Clinical and inflammation marker features of cancer patients with COVID-19: data of Istanbul, Turkey multicenter cancer patients (2020-2022).

OBJECTIVE: We aimed to identify a rapid, accurate, and accessible biomarker in the early stages of COVID-19 that can determine the prognosis of the disease in cancer patients. METHODS: A total number of 241 patients with solid cancers who had a COVID-19 diagnosis between March 2020 and February 2022 were included in the study. Factors and ten different markers of inflammation were analyzed by year of diagnosis of COVID-19 and grouped by severity of infection. RESULTS: Hospitalization, referral to the intensive care unit (ICU), mechanical ventilation, and death were more frequent in 2020 than in 2021 and 2022 (mortality rates, respectively, were 18.8%, 3.8%, and 2.5%). Bilateral lung involvement and chronic lung disease were independent risk factors for severe disease in 2020. In 2021-2022, only bilateral lung involvement was found as an independent risk factor for severe disease. The neutrophil-to-lymphocyte platelet ratio (NLPR) with the highest area under the curve (AUC) value in 2020 had a sensitivity of 71.4% and specificity of 73.3% in detecting severe disease (cut-off > 0.0241, Area Under the Curve (AUC) = 0.842, p <.001). In 2021-2022, the sensitivity of the C-reactive protein-to-lymphocyte ratio (CRP/L) with the highest AUC value was 70.0%, and the specificity was 73.3% (cut-off > 36.7, AUC = 0.829, p = .001). CONCLUSIONS: This is the first study to investigate the distribution and characteristics of cancer patients, with a focus on the years of their COVID-19 diagnosis. Based on the data from our study, bilateral lung involvement is an independent factor for severe disease, and the CRP/L inflammation index appears to be the most reliable prognostic marker.

[Informativeness estimation for the main clinical and laboratory parameters in patients with severe COVID-19].

AIM: To conduct a retrospective assessment of the clinical and laboratory data of patients with severe forms of COVID-19 hospitalized in the intensive care and intensive care unit, in order to assess the contribution of various indicators to the likelihood of death. MATERIALS AND METHODS: A retrospective assessment of data on 224 patients with severe COVID-19 admitted to the intensive care unit was carried out. The analysis included the data of biochemical, clinical blood tests, coagulograms, indicators of the inflammatory response. When transferring to the intensive care units (ICU), the indicators of the formalized SOFA and APACHE scales were recorded. Anthropometric and demographic data were downloaded separately. RESULTS: Analysis of obtained data, showed that only one demographic feature (age) and a fairly large number of laboratory parameters can serve as possible markers of an unfavorable prognosis. We identified 12 laboratory features the best in terms of prediction: procalcitonin, lymphocytes (absolute value), sodium (ABS), creatinine, lactate (ABS), D-dimer, oxygenation index, direct bilirubin, urea, hemoglobin, C-reactive protein, age, LDH. The combination of these features allows to provide the quality of the forecast at the level of AUC=0.85, while the known scales provided less efficiency (APACHE: AUC=0.78, SOFA: AUC=0.74). CONCLUSION: Forecasting the outcome of the course of COVID-19 in patients in ICU is relevant not only from the position of adequate distribution of treatment measures, but also from the point of view of understanding the pathogenetic mechanisms of the development of the disease.

Ovarian Cancer Biomarkers in the COVID-19 Era.

Ovarian Cancer (OC) diagnosis is entrusted to CA125 and HE4. Since the latter has been found increased in COVID-19 patients, in this study, we aimed to evaluate the influence of SARS-CoV-2 infection on OC biomarkers. HE4 values above the cut-off were observed in 65% of OC patients and in 48% of SARS-CoV-2-positive patients (not oncologic patients), whereas CA125 values above the cut-off were observed in 71% of OC patients and in 11% of SARS-CoV-2 patients. Hence, by dividing the HE4 levels into quartiles, we can state that altered levels of HE4 in COVID-19 patients were mostly detectable in quartile I (151-300 pmol/L), while altered levels in OC patients were mostly clustered in quartile III (>600, pmol/L). In light of these observations, in order to better discriminate women with ovarian cancer versus those with COVID-19, we established a possible HE4 cut-off of 328 pmol/L by means of a ROC curve. These results demonstrate that the reliability of HE4 as a biomarker in ovarian cancer remains unchanged, despite COVID-19 interference; moreover, it is important for a proper diagnosis that whether the patient has a recent history of SARS-CoV-2 infection is determined.

Performance of Early Warning Scoring Systems Regarding Adverse Events of Unanticipated Clinical Deterioration in Complementary and Alternative Medicine Hospitals.

PURPOSE: This study aims to examine the performance of early warning scoring systems regarding adverse events of unanticipated clinical deterioration in complementary and alternative medicine hospitals. METHODS: A medical record review of 500 patients from 5-year patient data in two traditional Korean medicine hospitals was conducted. Unanticipated clinical deterioration events included unexpected in-hospital mortality, cardiac arrest, and unplanned transfers to acute-care conventional medicine hospitals. Scores of the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and National Early Warning Score 2 (NEWS2) were calculated. Their performance was evaluated by calculating areas under the receiver-operating characteristic curve for the event occurrence. Multiple logistic regression analyses were performed to determine the factors associated with event occurrence. RESULTS: The incidence of unanticipated clinical deterioration events was 1.1% (225/21101). The area under the curve of MEWS, NEWS, and NEWS2 was .68, .72, and .72 at 24 hours before the events, respectively. NEWS and NEWS2, with almost the same performance, were superior to MEWS (p = .009). After adjusting for other variables, patients at low-medium risk (OR = 3.28; 95% CI = 1.02-10.55) and those at medium and high risk (OR = 25.03; 95% CI = 2.78-225.46) on NEWS2 scores were more likely to experience unanticipated clinical deterioration than those at low risk. Other factors associated with the event occurrence included frailty risk scores, clinical worry scores, primary medical diagnosis, prescribed medicine administration, acupuncture treatment, and clinical department. CONCLUSIONS: The three early warning scores demonstrated moderate-to-fair performance for clinical deterioration events. NEWS2 can be used for early identification of patients at high risk of deterioration in complementary and alternative medicine hospitals. Additionally, patient, care, and system factors need to be considered to improve patient safety.

Early biochemical analysis of COVID-19 patients helps severity prediction.

COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. These data has been obtained by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78 and an F1-score = 0.69. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course. This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease.

Does the ICU Requirement Score allow the poisoned patient to be safely managed without admission to the intensive care unit? - a validation cohort study.

BACKGROUND: The Intensive care unit (ICU) Requirement Score (IRS) has been defined as identifying poisoned patients on hospital admission who do not require ICU referral, in an effort to reduce health expenses. However, this score has been poorly validated. We aimed to evaluate the IRS in a large cohort of poisoned patients. METHODS: We performed a single-center retrospective cohort study. IRS was calculated using clinical parameters obtained on admission including age, systolic blood pressure, heart rate, Glasgow coma score, intoxication type, co-morbidities (i.e., arrhythmia, cirrhosis, and respiratory insufficiency), and the combination of the intoxication with another reason for ICU admission. We evaluated the ability of IRS < 6 determined on admission to predict the lack of need for ICU treatment, defined as the need for mechanical ventilation, vasopressors, and/or renal replacement therapy in the first 24 h post-admission and/or death during the hospital stay. This score was compared to the usual prognostic scores, i.e., SAPS II and III, SOFA score, and PSS. RESULTS: During the 10-year study period, 2,514 poisoned patients were admitted, 1,011 excluded as requiring ICU treatment on admission, and 1,503 included. Among these patients, 232 met the endpoint whereas only 23/510 patients with IRS < 6 (4.5%) presented the endpoint and one patient died. The area under the curve of the IRS ROC curve was 0.736 (95% confidence interval (CI), 0.702-0.770). The negative predictive value of IRS < 6 was 95% (95% CI, 93-97), sensitivity 89% (95% CI, 85-93), specificity 38% (95% CI, 36-41), and positive predictive value 21% (95% CI, 18-24). IRS performance was similar to those of the other tested scores, which are however not readily available on admission. CONCLUSION: Our data demonstrate the excellent negative predictive value of the IRS, allowing the exclusion of ICU requirements for poisoned patients with IRS < 6. IRS usefulness should be confirmed based on a prospective multicenter cohort study before extensive routine use.

The aggregate index of systemic inflammation may predict mortality in COVID-19 patients with chronic renal failure.

OBJECTIVE: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), was first detected in December 2019 and then spread globally, resulting in a pandemic. Initially, it was unknown if chronic kidney disease (CKD) contributed to the mortality caused by COVID-19. The immunosuppression associated with this disease may minimize the COVID-19-described hyper-inflammatory state or immunological dysfunction, and a high prevalence of comorbidities may lead to a poorer clinical prognosis. Patients with COVID-19 have abnormal circulating blood cells associated with inflammation. Risk stratification, diagnosis, and prognosis primarily rely on hematological features, such as white blood cells and their subpopulations, red cell distribution width, mean platelet volume, and platelet count, in addition to their combined ratios. In non-small-cell lung cancer, the aggregate index of systemic inflammation (AISI), (neutrophils x monocytes x platelets/lymphocytes) is evaluated. In light of the relevance of inflammation in mortality, the objective of this study is to determine the impact of AISI on the hospital mortality of CKD patients. PATIENTS AND METHODS: This study is an observational retrospective study. Data and test outcomes of all CKD patients, stages 3-5, hospitalized for COVID-19 and followed between April and October 2021 were analyzed. RESULTS: Patients were divided into two groups according to death (Group 1-Alive, Group 2-Died). Neutrophil count, AISI and C-reactive protein (CRP) levels were increased in Group-2 [10.3±4.6 vs. 7.65±4.22; p=0.001, 2,084.1 (364.8-2,577.5) vs. 628.9 (53.1-2,275); p=0.00 and 141.9 (20.5-318) vs. 84.75 (0.92-195); p=0.00; respectively]. Receiver operating characteristic (ROC) analysis demonstrated 621.1 as a cut-off value for AISI to predict hospital mortality with 81% sensitivity and 69.1% specificity [area under ROC curve 0.820 (95% CI: 0.733-0.907), p<.005]. Cox regression analysis was used to analyze the effect of risk variables on survival. In survival analysis, AISI and CRP were identified as important survival predictors [hazard ratio (HR): 1.001, 95% CI: 1-1.001; p=0.00 and HR: 1.009, 95% CI: 1.004-1.013; p=0.00]. CONCLUSIONS: This study demonstrated the discriminative effectiveness of AISI in predicting disease mortality in COVID-19 patients with CKD. Quantification of AISI upon admission might assist in the early detection and treatment of individuals with a bad prognosis.

Low serum level of citrullinated histone H3 in patients with dermatomyositis.

OBJECTIVES: We aimed at analyzing the serum levels of citrullinated histone H3 (CitH3) in patients with dermatomyositis (DM) and their association with disease activity. METHODS: Serum CitH3 levels were measured using enzyme-linked immunosorbent assays in serum samples obtained from 93 DM patients and 56 healthy controls (HCs). Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminant capacity of CitH3 and other disease variables. The association between CitH3 and disease variables was analyzed using Pearson's rank correlation. RESULTS: Serum CitH3 level was significantly lower in DM patients than in HCs (p < 0.001). The ROC curve analysis revealed that CitH3 strongly discriminated DM patients from HCs (area under the curve [AUC], 0.86), and a combination of CitH3 and the ratio of neutrophil to lymphocyte counts (NLR) showed a greater diagnostic value (AUC, 0.92). Serum CitH3 levels were markedly lower in DM patients with normal muscle enzyme levels than in HCs (all p < 0.001), and when compared to an elevated group, the CitH3 levels were comparable (all p > 0.05). The CitH3 levels showed no difference between DM in active and remission groups. However, in a paired test with 18 hospitalized DM patients, the CitH3 levels were higher in remission state than in active state. Moreover, the CitH3 levels showed no correlation with disease variables that were associated with the disease activity of DM. CONCLUSIONS: Serum CitH3 level may serve as a useful biochemical marker for screening patients with DM from HCs, while its role in monitoring DM disease activity requires further research.

Utility and limitations of patient-adjusted D-dimer cut-off levels for diagnosis of venous thromboembolism-A systematic review and meta-analysis.

PURPOSE: To systematically assess test performance of patient-adapted D-dimer cut-offs for the diagnosis of venous thromboembolism (VTE). METHODS: Systematic review and analysis of articles published in PubMed, Embase, ClinicalTrials.gov, and Cochrane Library databases. Investigations assessing patient-adjusted D-dimer thresholds for the exclusion of VTE were included. A hierarchical summary receiver operating characteristic model was used to assess diagnostic accuracy. Risk of bias was assessed by Quality Assessment of Diagnostic Accuracy Studies 2 score. RESULTS: A total of 68 studies involving 141,880 patients met the inclusion criteria. The standard cut-off revealed a sensitivity of 0.99 (95% confidence interval [CI] 0.98-0.99) and specificity of 0.23 (95% CI 0.16-0.31). Sensitivity was comparable to the standard cut-off for age-adjustment (0.97 [95% CI 0.96-0.98]) and YEARS algorithm (0.98 [95% CI 0.91-1.00]) but lower for pretest probability (PTP)-adjusted (0.95 [95% CI 0.89-0.98) and COVID-19-adapted thresholds (0.93 [95% CI 0.82-0.98]). Specificity was significantly higher across all adjustment strategies (age: 0.43 [95% CI 0.36-0.50]; PTP: 0.63 [95% CI 0.51-0.73]; YEARS algorithm: 0.65 [95% CI 0.39-0.84]; and COVID-19: 0.51 [95% CI 0.40-0.63]). The YEARS algorithm provided the best negative likelihood ratio (0.03 [95% CI 0.01-0.15]), followed by age-adjusted (both 0.07 [95% CI 0.05-0.09]), PTP (0.08 [95% CI 0.04-0.17), and COVID-19-adjusted thresholds (0.13 [95% CI 0.05-0.32]). CONCLUSIONS: This study indicates that adjustment of D-dimer thresholds to patient-specific factors is safe and embodies considerable potential for reduction of imaging. However, robustness, safety, and efficiency vary considerably among different adjustment strategies with a high degree of heterogeneity.

Baseline electrolyte disorders predict disease severity and mortality in patients with COVID-19.

Distinguishing critical laboratory biomarkers for disease severity at the time of hospital presentation is important for early identification of patients who are most likely to have poor outcomes and effective use of health resources. This study aimed to evaluate whether electrolyte imbalances on hospital admission predict severe disease and mortality in patients with coronavirus disease 2019 (COVID-19). We retrospectively collected data on the blood electrolyte concentrations of 286 COVID-19 patients at admission. The correlations between electrolyte imbalances, inflammation, and thrombosis markers in COVID-19 patients were also evaluated. We assessed the predictive performance of baseline blood electrolyte concentrations for severe disease and death using receiver operating characteristic curve analysis and multivariate logistic regression methods. Abnormalities in serum sodium, calcium, and potassium levels at admission were found at 20.6%, 14%, and 4.2%, respectively in this study. In the receiver operating characteristic curve analyses, hypocalcemia and hyponatremia effectively predicted disease progression to hospitalization (area under the curve 0.82, P < .001 and 0.81, P < .001, respectively) and 30-day mortality (area under the curve 0.85, P < .001 and 0.91, P < .001, respectively). In the multivariate logistic regression analysis, baseline hypocalcemia was identified as an independent risk factor associated with the risk of hospitalization (ß = 2.019, P = .01; odds ratio: 7.53). Baseline hypocalcemia and hyponatremia effectively predicted disease progression toward hospitalization and 30-day mortality in patients with COVID-19. Clinicians should closely follow up or reevaluate COVID-19 patients with baseline electrolyte disorders.

ICD2Vec: Mathematical representation of diseases.

BACKGROUND: The International Classification of Diseases (ICD) codes represent the global standard for reporting disease conditions. The current ICD codes connote direct human-defined relationships among diseases in a hierarchical tree structure. Representing the ICD codes as mathematical vectors helps to capture nonlinear relationships in medical ontologies across diseases. METHODS: We propose a universally applicable framework called "ICD2Vec" designed to provide mathematical representations of diseases by encoding corresponding information. First, we present the arithmetical and semantic relationships between diseases by mapping composite vectors for symptoms or diseases to the most similar ICD codes. Second, we investigated the validity of ICD2Vec by comparing the biological relationships and cosine similarities among the vectorized ICD codes. Third, we propose a new risk score called IRIS, derived from ICD2Vec, and demonstrate its clinical utility with large cohorts from the UK and South Korea. RESULTS: Semantic compositionality was qualitatively confirmed between descriptions of symptoms and ICD2Vec. For example, the diseases most similar to COVID-19 were found to be the common cold (ICD-10: J00), unspecified viral hemorrhagic fever (ICD-10: A99), and smallpox (ICD-10: B03). We show the significant associations between the cosine similarities derived from ICD2Vec and the biological relationships using disease-to-disease pairs. Furthermore, we observed significant adjusted hazard ratios (HR) and area under the receiver operating characteristics (AUROC) between IRIS and risks for eight diseases. For instance, the higher IRIS for coronary artery disease (CAD) can be the higher probability for the incidence of CAD (HR: 2.15 [95% CI 2.02-2.28] and AUROC: 0.587 [95% CI 0.583-0.591]). We identified individuals at substantially increased risk of CAD using IRIS and 10-year atherosclerotic cardiovascular disease risk (adjusted HR: 4.26 [95% CI 3.59-5.05]). CONCLUSIONS: ICD2Vec, a proposed universal framework for converting qualitatively measured ICD codes into quantitative vectors containing semantic relationships between diseases, exhibited a significant correlation with actual biological significance. In addition, the IRIS was a significant predictor of major diseases in a prospective study using two large-scale datasets. Based on this clinical validity and utility evidence, we suggest that publicly available ICD2Vec can be used in diverse research and clinical practices and has important clinical implications.

C-reactive protein to lymphocyte ratio is a significant predictive factor for poor short-term clinical outcomes of SARS-CoV-2 BA.2.2 patients.

Objective: The aim of the present study is to assess the utility of C-reactive protein to Lymphocyte Ratio (CLR) in predicting short-term clinical outcomes of patients infected by SARS-CoV-2 BA.2.2. Methods: This retrospective study was performed on 1,219 patients with laboratory-confirmed SARS-CoV-2 BA.2.2 to determine the association of CLR with short-term clinical outcomes. Independent Chi square test, Rank sum test, and binary logistic regression analysis were performed to calculate mean differences and adjusted odds ratios (aORs) with their 95% CI, respectively. Results: Over 8% of patients admitted due to SARS-CoV-2 BA.2.2. were critically ill. The best cut-off value of CLR was 21.25 in the ROC with a sensitivity of 72.3% and a specificity of 86%. After adjusting age, gender, and comorbidities, binary logistic regression analysis showed that elevated CLR was an independent risk factor for poor short-term clinical outcomes of COVID-19 patients. Conclusion: C-reactive protein to Lymphocyte Ratio is a significant predictive factor for poor short-term clinical outcomes of SARS-CoV-2 BA.2.2 inflicted patients.

[The predictive value of warning scores for intensive care unit admission in coronavirus disease 2019 patients].

To evaluate the predictive value of early warning scores for intensive care unit (ICU) admission in patients with coronavirus disease 2019 (COVID-19). For COVID-19 patients who were admitted to Shijiazhuang People's Hospital from January 2021 to February 2021, national early warning score (NEWS), national early warning score 2 (NEWS2), rapid emergency medicine score (REMS), quick sepsis-related organ failure (qSOFA), altered consciousness, blood urea nitrogen, respiratory rate, blood pressure, and age-65 (CURB-65) were used to evaluate the inpatient condition and the predictive value for ICU admission. A total of 368 patients were included, and 32 patients (8.7%) were transferred to the ICU. The median age was 49.0 (34.0,61.0) years. The scores of NEWS, NEWS2, REMS, and CURB-65 were 1 (0, 2), 1 (0, 2), 4 (2, 6) and 0 (0, 1), respectively. The receiver operating characteristic (ROC) cure (AUC) was used to evaluate the predictive value in detecting patients who are at risk of being transferred to the ICU. Area under the ROC AUC of NEWS was 0.756, sensitivity 65.6%, and specificity 71.3%. ROC AUC of NEWS2 was 0.732, sensitivity 62.5%, and specificity 61.3%. ROC AUC of REMS was 0.787, sensitivity 84.4%, and specificity 64.6%. ROC AUC of CURB-65 was 0.814, sensitivity 81.3%, and specificity 76.8%. The predictive value of NEWS and NEWS2 combined with age were significantly improved. The ROC AUC of NEWS combined with age was 0.885, sensitivity 85.1%, and specificity 75.0%. The ROC AUC of NEWS2 combined with age was 0.883, sensitivity 84.2%, and specificity 75.0%. NEWS and NEWS2 combined with age can be used as a predictive tool for whether COVID-19 patients will be admitted to the ICU.

Diagnostic performance of eNose technology in COVID-19 patients after hospitalization.

BACKGROUND: Volatile organic compounds (VOCs) produced by human cells reflect metabolic and pathophysiological processes which can be detected with the use of electronic nose (eNose) technology. Analysis of exhaled breath may potentially play an important role in diagnosing COVID-19 and stratification of patients based on pulmonary function or chest CT. METHODS: Breath profiles of COVID-19 patients were collected with an eNose device (SpiroNose) 3 months after discharge from the Leiden University Medical Centre and matched with breath profiles from healthy individuals for analysis. Principal component analysis was performed with leave-one-out cross validation and visualised with receiver operating characteristics. COVID-19 patients were stratified in subgroups with a normal pulmonary diffusion capacity versus patients with an impaired pulmonary diffusion capacity (DLCOc < 80% of predicted) and in subgroups with a normal chest CT versus patients with COVID-19 related chest CT abnormalities. RESULTS: The breath profiles of 135 COVID-19 patients were analysed and matched with 174 healthy controls. The SpiroNose differentiated between COVID-19 after hospitalization and healthy controls with an AUC of 0.893 (95-CI, 0.851-0.934). There was no difference in VOCs patterns in subgroups of COVID-19 patients based on diffusion capacity or chest CT. CONCLUSIONS: COVID-19 patients have a breath profile distinguishable from healthy individuals shortly after hospitalization which can be detected using eNose technology. This may suggest ongoing inflammation or a common repair mechanism. The eNose could not differentiate between subgroups of COVID-19 patients based on pulmonary diffusion capacity or chest CT.

The utility of the Rapid Emergency Medicine Score (REMS) compared with three other early warning scores in predicting in-hospital mortality among COVID-19 patients in the emergency department: a multicenter validation study.

BACKGROUND: Many early warning scores (EWSs) have been validated to prognosticate adverse outcomes of COVID-19 in the Emergency Department (ED), including the quick Sequential Organ Failure Assessment (qSOFA), the Modified Early Warning Score (MEWS), and the National Early Warning Score (NEWS). However, the Rapid Emergency Medicine Score (REMS) has not been widely validated for this purpose. We aimed to assess and compare the prognostic utility of REMS with that of qSOFA, MEWS, and NEWS for predicting mortality in emergency COVID-19 patients. METHODS: We conducted a multi-center retrospective study at five EDs of various levels of care in Thailand. Adult patients visiting the ED who tested positive for COVID-19 prior to ED arrival or within the index hospital visit between January and December 2021 were included. Their EWSs at ED arrival were calculated and analysed. The primary outcome was all-cause in-hospital mortality. The secondary outcome was mechanical ventilation. RESULTS: A total of 978 patients were included in the study; 254 (26%) died at hospital discharge, and 155 (15.8%) were intubated. REMS yielded the highest discrimination capacity for in-hospital mortality (the area under the receiver operator characteristics curves (AUROC) 0.771 (95% confidence interval (CI) 0.738, 0.804)), which was significantly higher than qSOFA (AUROC 0.620 (95%CI 0.589, 0.651); p < 0.001), MEWS (AUROC 0.657 (95%CI 0.619, 0.694); p < 0.001), and NEWS (AUROC 0.732 (95%CI 0.697, 0.767); p = 0.037). REMS was also the best EWS in terms of calibration, overall model performance, and balanced diagnostic accuracy indices at its optimal cutoff. REMS also performed better than other EWSs for mechanical ventilation. CONCLUSION: REMS was the early warning score with the highest prognostic utility as it outperformed qSOFA, MEWS, and NEWS in predicting in-hospital mortality in COVID-19 patients in the ED.

Development and validation of the nomogram to predict the risk of hospital drug shortages: A prediction model.

INTRODUCTION: Reasons for drug shortages are multi-factorial, and patients are greatly injured. So we needed to reduce the frequency and risk of drug shortages in hospitals. At present, the risk of drug shortages in medical institutions rarely used prediction models. To this end, we attempted to proactively predict the risk of drug shortages in hospital drug procurement to make further decisions or implement interventions. OBJECTIVES: The aim of this study is to establish a nomogram to show the risk of drug shortages. METHODS: We collated data obtained using the centralized procurement platform of Hebei Province and defined independent and dependent variables to be included in the model. The data were divided into a training set and a validation set according to 7:3. Univariate and multivariate logistic regression were used to determine independent risk factors, and discrimination (using the receiver operating characteristic curve), calibration (Hosmer-Lemeshow test), and decision curve analysis were validated. RESULTS: As a result, volume-based procurement, therapeutic class, dosage form, distribution firm, take orders, order date, and unit price were regarded as independent risk factors for drug shortages. In the training (AUC = 0.707) and validation (AUC = 0.688) sets, the nomogram exhibited a sufficient level of discrimination. CONCLUSIONS: The model can predict the risk of drug shortages in the hospital drug purchase process. The application of this model will help optimize the management of drug shortages in hospitals.

Platelet-albumin-bilirubin score and neutrophil-to-lymphocyte ratio predict intensive care unit admission in patients with end-stage kidney disease infected with the Omicron variant of COVID-19: a single-center prospective cohort study.

OBJECTIVES: The objective of this study was to develop clinical scores to predict the risk of intensive care unit (ICU) admission in patients with COVID-19 and end stage kidney disease (ESKD). METHODS: This was a prospective study in which 100 patients with ESKD were enrolled and divided into two groups: the ICU group and the non-ICU group. We utilized univariate logistic regression and nonparametric statistics to analyze the clinical characteristics and liver function changes of both groups. By plotting receiver operating characteristic curves, we identified clinical scores that could predict the risk of ICU admission. RESULTS: Out of the 100 patients with Omicron infection, 12 patients were transferred to the ICU due to disease aggravation, with an average of 9.08 days from hospitalization to ICU transfer. Patients transferred to the ICU more commonly experienced shortness of breath, orthopnea, and gastrointestinal bleeding. The peak liver function and changes from baseline in the ICU group were significantly higher, with p values <.05. We found that the baseline platelet-albumin-bilirubin score (PALBI) and neutrophil-to-lymphocyte ratio (NLR) were good predictors of ICU admission risk, with area under curve values of 0.713 and 0.770, respectively. These scores were comparable to the classic Acute Physiology and Chronic Health Evaluation II (APACHE-II) score (p > .05). CONCLUSION: Patients with ESKD and Omicron infection who are transferred to the ICU are more likely to have abnormal liver function. The baseline PALBI and NLR scores can better predict the risk of clinical deterioration and early transfer to the ICU for treatment.

Performance of point-of-care severity scores to predict prognosis in patients admitted through the emergency department with COVID-19.

BACKGROUND: Identifying COVID-19 patients at the highest risk of poor outcomes is critical in emergency department (ED) presentation. Sepsis risk stratification scores can be calculated quickly for COVID-19 patients but have not been evaluated in a large cohort. OBJECTIVE: To determine whether well-known risk scores can predict poor outcomes among hospitalized COVID-19 patients. DESIGNS, SETTINGS, AND PARTICIPANTS: A retrospective cohort study of adults presenting with COVID-19 to 156 Hospital Corporation of America (HCA) Healthcare EDs, March 2, 2020, to February 11, 2021. INTERVENTION: Quick Sequential Organ Failure Assessment (qSOFA), Shock Index, National Early Warning System-2 (NEWS2), and quick COVID-19 Severity Index (qCSI) at presentation. MAIN OUTCOME AND MEASURES: The primary outcome was in-hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and vasopressors receipt. Patients scored positive with qSOFA ≥ 2, Shock Index > 0.7, NEWS2 ≥ 5, and qCSI ≥ 4. Test characteristics and area under the receiver operating characteristics curves (AUROCs) were calculated. RESULTS: We identified 90,376 patients with community-acquired COVID-19 (mean age 64.3 years, 46.8% female). 17.2% of patients died in-hospital, 28.6% went to the ICU, 13.7% received mechanical ventilation, and 13.6% received vasopressors. There were 3.8% qSOFA-positive, 45.1% Shock Index-positive, 49.8% NEWS2-positive, and 37.6% qCSI-positive at ED-triage. NEWS2 exhibited the highest AUROC for in-hospital mortality (0.593, confidence interval [CI]: 0.588-0.597), ICU admission (0.602, CI: 0.599-0.606), mechanical ventilation (0.614, CI: 0.610-0.619), and vasopressor receipt (0.600, CI: 0.595-0.604). CONCLUSIONS: Sepsis severity scores at presentation have low discriminative power to predict outcomes in COVID-19 patients and are not reliable for clinical use. Severity scores should be developed using features that accurately predict poor outcomes among COVID-19 patients to develop more effective risk-based triage.

Association of inflammatory and coagulation biomarkers with mortality in patients aged 60 years or older and hospitalized with COVID-19 / Associação entre biomarcadores inflamatórios e da coagulação com mortalidade em pacientes com 60 anos de idade ou mais hospitalizados com COVID-19

Objectives: To analyze the association of inflammatory and coagulation biomarkers with mortality in geriatric patients with COVID-19. Methods: This is a retrospective cohort study of 206 patients aged 60 years or older who were hospitalized with COVID-19 at an intensive care unit. The analyzed variables were age, sex, length of hospital stay, and inflammatory biomarkers (C-reactive protein, neutrophil-to-lymphocyte ratio, procalcitonin, fibrinogen, ferritin, and d-dimer). We constructed a receiver operating characteristic curve and analyzed the area under the curve to evaluate the accuracy of biomarkers associated with mortality in patients with COVID-19. Results: Mean age was 72 (± 8) years. There were 101 deaths (49% of the total sample), which were significantly more frequent (p = 0.006) in the older age groups and were distributed as follows: 37.50% (60 ­ 69 years old); 50% (70 ­ 79 years old); 67.50% (80 ­ 89 years old); and 75% (over 90 years old). Mortality was associated with increased serum levels of procalcitonin, neutrophil-to-lymphocyte ratio, C-reactive protein, and d-dimer, and decreased fibrinogen levels. Neutrophil-to-lymphocyte ratio occupied the largest area under the receiver operating characteristic curve (area under the curve 0.859) in this group. Conclusions: In this study, inflammatory biomarkers neutrophil-to-lymphocyte ratio, procalcitonin, C-reactive protein, and d-dimer were associated with mortality in older patients with COVID-19 hospitalized at an intensive care unit, and neutrophil-to-lymphocyte ratio presented the best accuracy.

Objetivos: Analisar associação de biomarcadores inflamatórios e da coagulação com mortalidade em pacientes geriátricos com COVID-19. Metodologia: Estudo do tipo coorte retrospectiva de 206 pacientes com 60 anos de idade ou mais internados em unidade de terapia intensiva (UTI) com COVID-19. As variáveis analisadas foram idade, sexo, tempo de permanência hospitalar e biomarcadores inflamatórios, sendo esses proteína C reativa (PCR), relação neutrófilo-linfócitos (RNL), procalcitonina, fibrinogênio, ferritina e D-dímero. Empregou-se a curva ROC, com análise da área sob a curva (ACR), para avaliar a acurácia dos biomarcadores associados à mortalidade nos pacientes com COVID-19. Resultados: A média de idade foi de 72 (± 8) anos. Ocorreram 101 óbitos (49,02% da amostra total), significativamente mais frequente (p = 0,006) nas faixas etárias mais elevadas, distribuídos por faixa etária: 37,50% (60 ­ 69 anos); 50% (70 ­ 79 anos); 67,50% (80 ­ 89 anos); e 75% (nos maiores de 90 anos). A mortalidade foi associada a aumento dos níveis séricos dos biomarcadores procalcitonina, relação neutrófiloslinfócitos (RNL), proteína C reativa (PCR) e D-dímero, bem como diminuição dos níveis de fibrinogênio. A RNL ocupou a maior área sob a curva ROC (ACR 0,859) nesse grupo. Conclusões: Neste estudo, os biomarcadores inflamatórios RNL, procalcitonina, PCR e D-dímero foram associados com mortalidade em pacientes idosos portadores de COVID-19 internados em UTI, e a RNL foi a que apresentou a melhor acurácia.

Daily combined measurement of platelet count and presepsin concentration can predict in-hospital death of patients with severe coronavirus disease 2019 (COVID-19).

The purpose of this study was to classify patients with severe COVID-19 into more detailed risk groups using coagulation/fibrinolysis, inflammation/immune response, and alveolar/myocardial damage biomarkers, as well as to identify prognostic markers for these patients. These biomarkers were measured every day for eight intensive care unit days in 54 adult patients with severe COVID-19. The patients were classified into survivor (n = 40) and non-survivor (n = 14) groups. Univariate and multivariate analyses showed that the combined measurement of platelet count and presepsin concentrations may be the most valuable for predicting in-hospital death, and receiver operating characteristic curve analysis further confirmed this result (area under the curve = 0.832). Patients were consequently classified into three groups (high-, medium-, and low-risk) on the basis of their cutoff values (platelet count 53 × 103/µL, presepsin 714 pg/mL). The Kaplan-Meier curve for 90-day survival by each group showed that the 90-day mortality rate significantly increased as risk level increased (P < 0.01 by the log-rank test). Daily combined measurement of platelet count and presepsin concentration may be useful for predicting in-hospital death and classifying patients with severe COVID-19 into more detailed risk groups.