Association between time in range of relative normoglycemia and in-hospital mortality in critically ill patients: a single-center retrospective study.
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SUBMITTER: Okazaki T
PROVIDER: S-EPMC9277973 | biostudies-literature | 2022 Jul
REPOSITORIES: biostudies-literature
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Association between time in range of relative normoglycemia and in-hospital mortality in critically ill patients: a single-center retrospective study.
Okazaki Tomoya T Inoue Akihiko A Taira Takuya T Nakagawa Shun S Kawakita Kenya K Kuroda Yasuhiro Y
Scientific reports 20220713 1
The aim of this single-center retrospective study was to investigate the association between the time in range (TIR) of relative normoglycemia (RN) and in-hospital mortality. We defined RN as measured blood glucose in the range of 70-140% of A1C-derived average glucose and absolute normoglycemia (AN) as 70-140 mg/dL. We conducted multivariate logistic regression analyses to examine the association between TIR of RN > 80% or TIR of AN > 80% up to 72 h after ICU admission and in-hospital mortality ...[more]
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Project description:ObjectivesProthrombin time (PT) and PT-INR are independent predictors of mortality in patients with cancer. The PT and PT-INR of cancer patients are independent predictive variables of mortality. However, whether the PT or PT-INR is related to in-hospital mortality in severely ill patients with tumors remains unknown.DesignThis was a case-control study based on a multicenter public database.SettingsThis study is a secondary analysis of data extracted from 2014 to 2015 from the Electronic Intensive Care Unit Collaborative Research Database.ParticipantsThe data relevant to seriously ill patients with tumors were obtained from 208 hospitals spread throughout the USA. This research included a total of 200,859 participants. After the samples were screened for patients with combination malignancies and prolonged PT-INR or PT, the remaining 1745 and 1764 participants, respectively, were included in the final data analysis.Primary and secondary outcome measuresThe key evaluation methodology was the PT count and PT-INR, and the main outcome was the in-hospital mortality rate.ResultsAfter controlling for confounding variables, we found a curvilinear connection between PT-INR and in-hospital mortality (p < 0.001), and the inflection point was 2.5. When PT-INR was less than 2.5, an increase in PT-INR was positively associated with in-hospital mortality (OR 1.62, 95% CI 1.24 to 2.13), whereas when PT-INR was greater than 2.5, in-hospital mortality was relatively stable and higher than the baseline before the inflection point. Similarly, our study indicated that the PT exhibited a curvilinear connection with in-hospital mortality. On the left side of the inflection point (PT <22), a rise in the PT was positively linked with in-hospital mortality (OR 1.08, 95% CI 1.04 to 1.13, p < 0.001). On the right side of the inflection point, the baseline PT was above 22, and the in-hospital mortality was stable and higher than the PT count in the prior range (OR 1.01, 95% CI 0.97 to 1.04, 0.7056).ConclusionOur findings revealed that there is a curved rather than a linear link between the PT or PT-INR and in-hospital mortality in critically ill cancer patients. When these two laboratory results are below the inflection point, comprehensive therapy should be employed to reduce the count; when these two laboratory results are above the inflection point, every effort should be made to reduce the numerical value to a value below the inflection point.
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Project description:BackgroundWarfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the therapeutic dose. Warfarin sensitivity has been reported to be associated with increased incidence of international normalized ratio (INR) > 5. However, whether warfarin sensitivity is a risk factor for adverse outcomes in critically ill patients remains unknown. In the present study, we aimed to evaluate the utility of different machine learning algorithms for the prediction of warfarin sensitivity and to determine the impact of warfarin sensitivity on outcomes in critically ill patients.MethodsNine different machine learning algorithms for the prediction of warfarin sensitivity were tested in the International Warfarin Pharmacogenetic Consortium cohort and Easton cohort. Furthermore, a total of 7,647 critically ill patients was analyzed for warfarin sensitivity on in-hospital mortality by multivariable regression. Covariates that potentially confound the association were further adjusted using propensity score matching or inverse probability of treatment weighting.ResultsWe found that logistic regression (AUC = 0.879, 95% CI: 0.834-0.924) was indistinguishable from support vector machine with a linear kernel, neural network, AdaBoost and light gradient boosting trees, and significantly outperformed all the other machine learning algorithms. Furthermore, we found that warfarin sensitivity predicted by the logistic regression model was significantly associated with worse in-hospital mortality in critically ill patients with an odds ratio (OR) of 1.33 (95% CI, 1.01-1.77).ConclusionsOur data suggest that the logistic regression model is the best model for the prediction of warfarin sensitivity clinically and that warfarin sensitivity is likely to be a risk factor for adverse outcomes in critically ill patients.
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Project description:BackgroundWhile time in targeted blood glucose range (TIR) 70-140 mg/dL is a known factor associated with mortality in critically ill patients, it remains unclear whether TIR is associated with 28-day mortality under the glycemic control with a less tight target glucose range of 70-180 mg/dL. We aimed to examine whether TIR 70-180 mg/dL was associated with 28-day mortality.MethodsThis is a retrospective cohort study using data from a tertiary care center in Japan collected from January 2016 through October 2019. We included adult patients (aged ≥20 years) admitted to the ICU. We excluded patients 1) with diabetic ketoacidosis patients, 2) discharged within 48 hours, 3) with repeated ICU admissions. We calculated TIR 70-180 mg/dL using the measured blood glucose values (≥3 times per day). The primary outcome was 28-day mortality. We examined the association between TIR and 28-day mortality using a logistic regression and Cox proportional hazard models with a stratification by glycosylated hemoglobin (HbA1c) level of 6.5%. Additionally, we repeated the analyses using the TIR category to assess the optimal TIR. For the sensitivity analysis, we repeated the primary analysis using TIR during the first three days from ICU admission.ResultsOf 1,230 patients, the median age was 72 years, 65% were male, and 250 patients (20%) had HbA1c ≥6.5% on admission. In patients with HbA1c <6.5%, TIR <80% was associated with an increased risk of 28-day mortality, with an adjusted odds ratio (OR) of 1.88 (95%CI: 1.36-2.61). Likewise, when using 10% incremental TIR as a categorical variable, lower TIR was associated with a worse 28-day mortality compared with TIR ≥90% (e.g., adjusted OR of TIR <60%, 3.62 [95%CI 2.36-5.53]). Similar associations were found in the analyses using Cox proportional hazards model and using TIR during the first three days. By contrast, in patients with HbA1c ≥6.5%, there was no consistent association of TIR with 28-day mortality.ConclusionsWe found that lower TIR 70-180 mg/dL was associated with a higher 28-day mortality in critically ill patients with HbA1c <6.5%, whereas there was no consistent association in patients with HbA1c ≥6.5%.
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