Dataset Information

PERFORM: Pulmonary embolism risk score for mortality in computed tomographic pulmonary angiography-confirmed patients.

ABSTRACT:

Background

Current prognostic scores for pulmonary embolism (PE) were partly based on patients without PE confirmation via computed tomographic pulmonary angiography (CTPA), involving subjective parameters and complicated scoring methods. Therefore, we sought to develop an objective, accurate, and simple prognostic model in CTPA-confirmed patients to predict the risk of 30-day mortality.

Methods

We retrospectively evaluated 509 patients with objectively confirmed PE by CTPA from 2010 to 2017 in the Minhang Hospital, which is affiliated to Fudan University. Patients were randomly divided into the training and validation cohorts. The primary end point was 30-day mortality. The secondary end points were the time to recovery in 30 days and mortality in 15 days. We compared the predictive performance of Pulmonary Embolism Severity Index (PESI), simplified PESI (sPESI), and the PE risk score we developed, called PERFORM.

Findings

PERFORM (ranging from 0 to 12 score) is based on the patient's age, heart rate, and partial pressure of arterial oxygen. The area under the curve was 0.718 (95% confidence interval [CI], 0.627-0.809) for the training cohort and 0.906 (95% CI, 0.846-0.966) for the validation cohort. PERFORM was as good as PESI and sPESI in predicting mortality. Patients in the low-risk group (PERFORM score < 5) had a shorter time to recovery, whereas those in the high-risk group (PERFORM score ≥ 5) had a high mortality.

Interpretation

PERFORM in CTPA-confirmed patients is an objective, accurate, and simple tool to predict the risk of 30-day mortality.

Funding

Research Project of Shanghai Municipal Commission of Health and Family Planning (201740127), Shanghai Medical Key Subject Construction Project (ZK2019B08).

SUBMITTER: Yu S

PROVIDER: S-EPMC8181188 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportancePulmonary embolism (PE) is a life-threatening clinical problem, and computed tomographic imaging is the standard for diagnosis. Clinical decision support rules based on PE risk-scoring models have been developed to compute pretest probability but are underused and tend to underperform in practice, leading to persistent overuse of CT imaging for PE.ObjectiveTo develop a machine learning model to generate a patient-specific risk score for PE by analyzing longitudinal clinical data as clinical decision support for patients referred for CT imaging for PE.Design, setting, and participantsIn this diagnostic study, the proposed workflow for the machine learning model, the Pulmonary Embolism Result Forecast Model (PERFORM), transforms raw electronic medical record (EMR) data into temporal feature vectors and develops a decision analytical model targeted toward adult patients referred for CT imaging for PE. The model was tested on holdout patient EMR data from 2 large, academic medical practices. A total of 3397 annotated CT imaging examinations for PE from 3214 unique patients seen at Stanford University hospitals and clinics were used for training and validation. The models were externally validated on 240 unique patients seen at Duke University Medical Center. The comparison with clinical scoring systems was done on randomly selected 100 outpatient samples from Stanford University hospitals and clinics and 101 outpatient samples from Duke University Medical Center.Main outcomes and measuresPrediction performance of diagnosing acute PE was evaluated using ElasticNet, artificial neural networks, and other machine learning approaches on holdout data sets from both institutions, and performance of models was measured by area under the receiver operating characteristic curve (AUROC).ResultsOf the 3214 patients included in the study, 1704 (53.0%) were women from Stanford University hospitals and clinics; mean (SD) age was 60.53 (19.43) years. The 240 patients from Duke University Medical Center used for validation included 132 women (55.0%); mean (SD) age was 70.2 (14.2) years. In the samples for clinical scoring system comparisons, the 100 outpatients from Stanford University hospitals and clinics included 67 women (67.0%); mean (SD) age was 57.74 (19.87) years, and the 101 patients from Duke University Medical Center included 59 women (58.4%); mean (SD) age was 73.06 (15.3) years. The best-performing model achieved an AUROC performance of predicting a positive PE study of 0.90 (95% CI, 0.87-0.91) on intrainstitutional holdout data with an AUROC of 0.71 (95% CI, 0.69-0.72) on an external data set from Duke University Medical Center; superior AUROC performance and cross-institutional generalization of the model of 0.81 (95% CI, 0.77-0.87) and 0.81 (95% CI, 0.73-0.82), respectively, were noted on holdout outpatient populations from both intrainstitutional and extrainstitutional data.Conclusions and relevanceThe machine learning model, PERFORM, may consider multitudes of applicable patient-specific risk factors and dependencies to arrive at a PE risk prediction that generalizes to new population distributions. This approach might be used as an automated clinical decision-support tool for patients referred for CT PE imaging to improve CT use.