Purpose

Time and Risk Assessment are two valuable factors for the treatment and management of suspicious COVID-19 cases due to the high incidence of “death rate”. Before the arrival of RT (Reverse Transcript) PCR (Polymerase Chain Reaction) Test for confirmation of COVID-19, underlying risk of the suspicious patient can be assessed from the Lab and clinical data for the best possible treatment. Thus valuable lives could be saved by giving Interleukin blocker or glucocorticoids earlier and taking proper care of oxygen saturation.

Introduction

The epidemiology of agent, host, environment with bio-clinical characteristics of patients with COVID-19 has been described below:

COVID-19 virus is labeled as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which was previously, referred to as 2019-nCoV. The virus is a positive-strand RNA virus. Its structural proteins include spike glycoprotein (S), envelope small protein (E), membrane protein (M), and nucleocapsid phosphoprotein (N) (Fig. 1) (Fehr and Perlman 2015; Oostra et al. 2020).

Fig. 1
figure 1

SARS CoV-2

Full size image

Entree of the SARS CoV2 and subsequent change in the host environment are outlined: Researchers noticed that goblet and ciliated cells in the nose and nasopharynx have high levels of the receptor protein—ACE2 (Angiotensin-Converting Enzyme 2) and the TMPRSS2 protease (TransMembrane Protease, Serine 2) which can activate SARS-CoV-2 entry into the host cells (Li et al. 2003). The cornea of the eye, the lacrimal duct and the lining of the intestine may be alternative routes of infection (Oostra et al. 2020). After binding with the receptor, the virus enters into the host cell cytosol. The S-protein-receptor interaction is the primary determinant for the virus to infect a host cell and also directs the tissue tropism of the virus (Fig. 1).

An outline of replication of SARS-CoV-2 is described in Fig. 2: the next step in the SARS CoV-2 or COVID-19 lifecycle is the translation of the replicase gene from the virion genomic RNA. The replicase gene encodes ORFS (Open Reading Frames) (Stertz et al. 2007). SARS-CoV-2 encodes viral proteins up to 14 ORFS. The transcription works through the replication-transcription complex (RCT) organized in double-membrane vesicles and via the synthesis of subgenomic RNAs (sgRNAs) sequences. Following this assembly, budding occurs through rough ER (endoplasmic reticulum) as ERGIC (ER-golgi intermediate compartment) and in the golgi bodies virus replicates and is contained in the vesicle. Finally, by a process of exocytosis release of the virus may occur as early as 3 h after infection. Thus like photocopier machine host cells produce hundreds and thousands of virus for the next kill (Li et al. 2003; Stertz et al. 2007).

Fig. 2
figure 2

Entrée of SARS CoV2 and host reaction

Full size image

Pathogenesis after multiplication is described briefly in Figs. 3 and 4: Next target of attack of the virus is the hemoglobin. Hemoglobin (Hb) of the red blood corpuscle has two components namely Haem and Globin. Haem contains a porphyrin ring. The nitrogen molecules at the center of the porphyn ring are capable of “hosting” an iron molecule (Streitweiser and Heathcock 1981). Fe2+ attached with the porphyrin ring of haem is responsible for transport of the oxygen (O2) and CO2 in the tissues as Fe-O2 or Fe-CO2 complex (Fig. 3).

Fig. 3
figure 3

Hemoglobin (courtesy A Ganguly MRES,MSc)

Full size image
Fig. 4
figure 4

Pathogenesis

Full size image

Viruses that bind to porphyrins (which have two photon of energy) could gain energy through the light-induced method and static virus has become highly dynamic (2020). SARS CoV2 attacks the 1-beta of chain of hemoglobin hijacking this porphyrin ring and dislodging toxic Fe2+ ion. This Fe2+ ion fails to carry O2 and CO2. Due to the lack of O2/CO2 exchange in the lung, CO2 damages lung tissue along with the toxic ion of Fe2+ (Fig. 4) (Liu and Li 2020).

IL-6 (Interleukin 6), a multi-functional cytokine (a protein) produced by a range of cells, plays a central role in host defense mechanisms and is involved in the induction of B (lymphocyte) cell differentiation. There will be an accumulation of toxic iron ions, which will increase cytokine and in turn produces hyper-inflammation in the tissue with an increase in the C-reactive protein and albumin (Zandman-Goddard and Shoenfeld 2008; Mehta et al. 2020; Kernan and Carcillo 2017). To combat the increased Fe2+ concentration, the release of high serum Ferritin level as a part of immunity system causes a chain reaction with an appreciable change in the values such as high serum LDH (lactate dehydrogenase), low lymphocyte and platelet count and increased white blood cell and neutrophils (Mehta et al. 2020; Kernan and Carcillo 2017). Cytokine regulates ferritin synthesis by initiating the Ferritin regulatory gene, tissue necrosis, factor-alpha and interleukin-1 alpha at different levels (transcriptional, post-transcriptional, translational) during inflammation. Ferritin is a blood protein that combines with the Fe2+ ion. During active infection, increased Ferritin level signifies a vital host defense mechanism that attempts to remove viral growth and defends immune cell function (Li et al. 2003). Hyper inflammation due to cytokine produces “Cytokine Storm Syndrome” (Zandman-Goddard and Shoenfeld 2008; Mehta et al. 2020). A cytokine profile is associated with severity of COVID-19 disease, characterized by increased interleukin (IL)-2, IL-6 and granulocyte-colony, increased serum Ferritin, LDH, C Reactive protein, the rise of d-dimer and INR (Li et al. 2003; Stertz et al. 2007). Figure 4 illustrates the schematic outline of the pathogenesis.

Materials and Methods

Laboratory and clinical data of 91 Indian patients (49 patients from hospitals of Kolkata, West Bengal), 21 patients from Jaipur and 21 patients from Delhi and suburbs) were analyzed (Bhandari et al. 2020; Gupta et al. 2020; Rukmini 2020). By the help of Electronic Search engine (Google Scholar) and using NYP-CUIMC clinical data warehouse of Columbia University Irving Medical Center (CUIMC) full text of all the articles were accessed. According to these search criteria our meta analysis included only patients with or without the severe disease who have Lab data (Columbia University Irving Medical Center (CUIMC) 2020; Mehra et al. 2020; Geleris et al. 2020). Hematological and laboratory data of more than 10,000 COVID-19 patients, provided by several American, Italian and French researchers have also been studied (Columbia University Irving Medical Center (CUIMC) 2020; Mehra et al. 2020; Geleris et al. 2020).

Twenty-one COVID-19 positive patients who were admitted in S.M.S Hospital, Jaipur, Rajasthan have been reported by Bhandari et al. (2020). Most of the patients were foreigners from Italy, Spain, USA and UK. Study of twenty-one patients reported by Gupta et al. was included as a study cohort over a period of January–March 2020 in Safdarjanj Hospital, New Delhi. Thirteen (62%) patients had recent travel history outside India in the previous 30 days, two-thirds of whom had travelled to Italy (Gupta et al. 2020). Our sample study has also included the data provided by Mehra et al. of Brigham and Women’s Hospital of Harvard Medical School, Boston, USA (Mehra et al. 2020). Out of 8910 patients of COVID-19, 1536 patients (17.2%) hail from North America, 5755 (64.6%) patients from Europe, and 1619 (18.2%) from Asia (Columbia University Irving Medical Center (CUIMC) 2020). Geleris et al. had reported of 1446 consecutive COVID-19 patients of Newyork, USA (Mehra et al. 2020) who had a trial with Hydroxy Chloroquin. Retrospective cohort study of COVID-19 by Giulio Cavalli of 29 patients of the San Raffaele Hospital, a designated COVID-19, tertiary health-care centre in Milan, Italy was also included (Geleris et al. 2020).

Llitjos et al. (2020) noticed a high rate of thromboembolic events in COVID-19 patients treated with therapeutic anticoagulation, with 56% of Venous thrombo-embolism and 6 pulmonary embolisms out of 26 consecutive patients with severe COVID-19 admitted in two French intensive care units (ICU) from March 19th to April 11th of 2020. Gupta et al. analysed 2245 patients having severe acute respiratory illness (SARI) from 52 districts of 20 states of India out of which there were 104 COVID-19 patients (Gupta et al. 2020). Bhatnagar et al. (2020) studied and reported laboratory status of three patients in Kerala, India and recommended treatment protocol with Lopinivir. 61 patients of Brazil were reported by Nascimento et al. (2020). The data of these patients were compared with the analysis of pooled data of Fei Zhou of China and Giuseppe Lippi and Mario Plebani of Italy who pointed out the blood hematological and laboratory data in the early March of 2020 (Zhou et al. 2020; Lippi and Plebani 2020; Bataille et al. 2020). Finally, results were also compared with the various parameters reported in the WHO (World Health Organisation) web site by several Chinese workers after the first outbreak of the disease in China (Table 1) (World Health Organization 2020; Bataille et al. 2020; Centers for Disease Control andPrevention 2020; World Health Organization 2020; Zhou et al. 2020; Gorbalenya et al. 2020). From the various studies following parameters or Risk factors were identified which had an influence on the clinical course (critical or non critical) of COVID-19 patients (Table 1):

Table 1 Factors For PCA
Full size table

Physical factors—gender, age, days of illness.

Comorbidities—hypertension, diabetes, coronary heart disease, chronic obstructive lung disease, carcinoma/malignant disease, chronic kidney disease.

Clinical finding—temperature, cough, dyspnea.

Hematological and biochemical data: absolute total count of leucocyte and lymphocyte, platelets, neutrophil to lymphocyte ratio, INR. Serum ferritin, serum LDH, d-dimer, Interleukin 6, Cardiac Troponin, Imaging Features (Radiology—X-ray and HRCT of thorax).

Statistical evaluation: number of involvements (percentage), respective p and χ2 (Chi-Square Test) values of various factors were determined by a special Statistical Application tool “XL STAT” (trial version) of ADDINSOFT (France). To reduce the multi-dimensional facets of the variable/factors Principal Component Analysis (PCA) was applied which shows the relationship between factors (such as critical, noncritical, p values) and observations (Fig. 5).

Fig. 5
figure 5

PCA—Eigenvalue and Eigenvectors

Full size image

Further binary logical regression of the variables (Table 2) was done by XL STAT assigning COVID-19 Positive as “1” and negative case as “0”. Summary statistics of the Chi-square test of association evaluates relationships between categorical variables of rows and columns along with goodness of fit statistics (Fig. 6a, b). Artificial Intelligence (AI) with binary logical regression were applied with Y as quantitative variable and X as quantitative and qualitative variables (Fig. 7).

Table 2 Factors for logistic regression
Full size table
Fig. 6
figure 6

a Goodness of fit Statistics and b Type II analysis (Variable COVID-19)

Full size image
Fig. 7
figure 7

Summary statistics of logistic regression

Full size image

Y/Quantitative Variable: Binary—“0” as COVID-19 negative and “1” as positive—99 rows (observations) and 1 column.

X/Quantitative variables: 99 rows and 10 columns of various variables (factors) of hematological and lab data such as leucocyte, lymphocyte, platelet cell count, NLR (Neutrophil to lymphocyte ratio), INR, serum Ferritin, LDH, d-dimer and Interleukin 6 levels.

X/Qualitative variables: 99 rows and 2 columns of Risk (as low, potential risk, high risk and severely ill condition) and score of the risk was scaled (or graded) as 0–4 (Table 2).

Test of independence between the rows and the columns (Wilks'G2), Wald Chi-square, p value, alpha were noted from the statistical analysis along with Test interpretation indicating the Hypothesis (H0) and alternative Hypothesis (Ha) of the relationship between column and rows Test of the null hypothesis along with Goodness of fit statistics of Variables, Comparison of the categories of the qualitative variables (variable COVID-19) was also noted (Fig. 6b).

Preparation of application tool: scoring system after analyzing the interpretation of Logical regression of the variables (Table 2) positive predictive values of different variables or factors were assigned as scoring point system from 0 to 4 in the Excel data sheet (Fig. 8a, b), depending upon the values of the hematological and laboratory data such as leucocyte, lymphocyte cell count, platelet count, neutrophil lymphocyte ratio (NLR), serum ferritin, serum LDH, d-dimer, Interleukin 6 along with findings of Chest X-Ray and CT scan indicating the severity of the disease (Columbia University Irving Medical Center (CUIMC) 2020). Other clinical data (such as age, gender, temperature, respiratory distress) and associated Co-morbidities such as diabetes, heart, lung and kidney disease were considered for indirect added Risk assessment (Fig. 8b). If all these parameters are considered from their Dropdown Value system an accrued value is displayed in the Score Board (Fig. 8a). A high score (>30) would denote severe or critically ill patient. Less Critical patients or high risk are marked as 25–30 and potential COVID-19 patients (score of 21 to 24.) General or minimal risk patients would indicate a score of less than 20 points.

Fig. 8
figure 8

a High risk application. b Factors for high risk assessment

Full size image

Result and discussion

The median Indian deceased patients were older, at 57 yrs, compared to 76 yrs of Italian, Americans, British and Spanish patients (Rukmini 2020). Mandeep et al. (2020) analysed that out of total 8910 patients (80.90%) were below 60 years of age and cases admitted in various hospitals of Boston, 5.8% died in the hospital and 8395 patients survived. Most of the patients in our study cohort and reported by other workers were found to be male (61.40%.) in India (Bhandari et al. 2020). In India, USA, Italy, Spain, France, UK, China showed elevated serum ferritin (1295·6 ng/ml in non-survivors/ critical cases) vs 615·0 ng/ml in survivors /less critical (p < 0.001) and InterLeukin-6 > 146 pg/ml (p < 0.0001), Serum LDH level 780–2100, d-dimer level rises as high as 1600 ng/ml indicating that fatality may be due to hyper-inflammation initiated by the SARS CoV-2 virus (Fig. 9a, b) (Gupta et al. 2020). Rise of Neutrophil, Leucocyte level occur compared to diminished counts of most patients of china with a significant increase in the values of serum ferritin, erythrocyte sedimentation rate points out the day by day increase or decrease of the hematological or biochemical data level (Bhandari et al. 2020; Gupta et al. 2020; Mehra et al. 2020; Geleris et al. 2020).

Fig. 9
figure 9

a Biochemical and laboratory data. Serum Ferritin, serum LDH b Lymphocyte cell count, Interleikin 6, d-Dimer

Full size image

Lung Damage—X-Ray Chest or CT Scan Thorax (Fig. 10)—X-Ray is 63% sensitive compared to HRCT (High-Resolution CT) being 82% sensitive. Nodularities and Reticular changes to appear first. Then ground Glass Haze or Crazy Paving. Then Unilateral or Bilateral Consolidation appear (Mehra et al. 2020; Geleris et al. 2020).

Fig. 10
figure 10

CT scan of thorax and chest X-ray

Full size image

Other associated factors such as fever, respiratory distress and cough and Co-morbidities or associated disease such as hypertension, diabetes, coronary arterial disease, and chronic obstructive lung disease aggravate further seriousness of the disease (Bhandari et al. 2020; Gupta et al. 2020; Mehra et al. 2020).

High-Risk Application tool was applied to hematological and biochemical data of 18 patients of Kolkata who were also tested by real-time RT PCR (Table 3). Three cases showed High-Risk status who were also found COVID-19 positive (Fig. 8a). Other cases showed relatively low score and were tested COVID-19 negative (Fig. 8b). It is seen that the prediction of assessment of Risk of the negative and positive cases were 100% (Fig. 11).

Table 3 Data of patients: italics highlighted rows are COVID-19 patients’ data
Full size table
Fig. 11
figure 11

Prediction of success of interpretation of rate of application tool

Full size image

Conclusion

A pandemic caused by COVID-19 has created a devastating condition throughout the world claiming more than 3,70,000 lives so far in about 6 months. Risk assessment Score can predict confidently about the severity of the disease of suspicious COVID-19 infected patients warranting early attention and management before the availability of the RT PCR test for confirmation. Proper treatment and management could prevent early morbidity and death.