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 .

Ten women's decision-making experiences in threatened preterm labour: Qualitative findings from the EQUIPTT trial.

BACKGROUND: Clinical triage of women in threatened preterm labour (TPTL) could be improved through utilising the QUiPP App, as symptoms alone are poor predictors of early delivery. As most women in TPTL ultimately deliver at term, they must weigh this likelihood with their own personal considerations, and responsibilities. The importance of personal considerations was highlighted by the 2015 Montgomery ruling, and the significance of shared decision-making. AIMS: Through qualitative interviews, the primary aim was to explore women's decision-making experiences in TPTL through onset of symptoms, triage, clinical assessment, and discharge. METHODS: Qualitative interviews were undertaken as part of the EQUIPTT study (REC: 17/LO/1802) using a semi-structured interview schedule. Descriptive labels of the coding scheme were applied to the raw transcript data. This coding scheme was then increasingly refined into key themes and allowed parallels to be made within and between cases. RESULTS: Ten ethnically diverse women who presented at six different London hospitals sites in TPTL were interviewed. Three final themes emerged from the data incorporating 10 sub-themes, 'Seeking help', 'Being "assessed" vs making clinical decisions together', and 'End result.' CONCLUSION: Women described their busy lives and the need to juggle their commitments. Participants drew comparisons between their TPTL symptoms and 'period pain,' contrasting to typical medical terminology. Shared decision-making and the clinician-patient relationship could be improved through clinicians utilizing terminology women understand and relate to. Women used language that highlighted the clinician-patient power balance. While not fully involved in shared decision-making, women were overall satisfied with their care.

Impact of a medical mobile phone app (QUiPP) for predicting preterm birth on the anxiety and decisional conflicts faced by women in threatened preterm labour.

BACKGROUND: The QUiPP app is a free, validated mobile phone application (app) that supports clinical decision-making for women in threatened preterm labour by providing an individualised risk of delivery within clinically important time points. Alongside generating a percentage risk score, the QUiPP app also provides the risk score in an infographic donut chart, allowing the clinician to communicate with the woman in an easy to understand format. Informing women of their risk status using the QUIPP app may help to reduce anxiety in women and decrease decisional conflict. METHOD: A subset of participants from the EQUIPTT study [REC Ref. 17/LO/1802] were asked to complete a questionnaire booklet which was used to evaluate decisional conflict and anxiety. Seven sites were randomised to the QUiPP app intervention (to use as a decision and communication tool) and six sites were randomised to the control (continued their normal practice). The first section of the questionnaire booklet was completed by the woman before her assessment, and the second section after. The pre and postassessment anxiety scores utilised the Visual Analogue Scale for Anxiety (Hornblow and Kidson, 1976). The Decisional Conflict Scale (O'Connor, 1995) measured decisional conflict post assessment. The data were then analysed to determine the impact of the QUiPP App on the anxiety and decisional conflicts faced by women in threatened preterm labour. RESULTS: Questionnaires were completed by 221 women from 12 of the potential 13 sites. After exclusions 202 questionnaires were included in the analysis. There was a significant reduction in difference between anxiety scores before and after clinical assessment. While there were reductions in anxiety and decisional conflict for women who were aware of the QUiPP app use, this failed to reach statistical significance. CONCLUSIONS: The QUiPP app has potential to reduce anxiety and decisional conflict in women who are aware that it is being used in their care. Additional work is required to ensure clinicians are aware of the QUiPP app and optimise using it as a communication tool when counselling women.

Development and validation of predictive models for QUiPP App v.2: tool for predicting preterm birth in asymptomatic high-risk women.

OBJECTIVES: Accurate mid-pregnancy prediction of spontaneous preterm birth (sPTB) is essential to ensure appropriate surveillance of high-risk women. Advancing the QUiPP App prototype, QUiPP App v.2 aimed to provide individualized risk of delivery based on cervical length (CL), quantitative fetal fibronectin (qfFN) or both tests combined, taking into account further risk factors, such as multiple pregnancy. Here we report development of the QUiPP App v.2 predictive models for use in asymptomatic high-risk women, and validation using a distinct dataset in order to confirm the accuracy and transportability of the QUiPP App, overall and within specific clinically relevant time frames. METHODS: This was a prospective secondary analysis of data of asymptomatic women at high risk of sPTB recruited in 13 UK preterm birth clinics. Women were offered longitudinal qfFN testing every 2-4 weeks and/or transvaginal ultrasound CL measurement between 18 + 0 and 36 + 6 weeks' gestation. A total of 1803 women (3878 visits) were included in the training set and 904 women (1400 visits) in the validation set. Prediction models were created based on the training set for use in three groups: patients with risk factors for sPTB and CL measurement alone, with risk factors for sPTB and qfFN measurement alone, and those with risk factors for sPTB and both CL and qfFN measurements. Survival analysis was used to identify the significant predictors of sPTB, and parametric structures for survival models were compared and the best selected. The estimated overall probability of delivery before six clinically important time points (< 30, < 34 and < 37 weeks' gestation and within 1, 2 and 4 weeks after testing) was calculated for each woman and analyzed as a predictive test for the actual occurrence of each event. This allowed receiver-operating-characteristics curves to be plotted, and areas under the curve (AUC) to be calculated. Calibration was performed to measure the agreement between expected and observed outcomes. RESULTS: All three algorithms demonstrated high accuracy for the prediction of sPTB at < 30, < 34 and < 37 weeks' gestation and within 1, 2 and 4 weeks of testing, with AUCs between 0.75 and 0.90 for the use of qfFN and CL combined, between 0.68 and 0.90 for qfFN alone, and between 0.71 and 0.87 for CL alone. The differences between the three algorithms were not statistically significant. Calibration confirmed no significant differences between expected and observed rates of sPTB within 4 weeks and a slight overestimation of risk with the use of CL measurement between 22 + 0 and 25 + 6 weeks' gestation. CONCLUSIONS: The QUiPP App v.2 is a highly accurate prediction tool for sPTB that is based on a unique combination of biomarkers, symptoms and statistical algorithms. It can be used reliably in the context of communicating to patients the risk of sPTB. Whilst further work is required to determine its role in identifying women requiring prophylactic interventions, it is a reliable and convenient screening tool for planning follow-up or hospitalization for high-risk women. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Development and validation of a tool incorporating cervical length and quantitative fetal fibronectin to predict spontaneous preterm birth in asymptomatic high-risk women.

OBJECTIVE: To develop a predictive tool for spontaneous preterm birth (sPTB) in asymptomatic high-risk women that includes quantification of fetal fibronectin (fFN) along with cervical length (CL) measurement and other clinical factors. METHODS: Data were analyzed that had been collected prospectively from 1249 women at high risk for sPTB attending preterm surveillance clinics. Clinicians were blinded to quantitative measurements of fFN (qfFN), although they were aware of qualitative fFN results. Parametric survival models for sPTB, with time-updated covariates, were developed and the best was selected using the Akaike and Bayesian information criteria. The model was developed on the first 624 consecutive women and validated on the subsequent 625. Fractional polynomials were used to accommodate possible non-linear effects of qfFN and CL. The estimated probability of delivery before 30, 34 or 37 weeks' gestation and within 2 or 4 weeks of testing was calculated for each patient and analyzed as a predictive test for the actual occurrence of each event. Predictive statistics were calculated to compare training and validation sets. RESULTS: The final model that was selected used a log-normal survival curve with CL, √qfFN and previous sPTB/preterm prelabor rupture of membranes as predictors. Predictive statistics were similar for training and validation sets. Areas under the receiver-operating characteristics curves ranged from 0.77 to 0.99, indicating accurate prediction across all five delivery outcomes. CONCLUSIONS: sPTB in high-risk asymptomatic women can be predicted accurately using a model combining qfFN and CL, which supersedes the single-threshold fFN test, demographic information and obstetric history. This algorithm has been incorporated into an App (QUiPP) for widespread use.

Development and validation of a tool incorporating quantitative fetal fibronectin to predict spontaneous preterm birth in symptomatic women.

OBJECTIVE: To develop a reliable and validated tool for prediction of spontaneous preterm birth (sPTB) in symptomatic women that incorporates quantitative measurements of fetal fibronectin (qfFN) and other relevant risk factors. METHODS: Data were analyzed that had been collected prospectively from 382 women who presented at an emergency assessment unit between 22 + 0 and 35 + 6 weeks' gestation with symptoms of preterm labor. Clinicians were blinded to qfFN although they were aware of qualitative fFN results. Parametric survival models for sPTB, with time-updated covariates, were developed for combinations of predictors and the best was selected using the Akaike and Bayesian information criteria. The model was developed on the first 190 consecutive women and validated on the subsequent 192. The estimated probability of delivery before 30, 34 or 37 weeks' gestation and within 2 or 4 weeks of testing was calculated for each patient and was compared to actual event rates. Predictive statistics were calculated to compare training and validation sets. RESULTS: The final model that was selected used qfFN and previous sPTB/preterm prelabor rupture of membranes (PPROM) as predictors. Predictive statistics were similar for training and validation sets and there was good agreement between expected and observed sPTB for all outcomes. Areas under the receiver-operating characteristics curves ranged from 0.77 to 0.88, indicating accurate prediction across all five delivery outcomes. CONCLUSIONS: sPTB in symptomatic women can be predicted accurately using a model combining qfFN and previous sPTB/PPROM. Clinicians can use this model, which has been incorporated into an App (QUiPP), to determine accurately a woman's risk of sPTB and potentially tailor management decisions appropriately.

A report from Biopharm America 2013 - sixth Annual International Partnering Conference (September 16-19, 2013 - Boston, Massachusetts, USA).

The sixth Annual International Partnering Conference, Biopharm America 2013, took full advantage of its setting in Boston, Massachusetts, home of perhaps the greatest concentration of academic institutions in the U.S., if not the world. With a keynote address from a systems biologist from Harvard University, a networking reception at the Boston Museum of Science and a spirited 'Academic Innovators Showcase', the EBD Group's gathering paid homage to the links between a steady flow of new scientific ideas and a healthy biopharmaceutical pipeline.

A report from CPhI Worldwide 2013, Fifth Annual Pre-Connect Conference (October 22-24, 2013 - Frankfurt, Germany).

A day before the start of the 2013 Conference on Pharmaceutical Ingredients (CPhI) Worldwide, the world's leading pharmaceutical networking event, a number of attendees gathered for the Fifth Annual Pre-Connect Conference to discuss trends in business development, manufacturing and regulatory arenas. Of the six modules presented at the meeting, one was dedicated to the sourcing environment in emerging markets, with special attention paid to developments in India and China. Other modules evaluated the current trends in the creation of generics and supergenerics in emerging markets. Additionally, there were updates on issues surrounding the regulatory and development hurdles that biosimilars and biobetters are facing today. Common themes for both discussions include appropriate pricing and erosion demographics for generics and biosimilars, licensing scenarios, commercialization strategies, and how to stay competitive and find novel innovations within new delivery systems, improved formulations and modifications to create better quality active pharmaceutical ingredients.

The 'evil twin syndrome' in chronic pelvic pain: a systematic review of prevalence studies of bladder pain syndrome and endometriosis.

BACKGROUND: Chronic pelvic pain (CPP), a common gynaecological presentation, may be due to bladder pain syndrome (BPS) or the co-existence of BPS and endometriosis, known as the 'evil twins syndrome'. OBJECTIVES: To estimate the prevalence of BPS and the co-existence of BPS and endometriosis in women with CPP. DATA SOURCES: We searched until March 2012: The Cochrane Library, DARE (1997-2012), EMBASE (1980-2012), Medline (1950-2012), PSYCHINFO (1806-2012), Web of knowledge (1900-2012), LILACS (1982-2012) and SIGLE (1990-2012) with no language restrictions. We manually searched through bibliographies and conference proceedings of the International Continence Society. STUDY SELECTION: Observational studies of women suffering from CPP, who were not pregnant or suffering from cancer, who underwent a laparoscopy and cystoscopy to investigate their symptoms. Study selection, data extraction and quality assessment was performed independently by two reviewers. Statistical analysis was performed to estimate prevalence and confidence intervals (CI). RESULTS: Nine studies were included with 1016 patients with CPP. Study quality and diagnostic assessment varied. The mean prevalence of BPS was 61% (range 11-97%, CI 58-64%, I(2) = 98%). The mean prevalence of endometriosis was 70% (range 28-93%, CI 67-73%, I(2) = 93%) and co-existing BPS and endometriosis was 48% (range 16-78%, CI 44-51%, I(2) = 96%). CONCLUSION: Almost two thirds of women presenting with CPP have BPS. Large variations in prevalence may be due to variable study selection and quality. Clinicians need to actively investigate patients for BPS, a condition that appears to co-exist with endometriosis.