Global disease burden of and risk factors for acute lower respiratory infections caused by respiratory syncytial virus in preterm infants and young children in 2019: a systematic review and meta-analysis of aggregated and individual participant data.

BACKGROUND: Infants and young children born prematurely are at high risk of severe acute lower respiratory infection (ALRI) caused by respiratory syncytial virus (RSV). In this study, we aimed to assess the global disease burden of and risk factors for RSV-associated ALRI in infants and young children born before 37 weeks of gestation. METHODS: We conducted a systematic review and meta-analysis of aggregated data from studies published between Jan 1, 1995, and Dec 31, 2021, identified from MEDLINE, Embase, and Global Health, and individual participant data shared by the Respiratory Virus Global Epidemiology Network on respiratory infectious diseases. We estimated RSV-associated ALRI incidence in community, hospital admission, in-hospital mortality, and overall mortality among children younger than 2 years born prematurely. We conducted two-stage random-effects meta-regression analyses accounting for chronological age groups, gestational age bands (early preterm, <32 weeks gestational age [wGA], and late preterm, 32 to <37 wGA), and changes over 5-year intervals from 2000 to 2019. Using individual participant data, we assessed perinatal, sociodemographic, and household factors, and underlying medical conditions for RSV-associated ALRI incidence, hospital admission, and three severity outcome groups (longer hospital stay [>4 days], use of supplemental oxygen and mechanical ventilation, or intensive care unit admission) by estimating pooled odds ratios (ORs) through a two-stage meta-analysis (multivariate logistic regression and random-effects meta-analysis). This study is registered with PROSPERO, CRD42021269742. FINDINGS: We included 47 studies from the literature and 17 studies with individual participant-level data contributed by the participating investigators. We estimated that, in 2019, 1 650 000 (95% uncertainty range [UR] 1 350 000-1 990 000) RSV-associated ALRI episodes, 533 000 (385 000-730 000) RSV-associated hospital admissions, 3050 (1080-8620) RSV-associated in-hospital deaths, and 26 760 (11 190-46 240) RSV-attributable deaths occurred in preterm infants worldwide. Among early preterm infants, the RSV-associated ALRI incidence rate and hospitalisation rate were significantly higher (rate ratio [RR] ranging from 1·69 to 3·87 across different age groups and outcomes) than for all infants born at any gestational age. In the second year of life, early preterm infants and young children had a similar incidence rate but still a significantly higher hospitalisation rate (RR 2·26 [95% UR 1·27-3·98]) compared with all infants and young children. Although late preterm infants had RSV-associated ALRI incidence rates similar to that of all infants younger than 1 year, they had higher RSV-associated ALRI hospitalisation rate in the first 6 months (RR 1·93 [1·11-3·26]). Overall, preterm infants accounted for 25% (95% UR 16-37) of RSV-associated ALRI hospitalisations in all infants of any gestational age. RSV-associated ALRI in-hospital case fatality ratio in preterm infants was similar to all infants. The factors identified to be associated with RSV-associated ALRI incidence were mainly perinatal and sociodemographic characteristics, and factors associated with severe outcomes from infection were mainly underlying medical conditions including congenital heart disease, tracheostomy, bronchopulmonary dysplasia, chronic lung disease, or Down syndrome (with ORs ranging from 1·40 to 4·23). INTERPRETATION: Preterm infants face a disproportionately high burden of RSV-associated disease, accounting for 25% of RSV hospitalisation burden. Early preterm infants have a substantial RSV hospitalisation burden persisting into the second year of life. Preventive products for RSV can have a substantial public health impact by preventing RSV-associated ALRI and severe outcomes from infection in preterm infants. FUNDING: EU Innovative Medicines Initiative Respiratory Syncytial Virus Consortium in Europe.

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.

Development and initial evaluation of a clinical prediction model for risk of treatment resistance in first-episode psychosis: Schizophrenia Prediction of Resistance to Treatment (SPIRIT).

BACKGROUND: A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication. AIMS: To develop and evaluate a model that could predict the risk of TRS in routine clinical practice. METHOD: We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model. RESULTS: We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723-0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism. CONCLUSIONS: We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.

Calibration plots for multistate risk predictions models.

INTRODUCTION: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation. METHODS: We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. RESULTS: The pseudo-value, BLR-IPCW, and MLR-IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low-density regions of predicted transition probability. CONCLUSIONS: We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN.

Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults.

BACKGROUND: Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year. METHODS: Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal-external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups. RESULTS: The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal-external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, -0.87; 95% CI: -0.96 to -0.78). Clinical utility on external validation was improved after recalibration. CONCLUSION: The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.

Clinical prediction models and the multiverse of madness.

BACKGROUND: Each year, thousands of clinical prediction models are developed to make predictions (e.g. estimated risk) to inform individual diagnosis and prognosis in healthcare. However, most are not reliable for use in clinical practice. MAIN BODY: We discuss how the creation of a prediction model (e.g. using regression or machine learning methods) is dependent on the sample and size of data used to develop it-were a different sample of the same size used from the same overarching population, the developed model could be very different even when the same model development methods are used. In other words, for each model created, there exists a multiverse of other potential models for that sample size and, crucially, an individual's predicted value (e.g. estimated risk) may vary greatly across this multiverse. The more an individual's prediction varies across the multiverse, the greater the instability. We show how small development datasets lead to more different models in the multiverse, often with vastly unstable individual predictions, and explain how this can be exposed by using bootstrapping and presenting instability plots. We recommend healthcare researchers seek to use large model development datasets to reduce instability concerns. This is especially important to ensure reliability across subgroups and improve model fairness in practice. CONCLUSIONS: Instability is concerning as an individual's predicted value is used to guide their counselling, resource prioritisation, and clinical decision making. If different samples lead to different models with very different predictions for the same individual, then this should cast doubt into using a particular model for that individual. Therefore, visualising, quantifying and reporting the instability in individual-level predictions is essential when proposing a new model.

Calculating the power of a planned individual participant data meta-analysis to examine prognostic factor effects for a binary outcome.

Collecting data for an individual participant data meta-analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a planned IPDMA project aiming to synthesise multiple cohort studies to investigate the (unadjusted or adjusted) effects of potential prognostic factors for a binary outcome. We consider both binary and continuous factors and provide a three-step approach to estimating the power in advance of collecting IPD, under an assumption of the true prognostic effect of each factor of interest. The first step uses routinely available (published) aggregate data for each study to approximate Fisher's information matrix and thereby estimate the anticipated variance of the unadjusted prognostic factor effect in each study. These variances are then used in step 2 to estimate the anticipated variance of the summary prognostic effect from the IPDMA. Finally, step 3 uses this variance to estimate the corresponding IPDMA power, based on a two-sided Wald test and the assumed true effect. Extensions are provided to adjust the power calculation for the presence of additional covariates correlated with the prognostic factor of interest (by using a variance inflation factor) and to allow for between-study heterogeneity in prognostic effects. An example is provided for illustration, and Stata code is supplied to enable researchers to implement the method.

GRADE concept paper 8: judging the certainty of discrimination performance estimates of prognostic models in a body of validation studies.

BACKGROUND: Prognostic models incorporate multiple prognostic factors to estimate the likelihood of future events for individual patients based on their prognostic factor values. Evaluating these models crucially involves conducting studies to assess their predictive performance, like discrimination. Systematic reviews and meta-analyses of these validation studies play an essential role in selecting models for clinical practice. METHODS: In this paper, we outline 3 thresholds to determine the target for certainty rating in the discrimination of prognostic models, as observed across a body of validation studies. RESULTS AND CONCLUSION: We propose 3 thresholds when rating the certainty of evidence about a prognostic model's discrimination. The first threshold amounts to rating certainty in the model's ability to classify better than random chance. The other 2 approaches involve setting thresholds informed by other mechanisms for classification: clinician intuition or an alternative prognostic model developed for the same disease area and outcome. The choice of threshold will vary based on the context. Instead of relying on arbitrary discrimination cut-offs, our approach positions the observed discrimination within an informed spectrum, potentially aiding decisions about a prognostic model's practical utility.

SPIN-PM: a consensus framework to evaluate the presence of spin in studies on prediction models.

OBJECTIVES: To develop a framework to identify and evaluate spin practices and its facilitators in studies on clinical prediction model regardless of the modeling technique. STUDY DESIGN AND SETTING: We followed a three-phase consensus process: (1) premeeting literature review to generate items to be included; (2) a series of structured meetings to provide comments discussed and exchanged viewpoints on items to be included with a panel of experienced researchers; and (3) postmeeting review on final list of items and examples to be included. Through this iterative consensus process, a framework was derived after all panel's researchers agreed. RESULTS: This consensus process involved a panel of eight researchers and resulted in SPIN-Prediction Models which consists of two categories of spin (misleading interpretation and misleading transportability), and within these categories, two forms of spin (spin practices and facilitators of spin). We provide criteria and examples. CONCLUSION: We proposed this guidance aiming to facilitate not only the accurate reporting but also an accurate interpretation and extrapolation of clinical prediction models which will likely improve the reporting quality of subsequent research, as well as reduce research waste.

Community based complex interventions to sustain independence in older people: systematic review and network meta-analysis.

OBJECTIVE: To synthesise evidence of the effectiveness of community based complex interventions, grouped according to their intervention components, to sustain independence for older people. DESIGN: Systematic review and network meta-analysis. DATA SOURCES: Medline, Embase, CINAHL, PsycINFO, CENTRAL, clinicaltrials.gov, and International Clinical Trials Registry Platform from inception to 9 August 2021 and reference lists of included studies. ELIGIBILITY CRITERIA: Randomised controlled trials or cluster randomised controlled trials with ≥24 weeks' follow-up studying community based complex interventions for sustaining independence in older people (mean age ≥65 years) living at home, with usual care, placebo, or another complex intervention as comparators. MAIN OUTCOMES: Living at home, activities of daily living (personal/instrumental), care home placement, and service/economic outcomes at 12 months. DATA SYNTHESIS: Interventions were grouped according to a specifically developed typology. Random effects network meta-analysis estimated comparative effects; Cochrane's revised tool (RoB 2) structured risk of bias assessment. Grading of recommendations assessment, development and evaluation (GRADE) network meta-analysis structured certainty assessment. RESULTS: The review included 129 studies (74 946 participants). Nineteen intervention components, including "multifactorial action from individualised care planning" (a process of multidomain assessment and management leading to tailored actions), were identified in 63 combinations. For living at home, compared with no intervention/placebo, evidence favoured multifactorial action from individualised care planning including medication review and regular follow-ups (routine review) (odds ratio 1.22, 95% confidence interval 0.93 to 1.59; moderate certainty); multifactorial action from individualised care planning including medication review without regular follow-ups (2.55, 0.61 to 10.60; low certainty); combined cognitive training, medication review, nutritional support, and exercise (1.93, 0.79 to 4.77; low certainty); and combined activities of daily living training, nutritional support, and exercise (1.79, 0.67 to 4.76; low certainty). Risk screening or the addition of education and self-management strategies to multifactorial action from individualised care planning and routine review with medication review may reduce odds of living at home. For instrumental activities of daily living, evidence favoured multifactorial action from individualised care planning and routine review with medication review (standardised mean difference 0.11, 95% confidence interval 0.00 to 0.21; moderate certainty). Two interventions may reduce instrumental activities of daily living: combined activities of daily living training, aids, and exercise; and combined activities of daily living training, aids, education, exercise, and multifactorial action from individualised care planning and routine review with medication review and self-management strategies. For personal activities of daily living, evidence favoured combined exercise, multifactorial action from individualised care planning, and routine review with medication review and self-management strategies (0.16, -0.51 to 0.82; low certainty). For homecare recipients, evidence favoured addition of multifactorial action from individualised care planning and routine review with medication review (0.60, 0.32 to 0.88; low certainty). High risk of bias and imprecise estimates meant that most evidence was low or very low certainty. Few studies contributed to each comparison, impeding evaluation of inconsistency and frailty. CONCLUSIONS: The intervention most likely to sustain independence is individualised care planning including medicines optimisation and regular follow-up reviews resulting in multifactorial action. Homecare recipients may particularly benefit from this intervention. Unexpectedly, some combinations may reduce independence. Further research is needed to investigate which combinations of interventions work best for different participants and contexts. REGISTRATION: PROSPERO CRD42019162195.

Effect of a doctor working during the festive period on population health: natural experiment using 60 years of Doctor Who episodes (the TARDIS study).

OBJECTIVE: To examine the effect of a (fictional) doctor working during the festive period on population health. DESIGN: Natural experiment. SETTING: England, Wales, and the UK. MAIN OUTCOME MEASURES: Age standardised annual mortality rates in England, Wales, and the UK from 1963, when the BBC first broadcast Doctor Who, a fictional programme with a character called the Doctor who fights villains and intervenes to save others while travelling through space and time. Mortality rates were modelled in a time series analysis accounting for non-linear trends over time, and associations were estimated in relation to a new Doctor Who episode broadcast during the previous festive period, 24 December to 1 January. An interrupted time series analysis modelled the shift in mortality rates from 2005, when festive episodes of Doctor Who could be classed as a yearly Christmas intervention. RESULTS: 31 festive periods from 1963 have featured a new Doctor Who episode, including 14 broadcast on Christmas Day. In time series analyses, an association was found between broadcasts during the festive period and subsequent lower annual mortality rates. In particular, episodes shown on Christmas Day were associated with 0.60 fewer deaths per 1000 person years (95% confidence interval 0.21 to 0.99; P=0.003) in England and Wales and 0.40 fewer deaths per 1000 person years (0.08 to 0.73; P=0.02) in the UK. The interrupted time series analysis showed a strong shift (reduction) in mortality rates from 2005 onwards in association with the Doctor Who Christmas intervention, with a mean 0.73 fewer deaths per 1000 person years (0.21 to 1.26; P=0.01) in England and Wales and a mean 0.62 fewer deaths per 1000 person years (0.16 to 1.09; P=0.01) in the UK. CONCLUSIONS: A new Doctor Who episode shown every festive period, especially on Christmas Day, was associated with reduced mortality rates in England, Wales, and the UK, suggesting that a doctor working over the festive period could lower mortality rates. This finding reinforces why healthcare provision should not be taken for granted and may prompt the BBC and Disney+ to televise new episodes of Doctor Who every festive period, ideally on Christmas Day.

Moderators of the effect of therapeutic exercise for knee and hip osteoarthritis: a systematic review and individual participant data meta-analysis.

BACKGROUND: Many international clinical guidelines recommend therapeutic exercise as a core treatment for knee and hip osteoarthritis. We aimed to identify individual patient-level moderators of the effect of therapeutic exercise for reducing pain and improving physical function in people with knee osteoarthritis, hip osteoarthritis, or both. METHODS: We did a systematic review and individual participant data (IPD) meta-analysis of randomised controlled trials comparing therapeutic exercise with non-exercise controls in people with knee osteoathritis, hip osteoarthritis, or both. We searched ten databases from March 1, 2012, to Feb 25, 2019, for randomised controlled trials comparing the effects of exercise with non-exercise or other exercise controls on pain and physical function outcomes among people with knee osteoarthritis, hip osteoarthritis, or both. IPD were requested from leads of all eligible randomised controlled trials. 12 potential moderators of interest were explored to ascertain whether they were associated with short-term (12 weeks), medium-term (6 months), and long-term (12 months) effects of exercise on self-reported pain and physical function, in comparison with non-exercise controls. Overall intervention effects were also summarised. This study is prospectively registered on PROSPERO (CRD42017054049). FINDINGS: Of 91 eligible randomised controlled trials that compared exercise with non-exercise controls, IPD from 31 randomised controlled trials (n=4241 participants) were included in the meta-analysis. Randomised controlled trials included participants with knee osteoarthritis (18 [58%] of 31 trials), hip osteoarthritis (six [19%]), or both (seven [23%]) and tested heterogeneous exercise interventions versus heterogeneous non-exercise controls, with variable risk of bias. Summary meta-analysis results showed that, on average, compared with non-exercise controls, therapeutic exercise reduced pain on a standardised 0-100 scale (with 100 corresponding to worst pain), with a difference of -6·36 points (95% CI -8·45 to -4·27, borrowing of strength [BoS] 10·3%, between-study variance [τ2] 21·6) in the short term, -3·77 points (-5·97 to -1·57, BoS 30·0%, τ2 14·4) in the medium term, and -3·43 points (-5·18 to -1·69, BoS 31·7%, τ2 4·5) in the long term. Therapeutic exercise also improved physical function on a standardised 0-100 scale (with 100 corresponding to worst physical function), with a difference of -4·46 points in the short term (95% CI -5·95 to -2·98, BoS 10·5%, τ2 10·1), -2·71 points in the medium term (-4·63 to -0·78, BoS 33·6%, τ2 11·9), and -3·39 points in the long term (-4·97 to -1·81, BoS 34·1%, τ2 6·4). Baseline pain and physical function moderated the effect of exercise on pain and physical function outcomes. Those with higher self-reported pain and physical function scores at baseline (ie, poorer physical function) generally benefited more than those with lower self-reported pain and physical function scores at baseline, with the evidence most certain in the short term (12 weeks). INTERPRETATION: There was evidence of a small, positive overall effect of therapeutic exercise on pain and physical function compared with non-exercise controls. However, this effect is of questionable clinical importance, particularly in the medium and long term. As individuals with higher pain severity and poorer physical function at baseline benefited more than those with lower pain severity and better physical function at baseline, targeting individuals with higher levels of osteoarthritis-related pain and disability for therapeutic exercise might be of merit. FUNDING: Chartered Society of Physiotherapy Charitable Trust and the National Institute for Health and Care Research.