Addition of 25-hydroxyvitamin D levels to the Deyo-Charlson Comorbidity Index improves 90-day mortality prediction in critically ill patients

The definition of predictor and outcome variables in mortality prediction models: a scoping review and quality of reporting study

BACKGROUND AND OBJECTIVES

Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting.

METHODS

For this scoping review, MEDLINE, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials were searched. Studies developed within a general ICU population reporting on prediction models with mortality as a primary or secondary outcome were eligible. The selection criteria were adopted from a review by Keuning et al. Predictor and outcome variables, variable characteristics (defined as units, definitions, moments of measurement, and methods of measurement), and publication details (defined as first author, year of publication and title) were extracted from the included studies. Predictor and outcome variable categories were demographics, chronic disease, care logistics, acute diagnosis, clinical examination and physiological derangement, laboratory assessment, additional diagnostics, support and therapy, risk scores, and (mortality) outcomes.

RESULTS

A total of 56 mortality prediction models, containing 204 unique predictor and outcome variables, were included. The predictor variables most frequently included in the models were age (40 times), admission type (27 times), and mechanical ventilation (21 times). We observed that single variables were measured with different units, according to different definitions, at a different moment, and with a different method of measurement in different studies. The reporting of the unit was mostly complete (98% overall, 95% in the laboratory assessment category), whereas the definition of the variable (74% overall, 63% in the chronic disease category) and method of measurement (70% overall, 34% in the demographics category) were most often lacking.

CONCLUSION

Accurate and transparent reporting of predictor and outcome variables is paramount to enhance reproducibility, model performance in different contexts, and validity. Since unclarity about the required input data may introduce bias and thereby affect model performance, this study advocates that prognostic ICU models can be improved by transparent and clear reporting of predictor and outcome variables and their characteristics.

External Validation of Mortality Prediction Models for Critical Illness Reveals Preserved Discrimination but Poor Calibration

OBJECTIVES

In a recent scoping review, we identified 43 mortality prediction models for critically ill patients. We aimed to assess the performances of these models through external validation.

DESIGN

Multicenter study.

SETTING

External validation of models was performed in the Simple Intensive Care Studies-I (SICS-I) and the Finnish Acute Kidney Injury (FINNAKI) study.

PATIENTS

The SICS-I study consisted of 1,075 patients, and the FINNAKI study consisted of 2,901 critically ill patients.

MEASUREMENTS AND MAIN RESULTS

For each model, we assessed: 1) the original publications for the data needed for model reconstruction, 2) availability of the variables, 3) model performance in two independent cohorts, and 4) the effects of recalibration on model performance. The models were recalibrated using data of the SICS-I and subsequently validated using data of the FINNAKI study. We evaluated overall model performance using various indexes, including the (scaled) Brier score, discrimination (area under the curve of the receiver operating characteristics), calibration (intercepts and slopes), and decision curves. Eleven models (26%) could be externally validated. The Acute Physiology And Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS)-Reduced (SAPS-R)' and Simplified Mortality Score for the ICU models showed the best scaled Brier scores of 0.11' 0.10' 0.10' and 0.06' respectively. SAPS II, APACHE II, and APACHE IV discriminated best; overall discrimination of models ranged from area under the curve of the receiver operating characteristics of 0.63 (0.61-0.66) to 0.83 (0.81-0.85). We observed poor calibration in most models, which improved to at least moderate after recalibration of intercepts and slopes. The decision curve showed a positive net benefit in the 0-60% threshold probability range for APACHE IV and SAPS-R.

CONCLUSIONS

In only 11 out of 43 available mortality prediction models, the performance could be studied using two cohorts of critically ill patients. External validation showed that the discriminative ability of APACHE II, APACHE IV, and SAPS II was acceptable to excellent, whereas calibration was poor.

Mortality prediction models in the adult critically ill: A scoping review

BACKGROUND

Mortality prediction models are applied in the intensive care unit (ICU) to stratify patients into different risk categories and to facilitate benchmarking. To ensure that the correct prediction models are applied for these purposes, the best performing models must be identified. As a first step, we aimed to establish a systematic review of mortality prediction models in critically ill patients.

METHODS

Mortality prediction models were searched in four databases using the following criteria: developed for use in adult ICU patients in high-income countries, with mortality as primary or secondary outcome. Characteristics and performance measures of the models were summarized. Performance was presented in terms of discrimination, calibration and overall performance measures presented in the original publication.

RESULTS

In total, 43 mortality prediction models were included in the final analysis. In all, 15 models were only internally validated (35%), 13 externally (30%) and 10 (23%) were both internally and externally validated by the original researchers. Discrimination was assessed in 42 models (98%). Commonly used calibration measures were the Hosmer-Lemeshow test (60%) and the calibration plot (28%). Calibration was not assessed in 11 models (26%). Overall performance was assessed in the Brier score (19%) and the Nagelkerke's R2 (4.7%).

CONCLUSIONS

Mortality prediction models have varying methodology, and validation and performance of individual models differ. External validation by the original researchers is often lacking and head-to-head comparisons are urgently needed to identify the best performing mortality prediction models for guiding clinical care and research in different settings and populations.

How much does lumbar fusion change sagittal pelvic tilt in individuals receiving total hip arthroplasty?

Background

This study primarily aims to examine the effect of lumbar fusion on changes in sagittal pelvic tilt (SPT) in total hip arthroplasty (THA) patients.

Methods

We reviewed 19 hip osteoarthritic patients undergoing THA with or without lumbar fusion. The gender, age, primary disease, Deyo comorbidity score, and year of surgery were sorted and matched. All patients were followed up for at least 12 months. They were compared in terms of the SPT angle, Harris hip score (HHS) and complications.

Results

On average, the patients receiving lumbar fusion had a − 3.9 (95% CI − 7.7 to − 1.5) degrees of SPT before THA and − 2.7 (95% CI − 6.5 to 1.1) degrees postoperatively, and the THA patients without lumbar fusion averaged 2.5 (95% CI − 0.1 to 5.0) degrees and 4.2 (95% CI 2.0 to 6.4) degrees, respectively. In the lumbar fusion patients, the mean SPT was − 3.9 (95% CI − 9.9 to 2.0) degrees with L5S1 fusion and − 4.0(95% CI − 10.0 to 2.1) degrees without L5S1 fusion on the standing radiograph before THA (t = 0.01, P = 0.99). The mean SPT was − 1.2 (95% CI − 4.9 to 2.6) degrees with one- and two-segment fusion and − 10.0 (95% CI − 18.5 to 1.5) degrees with three- and four-segment fusion before THA (t = 2.60, P = 0.02). There was no statistically significant difference in cup inclination and cup anteversion after THA between the lumbar fusion and control groups. These patients in the two groups achieved a similar HHS 12 months after THA despite the fact that they had different SPT and HHS before THA.

Conclusion

Lumbar fusion appears to increase the posterior SPT by approximately 6 degrees in the patients undergoing THA. Lumbar fusion of more than two segments is a predictor of more posterior SPT changes, but fusion of L5S1 is not.

Low vitamin D at ICU admission is associated with cancer, infections, acute respiratory insufficiency, and liver failure

OBJECTIVES

Vitamin D deficiency may be associated with comorbidities and poor prognosis. However, this association in patients in the intensive care unit (ICU) has not been fully elucidated. The aim of this study was to investigate whether the serum concentrations of 25-hydroxyvitamin D (25[OH]D) within the first 48 h after ICU admission are associated with prognostic indicators (Acute Physiology and Chronic Health Evaluation [APACHE] II, Sequential Organ Failure Assessment [SOFA] score, Charlson comorbidity index [CCI]), clinical complications, serum C-reactive protein (CRP) concentrations, mechanical ventilation duration, and mortality.

METHODS

Seventy-one patients were admitted to the ICU, and their concentrations of 25(OH)D in the first 48 h were analyzed. To evaluate the prognostic factors in the ICU, APACHE II scores, SOFA scores, CCI questionnaires, mechanical ventilation time, CRP, and mortality were used.

RESULTS

The mean concentration of 25(OH)D was 17.7 ± 8.27 ng/mL (range 3.5-37.5 ng/mL), with 91.6% presenting with deficiency at admission. Although no associations were found between serum 25(OH)D concentrations with mechanical ventilation time, CRP, mortality, and APACHE II and SOFA severity scores, we found associations with the CCI when adjusted by age (model 1: odds ratio [OR], 1.64; 95% confidence interval [CI], 1.14-2.34) and by age, sex and body mass index (model 2: OR, 1.59; 95% CI, 1.10-2.34). In addition, among the comorbidities present, 25(OH)D concentrations were inversely associated with cancer (crude model OR, 3.42; 95% CI, 1.21-9.64) and liver disease (crude model OR, 9.64; 95% CI, 2.28-40.60).

CONCLUSION

We found a strong association between 25(OH)D concentrations and the prognostic indicator CCI and clinical complications (acute respiratory insufficiency, acute liver failure, and infections), but no associations with the prognostic indicators APACHE II and SOFA score, CRP, mechanical ventilation duration, or mortality. The main comorbidities associated with low 25(OH)D were cancer and liver disease, suggesting that the determination of 25(OH)vitamin D is relevant during the ICU stay.