A Modified TNM Classification for Primary Operable Colorectal Cancer

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 Validation of a Predictive Scoring System for Colorectal Cancer Patients With Liver Metastasis: A Population-Based Study

Frontiers in Oncology ◽

10.3389/fonc.2021.719638 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yinghao Cao ◽

Songqing Ke ◽

Shenghe Deng ◽

Lizhao Yan ◽

Junnan Gu ◽

...

Keyword(s):

Colorectal Cancer ◽

Liver Metastasis ◽

Lymph Nodes ◽

Validation Cohort ◽

Characteristic Curve ◽

External Validation ◽

Tumor Location ◽

Cancer Specific Survival ◽

Internal Validation ◽

Training Cohort

Liver metastasis in colorectal cancer (CRC) is common and has an unfavorable prognosis. This study aimed to establish a functional nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer liver metastasis (CRCLM). A total of 9,736 patients with CRCLM from 2010 to 2016 were randomly assigned to training, internal validation, and external validation cohorts. Univariate and multivariate Cox analyses were performed to identify independent clinicopathologic predictive factors, and a nomogram was constructed to predict CSS and OS. Multivariate analysis demonstrated age, tumor location, differentiation, gender, TNM stage, chemotherapy, number of sampled lymph nodes, number of positive lymph nodes, tumor size, and metastatic surgery as independent predictors for CRCLM. A nomogram incorporating the 10 predictors was constructed. The nomogram showed favorable sensitivity at predicting 1-, 3-, and 5-year OS, with area under the receiver operating characteristic curve (AUROC) values of 0.816, 0.782, and 0.787 in the training cohort; 0.827, 0.769, and 0.774 in the internal validation cohort; and 0.819, 0.745, and 0.767 in the external validation cohort, respectively. For CSS, the values were 0.825, 0.771, and 0.772 in the training cohort; 0.828, 0.753, and 0.758 in the internal validation cohort; and 0.828, 0.737, and 0.772 in the external validation cohort, respectively. Calibration curves and ROC curves revealed that using our models to predict the OS and CSS would add more benefit than other single methods. In summary, the novel nomogram based on significant clinicopathological characteristics can be conveniently used to facilitate the postoperative individualized prediction of OS and CSS in CRCLM patients.

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Plasma extracellular vesicle long RNA profiling identifies a diagnostic signature for the detection of pancreatic ductal adenocarcinoma

Gut ◽

10.1136/gutjnl-2019-318860 ◽

2019 ◽

Vol 69 (3) ◽

pp. 540-550 ◽

Cited By ~ 13

Author(s):

Shulin Yu ◽

Yuchen Li ◽

Zhuan Liao ◽

Zheng Wang ◽

Zhen Wang ◽

...

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Validation Cohort ◽

Characteristic Curve ◽

External Validation ◽

Extracellular Vesicle ◽

Carbohydrate Antigen ◽

Superior Performance ◽

Ductal Adenocarcinoma ◽

Support Vector ◽

Internal Validation

ObjectivePancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose at resectable stage. Recent studies have suggested that extracellular vesicles (EVs) contain long RNAs. The aim of this study was to develop a diagnostic (d-)signature for the detection of PDAC based on EV long RNA (exLR) profiling.DesignWe conducted a case-control study with 501 participants, including 284 patients with PDAC, 100 patients with chronic pancreatitis (CP) and 117 healthy subjects. The exLR profile of plasma samples was analysed by exLR sequencing. The d-signature was identified using a support vector machine algorithm and a training cohort (n=188) and was validated using an internal validation cohort (n=135) and an external validation cohort (n=178).ResultsWe developed a d-signature that comprised eight exLRs, including FGA, KRT19, HIST1H2BK, ITIH2, MARCH2, CLDN1, MAL2 and TIMP1, for PDAC detection. The d-signature showed high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.960, 0.950 and 0.936 in the training, internal validation and external validation cohort, respectively. The d-signature was able to identify resectable stage I/II cancer with an AUC of 0.949 in the combined three cohorts. In addition, the d-signature showed superior performance to carbohydrate antigen 19-9 in distinguishing PDAC from CP (AUC 0.931 vs 0.873, p=0.028).ConclusionThis study is the first to characterise the plasma exLR profile in PDAC and to report an exLR signature for the detection of pancreatic cancer. This signature may improve the prognosis of patients who would have otherwise missed the curative treatment window.

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Radiomics Based on Contrast-Enhanced MRI in Differentiation Between Fat-Poor Angiomyolipoma and Hepatocellular Carcinoma in Noncirrhotic Liver: A Multicenter Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.744756 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiangtian Zhao ◽

Yukun Zhou ◽

Yuan Zhang ◽

Lujun Han ◽

Li Mao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Diagnostic Accuracy ◽

Sensitivity And Specificity ◽

Validation Cohort ◽

Characteristic Curve ◽

External Validation ◽

Arterial Phase ◽

Combined Model ◽

Internal Validation ◽

Contrast Enhanced

ObjectiveThis study aims to develop and externally validate a contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics-based model for preoperative differentiation between fat-poor angiomyolipoma (fp-AML) and hepatocellular carcinoma (HCC) in patients with noncirrhotic livers and to compare the diagnostic performance with that of two radiologists.MethodsThis retrospective study was performed with 165 patients with noncirrhotic livers from three medical centers. The dataset was divided into a training cohort (n = 99), a time-independent internal validation cohort (n = 24) from one center, and an external validation cohort (n = 42) from the remaining two centers. The volumes of interest were contoured on the arterial phase (AP) images and then registered to the venous phase (VP) and delayed phase (DP), and a total of 3,396 radiomics features were extracted from the three phases. After the joint mutual information maximization feature selection procedure, four radiomics logistic regression classifiers, including the AP model, VP model, DP model, and combined model, were built. The area under the receiver operating characteristic curve (AUC), diagnostic accuracy, sensitivity, and specificity of each radiomics model and those of two radiologists were evaluated and compared.ResultsThe AUCs of the combined model reached 0.789 (95%CI, 0.579–0.999) in the internal validation cohort and 0.730 (95%CI, 0.563–0.896) in the external validation cohort, higher than the AP model (AUCs, 0.711 and 0.638) and significantly higher than the VP model (AUCs, 0.594 and 0.610) and the DP model (AUCs, 0.547 and 0.538). The diagnostic accuracy, sensitivity, and specificity of the combined model were 0.708, 0.625, and 0.750 in the internal validation cohort and 0.619, 0.786, and 0.536 in the external validation cohort, respectively. The AUCs for the two radiologists were 0.656 and 0.594 in the internal validation cohort and 0.643 and 0.500 in the external validation cohort. The AUCs of the combined model surpassed those of the two radiologists and were significantly higher than that of the junior one in both validation cohorts.ConclusionsThe proposed radiomics model based on triple-phase CE-MRI images was proven to be useful for differentiating between fp-AML and HCC and yielded comparable or better performance than two radiologists in different centers, with different scanners and different scanning parameters.

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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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Application of Thromboelastography to Predict Lung Cancer Stage

Technology in Cancer Research & Treatment ◽

10.1177/1533033820952351 ◽

2020 ◽

Vol 19 ◽

pp. 153303382095235

Author(s):

Yaning Zhou ◽

Yijun Guo ◽

Qing Cui ◽

Yun Dong ◽

Xiaoyue Cai ◽

...

Keyword(s):

Lung Cancer ◽

Tnm Classification ◽

Characteristic Curve ◽

Area Under The Curve ◽

Maximum Amplitude ◽

Stage Iv ◽

Tnm Stage ◽

Stage I ◽

Stage Iii ◽

Α Angle

Objective: Lung cancer is often associated with hypercoagulability. Thromboelastography provides integrated information on clot formation in whole blood. This study explored the possible relationship between thromboelastography and lung cancer. Methods: Lung cancer was staged according to the Tumor, Node, and Metastasis (TNM) classification system. Thromboelastography parameters in different stages of disease were compared. The value of thromboelastography for stage prediction was determined by area under the receiver operating characteristic curve analysis. Results: A total of 182 patients diagnosed with lung cancer were included. Thromboelastography parameters, including kinetics time, α-angle, and maximum amplitude, differed significantly between patients with metastatic and limited lung cancers ( P < 0.05). Kinetics time was significantly reduced and maximum amplitude was significantly increased in patients with stage I and II compared with stage III and IV tumors ( P < 0.05). TNM stage was significantly negatively correlated with kinetics time ( r = −0.186), and significantly positively correlated with α-angle ( r = 0.151) and maximum amplitude ( r = 0.251) (both P < 0.05). The area under the curve for kinetics time in patients with stage I cancer was 0.637 ( P < 0.05) and that for α-angle in stage ≥ II was 0.623 ( P < 0.05). The areas under the curves for maximum amplitude in stage ≥ III and stage IV cancer were 0.650 and 0.605, respectively (both P < 0.05). Thromboelastography parameters were more closely associated with TNM stage in patients with lung adenocarcinoma than in the whole lung cancer population. Conclusion: This study identified the diagnostic value of thromboelastography parameters for determining tumor stage in patients with lung cancer. Thromboelastography can be used as an independent predictive parameter for lung cancer severity.

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1573. Population Pharmacokinetic Analyses for Cefepime in Adult and Pediatric Patients

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.1437 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S574-S575

Author(s):

Jiajun Liu ◽

Michael Neely ◽

Jeffrey Lipman ◽

Fekade B Sime ◽

Jason Roberts ◽

...

Keyword(s):

Rate Constant ◽

Pediatric Patients ◽

Validation Cohort ◽

External Validation ◽

Compartment Model ◽

Volume Of Distribution ◽

Information Criteria ◽

Population Pharmacokinetic ◽

Final Model ◽

Two Compartment Model

Abstract Background Cefepime (CEF) is commonly used for adult and pediatric infections. Several studies have examined CEF’s pharmacokinetics (PK) in various populations; however, a unifying PK model for adult and pediatric subjects does not yet exist. We developed a combined population model for adult and pediatric patients and validated the model. Methods The initial model includes adult and pediatric patients with a rich cefepime sampling design. All adults received 2 g CEF while pediatric subjects received a mean of 49 (SD 5) mg/kg. One- and two-compartment models were considered as base models and were fit using a non-parametric adaptive grid algorithm within the Pmetrics package 1.5.2 (Los Angeles, CA) for R 3.5.1. Compartmental model selection was based on Akaike information criteria (AIC). Covariate relationships with PK parameters were visually inspected and mathematically assessed. Predictive performance was evaluated using bias and imprecision of the population and individual prediction models. External validation was conducted using a separate adult cohort. Results A total of 45 subjects (n = 9 adults; n = 36 pediatrics) were included in the initial PK model build and 12 subjects in the external validation cohort. Overall, the data were best described using a two-compartment model with volume of distribution (V) normalized to total body weight (TBW/70 kg) and an allometric scaled elimination rate constant (Ke) for pediatric subjects (AIC = 4,138.36). Final model observed vs. predicted plots demonstrated good fit (population R2 = 0.87, individual R2 = 0.97, Figure 1a and b). For the final model, the population median parameter values (95% credibility interval) were V0 (total volume of distribution), 11.7 L (10.2–14.6); Ke for adult, 0.66 hour−1 (0.38–0.78), Ke for pediatrics, 0.82 hour−1 (0.64–0.85), KCP (rate constant from central to peripheral compartment), 1.4 hour−1 (1.3–1.8), KPC (rate constant from peripheral to central compartment), 1.6 hour−1 (1.2–1.8). The validation cohort has 12 subjects, and the final model fit the data well (individual R2 = 0.75). Conclusion In this diverse group of adult and pediatrics, a two-compartment model described CEF PK well and was externally validated with a unique cohort. This model can serve as a population prior for real-time PK software algorithms. Disclosures All authors: No reported disclosures.

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Log Odds of Positive Lymph Node- (LODDS-) Based Competing-Risk Nomogram for Predicting Prognosis of Resected Rectal Cancer: A Development and Validation Study

Gastroenterology Research and Practice ◽

10.1155/2020/9706732 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Rui-zhe Zheng ◽

Jiang Xie ◽

Shui-qiang Zhang ◽

Wen Li ◽

Bo Dong ◽

...

Keyword(s):

Rectal Cancer ◽

Death Rate ◽

Clinical Decision Making ◽

Validation Cohort ◽

Tnm Classification ◽

Characteristic Curve ◽

Tnm Stage ◽

Competing Risk ◽

Brier Score ◽

Log Odds

Background and Aims. Cancer-specific survival (CSS) of rectal cancer (RC) is associated with several factors. We aimed to build an efficient competing-risk nomogram based on log odds of positive lymph nodes (LODDS) to predict RC survival. Methods. Medical records of 8754 patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database, of 4895 patients from SEER during 2011–2014 and of 478 patients from an Eastern center as a development cohort, validation cohort, and test cohort, respectively. Univariate and multivariate competing-risk analyses were performed to build competing-risk nomogram for predicting the CSS of RC patients. Prediction efficacy was evaluated and compared with reference to the 8th TNM classification using the factor areas under the receiver operating characteristic curve (AUC) and Brier score. Results. The competing-risk nomogram was based on 6 variables: size, M stage, LODDS, T stage, grade, and age. The competing-risk nomogram showed a higher AUC value in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.81 vs. 0.76), validation cohort (0.85 vs. 0.82), and test cohort (0.71 vs. 0.66). The competing-risk nomogram also showed a higher Brier score in predicting the 5-year death rate due to RC than the 8th TNM stage in the development cohort (0.120 vs. 0.127), validation cohort (0.123 vs. 0.128), and test cohort (0.202 vs. 0.226). Conclusion. We developed and validated a competing-risk nomogram for RC death, which could provide the probability of survival averting competing risk to facilitate clinical decision-making.

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Nomograms to predict serious adverse events (SAEs) in patients (pts) enrolled in phase II clinical trials of molecularly targeted agents (MTAs)

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.6601 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 6601-6601

Author(s):

G. R. Pond ◽

L. L. Siu ◽

M. J. Moore ◽

A. M. Oza ◽

H. Hirte ◽

...

Keyword(s):

Clinical Trials ◽

Adverse Event ◽

Phase Ii ◽

Statistical Significance ◽

Characteristic Curve ◽

External Validation ◽

Charlson Score ◽

Serious Adverse Events ◽

Internal Validation ◽

Ecog Ps

6601 Background: The likelihood of experiencing a SAE in clinical trials with a MTA is of interest for clinicians discussing treatment options. Adverse event data from clinical trials in the Princess Margaret Hospital Phase II Consortium [PMH2C] database were analyzed to address this question. Methods: All pts in the PMH2C database treated at the phase II dose level with either a MTA alone or in combination regimens since 2001 were included. Generalised estimating equations were used to construct optimal regression models predicting the increased/decreased odds of a SAE of all causalities (defined as a grade 3+ non-hematologic adverse event, or a grade 4+ hematologic adverse event) during the first cycle of treatment relative to a ‘reference’ pt. Nomograms were constructed to ease interpretation and internal validation explored using bootstrapping on trials larger than 35 pts. Results: 576 pts (median age=60, 55% male, ECOG PS 0:1:2=259:284:35) were accrued to 42 studies. In order of statistical significance, higher ECOG PS, increased LDH, decreased albumin, increased Charlson score, increased number of target lesions, not having prior radiotherapy and decreased age were predictive of increased odds of cycle 1 SAE. As an example, a 56-year old patient with ECOG 2, Charlson score=0, 5 target lesions, LDH=1.70x upper limit of normal [ULN], albumin=0.84xULN and no prior radiation would have ∼3 times increased odds of a SAE in cycle 1, compared to a 63-year old with ECOG 1, Charlson score=0, 1 target lesion, LDH=0.76xULN, albumin=0.68xULN and no prior radiation. Internal validation of the 4 largest studies indicated moderate-good accuracy (estimated area under the receiver operating characteristic curve = 0.57–0.86). Conclusions: A nomogram was produced allowing estimation of the increased odds of a SAE during cycle 1 of therapy in a phase II trial setting. Actual risk can then be further estimated by incorporating clinical judgment of risks for an average pt when given a particular MTA. This nomogram can potentially improve patient knowledge, risk estimates and the decision-making process. External validation of the model is still necessary to adequately assess model reliability. No significant financial relationships to disclose.

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External validation of risk prediction models for incident colorectal cancer using UK Biobank

British Journal of Cancer ◽

10.1038/bjc.2017.463 ◽

2018 ◽

Vol 118 (5) ◽

pp. 750-759 ◽

Cited By ~ 15

Author(s):

J A Usher-Smith ◽

A Harshfield ◽

C L Saunders ◽

S J Sharp ◽

J Emery ◽

...

Keyword(s):

Colorectal Cancer ◽

Prediction Models ◽

Characteristic Curve ◽

External Validation ◽

Risk Scores ◽

Prior History ◽

Uk Biobank ◽

Risk Models ◽

Potential Health ◽

Men And Women

Abstract Background: This study aimed to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include variables routinely available or easily obtainable via self-completed questionnaire. Methods: External validation of fourteen risk models from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries. Results: There were 1719 (0.46%) cases of incident CRC. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver and Ma all had an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals. Conclusions: Several risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening.

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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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