key: cord-0957616-2zx8bm0g
authors: Varol, Yelda; Hakoglu, Burcin; Kadri Cirak, Ali; Polat, Gulru; Komurcuoglu, Berna; Akkol, Berrin; Atasoy, Cagri; Bayramic, Eda; Balci, Gunseli; Ataman, Sena; Ermin, Sinem; Yalniz, Enver
title: The impact of charlson comorbidity index on mortality from SARS‐CoV‐2 virus infection and A novel COVID‐19 mortality index: CoLACD
date: 2020-12-07
journal: Int J Clin Pract
DOI: 10.1111/ijcp.13858
sha: 1de39af6302b5123a3b3f1414071e2d867ea6367
doc_id: 957616
cord_uid: 2zx8bm0g

OBJECTIVE: The aim of this study is to find out the potential risk factors including charlson comorbidity index (CCI) score associated with death in COVID‐19 patients hospitalised because of pneumonia and try to find a novel COVID‐19 mortality score for daily use. METHODS: All patients diagnosed as confirmed or probable COVID‐19 pneumonia whom hospitalised in our Chest Diseases Education and Research Hospital between March 11, 2020 and May 15, 2020 were enrolled. The optimal cut‐off values, sensitivity and specificity values and odds ratios to be used in mortality prediction of the novel scoring system created from these parameters were calculated by ROC analysis according to the area under the curve and Youden index. RESULTS: Over 383 patients (n: 33 deceased, n: 350 survivors) univariate and multivariate regression analysis showed that CCI and lymphocyte ratio were prognostic factors for COVID‐19‐related mortality. Using this analysis, a novel scoring model CoLACD (CoVID‐19 Lymphocyte ratio, Age, CCI score, Dyspnoea) was established. The cut‐off value of this scoring system, which determines the mortality risk in patients, was 2.5 points with 82% sensitivity and 73% specificity (AUC = 0.802, 95% CI 0.777‐0.886, P < .001). The risk of mortality was 11.8 times higher in patients with a CoLACD mortality score higher than 2.5 points than patients with a score lower than 2.5 (OR = 11.8 95% CI 4.7‐29.3 P < .001). CONCLUSION: This study showed that by using the CoLACD mortality score, clinicians may achieve a prediction of mortality in COVID‐19 patients hospitalised for pneumonia.

Analyses were performed with SPSS software v 25.5 (IBM, NY, USA).

To determine whether continuous data are normally distributed, Shapiro-Wilk and Kolmogorov-Smirnov normality tests were used.

Mann-Whitney U-test was used to compare parameters that were not normally distributed and χ 2 and Fisher's exact test were used for comparison of categorical data. Results were given as median (min-max), number and percentage (%). P value <.05 was considered statistically significant. The predictive values of the parameters for mortality were calculated with univariate and multivariate logistic regression analyses. The optimal cut-off values, sensitivity and specificity values and odds ratios to be used in mortality prediction of the scoring system created from these parameters were calculated by ROC analysis according to the area under the curve and Youden index. The results were presented with 95% confidence intervals. Figure 1 ). There was a male predominance in the cohort (57.2%). The median hospitalisation time was 6 (1-34) days in the cohort. Demographic data of the whole cohort, the characteristics of the deceased and survivors are showed in Table 1 . The median CCI score was 1 (0-11) in the cohort, the median score of the deceased groups was significantly higher compared with survivors [5 (0-11) to 1 (0-10)], (P < .001) (Table 1 ). If we look at the distribution of the age groups between deceased and survivors, there were older patients What's known?

• There are few scoring systems for predicting mortality in COVID-19 infected patients which were clinically impractical.

• We created a novel mortality model called CoLACD with four prognostic parameters only; CoVID-19 lymphocyte ratio, age, charlson comorbidity index score, dyspnoea.

• This study showed that by using the CoLACD mortality score, clinicians may achieve a prediction of mortality in COVID-19 patients hospitalised for pneumonia.

in the deceased group (P = .05). When we compared the 16 different symptoms on admission, only dyspnoea was significantly different between two groups (P < .001) ( Table 1) . Of the three physical examination findings on admission none of them were different between groups (Table 1) .

54.5% of the patients were RT-PCR confirmed COVID-19 pneumonia, being PCR confirmed was not different between the deceased and the survivor groups ( Table 2) . Number of leucocytes, number of lymphocytes and lymphocytes % were statistically significantly different between groups (Table 2 ). Other laboratory findings which are different between groups are mentioned in Table 2 . Of the whole cohort 68.9% of the patients had an abnormal finding on their Chest X-Ray, however, 95.6% had a High-Resolution Computerised Tomography (HRCT) finding.

When the predictive power of risk factors determined for mortality was evaluated with univariate analysis, it was found that patients with dyspnoea had a 7.3 times higher mortality risk (OR = 7.3 95% CI 3.1-17.3). Likewise, mortality risk of patients over 65 years of age was higher than other age groups ( Table 3 ). The cut-off value of lympho-cyte%, which determines the mortality risk, was 17.65% with 88% sensitivity and 63% specificity (AUC = 0.802, 95% CI 0.726-0.878, P < .001), while the cut-off value of the CCI score for mortality was determined as 2.5 with 78% sensitivity and 74% specificity (AUC 0.853, 95% CI 0.787-0.920, P < .001). Patients with lymphocyte% value below 17.65 had a 9.7 times higher mortality risk compared with patients with lymphocyte% above this percentage (OR = 9.7; 95%CI 3.7-25.8; P < .001). And the mortality risk of patients with a CCI score above 2.5 was 10.7 times higher than those with a CCI score of less than this value (OR = 10.7; 95%CI 4.5-25.6; P < .001) ( Table 3 ).

To create a simple score and facilitate clinical use, a novel scoring model was established CoLACD (CoVID-19 Lymphocyte ratio, Age, CCI score, Dyspnoea) mortality score which scores from 0 to 5 points (Table 4 ). The cut-off value of this scoring system, which determines the mortality risk in patients, was 2.5 points with 82% sensitivity and 73% specificity (AUC = 0.802, 95% CI 0.777-0.886, P < .001) ( Figure 2 ). The risk of mortality was 11.8 times higher in patients with a CoLACD mortality score higher than 2.5 points than patients with a score lower than 2.5 (OR = 11.8; 95% CI 4.7-29.3; P < .001).

When the predictive power of risk factors included in the CoLACD scoring system for mortality risk was evaluated by multivariate logistic regression analysis, CCI score and Lymphocyte% value was found to be important risk factors for mortality. (OR = 1.5; 95% CI 1.2-1.8; P < .001 and OR = 0.9; 95% CI 0.8-1.0; P = .002, respectively) ( Table 5) .

During the COVID-19 pandemic, with using a simple scoring system during the first admission, for the prediction of patients who will have a severe course, can be life-saving. Therefore, with this study a novel scoring model CoLACD, is developed for prediction of mortality at admission. This study showed that the risk of mortality was 11.8 times higher in patients with a CoLACD mortality score higher than 2.5 points than patients with a score lower than 2.5 points.

In several studies it has been shown that comorbidities play an important role in COVID-19 infected patients. Charlson comorbidity index which is a component of our novel score is valid and a reliable tool for predicting mortality. 5 However, its impact on COVID pneumonia is not studied properly. With this study, we showed that having a high comorbidity score increases the like hood of mortality 10.7 times. A cut off value of 2.5 (which means >3 points)

is an independent risk factor for mortality prediction. Our second component age is the basic factor of severity, which has become a consensus in the recent publications in COVID-19 and also in severe acute respiratory syndrome (SARS) infection. 9 some laboratory parameters such as LDH, D-Dimer, serum ferritin was absent in some patients. However, all components of CoLACD score were complete in the files and the hospital database system. Therefore, we tried to build a mortality score on basic laboratory parameters which is in routine use in first line health settings.

This study showed that a novel model including four parameters:

CCI score, Lymphocyte ratio, age and dyspnoea achieved a prediction of mortality in COVID-19 patients hospitalised for pneumonia.

If validated with prospective studies, CoLACD score can be used for effective utilisation of medical resources in the COVID-19 pandemic for decreasing mortality.

We would like to thank COVID Study Group of University of Health 

We do not have any financial or non-financial potential conflicts of interest. 

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Yelda Varol https://orcid.org/0000-0003-4604-7173

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