ICD-10-CM extension with ICD-9 diagnosis codes to support integrated access to clinical legacy data

Heart-Brain 346-7 Score: the development and validation of a simple mortality prediction score for carbon monoxide poisoning utilizing deep learning

INTRODUCTION

Acute mortality from carbon monoxide poisoning is 1-3%. The long-term mortality risk of survivors of carbon monoxide poisoning is doubled compared to age-matched controls. Cardiac involvement also increases mortality risk. We built a clinical risk score to identify carbon monoxide-poisoned patients at risk for acute and long-term mortality.

METHODS

We performed a retrospective analysis. We identified 811 adult carbon monoxide-poisoned patients in the derivation cohort, and 462 adult patients in the validation cohort. We utilized baseline demographics, laboratory values, hospital charge transactions, discharge disposition, and clinical charting information in the electronic medical record in Stepwise Akaike's Information Criteria with Firth logistic regression to determine optimal parameters to create a prediction model.

RESULTS

In the derivation cohort, 5% had inpatient or 1-year mortality. Three variables following the final Firth logistic regression minimized Stepwise Akaike's Information Criteria: altered mental status, age, and cardiac complications. The following predict inpatient or 1-year mortality: age > 67, age > 37 with cardiac complications, age > 47 with altered mental status, or any age with cardiac complications and altered mental status. The sensitivity of the score was 82% (95% confidence interval: 65-92%), the specificity was 80% (95% confidence interval: 77-83%), negative predictive value was 99% (95% confidence interval: 98-100%), positive predictive value 17% (95% confidence interval: 12-23%), and the area under the receiver operating characteristic curve was 0.81 (95% confidence interval: 0.74-0.87). A score above the cut-off point of -2.9 was associated with an odds ratio of 18 (95% confidence interval: 8-40). In the validation cohort (462 patients), 4% had inpatient death or 1-year mortality. The score performed similarly in the validation cohort: sensitivity was 72% (95% confidence interval: 47-90%), specificity was 69% (95% confidence interval: 63-73%), negative predictive value was 98% (95% confidence interval: 96-99%), positive predictive value was 9% (95% confidence interval: 5-15%) and the area under the receiver operating characteristic curve was 0.70 (95% confidence interval: 60%-81%).

CONCLUSIONS

We developed and validated a simple, clinical-based scoring system, the Heart-Brain 346-7 Score to predict inpatient and long-term mortality based on the following: age > 67, age > 37 with cardiac complications, age > 47 with altered mental status, or any age with cardiac complications and altered mental status. With further validation, this score will hopefully aid decision-making to identify carbon monoxide-poisoned patients with higher mortality risk.

Association of atrial fibrillation burden with in-hospital outcomes in patients with Takotsubo cardiomyopathy

BACKGROUND

The effects of atrial fibrillation (AF) and its burden on in-hospital mortality in patients with Takotsubo cardiomyopathy (TCM) are unclear. Here, we examined the effect of AF and paroxysmal AF on in-hospital outcomes in patients with TCM.

METHODS

We used ICD-10 codes to retrospectively identify patients with a primary diagnosis of TCM in the National Inpatient Sample database 2016-2018. We compared in-hospital outcomes in TCM patients with and without AF before and after propensity score matching. The effect of AF burden on outcomes was assessed in patients with paroxysmal AF and no AF.

RESULTS

Of the 4,733 patients with a primary diagnosis of TCM, 650 (13.7%) had AF, and 4,083 (86.3%) did not. Of TCM patients with AF, 368 (56.6%) had paroxysmal AF. In-hospital mortality was higher in patients with AF before (3.4% vs 1.2%, P <  0.001) and after propensity matching (3.4% vs 1.7%, P = 0.021) but did not differ between the paroxysmal AF and the no AF groups (P = 0.205). In the matched cohorts, both AF and paroxysmal AF groups were associated with a higher rate of cardiogenic shock (AF, P < 0.001; paroxysmal AF, P < 0.001), ventricular arrhythmia (AF, P = 0.002; paroxysmal AF, P = 0.02), acute kidney injury (AF, P = 0.007; paroxysmal AF, P = 0.008), and acute respiratory failure (AF, P < 0.001; paroxysmal AF, P < 0.001) compared with the no AF group.

CONCLUSIONS

Although AF was associated with increased in-hospital mortality, paroxysmal AF did not affect in-hospital mortality, suggesting a higher AF burden is associated with worse clinical outcome in patients with TCM.

Curation and expansion of Human Phenotype Ontology for defined groups of inborn errors of immunity

BACKGROUND

Accurate, detailed, and standardized phenotypic descriptions are essential to support diagnostic interpretation of genetic variants and to discover new diseases. The Human Phenotype Ontology (HPO), extensively used in rare disease research, provides a rich collection of vocabulary with standardized phenotypic descriptions in a hierarchical structure. However, to date, the use of HPO has not yet been widely implemented in the field of inborn errors of immunity (IEIs), mainly due to a lack of comprehensive IEI-related terms.

OBJECTIVES

We sought to systematically review available terms in HPO for the depiction of IEIs, to expand HPO, yielding more comprehensive sets of terms, and to reannotate IEIs with HPO terms to provide accurate, standardized phenotypic descriptions.

METHODS

We initiated a collaboration involving expert clinicians, geneticists, researchers working on IEIs, and bioinformaticians. Multiple branches of the HPO tree were restructured and extended on the basis of expert review. Our ontology-guided machine learning coupled with a 2-tier expert review was applied to reannotate defined subgroups of IEIs.

RESULTS

We revised and expanded 4 main branches of the HPO tree. Here, we reannotated 73 diseases from 4 International Union of Immunological Societies-defined IEI disease subgroups with HPO terms. We achieved a 4.7-fold increase in the number of phenotypic terms per disease. Given the new HPO annotations, we demonstrated improved ability to computationally match selected IEI cases to their known diagnosis, and improved phenotype-driven disease classification.

CONCLUSIONS

Our targeted expansion and reannotation presents enhanced precision of disease annotation, will enable superior HPO-based IEI characterization, and hence benefit both IEI diagnostic and research activities.

The epidemiologic characteristics and associated risk factors of preterm birth from 2004 to 2013 in Taiwan

Background

The rate of preterm birth has been increasing worldwide. Most preterm babies are at an increased risk of central nervous system impairments as well as respiratory and gastrointestinal complications. The aim of this study was to investigate the epidemiologic characteristics of and associated factors contributing to preterm birth in Taiwan.

Methods

Information on obstetric antecedents and risk factors for preterm birth in pregnant women was obtained from the National Health Insurance Research (NHIR) database provided by the Taiwan National Health Research Institute. All live births from 2004 to 2013 in Taiwan were included in this study.

Results

A total of 130,362 live births from 2004 to 2013 were included in this study. Overall, the average annual rate of preterm births increased by 5.3% (from 3.33% in 2004 to 5.11% in 2013). Multiple logistic regression analyses showed that nulliparous women, multifetal pregnancies, advanced mother age, history of preterm birth, history of maternal drug abuse/dependence, and maternal medical complications were positively associated with an increased risk of preterm birth (all p-values< 0.05).

Conclusion

The overall proportion of preterm births increased from 2004 to 2013 in Taiwan. Babies born preterm had a higher risk of developing morbidities and mortalities. The development of a comprehensive program to identify the high-risk group is needed for effective interventions to prevent premature birth.