The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19

Torres‐Macho, Juan; Ryan, Pablo; Valencia, Jorge; Pérez-Butragueño, Mario; Jiménez, Eva; Fontán-Vela, Mario; Izquierdo-García, Elsa; Fernández-Jiménez, Inés; Álvaro-Alonso, Elena Alba; Lazaro, Andrea; Alvarado, Marta; Notario, Helena; Resino, Salvador; Vélez-Serrano, Daniel; Meca, Alejandro Álvaro

doi:10.3390/jcm9103066

Cited by 39 publications

(58 citation statements)

References 18 publications

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“…The important characteristics and outcomes of the included articles were collated (Table 1). Of 40 articles, 19 were conducted in China 21,22,27,29,30,[37][38][39][40][41][42][43][44][45][46][47][48][49][50] , one in Kuwait 26 , one in Korea 28 , one in Mexico 51 , one in Japan 52 , two in Spain 53,54 , three in Italy [55][56][57] , and twelve in the USA 33,[58][59][60][61][62][63][64][65][66][67][68] . Most articles were retrospective studies.…”

Section: Search Results and Characteristics Of Studies Includedmentioning

confidence: 99%

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Smoking and risk of negative outcomes among COVID-19 patients: A systematic review and meta-analysis

Umnuaypornlert¹,

Kanchanasurakit²,

Lucero‐Prisno³

et al. 2021

Tob. Induc. Dis.

100

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INTRODUCTION COVID-19 has major effects on the clinical, humanistic and economic outcomes among patients, producing severe symptoms and death. Smoking has been reported as one of the factors that increases severity and mortality rate among COVID-19 patients. However, the effect of smoking on such medical outcomes is still controversial. This study conducted a comprehensive systematic review and meta-analysis (SR/MA) on the association between smoking and negative outcomes among COVID-19 patients. METHODS Electronic databases, including PubMed, EMBASE, Cochrane Library, Science Direct, Google Scholar, were systematically searched from the initiation of the database until 12 December 2020. All relevant studies about smoking and COVID-19 were screened using a set of inclusion and exclusion criteria. The Newcastle–Ottawa Scale was used to assess the methodological quality of eligible articles. Random meta-analyses were conducted to estimate odds ratios (ORs) with 95% confidence interval (CIs). Publication bias was assessed using the funnel plot, Begg’s test and Egger’s test. RESULTS A total of 1248 studies were retrieved and reviewed. A total of 40 studies were finally included for meta-analysis. Both current smoking and former smoking significantly increase the risk of disease severity (OR=1.58; 95% CI: 1.16–2.15, p=0.004; and OR=2.48; 95% CI: 1.64–3.77, p<0.001; respectively) with moderate appearance of heterogeneity. Similarly, current smoking and former smoking also significantly increase the risk of death (OR=1.35; 95% CI: 1.12–1.62, p=0.002; and OR=2.58; 95% CI: 2.15–3.09, p<0.001; respectively) with moderate appearance of heterogeneity. There was no evidence of publication bias, which was tested by the funnel plot, Begg’s test and Egger’s test. CONCLUSIONS Smoking, even current smoking or former smoking, significantly increases the risk of COVID-19 severity and death. Further causational studies on this association and ascertianing the underlying mechanisms of this relation is warranted.

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Section: Search Results and Characteristics Of Studies Includedmentioning

confidence: 99%

“…Nineteen studies defined outcomes as disease severity 22,[27][28][29][30][37][38][39]42,43,[46][47][48][49][50]58,60,62,63 . Seventeen studies defined outcomes as death 21,33,40,44,45,[51][52][53][54][55][56][57]59,[65][66][67][68] . Four studies used both disease severity and death 26,41,61,64 .…”

Section: Search Results and Characteristics Of Studies Includedmentioning

confidence: 99%

Smoking and risk of negative outcomes among COVID-19 patients: A systematic review and meta-analysis

Umnuaypornlert¹,

Kanchanasurakit²,

Lucero‐Prisno³

et al. 2021

Tob. Induc. Dis.

100

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“…Early identification in the ED of patients with COVID-19 disease who may develop critical illness, and therefore require hospital admission is of great importance, as it allows selecting patients who might benefit from an outpatient care model and thereby optimize the use of resources [ 17 – 19 ]. An integrated emergency—medical care at home system as an intermediate approach between outpatient control in primary care and hospital admission, is a new model for the management of cases of mild pneumonia in an overloaded health care system [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

An integrated emergency department/hospital at home model in mild COVID-19 pneumonia: feasibility and outcomes after discharge from the emergency department

et al. 2021

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To evaluate the effectiveness of an integrated emergency department (ED)/hospital at home (HH) medical care model in mild COVID-19 pneumonia and evaluate baseline predictors of major outcomes and potential savings. Retrospective cohort study with patients evaluated for COVID-19 pneumonia in the ED, from March 3 to April 30, 2020. All of them were discharged home and controlled by HH. The main outcomes were ED revisit and the need for deferred hospital admission (protocol failure). Outcome predictors were analyzed by simple logistic regression model (OR; 95% CI). Potential savings of this medical care model were estimated. Of the 377 patients attended in the ED, 109 were identified as having mild pneumonia and were included in the ED/HH medical care model. Median age was 50.0 years, 52.3% were males and 57.8% had Charlson index ≥ 1. The median HH stay was 8 (IQR 3.7–11) days. COVID-19-related ED revisit was 19.2% ( n = 21) within 6 days (IQR 3–12.5) after discharge from ED. Overall protocol failure (deferred hospital admission) was 6.4% ( n = 7), without ICU admission. The ED/HH model provided potential cost savings of 77% compared to traditional stay, due to the costs of home care entails 23% of the expenses generated by a conventional hospital stay. 789 days of hospital stay were avoided by HH, rather than hospital admission. An innovative ED/HH model for selected patients with mild COVID-19 pneumonia is feasible, safe and effective. Less than 6.5% of patients requiring deferred hospital admission and potential savings were generated due to hospitalization. Supplementary Information The online version contains supplementary material available at 10.1007/s11739-021-02661-8.

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“…The PANDEMYC score 5 showed that oxygen saturation was an important discriminatory factor in predicting survival or in-hospital deterioration.…”

Section: Discussionmentioning

confidence: 99%

Survival prediction algorithms for COVID‐19 patients admitted to a UK district general hospital

Fernandez

Obiechina

Koh

et al. 2021

Int. J. Clin. Pract.

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Objective To collect and review data from consecutive patients admitted to Queen’s Hospital, Burton on Trent for treatment of Covid‐19 infection, with the aim of developing a predictive algorithm that can help identify those patients likely to survive. Design Consecutive patient data were collected from all admissions to hospital for treatment of Covid‐19. Data were manually extracted from the electronic patient record for statistical analysis. Results Data, including outcome data (discharged alive/died), were extracted for 487 consecutive patients, admitted for treatment. Overall, patients who died were older, had very significantly lower Oxygen saturation (SpO2) on admission, required a higher inspired Oxygen concentration (IpO2) and higher CRP as evidenced by a Bonferroni‐corrected ( P < 0.0056). Evaluated individually, platelets and lymphocyte count were not statistically significant but when used in a logistic regression to develop a predictive score, platelet count did add predictive value. The 5‐parameter prediction algorithm we developed was: Conclusion Age, IpO2 on admission, CRP, platelets and number of lungs consolidated were effective marker combinations that helped identify patients who would be likely to survive. The AUC under the ROC Plot was 0.8129 (95% confidence interval 0.0.773 ‐ 0.853; P < .001).

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The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19

Cited by 39 publications

References 18 publications

Smoking and risk of negative outcomes among COVID-19 patients: A systematic review and meta-analysis

Smoking and risk of negative outcomes among COVID-19 patients: A systematic review and meta-analysis

An integrated emergency department/hospital at home model in mild COVID-19 pneumonia: feasibility and outcomes after discharge from the emergency department

Survival prediction algorithms for COVID‐19 patients admitted to a UK district general hospital

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