Low-Level Elevations of Procalcitonin Are Associated with Increased Mortality in Acute Heart Failure Patients, Independent of Concomitant Infection

We aimed to evaluate the prognostic value of procalcitonin (PCT) in acute heart failure (AHF) patients, especially in those without underlying infection. We enrolled patients presenting with acute dyspnea to the emergency department (ED) of Heidelberg University Hospital and studied the prognostic role of PCT on all-cause death. Of 312 patients, AHF was diagnosed in 139 patients. Of these, 125 patients had AHF without signs of infection, and 14 had AHF complicated by respiratory or other infection. The optimal prognostic PCT cutoff value for mortality prediction was calculated by a receiver operating characteristics curve. In patients with AHF, the prognostic PCT cutoff value was 0.08 ng/mL. The Kaplan–Meier survival analysis showed that AHF patients with PCT values > 0.08 ng/mL had a higher all-cause mortality at 120 days than those with PCT values ≤ 0.08 ng/mL (log-rank p = 0.0123). Similar results could be obtained after subdivision into AHF patients with and without signs of overt infection. In both cases, mortality was higher in patients with PCT levels above the prognostic PCT cutoff than in those with values ranging below this threshold. Moreover, we show that the prognostic PCT cutoff values for mortality prediction ranged below the established PCT cutoff for the guidance of antibiotic therapy. In conclusion, the data of our study revealed that low-level elevations of PCT were associated with an increased mortality in patients with AHF, irrespective of concomitant respiratory or other infection. PCT should thus be further used as a marker in the risk stratification of AHF.

Accuracy of Subclinical Ketosis Detection with Rapid Test Methods for BHBA in Blood in Commercial Dairy Farms

This study gives an overview of the performance and accuracy of devices used for the fast measurement of β-hydroxybutyrate (BHBA) in blood for the on-farm indication of subclinical ketosis. Data were collected on ten dairy farms. In each farm, blood samples were taken from ten cows on four test days (2, 4, 9 and 11), resulting in 400 samples. The reference method was the BHBA concentration in blood serum (BHBALAB). Four different devices that measure BHBA in whole blood were tested. The thresholds applied for identifying subclinical ketosis were ≥1.0, ≥1.2 and ≥1.4 mmol/L in blood serum. The BHBALAB was assigned in three classes: low—≤0.9 mmol/L; high—>0.9 mmol/L; and total—all values unclassified. Due to initial negative effects on the health and performance of cows with BHBA levels ≥0.9 mmol/L, this cut-off was chosen. The Passing–Bablok regression revealed different constant as well as absolute biases for each device in the aforementioned classes. The area under the receiver operating characteristics curve indicated highly accurate results, with 94–97% accuracy levels. As an overall conclusion, the performance of the devices was good and supports their use by farmers for the detection of subclinical ketotic cows in their herds.

Substantial clinical benefit and patient acceptable symptom states of the Forgotten Joint Score 12 after primary knee arthroplasty

Background and purpose — Knowing how to interpret values obtained with patient reported outcome measures (PROMs) is essential. We estimated the substantial clinical benefit (SCB) and patient acceptable symptom state (PASS) for Forgotten Joint Score 12 (FJS) and explored differences depending on methods used for the estimates. Patients and methods — The study was based on 195 knee arthroplasties (KA) performed at a university hospital. We used 1 item from the Knee injury and Osteoarthritis Outcome Score domain quality of life and satisfaction with surgery, obtained 1-year postoperatively, to assess SCB and PASS thresholds of the FJS with anchor-based methods. We used different combinations of anchor questions for SCB and PASS (satisfied, satisfied with no or mild knee difficulties, and satisfied with no knee difficulties). A novel predictive approach and receiver-operating characteristics curve were applied for the estimates. Results — 70 and 113 KAs were available for the SCB and PASS estimates, respectively. Depending on method, SCB of the FJS (range 0–100) was 28 (95% CI 21–35) and 22 (12–45) respectively. PASS was 31 (2–39) and 20 (10–29) for satisfied patients, 40 (31–47) and 38 (32–43) for satisfied patients with no/mild difficulties, and 76 (39–80) and 64 (55–74) for satisfied patients with no difficulties. The areas under the curve ranged from 0.82 to 0.88. Interpretation — Both the SCB and PASS thresholds varied depending on methodology. This may indicate a problem using meaningful values from other studies defining outcomes after KA. This study supports the premise of the FJS as a PROM with good discriminatory ability in patients undergoing KA.

Grip strength as a predictor of depressive symptoms among vulnerable elderly Europeans with musculoskeletal conditions

The present study aimed to investigate the grip strength (GS) discrimination capacity and cutoffs points for depressive symptoms among vulnerable elderly individuals with musculoskeletal conditions. The Survey of Health, Aging, and Retirement in Europe wave 6 was analyzed. GS was measured by a handgrip dynamometer, and EURO-D scale was used to assess depressive symptoms. GS cutoff values for depressive symptoms were calculated using the receiver operating characteristics curve. 2206 participants, mean age 74.0 (73.7–74.3), 78.8% with osteoarthritis/other rheumatism, enrolled in the study. Sensitivity varies between 0.44 (men, ≥ 85 years) and 0.82 (men, 75–84 years), and specificity varying between 0.35 (women, 75–84 years) and 0.70 (men 75–84 years). GS is associated with depressive symptoms just for women and it is not possible to use a GS cutoff point for screening depressive symptoms for vulnerable men and women with musculoskeletal conditions over the age of 65 years.

Growth Differentiation Factor 15 (GDF-15): A Novel Biomarker Associated with Poorer Respiratory Function in COVID-19

It is essential to find new biomarkers for severity stratification of patients with coronavirus disease (COVID-19). Growth differentiation factor 15 (GDF-15) is upregulated in pathological conditions that involve inflammation and/or oxidative stress. We determined circulating levels of GDF-15 and correlated them with clinical and laboratory parameters reflecting severity in 84 patients with COVID-19, finding that GDF-15 levels were higher in both patients than in 20 healthy controls and were higher in patients with poorer respiratory function. GDF-15 levels also correlated with interleukin-6, C-reactive protein, ferritin and D-dimer levels and with neutrophilia and lymphopenia. Of all the analysed biomarkers, GDF-15 showed the best area under the receiver operating characteristics curve in identifying patients with poor respiratory function. In conclusion, our data support GDF-15 as a biomarker associated with pulmonary impairment in COVID-19 and so can potentially be useful in stratifying COVID-19 cases by severity.

Plasma chemokines as immune biomarkers for diagnosis of pediatric tuberculosis

Background Diagnosing tuberculosis (TB) in children is challenging due to paucibacillary disease, and lack of ability for microbiologic confirmation. Hence, we measured the plasma chemokines as biomarkers for diagnosis of pediatric tuberculosis. Methods We conducted a prospective case control study using children with confirmed, unconfirmed and unlikely TB. Multiplex assay was performed to examine the plasma CC and CXC levels of chemokines. Results Baseline levels of CCL1, CCL3, CXCL1, CXCL2 and CXCL10 were significantly higher in active TB (confirmed TB and unconfirmed TB) in comparison to unlikely TB children. Receiver operating characteristics curve analysis revealed that CCL1, CXCL1 and CXCL10 could act as biomarkers distinguishing confirmed or unconfirmed TB from unlikely TB with the sensitivity and specificity of more than 80%. In addition, combiROC exhibited more than 90% sensitivity and specificity in distinguishing confirmed and unconfirmed TB from unlikely TB. Finally, classification and regression tree models also offered more than 90% sensitivity and specificity for CCL1 with a cutoff value of 28 pg/ml, which clearly classify active TB from unlikely TB. The levels of CCL1, CXCL1, CXCL2 and CXCL10 exhibited a significant reduction following anti-TB treatment. Conclusion Thus, a baseline chemokine signature of CCL1/CXCL1/CXCL10 could serve as an accurate biomarker for the diagnosis of pediatric tuberculosis.

Predicting bloodstream infection outcome using machine learning

Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We developed electronic medical record-based machine learning models that predict patient outcomes of BSI. The area under the receiver-operating characteristics curve was 0.82 for a full featured inclusive model, and 0.81 for a compact model using only 25 features. Our models were trained using electronic medical records that include demographics, blood tests, and the medical and diagnosis history of 7889 hospitalized patients diagnosed with BSI. Among the implications of this work is implementation of the models as a basis for selective rapid microbiological identification, toward earlier administration of appropriate antibiotic therapy. Additionally, our models may help reduce the development of BSI and its associated adverse health outcomes and complications.

A Study on Singapore’s Ageing Population in the Context of Eldercare Initiatives Using Machine Learning Algorithms

Ageing has always directly impacted the healthcare systems and, more specifically, the eldercare costs, as initiatives related to eldercare need to be addressed beyond the regular healthcare costs. This study aims to examine the general issues of eldercare in the Singapore context, as the population of the country is ageing rapidly. The main objective of the study is to examine the eldercare initiatives of the government and their likely impact on the ageing population. The methodology adopted in this study is Cross-Industry Standard Process for Data Mining (CRISP-DM). Reviews related to the impact of an ageing population on healthcare systems in the context of eldercare initiatives were studied. Analysis methods include correlation and machine learning algorithms, such as Decision Tree, Logistic Regression and Receiver Operating Characteristics curve analysis. Suggestions have been provided for various healthcare and eldercare systems’ initiatives and needs that are required to transform to cope with the ageing population.

Different Profiles of Cytokines, Chemokines and Coagulation Mediators Associated with Severity in Brazilian Patients Infected with Dengue Virus

The incidence of dengue in Latin America has increased dramatically during the last decade. Understanding the pathogenic mechanisms in dengue is crucial for the identification of biomarkers for the triage of patients. We aimed to characterize the profile of cytokines (IFN-γ, TNF-α, IL-1β, IL-6, IL-18 and IL-10), chemokines (CXCL8/IL-8, CCL2/MCP-1 and CXCL10/IP-10) and coagulation mediators (Fibrinogen, D-dimer, Tissue factor-TF, Tissue factor pathway inhibitor-TFPI and Thrombomodulin) during the dengue-4 epidemic in Brazil. Laboratory-confirmed dengue cases had higher levels of TNF-α (p < 0.001), IL-6 (p = 0.005), IL-10 (p < 0.001), IL-18 (p = 0.001), CXCL8/IL-8 (p < 0.001), CCL2/MCP-1 (p < 0.001), CXCL10/IP-10 (p = 0.001), fibrinogen (p = 0.037), D-dimer (p = 0.01) and TFPI (p = 0.042) and lower levels of TF (p = 0.042) compared to healthy controls. A principal component analysis (PCA) distinguished between two profiles of mediators of inflammation and coagulation: protective (TNF-α, IL-1β and CXCL8/IL-8) and pathological (IL-6, TF and TFPI). Lastly, multivariate logistic regression analysis identified high aspartate aminotransferase-to-platelet ratio index (APRI) as independent risk factors associated with severity (adjusted OR: 1.33; 95% CI 1.03–1.71; p = 0.027), the area under the receiver operating characteristics curve (AUC) was 0.775 (95% CI 0.681–0.869) and an optimal cutoff value was 1.4 (sensitivity: 76%; specificity: 79%), so it could be a useful marker for the triage of patients attending primary care centers.

Establishment of a prognostic model for patients with sepsis based on SOFA: a retrospective cohort study

Objective To construct a nomogram based on the Sequential Organ Failure Assessment (SOFA) that is more accurate in predicting 30-, 60-, and 90-day mortality risk in patients with sepsis. Methods Data from patients with sepsis were retrospectively collected from the Medical Information Mart for Intensive Care (MIMIC) database. Included patients were randomly divided into training and validation cohorts. Variables were selected using a backward stepwise selection method with Cox regression, then used to construct a prognostic nomogram. The nomogram was compared with the SOFA model using the concordance index (C-index), area under the time-dependent receiver operating characteristics curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA). Results A total of 5240 patients were included in the study. Patient’s age, SOFA score, metastatic cancer, SpO2, lactate, body temperature, albumin, and red blood cell distribution width were included in the nomogram. The C-index, AUC, NRI, IDI, and DCA of the nomogram showed that it performs better than the SOFA alone. Conclusion A nomogram was established that performed better than the SOFA in predicting 30-, 60-, and 90-day mortality risk in patients with sepsis.