Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText.
Kick-your-searchterm to multiple Engines kick-your-query now !>
A dictionary by aggregated review articles of nephrology, medicine and the life sciences
Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

suck abstract from ncbi


10.18632/aging.103980

http://scihub22266oqcxt.onion/10.18632/aging.103980
suck pdf from google scholar
33170150!7695402!33170150
unlimited free pdf from europmc33170150    free
PDF from PMC    free
html from PMC    free

suck abstract from ncbi


Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534

Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
pmid33170150      Aging+(Albany+NY) 2020 ; 12 (21): 20982-20996
Nephropedia Template TP

gab.com Text

Twit Text FOAVip

Twit Text #

English Wikipedia


  • A predictive model for the severity of COVID-19 in elderly patients #MMPMID33170150
  • Zeng F; Deng G; Cui Y; Zhang Y; Dai M; Chen L; Han D; Li W; Guo K; Chen X; Shen M; Pan P
  • Aging (Albany NY) 2020[Nov]; 12 (21): 20982-20996 PMID33170150show ga
  • Elderly patients with coronavirus disease 2019 (COVID-19) are more likely to develop severe or critical pneumonia, with a high fatality rate. To date, there is no model to predict the severity of COVID-19 in elderly patients. In this study, patients who maintained a non-severe condition and patients who progressed to severe or critical COVID-19 during hospitalization were assigned to the non-severe and severe groups, respectively. Based on the admission data of these two groups in the training cohort, albumin (odds ratio [OR] = 0.871, 95% confidence interval [CI]: 0.809 - 0.937, P < 0.001), d-dimer (OR = 1.289, 95% CI: 1.042 - 1.594, P = 0.019) and onset to hospitalization time (OR = 0.935, 95% CI: 0.895 - 0.977, P = 0.003) were identified as significant predictors for the severity of COVID-19 in elderly patients. By combining these predictors, an effective risk nomogram was established for accurate individualized assessment of the severity of COVID-19 in elderly patients. The concordance index of the nomogram was 0.800 in the training cohort and 0.774 in the validation cohort. The calibration curve demonstrated excellent consistency between the prediction of our nomogram and the observed curve. Decision curve analysis further showed that our nomogram conferred significantly high clinical net benefit. Collectively, our nomogram will facilitate early appropriate supportive care and better use of medical resources and finally reduce the poor outcomes of elderly COVID-19 patients.
  • |*COVID-19/diagnosis/mortality/physiopathology/therapy[MESH]
  • |*Pneumonia, Viral/diagnosis/etiology/mortality[MESH]
  • |Aged[MESH]
  • |China/epidemiology[MESH]
  • |Critical Illness/*mortality[MESH]
  • |Geriatric Assessment/methods[MESH]
  • |Hospitalization/statistics & numerical data[MESH]
  • |Humans[MESH]
  • |Patient Selection[MESH]
  • |Predictive Value of Tests[MESH]
  • |Prognosis[MESH]
  • |Risk Assessment/*methods[MESH]
  • |SARS-CoV-2/isolation & purification[MESH]


  • DeepDyve
  • Pubget Overpricing
  • suck abstract from ncbi

    Linkout box