A risk stratification model to predict adverse neonatal outcome in labor

The Golden Hours of Fetal Heart Rate Monitoring: Systematic Approach to the Critical Times of Labor and Delivery

The first hour after admission and the last hour before delivery are critical times for identifying and preventing hypoxic-ischemic encephalopathy. These are times of transition that require coordinated steps to identify fetuses at risk, institute effective plans for fetal heart rate monitoring, and to establish situational awareness. Interpretation and intervention based on fetal heart rate monitoring is an important part of the care provided during these crucial times. We present checklists for the first and last hour of labor for use on labor and delivery to help standardize and optimize the approach to care during these times.

Interpretation of Fetal Heart Rate Monitoring in the Clinical Context

Use of intrapartum fetal heart rate (FHR) monitoring has had limited success in preventing hypoxic injury to neonates. One of the most common limitations of FHR interpretation is the failure to consider chronic and acute clinical factors that may increase the risk of evolving acidemia. This manuscript reviews common clinical factors that may affect the FHR and should be considered when determining the need for early intervention based on changes in the FHR.

Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study

OBJECTIVE

To create a personalised machine learning model for prediction of severe adverse neonatal outcomes (SANO) during the second stage of labour.

DESIGN

Retrospective Electronic-Medical-Record (EMR) -based study.

POPULATION

A cohort of 73 868 singleton, term deliveries that reached the second stage of labour, including 1346 (1.8%) deliveries with SANO.

METHODS

A gradient boosting model was created, analysing 21 million data points from antepartum features (e.g. gravidity and parity) gathered at admission to the delivery unit, and intrapartum data (e.g. cervical dilatation and effacement) gathered during the first stage of labour. Deliveries were allocated to high-risk and low-risk groups based on the Youden index to maximise sensitivity and specificity.

MAIN OUTCOME MEASURES

SANO was defined as either umbilical cord pH levels ≤7.1 or 1-minute or 5-minute Apgar score ≤7.

RESULTS

The model for prediction of SANO yielded an area under the receiver operating curve (AUC) of 0.761 (95% CI 0.748-0.774). A third of the cohort (33.5%, n = 24 721) were allocated to a high-risk group for SANO, which captured up to 72.1% of these cases (odds ratio 5.3, 95% CI 4.7-6.0; high-risk versus low-risk groups).

CONCLUSIONS

Data acquired throughout the first stage of labour can be used to predict SANO during the second stage of labour using a machine learning model. Stratifying parturients at the beginning of the second stage of labour in a 'time out' session, can direct a personalised approach to management of this challenging aspect of labour, as well as improve allocation of staff and resources.

TWEETABLE ABSTRACT

Personalised prediction score for severe adverse neonatal outcomes in labour using machine learning model.

Perinatal mortality or severe neonatal encephalopathy among normally formed singleton pregnancies according to obstetric risk status:" is low risk the new high risk?" A population-based cohort study

OBJECTIVE

To evaluate the capacity of the current system of obstetric risk stratification at the outset of pregnancy to predict severe adverse perinatal outcome.

STUDY DESIGN

This retrospective cohort study of singleton pregnancies over a five year period (2009-2013) was performed at the Rotunda Hospital, Dublin, Ireland. High-risk or low-risk status was assigned retrospectively to a large consecutive cohort of women with a normally-formed singleton pregnancy on the basis of factors analyzed at the first prenatal hospital visit. The incidence of severe perinatal morbidity and mortality were compared between high- and low-risk groups to determine the predictive utility of risk stratification at the outset of pregnancy for severe perinatal morbidity.

RESULTS

During the study period, 41,044 patients registered for prenatal care. 25,702;(63%) were deemed low-risk and 15,342;(37%) high-risk. Low-risk women were statistically more likely to be nulliparous (p < 0.0001) and to have a spontaneous or operative vaginal delivery (p < 0.0001). High-risk women were more likely to be multiparous and to undergo Caesarean delivery (p < 0.0001). The perinatal mortality rate was 3.8 per-1000 in low-risk pregnancies and 6.1 per-1000 in the a priori high-risk group (p = 0.012). The incidence of severe neonatal encephalopathy (NNE) was 1.8 and 0.65 per-1000 in the low and high-risk groups respectively (p = 0.0025).

CONCLUSION

Where low-risk status is assigned at registration, neonatal encephalopathy is more prevalent. This data is relevant for the design of prenatal care models and demonstrates that assignment of low obstetric risk on the basis of maternal or pre-pregnancy factors alone may erroneously be interpreted as conferring low-risk status to the fetus.

Early inpatient calculation of laboratory-based 30-day readmission risk scores empowers clinical risk modification during index hospitalization

Improving 30-day readmission continues to be problematic for most hospitals. This study reports the creation and validation of sex-specific inpatient (i) heart failure (HF) risk scores using electronic data from the beginning of inpatient care for effective and efficient prediction of 30-day readmission risk.

METHODS

HF patients hospitalized at Intermountain Healthcare from 2005 to 2012 (derivation: n=6079; validation: n=2663) and Baylor Scott & White Health (North Region) from 2005 to 2013 (validation: n=5162) were studied. Sex-specific iHF scores were derived to predict post-hospitalization 30-day readmission using common HF laboratory measures and age. Risk scores adding social, morbidity, and treatment factors were also evaluated.

RESULTS

The iHF model for females utilized potassium, bicarbonate, blood urea nitrogen, red blood cell count, white blood cell count, and mean corpuscular hemoglobin concentration; for males, components were B-type natriuretic peptide, sodium, creatinine, hematocrit, red cell distribution width, and mean platelet volume. Among females, odds ratios (OR) were OR=1.99 for iHF tertile 3 vs. 1 (95% confidence interval [CI]=1.28, 3.08) for Intermountain validation (P-trend across tertiles=0.002) and OR=1.29 (CI=1.01, 1.66) for Baylor patients (P-trend=0.049). Among males, iHF had OR=1.95 (CI=1.33, 2.85) for tertile 3 vs. 1 in Intermountain (P-trend <0.001) and OR=2.03 (CI=1.52, 2.71) in Baylor (P-trend < 0.001). Expanded models using 182-183 variables had predictive abilities similar to iHF.

CONCLUSIONS

Sex-specific laboratory-based electronic health record-delivered iHF risk scores effectively predicted 30-day readmission among HF patients. Efficient to calculate and deliver to clinicians, recent clinical implementation of iHF scores suggest they are useful and useable for more precise clinical HF treatment.