Computerised interpretation of the fetal heart rate during labour: a randomised controlled trial (INFANT)

A Scoping Review of Item-Level Missing Data in Within-Trial Cost-Effectiveness Analysis

OBJECTIVES

Cost-effectiveness analysis (CEA) alongside randomized controlled trials often relies on self-reported multi-item questionnaires that are invariably prone to missing item-level data. The purpose of this study is to review how missing multi-item questionnaire data are handled in trial-based CEAs.

METHODS

We searched the National Institute for Health Research journals to identify within-trial CEAs published between January 2016 and April 2021 using multi-item instruments to collect costs and quality of life (QOL) data. Information on missing data handling and methods, with a focus on the level and type of imputation, was extracted.

RESULTS

A total of 87 trial-based CEAs were included in the review. Complete case analysis or available case analysis and multiple imputation (MI) were the most popular methods, selected by similar numbers of studies, to handle missing costs and QOL in base-case analysis. Nevertheless, complete case analysis or available case analysis dominated sensitivity analysis. Once imputation was chosen, missing costs were widely imputed at item-level via MI, whereas missing QOL was usually imputed at the more aggregated time point level during the follow-up via MI.

CONCLUSIONS

Missing costs and QOL tend to be imputed at different levels of missingness in current CEAs alongside randomized controlled trials. Given the limited information provided by included studies, the impact of applying different imputation methods at different levels of aggregation on CEA decision making remains unclear.

An exploration of expectations and perceptions of practicing physicians on the implementation of computerized clinical decision support systems using a Qsort approach

Background

There is increasing interest in incorporating clinical decision support (CDS) into electronic healthcare records (EHR). Successful implementation of CDS systems depends on acceptance of them by healthcare workers. We used a mix of quantitative and qualitative methods starting from Qsort methodology to explore expectations and perceptions of practicing physicians on the use of CDS incorporated in EHR.

Methods

The study was performed in a large tertiary care academic hospital. We used a mixed approach with a Q-sort based classification of pre-defined reactions to clinical case vignettes combined with a thinking-aloud approach, taking into account COREQ recommendations The open source software of Ken-Q Analysis version 1.0.6. was used for the quantitative analysis, using principal components and a Varimax rotation. For the qualitative analysis, a thematic analysis based on the four main themes was performed based on the audiotapes and field notes.

Results

Thirty physicians were interviewed (7 in training, 8 junior staff and 15 senior staff; 16 females). Nearly all respondents were strongly averse towards interruptive messages, especially when these also were obstructive. Obstructive interruption was considered to be acceptable only when it increases safety, is adjustable to user expertise level and/or allows deviations when the end-user explains why a deviation is desirable in the case at issue. Transparency was deemed an essential feature, which seems to boil down to providing sufficient clarification on the factors underlying the recommendations of the CDS, so that these can be compared against the physicians’ existing knowledge, beliefs and convictions.

Conclusion

Avoidance of disruptive workflows and transparency of the underlying decision processes are important points to consider when developing CDS systems incorporated in EHR.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01933-3.

Current practice in the measurement and interpretation of intervention adherence in randomised controlled trials: A systematic review

BACKGROUND

Ideally all participants in a randomised controlled trial (RCT) should fully receive their allocated intervention; however, this rarely occurs in practice. Intervention adherence affects Type II error so influences the interpretation of trial results and subsequent implementation. We aimed to describe current practice in the definition, measurement, and reporting of intervention adherence in non-pharmacological RCTs, and how this data is incorporated into a trial's interpretation and conclusions.

METHODS

We conducted a systematic review of phase III RCTs published between January 2018 and June 2020 in the National Institute for Health Research Journals Library for the Health Technology Assessment, Programme Grants for Applied Research, and Public Health Research funding streams.

RESULTS

Of 237 reports published, 76 met the eligibility criteria and were included. Most RCTs (n = 68, 89.5%) reported adherence, though use of terminology varied widely; nearly three quarters of these (n = 49, 72.1%) conducted a sensitivity analysis. Adherence measures varied between intervention types: behavioural change (n = 10, 43.5%), psychological therapy (n = 5, 83.3%) and physiotherapy/rehabilitation (n = 8, 66.7%) interventions predominately measured adherence based on session attendance. Whereas medical device and surgical interventions (n = 17, 73.9%) primarily record the number of participants receiving the allocated intervention, a third (n = 33, 67.3%) of studies reported a difference in findings between primary and sensitivity analyses.

CONCLUSIONS

Although most trials report elements of adherence, terminology was inconsistent, and there was no systematic approach to its measurement, analyses, interpretation, or reporting. Given the importance of adherence within clinical trials, there is a pressing need for a standardised approach or framework.

"Many roads lead to Rome and the Artificial Intelligence only shows me one road": an interview study on physician attitudes regarding the implementation of computerised clinical decision support systems

Research regarding the drivers of acceptance of clinical decision support systems (CDSS) by physicians is still rather limited. The literature that does exist, however, tends to focus on problems regarding the user-friendliness of CDSS. We have performed a thematic analysis of 24 interviews with physicians concerning specific clinical case vignettes, in order to explore their underlying opinions and attitudes regarding the introduction of CDSS in clinical practice, to allow a more in-depth analysis of factors underlying (non-)acceptance of CDSS. We identified three general themes from the results. First, 'the perceived role of the AI', including items referring to the tasks that may properly be assigned to the CDSS according to the respondents. Second, 'the perceived role of the physician', referring to the aspects of clinical practice that were seen as being fundamentally 'human' or non-automatable. Third, 'concerns regarding AI', including items referring to more general issues that were raised by the respondents regarding the introduction of CDSS in general and/or in clinical medicine in particular. Apart from the overall concerns expressed by the respondents regarding user-friendliness, we will explain how our results indicate that our respondents were primarily occupied by distinguishing between parts of their job that should be automated and aspects that should be kept in human hands. We refer to this distinction as 'the division of clinical labor.' This division is not based on knowledge regarding AI or medicine, but rather on which parts of a physician's job were seen by the respondents as being central to who they are as physicians and as human beings. Often the respondents' view that certain core parts of their job ought to be shielded from automation was closely linked to claims concerning the uniqueness of medicine as a domain. Finally, although almost all respondents claimed that they highly value their final responsibility, a closer investigation of this concept suggests that their view of 'final responsibility' was not that demanding after all.

The potential of big data for obstetrics discovery

PURPOSE OF REVIEW

The purpose of this article is to introduce the concept of 'Big Data' and review its potential to advance scientific discovery in obstetrics.

RECENT FINDINGS

Big Data is now ubiquitous in medicine, being used in many specialties to understand the pathophysiology, risk factors, and treatment for many diseases. Big Data analyses often employ machine learning methods to understand the complex relationships that may exist within these sources. We review the basic principles of supervised and unsupervised machine learning methods, including deep learning. We highlight how these methods have been used to study genetic risk factors for preterm birth, interpreting electronic fetal heart rate tracings, and predict adverse maternal and neonatal outcomes during pregnancy and delivery. Despite its promise, there are challenges with using Big Data, including data integrity, generalizability (namely the concerns about perpetuating inequalities), and confidentiality.

SUMMARY

The combination of new data and enhanced methods present a synergistic opportunity to explore the complex relationships common to human illness and medical practice, including obstetrics. With prediction as a primary objective instead of the more familiar goals of hypothesis testing, these analytic methods can capture multifaceted, rare, and nuanced relationships between exposures and outcomes that exist within these large data sets.

Economic evaluations of big data analytics for clinical decision-making: a scoping review

OBJECTIVE

Much has been invested in big data analytics to improve health and reduce costs. However, it is unknown whether these investments have achieved the desired goals. We performed a scoping review to determine the health and economic impact of big data analytics for clinical decision-making.

MATERIALS AND METHODS

We searched Medline, Embase, Web of Science and the National Health Services Economic Evaluations Database for relevant articles. We included peer-reviewed papers that report the health economic impact of analytics that assist clinical decision-making. We extracted the economic methods and estimated impact and also assessed the quality of the methods used. In addition, we estimated how many studies assessed "big data analytics" based on a broad definition of this term.

RESULTS

The search yielded 12 133 papers but only 71 studies fulfilled all eligibility criteria. Only a few papers were full economic evaluations; many were performed during development. Papers frequently reported savings for healthcare payers but only 20% also included costs of analytics. Twenty studies examined "big data analytics" and only 7 reported both cost-savings and better outcomes.

DISCUSSION

The promised potential of big data is not yet reflected in the literature, partly since only a few full and properly performed economic evaluations have been published. This and the lack of a clear definition of "big data" limit policy makers and healthcare professionals from determining which big data initiatives are worth implementing.

Economic evaluation of computerised interpretation of fetal heart rate during labour: a cost-consequence analysis alongside the INFANT study

OBJECTIVE

Economic evaluation of computerised decision-support software intended to assist in the interpretation of a cardiotocography (CTG) during birth.

DESIGN

Individual patient level data from the INFANT study (an unmasked randomised controlled trial).

SETTING

Maternity units in the UK and Ireland.

POPULATION

Singleton or twin pregnancy women of 35 weeks' gestation or more and receiving continuous electronic fetal monitoring during labour.

INTERVENTION

Computerised decision-support software.

METHODS

Cost-consequence analysis presenting costs and outcomes with a time horizon of 2 years from a government healthcare perspective. Unit cost data collected from a combination of primary and secondary sources.

MAIN OUTCOME MEASURES

Primary clinical outcomes were (i) composite 'poor neonatal outcome' and (ii) developmental assessment at age 2 years in a subset of surviving children. Mean cost per mother and infant dyad from birth to hospital discharge, and from hospital discharge to 24 months follow-up. Maternal health-related quality of life was assessed at 12 and 24 months follow-up using the EuroQol three-level health-related quality of life instrument (EQ-5D-3L).

RESULTS

Data were analysed for 46 042 women and 46 614 infants. No statistically significant differences were detected between trial arms in any of the primary clinical outcomes or maternal quality of life. No statistically significant differences in costs were detected in maternal or infant costs from trial entry to hospital discharge or overall from hospital discharge to 2-year follow-up.

CONCLUSIONS

Decision-support software during labour is not associated with additional maternal or infant benefits and over a 2-year period the software did not lead to additional costs or savings to the National Health Service.

TRIAL REGISTRATION NUMBER

ISRCTN98680152.

Evaluation of 3-tier and 5-tier FHR pattern classifications using umbilical blood pH and base excess at delivery

Objective

The relevance between time-series fetal heart rate (FHR) pattern changes during labor and outcomes such as arterial blood gas data at delivery has not been studied. Using 3-tier and 5-tier classification systems, we studied the relationship between time-series FHR pattern changes before delivery and umbilical artery blood gas data at delivery.

Methods

The subjects were 1,909 low-risk women with vaginal delivery (age: 29.1 ± 4.4 years, parity: 1.7 ± 0.8). FHR patterns were classified by a skilled obstetrician based on each 10 min-segment of the last 60 min before delivery from continuous CTG records in an obstetric clinic.

Results

The relationship between each 10 min-segment FHR pattern classification from 60 minutes before delivery and umbilical artery blood pH and base excess (BE) values at delivery changed with time. In the 3-tier classification, mean pH of Category I group in each 10 min-segment was significantly higher than that of Category II group. For Category I groups in each 10-minute segment, its number decreased and its average pH increased as the delivery time approached. In the 5-tier classification, there was the same tendency. About each level group in 10 min-segment, the higher the level, the lower the blood gas values, and mean pH of higher level groups decreased as the delivery time approached.

Conclusions

The relationship between classifications and outcomes was clear at any time from 60 min before delivery in 3- and 5-tier classifications, and the 5-tier classification was more relevant.