Dataset Information

Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients.

ABSTRACT: Accurately measuring the risk of pressure injury remains the most important step for effective prevention and intervention. Time-dependent risk factors for pressure injury development in the adult intensive care unit setting are not well understood.

Objectives

To develop and validate a dynamic risk prediction model to estimate the risk of developing a hospital-acquired pressure injury among adult ICU patients.

Design

ICU admission data were split into training and validation sets. With death as a competing event, both static and dynamic Fine-Gray models were developed to predict hospital-acquired pressure injury development less than 24, 72, and 168 hours postadmission. Model performance was evaluated using Wolbers' concordance index, Brier score, net reclassification improvement, and integrated discrimination improvement.

Setting and participants

We performed a retrospective cohort study of ICU patients in a tertiary care hospital located in San Francisco, CA, from November 2013 to August 2017.

Main outcomes and measures

Data were extracted from electronic medical records of 18,019 ICU patients (age ≥ 18 yr; 21,220 encounters). Record of hospital-acquired pressure injury data was captured in our institution's incident reporting system. The information is periodically reviewed by our wound care team. Presence of hospital-acquired pressure injury during an encounter and hospital-acquired pressure injury diagnosis date were provided.

Results

The dynamic model predicting hospital-acquired pressure injury more than 24 hours postadmission, including predictors age, body mass index, lactate serum, Braden scale score, and use of vasopressor and antifungal medications, had adequate discrimination ability within 6 days from time of prediction (c = 0.73). All dynamic models produced more accurate risk estimates than static models within 26 days postadmission. There were no significant differences in Brier scores between dynamic and static models.

Conclusions and relevance

A dynamic risk prediction model predicting hospital-acquired pressure injury development less than 24 hours postadmission in ICU patients for up to 7 days postadmission was developed and validated using a large dataset of clinical variables readily available in the electronic medical record.

SUBMITTER: Shui AM

PROVIDER: S-EPMC8613355 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeThe purpose of this study was to examine clinical characteristics and risk factors for critically ill patients who develop pressure injuries and identify the proportion of validated unavoidable pressure injuries associated with the proposed risk factors for acute skin failure (ASF).DesignRetrospective case-control comparative study.Subjects and settingThe sample comprised adult critically ill participants hospitalized in critical care units such as surgical, trauma, cardiovascular surgical, cardiac, neuro, and medical intensive care and corresponding progressive care units in 5 acute care hospitals within a large Midwestern academic/teaching healthcare system. Participants who developed hospital-acquired pressure injuries (HAPIs) and patients without HAPIs (controls) were included.MethodsA secondary analysis of data from a previous study with HAPIs and matching data for the control sample without HAPIs were obtained from the electronic health record. Descriptive and multivariate logistic regression analyses were conducted.ResultsThe sample comprised 475 participants; 165 experienced a HAPI and acted as cases, whereas the remaining 310 acted as controls. Acute Physiology and Chronic Health Evaluation (APACHE II) mean score (23.8, 8.7%; P < .001), mortality (n = 45, 27.3%; P = .002), history of liver disease (n = 28, 17%; P < .001), and unintentional loss of 10 lb or more in 1 month (n = 20, 12%; P = .002) were higher in the HAPI group. Multivariate logistic regression analysis identified participants with respiratory failure (odds ratio [OR] = 3.00; 95% confidence interval [CI], 1.27-7.08; P = .012), renal failure (OR = 7.48; 95% CI, 3.49-16.01; P < .001), cardiac failure (OR = 4.50; 95% CI, 1.76-11.51; P = .002), severe anemia (OR = 10.89; 95% CI, 3.59-33.00; P < .001), any type of sepsis (OR = 3.15; 95% CI, 1.44-6.90; P = .004), and moisture documentation (OR = 11.89; 95% CI, 5.27-26.81; P <.001) were more likely to develop a HAPI. No differences between unavoidable HAPI, avoidable HAPI, or the control group were identified based on the proposed ASF risk factors.ConclusionThis study provides important information regarding avoidable and unavoidable HAPIs and ASF. Key clinical characteristics and risk factors, such as patient acuity, organ failure, tissue perfusion, sepsis, and history of prior pressure injury, are associated with avoidable and unavoidable HAPI development. In addition, we were unable to support a relationship between unavoidable HAPIs and the proposed risk factors for ASF. Unavoidability of HAPIs rests with the documentation of appropriate interventions and not necessarily with the identification of clinical risk factors.

Project description:Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care.To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization.Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities.Nontrauma, non-cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144,913) were linked to hospital discharge data and randomly split into development (n = 87,266 [60%]) and validation (n = 57,647 [40%]) cohorts.Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization.Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17).In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent population.

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