Machine learning-based clinical decision support using laboratory data.

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

Coagulopathy is Initiated with Endothelial Dysfunction and Disrupted Fibrinolysis in Patients with COVID-19 Disease.

A substantial group of patients suffer from Covid-19 (CAC) coagulopathy and are presented with thrombosis. The pathogenesis involved in CAC is not fully understood. We evaluated the hemostatic and inflammatory parameters of 51 hospitalized Covid-19 adult patients and 21 controls. The parameters analyzed were danger signal molecule (High molecular weight group box protein-1/HMGBP-1), platelet count, prothrombin time (PT), activated partial thromboplastin time (aPTT), D-dimer, fibrinogen, endothelial protein C receptor (EPCR), soluble E-selectin, soluble P-selectin, thrombomodulin, tissue plasminogen activator (TPA), plasminogen activator inhibitor-1 (PAI-1), soluble fibrin monomer complex (SFMC), platelet-derived microparticles (PDMP), ß-thromboglobulin, antithrombin and protein C. The main objective of our study was to investigate which part of the hemostatic system was mostly affected at the admission of Covid-19 patients and whether these parameters could differentiate intensive care unit (ICU) and non-ICU patients. In this prospective case-control study, 51 patients ≥ 18 years who are hospitalized with the diagnosis of Covid-19 and 21 healthy control subjects were included. We divided the patients into two groups according to their medical progress, either in ICU or non-ICU group. Regarding the outcome, patients were again categorized as a survivor and non-survivor groups. Blood samples were collected from patients at admission at the time of hospitalization before the administration of any treatment for Covid-19. The analyzes of the study were made with the IBM SPSS V22 program. p < 0.05 was considered statistically significant. A total of 51 adult patients (F/M: 24/27) (13 ICU and 38 non-ICU) were included in the study cohort. The mean age of the patients was 68.1 ± 14.4 years. The control group consisted of 21 age and sex-matched healthy individuals. All of the patients were hospitalized. In a group of 13 patients, Covid-19 progressed to a severe form, and were hospitalized in ICU. We found out that the levels of fibrinogen, prothrombin time (PT), endothelial protein-C receptor (EPCR), D-dimer, soluble E-selectin, soluble P-selectin, plasminogen activator inhibitor-1 (PAI-1), and tissue plasminogen activator (TPA) were increased; whereas, the levels of soluble fibrin monomer complex (SFMC), platelet-derived microparticles (PDMP), antithrombin and protein-C were decreased in Covid-19 patients compared to the control group at hospital admission. Tissue plasminogen activator was the only marker with a significantly different median level between ICU and non-ICU groups (p < 0.001). In accordance with the previous literature, we showed that Covid-19 associated coagulopathy is distinct from sepsis-induced DIC with prominent early endothelial involvement and fibrinolytic shut-down. Reconstruction of endothelial function at early stages of infection may protect patients from progressing to ICU hospitalization. We believe that after considering the patient's bleeding risk, early administration of LMWH therapy for Covid-19, even in an outpatient setting, may be helpful both for restoring endothelial function and anticoagulation. The intensity of anticoagulation in non-ICU and ICU Covid-19 patients should be clarified with further studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s12291-023-01118-3.

Peri-implant crevicular fluid and serum levels of soluble ST2 in peri-implant diseases: A pilot study.

BACKGROUND AND OBJECTIVE: Soluble ST2 (sST2) is a current biomarker of cardiovascular disease. It is used to predict susceptibility to cardiovascular diseases and to analyze their prognosis. Serum sST2 level increases in inflammatory diseases such as periodontitis. However, the level of sST2 in peri-implant diseases and crevicular fluid has not been investigated yet. Thus, the aim of this cross-sectional study is to analyze the level of sST2 in peri-implant health and diseases. METHODS: Sixty-nine participants were divided into 3 groups as peri-implant health (PH), peri-implant mucositis (PM), and peri-implantitis (P-I). Peri-implant crevicular fluid (PICF) and serum samples were collected from each participant. The levels of sST2 and IL-6 in PICF and sST2, IL-6, and CRP in serum were compared between the groups. Pocket depth (PD), modified bleeding index (mBI), modified plaque index (mPI), keratinized mucosa index (KTW), and gingival/mucosal recession (REC) were recorded as clinical parameters. Biomarkers in the serum and PICF were analyzed by ELISA kit. RESULTS: Sixty-nine patients were included in the study. The differences in the following parameters were statistically significant between groups: age (p = .009), implant function time (p = .027), PD (p < .001), mBI (p < .001), mPI (p < .001), and KTW (p = .043). The PICF volume of P-I and PM groups were statistically higher than PH (p < .001). The amount of sST2 in P-I and PM groups were higher than PH (p = .043). Serum CRP was higher in the P-I group than in other groups (p = .034). There were no significant differences in serum sST2 (p = .247) and IL-6 (p = .110) levels between groups. CONCLUSION: The PICF levels of sST2 were significantly higher in PM and P-I groups compared to the healthy group. However, no significant difference was observed between the groups in terms of serum sST2 level.

Effect of rosuvastatin on spatial learning, memory, and anxiety-like behaviour in ovariectomized rats.

The effect of rosuvastatin (Ros) on cognitive function and anxiety-like behaviour in ovariectomized rats were evaluated. Eighteen female Wistar rats (218-310 g, 6-8 months old) were allocated into sham (n = 6), ovariectomy (Ovx, n = 6) or Ovx + Ros (up to eighth week n = 6, then n = 4) groups. Ros was administered at 20 mg/kg/day by oral gavage for 12 weeks. Behavioural tests were performed at 4, 8 and 12 weeks following Ovx. At 12 weeks, Ovx group had significantly longer escape latency than the sham group at the first day of the four-day training period of the Morris Water Maze test (p < .01). In the Elevated Plus Maze test, Ovx group spent significantly more time in the closed arms than the sham group (p < .01), and this anxiety-like behavioural effect of Ovx was prevented by 12-weeks Ros treatment (p < .05). In conclusion, Ros prevents memory deficit and anxiety-like behaviour in the ovariectomized rats, a model for human surgical menopause. Impact StatementWhat is already known on this subject? Reduced levels of oestrogen in surgical postmenopausal period has been linked to an increased risk of cognitive dysfunction. Although statins have been shown to improve cognitive function in experimental and clinical studies, there are limited studies evaluating the effect of statins on the cognitive decline and anxiety-like behaviour associated with surgical menopause.What do the results of this study add? Rosuvastatin, a long-acting statin, prevents learning and memory deficit and anxiety-like behaviour in the ovariectomized rat model.What are the implications of these findings for future clinical practice and/or future clinical research? These findings will form the basis for further experimental and clinical research on the effects of statins on cognitive functions and anxiety-like behaviour in the surgical menopause.

Searching for the urine osmolality surrogate: an automated machine learning approach.

OBJECTIVES: Automated machine learning (AutoML) tools can help clinical laboratory professionals to develop machine learning models. The objective of this study was to develop a novel formula for the estimation of urine osmolality using an AutoML tool and to determine the efficiency of AutoML tools in a clinical laboratory setting. METHODS: Three hundred routine urinalysis samples were used for reference osmolality and urine clinical chemistry analysis. The H2O AutoML engine completed the machine learning development steps with minimum human intervention. Four feature groups were created, which include different urinalysis measurements according to the Boruta feature selection algorithm. Method comparison statistics including Spearman's correlation, Passing-Bablok regression analysis were performed, and Bland Altman plots were created to compare model predictions with the reference method. The minimum allowable bias (24.17%) from biological variation data was used as the limit of agreement. RESULTS: The AutoML engine developed a total of 183 ML models. Conductivity and specific gravity had the highest variable importance. Models that include conductivity, specific gravity, and other urinalysis parameters had the highest R2 (0.70-0.83), and 70-84% of results were within the limit of agreement. CONCLUSIONS: Combining urinary conductivity with other urinalysis parameters using validated machine learning models can yield a promising surrogate. Additionally, AutoML tools facilitate the machine learning development cycle and should be considered for developing ML models in clinical laboratories.

Estimation of Low-Density Lipoprotein Cholesterol Concentration Using Machine Learning.

OBJECTIVE: Low-density lipoprotein cholesterol (LDL-C) can be estimated using the Friedewald and Martin-Hopkins formulas. We developed LDL-C prediction models using multiple machine learning methods and investigated the validity of the new models along with the former formulas. METHODS: Laboratory data (n = 59,415) on measured LDL-C, high-density lipoprotein cholesterol, triglycerides (TG), and total cholesterol were partitioned into training and test data sets. Linear regression, gradient-boosted trees, and artificial neural network (ANN) models were formed based on the training data. Paired-group comparisons were performed using a t-test and the Wilcoxon signed-rank test. We considered P values <.001 with an effect size >.2 to be statistically significant. RESULTS: For TG ≥177 mg/dL, the Friedewald formula underestimated and the Martin-Hopkins formula overestimated the LDL-C (P <.001), which was more significant for LDL-C <70 mg/dL. The linear regression, gradient-boosted trees, and ANN models outperformed the aforementioned formulas for TG ≥177 mg/dL and LDL-C <70 mg/dL based on a comparison with a homogeneous assay (P >.001 vs. P <.001) and classification accuracy. CONCLUSION: Linear regression, gradient-boosted trees, and ANN models offer more accurate alternatives to the aforementioned formulas, especially for TG 177 to 399 mg/dL and LDL-C <70 mg/dL.

Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests.

OBJECTIVES: The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. METHODS: We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. RESULTS: The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). CONCLUSIONS: ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.

Serum growth differentiation factor-15 analysis as a malnutrition marker in hemodialysis patients

Background/aim: Growth differentiation factor (GDF)-15 is related to inflammation and mortality in many conditions. We aimed to determine if an elevated serum GDF-15 level is related to nutritional status of patients on hemodialysis (HD) and mortality. Materials and methods: Routine HD patients (n = 158) were included in the study and followed for 18 months. Some malnutrition/ inflammation scoring indexes (malnutrition/inflammation score (MIS), controlling nutritional status (CONUT) score, and prognostic nutritional index (PNI)), biochemical parameters, and GDF-15 were used to build Cox regression multivariate models to study the association with mortality. Results: Among the patients, 90 (57 %) had a high MIS (≥8), which associates with worse status. The serum GDF-15 level was higher in the same group (p = 0.003). The serum GDF-15 level differentiated malnutrition/inflammation according to the MIS (p = 0.031). Age, GDF15, and C-reactive protein (CRP) were significantly associated with higher all-cause mortality risk. Patients with both age and GDF-15 above the mean had a hazard ratio of 2.76 (p = 0.006) when compared with those both < mean. Conclusion: In HD patients, the GDF-15 level is increased in worse nutritional status. Beyond the MIS, age, GDF-15 and CRP would be used together to estimate the worse clinical outcome in these patients.

A model to establish autoverification in the clinical laboratory.

OBJECTIVES: Autoverification is the process of evaluating and validating laboratory results using predefined computer-based algorithms without human interaction. By using autoverification, all reports are validated according to the standard evaluation criteria with predefined rules, and the number of reports per laboratory specialist is reduced. However, creating and validating these rules are the most demanding steps for setting up an autoverification system. In this study, we aimed to develop a model for helping users establish autoverification rules and evaluate their validity and performance. DESIGN & METHODS: The proposed model was established by analyzing white papers, previous study results, and national/international guidelines. An autoverification software (myODS) was developed to create rules according to the model and to evaluate the rules and autoverification rates. The simulation results that were produced by the software were used to demonstrate that the determined framework works as expected. Both autoverification rates and step-based evaluations were performed using actual patient results. Two algorithms defined according to delta check usage (Algorithm A and B) and three review limits were used for the evaluation. RESULTS: Six hundred seventeen rules were created according to the proposed model. 1,976 simulation results were created for validation. Our results showed that manual review limits are the most critical step in determining the autoverification rate, and delta check evaluation is especially important for evaluating inpatients. Algorithm B, which includes consecutive delta check evaluation, had higher AV rates. CONCLUSIONS: Systemic rule formation is a critical factor for successful AV. Our proposed model can help laboratories establish and evaluate autoverification systems. Rules created according to this model could be used as a starting point for different test groups.

Tacrolimus intrapatient variability in BK virus nephropathy and chronic calcineurin toxicity in kidney transplantation.

Intrapatient variability (IPV) in tacrolimus has been increasingly acknowledged as a risk factor for poor graft survival after kidney transplantation. Although past studies have mainly accounted for IPV in acute or chronic rejection states as due to underimmunosuppression, this is not yet clear. So far, tacrolimus IPV for BK virus-associated nephropathy (BKVN) and chronic calcineurin inhibitor toxicity (CNIT) has not been investigated. Here, we evaluated IPV in tacrolimus for BKVN and chronic CNIT, which are mainly considered as overimmunosuppression states. In this case-control study, kidney allograft biopsies conducted between 1998 and 2018 were included, with patients grouped by biopsy results as BKVN alone group, CNIT alone group, and normal graft function (control group). IPV was estimated as mean absolute deviation. Our study groups included 25 kidney transplant recipients with BKVN alone, 91 patients with CNIT alone, and 60 patients with normal 5-year graft survival (control group). In analyses of IPV in tacrolimus six months before graft biopsy, IPV was highest in the BKVN group (P = 0.001). The BKVN group also had the highest IPV in tacrolimus at 12 months after biopsy (P = 0.001), with all pairwise comparisons statistically different between groups. At 12 months after biopsy, five patients (20%) in the BKVN group and 10 patients (10.9%) in the CNIT group had graft loss. Among other risk factors, BKVN and chronic CNIT are consequences related to high IPV. Quantification of IVP for tacrolimus in clinical practice would help to optimize kidney transplant outcomes.

The Impact of Serum 25-Hydroxyvitamin D3 Levels on Allergic Rhinitis.

We aimed to clarify the relation between allergic rhinitis and the serum levels of 25-hydroxivitamin D in the adult population. The study group consisted of 86 patients with allergic rhinitis who were diagnosed with the help of history of allergy, positive signs for allergy, blood samples, and positive skin prick tests; while the control group included 43 age- and sex-matched healthy volunteers with negative skin prick tests. The demographic data, medical history, findings in the physical examinations, serum levels of total immunoglobulin E (IgE) and 25-hydroxyvitamin D, and skin prick test results of the groups were noted. A total of 129 patients fulfilling the necessary criteria were enrolled. The median serum 25-hydroxyvitamin D levels in the study group were significantly lower compared to the control group (P = .014). In the study group, median serum vitamin D levels were significantly higher in men, compared to women (P = .03). There was a significant negative correlation between IgE and vitamin D levels in the allergic rhinitis group (P = .028, r = -0.246). This study showed that patients with allergic rhinitis might be more vulnerable to have lower serum levels of vitamin D. Thus, vitamin D supplementation as an adjunctive therapy may be considered in those patients.

The effect of jet-lag on serum concentrations of thyroid stimulating hormone and prolactin: A case report.

INTRODUCTION: This case report is about the importance of sleeping status for analysis of thyroid hormone stimulating hormone (TSH) and prolactin (PRL) which arose from discordant results of a patient who was referred for serum TSH and PRL testing within 12-hour period after an intercontinental flight. CASE DESCRIPTION: An adult male patient was admitted to our laboratory for serum TSH and PRL tests and came back questioning the accuracy of his previous results. FURTHER INVESTIGATIONS: A new analysis with a new sample was offered. His new results were not consistent with his previous results. WHAT HAPPENED: It was revealed that the night before the first sampling, he travelled back to Turkey from The United States of America and came to testing within 12 hours after the arrival. DISCUSSION: Sleeping status is one of the factors that can affect laboratory results. Intercontinental flights causing jet-lag can alter the secretions of TSH and PRL which are predominantly modulated by thyrotropin-releasing hormone (TRH). MAIN LESSON: Travel history and sleeping status are important factors to be evaluated prior sampling for hormone analysis. Patients must be informed about the importance of sampling timing.

Does on-pump normothermic beating-heart valve surgery with low tidal volume ventilation protect the lungs?

BACKGROUND: Postoperative pulmonary dysfunction following cardiopulmonary bypass (CPB) usually develops secondary to the inflammatory process with contact activation, hypothermia, operative trauma, general anesthesia, atelectasis, pain, and pulmonary ischemia/reperfusion due to cross-clamping. The aim of the present study was to evaluate the effects of an on-pump, normothermic, and beating-heart technique and of low-volume ventilation on lung injury. METHODS: We compared the results for 20 patients who underwent operations with an on-pump, normothermic, and beating-heart technique of mitral valve surgery with low-volume ventilation (group 1) with the results for 23 patients who underwent their operations with an on-pump, hypothermic cardiac-arrest technique (group 2). In both groups, blood samples were collected from the right superior pulmonary vein, and inflammation and oxidative stress markers (malondialdehyde, lactic acid, platelet-activating factor, and myeloperoxidase) were studied. RESULTS: Malondialdehyde, myeloperoxidase, and lactate values were significantly lower in group 1 than in group 2 just before the termination of CPB (P < .05). We observed no differences between the 2 groups with regard to values for platelet-activating factor. CONCLUSIONS: Inflammation and oxidative stress markers were lower in the group of patients who underwent beating-heart valve surgery with low-volume ventilation. These results reflect less of an ischemic insult and lower inflammation compared with the results for the patients who underwent conventional operations.