Novel agents (NAs)  were first introduced in 2013 as therapeutic options to treat chronic lymphocytic leukemia (CLL).OBJECTIVES: To determine if the uptake of NAs for first-line treatment was similar in Black and White patients with CLL treated in the Department of Veterans Affairs (VA).P < 0.01]) but had similar median baseline Charlson comorbidity scores (4 vs 5).We conducted a retrospective cohort study including adults with CLL managed in the VA from October 1, 2013, to September 30, 2017. Descriptive statistics were used to summarize demographic data, and appropriate bivariable statistical tests were used to compare NA use, baseline characteristics, health outcomes, and complications. A multivariable logistic regression model was used to identify factors associated with uptake of NAs. The study included 565 patients; 86% were White and 14% were Black. Black patients were younger than White patients . The gap narrowed over the study period: 4% vs 17% (2014), 13% vs 25% (2015), 17% vs 33% (2016), and 31% vs 33% (2017). Black race (P = 0.02) and fiscal year (P < 0.01) were the only variables significantly associated with NA use in the multivariable model. Health outcomes and most complications were similar for Black and White patients despite the difference in prescribing patterns.Overall, Black patients were less likely to receive NAs than White patients  were less likely to receive the newest CLL treatments as first-line compared with White patients with CLL, even after using special statistical methods to account for other important differences between the groups. Fortunately, health outcomes and most complications were similar for Black and White patients despite the difference in prescribing patterns. Implications for managed care pharmacyThis study demonstrates that Black patients were less likely to receive novel therapies for CLL, at least initially. Managed care providers should consider racial, socioeconomic, and cultural factors that may drive disparities, such as these, so that they can design and implement strategies to prevent and mitigate these disparities.1 Older White male individuals have the greatest incidence of CLL, but Black male individuals are also at risk, with Black patients diagnosed at a younger age than White patients (mean age at diagnosis of 67 vs 70 years).1 The US veteran population mirrors these CLL risk factors: 46% of veterans are at least aged 65years, 82% are White, and 91% are male.2 The heterogeneous nature of CLL ranges from an indolent course, requiring a watch-and-wait method in which treatment is not initiated for many years, to an aggressive disease in which treatment is initiated soon after diagnosis. In the United States, overall age-adjusted 5-year survival estimates have markedly improved to greater than 80%.1Chronic lymphocytic leukemia (CLL) is the most common form of adult leukemia, with a median age of diagnosis of 70 years.3 Black patients have also presented with factors associated with worse outcomes, including lower median hemoglobin levels, higher β2-microglobulin levels, and more unmutated IGHV gene expression, ZAP70 expression, and chromosome 17p or 11q deletion.4Although survival rates have improved overall, outcomes in CLL demonstrate inferior survival among Black patients, with a 5-year survival rate of only 64%.4 With respect to racial disparities in localized melanoma and renal cell carcinoma treatment since the introduction of novel agents (NAs), a 2016 analysis found disproportionate benefit favoring non-Hispanic White patients and thereby broadening the survival gap with minority populations.5 However, the study did not observe any racial disparities related to the use of NAs but rather that Black patients had worse outcomes regardless of the treatment modality. Given that Black race is associated with several high-risk features, Black patients might need to receive NAs in higher proportions that exceed that of non-Hispanic White patients. The differences in outcomes appear to be principally related to limitations in access to medical care, later stage at diagnosis, economic factors, and disparities in treatment. In a study of men with prostate cancer,6 most participants were willing to discuss clinical trial participation, but Black men were significantly less willing than White men, thus less likely to enroll and potentially benefit from NAs in clinical trials. The differences in outcomes might also be related to limitations in access to medical care.Black race has been associated with worse outcomes in other malignancies as well. For instance, the 5-year survival rate for Black patients with hematologic malignancies remains inferior to the general population.7 Non-Hispanic White patients received NAs in similar proportions regardless of whether they had Medicare, Medicaid, or private insurance. In contrast, patients of other races were less likely to receive NAs if they had Medicare or Medicaid insurance vs private insurance. Similarly, patients of other races were less likely to receive transplantation if they had Medicare or Medicaid insurance vs private insurance.In another study of patients with multiple myeloma, type of insurance was significantly associated with receipt of NAs.8 idelalisib ,9 and venetoclax 10 function at the gateway of dysregulated enzyme pathways. Randomized clinical trials, including RESONATE (relapsed/refractory ibrutinib) and RESONATE-2 (first-line ibrutinib),12 have demonstrated the ability of ibrutinib to improve survival compared with chemotherapy (CT)-based treatments. A follow-up 6-year analysis of the RESONATE trial reported statistically significant improvement in progression-free survival, overall survival, and overall response rate with ibrutinib.13 Importantly, these NAs are available as oral therapies, as opposed to traditional chemotherapies, most of which are intravenous only.With regard to CLL, the “NAs” ibrutinib ,17 For patients with high-risk disease , regardless of age and comorbidities, the use of NAs is recommended.18 As the incidence of CLL increases, with an older, more diverse population undergoing treatment, use of NAs is expected to rise.NAs are now featured prominently in the CLL clinical practice and treatment guidelines.19 Among Black patients, the higher prevalence of high-risk features for which NAs are recommended might predict a higher rate of novel treatment receipt compared with White patients. The Veterans Affairs (VA) is the largest equal-access health care system in the United States, so it is expected that VA patients should have equal access to these NAs. We hypothesized that the uptake of NAs for first-line treatment of CLL would be similar in Black and White patients managed in the VA.Despite the increasing number of treatment options for CLL, few studies have focused on racial disparities between Black and White patients regarding emerging CLL treatments, like the NAs. Black and White patients benefit equally from traditional CLL treatments.Supplementary Figure 1, available in online article). The study period was based on date of first NA approval for CLL, regardless of line of therapy, to the end of the latest full fiscal year (FY) of data available for the VA at the time of analysis. This study was approved by the University of Texas Health San Antonio Institutional Review Board (HSC-18-712H) and the South Texas Veterans Health Care System Research & Development Committee (VA0003). Electronic data were extracted from the VA electronic medical record. Start and stop dates for 9 common CLL therapies were reviewed manually by a pharmacist to determine first-line therapies.This was a retrospective observational study, which included adult patients with a confirmed diagnosis of CLL managed in the 18 Veterans Integrated Service Networks in the national VA from October 1, 2013, to September 30, 2017  system, which includes administrative, clinical, laboratory, and pharmacy data repositories. These repositories include data from both hospital and clinic settings. Furthermore, the VA system maintains a vital status file that enables investigators to determine patient mortality, even when it occurs outside of the clinic or hospital. Additionally, the VA captures disability and socioeconomic status as VA Priority Group; Group 1 has the greatest disability, Groups 2-6 have the lowest income, and Groups 7-8 have the highest income.20A thorough description of the data source was previously published.Four national VA databases, namely the VA Medical SAS Datasets (both inpatient and outpatient), the VA Vital Status File, the VA Decision Support System Datasets, and the VA Assistant Deputy Under Secretary of Health Annual Enrollment Files, were linked together using a unique patient identifier to develop an analytic dataset of patient data. More than 100 variables of interest were designed and created from the electronic data systems, which were populated with existing data generated during patient care and billing.International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes 204.10, 204.11, or 204.12 or ICD-10-CM codes C91.10, C91.11, or C91.12) at a VA facility from October 1, 2013, to September 30, 2017, received at least 1 of 9 therapies for CLL, and were at least aged 18 years at the date of CLL diagnosis. Patients in clinical trials and those who did not have at least 6 months of follow-up were excluded from this analysis. We chose a minimum follow-up period of 6 months based on a prior real-world study by Seung et al.21 Each patient chart was reviewed manually, by trained data abstractors, to confirm the CLL diagnosis and to collect information that could not be readily obtained by electronic means, including confirmation of line of therapy. Baseline demographics, health outcomes, and complications were determined for these patients. Data from the 20 years prior to the CLL treatment initiation date were used to define the baseline variables. Data in the 6 months after CLL treatment initiation were used to identify health outcomes  and complications ; patients who did not have at least 6 months of follow-up were excluded from this analysis.Adult patients (aged ≥18 years) with CLL, in the VA, who initiated first-line treatment with 1 of 9 CLL treatments (3 NAs and 6 CT/chemoimmunotherapy [CIT] treatments), from October 1, 2013, to September 30, 2017 (index period), were included in this analysis. NAs were ibrutinib, idelalisib, and venetoclax. CT/CIT therapies were bendamustine + rituximab; fludarabine + rituximab: fludarabine + cyclophosphamide + rituximab; pentostatin + cyclophosphamide + rituximab; chlorambucil; CD20 (rituximab or ofatumumab or obinutuzumab); and other . Patients were included if they had a diagnosis code for CLL  and clinician judgment about additional variables  that warranted inclusion in the model. Ultimately, Black race, patient age, male sex, peptic ulcer disease, deep vein thrombosis, intestinal disorders, VA Priority Group 7-8 (a marker of socioeconomic status), antihypertensives, and FY were included as covariates in the multivariable model.Descriptive statistics were used to summarize and compare baseline characteristics, health outcomes, and complications. A nominal logistic regression model was used to determine the impact of Black race  on receipt of NAs . Baseline patient characteristics for the 2 groups (Black vs White) were compared using the chi-square test and Fisher exact test for categorical variables and t-test and Mann-Whitney U-test for continuous variables. Covariates for the multivariable regression model were based on differences in baseline characteristics . The use of data from a structured EHR database reduced, but did not eliminate, missing values. When comparing specific characteristics between groups of interest, variables with unknown/missing data were not included in the analyses.The study included 565 patients; 86% White and 14% Black . PatientP = 0.02). The proportion of patients receiving a NA increased over time for both groups, and the gap between groups narrowed/closed over the study period , male sex (P = 0.09), peptic ulcer disease (P = 0.10), deep vein thrombosis (P = 0.10), intestinal disorders (P = 0.03), Priority Group 7-8 (P = 0.06), and antihypertensives (P = 0.03) were different in Black and White patients at baseline (P < 0.1) and were therefore used as model covariates. Additionally, FY was added as an additional covariate to the model because of the association between NA use and FY. In the multivariable model, with NA use as the dependent variable, Black race as the independent variable, and the aforementioned variables as the covariates, the only variables that were independently predictive of NA use were Black race and FY (Patient age (e and FY .P < 0.01)  .19 equal access to new CLL treatments is critical. Fortunately, in this study, observed health outcomes and complications, except for prostate cancer, were similar for Black and White patients despite the difference in receipt of NAs. This study did not assess quality of life (QOL) for the 2 groups, and study authors were unable to find existing literature regarding QOL for Black and White patients treated with NAs for CLL in the VA. This study did not assess other health outcomes, including response to therapy, progression-free survival, or QOL. The observed disparity between Black and White patients was greatest for the first study year and progressively narrowed/closed in subsequent years.This is the first study to identify a potential health disparity with respect to uptake of NAs among Black and White patients treated for CLL in the VA. Because Black and White patients benefit equally from traditional CLL treatments,4 and is possibly attributable to differences in patient age between the groups. This association between patient age and race led to our inclusion of age as a covariate in our multivariable model.Prior to the introduction of NAs in CLL management, CT/CIT was the standard of care. Notably, in our study, more Black patients received CT/CIT and more White patients received NAs as first-line therapy over the study period. This difference in CLL treatment for Black and White patients is consistent with a prior CLL study22 In contrast, studies in non-VA populations generally report more high-risk features, worse disease biology, medical comorbidities, and higher Charlson scores in Black patients compared with White patients.4,22,23 However, the VA population as a whole generally has more comorbid conditions than the general US population, which may limit the generalizability of these findings to non-VA populations.24Higher comorbidities in Black patients has been postulated as a potential reason for racial disparities in cancer care; however, in our study, Black and White patients had a similar age-adjusted Charlson comorbidity score. Other studies have also demonstrated similar mean comorbidity scores between Black and White patients within the VA.Socioeconomic status is frequently discussed as a potential prognostic factor because of its potential to influence several important components in the management of patients with cancer. It has been suggested that patients with cancer belonging to minority racial or ethnic groups, including Black patients, may have poorer access to health care facilities, leading to potential delay in diagnosis and more advanced disease at presentation. In this study, Black and White patients were statistically similar with respect to VA Priority Groups (a marker of socioeconomic status).25 Utilization of NAs was lower among Black patients than White patients. However, there was no statistical difference when compared by patient age and comorbidities, as Black patients possessed comorbidities that were associated with less use of NAs.25 In our study, even after controlling for several covariates, including patient age and divergent comorbidities, a statistically significant difference remained in the uptake of NAs.Racial disparities regarding NA use has been documented in patients with multiple myeloma.26 Other studies have also reported differences in the uptake of CT by race. In patients with acute myeloid leukemia, lower proportions of Black patients received appropriate treatment with CT compared with White patients.27 In another study, the likelihood of undergoing hematopoietic stem cell transplant for leukemia, lymphoma, and multiple myeloma was shown to be significantly lower for Black patients compared with White patients.28 The disparity in the uptake of NAs is likely multifactorial, including barriers at the level of patients, clinicians, and institutions.31Long before the advent of NAs, racial disparities regarding therapeutic agent use have been reported for other hematologic malignancies. A study regarding patients with diffuse large B-cell lymphoma found similar trends in the use of CIT after the introduction of NAs. Over time, an increase in use occurred for patients across all racial categories; however, Black patients were still less likely to receive CIT as the years progressed.32 Black Americans that lived in neighborhoods separate from White Americans, as well as in inner cities, had less access to primary care providers.32 This example of structural discrimination creates social risk factors that adversely affect health outcomes. The complexity of patients seeking cancer care and fragmentation of the health care system can also pose a challenging dilemma. Providers’ contribution to racial disparities has not been well established. However, one hypothesis describes the process of medical decision-making that providers go through and the implicit bias (both unconscious and conscious) about individuals that may result in disparate treatments.33 This theory, still in its infancy, possesses its own obstacles as no provider wants to be subject to underlying racial bias, but it must be recognized and addressed.It is evident that across and within sociodemographic groups there are major differences in the environment where people live. For example, neighborhoods have often been seen as the result of sorting that occurred secondary to socioeconomic differences. One study highlights the importance of residential segregation in health care disparities.34 Some of the clinical factors we lacked  are critical in the choice of targeted therapies.The current study provides insights into potential racial disparities in the uptake of NAs between Black and White patients treated for CLL in the VA; however, we exercise caution in making conclusive inferences because of the following limitations. There is insufficient sample size to achieve a study power of 80% for the 2 groups of patients in each FY. This creates the potential for type 2 error when we compared the individual FYs. Nevertheless, the magnitude of the effect sizes  suggests that this finding is clinically important. Race and ethnicity in the VA EHR are assigned by VA providers and are not self-reported, which may introduce bias. Additionally, the VA EHR is unable to capture a multiracial identity. Race is a social construct and should be considered with other sociodemographic factors; we included a socioeconomic assessment based on VA Priority Group, which accounts for level of disability and income, but other factors were unable to be assessed, such as economic stability, education, and employment. Furthermore, our study cannot determine other potential drivers of the observed disparity. Such drivers could include physician experience, clinical factors not accessible in the data, or patient factors such as historical mistrust of the medical community and health literacy.33 Furthermore, we did not have access to detailed information on VA facility, state, or physician prescribing behavior. Availability of these additional variables could have improved the multivariable regression model and enabled multilevel modeling and cluster analysis.Patient preference and logistical differences in administration of these therapies  may have also influenced patient or physician preference to choose one therapy over another. Unfortunately, this was a retrospective study and, therefore, could not capture information on patient or physician thoughts or preferences regarding method of administration and if it influenced treatment selection.We did not collect data on patients who were excluded from the study; therefore, we cannot determine if included and excluded patients were similar. There were no missing data for any of the baseline, health outcomes, and complications variables depicted in Tables 1 and 2, except for 7 patients who were missing the exact date of CLL diagnosis. Those 7 patients were excluded from the “time from diagnosis to treatment initiation” calculation.Some baseline variables, like patient age, were calculated at the time of CLL treatment initiation, using dates. Other variables, like patient comorbidities, were obtained from an EHR that was built for patient care, not research. A patient’s problem list for a given health encounter contains the problems managed during that encounter. Not all chronic conditions are listed for every encounter. Therefore, one gets a much more complete picture of the patient by looking at the problem list from many encounters. In this study, we looked at the problem lists from all inpatient and outpatient encounters for many years prior to CLL treatment initiation to get a much more complete picture of the patient’s comorbidities. This is a strength of the study.35 However, our results indicate that racial disparities exist, even with the elimination of health care access–related factors, such as the ability to pay for care, suggesting that racial disparities in the uptake of NAs within the VA is likely multifactorial and could include factors related to patient and/or physician preference in regards to the use and prescribing of newly approved agents. This could be why the prescribing gap was greater in the early study years but narrowed/closed by the final study year. The uptake of NAs used throughout the study period was similar to other studies within the time period.36 With the rapid change in survival outcomes, fixed treatment durations, and improved toxicity profiles, NAs have completely shifted the treatment landscape of first-line treatment for CLL in the last decade.Studies of clinical trials and equal-access systems provide support for the idea that equal treatment will yield similar outcomes between patients of racial and ethnic minority groups and White patients with similar diseases.This is the first study to identify a potential health disparity with respect to use of NAs among Black and White patients treated with CLL in the VA. Fortunately, health outcomes and most complications were similar for Black and White patients despite the difference in prescribing patterns. Further studies should explore other socioeconomic and cultural factors that may drive these racial disparities, as well as the safety and effectiveness of treatments by race, to better understand how racial disparities might impact patient outcomes. The VA should consider the findings of this study and other studies to design and implement programs and policies to eliminate this disparity.


Multiple myeloma (MM) is a hematological malignancy that is consistently preceded by an asymptomatic condition, monoclonal gammopathy of undetermined significance (MGUS). Disparities by age, gender, and race/ethnicity in both MGUS and MM are well-established. However, it remains unclear whether these disparities can be explained by increased incidence of MGUS and/or accelerated progression from MGUS to MM. Here, we fit a mathematical model to nationally representative data from the United States and showed that the difference in MM incidence can be explained by an increased incidence of MGUS among male and non-Hispanic Black populations. We did not find evidence showing differences in the rate of progression from MGUS to MM by either gender or race/ethnicity. Our results suggest that screening for MGUS among high-risk groups  may hold promise as a strategy to reduce the burden and MM health disparities. Multiple myeloma (MM) is a haematological malignancy that is preceded by monoclonal gammopathy of undetermined significance (MGUS). Here, the authors use a mechanistic model fitted to surveillance data from the United States to investigate whether variation in MM is best explained by incidence of MGUS or rate of progression to MM. MM is consistently preceded by a premalignant condition, monoclonal gammopathy of undetermined significance (MGUS)2. A prior cohort study of a predominately white population in Olmsted County, Minnesota suggested that MGUS progresses to MM at a rate of approximately 1% per year with the 20-year cumulative risk totaling 18%3. However, due to the absence of population-based screening and treatment recommendations for MGUS5, most MGUS cases are detected incidentally6, and, thus, our understanding of MGUS and MM are primarily informed by clinical studies. Further work is needed to understand the natural history of MM and how it varies across age, gender, and race/ethnicity.Multiple myeloma (MM) is a malignant transformation of plasma cells that caused an estimated 34,920 diagnoses and 12,410 deaths in the United States in 2021 alone7. There is a greater burden of MM among men compared to women and non-Hispanic Black people compared to non-Hispanic white people. Additionally, both men and non-Hispanics Black people have a higher prevalence of MGUS10 and develop MM earlier than women and non-Hispanic white people12. However, it remains unclear whether the increased incidence of MM can be attributed to an increased incidence of MGUS, to an increased rate of progression from MGUS to MM, or a combination of both7. An improved understanding of the cause of these observed disparities can potentially guide future screening and treatment strategies aiming to reduce these disparities13.One unresolved aspect of the natural history of MM concerns the observed disparities in MM incidence that exist by gender and race/ethnicity11. Landgren et al.9 leveraged the National Health and Nutritional Examination Survey and found that high-risk features of MGUS were more common among non-Hispanic Black people as compared to non-Hispanic white people, suggesting an increased rate of progression from MGUS to MM among non-Hispanic Black people. However, this study lacked long-term outcomes on MM progression and was unable to confirm this finding. Using the Veterans Health Administration database, Landgren et al.10 examined veterans diagnosed with MGUS during 1980–1996 and found no difference in rate of progression from MGUS to MM among non-Hispanic white people and non-Hispanic Black people. Therefore, they concluded that the higher MM incidence among non-Hispanic Black people could be explained by an increased incidence of MGUS. A more recent analysis using the same database demonstrated progression to MM occurred at a younger age among non-Hispanic Black people as compared to non-Hispanic white people in patients diagnosed with MGUS, which may be attributed to the higher incidence of MGUS, the higher progression rate of MGUS to MM, or both in non-Hispanic Black people13. Nevertheless, reconciling the conclusions between these two studies is challenging, and, importantly, both studies relied upon study populations with clinically diagnosed MGUS. However, MGUS is primarily asymptomatic, so examining the progression of MM among patients with incidentally diagnosed MGUS excludes those with undiagnosed MGUS and therefore may provide an incomplete picture of the natural history of MM.Prior studies have aimed to understand racial and ethnic disparities in MM incidence16. We constructed a compartmental model of the natural history of MM and fit this model using the aforementioned MGUS prevalence and MM incidence. We then used the fitted model to isolate the contributions of age, gender, and race/ethnicity to the observed disparities in MM incidence. Finally, we predicted how the preclinical dwell time, defined as the time from MGUS onset to MM onset, is likely to vary by age, gender, and race/ethnicity.To address this, we leveraged two nationally representative databases from the United States on MGUS prevalence using data from the National Health and Nutritional Examination Survey (NHANES) and MM incidence using data from the Surveillance, Epidemiology, and End Results (SEER) program. Importantly, the NHANES tested all study participants for MGUS using serum protein electrophoresis irrespective of underlying comorbidities, providing more representative measures of MGUS prevalence than would be obtained using clinically diagnosed MGUSThe fitted mathematical model of the natural history of MM shows that five independent MCMC chains were well-mixed Fig. S, and theThe fitted model captured the trends in MGUS prevalence and MM incidence across age, gender, and race/ethnicity Fig. . Consist−1 at age 60 to 0.0034 (0.0022–0.0049) yr−1 by age 80. By comparison, the rate of progression to MM among MGUS-positive individuals monotonically increased up to age 71 (95% CI: 67–77) and then monotonically decreased. Modeling the rate of MM progression as a quadratic relationship with age allowed us to capture the declines in MM incidence seen in the 80–84 and 85+ age groups in the SEER data  Fig. . SimilarThe preclinical dwell time, defined as the time from MGUS onset to MM onset, decreased with increasing age of MGUS onset Fig. . For nonWe estimated that the preclinical dwell time was affected by the gender and race/ethnicity of each cohort. Female gender was associated with an increased dwell time across all ages of MGUS onset. For example, the expected dwell time at age 50 was 17 (95% CI: 15–19) years for non-Hispanic white women versus 16 (95% CI: 14–17) years for non-Hispanic white men and 16 (95% CI: 14–18) years for non-Hispanic Black women versus 15 (95% CI: 13–16) years for non-Hispanic Black men. Furthermore, independent of gender, non-Hispanic Black race/ethnicity was associated with a shorter preclinical dwell time. For instance, at age 70, the expected dwell times were 6.6 (95% CI: 6.1–7.1) years for non-Hispanic white men compared to 6.1 (95% CI: 5.6–6.5) years for non-Hispanic Black men and 7.5 (95% CI: 6.9–8.2) years for non-Hispanic white women compared to 7.0 (95% CI: 6.4–7.5) years for non-Hispanic Black women.2. Therefore, clinical studies, which examine progression to MM among patients with diagnosed MGUS exclude those with undiagnosed MGUS, likely biasing their analyses towards MGUS-positive patients with the greatest number of comorbidities13. By leveraging nationally representative data on MGUS prevalence16, our study calibrated a discrete-time, multistate compartmental model of the natural history of MM that was able to uncover whether the higher incidence of MGUS, the progression rate of MGUS to MM, or both contributed to MM health disparities across age, gender, and race/ethnicity.Obtaining a detailed understanding of the drivers of MM disparities is challenging, because MGUS, the preclinical state, is asymptomatic and most often detected incidentally18. Our fitted model suggests that these disparities in MM incidence can be explained by an increased incidence of MGUS among healthy men and non-Hispanic Black people. Importantly, we found no statistically significant difference in the rate of progression from MGUS to MM, and these results were robust to multiple supplementary analyses that considered alternative models, including one in which there was no effect of MGUS on mortality, as well as alternative years of SEER MM incidence data. That the disparities in MM incidence can be explained by differential rate of development of MGUS suggests that strategies aiming to reduce disparities by gender and race/ethnicity should emphasize interventions that reduce the development of MGUS among high-risk groups. Although we identified an increased rate of development of MGUS amongst men and non-Hispanic Black people, our study cannot provide a mechanistic explanation for this phenomenon. It has been previously suggested that greater background plasma cell activity among non-Hispanic Black people may predispose them to developing MGUS and ultimately MM19, and mutational signatures may be detectable in the early decades of life20. Alternatively, differences by race/ethnicity may be explained by socio-contextual factors7 and differences in the distribution of known risk factors, such as obesity13. Future investigation that accounts for these and other hypotheses may eliminate the practice of essentializing race/ethnicity in cancer risk prediction models21.Prior studies have revealed that male gender and non-Hispanic Black race/ethnicity are risk factors for MGUS and MM, independent of other factors, such as age and socioeconomic status22. Furthermore, we estimated that the rate of progression from MGUS to MM peaked at approximately 71 years of age and subsequently declined, which mirrors the observed decline in MM incidence at higher age groups and may reflect a subset of older individuals with a more indolent presentation of MGUS and thus lower overall risk of progression to MM. Taken together, these results suggest that, if implemented, prevention strategies for MGUS may be cost-effective at all age groups, whereas prevention of MM among MGUS-positive individuals using pharmacological management, such as metformin23 or aspirin24, and non-pharmacological management, such as weight loss13, may not be cost-effective beyond 71 years of age.Independent of gender and race/ethnicity, we estimated the rates of developing MGUS and progressing from MGUS to MM increase nonlinearly with age. The rate of MGUS development monotonically increased with increasing age, suggesting that the observed increase in MGUS prevalence with age reflects a concomitant increase in MGUS incidence. This confirms a finding from a prior modeling study fitted to a predominately white cohort in Olmsted County, Minnesota and reveals that this finding is maintained across race/ethnicity as well as genderWe estimated that the preclinical dwell time, defined as the time from MGUS onset to MM onset, declined nonlinearly with increasing age of MGUS onset. Because the preclinical dwell time requires that individuals with MGUS survive long enough to develop MGUS, we attribute this phenomenon to two competing effects: (1) the rate of progression from MGUS to MM and (2) the baseline mortality rate. The rate of progression from MGUS to MM increases nonlinearly up to age 71, resulting in a concomitant decline in the preclinical dwell time. Additionally, as individuals age, they are subject to a greater competing risk of mortality. This implies that, at higher ages of MGUS onset, those individuals that survive to progress to MM do so much more quickly, thereby shortening the average preclinical dwell time. This explains apparent differences in the preclinical dwell time between non-Hispanic Black people and non-Hispanic white people. Although we identified no statistically significant difference in the rate of progression from MGUS to MM across race/ethnicity, non-Hispanic Black people are subject to a higher mortality, resulting in shorter preclinical dwell times as compared to non-Hispanic white people.25. Second, the data from NHANES and SEER are not collected from a single cohort. In order to utilize these distinct data sources within our modeling framework, we assumed that these data sources are nationally representative and thus reflect samples from the true MGUS prevalence and MM incidence within the U.S. population. Given the extensive geographic coverage and large samples sizes of these data sources26, we believe the assumption is valid. However, the NHANES database is subject to non-response bias, which could affect our conclusions if the prevalence of MGUS was significantly different among those that responded as compared to those that did not respond. MGUS prevalence and MM incidence could also be underreported due to ascertainment bias. This would bias our estimates of the rates of MGUS development and progression from MGUS to MM downward and cause us to underestimate the preclinical dwell time. Additionally, extensions to our modeling framework could simulate multiple cohorts to account for period effects that likely shape MGUS prevalence and MM incidence. We considered only a single year of MGUS prevalence and MM incidence data. Our results were robust to the year of MM incidence data considered, but future work could assess whether increases in MM incidence over time can be attributed to an aging population or instead reflect an increase in MM risk over time. Finally, although we identified differences in MGUS development by gender and race/ethnicity, future work is needed to identify whether these differences can be explained by differences in the distribution of known risk factors, such as obesity18, across gender and race/ethnicity.Our study is subject to a number of limitations. First, we lacked data on smoldering multiple myeloma (SMM), an intermediate state between MGUS and MM, so we are unable to assess how progression to SMM varies across age, gender, and race/ethnicityThis study found that disparities in MM incidence can be explained by an increased incidence of MGUS, not an increased rate of progression to MM, among healthy men and non-Hispanic Black people. Future studies are needed to identify whether these differences can be explained by differences in the distribution of known risk factors.Ethical approval was obtained from Washington University School of Medicine in St. Louis (IRB 202110041). All analyses were performed in accordance with relevant guidelines and regulations.22, we constructed a discrete-time, multistate compartmental model consisting of four health states: healthy (H), monoclonal gammopathy of undetermined significance (MGUS), multiple myeloma (MM), and death (D)  Fig. .Fig. 4Scs and race/ethnicity r, the proportion P of the cohort in each of these states at a given age a is defined by the following set of differential equations:For a given birth cohort of gender a, gender s, and race/ethnicity r develops MGUS, which we computed asIn Eq. , \documea, gender s, and race/ethnicity r with MGUS develops MM and is calculated as 22 by defining distinct mortality rates for individuals with MGUS and MM.In Eq. , \documeentclass1pt{minima in Eqs. , \documes and race/ethnicity r that occupies each state at age a.Equations –4 cannotp of MGUS and MM among individuals of gender s and race/ethnicity r, we conditioned upon the proportion of the birth cohort that was alive at age a, such thatThe quantities mentclass2pt{minimrom Eqs.  do not ri of MGUS and MM among individuals of gender s and race/ethnicity r required that we condition upon the proportion of the birth cohort that was alive at age a. Therefore, we calculated Similarly, calculating age-stratified incidence mentclass2pt{minimrom Eqs.  as8\docua, gender s, and race/ethnicity r, which we computed as In Eq. , \documerom Eqs. .16, we obtained empirical estimates of MGUS prevalence for 4,355 individuals 50 years of age and older stratified by age, gender, and race/ethnicity  and the 55–59 age group . To confirm that our conclusions were robust to the year of MM incidence data, we performed an alternative analysis in which used SEER data from 2004 , 1999–200427. For individuals 85 years of age or greater, population estimates were aggregated. To disaggregate population estimates for 85+ years, we assumed that the population distribution of individuals 85–99 years of age in 2010 was equivalent to the distribution in 2014, the first year in which CDC WONDER did not aggregate this age group28.We obtained population estimates by age, gender, and race/ethnicity in 2010 from Centers for Disease Control (CDC) WONDER database29. For MGUS-positive individuals, we followed Therneau et al.22 and assumed that mortality in MGUS-positive individuals was 1.25 times greater than the baseline age- and race/ethnicity-specific mortality rate for men and 1.11 times greater than the baseline age- and race/ethnicity-specific mortality rate for women  MGUS prevalence and (2) MM incidence. Because we adopt a fully Bayesian approach, we do not specify a weight for each data type during model fitting.a, gender s, and race/ethnicity r were MGUS-positive among a, gender s, and race/ethnicity r from NHANES as a binomial process, such thatWe modeled the probability that a, gender s, and race/ethnicity r from Eqs.  algorithmAfter fitting the model and comparing the model predictions to the NHANES and SEER data, we used the fitted model to explore the epidemiology of MGUS and MM by age, gender, and race/ethnicity. First, we analyzed the estimated model parameters to isolate the contributions of age, gender, and race/ethnicity to the rates of progression from healthy to MGUS and from MGUS to MM. Next, we computed the expected duration of the preclinical dwell time  by age, gender, and race/ethnicity. The preclinical dwell time depends upon the rate of progression from MGUS to MM and the competing baseline mortality rate, both of which depend upon age, gender, and race/ethnicity. This relationship between the preclinical dwell time and the baseline mortality rate exists because individuals with MGUS must survive sufficiently long to progress to MM.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Naldemedine is structurally designed to prevent passage across the blood–brain barrier (BBB), resulting in the attenuation of opioid-induced constipation without interfering with the analgesic effects of opioids. However, the influence of brain metastasis (BM), as one indicator of BBB disruption, on the analgesic effects of opioids in patients treated with naldemedine remains unclear. To examine whether the analgesic effects of opioids following naldemedine treatment are lower in patients with BM than in those without BM, we surveyed inpatients with lung and breast cancers treated with naldemedine at Fujita Health University Hospital between April 2017 and March 2022. Changes in the numeric rating scale (NRS) scores, morphine milligram equivalents (MMEs), and the number of rescues were assessed as analgesia-related outcomes during the first 7 days of naldemedine treatment in patients with or without BM, matched by the propensity score. In total, 172 patients were enrolled. After propensity-score matching, 30 patients with BM and 60 patients without BM were included in the analysis. Changes in NRS scores, MMEs, and the number of rescues did not differ between patients with and without BM. In the linear mixed-effects model, the coefficient of interaction between patients with or without BM and the days for each outcome was not statistically significant. BM does not influence the analgesic effect of opioids in patients with lung and breast cancers treated with naldemedine. Naldemedine may be useful for treating BM. Opioid analgesics act on the μ-opioid receptor (MOR) in the central nervous system (CNS) to moderate cancer pain. However, MOR activation expressed in the peripheral nervous system by opioid analgesics results in several adverse events, particularly bowel dysfunction, such as opioid-induced constipation (OIC) . OIC is Osaka et al. reported that naldemedine did not change the numeric rating scale (NRS) scores in a subgroup of patients with BM in two randomized controlled trials of naldemedine for patients with cancer with OIC . HoweverBM preferentially originates from lung and breast cancers . To miniJapanese inpatients with lung and breast cancers treated with naldemedine at Fujita Health University Hospital between April 2017 and March 2022 were surveyed in this retrospective observational study . The inclusion criteria were as follows: patients diagnosed with lung or breast cancers and inpatients treated with naldemedine and opioid analgesics during the study periods. The exclusion criteria were as follows: patients treated with naldemedine before hospitalization, those treated with naldemedine within 2 days, those treated with naldemedine without opioid use, and those treated with naldemedine as needed.Of the 240 patients, 172 were included in this study after excluding 68 patients for the following reasons: 57 patients were treated with naldemedine before hospitalization, 8 patients were treated with naldemedine within 2 days, 2 patients were treated with naldemedine without opioid use, and 1 patient was treated with naldemedine as needed. To investigate whether naldemedine reduced the analgesic effects of opioids in patients with BM, 172 patients were divided into 2 groups: patients with or without BM. After propensity-score matching (PSM), as described in the statistical analysis, patients with BM  and without BM  were identified for the analysis.The data were collected from electronic medical records at Fujita Health University Hospital. The day of the first naldemedine treatment was defined as day 1, and the data regarding NRS scores, MMEs, and the number of rescues from day 0  to day 7 were collected as analgesia-related outcomes. MMEs were converted to opioid doses according to the conversion factor table by referring to the CDC’s standard conversion factor, clinical guidelines for the pharmacological management of cancer pain in 2020 published by The Japanese Society of Palliative Medicine, and package product labeling of each opioid analgesic in Japan . The numwww.r-project.org).Patient characteristics are presented as medians and interquartile ranges for continuous variables and counts and proportions for categorical variables. To ensure comparability between the BM and non-BM groups, a 1:2 PSM was performed. Propensity scores were estimated using logistic regression models with the group as the objective variable and age; sex; height; body weight; body mass index; cancer type; stage ; first occurrence or recurrence of cancer; presence of bone metastasis; radiotherapy for pain relief; coexistence of hypertension, stroke, schizophrenia, depression, or epilepsy; NRS scores; MMEs; and the number of rescues for day 0 as explanatory variables. Caliper values were set using the SD/4 criterion for linearly predicted values from the logistic regression model. Multiple imputation methods were used because missing values were identified as covariates. The least square (LS) means of the outcomes were calculated for each group on each day, and comparisons were conducted between the groups. A linear mixed-effects model was used to evaluate differences over time in the effect of naldemedine on NRS scores, MMEs, and the number of rescues during the first 7 days of naldemedine treatment (changes from day 0) between the BM and non-BM groups. Groups, days, and their interaction terms were included in the model as fixed effects, and a randomly varying intercept between patients was applied. If the interaction between the group and days was statistically significant, the BM was used to modify the effect of naldemedine. A two-sided significance level of 5% was considered. R version 4.2.2 was used for all analyses  and −0.30 , respectively. Changes in MMEs increased day by day in both groups, and the LS mean difference between the BM and non-BM groups on days 3 and 7 was −4.32  and −5.57 , respectively. The number of rescues did not alter over the 7 days, and the LS mean difference between the BM and non-BM groups on days 3 and 7 was −0.13  and 0.15 , respectively.Changes in the NRS scores, MMEs, and number of rescues from day 0 and LS means on days 3 and 7 during the first 7 days of naldemedine treatment are shown in p = 0.435; MMEs, −0.31 , p = 0.519; number of rescues, 0.07 , p = 0.195). Thus, the changes in each outcome over time did not differ between the BM and non-BM groups.The coefficient of the group, days, interaction between the group and days for the changes from day 0 during the first 7 days of naldemedine treatment in terms of NRS scores, MMEs, and the number of rescues by the linear mixed-effect model are shown in Changes in analgesia-related outcomes, such as NRS scores, MMEs, and the number of rescues in patients with lung and breast cancers with or without BM treated with naldemedine, were analyzed in the PSM population in this study. Patients with BM did not show higher MMEs, number of rescues, or NRS scores with naldemedine treatment than those without BM.Osaka et al. reported that naldemedine had no effect on NRS score changes in patients with BM in a subgroup post hoc analysis of two randomized studies . In addiNaldemedine is a derivative of naltrexone with a carbamoyl group that increases the polarity and molecular weight, resulting in an inability to cross the BBB . HoweverOur study had several limitations. First, the target cancer types were limited to patients with lung and breast cancers who frequently had BM. The structural integrity and permeability of the BBB were heterogeneous between metastatic lesions and different types of primary tumors . Thus, tIn conclusion, analgesia-related outcomes in patients with lung and breast cancers with BM were not influenced by naldemedine treatment. Although further investigations are needed, the present results provide evidence that naldemedine may be used for OIC in patients with BM without compromising the analgesic effects of opioids.


Background: Ingestible capsule (IC) systems can assess gastrointestinal (GI) transit times as a surrogate for gut motility for extended periods of time within a minimally invasive, radiation-free and ambulatory setting. Methods: A literature review of IC systems and a systematic review of studies utilizing IC systems to measure GI transit times in healthy volunteers was performed. Screening for eligible studies, data extraction and bias assessments was performed by two reviewers. A narrative synthesis of the results was performed. Results: The literature review identified 23 different IC systems. The systematic review found 6892 records, of which 22 studies were eligible. GI transit time data were available from a total of 1885 healthy volunteers. Overall, seventeen included studies reported gastric emptying time (GET) and small intestinal transit time (SITT). Colonic transit time (CTT) was reported in nine studies and whole gut transit time (WGTT) was reported in eleven studies. GI transit times in the included studies ranged between 0.4 and 15.3 h for GET, 3.3–7 h for SITT, 15.9–28.9 h for CTT and 23.0–37.4 h for WGTT. GI transit times, notably GET, were influenced by the study protocol. Conclusions: This review provides an up-to-date overview of IC systems and reference ranges for GI transit times. It also highlights the need to standardise protocols to differentiate between normal and pathological function. Ingestible capsule (IC) systems have been used to measure gastrointestinal (GI) function since the late 1950s . Subsequa literature review of IC systems used clinically and in research for the assessment of GI motility;a systematic review of studies utilising IC systems to measure and report gastric emptying time (GET), small intestinal transit time (SITT), colonic transit time (CTT) and whole-gut transit time (WGTT) in healthy volunteers.Establishing robust normative reference values for whole-gut or segmental transit times in healthy volunteers is key to the application of any GI motility test, and although these have been reported for individual IC systems ,6, to thA secondary objective of the systematic review was to evaluate the influence of the study protocol on the transit time estimates.www.crd.york.ac.uk/PROSPERO (accessed on 8 October 2021): registration number CRD42021271289). The review was conducted in line with the protocol and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [The protocol for this systematic review was registered on PROSPERO , are provided in PubMed and EMBASE were used to search for eligible English-language studies. There were no restrictions on the study publication date. The search end date was 3 February 2023. The reference lists of included studies were reviewed for any additional eligible studies. The full search strategy consisted of three queries, as outlined in The search was performed by one author (P.C.), who extracted all the results into a spreadsheet . After the removal of duplicate records, the remaining study titles and abstracts were independently screened by two authors (G.K.N. and P.C.) against the eligibility criteria. Full-text articles of potentially eligible studies were retrieved and reviewed independently by two authors (G.K.N. and P.C.) with any disagreements resolved by the senior author (S.M.S.).Study characteristics and outcome data were extracted by G.K.N. and verified by P.C. The following data were extracted from the eligible studies: publication year, country of origin, study type, healthy volunteer sample size and final sample size , number of female subjects, mean or median age of subjects, type of IC system used, any bowel preparation procedures performed prior to capsule ingestion, capsule ingestion protocol  where available, the ingestion meal calorie content, fasting duration following capsule ingestion and GI transit times.https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools (accessed on 23 February 2023)). The assessment was carried out by two reviewers (G.K.N. and P.C.) independently, with any disagreements discussed and resolved with the senior author (S.M.S.). Depending on the tool used, each study was assessed against a series of questions which required reviewers to answer “yes”, “no” or “cannot determine/not applicable/not reported”. Any “yes” answers scored 1 point, whereas “no” or “cannot determine/not reported” scored 0, thereby indicating a potential flaw in the study. The quality of a study was graded on the percentage of points scored such that a score between 0% and 33% indicated a high risk of bias, ≥34–66% indicated a moderate risk of bias and ≥67% indicated a low risk of bias.Risk of bias was assessed using the National Heart, Lung and Blood Institute (NHLBI) quality assessment tools for studies  calculated for studies that reported means and standard deviations. All estimates of transit times, regardless of whether the summary statistics were reported as means or medians, were presented in bubble charts, with the study reference on the x-axis, the transit time estimate on the y-axis and the size of the bubble representing the sample size. The 95% CI for the mean estimates of transit times were presented as error bars, and for studies reporting medians, the interquartile ranges or 95% confidence intervals for medians were used, where available. Where studies reported multiple results due to the ingestion of multiple capsules or variations in the protocol, the results were superimposed vertically in the bubble charts to indicate that the results were from the same study. The effect of the study protocol on transit times was illustrated in swarm plots. Due to heterogeneity in the design of the eligible studies and differences in the inclusion criteria, pooling of the results was not considered to be appropriate. Therefore, a meta-analysis was not performed. A narrative synthesis was carried out instead.The literature review identified 23 different IC systems, which were grouped into 5 categories: (1) intraluminal imaging systems; (2) pH, temperature and pressure-sensing systems; (3) single-sensor temperature-sensing systems; (4) magnetic tracking systems; and (5) gas sensing systems—see The study search and selection process are described in the PRISMA flow diagram . A totalThree studies ,65,66 weThe characteristics of the included studies are provided in Nine studies utilised imaging systems, seven used pH-sensing systems, four used magnetic/electromagnetic system, one study used a single-sensor temperature sensing system and one study used the gas-sensing system alongside a pH sensing system. All but two of the studies utilised one capsule per subject; Haase et al.  utilisedFourteen 64%) studies required the subjects to ingest capsules in the morning. One study  required4% studieThe results of the risk of bias assessment can be found in the Seventeen 77%) of the twenty-two included studies reported GET , providi% of the In eight studies ,68,77,79In 12 studies ,74,77,79SITT estimates were reported by seventeen 77%) of the included studies , providi% of the Of the twenty-two included studies, only nine (41%) reported CTT estimates , providiEleven 50%) of the twenty-two included studies see  reported0% of theThe primary objectives of this review were to: firstly, provide an overview of the currently available IC systems, and secondly, perform a systematic review of studies utilising these systems to report GI transit times in healthy volunteers. Two systematic reviews were identified on a similar topic as part of the search ,84; howeThe majority of included studies reported GET values, albeit over a large range. This appears to be due to variations in the protocol, which have an impact on GET, especially if capsules are ingested with a meal. Non-digestible solids have been shown to empty from the stomach in its fasting state, when powerful antral phase III MMC contractions occur, which expel undigested content from the stomach ,86. For Variations in the study protocol make it difficult to provide concise reference ranges for GET as measured using IC systems. However, it is not possible for all capsule studies to adopt the same protocol for the purposes of measuring GI transit times as, for some systems, such as the imaging capsules, transit times are only a secondary measure; therefore, capsules need to be ingested without a meal to ensure clear images of the gut mucosa are acquired. Nonetheless, the results for GET from the included studies can be grouped based on two similar protocols if the results from Koziolek et al.  and the SITT measurements were readily available from most of the included studies, with the reported values falling over a relatively large range. Interestingly, the studies that used imaging capsules reported shorter SITT values (<4 h) than those that used electromagnetic or pH sensing systems, which reported values > 4 h. This may be due to variations in the study protocol as the majority of studies that used imaging capsules did not specify whether the capsule was ingested with a meal. It is highly likely that these capsules were ingested without a meal for the purposes of obtaining clearer images. Therefore, similar to GET, the shorter SITT values obtained from imaging capsules may perhaps be due to the activity of the small intestine’s MMC, which may speed up the progress of non-digestible solids in the absence of any food or liquids that would have otherwise disrupted the activity in the small intestine . HoweverNormal reference values for CTT have been shown to fall over a large range of ≤70 h in mixed populations . AdditioROM studies are the established methods for the assessment of WGTT , with reThere are several limitations to this systematic review. Firstly, it was not possible to perform a quantitative meta-analysis of GI transit times due to the heterogeneity of the included studies and associated methodologies. Therefore, we were not able to determine effect sizes or quantitatively analyse the influence of factors such as the ingestion protocol on GI transit times. The study quality was also a limiting factor as only five of the twenty-two included studies were of a high quality. The limitation on the sample size of the eligible studies to twenty subjects was chosen for pragmatic reasons; however, six of the included studies ,73,75,77Finally, only two ,67 of thThe ambulatory monitoring of GI motility over extended periods of time outside clinical or laboratory environments has been made possible by IC systems, thereby expanding our knowledge of normal and pathological GI function. This systematic review has shown that there are a variety of IC systems available for the assessment of GI motility, and despite the differences in the sensor technologies employed, all of the systems appear to be capable of measuring GI transit times within a minimally invasive and radiation-free setting. However, a lack of standardisation of the protocols, even for the same type of IC system, makes it difficult to compare and combine the results. Nevertheless, reference ranges for GI transit times in healthy volunteers as determined using IC systems have been produced , which s


The popularization of the internet provides the possibility to improve the depressive symptoms (DS) and social isolation of middle-aged and older adults with functional disability. There was a significant correlation between internet use and DS in middle-aged and older adults with functional disability, but the relationship between internet use, social isolation, and DS remains to be confirmed.Data were obtained from 7,955 middle-aged and older adults aged 45 years and older from the 2018 China Health and Retirement Longitudinal Study (CHARLS). Multiple linear regression models were used to analyze the association between internet use and DS, and the Sobel test was used to explore mediating models.p = 0.041), and information reception  were significantly related to DS in middle-aged and older adults. Only information reception  was significantly related to DS among middle-aged and older adults with functional disability. Social isolation partially mediated the association between internet use and DS in middle-aged and older adults without functional disability, whereas it was fully mediated in middle-aged and older adults with functional disability.Results showed that internet use was negatively associated with DS among Chinese middle-aged and older adults. Among them, social needs (B = −0.770, This study indicates that when formulating health policies to promote the mental health of Chinese middle-aged and older adults, the use of the internet should not be overlooked. DS are Internet use is both a challenge and an opportunity for middle-aged and older adults with functional disability. With the increase of internet penetration rate, the scale of internet use among middle-aged and older adults is continuing to expand. There is growing concern about the potential adverse effects of age-related changes in physical functioning and the digital divide on internet use among middle-aged and older adults . StudiesInternet penetration will have a certain degree of impact on mental health due to the effects of illness and functional disability in middle-aged and older adults. According to the internet compensation theory, internet use can help reduce DS by meeting people’s unmet psychosocial needs in real life . StudiesInternet use has been shown to have a significant impact on DS in middle-aged and older adults, yet few studies have explored the potential mediating mechanisms by which internet use affects DS. In studying the relationship between internet use and DS, some scholars have studied how internet use affects DS. They found that internet use improved DS by enhancing factors including social capital, such as social engagement, social support, and interpersonal relationships . The exiResearch remains divided on whether internet use has an impact on the health of middle-aged and older adults with functional disability. The purpose of the study is to explore the role of social isolation in the relationship between internet use and DS in Chinese middle-aged and older adults with functional disability. At the same time, the study will explore how they interact to reduce the risk of DS. The following research hypotheses are proposed: (1) Internet use is significantly associated with DS in middle-aged and older adults; (2) Social isolation mediates, to some extent, the relationship between internet use and DS in middle-aged and older adults with functional disability.2.2.1.This study is based on the data from Wave 4 of the 2018 China Health and Retirement Longitudinal Study (CHARLS). The nationally representative longitudinal survey began in 2011 and followed the sample in 2013, 2015, and 2018. The purpose of the survey is to investigate the health and economic adjustment of China’s rapidly aging population, including demographic characteristics, socioeconomic status, family relationships, health status, and health care. A multi-stage probability sampling method was used to select the sample, with the final sample drawn from 150 counties in 28 provinces in China. The further detailed description of the CHARLS data can be found in the cohort profile .The 2018 CHARLS data selected for the study is the most recent wave of data available, covering 19,816 respondents aged 45 years and older in 450 communities (villages) in China. Considering the needs and objectives of the study, a total of 7,955 participants, including 3,845 males and 4,110 females, were included after excluding respondents under 45 years of age (age < 45 or missing date: 261) and participants with missing key variables . All interviewees were required to sign informed consent. Ethics approval for the data collection in CHARLS was obtained from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015).2.2.2.2.1.Internet use was selected as the independent variable. In the CHARLS 2018 questionnaire, the following question was used to measure internet use, where respondents were asked to answer the question “In the past month, have you used the internet?” By answering “yes” or “no.” We treated their responses as a dichotomous variable, recorded as “yes = 1” and “no = 0” . The typ2.2.2.Researchers used the Center for Epidemiologic Studies Depression Scale (CES-D-10) to assess the severity of individual DS. A total of 10 items on the CES-D-10 assessed participants’ feelings during the previous week, such as worry, hope, fear, loneliness, unhappiness, attention deficits, and sleep disturbances. Respondents were asked to rate the frequency of each item by choosing one of four response options ranging from “0 = little or no time” to “3 = most or all of the time.” The sum of the scores ranges from 0 to 30, with higher scores indicating greater severity of DS. In this study, respondents with CES-D-10 ≥ 10 were categorized as having DS, whereas respondents with CES-D-10 < 10 were categorized as not having DS .2.2.3.The four items were combined to form a score for social isolation . A score2.2.4.Based on previous literature, we controlled for possible covariates associated with DS in CHARLS. The respondents’ demographic information including gender, age , education level , self-reported health status , life satisfaction , chronic disease status (no and yes), activities of daily living (as measured by the Katz Index of Independence in Activities of Daily Living), instrumental activities of daily living , smoking , and drinking status .2.3.p < 0.05. The mediation analysis also controlled for all covariates.SPSS 26.0 and SPSS PROCESS  were use3.3.1.As shown in 3.2.The results of the correlation analysis are shown in 3.3.p < 0.01), −0.931(p < 0.01), and − 0.493(p < 0.05) shown in Model 2, Model 3, and Model 4, respectively, still showed a significant association with DS in middle-aged and older adults. The results of this study support the conclusion that internet use can reduce DS in middle-aged and older adults. In addition, Model 4 shows the results of the effects of other control variables on DS in middle-aged and older adults. The effect of internet use on DS in middle-aged and older adults remained significant (p < 0.05). Regression results showed that gender, age, education level, self-rated health, life satisfaction, chronic disease status, ADL/IADL status, and social isolation were all associated with DS in middle-aged and older adults. Among them, females had higher levels of DS than males. The lower the level of education, the more severe the DS. The worse the self-rated health status and life satisfaction, the more pronounced the DS in middle-aged and older adults. The more severe the DS manifested in middle-aged and older adults with ADL/IADL disorders and high social isolation.The regression results of Model 1 showed that internet use was significantly and negatively associated with DS in middle-aged and older adults. After adding the personal characteristics and social characteristics variables sequentially, the regression coefficients of internet use are −0.993(3.4.p = 0.041), and information reception  were negatively associated with DS in middle-aged and older adults. However, only Information reception  was significantly negatively associated with DS in middle-aged and older adults with functional disability.As shown in 3.5.Mediating analysis was performed according to the presence or absence of ADL/IADL disabilities. The mediating effect of social isolation on internet use and DS in middle-aged and older adults is shown in p < 0.001, total effect = −1.881, p = 0.016). Among middle-aged and older participants without functional disability, social isolation partially mediated the effect between internet use and DS .4.This study examined the impact of internet use on psychosocial health outcomes among middle-aged and older adults in the context of digital convergence and global aging. The findings suggest that internet use is associated with DS. Providing interventions to improve social isolation may help directly or indirectly reduce the degree of risk for DS in middle-aged and older adults with functional disability.The study results showed that internet use had a significant effect on reducing the risk of DS in middle-aged and older adults, which is consistent with previous studies . This stMultifunctional internet use can potentially improve the DS of middle-aged and older adults. The study found that the social needs and information reception functions of internet use significantly reduced DS in middle-aged and older adults, with information reception being the most significant among functional disability groups. Research has shown that social media alone may not improve mental health in older adults . TouchscSocial isolation fully mediated the relationship between internet use and DS in Chinese middle-aged and older adults with functional disability and partially mediated the relationship in middle-aged and older adults without functional disability. This implies that the internet reduces the risk of DS by improving social isolation in middle-aged and older adults with functional disability, which is similar to previous research findings . Middle-There are certain limitations of the study. First, the use of cross-sectional studies cannot monitor changes in mental health dynamics in middle-aged and older adults, and further longitudinal studies are needed to confirm this in the future to deepen the understanding of mental health. Second, DS in middle-aged and older adults are influenced by numerous factors. Further exploration of the different components of internet use on the health of middle-aged and older adults is needed to more clearly elucidate the potential mechanisms of internet use on mental health. Furthermore, participants’ DS were based on self-report measures, which may be influenced by personal feelings at the time, and thus bias may affect the results.5.Under the background of accelerating global aging and expanding the scale of the older population, speeding up the digital integration of older adults, and narrowing the digital divide for older adults are the new demands and practical contents of promoting active aging in the digital society. Our findings determined that the impact of internet use on the mental health status of middle-aged and older adults can be predicted by social isolation status. For middle-aged and older adults with functional disability, internet use is critical for enhancing social relationships and maintaining their mental health status. The findings of this study highlight the need to develop and implement strategies or policies that promote or popularize internet use to mitigate mental health disparities among older adults in an internet society. Further research into the experiences of people with functional disability could lead to a broader survey of health care and policy interventions to address the health needs that may exist in the current global health crisis.http://charls.pku.edu.cn/pages/data/2018-charls-wave4/zh-cn.html.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: MW, CL, XZ, TH, LZ, and YY conceived and designed the study, performed all statistical analyses and interpreted the data, drafted and revised all parts of the manuscript. MW drafted and revised the manuscript. MW, FY, and YH revised the discussion part and data processing of result part . YH revised and provided financial support for the progress of the manuscript. All authors contributed to the article and approved the submitted version.This research was funded by Hubei Provincial Department of Education Philosophy and Social Science Foundation (22Y089).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Both steps were accompanied by quantitative surveys (mixed methods approach). The qualitative data were analyzed using content analysis. A total of 11 needs were identified, which were prioritized differently. Highest priority was given to: data transmission, video consultation, access to patient records, symptom questionnaires, and communication support. The concerns identified were related to the assumption of deterioration of the status quo. Potential causes of deterioration were thought to be the negative impact on patient care, inappropriate user behavior, or a high level of technical requirements. As a conclusion, we define six recommendations for telehealth in PPHC.Pediatric palliative home care (PPHC) provides care for children, adolescents, and young adults with life-limiting illnesses in their own homes. Home care often requires long travel times for the PPHC team, which is available to the families 24/7 during crises. The complementary use of telehealth may improve the quality of care. In this pilot study we identify the needs and concerns of patients, teams, and other stakeholders regarding the introduction of telehealth. As a first step, focus groups were conducted in three teams. For the second step, semi-structured interviews were conducted with patients and their families ( In Germany, children, adolescents, and young adults (hereafter referred to as “children”) with life-limiting or life-threatening illnesses are cared for at home by Pediatric Palliative Home Care (PPHC). PPHC provides 24/7 on-call palliative care at home. This improves the quality of life of children with life-limiting illnesses . The higWith PPHC, weekly home visits enable the team to assess the patient in the context of their complex illness, their care network, and their family system. Moreover, this helps build a relationship of trust, including the confidence the patients’ and caregivers’ feel towards the PPHC services ,3. BasedTelehealth is an umbrella term for the exchange of medical information via electronic communication. It includes the aforementioned direct exchange between health care professionals, such as the multidisciplinary PPHC teams, and families and is also referred to as telemedicine or telenursing/telecare ,10,11. IVideo consultation is already being used to provide care in general pediatrics and for critically and chronically ill children with dermatologic, cardiac, neurologic, psychiatric, and genetic conditions, such as those cared for in PPHC ,9,12,13.Initial reviews of pilot projects indicate that the use of telehealth in PPHC may be feasible and acceptable, especially in pediatrics, as most caregivers are younger adults with an increased ability regarding use of digital technology. However, a high-quality, controlled study is missing ,6,16. Toinformation about PPHC for new patients;care network coordination;support services .This article focuses on the needs assessment prior to the planned, complementary use of telehealth in PPHC. In a presentation, Lotstein et al.  proposedTo our knowledge, there is no further research on needs and functional scope of telehealth in PPHC.Following Lotstein et al. , we examInformation about the other members of the research team and the objectives of the focus groups and interviews were provided, and the results were made available to all participants after analysis. Authors L.N., M.L.H., H.M. and J.Z. are psychologists with experience in empirical social research, especially interviews and focus groups. Authors M.J.D., M.F.-J., H.H., M.N., S.K., T.V. and V.V. have proven expertise in pediatric palliative care and health services research. The authors M.J.D., M.L.H., L.N. and J.Z. received an interviewer training for this study to conduct interviews and focus groups. The study is registered in the German Clinical Trials Register under DRKS00030546. Approvals have been obtained from the Ethics Committees of the Universities of Kassel (EK-Nr. 202213) and Giessen (AZ 64/22).Focus groups were used as an economic method to assess the teams’ needs and concerns. In PPHC, the multidisciplinary team collectively establishes the everyday patient care decisions. Focus groups facilitate a dynamic data collection in which the relevance of a need or concern is discussed between the different professionals who know the actual conditions of their work. The individual opinions of specific members would not have been as able to capture the relevant needs and concerns. Accordingly, for the needs assessment of the teams, three members of the author team  conducted one focus group each for all of the teams in Hesse. Participation in the three focus groups was voluntary and took place on site  and via video conferencing (focus group F) during regular working hours. All staff on duty participated. The participants were already familiar with the TelPa_kids project and author M.J.D.We developed the focus group guide based on a collaborative brainstorming session with the entire research team. The principal investigator (M.L.H.) had no contact with the teams prior to the study. The guide included information about (1) the process, (2) the framework, and (3) specific guiding questions. Three people  moderated the three focus groups, each of which lasted approximately 90 min.The first of the two guiding questions was: “What are typical problems in everyday work where digitalization could help?” The second was: “What concerns do you have about the application of digitalization?” see  The focuTo assess the needs of patients and families, we conducted a brief online survey using multiple choice answers and free text followed by semi-structured interviews (question guide can be requested from the authors). Participants were selected to ensure diversity of sociodemographic background and underlying conditions  was unknown to the participants. Information about the aims of the survey, the TelPa_kids project, and the interviewer was provided in advance.n = 10), additional interviews were conducted until no new needs or concerns were raised by the families.Between March and June 2022, patients and relatives were interviewed by means of an online questionnaire. The questionnaire included 9 items on sociodemographic information, 8 items on the care situation, and 9 items on needs for a telehealth app. This was followed by online interviews via Zoom “on premises” until June 2022 . The aveBased on the audio recordings and field notes of the focus groups and interviews, a structuring content analysis ,20 and anκ = 0.85–0.90 for the needs. Overall, these values indicate good-to-very-good intersubjective comprehensibility of the code book.For quality control, 10% of the material was re-coded by another member of the author team based on the code book and the intercoder reliability was determined. The Cohen’s kappa according to Brennan and Prediger  was κn =To identify the needs of other stakeholders involved in the outpatient care of chronically ill children, the authors M.J.D., M.F., H.H., S.K. and V.V. conducted a survey. Cooperation partners of the three teams in Hesse were interviewed about their needs regarding the complementary use of telehealth. Results were reported to M.J.D. and analyzed.Quantitative analysis of the patient and parent survey, focus group member checks, and stakeholder survey were performed using MS Excel and SPSS version 26 .n = 28) and consisted of physicians and nurses, supplemented by social workers, psychologists, and a team secretary.Overall, the three focus groups conducted in Study I had between 7 and 11 participants . The other sociodemographic characteristics and diagnoses  were comn = 1), pediatricians in private practice (n = 4), ambulatory care nursing services (n = 3), nursing homes and hospices (n = 5), and providers of medical aids (n = 1).For Study III, we surveyed neuropediatric outpatient clinics  was not willing to do so. In addition, the data provided insight into the preferences of individual stakeholders. For example, the three young adult patients showed the highest willingness to communicate with teams via messenger. The three bereaved caregivers were the only stakeholders who unanimously agreed that educational materials on “recognizing the dying process” and “actions in case of death” should be made available see .In addition, families also mentioned other features such as an emergency button with a locating function (5 families) or a “Frequently Asked Questions” relating to their own child to facilitate the communication of important information (5 families). Contrary to the authors’ expectations, telemetric transmission of vital signs  was not desired by the teams. The integration of an electronic stethoscope was seen as critical regarding technical feasibility. As an anecdote, we can report that after the needs assessment we tested the use of a Bluetooth-based electronic stethoscope by family members during video consultations with each of the three teams. A total of 9 patients receiving PPHC were evaluated, and 18 lungs examined. Of these, 7 lungs showed vesicular breath sounds, 6 lungs showed fine to coarse crackles, 2 lungs showed increased intensity of breathing sounds and one lung showed a lack of breathing sounds in its basal region. Only in one patient did audio quality prevent an evaluation of the lungs. However, the greatest obstacles proved to be the time lag between video stream and an auscultation signal, which itself also takes some time to get used to.The other stakeholders in the care of chronically ill children were open to networking via an app, but no additional needs were mentioned. A company providing rehabilitation and orthopedic aids for children, nursing homes, and a hospice showed great interest in enhancing communication with the PPHC team, especially using video consultation, the electronic stethoscope, and access to the patient record. This need was not expressed by the neuropediatric outpatient clinic or the pediatricians in private practice.In a more in-depth study, four basic needs for telehealth were identified through content analysis, which aggregate the above needs. These can be generalized to both families and PPHC teams: (1) support: simplification of organizational and bureaucratic tasks; (2) support: more security in symptom reporting by the family; (3) overview and coordination of the care network; and (4) more effective and flexible communication.Barriers reported regarding implementation of telehealth in PPHC are shown in There was also a concern that digitalization would replace traditional and ritual norms of patient care. One physician warned about the introduction of remote auscultation using electronic stethoscopes: “The auscultation with a stethoscope is a sacred act for patients. It makes a difference whether it is performed by a layman or a saint—the doctor is the best medicine!” Interestingly, families and teams did not express any concerns about data protection. Instead, they rated it as an obvious requirement for the app and expected that it would be sufficiently implemented.The use of telehealth in PPHC has been reported in pilot projects in hospices as well as in PPHC in Australia, where vast distances are a common factor . These rFrom the most relevant needs for both user groups, only ‘video consultation’ and ‘symptom questionnaires’ have previously been reported as relevant for PPHC. Furthermore, in adult Palliative Home Care (adult PHC), the lack of a messenger function has been reported, whereas the high priority needs ‘data transfer’ and ‘access to patient records’ are new findings for PPHC ,23. The Among the other stakeholders in the outpatient care for palliative children, the non-physician collaborators were particularly open to telehealth, especially video consultation with the teams. Our teams also identified telehealth and especially video consultations as an important tool for (inter-)professional exchange as described for other settings ,15. DataConcerns about the implementation of telehealth in PPHC and pediatrics lay, as already previously reported, with the providers which worry about the negative effects on patient care ,7. Our fVideo consultation is one of the most important needs. It presents the possibility of fundamentally changing the current standard of care by replacing all or part of the home visits with video consultation . AccordiThese results provide concrete information for the addressing of telehealth needs in PPHC and will lead to the technical implementation of an app in the next project phase. In addition, we plan to add the ability to collect data for clinical trials to the feature set of the telehealth app. Furthermore, our data indicate that future studies on telehealth in PPHC should assess the familial and teams’ sense of security of care during PPHC as well as familial empowerment as already discussed for adult PHC . HoweverConsidering the systematic stakeholder needs assessment, we have derived six recommendations for the complementary use of telehealth in PPHC. First, key functions should include data transmission, 3-party video consultations, access to patient records, symptom questionnaires, and communication support. Second, telehealth should be integrated into the team’s medical record. Third, telehealth apps should provide the ability for customized, personalized functions. Fourth, the impact of telehealth warrants review and adaptation of current practices as well as clear rules for families and teams regarding its use for home-based support. Fifth, palliative care assessments should be conducted as face-to-face contacts. Sixth, teams should network with other teams to ensure up-to-date and relevant information material and to conduct future controlled telehealth studies.


Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.  Keratoconus (KCN) is a non-inflammatory disease that causes thinning and eventual bulging of the cornea affecting one or both eyes , and canMachine learning (ML) algorithms  have beeOne such DL model  was deveWhile the previous studies utilized Pentacam maps, other attempts have been developed on synthetic data. In one such study , the autDetecting subclinical KCN is challenging for expert clinicians, as there may be no clear manifestation of signs. This problem is even more challenging for less experienced corneal experts. Hence, DL models may play an important role in detecting suspect  KCN .In this study, our aim was to improve both the accuracy and robustness of detecting subclinical forms of KCN, based on an ensemble of DL features, which is an emerging approach in which the features are extracted from a pretrained DL network and fed into a machine learning classifier, such as SVM, to make the final classification. This approach has previously been applied to detect two corneal classes  with four corneal topographic maps and Alexnet with SVM , and hasIn order to harvest the power of both DL features and ML classifier, we propose a novel fusion DL method based on Xception  and InceTwo datasets were used in this study: the first dataset was acquired with the Pentacam instrument in Egypt and the second dataset was collected with the Pentacam instrument in Iraq. Both studies were conducted according to the ethical codes of the declaration of Helsinki.The first dataset included images from normal, suspect, and established keratoconus, which were also utilized in Abdolmotaal et al. . It inclThe second dataset included corneal maps from normal and KCN subjects. It was originally collected at Al-Amal eye Clinical in Baghdad Iraq, and was used in previous study as well . The diaFor the 3-class dataset, all maps have black numbers and annotations generated by the measurement equipment. Transfer learning is a technique to learn from a large dataset and then transfer the knowledge to a small dataset . One of Machine learning (ML) classifiers’ performance may be improved using descriptive features. Therefore, in this paper, the feature fusion technique  has beenIn this scenario, we trained each model (Xception and InceptionResNetV2 here) separately, then used those models to extract features from the new training and testing subsets. The extracted features from models are fused in one pool to train conventional ML classifiers. Several ML classifiers were trained and tested, including decision trees (DT), quadratic discriminant analysis, SVMs, and K-nearest neighbor (KNN). Using the classification learner application on Matlab, the best performing ML model was selected and the results were reported.Scenario 1 (S1—2-class): training the models with two classes of normal and KCN.Scenario 2 (S2—3-class): training the models with three classes of normal, suspect, and KCN.All used datasets were divided into three sets: training, validation, and testing. The percentage of split was equal to 80:10:10 . The used datasets were trained in two different scenarios as follows:To train the networks in this study, the following settings were used. The training options included an Adam optimizer, a mini-batch size of 15, max epochs set at 100, shuffled for every epoch, and the initial learning rate was 0.001. The processor properties used in this experiment were Intel (R) Core i7/32 GB/1 TB/Nvidia RTX A3000 12 GB. Matlab 2022a was used to develop the experiments.Both the pre-trained models and the ML classifiers were evaluated based on accuracy, specificity, recall, precision, and F1-score metrics. These evaluation metrics were computed based on the TN, TP, FN, and FP values. The TN and TP implied precisely categorized negative and positive instances, whereas FN and FP denoted misclassified positive and negative cases, respectively. Every evaluation metric equation is presented as follows:In order to examine the network activations, Grad-Cams  were alsThe first dataset included 4113 corneal maps from 1371 eyes. A total of 500 eyes were normal, 500 suspect, and 371 KCN. The mean age of the participants was .The second dataset included 639 corneal maps from 213 eyes. A total of 114 eyes were normal, and 99 KCN. The mean age of all participants was 31.4 ± 9.2 years for the normal eyes and 32.95 ± 10.86 years for KCN, mean ± SD).Once we trained, tuned, and tested the model on 80/10/10  split of the dataset from Egypt, the Xception model generated an accuracy of 99.0% . Only twThe DL model was trained, tuned, and tested based on the 80/10/10 split of the Egypt dataset, and the Xception model generated an accuracy of 99.7% . The IncFigure 5The ElvF map will be considered in this section. Scenario 2 was first evaluated by training testing both models (Xception and InceptionResNetV2) as listed in In order to overcome the misclassified samples, features that were extracted by both models have been combined to train several ML classifiers. The highest results were achieved by two classifiers: SVM and K-nearest neighbor. SVM was chosen to be listed as shown in Although the feature fusion with SVM improved the results compared to both models, four samples were misclassified, as shown in In order to validate the clinical relevance of the models, class activation maps (CAMs) were generated to show the regions of the corneal map that were more important for the model to make decision. To further understand how feature extraction, based on the Xception model, impacts the final KCN decision, and to assess the clinical relevance of the features, we visualized the filters that the Xception model learned from input corneal maps. To assess how the model is generalizable to unseen corneal maps from different settings and ethnicities, we re-evaluated the performance of the model based on an independent dataset. This dataset was collected from an eye clinic in Baghdad, Iraq, which represents corneal data from different settings with likely different settings. We developed several conventional CNN models, based on state-of-the-art architectures, including Xception and InceptionResNetV2, which were able to detect corneas with suspect or established KCN. We observed that the accuracy of these CNN architectures to identify normal eyes from eyes with established KCN based on different corneal maps, reached around 99%. A similar efficacy was observed in the same CNN models for distinguishing normal eyes, eyes with suspect KCN, and eyes with established KCN. While these results were highly promising, the testing subset was selected from the same dataset as the model training.To assess the generalizability, we evaluated the accuracy of the CNN models based on another independent dataset collected from a Pentacam instrument in another country, with likely different device settings. For the S1 (two-class) problem, we achieved an AUC and accuracy of 0.99 and 100, respectively, with the CorPac map. For the S2 (three-class) problem, we obtained an accuracy of 100 with the Sag map. Our findings suggest that our CNN models are generalizable to unseen corneal maps from other instruments from other countries with likely different settings, and corneal maps from different populations.With our DL fusion models (Xception and InceptionResNetV2), different classifiers were utilized including DT, quadratic discriminant analysis, KNN, and SVM. SVM and DT were the best performing classifiers compared to the other ML models, with an accuracy of 99.1 and 100, respectively.Examining Based on the results in A comparison of our model with other models previously published is shown in As shown in the learnable filters displayed in While we used relatively large datasets of corneal maps from two datasets collected from two different countries, additional datasets with larger number of corneal maps from other ethnicities/races are desirable to further validate the proposed CNN models. Additionally, our CNN models are limited to corneal data from Pentacam. Therefore, the use of innovative device-agnostic CNN models is warranted to exploit data from other corneal topography instruments, such as CASIA, Orbscan, and Galilei.DL fusion-based methods were proposed and developed for KCN detection. While single CNN architecture achieves good performance, the fusion of two DL models (Xception and InceptionResNetV2) with features from these models and an SVM classifier is particularly suited to meet the challenge of identification of earlier stage, clinical suspect KCN, distinct from established KCN. We validated our proposed method on a three-class dataset and achieved an accuracy of 97–100%. Furthermore, independent evaluation of a two-class dataset from a different country showed an accuracy of 88–92% and an AUC 0.91–0.96 for the three corneal maps.