Dataset Information

Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and Current Procedural Terminology set embeddings.

ABSTRACT:

Background

Preoperative knowledge of surgical risks can improve perioperative care and patient outcomes. However, assessments requiring clinician examination of patients or manual chart review can be too burdensome for routine use.

Methods

We conducted a multicentre retrospective study of 243 479 adult noncardiac surgical patients at four hospitals within the Mass General Brigham (MGB) system in the USA. We developed a machine learning method using routinely collected coding and patient characteristics data from the electronic health record which predicts 30-day mortality, 30-day readmission, discharge to long-term care, and hospital length of stay.

Results

Our method, the Flexible Surgical Set Embedding (FLEX) score, achieved state-of-the-art performance to identify comorbidities that significantly contribute to the risk of each adverse outcome. The contributions of comorbidities are weighted based on patient-specific context, yielding personalised risk predictions. Understanding the significant drivers of risk of adverse outcomes for each patient can inform clinicians of potential targets for intervention.

Conclusions

FLEX utilises information from a wider range of medical diagnostic and procedural codes than previously possible and can adapt to different coding practices to accurately predict adverse postoperative outcomes.

SUBMITTER: Liu R

PROVIDER: S-EPMC10870132 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and Current Procedural Terminology set embeddings.

Liu Ran R Stone Tom A D TAD Raje Praachi P Mather Rory V RV Santa Cruz Mercado Laura A LA Bharadwaj Kishore K Johnson Jasmine J Higuchi Masaya M Nipp Ryan D RD Kunitake Hiroko H Purdon Patrick L PL

British journal of anaesthesia 20240106 3

<h4>Background</h4>Preoperative knowledge of surgical risks can improve perioperative care and patient outcomes. However, assessments requiring clinician examination of patients or manual chart review can be too burdensome for routine use.<h4>Methods</h4>We conducted a multicentre retrospective study of 243 479 adult noncardiac surgical patients at four hospitals within the Mass General Brigham (MGB) system in the USA. We developed a machine learning method using routinely collected coding and p ...[more]

PMID: 38184474

Similar Datasets

Project description:IntroductionUnlike antibiotic and perfusion support, guidelines for sepsis source control lack high-quality evidence and are ungraded. Internally valid administrative data methods are needed to identify cases representing source control procedures to evaluate outcomes.MethodsOver five modified Delphi rounds, two independent reviewers identified Current Procedural Terminology (CPT) codes pertinent to source control. In each round, codes with perfect agreement were retained or excluded, whereas disagreements were reviewed by the panelists. Manual review of 400 patient records meeting Sepsis-3 criteria (2010-2017) clinically adjudicated which encounters included source control procedures (gold standard). The performance of consensus codes was compared with the gold standard to assess sensitivity, specificity, predictive values, and likelihood ratios.ResultsOf 5752 CPT codes, 609 consensus codes represented source control procedures. Of 400 hospitalizations for sepsis, 39 (9.8%; 95% confidence interval [CI] 7.0%-13.1%) underwent gold standard source control procedures and 29 (7.3%; 95% CI 4.9-10.3%) consensus code-defined source control procedures. Thirty consensus codes were identified (20.0% gastrointestinal/intraabdominal, 10.0% genitourinary, 13.3% hepatopancreatobiliary, 23.3% orthopedic/cranial, 23.3% soft tissue, and 10.0% intrathoracic), which had 61.5% (95% CI 44.6%-76.6%) sensitivity, 98.6% (95% CI 96.8%-99.6%) specificity, 83.2% (95% CI 66.6%-92.4%) positive, and 95.9% (95% CI 93.9%-97.2%) negative predictive values. With pretest probability at sample prevalence, an identified consensus code had a posttest probability of 83.0% (95% CI 66.0%-92.0%), whereas consensus code absence had a probability of 4.0% (95% CI 3.0-6.0) for undergoing a source control procedure.ConclusionsUsing modified Delphi methodology, we created and validated CPT codes identifying source control procedures, providing a framework for evaluation of the surgical care of patients with sepsis.

Project description:BACKGROUND:Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language processing, offer opportunities to develop classification tools for Current Procedural Terminology codes across anesthesia procedures. METHODS:Models were created using a Train/Test dataset including 1,164,343 procedures from 16 academic and private hospitals. Five supervised machine learning models were created to classify anesthesiology Current Procedural Terminology codes, with accuracy defined as first choice classification matching the institutional-assigned code existing in the perioperative database. The two best performing models were further refined and tested on a Holdout dataset from a single institution distinct from Train/Test. A tunable confidence parameter was created to identify cases for which models were highly accurate, with the goal of at least 95% accuracy, above the reported 2018 Centers for Medicare and Medicaid Services (Baltimore, Maryland) fee-for-service accuracy. Actual submitted claim data from billing specialists were used as a reference standard. RESULTS:Support vector machine and neural network label-embedding attentive models were the best performing models, respectively, demonstrating overall accuracies of 87.9% and 84.2% (single best code), and 96.8% and 94.0% (within top three). Classification accuracy was 96.4% in 47.0% of cases using support vector machine and 94.4% in 62.2% of cases using label-embedding attentive model within the Train/Test dataset. In the Holdout dataset, respective classification accuracies were 93.1% in 58.0% of cases and 95.0% among 62.0%. The most important feature in model training was procedure text. CONCLUSIONS:Through application of machine learning and natural language processing techniques, highly accurate real-time models were created for anesthesiology Current Procedural Terminology code classification. The increased processing speed and a priori targeted accuracy of this classification approach may provide performance optimization and cost reduction for quality improvement, research, and reimbursement tasks reliant on anesthesiology procedure codes.

Project description:BackgroundAdministrative costs for billing and insurance-related activities in the United States are substantial. One critical cause of the high overhead of administrative costs is medical billing errors. With advanced deep learning techniques, developing advanced models to predict hospital and professional billing codes has become feasible. These models can be used for administrative cost reduction and billing process improvements.ObjectiveIn this study, we aim to develop an automated anesthesiology current procedural terminology (CPT) prediction system that translates manually entered surgical procedure text into standard forms using neural machine translation (NMT) techniques. The standard forms are calculated using similarity scores to predict the most appropriate CPT codes. Although this system aims to enhance medical billing coding accuracy to reduce administrative costs, we compare its performance with that of previously developed machine learning algorithms.MethodsWe collected and analyzed all operative procedures performed at Michigan Medicine between January 2017 and June 2019 (2.5 years). The first 2 years of data were used to train and validate the existing models and compare the results from the NMT-based model. Data from 2019 (6-month follow-up period) were then used to measure the accuracy of the CPT code prediction. Three experimental settings were designed with different data types to evaluate the models. Experiment 1 used the surgical procedure text entered manually in the electronic health record. Experiment 2 used preprocessing of the procedure text. Experiment 3 used preprocessing of the combined procedure text and preoperative diagnoses. The NMT-based model was compared with the support vector machine (SVM) and long short-term memory (LSTM) models.ResultsThe NMT model yielded the highest top-1 accuracy in experiments 1 and 2 at 81.64% and 81.71% compared with the SVM model (81.19% and 81.27%, respectively) and the LSTM model (80.96% and 81.07%, respectively). The SVM model yielded the highest top-1 accuracy of 84.30% in experiment 3, followed by the LSTM model (83.70%) and the NMT model (82.80%). In experiment 3, the addition of preoperative diagnoses showed 3.7%, 3.2%, and 1.3% increases in the SVM, LSTM, and NMT models in top-1 accuracy over those in experiment 2, respectively. For top-3 accuracy, the SVM, LSTM, and NMT models achieved 95.64%, 95.72%, and 95.60% for experiment 1, 95.75%, 95.67%, and 95.69% for experiment 2, and 95.88%, 95.93%, and 95.06% for experiment 3, respectively.ConclusionsThis study demonstrates the feasibility of creating an automated anesthesiology CPT classification system based on NMT techniques using surgical procedure text and preoperative diagnosis. Our results show that the performance of the NMT-based CPT prediction system is equivalent to that of the SVM and LSTM prediction models. Importantly, we found that including preoperative diagnoses improved the accuracy of using the procedure text alone.

Project description:BackgroundPathology reports serve as an auditable trial of a patient's clinical narrative, containing text pertaining to diagnosis, prognosis, and specimen processing. Recent works have utilized natural language processing (NLP) pipelines, which include rule-based or machine-learning analytics, to uncover textual patterns that inform clinical endpoints and biomarker information. Although deep learning methods have come to the forefront of NLP, there have been limited comparisons with the performance of other machine-learning methods in extracting key insights for the prediction of medical procedure information, which is used to inform reimbursement for pathology departments. In addition, the utility of combining and ranking information from multiple report subfields as compared with exclusively using the diagnostic field for the prediction of Current Procedural Terminology (CPT) codes and signing pathologists remains unclear.MethodsAfter preprocessing pathology reports, we utilized advanced topic modeling to identify topics that characterize a cohort of 93,039 pathology reports at the Dartmouth-Hitchcock Department of Pathology and Laboratory Medicine (DPLM). We separately compared XGBoost, SVM, and BERT (Bidirectional Encoder Representation from Transformers) methodologies for the prediction of primary CPT codes (CPT 88302, 88304, 88305, 88307, 88309) as well as 38 ancillary CPT codes, using both the diagnostic text alone and text from all subfields. We performed similar analyses for characterizing text from a group of the 20 pathologists with the most pathology report sign-outs. Finally, we uncovered important report subcomponents by using model explanation techniques.ResultsWe identified 20 topics that pertained to diagnostic and procedural information. Operating on diagnostic text alone, BERT outperformed XGBoost for the prediction of primary CPT codes. When utilizing all report subfields, XGBoost outperformed BERT for the prediction of primary CPT codes. Utilizing additional subfields of the pathology report increased prediction accuracy across ancillary CPT codes, and performance gains for using additional report subfields were high for the XGBoost model for primary CPT codes. Misclassifications of CPT codes were between codes of a similar complexity, and misclassifications between pathologists were subspecialty related.ConclusionsOur approach generated CPT code predictions with an accuracy that was higher than previously reported. Although diagnostic text is an important source of information, additional insights may be extracted from other report subfields. Although BERT approaches performed comparably to the XGBoost approaches, they may lend valuable information to pipelines that combine image, text, and -omics information. Future resource-saving opportunities exist to help hospitals detect mis-billing, standardize report text, and estimate productivity metrics that pertain to pathologist compensation (RVUs).

Dataset Information

Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and Current Procedural Terminology set embeddings.

Background

Methods

Results

Conclusions

Publications

Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and Current Procedural Terminology set embeddings.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets