Measurable Residual FLT3 Internal Tandem Duplication Before Allogeneic Transplant for Acute Myeloid Leukemia.

Importance: Persistence of FLT3 internal tandem duplication (ITD) in adults with acute myeloid leukemia (AML) in first complete remission (CR) prior to allogeneic hematopoietic cell transplant (HCT) is associated with increased relapse and death after transplant, but the association between the level of measurable residual disease (MRD) detected and clinical outcome is unknown. Objective: To examine the association between pre-allogeneic HCT MRD level with relapse and death posttransplant in adults with AML in first CR. Design, Setting, and Participants: In this cohort study, DNA sequencing was performed on first CR blood from patients with FLT3-ITD AML transplanted from March 2013 to February 2019. Clinical follow-up was through May 2022. Data were analyzed from October 2022 to December 2023. Exposure: Centralized DNA sequencing for FLT3-ITD in pre-allogeneic HCT first CR blood using a commercially available kit. Main Outcomes and Measures: The primary outcomes were overall survival and cumulative incidence of relapse, with non-relapse-associated mortality as a competing risk post-allogeneic HCT. Kaplan-Meier estimations (log-rank tests), Cox proportional hazards models, and Fine-Gray models were used to estimate the end points. Results: Of 537 included patients with FLT3-ITD AML from the Pre-MEASURE study, 296 (55.1%) were female, and the median (IQR) age was 55.6 (42.9-64.1) years. Using the variant allele fraction (VAF) threshold of 0.01% or greater for MRD positivity, the results closely aligned with those previously reported. With no VAF threshold applied (VAF greater than 0%), 263 FLT3-ITD variants (median [range] VAF, 0.005% [0.0002%-44%]), and 177 patients (33.0%) with positive findings were identified. Multivariable analyses showed that residual FLT3-ITD was the variable most associated with relapse and overall survival, with a dose-dependent correlation. Patients receiving reduced-intensity conditioning without melphalan or nonmyeloablative conditioning had increased risk of relapse and death at any given level of MRD compared with those receiving reduced-intensity conditioning with melphalan or myeloablative conditioning. Conclusions and Relevance: This study provides generalizable and clinically applicable evidence that the detection of residual FLT3-ITD in the blood of adults in first CR from AML prior to allogeneic HCT is associated with an increased risk of relapse and death, particularly for those with a VAF of 0.01% or greater. While transplant conditioning intensification, an intervention not available to all, may help mitigate some of this risk, alternative approaches will be necessary for this high-risk population of patients who are underserved by the current standard of care.

Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment.

ABSTRACT: Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity.

Glucose Color Index: Development and Validation of a Novel Measure of the Shape of Glycemic Variability.

BACKGROUND: Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management. METHODS: We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study. RESULTS: The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated (p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders. CONCLUSION: We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information.

Place, Race, and Lapses in Diabetic Retinopathy Care.

This cohort study investigates the association of neighborhood-level social determinants of health with lapses in diabetic retinopathy care by race and ethnicity.

Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study.

PURPOSE: To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab. DESIGN: Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network. SUBJECTS: Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion). METHODS: The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate. MAIN OUTCOME MEASURES: Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure. RESULTS: Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60). CONCLUSIONS: There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Social Risk Groups in Patients With Diabetes With Differing Eye Care Utilization and Vision Outcomes.

Purpose: To evaluate whether latent class analysis on social determinants of health (SDoH) data can identify social risk groups that differ by adverse SDoH and vision outcomes in patients with diabetes. Methods: This was a prospective cohort study of adults ≥18 years with diabetes who completed a SDoH survey. Latent class analysis was used to cluster patients into social risk groups. The association of social risk group and severity of diabetic retinopathy, history of lapses in diabetic retinopathy care, and visual acuity was evaluated. Results: A total of 1006 participants were included. The three social risk groups differed by sociodemographic characteristics. The average age was 65, 60, and 54 in Groups 1, 2, and 3 respectively. Most (51%) patients in group 1 were non-Hispanic White, 66% in group 2 were non-Hispanic Black, and 80% in group 3 were Hispanic. Group 1 had the lowest burden of adverse SDoH per person (average 3.6), group 2 had 8.2, and group 3 had 10.5. In general, group 1 lacked diabetic retinopathy knowledge, group 2 had financial insecurity and difficulties with transportation, and group 3 had financial insecurity and did not have health insurance. Social risk group was associated with a history of lapses in diabetic retinopathy care, and presenting with worse vision. Conclusions and Translational Relevance: We identified distinct social risk groups among patients seeking care for diabetic retinopathy that differed by social needs, eye care utilization, and vision. Identifying these groups and their specific needs can help guide interventions to effectively address adverse SDoH and improve eye care utilization and vision outcomes among patients with diabetes.

The HOPE4MCI study: A randomized double-blind assessment of AGB101 for the treatment of MCI due to AD.

INTRODUCTION: In addition to the accumulation of amyloid plaques and neurofibrillary tangles, the presence of excess neural activity is a pathological hallmark of Alzheimer's disease (AD) and a prognostic indicator for progression of AD pathology and clinical/cognitive worsening in mild cognitive impairment due to Alzheimer's disease (MCI due to AD). The HOPE4MCI clinical study tested the efficacy of a therapeutic with demonstrated ability to normalize heightened neural activity in the hippocampus in a randomized controlled trial of 78 weeks duration in patients with MCI due to AD. METHODS: One hundred and sixty-four participants were randomized to placebo (n = 83) or AGB101 (n = 81), an extended-release formulation of low dose (220 mg) levetiracetam. The primary endpoint was the change in Clinical Dementia Rating Scale Sum of Boxes score (CDR-SB) comparing follow up at 18 months to baseline. The goal of the primary efficacy analysis was to estimate the difference between the AGB101 and placebo arms in the mean change of the primary endpoint. RESULTS: The mean change in CDR-SB was estimated to be 1.12 (95% confidence interval [CI]: 0.66, 1.69) for the AGB101 arm and 1.22 (95% CI: 0.75, 1.78) for the placebo arm. The estimated difference between arms is -0.10 (95% CI: -0.85, 0.58), which was not statistically significant. In a prespecified analysis, the difference was -0.45 (95% CI: -1.43, 0.53) for ApoE-4 noncarriers and -0.10 (95% CI: -0.92, 0.72) for apolipoprotein E (ApoE)-4 carriers. DISCUSSION: The possibility that ApoE-4 carriers and noncarriers will respond differently to therapeutic intervention is consistent with recently reported findings from biologics and the present results show further testing of AGB101 in patients with MCI due to AD who are noncarriers of the ApoeE-4 allele is warranted. Conclusions from the HOPE4MCI study are limited primarily due to the small sample size and results can only be regarded as a guide to future research.

Distinct Scleroderma Autoantibody Profiles Stratify Patients for Cancer Risk at Scleroderma Onset and During the Disease Course.

OBJECTIVES: We examined whether an array of scleroderma autoantibodies associates with risk of cancer and could be useful tools for risk stratification. METHODS: Scleroderma cancer cases and scleroderma controls without cancer from the Johns Hopkins Scleroderma Center and the University of Pittsburgh Scleroderma Center were studied. Sera were assayed by Lineblot and enzyme-linked immunosorbent assay (ELISA) for autoantibodies against centromere, topoisomerase 1, RNA polymerase (POLR) 3, PM/Scl, Th/To, NOR90, U3 RNP, Ku, Ro52, U1RNP, and RNPC3. Logistic regression models were constructed to examine whether distinct autoantibodies associated with overall cancer at any time and cancer-associated scleroderma (cancer occurring three years before and after scleroderma onset). The effects of having more than one autoantibody on cancer were further examined using random forest analysis. RESULTS: A total of 676 cases and 687 controls were studied. After adjusting for relevant covariates, anti-POLR3 (odds ratio [OR] 1.47, 95% confidence interval [CI] 1.03-2.11) and monospecific anti-Ro52 (OR 2.19, 95% CI 1.29-3.74) were associated with an increased overall cancer risk, whereas anticentromere (OR 0.69, 95% CI 0.51-0.93) and anti-U1RNP (OR 0.63, 95% CI 0.43-0.93) were associated with lower risk. When examining risk of cancer-associated scleroderma, these immune responses remained associated with increased or decreased risk: anti-POLR3 (OR 2.28, 95% CI 1.33-3.91), monospecific anti-Ro52 (OR 2.58, 95% CI 1.05-6.30), anticentromere (OR 0.39, 95% CI 0.20-0.74), and anti-U1RNP (OR 0.32, 95% CI 0.11-0.93). Anti-Ro52 plus anti-U1RNP or anti-Th/To was associated with decreased cancer risk compared with anti-Ro52 alone. CONCLUSIONS: These data suggest that five distinct scleroderma immune responses, alone or in combination, may be useful tools to stratify the risk of cancer for scleroderma patients. Further study examining cancer risk in autoantibody subgroups relative to the general population is warranted.

Application of machine learning models to identify serological predictors of COVID-19 severity and outcomes.

Critically ill people with COVID-19 have greater antibody titers than those with mild to moderate illness, but their association with recovery or death from COVID-19 has not been characterized. In 178 COVID-19 patients, 73 non-hospitalized and 105 hospitalized patients, mucosal swabs and plasma samples were collected at hospital enrollment and up to 3 months post-enrollment (MPE) to measure virus RNA, cytokines/chemokines, binding antibodies, ACE2 binding inhibition, and Fc effector antibody responses against SARS-CoV-2. The association of demographic variables and >20 serological antibody measures with intubation or death due to COVID-19 was determined using machine learning algorithms. Predictive models revealed that IgG binding and ACE2 binding inhibition responses at 1 MPE were positively and C1q complement activity at enrollment was negatively associated with an increased probability of intubation or death from COVID-19 within 3 MPE. Serological antibody measures were more predictive than demographic variables of intubation or death among COVID-19 patients.

Association between antenatal care facility readiness and provision of care at the client level and facility level in five low- and middle-income countries.

BACKGROUND: Despite growing interest in monitoring improvements in quality of care, data on service quality in low-income and middle-income countries (LMICs) is limited. While health systems researchers have hypothesized the relationship between facility readiness and provision of care, there have been few attempts to quantify this relationship in LMICs. This study assesses the association between facility readiness and provision of care for antenatal care at the client level and facility level. METHODS: To assess the association between provision of care and various facility readiness indices for antenatal care, we used multilevel, multivariable random-effects linear regression models. We tested an inflection point on readiness scores by fitting linear spline models. To compare the coefficients between models, we used a bootstrapping approach and calculated the mean difference between all pairwise comparisons. Analyses were conducted at client and facility levels. RESULTS: Our results showed a small, but significant association between facility readiness and provision of care across countries and most index constructions. The association was most evident in the client-level analyses that had a larger sample size and were adjusted for factors at the facility, health worker, and individual levels. In addition, spline models at a facility readiness score of 50 better fit the data, indicating a plausible threshold effect. CONCLUSIONS: The results of this study suggest that facility readiness is not a proxy for provision of care, but that there is an important association between facility readiness and provision of care. Data on facility readiness is necessary for understanding the foundations of health systems particularly in countries with the lowest levels of service quality. However, a comprehensive view of quality of care should include both facility readiness and provision of care measures.

Risk factors for neonatal mortality: an observational cohort study in Sarlahi district of rural southern Nepal.

OBJECTIVES: To assess the association between maternal characteristics, adverse birth outcomes (small-for-gestational-age (SGA) and/or preterm) and neonatal mortality in rural Nepal. DESIGN: This is a secondary observational analysis to identify risk factors for neonatal mortality, using data from a randomised trial to assess the impact of newborn massage with different oils on neonatal mortality in Sarlahi district, Nepal. SETTING: Rural Sarlahi district, Nepal. PARTICIPANTS: 40 119 pregnant women enrolled from 9 September 2010 to 16 January 2017. MAIN OUTCOME: The outcome variable is neonatal death. Cox regression was used to estimate adjusted Hazard Ratios (aHRs) to assess the association between adverse birth outcomes and neonatal mortality. RESULTS: There were 32 004 live births and 998 neonatal deaths. SGA and/or preterm birth was strongly associated with increased neonatal mortality: SGA and preterm (aHR: 7.09, 95% CI: (4.44 to 11.31)), SGA and term/post-term (aHR: 2.12, 95% CI: (1.58 to 2.86)), appropriate-for-gestational-age/large-for-gestational-age and preterm (aHR: 3.23, 95% CI: (2.30 to 4.54)). Neonatal mortality was increased with a history of prior child deaths (aHR: 1.53, 95% CI: (1.24 to 1.87)), being a twin or triplet (aHR: 5.64, 95% CI: (4.25 to 7.48)), births at health posts/clinics or in hospital (aHR: 1.34, 95% CI: (1.13 to 1.58)) and on the way to facilities or outdoors (aHR: 2.26, 95% CI: (1.57 to 3.26)). Risk was lower with increasing maternal height from <145 cm to 145-150 cm (aHR: 0.78, 95% CI: (0.65 to 0.94)) to ≥150 cm (aHR: 0.57, 95% CI: (0.47 to 0.68)), four or more antenatal care (ANC) visits (aHR: 0.67, 95% CI: (0.53 to 0.86)) and education >5 years (aHR: 0.75, 95% CI: (0.62 to 0.92)). CONCLUSION: SGA and/or preterm birth are strongly associated with increased neonatal mortality. To reduce neonatal mortality, interventions that prevent SGA and preterm births by promoting ANC and facility delivery, and care of high-risk infants after birth should be tested. TRIAL REGISTRATION NUMBER: NCT01177111.

Measurable Residual IDH1 before Allogeneic Transplant for Acute Myeloid Leukemia.

Measurable residual disease (MRD) in adults with acute myeloid leukemia (AML) in complete remission is an important prognostic marker, but detection methodology requires optimization. The persistence of mutated NPM1 or FLT3-ITD in the blood of adult patients with AML in first complete remission (CR1) prior to allogeneic hematopoetic cell transplant (alloHCT) has been established as associated with increased relapse and death after transplant. The prognostic implications of persistence of other common AML-associated mutations, such as IDH1, at this treatment landmark however remains incompletely defined. We performed testing for residual IDH1 variants (IDH1m) in pre-transplant CR1 blood of 148 adult patients undergoing alloHCT for IDH1-mutated AML at a CIBMTR site between 2013-2019. No post-transplant differences were observed between those testing IDH1m positive (n=53, 36%) and negative pre-transplant (overall survival: p = 0.4; relapse: p = 0.5). For patients with IDH1 mutated AML co-mutated with NPM1 and/or FLT3-ITD, only detection of persistent mutated NPM1 and/or FLT3-ITD was associated with significantly higher rates of relapse (p = 0.01). These data, from the largest study to date, do not support the detection of IDH1 mutation in CR1 blood prior to alloHCT as evidence of AML MRD or increased post-transplant relapse risk.

Multi-Cause Calibration of Verbal Autopsy-Based Cause-Specific Mortality Estimates of Children and Neonates in Mozambique.

The Countrywide Mortality Surveillance for Action platform is collecting verbal autopsy (VA) records from a nationally representative sample in Mozambique. These records are used to estimate the national and subnational cause-specific mortality fractions (CSMFs) for children (1-59 months) and neonates (1-28 days). Cross-tabulation of VA-based cause-of-death (COD) determination against that from the minimally invasive tissue sampling (MITS) from the Child Health and Mortality Prevention project revealed important misclassification errors for all the VA algorithms, which if not accounted for will lead to bias in the estimates of CSMF from VA. A recently proposed Bayesian VA-calibration method is used that accounts for this misclassification bias and produces calibrated estimates of CSMF. Both the VA-COD and the MITS-COD can be multi-cause (i.e., suggest more than one probable COD for some of the records). To fully use this probabilistic COD data, we use the multi-cause VA calibration. Two different computer-coded VA algorithms are considered-InSilicoVA and EAVA-and the final CSMF estimates are obtained using an ensemble calibration that uses data from both the algorithms. The calibrated estimates consistently offer a better fit to the data and reveal important changes in the CSMF for both children and neonates in Mozambique after accounting for VA misclassification bias.

Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates.

Verbal autopsies (VAs) are extensively used to determine cause of death (COD) in many low- and middle-income countries. However, COD determination from VA can be inaccurate. Computer coded verbal autopsy (CCVA) algorithms used for this task are imperfect and misclassify COD for a large proportion of deaths. If not accounted for, this misclassification leads to biased estimates of cause-specific mortality fractions (CSMFs), a critical piece in health-policy making. Recent work has demonstrated that the knowledge of the CCVA misclassification rates can be used to calibrate raw VA-based CSMF estimates to account for the misclassification bias. In this manuscript, we review the current practices and issues with raw COD predictions from CCVA algorithms and provide a complete primer on how to use the VA calibration approach with the calibratedVA software to correct for verbal autopsy misclassification bias in cause-specific mortality estimates. We use calibratedVA to obtain CSMFs for child (1-59 months) and neonatal deaths using VA data from the Countrywide Mortality Surveillance for Action project in Mozambique.

Countrywide Mortality Surveillance for Action in Mozambique: Results from a National Sample-Based Vital Statistics System for Mortality and Cause of Death.

Sub-Saharan Africa lacks timely, reliable, and accurate national data on mortality and causes of death (CODs). In 2018 Mozambique launched a sample registration system (Countrywide Mortality Surveillance for Action [COMSA]-Mozambique), which collects continuous birth, death, and COD data from 700 randomly selected clusters, a nationally representative population of 828,663 persons. Verbal and social autopsy interviews are conducted for COD determination. We analyzed data collected in 2019-2020 to report mortality rates and cause-specific fractions. Cause-specific results were generated using computer-coded verbal autopsy (CCVA) algorithms for deaths among those age 5 years and older. For under-five deaths, the accuracy of CCVA results was increased through calibration with data from minimally invasive tissue sampling. Neonatal and under-five mortality rates were, respectively, 23 (95% CI: 18-28) and 80 (95% CI: 69-91) deaths per 1,000 live births. Mortality rates per 1,000 were 18 (95% CI: 14-21) among age 5-14 years, 26 (95% CI: 20-31) among age 15-24 years, 258 (95% CI: 230-287) among age 25-59 years, and 531 (95% CI: 490-572) among age 60+ years. Urban areas had lower mortality rates than rural areas among children under 15 but not among adults. Deaths due to infections were substantial across all ages. Other predominant causes by age group were prematurity and intrapartum-related events among neonates; diarrhea, malaria, and lower respiratory infections among children 1-59 months; injury, malaria, and diarrhea among children 5-14 years; HIV, injury, and cancer among those age 15-59 years; and cancer and cardiovascular disease at age 60+ years. The COMSA-Mozambique platform offers a rich and unique system for mortality and COD determination and monitoring and an opportunity to build a comprehensive surveillance system.

Health Disparities in Lapses in Diabetic Retinopathy Care.

Objective: To develop a novel methodology to identify lapses in diabetic retinopathy care in electronic health records (EHRs) and evaluate health disparities by race and ethnicity. Design: Retrospective cohort study. Subjects: Adult patients with diabetes mellitus who were evaluated at the Wilmer Eye Institute from January 1, 2013 to April 2, 2022. Methods: The methodology to identify lapses in care first identified diabetic retinopathy screening or treatment visits and then compared the providers' recommended follow-up timeframe with the patient's actual time to next encounter. The association of race and ethnicity with odds of lapses in care was evaluated using a mixed-effects logistic regression model controlling for age, sex, insurance, severity of diabetic retinopathy, presence of other retinal disorders, and glaucoma. Main Outcome Measures: Lapses in diabetic retinopathy care. Results: The methodology to identify diabetic retinopathy-related visits had a 95.0% (95% confidence interval, 93.0-96.6) sensitivity and 98.8% (98.1-99.3) specificity as compared with a gold standard grader. The methodology resulted in a 97.3% (96.2-98.4) sensitivity and 98.1% (97.3-98.9) specificity for detecting a follow-up recommendation, with an average error of -0.05 (-0.31 to 0.21) weeks in extracting the precise timeframe. A total of 39 561 patients with 91 104 office visits were included in the analysis. The average age was 61.4 years. More than 3 (77.6%) in 4 patients had a lapse in care. In multivariable analysis, non-Hispanic Black patients had 1.24 (1.19-1.30) odds and Hispanic patients had 1.26 (1.13-1.40) odds of ever having a lapse in care compared with non-Hispanic White patients (P < 0.001, respectively). Conclusions: We have developed a reliable methodology for identifying lapses in diabetic retinopathy care that is tailored to a provider's recommended follow-up. Using this approach, we find that 3 in 4 patients experience a lapse in diabetic retinopathy care and that these rates are higher among non-Hispanic Black and Hispanic patients. Deploying this methodology in the EHR is one potential means by which to identify and mitigate lapses in critical ophthalmic care in patients with diabetes. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

DNA Sequencing to Detect Residual Disease in Adults With Acute Myeloid Leukemia Prior to Hematopoietic Cell Transplant.

Importance: Preventing relapse for adults with acute myeloid leukemia (AML) in first remission is the most common indication for allogeneic hematopoietic cell transplant. The presence of AML measurable residual disease (MRD) has been associated with higher relapse rates, but testing is not standardized. Objective: To determine whether DNA sequencing to identify residual variants in the blood of adults with AML in first remission before allogeneic hematopoietic cell transplant identifies patients at increased risk of relapse and poorer overall survival compared with those without these DNA variants. Design, Setting, and Participants: In this retrospective observational study, DNA sequencing was performed on pretransplant blood from patients aged 18 years or older who had undergone their first allogeneic hematopoietic cell transplant during first remission for AML associated with variants in FLT3, NPM1, IDH1, IDH2, or KIT at 1 of 111 treatment sites from 2013 through 2019. Clinical data were collected, through May 2022, by the Center for International Blood and Marrow Transplant Research. Exposure: Centralized DNA sequencing of banked pretransplant remission blood samples. Main Outcomes and Measures: The primary outcomes were overall survival and relapse. Day of transplant was considered day 0. Hazard ratios were reported using Cox proportional hazards regression models. Results: Of 1075 patients tested, 822 had FLT3 internal tandem duplication (FLT3-ITD) and/or NPM1 mutated AML (median age, 57.1 years, 54% female). Among 371 patients in the discovery cohort, the persistence of NPM1 and/or FLT3-ITD variants in the blood of 64 patients (17.3%) in remission before undergoing transplant was associated with worse outcomes after transplant (2013-2017). Similarly, of the 451 patients in the validation cohort who had undergone transplant in 2018-2019, 78 patients (17.3%) with residual NPM1 and/or FLT3-ITD variants had higher rates of relapse at 3 years (68% vs 21%; difference, 47% [95% CI, 26% to 69%]; HR, 4.32 [95% CI, 2.98 to 6.26]; P < .001) and decreased survival at 3 years (39% vs 63%; difference, -24% [2-sided 95% CI, -39% to -9%]; HR, 2.43 [95% CI, 1.71 to 3.45]; P < .001). Conclusions and Relevance: Among patients with acute myeloid leukemia in first remission prior to allogeneic hematopoietic cell transplant, the persistence of FLT3 internal tandem duplication or NPM1 variants in the blood at an allele fraction of 0.01% or higher was associated with increased relapse and worse survival compared with those without these variants. Further study is needed to determine whether routine DNA-sequencing testing for residual variants can improve outcomes for patients with acute myeloid leukemia.

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients.

As clinicians are faced with a deluge of clinical data, data science can play an important role in highlighting key features driving patient outcomes, aiding in the development of new clinical hypotheses. Insight derived from machine learning can serve as a clinical support tool by connecting care providers with reliable results from big data analysis that identify previously undetected clinical patterns. In this work, we show an example of collaboration between clinicians and data scientists during the COVID-19 pandemic, identifying sub-groups of COVID-19 patients with unanticipated outcomes or who are high-risk for severe disease or death. We apply a random forest classifier model to predict adverse patient outcomes early in the disease course, and we connect our classification results to unsupervised clustering of patient features that may underpin patient risk. The paradigm for using data science for hypothesis generation and clinical decision support, as well as our triaged classification approach and unsupervised clustering methods to determine patient cohorts, are applicable to driving rapid hypothesis generation and iteration in a variety of clinical challenges, including future public health crises.

Impact of Severe Acute Respiratory Syndrome Coronavirus 2 Variants on Inpatient Clinical Outcome.

BACKGROUND: Prior observation has shown differences in COVID-19 hospitalization risk between SARS-CoV-2 variants, but limited information describes hospitalization outcomes. METHODS: Inpatients with COVID-19 at 5 hospitals in the eastern United States were included if they had hypoxia, tachypnea, tachycardia, or fever, and SARS-CoV-2 variant data, determined from whole-genome sequencing or local surveillance inference. Analyses were stratified by history of SARS-CoV-2 vaccination or infection. The average effect of SARS-CoV-2 variant on 28-day risk of severe disease, defined by advanced respiratory support needs, or death was evaluated using models weighted on propensity scores derived from baseline clinical features. RESULTS: Severe disease or death within 28 days occurred for 977 (29%) of 3369 unvaccinated patients and 269 (22%) of 1230 patients with history of vaccination or prior SARS-CoV-2 infection. Among unvaccinated patients, the relative risk of severe disease or death for Delta variant compared with ancestral lineages was 1.30 (95% confidence interval [CI]: 1.11-1.49). Compared with Delta, the risk for Omicron patients was .72 (95% CI: .59-.88) and compared with ancestral lineages was .94 (.78-1.1). Among Omicron and Delta infections, patients with history of vaccination or prior SARS-CoV-2 infection had half the risk of severe disease or death (adjusted hazard ratio: .40; 95% CI: .30-.54), but no significant outcome difference by variant. CONCLUSIONS: Although risk of severe disease or death for unvaccinated inpatients with Omicron was lower than with Delta, it was similar to ancestral lineages. Severe outcomes were less common in vaccinated inpatients, with no difference between Delta and Omicron infections.

Within-Person and Between-Sensor Variability in Continuous Glucose Monitoring Metrics.

BACKGROUND: The within-person and between-sensor variability of metrics from different interstitial continuous glucose monitoring (CGM) sensors in adults with type 2 diabetes not taking insulin is unclear. METHODS: Secondary analysis of data from 172 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea randomized clinical trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors for up to 2 weeks at baseline and again at the 3-month follow-up visit. RESULTS: At baseline (up to 2 weeks of CGM), mean glucose for both the Abbott and Dexcom sensors was approximately 150 mg/dL (8.3 mmol/L) and time in range (70180 mg/dL [3.910.0 mmol/L]) was just below 80. When comparing the same sensor at 2 different time points (two 2-week periods, 3 months apart), the within-person coefficient of variation (CVw) in mean glucose was 17.4 (Abbott) and 14.2 (Dexcom). CVw for percent time in range: 20.1 (Abbott) and 18.6 (Dexcom). At baseline, the Pearson correlation of mean glucose from the 2 sensors worn simultaneously was r 0.86, root mean squared error (RMSE), 13 mg/dL (0.7 mmol/L); for time in range, r 0.88, RMSE, 8 percentage points. CONCLUSIONS: Substantial variation was observed within sensors over time and across 2 different sensors worn simultaneously on the same individuals. Clinicians should be aware of this variability when using CGM technology to make clinical decisions.ClinicalTrials.gov Identifier: NCT02454153.