Is the 4C Score Still a Valid Item to Predict In-Hospital Mortality in People with SARS-CoV-2 Infections in the Omicron Variant Era?

Since the start of the SARS-CoV-2 pandemic, several scores have been proposed to identify infected individuals at a higher risk of progression and death. The most famous is the 4C score. However, it was developed in early 2020. Our study aimed to evaluate the accuracy of the 4C score during the wave in which the Omicron variant was prevalent. An observational study was conducted at an Italian University Hospital between 1 January and 31 July 2022. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the ability of the 4C score to predict mortality. Overall, 1186 people were recruited, of which 160 (13.5%) died. According to the 4C score, 177 (11.6%) were classified as having a low risk of mortality, 302 (25.5%) were intermediate, 596 (50.3%) were high, and 151 (12.7%) were very high. The ROC curve of the 4C score showed an AUC (95% CI) value of 0.78 (0.74−0.82). At the criterion value of > 10, the sensitivity was 76.2% and the specificity was 62.67%. Similar to previous studies, the 4C mortality score performed well in our sample, and it is still a useful tool for clinicians to identify patients with a high risk of progression. However, clinicians must be aware that the mortality rate reported in the original studies was higher than that observed in our study.

Identifying the Target Population for Primary Respiratory Syncytial Virus Two-Step Prevention in Infants: Normative Outcome of Hospitalisation Assessment for Newborns (NOHAN).

BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of acute respiratory infection- related hospitalisations in infants (RSVh). Most of these infants are younger than 6 months old with no known risk factors. An efficient RSVh prevention program should address both mothers and infants, relying on Non-Pharmaceutical (NPI) and Pharmaceutical Interventions (PI). This study aimed at identifying the target population for these two interventions. METHODS: Laboratory-confirmed RSV-infected infants hospitalised during the first 6 months of life were enrolled from the Hospices Civils de Lyon birth cohort (2014 to 2018). Clinical variables related to pregnancy and birth (sex, month of birth, birth weight, gestational age, parity) were used for descriptive epidemiology, multivariate logistic regression, and predictive score development. RESULTS: Overall, 616 cases of RSVh in 45,648 infants were identified. Being born before the epidemic season, prematurity, and multiparity were independent predictors of RSVh. Infants born in January or June to August with prematurity and multiparity, and those born in September or December with only one other risk factor (prematurity or multiparity) were identified as moderate-risk, identifying the mothers as candidates for a first-level NPI prevention program. Infants born in September or December with prematurity and multiparity, and those born in October or November were identified as high-risk, identifying the mothers and infants as candidates for a second-level (NPI and PI) intervention. CONCLUSIONS: It is possible to determine predictors of RSVh at birth, allowing early enrollment of the target population in a two-level RSV prevention intervention.

A Convenient Risk Prediction Score for COVID-19 for Determining Whether or Not Hospitalization Is Recommended: Kanagawa Admission Priority Assessment Score.

Objective Coronavirus disease 2019 (COVID-19) has caused a collapse of the medical care system, with effective triage proving vital. The Kanagawa admission priority assessment score, version-1 (KAPAS-1) and version-2 (KAPAS-2), was developed to determine the need for hospitalization. Patients with a high KAPAS (≥5) are recommended for hospitalization. We retrospectively investigated the correlation between the KAPAS and oxygen requirement during hospitalization. Methods We collected the clinical data of COVID-19 patients admitted between February 5 and December 6, 2020. Patients were divided into two groups: those who required oxygen therapy during hospitalization (OXY) and those who did not (NOXY). We assessed the correlations between the groups and KAPAS-1 and KAPAS-2. Results Overall, 117 COVID-19 patients were analyzed, including 20 OXY and 97 NOXY and 54 high KAPAS-1 and 63 high KAPAS-2. The median KAPAS-1 and KAPAS-2 were significantly higher in OXY than in NOXY (6.5 vs. 3, and 9 vs. 4, respectively). The areas under the receiver operating characteristic curves of KAPAS-1 and KAPAS-2 for oxygen requirement were 0.777 and 0.825, respectively, and the maximum values of Youden's index were 4 and 6, respectively. The proportions of high KAPAS-1 and high KAPAS-2 were significantly higher in OXY than in NOXY (90.0% vs. 37.1%, and 90.0% vs. 46.4%, respectively). Conclusion The KAPAS was significantly correlated with oxygen requirement. Furthermore, the KAPAS may be useful for deciding which patients are most likely to require hospitalization and for selecting non-hospitalized patients who should be carefully monitored.

A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV19-ID score.

BACKGROUND: While several studies aimed to identify risk factors for severe COVID-19 cases to better anticipate intensive care unit admissions, very few have been conducted on self-reported patient symptoms and characteristics, predictive of RT-PCR test positivity. We therefore aimed to identify those predictive factors and construct a predictive score for the screening of patients at admission. METHODS: This was a monocentric retrospective analysis of clinical data from 9081 patients tested for SARS-CoV-2 infection from August 1 to November 30 2020. A multivariable logistic regression using least absolute shrinkage and selection operator (LASSO) was performed on a training dataset (60% of the data) to determine associations between self-reported patient characteristics and COVID-19 diagnosis. Regression coefficients were used to construct the Coronavirus 2019 Identification score (COV19-ID) and the optimal threshold calculated on the validation dataset (20%). Its predictive performance was finally evaluated on a test dataset (20%). RESULTS: A total of 2084 (22.9%) patients were tested positive to SARS-CoV-2 infection. Using the LASSO model, COVID-19 was independently associated with loss of smell (Odds Ratio, 6.4), fever (OR, 2.7), history of contact with an infected person (OR, 1.7), loss of taste (OR, 1.5), muscle stiffness (OR, 1.5), cough (OR, 1.5), back pain (OR, 1.4), loss of appetite (OR, 1.3), as well as male sex (OR, 1.05). Conversely, COVID-19 was less likely associated with smoking (OR, 0.5), sore throat (OR, 0.9) and ear pain (OR, 0.9). All aforementioned variables were included in the COV19-ID score, which demonstrated on the test dataset an area under the receiver-operating characteristic curve of 82.9% (95% CI 80.6%-84.9%), and an accuracy of 74.2% (95% CI 74.1%-74.3%) with a high sensitivity (80.4%, 95% CI [80.3%-80.6%]) and specificity (72.2%, 95% CI [72.2%-72.4%]). CONCLUSIONS: The COV19-ID score could be useful in early triage of patients needing RT-PCR testing thus alleviating the burden on laboratories, emergency rooms, and wards.

Clinical Features of Early Stage COVID-19 in a Primary Care Setting.

Background: The coronavirus disease 2019 (COVID-19) pandemic remains a global healthcare crisis. Nevertheless, the majority of COVID-19 cases involve mild to moderate symptoms in the early stages. The lack of information relating to these cases necessitates further investigation. Methods: Patients visiting the outpatient clinic at the Kamagaya General Hospital were screened by interview and body temperature check. After initial screening, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was suspected in 481 patients who then underwent blood tests and the loop-mediated isothermal amplification (LAMP) test for SARS-CoV-2. Clinical characteristics between positive and negative SARS-CoV-2 groups were compared. Further, the novel predictive value of routine blood test results for SARS-CoV-2 infection was evaluated using ROC analysis. Results: A total of 15,560 patients visited our hospital during the study period. After exclusion and initial screening by interview, 481 patients underwent the LAMP test and routine blood tests. Of these patients, 69 (14.3%) were positive for SARS-CoV-2 and diagnosed with COVID-19 (positive group), and 412 (85.7%) were negative (negative group). The median period between the first onset of symptoms and visit to our hospital was 3.4 and 2.9 days in the negative and positive groups, respectively. Cough (p = 0.014), rhinorrhea (p = 0.039), and taste disorders (p < 0.001) were significantly more common in the positive group, while gastrointestinal symptoms in the negative group (p = 0.043). The white blood cell count (p < 0.001), neutrophil count (p < 0.001), and percentage of neutrophils (p < 0.001) were higher in the negative group. The percentage of monocytes (p < 0.001) and the levels of ferritin (p < 0.001) were higher in the positive group. As per the predictive values for COVID-19 using blood tests, the values for the area under the curve for the neutrophil-to-monocyte ratio (NMR), white blood cell-to-hemoglobin ratio (WHR), and the product of the two (NMWH) were 0.857, 0.837, and 0.887, respectively. Conclusion: Symptoms in early stage COVID-19 patients were similar to those in previous reports. Some blood test results were not consistent with previous reports. NMR, WHR, and NMWH are novel diagnostic scores in early-stage mild-symptom COVID-19 patients in primary care settings.

Impact of Acute Confusional State in Patients With COVID-19 and a Predictive Score.

BACKGROUND: Acute confusional state (ACS) in COVID-19 is shown to be associated with poor clinical outcomes. METHODS: We assessed the impact of ACS - defined as a documented deterioration of mental status from baseline on the alertness and orientation to time, place, and person - on inpatient mortality and the need for intensive care unit (ICU) transfer in inpatient admissions with active COVID-19 infection in a single-center retrospective cohort of inpatient admissions from a designated COVID-19 tertiary care center using an electronic health record system. Furthermore, we developed and validated a neurological history and symptom-based predictive score of developing ACS. RESULTS: Thirty seven out of 245 (15%) patients demonstrated ACS. Nineteen (51%) patients had multifactorial ACS, followed by 11 (30%) patients because of hypoxemia. ACS patients were significantly older (80 [70-85] years vs 50.5 [38-69] years, p < 0.001) and demonstrated more frequent history of dementia (43% vs 9%, p < 0.001) and epilepsy (16% vs 2%, p = 0.001). ACS patients observed significantly higher in-hospital mortality (45.9% vs 1.9%, aOR [adjusted odds ratio]: 15.7, 95% CI = 3.6-68.0, p < 0.001) and need for ICU transfer (64.9% vs 35.1%, aOR: 2.7, 95% CI = 1.2-6.1, p = 0.015). In patients who survived hospitalization, ACS was associated with longer hospital stay (6 [3.5-10.5] days vs 3 [2-7] day, p = 0.012) and numerically longer ICU stay (6 [4-10] days vs 3 [2-6] days, p = 0.078). A score to predict ACS demonstrated 75.68% sensitivity and 81.73% specificity at a cutoff of ≥3. CONCLUSION: A high prevalence of ACS was found in patients with COVID-19 in our study cohort. Patients with ACS demonstrated increased mortality and need for ICU care. An internally validated score to predict ACS demonstrated high sensitivity and specificity in our cohort.

COVID-19 epidemiological, sociological and anthropological investigation: study protocol for a multidisciplinary mixed methods research in Burkina Faso.

BACKGROUND: The world has high hopes of vaccination against COVID-19 to protect the population, boost economies and return to normal life. Vaccination programmes are being rolled out in high income countries, but the pandemic continues to progress in many low-and middle-income countries (LMICs) despite implementation of strict hygiene measures. We aim to present a comprehensive research protocol that will generate epidemiological, sociological and anthropological data about the COVID-19 epidemic in Burkina Faso, a landlocked country in West Africa with scarce resources. METHODS: We will perform a multidisciplinary research using mixed methods in the two main cities in Burkina Faso (Ouagadougou and Bobo-Dioulasso). Data will be collected in the general population and in COVID-19 patients, caregivers and health care professionals in reference care centers: (i) to determine cumulative incidence of SARS-CoV-2 infection in the Burkinabe population using blood samples collected from randomly selected households according to the WHO-recommended protocol; (ii) develop a score to predict severe complications of COVID-19 in persons infected with SARS-CoV-2 using retrospective and prospective data; (iii) perform semi-structured interviews and direct observation on site, to describe and analyze the healthcare pathways and experiences of patients with COVID-19 attending reference care centers, and to identify the perceptions, acceptability and application of preventive strategies among the population. DISCUSSION: This study will generate comprehensive data that will contribute to improving COVID-19 response strategies in Burkina Faso. The lessons learned from the management of this epidemic may serve as examples to the country authorities to better design preventive strategies in the case of future epidemics or pandemics. The protocol was approved by the Ministry for Health (N° 2020-00952/MS/CAB/INSP/CM) and the Health Research Ethics Committee in Burkina Faso (N° 2020-8-140).

A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data.

OBJECTIVES: Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. METHODS: This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. RESULTS: A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count ≤7.7×103mm-3, LDH >273U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77-0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75-0.90). CONCLUSIONS: Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations.