Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients

Shanbhag, Vishal; Arjun, N R; Chaudhuri, Souvik; Pandey, Akhilesh

doi:10.5005/jp-journals-10071-23946

Cited by 10 publications

(10 citation statements)

References 28 publications

(36 reference statements)

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“…All the clinical scores we used for prognosis stratification in our cohort (CCI, NEWS2 and CoLACD scores) proved to correlate with ICU admission and in-hospital mortality in patients without ASCVD, while only NEWS2 was correlated with ICU admission and death in patients with a history of ASCVD. Our results are in line with other studies, which reported that an age adjusted CCI is predictive for mortality and invasive mechanical ventilation [ 47 ], and NEWS2 score can identify inpatients deterioration [ 19 ]. The CoLACD score proved to be useful as a mortality predictor in COVID-19 patients [ 18 ].…”

Section: Discussionsupporting

confidence: 93%

Cardiac Biomarkers and Risk Scores in Relation with History of Atherosclerotic Cardiovascular Disease in Patients Admitted with COVID-19: The Experience of an Eastern European Center

Lionte

Șorodoc

Haliga

et al. 2022

JCM

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Background: Data regarding the combined prognostic role of biomarkers and risk scores in relation with the history of atherosclerotic cardiovascular disease (ASCVD) in COVID-19 patients are lacking. Methods: The aim of this observational cohort study was to evaluate the combined prognostic value of N-terminal pro B-type natriuretic peptide (NT-pro BNP), troponin and risk scores in relation with ASCVD history in hospitalized COVID-19 patients. The primary composite endpoint was Intensive Care Unit (ICU) admission and death. Results: From April 2020 to June 2022, 1066 consecutive COVID-19 patients with available biomarkers upon admission were included. During a median follow-up period of 12 days, 176 patients (16.5%) died. Independent predictors of ICU admission and death in patients with ASCVD were NT-pro BNP (HR 2.63; 95% CI, 1.65–4.18) and troponin (HR 1.51; 95% CI, 1.13–2.03). In patients without ASCVD, only NT-pro BNP was predictive for the primary endpoint (HR 1.66; 95% CI, 1.10–2.53). This remained significant after adjustment for other relevant covariates (HR 3.54; 95% CI, 1.98–6.33) in patients with ASCVD and in patients without ASCVD (HR 1.82; 95% CI, 1.02–3.26). Conclusions: These data showed the combined prognostic accuracy of NT-pro BNP and troponin in relation with ASCVD history for ICU admission and death in COVID-19 patients.

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Section: Discussionsupporting

confidence: 93%

Cardiac Biomarkers and Risk Scores in Relation with History of Atherosclerotic Cardiovascular Disease in Patients Admitted with COVID-19: The Experience of an Eastern European Center

Lionte

Șorodoc

Haliga

et al. 2022

JCM

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“…The COVID-19 disease has affected millions of human beings and caused more death rates worldwide. Therefore, Shanbhag et al [ 30 ] have designed the ML-based mortality prediction model, which was applied to the clinical data set. Here, 961 data sets are worn for the preparation process, and 249 data sets are used as the testing procedure.…”

Section: Related Workmentioning

confidence: 99%

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System

Mary¹,

Kumar

Venkatesan³

et al. 2022

Interdiscip Sci Comput Life Sci

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In today's scenario, many scientists and medical researchers have been involved in deep research for discovering the desired medicine to reduce the spread of COVID-19 disease. However, still, it is not the end. Hence, predicting the COVID possibility in an early stage is the most required matter to reduce the death risks. Therefore, many researchers have focused on designing an early prediction mechanism in the basis of deep learning (DL), machine learning (Ml), etc., on detecting the COVID virus and severity in the human body in an earlier stage. However, the complexity of X-ray images has made it difficult to attain the finest prediction accuracy. Hence, the present research work has aimed to develop a novel Vulture Based Adaboost-Feedforward Neural (VbAFN) scheme to forecast the COVID-19 severity early. Here, the chest X-ray images were employed to identify the COVID risk feature in humans. The preprocessing function is done in the initial phase; the error-free data is imported to the classification layer for the feature extraction and segmentation process. This investigation aims to track and segment the affected parts from the trained X-ray images by the vulture fitness and to segment them with a good exactness rate. Subsequently, the designed model has gained a better segmentation accuracy of 99.9% and a lower error rate of 0.0145, which is better than other compared models. Hence, this proposed model in medical applications will offer the finest results. Graphical abstract

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“…For this purpose, various scoring and evaluation systems have been used in many studies. Of these, the Charlson comorbidity index has been reported as an important prognostic marker (Bannay et al, 2016;Christensen et al, 2020;Imam et al, 2020;Shanbhag et al, 2021;Sabaz and Aşar, 2021). It is a simple and easy scoring system that evaluates the total comorbidity burden.…”

Section: Black Sea Journal Of Health Sciencementioning

confidence: 99%

“…In patients diagnosed with COVID-19; studies have been conducted to show the impact of clinical, demographic, laboratory, epidemiological and radiological characteristics on mortality (Li et al, 2020). Many descriptive observational studies have also found that patients with comorbidities are disproportionately affected by COVID-19 and are associated with poorer clinical outcomes (Christensen et al, 2020;Guan et al, 2020;Shanbhag et al, 2021). Therefore, comprehensive assessment of comorbidities for risk stratification of hospitalized patients with COVID-19 and accurate prediction of prognosis are important for clinical management and outcomes.…”

Section: Introductionmentioning

confidence: 99%

Effect of APACHE-II and the Age-Adjusted CHARLSON Comorbidity Index at Predicting Mortality in Patients with COVID-19

Eminoğlu

Özgünay

2022

Black Sea Journal of Health Science

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The COVID-19 pandemic disproportionately affects patients with comorbidities. Comprehensive comorbidity assessment is important in establishing the risk stratification of patients with COVID-19 after hospital admission. In this study, our aim is to investigate the effectiveness of Acute Physiology and Chronic Health Assessment II (APACHE-II) and Age Adjusted Charlson Comorbidity Index (ACCI) in predicting mortality in COVID-19 patients admitted to the Intensive Care Unit (ICU). Patients aged >18 years who were admitted to the intensive care unit with the diagnosis of COVID-19 pneumonia in the Health Sciences University Bursa Yüksek İhtisas Training and Training Hospital between July 2021 and September 2021 were included in the study. The medical records of the patients were then scanned into the hospital automation system. Demographics, comorbidities, clinical features, laboratory parameters, APACHE-II score, treatments, and outcomes were recorded in a standard form. ACCI score was calculated from the data and recorded. The 276 patients analyzed were divided into two groups as surviving (n=129) and developing mortality (n=147). The mortality rate was 58.93%, mostly male (58%), median age 65 years, ACCI score 1 (IQR.3) and APACHE-II score 2 (IQR.8). There was no difference between the groups in terms of age, gender distribution and APACHI-II score (P= 0.519, P= 0.927, P= 0.364, respectively). The groups did not differ in terms of comorbidity except for chronic renal failure (CRF), and CRF was significantly higher in patients who developed mortality (P= 0.037). The ACCI score was found to be higher in patients who developed mortality (P= 0.034). Death risk; Those with an ACCI score of >2 were 2.26 times higher than those with an ACCI score of ≤2 (P= 0.021). The APACHI-II score did not differ between the groups in terms of mortality (P= 0.380). As a result, high ACCI score was found to be effective in predicting mortality. It could potentially be used to identify at-risk patients infected with COVID-19 and to predict their clinical status.

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Utility of Age-adjusted Charlson Comorbidity Index as a Predictor of Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Survival in COVID-19 Patients

Cited by 10 publications

References 28 publications

Cardiac Biomarkers and Risk Scores in Relation with History of Atherosclerotic Cardiovascular Disease in Patients Admitted with COVID-19: The Experience of an Eastern European Center

Cardiac Biomarkers and Risk Scores in Relation with History of Atherosclerotic Cardiovascular Disease in Patients Admitted with COVID-19: The Experience of an Eastern European Center

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System

Effect of APACHE-II and the Age-Adjusted CHARLSON Comorbidity Index at Predicting Mortality in Patients with COVID-19

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