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

The Role of Intermediate Care in Supporting Critically Ill Patients and Critical Care Infrastructure

Intermediate care (IC) is used for patients who do not require the human and technological support of the intensive care unit (ICU) yet require more care and monitoring than can be provided on general wards. Though prevalent in many countries, there is marked variability in models of organization and staffing, as well as monitoring and interventions provided. In this article, the authors will discuss the historical background of IC, review the impact of IC on ICU and IC patient outcomes, and highlight where future studies can shed light on how to optimize IC organization and outcomes.

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

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.

Development and verification of a nomogram for predicting short-term mortality in elderly ischemic stroke populations

Stroke is a major healthcare problem worldwide, particularly in the elderly population. Despite limited research on the development of prediction models for mortality in elderly individuals with ischemic stroke, our study aimed to address this knowledge gap. By leveraging data from the Medical Information Mart for Intensive Care IV database, we collected comprehensive raw data pertaining to elderly patients diagnosed with ischemic stroke. Through meticulous screening of clinical variables associated with 28-day mortality, we successfully established a robust nomogram. To assess the performance and clinical utility of our nomogram, various statistical analyses were conducted, including the concordance index, integrated discrimination improvement (IDI), net reclassification index (NRI), calibration curves and decision curve analysis (DCA). Our study comprised a total of 1259 individuals, who were further divided into training (n = 894) and validation (n = 365) cohorts. By identifying several common clinical features, we developed a nomogram that exhibited a concordance index of 0.809 in the training dataset. Notably, our findings demonstrated positive improvements in predictive performance through the IDI and NRI analyses in both cohorts. Furthermore, calibration curves indicated favorable agreement between the predicted and actual incidence of mortality (P > 0.05). DCA curves highlighted the substantial net clinical benefit of our nomogram compared to existing scoring systems used in routine clinical practice. In conclusion, our study successfully constructed and validated a prognostic nomogram, which enables accurate short-term mortality prediction in elderly individuals with ischemic stroke.

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

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.

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

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.