2022
DOI: 10.3389/fendo.2022.1034251
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Machine learning models to predict in-hospital mortality in septic patients with diabetes

Abstract: BackgroundSepsis is a leading cause of morbidity and mortality in hospitalized patients. Up to now, there are no well-established longitudinal networks from molecular mechanisms to clinical phenotypes in sepsis. Adding to the problem, about one of the five patients presented with diabetes. For this subgroup, management is difficult, and prognosis is difficult to evaluate.MethodsFrom the three databases, a total of 7,001 patients were enrolled on the basis of sepsis-3 standard and diabetes diagnosis. Input vari… Show more

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Cited by 6 publications
(5 citation statements)
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“…Although our identified models have a ‘fair’ predictive capacity (close to ‘good’), their estimated AUC is generally lower than the previous studies 9 , 12 , 14 , 15 . This state of affairs can be attributed in part to the availability of clinical information regarding the laboratory tests and vital signs that were used in the previous investigations, whereas in our study none of these information were used.…”
Section: Discussionmentioning
confidence: 51%
See 3 more Smart Citations
“…Although our identified models have a ‘fair’ predictive capacity (close to ‘good’), their estimated AUC is generally lower than the previous studies 9 , 12 , 14 , 15 . This state of affairs can be attributed in part to the availability of clinical information regarding the laboratory tests and vital signs that were used in the previous investigations, whereas in our study none of these information were used.…”
Section: Discussionmentioning
confidence: 51%
“…It can be partly explained by the reverse epidemiological phenomenon of standard risk factors in chronic diseases and chronic infections such as HIV/AIDS 25 , 26 . Previous studies 9 15 have reported various predictors of mortality in diabetes; however, the identified factors have not been consistently replicated across studies, as summarized in Table 4 . Age is the only predictor that was consistently shown to be significant in several studies, as well as in ours.…”
Section: Discussionmentioning
confidence: 97%
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“…The number of studies on sepsis phenotyping continues to rise, taking into consideration the site of infection [100,101], the type of organ dysfunction, e.g., liver [102], kidney [103][104][105][106] lung [107,108] or coagulation [109][110][111][112], and the type of disease, e.g., COVID-19 [113][114][115][116][117][118][119][120][121][122][123][124][125][126][127][128] or non-COVID-19 ARDS ; they try to correlate phenotypes and associated harms of delayed time-to-antibiotics [154,155], and even to predict the response to treatment or the outcome [156][157][158][159]. Different phenotypes were also identified in septic children [160][161][162][163][164][165][166][167][168][169]…”
Section: Sepsis and Phenotypes: There Is More To Comementioning
confidence: 99%