The impact of prepregnancy body mass index on pregnancy and neonatal outcomes.

CONTEXT: The obesity epidemic in the United States is continuing to worsen. Obesity is a known risk factor for pregnancy morbidity. However, many studies use the patient's body mass index (BMI) at the time of delivery, do not stratify by class of obesity, or utilize billing codes as the basis of their study, which are noted to be inaccurate. OBJECTIVES: This study aims to investigate the prepregnancy BMI class specific risks for pregnancy and neonatal complications based on a prepregnancy BMI class. METHODS: We conducted a retrospective cohort study of 40,256 pregnant women with 55,202 singleton births between October 16, 2007 and December 3, 2023. We assessed the risk of pregnancy and neonatal morbidity based on the maternal prepregnancy BMI category. The primary outcome was composite maternal morbidity, including hypertensive disorders of pregnancy (i.e., gestational hypertension [GHTN] and preeclampsia), and gestational diabetes mellitus (GDM), adjusted for pregestational diabetes mellitus and chronic hypertension (cHTN). Secondary maternal outcomes included preterm premature rupture of membranes (PPROM), preterm delivery (PTD<37 and <32 weeks), induction of labor (IOL), cesarean delivery (CD), and postpartum hemorrhage (PPH). Neonatal outcomes included a composite adverse outcome (including stillbirth, intraventricular hemorrhage (IVH), hypoglycemia, respiratory distress syndrome [RDS], APGAR [Appearance, Pulse, Grimace, Activity, and Respiration] <7 at 5 min, and neonatal intensive care unit [NICU] admission), birthweight, fetal growth restriction (FGR), and macrosomia. RESULTS: Composite maternal morbidity (odds ratio [OR] 4.40, confidence interval [CI] 3.70-5.22 for class III obesity [BMI≥40.0 kg/m2] compared with normal BMI), hypertensive disorders of pregnancy (HDP), GDM, PTD, IOL, CD, PPH, neonatal composite morbidity, hypoglycemia, RDS, APGAR<7 at 5 min, NICU admission, and macrosomia showed a significant increasing test of trend among BMI classes. Increased BMI was protective for FGR. CONCLUSIONS: Our data provides BMI-class specific odds ratios (ORs) for adverse pregnancy outcomes. Increased BMI class significantly increases the risk of HDP, GDM, IOL, CD, composite adverse neonatal outcomes, and macrosomia, and decreases the risk of FGR. Attaining a healthier BMI category prior to conception may lower pregnancy morbidity.

Genetics Visit Uptake Among Individuals Receiving Clinically Actionable Genomic Screening Results.

Importance: Screening unselected populations for clinically actionable genetic disease risk can improve ascertainment and facilitate risk management. Genetics visits may encourage at-risk individuals to perform recommended management, but little has been reported on genetics visit completion or factors associated with completion in genomic screening programs. Objective: To identify factors associated with postdisclosure genetics visits in a genomic screening cohort. Design, Setting, and Participants: This was a cohort study of biobank data in a health care system in central Pennsylvania. Participants' exome sequence data were reviewed for pathogenic or likely pathogenic (P/LP) results in all genes on the American College of Medical Genetics and Genomics Secondary Findings list. Clinically confirmed results were disclosed by phone and letter. Participants included adult MyCode biobank participants who received P/LP results between July 2015 and November 2019. Data were analyzed from May 2021 to March 2022. Exposure: Clinically confirmed P/LP result disclosed by phone or letter. Main Outcomes and Measures: Completion of genetics visit in which the result was discussed and variables associated with completion were assessed by electronic health record (EHR) review. Results: Among a total of 1160 participants (703 [60.6%] female; median [IQR] age, 57.0 [42.1-68.5] years), fewer than half of participants (551 of 1160 [47.5%]) completed a genetics visit. Younger age (odds ratio [OR] for age 18-40 years, 2.98; 95% CI, 1.40-6.53; OR for age 41-65 years, 2.36; 95% CI, 1.22-4.74; OR for age 66-80 years, 2.60; 95% CI, 1.41-4.98 vs age ≥81 years); female sex (OR, 1.49; 95% CI, 1.14-1.96); being married (OR, 1.74; 95% CI, 1.23-2.47) or divorced (OR, 1.80; 95% CI, 1.11-2.91); lower Charlson comorbidity index (OR for score of 0-2, 1.76; 95% CI, 1.16-2.68; OR for score of 3-4, 1.73; 95% CI, 1.18-2.54 vs score of ≥5); EHR patient portal use (OR, 1.42; 95% CI, 1.06-1.89); living closer to a genetics clinic (OR, 1.64; 95% CI, 1.14-2.36 for <8.9 miles vs >20.1 miles); successful results disclosure (OR for disclosure by genetic counselor, 16.32; 95% CI, 8.16-37.45; OR for disclosure by research assistant, 20.30; 95% CI, 10.25-46.31 vs unsuccessful phone disclosure); and having a hereditary cancer result (OR, 2.13; 95% CI, 1.28-3.58 vs other disease risk) were significantly associated with higher rates of genetics visit completion. Preference to follow up with primary care was the most common reported reason for declining a genetics visit (68 of 152 patients [44.7%]). Conclusions and Relevance: This cohort study of a biobank-based population genomic screening program suggests that targeted patient engagement, improving multidisciplinary coordination, and reducing barriers to follow-up care may be necessary for enhancing genetics visit uptake.

Comparison of three antepartum risk assessment tools to predict significant postpartum hemorrhage in livebirths.

BACKGROUND: To adequately predict significant postpartum hemorrhage (PPH) at hospital admission, we evaluated and compared the accuracy of three risk assessment tools: 1. California Maternal Quality Care Collaborative (CMQCC), 2. American College of Obstetrics and Gynecology Safe Motherhood Initiative (ACOG SMI) and 3. Association of Women's Health, Obstetric and Neonatal Nurses (AWHONN). STUDY DESIGN AND METHODS: This is a retrospective cohort study of people who delivered liveborn infants from January 2018 to June 2021 at our center. Patients with comorbidities necessitating higher hemoglobin values, those who refused blood transfusions, and missing pertinent data were excluded. Significant PPH was defined as a blood transfusion within 48 hours following delivery. Diagnostic statistics were calculated for each tool. RESULTS: Of the 11,679 included pregnancies, 232 (1.9%) people had significant PPH. Amongst those diagnosed as high-risk by the CMQCC tool, 67/1485 (4.5%) had significant PPH; 62/1672 (3.7%) by the ACOG SMI tool, and 85/1864 (4.6%) by the AWHONN tool had significant PPH. All tools have low sensitivity and high negative predictive values. The area under the receiver operating characteristics curve of the three tools is moderately poor (CMQCC: 0.58, ACOG SMI: 0.55, AWHONN:0.61). DISCUSSION: Upon admission to labor and delivery, all three studied tools are poor predictors of significant PPH. The development and validation of better PPH risk stratification tools are required with the inclusion of additional important variables.

Retrospective application of algorithms to improve identification of pregnancy outcomes from the electronic health record.

OBJECTIVE: To improve upon the accuracy of ICD codes for identifying maternal and neonatal outcomes by developing algorithms that incorporate readily available EHR data. STUDY DESIGN: Algorithms were developed for gestational hypertension (GHTN), pre-eclampsia (PreE), gestational diabetes mellitus (GDM) and were compared to ICD codes and chart review. Accuracy and sensitivity analyses were calculated with their respective 95% confidence limits for each of the comparisons between algorithms, ICD codes alone, and chart review. RESULTS: Sensitivity of GHTN ICD codes was 8.1% vs. 83.8% for the algorithm when compared to chart review. In comparison to chart review, sensitivity of ICD codes for PreE was 7.5% vs. 71.4% for the algorithm. GDM had similar sensitivity rates for both ICD codes and the algorithm. CONCLUSION: Application of algorithms, validated by chart review, enhanced capture of several outcomes. Algorithms should be obligatory adjunct tools to the ICD codes for identification of outcomes of interest.