Transferability of clinical laboratory data within a health care region

1992 ◽  
Vol 52 (7) ◽  
pp. 679-687 ◽  
Author(s):  
E. Olafsdottir ◽  
T. Aronsson ◽  
T. Groth ◽  
C.-H. de Verdier
Keyword(s):  
Health Care ◽  
Clinical Laboratory ◽  
Laboratory Data ◽  
Health Care Region

scholarly journals Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Juan L Domínguez-Olmedo ◽  
Álvaro Gragera-Martínez ◽  
Jacinto Mata ◽  
Victoria Pachón Álvarez
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Human Resources ◽  
Clinical Laboratory ◽  
Severe Disease ◽  
Laboratory Data ◽  
Gradient Boosting ◽  
Lactate Dehydrogenase Activity ◽  
Short Period ◽  
Extreme Gradient Boosting

BACKGROUND The COVID-19 pandemic is probably the greatest health catastrophe of the modern era. Spain’s health care system has been exposed to uncontrollable numbers of patients over a short period, causing the system to collapse. Given that diagnosis is not immediate, and there is no effective treatment for COVID-19, other tools have had to be developed to identify patients at the risk of severe disease complications and thus optimize material and human resources in health care. There are no tools to identify patients who have a worse prognosis than others. OBJECTIVE This study aimed to process a sample of electronic health records of patients with COVID-19 in order to develop a machine learning model to predict the severity of infection and mortality from among clinical laboratory parameters. Early patient classification can help optimize material and human resources, and analysis of the most important features of the model could provide more detailed insights into the disease. METHODS After an initial performance evaluation based on a comparison with several other well-known methods, the extreme gradient boosting algorithm was selected as the predictive method for this study. In addition, Shapley Additive Explanations was used to analyze the importance of the features of the resulting model. RESULTS After data preprocessing, 1823 confirmed patients with COVID-19 and 32 predictor features were selected. On bootstrap validation, the extreme gradient boosting classifier yielded a value of 0.97 (95% CI 0.96-0.98) for the area under the receiver operator characteristic curve, 0.86 (95% CI 0.80-0.91) for the area under the precision-recall curve, 0.94 (95% CI 0.92-0.95) for accuracy, 0.77 (95% CI 0.72-0.83) for the F-score, 0.93 (95% CI 0.89-0.98) for sensitivity, and 0.91 (95% CI 0.86-0.96) for specificity. The 4 most relevant features for model prediction were lactate dehydrogenase activity, C-reactive protein levels, neutrophil counts, and urea levels. CONCLUSIONS Our predictive model yielded excellent results in the differentiating among patients who died of COVID-19, primarily from among laboratory parameter values. Analysis of the resulting model identified a set of features with the most significant impact on the prediction, thus relating them to a higher risk of mortality.

scholarly journals Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation

10.2196/26211 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e26211
Author(s):  
Juan L Domínguez-Olmedo ◽  
Álvaro Gragera-Martínez ◽  
Jacinto Mata ◽  
Victoria Pachón Álvarez
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Human Resources ◽  
Clinical Laboratory ◽  
Severe Disease ◽  
Laboratory Data ◽  
Gradient Boosting ◽  
Lactate Dehydrogenase Activity ◽  
Short Period ◽  
Extreme Gradient Boosting

Background The COVID-19 pandemic is probably the greatest health catastrophe of the modern era. Spain’s health care system has been exposed to uncontrollable numbers of patients over a short period, causing the system to collapse. Given that diagnosis is not immediate, and there is no effective treatment for COVID-19, other tools have had to be developed to identify patients at the risk of severe disease complications and thus optimize material and human resources in health care. There are no tools to identify patients who have a worse prognosis than others. Objective This study aimed to process a sample of electronic health records of patients with COVID-19 in order to develop a machine learning model to predict the severity of infection and mortality from among clinical laboratory parameters. Early patient classification can help optimize material and human resources, and analysis of the most important features of the model could provide more detailed insights into the disease. Methods After an initial performance evaluation based on a comparison with several other well-known methods, the extreme gradient boosting algorithm was selected as the predictive method for this study. In addition, Shapley Additive Explanations was used to analyze the importance of the features of the resulting model. Results After data preprocessing, 1823 confirmed patients with COVID-19 and 32 predictor features were selected. On bootstrap validation, the extreme gradient boosting classifier yielded a value of 0.97 (95% CI 0.96-0.98) for the area under the receiver operator characteristic curve, 0.86 (95% CI 0.80-0.91) for the area under the precision-recall curve, 0.94 (95% CI 0.92-0.95) for accuracy, 0.77 (95% CI 0.72-0.83) for the F-score, 0.93 (95% CI 0.89-0.98) for sensitivity, and 0.91 (95% CI 0.86-0.96) for specificity. The 4 most relevant features for model prediction were lactate dehydrogenase activity, C-reactive protein levels, neutrophil counts, and urea levels. Conclusions Our predictive model yielded excellent results in the differentiating among patients who died of COVID-19, primarily from among laboratory parameter values. Analysis of the resulting model identified a set of features with the most significant impact on the prediction, thus relating them to a higher risk of mortality.

A STUDY OF HEPATITIS B VIRUS GENOTYPES IN CHRONIC HEPATITIS B PATIENTS

2011 ◽  
pp. 25-29
Author(s):  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Clinical Laboratory ◽  
Laboratory Data ◽  
Hbv Dna ◽  
Hbv Genotypes ◽  
B Virus ◽  
Virus Genotypes ◽  
Genotype C

Aims: To measure the prevalence of HBV genotypes in chronic hepatitis B patients and their relation to HBeAg and HBV DNA level. Methods: 81 patients were enrolled in this study from January 2009 to December 2010. Clinical, laboratory data were collected during the patient’s hospitalization. Sera were quantitatively tested for HBeAg and HBV DNA. HBV genotyping was made by real-time PCR. Results: Among the 81 patients, 60.5% had genotype B, 26.7% had genotype C and 8.6% had mixed genotype B-C. Prevalence of symptoms (fatigue, anorexia, insomnia...) was higher in genotype C than in genotype B. Genotype C patients had positivity higher HBeAg than genotype B patients (56% vs. 38,8%, p <0.05). The rate of HBV DNA > 107 copies/mL was higher in genotype C group than in genotype B group (36% vs. 28,6%, p > 0.05). Conclusions: Most of the patients had genotypes B or C. Patients with genotype C had positive HBeAg and may be related to higher serological HBV DNA level than in genotype B.

scholarly journals Analyses of longitudinal, hospital clinical laboratory data with application to blood glucose concentrations

2011 ◽  
Vol 30 (27) ◽  
pp. 3208-3220 ◽  
Author(s):  
Jonathan S. Schildcrout ◽  
Sebastien Haneuse ◽  
Josh F. Peterson ◽  
Joshua C. Denny ◽  
Michael E. Matheny ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Clinical Laboratory ◽  
Laboratory Data ◽  
Blood Glucose Concentrations

scholarly journals The association between clinical laboratory data and chest CT findings explains disease severity in a large Italian cohort of COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Simone Canovi ◽  
◽  
Giulia Besutti ◽  
Efrem Bonelli ◽  
Valentina Iotti ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Laboratory Data ◽  
Organ Damage ◽  
Cross Sectional Study ◽  
Chest Ct ◽  
Arterial Oxygen ◽  
Emergency Rooms ◽  
Cross Sectional ◽  
Ct Findings ◽  
Patient Prognosis

Abstract Background Laboratory data and computed tomography (CT) have been used during the COVID-19 pandemic, mainly to determine patient prognosis and guide clinical management. The aim of this study was to evaluate the association between CT findings and laboratory data in a cohort of COVID-19 patients. Methods This was an observational cross-sectional study including consecutive patients presenting to the Reggio Emilia (Italy) province emergency rooms for suspected COVID-19 for one month during the outbreak peak, who underwent chest CT scan and laboratory testing at presentation and resulted positive for SARS-CoV-2. Results Included were 866 patients. Total leukocytes, neutrophils, C-reactive protein (CRP), creatinine, AST, ALT and LDH increase with worsening parenchymal involvement; an increase in platelets was appreciable with the highest burden of lung involvement. A decrease in lymphocyte counts paralleled worsening parenchymal extension, along with reduced arterial oxygen partial pressure and saturation. After correcting for parenchymal extension, ground-glass opacities were associated with reduced platelets and increased procalcitonin, consolidation with increased CRP and reduced oxygen saturation. Conclusions Pulmonary lesions induced by SARS-CoV-2 infection were associated with raised inflammatory response, impaired gas exchange and end-organ damage. These data suggest that lung lesions probably exert a central role in COVID-19 pathogenesis and clinical presentation.

scholarly journals Defining Kawasaki disease and pediatric inflammatory multisystem syndrome-temporally associated to SARS-CoV-2 infection during SARS-CoV-2 epidemic in Italy: results from a national, multicenter survey

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Marco Cattalini ◽  
◽  
Sara Della Paolera ◽  
Fiammetta Zunica ◽  
Claudia Bracaglia ◽  
...  
Keyword(s):  
Kawasaki Disease ◽  
Clinical Laboratory ◽  
Troponin T ◽  
Inflammatory Diseases ◽  
Age At Onset ◽  
Laboratory Data ◽  
Clinical Manifestations ◽  
Chi Square ◽  
Pediatric Society ◽  
Multicenter Survey

Abstract Background There is mounting evidence on the existence of a Pediatric Inflammatory Multisystem Syndrome-temporally associated to SARS-CoV-2 infection (PIMS-TS), sharing similarities with Kawasaki Disease (KD). The main outcome of the study were to better characterize the clinical features and the treatment response of PIMS-TS and to explore its relationship with KD determining whether KD and PIMS are two distinct entities. Methods The Rheumatology Study Group of the Italian Pediatric Society launched a survey to enroll patients diagnosed with KD (Kawasaki Disease Group – KDG) or KD-like (Kawacovid Group - KCG) disease between February 1st 2020, and May 31st 2020. Demographic, clinical, laboratory data, treatment information, and patients’ outcome were collected in an online anonymized database (RedCAP®). Relationship between clinical presentation and SARS-CoV-2 infection was also taken into account. Moreover, clinical characteristics of KDG during SARS-CoV-2 epidemic (KDG-CoV2) were compared to Kawasaki Disease patients (KDG-Historical) seen in three different Italian tertiary pediatric hospitals (Institute for Maternal and Child Health, IRCCS “Burlo Garofolo”, Trieste; AOU Meyer, Florence; IRCCS Istituto Giannina Gaslini, Genoa) from January 1st 2000 to December 31st 2019. Chi square test or exact Fisher test and non-parametric Wilcoxon Mann-Whitney test were used to study differences between two groups. Results One-hundred-forty-nine cases were enrolled, (96 KDG and 53 KCG). KCG children were significantly older and presented more frequently from gastrointestinal and respiratory involvement. Cardiac involvement was more common in KCG, with 60,4% of patients with myocarditis. 37,8% of patients among KCG presented hypotension/non-cardiogenic shock. Coronary artery abnormalities (CAA) were more common in the KDG. The risk of ICU admission were higher in KCG. Lymphopenia, higher CRP levels, elevated ferritin and troponin-T characterized KCG. KDG received more frequently immunoglobulins (IVIG) and acetylsalicylic acid (ASA) (81,3% vs 66%; p = 0.04 and 71,9% vs 43,4%; p = 0.001 respectively) as KCG more often received glucocorticoids (56,6% vs 14,6%; p < 0.0001). SARS-CoV-2 assay more often resulted positive in KCG than in KDG (75,5% vs 20%; p < 0.0001). Short-term follow data showed minor complications. Comparing KDG with a KD-Historical Italian cohort (598 patients), no statistical difference was found in terms of clinical manifestations and laboratory data. Conclusion Our study suggests that SARS-CoV-2 infection might determine two distinct inflammatory diseases in children: KD and PIMS-TS. Older age at onset and clinical peculiarities like the occurrence of myocarditis characterize this multi-inflammatory syndrome. Our patients had an optimal response to treatments and a good outcome, with few complications and no deaths.

scholarly journals Moraxella lacunata infection accompanied by acute glomerulonephritis

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 962-967
Author(s):  
Nami Sawada ◽  
Tamaki Morohashi ◽  
Tomokazu Mutoh ◽  
Tsukasa Kuwana ◽  
Junko Yamaguchi ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Case Reports ◽  
Laboratory Data ◽  
Inflammatory Cells ◽  
Final Diagnosis ◽  
Renal Tissue ◽  
Acute Glomerulonephritis ◽  
Pathological Findings ◽  
Complement Activity ◽  
Moraxella Lacunata

AbstractMoraxella lacunata (M. lacunata) is a Gram-negative bacterium, which rarely causes serious infection. This is a rare case report of acute glomerulonephritis diagnosed by pathological findings in a child accompanied by M. lacunata infection. The patient showed hematuria, proteinuria and hyperkalemia requiring emergency hemodialysis. After hospitalization, M. lacunata bacteremia became apparent. Pathological findings showed an increase in glomerulus inflammatory cells and glomerular C3 deposition was observed in the renal tissue biopsy. Final diagnosis was endocapillary proliferative glomerulonephritis. Clinical reports of M. lacunata infection requiring emergency hemodialysis in children are rare. Previous reports have suggested that lowered immune competency with chronic kidney disease may be a risk factor associated with serious invasive cases of M. lacunata infection. However, detailed clinical laboratory data and pathological findings have not been identified in previous case reports. Our case directly indicated complement activity and acute glomerulonephritis with M. lacunata infection. Although there are various causes for acute glomerulonephritis, infection-related glomerulonephritis (IRGN) is an important concept. M. lacunata infection might have a potential risk for IRGN with dysregulation of complement activity leading to serious and invasive clinical conditions than previously considered.

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