Dataset Information

Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage.

ABSTRACT:

Introduction

Postpartum hemorrhage remains a leading cause of maternal morbidity and mortality worldwide. Therefore, cumulative incidence of postpartum hemorrhage and severe postpartum hemorrhage are commonly monitored within and compared across maternity hospitals or countries for obstetrical safety improvement. These indicators are usually based on hospital discharge data though their accuracy is seldom assessed. We aimed to measure postpartum hemorrhage and severe postpartum hemorrhage using electronic health records and hospital discharge data separately and compare the detection accuracy of these methods to manual chart review, and to examine the temporal trends in cumulative incidence of these potentially avoidable adverse outcomes.

Materials and methods

We analyzed routinely collected data of 7904 singleton deliveries from a large Swiss university hospital for a three year period (2014-2016). We identified postpartum hemorrhage and severe postpartum hemorrhage in electronic health records by text mining discharge letters and operative reports and calculating drop in hemoglobin from laboratory tests. Diagnostic and procedure codes were used to identify cases in hospital discharge data. A sample of 334 charts was reviewed manually to provide a reference-standard and evaluate the accuracy of the other detection methods.

Results

Sensitivities of detection algorithms based on electronic health records and hospital discharge data were 95.2% (95% CI: 92.6% 97.8%) and 38.2% (33.3% to 43.0%), respectively for postpartum hemorrhage, and 87.5% (85.2% to 89.8%) and 36.2% (26.3% to 46.1%) for severe postpartum hemorrhage. Postpartum hemorrhage cumulative incidence based on electronic health records decreased from 15.6% (13.1% to 18.2%) to 8.5% (6.7% to 10.5%) from the beginning of 2014 to the end of 2016, with an average of 12.5% (11.8% to 13.3%). The cumulative incidence of severe postpartum hemorrhage remained at approximately 4% (3.5% to 4.4%). Hospital discharge data-based algorithms provided significantly underestimated incidences.

Conclusions

Hospital discharge data is not accurate enough to assess the incidence of postpartum hemorrhage at hospital or national level. Instead, automated algorithms based on structured and textual data from electronic health records should be considered, as they provide accurate and timely estimates for monitoring and improvement in obstetrical safety. Furthermore, they have the potential to better code for postpartum hemorrhage thus improving hospital reimbursement.

SUBMITTER: Walther D

PROVIDER: S-EPMC7857548 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage.

Walther Diana D Halfon Patricia P Tanzer Romain R Burnand Bernard B Robertson Moira M Vial Yvan Y Desseauve David D Le Pogam Marie-Annick MA

PloS one 20210203 2

<h4>Introduction</h4>Postpartum hemorrhage remains a leading cause of maternal morbidity and mortality worldwide. Therefore, cumulative incidence of postpartum hemorrhage and severe postpartum hemorrhage are commonly monitored within and compared across maternity hospitals or countries for obstetrical safety improvement. These indicators are usually based on hospital discharge data though their accuracy is seldom assessed. We aimed to measure postpartum hemorrhage and severe postpartum hemorrhag ...[more]

PMID: 33534862

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Project description:Background: Postpartum hemorrhage (PPH) is a leading cause of maternal morbidity and mortality worldwide but the incidence and its risk factors in China is limited. The objective of this study is to investigate the incidence and the risk factors of PPH in Chinese women. Methods: A multi-center retrospective study of pregnant women at ≥28 weeks of gestation was conducted. Logistic regression was used to identify potential risk factors of PPH and receiver operating characteristic curve was used to evaluate the predictive performance of the identified risk factors. Subgroup analysis focusing on the number of fetus and the mode of delivery was conducted. Results: A total of 99,253 pregnant women were enrolled and 804 (0.81%) experienced PPH. The subgroup analysis revealed that the incidence of PPH was 0.75, 2.65, 1.40, and 0.31% in singletons, twin pregnancies, cesarean sections, and vaginal deliveries, respectively. Placenta previa and placenta accreta were the predominant risk factors of PPH in the overall population and all subgroups. A twin pregnancy was a risk factor for PPH regardless of the mode of delivery. Obesity, and multiparity were risk factors for PPH in both singletons and cesarean section cases, but the latter predicted a reduced probability of PPH in vaginal deliveries. Macrosomia was associated with increased risk of PPH in singletons or vaginal deliveries. In women who delivered vaginally, preeclampsia was associated with a higher risk of PPH. The areas under the curve for the overall cohort, singletons, twin pregnancies, cesarean section cases, and vaginal deliveries were 0.832 (95% confidence interval [CI] 0.813-0.851), 0.824 (95% CI 0.803-0.845), 0.686 (95% CI 0.617-0.755), 0.854 (95% CI 0.834-0.874), and 0.690 (95% CI 0.646-0.735), respectively. Conclusions: The risk factors of PPH varied slightly based on the number of fetuses and the mode of delivery, while placenta previa and placenta accreta were the two major risk factors. A combination of the identified risk factors yielded a satisfactory predictive performance in determining PPH in the overall cohort, singletons pregnancies, and women who delivered by cesarean section, whereas the performance was moderate in twin pregnancies and in women delivering vaginally.

Project description:BACKGROUND: Administrative data are widely used to study health systems and make important health policy decisions. Yet little is known about the influence of coder characteristics on administrative data validity in these studies. Our goal was to describe the relationship between several measures of validity in coded hospital discharge data and 1) coders' volume of coding (> or = 13,000 vs. <13,000 records), 2) coders' employment status (full- vs. part-time), and 3) hospital type. METHODS: This descriptive study examined 6 indicators of face validity in ICD-10 coded discharge records from 4 hospitals in Calgary, Canada between April 2002 and March 2007. Specifically, mean number of coded diagnoses, procedures, complications, Z-codes, and codes ending in 8 or 9 were compared by coding volume and employment status, as well as hospital type. The mean number of diagnoses was also compared across coder characteristics for 6 major conditions of varying complexity. Next, kappa statistics were computed to assess agreement between discharge data and linked chart data reabstracted by nursing chart reviewers. Kappas were compared across coder characteristics. RESULTS: 422,618 discharge records were coded by 59 coders during the study period. The mean number of diagnoses per record decreased from 5.2 in 2002/2003 to 3.9 in 2006/2007, while the number of records coded annually increased from 69,613 to 102,842. Coders at the tertiary hospital coded the most diagnoses (5.0 compared with 3.9 and 3.8 at other sites). There was no variation by coder or site characteristics for any other face validity indicator. The mean number of diagnoses increased from 1.5 to 7.9 with increasing complexity of the major diagnosis, but did not vary with coder characteristics. Agreement (kappa) between coded data and chart review did not show any consistent pattern with respect to coder characteristics. CONCLUSIONS: This large study suggests that coder characteristics do not influence the validity of hospital discharge data. Other jurisdictions might benefit from implementing similar employment programs to ours, e.g.: a requirement for a 2-year college training program, a single management structure across sites, and rotation of coders between sites. Limitations include few coder characteristics available for study due to privacy concerns.

Project description:BackgroundNeonatal morbidity is associated with lifelong impairments, but the absence of a consensual definition and the need for large data sets limit research.ObjectivesTo inform initiatives to define standard outcomes for research, we reviewed composite neonatal morbidity indicators derived from routine hospital discharge data.Data sourcesPubMed (updated on October 12, 2018). The search algorithm was based on three components: "morbidity," "neonatal," and "hospital discharge data."Study selection and data extractionStudies investigating neonatal morbidity using a composite indicator based on hospital discharge data were included. Indicators defined for specific conditions (eg congenital anomalies, maternal addictions) were excluded. The target population, objectives, component morbidities, diagnosis and procedure codes, validation methods, and prevalence of morbidity were extracted.SynthesisFor each study, we assessed construct validity by describing the methods used to select the indicator components and evaluated whether the authors assessed internal and external validity. We also calculated confidence intervals for the prevalence of the morbidity composite.ResultsSeventeen studies fulfilled inclusion criteria. Indicators targeted all (n = 4), low-/moderate-risk (n = 9), and very preterm (VPT, n = 4) infants. Components were similar for VPT infants, but domains and diagnosis codes within domains varied widely for all and low-/moderate-risk infants. Component selection was described for 8/17 indicators and some form of validation reported for 12/17. Neonatal morbidity prevalence ranged from 4.6% to 9.0% of all infants, 0.4% to 8.0% of low-/moderate-risk infants, and 17.8% to 61.0% of VPT infants.ConclusionsMultiple neonatal morbidity indicators based on hospital discharge data have been used for research, but their heterogeneity limits comparisons between studies. Standard neonatal outcome measures are needed for benchmarking and synthesis of research results.

Project description:BackgroundPostpartum hemorrhage (PPH) is the leading cause of direct maternal mortality globally and in Suriname. We aimed to study the prevalence, risk indicators, causes, and management of PPH to identify opportunities for PPH reduction.MethodsA nationwide retrospective descriptive study of all hospital deliveries in Suriname in 2017 was performed. Logistic regression analysis was applied to identify risk indicators for PPH (≥ 500ml blood loss). Management of severe PPH (blood loss ≥1,000ml or ≥500ml with hypotension or at least three transfusions) was evaluated via a criteria-based audit using the national guideline.ResultsIn 2017, the prevalence of PPH and severe PPH in Suriname was 9.2% (n = 808/8,747) and 2.5% (n = 220/8,747), respectively. PPH varied from 5.8% to 15.8% across the hospitals. Risk indicators associated with severe PPH included being of African descent (Maroon aOR 2.1[95%CI 1.3-3.3], Creole aOR 1.8[95%CI 1.1-3.0]), multiple pregnancy (aOR 3.4[95%CI 1.7-7.1]), delivery in Hospital D (aOR 2.4[95%CI 1.7-3.4]), cesarean section (aOR 3.9[95%CI 2.9-5.3]), stillbirth (aOR 6.4 [95%CI 3.4-12.2]), preterm birth (aOR 2.1[95%CI 1.3-3.2]), and macrosomia (aOR 2.8 [95%CI 1.5-5.0]). Uterine atony (56.7%, n = 102/180[missing 40]) and retained placenta (19.4%, n = 35/180[missing 40]), were the main causes of severe PPH. A criteria-based audit revealed that women with severe PPH received prophylactic oxytocin in 61.3% (n = 95/155[missing 65]), oxytocin treatment in 68.8% (n = 106/154[missing 66]), and tranexamic acid in 4.9% (n = 5/103[missing 117]).ConclusionsPPH prevalence and risk indicators in Suriname were similar to international and regional reports. Inconsistent blood loss measurement, varied maternal and perinatal characteristics, and variable guideline adherence contributed to interhospital prevalence variation. PPH reduction in Suriname can be achieved through prevention by practicing active management of the third stage of labor in every birth and considering risk factors, early recognition by objective and consistent blood loss measurement, and prompt treatment by adequate administration of oxytocin and tranexamic acid according to national guidelines.

Dataset Information

Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage.

Introduction

Materials and methods

Results

Conclusions

Publications

Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage.

Similar Datasets

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