The laboratory parameters-derived CoLab score as an indicator of the host response in ICU COVID-19 patients decreases over time: a prospective cohort study.

The CoLab score was developed and externally validated to rule out COVID-19 among suspected patients presenting at the emergency department. We hypothesized a within-patient decrease in the CoLab score over time in an intensive care unit (ICU) cohort. Such a decrease would create the opportunity to potentially rule out the need for isolation when the infection is overcome. Using linear mixed-effects models, data from the Maastricht Intensive Care COVID (MaastrICCht) cohort were used to investigate the association between time and the CoLab score. Models were adjusted for sex, APACHE II score, ICU mortality, and daily SOFA score. The CoLab score decreased by 0.30 points per day (95% CI - 0.33 to - 0.27), independent of sex, APACHE II, and Mortality. With increasing SOFA score over time, the CoLab score decreased more strongly (- 0.01 (95% CI - 0.01 to - 0.01) additional decrease per one-point increase in SOFA score.) The CoLab score decreased in ICU patients on mechanical ventilation for COVID-19, with a one-point reduction per three days, independent of sex, APACHE II, and ICU mortality, and somewhat stronger with increasing multi-organ failure over time. This suggests that the CoLab score would decrease below a threshold where COVID-19 can be excluded.

The CoLab score is associated with SARS-CoV-2 viral load during admission in individuals admitted to the intensive care unit: the CoLaIC cohort study.

OBJECTIVES: The present study examines the temporal association between the changes in SARS-CoV-2 viral load during infection and whether the CoLab-score can facilitate de-isolation. METHODS: Nasal swabs and blood samples were collected from ICU-admitted SARS-CoV-2 positive patients at Maastricht UMC+ from March 25, 2020 to October 1, 2021. The CoLab-score was calculated based on 10 blood parameters and age and can range from -43 to 6. Three mixed effects analyses compared patient categories based on initial PCR Ct values (low; Ct≤20, mid; 20>Ct≤30, high; Ct>30), serial PCR Ct values to CoLab-scores over time, and the association between within-patient delta Ct values and CoLab-scores. RESULTS: In 324 patients, the median Ct was 33, and the median CoLab-score was -1.78. Mid (n=110) and low (n=41) Ct-categories had higher CoLab-scores over time (+0.60 points, 95 % CI; 0.04-1.17, and +0.28 points, 95 % CI -0.49 to 1.04) compared to the high Ct (n=87) category. Over time, higher serial Ct values were associated with lower serial CoLab-scores, decreasing by -0.07 points (95 % CI; -0.11 to -0.02) per day. Increasing delta Ct values were associated with a decreasing delta CoLab-score of -0.12 (95 % CI; -0.23; -0.01). CONCLUSIONS: The study found an association between lower viral load on admission and reduced CoLab-score. Additionally, a decrease in viral load over time was associated with a decrease in CoLab-score. Therefore, the CoLab-score may make patient de-isolation an option based on the CoLab-score.

Development and validation of an early warning score to identify COVID-19 in the emergency department based on routine laboratory tests: a multicentre case-control study.

OBJECTIVES: Identifying patients with a possible SARS-CoV-2 infection in the emergency department (ED) is challenging. Symptoms differ, incidence rates vary and test capacity may be limited. As PCR-testing all ED patients is neither feasible nor effective in most centres, a rapid, objective, low-cost early warning score to triage ED patients for a possible infection is developed. DESIGN: Case-control study. SETTING: Secondary and tertiary hospitals in the Netherlands. PARTICIPANTS: The study included patients presenting to the ED with venous blood sampling from July 2019 to July 2020 (n=10 417, 279 SARS-CoV-2-positive). The temporal validation cohort covered the period from July 2020 to October 2021 (n=14 080, 1093 SARS-CoV-2-positive). The external validation cohort consisted of patients presenting to the ED of three hospitals in the Netherlands (n=12 061, 652 SARS-CoV-2-positive). PRIMARY OUTCOME MEASURES: The primary outcome was one or more positive SARS-CoV-2 PCR test results within 1 day prior to or 1 week after ED presentation. RESULTS: The resulting 'CoLab-score' consists of 10 routine laboratory measurements and age. The score showed good discriminative ability (AUC: 0.930, 95% CI 0.909 to 0.945). The lowest CoLab-score had high sensitivity for COVID-19 (0.984, 95% CI 0.970 to 0.991; specificity: 0.411, 95% CI 0.285 to 0.520). Conversely, the highest score had high specificity (0.978, 95% CI 0.973 to 0.983; sensitivity: 0.608, 95% CI 0.522 to 0.685). The results were confirmed in temporal and external validation. CONCLUSIONS: The CoLab-score is based on routine laboratory measurements and is available within 1 hour after presentation. Depending on the prevalence, COVID-19 may be safely ruled out in over one-third of ED presentations. Highly suspect cases can be identified regardless of presenting symptoms. The CoLab-score is continuous, in contrast to the binary outcome of lateral flow testing, and can guide PCR testing and triage ED patients.

Use of an algorithm based on routine blood laboratory tests to exclude COVID-19 in a screening-setting of healthcare workers.

BACKGROUND: COVID-19 is an ongoing pandemic leading to exhaustion of the hospital care system. Our health care system has to deal with a high level of sick leave of health care workers (HCWs) with COVID-19 related complaints, in whom an infection with SARS-CoV-2 has to be ruled out before they can return back to work. The aim of the present study is to investigate if the recently described CoLab-algorithm can be used to exclude COVID-19 in a screening setting of HCWs. METHODS: In the period from January 2021 till March 2021, HCWs with COVID-19-related complaints were prospectively collected and included in this study. Next to the routinely performed SARS-CoV-2 RT-PCR, using a set of naso- and oropharyngeal swab samples, two blood tubes (one EDTA- and one heparin-tube) were drawn for analysing the 10 laboratory parameters required for running the CoLab-algorithm. RESULTS: In total, 726 HCWs with a complete CoLab-laboratory panel were included in this study. In this group, 684 HCWs were tested SARS-CoV-2 RT-PCR negative and 42 cases RT-PCR positive. ROC curve analysis showed an area under the curve (AUC) of 0.853 (95% CI: 0.801-0.904). At a safe cut-off value for excluding COVID-19 of -6.525, the sensitivity was 100% with a specificity of 34% (95% CI: 21 to 49%). No SARS-CoV-2 RT-PCR cases were missed with this cut-off and COVID-19 could be safely ruled out in more than one third of HCWs. CONCLUSION: The CoLab-score is an easy and reliable algorithm that can be used for screening HCWs with COVID-19 related complaints. A major advantage of this approach is that the results of the score are available within 1 hour after collecting the samples. This results in a faster return to labour process of a large part of the COVID-19 negative HCWs (34%), next to a reduction in RT-PCR tests (reagents and labour costs) that can be saved.

Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study.

BACKGROUND: Measurement of heart rate (HR) through an unobtrusive, wrist-worn optical HR monitor (OHRM) could enable earlier recognition of patient deterioration in low acuity settings and enable timely intervention. OBJECTIVE: The goal of this study was to assess the agreement between the HR extracted from the OHRM and the gold standard 5-lead electrocardiogram (ECG) connected to a patient monitor during surgery and in the recovery period. METHODS: In patients undergoing surgery requiring anesthesia, the HR reported by the patient monitor's ECG module was recorded and stored simultaneously with the photopletysmography (PPG) from the OHRM attached to the patient's wrist. The agreement between the HR reported by the patient's monitor and the HR extracted from the OHRM's PPG signal was assessed using Bland-Altman analysis during the surgical and recovery phase. RESULTS: A total of 271.8 hours of data in 99 patients was recorded simultaneously by the OHRM and patient monitor. The median coverage was 86% (IQR 65%-95%) and did not differ significantly between surgery and recovery (Wilcoxon paired difference test P=.17). Agreement analysis showed the limits of agreement (LoA) of the difference between the OHRM and the ECG HR were within the range of 5 beats per minute (bpm). The mean bias was -0.14 bpm (LoA between -3.08 bpm and 2.79 bpm) and -0.19% (LoA between -5 bpm to 5 bpm) for the PPG- measured HR compared to the ECG-measured HR during surgery; during recovery, it was -0.11 bpm (LoA between -2.79 bpm and 2.59 bpm) and -0.15% (LoA between -3.92% and 3.64%). CONCLUSIONS: This study shows that an OHRM equipped with a PPG sensor can measure HR within the ECG reference standard of -5 bpm to 5 bpm or -10% to 10% in the perioperative setting when the PPG signal is of sufficient quality. This implies that an OHRM can be considered clinically acceptable for HR monitoring in low acuity hospitalized patients.

Detecting patients with PMI post-CABG based on cardiac troponin-T profiles: A latent class mixed modeling approach.

BACKGROUND: Diagnosis of perioperative myocardial infarction (PMI) after coronary artery bypass grafting (CABG) is fraught with complexity since it is primarily based on a single cut-off value for cardiac troponin (cTn) that is exceeded in over 90% of CABG patients, including non-PMI patients. In this study we applied an unsupervised statistical modeling approach to uncover clinically relevant cTn release profiles post-CABG, including PMI, and used this to improve diagnostic accuracy of PMI. METHODS: In 624 patients that underwent CABG, cTnT concentration was serially measured up to 24 h post aortic cross clamping. 2857 cTnT measurements were available to fit latent class linear mixed models (LCMMs). RESULTS: Four classes were found, described by: normal, high, low and rising cTnT release profiles. With the clinical diagnosis of PMI as golden standard, the rising profile had a diagnostic accuracy of 97%, compared to 83% for an optimally chosen cut-off and 21% for the guideline recommended cut-off value. CONCLUSION: Clinically relevant subgroups, including patients with PMI, can be uncovered using serially measured cTnT and a LCMM. The LCMM showed superior diagnostic accuracy of PMI. A rising cTnT profile is potentially a better criterion than a single cut-off value in diagnosing PMI post-CABG.

Metabolic Health Index (MHI): Assessment of Comorbidity in Bariatric Patients Based on Biomarkers.

PURPOSE: The focus of bariatric surgery is reduction of weight, reflected in body mass index (BMI). However, the resolution of comorbidity is a second important outcome indicator. The degree of comorbidity is hard to quantify objectively as comorbidities develop gradually and are interdependent. Multiple scoring systems quantifying comorbidity exist but they lack continuity and objectivity. In analogy with BMI as index for weight, the Metabolic Health Index (MHI) is developed as objective quantification of metabolic health status. Laboratory data were used as comorbidities affect biomarkers. Conversely, laboratory data can be used as objectively obtained variables to describe comorbidity. METHODS: Laboratory data were collected and crosschecked by national quality registry entries. Machine learning was applied to develop an ordinal logistic regression model, using 4 clinical and 32 laboratory input variables. The output was mathematically transformed into a continuous score for intuitive interpretation, ranging from 1 to 6 (MHI). RESULTS: In total, 4778 data records of 1595 patients were used. The degree of comorbidity is best described by age at phlebotomy, estimated Glomerular Filtration Rate (eGFR), and concentrations of glycated hemoglobin (HbA1c), triglycerides, and potassium. The model is independent of day of sampling and type of surgery. Mean MHI was significantly different between patient subgroups with increasing number of comorbidities. CONCLUSION: The MHI reflects severity of comorbidity, enabling objective assessment of a bariatric patient's metabolic health state, regardless day of sampling and surgery type. Next to weight-focused outcome measures like %TWL, the MHI can serve as outcome measure for metabolic health.