Advertisement

 

 

Development of a risk-prediction model for Middle East respiratory syndrome coronavirus infection in dialysis patients.

Development of a risk-prediction model for Middle East respiratory syndrome coronavirus infection in dialysis patients.
Author Information (click to view)

Ahmed AE, Alshukairi AN, Al-Jahdali H, Alaqeel M, Siddiq SS, Alsaab HA, Sakr EA, Alyahya HA, Alandonisi MM, Subedar AT, Aloudah NM, Baharoon S, Alsalamah MA, Al Johani S, Alghamdi MG,


Ahmed AE, Alshukairi AN, Al-Jahdali H, Alaqeel M, Siddiq SS, Alsaab HA, Sakr EA, Alyahya HA, Alandonisi MM, Subedar AT, Aloudah NM, Baharoon S, Alsalamah MA, Al Johani S, Alghamdi MG, (click to view)

Ahmed AE, Alshukairi AN, Al-Jahdali H, Alaqeel M, Siddiq SS, Alsaab HA, Sakr EA, Alyahya HA, Alandonisi MM, Subedar AT, Aloudah NM, Baharoon S, Alsalamah MA, Al Johani S, Alghamdi MG,

Advertisement

Hemodialysis international. International Symposium on Home Hemodialysis 2018 04 14() doi 10.1111/hdi.12661
Abstract

Introduction The Middle East respiratory syndrome coronavirus (MERS-CoV) infection can cause transmission clusters and high mortality in hemodialysis facilities. We attempted to develop a risk-prediction model to assess the early risk of MERS-CoV infection in dialysis patients. Methods This two-center retrospective cohort study included 104 dialysis patients who were suspected of MERS-CoV infection and diagnosed with rRT-PCR between September 2012 and June 2016 at King Fahd General Hospital in Jeddah and King Abdulaziz Medical City in Riyadh. We retrieved data on demographic, clinical, and radiological findings, and laboratory indices of each patient. Findings A risk-prediction model to assess early risk for MERS-CoV in dialysis patients has been developed. Independent predictors of MERS-CoV infection were identified, including chest pain (OR = 24.194; P = 0.011), leukopenia (OR = 6.080; P = 0.049), and elevated aspartate aminotransferase (AST) (OR = 11.179; P = 0.013). The adequacy of this prediction model was good (P = 0.728), with a high predictive utility (area under curve [AUC] = 76.99%; 95% CI: 67.05% to 86.38%). The prediction of the model had optimism-corrected bootstrap resampling AUC of 71.79%. The Youden index yielded a value of 0.439 or greater as the best cut-off for high risk of MERS infection. Discussion This risk-prediction model in dialysis patients appears to depend markedly on chest pain, leukopenia, and elevated AST. The model accurately predicts the high risk of MERS-CoV infection in dialysis patients. This could be clinically useful in applying timely intervention and control measures to prevent clusters of infections in dialysis facilities or other health care settings. The predictive utility of the model warrants further validation in external samples and prospective studies.

Submit a Comment

Your email address will not be published. Required fields are marked *

one × 2 =

[ HIDE/SHOW ]