A cluster-randomized trial to reduce major perinatal morbidity among women with one prior cesarean delivery in Québec (PRISMA trial): study protocol for a randomized controlled trial

Chaillet, Nils; Bujold, Emmanuel; Mâsse, Benoı̂t; Grobman, William A.; Rozenberg, Patrick; Pasquier, Jean-Charles; Shorten, Allison; Johri, Mira; Beaudoin, François; Abenhaim, Haim A.; Demers, Suzanne; Fraser, William D.; Dugas, Marylène; Blouin, Simon; Dubé, Évemie; Gauthier, Robert

doi:10.1186/s13063-017-2150-x

Cited by 9 publications

(5 citation statements)

References 68 publications

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“…Whilst there are many examples of national epidemiological research and audit conducted using EPR data over the past 20 years [ 2 ], clinical trials using data collected from EPR have only become more prevalent in the last 5 years. Examples in maternal and perinatal medicine come from Canada [ 3 ], Sweden [ 4 ], the Netherlands [ 5 ], Scotland [ 6 , 7 ], Wales [ 8 ] and England [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

Relph

Elstad

Coker

et al. 2021

Trials

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Background The use of electronic patient records for assessing outcomes in clinical trials is a methodological strategy intended to drive faster and more cost-efficient acquisition of results. The aim of this manuscript was to outline the data collection and management considerations of a maternity and perinatal clinical trial using data from electronic patient records, exemplifying the DESiGN Trial as a case study. Methods The DESiGN Trial is a cluster randomised control trial assessing the effect of a complex intervention versus standard care for identifying small for gestational age foetuses. Data on maternal/perinatal characteristics and outcomes including infants admitted to neonatal care, parameters from foetal ultrasound and details of hospital activity for health-economic evaluation were collected at two time points from four types of electronic patient records held in 22 different electronic record systems at the 13 research clusters. Data were pseudonymised on site using a bespoke Microsoft Excel macro and securely transferred to the central data store. Data quality checks were undertaken. Rules for data harmonisation of the raw data were developed and a data dictionary produced, along with rules and assumptions for data linkage of the datasets. The dictionary included descriptions of the rationale and assumptions for data harmonisation and quality checks. Results Data were collected on 182,052 babies from 178,350 pregnancies in 165,397 unique women. Data availability and completeness varied across research sites; each of eight variables which were key to calculation of the primary outcome were completely missing in median 3 (range 1–4) clusters at the time of the first data download. This improved by the second data download following clarification of instructions to the research sites (each of the eight key variables were completely missing in median 1 (range 0–1) cluster at the second time point). Common data management challenges were harmonising a single variable from multiple sources and categorising free-text data, solutions were developed for this trial. Conclusions Conduct of clinical trials which use electronic patient records for the assessment of outcomes can be time and cost-effective but still requires appropriate time and resources to maximise data quality. A difficulty for pregnancy and perinatal research in the UK is the wide variety of different systems used to collect patient data across maternity units. In this manuscript, we describe how we managed this and provide a detailed data dictionary covering the harmonisation of variable names and values that will be helpful for other researchers working with these data. Trial registration Primary registry and trial identifying number: ISRCTN 67698474. Registered on 02/11/16.

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Section: Introductionmentioning

confidence: 99%

Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

Relph

Elstad

Coker

et al. 2021

Trials

View full text Add to dashboard Cite

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“…Another trial, the PRISMA cluster randomized trial, is currently evaluating the combination of both TA-US and TV-US alongside the use of a partogram during labor with the aim of reducing the risk of uterine rupture and perinatal morbidity. 22 In conclusion, TA-US leads to thicker LUS measurement than TV-US; therefore, the same cut-off cannot be used for both. Taken together, existing literature and the current study suggest that the combination of both TA-US and TV-US is superior to TA-US alone and therefore, we recommend the use of both approaches in clinical practice for the detection of uterine scar dehiscence and the prevention of uterine rupture.…”

Section: Discussionmentioning

confidence: 95%

“…We observed no cases of symptomatic uterine rupture out of 984 TOLAC, which suggests that incorporating this approach in delivery planning may help to prevent uterine rupture. Another trial, the PRISMA cluster randomized trial, is currently evaluating the combination of both TA‐US and TV‐US alongside the use of a partogram during labor with the aim of reducing the risk of uterine rupture and perinatal morbidity 22 …”

Section: Discussionmentioning

confidence: 99%

Measurement of Lower Uterine Segment Thickness to Detect Uterine Scar Defect: Comparison of Transabdominal and Transvaginal Ultrasound

Marchant

Lessard

Bergeron

et al. 2023

J of Ultrasound Medicine

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Objectives Lower uterine segment (LUS) thickness measurement using transabdominal ultrasound (TA‐US), transvaginal ultrasound (TV‐US), or the combination of both methods can detect scar defect in women with prior cesarean. We aimed to compare the sensitivity of three approaches. Methods Women with prior cesarean underwent LUS thickness measurement at 34–38 weeks' gestation. Among those who underwent repeat cesarean before labor, we compared the accuracy of TA‐US, TV‐US, and the thinner of the two measurements (the “combined measurement”) for uterine scar dehiscence using the area under the curve (AUC) of receiver operating curves with their 95% confidence intervals (CI). We calculated the sensitivity and specificity of the three approaches using a cut‐off of 2.3 mm based on prior literature. Results We included 747 participants. The mean LUS thickness was greater with TA‐US (3.8 ± 1.6 mm) compared with TV‐US (3.5 ± 1.9 mm) or the combined measurement (3.2 ± 1.5 mm; P < .001). The AUC was 78% (95% CI: 69%–87%), 85% (95% CI: 79%–91%), and 88% (95% CI: 82%–93%), respectively (all with P < .001). The AUC difference between TA‐US and the combined measurement was not significant (P = .057). A LUS below 2.3 mm would have predicted 9 (45%) of the 20 cases of uterine scar dehiscence using TA‐US, 17 (85%) using TV‐US, and 18 (90%) using the combined measurement (P < .01). Conclusion The choice of ultrasound approach influences the measurement of the LUS thickness. The combination of the TA‐US and TV‐US seems to be superior for the detection of uterine dehiscence.

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“…A slightly different design was adopted by the PRISMA trial of a multifaceted intervention to reduce perinatal morbidity among women with a previous caesarean delivery in Québec. 7 In this trial, 40 hospitals were randomised 1:1 to either intervention or routine care. The trial used routinely collected data at the participating hospitals.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of cluster randomised trials with continuous recruitment and prospective baseline period

Hooper

Copas

2021

Clinical Trials

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Background: Cluster randomised trials, like individually randomised trials, may benefit from a baseline period of data collection. We consider trials in which clusters prospectively recruit or identify participants as a continuous process over a given calendar period, and ask whether and for how long investigators should collect baseline data as part of the trial, in order to maximise precision. Methods: We show how to calculate and plot the variance of the treatment effect estimator for different lengths of baseline period in a range of scenarios, and offer general advice. Results: In some circumstances it is optimal not to include a baseline, while in others there is an optimal duration for the baseline. All other things being equal, the circumstances where it is preferable not to include a baseline period are those with a smaller recruitment rate, smaller intracluster correlation, greater decay in the intracluster correlation over time, or wider transition period between recruitment under control and intervention conditions. Conclusion: The variance of the treatment effect estimator can be calculated numerically, and plotted against the duration of baseline to inform design. It would be of interest to extend these investigations to cluster randomised trial designs with more than two randomised sequences of control and intervention condition, including stepped wedge designs.

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A cluster-randomized trial to reduce major perinatal morbidity among women with one prior cesarean delivery in Québec (PRISMA trial): study protocol for a randomized controlled trial

Cited by 9 publications

References 68 publications

Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

Using electronic patient records to assess the effect of a complex antenatal intervention in a cluster randomised controlled trial—data management experience from the DESiGN Trial team

Measurement of Lower Uterine Segment Thickness to Detect Uterine Scar Defect: Comparison of Transabdominal and Transvaginal Ultrasound

Optimal design of cluster randomised trials with continuous recruitment and prospective baseline period

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