dxpr: an R package for generating analysis-ready data from electronic health records-diagnoses and procedures

lab: an R package for generating analysis-ready data from laboratory records

Background

Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses.

Methods

To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies.

Results

Using the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series-analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area under the receiver operating characteristic curve of 0.83 for predicting 30-day in-hospital mortality in model training. These findings demonstrate the lab package's effectiveness in analyzing disease progression.

Conclusions

The proposed lab package simplifies and expedites the workflow involved in laboratory records extraction. This tool is particularly valuable in assisting clinical data analysts in overcoming the obstacles associated with heterogeneous and sparse laboratory records.

Clinical characteristics and outcomes of mixed virus or bacterial infection in children with laboratory-confirmed influenza infection

BACKGROUND/PURPOSE

This study investigated the demographic characteristics and influenza complications of paediatric patients and explored the association of different influenza virus types and viral and bacterial coinfections with disease severity.

METHODS

This retrospective cohort study used data collected in 2010-2016 from the Chang Gung Research Database (CGRD), the largest collection of multi-institutional electronic medical records in Taiwan. Data were retrieved for children aged 0-18 years with laboratory-confirmed influenza. We extracted and analysed the demographic characteristics and the data on clinical features, complications, microbiological information, and advanced therapies of each case.

RESULTS

We identified 6193 children with laboratory-confirmed influenza, of whom 1964 (31.7%) were hospitalised. The age of patients with influenza A infection was lower than that of patients with influenza B (4.48 vs. 6.68, p < 0.001). Patients with influenza B infection had a higher incidence of myositis or rhabdomyolysis (4.4%, p < 0.001) and a higher need for advanced therapies (OR, 1.96; 95% CI, 1.32-2.9, p < 0.001). In addition to bacterial (OR, 9.07; 95% CI, 5.29-15.54, p < 0.001) and viral coinfection (OR, 7.73; 95% CI, 5.4-11.07, p < 0.001), dual influenza A and B infection was also a risk factor for influenza complications (OR, 2.13; 95% CI, 1.47-3.09, p < 0.001).

CONCLUSION

Dual influenza A and B infection and bacterial coinfection can contribute to influenza complications. Early recognition of any influenza complication is critical for the timely initiation of organ-specific advanced therapies to improve influenza-associated outcomes.