Dataset Information

Development and Validation of a 30-Day In-hospital Mortality Model Among Seriously Ill Transferred Patients: a Retrospective Cohort Study.

ABSTRACT:

Background

Predicting the risk of in-hospital mortality on admission is challenging but essential for risk stratification of patient outcomes and designing an appropriate plan-of-care, especially among transferred patients.

Objective

Develop a model that uses administrative and clinical data within 24 h of transfer to predict 30-day in-hospital mortality at an Academic Health Center (AHC).

Design

Retrospective cohort study. We used 30 putative variables in a multiple logistic regression model in the full data set (n = 10,389) to identify 20 candidate variables obtained from the electronic medical record (EMR) within 24 h of admission that were associated with 30-day in-hospital mortality (p < 0.05). These 20 variables were tested using multiple logistic regression and area under the curve (AUC)-receiver operating characteristics (ROC) analysis to identify an optimal risk threshold score in a randomly split derivation sample (n = 5194) which was then examined in the validation sample (n = 5195).

Participants

Ten thousand three hundred eighty-nine patients greater than 18 years transferred to the Indiana University (IU)-Adult Academic Health Center (AHC) between 1/1/2016 and 12/31/2017.

Main measures

Sensitivity, specificity, positive predictive value, C-statistic, and risk threshold score of the model.

Key results

The final model was strongly discriminative (C-statistic = 0.90) and had a good fit (Hosmer-Lemeshow goodness-of-fit test [X² (8) =6.26, p = 0.62]). The positive predictive value for 30-day in-hospital death was 68%; AUC-ROC was 0.90 (95% confidence interval 0.89-0.92, p < 0.0001). We identified a risk threshold score of -2.19 that had a maximum sensitivity (79.87%) and specificity (85.24%) in the derivation and validation sample (sensitivity: 75.00%, specificity: 85.71%). In the validation sample, 34.40% (354/1029) of the patients above this threshold died compared to only 2.83% (118/4166) deaths below this threshold.

Conclusion

This model can use EMR and administrative data within 24 h of transfer to predict the risk of 30-day in-hospital mortality with reasonable accuracy among seriously ill transferred patients.

SUBMITTER: Mahendraker N

PROVIDER: S-EPMC7840078 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveAlbumin-corrected calcium is usually calculated to reflect the real serum calcium level of the whole body by physicians. However, studies on the association between albumin-corrected calcium and 30-day in-hospital mortality in Intensive Care Unit (ICU) patients are rare. The purpose of our study was to explore the association between baseline albumin-corrected calcium and 30-day in-hospital mortality in the American ICU population.MethodsA multicenter retrospective cohort study of 102,245 ICU patients in the eICU-CRD v2.0 from the USA during 2014-2015 was performed. The average age was 63.7 ± 16.9 years, of which 55,313 (53.7%) were men and 47,758 (46.3%) were women. The association between albumin-corrected calcium and 30-day in-hospital mortality was analyzed by Cox proportional-hazards regression, smooth curve fitting, piecewise linear regression, subgroup analyses, and a series of sensitivity analyses.ResultsWe found that among ICU patients with calcium abnormalities, more than 95% were mild hypocalcemia or mild hypercalcemia. The risk of 30-day in-hospital mortality will increase by 10% in the ≥7.5-< 8.5 mg/dl subgroup (OR=1.1, 95% CI 1.0-1.3) or 20% in the ≥10.3-<12 mg/dl subgroup (OR=1.2, 95% CI 1.1-1.3) when the albumin-corrected calcium level increases by 1 mg/dl. Additionally, the relationship between albumin-corrected calcium and 30-day in-hospital mortality was U shaped; the inflection point was 8.9 mg/dl (log likelihood ratio test P = 0.005). Finally, after a series of sensitivity analyses, we found that the relationship between albumin-corrected calcium and 30-day in-hospital mortality remained significant.ConclusionIn a large nationally representative cohort of ICU patients, abnormalities in albumin-corrected calcium, particularly slight hypocalcemia or slight hypercalcemia, were associated with an increased 30-day in-hospital mortality risk, and yet the findings in this study need to be further confirmed by prospective studies.

Project description:BackgroundThis study aimed to investigate the association between preadmission glucocorticoid (GC) therapy and 30-day mortality in critically ill patients following admission to an intensive care unit (ICU). We aimed to determine whether this association differed according to daily GC dosage and type. We conducted a retrospective cohort study of adult patients admitted to a single tertiary academic hospital ICU from January 2012 to December 2017. We classified the patients regularly undergoing oral GC therapy as preadmission GC users, and those with no history of GC use were classified as non-GC users.ResultsThe study included 24,929 patients, of whom 816 (3.3%) were preadmission GC users. Thirty-day mortality in preadmission GC users (173 of 816 patients) was 21.2% compared to 8.8% (2113 of 24,113 patients) in non-GC users. Multivariate Cox regression analysis showed that preadmission GC users had a 1.62-fold increase in 30-day mortality compared to non-GC users [hazard ratio (HR) 1.62, 95% confidence interval (CI) 1.29-2.03, P?<?0.001]. When comparing preadmission GC users with diabetes mellitus to non-GC users, a 2.29-fold increase in 30-day mortality was noted (HR 2.29, 95% CI 1.08-4.86, P?=?0.031). In the sensitivity analysis, compared to non-GC users, daily dosages of???5 and?>?5 mg of prednisolone in preadmission GC users showed 1.45-fold (HR 1.45, 95% CI 1.03-2.03, P?=?0.033) and 1.67-fold (HR 1.67, 95% CI 1.25-2.24, P?=?0.001) increases, respectively, in 30-day mortality after ICU admission. Moreover, prednisolone, methylprednisolone, and dexamethasone users in the preadmission GC users group showed 1.56-fold (HR 1.56, 95% CI 1.21-2.01, P?=?0.001), 1.90-fold (HR 1.90, 95% CI 1.12-3.25, P?=?0.018), and 1.30-fold (HR 1.30, 95% CI 1.05-1.50, P?=?0.042) increases, respectively, in 30-day mortality compared to non-GC users.ConclusionPreadmission GC use among critically ill patients was associated with an increased 30-day mortality after ICU admission compared to non-GC use. This association was more prevalent in preadmission GC users with diabetes mellitus and in preadmission GC users who took?>?5 mg/day of prednisolone and methylprednisolone.

Project description:OBJECTIVES:To identify characteristics that predict 30-day mortality among patients critically ill with coronavirus disease 2019 in England, Wales, and Northern Ireland. DESIGN:Observational cohort study. SETTING:A total of 258 adult critical care units. PATIENTS:A total of 10,362 patients with confirmed coronavirus disease 2019 with a start of critical care between March 1, 2020, and June 22, 2020, of whom 9,990 were eligible (excluding patients with a duration of critical care less than 24 hr or missing core variables). MEASUREMENTS AND MAIN RESULTS:The main outcome measure was time to death within 30 days of the start of critical care. Of 9,990 eligible patients (median age 60 yr, 70% male), 6,027 died within 30 days of the start of critical care. As of July 22, 2020, 189 patients were still receiving critical care and a further 446 were still in acute hospital. Data were missing for between 0.1% and 7.2% of patients across prognostic factors. We imputed missing data ten-fold, using fully conditional specification and continuous variables were modeled using restricted cubic splines. Associations between the candidate prognostic factors and time to death within 30 days of the start of critical care were determined after adjustment for multiple variables with Cox proportional hazards modeling. Significant associations were identified for age, ethnicity, deprivation, body mass index, prior dependency, immunocompromise, lowest systolic blood pressure, highest heart rate, highest respiratory rate, PaO2/FIO2 ratio (and interaction with mechanical ventilation), highest blood lactate concentration, highest serum urea, and lowest platelet count over the first 24 hours of critical care. Nonsignificant associations were found for sex, sedation, highest temperature, and lowest hemoglobin concentration. CONCLUSIONS:We identified patient characteristics that predict an increased likelihood of death within 30 days of the start of critical care for patients with coronavirus disease 2019. These findings may support development of a prediction model for benchmarking critical care providers.

Project description:The association between hospital volume and the death rate for patients who are hospitalized for acute myocardial infarction, heart failure, or pneumonia remains unclear. It is also not known whether a volume threshold for such an association exists.We conducted cross-sectional analyses of data from Medicare administrative claims for all fee-for-service beneficiaries who were hospitalized between 2004 and 2006 in acute care hospitals in the United States for acute myocardial infarction, heart failure, or pneumonia. Using hierarchical logistic-regression models for each condition, we estimated the change in the odds of death within 30 days associated with an increase of 100 patients in the annual hospital volume. Analyses were adjusted for patients' risk factors and hospital characteristics. Bootstrapping procedures were used to estimate 95% confidence intervals to identify the condition-specific volume thresholds above which an increased volume was not associated with reduced mortality.There were 734,972 hospitalizations for acute myocardial infarction in 4128 hospitals, 1,324,287 for heart failure in 4679 hospitals, and 1,418,252 for pneumonia in 4673 hospitals. An increased hospital volume was associated with reduced 30-day mortality for all conditions (P<0.001 for all comparisons). For each condition, the association between volume and outcome was attenuated as the hospital's volume increased. For acute myocardial infarction, once the annual volume reached 610 patients (95% confidence interval [CI], 539 to 679), an increase in the hospital volume by 100 patients was no longer significantly associated with reduced odds of death. The volume threshold was 500 patients (95% CI, 433 to 566) for heart failure and 210 patients (95% CI, 142 to 284) for pneumonia.Admission to higher-volume hospitals was associated with a reduction in mortality for acute myocardial infarction, heart failure, and pneumonia, although there was a volume threshold above which an increased condition-specific hospital volume was no longer significantly associated with reduced mortality.