Short interpregnancy intervals and adverse pregnancy outcomes by maternal age in the United States

Purpose: The purpose of the article was to examine the association between short interpregnancy intervals and adverse outcomes by maternal age among U.S. women.Methods: Using publicly available natality files for 2013-2016 singleton births, we compared the risks of preterm birth, gestational diabetes, gestational hypertension, and maternal morbidity (delivery-related complications) for less than 6-month, 6 to 11-month, and 12 to 17-month to 18- to 23-month interpregnancy intervals, overall and by maternal age. Models adjusted for maternal demographics, conditions, and behaviors.Results: Among 2,365,219 births, adjusted risk ratios (aRR) for preterm birth overall for intervals less than 6, 6-11, and 12-17 months were 1.62 (95% confidence interval: 1.60, 1.65), 1.16 (1.15, 1.18), and 1.03 (1.02, 1.05), respectively, compared with 18-23 months. Intervals less than 6, 6-11, and 12-17 months were significantly protective overall for gestational diabetes (aRR range: 0.89-0.98), gestational hypertension (aRR range: 0.93-0.95), and maternal morbidity (aRR range: 0.93-1.08). All aRRs attenuated or remained flat with increasing maternal age.Conclusion: Interpregnancy intervals less than 18 months showed different patterns of association for preterm birth compared with maternal outcomes, overall and across age. This suggests that increasing maternal age may have discordant effects on associations between short interpregnancy interval and adverse perinatal and maternal outcomes

Supervised Text Classification System Detects Fontan Patients in Electronic Records With Higher Accuracy Than ICD Codes

Background The Fontan operation is associated with significant morbidity and premature mortality. Fontan cases cannot always be identified by International Classification of Diseases (ICD) codes, making it challenging to create large Fontan patient cohorts. We sought to develop natural language processing–based machine learning models to automatically detect Fontan cases from free texts in electronic health records, and compare their performances with ICD code–based classification. Methods and Results We included free‐text notes of 10 935 manually validated patients, 778 (7.1%) Fontan and 10 157 (92.9%) non‐Fontan, from 2 health care systems. Using 80% of the patient data, we trained and optimized multiple machine learning models, support vector machines and 2 versions of RoBERTa (a robustly optimized transformer‐based model for language understanding), for automatically identifying Fontan cases based on notes. For RoBERTa, we implemented a novel sliding window strategy to overcome its length limit. We evaluated the machine learning models and ICD code–based classification on 20% of the held‐out patient data using the F1 score metric. The ICD classification model, support vector machine, and RoBERTa achieved F1 scores of 0.81 (95% CI, 0.79–0.83), 0.95 (95% CI, 0.92–0.97), and 0.89 (95% CI, 0.88–0.85) for the positive (Fontan) class, respectively. Support vector machines obtained the best performance (P<0.05), and both natural language processing models outperformed ICD code–based classification (P<0.05). The sliding window strategy improved performance over the base model (P<0.05) but did not outperform support vector machines. ICD code–based classification produced more false positives. Conclusions Natural language processing models can automatically detect Fontan patients based on clinical notes with higher accuracy than ICD codes, and the former demonstrated the possibility of further improvement

How Well Do ICD‐9‐CM Codes Predict True Congenital Heart Defects? A Centers for Disease Control and Prevention‐Based Multisite Validation Project

Background The Centers for Disease Control and Prevention's Surveillance of Congenital Heart Defects Across the Lifespan project uses large clinical and administrative databases at sites throughout the United States to understand population‐based congenital heart defect (CHD) epidemiology and outcomes. These individual databases are also relied upon for accurate coding of CHD to estimate population prevalence. Methods and Results This validation project assessed a sample of 774 cases from 4 surveillance sites to determine the positive predictive value (PPV) for identifying a true CHD case and classifying CHD anatomic group accurately based on 57 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes. Chi‐square tests assessed differences in PPV by CHD severity and age. Overall, PPV was 76.36% (591/774 [95% CI, 73.20–79.31]) for all sites and all CHD‐related ICD‐9‐CM codes. Of patients with a code for complex CHD, 89.85% (177/197 [95% CI, 84.76–93.69]) had CHD; corresponding PPV estimates were 86.73% (170/196 [95% CI, 81.17–91.15]) for shunt, 82.99% (161/194 [95% CI, 76.95–87.99]) for valve, and 44.39% (83/187 [95% CI, 84.76–93.69]) for “Other” CHD anatomic group (X2=142.16, P64 years of age, (X2=4.23, P<0.0001). Conclusions While CHD ICD‐9‐CM codes had acceptable PPV (86.54%) (508/587 [95% CI, 83.51–89.20]) for identifying whether a patient has CHD when excluding patients with ICD‐9‐CM codes for “Other” CHD and code 745.5, further evaluation and algorithm development may help inform and improve accurate identification of CHD in data sets across the CHD ICD‐9‐CM code groups