Predicting COVID-19 Using Retrospective Data: Impact of Obesity on Outcomes of Adult Patients With Viral Pneumonia

Raghavan, Sairam; Trelles-Garcia, Valeria Patricia; Trelles-Garcia, Daniela Patricia; Abusalim, Abdul-Rahman I.; Parfieniuk, Agata; Ojemolon, Pius; Azubuike, Clark

doi:10.7759/cureus.10291

Cited by 8 publications

(13 citation statements)

References 16 publications

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“…In this context, respiratory insufficiency with acute respiratory distress syndrome (ARDS) was recently more observed in patients with severe obesity BMI > 35 kg/m 2 [ 21 ]. In addition, the rate of viral pneumonia was higher in obesity as compared to non-obesity [ 22 , 23 ]. Obesity seems to decrease chest-wall elastance, which leads to lower total respiratory compliance with a reduction of expiratory reserve volume and a higher susceptibility for infection [ 24 ].…”

Section: Resultsmentioning

confidence: 99%

Does there exist an obesity paradox in COVID-19? Insights of the international HOPE-COVID-19-registry

Abumayyaleh

Gil

El‐Battrawy

et al. 2021

Obesity Research & Clinical Practice

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Background Obesity has been described as a protective factor in cardiovascular and other diseases being expressed as ‘obesity paradox’. However, the impact of obesity on clinical outcomes including mortality in COVID-19 has been poorly systematically investigated until now. We aimed to compare clinical outcomes among COVID-19 patients divided into three groups according to the body mass index (BMI). Methods We retrospectively collected data up to May 31 st , 2020. 3635 patients were divided into three groups of BMI (<25 kg/m 2 ; n = 1110, 25-30 kg/m 2 ; n = 1464, and >30 kg/m 2 ; n = 1061). Demographic, in-hospital complications, and predictors for mortality, respiratory insufficiency, and sepsis were analyzed. Results The rate of respiratory insufficiency was more recorded in BMI 25-30 kg/m 2 as compared to BMI < 25 kg/m 2 (22.8% vs. 41.8%; p < 0.001), and in BMI > 30 kg/m 2 than BMI < 25 kg/m 2 , respectively (22.8% vs. 35.4%; p < 0.001). Sepsis was more observed in BMI 25-30 kg/m 2 and BMI > 30 kg/m 2 as compared to BMI < 25 kg/m 2 , respectively (25.1% vs. 42.5%; p = 0.02) and (25.1% vs. 32.5%; p = 0.006). The mortality rate was higher in BMI 25-30 kg/m 2 and BMI > 30 kg/m 2 as compared to BMI < 25 kg/m 2 , respectively (27.2% vs. 39.2%; p = 0.31) (27.2% vs. 33.5%; p = 0.004). In the Cox multivariate analysis for mortality, BMI < 25 kg/m 2 and BMI > 30 kg/m 2 did not impact the mortality rate (HR 1.15, 95% CI: 0.889-1.508; p = 0.27) (HR 1.15, 95% CI: 0.893-1.479; p = 0.27). In multivariate logistic regression analyses for respiratory insufficiency and sepsis, BMI < 25 kg/m 2 is determined as an independent predictor for reduction of respiratory insufficiency (OR 0.73, 95% CI: 0.538-1.004; p = 0.05). Conclusions HOPE COVID-19-Registry revealed no evidence of obesity paradox in patients with COVID-19. However, Obesity was associated with a higher rate of respiratory insufficiency and sepsis but was not determined as an independent predictor for a high mortality.

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Section: Resultsmentioning

confidence: 99%

Does there exist an obesity paradox in COVID-19? Insights of the international HOPE-COVID-19-registry

Abumayyaleh

Gil

El‐Battrawy

et al. 2021

Obesity Research & Clinical Practice

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“…A model based on CCSR categories present in the DPG file was mapped from disease groups in the Sundararajan's adaptation of the modified Deyo's CCI [15]. We included smoking history, obesity, malnutrition, and anemia as variables that have impacted mortality in prior HCUP studies [20][21][22]. Mortality is a common outcome of administrative database analysis, which has demonstrated high reliability in coding [23].…”

Section: Outcome Measuresmentioning

confidence: 99%

A Revised Comorbidity Model for Administrative Databases Using Clinical Classifications Software Refined Variables

Edigin¹

2021

Cureus

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Background and objectiveDatabase research has shaped policies, identified trends, and informed healthcare guidelines for numerous disease conditions. However, despite their abundant uses and vast potential, administrative databases have several limitations. Adjusting outcomes for comorbidities is often needed during database analysis as a means of overcoming non-randomization. We sought to obtain a model for comorbidity adjustment based on Clinical Classifications Software Refined (CCSR) variables and compare this with current models. Our aim was to provide a simplified, adaptable, and accurate measure for comorbidities in the Agency for Healthcare Research and Quality (AHRQ) databases, in order to strengthen the validity of outcomes. MethodsThe Nationwide Inpatient Sample (NIS) database for 2018 was the data source. We obtained the mortality rate among all included hospitalizations in the dataset. A model based on CCSR categories was mapped from disease groups in Sundararajan's adaptation of the modified Deyo's Charlson Comorbidity Index (CCI). We employed logistic regression analysis to obtain the final model using CCSR variables as binary variables. We tested the final model on the 10 most common reasons for hospitalizations. ResultsThe model had a higher area under the curve (AUC) compared to the three modalities of the CCI studied in all the categories. Also, the model had a higher AUC compared to the Elixhauser model in 8/10 categories. However, the model did not have a higher AUC compared to a model made from stepwise backward regression analysis of the original 21-variable model. ConclusionWe developed a 15-CCSR-variable model that showed good discrimination for inpatient mortality compared to prior models.

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“…It encompasses both undernutrition or protein-energy malnutrition (PEM) and overnutrition, including obesity. Malnutrition has been reported to increase the risk of infection and impacts the hospital outcomes of various disease conditions [2][3][4][5][6][7][8][9]. The World Health Organization (WHO) estimates that 462 million adults have PEM [10].…”

Section: Introductionmentioning

confidence: 99%

Protein-Energy Malnutrition Increases Mortality in Patients Hospitalized With Bacterial Pneumonia: A Retrospective Nationwide Database Analysis

Gonakoti¹,

Osifo²

2021

Cureus

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Predicting COVID-19 Using Retrospective Data: Impact of Obesity on Outcomes of Adult Patients With Viral Pneumonia

Cited by 8 publications

References 16 publications

Does there exist an obesity paradox in COVID-19? Insights of the international HOPE-COVID-19-registry

Does there exist an obesity paradox in COVID-19? Insights of the international HOPE-COVID-19-registry

A Revised Comorbidity Model for Administrative Databases Using Clinical Classifications Software Refined Variables

Protein-Energy Malnutrition Increases Mortality in Patients Hospitalized With Bacterial Pneumonia: A Retrospective Nationwide Database Analysis

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