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Study design and setting

This retrospective and validation study was executed across from 3 ED in Korea (A, B and C). A, B and C are tertiary hospitals located in a metropolitan city in Korea. Respectively, the hospital has approximately 2000, 1000, and 1000 inpatient beds. Approximately more than 80,000, 90,000 and 50,000 patients visit the ED annually. There are 16, 20 and 7 specialists working at each institution, respectively. All data were mapped to the Observational Medical Outcome Partnership Common Data Model (OMOP-CDM) for the multicenter study. This study was approved by the Samsung Medical Center Institutional Review Board (2023-02-036), and a waiver of informed consent was granted for EHR data collection and analysis because of the retrospective and de-identified nature of the data. All methods were performed in accordance with the relevant guidelines and regulations.

Initially, ED patients from 2016 to 2017 were included for each hospital. Patient older than 18 with disease patients were included. We also excluded patient with left without being seen or death on arrival/cardiopulmonary resuscitation patients. We split into two cohort: development (70%) cohort for training the interpretable ML model and test (30%) for evaluation from each hospital.

We extracted data from each hospitals electronic medical records system which all patient information was deidentified. Candidate input variables were considered with available features at the stage of ED triage including demographic characteristics such as age, gender, administrative variables including time of ED visit and clinical variables such as severity index, consciousness, and initial vital sign. Comorbidities were also obtained from hospital diagnosis records in the preceding 5years before patients emergency visit and compared for each hospital. They were extracted from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision(ICD-10). The list and description of candidate predictors and comorbidities are given in the supplementary Tables6 and 7.

Emergency patients with semi-acute conditions typically undergo surgical procedure or are admitted to Intensive care unit (ICU) following emergency room treatment and given the imperative for patients to survive. Our primary outcome was 2-day mortality which was the target feature for analysis to build the interpretable ML model for each hospital.

For the multicenter study, we adopted OMOP CDM from the research network Observational Health Data Sciences and Informatics (OHDSI)28 for standardized structure and vocabularies to map emergency department data based on Systematized Nomenclature of MedicineClinical Terms (SNOMED-CT) and Logical Observation Identifiers Names and Codes (LOINC) as example shown Supplementary Fig.1. Extract, Transformation and Load (ETL) process was performed with structured query language. Each ED care and diagnosis related information was mapped into proper CDM tables as shown in Fig.2. For example, patient demographics and vital sign are mapped to Person and Measurement table, respectively. After transformation was completed into CDM format, all hospital can get the same structure and vocabularies, for executing same research query. All details of transformation and code are accessible on Gitgub29.

Table mapping for converting clinical to common data model tables. CDM: common data model; ED: Emergency department.

AutoScore Framework is a machine learning-based clinical score generator, consisting of six modules developed from Singapore12. Module 1 uses a random forest for ranking variables according to their importance. Module 2 transforms variables by categorizing continuous variables to improve interpretation with quantile information. Module 3 makes scores for each variable based on a logistic regression coefficient. Module 4 selects which variables could be included in the scoring model. In Module 5, clinical domain knowledge is incorporated to the score and cutoff points can be defined when categorizing continuous variables. Module 6 evaluates the performance of the score in a separate test dataset. The AutoScore framework provides a systematic and automated approach to develop score automatically, combining of advantage of machine learning for discriminating and the strength of logistic regression in its interpretability. For the overall score generation, We considered weighted average scores across all institutions. For each institutionsi, a weight({w}_{i})was formulated as ({w}_{i})=(left(sqrt{{(AUC}_{i})} times {N}_{i}^{3}right))/({sum }_{i=1}^{M}sqrt{{(AUC}_{i})} times {N}_{i}^{3}))100%where({N}_{i})was the sample size,({AUC}_{i}) was the AUC value obtained based on the validation set, andMwas the total number of institutions. Overall score was calculated with weighted score based on ({w}_{i}).

We defined our new novel framework CDM Autoscore for ED, combination of CDM based standardized format and autoscore based interpretable framework shown in Fig.3. The analysis and preparation code using CDM format was also shared on GitHub29.

Overall process of CDM Autoscore for ED. Each Institutions conducted Extract, Transformation and Load process for converting local data into CDM format. Algorithms from each of institution were derived using interpretable machine learning framework and validated inter-and intra- institutionally. EMR: Electronic medical records; ETL: Extract, transformation and Load; OMOP CDM: Observational Medical Outcome Partnership Common Data Model.

Categorical features were expressed as frequency and percentages and continuous features were expressed as means and standard deviations. Comparison tests for each hospital were performed with analysis of variance and chi-square tests at 5% significance levels. Standardized mean difference (SMD) was also calculated for comparing each hospital. Two types of validations for this study were conducted. First, we executed internal-institutional validation for each hospitals score. We also performed intra-institutional validation pair-wisely for the external validation. Area under the curve in the receiver operating characteristic (AUROC) and 95% confidence interval (CI) with 1000 times of bootstrap was reported. Other metrics including accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were also reported. SMOTE was conducted for handling the imbalance problem. Twice of minority was oversampled and same number of majorities according to the number of minority was sampled with fixed seed number.

Read more:
Inter hospital external validation of interpretable machine learning based triage score for the emergency department ... - Nature.com

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