Validation of the YEARS algorithm and Wells' score with the age-adjusted cut-off to exclude pulmonary embolism in COVID-19 patients.
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ABSTRACT:
SUBMITTER: Speksnijder EM
PROVIDER: S-EPMC9474969 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
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Validation of the YEARS algorithm and Wells' score with the age-adjusted cut-off to exclude pulmonary embolism in COVID-19 patients.
Speksnijder Esther M EM Hessels Lisa M LM Muusses Linda L Büller Harry R HR Boersma Wim G WG Simsek Suat S
Thrombosis research 20220915
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Project description:BackgroundAcute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.MethodsThis is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Naïve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis.ResultsThe ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726.ConclusionsBased on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.
| S-EPMC10950185 | biostudies-literature
Project description:BackgroundComputed tomography pulmonary angiography (CTPA) is frequently used in the emergency department (ED) for the diagnosis of pulmonary embolism (PE), while posing risk for contrast-induced nephropathy and radiation-induced malignancy.ObjectiveWe aimed to create an automated process to calculate the Wells score for pulmonary embolism for patients in the ED, which could potentially reduce unnecessary CTPA testing.MethodsWe designed an automated process using electronic health records data elements, including using a combinatorial keyword search method to query free-text fields, and calculated automated Wells scores for a sample of all adult ED encounters that resulted in a CTPA study for PE at 2 tertiary care hospitals in New York, over a 2-month period. To validate the automated process, the scores were compared to those derived from a 2-clinician chart review.ResultsA total of 202 ED encounters resulted in a completed CTPA to form the retrospective study cohort. Patients classified as "PE likely" by the automated process (126/202, 62%) had a PE prevalence of 15.9%, whereas those classified as "PE unlikely" (76/202, 38%; Wells score >4) had a PE prevalence of 7.9%. With respect to classification of the patient as "PE likely," the automated process achieved an accuracy of 92.1% when compared with the chart review, with sensitivity, specificity, positive predictive value, and negative predictive value of 93%, 90.5%, 94.4%, and 88.2%, respectively.ConclusionsThis was a successful development and validation of an automated process using electronic health records data elements, including free-text fields, to classify risk for PE in ED visits.
| S-EPMC8922138 | biostudies-literature
Project description:BackgroundPulmonary embolism (PE) is a common and potentially life-threatening condition. Since it is considered a 'do not miss' diagnosis, PE tends to be over-investigated beyond the evidence-based clinical decision support systems (CDSS), which in turn subjects patients to unnecessary radiation and contrast agent exposure with no apparent benefits in terms of outcome. The purpose of this study was to evaluate the yield of 'clinical hunch' (gestalt) and four CDSS: the PERC Rule, Wells score, revised Geneva score, and Years criteria.MethodsA review was conducted on the Electronic Medical Records (EMR) of 1566 patients from the Emergency Department at a tertiary teaching hospital who underwent CTPA from the 1st of January 2018 to the 31st of December 2019. The scores for the four CDSS were calculated retrospectively from the EMR data. We considered that a CTPA had been ordered on a clinical hunch when there was no mention of CDSS in the EMR, and no D-dimer test. A bypass of CDSS was confirmed when any step of the diagnostic algorithms was not followed.ResultsOf the total 1566 patients who underwent CTPA, 265 (17%) were positive for PE. The diagnosis yield from the five decision groups (clinical hunch and four CDSS) was as follows-clinical hunch, 15%; PERC rule, 18% (6% when bypassed); Wells score, 19% (11% when bypassed); revised Geneva score, 26% (13% when bypassed); and YEARS criteria, 18% (6% when bypassed).ConclusionClinicians should trust the evidence-based clinical decision support systems in line with the international guidelines to diagnose PE.
| S-EPMC9682736 | biostudies-literature
Project description:Subpleural consolidations have been found in lung ultrasound in patients with COVID-19, possibly deriving from pulmonary embolism (PE). The diagnostic utility of impact of lung ultrasound in critical-ill patients with COVID-19 for PE diagnostics however is unclear. We retrospectively evaluated all SARS-CoV2-associated ARDS patients admitted to our ICU between March 8th and May 31th 2020. They were enrolled in this study, when a lung ultrasound and a computed tomography pulmonary angiography (CTPA) were documented. In addition, wells score was calculated to estimate the probability of PE. The CTPA was used as the gold standard for the detection of PE. Twenty out of 25 patients met the inclusion criteria. In 12/20 patients (60%) (sub-) segmental PE were detected by CT-angiography. Lung ultrasound found subpleural consolidations in 90% of patients. PE-typical large supleural consolidations with a size ≥ 1 cm were detectable in 65% of patients and were significant more frequent in patients with PE compared to those without (p = 0.035). Large consolidations predicted PE with a sensitivity of 77% and a specificity of 71%. The Wells score was significantly higher in patients with PE compared to those without (2.7 ± 0.8 and 1.7 ± 0.5, respectively, p = 0.042) and predicted PE with an AUC of 0.81. When combining the two modalities, comparing patients with considered/probable PE using LUS plus a Wells score ≥ 2 to patients with possible/unlikely PE in LUS plus a Wells score < 2, PE could be predicted with a sensitivity of 100% and a specificity of 80%. Large consolidations detected in lung ultrasound were found frequently in COVID-19 ARDS patients with pulmonary embolism. In combination with a Wells score > 2, this might indicate a high-risk for PE in COVID-19.
| S-EPMC7608377 | biostudies-literature
Project description:Acute pulmonary embolism (PE) is a frequently diagnosed condition. Prediction of in-hospital deterioration is challenging with current risk models. The Calgary Acute Pulmonary Embolism (CAPE) score was recently derived to predict in-hospital adverse PE outcomes but has not yet been externally validated. Retrospective cohort study of normotensive acute pulmonary embolism cases diagnosed in our emergency department between 2017 and 2019. An external validation of the CAPE score was performed in this population for prediction of in-hospital adverse outcomes and a secondary outcome of 30-day all-cause mortality. Performance of the simplified Pulmonary Embolism Severity Index (sPESI) and Bova score was also evaluated. 712 patients met inclusion and exclusion criteria, with 536 patients having a sPESI score of 1 or more. Among this population, the CAPE score had a weak discriminative power to predict in-hospital adverse outcomes, with a calculated c-statistic of 0.57. In this study population, an external validation study found weak discriminative power of the CAPE score to predict in-hospital adverse outcomes among normotensive PE patients. Further efforts are needed to define risk assessment models that can identify normotensive PE patients at risk for in hospital deterioration. Identification of such patients will better guide intensive care utilization and invasive procedural management of PE.
| S-EPMC10641891 | biostudies-literature
Project description:Sequential diagnostic algorithms are used in the case of suspected pulmonary embolism (PE). The PEGeD study proposed a new diagnostic strategy to reduce the use of computed tomography pulmonary angiography (CTPA). We aimed to externally validate this diagnostic strategy in an independent cohort. We analyzed data from 3 prospective studies of outpatients with suspected PE. As per the PEGeD algorithm, patients were classified as having a low, moderate, or high clinical pretest probability (C-PTP). PE was excluded with a D-dimer <1000 ng/mL in case of low C-PTP and <500 ng/mL in case of moderate C-PTP. We assessed the yield and safety of this approach and compared them with those of previously validated algorithms. Among the 3308 evaluated patients, 1615 (49%) patients could have had PE excluded according to the PEGeD algorithm, without the need for imaging. Of these patients, 38 (2.3%; 95% confidence interval [CI], 1.7-3.2) were diagnosed with a symptomatic PE at initial testing or during the 3-month follow-up. On further analysis, 36 patients out of these 38 patients had a positive age-adjusted D-dimer. The risk of venous thromboembolic events among the 414 patients with a D-dimer <1000 ng/mL but above the age-adjusted D-dimer cut-off was 36 of 414 (8.7%; 95% CI, 6.4-11.8). We provide external validation of the PEGeD algorithm in an independent cohort. Compared with standard algorithms, the PEGeD decreased the number of CTPA examinations. However, caution is required in patients with a low C-PTP and a D-dimer <1000 ng/mL but above their age-adjusted D-dimer cut-off.
| S-EPMC10410134 | biostudies-literature
Project description:This systematic review and meta-analysis aimed to assess the diagnostic accuracy of the YEARS algorithm in excluding pulmonary embolism, as well as to compare the utilisation of advanced imaging modalities between the YEARS approach and standard clinical practice. Eligible studies were identified from multiple databases spanning July 2017 to September 2022, following the Joanna Briggs Institute methodology for diagnostic accuracy reviews. A total of 10 studies, involving approximately 14,000 participants, were included in the analysis. The YEARS algorithm demonstrated a sensitivity of 96% (95% confidence interval [CI]: 93-98%) and a specificity of 50% (95% CI: 33-67%). Additonally, the risk ratio for advanced imaging utilisation was 0.78 (95% CI: 67-90), indicating a significant reduction in imaging use. These findings suggest that the YEARS is an effective and safe strategy for managing patients with suspected pulmonary embolism.
| S-EPMC11614012 | biostudies-literature
Project description: Not available
| S-EPMC10912690 | biostudies-literature
Project description:ObjectiveAccording to the 2019 European Society of Cardiology (ESC) guidelines on pulmonary embolism (PE), prognosis is calculated using the Pulmonary Embolism Severity Index (PESI), a complex score with debated validity, or simplified PESI (sPESI). We have developed and validated a new risk score for in-hospital mortality (IHM) of patients with PE in the emergency department.MethodsThis retrospective, dual-center cohort study was conducted in the emergency departments of two third-level university hospitals. Patients aged >18 years with a contrast-enhanced computed tomography-confirmed PE were included. Clinical variables and laboratory tests were evaluated blindly to IHM. Multivariable logistic regression was performed to identify the new score's predictors, and the new score was compared with the PESI, sPESI, and shock index.ResultsA total of 1,358 patients were included in this study: 586 in the derivation cohort and 772 in the validation cohort, with a global 10.6% of IHM. The PATHOS scores were developed using independent variables to predict mortality: platelet count, age, troponin, heart rate, oxygenation, and systolic blood pressure. The PATHOS score showed good calibration and high discrimination, with an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [CI], 0.77-0.89) in the derivation population and 0.74 (95% CI, 0.68-0.80) in the validation cohort, which is significantly higher than the PESI, sPESI, and shock index in both cohorts (P<0.01 for all comparisons).ConclusionPATHOS is a simple and effective prognostic score for predicting IHM in patients with PE in an emergency setting.
| S-EPMC10090728 | biostudies-literature
Project description:BackgroundRecent studies demonstrate an improved prognostic performance of the 2014 European Society of Cardiology (ESC) algorithm for risk stratification of patients with pulmonary embolism (PE) compared to the 2008 ESC algorithm. The modified FAST and Bova scores appear especially helpful to identify PE patients at intermediate-high risk.MethodsWe validated the prognostic performance of the modified FAST score compared to other scores for risk stratification in a post-hoc analysis of 868 normotensive PE patients included in the prospective Italian Pulmonary Embolism Registry. In-hospital adverse outcome was defined as PE-related death, mechanical ventilation, cardiopulmonary resuscitation or administration of catecholamines.ResultsOverall, 27 patients (3.1%) had an adverse outcome and 32 patients (3.7%) died. The rate of an adverse outcome was highest in the intermediate-high risk classes of the 2019 ESC algorithm (7.5%) and the modified FAST score (5.3%) while the Bova score failed to discriminate between intermediate-low and intermediate-high-risk patients. Patients classified as intermediate-high risk by the 2019 ESC algorithm (Odds Ratio [OR], 4.2 [95% CI, 1.9-9.0]) and modified FAST score (OR, 2.8 [1.3-6.2]) had a higher risk of an adverse outcome compared to patients classified by the Bova score (OR, 1.6 [0.7-3.7]). The c-index was higher for the 2019 ESC algorithm and the modified FAST score (AUC, 0.69 [0.58-0.79] and 0.67 [0.59-0.76]) compared to the Bova score (AUC, 0.64 [0.55-0.73]).ConclusionsThe 2019 ESC algorithm provided the best prognostic performance, but also the modified FAST score accurately stratified normotensive PE patients in different risk classes while the Bova score failed to identify patients at highest risk.
| S-EPMC7376081 | biostudies-literature