Performance of Critical Care Outcome Prediction Models in an Intermediate Care Unit

Brusca, Rebeccah M.; Simpson, Carol; Sahetya, Sarina K.; Noorain, Zeba; Tanykonda, Varshitha; Stephens, Robert S.; Hager, David N.

doi:10.1177/0885066619882675

Cited by 7 publications

(4 citation statements)

References 45 publications

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“…If intermediate care patients were distributed across many floors, respiratory therapy would no longer be immediately available to the 30% of IMCU admissions with respiratory insufficiency. 14 Moreover, while DOM services each carry an average of 2 to 3 intermediate care patients, this varies from day to day with no such patients on some days and more than 3 on others. On days a clinical team had no such patients, these beds would either not be used, or would be used for floor level patients.…”

Section: Resultsmentioning

confidence: 99%

Models of Intermediate Care Organization and Staffing at an Academic Medical Center: Considerations of an Inpatient Planning Committee

Hager

Dezube

Disney

et al. 2022

J Intensive Care Med

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Rationale: Geographic co-localization of patients and provider teams (geography) may improve care efficiency and quality. Patients requiring intermediate care present a unique challenge to the geographic model. Objective: Identify the best organizational and staffing model for intermediate care at our academic medical center. Methods: A modified nominal group technique was employed to assess the benefits and limitations of an existing model of intermediate care, identify and review potential alternative models, and choose a new model. Results: In addition to the institution's current model, the benefits and limitations of six alternative organizational and staffing models were characterized. The anticipated impact of each model on nurse: provider communication, maintenance of nursing competencies, nurse satisfaction, efficient utilization of technical and human resources, triage of patients to the unit, care continuity, and the impact on trainee education are described. After considering these features, stakeholders ranked a closed provider staffing model on a unit dedicated to intermediate care highest of the six alternative models. Important outcomes to monitor following transition to a closed staffing model included patient outcomes, nursing job satisfaction and retention, provider and trainee experience, unexpected patient transfers to higher or lower levels of care, and administrative costs. Conclusions: After considering six alternative staffing models for intermediate care, stakeholders ranked a closed provider staffing model highest. Further qualitative and quantitative comparisons to determine optimal models of intermediate care are needed.

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Section: Resultsmentioning

confidence: 99%

Models of Intermediate Care Organization and Staffing at an Academic Medical Center: Considerations of an Inpatient Planning Committee

Hager

Dezube

Disney

et al. 2022

J Intensive Care Med

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“…SAPS II, an evolution of the original SAPS by Le Gall et al [ 27 ], represents a seminal advancement in critical care prognostication, with subsequent iterations culminating in SAPS III. However, empirical investigations have revealed a propensity for the SAPS III scoring schema to overestimate mortality rates among ICU patients afflicted with internal disorders when juxtaposed against its predecessor, SAPS II [ 28 ]. Of particular significance is the discernible superiority of our nomogram in predicting 28-day mortality within the ischemic stroke cohort when compared against SAPS II, an instrument encompassing a comprehensive array of 15 variables.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a nomogram for predicting 28-day mortality in patients with ischemic stroke

Fang,

Zhou,

Mao

et al. 2024

PLoS ONE

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Background/aim We aimed to construct a validated nomogram model for predicting short-term (28-day) ischemic stroke mortality among critically ill populations. Materials and methods We collected raw data from the Medical Information Mart for Intensive Care IV database, a comprehensive repository renowned for its depth and breadth in critical care information. Subsequently, a rigorous analytical framework was employed, incorporating a 10-fold cross-validation procedure to ensure robustness and reliability. Leveraging advanced statistical methodologies, specifically the least absolute shrinkage and selection operator regression, variables pertinent to 28-day mortality in ischemic stroke were meticulously screened. Next, binary logistic regression was utilized to establish nomogram, then applied concordance index to evaluate discrimination of the prediction models. Predictive performance of the nomogram was assessed by integrated discrimination improvement (IDI) and net reclassification index (NRI). Additionally, we generated calibration curves to assess calibrating ability. Finally, we evaluated the nomogram’s net clinical benefit using decision curve analysis (DCA), in comparison with scoring systems clinically applied under common conditions. Results A total of 2089 individuals were identified and assigned into training (n = 1443) or validation (n = 646) cohorts. Various identified risk factors, including age, ethnicity, marital status, underlying metastatic solid tumor, Charlson comorbidity index, heart rate, Glasgow coma scale, glucose concentrations, white blood cells, sodium concentrations, potassium concentrations, mechanical ventilation, use of heparin and mannitol, were associated with short-term (28-day) mortality in ischemic stroke individuals. A concordance index of 0.834 was obtained in the training dataset, indicating that our nomogram had good discriminating ability. Results of IDI and NRI in both cohorts proved that our nomogram had positive improvement of predictive performance, compared to other scoring systems. The actual and predicted incidence of mortality showed favorable concordance on calibration curves (P > 0.05). DCA curves revealed that, compared with scoring systems clinically used under common conditions, the constructed nomogram yielded a greater net clinical benefit. Conclusions Utilizing a comprehensive array of fourteen readily accessible variables, a prognostic nomogram was meticulously formulated and rigorously validated to provide precise prognostication of short-term mortality within the ischemic stroke cohort.

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“…They use 14 or 17 variables, also making their clinical practice not convenient. SOFA, another confirmed useful predictive model of critical illness, which is much simpler than APACHE II score and SAPS II, also measures six variables (4)(5)(6). Even so, the discrimination of these models remains unsatisfactory with AUCs varied from 0.7 to 0.9 in previous studies (24).…”

Section: Discussionmentioning

confidence: 99%

“…The main prognostic models for assessing the overall severity of illness in critically ill adults are Acute Physiology and Chronic Health Evaluation (APACHE), Simplified Acute Physiology Score (SAPS), and Mortality Prediction Model (MPM) (2). These models, as well as Sequential Organ Failure Assess (SOFA), which was primarily designed to describe the degree of organ dysfunction in critically ill patients (3), have been found to predict mortality effectively in different clinical conditions (4)(5)(6). However, limitations for these scoring systems do exist since they are all obtained by calculating a lot of components, which makes their clinical practice complicated.…”

Section: Introductionmentioning

confidence: 99%

Elevated Serum Tenascin-C Predicts Mortality in Critically Ill Patients With Multiple Organ Dysfunction

Gao

et al. 2021

Front. Med.

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Background: Multiple organ dysfunction is a complex and lethal clinical feature with heterogeneous causes and is usually characterized by tissue injury of multiple organs. Tenascin-C (TNC) is a matricellular protein that is rarely expressed in most of the adult tissues, but re-induced following injury. This study aimed to evaluate serum TNC in predicting mortality in critically ill patients with multiple organ dysfunction.Methods: Adult critically ill patients with at least two organs dysfunction and an increase of Sequential Organ Failure Assess (SOFA) score ≥ 2 points within 7 days were prospectively enrolled into two independent cohorts. The emergency (derivation) cohort was a consecutive series and the patients were from Emergency Department. The inpatient (validation) cohort was a convenience series and the patients were from medical wards. Their serum samples at the first 24 h after enrollment were collected and subjected to TNC measurement using ELISA. The association between serum TNC level and 28-day all-cause mortality was investigated, and then the predictive value of serum TNC was analyzed.Results: A total of 110 patients with a median age of 64 years (53, 73) were enrolled in the emergency cohort. Compared to the survivors, serum TNC in the non-survivors was significantly higher (467.7 vs. 197.5 ng/ml, p < 0.001). Multivariate logistic regression analysis revealed that the association between serum TNC and 28-day mortality was independent of sepsis or critical illness scores such as SOFA, Acute Physiology and Chronic Health Evaluation (APACHE II), and Simplified Acute Physiology Score (SAPS II), respectively (p < 0.001 for each). The area under receiver operating characteristic curve of serum TNC for predicting mortality was 0.803 (0.717–0.888) (p < 0.001), similar with SOFA 0.808 (0.725–0.891), APACHE II 0.762 (0.667–0.857), and SAPS II 0.779 (0.685–0.872). The optimal cut-off value of serum TNC was 298.2 ng/ml. Kaplan–Meier analysis showed that the survival of patients with serum TNC ≥ 300 ng/ml was significantly worse than that of patients with serum TNC < 300 ng/ml. This result was validated in the inpatient cohort. The sensitivity and specificity of serum TNC ≥ 300 ng/ml for predicting mortality were 74.3 and 74.7% in the emergency cohort, and 63.0 and 70.1% in the inpatient cohort, respectively.Conclusion: Serum TNC was associated with mortality in critically ill patients with multiple organ dysfunction, and would be used as a prognostic tool for predicting mortality in this population.

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Performance of Critical Care Outcome Prediction Models in an Intermediate Care Unit

Cited by 7 publications

References 45 publications

Models of Intermediate Care Organization and Staffing at an Academic Medical Center: Considerations of an Inpatient Planning Committee

Models of Intermediate Care Organization and Staffing at an Academic Medical Center: Considerations of an Inpatient Planning Committee

Development and validation of a nomogram for predicting 28-day mortality in patients with ischemic stroke

Elevated Serum Tenascin-C Predicts Mortality in Critically Ill Patients With Multiple Organ Dysfunction

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