mjøen ◽

Ingela Ferhman-Ekholm ◽

...

Keyword(s):

Decision Making ◽

Glomerular Filtration ◽

Validation Cohort ◽

External Validation ◽

Healthy Individuals ◽

Kidney Donors ◽

Internal Validation ◽

Living Kidney Donors ◽

Multi Center Study ◽

Elderly Living

Abstract Objectives Most data on glomerular filtration rate (GFR) originate from subjects <65 years old, complicating decision-making in elderly living kidney donors. In this retrospective multi-center study, we calculated percentiles of measured GFR (mGFR) in donors <65 years old and extrapolated these to donors ≥65 years old. Methods mGFR percentiles were calculated from a development cohort of French/Belgian living kidney donors <65 years (n=1,983), using quantiles modeled as cubic splines (two linear parts joining at 40 years). Percentiles were extrapolated and validated in an internal cohort of donors ≥65 years (n=147, France) and external cohort of donors and healthy subjects ≥65 years (n=329, Germany, Sweden, Norway, France, The Netherlands) by calculating percentages within the extrapolated 5th–95th percentile (P5–P95). Results Individuals in the development cohort had a higher mGFR (99.9 ± 16.4 vs. 86.4 ± 14 and 82.7 ± 15.5 mL/min/1.73 m2) compared to the individuals in the validation cohorts. In the internal validation cohort, none (0%) had mGFR below the extrapolated P5, 12 (8.2%) above P95 and 135 (91.8%) between P5–P95. In the external validation cohort, five subjects had mGFR below the extrapolated P5 (1.5%), 25 above P95 (7.6%) and 299 (90.9%) between P5–P95. Conclusions We demonstrate that extrapolation of mGFR from younger donors is possible and might aid with decision-making in elderly donors.

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A Modified TNM Classification for Primary Operable Colorectal Cancer

JNCI Cancer Spectrum ◽

10.1093/jncics/pkaa093 ◽

2020 ◽

Author(s):

Chundong Zhang ◽

Zubing Mei ◽

Junpeng Pei ◽

Masanobu Abe ◽

Xiantao Zeng ◽

...

Keyword(s):

Colorectal Cancer ◽

Validation Cohort ◽

Tnm Classification ◽

Characteristic Curve ◽

External Validation ◽

Model Fitting ◽

Information Criteria ◽

Stage I ◽

Internal Validation ◽

Clinical Benefits

Abstract Background The American Joint Committee on Cancer (AJCC) 8th tumor/node/metastasis (TNM) classification for colorectal cancer (CRC) has limited ability to predict prognosis. Methods We included 45,379 eligible stage I-III CRC patients from the Surveillance, Epidemiology, and End Results Program. Patients were randomly assigned individually to a training (N =31,772) or an internal validation cohort (N =13,607). External validation was performed in 10,902 additional patients. Patients were divided according to T and N stage permutations. Survival analyses were conducted by a Cox proportional hazard model and Kaplan-Meier analysis, with T1N0 as the reference. Area under receiver operating characteristic curve (AUC) and Akaike information criteria (AIC) were applied for prognostic discrimination and model-fitting, respectively. Clinical benefits were further assessed by decision curve analyses. Results We created a modified TNM (mTNM) classification: stages I (T1-2N0-1a), IIA (T1N1b, T2N1b, T3N0), IIB (T1-2N2a-2b, T3N1a-1b, T4aN0), IIC (T3N2a, T4aN1a-2a, T4bN0), IIIA (T3N2b, T4bN1a), IIIB (T4aN2b, T4bN1b), and IIIC (T4bN2a-2b). In the internal validation cohort, compared to the AJCC 8th TNM classification, the mTNM classification showed superior prognostic discrimination (AUC = 0.675 vs. 0.667, respectively; two-sided P <0.001) and better model-fitting (AIC = 70,937 vs. 71,238, respectively). Similar findings were obtained in the external validation cohort. Decision curve analyses revealed that the mTNM had superior net benefits over the AJCC 8th TNM classification in the internal and external validation cohorts. Conclusions The mTNM classification provides better prognostic discrimination than AJCC 8th TNM classification, with good applicability in various populations and settings, to help better stratify stage I-III CRC patients into prognostic groups.

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Development and external validation of a clinical prediction model for MRSA carriage at hospital admission in Southeast Lower Saxony, Germany

Scientific Reports ◽

10.1038/s41598-020-75094-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Gabriele Raschpichler ◽

Heike Raupach-Rosin ◽

Manas K. Akmatov ◽

Stefanie Castell ◽

Nicole Rübsamen ◽

...

Keyword(s):

Hospital Admission ◽

External Validation ◽

Model Development ◽

Cost Effective ◽

Risk Groups ◽

Validation Dataset ◽

Lower Saxony ◽

Screening Programs ◽

Lower Specificity ◽

History Of

Abstract In countries with low endemic Methicillin-resistant Staphylococcus aureus (MRSA) prevalence, identification of risk groups at hospital admission is considered more cost-effective than universal MRSA screening. Predictive statistical models support the selection of suitable stratification factors for effective screening programs. Currently, there are no universal guidelines in Germany for MRSA screening. Instead, a list of criteria is available from the Commission for Hospital Hygiene and Infection Prevention (KRINKO) based on which local strategies should be adopted. We developed and externally validated a model for individual prediction of MRSA carriage at hospital admission in the region of Southeast Lower Saxony based on two prospective studies with universal screening in Braunschweig (n = 2065) and Wolfsburg (n = 461). Logistic regression was used for model development. The final model (simplified to an unweighted score) included history of MRSA carriage, care dependency and cancer treatment. In the external validation dataset, the score showed a sensitivity of 78.4% (95% CI: 64.7–88.7%), and a specificity of 70.3% (95% CI: 65.0–75.2%). Of all admitted patients, 25.4% had to be screened if the score was applied. A model based on KRINKO criteria showed similar sensitivity but lower specificity, leading to a considerably higher proportion of patients to be screened (49.5%).

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The Monocyte-to-Lymphocyte Ratio at Hospital Admission Is a Novel Predictor for Acute Traumatic Intraparenchymal Hemorrhage Expansion after Cerebral Contusion

Mediators of Inflammation ◽

10.1155/2020/5483981 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Jiangtao Sheng ◽

Tian Li ◽

Dongzhou Zhuang ◽

Shirong Cai ◽

Jinhua Yang ◽

...

Keyword(s):

Hospital Admission ◽

Validation Cohort ◽

External Validation ◽

Multivariate Model ◽

Hematoma Expansion ◽

Combined Model ◽

Cerebral Contusion ◽

Long Term Outcome ◽

Lymphocyte Ratio ◽

Mlr Model

Purpose. To explore the potential of monocyte-to-lymphocyte ratio (MLR) at hospital admission for predicting acute traumatic intraparenchymal hematoma (tICH) expansion in patients with cerebral contusion. Patients and Methods. This multicenter, observational study included patients with available at-hospital admission (baseline) and follow-up computed tomography for volumetric analysis (retrospective development cohort: 1146 patients; prospective validation cohort: 207 patients). Semiautomated software assessed tICH expansion (defined as ≥33% or 5 mL absolute growth). MLR was acquired from routine blood tests upon admission. We constructed two predictive models: basic combined model of clinical and imaging variables and MLR combined model of both MLR and other variables in the basic model. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to estimate the performance of MLR for predicting acute tICH expansion. Results. MLR was significantly larger in patients with acute tICH expansion compared to those without acute tICH expansion (mean [SD], 1.08 [1.05] vs. 0.59 [0.37], P < 0.001 ). A nonlinear positive relationship between MLR and the incidence of acute tICH expansion was observed. Multivariate logistic regression indicated MLR as an independent risk factor for acute tICH expansion (odds ratio (OR), 5.88; 95% confidence interval (CI), 4.02-8.61). The power of the multivariate model for predicting acute tICH expansion was substantially improved with the inclusion of MLR (AUC 0.86 vs. AUC 0.74, P < 0.001 ), as was also observed in an external validation cohort (AUC 0.83 vs. AUC 0.71, P < 0.001 ). The net benefit of MLR model was higher between threshold probabilities of 20-100% in DCA. For clinical application, a nomogram derived from the multivariate model with MLR was introduced. In addition, MLR was positively associated with 6-month unfavorable outcome. Conclusion. MLR is a novel predictor for traumatic parenchymatous hematoma expansion. A nomogram derived from the MLR model may provide an easy-to-use tool for predicting acute tICH expansion and promoting the individualized treatment of patients with hemorrhagic cerebral contusion. MLR is associated with long-term outcome after cerebral contusion.

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Deep Learning Model for Screening Sepsis Using Electrocardiography

10.21203/rs.3.rs-186976/v1 ◽

2021 ◽

Author(s):

Joon-myoung Kwon ◽

Ye Rang Lee ◽

Min-Seung Jung ◽

Yoon-Ji Lee ◽

Yong-Yeon Jo ◽

...

Keyword(s):

Septic Shock ◽

Deep Learning ◽

Characteristic Curve ◽

External Validation ◽

Medical Emergency ◽

Validation Dataset ◽

Internal Validation ◽

Significant Difference ◽

Life Threatening ◽

Sepsis And Septic Shock

Abstract Background: Sepsis is a life-threatening organ dysfunction and is a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, it is difficult to screen the occurrence of sepsis. In this study, we propose an artificial intelligence based on deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG).Methods: This retrospective cohort study included 46,017 patients who admitted to two hospitals. 1,548 and 639 patients underwent sepsis and septic shock. The DLM was developed using 73,727 ECGs of 18,142 patients and internal validation was conducted using 7,774 ECGs of 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs of 20,101 patients from another hospital to verify the applicability of the DLM across centers.Results: During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of an DLM using 12-lead ECG for screening sepsis were 0.901 (95% confidence interval 0.882–0.920) and 0.863 (0.846–0.879), respectively. During internal and external validation, AUC of an DLM for detecting septic shock were 0.906 (95% CI = 0.877–0.936) and 0.899 (95% CI = 0.872–0.925), respectively. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs were 0.845–0.882. A sensitivity map showed that the QRS complex and T wave was associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who admitted with infectious disease, The AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs 0.574, p<0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs 0.725, p=0.018).Conclusions: The DLM demonstrated reasonable performance for screening sepsis using 12-, 6-, and single-lead ECG. The results suggest that sepsis can be screened using not only conventional ECG devices, but also diverse life-type ECG machine employing the DLM, thereby preventing irreversible disease progression and mortality.

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Effect of Aging-Related Genes on the Prognosis of Colon Cancer

10.21203/rs.3.rs-984927/v1 ◽

2021 ◽

Author(s):

Yushu Liu ◽

Jiantao Gong ◽

Yanyi Huang ◽

Qunguang Jiang

Keyword(s):

Colon Cancer ◽

Cancer Patients ◽

Clinical Characteristics ◽

Cox Regression ◽

Validation Cohort ◽

External Validation ◽

Predictive Ability ◽

Lasso Regression ◽

Internal Validation ◽

Cox Regression Analysis

Abstract Background:Colon cancer is a common malignant cancer with high incidence and poor prognosis. Cell senescence and apoptosis are important mechanisms of tumor occurrence and development, in which aging-related genes(ARGs) play an important role. This study aimed to establish a prognostic risk model based on ARGs for diagnosis and prognosis prediction of colon cancer .Methods: We downloaded transcriptome data and clinical information of colon cancer patients from the Cancer Genome Atlas(TCGA) database and the microarray dataset(GSE39582) from the Gene Expression Omnibus(GEO) database. Univariate COX, least absolute shrinkage and selection operator(LASSO) regression algorithm and multivariate COX regression analysis were used to construct a 6-ARG prognosis model and calculated the riskScore. The prognostic signatures is validated by internal validation cohort and external validation cohort(GSE39582).In addition, functional enrichment pathways and immune microenvironment of aging-related genes(ARGs) were also analyzed. We also analyzed the correlation between rsikScore and clinical features and constructed a nomogram based on riskScore. We are the first to construct prognostic nomogram based on ARGs.Results: Through univariate COX,LASSO regression algorithm and multivariate COX regression analysis,6 prognostic ARGs (PDPK1,RAD52,GSR,IL7,BDNF and SERPINE1) were screened out and riskScore was constructed. We have verified that riskScore has good prognostic value in both internal validation cohort and external validation cohort. Pathway enrichment and immunoanalysis of ARGs provide a direction for the treatment of colon cancer patients. We also found that riskScore was closely related to the clinical characteristics of patients. Based on riskScore and related clinical features, we constructed a nomogram, which has good predictive performance.Conclusion: The 6-ARG prognostic signature we constructed has a certain clinical predictive ability. Its riskScore is also closely related to clinical characteristics, and nomogram based on this has stronger predictive ability than a single indicator. ARGs and the nomogram we constructed may provide a promising treatment for colon cancer patients.

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