Reducing unnecessary routine laboratory testing for noncritically ill patients with COVID-19.

BACKGROUND: Reducing unnecessary routine laboratory testing is a Choosing Wisely® recommendation, and new areas of overuse were noted during the COVID-19 pandemic. OBJECTIVE: To reduce unnecessary repetitive routine laboratory testing for patients with COVID-19 during the pandemic across a large safety net health system. DESIGNS, SETTINGS AND PARTICIPANTS: This quality improvement initiative was initiated by the System High-Value Care Council at New York City Health + Hospitals (H + H), the largest public healthcare system in the United States consisting of 11 acute care hospitals. INTERVENTION: four overused laboratory tests in noncritically ill hospitalized patients with COVID-19 were identified: C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), and procalcitonin. A two-pronged electronic health record intervention was implemented consisting of (1) nonintrusive, informational nudge statements placed on selected order sets, and (2) a forcing function of one consecutive day limit on ordering. MAIN OUTCOME AND MEASURES: The average of excess tests per encounter days (ETPED) for each of four target laboratory testing only in patients with COVID-19. OBJECTIVE: Interdisciplinary System High-Value Care Council identified four overused laboratory tests (inflammatory markers) in noncritically ill hospitalized patients with COVID-19: C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), and procalcitonin. Within an 11-hospital safety net health system, a two-pronged electronic health record intervention was implemented consisting of (1) nonintrusive, informational nudge statements placed on selected order sets, and (2) a forcing function of one consecutive day limit on ordering. The preintervention period (March 16, 2020 to January 24, 2021) was compared to the postintervention period (January 25, 2021 to March 22, 2022). RESULTS: Time series linear regression showed decreases in CRP (-17.9%, p < .05), ferritin (-37.6%, p < .001), and LDH (-30.1%, p < .001). Slope differences were significant (CRP, ferritin, and LDH p < 0.001; procalcitonin p < 0.05). Decreases were observed across weekly averages: CRP (-19%, p < .01), ferritin (-37.9%, p < .001), LDH (-28.7%, p < .001), and procalcitonin (-18.4%, p < .05). CONCLUSION: This intervention was associated with reduced routine inflammatory marker testing in non-intensive care unit COVID-19 hospitalized patients across 11 hospitals. Variation was high among individual hospitals.

Biomarkers and short-term prognosis in COVID-19.

PURPOSE: The aim of our study was to analyse the short-term prognostic value of different biomarkers in patients with COVID-19. METHODS: We included patients admitted to emergency department with COVID-19 and available concentrations of cardiac troponin I (cTnI), D-dimer, C-reactive protein (CRP) and lactate dehydrogenase (LDH). Patients were classified for each biomarker into two groups (low vs. high concentrations) according to their best cut-off point, and 30-day all-cause death was evaluated. RESULTS: After multivariate adjustment, cTnI ≥21 ng/L, D-dimer ≥1112 ng/mL, CRP ≥10 mg/dL and LDH ≥334 U/L at admission were associated with an increased risk of 30-day all-cause death (hazard ratio (HR) 4.30; 95% CI 1.74-10.58; p = 0.002; HR 3.35; 95% CI 1.58-7.13; p = 0.002; HR 2.25; 95% CI 1.13-4.50; p = 0.021; HR 2.00; 95% CI 1.04-3.84; p = 0.039, respectively). The area under the curve for cTnI was 0.825 (95% CI 0.759-0.892) and, in comparison, was significantly better than CRP (0.685; 95% CI 0.600-0.770; p = 0.009) and LDH (0.643; 95% CI 0.534-0.753; p = 0.006) but non-significantly better than D-dimer (0.756; 95% CI 0.674-0.837; p = 0.115). CONCLUSIONS: In patients with COVID-19, increased concentrations of cTnI, D-dimer, CRP and LDH are associated with short-term mortality. Of these, cTnI provides better mortality risk prediction. However, differences with D-dimer were non-significant.

A cohort study of 676 patients indicates D-dimer is a critical risk factor for the mortality of COVID-19.

Coronavirus Disease 2019 (COVID-19) has recently become a public emergency and a worldwide pandemic. However, the information on the risk factors associated with the mortality of COVID-19 and of their prognostic potential is limited. In this retrospective study, the clinical characteristics, treatment and outcome data were collected and analyzed from 676 COVID-19 patients stratified into 140 non-survivors and 536 survivors. We found that the levels of Dimerized plasmin fragment D (D-dimer), C-reactive protein (CRP), lactate dehydrogenase (LDH), procalcitonin (PCT) were significantly higher in non-survivals on admission (non-survivors vs. survivors: D-Dimer ≥ 0.5 mg/L, 83.2% vs. 44.9%, P<0.01; CRP ≥10 mg/L, 50.4% vs. 6.0%, P<0.01; LDH ≥ 250 U/L, 73.8% vs. 20.1%, P<0.01; PCT ≥ 0.5 ng/ml, 27.7% vs. 1.8%, P<0.01). Moreover, dynamic tracking showed D-dimer kept increasing in non-survivors, while CRP, LDH and PCT remained relatively stable after admission. D-dimer has the highest C-index to predict in-hospital mortality, and patients with D-dimer levels ≥0.5 mg/L had a higher incidence of mortality (Hazard Ratio: 4.39, P<0.01). Our study suggested D-dimer could be a potent marker to predict the mortality of COVID-19, which may be helpful for the management of patients.

Development and validation of the HNC-LL score for predicting the severity of coronavirus disease 2019.

BACKGROUND: Information regarding risk factors associated with severe coronavirus disease (COVID-19) is limited. This study aimed to develop a model for predicting COVID-19 severity. METHODS: Overall, 690 patients with confirmed COVID-19 were recruited between 1 January and 18 March 2020 from hospitals in Honghu and Nanchang; finally, 442 patients were assessed. Data were categorised into the training and test sets to develop and validate the model, respectively. FINDINGS: A predictive HNC-LL (Hypertension, Neutrophil count, C-reactive protein, Lymphocyte count, Lactate dehydrogenase) score was established using multivariate logistic regression analysis. The HNC-LL score accurately predicted disease severity in the Honghu training cohort (area under the curve [AUC]=0.861, 95% confidence interval [CI]: 0.800-0.922; P<0.001); Honghu internal validation cohort (AUC=0.871, 95% CI: 0.769-0.972; P<0.001); and Nanchang external validation cohort (AUC=0.826, 95% CI: 0.746-0.907; P<0.001) and outperformed other models, including CURB-65 (confusion, uraemia, respiratory rate, BP, age ≥65 years) score model, MuLBSTA (multilobular infiltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hypertension, and age) score model, and neutrophil-to-lymphocyte ratio model. The clinical significance of HNC-LL in accurately predicting the risk of future development of severe COVID-19 was confirmed. INTERPRETATION: We developed an accurate tool for predicting disease severity among COVID-19 patients. This model can potentially be used to identify patients at risks of developing severe disease in the early stage and therefore guide treatment decisions. FUNDING: This work was supported by the National Nature Science Foundation of China (grant no. 81972897) and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2015).

A simple algorithm helps early identification of SARS-CoV-2 infection patients with severe progression tendency.

OBJECTIVES: We aimed to develop a simple algorithm to help early identification of SARS-CoV-2 infection patients with severe progression tendency. METHODS: The univariable and multivariable analysis were computed to identify the independent predictors of COVID-19 progression. The prediction model was established in a retrospective training set of 322 COVID-19 patients and was re-evaluated in a prospective validation set of 317 COVID-19 patients. RESULTS: The multivariable analysis identified age (OR = 1.061, p = 0.028), lactate dehydrogenase (LDH) (OR = 1.006, p = 0.037), and CD4 count (OR = 0.993, p = 0.006) as the independent predictors of COVID-19 progression. Consequently, the age-LDH-CD4 algorithm was derived as (age × LDH)/CD4 count. In the training set, the area under the ROC curve (AUROC) of age-LDH-CD4 model was significantly higher than that of single CD4 count, LDH, or age (0.92, 0.85, 0.80, and 0.75, respectively). In the prospective validation set, the AUROC of age-LDH-CD4 model was also significantly higher than that of single CD4 count, LDH, or age (0.92, 0.75, 0.81, and 0.82, respectively). The age-LDH-CD4 ≥ 82 has high sensitive (81%) and specific (93%) for the early identification of COVID-19 patients with severe progression tendency. CONCLUSIONS: The age-LDH-CD4 model is a simple algorithm for early identifying patients with severe progression tendency following SARS-CoV-2 infection, and warrants further validation.

Routine blood tests as a potential diagnostic tool for COVID-19.

Objectives The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to date, the epidemic has gradually spread to 209 countries worldwide with more than 1.5 million infected people and 100,000 deaths. Amplification of viral RNA by rRT-PCR serves as the gold standard for confirmation of infection, yet it needs a long turnaround time (3-4 h to generate results) and shows false-negative rates as large as 15%-20%. In addition, the need of certified laboratories, expensive equipment and trained personnel led many countries to limit the rRT-PCR tests only to individuals with pronounced respiratory syndrome symptoms. Thus, there is a need for alternative, less expensive and more accessible tests. Methods We analyzed the plasma levels of white blood cells (WBCs), platelets, C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transpeptidase (GGT), alkaline phosphatase and lactate dehydrogenase (LDH) of 207 patients who, after being admitted to the emergency room of the San Raffaele Hospital (Milan, Italy) with COVID-19 symptoms, were rRT-PCR tested. Of them, 105 tested positive, whereas 102 tested negative. Results Statistically significant differences were observed for WBC, CRP, AST, ALT and LDH. Empirical thresholds for AST and LDH allowed the identification of 70% of either COVID-19-positive or -negative patients on the basis of routine blood test results. Conclusions Combining appropriate cutoffs for certain hematological parameters could help in identifying false-positive/negative rRT-PCR tests. Blood test analysis might be used as an alternative to rRT-PCR for identifying COVID-19-positive patients in those countries which suffer from a large shortage of rRT-PCR reagents and/or specialized laboratory.