Evaluating the use of the QUiPP app and its impact on the management of threatened preterm labour: A cluster randomised trial

Clinical decision support systems for maternity care: a systematic review and meta-analysis

Background

The use of Clinical Decision Support Systems (CDSS) is increasing throughout healthcare and may be able to improve safety and outcomes in maternity care, but maternity care has key differences to other disciplines that complicate the use of CDSS. We aimed to identify evaluated CDSS and synthesise evidence of their impact on maternity care.

Methods

We conducted a systematic review for articles published before 24th May 2024 that described i) CDSS that ii) investigated the impact of their use iii) in maternity settings. Medline, CINAHL, CENTRAL and HMIC were searched for articles relating to evaluations of CDSS in maternity settings, with forward- and backward-citation tracing conducted for included articles. Risk of bias was assessed using the Mixed Methods Assessment Tool, and CDSS were described according to the clinical problem, purpose, design, and technical environment. Quantitative results from articles reporting appropriate data were meta-analysed to estimate odds of a CDSS achieving its desired outcome using a multi-level random effects model, first by individual CDSS and then across all CDSS. PROSPERO ID: CRD42022348157.

Findings

We screened 12,039 papers and included 87 articles describing 47 unique CDSS. 24 articles (28%) described randomised controlled trials, 30 (34%) described non-randomised interventional studies, 10 (11%) described mixed methods studies, 10 (11%) described qualitative studies, 7 (8%) described quantitative descriptive studies, and 7 (8%) described economic evaluations. 49 (56%) were in High-Income Countries and 38 (44%) in Low- and Middle-Income countries, with no CDSS trialled in both income categories. Meta-analysis of 35 included studies found an odds ratio for improved outcomes of 1.69 (95% confidence interval 1.24-2.30). There was substantial variation in effects, aims, CDSS types, context, study designs, and outcomes.

Interpretation

Most CDSS evaluations showed improvements in outcomes, but there was heterogeneity in all aspects of design and evaluation of systems. CDSS are increasingly important in delivering healthcare, and Electronic Health Records and mHealth will increase their availability, but traditional epidemiological methods may be limited in guiding design and demonstrating effectiveness due to rapid CDSS development lifecycles and the complex systems in which they are embedded. Development methods that are attentive to context, such as Human Centred Design, will help to meet this need.

Funding

None.

Socioeconomic and ethnic disparities in preterm births in an English maternity setting: a population-based study of 1.3 million births

BACKGROUND

Preterm birth is a major cause of infant mortality and morbidity and accounts for 7-8% of births in the UK. It is more common in women from socially deprived areas and from minority ethnic groups, but the reasons for this disparity are poorly understood. To inform interventions to improve child survival and their quality of life, this study examined the socioeconomic and ethnic inequalities in preterm births (< 37 weeks of gestation at birth) within Health Trusts in England.

METHODS

This study investigated socioeconomic and ethnic inequalities in preterm birth rates across the National Health Service (NHS) in England. The NHS in England can be split into different units known as Trusts. We visualised between-Trust differences in preterm birth rates. Health Trusts were classified into five groups based on their standard deviation (SD) variation from the average national preterm birth rate. We used modified Poisson regression to compute risk ratios (RR) and 95% confidence intervals (95% CI) with generalised estimating equations.

RESULTS

The preterm birth rate ranged from 6.8/100 births for women living in the least deprived areas to 8.8/100 births for those living in the most deprived areas. Similarly, the preterm birth rate ranged from 7.8/100 births for white women, up to 8.6/100 births for black women. Some Health Trusts had lower than average preterm birth rates in white women whilst concurrently having higher than average preterm birth rates in black and Asian women. The risk of preterm birth was higher for women living in the most deprived areas and ethnicity (Asian).

CONCLUSIONS

There was evidence of variation in rates of preterm birth by ethnic group, with some Trusts reporting below average rates in white ethnic groups whilst concurrently reporting well above average rates for women from Asian or black ethnic groups. The risk of preterm birth varied substantially at the intersectionality of maternal ethnicity and the level of socioeconomic deprivation of their residency. In the absence of other explanations, these findings suggest that even within the same Health Trust, maternity care may vary depending on the women's ethnicity and/or whether she lives in an area of high socioeconomic deprivation. Thus, social factors are likely key determinants of inequality in preterm birth rather than provision of maternity care alone.

Preterm Birth: Screening and Prediction

Preterm birth (PTB) affects approximately 10% of births globally each year and is the most significant direct cause of neonatal death and of long-term disability worldwide. Early identification of women at high risk of PTB is important, given the availability of evidence-based, effective screening modalities, which facilitate decision-making on preventative strategies, particularly transvaginal sonographic cervical length (CL) measurement. There is growing evidence that combining CL with quantitative fetal fibronectin (qfFN) and maternal risk factors in the extensively peer-reviewed and validated QUanititative Innovation in Predicting Preterm birth (QUiPP) application can aid both the triage of patients who present as emergencies with symptoms of preterm labor and high-risk asymptomatic women attending PTB surveillance clinics. The QUiPP app risk of delivery thus supports shared decision-making with patients on the need for increased outpatient surveillance, in-patient treatment for preterm labor or simply reassurance for those unlikely to deliver preterm. Effective triage of patients at preterm gestations is an obstetric clinical priority as correctly timed administration of antenatal corticosteroids will maximise their neonatal benefits. This review explores the predictive capacity of existing predictive tests for PTB in both singleton and multiple pregnancies, including the QUiPP app v.2. and discusses promising new research areas, which aim to predict PTB through cervical stiffness and elastography measurements, metabolomics, extracellular vesicles and artificial intelligence.

The web-based application "QUiPP v.2" for the prediction of preterm birth in symptomatic women is not yet ready for worldwide clinical use: ten reflections on development, validation and use

PURPOSE

In this correspondence, we highlight general and domain-specific caveats in the development and validation of prediction models.

METHODS

Development and use of the "QUiPP" application, a tool for preterm birth prediction which is supported by the United Kingdom National Health Service, is scrutinised and commented on.

RESULTS

We highlight and elaborate ten points which may be perceived to be unclear or potentially misleading.

CONCLUSION

While the QUiPP application has high potential, it lacks transparency (on certain aspects related to model development) and proper validation. This precludes transportability to settings with other treatment policies and to other countries where the app has been made publicly available.

Clinicians' experiences of using and implementing a medical mobile phone app (QUiPP V2) designed to predict the risk of preterm birth and aid clinical decision making

Background

As the vast majority of women who present in threatened preterm labour (TPTL) will not deliver early, clinicians need to balance the risks of over-medicalising the majority of women, against the potential risk of preterm delivery for those discharged home. The QUiPP app is a free, validated app which can support clinical decision-making as it produces individualised risks of delivery within relevant timeframes. Recent evidence has highlighted that clinicians would welcome a decision-support tool that accurately predicts preterm birth.

Methods

Qualitative interviews were undertaken as part of the EQUIPTT study (The Evaluation of the QUiPP app for Triage and Transfer) (REC: 17/LO/1802) which aimed to evaluate the impact of the QUiPP app on management of TPTL. Individual semi-structured telephone interviews were used to explore clinicians’ (obstetricians’ and midwives’) experiences of using the QUiPP app and how it was implemented at their hospital sites. Thematic analysis was chosen to explore the meaning of the data, through a framework approach.

Results

Nineteen participants from 10 hospital sites in England took part. Data analysis revealed three overarching themes which were: ‘experience of using the app’, ‘how QUiPP risk changes practice’ and ‘successfully adopting QUiPP: context is everything’. With these final themes we appeared to have achieved our aim of exploring the clinicians’ experiences of using and implementing the QUiPP app.

Conclusion

This study explored different clinician’s experiences of implementing the app. The organizational and cultural context at different sites appeared to have a large impact on how well the QUiPP app was implemented. Future work needs to be undertaken to understand how best to embed the intervention within different settings. This will inform scale up of QUiPP app use across the UK and ensure that clinicians have access to this free, easy-to-use tool which can positively aid clinical decision making when caring for women in TPTL.

Clinical trial registry and registration number

ISRCTN 17846337, registered 08th January 2018, https://doi.org/10.1186/ISRCTN17846337.