A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay

<p>Abstract</p> <p>Background</p> <p>Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay.</p> <p>Methods</p> <p>We performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model.</p> <p>Results</p> <p>The variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO₂: FiO₂ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r²was 20.2% across individuals and 44.3% across units.</p> <p>Conclusions</p> <p>A model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay.</p

Timing of Mental Health Service Use After a Pediatric Firearm Injury

OBJECTIVES: To examine how timing of the first outpatient mental health (MH) visit after a pediatric firearm injury varies by sociodemographic and clinical characteristics. METHODS: We retrospectively studied children aged 5 to 17 years with a nonfatal firearm injury from 2010 to 2018 using the IBM Watson MarketScan Medicaid database. Logistic regression estimated the odds of MH service use in the 6 months after injury, adjusted for sociodemographic and clinical characteristics. Cox proportional hazard models, stratified by previous MH service use, evaluated variation in timing of the first outpatient MH visit by sociodemographic and clinical characteristics. RESULTS: After a firearm injury, 958 of 2613 (36.7%) children used MH services within 6 months; of these, 378 of 958 (39.5%) had no previous MH service use. The adjusted odds of MH service use after injury were higher among children with previous MH service use (adjusted odds ratio, 10.41; 95% confidence interval [CI], 8.45-12.82) and among non-Hispanic white compared with non-Hispanic Black children (adjusted odds ratio, 1.29; 95% CI, 1.02-1.63). The first outpatient MH visit after injury occurred sooner among children with previous MH service use (adjusted hazard ratio, 6.32; 95% CI, 5.45-7.32). For children without previous MH service use, the first MH outpatient visit occurred sooner among children with an MH diagnosis made during the injury encounter (adjusted hazard ratio, 2.72; 95% CI, 2.04-3.65). CONCLUSIONS: More than 3 in 5 children do not receive MH services after firearm injury. Previous engagement with MH services and new detection of MH diagnoses during firearm injury encounters may facilitate timelier connection to MH services after injury

Mental Healthcare Following Firearm and Motor Vehicle Related Injuries: Differences Impacting Our Treatment Strategies

OBJECTIVE: To compare new mental health diagnoses (NMHD) in children after a firearm injury versus following a motor vehicle collision (MVC). SUMMARY BACKGROUND DATA: A knowledge gap exists regarding childhood mental health diagnoses following firearm injuries, notably in comparison to other forms of traumatic injury. METHODS: We utilized Medicaid MarketScan claims (2010-2016) to conduct a matched case-control study of children ages 3-17 years. Children with firearm injuries were matched with up to three children with MVC injuries. Severity was determined by injury severity score (ISS) and emergency department (ED) disposition. We used multivariable logistic regression to measure the association of acquiring a NMHD diagnosis in the year post-injury after firearm and MVC mechanisms. RESULTS: We matched 1450 children with firearm injuries to 3691 children with MVC injuries. Compared to MVC injuries, children with firearm injuries were more likely to be black, have higher ISS, and receive hospital admission from the ED (P\u3c0.001). The adjusted odds ratio (aOR) of NMHD diagnosis was 1.55 [95% CI 1.33,1.80] greater after firearm injuries compared to MVC injuries. The odds of a NMHD were higher among children admitted to the hospital compared to those discharged. The increased odds of NMHD after firearm injuries was driven by increases in substance-related and addictive disorders (aOR 2.08 [95% CI 1.63, 2.64]) and trauma and stressor-related disorders (aOR 2.07 [95% CI 1.55, 2.76]). CONCLUSIONS: Children were found to have 50% increased odds of having a NMHD in the year following a firearm injury as compared to MVC. Programmatic interventions are needed to address children\u27s mental health following firearm injuries