Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (ML) have been playing a pivotal role in revolutionizing medical diagnostics. This research presents a novel approach of using ML algorithms for screening hematologic malignancies using CPD. The data collection was done at Konkuk University Medical Center, Seoul. A total of (882 cases: 457 hematologic malignancy and 425 hematologic nonmalignancy) were used for analysis. In our study, seven machine learning models, i.e., SGD, SVM, RF, DT, Linear model, Logistic regression, and ANN, were used. In order to measure the performance of our ML models, stratified 10-fold cross validation was performed, and metrics, such as accuracy, precision, recall, and AUC were used. We observed outstanding performance by the ANN model as compared to other ML models. The diagnostic ability of ANN achieved the highest accuracy, precision, recall, and AUC ± Standard Deviation as follows: 82.8%, 82.8%, 84.9%, and 93.5% ± 2.6 respectively. ANN algorithm based on CPD appeared to be an efficient aid for clinical laboratory screening of hematologic malignancies. Our results encourage further work of applying ML to wider field of clinical practice.ope

Frequency and Factors of Indeterminate QuantiFERON-TB Gold In-Tube and QuantiFERON-TB Gold PLUS Test Results in Rheumatic Diseases

We compared the results and differences of indeterminate rates between the QuantiFERON-TB Gold In-Tube (QFT-GIT) and QuantiFERON-TB Gold PLUS (QFT-PLUS) tests in patients with rheumatic diseases and analyzed the associated factors. Data of patients with rheumatic diseases who had undergone the QFT-GIT or QFT-PLUS test were used, and information regarding patient demographics, primary diagnosis, laboratory results, and medications was collected. Furthermore, indeterminate result rates of the patient cohort and healthy controls were also compared. A total of 177 (43.4%) and 231 (56.6%) patients had undergone QFT-GIT and QFT-PLUS tests, respectively. Among them, four (2.3%) and seven (3.0%) patients had indeterminate results, which did not differ between the QFT-GIT and QFT-PLUS groups. Indeterminate results were significantly higher among patients with rheumatic diseases than in healthy controls (2.7% vs. 0.2%, p < 0.001). Multivariate logistic regression revealed that the lymphocyte count (hazard ratio (HR) 0.998, 95% confidence interval (CI) 0.997, 1.000; p = 0.012) and albumin level (HR 0.366, 95% CI 0.150, 0.890; p = 0.027) were predictive of indeterminate results. A lymphocyte count of ≤810/mm3 and an albumin level of ≤3.7 mg/dL were capable of discriminating between indeterminate and determinate results. The QFT-GIT and QFT-PLUS tests have comparable diagnostic performances in patients with rheumatic diseases. Decreased lymphocyte and albumin levels contribute to indeterminate results.ope

Risk of cancer, tuberculosis and serious infections in patients with ankylosing spondylitis, psoriatic arthritis and psoriasis treated with IL-17 and TNF-α inhibitors: a nationwide nested case-control analysis

Objectives: Targeting interleukin (IL)-17 and tumour necrosis factor (TNF)-α is recommended for the management of severe/refractory ankylosing spondylitis (AS), psoriatic arthritis (PsA), and psoriasis (PsO); however, safety data comparing these agents, especially in a large Asian population are unavailable. Methods: Patients with AS, PsA and PsO were searched using the Health Insurance Review and Assessment Service database, defined according to the International Classification of Diseases-10 and unique insurance codes for rare diseases. By including patients newly diagnosed with AS, PsA, and PsO between 2010-2020, the outcomes of cancer, tuberculosis (TB) and serious infections following IL-17 and TNF-α inhibitor usage were evaluated. To investigate the association between treatments and outcomes, nested case-control analyses matching patients to controls (maximum of 1:10 ratio) according to index age, sex, index year, and follow-up duration were performed. Results: Among 40322, 4953, and 5347 patients with AS, PsA, and PsO, respectively, three different datasets were generated to evaluate incidence of outcomes. Conditional logistic regression analysis revealed that cyclosporine use (odds ratio [OR] 2.286, p=0.0176) increased cancer, and a higher Charlson Comorbidity Index (CCI) score (OR 1.085, p=0.0406) and IL-17 inhibitor use only (OR 0.126, p=0.0457) showed a positive and negative association with TB, respectively. Serious infections increased in patients with high CCI scores (OR 1.117, p<0.0001), cyclosporine users (OR 1.445, p=0.0098), and medical-aided individuals (OR 1.667, p<0.0001). Conclusions: In this nationwide cohort of IL-17 and TNF-α inhibitor users, both treatments conferred comparable risk of cancer and serious infections, while IL-17 inhibitors may be advantageous for TB.ope

Inflammation Alters Relationship Between High-Density Lipoprotein Cholesterol and Cardiovascular Risk in Patients With Chronic Kidney Disease: Results From KNOW-CKD

Background The function of high-density lipoprotein can change from protective to proatherosclerotic under inflammatory conditions. Herein, we studied whether inflammation could modify the relationship between high-density lipoprotein level and risk of adverse outcomes in patients with chronic kidney disease . Methods and Results In total, 1864 patients from the prospective KNOW-CKD (Korean Cohort Study for Outcome in Patients With Chronic Kidney Disease) were enrolled. The main predictor was high-density lipoprotein cholesterol (HDL-C) level. Presence of inflammation was defined by hs-CRP (high-sensitivity C-reactive protein) level of ≥1.0 mg/L. The primary outcome was extended major adverse cardiovascular events. During 9231.2 person-years of follow-up, overall incidence of the primary outcome was 15.8 per 1000 person-years. In multivariable Cox analysis after adjusting for confounders, HDL-C level was not associated with the primary outcome. There was a significant interaction between the inflammatory status and HDL-C for risk of extended major adverse cardiovascular events (P=0.003). In patients without inflammation, the hazard ratios (HRs) (95% CIs) for HDL-C levels <40, 50 to 59, and ≥60 mg/dL were 1.10 (0.50-1.82), 0.95 (0.50-1.82), and 0.42 (0.19-0.95), respectively, compared with HDL-C of 40 to 49 mg/dL. However, the significant association for HDL-C ≥60 mg/dL was not seen after Bonferroni correction. In patients with inflammation, we observed a trend toward increased risk of extended major adverse cardiovascular events in higher HDL-C groups (HRs [95% CIs], 0.73 [0.37-1.43], 1.24 [0.59-2.61], and 1.56 [0.71-3.45], respectively), but without statistical significance. Conclusions The association between HDL-C level and adverse cardiovascular outcomes showed reverse trends based on inflammation status in Korean patients with chronic kidney disease. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01630486.ope

Impact of iron status on kidney outcomes in kidney transplant recipients

Iron plays an important role in hemodynamics and the immunity, independent of anemia. Since dynamic changes occur in iron storage after kidney transplantation (KT), we investigated the association between iron status and kidney outcomes in KT patients. We analyzed data from the KoreaN cohort study for Outcome in patients With KT (KNOW-KT). The iron status was classified into three groups based on ferritin or transferrin saturation (TSAT) levels one year after KT, with reference ranges of 20‒35% and 100‒300 ng/mL for TSAT and ferritin, respectively. The primary outcome was the composite outcome, which consisted of death, graft failure, and an estimated glomerular filtration rate decline ≥ 50%. In total, 895 patients were included in the final analysis. During a median follow-up of 5.8 years, the primary outcome occurred in 94 patients (19.8/1000 person-years). TSAT levels decreased one year after KT and thereafter gradually increased, whereas ferritin levels were maintained at decreased levels. The adjusted hazard ratios (95% confidence intervals) for the composite outcome were 1.67 (1.00-2.77) and 1.20 (0.60-2.40) in the TSAT > 35% and ferritin > 300 ng/mL groups, respectively. High iron status with high TSAT levels increases the risk of graft failure or kidney functional deterioration after KT.ope

Dialysis adequacy predictions using a machine learning method

Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting [XGBoost]) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman's rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.ope

Comparison of early and late Pneumocystis jirovecii Pneumonia in kidney transplant patients: the Korean Organ Transplantation Registry (KOTRY) Study

Late Pneumocystis jirovecii pneumonia (PJP) is not rare in the era of universal prophylaxis after kidney transplantation. We aimed to determine the nationwide status of PJP prophylaxis in Korea and compare the incidence, risk factors, and outcomes of early and late PJP using data from the Korean Organ Transplantation Registry (KOTRY), a nationwide Korean transplant cohort. We conducted a retrospective analysis using data of 4,839 kidney transplant patients from KOTRY between 2014 and 2018, excluding patients who received multi-organ transplantation or were under 18 years old. Cox regression analysis was performed to determine risk factors for early and late PJP. A total of 50 patients developed PJP. The number of patients who developed PJP was same between onset before 6 months and onsets after 6 months. There were no differences in the rate, duration, or dose of PJP prophylaxis between early and late PJP. Desensitization, higher tacrolimus dose at discharge, and acute rejection were associated with early PJP. In late PJP, old age as well as acute rejection were significant risk factors. In conclusion late PJP is as common and risky as early PJP and requires individualized risk-based prophylaxis, such as prolonged prophylaxis for old patients with a history of rejection.ope

Creatinine-cystatin C ratio and mortality in cancer patients: a retrospective cohort study

Background: Muscle wasting is prevalent in cancer patients, and early recognition of this phenomenon is important for risk stratification. Recent studies have suggested that the creatinine-cystatin C ratio may correlate with muscle mass in several patient populations. The association between creatinine-cystatin C ratio and survival was assessed in cancer patients. Methods: A total of 3060 patients who were evaluated for serum creatinine and cystatin C levels at the time of cancer diagnosis were included. The primary outcome was 6-month mortality. The 1-year mortality, and length of intensive care unit (ICU) and hospital stay were also evaluated. Results: The mean age was 61.6 ± 13.5 years, and 1409 patients (46.0%) were female. The median creatinine and cystatin C levels were 0.9 (interquartile range [IQR], 0.6-1.3) mg/dL and 1.0 (IQR, 0.8-1.5) mg/L, respectively, with a creatinine-cystatin C ratio range of 0.12-12.54. In the Cox proportional hazards analysis, an increase in the creatinine-cystatin C ratio was associated with a significant decrease in the 6-month mortality (per 1 creatinine-cystatin C ratio, hazard ratio [HR] 0.35; 95% confidence interval [CI], 0.28-0.44). When stratified into quartiles, the risk of 6-month mortality was significantly lower in the highest quartile (HR 0.30; 95% CI, 0.24-0.37) than in the lowest quartile. Analysis of 1-year mortality outcomes revealed similar findings. These associations were independent of confounding factors. The highest quartile was also associated with shorter lengths of ICU and hospital stay (both P < 0.001). Conclusions: The creatinine-cystatin C ratio at the time of cancer diagnosis significantly associates with survival and hospitalization in cancer patients.ope

Comparison of high-dose IVIG and rituximab versus rituximab as a preemptive therapy for de novo donor-specific antibodies in kidney transplant patients

De novo donor-specific antibody (dnDSA) is associated with a higher risk of kidney graft failure. However, it is unknown whether preemptive treatment of subclinical dnDSA is beneficial. Here, we assessed the efficacy of high-dose intravenous immunoglobulin (IVIG) and rituximab combination therapy for subclinical dnDSA. An open-label randomized controlled clinical trial was conducted at two Korean institutions. Adult (aged ≥ 19 years) kidney transplant patients with subclinical class II dnDSA (mean fluorescence intensity ≥ 1000) were enrolled. Eligible participants were randomly assigned to receive rituximab or rituximab with IVIG at a 1:1 ratio. The primary endpoint was the change in dnDSA titer at 3 and 12 months after treatment. A total of 46 patients (24 for rituximab and 22 for rituximab with IVIG) were included in the analysis. The mean baseline estimated glomerular filtration rate was 66.7 ± 16.3 mL/min/1.73 m2. The titer decline of immune-dominant dnDSA at 12 months in both the preemptive groups was significant. However, there was no difference between the two groups at 12 months. Either kidney allograft function or proteinuria did not differ between the two groups. No antibody-mediated rejection occurred in either group. Preemptive treatment with high-dose IVIG combined with rituximab did not show a better dnDSA reduction compared with rituximab alone. Trial registration : IVIG/Rituximab versus Rituximab in Kidney Transplant With de Novo Donor-specific Antibodies (ClinicalTrials.gov Identifier: NCT04033276, first trial registration (26/07/2019). © 2023, The Author(s).ope

Deep Learning Model for Predicting Intradialytic Hypotension Without Privacy Infringement: A Retrospective Two-Center Study

Objective: Previously developed Intradialytic hypotension (IDH) prediction models utilize clinical variables with potential privacy protection issues. We developed an IDH prediction model using minimal variables, without the risk of privacy infringement. Methods: Unidentifiable data from 63,640 hemodialysis sessions (26,746 of 79 patients for internal validation, 36,894 of 255 patients for external validation) from two Korean hospital hemodialysis databases were finally analyzed, using three IDH definitions: (1) systolic blood pressure (SBP) nadir <90 mmHg (Nadir90); (2) SBP decrease ≥20 mmHg from baseline (Fall20); and (3) SBP decrease ≥20 mmHg and/or mean arterial pressure decrease ≥10 mmHg (Fall20/MAP10). The developed models use 30 min information to predict an IDH event in the following 10 min window. Area under the receiver operating characteristic curves (AUROCs) and precision-recall curves were used to compare machine learning and deep learning models by logistic regression, XGBoost, and convolutional neural networks. Results: Among 344,714 segments, 9,154 (2.7%), 134,988 (39.2%), and 149,674 (43.4%) IDH events occurred according to three different IDH definitions (Nadir90, Fall20, and Fall20/MAP10, respectively). Compared with models including logistic regression, random forest, and XGBoost, the deep learning model achieved the best performance in predicting IDH (AUROCs: Nadir90, 0.905; Fall20, 0.864; Fall20/MAP10, 0.863) only using measurements from hemodialysis machine during dialysis session. Conclusions: The deep learning model performed well only using monitoring measurement of hemodialysis machine in predicting IDH without any personal information that could risk privacy infringement.ope