Rare diseases: why is a rapid referral to an expert center so important?

BACKGROUND: Patients with rare diseases usually go through years of diagnostic odysseys. The large number of rare diseases and the associated lack of expertise pose a major challenge to physicians. There are few physicians dealing with patients with rare diseases and they usually work in a limited number of specialized centers. The aim of this study was to evaluate the diagnostic efficiency of an expert center. METHODS: The diagnostic pathway of 78 patients of the outpatient clinic for rare inflammatory systemic diseases with renal involvement was analyzed retrospectively. For this purpose, each examination day was documented with the corresponding examinations performed from the onset of initial symptoms. Three time points were considered: The time when patients first visited a physician with symptoms, the time when patients consulted an expert, and the time when they received the correct diagnosis. In addition, it was documented whether the diagnosis could be made without the expert, or only with the help of the expert. The examinations that confirmed the diagnosis were also documented for each patient. RESULTS: A correct diagnosis was made without the help of the expert in only 21% of cases. Each patient visited an average of 6 physicians before consulting the expert. Targeted diagnostics enabled the expert to make the correct diagnosis with an average of seven visits, or one inpatient stay. However, referral to the expert took an average of 4 years. CONCLUSION: The data show that rapid and targeted diagnostics were possible in the expert center due to the available expertise and the interdisciplinary exchange. Early diagnosis is of great importance for many patients, as an early and correct therapy can be decisive for the course of the disease.

Health economic benefits through the use of diagnostic support systems and expert knowledge.

BACKGROUND: Rare diseases are difficult to diagnose. Due to their rarity, heterogeneity, and variability, rare diseases often result not only in extensive diagnostic tests and imaging studies, but also in unnecessary repetitions of examinations, which places a greater overall burden on the healthcare system. Diagnostic decision support systems (DDSS) optimized by rare disease experts and used early by primary care physicians and specialists are able to significantly shorten diagnostic processes. The objective of this study was to evaluate reductions in diagnostic costs incurred in rare disease cases brought about by rapid referral to an expert and diagnostic decision support systems. METHODS: Retrospectively, diagnostic costs from disease onset to diagnosis were analyzed in 78 patient cases from the outpatient clinic for rare inflammatory systemic diseases at Hannover Medical School. From the onset of the first symptoms, all diagnostic measures related to the disease were taken from the patient files and documented for each day. The basis for the health economic calculations was the Einheitlicher Bewertungsmaßstab (EBM) used in Germany for statutory health insurance, which assigns a fixed flat rate to the various medical services. For 76 cases we also calculated the cost savings that would have been achieved by the diagnosis support system Ada DX applied by an expert. RESULTS: The expert was able to achieve significant savings for patients with long courses of disease. On average, the expert needed only 27 % of the total costs incurred in the individual treatment odysseys to make the correct diagnosis. The expert also needed significantly less time and avoided unnecessary examination repetitions. If a DDSS had been applied early in the 76 cases studied, only 51-68 % of the total costs would have incurred and the diagnosis would have been made earlier. Earlier diagnosis would have significantly reduced costs. CONCLUSION: The study showed that significant savings in the diagnostic process of rare diseases can be achieved through rapid referral to an expert and the use of DDSS. Faster diagnosis not only achieves savings, but also enables the right therapy and thus an increase in the quality of life for patients.

When performance is not enough-A multidisciplinary view on clinical decision support.

Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions. This work presents a multidisciplinary view of machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. The target audience is computer scientists, who plan to do research in a clinical context. The paper starts from a relatively straightforward risk prediction system in the subspecialty nephrology that was evaluated on historic patient data both intrinsically and based on a reader study with medical doctors. Although the results were quite promising, the focus of this article is not on the model itself or potential performance improvements. Instead, we want to let other researchers participate in the lessons we have learned and the insights we have gained when implementing and evaluating our system in a clinical setting within a highly interdisciplinary pilot project in the cooperation of computer scientists, medical doctors, ethicists, and legal experts.

Influence of Belatacept- vs. CNI-Based Immunosuppression on Vascular Stiffness and Body Composition.

BACKGROUND: Arterial stiffness and phase angle (PhA) have gained importance as a diagnostic and prognostic parameter in the management of cardiovascular disease. There are few studies regarding the differences in arterial stiffness and body composition between renal transplant recipients (RTRs) receiving belatacept (BELA) vs. calcineurin inhibitors (CNI). Therefore, we investigated the differences in arterial stiffness and body composition between RTRs treated with different immunosuppressants, including BELA. METHODS: In total, 325 RTRs were enrolled in the study (mean age 52.2 years, M -62.7%). Arterial stiffness was determined with an automated oscillometric device. All body composition parameters were assessed, based on bioelectrical impedance analysis (BIA), and laboratory parameters were obtained from the medical files of the patients. RESULTS: We did not detect any significant difference in terms of arterial stiffness and PhA in RTRs undergoing different immunosuppressive regimens, based on CsA, Tac, or BELA. Age was an essential risk factor for greater arterial stiffness. The PhA was associated with age, BMI, time of dialysis before transplantation, and kidney graft function. CONCLUSION: No significant differences in arterial stiffness and PhA were observed in RTRs under different immunosuppressive regimens. While our data provide additional evidence for arterial stiffness and PhA in RTRs, more research is needed to fully explore these cardiovascular risk factors and the impact of different immunosuppressive regimens.

Development and validation of multivariable prediction models of serological response to SARS-CoV-2 vaccination in kidney transplant recipients.

Repeated vaccination against SARS-CoV-2 increases serological response in kidney transplant recipients (KTR) with high interindividual variability. No decision support tool exists to predict SARS-CoV-2 vaccination response to third or fourth vaccination in KTR. We developed, internally and externally validated five different multivariable prediction models of serological response after the third and fourth vaccine dose against SARS-CoV-2 in previously seronegative, COVID-19-naïve KTR. Using 20 candidate predictor variables, we applied statistical and machine learning approaches including logistic regression (LR), least absolute shrinkage and selection operator (LASSO)-regularized LR, random forest, and gradient boosted regression trees. For development and internal validation, data from 590 vaccinations were used. External validation was performed in four independent, international validation cohorts comprising 191, 184, 254, and 323 vaccinations, respectively. LASSO-regularized LR performed on the whole development dataset yielded a 20- and 10-variable model, respectively. External validation showed AUC-ROC of 0.840, 0.741, 0.816, and 0.783 for the sparser 10-variable model, yielding an overall performance 0.812. A 10-variable LASSO-regularized LR model predicts vaccination response in KTR with good overall accuracy. Implemented as an online tool, it can guide decisions whether to modulate immunosuppressive therapy before additional active vaccination, or to perform passive immunization to improve protection against COVID-19 in previously seronegative, COVID-19-naïve KTR.

Poor Outcomes in Patients With Transplant Glomerulopathy Independent of Banff Categorization or Therapeutic Interventions.

Background: Transplant glomerulopathy (TG) may indicate different disease entities including chronic AMR (antibody-mediated rejection). However, AMR criteria have been frequently changed, and long-term outcomes of allografts with AMR and TG according to Banff 2017 have rarely been investigated. Methods: 282 kidney allograft recipients with biopsy-proven TG were retrospectively investigated and diagnosed according to Banff'17 criteria: chronic AMR (cAMR, n = 72), chronic active AMR (cAAMR, n = 76) and isolated TG (iTG, n = 134). Of which 25/72 (34.7%) patients of cAMR group and 46/76 (60.5%) of cAAMR group were treated with antihumoral therapy (AHT). Results: Up to 5 years after indication biopsy, no statistically significant differences were detected among iTG, cAMR and cAAMR groups in annual eGFR decline (-3.0 vs. -2.0 vs. -2.8 ml/min/1.73 m2 per year), 5-year median eGFR (21.5 vs. 16.0 vs. 20.0 ml/min/1.73 m2), 5-year graft survival rates (34.1 vs. 40.6 vs. 31.8%) as well as urinary protein excretion during follow-up. In addition, cAMR and cAAMR patients treated with AHT had similar graft and patient survival rates in comparison with those free of AHT, and similar comparing with iTG group. The TG scores were not associated with 5-year postbiopsy graft failure; whereas the patients with higher scores of chronic allograft scarring (by mm-, ci- and ct-lesions) had significantly lower graft survival rates than those with mild scores. The logistic-regression analysis demonstrated that Banff mm-, ah-, t-, ci-, ct-lesions and the eGFR level at biopsy were associated with 5-year graft failure. Conclusions: The occurrence of TG is closely associated with graft failure independent of disease categories and TG score, and the long-term clinical outcomes were not influenced by AHT. The Banff lesions indicating progressive scarring might be better suited to predict an unfavorable outcome.

Declining Course of Humoral Immune Response in Initially Responding Kidney Transplant Recipients after Repeated SARS-CoV-2 Vaccination.

The immunogenicity of SARS-CoV-2 vaccines in kidney transplant recipients is limited, resulting in inadequately low serological response rates and low immunoglobulin (Ig) levels, correlating with reduced protection against death and hospitalization from COVID-19. We retrospectively examined the time course of anti-SARS-CoV-2 Ig antibody levels after up to five repeated vaccinations in 644 previously nonresponding kidney transplant recipients. Using anti SARS-CoV-2 IgG/IgA ELISA and the total Ig ECLIA assays, we compared antibody levels at 1 month with levels at 2 and 4 months, respectively. Additionally, we correlated the measurements of the used assays. Between 1 and 2 months, and between 1 and 4 months, mean anti-SARS-CoV-2 Ig levels in responders decreased by 14% and 25%, respectively, depending on the assay. Absolute Ig values and time course of antibody levels showed high interindividual variability. Ig levels decreased by at least 20% in 77 of 148 paired samples with loss of sufficient serological protection over time occurring in 18 out of 148 (12.2%). IgG ELISA and total Ig ECLIA assays showed a strong positive correlation (Kendall's tau = 0.78), yet the two assays determined divergent results in 99 of 751 (13.2%) measurements. IgG and IgA assays showed overall strong correlation but divergent results in 270 of 1.173 (23.0%) cases and only weak correlation of antibody levels in positive samples. Large interindividual variability and significant loss of serological response after 4 months supports repeated serological sampling and consideration of shorter vaccination intervals in kidney transplant recipients.

Serological Response to Three, Four and Five Doses of SARS-CoV-2 Vaccine in Kidney Transplant Recipients.

Mortality from COVID-19 among kidney transplant recipients (KTR) is high, and their response to three vaccinations against SARS-CoV-2 is strongly impaired. We retrospectively analyzed the serological response of up to five doses of the SARS-CoV-2 vaccine in KTR from 27 December 2020 until 31 December 2021. Particularly, the influence of the different dose adjustment regimens for mycophenolic acid (MPA) on serological response to fourth vaccination was analyzed. In total, 4277 vaccinations against SARS-CoV-2 in 1478 patients were analyzed. Serological response was 19.5% after 1203 basic immunizations, and increased to 29.4%, 55.6%, and 57.5% in response to 603 third, 250 fourth, and 40 fifth vaccinations, resulting in a cumulative response rate of 88.7%. In patients with calcineurin inhibitor and MPA maintenance immunosuppression, pausing MPA and adding 5 mg prednisolone equivalent before the fourth vaccination increased the serological response rate to 75% in comparison to the no dose adjustment (52%) or dose reduction (46%). Belatacept-treated patients had a response rate of 8.7% (4/46) after three vaccinations and 12.5% (3/25) after four vaccinations. Except for belatacept-treated patients, repeated SARS-CoV-2 vaccination of up to five times effectively induces serological response in kidney transplant recipients. It can be enhanced by pausing MPA at the time of vaccination.

Glucocorticoid Effects on Tissue Residing Immune Cells in Giant Cell Arteritis: Importance of GM-CSF.

Giant cell arteritis (GCA) is a systemic granulomatous vasculitis clinically characterized by a prompt response to glucocorticoid therapy. Dendritic cells (DCs) play a central role in the pathogenesis of the disease and are increased in temporal arteries from GCA patients. The aim of this study was to determine the effects of glucocorticoid therapy on granulomatous infiltrates and on peripheral DCs of GCA patients. Immunohistochemical staining of temporal artery specimens from 41 GCA patients revealed a rapid reduction of the number of DCs after initiation of glucocorticoid treatment. TUNEL staining was performed to quantify apoptotic S100+ DC, CD3+ T cells, and CD68+ macrophages in the granulomatous infiltrates. An increase of apoptotic cells up to 9 ± 2% after 4-5 days of glucocorticoid therapy and up to 27 ± 5% (p < 0.001, compared to earlier timepoints) after 6-10 days was detected. A decrease of CCL19 and CCL21 expression was observed after starting glucocorticoid therapy. Granulocyte-macrophage colony-stimulating factor (GM-CSF) expression also significantly decreased under glucocorticoid therapy. No GM-CSF expression was detected in the control specimens. Glucocorticoid therapy leads to a rapid, time-dependent reduction of DCs in temporal arteries from GCA patients and reduction of mediators for cell migration. Our data suggest GM-CSF as a novel therapeutic target of GCA.

Poor Long-Term Renal Allograft Survival in Patients with Chronic Antibody-Mediated Rejection, Irrespective of Treatment-A Single Center Retrospective Study.

The Banff 2017 report permits the diagnosis of pure chronic antibody-mediated rejection (cAMR) in absence of microcirculation inflammation. We retrospectively investigated renal allograft function and long-term outcomes of 67 patients with cAMR, and compared patients who received antihumoral therapy (cAMR-AHT, n = 21) with patients without treatment (cAMRwo, n = 46). At baseline, the cAMR-AHT group had more concomitant T-cell-mediated rejection (9/46 (19.2%) vs. 10/21 (47.6%); p = 0.04), a higher g-lesion score (0.4 ± 0.5 versus 0.1 ± 0.3; p = 0.01) and a higher median eGFR decline in the six months prior to biopsy (6.6 vs. 3.0 mL/min; p = 0.04). The median eGFR decline six months after biopsy was comparable (2.6 vs. 4.9 mL/min, p = 0.61) between both groups, and three-year graft survival after biopsy was statistically lower in the cAMR-AHT group (35.0% vs. 61.0%, p = 0.03). Patients who received AHT had more infections (0.38 vs. 0.20 infections/patient; p = 0.04). Currently, antihumoral therapy is more often administered to patients with cAMR and rapidly deteriorating renal function or concomitant TCMR. However, long-term graft outcomes remain poor, despite treatment.

Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study.

BACKGROUND: Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases. RESULTS: Ada DX suggested the correct disease earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score -6.68, respective -5.71, α=0.05, p-value <0.001). CONCLUSION: Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user, and the optimization of the knowledge base during the course of the study. Results pertaining to the system's accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies.