Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis.

Background: Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes. Objectives: To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data. Design: Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis. Participants: Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies). Predictors: Maternal clinical characteristics, biochemical and ultrasound markers. Primary outcomes: fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight. Analysis: First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model. Results: Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval -154.3 g to 173.8 g). Limitations: We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data. Future work: International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation. Conclusion: The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management. Study registration: This study is registered as PROSPERO CRD42019135045. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.

One in ten babies is born small for their age. A third of such small babies are considered to be 'growth-restricted' as they have complications such as dying in the womb (stillbirth) or after birth (newborn death), cerebral palsy, or needing long stays in hospital. When growth restriction is suspected in fetuses, they are closely monitored and often delivered early to avoid complications. Hence, it is important that we identify growth-restricted babies early to plan care. Our goal was to provide personalised and accurate estimates of the mother's chances of having a growth-restricted baby and predict the baby's weight if delivered at various time points in pregnancy. To do so, first we tested how accurate existing risk calculators ('prediction models') were in predicting growth restriction and birthweight. We then developed new risk-calculators and studied their clinical and economic benefits. We did so by accessing the data from individual pregnant women and their babies in our large database library (International Prediction of Pregnancy Complications). Published risk-calculators had various definitions of growth restriction and none predicted the chances of having a growth-restricted baby using our definition. One predicted baby's birthweight. This risk-calculator performed well, but underpredicted the birthweight by up to 143 g. We developed two new risk-calculators to predict growth-restricted babies (International Prediction of Pregnancy Complications-fetal growth restriction) and birthweight (International Prediction of Pregnancy Complications-birthweight). Both calculators accurately predicted the chances of the baby being born with growth restriction, and its birthweight. The birthweight was underpredicted by <9.7 g. The calculators performed well in both mothers predicted to be low and high risk. Further research is needed to determine the impact of using these calculators in practice, and challenges to implementing them in practice. Both International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight risk calculators will inform healthcare professionals and empower parents make informed decisions on monitoring and timing of delivery.

Development and validation of a prognostic model to predict birth weight: individual participant data meta-analysis.

Objective: To predict birth weight at various potential gestational ages of delivery based on data routinely available at the first antenatal visit. Design: Individual participant data meta-analysis. Data sources: Individual participant data of four cohorts (237 228 pregnancies) from the International Prediction of Pregnancy Complications (IPPIC) network dataset. Eligibility criteria for selecting studies: Studies in the IPPIC network were identified by searching major databases for studies reporting risk factors for adverse pregnancy outcomes, such as pre-eclampsia, fetal growth restriction, and stillbirth, from database inception to August 2019. Data of four IPPIC cohorts (237 228 pregnancies) from the US (National Institute of Child Health and Human Development, 2018; 233 483 pregnancies), UK (Allen et al, 2017; 1045 pregnancies), Norway (STORK Groruddalen research programme, 2010; 823 pregnancies), and Australia (Rumbold et al, 2006; 1877 pregnancies) were included in the development of the model. Results: The IPPIC birth weight model was developed with random intercept regression models with backward elimination for variable selection. Internal-external cross validation was performed to assess the study specific and pooled performance of the model, reported as calibration slope, calibration-in-the-large, and observed versus expected average birth weight ratio. Meta-analysis showed that the apparent performance of the model had good calibration (calibration slope 0.99, 95% confidence interval (CI) 0.88 to 1.10; calibration-in-the-large 44.5 g, -18.4 to 107.3) with an observed versus expected average birth weight ratio of 1.02 (95% CI 0.97 to 1.07). The proportion of variation in birth weight explained by the model (R2) was 46.9% (range 32.7-56.1% in each cohort). On internal-external cross validation, the model showed good calibration and predictive performance when validated in three cohorts with a calibration slope of 0.90 (Allen cohort), 1.04 (STORK Groruddalen cohort), and 1.07 (Rumbold cohort), calibration-in-the-large of -22.3 g (Allen cohort), -33.42 (Rumbold cohort), and 86.4 g (STORK Groruddalen cohort), and observed versus expected ratio of 0.99 (Rumbold cohort), 1.00 (Allen cohort), and 1.03 (STORK Groruddalen cohort); respective pooled estimates were 1.00 (95% CI 0.78 to 1.23; calibration slope), 9.7 g (-154.3 to 173.8; calibration-in-the-large), and 1.00 (0.94 to 1.07; observed v expected ratio). The model predictions were more accurate (smaller mean square error) in the lower end of predicted birth weight, which is important in informing clinical decision making. Conclusions: The IPPIC birth weight model allowed birth weight predictions for a range of possible gestational ages. The model explained about 50% of individual variation in birth weights, was well calibrated (especially in babies at high risk of fetal growth restriction and its complications), and showed promising performance in four different populations included in the individual participant data meta-analysis. Further research to examine the generalisability of performance in other countries, settings, and subgroups is required. Trial registration: PROSPERO CRD42019135045.

Musculoskeletal Health and Work: Development and Internal-External Cross-Validation of a Model to Predict Risk of Work Absence and Presenteeism in People Seeking Primary Healthcare.

PURPOSE: To develop and validate prediction models for the risk of future work absence and level of presenteeism, in adults seeking primary healthcare with musculoskeletal disorders (MSD). METHODS: Six studies from the West-Midlands/Northwest regions of England, recruiting adults consulting primary care with MSD were included for model development and internal-external cross-validation (IECV). The primary outcome was any work absence within 6 months of their consultation. Secondary outcomes included 6-month presenteeism and 12-month work absence. Ten candidate predictors were included: age; sex; multisite pain; baseline pain score; pain duration; job type; anxiety/depression; comorbidities; absence in the previous 6 months; and baseline presenteeism. RESULTS: For the 6-month absence model, 2179 participants (215 absences) were available across five studies. Calibration was promising, although varied across studies, with a pooled calibration slope of 0.93 (95% CI: 0.41-1.46) on IECV. On average, the model discriminated well between those with work absence within 6 months, and those without (IECV-pooled C-statistic 0.76, 95% CI: 0.66-0.86). The 6-month presenteeism model, while well calibrated on average, showed some individual-level variation in predictive accuracy, and the 12-month absence model was poorly calibrated due to the small available size for model development. CONCLUSIONS: The developed models predict 6-month work absence and presenteeism with reasonable accuracy, on average, in adults consulting with MSD. The model to predict 12-month absence was poorly calibrated and is not yet ready for use in practice. This information may support shared decision-making and targeting occupational health interventions at those with a higher risk of absence or presenteeism in the 6 months following consultation. Further external validation is needed before the models' use can be recommended or their impact on patients can be fully assessed.

Trends in clinical workload in UK primary care 2005-2019: a retrospective cohort study.

BACKGROUND: Substantial increases in UK consulting rates, mean consultation duration, and clinical workload were observed between 2007 and 2014. To the authors' knowledge, no analysis of more recent trends in clinical workload has been published to date. This study updates and builds on previous research, identifying underlying changes in population morbidity levels affecting demand for primary health care. AIM: To describe the changes in clinical workload in UK primary care since 2005. DESIGN AND SETTING: Retrospective cohort study using GP primary care electronic health records data from 824 UK general practices. METHOD: Over 500 million anonymised electronic health records were obtained from IQVIA Medical Research Data to examine consulting rates with GPs and practice nurses together with the duration of these consultations to determine total patient-level workload per person-year. RESULTS: Age-standardised mean GP direct (face-to-face and telephone) consulting rates fell steadily by 2.0% a year from 2014 to 2019. Between 2005 and 2019 mean GP direct consulting rates fell by 5.8% overall whereas mean workload per person-year increased by 25.8%, owing in part to a 36.9% increase in mean consultation duration. Indirect GP workload almost tripled over the 15 years, contributing to a 48.3% increase in overall clinical workload per person-year. The proportion of the study population with ≥3 serious chronic conditions increased from 9.7% to 16.1%, accounting for over a third of total clinical workload in 2019. CONCLUSION: Findings show sustained increases in consulting rates, consultation duration, and clinical workload until 2014. From 2015, however, rising demand for health care and a larger administrative workload have led to capacity constraints as the system nears saturation.

Association between pregnancy-related complications and development of type 2 diabetes and hypertension in women: an umbrella review.

BACKGROUND: Despite many systematic reviews and meta-analyses examining the associations of pregnancy complications with risk of type 2 diabetes mellitus (T2DM) and hypertension, previous umbrella reviews have only examined a single pregnancy complication. Here we have synthesised evidence from systematic reviews and meta-analyses on the associations of a wide range of pregnancy-related complications with risk of developing T2DM and hypertension. METHODS: Medline, Embase and Cochrane Database of Systematic Reviews were searched from inception until 26 September 2022 for systematic reviews and meta-analysis examining the association between pregnancy complications and risk of T2DM and hypertension. Screening of articles, data extraction and quality appraisal (AMSTAR2) were conducted independently by two reviewers using Covidence software. Data were extracted for studies that examined the risk of T2DM and hypertension in pregnant women with the pregnancy complication compared to pregnant women without the pregnancy complication. Summary estimates of each review were presented using tables, forest plots and narrative synthesis and reported following Preferred Reporting Items for Overviews of Reviews (PRIOR) guidelines. RESULTS: Ten systematic reviews were included. Two pregnancy complications were identified. Gestational diabetes mellitus (GDM): One review showed GDM was associated with a 10-fold higher risk of T2DM at least 1 year after pregnancy (relative risk (RR) 9.51 (95% confidence interval (CI) 7.14 to 12.67) and although the association differed by ethnicity (white: RR 16.28 (95% CI 15.01 to 17.66), non-white: RR 10.38 (95% CI 4.61 to 23.39), mixed: RR 8.31 (95% CI 5.44 to 12.69)), the between subgroups difference were not statistically significant at 5% significance level. Another review showed GDM was associated with higher mean blood pressure at least 3 months postpartum (mean difference in systolic blood pressure: 2.57 (95% CI 1.74 to 3.40) mmHg and mean difference in diastolic blood pressure: 1.89 (95% CI 1.32 to 2.46) mmHg). Hypertensive disorders of pregnancy (HDP): Three reviews showed women with a history of HDP were 3 to 6 times more likely to develop hypertension at least 6 weeks after pregnancy compared to women without HDP (meta-analysis with largest number of studies: odds ratio (OR) 4.33 (3.51 to 5.33)) and one review reported a higher rate of T2DM after HDP (hazard ratio (HR) 2.24 (1.95 to 2.58)) at least a year after pregnancy. One of the three reviews and five other reviews reported women with a history of preeclampsia were 3 to 7 times more likely to develop hypertension at least 6 weeks postpartum (meta-analysis with the largest number of studies: OR 3.90 (3.16 to 4.82) with one of these reviews reporting the association was greatest in women from Asia (Asia: OR 7.54 (95% CI 2.49 to 22.81), Europe: OR 2.19 (95% CI 0.30 to 16.02), North and South America: OR 3.32 (95% CI 1.26 to 8.74)). CONCLUSIONS: GDM and HDP are associated with a greater risk of developing T2DM and hypertension. Common confounders adjusted for across the included studies in the reviews were maternal age, body mass index (BMI), socioeconomic status, smoking status, pre-pregnancy and current BMI, parity, family history of T2DM or cardiovascular disease, ethnicity, and time of delivery. Further research is needed to evaluate the value of embedding these pregnancy complications as part of assessment for future risk of T2DM and chronic hypertension.

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed.

OBJECTIVES: Risk of bias assessments are important in meta-analyses of both aggregate and individual participant data (IPD). There is limited evidence on whether and how risk of bias of included studies or datasets in IPD meta-analyses (IPDMAs) is assessed. We review how risk of bias is currently assessed, reported, and incorporated in IPDMAs of test accuracy and clinical prediction model studies and provide recommendations for improvement. STUDY DESIGN AND SETTING: We searched PubMed (January 2018-May 2020) to identify IPDMAs of test accuracy and prediction models, then elicited whether each IPDMA assessed risk of bias of included studies and, if so, how assessments were reported and subsequently incorporated into the IPDMAs. RESULTS: Forty-nine IPDMAs were included. Nineteen of 27 (70%) test accuracy IPDMAs assessed risk of bias, compared to 5 of 22 (23%) prediction model IPDMAs. Seventeen of 19 (89%) test accuracy IPDMAs used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), but no tool was used consistently among prediction model IPDMAs. Of IPDMAs assessing risk of bias, 7 (37%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided details on the information sources (e.g., the original manuscript, IPD, primary investigators) used to inform judgments, and 4 (21%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided information or whether assessments were done before or after obtaining the IPD of the included studies or datasets. Of all included IPDMAs, only seven test accuracy IPDMAs (26%) and one prediction model IPDMA (5%) incorporated risk of bias assessments into their meta-analyses. For future IPDMA projects, we provide guidance on how to adapt tools such as Prediction model Risk Of Bias ASsessment Tool (for prediction models) and QUADAS-2 (for test accuracy) to assess risk of bias of included primary studies and their IPD. CONCLUSION: Risk of bias assessments and their reporting need to be improved in IPDMAs of test accuracy and, especially, prediction model studies. Using recommended tools, both before and after IPD are obtained, will address this.

Factors affecting the implementation of calcium supplementation strategies during pregnancy to prevent pre-eclampsia: a mixed-methods systematic review.

OBJECTIVES: Daily calcium supplements are recommended for pregnant women from 20 weeks' gestation to prevent pre-eclampsia in populations with low dietary calcium intake. We aimed to improve understanding of barriers and facilitators for calcium supplement intake during pregnancy to prevent pre-eclampsia. DESIGN: Mixed-method systematic review, with confidence assessed using the Grading of Recommendations, Assessment, Development and Evaluations-Confidence in the Evidence from Reviews of Qualitative research approach. DATA SOURCES: MEDLINE and EMBASE (via Ovid), CINAHL and Global Health (via EBSCO) and grey literature databases were searched up to 17 September 2022. ELIGIBILITY CRITERIA: We included primary qualitative, quantitative and mixed-methods studies reporting implementation or use of calcium supplements during pregnancy, excluding calcium fortification and non-primary studies. No restrictions were imposed on settings, language or publication date. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers extracted data and assessed risk of bias. We analysed the qualitative data using thematic synthesis, and quantitative findings were thematically mapped to qualitative findings. We then mapped the results to behavioural change frameworks to identify barriers and facilitators. RESULTS: Eighteen reports from nine studies were included in this review. Women reported barriers to consuming calcium supplements included limited knowledge about calcium supplements and pre-eclampsia, fears and experiences of side effects, varying preferences for tablets, dosing, working schedules, being away from home and taking other supplements. Receiving information regarding pre-eclampsia and safety of calcium supplement use from reliable sources, alternative dosing options, supplement reminders, early antenatal care, free supplements and support from families and communities were reported as facilitators. Healthcare providers felt that consistent messaging about benefits and risks of calcium, training, and ensuring adequate staffing and calcium supply is available would be able to help them in promoting calcium. CONCLUSION: Relevant stakeholders should consider the identified barriers and facilitators when formulating interventions and policies on calcium supplement use. These review findings can inform implementation to ensure effective and equitable provision and scale-up of calcium interventions. PROSPERO REGISTRATION NUMBER: CRD42021239143.

Predictors of outcome in sciatica patients following an epidural steroid injection: the POiSE prospective observational cohort study protocol.

INTRODUCTION: Sciatica can be very painful and, in most cases, is due to pressure on a spinal nerve root from a disc herniation with associated inflammation. For some patients, the pain persists, and one management option is a spinal epidural steroid injection (ESI). The aim of an ESI is to relieve leg pain, improve function and reduce the need for surgery. ESIs work well in some patients but not in others, but we cannot identify these patient subgroups currently. This study aims to identify factors, including patient characteristics, clinical examination and imaging findings, that help in predicting who does well and who does not after an ESI. The overall objective is to develop a prognostic model to support individualised patient and clinical decision-making regarding ESI. METHODS: POiSE is a prospective cohort study of 439 patients with sciatica referred by their clinician for an ESI. Participants will receive weekly text messages until 12 weeks following their ESIand then again at 24 weeks following their ESI to collect data on leg pain severity. Questionnaires will be sent to participants at baseline, 6, 12 and 24 weeks after their ESI to collect data on pain, disability, recovery and additional interventions. The prognosis for the cohort will be described. The primary outcome measure for the prognostic model is leg pain at 6 weeks. Prognostic models will also be developed for secondary outcomes of disability and recovery at 6 weeks and additional interventions at 24 weeks following ESI. Statistical analyses will include multivariable linear and logistic regression with mixed effects model. ETHICS AND DISSEMINATION: The POiSE study has received ethical approval (South Central Berkshire B Research Ethics Committee 21/SC/0257). Dissemination will be guided by our patient and public engagement group and will include scientific publications, conference presentations and social media.

Development and External Validation of Individualized Prediction Models for Pain Intensity Outcomes in Patients With Neck Pain, Low Back Pain, or Both in Primary Care Settings.

OBJECTIVE: The purpose of this study was to develop and externally validate multivariable prediction models for future pain intensity outcomes to inform targeted interventions for patients with neck or low back pain in primary care settings. METHODS: Model development data were obtained from a group of 679 adults with neck or low back pain who consulted a participating United Kingdom general practice. Predictors included self-report items regarding pain severity and impact from the STarT MSK Tool. Pain intensity at 2 and 6 months was modeled separately for continuous and dichotomized outcomes using linear and logistic regression, respectively. External validation of all models was conducted in a separate group of 586 patients recruited from a similar population with patients' predictor information collected both at point of consultation and 2 to 4 weeks later using self-report questionnaires. Calibration and discrimination of the models were assessed separately using STarT MSK Tool data from both time points to assess differences in predictive performance. RESULTS: Pain intensity and patients reporting their condition would last a long time contributed most to predictions of future pain intensity conditional on other variables. On external validation, models were reasonably well calibrated on average when using tool measurements taken 2 to 4 weeks after consultation (calibration slope = 0.848 [95% CI = 0.767 to 0.928] for 2-month pain intensity score), but performance was poor using point-of-consultation tool data (calibration slope for 2-month pain intensity score of 0.650 [95% CI = 0.549 to 0.750]). CONCLUSION: Model predictive accuracy was good when predictors were measured 2 to 4 weeks after primary care consultation, but poor when measured at the point of consultation. Future research will explore whether additional, nonmodifiable predictors improve point-of-consultation predictive performance. IMPACT: External validation demonstrated that these individualized prediction models were not sufficiently accurate to recommend their use in clinical practice. Further research is required to improve performance through inclusion of additional nonmodifiable risk factors.

Calcium supplementation to prevent pre-eclampsia: protocol for an individual participant data meta-analysis, network meta-analysis and health economic evaluation.

INTRODUCTION: Low dietary calcium intake is a risk factor for pre-eclampsia, a major contributor to maternal and perinatal mortality and morbidity worldwide. Calcium supplementation can prevent pre-eclampsia in women with low dietary calcium. However, the optimal dose and timing of calcium supplementation are not known. We plan to undertake an individual participant data (IPD) meta-analysis of randomised trials to determine the effects of various calcium supplementation regimens in preventing pre-eclampsia and its complications and rank these by effectiveness. We also aim to evaluate the cost-effectiveness of calcium supplementation to prevent pre-eclampsia. METHODS AND ANALYSIS: We will identify randomised trials on calcium supplementation before and during pregnancy by searching major electronic databases including Embase, CINAHL, MEDLINE, CENTRAL, PubMed, Scopus, AMED, LILACS, POPLINE, AIM, IMSEAR, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform, without language restrictions, from inception to February 2022. Primary researchers of the identified trials will be invited to join the International Calcium in Pregnancy Collaborative Network and share their IPD. We will check each study's IPD for consistency with the original authors before standardising and harmonising the data. We will perform a series of one-stage and two-stage IPD random-effect meta-analyses to obtain the summary intervention effects on pre-eclampsia with 95% CIs and summary treatment-covariate interactions (maternal risk status, dietary intake, timing of intervention, daily dose of calcium prescribed and total intake of calcium). Heterogeneity will be summarised using tau2, I2 and 95% prediction intervals for effect in a new study. Sensitivity analysis to explore robustness of statistical and clinical assumptions will be carried out. Minor study effects (potential publication bias) will be investigated using funnel plots. A decision analytical model for use in low-income and middle-income countries will assess the cost-effectiveness of calcium supplementation to prevent pre-eclampsia. ETHICS AND DISSEMINATION: No ethical approvals are required. We will store the data in a secure repository in an anonymised format. The results will be published in peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42021231276.

Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI.

BACKGROUND: Antihypertensives reduce the risk of cardiovascular disease but are also associated with harms including acute kidney injury (AKI). Few data exist to guide clinical decision making regarding these risks. AIM: To develop a prediction model estimating the risk of AKI in people potentially indicated for antihypertensive treatment. DESIGN AND SETTING: Observational cohort study using routine primary care data from the Clinical Practice Research Datalink (CPRD) in England. METHOD: People aged ≥40 years, with at least one blood pressure measurement between 130 mmHg and 179 mmHg were included. Outcomes were admission to hospital or death with AKI within 1, 5, and 10 years. The model was derived with data from CPRD GOLD (n = 1 772 618), using a Fine-Gray competing risks approach, with subsequent recalibration using pseudo-values. External validation used data from CPRD Aurum (n = 3 805 322). RESULTS: The mean age of participants was 59.4 years and 52% were female. The final model consisted of 27 predictors and showed good discrimination at 1, 5, and 10 years (C-statistic for 10-year risk 0.821, 95% confidence interval [CI] = 0.818 to 0.823). There was some overprediction at the highest predicted probabilities (ratio of observed to expected event probability for 10-year risk 0.633, 95% CI = 0.621 to 0.645), affecting patients with the highest risk. Most patients (>95%) had a low 1- to 5-year risk of AKI, and at 10 years only 0.1% of the population had a high AKI and low CVD risk. CONCLUSION: This clinical prediction model enables GPs to accurately identify patients at high risk of AKI, which will aid treatment decisions. As the vast majority of patients were at low risk, such a model may provide useful reassurance that most antihypertensive treatment is safe and appropriate while flagging the few for whom this is not the case.

Diabetic retinopathy progression in patients under monitoring for treatment or vision loss: external validation and update of a multivariable prediction model.

INTRODUCTION: The number of people with diabetes mellitus is increasing globally and consequently so too is diabetic retinopathy (DR). Most patients with diabetes are monitored through the diabetic eye screening programme (DESP) until they have signs of retinopathy and these changes progress, requiring referral into hospital eye services (HES). Here, they continue to be monitored until they require treatment. Due to current pressures on HES, delays can occur, leading to harm. There is a need to triage patients based on their individual risk. At present, patients are stratified according to retinopathy stage alone, yet other risk factors like glycated haemoglobin (HbA1c) may be useful. Therefore, a prediction model that combines multiple prognostic factors to predict progression will be useful for triage in this setting to improve care.We previously developed a Diabetic Retinopathy Progression model to Treatment or Vision Loss (DRPTVL-UK) using a large primary care database. The aim of the present study is to externally validate the DRPTVL-UK model in a secondary care setting, specifically in a population under care by HES. This study will also provide an opportunity to update the model by considering additional predictors not previously available. METHODS AND ANALYSIS: We will use a retrospective cohort of 2400 patients with diabetes aged 12 years and over, referred from DESP to the NHS hospital trusts with referable DR between 2013 and 2016, with follow-up information recorded until December 2021.We will evaluate the external validity of the DRPTVL-UK model using measures of discrimination, calibration and net benefit. In addition, consensus meetings will be held to agree on acceptable risk thresholds for triage within the HES system. ETHICS AND DISSEMINATION: This study was approved by REC (ref 22/SC/0425, 05/12/2022, Hampshire A Research Ethics Committee). The results of the study will be published in a peer-reviewed journal, presented at clinical conferences. TRIAL REGISTRATION NUMBER: ISRCTN 10956293.

Prognostic factors associated with outcome following an epidural steroid injection for disc-related sciatica: a systematic review and narrative synthesis.

PURPOSE: Clinical guidelines recommend epidural steroid injection (ESI) as a treatment option for severe disc-related sciatica, but there is considerable uncertainty about its effectiveness. Currently, we know very little about factors that might be associated with good or poor outcomes from ESI. The aim of this systematic review was to synthesise and appraise the evidence investigating prognostic factors associated with outcomes following ESI for patients with imaging confirmed disc-related sciatica. METHODS: The search strategy involved the electronic databases Medline, Embase, CINAHL Plus, PsycINFO and reference lists of eligible studies. Selected papers were quality appraised independently by two reviewers using the Quality in Prognosis Studies tool. Between-study heterogeneity precluded statistical pooling of results. RESULTS: 3094 citations were identified; 15 studies were eligible. Overall study quality was low with all judged to have moderate or high risk of bias. Forty-two prognostic factors were identified but were measured inconsistently. The most commonly assessed prognostic factors were related to pain and function (n = 10 studies), imaging features (n = 8 studies), patient socio-demographics (n = 7 studies), health and lifestyle (n = 6 studies), clinical assessment findings (n = 4 studies) and injection level (n = 4 studies). No prognostic factor was found to be consistently associated with outcomes following ESI. Most studies found no association or results that conflicted with other studies. CONCLUSIONS: There is little, and low quality, evidence to guide practice in terms of factors that predict outcomes in patients following ESI for disc-related sciatica. The results can help inform some of the decisions about potential prognostic factors that should be assessed in future well-designed prospective cohort studies.

Anticholinergic burden measures, symptoms, and fall-associated risk in older adults with polypharmacy: Development and validation of a prognostic model.

BACKGROUND: Anticholinergic burden has been associated with adverse outcomes such as falls. To date, no gold standard measure has been identified to assess anticholinergic burden, and no conclusion has been drawn on which of the different measure algorithms best predicts falls in older patients from general practice. This study compared the ability of five measures of anticholinergic burden to predict falls. To account for patients' individual susceptibility to medications, the added predictive value of typical anticholinergic symptoms was further quantified in this context. METHODS AND FINDINGS: To predict falls, models were developed and validated based on logistic regression models created using data from two German cluster-randomized controlled trials. The outcome was defined as "≥ 1 fall" vs. "no fall" within a 6-month follow-up period. Data from the RIME study (n = 1,197) were used in model development, and from PRIMUM (n = 502) for external validation. The models were developed step-wise in order to quantify the predictive ability of anticholinergic burden measures, and anticholinergic symptoms. In the development set, 1,015 patients had complete data and 188 (18.5%) experienced ≥ 1 fall within the 6-month follow-up period. The overall predictive value of the five anticholinergic measures was limited, with neither the employed anticholinergic variable (binary / count / burden), nor dose-dependent or dose-independent measures differing significantly in their ability to predict falls. The highest c-statistic was obtained using the German Anticholinergic Burden Score (0.73), whereby the optimism-corrected c-statistic was 0.71 after interval validation using bootstrapping and 0.63 in the external validation. Previous falls and dizziness / vertigo had the strongest prognostic value in all models. CONCLUSIONS: The ability of anticholinergic burden measures to predict falls does not appear to differ significantly, and the added value they contribute to risk classification in fall-prediction models is limited. Previous falls and dizziness / vertigo contributed most to model performance.

Development and external validation of a risk prediction model for falls in patients with an indication for antihypertensive treatment: retrospective cohort study.

OBJECTIVE: To develop and externally validate the STRAtifying Treatments In the multi-morbid Frail elderlY (STRATIFY)-Falls clinical prediction model to identify the risk of hospital admission or death from a fall in patients with an indication for antihypertensive treatment. DESIGN: Retrospective cohort study. SETTING: Primary care data from electronic health records contained within the UK Clinical Practice Research Datalink (CPRD). PARTICIPANTS: Patients aged 40 years or older with at least one blood pressure measurement between 130 mm Hg and 179 mm Hg. MAIN OUTCOME MEASURE: First serious fall, defined as hospital admission or death with a primary diagnosis of a fall within 10 years of the index date (12 months after cohort entry). Model development was conducted using a Fine-Gray approach in data from CPRD GOLD, accounting for the competing risk of death from other causes, with subsequent recalibration at one, five, and 10 years using pseudo values. External validation was conducted using data from CPRD Aurum, with performance assessed through calibration curves and the observed to expected ratio, C statistic, and D statistic, pooled across general practices, and clinical utility using decision curve analysis at thresholds around 10%. RESULTS: Analysis included 1 772 600 patients (experiencing 62 691 serious falls) from CPRD GOLD used in model development, and 3 805 366 (experiencing 206 956 serious falls) from CPRD Aurum in the external validation. The final model consisted of 24 predictors, including age, sex, ethnicity, alcohol consumption, living in an area of high social deprivation, a history of falls, multiple sclerosis, and prescriptions of antihypertensives, antidepressants, hypnotics, and anxiolytics. Upon external validation, the recalibrated model showed good discrimination, with pooled C statistics of 0.833 (95% confidence interval 0.831 to 0.835) and 0.843 (0.841 to 0.844) at five and 10 years, respectively. Original model calibration was poor on visual inspection and although this was improved with recalibration, under-prediction of risk remained (observed to expected ratio at 10 years 1.839, 95% confidence interval 1.811 to 1.865). Nevertheless, decision curve analysis suggests potential clinical utility, with net benefit larger than other strategies. CONCLUSIONS: This prediction model uses commonly recorded clinical characteristics and distinguishes well between patients at high and low risk of falls in the next 1-10 years. Although miscalibration was evident on external validation, the model still had potential clinical utility around risk thresholds of 10% and so could be useful in routine clinical practice to help identify those at high risk of falls who might benefit from closer monitoring or early intervention to prevent future falls. Further studies are needed to explore the appropriate thresholds that maximise the model's clinical utility and cost effectiveness.