Trends in severe maternal morbidity following an institutional team goal strategy for disparity reduction.
Racial disparities in maternal pregnancy outcomes, specifically in morbidity and mortality, are persistent in the U.S., and a multifaceted approach to mitigating these disparate outcomes is critical. In 2020, our health system committed to reducing severe maternal morbidity (SMM) in Black patients, employing multiple strategic interventions including implicit bias training, regular reporting of a composite SMM metric stratified by race and ethnicity, standardization of best practices, focused efforts for hemorrhage risk reduction, and system-wide team building.
The goal of this study is to investigate trends in SMM by race across this period of concentrated interventions to improve maternal outcomes overall, and specifically for Black patients.
This is a retrospective cohort study evaluating all delivery admissions at an academic, urban, tertiary-care hospital in Philadelphia-one site of a health system encompassing five delivery hospitals-over a 3-year period from 2019 to 2021. Data including patient demographics, clinical features, and outcomes were extracted from the electronic medical record (EMR). Self-reported race was categorized as Black vs non-Black as documented in the EMR. SMM was defined according to established CDC indicators as well as additional codes identified by Vizient for common sources of SMM including hemorrhage, infection, and embolism. Data were analyzed by year with a multivariable logistic regression model including insurance type and obstetric comorbidity index (OB-CMI), a weighted scoring system accounting for numerous chronic medical conditions and antepartum pregnancy complications.
In total, 12,339 deliveries were included, 64.6% (N=8012) of which were to Black patients. Median OB-CMI score was higher for Black patients at 3 (interquartile range [IQR] 1-5) compared to 2 (IQR 1-4) for non-Black patients, P<.01. There was a significant decrease in SMM for the entire cohort over the study period (8.5% in 2019 to 6.5% in 2021, P=.001), driven by a decreased rate specifically among Black patients (8.9% in 2019 to 6.6% in 2021, P=.005) with a nonsignificant decrease for non-Black patients (7.8% in 2019 to 6.3% in 2021, P=.21). The adjusted model similarly demonstrated decreased risk of SMM over time for Black patients (2020 vs 2019 adjusted odds ratio [aOR] = 0.81, 95% confidence interval [CI] 0.69-0.96; 2021 vs 2019 aOR 0.73, 95% CI 0.62-0.86).
Dedicated efforts to improve equity in maternal outcomes over a 2-year period (2020-2021) in this hospital serving a Black patient majority were associated with a significant decline in SMM, especially among Black patients. This finding demonstrates the success of a high-level, coordinated, and systematic approach in reducing SMM and associated disparities, and is highly consequential in light of the ongoing major epidemic of racial disparities in obstetric outcomes.
Kern-Goldberger AR
，Hirshberg A
，James A
，Levine LD
，Howell E
，Harbuck E
，Srinivas SK
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Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.
Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided.
(1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS?
Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses.
Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS.
Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments.
Level III, diagnostic study.
Lee CC
，Chen CW
，Yen HK
，Lin YP
，Lai CY
，Wang JL
，Groot OQ
，Janssen SJ
，Schwab JH
，Hsu FM
，Lin WH
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Hospital-level variation in racial disparities in low-risk nulliparous cesarean delivery rates.
Nationally, rates of cesarean delivery are highest among Black patients compared with other racial/ethnic groups. These observed inequities are a relatively new phenomenon (in the 1980s, cesarean delivery rates among Black patients were lower than average), indicating an opportunity to narrow the gap. Cesarean delivery rates vary greatly among hospitals, masking racial disparities that are unseen when rates are reported in aggregate.
This study aimed to explore reasons for the current large Black-White disparity in first-birth cesarean delivery rates by first examining the hospital-level variation in first-birth cesarean delivery rates among different racial/ethnic groups. We then identified hospitals that had low first-birth cesarean delivery rates among Black patients and compared them with hospitals with high rates. We sought to identify differences in facility or patient characteristics that could explain the racial disparity.
A population cross-sectional study was performed on 1,267,493 California live births from 2018 through 2020 using birth certificate data linked with maternal patient discharge records. Annual nulliparous term singleton vertex cesarean delivery (first-birth) rates were calculated for the most common racial/ethnic groups statewide and for each hospital. Self-identified race/ethnicity categories as selected on the birth certificate were used. Relative risk and 95% confidence intervals for first-birth cesarean delivery comparing 2019 with 2015 were estimated using a log-binomial model for each racial/ethnic group. Patient and hospital characteristics were compared between hospitals with first-birth cesarean delivery rates <23.9% for Black patients and hospitals with rates ≥23.9% for Black patients.
Hospitals with at least 30 nulliparous term singleton vertex Asian, Black, Hispanic, and White patients each were identified. Black patients had a very different distribution, with a significantly higher rate (28.4%) and wider standard deviation (7.1) and interquartile range (6.5) than other racial groups (P<.01). A total of 29 hospitals with a low first-birth cesarean delivery rate among Black patients were identified using the Healthy People 2020 target of 23.9% and compared with 106 hospitals with higher rates. The low-rate group had a cesarean delivery rate of 19.9%, as opposed to 30.7% in the higher-rate group. There were no significant differences between the groups in hospital characteristics (ownership, delivery volume, neonatal level of care, proportion of midwife deliveries) or patient characteristics (age, education, insurance, onset of prenatal care, body mass index, hypertension, diabetes mellitus). Among the 106 hospitals that did not meet the target for Black patients, 63 met it for White patients with a mean rate of 21.4%. In the same hospitals, the mean rate for Black patients was 29.5%. Among Black patients in the group that did not meet the 23.9% target, there were significantly higher rates of all cesarean delivery indications: labor dystocia, fetal concern (spontaneous labor), and no labor (eg, macrosomia), which are all indications with a high degree of subjectivity.
The statewide cesarean delivery rate of Black patients is significantly higher and has substantially greater hospital variation compared with other racial or ethnic groups. The lack of difference in facility or patient characteristics between hospitals with low cesarean delivery rates among Black patients and those with high rates suggests that unconscious bias and structural racism potentially play important roles in creating these racial differences.
Main EK
，Chang SC
，Tucker CM
，Sakowski C
，Leonard SA
，Rosenstein MG
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ResearchGate
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钛学术
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