Prevalence of and Recovery From Anemia Following Hospitalization for Critical Illness Among Adults

Warner, Matthew A.; Hanson, Andrew C.; Frank, Ryan D.; Schulte, Phillip J.; Go, Ronald S.; Storlie, Curtis B.; Kor, Daryl J.

doi:10.1001/jamanetworkopen.2020.17843

Cited by 50 publications

(63 citation statements)

References 38 publications

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“…Likewise, the rate of anemia increased the longer they remained in the ICU. In a recent study published by Warner et al [22], in a cohort of 6901 adults hospitalized in the ICU, 41% had anemia before hospitalization, a value slightly lower than that obtained in our study. However, as we have commented previously, the longer they remained in the ICU, the more the prevalence of anemia increased.…”

Section: Discussioncontrasting

confidence: 90%

Related Factors of Anemia in Critically Ill Patients: A Prospective Multicenter Study

Juárez‐Vela

Andrés-Esteban

Gea-Caballero

et al. 2022

JCM

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Anemia is common in critically ill patients; almost 95% of patients admitted to intensive care units (ICUs) have hemoglobin levels below normal. Several causes may explain this phenomenon as well as the tendency to transfuse patients without adequate cause: due to a lack of adherence to protocols, lack of supervision, incomplete transfusion request forms, or a lack of knowledge about the indications, risks, and costs of transfusions. Daily sampling to monitor the coagulation parameters and the acid–base balance can aggravate anemia as the main iatrogenic factor in its production. We studied the association and importance of iatrogenic blood loss and other factors in the incidence of anemia in ICUs. We performed a prospective, observational, multicenter study in five Spanish hospitals. A total of 142 patients with a median age of 58 years (IQI: 48–69), 71.83% male and 28.17% female, were admitted to ICUs without a diagnosis of iatrogenic anemia. During their ICU stay, anemia appeared in 66.90% of the sample, 95 patients, (95% CI: 58.51–74.56%). Risk factors associated with the occurrence of iatrogenic anemia were arterial catheter insertion (72.63% vs. 46.81%, p-value = 0.003), venous catheter insertion (87.37% vs. 72.34%, p-value = 0.023), drainages (33.68% vs. 12. 77%, p-value = 0.038), and ICU stay, where the longer the stay, the higher the rate of iatrogenic anemia (p-value < 0.001). We concluded that there was a statistical significance in the production of iatrogenic anemia due to the daily sampling for laboratory monitoring and critical procedures in intensive care units. The implementation of patient blood management programs could address these issues.

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Section: Discussioncontrasting

confidence: 90%

Related Factors of Anemia in Critically Ill Patients: A Prospective Multicenter Study

Juárez‐Vela

Andrés-Esteban

Gea-Caballero

et al. 2022

JCM

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“…Similarly, the COVID-ICU study group recently reported a number of sequelae after ICU discharge, and the mortality gradually reached approximately 31% (1298/4244) at 90 days after ICU admission for COVID-19 infection [ 3 ]. A number of recent studies, including our study focusing on the long-term impact of early fluid balance on 1-year mortality in critically ill cancer patients, have identified factors associated with the long-term mortality in critically ill patients [ 4 , 5 ]. These evidence highlight the essential need to explore early determinants and early risk stratification for long-term outcome in critically ill patients.…”

Section: Introductionmentioning

confidence: 99%

Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

Chan

Pai

et al. 2022

BMC Med Inform Decis Mak

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Background Machine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patients. Methods We retrospectively included patients who were admitted to intensive care units during 2015–2018 at a tertiary hospital in central Taiwan and linked with the Taiwanese nationwide death registration data. Three ML models, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR), were used to establish mortality prediction model. Furthermore, we used feature importance, Shapley Additive exPlanations (SHAP) plot, partial dependence plot (PDP), and local interpretable model-agnostic explanations (LIME) to explain the established model. Results We enrolled 6994 patients and found the accuracy was similar among the three ML models, and the area under the curve value of using XGBoost to predict 30-day, 90-day and 1-year mortality were 0.858, 0.839 and 0.816, respectively. The calibration curve and decision curve analysis further demonstrated accuracy and applicability of models. SHAP summary plot and PDP plot illustrated the discriminative point of APACHE (acute physiology and chronic health exam) II score, haemoglobin and albumin to predict 1-year mortality. The application of LIME and SHAP force plots quantified the probability of 1-year mortality and algorithm of key features at individual patient level. Conclusions We used an explainable ML approach, mainly XGBoost, SHAP and LIME plots to establish an explainable 1-year mortality prediction ML model in critically ill ventilated patients.

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“…More recently, a population‐based study from the USA by Warner et al. found a prevalence of anaemia at 6 months among ICU survivors of 52% and a significant association between lower hospital discharge Hb and increased mortality [5]. Although data are limited, anaemia in ICU survivors has also been associated with subsequent impairment in physical function [6].…”

Section: Introductionmentioning

confidence: 99%

“…Historical data suggest that up to half of patients who are anaemic at ICU discharge remain anaemic at 6 months and experience markedly reduced health-related quality of life [4]. More recently, a population-based study from the USA by Warner et al found a prevalence of anaemia at 6 months among ICU survivors of 52% and a significant association between lower hospital discharge Hb and increased mortality [5].…”

Section: Introductionmentioning

confidence: 99%

Anaemia among intensive care unit survivors and association with days alive and at home: an observational study

et al. 2021

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Summary Anaemia is highly prevalent at the time of intensive care unit discharge and is persistent for a high proportion of intensive care unit survivors. Whether anaemia is a driver of impaired recovery after critical illness is uncertain. The aim of this study was to test the hypothesis that, in adult intensive care survivors, anaemia at the time of intensive care unit discharge independently predicts decreased days at home‐90. This retrospective cohort study was conducted in a tertiary intensive care unit in Perth, Western Australia. All patients aged ≥ 16 years, discharged alive from their index intensive care unit admission and without documented treatment limitations were included. Median (IQR [range]) age of the 6358 participants was 61 (46–72 [16–95]) years and included 3385 (53.2%) unplanned admissions. Intensive care unit discharge with a haemoglobin concentration < 100 g.l‐1 occurred in 2886 (45.4%) patients, a threshold that identified a cohort with significantly lower days at home‐90 (median (IQR [range]) 80 (64–85 [0–90]) days vs. 85 (77–88 [0–90]) days (median difference 5 days, 95%CI 4.4–5.5, p < 0.0001). The association followed a severity‐response relationship with more severe anaemia predicting lower days at home‐90. When accounting for prespecified covariates including admission haemoglobin concentration and red blood cell transfusion, anaemia at intensive care unit discharge remained a significant predictor of decreased days at home‐90, relative risk 0.96 (0.93–0.98), p < 0.002. These findings support the need for interventional trials investigating whether this risk is modifiable.

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Prevalence of and Recovery From Anemia Following Hospitalization for Critical Illness Among Adults

Cited by 50 publications

References 38 publications

Related Factors of Anemia in Critically Ill Patients: A Prospective Multicenter Study

Related Factors of Anemia in Critically Ill Patients: A Prospective Multicenter Study

Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

Anaemia among intensive care unit survivors and association with days alive and at home: an observational study

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