Predictive models in emergency medicine and their missing data strategies: a systematic review

Injury severity bias in missing prehospital vital signs: Prevalence and implications for trauma registries

BACKGROUND

Vital signs are important factors in assessing injury severity and guiding trauma resuscitation, especially among severely injured patients. Despite this, physiological data are frequently missing from trauma registries. This study aimed to evaluate the extent of missing prehospital data in a hospital-based trauma registry and to assess the associations between prehospital physiological data completeness and indicators of injury severity.

METHODS

A retrospective review was conducted on all adult trauma patients brought directly to a level 1 trauma center in Toronto, Ontario by paramedics from January 1, 2015, to December 31, 2019. The proportion of missing data was evaluated for each variable and patterns of missingness were assessed. To investigate the associations between prehospital data completeness and injury severity factors, descriptive and unadjusted logistic regression analyses were performed.

RESULTS

A total of 3,528 patients were included. We considered prehospital data missing if any of heart rate, systolic blood pressure, respiratory rate or oxygen saturation were incomplete. Each individual variable was missing from the registry in approximately 20 % of patients, with oxygen saturation missing most frequently (n = 831; 23.6 %). Over 25 % (n = 909) of patients were missing at least one prehospital vital sign, of which 69.1 % (n = 628) were missing all four of these variables. Patients with incomplete data were more severely injured, had higher mortality, and more frequently received lifesaving interventions such as blood transfusion and intubation. Patients were most likely to have missing prehospital physiological data if they died in the trauma bay (unadjusted OR: 9.79; 95 % CI: 6.35-15.10), did not survive to discharge (unadjusted OR: 3.55; 95 % CI: 2.76-4.55), or had a prehospital GCS less than 9 (OR: 3.24; 95 % CI: 2.59-4.06).

CONCLUSION

In this single center trauma registry, key prehospital variables were frequently missing, particularly among more severely injured patients. Patients with missing data had higher mortality, more severe injury characteristics and received more life-saving interventions in the trauma bay, suggesting an injury severity bias in prehospital vital sign missingness. To ensure the validity of research based on trauma registry data, patterns of missingness must be carefully considered to ensure missing data is appropriately addressed.

Quality assessment of radiomics models in carotid plaque: a systematic review

Background

Although imaging techniques provide information about the morphology and stability of carotid plaque, they are operator dependent and may miss certain subtleties. A variety of radiomics models for carotid plaque have recently been proposed for identifying vulnerable plaques and predicting cardiovascular and cerebrovascular diseases. The purpose of this review was to assess the risk of bias, reporting, and methodological quality of radiomics models for carotid atherosclerosis plaques.

Methods

A systematic search was carried out to identify available literature published in PubMed, Web of Science, and the Cochrane Library up to March 2023. Studies that developed and/or validated machine learning models based on radiomics data to identify and/or predict unfavorable cerebral and cardiovascular events in carotid plaque were included. The basic information of each piece of included literature was identified, and the reporting quality, risk of bias, and radiomics methodology quality were assessed according the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) checklist, the Prediction Model Risk of Bias Assessment Tool (PROBAST), and the radiomics quality score (RQS), respectively.

Results

A total of 2,738 patients from 19 studies were included. The mean overall TRIPOD adherence rate was 66.1% (standard deviation 12.8%), with a range of 45-87%. All studies had a high overall risk of bias, with the analysis domain being the most common source of bias. The mean RQS was 9.89 (standard deviation 5.70), accounting for 27.4% of the possible maximum value of 36. The mean area under the curve for diagnostic or predictive properties of these included radiomics models was 0.876±0.09, with a range of 0.741-0.989.

Conclusions

Radiomics models may have value in the assessment of carotid plaque, the overall scientific validity and reporting quality of current carotid plaque radiomics reports are still lacking, and many barriers must be overcome before these models can be applied in clinical practice.