Ruben Deneer

Routinely used statistical hypothesis tests are often inadequate for longitudinal flow cytometry data, necessitating more sophisticated modeling approaches. Analyzing dynamic biomarker expression across hematopoietic maturation presents several analytical challenges, notably nonlinearity, serial correlation between subsequent stages, and varying total cell counts. This study addresses these issues by comparing traditional stage‐by‐stage hypothesis tests with two marginal modeling approaches: Generalized least squares (GLS) and a generalized linear model fitted with generalized estimating equations (GEE‐GLM). We analyzed cell fractions expressing the dynamic biomarkers Bcl‐2 (anti‐apoptotic marker) and Ki‐67 (proliferation marker), as well as the Bcl‐2:Ki‐67 ratio, across 20 stages of erythropoietic maturation in bone marrow aspirates from 25 patients with myelodysplastic syndromes (MDS), 25 patients with acute myeloid leukemia (AML), and 50 nonmalignant controls. The GLS modeling approach accounts for nonlinearity and correlation between subsequent maturation stages, while GEE‐GLM additionally accounts for varying total cell counts by assigning greater weight to observations with larger cell counts. Instead of repeated stage‐by‐stage testing, GLS and GEE‐GLM model the average maturation profile across different sub‐populations, enabling visualization and robust population‐level statistical inference. We demonstrate that variations in the total number of cells influence the estimated population mean profiles and their confidence intervals. This effect is particularly pronounced during early maturation stages, where low Ki‐67+ cell counts can lead to substantially different estimates and conclusions when using the GEE‐GLM approach. Conversely, the GLS approach offers the flexibility to model the unbounded Bcl‐2:Ki‐67 ratio, revealing differences between AML and nonmalignant patients across all 20 stages. The computational workflow and scripts are provided to facilitate reproducibility and application to similar time‐dependent flow cytometry datasets.

Intensive care unit (ICU) patients with COVID-19 are isolated until two subsequent SARS-CoV-2 PCR tests have become negative. The question is whether the viral RNA detected here represents intact or compromised viral particles. A viability PCR method detects only intact infectious viruses and disregards RNA from incomplete non-infectious viruses or RNA remnants. We hypothesize that the time to a negative PCR is shorter using the viability PCR compared to conventional SARS-CoV-2 PCR. Patients admitted to the ICU in the Maastricht University Medical Center within an ongoing prospective cohort were included, and respiratory samples were collected three times weekly. Samples were aliquoted for viability PCR into two aliquots, one of which was treated with propidium monoazide (PMA) to eliminate incomplete viruses. The PMA-untreated sample corresponded to the traditional SARS-CoV-2 PCR used in routine diagnostics. A mean time difference between viability and conventional PCR was estimated, assuming a negative binomial distribution. One hundred two ICU patients were included in the analysis. The time to a negative PCR test result differed significantly between viability PCR and conventional PCR (Paired Prentice-Wilcoxon test P -value = 0.0001). On average, the first negative viability PCR test result occurred 2.4 days (95% CI 1.5–4.2) before the first negative conventional PCR test result. The application of a viability PCR technique for SARS-CoV-2 provided an earlier indication of the absence of intact virus, suggesting reduced infectivity, potentially enabling earlier de-isolation of ICU patients. IMPORTANCE: This study demonstrated that, within patients, on average, viability PCR became negative 2.4 days earlier than conventional PCR, indicating that viability PCR could potentially assist in de-isolating patients 2.4 days earlier. Future use of this assay could thus aid in improving routine COVID-19 diagnostics and prognostics related to the timing of de-isolation. Additionally, future development of a similar approach for other respiratory viruses could be of interest.

This study describes and compares the performance of several semi-parametric statistical modeling approaches to dynamically classify subjects into two groups, based on an irregularly and sparsely sampled curve. The motivating example of this study is the diagnosis of a complication following cardiac surgery, based on repeated measures of a single cardiac biomarker where early detection enables prompt intervention by clinicians. We first simulate data to compare the dynamic predictive performance over time for growth charts, conditional growth charts, a varying-coefficient model, a generalized functional linear model and longitudinal discriminant analysis. Our results demonstrate that functional regression approaches that implicitly incorporate historic information through random effects, provide superior discriminative ability compared to approaches that do not take historic information into account or explicitly model historic information through autoregressive terms. Semi-parametric modeling approaches show a benefit in terms of dynamic discriminative ability compared to the clinical practice of using a fixed threshold on the raw measured value. Under high degrees of sparsity the functional regression approaches are less advantageous compared to varying-coefficient models or quantile regression. The class imbalance of the outcome affects the historic and non-historic approaches in equal measure, with lower event rates reducing performance. Finally, the functional regression and varying-coefficient model were applied to a real-world clinical dataset to demonstrate their performance and application.

The relationship between apoptosis inhibition and cell proliferation was studied during the maturation stages of erythropoiesis, granulopoiesis, and monopoiesis in patients with myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). The anti‐apoptotic and proliferative cell fractions were determined in bone marrow aspirates derived from 25 MDS patients and 25 AML patients and compared to 50 nonmalignant cases, using antibodies to Bcl‐2 and Ki‐67, respectively. These were applied along with ten‐color flow cytometry of maturation markers for the three hematopoietic cell lineages. Next, the Bcl‐2:Ki‐67 ratio was determined as the ratio between the Bcl‐2+ and Ki‐67+ cell fractions of the specific hematopoietic cell lineages, both in total and during the different stages of maturation. Bone marrow samples from MDS and AML patients showed a significant increase in the anti‐apoptotic cell fraction and a reduced proliferative cell compartment compared to non‐malignant cases. Overall, the anti‐apoptotic cell fraction was particularly increased in the more mature stages, while the proliferative cell fractions were decreased more frequently in the immature stages. These changes varied among different hematopoietic cell lineages. The erythropoietic maturation process showed the most significant differences in both Ki‐67+ and Bcl‐2+ cell fractions when comparing MDS and AML to non‐malignant cases. This difference was restricted to that of the Bcl‐2+ cell fractions in the granulopoiesis and that of the Ki‐67+ cell fraction of the monopoiesis. All three hematopoietic cell lineages encompass a small fraction of cells (up to 10%) that concurrently exhibit anti‐apoptotic and proliferative marker expression. Although MDS and AML patients displayed considerable variability in their anti‐apoptotic and proliferation index, the Bcl‐2:Ki‐67 ratio resulted in a clear separation between the malignant and non‐malignant cases. Incorporating this combination of the Bcl‐2 and Ki‐67 markers into the study of MDS and AML and in future diagnostic workups may provide important insights into the biological behavior of these blood‐borne neoplasms and facilitate personalized therapy decisions for patients.

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.

Background Rapid identification of emergency department (ED) patients with a possible COVID-19 infection is needed. PCR-testing all ED patients is neither feasible nor effective in most centers, therefore a rapid, objective, low-cost screening tool to triage ED patients is necessary. Methods Results from all routine lab tests from ED patients at the Catharina Hospital were collected from July 2019 to July 2020 and used in a statistical model to obtain the CoLab-score. The score was validated temporally and externally in three independent centers. Results The CoLab-score consists of 10 routine lab results and can be used to safely rule-out a COVID-19 infection in more than one third of ED presentations with a negative predictive value of 0.997 (95% CI: 0.994 – 0.999). Conclusions The CoLab-score is a valuable tool to rule out COVID-19, guide PCR testing and is available to any center with access to routine laboratory tests.

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.

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.

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.