Mortality Outcomes in a Large Population with and without Covert Cerebrovascular Disease.

Covert cerebrovascular disease (CCD) is frequently reported on neuroimaging and associates with increased dementia and stroke risk. We aimed to determine how incidentally-discovered CCD during clinical neuroimaging in a large population associates with mortality. We screened CT and MRI reports of adults aged ≥50 in the Kaiser Permanente Southern California health system who underwent neuroimaging for a non-stroke clinical indication from 2009-2019. Natural language processing identified incidental covert brain infarcts (CBI) and/or white matter hyperintensities (WMH), grading WMH as mild/moderate/severe. Models adjusted for age, sex, ethnicity, multimorbidity, vascular risks, depression, exercise, and imaging modality. Of n=241,028, the mean age was 64.9 (SD=10.4); mean follow-up 4.46 years; 178,554 (74.1%) had CT; 62,474 (25.9%) had MRI; 11,328 (4.7%) had CBI; and 69,927 (29.0%) had WMH. The mortality rate per 1,000 person-years with CBI was 59.0 (95%CI 57.0-61.1); with WMH=46.5 (45.7-47.2); with neither=17.4 (17.1-17.7). In adjusted models, mortality risk associated with CBI was modified by age, e.g. HR 1.34 [1.21-1.48] at age 56.1 years vs HR 1.22 [1.17-1.28] at age 72 years. Mortality associated with WMH was modified by both age and imaging modality e.g., WMH on MRI at age 56.1 HR = 1.26 [1.18-1.35]; WMH on MRI at age 72 HR 1.15 [1.09-1.21]; WMH on CT at age 56.1 HR 1.41 [1.33-1.50]; WMH on CT at age 72 HR 1.28 [1.24-1.32], vs. patients without CBI or without WMH, respectively. Increasing WMH severity associated with higher mortality, e.g. mild WMH on MRI had adjusted HR=1.13 [1.06-1.20] while severe WMH on CT had HR=1.45 [1.33-1.59]. Incidentally-detected CBI and WMH on population-based clinical neuroimaging can predict higher mortality rates. We need treatments and healthcare planning for individuals with CCD.

Is Incidentally Discovered Covert Cerebrovascular Disease Ignorable?

This Viewpoint discusses the clinical implications of incidentally discovered covert cerebrovascular disease.

A Clinical Model to Predict the Occurrence of Select High-risk Infections in the First Year Following Heart Transplantation.

Background: Invasive infection remains a dangerous complication of heart transplantation (HT). No objectively defined set of clinical risk factors has been established to reliably predict infection in HT. The aim of this study was to develop a clinical prediction model for use at 1 mo post-HT to predict serious infection by 1 y. Methods: A retrospective cohort study of HT recipients (2000-2018) was performed. The composite endpoint included cytomegalovirus (CMV), herpes simplex or varicella zoster virus infection, blood stream infection, invasive fungal, or nocardial infection occurring 1 mo to 1 y post-HT. A least absolute shrinkage and selection operator regression model was constructed using 10 candidate variables. A concordance statistic, calibration curve, and mean calibration error were calculated. A scoring system was derived for ease of clinical application. Results: Three hundred seventy-five patients were analyzed; 93 patients experienced an outcome event. All variables remained in the final model: aged 55 y or above, history of diabetes, need for renal replacement therapy in first month, CMV risk derived from donor and recipient serology, use of induction and/or early lymphodepleting therapy in the first month, use of trimethoprim-sulfamethoxazole prophylaxis at 1 mo, lymphocyte count under 0.75 × 103cells/µL at 1 mo, and inpatient status at 1 mo. Good discrimination (C-index 0.80) and calibration (mean absolute calibration error 3.6%) were demonstrated. Conclusion: This model synthesizes multiple highly relevant clinical parameters, available at 1 mo post-HT, into a unified, objective, and clinically useful prediction tool for occurrence of serious infection by 1 y post-HT.

Stratifying future stroke risk with incidentally-discovered white matter disease severity and covert brain infarct site.

BACKGROUND: Covert cerebrovascular disease (CCD) includes white matter disease (WMD) and covert brain infarction (CBI). Incidentally-discovered CCD is associated with increased risk of subsequent symptomatic stroke. However, it is unknown whether the severity of WMD or the location of CBI predicts risk. OBJECTIVES: To examine the association of incidentally-discovered WMD severity and CBI location with risk of subsequent symptomatic stroke. METHOD: This retrospective cohort study includes patients 50 years old in the Kaiser Permanente Southern California health system who received neuroimaging for a non-stroke indication between 2009-2019. Incidental CBI and WMD were identified via natural language processing of the neuroimage report, and WMD severity was classified into grades. RESULTS: 261,960 patients received neuroimaging; 78,555 (30.0%) were identified to have incidental WMD, and 12,857 (4.9%) to have incidental CBI. Increasing WMD severity is associated with increased incidence rate of future stroke. However, the stroke incidence rate in CT-identified WMD is higher at each level of severity compared to rates in MRI-identified WMD. Patients with mild WMD via CT have a stroke incidence rate of 24.9 per 1,000 person-years, similar to that of patients with severe WMD via MRI. Among incidentally-discovered CBI patients with a determined CBI location, 97.9% are subcortical rather than cortical infarcts. CBI confers a similar risk of future stroke, whether cortical or subcortical, or whether MRI- or CT-detected. CONCLUSIONS: Increasing severity of incidental WMD is associated with an increased risk of future symptomatic stroke, dependent on the imaging modality. Subcortical and cortical CBI conferred similar risks.

Integrated care in patients with atrial fibrillation- a predictive heterogeneous treatment effect analysis of the ALL-IN trial.

INTRODUCTION: Integrated care is effective in reducing all-cause mortality in patients with atrial fibrillation (AF) in primary care, though time and resource intensive. The aim of the current study was to assess whether integrated care should be directed at all AF patients equally. METHODS: The ALL-IN trial (n = 1,240 patients, median age 77 years) was a cluster-randomized trial in which primary care practices were randomized to provide integrated care or usual care to AF patients aged 65 years and older. Integrated care comprised of (i) anticoagulation monitoring, (ii) quarterly checkups and (iii) easy-access consultation with cardiologists. For the current analysis, cox proportional hazard analysis with all clinical variables from the CHA2DS2-VASc score was used to predict all-cause mortality in the ALL-IN trial. Subsequently, the hazard ratio and absolute risk reduction were plotted as a function of this predicted mortality risk to explore treatment heterogeneity. RESULTS: Under usual care, after a median of 2 years follow-up the absolute risk of all-cause mortality in the highest-risk quarter was 31.0%, compared to 4.6% in the lowest-risk quarter. On the relative scale, there was no evidence of treatment heterogeneity (p for interaction = 0.90). However, there was substantial treatment heterogeneity on the absolute scale: risk reduction in the lowest risk- quarter of risk 3.3% (95% CI -0.4% - 7.0) compared to 12.0% (95% CI 2.7% - 22.0) in the highest risk quarter. CONCLUSION: While the relative degree of benefit from integrated AF care is similar in all patients, patients with a high all-cause mortality risk have a greater benefit on an absolute scale and should therefore be prioritized when implementing integrated care.

Assessing heterogeneity of treatment effect in multiple sclerosis trials.

Multiple sclerosis (MS) is heterogeneous with respect to outcomes, and evaluating possible heterogeneity of treatment effect (HTE) is of high interest. HTE is non-random variation in the magnitude of a treatment effect on a clinical outcome across levels of a covariate (i.e. a patient attribute or set of attributes). Multiple statistical techniques can evaluate HTE. The simplest but most bias-prone is conventional one variable-at-a-time subgroup analysis. Recently, multivariable predictive approaches have been promoted to provide more patient-centered results, by accounting for multiple relevant attributes simultaneously. We review approaches used to estimate HTE in clinical trials of MS.

Management of covert brain infarction survey: A call to care for and trial this neglected population.

BACKGROUND: Covert brain infarction (CBI) is highly prevalent and linked with stroke risk factors, increased mortality, and morbidity. Evidence to guide management is sparse. We sought to gain information on current practice and attitudes toward CBI and to compare differences in management according to CBI phenotype. METHODS: We conducted a web-based, structured, international survey from November 2021 to February 2022 among neurologists and neuroradiologists. The survey captured respondents' baseline characteristics, general approach toward CBI and included two case scenarios designed to evaluate management decisions taken upon incidental detection of an embolic-phenotype and a small-vessel-disease phenotype. RESULTS: Of 627 respondents (38% vascular neurologists, 24% general neurologists, and 26% neuroradiologists), 362 (58%) had a partial, and 305 (49%) a complete response. Most respondents were university hospital senior faculty members experienced in stroke, mostly from Europe and Asia. Only 66 (18%) of respondents had established institutional written protocols to manage CBI. The majority indicated that they were uncertain regarding useful investigations and further management of CBI patients (median 67 on a slider 0-100, 95% CI 35-81). Almost all respondents (97%) indicated that they would assess vascular risk factors. Although most would investigate and treat similarly to ischemic stroke for both phenotypes, including initiating antithrombotic treatment, there was considerable diagnostic and therapeutic heterogeneity. Less than half of respondents (42%) would assess cognitive function or depression. CONCLUSIONS: There is a high degree of uncertainty and heterogeneity regarding management of two common types of CBI, even among experienced stroke physicians. Respondents were more proactive regarding the diagnostic and therapeutic management than the minimum recommended by current expert opinions. More data are required to guide management of CBI; meantime, more consistent approaches to identification and consistent application of current knowledge, that also consider cognition and mood, would be promising first steps to improve consistency of care.

Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials.

BACKGROUND: People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). METHODS AND FINDINGS: We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. CONCLUSIONS: Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Overall average treatment effects from clinical trials, one-variable-at-a-time subgroup analyses and predictive approaches to heterogeneous treatment effects: Toward a more patient-centered evidence-based medicine.

Despite the predominance of the evidence-based medicine paradigm, a fundamental incongruity remains: Evidence is derived from groups of people, yet medical decisions are made by and for individuals. Randomization ensures the comparability of treatment groups within a clinical trial, which allows for unbiased estimation of average treatment effects. If we treated groups of patients instead of individuals, or if patients with the same disease were identical to one another in all factors that determined the harms and the benefits of therapy, then these group-level averages would make a perfectly sound foundation for medical decision-making. But patients differ from one another in many ways that determine the likelihood of an outcome, both with and without a treatment. Nevertheless, popular approaches to evidence-based medicine have encouraged a reliance on the average treatment effects estimated from clinical trials and meta-analysis as guides to decision-making for individuals. Here, we discuss the limitations of this approach as well as limitations of conventional, one-variable-at-a-time subgroup analysis; finally, we discuss the rationale for "predictive" approaches to heterogeneous treatment effects. Predictive approaches to heterogeneous treatment effects combine methods for causal inference (e.g. randomization) with methods for prediction that permit inferences about which patients are likely to benefit and which are not, taking into account multiple relevant variables simultaneously to yield "personalized" estimates of benefit-harm trade-offs. We focus on risk modeling approaches, which rely on the mathematical dependence of the absolute treatment effect with the baseline risk, which varies substantially "across patients" in most trials. While there are a number of examples of risk modeling approaches that have been practice-changing, risk modeling does not provide ideal estimates of individual treatment effects, since risk modeling does not account for how individual variables might modify the effects of therapy. In "effect modeling," prediction models are developed directly on clinical trial data, including terms for treatment and treatment effect interactions. These more flexible approaches may better uncover individualized treatment effects, but are also prone to overfitting when dimensionality is high, power is low, and there is limited prior knowledge about effect modifiers.

Systematic metareview of prediction studies demonstrates stable trends in bias and low PROBAST inter-rater agreement.

OBJECTIVES: To (1) explore trends of risk of bias (ROB) in prediction research over time following key methodological publications, using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and (2) assess the inter-rater agreement of the PROBAST. STUDY DESIGN AND SETTING: PubMed and Web of Science were searched for reviews with extractable PROBAST scores on domain and signaling question (SQ) level. ROB trends were visually correlated with yearly citations of key publications. Inter-rater agreement was assessed using Cohen's Kappa. RESULTS: One hundred and thirty nine systematic reviews were included, of which 85 reviews (containing 2,477 single studies) on domain level and 54 reviews (containing 2,458 single studies) on SQ level. High ROB was prevalent, especially in the Analysis domain, and overall trends of ROB remained relatively stable over time. The inter-rater agreement was low, both on domain (Kappa 0.04-0.26) and SQ level (Kappa -0.14 to 0.49). CONCLUSION: Prediction model studies are at high ROB and time trends in ROB as assessed with the PROBAST remain relatively stable. These results might be explained by key publications having no influence on ROB or recency of key publications. Moreover, the trend may suffer from the low inter-rater agreement and ceiling effect of the PROBAST. The inter-rater agreement could potentially be improved by altering the PROBAST or providing training on how to apply the PROBAST.

Association of Preexisting Heart Failure With Outcomes in Older Patients With Diffuse Large B-Cell Lymphoma.

Importance: Anthracycline-containing regimens are highly effective for diffuse large B-cell lymphoma (DLBCL); however, patients with preexisting heart failure (HF) may be less likely to receive anthracyclines and may be at higher risk of lymphoma mortality. Objective: To assess the prevalence of preexisting HF in older patients with DLBCL and its association with treatment patterns and outcomes. Design, Setting, and Participants: This longitudinal cohort study used data from the Surveillance, Epidemiology, and End Results (SEER)-Medicare registry from 1999 to 2016. The SEER registry is a system of population-based cancer registries, capturing more than 25% of the US population. Linkage to Medicare offers additional information from billing claims. This study included individuals 65 years and older with newly diagnosed DLBCL from 2000 to 2015 with Medicare Part A or B continuously in the year prior to lymphoma diagnosis. Data were analyzed from September 2020 to December 2022. Exposures: Preexisting HF in the year prior to DLBCL diagnosis ascertained from billing codes required one of the following: (1) 1 primary inpatient discharge diagnosis, (2) 2 outpatient diagnoses, (3) 3 secondary inpatient discharge diagnoses, (4) 3 emergency department diagnoses, or (5) 2 secondary inpatient discharge diagnoses plus 1 outpatient diagnosis. Main Outcomes and Measures: The primary outcome was anthracycline-based treatment. The secondary outcomes were (1) cardioprotective medications and (2) cause-specific mortality. The associations between preexisting HF and cancer treatment were estimated using multivariable logistic regression. The associations between preexisting HF and cause-specific mortality were evaluated using cause-specific Cox proportional hazards models with adjustment for comorbidities and cancer treatment. Results: Of 30 728 included patients with DLBCL, 15 474 (50.4%) were female, and the mean (SD) age was 77.8 (7.2) years. Preexisting HF at lymphoma diagnosis was present in 4266 patients (13.9%). Patients with preexisting HF were less likely to be treated with an anthracycline (odds ratio, 0.55; 95% CI, 0.49-0.61). Among patients with preexisting HF who received an anthracycline, dexrazoxane or liposomal doxorubicin were used in 78 of 1119 patients (7.0%). One-year lymphoma mortality was 41.8% (95% CI, 40.5-43.2) with preexisting HF and 29.6% (95% CI, 29.0%-30.1%) without preexisting HF. Preexisting HF was associated with higher lymphoma mortality in models adjusting for baseline and time-varying treatment factors (hazard ratio, 1.24; 95% CI, 1.18-1.31). Conclusions and Relevance: In this study, preexisting HF in patients with newly diagnosed DLBCL was common and was associated with lower use of anthracyclines and lower use of any chemotherapy. Trials are needed for this high-risk population.

A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases.

Treatment effects are often anticipated to vary across groups of patients with different baseline risk. The Predictive Approaches to Treatment Effect Heterogeneity (PATH) statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessment of treatment effect heterogeneity in a randomized controlled trial. The aim of this study is to extend this approach to the observational setting using a standardized scalable framework. The proposed framework consists of five steps: (1) definition of the research aim, i.e., the population, the treatment, the comparator and the outcome(s) of interest; (2) identification of relevant databases; (3) development of a prediction model for the outcome(s) of interest; (4) estimation of relative and absolute treatment effect within strata of predicted risk, after adjusting for observed confounding; (5) presentation of the results. We demonstrate our framework by evaluating heterogeneity of the effect of thiazide or thiazide-like diuretics versus angiotensin-converting enzyme inhibitors on three efficacy and nine safety outcomes across three observational databases. We provide a publicly available R software package for applying this framework to any database mapped to the Observational Medical Outcomes Partnership Common Data Model. In our demonstration, patients at low risk of acute myocardial infarction receive negligible absolute benefits for all three efficacy outcomes, though they are more pronounced in the highest risk group, especially for acute myocardial infarction. Our framework allows for the evaluation of differential treatment effects across risk strata, which offers the opportunity to consider the benefit-harm trade-off between alternative treatments.

Estimating individualized treatment effects from randomized controlled trials: a simulation study to compare risk-based approaches.

BACKGROUND: Baseline outcome risk can be an important determinant of absolute treatment benefit and has been used in guidelines for "personalizing" medical decisions. We compared easily applicable risk-based methods for optimal prediction of individualized treatment effects. METHODS: We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk, the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the prognostic index). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the prognostic index; models including a linear interaction of treatment with the prognostic index; models including an interaction of treatment with a restricted cubic spline transformation of the prognostic index; an adaptive approach using Akaike's Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit. RESULTS: The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size (N = 4,250; ~ 785 events). The restricted cubic splines model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger (N = 17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial. CONCLUSIONS: An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions.

US and Dutch Perspectives on the Use of COVID-19 Clinical Prediction Models: Findings from a Qualitative Analysis.

INTRODUCTION: Clinical prediction models (CPMs) for coronavirus disease 2019 (COVID-19) may support clinical decision making, treatment, and communication. However, attitudes about using CPMs for COVID-19 decision making are unknown. METHODS: Online focus groups and interviews were conducted among health care providers, survivors of COVID-19, and surrogates (i.e., loved ones/surrogate decision makers) in the United States and the Netherlands. Semistructured questions explored experiences about clinical decision making in COVID-19 care and facilitators and barriers for implementing CPMs. RESULTS: In the United States, we conducted 4 online focus groups with 1) providers and 2) surrogates and survivors of COVID-19 between January 2021 and July 2021. In the Netherlands, we conducted 3 focus groups and 4 individual interviews with 1) providers and 2) surrogates and survivors of COVID-19 between May 2021 and July 2021. Providers expressed concern about CPM validity and the belief that patients may interpret CPM predictions as absolute. They described CPMs as potentially useful for resource allocation, triaging, education, and research. Several surrogates and people who had COVID-19 were not given prognostic estimates but believed this information would have supported and influenced their decision making. A limited number of participants felt the data would not have applied to them and that they or their loved ones may not have survived, as poor prognosis may have suggested withdrawal of treatment. CONCLUSIONS: Many providers had reservations about using CPMs for people with COVID-19 due to concerns about CPM validity and patient-level interpretation of the outcome predictions. However, several people who survived COVID-19 and their surrogates indicated that they would have found this information useful for decision making. Therefore, information provision may be needed to improve provider-level comfort and patient and surrogate understanding of CPMs. HIGHLIGHTS: While clinical prediction models (CPMs) may provide an objective means of assessing COVID-19 prognosis, provider concerns about CPM validity and the interpretation of CPM predictions may limit their clinical use.Providers felt that CPMs may be most useful for resource allocation, triage, research, or educational purposes for COVID-19.Several survivors of COVID-19 and their surrogates felt that CPMs would have been informative and may have aided them in making COVID-19 treatment decisions, while others felt the data would not have applied to them.

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis.

BACKGROUND: Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. METHODS AND FINDINGS: Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631. CONCLUSIONS: Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. TRIAL REGISTRATION: A prespecified protocol was registered on PROSPERO (CRD42018048202).

Stratifying Future Stroke Risk with Incidentally Discovered White Matter Disease Severity and Covert Brain Infarct Site.

BACKGROUND: Covert cerebrovascular disease (CCD) includes white matter disease (WMD) and covert brain infarction (CBI). Incidentally discovered CCD is associated with increased risk of subsequent symptomatic stroke. However, it is unknown whether the severity of WMD or the location of CBI predicts risk. OBJECTIVES: The aim of this study was to examine the association of incidentally discovered WMD severity and CBI location with risk of subsequent symptomatic stroke. METHOD: This retrospective cohort study includes patients aged ≥50 years old in the Kaiser Permanente Southern California health system who received neuroimaging for a nonstroke indication between 2009 and 2019. Incidental CBI and WMD were identified via natural language processing of the neuroimage report, and WMD severity was classified into grades. RESULTS: A total of 261,960 patients received neuroimaging; 78,555 patients (30.0%) were identified to have incidental WMD and 12,857 patients (4.9%) to have incidental CBI. Increasing WMD severity is associated with an increased incidence rate of future stroke. However, the stroke incidence rate in CT-identified WMD is higher at each level of severity compared to rates in MRI-identified WMD. Patients with mild WMD via CT have a stroke incidence rate of 24.9 per 1,000 person-years, similar to that of patients with severe WMD via MRI. Among incidentally discovered CBI patients with a determined CBI location, 97.9% are subcortical rather than cortical infarcts. CBI confers a similar risk of future stroke, whether cortical or subcortical or whether MRI- or CT-detected. CONCLUSIONS: Increasing severity of incidental WMD is associated with an increased risk of future symptomatic stroke, dependent on the imaging modality. Subcortical and cortical CBI conferred similar risks.

Association of Incidentally Discovered Covert Cerebrovascular Disease Identified Using Natural Language Processing and Future Dementia.

Background Covert cerebrovascular disease (CCD) has been shown to be associated with dementia in population-based studies with magnetic resonance imaging (MRI) screening, but dementia risk associated with incidentally discovered CCD is not known. Methods and Results Individuals aged ≥50 years enrolled in the Kaiser Permanente Southern California health system receiving head computed tomography (CT) or MRI for nonstroke indications from 2009 to 2019, without prior ischemic stroke/transient ischemic attack, dementia/Alzheimer disease, or visit reason/scan indication suggestive of cognitive decline or stroke were included. Natural language processing identified incidentally discovered covert brain infarction (id-CBI) and white matter disease (id-WMD) on the neuroimage report; white matter disease was characterized as mild, moderate, severe, or undetermined. We estimated risk of dementia associated with id-CBI and id-WMD. Among 241 050 qualified individuals, natural language processing identified 69 931 (29.0%) with id-WMD and 11 328 (4.7%) with id-CBI. Dementia incidence rates (per 1000 person-years) were 23.5 (95% CI, 22.9-24.0) for patients with id-WMD, 29.4 (95% CI, 27.9-31.0) with id-CBI, and 6.0 (95% CI, 5.8-6.2) without id-CCD. The association of id-WMD with future dementia was stronger in younger (aged <70 years) versus older (aged ≥70 years) patients and for CT- versus MRI-discovered lesions. For patients with versus without id-WMD on CT, the adjusted HR was 2.87 (95% CI, 2.58-3.19) for older and 1.87 (95% CI, 1.79-1.95) for younger patients. For patients with versus without id-WMD on MRI, the adjusted HR for dementia risk was 2.28 (95% CI, 1.99-2.62) for older and 1.48 (95% CI, 1.32-1.66) for younger patients. The adjusted HR for id-CBI was 2.02 (95% CI, 1.70-2.41) for older and 1.22 (95% CI, 1.15-1.30) for younger patients for either modality. Dementia risk was strongly correlated with id-WMD severity; adjusted HRs compared with patients who were negative for id-WMD by MRI ranged from 1.41 (95% CI, 1.25-1.60) for those with mild disease on MRI to 4.11 (95% CI, 3.58-4.72) for those with severe disease on CT. Conclusions Incidentally discovered CCD is common and associated with a high risk of dementia, representing an opportunity for prevention. The association is strengthened when discovered at younger age, by increasing id-WMD severity, and when id-WMD is detected by CT scan rather than MRI.

Predicting Chronic Opioid Use Among Patients With Osteoarthritis Using Electronic Health Record Data.

OBJECTIVE: To estimate the risk of a patient with osteoarthritis (OA) developing chronic opioid use (COU) within 1 year of a new opioid prescription by using electronic health record (EHR) data and predictive models. METHODS: We used EHR data from 13 health care organizations to identify patients with OA with an opioid prescription between March 1, 2017 and February 28, 2019 and no record of opioid use in the prior 6 months. We evaluated 4 machine learning models to estimate patients' risk of COU (≥3 prescriptions ≥84 days, maximum gap ≤60 days). We also estimated the transportability of models to organizations outside the training set. RESULTS: The cohort consisted of 33,894 patients with OA, of whom 2,925 (8.6%) developed COU within 1 year. All models demonstrated good discrimination, with the best-performing model (random forest) achieving an area under the receiver operating characteristic curve (AUC) of 0.728 (95% CI 0.711-0.745), but the simplest regression model performed nearly as well (AUC 0.717 [95% CI 0.699-0.734]). Predicted risk deciles spanned a range of 2% risk for the 10th percentile to 18% risk for the 90th percentile for well-calibrated models. Models showed highly variable discrimination across organizations (AUC 0.571-0.842). CONCLUSIONS: We found that EHR-based predictive models could estimate the risk of future COU among patients with OA to help inform care decisions. Black-box methods did not have significant advantages over more interpretable models. Care should be taken when extending all models into organizations not included in model training because of a high variability in performance across held-out organizations.

Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients.

Clinical prognostic models can assist patient care decisions. However, their performance can drift over time and location, necessitating model monitoring and updating. Despite rapid and significant changes during the pandemic, prognostic models for COVID-19 patients do not currently account for these drifts. We develop a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts. Our findings suggest that, using this framework, models remain accurate and well-calibrated across various waves, variants, race and sex and yield positive net-benefits.