Temporal trends of in-hospital mortality and its determinants following percutaneous coronary intervention in patients with acute coronary syndrome in England and Wales: A population-based study between 2006 and 2021.

BACKGROUND: There is limited data around drivers of changes in mortality over time. We aimed to examine the temporal changes in mortality and understand its determinants over time. METHODS: 743,149 PCI procedures for patients from the British Cardiovascular Intervention Society (BCIS) database who were aged between 18 and 100 years and underwent Percutaneous Coronary Intervention (PCI) for Acute Coronary Syndrome (ACS) in England and Wales between 2006 and 2021 were included. We decomposed the contributing factors to the difference in the observed mortality proportions between 2006 and 2021 using Fairlie decomposition method. Multiple imputation was used to address missing data. RESULTS: Overall, there was an increase in the mortality proportion over time, from 1.7% (95% CI: 1.5% to 1.9%) in 2006 to 3.1% (95% CI: 3.0% to 3.2%) in 2021. 61.2% of this difference was explained by the variables included in the model. ACS subtypes (percentage contribution: 14.67%; 95% CI: 5.76% to 23.59%) and medical history (percentage contribution: 13.50%; 95% CI: 4.33% to 22.67%) were the strongest contributors to the difference in the observed mortality proportions between 2006 and 2021. Also, there were different drivers to mortality changes between different time periods. Specifically, ACS subtypes and severity of presentation were amongst the strongest contributors between 2006 and 2012 while access site and demographics were the strongest contributors between 2012 and 2021. CONCLUSIONS: Patient factors and the move towards ST-elevated myocardial infarction (STEMI) PCI have driven the short-term mortality changes following PCI for ACS the most.

10-year multimorbidity patterns among people with and without rheumatic and musculoskeletal diseases: an observational cohort study using linked electronic health records from Wales, UK.

OBJECTIVES: To compare the patterns of multimorbidity between people with and without rheumatic and musculoskeletal diseases (RMDs) and to describe how these patterns change by age and sex over time, between 2010 and 2019. PARTICIPANTS: 103 426 people with RMDs and 2.9 million comparators registered in 395 Wales general practices (GPs). Each patient with an RMD aged 0-100 years between January 2010 and December 2019 registered in Clinical Practice Research Welsh practices was matched with up to five comparators without an RMD, based on age, gender and GP code. PRIMARY OUTCOME MEASURES: The prevalence of 29 Elixhauser-defined comorbidities in people with RMDs and comparators categorised by age, gender and GP practices. Conditional logistic regression models were fitted to calculate differences (OR, 95% CI) in associations with comorbidities between cohorts. RESULTS: The most prevalent comorbidities were cardiovascular risk factors, hypertension and diabetes. Having an RMD diagnosis was associated with a significantly higher odds for many conditions including deficiency anaemia (OR 1.39, 95% CI (1.32 to 1.46)), hypothyroidism (OR 1.34, 95% CI (1.19 to 1.50)), pulmonary circulation disorders (OR 1.39, 95% CI 1.12 to 1.73) diabetes (OR 1.17, 95% CI (1.11 to 1.23)) and fluid and electrolyte disorders (OR 1.27, 95% CI (1.17 to 1.38)). RMDs have a higher proportion of multimorbidity (two or more conditions in addition to the RMD) compared with non-RMD group (81% and 73%, respectively in 2019) and the mean number of comorbidities was higher in women from the age of 25 and 50 in men than in non-RMDs group. CONCLUSION: People with RMDs are approximately 1.5 times as likely to have multimorbidity as the general population and provide a high-risk group for targeted intervention studies. The individuals with RMDs experience a greater load of coexisting health conditions, which tend to manifest at earlier ages. This phenomenon is particularly pronounced among women. Additionally, there is an under-reporting of comorbidities in individuals with RMDs.

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.

Pre-Operative Measures of Systemic Inflammation Predict Survival After Surgery for Primary Lung Cancer.

BACKGROUND: Measures of systemic inflammation (MSIs) have been developed and shown to help predict prognosis in patients with lung cancer. However, studies investigating the impact of MSIs on outcomes solely in cohorts of patients undergoing curative-intent resection of NSCLC are lacking. In the era of individualized therapies, targeting inflammatory pathways could represent a novel addition to the armamentarium of lung cancer treatment. METHODS: A multicentre retrospective review of patients who underwent primary lung cancer resection between 2012 and 2018 was undertaken. MSIs assessed were neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), systemic immune inflammation index (SII), advanced lung cancer inflammation index (ALI), prognostic nutritional index (PNI) and haemoglobin albumin lymphocyte platelet (HALP) score. Cox regression analysis was performed to assess the impact of MSIs on overall survival. RESULTS: A total of 5029 patients were included in the study. Overall 90-day mortality was 3.7% (n = 185). All MSIs were significantly associated with overall survival on univariable analysis. After multivariable Cox regression analyses, lower ALI (expressed as a continuous variable) (HR 1.000, 95% CI 1.000-1.000, P = .049) and ALI <366.43 (expressed as a dichotomous variable) (HR 1.362, 95% CI 1.137-1.631, P < .001) remained independently associated with reduced overall survival. CONCLUSIONS: MSIs have emerged in this study as potentially important factors associated with survival following lung resection for NSCLC with curative intent. In particular, ALI has emerged as independently associated with long-term outcomes. The role of MSIs in the clinical management of patients with primary lung cancer requires further investigation.

Association of a device-based remote management heart failure pathway with outcomes: TriageHF Plus real-world evaluation.

AIMS: Clinical pathways have been shown to improve outcomes in patients with heart failure (HF). Although patients with HF often have a cardiac implantable electronic device, few studies have reported the utility of device-derived risk scores to augment and organize care. TriageHF Plus is a device-based HF clinical pathway (DHFP) that uses remote monitoring alerts to trigger structured telephone assessment for HF stability and optimization. We aimed to evaluate the impact of TriageHF Plus on hospitalizations and describe the associated workforce burden. METHODS AND RESULTS: TriageHF Plus was a multi-site, prospective study that compared outcomes for patients recruited between April 2019 and February 2021. All alert-triggered assessments were analysed to determine the appropriateness of the alert and the workload burden. A negative-binomial regression with inverse probability treatment weighting using a time-matched usual care cohort was applied to estimate the effect of TriageHF Plus on non-elective hospitalizations. A post hoc pre-COVID-19 sensitivity analysis was also performed. The TriageHF Plus cohort (n = 443) had a mean age of 68.8 ± 11.2 years, 77% male (usual care cohort: n = 315, mean age of 66.2 ± 14.5 years, 65% male). In the TriageHF Plus cohort, an acute medical issue was identified following an alert in 79/182 (43%) cases. Fifty assessments indicated acute HF, requiring clinical action in 44 cases. At 30 day follow-up, 39/66 (59%) of initially symptomatic patients reported improvement, and 20 (19%) initially asymptomatic patients had developed new symptoms. On average, each assessment took 10 min. The TriageHF Plus group had a 58% lower rate of hospitalizations across full follow-up [incidence relative ratio: 0.42, 95% confidence interval (CI): 0.23-0.76, P = 0.004]. Across the pre-COVID-19 window, hospitalizations were 31% lower (0.69, 95% CI: 0.46-1.04, P = 0.077). CONCLUSIONS: These data represent the largest real-world evaluation of a DHFP based on multi-parametric risk stratification. The TriageHF Plus clinical pathway was associated with an improvement in HF symptoms and reduced all-cause hospitalizations.

External validation of the computer aided risk scoring system in predicting in-hospital mortality following emergency medical admissions.

BACKGROUND: Clinical prediction models have the potential to improve the quality of care and enhance patient safety outcomes. A Computer-aided Risk Scoring system (CARSS) was previously developed to predict in-hospital mortality following emergency admissions based on routinely collected blood tests and vitals. We aimed to externally validate the CARSS model. METHODS: In this retrospective external validation study, we considered all adult (≥18 years) emergency medical admissions discharged between 11/11/2020 and 11/11/2022 from The Rotherham Foundation Trust (TRFT), UK. We assessed the predictive performance of the CARSS model based on its discriminative (c-statistic) and calibration characteristics (calibration slope and calibration plots). RESULTS: Out of 32,774 admissions, 20,422 (62.3 %) admissions were included. The TRFT sample had similar demographic characteristics to the development sample but had higher mortality (6.1 % versus 5.7 %). The CARSS model demonstrated good discrimination (c-statistic 0.87 [95 % CI 0.86-0.88]) and good calibration to the TRFT dataset (slope = 1.03 [95 % CI 0.98-1.08] intercept = 0 [95 % CI -0.06-0.07]) after re-calibrating for differences in baseline mortality (intercept = 0.96 [95 % CI 0.90-1.03] before re-calibration). CONCLUSION: In summary, the CARSS model is externally validated after correcting the baseline risk of death between development and validation datasets. External validation of the CARSS model showed that it under-predicted in-hospital mortality. Re-calibration of this model showed adequate performance in the TRFT dataset.

Indirect impact of the war in Ukraine on primary percutaneous coronary intervention for ST-segment elevation myocardial infarction in Poland.

INTRODUCTION: The Russian invasion of Ukraine in February 2022 resulted in displacement of approximately 12.5 million refugees to adjacent countries, including Poland, which may have strained health care service delivery. OBJECTIVES: Using the ST­segment elevation myocardial infarction (STEMI) data, we aimed to evaluate whether the Russian invasion of Ukraine has indirectly impacted delivery of acute cardiovascular care in Poland. PATIENTS AND METHODS: We analyzed all adult patients undergoing percutaneous coronary interventions (PCIs) for STEMI across Poland between February 25, 2017 and May 24, 2022. The investigated health care centers were allocated to regions below and over 100 km from the Polish-Ukrainian border. Mixed­effect generalized linear regression models with random effects per hospital were used to explore the associations between the war in Ukraine and several parameters, and whether these associations differed across the regions below and over 100 km from the border. RESULTS: A total of 90 115 procedures were included in the analysis. The average number of procedures per month was similar to the predicted volume for centers over 100 km from the border, while it was higher than expected (by an estimated median of 15 [interquartile range, 11-19]) for the region below 100 km from the border. There was no difference in adjusted fatality rate or quality of care outcomes for pre- and during­war time in both regions, with no evidence of a difference­in­difference across the regions. CONCLUSIONS: Following the Russian invasion of Ukraine, there was only a modest and temporary increase in the number of primary PCIs, predominantly in the centers situated within 100 km of the Polish-Ukrainian border, although no significant impact on in­hospital fatality rate was found.

Sepsis and case fatality rates and associations with deprivation, ethnicity, and clinical characteristics: population-based case-control study with linked primary care and hospital data in England.

PURPOSE: Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. The purpose of the study was to measure the associations of specific exposures (deprivation, ethnicity, and clinical characteristics) with incident sepsis and case fatality. METHODS: Two research databases in England were used including anonymized patient-level records from primary care linked to hospital admission, death certificate, and small-area deprivation. Sepsis cases aged 65-100 years were matched to up to six controls. Predictors for sepsis (including 60 clinical conditions) were evaluated using logistic and random forest models; case fatality rates were analyzed using logistic models. RESULTS: 108,317 community-acquired sepsis cases were analyzed. Severe frailty was strongly associated with the risk of developing sepsis (crude odds ratio [OR] 14.93; 95% confidence interval [CI] 14.37-15.52). The quintile with most deprived patients showed an increased sepsis risk (crude OR 1.48; 95% CI 1.45-1.51) compared to least deprived quintile. Strong predictors for sepsis included antibiotic exposure in prior 2 months, being house bound, having cancer, learning disability, and diabetes mellitus. Severely frail patients had a case fatality rate of 42.0% compared to 24.0% in non-frail patients (adjusted OR 1.53; 95% CI 1.41-1.65). Sepsis cases with recent prior antibiotic exposure died less frequently compared to non-users (adjusted OR 0.7; 95% CI 0.72-0.76). Case fatality strongly decreased over calendar time. CONCLUSION: Given the variety of predictors and their level of associations for developing sepsis, there is a need for prediction models for risk of developing sepsis that can help to target preventative antibiotic therapy.

Making predictions under interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care.

Background: Most existing clinical prediction models do not allow predictions under interventions. Such predictions allow predicted risk under different proposed strategies to be compared and are therefore useful to support clinical decision making. We aimed to compare methodological approaches for predicting individual level cardiovascular risk under three interventions: smoking cessation, reducing blood pressure, and reducing cholesterol. Methods: We used data from the PREDICT prospective cohort study in New Zealand to calculate cardiovascular risk in a primary care setting. We compared three strategies to estimate absolute risk under intervention: (a) conditioning on hypothetical interventions in non-causal models; (b) combining existing prediction models with causal effects estimated using observational causal inference methods; and (c) combining existing prediction models with causal effects reported in published literature. Results: The median absolute cardiovascular risk among smokers was 3.9%; our approaches predicted that smoking cessation reduced this to a median between a non-causal estimate of 2.5% and a causal estimate of 2.8%, depending on estimation methods. For reducing blood pressure, the proposed approaches estimated a reduction of absolute risk from a median of 4.9% to a median between 3.2% and 4.5% (both derived from causal estimation). Reducing cholesterol was estimated to reduce median absolute risk from 3.1% to between 2.2% (non-causal estimate) and 2.8% (causal estimate). Conclusions: Estimated absolute risk reductions based on non-causal methods were different to those based on causal methods, and there was substantial variation in estimates within the causal methods. Researchers wishing to estimate risk under intervention should be explicit about their causal modelling assumptions and conduct sensitivity analysis by considering a range of possible approaches.

External Validation of Prognostic Models in Critical Care: A Cautionary Tale From COVID-19 Pneumonitis.

OBJECTIVES BACKGROUND: To externally validate clinical prediction models that aim to predict progression to invasive ventilation or death on the ICU in patients admitted with confirmed COVID-19 pneumonitis. DESIGN: Single-center retrospective external validation study. DATA SOURCES: Routinely collected healthcare data in the ICU electronic patient record. Curated data recorded for each ICU admission for the purposes of the U.K. Intensive Care National Audit and Research Centre (ICNARC). SETTING: The ICU at Manchester Royal Infirmary, Manchester, United Kingdom. PATIENTS: Three hundred forty-nine patients admitted to ICU with confirmed COVID-19 Pneumonitis, older than 18 years, from March 1, 2020, to February 28, 2022. Three hundred two met the inclusion criteria for at least one model. Fifty-five of the 349 patients were admitted before the widespread adoption of dexamethasone for the treatment of severe COVID-19 (pre-dexamethasone patients). OUTCOMES: Ability to be externally validated, discriminate, and calibrate. METHODS: Articles meeting the inclusion criteria were identified, and those that gave sufficient details on predictors used and methods to generate predictions were tested in our cohort of patients, which matched the original publications' inclusion/exclusion criteria and endpoint. RESULTS: Thirteen clinical prediction articles were identified. There was insufficient information available to validate models in five of the articles; a further three contained predictors that were not routinely measured in our ICU cohort and were not validated; three had performance that was substantially lower than previously published (range C-statistic = 0.483-0.605 in pre-dexamethasone patients and C = 0.494-0.564 among all patients). One model retained its discriminative ability in our cohort compared with previously published results (C = 0.672 and 0.686), and one retained performance among pre-dexamethasone patients but was poor in all patients (C = 0.793 and 0.596). One model could be calibrated but with poor performance. CONCLUSIONS: Our findings, albeit from a single center, suggest that the published performance of COVID-19 prediction models may not be replicated when translated to other institutions. In light of this, we would encourage bedside intensivists to reflect on the role of clinical prediction models in their own clinical decision-making.

Rapid systematic review on risks and outcomes of sepsis: the influence of risk factors associated with health inequalities.

BACKGROUND AND AIMS: Sepsis is a serious and life-threatening condition caused by a dysregulated immune response to an infection. Recent guidance issued in the UK gave recommendations around recognition and antibiotic treatment of sepsis, but did not consider factors relating to health inequalities. The aim of this study was to summarise the literature investigating associations between health inequalities and sepsis. METHODS: Searches were conducted in Embase for peer-reviewed articles published since 2010 that included sepsis in combination with one of the following five areas: socioeconomic status, race/ethnicity, community factors, medical needs and pregnancy/maternity. RESULTS: Five searches identified 1,402 studies, with 50 unique studies included in the review after screening (13 sociodemographic, 14 race/ethnicity, 3 community, 3 care/medical needs and 20 pregnancy/maternity; 3 papers examined multiple health inequalities). Most of the studies were conducted in the USA (31/50), with only four studies using UK data (all pregnancy related). Socioeconomic factors associated with increased sepsis incidence included lower socioeconomic status, unemployment and lower education level, although findings were not consistent across studies. For ethnicity, mixed results were reported. Living in a medically underserved area or being resident in a nursing home increased risk of sepsis. Mortality rates after sepsis were found to be higher in people living in rural areas or in those discharged to skilled nursing facilities while associations with ethnicity were mixed. Complications during delivery, caesarean-section delivery, increased deprivation and black and other ethnic minority race were associated with post-partum sepsis. CONCLUSION: There are clear correlations between sepsis morbidity and mortality and the presence of factors associated with health inequalities. To inform local guidance and drive public health measures, there is a need for studies conducted across more diverse setting and countries.

Identifying the prevalence of genital injuries amongst patients attending Saint Mary's sexual assault referral centre following an allegation of digital penetration.

This study aimed to (1) add to the limited evidence base regarding genital injury associated with digital vaginal penetration and (2) identify predisposing or protective factors to the identification of a genital injury. Data collection was performed retrospectively on the paper case files of 120 female adult (>18 years) patients alleging digital vaginal penetration with no penile vaginal penetration that had an acute FME at Saint Mary's Sexual Assault Referral Centre (SARC) Manchester. Descriptive statistics were used to investigate differences in the demographics of those reporting digital penetration, with and without injuries. Overall, 18% had genital injuries noted at the time of the FME. Posterior fourchette was the most common location of genital injury and abrasion was the most common injury type. It is worth further noting that all 22 patients where an injury was noted were of white ethnicity, only 12 patients in the sample were not white so caution is needed in interpretating this finding of a non-significant difference. Future research should consider injury and ethnicity more specifically. The findings from this study add to the existing evidence base and should prove useful to expert witnesses when called upon to interpret examination findings of sexual assault complainants as they relate to an allegation of digital penetration.

Comparing Predictive Performance of Time Invariant and Time Variant Clinical Prediction Models in Cardiac Surgery.

Clinical prediction models are increasingly used across healthcare to support clinical decision making. Existing methods and models are time-invariant and thus ignore the changes in populations and healthcare practice that occur over time. We aimed to compare the performance of time-invariant with time-variant models in UK National Adult Cardiac Surgery Audit data from Manchester University NHS Foundation Trust between 2009 and 2019. Data from 2009-2011 were used for initial model fitting, and data from 2012-2019 for validation and updating. We fitted four models to the data: a time-invariant logistic regression model (not updated), a logistic model which was updated every year and validated it in each subsequent year, a logistic regression model where the intercept is a function of calendar time (not updated), and a continually updating Bayesian logistic model which was updated with each new observation and continuously validated. We report predictive performance over the complete validation cohort and for each year in the validation data. Over the complete validation data, the Bayesian model had the best predictive performance.

Compatibility in Missing Data Handling Across the Prediction Model Pipeline: A Simulation Study.

Careful handling of missing data is crucial to ensure that clinical prediction models are developed, validated, and implemented in a robust manner. We determined the bias in estimating predictive performance of different combinations of approaches for handling missing data across validation and implementation. We found four strategies that are compatible across the model pipeline and have provided recommendations for handling missing data between model validation and implementation under different missingness mechanisms.

Development and Internal Validation of a Multivariable Prediction Model to Predict Repeat Attendances in the Pediatric Emergency Department: A Retrospective Cohort Study.

OBJECTIVE: Unplanned reattendances to the pediatric emergency department (PED) occur commonly in clinical practice. Multiple factors influence the decision to return to care, and understanding risk factors may allow for better design of clinical services. We developed a clinical prediction model to predict return to the PED within 72 hours from the index visit. METHODS: We retrospectively reviewed all attendances to the PED of Royal Manchester Children's Hospital between 2009 and 2019. Attendances were excluded if they were admitted to hospital, aged older than 16 years or died in the PED. Variables were collected from Electronic Health Records reflecting triage codes. Data were split temporally into a training (80%) set for model development and a test (20%) set for internal validation. We developed the prediction model using LASSO penalized logistic regression. RESULTS: A total of 308,573 attendances were included in the study. There were 14,276 (4.63%) returns within 72 hours of index visit. The final model had an area under the receiver operating characteristic curve of 0.64 (95% confidence interval, 0.63-0.65) on temporal validation. The calibration of the model was good, although with some evidence of miscalibration at the high extremes of the risk distribution. After-visit diagnoses codes reflecting a nonspecific problem ("unwell child") were more common in children who went on to reattend. CONCLUSIONS: We developed and internally validated a clinical prediction model for unplanned reattendance to the PED using routinely collected clinical data, including markers of socioeconomic deprivation. This model allows for easy identification of children at the greatest risk of return to PED.

Developing and externally validating multinomial prediction models for methotrexate treatment outcomes in patients with rheumatoid arthritis: results from an international collaboration.

OBJECTIVES: In rheumatology, there is a clinical need to identify patients at high risk (>50%) of not responding to the first-line therapy methotrexate (MTX) due to lack of disease control or discontinuation due to adverse events (AEs). Despite this need, previous prediction models in this context are at high risk of bias and ignore AEs. Our objectives were to (i) develop a multinomial model for outcomes of low disease activity and discontinuing due to AEs 6 months after starting MTX, (ii) update prognosis 3-month following treatment initiation, and (iii) externally validate these models. STUDY DESIGN AND SETTING: A multinomial model for low disease activity (submodel 1) and discontinuing due to AEs (submodel 2) was developed using data from the UK Rheumatoid Arthritis Medication Study, updated using landmarking analysis, internally validated using bootstrapping, and externally validated in the Norwegian Disease-Modifying Antirheumatic Drug register. Performance was assessed using calibration (calibration-slope and calibration-in-the-large), and discrimination (concordance-statistic and polytomous discriminatory index). RESULTS: The internally validated model showed good calibration in the development setting with a calibration-slope of 1.01 (0.87, 1.14) (submodel 1) and 0.83 (0.30, 1.34) (submodel 2), and moderate discrimination with a c-statistic of 0.72 (0.69, 0.74) and 0.53 (0.48, 0.59), respectively. Predictive performance decreased after external validation (calibration-slope 0.78 (0.64, 0.93) (submodel 1) and 0.86 (0.34, 1.38) (submodel 2)), which may be due to differences in disease-specific characteristics and outcome prevalence. CONCLUSION: We addressed previously identified methodological limitations of prediction models for outcomes of MTX therapy. The multinomial approach predicted outcomes of disease activity more accurately than AEs, which should be addressed in future work to aid implementation into clinical practice.

A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods.

OBJECTIVES: Multimorbidity, the presence of two or more long-term conditions, is a growing public health concern. Many studies use analytical methods to discover multimorbidity patterns from data. We aimed to review approaches used in published literature to validate these patterns. STUDY DESIGN AND SETTING: We systematically searched PubMed and Web of Science for studies published between July 2017 and July 2023 that used analytical methods to discover multimorbidity patterns. RESULTS: Out of 31,617 studies returned by the searches, 172 were included. Of these, 111 studies (64%) conducted validation, the number of studies with validation increased from 53.13% (17 out of 32 studies) to 71.25% (57 out of 80 studies) in 2017-2019 to 2022-2023, respectively. Five types of validation were identified: assessing the association of multimorbidity patterns with clinical outcomes (n = 79), stability across subsamples (n = 26), clinical plausibility (n = 22), stability across methods (n = 7) and exploring common determinants (n = 2). Some studies used multiple types of validation. CONCLUSION: The number of studies conducting a validation of multimorbidity patterns is clearly increasing. The most popular validation approach is assessing the association of multimorbidity patterns with clinical outcomes. Methodological guidance on the validation of multimorbidity patterns is needed.

Overweight-years and cancer risk: A prospective study of the association and comparison of predictive performance with body mass index (Atherosclerosis Risk in Communities Study).

Excess body mass index (BMI) is associated with a higher risk of at least 13 cancers, but it is usually measured at a single time point. We tested whether the overweight-years metric, which incorporates exposure time to BMI ≥25 kg/m2 , is associated with cancer risk and compared this with a single BMI measure. We used adulthood BMI readings in the Atherosclerosis Risk in Communities (ARIC) study to derive the overweight-years metric. We calculated associations between the metric and BMI and the risk of cancers using Cox proportional hazards models. Models that either included the metric or BMI were compared using Harrell's C-statistic. We included 13,463 participants, with 3,876 first primary cancers over a mean of 19 years (SD 7) of cancer follow-up. Hazard ratios for obesity-related cancers per standard deviation overweight-years were 1.15 (95% CI: 1.05-1.25) in men and 1.14 (95% CI: 1.08-1.20) in women. The difference in the C-statistic between models that incorporated BMI, or the overweight-years metric was non-significant in men and women. Overweight-years was associated with the risk of obesity-related cancers but did not outperform a single BMI measure in association performance characteristics.