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Unlike the PSI and MEDS, however, the upper 95% confidence interval for the STSS was <0.80 and the specificity for identifying patients at risk of in-hospital mortality was poor at 15%.
However, the AUC for the prediction of 90-day mortality was poor.
The predictive value of the individual PCT samples for mortality was poor, but a prompt 50% decrease in PCT indicating resolving infection was associated with a favorable outcome.
Despite the strong association between SBP and mortality, the performance of SBP in predicting 7-day mortality was poor across all three cohorts and SBP by itself is not an adequate tool for risk stratification in the ED or in the prehospital population.
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Other variables associated with in-hospital mortality were poor performance status, severity on admission as assessed by SOFA score, allogeneic hematopoietic stem cell transplantation, increased time to ICU admission and invasive aspergillosis.
These indicators, including maternal and infant morbidity and mortality, are poorest among low-income populations.
Universally, these indicators, and especially maternal and infant morbidity and mortality, are poorest among low-income populations.
One of the domains we believe may be a factor in driving the large difference in mortality is poorer access and worse quality of clinical care in rural counties.
Adjusted mortality was equally poor among patients with severe renal dysfunction and on dialysis.
However, the receiver operating characteristic (ROC) curve of the HGI revealed an AUC of only 0.64, indicating that capacity to predict mortality was relatively poor although perhaps statistically a little better than for the other markers of blood glucose control.
CONCLUSIONS: Agreement between different methods of ranking hospital-based quality of care and 30-day mortality or readmission rankings was poor.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com