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.

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.

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.

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.

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.

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.

Risk-stratified monitoring for sulfasalazine toxicity: prognostic model development and validation.

BACKGROUND: Sulfasalazine-induced cytopenia, nephrotoxicity and hepatotoxicity is uncommon during long-term treatment. Some guidelines recommend 3 monthly monitoring blood tests indefinitely during long-term treatment while others recommend stopping monitoring after 1 year. To rationalise monitoring, we developed and validated a prognostic model for clinically significant blood, liver or kidney toxicity during established sulfasalazine treatment. DESIGN: Retrospective cohort study. SETTING: UK primary care. Data from Clinical Practice Research Datalink Gold and Aurum formed independent development and validation cohorts. PARTICIPANTS: Age ≥18 years, new diagnosis of an inflammatory condition and sulfasalazine prescription. STUDY PERIOD: 1 January 2007 to 31 December 2019. OUTCOME: Sulfasalazine discontinuation with abnormal monitoring blood-test result. ANALYSIS: Patients were followed up from 6 months after first primary care prescription to the earliest of outcome, drug discontinuation, death, 5 years or 31 December 2019. Penalised Cox regression was performed to develop the risk equation. Multiple imputation handled missing predictor data. Model performance was assessed in terms of calibration and discrimination. RESULTS: 8936 participants were included in the development cohort (473 events, 23 299 person-years) and 5203 participants were included in the validation cohort (280 events, 12 867 person-years). Nine candidate predictors were included. The optimism adjusted R2 D and Royston D statistic in the development data were 0.13 and 0.79, respectively. The calibration slope (95% CI) and Royston D statistic (95% CI) in validation cohort was 1.19 (0.96 to 1.43) and 0.87 (0.67 to 1.07), respectively. CONCLUSION: This prognostic model for sulfasalazine toxicity uses readily available data and should be used to risk-stratify blood-test monitoring during established sulfasalazine treatment.

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.

Medical students' perceptions of an artificial intelligence (AI) assisted diagnosing program.

As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians.

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.

Prognostic factors for liver, blood and kidney adverse events from glucocorticoid sparing immune-suppressing drugs in immune-mediated inflammatory diseases: a prognostic systematic review.

BACKGROUND: Immune-suppressing drugs can cause liver, kidney or blood toxicity. Prognostic factors for these adverse-events are poorly understood. PURPOSE: To ascertain prognostic factors associated with liver, blood or kidney adverse-events in people receiving immune-suppressing drugs. DATA SOURCES: MEDLINE, Web of Science, EMBASE and the Cochrane library (01 January 1995 to 05 January 2023), and supplementary sources. DATA EXTRACTION AND SYNTHESIS: Data were extracted by one reviewer using a modified CHARMS-PF checklist and validated by another. Two independent reviewers assessed risk of bias using Quality in Prognostic factor Studies tool and assessed the quality of evidence using a Grading of Recommendations Assessment, Development and Evaluation-informed framework. RESULTS: Fifty-six studies from 58 papers were included. High-quality evidence of the following associations was identified: elevated liver enzymes (6 studies) and folate non-supplementation (3 studies) are prognostic factors for hepatotoxicity in those treated with methotrexate; that mercaptopurine (vs azathioprine) (3 studies) was a prognostic factor for hepatotoxicity in those treated with thiopurines; that mercaptopurine (vs azathioprine) (3 studies) and poor-metaboliser status (4 studies) were prognostic factors for cytopenia in those treated with thiopurines; and that baseline elevated liver enzymes (3 studies) are a prognostic factor for hepatotoxicity in those treated with anti-tumour necrosis factors. Moderate and low quality evidence for several other demographic, lifestyle, comorbidities, baseline bloods/serologic or treatment-related prognostic factors were also identified. LIMITATIONS: Studies published before 1995, those with less than 200 participants and not published in English were excluded. Heterogeneity between studies included different cut-offs for prognostic factors, use of different outcome definitions and different adjustment factors. CONCLUSIONS: Prognostic factors for target-organ damage were identified which may be further investigated for their potential role in targeted (risk-stratified) monitoring. PROSPERO REGISTRATION NUMBER: CRD42020208049.

Open science practices need substantial improvement in prognostic model studies in oncology using machine learning.

OBJECTIVE: To describe the frequency of open science practices in a contemporary sample of studies developing prognostic models using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING: We conducted a systematic review, searching the MEDLINE database between December 1, 2022, and December 31, 2022, for studies developing a multivariable prognostic model using machine learning methods (as defined by the authors) in oncology. Two authors independently screened records and extracted open science practices. RESULTS: We identified 46 publications describing the development of a multivariable prognostic model. The adoption of open science principles was poor. Only one study reported availability of a study protocol, and only one study was registered. Funding statements and conflicts of interest statements were common. Thirty-five studies (76%) provided data sharing statements, with 21 (46%) indicating data were available on request to the authors and seven declaring data sharing was not applicable. Two studies (4%) shared data. Only 12 studies (26%) provided code sharing statements, including 2 (4%) that indicated the code was available on request to the authors. Only 11 studies (24%) provided sufficient information to allow their model to be used in practice. The use of reporting guidelines was rare: eight studies (18%) mentioning using a reporting guideline, with 4 (10%) using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis statement, 1 (2%) using Minimum Information About Clinical Artificial Intelligence Modeling and Consolidated Standards Of Reporting Trials-Artificial Intelligence, 1 (2%) using Strengthening The Reporting Of Observational Studies In Epidemiology, 1 (2%) using Standards for Reporting Diagnostic Accuracy Studies, and 1 (2%) using Transparent Reporting of Evaluations with Nonrandomized Designs. CONCLUSION: The adoption of open science principles in oncology studies developing prognostic models using machine learning methods is poor. Guidance and an increased awareness of benefits and best practices of open science are needed for prediction research in oncology.

Discontinuation of anti-tumour necrosis factor alpha treatment owing to blood test abnormalities, and cost-effectiveness of alternate blood monitoring strategies.

BACKGROUND: There is no evidence base to support the use of 6-monthly monitoring blood tests for the early detection of liver, blood and renal toxicity during established anti-tumour necrosis factor alpha (TNFα) treatment. OBJECTIVES: To evaluate the incidence and risk factors of anti-TNFα treatment cessation owing to liver, blood and renal side-effects, and to estimate the cost-effectiveness of alternate intervals between monitoring blood tests. METHODS: A secondary care-based retrospective cohort study was performed. Data from the British Association of Dermatologists Biologic and Immunomodulators Register (BADBIR) were used. Patients with at least moderate psoriasis prescribed their first anti-TNFα treatment were included. Treatment discontinuation due to a monitoring blood test abnormality was the primary outcome. Patients were followed-up from start of treatment to the outcome of interest, drug discontinuation, death, 31 July 2021 or up to 5 years, whichever came first. The incidence rate (IR) and 95% confidence intervals (CIs) of anti-TNFα discontinuation with monitoring blood test abnormality was calculated. Multivariate Cox regression was used to examine the association between risk factors and outcome. A mathematical model evaluated costs and quality-adjusted life years (QALYs) associated with increasing the length of time between monitoring blood tests during anti-TNFα treatment. RESULTS: The cohort included 8819 participants [3710 (42.1%) female, mean (SD) age 44.76 (13.20) years] that contributed 25 058 person-years (PY) of follow-up and experienced 125 treatment discontinuations owing to a monitoring blood test abnormality at an IR of 5.85 (95% CI 4.91-6.97)/1000 PY. Of these, 64 and 61 discontinuations occurred within the first year and after the first year of treatment start, at IRs of 8.62 (95% CI 6.74-11.01) and 3.44 (95% CI 2.67-4.42)/1000 PY, respectively. Increasing age (in years), diabetes and liver disease were associated with anti-TNFα discontinuation after a monitoring blood test abnormality [adjusted hazard ratios of 1.02 (95% CI 1.01-1.04), 1.68 (95% CI 1.00-2.81) and 2.27 (95% CI 1.26-4.07), respectively]. Assuming a threshold of £20 000 per QALY gained, no monitoring was most cost-effective, but all extended periods were cost-effective vs. 3- or 6-monthly monitoring. CONCLUSIONS: Anti-TNFα drugs were uncommonly discontinued owing to abnormal monitoring blood tests after the first year of treatment. Extending the duration between monitoring blood tests was cost-effective. Our results produce evidence for specialist society guidance to reduce patient monitoring burden and healthcare costs.