Predictors of severity and mortality among patients hospitalized with COVID-19 in Rhode Island

Pandita, Aakriti; Gillani, Fizza S.; Shi, Yiyun; Hardesty, Anna; McCarthy, Meghan; Aridi, Jad; Farmakiotis, Dimitrios; Chiang, Silvia S.; Beckwith, Curt G.

doi:10.1371/journal.pone.0252411

Cited by 19 publications

(23 citation statements)

References 43 publications

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“…Disease severity on presentation was defined based on initial signs that have been shown to predict subsequent mortality. These included a) initial respiratory symptoms (defined as presence of shortness of breath, cough, chest pain or congestion), b) tachypnea (initial respiratory rate >24/min), c) initial hypoxia (<90% oxygen saturation), or d) initial hypotension (first systolic blood pressure of <90 mm Hg) [31,32]. Time from initial symptom onset has also been shown to be a predictor of disease severity [2].…”

Section: Variable Definitionsmentioning

confidence: 99%

Association of renalase with clinical outcomes in hospitalized patients with COVID-19

et al. 2022

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Renalase is a secreted flavoprotein with anti-inflammatory and pro-cell survival properties. COVID-19 is associated with disordered inflammation and apoptosis. We hypothesized that blood renalase levels would correspond to severe COVID-19 and survival. In this retrospective cohort study, clinicopathologic data and blood samples were collected from hospitalized COVID-19 subjects (March—June 2020) at a single institution tertiary hospital. Plasma renalase and cytokine levels were measured and clinical data abstracted from health records. Of 3,450 COVID-19 patients, 458 patients were enrolled. Patients were excluded if <18 years, or opted out of research. The primary composite outcome was intubation or death within 180 days. Secondary outcomes included mortality alone, intensive care unit admission, use of vasopressors, and CPR. Enrolled patients had mean age 64 years (SD±17), were 53% males, and 48% non-whites. Mean renalase levels was 14,108·4 ng/ml (SD±8,137 ng/ml). Compared to patients with high renalase, those with low renalase (< 8,922 ng/ml) were more likely to present with hypoxia, increased ICU admission (54% vs. 33%, p < 0.001), and cardiopulmonary resuscitation (10% vs. 4%, p = 0·023). In Cox proportional hazard model, every 1000 ng/ml increase in renalase decreased the risk of death or intubation by 5% (HR 0·95; 95% CI 0·91–0·98) and increased survival alone by 6% (HR 0·95; CI 0·90–0·98), after adjusting for socio-demographics, initial disease severity, comorbidities and inflammation. Patients with high renalase-low IL-6 levels had the best survival compared to other groups (p = 0·04). Renalase was independently associated with reduced intubation and mortality in hospitalized COVID-19 patients. Future studies should assess the pathophysiological relevance of renalase in COVID-19 disease.

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Section: Variable Definitionsmentioning

confidence: 99%

Association of renalase with clinical outcomes in hospitalized patients with COVID-19

et al. 2022

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“…The SARS-CoV-2 pandemic began in Wuhan, China, in December 2019, and continues to pose a challenge to the healthcare system worldwide [ 1 ]. According to the WHO’s most recent update, there have been over 255 million cases worldwide, with over 5 million deaths reported.…”

Section: Introductionmentioning

confidence: 99%

Predictors of severity and mortality in COVID-19 patients

et al. 2022

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Background Due to limited capacity, health care systems worldwide have been put in challenging situations since the emergence of COVID-19. To prioritize patients who need hospital admission, a better understanding of the clinical predictors of disease severity is required. In the current study, we investigated the predictors of mortality and severity of illness in COVID-19 from a single center in Cairo, Egypt. Methods This retrospective cohort study included 175 patients hospitalized with COVID-19 pneumonia and had positive real-time polymerase chain reaction (RT-PCR) results for SARS-CoV-2 from 1 May 2020 to 1 December 2020. Severe COVID-19 was defined as requiring high-flow oxygen (flow rate of more than 8 L/min or use of high flow oxygen cannula), noninvasive ventilation, or invasive mechanical ventilation at any time point during the hospitalization. We used univariate and multivariate regression analyses to examine the differences in patient demographics and clinical and laboratory data collected during the first 24 h of hospitalization related to severe disease or death in all 175 patients. Results Sixty-seven (38.3%) of the study subjects had a severe or critical disease. Elevated d-dimer, leukocytosis, and elevated CRP were found to be independent predictors of severe disease. In-hospital mortality occurred in 34 (19.4%) of the cases. Elevated TLC, urea, the use of invasive mechanical ventilation, and the presence of respiratory bacterial co-infection were found to be independently associated with mortality. Conclusion Clinical and laboratory data of COVID-19 patients at their hospital admission may aid clinicians in the early identification and triage of high-risk patients.

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“…www.nature.com/scientificreports/ Numerous prognostic models have been developed for COVID-19, using both traditional statistics and ML. Blood parameters, like ferritin 22 , troponin, and myoglobin [23][24][25][26] , demographic parameters, including Charlson comorbidity index score 27 , age and gender 28,29 , chest CT images 30 , routine chest X-rays 31 , age-related dementia 32 , cardiac auscultation 33 and other factors are suggested to predict severity or death in patients with COVID-19. However, all these studies were aimed at a single-time assessment of patients' prognosis, close to admission.…”

Section: Discussionmentioning

confidence: 99%

Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19

Kogan

Robinson

Itelman

et al. 2022

Sci Rep

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Our study was aimed at developing and validating a new approach, embodied in a machine learning-based model, for sequentially monitoring hospitalized COVID-19 patients and directing professional attention to patients whose deterioration is imminent. Model development employed real-world patient data (598 prediction events for 210 patients), internal validation (315 prediction events for 97 patients), and external validation (1373 prediction events for 307 patients). Results show significant divergence in longitudinal values of eight routinely collected blood parameters appearing several days before deterioration. Our model uses these signals to predict the personal likelihood of transition from non-severe to severe status within well-specified short time windows. Internal validation of the model's prediction accuracy showed ROC AUC of 0.8 and 0.79 for prediction scopes of 48 or 96 h, respectively; external validation showed ROC AUC of 0.7 and 0.73 for the same prediction scopes. Results indicate the feasibility of predicting the forthcoming deterioration of non-severe COVID-19 patients by eight routinely collected blood parameters, including neutrophil, lymphocyte, monocyte, and platelets counts, neutrophil-to-lymphocyte ratio, CRP, LDH, and D-dimer. A prospective clinical study and an impact assessment will allow implementation of this model in the clinic to improve care, streamline resources and ease hospital burden by timely focusing the medical attention on potentially deteriorating patients.

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Predictors of severity and mortality among patients hospitalized with COVID-19 in Rhode Island

Cited by 19 publications

References 43 publications

Association of renalase with clinical outcomes in hospitalized patients with COVID-19

Association of renalase with clinical outcomes in hospitalized patients with COVID-19

Predictors of severity and mortality in COVID-19 patients

Developing and validating a machine learning prognostic model for alerting to imminent deterioration of hospitalized patients with COVID-19

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