Dataset Information

Prediction of Inhospital Mortality in Critically Ill Patients With Sepsis: Confirmation of the Added Value of 24-Hour Lactate to Acute Physiology and Chronic Health Evaluation IV.

ABSTRACT: We previously reported the added value of 24-hour lactate concentration alone and in combination with 24-hour lactate clearance and lactate concentration at admission for the prediction of inhospital mortality in critically ill patients with sepsis. We aimed to validate this finding.

Derivation cohort

The derivation cohort from Leiden, The Netherlands, consisted of 451 critically ill patients with sepsis.

Validation cohort

The validation cohort consisted of 4,440 critically ill adult patients with sepsis from the Medical Information Mart for Intensive Care cohort admitted to the ICU of Beth Israel Deaconness Medical Center, Boston, MA, between January 2006 and 2018.

Prediction model

Predictors of mortality were: age, chronic comorbidities, length of stay pre-ICU, Glasgow Coma Scale, and Acute Physiology Score. Lactate concentration at 24-hour alone, in combination with 24-hour lactate clearance and in combination with lactate concentration at admission, was added to assess improvement of the prediction model. The outcome was inhospital mortality.

Results

Inhospital mortality occurred in 160 patients (36%) in the derivation cohort and in 2,347 patients (53%) in the validation cohort. The Acute Physiology and Chronic Health Evaluation (APACHE) IV model had a moderate discriminative performance (recalibrated C-statistic, 0.62; 95% CI, 0.60-0.63). Addition of 24-hour lactate concentration increased the recalibrated C-statistic to 0.64 (95% CI, 0.62-0.66). The model with 24-hour lactate concentration and lactate concentration at admission showed the best fit as depicted by the smallest Akaike Information Criterion in both the derivation and validation data.

Conclusion

The 24-hour lactate concentration and lactate concentration at admission contribute modestly to prediction of inhospital mortality in critically ill patients with sepsis. Future updates and possible modification of APACHE IV should consider the incorporation of lactate concentration at baseline and at 24 hours.

SUBMITTER: Baysan M

PROVIDER: S-EPMC9444407 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Prediction of Inhospital Mortality in Critically Ill Patients With Sepsis: Confirmation of the Added Value of 24-Hour Lactate to Acute Physiology and Chronic Health Evaluation IV.

Baysan Meryem M Arbous Mendi S MS Steyerberg Ewout W EW van der Bom Johanna G JG

Critical care explorations 20220902 9

We previously reported the added value of 24-hour lactate concentration alone and in combination with 24-hour lactate clearance and lactate concentration at admission for the prediction of inhospital mortality in critically ill patients with sepsis. We aimed to validate this finding.<h4>Derivation cohort</h4>The derivation cohort from Leiden, The Netherlands, consisted of 451 critically ill patients with sepsis.<h4>Validation cohort</h4>The validation cohort consisted of 4,440 critically ill adu ...[more]

PMID: 36082375

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Project description:We investigated the added predictive value of lactate and lactate clearance to the Acute Physiology and Chronic Health Evaluation IV model for predicting in-hospital mortality in critically ill patients with sepsis.DesignRetrospective observational cohort study.SettingMixed ICU of Leiden University Medical Center, The Netherlands.PatientsCritically ill patients adult patients with sepsis who have been admitted to the ICU of Leiden University Medical Center, The Netherlands, from 2006 to January 2018.InterventionsNone.Measurements and main resultsWe fitted a baseline model with the Acute Physiology and Chronic Health Evaluation IV predictors and added 13 prespecified combinations of lactate and lactate clearance at 0, 6 and 24 hours after admission to create a set of extended models to compare with the baseline Acute Physiology and Chronic Health Evaluation IV model. Among 603 ICU admissions, 451 patients met the inclusion criteria. A total of 160 patients died in-hospital, of which 106 died in the ICU. Their lactate and lactate clearance measurements were higher at all time points than those of survivors. The Akaike Information Criterion score improved in 10 of 13 prespecified extended models, with best performance for models that included lactate at 24 hours, alone or in combination with lactate at admission or lactate clearance at 24 hours. We compared the observed and predicted probabilities of in-hospital mortality of the baseline Acute Physiology and Chronic Health Evaluation IV model with the best model in our data, lactate at 24 hours added to the Acute Physiology and Chronic Health Evaluation IV model. This resulted in an increase in specificity of 29.9% (95% CI, 18.9-40.9%).ConclusionsLactate measurements at 24 hours after admission add predictive value to the prediction of mortality with Acute Physiology and Chronic Health Evaluation IV among ICU patients with sepsis. External validation is needed to develop extended prediction models.

Project description:To characterize prearrest hemodynamic trajectories of children suffering inhospital cardiac arrest.DesignExploratory retrospective analysis of arterial blood pressure and electrocardiogram waveforms.SettingPICU and cardiac critical care unit in a tertiary-care children's hospital.PatientsTwenty-seven children with invasive blood pressure monitoring who suffered a total of 31 inhospital cardiac arrest events between June 2017 and June 2019.InterventionsNone.Measurements and main resultsWe assessed changes in cardiac output, systemic vascular resistance, stroke volume, and heart rate derived from arterial blood pressure waveforms using three previously described estimation methods. We observed substantial prearrest drops in cardiac output (population median declines of 65-84% depending on estimation method) in all patients in the 10 minutes preceding inhospital cardiac arrest. Most patients' mean arterial blood pressure also decreased, but this was not universal. We identified three hemodynamic patterns preceding inhospital cardiac arrest: subacute pulseless arrest (n = 18), acute pulseless arrest (n = 7), and bradycardic arrest (n = 6). Acute pulseless arrest events decompensated within seconds, whereas bradycardic and subacute pulseless arrest events deteriorated over several minutes. In the subacute and acute pulseless arrest groups, decreases in cardiac output were primarily due to declines in stroke volume, whereas in the bradycardic group, the decreases were primarily due to declines in heart rate.ConclusionsCritically ill children exhibit distinct physiologic behaviors prior to inhospital cardiac arrest. All events showed substantial declines in cardiac output shortly before inhospital cardiac arrest. We describe three distinct prearrest patterns with varying rates of decline and varying contributions of heart rate and stroke volume changes to the fall in cardiac output. Our findings suggest that monitoring changes in arterial blood pressure waveform-derived heart rate, pulse pressure, cardiac output, and systemic vascular resistance estimates could improve early detection of inhospital cardiac arrest by up to several minutes. Further study is necessary to verify the patterns witnessed in our cohort as a step toward patient rather than provider-centered definitions of inhospital cardiac arrest.

Project description:Lactate clearance is a standard resuscitation goal in patients in nontraumatic shock but has not been investigated adequately as a tool to identify trauma patients at risk of dying. Our objective was to determine if trauma patients with impaired lactate clearance have a higher 24-hour mortality rate than patients whose lactate concentration normalizes.A retrospective chart review identified patients who were admitted directly from the scene of injury to an urban trauma center between 2010 and 2013 and who had at least one lactate concentration measurement within 24 hours. Transfers, patients without lactate measurement, and those who were dead on arrival were excluded. Of the 26,545 screened patients, 18,304 constituted the initial lactate measurement population, and 3,887 were the lactate clearance cohorts.Initial lactate had an area under the receiver operating characteristic curve of 0.86 and 0.73 for mortality at 24 hours and in the hospital, respectively. An initial concentration of 3 mmol/L or greater had a sensitivity of 0.86 and a specificity of 0.73 for mortality at 24 hours. The mortality rate among patients with elevated lactate concentrations (n = 2,381; 5.6 [2.8] mmol/L) that did not decline to less than 2.0 mmol/L in response to resuscitative efforts (mean [SD] second measurement, 3.7 [1.9] mmol/L) was nearly seven times higher (4.1% vs. 0.6%, p < 0.001) than among those with an elevated concentration (n = 1,506, 5.3 [2.7]?mmol/L) that normalized (1.4 [0.4]?mmol/L). Logistic regression analysis showed that failure to clear lactate was associated with death more than any other feature (odds ratio, 7.4; 95% confidence interval, 1.5-35.5), except having an Injury Severity Score (ISS) greater than 25 (odds ratio, 8.2; 95% confidence interval, 2.7-25.2).Failure to clear lactate is a strong negative prognostic marker after injury. An initial lactate measurement combined with a second measurement for high-risk individuals might constitute a useful method of risk stratifying injured patients.Prognostic study, level III.

Project description:BackgroundDetermining kidney function in critically ill patients is paramount for the dose adjustment of several medications. When assessing kidney function, the glomerular filtration rate (GFR) is generally estimated either by calculating urine creatinine clearance (UCrCl) or using a predictive equation. Unfortunately, all predictive equations have been derived for medical outpatients. Therefore, the validity of predictive equations is of concern when compared with that of the UCrCl method, particularly in medical critically ill patients. Therefore, we conducted this study to assess the agreement of the estimated GFR (eGFR) using common predictive equations and UCrCl in medical critical care setting.MethodsThis was the secondary analysis of a nutrition therapy study. Urine was collected from participating patients over 24 h for urine creatinine, urine nitrogen, urine volume, and serum creatinine measurements on days 1, 3, 5, and 14 of the study. Subsequently, we calculated UCrCl and eGFR using four predictive equations, the Cockcroft-Gault (CG) formula, the four and six-variable Modification of Diet in Renal Disease Study (MDRD-4 and MDRD-6) equations, and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. The correlation and agreement between eGFR and UCrCl were determined using the Spearman rank correlation coefficient and Bland-Altman plot with multiple measurements per subject, respectively. The performance of each predictive equation for estimating GFR was reported as bias, precision, and absolute percentage error (APE).ResultsA total of 49 patients with 170 urine samples were included in the final analysis. Of 49 patients, the median age was 74 (21-92) years-old and 49% was male. All patients were hemodynamically stable with mean arterial blood pressure of 82 (65-108) mmHg. Baseline serum creatinine was 0.93 (0.3-4.84) mg/dL and baseline UCrCl was 46.69 (3.40-165.53) mL/min. The eGFR from all the predictive equations showed modest correlation with UCrCl (r: 0.692 to 0.759). However, the performance of all the predictive equations in estimating GFR compared to that of UCrCl was poor, demonstrating bias ranged from -8.36 to -31.95 mL/min, precision ranged from 92.02 to 166.43 mL/min, and an unacceptable APE (23.01% to 47.18%). Nevertheless, the CG formula showed the best performance in estimating GFR, with a small bias (-2.30 (-9.46 to 4.86) mL/min) and an acceptable APE (14.72% (10.87% to 23.80%)), especially in patients with normal UCrCl.ConclusionFrom our finding, CG formula was the best eGFR formula in the medical critically ill patients, which demonstrated the least bias and acceptable APE, especially in normal UCrCl patients. However, the predictive equation commonly used to estimate GFR in critically ill patients must be cautiously applied due to its large bias, wide precision, and unacceptable error, particularly in renal function impairment.

Project description:BackgroundAcute kidney injury (AKI) is one of the most common complications for critically ill patients with cirrhosis, but it has remained unclear whether urine output fluctuations are associated with the risk of AKI in such patients. Thus, we explored the influence of 24-h urine-output trajectory on AKI in patients with cirrhosis through latent category trajectory modeling.Materials and methodsThis retrospective cohort study examined patients with cirrhosis using the MIMIC-IV database. Changes in the trajectories of urine output within 24 h after admission to the intensive care unit (ICU) were categorized using latent category trajectory modeling. The outcome examined was the occurrence of AKI during ICU hospitalization. The risk of AKI in patients with different trajectory classes was explored using the cumulative incidence function (CIF) and the Fine-Gray model with the sub-distribution hazard ratio (SHR) and the 95% confidence interval (CI) as size effects.ResultsThe study included 3,562 critically ill patients with cirrhosis, of which 2,467 (69.26%) developed AKI during ICU hospitalization. The 24-h urine-output trajectories were split into five classes (Classes 1-5). The CIF curves demonstrated that patients with continuously low urine output (Class 2), a rapid decline in urine output after initially high levels (Class 3), and urine output that decreased slowly and then stabilized at a lower level (Class 4) were at higher risk for AKI than those with consistently moderate urine output (Class 1). After fully adjusting for various confounders, Classes 2, 3, and 4 were associated with a higher risk of AKI compared with Class 1, and the respective SHRs (95% CIs) were 2.56 (1.87-3.51), 1.86 (1.34-2.59), and 1.83 1.29-2.59).ConclusionsThe 24-h urine-output trajectory is significantly associated with the risk of AKI in critically ill patients with cirrhosis. More attention should be paid to the dynamic nature of urine-output changes over time, which may help guide early intervention and improve patients' prognoses.

Dataset Information

Prediction of Inhospital Mortality in Critically Ill Patients With Sepsis: Confirmation of the Added Value of 24-Hour Lactate to Acute Physiology and Chronic Health Evaluation IV.

Derivation cohort

Validation cohort

Prediction model

Results

Conclusion

Publications

Prediction of Inhospital Mortality in Critically Ill Patients With Sepsis: Confirmation of the Added Value of 24-Hour Lactate to Acute Physiology and Chronic Health Evaluation IV.

Similar Datasets

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